Functional Brain Imaging

resonance imaging. IEEE Transactions on Medical Imaging. 2004;23(2):137-152

Neuroimaging - Structure, Function and Mind

information maximisation approach to

deconvolution. Neural Computation.

[36] Kim TS, Zhou Y, Kim S, Singh M. EEG distributed source imaging with a realistic finite-element head model. IEEE Transactions on Nuclear Science.

[37] Makeig S, Westerfield M, Jung TP, Enghoff S, Townsend J, Courchesne E, Sejnowski TJ. Dynamic brain sources of visual evoked responses. Science. 2002;

[38] Neeman M, Dafni H. Structural, functional, and molecular MR imaging of the. Microvasculature. Annual Review of Biomedical Engineering.

[39] Fox PT, Raichle ME. Focal physiological uncoupling of cerebral blood flow and oxidative metabolism during somatosensory stimulation in human subjects. Proceeding of National Academic Science. 1986;83:1140-1144

[40] Zhou Y, Functional Neuroimaging Methods and Frontiers. Nova Science

[35] Bell AJ, Sejnowski TJ. An

blind separation and blind

1995;7(6):1129-1159

2002;49(3):745-752

295(5555):690-694

2003;5:29-56

Publishers. 2018

68

**71**

**Chapter 4**

Areas

**Abstract**

and enhanced interactions.

**1. Introduction**

language area, mirror neuron, default mode network

Simultaneous Smelling an Incense

Outdoor and Putting the Hands

Together Activate Specific Brain

Mirror neurons are involved in imitation of habitual behaviors. To increase understanding of the theory of mirror neurons and the default mode network, brain activation was explored in 11 healthy adult volunteers who did or did not have a habit of putting their hands together as if praying. Magnetoencephalography (MEG) data were recorded while the participants simultaneously smelled an odor in two kinds of incenses outdoor and/or while they moved to putting their hands together. A magnetoencephalographic contour map of the recorded findings was drawn and an estimated current dipole (ECD) was set. Regardless of a habit of putting their hands together or not, the inner lobe of the frontal area, anterior area in the temporal lobe, and F5 language area in the left frontal lobe and so on were specifically activated. We used cortisol value as an index of the stress state measured in every state (before and after smelling two different incenses outdoor). These experiments suggest that simultaneous smelling an incense outdoor and the behavior of putting their hands together increased the activity of these specific areas in the human brain due to mutual interactions

**Keywords:** incense outdoor, putting the hands together, habit/no habit, MEG, F5

In the olfactory neural processing in humans, evoked magnetic fields by odorant synchronized with respiration and sniffing odors are found in orbito-frontal cortex (OFC) and inferior temporal lobe [1–6]. On the other hand, mirror neurons in the brain are known to activate the inner prefrontal lobe and F5 area which have the function of imitation of behavior in daily life [7–9]. Therefore, mirror neurons are considered to have the function for imitation of habit [10–12]. Super mirror neurons are concerned with determination of values, recognition of oneself and others, and reward from one's work. The inner default mode network controls the fundamental activity of daily movements and the resting state of the human brain [13–15]. Because this default mode network is strongly related to super mirror neurons, discrimination of oneself from others and the determination of social

*Mitsuo Tonoike and Takuto Hayashi*

#### **Chapter 4**

## Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific Brain Areas

*Mitsuo Tonoike and Takuto Hayashi*

### **Abstract**

Mirror neurons are involved in imitation of habitual behaviors. To increase understanding of the theory of mirror neurons and the default mode network, brain activation was explored in 11 healthy adult volunteers who did or did not have a habit of putting their hands together as if praying. Magnetoencephalography (MEG) data were recorded while the participants simultaneously smelled an odor in two kinds of incenses outdoor and/or while they moved to putting their hands together. A magnetoencephalographic contour map of the recorded findings was drawn and an estimated current dipole (ECD) was set. Regardless of a habit of putting their hands together or not, the inner lobe of the frontal area, anterior area in the temporal lobe, and F5 language area in the left frontal lobe and so on were specifically activated. We used cortisol value as an index of the stress state measured in every state (before and after smelling two different incenses outdoor). These experiments suggest that simultaneous smelling an incense outdoor and the behavior of putting their hands together increased the activity of these specific areas in the human brain due to mutual interactions and enhanced interactions.

**Keywords:** incense outdoor, putting the hands together, habit/no habit, MEG, F5 language area, mirror neuron, default mode network

#### **1. Introduction**

In the olfactory neural processing in humans, evoked magnetic fields by odorant synchronized with respiration and sniffing odors are found in orbito-frontal cortex (OFC) and inferior temporal lobe [1–6]. On the other hand, mirror neurons in the brain are known to activate the inner prefrontal lobe and F5 area which have the function of imitation of behavior in daily life [7–9]. Therefore, mirror neurons are considered to have the function for imitation of habit [10–12]. Super mirror neurons are concerned with determination of values, recognition of oneself and others, and reward from one's work. The inner default mode network controls the fundamental activity of daily movements and the resting state of the human brain [13–15]. Because this default mode network is strongly related to super mirror neurons, discrimination of oneself from others and the determination of social

cognition are considered important in human daily life [16, 17]. The purpose of this study is to clarify that simultaneous smelling an incense outdoor and putting the hands together activate the human brain and to show where specific areas are activated.

#### **2. Materials and methods**

#### **2.1 Incense sticks**

In this MEG experiment, two types of incense sticks (A: SEIUN-Violet Smokeless, and B: MAINICHI-Kou Sandalwood), which are produced by Nippon Kodo Co. Ltd. in Japan, were used as odors.

#### **2.2 Subjects**

Eleven Japanese volunteer subjects (six males, five females) between the ages of 22 and 58 years (mean age 41 ± 11 years) without significant smell loss or a neurologic history participated. All subjects were right-handed and were given the informed consent in accordance with guidelines set by the ethical committee on human studies in both Aino University and the Kansai center in AIST in Japan.

#### *2.2.1 Preparation of subjects*

All subjects used non-magnetic clothes, and answered no problem for the questionnaire to exclude metal artifacts. Before the MEG experiments, an individual subject was shown the essence of instructions and possible debriefing for the experiments.

All subjects were given informed consent in accordance with the acceptance for measuring MEG and individual anatomical MRI for each individual brain structure to the experiments.

Participants were requested in seated during MEG experiments, and the head of the participant was positioned in the MEG helmet under the gantry of MEG system in the magnetically shielded room.

Ten of these volunteers (except for one male) were separated into two groups, the A-group, which included individuals with a habit of putting their hands together in their daily life (similar to praying), and the B-group, which included individuals who are not in the habit of putting their hands together or who do not pray.

One person was not included in either group, because he had experience putting his hands together and sometimes prayed. In this MEG experiment, he did not use a burning incense stick and instead directly sniffed his hands, which were painted with a liquid odorant containing the same ingredients as the incense stick.

#### **2.3 Experimental design**

#### *2.3.1 MEG system*

This MEG system is Neuro-magnetometer with 122 channel DC-SQUID sensors, whole-cortex type system (Neuromag-122™, Electa Co. Ltd., made in Finland).

**73**

of data.

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific…*

Neuromag-Aquis122-Ver.3. Sampling frequency was used Max 600 Hz, with an

As the location of the head relative to MEG sensors differ across participants, projection onto a common source space would address this issue through wellestablished techniques for spatial normalization [18], although realignment of the

This MRI system is 0.4T Hitachi open type MRI system (AIRIS-Light MRI

1.*EOG/ECG/EMG*: EOG/ECG/EMG were measured to test for subject's seating state on the chair in MEG system before the experiments, however these data were not used in MEG experiments because no artifacts and no noise for MEG

2.*Head shape system*: this MEG system used Head Position Indicator (HPI) for the

3.*Head movements*: head movements of MEG were recorded continuously by using advanced HPI system, and the head movement compensation algorithm was applied [22]. The difference of between head positions before and after

4.*Position of participants*: participants were in seated in MEG experiments, and the head of the participant was positioned in the MEG helmet under the gantry

5.*External stimulation and recording devices*: this MEG system has photodiode devices to determine visual stimulus onset with respect to MEG trigger, and MEG has delays of a few msec. MEG data were corrected for these delays.

6.*Coregistration*: this MEG system has the following coregistration procedure. Anatomical MRIs were used individually to apply to individual own MEG data only by oneself. The method section is described for the preprocessing of the MEG study as the following, and the order of these preprocessing steps were

7.*Bad MEG sensors*: in this MEG system there are sometimes a few bad MEG sensors. This MEG system has tuning program for all 122 sensor's tune, and after tuning processing a few bad sensors were found, and a few bad sensors were excluded during acquisition or analysis. The signals of bad sensors were interpolated to the signal estimation by using signal estimation software.

8.*Filtering processing*: in this MEG experiments we applied the following filtering. We used the digital band-pass filtering (0.3–40 Hz) the second order forward

9.*IAC algorithms*: ICA program was applied to input data of MEG. The number of components was five for the estimation. Criteria of ICA estimation on the total five components for selecting are determined to 85% to all components

system: permanent magnetic type, made in Hitachi Co. Ltd. in Japan).

digital value of the own head shape for individual subjects.

analog pass-band filter of 0.01–200 Hz for acquisition filters.

*DOI: http://dx.doi.org/10.5772/intechopen.81624*

data could also be done in sensor space [19–21].

the run of MEG was recorrected.

of MEG system in the magnetic shielded room.

butterworth filtering with the windows algorithms.

*2.3.2 MRI system*

data.

carried out.

SQUID sensor is planner DC-SQUID type. Inner helmet of the head, at the 62 points which were selected around the whole head two the first derivative DC-SQUID sensors were located individually (so, the number of total sensors are 122 = 62 × 2). This system's version of the acquisition software is

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific… DOI: http://dx.doi.org/10.5772/intechopen.81624*

Neuromag-Aquis122-Ver.3. Sampling frequency was used Max 600 Hz, with an analog pass-band filter of 0.01–200 Hz for acquisition filters.

As the location of the head relative to MEG sensors differ across participants, projection onto a common source space would address this issue through wellestablished techniques for spatial normalization [18], although realignment of the data could also be done in sensor space [19–21].

#### *2.3.2 MRI system*

*Neuroimaging - Structure, Function and Mind*

**2. Materials and methods**

*2.2.1 Preparation of subjects*

Kodo Co. Ltd. in Japan, were used as odors.

**2.1 Incense sticks**

**2.2 Subjects**

experiments.

to the experiments.

in the magnetically shielded room.

**2.3 Experimental design**

*2.3.1 MEG system*

activated.

cognition are considered important in human daily life [16, 17]. The purpose of this study is to clarify that simultaneous smelling an incense outdoor and putting the hands together activate the human brain and to show where specific areas are

In this MEG experiment, two types of incense sticks (A: SEIUN-Violet Smokeless, and B: MAINICHI-Kou Sandalwood), which are produced by Nippon

Eleven Japanese volunteer subjects (six males, five females) between the ages of 22 and 58 years (mean age 41 ± 11 years) without significant smell loss or a neurologic history participated. All subjects were right-handed and were given the informed consent in accordance with guidelines set by the ethical committee on human studies in both Aino University and the Kansai center in AIST in Japan.

All subjects used non-magnetic clothes, and answered no problem for the questionnaire to exclude metal artifacts. Before the MEG experiments, an individual subject was shown the essence of instructions and possible debriefing for the

All subjects were given informed consent in accordance with the acceptance for measuring MEG and individual anatomical MRI for each individual brain structure

Participants were requested in seated during MEG experiments, and the head of the participant was positioned in the MEG helmet under the gantry of MEG system

Ten of these volunteers (except for one male) were separated into two groups, the A-group, which included individuals with a habit of putting their hands together in their daily life (similar to praying), and the B-group, which included individuals who

One person was not included in either group, because he had experience putting his hands together and sometimes prayed. In this MEG experiment, he did not use a burning incense stick and instead directly sniffed his hands, which were painted

This MEG system is Neuro-magnetometer with 122 channel DC-SQUID sensors,

whole-cortex type system (Neuromag-122™, Electa Co. Ltd., made in Finland). SQUID sensor is planner DC-SQUID type. Inner helmet of the head, at the 62 points which were selected around the whole head two the first derivative DC-SQUID sensors were located individually (so, the number of total sensors are 122 = 62 × 2). This system's version of the acquisition software is

are not in the habit of putting their hands together or who do not pray.

with a liquid odorant containing the same ingredients as the incense stick.

**72**

This MRI system is 0.4T Hitachi open type MRI system (AIRIS-Light MRI system: permanent magnetic type, made in Hitachi Co. Ltd. in Japan).


10.*Trials and segments*: trials and segments were anyways applied to reject under the criteria when the external bigger noises mix the income to the MEG data and the subject's unforecasted artifacts of movements.

In this MEG experiment, each subject's head was placed in a helmet with wholecortex type SQUID sensors (Neuromag-122™, Electa Co. Ltd.). Three-dimensional orthogonal coordinates were determined in the helmet of the neuromagnetometer. Experiments were performed in the Kansai Center in Ikeda city, National Institute of Advanced Industrial Science and Technology (AIST) in Japan.

An incense outdoor was freely presented to the subject by means of a burning incense stick on a holder that was naturally held in front of the subject while seated in a chair in a magnetically shielded room.

#### *2.3.3 Experiments of the stress state using subject's saliva*

In these experiments, magnetoencephalography (MEG) was performed, and the cortisol value in the subject's saliva was measured in every state (before and after smelling two different incense outdoors (A and B)).

#### **2.4 MEG experiments for four mode state**

MEG response data were measured at the following four mode states, (1): control mode, (2): simple mode of putting the hands together, (3): smelling mode with putting the hands together, (4): only smelling mode. MEG data were added with 100 times averaging with the random sampling method. The subject pushed an optical sensor button with his or her own thumb.


By using the above two modes, we tried to measure the subject's own singular characteristic active area on the control state and to obtain the brain area activated by putting the hand together and we have examined to compare how the brain activity is different for the habit and no habit behavior of putting the hand together in daily life.


**75**

(e.g., FSL atlas).

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific…*

Both the control mode and simple mode of putting the hands together were recorded in the absence of the burning incense odor. After one incense odor was tested, the room air including the odor in the magnetically shielded room was exchanged completely with fresh air by using a large fan for about 10 minutes.

For the purpose of observing brain activity with greater accuracy, we used a whole-head 122-sensor neuromagnetometer (a DC-SQUID device of the first order differential planar type, by Neuromag, Finland). With an attached digital band filter capable of passing only measurements in the bandwidths of 0.3–40 Hz, only valid readings were collected at an actual sampling rate of 400 Hz and converted into digital values. To observe brain functions in several experimental modes, we used a whole-head type DC-SQUID, which allowed us to detect cortical current directly and to monitor brain activities [24]. This detection method is called MEG. The analog readings detected in this manner of the brain magnetic field were digitized at a sampling rate of 400 Hz with an A/D converter, downloaded, and

The 122-channel neuromagnetometer of the Planar Type Gradiometer can calculate the first derivative of the magnetic vector field Bz through individual SQUID sensors installed on the helmet, or it can calculate {(*∂Bz*/*∂x*)*i*, (*∂Bz*/*∂y*)*i*} about

\_\_\_

tions of longitude and latitude, respectively. A total of 122 sensor elements on the helmet were paired with the x- and y-axes, and each pair was assigned to measure one part of the head surface. A total of 61 sets (122 data points total) of magnetic field data can be detected, recorded at a particular interval (j), and calculated using

The advantage of planar gradiometer is the ability to manufacture them using standard thin-film techniques developed for the semiconductor this can reduce manufacturing costs and increase the precision with which the coils can be made since slight imperfections in the size or orientation of the two loops can reduce their

Signal processing method for noise reduction to this MEG system is Signal Space

In general, we use the volume conductor model of the subject's head (e.g., Sphere model, BEM, FEM) individually and the lead fields algorithms for magnetic fields [26]. Normalization procedure was also used for spatial normalization after source localization by using SPM-12 of MRI software. The coordinates of subject's brain are linked to individual subject's brain structures using the source of the lookup table

Separation (SSS)n which reduces environmental noise [25]. This method mathematically decomposes the magnetic field recorded from a spherically distributed array of sensors into a series expansion composed of internal and external terms that represent the proportion of the measured fields arising from inside and outside the sphere, respectively. The measured signal is reconstructed using only the

*Hz*. The x- and y-axes represent the direc-

*2.5.1 122-channel neuromagnetometer of the Planar Type Gradiometer*

the formula {(*∂Bz*/*∂x*)*i,j,* (*∂Bz*/*∂y*)*i,j*, (*i* = 1, 2, …, *t; j* = 1, 2, …, *t*)}.

internal terms to discard the environmental noise [19, 20].

*DOI: http://dx.doi.org/10.5772/intechopen.81624*

SQUID sensor *i*. Its dimension is *fT*/*cm* <sup>√</sup>

ability to perfectly reject the zero-order field.

*2.5.2 Signal Space Separation (SSS) system*

*2.5.3 Source reconstruction*

**2.5 MEG and data analysis**

stored in a PC.

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific… DOI: http://dx.doi.org/10.5772/intechopen.81624*

Both the control mode and simple mode of putting the hands together were recorded in the absence of the burning incense odor. After one incense odor was tested, the room air including the odor in the magnetically shielded room was exchanged completely with fresh air by using a large fan for about 10 minutes.

#### **2.5 MEG and data analysis**

*Neuroimaging - Structure, Function and Mind*

in a chair in a magnetically shielded room.

**2.4 MEG experiments for four mode state**

*2.3.3 Experiments of the stress state using subject's saliva*

smelling two different incense outdoors (A and B)).

optical sensor button with his or her own thumb.

the control state [23].

10.*Trials and segments*: trials and segments were anyways applied to reject under the criteria when the external bigger noises mix the income to the MEG data

In this MEG experiment, each subject's head was placed in a helmet with wholecortex type SQUID sensors (Neuromag-122™, Electa Co. Ltd.). Three-dimensional orthogonal coordinates were determined in the helmet of the neuromagnetometer. Experiments were performed in the Kansai Center in Ikeda city, National Institute

An incense outdoor was freely presented to the subject by means of a burning incense stick on a holder that was naturally held in front of the subject while seated

In these experiments, magnetoencephalography (MEG) was performed, and the cortisol value in the subject's saliva was measured in every state (before and after

MEG response data were measured at the following four mode states, (1): control mode, (2): simple mode of putting the hands together, (3): smelling mode with putting the hands together, (4): only smelling mode. MEG data were added with 100 times averaging with the random sampling method. The subject pushed an

1.In the control mode, the subject sat quietly and naturally in a chair with his or her eyes open and freely pushed the button of the optical fiber sensor at random times with the right thumb in synchronization with active inspiration (i.e., sniffing with the nose) of his or her own respiration rate, and the average MEG brain waves were obtained from raw data collected about 100 times in

2.For the next mode, the simple mode for the behavior of putting the hands together was performed as the experimental task, regardless of whether the subject did or did not have the habit of putting his or her hands together or praying in daily life. During this simple mode of putting the hands together, the subject held the optical sensor between the hands and pushed the button with the right thumb at random times while putting the hands together. By using the above two modes, we tried to measure the subject's own singular characteristic active area on the control state and to obtain the brain area activated by putting the hand together and we have examined to compare how the brain activity is different for the habit and no habit behavior of putting the hand together in daily life.

3.In the next mode that included smelling and putting the hands together, we measured the MEG response of both brain activities: smelling the odor in synchronization with active inspiration (i.e., sniffing and smelling the incense

4.In the last smelling mode, when the subject smelled only the incense odor

without putting the hands together, the averaged MEG response was measured by adding the raw MEG data collected about 100 times by pushing the optical

odor) and the behavior of putting the hands together [6].

and the subject's unforecasted artifacts of movements.

of Advanced Industrial Science and Technology (AIST) in Japan.

**74**

sensor button.

For the purpose of observing brain activity with greater accuracy, we used a whole-head 122-sensor neuromagnetometer (a DC-SQUID device of the first order differential planar type, by Neuromag, Finland). With an attached digital band filter capable of passing only measurements in the bandwidths of 0.3–40 Hz, only valid readings were collected at an actual sampling rate of 400 Hz and converted into digital values. To observe brain functions in several experimental modes, we used a whole-head type DC-SQUID, which allowed us to detect cortical current directly and to monitor brain activities [24]. This detection method is called MEG. The analog readings detected in this manner of the brain magnetic field were digitized at a sampling rate of 400 Hz with an A/D converter, downloaded, and stored in a PC.

#### *2.5.1 122-channel neuromagnetometer of the Planar Type Gradiometer*

The 122-channel neuromagnetometer of the Planar Type Gradiometer can calculate the first derivative of the magnetic vector field Bz through individual SQUID sensors installed on the helmet, or it can calculate {(*∂Bz*/*∂x*)*i*, (*∂Bz*/*∂y*)*i*} about SQUID sensor *i*. Its dimension is *fT*/*cm* <sup>√</sup> \_\_\_ *Hz*. The x- and y-axes represent the directions of longitude and latitude, respectively. A total of 122 sensor elements on the helmet were paired with the x- and y-axes, and each pair was assigned to measure one part of the head surface. A total of 61 sets (122 data points total) of magnetic field data can be detected, recorded at a particular interval (j), and calculated using the formula {(*∂Bz*/*∂x*)*i,j,* (*∂Bz*/*∂y*)*i,j*, (*i* = 1, 2, …, *t; j* = 1, 2, …, *t*)}.

The advantage of planar gradiometer is the ability to manufacture them using standard thin-film techniques developed for the semiconductor this can reduce manufacturing costs and increase the precision with which the coils can be made since slight imperfections in the size or orientation of the two loops can reduce their ability to perfectly reject the zero-order field.

#### *2.5.2 Signal Space Separation (SSS) system*

Signal processing method for noise reduction to this MEG system is Signal Space Separation (SSS)n which reduces environmental noise [25]. This method mathematically decomposes the magnetic field recorded from a spherically distributed array of sensors into a series expansion composed of internal and external terms that represent the proportion of the measured fields arising from inside and outside the sphere, respectively. The measured signal is reconstructed using only the internal terms to discard the environmental noise [19, 20].

#### *2.5.3 Source reconstruction*

In general, we use the volume conductor model of the subject's head (e.g., Sphere model, BEM, FEM) individually and the lead fields algorithms for magnetic fields [26]. Normalization procedure was also used for spatial normalization after source localization by using SPM-12 of MRI software. The coordinates of subject's brain are linked to individual subject's brain structures using the source of the lookup table (e.g., FSL atlas).

#### *2.5.4 Dipole fitting*

The solutions obtained with dipole fitting approaches depend heavily on the choice that is made by the researcher. Therefore, this choice must be selected in with no intention. The reported solution for dipoles was chosen over a few alternative models. And the minimum current estimation method was used in our dipole fitting to MEG [27]. For example, the choices have to be made about the number of dipoles, time windows (single latency, multiple latencies), exact dipole models (moving, rotating, fixed dipole) for this process are shown as the following [28–32] and the fitting of the best cost function for the stability of solution [28, 33].

#### *2.5.5 Single current dipole tracing method (single sphere model)*

The single current dipole tracing method is a common technique for estimating a single source of magnetic field distribution that emerges on the head surface (on the outer surface of the helmet). Given the hypothesis that the brain magnetic field is not distorted, we surmised that the influence of the distribution current (the so-called "volume current") is balanced by spatial symmetry and that the first order approximation of reading values is not affected, based on Biot-Savart's law. If these presumptions are valid, an equivalent current dipole, as displayed in threedimensional vectors, should emerge in the brains.

A critical step in the use of the single sphere model is the choice of the sphere center. The flow of volume currents would be most influenced by the boundary with the largest change in conductivity, the highly resistive inner skull surface is thought to be the optimal choice for defining the sphere surface. A best-fit sphere superimposed on an individual's structural MRI scan and obtained from performing a least-squares minimization. We can achieve a relatively good fit of a sphere to the superior and lateral aspects of the inner skull, suggesting that a single sphere model is well justified for modeling sources in the central and lateral portions of the brain.

Still, for more nonspherical portions of intracranial space, such as near the inferior frontal and temporal regions, large deviations from sphericity can introduce errors into solutions [34–36]. The distortion of volume currents should be taken in consideration. A variant of the spherical head model that is widely used in clinical MEG applications is the model of local or overlapping spheres. Instead of using a single sphere model, spheres of different curvature are fit to the various areas of the skull underlying each MEG sensor. The individual sphere centers are then used in the forward model to better model local distortions in the volume currents based on the assumption that the local curvature influences the volume currents for nearby sensors more than for distant sensors.

The current dipole can be estimated by solving the inverse problem of the magnetic field distribution as projected on the head surface. For estimation, we first drew a magnetic field contour map in reference to the measured values of (*∂Bz*/*∂z*)*i,j* or in reference to the values of {(*∂Bz/∂x*)*i,j* and (*∂Bz*/*∂y*)*i,j*} with the inner estimation method. This magnetic field contour map allowed us to estimate a single source by following the least-squares estimation method. Using this method, the signal source can be defined as in the middle position of the extreme and the sink identified on the magnetic field. A single current dipole tracing method relies on the common notion that a higher parameter G value (goodness of fit: GOF) guarantees a higher accuracy in the least-squares estimation, and an estimated single source should therefore be closer to the actual value.

**77**

brain.

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific…*

The statistical cost function measures the goodness of fit (GOF) between the magnetic field predicted by the dipole location and moment and the measured field. Typical statistical cost functions include the percent of variance unexplained

Most common approaches for MEG source estimation, and the dominated the field for many decades, is to specify only one or a few equivalent current dipoles (ECDs) to represent the solution. The strength (dipole moment) of ECDs ranges

which have typical source moments ranging from 10 to 30 nAm, may involve the

For highly dipolar field patterns with high SNR, such as the early components of sensory responses, ECD solutions can reach a greater than 90% goodness of fit, with good correspondence to the corresponding sensory projection areas of the

In general, the single current dipole tracing method is extremely useful if only one single cortical current is observed at a given instance as a result of brain activity. The method is not as valuable, however, if the entire brain is perceptively active and cortical current emerges at multiple points on the head surface. In such a case, use of the multi-current dipoles tracing method may provide a solution, as it presumes the appropriate number of dipoles likely to exist and estimates various current sources that may be occurring in the brain. Using this method, the parameter GOF becomes high only if the presumed number of dipoles is

The ECD modeling approach was extended to more complex patterns of the brain activity by adding more dipole sources to the model. One solution is to keep adding dipoles until there is little or no improvement in the goodness of fit (GOF) measure or if the percent of variance obtained reaches a criterion. An alternative is to use an objective measure of signal complexity, such as the number of principal

To further stabilize the solutions, constraints can be applied (fixing the location of one source while allowing additional sources to have free parameters) such that

However, if it is not, the resulting estimate in the real clinical MEG is not close to the actual value. Because of the constraints in determining the propriety of the presumed number of dipoles and because of the subsequent, laborious calculations, the multi-current dipoles tracing method is usually deemed relatively unrealistic

*2.5.8 Estimation of the current source by observing the magnetic field distribution*

Unlike an experimental observing the spontaneous control state, the task of smelling state and putting the hands together state were designed to activate

As the current dipole method was not originally intended to detect such a spontaneous control state, and because dipoles of the magnetic field are expressed in rather complicated patterns by this method, we traced the variations of the magnetic field distribution by their progress over time, as well as at given intervals.

Am (or 1–100 nAm). The evoked magnetic responses,

of cortex and are therefore reasonably well modeled as

(residual variance) or the corresponding chi-square statistic value [37, 38].

*DOI: http://dx.doi.org/10.5772/intechopen.81624*

to 10<sup>−</sup><sup>7</sup>

anywhere from 10<sup>−</sup><sup>9</sup>

a single ECD.

appropriate.

brain.

activation of less than 1 cm<sup>2</sup>

*2.5.6 Evaluation method using statistical cost function (GOF)*

*2.5.7 Multi-current dipoles tracing method (multi source models)*

components requested to account for a criterion power.

very complex source models can sometimes be attained.

and impractical to the realistic clinical MEG.

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific… DOI: http://dx.doi.org/10.5772/intechopen.81624*

#### *2.5.6 Evaluation method using statistical cost function (GOF)*

*Neuroimaging - Structure, Function and Mind*

The solutions obtained with dipole fitting approaches depend heavily on the choice that is made by the researcher. Therefore, this choice must be selected in with no intention. The reported solution for dipoles was chosen over a few alternative models. And the minimum current estimation method was used in our dipole fitting to MEG [27]. For example, the choices have to be made about the number of dipoles, time windows (single latency, multiple latencies), exact dipole models (moving, rotating, fixed dipole) for this process are shown as the following [28–32] and the fitting of the best cost function for the stability of solution

The single current dipole tracing method is a common technique for estimating a single source of magnetic field distribution that emerges on the head surface (on the outer surface of the helmet). Given the hypothesis that the brain magnetic field is not distorted, we surmised that the influence of the distribution current (the so-called "volume current") is balanced by spatial symmetry and that the first order approximation of reading values is not affected, based on Biot-Savart's law. If these presumptions are valid, an equivalent current dipole, as displayed in three-

A critical step in the use of the single sphere model is the choice of the sphere center. The flow of volume currents would be most influenced by the boundary with the largest change in conductivity, the highly resistive inner skull surface is thought to be the optimal choice for defining the sphere surface. A best-fit sphere superimposed on an individual's structural MRI scan and obtained from performing a least-squares minimization. We can achieve a relatively good fit of a sphere to the superior and lateral aspects of the inner skull, suggesting that a single sphere model is well justified for modeling sources in the central and lateral portions of the

Still, for more nonspherical portions of intracranial space, such as near the inferior frontal and temporal regions, large deviations from sphericity can introduce errors into solutions [34–36]. The distortion of volume currents should be taken in consideration. A variant of the spherical head model that is widely used in clinical MEG applications is the model of local or overlapping spheres. Instead of using a single sphere model, spheres of different curvature are fit to the various areas of the skull underlying each MEG sensor. The individual sphere centers are then used in the forward model to better model local distortions in the volume currents based on the assumption that the local curvature influences the volume currents for nearby

The current dipole can be estimated by solving the inverse problem of the magnetic field distribution as projected on the head surface. For estimation, we first drew a magnetic field contour map in reference to the measured values of (*∂Bz*/*∂z*)*i,j* or in reference to the values of {(*∂Bz/∂x*)*i,j* and (*∂Bz*/*∂y*)*i,j*} with the inner estimation method. This magnetic field contour map allowed us to estimate a single source by following the least-squares estimation method. Using this method, the signal source can be defined as in the middle position of the extreme and the sink identified on the magnetic field. A single current dipole tracing method relies on the common notion that a higher parameter G value (goodness of fit: GOF) guarantees a higher accuracy in the least-squares estimation, and an estimated single source should therefore be closer to the actual

*2.5.5 Single current dipole tracing method (single sphere model)*

dimensional vectors, should emerge in the brains.

sensors more than for distant sensors.

*2.5.4 Dipole fitting*

[28, 33].

brain.

**76**

value.

The statistical cost function measures the goodness of fit (GOF) between the magnetic field predicted by the dipole location and moment and the measured field. Typical statistical cost functions include the percent of variance unexplained (residual variance) or the corresponding chi-square statistic value [37, 38].

Most common approaches for MEG source estimation, and the dominated the field for many decades, is to specify only one or a few equivalent current dipoles (ECDs) to represent the solution. The strength (dipole moment) of ECDs ranges anywhere from 10<sup>−</sup><sup>9</sup> to 10<sup>−</sup><sup>7</sup> Am (or 1–100 nAm). The evoked magnetic responses, which have typical source moments ranging from 10 to 30 nAm, may involve the activation of less than 1 cm<sup>2</sup> of cortex and are therefore reasonably well modeled as a single ECD.

For highly dipolar field patterns with high SNR, such as the early components of sensory responses, ECD solutions can reach a greater than 90% goodness of fit, with good correspondence to the corresponding sensory projection areas of the brain.

#### *2.5.7 Multi-current dipoles tracing method (multi source models)*

In general, the single current dipole tracing method is extremely useful if only one single cortical current is observed at a given instance as a result of brain activity. The method is not as valuable, however, if the entire brain is perceptively active and cortical current emerges at multiple points on the head surface. In such a case, use of the multi-current dipoles tracing method may provide a solution, as it presumes the appropriate number of dipoles likely to exist and estimates various current sources that may be occurring in the brain. Using this method, the parameter GOF becomes high only if the presumed number of dipoles is appropriate.

The ECD modeling approach was extended to more complex patterns of the brain activity by adding more dipole sources to the model. One solution is to keep adding dipoles until there is little or no improvement in the goodness of fit (GOF) measure or if the percent of variance obtained reaches a criterion. An alternative is to use an objective measure of signal complexity, such as the number of principal components requested to account for a criterion power.

To further stabilize the solutions, constraints can be applied (fixing the location of one source while allowing additional sources to have free parameters) such that very complex source models can sometimes be attained.

However, if it is not, the resulting estimate in the real clinical MEG is not close to the actual value. Because of the constraints in determining the propriety of the presumed number of dipoles and because of the subsequent, laborious calculations, the multi-current dipoles tracing method is usually deemed relatively unrealistic and impractical to the realistic clinical MEG.

#### *2.5.8 Estimation of the current source by observing the magnetic field distribution*

Unlike an experimental observing the spontaneous control state, the task of smelling state and putting the hands together state were designed to activate brain.

As the current dipole method was not originally intended to detect such a spontaneous control state, and because dipoles of the magnetic field are expressed in rather complicated patterns by this method, we traced the variations of the magnetic field distribution by their progress over time, as well as at given intervals. Drawing a contour map of the recorded findings, we identified extremes (maxima) and sinks (minima) found in pairs respectively on the magnetic field. We then set a virtual current vector in the middle position between each pair of extremes and sinks and traced the variations of the vector over time. Although this method has not been established for signal estimation and can only give approximations, no other available method seems more practical or acceptable for evaluation of the spontaneous control state, where neither the single dipole method nor the multidipole method is useful.

We observed a combination of extremes and sinks on the brain magnetic field contour map, vertically upward from the vertex. Extremes and sinks were aligned in such a way that their magnetic fields were tangential to each other. In between, the cortical current ran in the direction of the tangent vector in accordance with Biot-Savart's Law [39, 40].

We calculated the magnetic field contour map at a single time, with all three cortical currents in clear view. The test results were analyzed using the method of contrast between extreme and sink. The pattern recognition analysis of the inverse problem method is also available and more precise; however, this method was too time-consuming considering the number of cortical currents we needed to observe [41]. With respect to our test objective, we prioritized efficiency over numerical precision, which is normally preferred in localizing brain functional foci.

In order to reliable ECD fit, we must have fewer models. Another popular approach that has been used in MEG source modeling is the so-called "Spatiotemporal Dipole Fit" introduced Scherg and Von Cramon [38] in which the time-varying amplitude (time course) of each dipole is used as additional information to constrain the solutions.

#### *2.5.9 Data acquisition, processing and analysis*

We traced the cortical current using the first-order differential planar type of DC-SQUID. This device enables us to detect the current source of brain activity directly under its sensor, revealing the maximum of the absolute values. This is the greatest advantage of using the differential planar type device, which has a dimension of *fT*/*cm* <sup>√</sup> \_\_\_ *Hz*. When using neuromagnetometers of the axial type, as explained in Section 2.5.2 above (the single current dipole tracing method), we can estimate the current source as defined as the middle position between the minima and maxima of the cranial nerve magnetic field distribution [42].

Neuromagnetometers of the planar type are useful for determining where the current source of brain activity exists by detecting the maximum of absolute magnetic field values. We therefore used these readings to map the distributions of the cranial nerve magnetic field using MATLAB software, illustrating how the magnetic field varies over time [43]. Data acquisition began at the moment of the signal, although the data we actually used began 500 ms after the starting signal. Thus, we sampled the experimental activities of the brains. In the olfactory neural processing in humans, the responses of event related magnetic fields and evoked magnetic fields were obtained within about 250 ms in healthy subjects. In our MEG experiments, subjects sniff an incense odor actively by using his own nose and when starting to sniff he pushes the optical sensor button as a trigger signal. Therefore, to record the more precise changing of MEG we used the sampling interval with every 50 ms. So, measurements MEG responding data were analyzed by every 50 ms. By observing these cranial nerve magnetic field distributions on the surface of the head, we traced and recorded variations in the current source at each particular moment.

**79**

**Figure 1.**

*Real-time active state at a control in our brain.*

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific…*

**3.1 Result of signal source estimation of MEG in the brain obtained with the** 

This single current dipole tracing method has the advantage of directly obtaining real-time responses of the brain's neural activities. This is different from fMRI and PET methods, which measure metabolism of physiologically active substances. We obtained changing activities of the signal source and estimated the active regions in the brain with analysis using the single current dipole tracing method. In single current dipole tracing method, the first main current dipole is the largest dipole. This current dipole was obtained in the middle position of extreme center and sink center identified on magnetic field. The second and the third current dipoles were smaller and weaker than the first main current dipole. Using this single current dipole tracing method, we can estimate only one current signal source (magnitude,

*3.1.1 Advantage of the real-time response of the brain's neural activities by analysis of millisecond-time resolution using the single current dipole tracing method of* 

direction, and location) as the most reliable neural activity in the brain.

*single current dipole tracing method of MEG data*

mapping of MEG response at a control state.

*3.1.2 Mechanism of the real-time estimation method of the active area using the* 

**Figure 1** shows the real-time estimation method for obtaining the active area in the subject's brain. **Figure 1(a)** shows an example of a MEG response to random activities such as the control state before putting the hands together as assessed with the single current dipole tracing method. We could not obtain the dipole completely, and thus, we could not identify the generally active area in this control state (with no smelling odor and no putting the hands). **Figure 1(b)** shows contour

**Figure 1(a)** shows over head vision, upper is anterior, lower is posterior of the head. Each curves show 122-channel MEG averaging response waves of duration 0.2 s time. A red vertical line shows starting time point for the inspiration of odorless air.

*DOI: http://dx.doi.org/10.5772/intechopen.81624*

**single current dipole tracing method**

**3. Results**

*MEG*

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific… DOI: http://dx.doi.org/10.5772/intechopen.81624*

#### **3. Results**

*Neuroimaging - Structure, Function and Mind*

dipole method is useful.

Biot-Savart's Law [39, 40].

tion to constrain the solutions.

\_\_\_

*2.5.9 Data acquisition, processing and analysis*

functional foci.

sion of *fT*/*cm* <sup>√</sup>

Drawing a contour map of the recorded findings, we identified extremes (maxima) and sinks (minima) found in pairs respectively on the magnetic field. We then set a virtual current vector in the middle position between each pair of extremes and sinks and traced the variations of the vector over time. Although this method has not been established for signal estimation and can only give approximations, no other available method seems more practical or acceptable for evaluation of the spontaneous control state, where neither the single dipole method nor the multi-

We observed a combination of extremes and sinks on the brain magnetic field contour map, vertically upward from the vertex. Extremes and sinks were aligned in such a way that their magnetic fields were tangential to each other. In between, the cortical current ran in the direction of the tangent vector in accordance with

We calculated the magnetic field contour map at a single time, with all three cortical currents in clear view. The test results were analyzed using the method of contrast between extreme and sink. The pattern recognition analysis of the inverse problem method is also available and more precise; however, this method was too time-consuming considering the number of cortical currents we needed to observe [41]. With respect to our test objective, we prioritized efficiency over numerical precision, which is normally preferred in localizing brain

In order to reliable ECD fit, we must have fewer models. Another popular approach that has been used in MEG source modeling is the so-called

"Spatiotemporal Dipole Fit" introduced Scherg and Von Cramon [38] in which the time-varying amplitude (time course) of each dipole is used as additional informa-

We traced the cortical current using the first-order differential planar type of DC-SQUID. This device enables us to detect the current source of brain activity directly under its sensor, revealing the maximum of the absolute values. This is the greatest advantage of using the differential planar type device, which has a dimen-

in Section 2.5.2 above (the single current dipole tracing method), we can estimate the current source as defined as the middle position between the minima and

Neuromagnetometers of the planar type are useful for determining where the current source of brain activity exists by detecting the maximum of absolute magnetic field values. We therefore used these readings to map the distributions of the cranial nerve magnetic field using MATLAB software, illustrating how the magnetic field varies over time [43]. Data acquisition began at the moment of the signal, although the data we actually used began 500 ms after the starting signal. Thus, we sampled the experimental activities of the brains. In the olfactory neural processing in humans, the responses of event related magnetic fields and evoked magnetic fields were obtained within about 250 ms in healthy subjects. In our MEG experiments, subjects sniff an incense odor actively by using his own nose and when starting to sniff he pushes the optical sensor button as a trigger signal. Therefore, to record the more precise changing of MEG we used the sampling interval with every 50 ms. So, measurements MEG responding data were analyzed by every 50 ms. By observing these cranial nerve magnetic field distributions on the surface of the head, we traced and recorded variations in the current source at each

maxima of the cranial nerve magnetic field distribution [42].

*Hz*. When using neuromagnetometers of the axial type, as explained

**78**

particular moment.

#### **3.1 Result of signal source estimation of MEG in the brain obtained with the single current dipole tracing method**

*3.1.1 Advantage of the real-time response of the brain's neural activities by analysis of millisecond-time resolution using the single current dipole tracing method of MEG*

This single current dipole tracing method has the advantage of directly obtaining real-time responses of the brain's neural activities. This is different from fMRI and PET methods, which measure metabolism of physiologically active substances. We obtained changing activities of the signal source and estimated the active regions in the brain with analysis using the single current dipole tracing method. In single current dipole tracing method, the first main current dipole is the largest dipole. This current dipole was obtained in the middle position of extreme center and sink center identified on magnetic field. The second and the third current dipoles were smaller and weaker than the first main current dipole. Using this single current dipole tracing method, we can estimate only one current signal source (magnitude, direction, and location) as the most reliable neural activity in the brain.

*3.1.2 Mechanism of the real-time estimation method of the active area using the single current dipole tracing method of MEG data*

**Figure 1** shows the real-time estimation method for obtaining the active area in the subject's brain. **Figure 1(a)** shows an example of a MEG response to random activities such as the control state before putting the hands together as assessed with the single current dipole tracing method. We could not obtain the dipole completely, and thus, we could not identify the generally active area in this control state (with no smelling odor and no putting the hands). **Figure 1(b)** shows contour mapping of MEG response at a control state.

**Figure 1(a)** shows over head vision, upper is anterior, lower is posterior of the head. Each curves show 122-channel MEG averaging response waves of duration 0.2 s time. A red vertical line shows starting time point for the inspiration of odorless air.

**Figure 1.** *Real-time active state at a control in our brain.*

**Figure 1(b)** shows the contour mapping of real time MEG response at a control state. We could not almost obtain a constricted dipole completely, then we could not find out the active brain area generally in this control state.

#### **(a) The simple mode of putting the hands together without smelling**

#### *3.1.3 The theory of mirror neurons and the default mode network*

In this experimental task, the subjects put their hands together or mimicked praying without smelling. We obtained the subject's type as an individual variation for the priority of brain laterality regarding putting the hands together or praying in daily life. **Figure 2** shows an example of the MEG response for the active area obtained with the single current dipole tracing method for this experimental condition. We analyzed estimated active areas continuously using a real-time estimation method. **Figure 2(b)** shows an MEG response on active area of left side brain as a left priority type after only putting the hands together (with no smelling odor). **Figure 2(c)** shows a vector of single current dipole estimated in the brain using 3-D coordinates.

**Figure 2(c)** shows a vector of single current dipole estimated in the brain using 3-D coordinates after putting the hands together. X-axis is the horizontal line of right to left ear, and Y-axis is the line from nasion to inion, and Z-axis is the upper to lower line of the vertical of the brain.

**Figure 2.** *Real-time estimation of the active area in our brain after only putting the hands together.*

**81**

**Table 1.**

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific…*

**Table 1** shows an estimated ECD dipole each subjects for latency tie window (210–1100 ms), priority of the laterality (right or left), activated region, and GOF (statistical goodness of fit, %) for the simple mode of only putting the hands

Five of the 11 subjects had the right priority brain type for laterality. Three of these five persons regularly put their hands together in their daily life, and the other

Six of the 11 persons had the left priority brain type for laterality. Two of six subjects regularly put their hands together in their daily life, and the other three did not. Only one subject of the 11 was not classified in these two groups, and this person

(N1) had the left priority brain type estimated in central temporal gyrus (N1:

The priorities of brain laterality are considered important for obtaining the characteristic laterality of the active brain in daily life as described below, regardless

As shown in **Table 1**, in the A-group which had the habit of putting the hands together in daily life, the main active areas in the brain were generally estimated to be on the right near the superior regions (A1: latency 309.2 ms, GOF 50.2%; A4: latency 405.6 ms, GOF 47.4%) or the left near central (A5: latency 1065.3 ms, GOF 57.6%) or left caudal regions (A2: latency 613 ms, GOF 47.9%) in the temporal gyrus. The right prefrontal area was activated in only one subject (A3: latency

*Results of MEG experiments for the simple modes (a) of only putting the hands together without smelling.*

latency 579.0 ms, GOF 32.8%) as shown in the above **Table 1**.

*3.1.3.3 A-group: (A1–A5) habit of putting the hands together or praying*

of putting the hands together and praying or not.

*DOI: http://dx.doi.org/10.5772/intechopen.81624*

together without smelling.

two did not.

974 ms, GOF 47.9%).

*3.1.3.1 a-1. Right priority brain type*

*3.1.3.2 a-2. Left priority brain type*

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific… DOI: http://dx.doi.org/10.5772/intechopen.81624*

**Table 1** shows an estimated ECD dipole each subjects for latency tie window (210–1100 ms), priority of the laterality (right or left), activated region, and GOF (statistical goodness of fit, %) for the simple mode of only putting the hands together without smelling.

#### *3.1.3.1 a-1. Right priority brain type*

*Neuroimaging - Structure, Function and Mind*

lower line of the vertical of the brain.

find out the active brain area generally in this control state.

*3.1.3 The theory of mirror neurons and the default mode network*

*Real-time estimation of the active area in our brain after only putting the hands together.*

**Figure 1(b)** shows the contour mapping of real time MEG response at a control state. We could not almost obtain a constricted dipole completely, then we could not

In this experimental task, the subjects put their hands together or mimicked praying without smelling. We obtained the subject's type as an individual variation for the priority of brain laterality regarding putting the hands together or praying in daily life. **Figure 2** shows an example of the MEG response for the active area obtained with the single current dipole tracing method for this experimental condition. We analyzed estimated active areas continuously using a real-time estimation method. **Figure 2(b)** shows an MEG response on active area of left side brain as a left priority type after only putting the hands together (with no smelling odor). **Figure 2(c)** shows a vector of single current dipole estimated in the brain using 3-D coordinates. **Figure 2(c)** shows a vector of single current dipole estimated in the brain using 3-D coordinates after putting the hands together. X-axis is the horizontal line of right to left ear, and Y-axis is the line from nasion to inion, and Z-axis is the upper to

**(a) The simple mode of putting the hands together without smelling**

**80**

**Figure 2.**

Five of the 11 subjects had the right priority brain type for laterality. Three of these five persons regularly put their hands together in their daily life, and the other two did not.

#### *3.1.3.2 a-2. Left priority brain type*

Six of the 11 persons had the left priority brain type for laterality. Two of six subjects regularly put their hands together in their daily life, and the other three did not.

Only one subject of the 11 was not classified in these two groups, and this person (N1) had the left priority brain type estimated in central temporal gyrus (N1: latency 579.0 ms, GOF 32.8%) as shown in the above **Table 1**.

The priorities of brain laterality are considered important for obtaining the characteristic laterality of the active brain in daily life as described below, regardless of putting the hands together and praying or not.

#### *3.1.3.3 A-group: (A1–A5) habit of putting the hands together or praying*

As shown in **Table 1**, in the A-group which had the habit of putting the hands together in daily life, the main active areas in the brain were generally estimated to be on the right near the superior regions (A1: latency 309.2 ms, GOF 50.2%; A4: latency 405.6 ms, GOF 47.4%) or the left near central (A5: latency 1065.3 ms, GOF 57.6%) or left caudal regions (A2: latency 613 ms, GOF 47.9%) in the temporal gyrus. The right prefrontal area was activated in only one subject (A3: latency 974 ms, GOF 47.9%).


**Table 1.**

*Results of MEG experiments for the simple modes (a) of only putting the hands together without smelling.*

#### *3.1.3.4 B-group: (B1–B5) no habit of putting the hands together or praying*

As shown in **Table 1**, in the B-group, which did not have the habit of putting the hands together or praying, the main active areas in the brain were generally estimated to be the right posterior regions (B1: latency 215.0 ms, GOF 28.1%; B4: latency 419.4 ms) in the frontal gyrus and left central region (B2: latency 236.0 ms, GOF 68.6%) and left caudal regions (B3: latency 303.4 ms, GOF 30.5%; B5: latency 366.3 ms, GOF 27.3%) in the frontal gyrus.

#### **(b) Simultaneous smelling an incense outdoor and putting the hands together mode**

All 11 subjects were separated into two groups. The A-group had the habit of putting the hands together or praying according to the Japanese traditional conventional style of putting the hands together for a few minutes every day in their daily life. The B-group did not have this habit.

**Table 2** shows an estimated ECD dipole for each subjects for latency time (290–1900 ms), priority of the laterality (right or left), and activated region, and GOF (statistical goodness of fit, %) for simultaneous smelling an incense outdoor and putting the hands together mode.

**Figure 3** shows that the estimated current dipoles of four subjects were obtained at the F5 language area of the inner region (A5: right priority, latency 981.0 ms, GOF 68.0%; B3: left priority, latency 557.3 ms, GOF 28.3%; B4: left priority, latency 423.9 ms, GOF 58.0%; B5: left priority, latency 328.4 ms, GOF 34.5%) of the frontal gyrus in a simultaneous state of the smelling an incense outdoor and putting the hands together. These responses were presented in two subjects, one is OFC area (A3: right priority, latency 974.1 ms, GOF 33.1%) and another is F5 area (A5: left priority, latency 981.0 ms, GOF 68.0%) in the A-group and four subjects in the B-group after smelling incense odors A and B.

**Figure 4** shows that the responses of another two subjects (A2: left priority, latency 627.8 ms, GOF 55.1%; A4: right priority, latency 309.3 ms, GOF 33.7%) in the A-group were obtained at the V1 visual area in the calcarine sulcus in the right or left occipital lobe after smelling incense odors A and B with putting the hands together. These V1 responses were not found in the B-group.


**Table 2.**

*Results of MEG experiments for simultaneous smelling an incense outdoor and putting the hands together mode.*

**83**

**Figure 3.**

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific…*

Only one in the 11 subjects was classified in neither the A- nor B-group, and this only one subject (N1) was used by coating smell method. He had the left priority brain type. His estimated current dipole was obtained at the OFC orbitofrontal gyrus (**Figure 5**) (N1: left priority, latency 974.1 ms, GOF 67.6%) when he

*F5 language area estimated by the simultaneous smelling an incense outdoor and putting the hands together.*

*DOI: http://dx.doi.org/10.5772/intechopen.81624*

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific… DOI: http://dx.doi.org/10.5772/intechopen.81624*

Only one in the 11 subjects was classified in neither the A- nor B-group, and this only one subject (N1) was used by coating smell method. He had the left priority brain type. His estimated current dipole was obtained at the OFC orbitofrontal gyrus (**Figure 5**) (N1: left priority, latency 974.1 ms, GOF 67.6%) when he

*Neuroimaging - Structure, Function and Mind*

366.3 ms, GOF 27.3%) in the frontal gyrus.

life. The B-group did not have this habit.

and putting the hands together mode.

B-group after smelling incense odors A and B.

together. These V1 responses were not found in the B-group.

**together mode**

*3.1.3.4 B-group: (B1–B5) no habit of putting the hands together or praying*

As shown in **Table 1**, in the B-group, which did not have the habit of putting the hands together or praying, the main active areas in the brain were generally estimated to be the right posterior regions (B1: latency 215.0 ms, GOF 28.1%; B4: latency 419.4 ms) in the frontal gyrus and left central region (B2: latency 236.0 ms, GOF 68.6%) and left caudal regions (B3: latency 303.4 ms, GOF 30.5%; B5: latency

**(b) Simultaneous smelling an incense outdoor and putting the hands** 

All 11 subjects were separated into two groups. The A-group had the habit of putting the hands together or praying according to the Japanese traditional conventional style of putting the hands together for a few minutes every day in their daily

**Table 2** shows an estimated ECD dipole for each subjects for latency time (290–1900 ms), priority of the laterality (right or left), and activated region, and GOF (statistical goodness of fit, %) for simultaneous smelling an incense outdoor

at the F5 language area of the inner region (A5: right priority, latency 981.0 ms, GOF 68.0%; B3: left priority, latency 557.3 ms, GOF 28.3%; B4: left priority, latency 423.9 ms, GOF 58.0%; B5: left priority, latency 328.4 ms, GOF 34.5%) of the frontal gyrus in a simultaneous state of the smelling an incense outdoor and putting the hands together. These responses were presented in two subjects, one is OFC area (A3: right priority, latency 974.1 ms, GOF 33.1%) and another is F5 area (A5: left priority, latency 981.0 ms, GOF 68.0%) in the A-group and four subjects in the

**Figure 4** shows that the responses of another two subjects (A2: left priority, latency 627.8 ms, GOF 55.1%; A4: right priority, latency 309.3 ms, GOF 33.7%) in the A-group were obtained at the V1 visual area in the calcarine sulcus in the right or left occipital lobe after smelling incense odors A and B with putting the hands

*Results of MEG experiments for simultaneous smelling an incense outdoor and putting the hands together mode.*

**Figure 3** shows that the estimated current dipoles of four subjects were obtained

**82**

**Table 2.**

performed the special activities of directly coating smelling both hands that were coated with the liquid incense odor A and putting his hands together.

As shown in the **Table 2**, another one subject (A1: right priority, latency 443.5 ms, GOF 62.5%) in the A-group with the habit of putting the hands together

**85**

**Figure 5.**

*and B group.*

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific…*

or praying showed the activity in the inner central temporal area in the right insula, and two subjects (B1: right priority, latency 296.1 ms, GOF 36.7%; B2: left priority, latency 1851.0 ms, GOF 64.6%) in the B-group without this habit also showed activ-

**Figure 6** shows the estimated current dipoles of three subjects obtained in insula regions in the right and left temporal gyrus in both the groups after simultaneous

*Orbito-frontal area estimated by the coating smell and putting the hands together in only one subject without A* 

*DOI: http://dx.doi.org/10.5772/intechopen.81624*

ity in the inner area in the right and left insula.

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific… DOI: http://dx.doi.org/10.5772/intechopen.81624*

or praying showed the activity in the inner central temporal area in the right insula, and two subjects (B1: right priority, latency 296.1 ms, GOF 36.7%; B2: left priority, latency 1851.0 ms, GOF 64.6%) in the B-group without this habit also showed activity in the inner area in the right and left insula.

**Figure 6** shows the estimated current dipoles of three subjects obtained in insula regions in the right and left temporal gyrus in both the groups after simultaneous

**Figure 5.** *Orbito-frontal area estimated by the coating smell and putting the hands together in only one subject without A and B group.*

*Neuroimaging - Structure, Function and Mind*

performed the special activities of directly coating smelling both hands that were

As shown in the **Table 2**, another one subject (A1: right priority, latency 443.5 ms, GOF 62.5%) in the A-group with the habit of putting the hands together

*V1 visual area estimated by the simultaneous smelling an incense outdoor and putting the hands together.*

coated with the liquid incense odor A and putting his hands together.

**84**

**Figure 4.**

smelling incense odors A and B outdoor and putting the hands together. In particular, the responses of almost all subjects in the B-group were found in temporal areas very close to the same regions as during the simple mode of only putting the hands together without smelling.

From the above analyses, in this task of simultaneous smelling an incense outdoor and putting the hands together mode, the brains of four subjects were activated in the F5 language area in the left frontal lobe. Two of four subjects had the right priority brain type and did not have the habit of putting the hands together in their daily life. However, their F5 language area in the left frontal lobe was activated after this task when simultaneous smelling an incense odor outdoor and putting their hands together. On the other hand, in two other persons with a habit of putting their hands together or praying in daily life, the right and left calcarine sulci of the V1 visual area in the occipital lobe were activated after the task of simultaneous smelling the odor outdoor and putting their hands together. From these all results, we consider that the F5 language area in the left frontal lobe and V1 visual area in the right and left occipital lobes were activated by the task of simultaneous smelling an incense outdoor and putting their hands together regardless of whether they had the habit of putting their hands together in their daily life. These phenomena are considered to be guided by the activation of mirror neurons and the default mode neural network's function.

#### **(c) The mode of smelling only and not putting the hands together**

**Table 3** shows an estimated ECD dipole for each subjects for latency time (230–1100 ms), priority of the laterality (right or left), activated region and GOF (statistical goodness of fit, %) for the mode (c) of smelling an incense outdoor only and not putting the hands together.

#### *3.1.4 One person (N1) not classified in the A- or B-group*

#### *3.1.4.1 c-1. Orbito-frontal lobe area*

AS shown in the above **Table 3**, only one subject was not classified in either the A- or B-group, and this person (N1: right priority, latency 414.0 ms, GOF 43.0%) had the right priority brain type. His estimated current dipole was also obtained at the left or right orbito-frontal lobe when he performed only the mode of smelling both hands, which were coated with liquid odor A or B, without putting his hands together. In this experiment, he could smell and clearly perceive the odorants on both hands.

#### *3.1.4.2 A-group: habit of putting the hands together or praying*

AS shown in **Table 3**, one female subject had the right priority brain type. Her estimated current dipole (A4: right priority, latency 473.0 ms, GOF 35.5%) were obtained in the right insula in the temporal gyrus when she performed the mode of smelling only odor A or B without putting her hands together. Also, the estimated current dipoles of a male subject (A3: left priority, latency 563.2 ms, GOF 53.7%) and another female (A5: left priority, latency 520.2 ms, GOF 58.3%) who had the left priority brain type were obtained at the left amygdala in the olfactory nervous pathway system when they performed the mode of smelling odor B without putting their hands together. Another male subject (A1: right priority, latency 1060.3 ms, GOF 28.9%) was obtained at the posterior frontal gyrus and another female subject (A2: left priority, latency 598.3 ms, GOF 21.9 5) was obtained at trigonum olfactorium in the olfactory pathway system in A-group.

**87**

**Figure 6.**

*hands together in almost all B group.*

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific…*

They could smell and clearly perceive odor A or B, and therefore, we could obtain their nervous pathway system and active area through olfactory nerve

*Anterior area in the temporal lobe estimated by simultaneous smelling an incense outdoor and putting the* 

*DOI: http://dx.doi.org/10.5772/intechopen.81624*

projection regions.

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific… DOI: http://dx.doi.org/10.5772/intechopen.81624*

They could smell and clearly perceive odor A or B, and therefore, we could obtain their nervous pathway system and active area through olfactory nerve projection regions.

**Figure 6.**

*Anterior area in the temporal lobe estimated by simultaneous smelling an incense outdoor and putting the hands together in almost all B group.*

*Neuroimaging - Structure, Function and Mind*

together without smelling.

neural network's function.

and not putting the hands together.

*3.1.4.1 c-1. Orbito-frontal lobe area*

*3.1.4 One person (N1) not classified in the A- or B-group*

*3.1.4.2 A-group: habit of putting the hands together or praying*

rium in the olfactory pathway system in A-group.

smelling incense odors A and B outdoor and putting the hands together. In particular, the responses of almost all subjects in the B-group were found in temporal areas very close to the same regions as during the simple mode of only putting the hands

From the above analyses, in this task of simultaneous smelling an incense outdoor and putting the hands together mode, the brains of four subjects were activated in the F5 language area in the left frontal lobe. Two of four subjects had the right priority brain type and did not have the habit of putting the hands together in their daily life. However, their F5 language area in the left frontal lobe was activated after this task when simultaneous smelling an incense odor outdoor and putting their hands together. On the other hand, in two other persons with a habit of putting their hands together or praying in daily life, the right and left calcarine sulci of the V1 visual area in the occipital lobe were activated after the task of simultaneous smelling the odor outdoor and putting their hands together. From these all results, we consider that the F5 language area in the left frontal lobe and V1 visual area in the right and left occipital lobes were activated by the task of simultaneous smelling an incense outdoor and putting their hands together regardless of whether they had the habit of putting their hands together in their daily life. These phenomena are considered to be guided by the activation of mirror neurons and the default mode

**(c) The mode of smelling only and not putting the hands together Table 3** shows an estimated ECD dipole for each subjects for latency time (230–1100 ms), priority of the laterality (right or left), activated region and GOF (statistical goodness of fit, %) for the mode (c) of smelling an incense outdoor only

AS shown in the above **Table 3**, only one subject was not classified in either the A- or B-group, and this person (N1: right priority, latency 414.0 ms, GOF 43.0%) had the right priority brain type. His estimated current dipole was also obtained at the left or right orbito-frontal lobe when he performed only the mode of smelling both hands, which were coated with liquid odor A or B, without putting his hands together. In this experiment, he could smell and clearly perceive the odorants on

AS shown in **Table 3**, one female subject had the right priority brain type. Her estimated current dipole (A4: right priority, latency 473.0 ms, GOF 35.5%) were obtained in the right insula in the temporal gyrus when she performed the mode of smelling only odor A or B without putting her hands together. Also, the estimated current dipoles of a male subject (A3: left priority, latency 563.2 ms, GOF 53.7%) and another female (A5: left priority, latency 520.2 ms, GOF 58.3%) who had the left priority brain type were obtained at the left amygdala in the olfactory nervous pathway system when they performed the mode of smelling odor B without putting their hands together. Another male subject (A1: right priority, latency 1060.3 ms, GOF 28.9%) was obtained at the posterior frontal gyrus and another female subject (A2: left priority, latency 598.3 ms, GOF 21.9 5) was obtained at trigonum olfacto-

**86**

both hands.


**Table 3.**

*Results of MEG experiments for the mode (c) of smelling an incense outdoor only and not putting the hands together.*

#### *3.1.4.3 B-group: no habit of putting the hands together or praying*

As shown in **Table 3**, two female subjects (B2: left priority, latency 509.7 ms, GOF 38.2; B4: right priority, latency 252.2 ms, GOF 35.2%) and one male subject (B3: left priority, latency 237.1 ms, GOF 57.0%) had the response at insula regions. Their estimated current dipoles were obtained in insula regions at the temporal gyrus when they performed the mode of smelling only odor B without putting their hands together.

On the other hand, other two male subjects had the left priority brain type. Their estimated current dipoles (B1: left priority, latency 502.5 ms, GOF 55.0%; B5: left priority, latency 303.4 ms, GOF 45.3%) were obtained at the left amygdala in the olfactory pathway system when they performed the mode of smelling only odor B without putting their hands together.

Although these subjects did not have the habit of putting the hands together or praying in their daily life, they could smell and clearly perceive odors A and B. Therefore, we could obtain the responses of their olfactory nervous pathway system and active areas through olfactory nerve projection regions.

#### **3.2 Results of statistical analysis of the cortisol level in the saliva of each of the 11 subjects**


The cortisol value (μg/dL) is an index of the state of stress.

**Table 4** shows the result of statistical analysis of each value, and the mean and standard deviation of the cortisol value were calculated for all 11 subjects, and for ten subjects, five subjects in the A-group and another five subjects in the B-group.

**89**

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific…*

1.No significant difference was found among the mean cortisol value of the conditions 1: before smelling the odor, 2: after smelling incense odor A, and 3:

2.The average cortisol value tended to decrease in the order of 1: before smelling the odor, 2: after smelling incense odor A, and 3: after smelling incense odor B

4.The average cortisol value tended to decrease in the order of (1) after smelling incense odor A (2), before smelling the odor, and (3) after smelling incense

5.A different tendency in the average cortisol value was observed between the A-group and B-group. In particular, an effect of stress was observed for smell-

6.All subjects perceived and smelled incense odor B, which had no effect regard-

7.For individual subjects, the cortisol value tended to decrease in the order of 1: before smelling the incense odor, 2: after smelling incense odor A, 3: after

8.For individual subjects, the cortisol value tended to decrease in the order of 1: after smelling incense odor A, 2: before smelling the odor, 3: after smelling

9.For individual subjects, especially in the one subject who was different from the other subjects in the B-group whose cortisol value tended to decrease, the cortisol value tended to decrease in the order of 1: before smelling the odor, 2: after smelling incense odor A, 3: after smelling incense odor B, similar to the

**3.3 Relation between the impression of the subject about the incense outdoor and** 

Almost all subjects in the A-group, except for one female, felt that incense odor B was more familiar than incense odor A in daily life. However, both incense odors were pleasant for all subjects in the A-group according to psychological inquiries. In these cases, the cortisol value for almost all subjects except this female decreased in the order of 1: before smelling the odor, 2: after smelling incense odor A, and 3: after smelling incense odor B. In other words, almost all subjects except this female reported a decrease in stress in the order 1: odor B, 2: odor A. 3: no odor.

smelling incense odor B in five subjects in the A-group.

incense odor B in three subjects in the B-group.

**stress measured by the cortisol value**

*3.3.1 A-group: habit of putting their hands together or praying*

Next, statistical t-tests were performed to compare the cortisol values of each condition in all 10 subjects and each five subjects classified in the A- or B-group,

after smelling incense odor B for all 10 subjects (see **Figure 7**).

odor B for the five subjects in the B-group.

ing incense odor A.

ing stress.

A-group.

in all 10 subjects and the five subjects in the A-group (see **Figure 8**).

3.A significant difference (p < 0.078) was found between the mean cortisol value of the condition after smelling incense odor A (2) and after smelling incense odor B (3) for the five subjects in the B-group (see **Figure 9**).

*DOI: http://dx.doi.org/10.5772/intechopen.81624*

respectively.

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific… DOI: http://dx.doi.org/10.5772/intechopen.81624*

Next, statistical t-tests were performed to compare the cortisol values of each condition in all 10 subjects and each five subjects classified in the A- or B-group, respectively.


#### **3.3 Relation between the impression of the subject about the incense outdoor and stress measured by the cortisol value**

#### *3.3.1 A-group: habit of putting their hands together or praying*

Almost all subjects in the A-group, except for one female, felt that incense odor B was more familiar than incense odor A in daily life. However, both incense odors were pleasant for all subjects in the A-group according to psychological inquiries. In these cases, the cortisol value for almost all subjects except this female decreased in the order of 1: before smelling the odor, 2: after smelling incense odor A, and 3: after smelling incense odor B. In other words, almost all subjects except this female reported a decrease in stress in the order 1: odor B, 2: odor A. 3: no odor.

*Neuroimaging - Structure, Function and Mind*

*3.1.4.3 B-group: no habit of putting the hands together or praying*

As shown in **Table 3**, two female subjects (B2: left priority, latency 509.7 ms, GOF 38.2; B4: right priority, latency 252.2 ms, GOF 35.2%) and one male subject (B3: left priority, latency 237.1 ms, GOF 57.0%) had the response at insula regions. Their estimated current dipoles were obtained in insula regions at the temporal gyrus when they performed the mode of smelling only odor B without putting their

*Results of MEG experiments for the mode (c) of smelling an incense outdoor only and not putting the hands* 

On the other hand, other two male subjects had the left priority brain type. Their estimated current dipoles (B1: left priority, latency 502.5 ms, GOF 55.0%; B5: left priority, latency 303.4 ms, GOF 45.3%) were obtained at the left amygdala in the olfactory pathway system when they performed the mode of smelling only odor

Although these subjects did not have the habit of putting the hands together or praying in their daily life, they could smell and clearly perceive odors A and B. Therefore, we could obtain the responses of their olfactory nervous pathway

**3.2 Results of statistical analysis of the cortisol level in the saliva of each of the** 

**Table 4** shows the result of statistical analysis of each value, and the mean and standard deviation of the cortisol value were calculated for all 11 subjects, and for ten subjects, five subjects in the A-group and another five subjects in the B-group.

system and active areas through olfactory nerve projection regions.

1.Cortisol value before smelling the odor and MEG experiments

The cortisol value (μg/dL) is an index of the state of stress.

**88**

hands together.

**Table 3.**

*together.*

**11 subjects**

B without putting their hands together.

2.Cortisol value after smelling incense A

3.Cortisol value after smelling incense B



**91**

**Figure 7.**

**Figure 8.**

order 1: no odor, 2: odor A, odor B.

*Statistical analysis of cortisol value for habit group A.*

*3.3.2 B-group: no habit of putting the hands together or praying*

the order 1: incense odor A, 2: no odor, 3: incense odor B.

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific…*

Only one female subject was different from the other subjects in the A-group. She liked incense odor A more than incense odor B. Therefore, she felt not more stress from incense odor A than incense odor B. Her cortisol value decreased in the

Almost all subjects in the B-group reported feeling more stress for incense odor A than incense odor B, because incense odor B was considered more familiar in their daily life. In contrast, almost all B-group subjects felt stress for unfamiliar odor A more than the state of no odor before smelling. Their cortisol value decreased in

*DOI: http://dx.doi.org/10.5772/intechopen.81624*

*Statistical analysis of cortisol value for all 10 subjects.*

*Results of cortisol value (μg/dL) in the saliva for each 11 subjects.*

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific… DOI: http://dx.doi.org/10.5772/intechopen.81624*

**Figure 7.**

*Neuroimaging - Structure, Function and Mind*

**90**

**Cortisol value (μg/dL)**

**Subject** Before experiment

After incense A After incense B

Average Standard deviation

**Table 4.**

*Results of cortisol value (μg/dL) in the saliva for each 11 subjects.*

0.057

0.035

0.021

0.057

0.042

0.007

0

0.021

0.177

0.064

0.042

0.34 0.32 0.26 0.31

0.09

0.08

0.11

0.1

0.09

0.16

0.17

0.19

0.15

0.06

0.07

0.07

0.07

0.07

0.07

0.16

0.12

0.09

0.16

0.03

0.11

0.163

0.07

0.08

0.1

0.1

0.12

0.16

0.25

0.15

0.23

0.06

0.15

0.184

0.12

0.1

0.15

0.13

0.08

0.16

0.15

0.34

0.07

0.09

0.16

0.177

**A1**

**A2**

**A3**

**A4**

**A5**

**B1**

**B2**

**B3**

**B4**

**B5**

**N1**

**Average**

**Standard deviation**

**A-group (habit group)**

**B-group (no-habit group)**

*Statistical analysis of cortisol value for all 10 subjects.*


#### **Figure 8.**

*Statistical analysis of cortisol value for habit group A.*

Only one female subject was different from the other subjects in the A-group. She liked incense odor A more than incense odor B. Therefore, she felt not more stress from incense odor A than incense odor B. Her cortisol value decreased in the order 1: no odor, 2: odor A, odor B.

#### *3.3.2 B-group: no habit of putting the hands together or praying*

Almost all subjects in the B-group reported feeling more stress for incense odor A than incense odor B, because incense odor B was considered more familiar in their daily life. In contrast, almost all B-group subjects felt stress for unfamiliar odor A more than the state of no odor before smelling. Their cortisol value decreased in the order 1: incense odor A, 2: no odor, 3: incense odor B.

#### **Figure 9.**

*Statistical analysis of cortisol value for no habit group B.*

From these analyses about the relationship between the impression of the odor and the measured cortisol value, the nature of the state of stress was different in the A-group and B-group.

#### **3.4 Summary of results**

#### *3.4.1 The specific and distinct mirror neuron activities without the error activity on the hand motor system by putting the hands together*

Our MEG experiments of the above results using the methods of (1) Control mode in section 2.4 as the obtained **Figure 1(a)** and **(b)** showed the distinct and objective activities of our brain on the control state of non-motor system's activity clinically. However, in the MEG experiments of only putting the hands together without smelling using methods of (2) Simple mode of putting the hands together in section 2.4 as shown at (a) in **Figure 2(a)–(c)** we obtained the MEG local estimated signal response areas for the distinct mirror neuron activity. In our MEG experimental results for only putting the hands together without smelling an incense outdoor, 11 subject's detailed responses were obtained as **Table 1** in which A-group subjects were obtained in superior and anterior temporal gyrus or central and caudal temporal and frontal gyrus, on the other hand B-group subjects were obtained also the same temporal and frontal areas. These results show that the estimated local activated regions of B-group having the no habit of putting the hands together or praying are almost all resemble to the activated areas in brain of A-group having the habit of putting the hands together in daily lives. These results of the coincidence active areas in A-group and B-group in the behavioral action for putting the hands together show the distinct activities of mirror neurons activities as the imitation in the brain without the simple artifacts of moving error activities in moving neuronal system.

#### *3.4.2 The simultaneous new specific stronger effects of both the distinct mirror neuron's activity putting the hands together and the activities of smelling an incense outdoor at the same time*

Our MEG experiments of the above results using the methods of (3) Smelling mode with putting the hands together in section 2.4 as the obtained (b) in **Figure 3(a–c)**

**93**

A- and B-group.

**4. Discussions**

with higher cognitive function.

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific…*

showed the distinct and objective activities of our brain on the state of simultaneous responses of putting the hands together and at the same time smelling an incense outdoor. In this simultaneous status mode of our MEG experiments, this specific active area in **Figure 3** were shown in distinct F5 language areas of the inner regions of the left frontal lobe or orbito-frontal gyrus (OFC) clinically. These responses were presented in two subjects in A-group and four subjects in B-group. These specific results show the simultaneous new distinct stronger effects of both the mirror neuronal activities as the imitation without the artifacts of the simple moving error activities and olfactory activated effects. The specific responses of another two subjects in A-group showed the simultaneous other new specific stronger effects of both the mirror neuron's activities putting the hands together and the activities of smelling an incense outdoor at the same time in V1 visual areas in the calcarine sulcus in occipital lobe clinically as the another distinct active areas as shown in **Figure 4(a)** and **(b)**. Only one person of 11 subjects in neither A- nor B-group who used by the direct coating strong smell over the hands showed the specific simultaneous activities in the orbito-frontal lobe as shown in **Figure 5(a)** and **(b)**. And the simultaneous specific activities in the brain both the putting the hands together and smelling an incense outdoor at the same time of other five subjects were obtained in anterior and posterior areas in the temporal lobes as shown in **Figure 6(a)–(c)**. These detailed MEG response data are shown in **Table 2** for simultaneous smelling an incense outdoor and putting the hands together and these results show the specific new strong effects of simultaneous responses in the relation of

both the mirror neuron activities and olfactory effects at the same time.

**4.1 The inverse problem: source estimation models**

sensor geometry, including registration of sensors to the head.

tivity and can be modeled more accurately in future [44].

*3.4.3 The mode of smelling an incense outdoor only without putting the hands together (olfactory response with non-mirror neuron activity)*

The detailed responses of our MEG experiments of the above results in the mode of smelling an incense outdoor only without putting the hands together (non-mirror neuron activity) were shown in **Table 3** with almost all subject's data. From these clinical and objective MEG measurements and analysis we obtained the distinct olfactory activities clearly such as the frontal and temporal regions in the olfactory nervous projection areas and olfactory nervous pathways nevertheless

We used dipole models for the source estimation of the recorded MEG signals. The simpler spherical model for the head is adequate for MEG source modeling in most cases. In addition, MEG benefits from very precise knowledge of the real

However, source modeling in MEG remains a challenging mathematical problem, especially for more complex configurations of neuronal sources associated

As a realistic clinical tool to for the spatio-temporal localization of the evoked brain activity by simultaneous smelling an incense outdoor and putting the hands together. A variety of methods have been applied to the MEG source estimation problem to overcome the limitations. Using the individual's MRI scan of every subjects, template can provide good approximations for realistic head modeling. For example, finite element models (FEMs) could be applied to drastic changes in tissue conduc-

*DOI: http://dx.doi.org/10.5772/intechopen.81624*

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific… DOI: http://dx.doi.org/10.5772/intechopen.81624*

showed the distinct and objective activities of our brain on the state of simultaneous responses of putting the hands together and at the same time smelling an incense outdoor. In this simultaneous status mode of our MEG experiments, this specific active area in **Figure 3** were shown in distinct F5 language areas of the inner regions of the left frontal lobe or orbito-frontal gyrus (OFC) clinically. These responses were presented in two subjects in A-group and four subjects in B-group. These specific results show the simultaneous new distinct stronger effects of both the mirror neuronal activities as the imitation without the artifacts of the simple moving error activities and olfactory activated effects. The specific responses of another two subjects in A-group showed the simultaneous other new specific stronger effects of both the mirror neuron's activities putting the hands together and the activities of smelling an incense outdoor at the same time in V1 visual areas in the calcarine sulcus in occipital lobe clinically as the another distinct active areas as shown in **Figure 4(a)** and **(b)**. Only one person of 11 subjects in neither A- nor B-group who used by the direct coating strong smell over the hands showed the specific simultaneous activities in the orbito-frontal lobe as shown in **Figure 5(a)** and **(b)**. And the simultaneous specific activities in the brain both the putting the hands together and smelling an incense outdoor at the same time of other five subjects were obtained in anterior and posterior areas in the temporal lobes as shown in **Figure 6(a)–(c)**. These detailed MEG response data are shown in **Table 2** for simultaneous smelling an incense outdoor and putting the hands together and these results show the specific new strong effects of simultaneous responses in the relation of both the mirror neuron activities and olfactory effects at the same time.

#### *3.4.3 The mode of smelling an incense outdoor only without putting the hands together (olfactory response with non-mirror neuron activity)*

The detailed responses of our MEG experiments of the above results in the mode of smelling an incense outdoor only without putting the hands together (non-mirror neuron activity) were shown in **Table 3** with almost all subject's data. From these clinical and objective MEG measurements and analysis we obtained the distinct olfactory activities clearly such as the frontal and temporal regions in the olfactory nervous projection areas and olfactory nervous pathways nevertheless A- and B-group.

#### **4. Discussions**

*Neuroimaging - Structure, Function and Mind*

A-group and B-group.

*Statistical analysis of cortisol value for no habit group B.*

**Figure 9.**

**3.4 Summary of results**

in moving neuronal system.

*incense outdoor at the same time*

From these analyses about the relationship between the impression of the odor and the measured cortisol value, the nature of the state of stress was different in the

*3.4.1 The specific and distinct mirror neuron activities without the error activity on* 

*3.4.2 The simultaneous new specific stronger effects of both the distinct mirror* 

*neuron's activity putting the hands together and the activities of smelling an* 

with putting the hands together in section 2.4 as the obtained (b) in **Figure 3(a–c)**

Our MEG experiments of the above results using the methods of (3) Smelling mode

Our MEG experiments of the above results using the methods of (1) Control mode in section 2.4 as the obtained **Figure 1(a)** and **(b)** showed the distinct and objective activities of our brain on the control state of non-motor system's activity clinically. However, in the MEG experiments of only putting the hands together without smelling using methods of (2) Simple mode of putting the hands together in section 2.4 as shown at (a) in **Figure 2(a)–(c)** we obtained the MEG local estimated signal response areas for the distinct mirror neuron activity. In our MEG experimental results for only putting the hands together without smelling an incense outdoor, 11 subject's detailed responses were obtained as **Table 1** in which A-group subjects were obtained in superior and anterior temporal gyrus or central and caudal temporal and frontal gyrus, on the other hand B-group subjects were obtained also the same temporal and frontal areas. These results show that the estimated local activated regions of B-group having the no habit of putting the hands together or praying are almost all resemble to the activated areas in brain of A-group having the habit of putting the hands together in daily lives. These results of the coincidence active areas in A-group and B-group in the behavioral action for putting the hands together show the distinct activities of mirror neurons activities as the imitation in the brain without the simple artifacts of moving error activities

*the hand motor system by putting the hands together*

**92**

#### **4.1 The inverse problem: source estimation models**

We used dipole models for the source estimation of the recorded MEG signals. The simpler spherical model for the head is adequate for MEG source modeling in most cases. In addition, MEG benefits from very precise knowledge of the real sensor geometry, including registration of sensors to the head.

However, source modeling in MEG remains a challenging mathematical problem, especially for more complex configurations of neuronal sources associated with higher cognitive function.

As a realistic clinical tool to for the spatio-temporal localization of the evoked brain activity by simultaneous smelling an incense outdoor and putting the hands together.

A variety of methods have been applied to the MEG source estimation problem to overcome the limitations. Using the individual's MRI scan of every subjects, template can provide good approximations for realistic head modeling. For example, finite element models (FEMs) could be applied to drastic changes in tissue conductivity and can be modeled more accurately in future [44].

#### **4.2 Mirror neurons and the default mode network**

The concept of mirror neurons was described by Marco Iacoboni. These neurons are located in the F5 inner area of the prefrontal lobe [7]. In general, the motion of putting the hands together and mimicking behavior are considered to activate the mirror neuron mechanism [8, 45, 46] and the default mode network in the human brain [9, 47–49]. These neural effects are considered to increase activity in the central areas of the temporal lobe and the caudal area of the frontal lobe according to the imitation principal [50–54].

The theory of these mirror neurons revealed the principal of imitation of behavior. Although these F5 areas in the left side of the human brain are in the same areas as Broca's language regions, F5 areas of both sides of the brain function to mimic motion and behavior. From anatomical research, F5 areas are connected to pre-motor areas and supplemental areas in movement regions in the brain.

Mirror neurons are thus considered to function for imitation of the habit of putting the hands together or praying, which is also performed with both hands by almost all elderly Japanese people in their daily life.

Super mirror neurons are concerned with determination of values, recognition of oneself and others, and reward from one's work. The inner default mode network controls the fundamental activity of daily movements and the resting state of the human brain. Because this default mode network is strongly related to super mirror neurons, discrimination of oneself from others and the determination of social cognition are considered important in human daily life.

#### **4.3 The meaning of simultaneous smelling an incense outdoor and putting the hands together**

Odorants stimulate activity in the olfactory nervous center, orbito-frontal areas, and others in the human brain [1–4]. Neurophysiological experiments in monkeys have shown that the olfactory nervous center and olfactory pathway project to the orbitofrontal cortex [55–57]. In humans, olfactory event-related potentials and magnetic fields evoked by odorant pulses synchronized with respiration are also found in the orbitofrontal area [5, 6, 58, 59].

In this experiment, only one subject was not in the A- or B-group and smelled his hands that were coated with liquid odor. By performing this behavior, he clearly experienced strong A and B odors. We estimated that the areas activated by his sniffing of both the A and B odors were the prefrontal area and the right or left orbito-frontal area.

In habits of daily life, the brain of A-group people after smelling incense odors and putting their hands together or praying was activated at the inner lobe of the frontal area, F5 language area, anterior area in the temporal lobe, orbito-frontal area, and others.

The brain of B-group individuals who did not have the habit of smelling incense odor or putting their hands together or praying in their daily life was also activated at the inner lobe of the frontal area, anterior area in the temporal lobe, and F5 language area in the left frontal lobe, similar to the A-group.

These results suggest that mirror neurons or the super mirror neuron system and the default mode network system in the brain of B-group subjects were activated by both smelling the incense odor and their imitation of putting their hands together, although they did not have the habit of smelling incense odors or putting their hands together or praying in their daily life.

From the above analyses, in the task involving simultaneous smelling an incense outdoor and putting the hands together, four person's brains were activated in the

**95**

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific…*

F5 language area in the left frontal lobe. Two of four subjects had the right priority brain type and no habit of putting their hands together in their daily life. However,

On the other hand, in two persons with a habit of putting their hands together or praying in their daily life, the right and left calcarine sulci of the V1 visual area in the occipital lobe were activated after the task of simultaneous smelling an incense

This research revealed that simultaneous smelling an incense outdoor and putting hands together increased the activity of specific brain areas, for example inner areas of the prefrontal cortex and F5 regions of the human brain. In our experiments, evoked neuronal activity was recorded by the MEG and the cortisol value in the subject's saliva was measured in every experimental stage. From a few previous researches, it is known that F5 area is activated during observation of certain actions, during action execution etc. and these results show F5 have multimodal and different type of neurons. Moreover, the F5p is also known as a hand-related area that encoded goal-directed actions, not only mimic or autonomic actions. Our results demonstrated that the sources of MEG which are postsynaptic signals synchronized activation of intracellular currents across dendrites of cortical pyramidal neurons link strongly with anatomic position of mirror neurons. Mirror neurons in our experiment case are considered to have the function for imitation of the habit of putting the hands together or praying, which almost all elderly Japanese peoples often practice in their daily life. Super mirror neurons are concerned with determination of values, recognition of oneself and others, and reward from one's work. The inner default mode network controls the fundamental activity of daily movements and the resting state of the human brain. Because this default mode network is strongly related to super mirror neurons, discrimination of oneself from others and the determination of social cognition are considered important in human daily life. From these mirror neuron theories and the above summary of our results (1). We can conclude the distinct activities as follows. From these concerns and the above summary results (2) and (3), it can be considered that the specific regions in the brain such as the F5 language area in the left frontal lobe and the V1 visual area in the right and left occipital lobes were distinctly activated by the simultaneous new stronger effects increased with the task of smelling an odor and putting their hands together regardless of the habit in daily lives. These results show that the sources of MEG strongly link with the anatomic positions of mirror neurons and their types. Especially, these phenomena are considered to be guided by the simultaneous new stronger effects increased by both the olfactory activities of smelling an incense outdoor accompanied with the activation of mirror neurons and the default mode neural network [64–66] for the imitation behavior of putting hands together. From the above results, we consider that the F5 language area in the left frontal lobe and V1 visual area in the right and left occipital lobes and other specific brain areas were activated distinctly by the task of simultaneous smelling an incense outdoor and putting their hands together regardless of whether they had the habit of putting their hands together in their daily life. From our experiments, the cortisol value in saliva for the stress and the specific mirror neuron theories. we conclude that the simultaneous new specific effects both the smelling an incense outdoor and the imitating the behavior of putting the hands together can be

considered to increase the activities of these areas in the human brain due to mutual

interactions, reciprocal connections, or alternative actions.

their F5 language area in the left frontal lobe was activated after this task.

*DOI: http://dx.doi.org/10.5772/intechopen.81624*

outdoor and putting the hands together [60–63].

**5. Conclusions**

#### *Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific… DOI: http://dx.doi.org/10.5772/intechopen.81624*

F5 language area in the left frontal lobe. Two of four subjects had the right priority brain type and no habit of putting their hands together in their daily life. However, their F5 language area in the left frontal lobe was activated after this task.

On the other hand, in two persons with a habit of putting their hands together or praying in their daily life, the right and left calcarine sulci of the V1 visual area in the occipital lobe were activated after the task of simultaneous smelling an incense outdoor and putting the hands together [60–63].

#### **5. Conclusions**

*Neuroimaging - Structure, Function and Mind*

to the imitation principal [50–54].

**hands together**

orbito-frontal area.

area, and others.

**4.2 Mirror neurons and the default mode network**

almost all elderly Japanese people in their daily life.

cognition are considered important in human daily life.

found in the orbitofrontal area [5, 6, 58, 59].

hands together or praying in their daily life.

The concept of mirror neurons was described by Marco Iacoboni. These neurons are located in the F5 inner area of the prefrontal lobe [7]. In general, the motion of putting the hands together and mimicking behavior are considered to activate the mirror neuron mechanism [8, 45, 46] and the default mode network in the human brain [9, 47–49]. These neural effects are considered to increase activity in the central areas of the temporal lobe and the caudal area of the frontal lobe according

The theory of these mirror neurons revealed the principal of imitation of behavior. Although these F5 areas in the left side of the human brain are in the same areas as Broca's language regions, F5 areas of both sides of the brain function to mimic motion and behavior. From anatomical research, F5 areas are connected to pre-motor areas and supplemental areas in movement regions in the brain.

Mirror neurons are thus considered to function for imitation of the habit of putting the hands together or praying, which is also performed with both hands by

**4.3 The meaning of simultaneous smelling an incense outdoor and putting the** 

Odorants stimulate activity in the olfactory nervous center, orbito-frontal areas, and others in the human brain [1–4]. Neurophysiological experiments in monkeys have shown that the olfactory nervous center and olfactory pathway project to the orbitofrontal cortex [55–57]. In humans, olfactory event-related potentials and magnetic fields evoked by odorant pulses synchronized with respiration are also

In this experiment, only one subject was not in the A- or B-group and smelled his hands that were coated with liquid odor. By performing this behavior, he clearly experienced strong A and B odors. We estimated that the areas activated by his sniffing of both the A and B odors were the prefrontal area and the right or left

In habits of daily life, the brain of A-group people after smelling incense odors and putting their hands together or praying was activated at the inner lobe of the frontal area, F5 language area, anterior area in the temporal lobe, orbito-frontal

The brain of B-group individuals who did not have the habit of smelling incense odor or putting their hands together or praying in their daily life was also activated at the inner lobe of the frontal area, anterior area in the temporal lobe, and F5

These results suggest that mirror neurons or the super mirror neuron system and the default mode network system in the brain of B-group subjects were activated by both smelling the incense odor and their imitation of putting their hands together, although they did not have the habit of smelling incense odors or putting their

From the above analyses, in the task involving simultaneous smelling an incense outdoor and putting the hands together, four person's brains were activated in the

language area in the left frontal lobe, similar to the A-group.

Super mirror neurons are concerned with determination of values, recognition of oneself and others, and reward from one's work. The inner default mode network controls the fundamental activity of daily movements and the resting state of the human brain. Because this default mode network is strongly related to super mirror neurons, discrimination of oneself from others and the determination of social

**94**

This research revealed that simultaneous smelling an incense outdoor and putting hands together increased the activity of specific brain areas, for example inner areas of the prefrontal cortex and F5 regions of the human brain. In our experiments, evoked neuronal activity was recorded by the MEG and the cortisol value in the subject's saliva was measured in every experimental stage. From a few previous researches, it is known that F5 area is activated during observation of certain actions, during action execution etc. and these results show F5 have multimodal and different type of neurons. Moreover, the F5p is also known as a hand-related area that encoded goal-directed actions, not only mimic or autonomic actions. Our results demonstrated that the sources of MEG which are postsynaptic signals synchronized activation of intracellular currents across dendrites of cortical pyramidal neurons link strongly with anatomic position of mirror neurons. Mirror neurons in our experiment case are considered to have the function for imitation of the habit of putting the hands together or praying, which almost all elderly Japanese peoples often practice in their daily life. Super mirror neurons are concerned with determination of values, recognition of oneself and others, and reward from one's work. The inner default mode network controls the fundamental activity of daily movements and the resting state of the human brain. Because this default mode network is strongly related to super mirror neurons, discrimination of oneself from others and the determination of social cognition are considered important in human daily life. From these mirror neuron theories and the above summary of our results (1). We can conclude the distinct activities as follows. From these concerns and the above summary results (2) and (3), it can be considered that the specific regions in the brain such as the F5 language area in the left frontal lobe and the V1 visual area in the right and left occipital lobes were distinctly activated by the simultaneous new stronger effects increased with the task of smelling an odor and putting their hands together regardless of the habit in daily lives. These results show that the sources of MEG strongly link with the anatomic positions of mirror neurons and their types. Especially, these phenomena are considered to be guided by the simultaneous new stronger effects increased by both the olfactory activities of smelling an incense outdoor accompanied with the activation of mirror neurons and the default mode neural network [64–66] for the imitation behavior of putting hands together. From the above results, we consider that the F5 language area in the left frontal lobe and V1 visual area in the right and left occipital lobes and other specific brain areas were activated distinctly by the task of simultaneous smelling an incense outdoor and putting their hands together regardless of whether they had the habit of putting their hands together in their daily life. From our experiments, the cortisol value in saliva for the stress and the specific mirror neuron theories. we conclude that the simultaneous new specific effects both the smelling an incense outdoor and the imitating the behavior of putting the hands together can be considered to increase the activities of these areas in the human brain due to mutual interactions, reciprocal connections, or alternative actions.

#### **Acknowledgements**

We thank Mr. Ippei Torige, Mr. Kimiyoshi Yoshino, and Mr. Masaru Yamamoto in Nippon Kodo Co. Ltd. for expert help during the experiments in subject's attendance and in the preparations of incense odorants.

This study was supported by the Grants for the Alzheimer's Disease in Osaka Research Association and the Awards of Osaka-Gas for the Research of Alzheimer's Disease in Japan.

### **Author details**

Mitsuo Tonoike\* and Takuto Hayashi Department of Medical Engineering, Faculty of Health and Science, Aino University, Ibaraki, Osaka, Japan

\*Address all correspondence to: gah00161@nifty.ne.jp

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**97**

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific…*

The Imitative Mind. Development, Evolution, and Brain Bases. Cambridge: Cambridge University Press; 2002.

[9] Arbib M. The Mirror System Hypothesis. Linking Language to Theory of Mind. 2005. Retrieved

[10] Ramachandran VS, Vilayanur S. Mirror neurons and imitation learning as the driving force behind "the great leap forward" in human evolution. 2005. http://www.edge.org/3rd\_culture/ ramachandran/ramachandran\_p1.html

[11] Rizzolatti G, Fogassi L, Gallese V. Neurophysiological mechanisms underlying the understanding and imitation of action. Nature Reviews Neuroscience. 2001;**2**:661-670

[12] Rizzolatti G, Craighero L. The mirror neuron system. Annual Reviews of Neuroscience. 2004;**27**:169-192

[13] Raiche ME, MacLeod AM, Snyder AZ, et al. A default mode of brain function. Proceedigs of the National Academy of Sciences USA.

[14] Cherkassky VL, Kana RK, Keller TA, Just MA. Functional connectivity in a baseline resting-state network in autism. Neuroreport. 2006;**17**(16):1687-1690

[15] Gusnard DA, Raiche ME. Searching for a baseline: Functional imaging and the resting human brain. Nature Reviews Neuroscience. 2001;**2**:

[16] Xu X, Yuan H, Lei X. Activation and connectivity within the default mode network contribute independently to future-oriented thought. Scientific Reports. 2016;**6**:21001. DOI: 10.1038/

2001;**98**:676-682

685-694

srep21001

[Accessed: June 15th, 2005]

pp. 247-266

2006-02-17

*DOI: http://dx.doi.org/10.5772/intechopen.81624*

[1] Takagi SF. Human Olfaction. Tokyo, Japan: University of Tokyo Press; 1989

[2] Takagi SF. Olfactory frontal cortex and multiple olfactory processing in primates. In: Peters A, Ones EG, editors. Cerebral Cortex. Vol. 9. New York:

[3] Sobel N, Pranhakaran V, Desmond JE, Glovere GH, Goode RL, Sullivan EV, et al. Sniffing and smelling: Separate subsystems in the human olfactory cortex. Nature. 1998;**392**:282-286

[4] Zatorre RJ, Jones-Gotman M, Evans AC, Meyer E. Functional localization and lateralization of human olfactory cortex. Nature. 1992;**360**:339-340

[5] Tonoike M, Yamaguchi M, Kaetsu I, Kida H, Seo R, Koizuka I. Ipsilateral dominance of human olfactory activated centers estimated from event-related magnetic fields measured by 122-channel whole-head neuromagnetometer using odorant stimuli synchronized with respirations. In: Murphy C, editor. Olfaction and Taste XII. Vol. 855. New York: New York

Academy of Sciences; 1998.

[6] Tonoike M, Yamaguchi M, Hamada T, Kaetsu I, Koizuka I, Seo R. Odorant perception and active olfaction: A study of olfactory magnetic fields evoked by odorant pulse stimuli synchronized with respiratory cycle. Proceedings of 20th Annual International Conference IEEE/EMBS'98. 1998;**20**(4):2213-2216

[7] Iacoboni M, Woods RP, Brass M, et al. Cortical mechanisms of human imitation. Science. 1999;**286**:2526-2528

[8] Rizzolatti G, Fadiga L, Fogassi L, Gallese V. From mirror neurons to imitation: Facts and speculations. In: Meltzoff AN, Prinz W, editors.

pp. 579-590

Plenum; 1991. pp. 133-152

**References**

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific… DOI: http://dx.doi.org/10.5772/intechopen.81624*

#### **References**

*Neuroimaging - Structure, Function and Mind*

dance and in the preparations of incense odorants.

We thank Mr. Ippei Torige, Mr. Kimiyoshi Yoshino, and Mr. Masaru Yamamoto in Nippon Kodo Co. Ltd. for expert help during the experiments in subject's atten-

This study was supported by the Grants for the Alzheimer's Disease in Osaka Research Association and the Awards of Osaka-Gas for the Research of Alzheimer's

**Acknowledgements**

Disease in Japan.

**96**

**Author details**

provided the original work is properly cited.

Mitsuo Tonoike\* and Takuto Hayashi

Aino University, Ibaraki, Osaka, Japan

\*Address all correspondence to: gah00161@nifty.ne.jp

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Department of Medical Engineering, Faculty of Health and Science,

[1] Takagi SF. Human Olfaction. Tokyo, Japan: University of Tokyo Press; 1989

[2] Takagi SF. Olfactory frontal cortex and multiple olfactory processing in primates. In: Peters A, Ones EG, editors. Cerebral Cortex. Vol. 9. New York: Plenum; 1991. pp. 133-152

[3] Sobel N, Pranhakaran V, Desmond JE, Glovere GH, Goode RL, Sullivan EV, et al. Sniffing and smelling: Separate subsystems in the human olfactory cortex. Nature. 1998;**392**:282-286

[4] Zatorre RJ, Jones-Gotman M, Evans AC, Meyer E. Functional localization and lateralization of human olfactory cortex. Nature. 1992;**360**:339-340

[5] Tonoike M, Yamaguchi M, Kaetsu I, Kida H, Seo R, Koizuka I. Ipsilateral dominance of human olfactory activated centers estimated from event-related magnetic fields measured by 122-channel whole-head neuromagnetometer using odorant stimuli synchronized with respirations. In: Murphy C, editor. Olfaction and Taste XII. Vol. 855. New York: New York Academy of Sciences; 1998. pp. 579-590

[6] Tonoike M, Yamaguchi M, Hamada T, Kaetsu I, Koizuka I, Seo R. Odorant perception and active olfaction: A study of olfactory magnetic fields evoked by odorant pulse stimuli synchronized with respiratory cycle. Proceedings of 20th Annual International Conference IEEE/EMBS'98. 1998;**20**(4):2213-2216

[7] Iacoboni M, Woods RP, Brass M, et al. Cortical mechanisms of human imitation. Science. 1999;**286**:2526-2528

[8] Rizzolatti G, Fadiga L, Fogassi L, Gallese V. From mirror neurons to imitation: Facts and speculations. In: Meltzoff AN, Prinz W, editors.

The Imitative Mind. Development, Evolution, and Brain Bases. Cambridge: Cambridge University Press; 2002. pp. 247-266

[9] Arbib M. The Mirror System Hypothesis. Linking Language to Theory of Mind. 2005. Retrieved 2006-02-17

[10] Ramachandran VS, Vilayanur S. Mirror neurons and imitation learning as the driving force behind "the great leap forward" in human evolution. 2005. http://www.edge.org/3rd\_culture/ ramachandran/ramachandran\_p1.html [Accessed: June 15th, 2005]

[11] Rizzolatti G, Fogassi L, Gallese V. Neurophysiological mechanisms underlying the understanding and imitation of action. Nature Reviews Neuroscience. 2001;**2**:661-670

[12] Rizzolatti G, Craighero L. The mirror neuron system. Annual Reviews of Neuroscience. 2004;**27**:169-192

[13] Raiche ME, MacLeod AM, Snyder AZ, et al. A default mode of brain function. Proceedigs of the National Academy of Sciences USA. 2001;**98**:676-682

[14] Cherkassky VL, Kana RK, Keller TA, Just MA. Functional connectivity in a baseline resting-state network in autism. Neuroreport. 2006;**17**(16):1687-1690

[15] Gusnard DA, Raiche ME. Searching for a baseline: Functional imaging and the resting human brain. Nature Reviews Neuroscience. 2001;**2**: 685-694

[16] Xu X, Yuan H, Lei X. Activation and connectivity within the default mode network contribute independently to future-oriented thought. Scientific Reports. 2016;**6**:21001. DOI: 10.1038/ srep21001

[17] Maresh EL, Allen JP, Coan JA. Increased default mode network activity in socially anxious individuals during reward processing. Biology of Mood & Anxiety Disorders. 2014;**4**:7. DOI: 10.1186/2045-5380-4-7

[18] John A, Friston KJ. Unified segmentation. NeuroImage. 2005;**26**:839-851

[19] Taulu S, Kajola M, Simola J. The signal space separation method. In: 14th Conference of the International Society for Brain Electromagnetic Topography; Santa Fe, NM

[20] Taulu S, Simola J, Kajola M. Applications of the signal space separation method. IEEE Transactions on Signal Processing. 2005;**53**:3359-3372

[21] Taulu S, Simola J. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Physics in Medicine and Biology. 2006;**51**(7):1759-1768

[22] Uutela K, Taulu S, Hamalainen M. Detecting and correcting for head movements. NeuroImage. 2001;**14**:1424-1431

[23] Tonoike M, Yamaguchi M, Kaetsu I, Kida H, Seo R, Koizuka I. Ipsilateral dominance of human olfactory activated centers estimated from event-related magnetic fields measured by 122-channel whole-head neuromagnetometer using stimuli synchronized with respirations. In: Murphy C, editor. Olfaction and Taste Xll. Vol. **855**. New York: New York Academy of Sciences; 1998. pp. 579-590

[24] Williamson S, Kaufman L. Biomagnetism. Journal of Magnetism and Magnetic Materials. 1981;**22**:129

[25] Uusitalo MA, Ilmoniemi RJ. Signalspace projection method. Medical & Biological Engineering. 1997;**32**:35-42

[26] Uutela K, Hamalainen M, Salmelin R. Global optimization in the localization of neuromagnetic sources. IEEE Transactions on Biomedical Engineering. 1998;**45**(6):716-723

[27] Uutela K, Hamalainen M, Salmelin R. Visualization of magnetoencepalographyic data using minimum current estimates. NeuroImage. 1999;**10**(2):173-180

[28] Kiebel SJ, Daunizeau J, Friston KJ. A hierarchy of time-scales and the brain. PLOS Computational Biology. 2008;**4**(11):1-12, 4e1000209

[29] Kiebel SJ, Garrido MI, Moran R, Chen CC, Friston KJ. Dynamic causal modeling for EEG and MEG. Human Brain Mapping. 2009;**30**:1866-1876

[30] Sanja JG, Susac A, Grilj V, et al. Size matters: MEG empirical and simulation study on source localization of the earliest visual activity in the occipital cortex. Medical & Biological Engineering & Computing. 2011;**49**(5):545-554

[31] Sanja JG, Aine Cheryl J, Stephen Julia M, Adair John C, Knoefel Janice E, Selma S. Modulatory role of the prefrontal generator within the auditory M50 network. NeuroImage. 2014;**92**:120-131

[32] Sanja JG, Aine Cheryl J, Stephen Julia M, Adair John C, Knoefel Janice E, Selma S. MEG biomarker of Alzheimer's disease: Absence of a prefrontal generator during auditory sensory gating. Human Brain Mapping. 2017;**38**:5180-5194

[33] Darvas F, Pantazis D, Kucukaltun-Yildirim E, Leahy RM. Mapping human brain function with MEG and EEG: Methods and validation. Neuromag. 2004;**23**(suppl.1):S289-S229

[34] Hamalainen M, Sarvas J. Feasibility of the homogenous head model in

**99**

pp. 2617-2621

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific…*

[43] Nishimura K, Tobinaga Y, Tonoike M. Detection of neural activity associated with thinking in frontal lobe by magnetoencephalography. Progress of Theoretical Physics. 2002;**173**(Suppl):332-341

[44] Haueisen J, Ramon C, Eiselt M, Brauer H, Nowak N. Influence of tissue resistivities on neuromagnetic

fields and electric potentials studied with a finite element model of the head. IEEE Transactions on Biomedical Engineering.

[45] Dapretto M. Understanding emotions in others: Mirror neuron dysfunction in children with autism spectrum disorders. Nature Neuroscience. 2006;**9**(1):28-30

[46] Bastiaansen JACJ, Thioux M, Keysers C. Evidence for mirror systems in emotions. Philosophical Transactions of the Royal Society Biological Science.

1997;**44**(8):727-735

2009;**364**:2391-2404

2005;**24**(2):190-198

2007;**2**:62-66

[47] Oberman LM, Hubbard EM, McCleery JP, Altschuler EL, Ramachandran VS, Pineda JA. EEG evidence for mirror neuron dysfunction in autism spectral disorders. Brain Research. Cognitive Brain Research.

[48] Oberman LM, Pineda JA,

2011;**5**:1-13. DOI: 10.3389/

fsnys.2011.00080

2608-2611

Ramachandran VS. The human mirror neuron system: A link between action observation and social skills. SCAN.

[49] Kiebel SJ, Friston KJ. Free energy and dendritic self-organization. Frontiers in System Neuroscience.

[50] Fadiga L, Fogassi L, Pavesi G, Rizzolatti G. Motor facilitation during action observating: A magnetic stimulation study. Journal of Neurophysiology. 1995;**73**:

*DOI: http://dx.doi.org/10.5772/intechopen.81624*

the interpretation of neuromagnetic fields. Physics in Medicine and Biology.

[35] Hamalainen M, Hari R, Ilmoniemi RJ, Knuutila JET, Lounasmaa OV. Magnetoencephalography-theory, instrumentation, and application to noninvasive studies of the working human brain. Reviews of Modern

1987;**32**:91-97

Physics. 1993;**65**:413-497

[36] Hamalainen M, Ilmoniemi RJ. Interpreting magnetic fields of the brain: Minimun norm estimates.

Computing. 1994;**32**(1):35-42

on Biomedical Engineering.

[38] Scherg M, Von Cramon D. Evoked dipole source potentials of the human auditory cortex. Electroencephalography and Clinical Neurophysiology. 1986;**65**(5):344-360

[39] Ilmoniemi RJ. Neuromagnetism: Theory, techniques, and measurement

[PhD thesis]. Helsinki Univ. of

[40] Sarvas J. Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Physics in Medicine and Biology. 1987;**32**(1):11-22

[41] Ioannides A, Liu LC, Kwapien J, Drozdz S, Streit M. Coupling of regional activations in human brain during an object and face affect recognition task. Human Brain Mapping. 2000;**11**:77-92

[42] Tonoike M, Nishimura K, Tobinaga Y. Detection of thinking in human by magnetoencephalography, world congress of medical physics and biological engineering. In: IFMBE Proceedings. Vol. 14. 2006.

Technology; 1985

1985;**32**(11):905-910

[37] Cuffin BN. A comparison of moving dipole inverse solution using EEG's and MEG's. IEEE Transactions

Medical and Biological Engineering and

*Simultaneous Smelling an Incense Outdoor and Putting the Hands Together Activate Specific… DOI: http://dx.doi.org/10.5772/intechopen.81624*

the interpretation of neuromagnetic fields. Physics in Medicine and Biology. 1987;**32**:91-97

*Neuroimaging - Structure, Function and Mind*

[26] Uutela K, Hamalainen M,

[27] Uutela K, Hamalainen M, Salmelin R. Visualization of magnetoencepalographyic data using minimum current estimates. NeuroImage. 1999;**10**(2):173-180

[28] Kiebel SJ, Daunizeau J, Friston KJ. A hierarchy of time-scales and the brain. PLOS Computational Biology.

[29] Kiebel SJ, Garrido MI, Moran R, Chen CC, Friston KJ. Dynamic causal modeling for EEG and MEG. Human Brain Mapping. 2009;**30**:1866-1876

[30] Sanja JG, Susac A, Grilj V, et al. Size matters: MEG empirical and simulation study on source localization

[31] Sanja JG, Aine Cheryl J, Stephen Julia M, Adair John C, Knoefel Janice E, Selma S. Modulatory role of the prefrontal generator within the auditory M50 network. NeuroImage.

[32] Sanja JG, Aine Cheryl J, Stephen Julia M, Adair John C, Knoefel Janice E, Selma S. MEG biomarker of Alzheimer's disease: Absence of a prefrontal generator during auditory sensory gating. Human Brain Mapping.

[33] Darvas F, Pantazis D, Kucukaltun-Yildirim E, Leahy RM. Mapping human brain function with MEG and EEG: Methods and validation. Neuromag.

[34] Hamalainen M, Sarvas J. Feasibility of the homogenous head model in

2004;**23**(suppl.1):S289-S229

of the earliest visual activity in the occipital cortex. Medical & Biological Engineering & Computing.

2011;**49**(5):545-554

2014;**92**:120-131

2017;**38**:5180-5194

2008;**4**(11):1-12, 4e1000209

Salmelin R. Global optimization in the localization of neuromagnetic sources. IEEE Transactions on Biomedical Engineering. 1998;**45**(6):716-723

[17] Maresh EL, Allen JP, Coan JA. Increased default mode network activity in socially anxious individuals during reward processing. Biology of Mood & Anxiety Disorders. 2014;**4**:7.

DOI: 10.1186/2045-5380-4-7

2005;**26**:839-851

Santa Fe, NM

[18] John A, Friston KJ. Unified segmentation. NeuroImage.

[19] Taulu S, Kajola M, Simola J. The signal space separation method. In: 14th Conference of the International Society for Brain Electromagnetic Topography;

[20] Taulu S, Simola J, Kajola M. Applications of the signal space separation method. IEEE Transactions on Signal Processing. 2005;**53**:3359-3372

[21] Taulu S, Simola J. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Physics in Medicine and

Biology. 2006;**51**(7):1759-1768

[23] Tonoike M, Yamaguchi M, Kaetsu I, Kida H, Seo R, Koizuka I. Ipsilateral dominance of human olfactory activated centers estimated from event-related magnetic fields measured by 122-channel whole-head neuromagnetometer using stimuli synchronized with respirations. In: Murphy C, editor. Olfaction and Taste Xll. Vol. **855**. New York: New York Academy of Sciences; 1998.

[24] Williamson S, Kaufman L. Biomagnetism. Journal of Magnetism and Magnetic Materials. 1981;**22**:129

[25] Uusitalo MA, Ilmoniemi RJ. Signalspace projection method. Medical & Biological Engineering. 1997;**32**:35-42

2001;**14**:1424-1431

[22] Uutela K, Taulu S, Hamalainen M. Detecting and correcting for head movements. NeuroImage.

**98**

pp. 579-590

[35] Hamalainen M, Hari R, Ilmoniemi RJ, Knuutila JET, Lounasmaa OV. Magnetoencephalography-theory, instrumentation, and application to noninvasive studies of the working human brain. Reviews of Modern Physics. 1993;**65**:413-497

[36] Hamalainen M, Ilmoniemi RJ. Interpreting magnetic fields of the brain: Minimun norm estimates. Medical and Biological Engineering and Computing. 1994;**32**(1):35-42

[37] Cuffin BN. A comparison of moving dipole inverse solution using EEG's and MEG's. IEEE Transactions on Biomedical Engineering. 1985;**32**(11):905-910

[38] Scherg M, Von Cramon D. Evoked dipole source potentials of the human auditory cortex. Electroencephalography and Clinical Neurophysiology. 1986;**65**(5):344-360

[39] Ilmoniemi RJ. Neuromagnetism: Theory, techniques, and measurement [PhD thesis]. Helsinki Univ. of Technology; 1985

[40] Sarvas J. Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Physics in Medicine and Biology. 1987;**32**(1):11-22

[41] Ioannides A, Liu LC, Kwapien J, Drozdz S, Streit M. Coupling of regional activations in human brain during an object and face affect recognition task. Human Brain Mapping. 2000;**11**:77-92

[42] Tonoike M, Nishimura K, Tobinaga Y. Detection of thinking in human by magnetoencephalography, world congress of medical physics and biological engineering. In: IFMBE Proceedings. Vol. 14. 2006. pp. 2617-2621

[43] Nishimura K, Tobinaga Y, Tonoike M. Detection of neural activity associated with thinking in frontal lobe by magnetoencephalography. Progress of Theoretical Physics. 2002;**173**(Suppl):332-341

[44] Haueisen J, Ramon C, Eiselt M, Brauer H, Nowak N. Influence of tissue resistivities on neuromagnetic fields and electric potentials studied with a finite element model of the head. IEEE Transactions on Biomedical Engineering. 1997;**44**(8):727-735

[45] Dapretto M. Understanding emotions in others: Mirror neuron dysfunction in children with autism spectrum disorders. Nature Neuroscience. 2006;**9**(1):28-30

[46] Bastiaansen JACJ, Thioux M, Keysers C. Evidence for mirror systems in emotions. Philosophical Transactions of the Royal Society Biological Science. 2009;**364**:2391-2404

[47] Oberman LM, Hubbard EM, McCleery JP, Altschuler EL, Ramachandran VS, Pineda JA. EEG evidence for mirror neuron dysfunction in autism spectral disorders. Brain Research. Cognitive Brain Research. 2005;**24**(2):190-198

[48] Oberman LM, Pineda JA, Ramachandran VS. The human mirror neuron system: A link between action observation and social skills. SCAN. 2007;**2**:62-66

[49] Kiebel SJ, Friston KJ. Free energy and dendritic self-organization. Frontiers in System Neuroscience. 2011;**5**:1-13. DOI: 10.3389/ fsnys.2011.00080

[50] Fadiga L, Fogassi L, Pavesi G, Rizzolatti G. Motor facilitation during action observating: A magnetic stimulation study. Journal of Neurophysiology. 1995;**73**: 2608-2611

[51] Gallese V, Goldman A. Mirror neurons and the simulation theory of mindreading. Trends in Cognitive Sciences. 1998;**2**:493-501

[52] McClure SM, Li J, Tomlin D, et al. Neural correlates of behavioral preference for culturally familiar drinks. Neuron. 2004;**44**:379-387

[53] Mukamel R, Ekstrom AD, Kaplan J, Iacoboni M, Fried I. Singleneuron responses in humans during execution and observation of actions. Current Biology. 2010;**20**(8):750-756

[54] Hari R, Forss N, Avikainen S, et al. Activation of human primary motor cortex during action observation: A Neuromagnetic study. Proceedings of the National Academy of Sciences of United States of America. 1998;**95**(25):15061-15065

[55] Yarita H, Iino M, Tanabe T, Kogure S, Takagi SF. A transthalamic olfactory pathway to orbitofrontal cortex in the monkey. Journal of Neurophysiology. 1980;**45**:69-85

[56] Tanabe T, Yarita H, Iino M, Ooshima Y, Takagi SF. An olfactory projection area in orbitofrontal cortex of the monkey. Journal of Neurophysiology. 1975;**38**:1269-1283

[57] Seubert J, Gregory KK, Chamberland J, Dessirier JM, Lundstrom JN. Odor valence linearly modulates attractiveness, but not age assessment, of invariant facial features in a memory-based rating task. PLoS One. 2014;**9**:e98347. DOI: 10.1371/ journal.pone.0098347

[58] Tonoike M, Yoshida T, Sakuma H, Wang L-Q. fMRI measurement of integrative effects of visual and chemical senses stimuli in humans. Journal of Integrative Neuroscience. 2013;**12**(3):369-384

[59] Tonoike M. Odor perception: The mechanism of how odor is perceived, human olfactory displays and interfaces: Odor sensing and presentation. In: IGI Global Disseminator and Knowledge. 2013. pp. 44-59

[60] Zhou W, Jiang Y, He S, Chen D. Olfaction modulates visual perception in binocular rivalry. Current Biology. 2010;**20**:1356-1358

[61] Calvert GA, Campbell R, Braer MJ. Evidence from functional magnetic resonance imaging of crossmodal binding in the human heteromodal cortex. Current Biology: CB. 2000;**10**:649-657

[62] Calvert GA. Crossmodal processing in the human brain: Insights from functional neuroimaging studies. Cerebral Cortex. 2011;**11**:1110-1123

[63] Davis MH. Measuring indivisual differences in emphathy: Evidence for a multidimensional approach. Journal of Personality & Social Psychology. 1983;**44**:113-126

[64] Simpson JR, Snyder AZ, Gusnard DA, Raichle ME. Emotional-induced changes in human medial prefrontal cortex: 1. During cognitive task performance. Proceedings of the National Academy of Sciences of the United States of America. 2001;**98**:683-687

[65] Buccino G, Binkofski F, Fink GR, Fadiga L, Fogassi L, Gallese V. Action observation activates premotor and parietal areas in a somatotopic manner: An fMRI study. European Journal of Neuroscience. 2001;**13**:400-404

[66] Cherkassky VL, Kana RK, Keller TA, Just MA. Functional connectivity in a baseline resting-state network in autism. Neuroreport. 2006;**17**(16):1787-1690

**101**

**Chapter 5**

*Agnieszka A. Reid*

genetics, multiple deficit model

**1. Introduction**

**disorders**

**Abstract**

Neuroimaging Reveals

Multiple Deficit Model

Heterogeneous Neural Correlates

of Reading Deficit in Individuals

with Dyslexia Consistent with a

Neuroimaging has become a powerful way of studying in vivo brain function and structure. The aim here is to comprehensively review Reid's fMRI study which is the first to use a multiple case approach to investigate individual differences among 18 participants with dyslexia (DPs) and 16 control participants (CPs) and to directly test the predictions of the main dyslexia theories on reading deficit. The results show that the neural correlates of reading deficit for all DPs (except one) are consistent with more than one theory, supporting a multiple deficit model. Striking individual differences between DPs were found; even if the neural correlates of reading deficit in two DPs were consistent with the same theory, the affected brain areas could differ. To make progress, research on causes of reading deficit in dyslexia would need to (1) focus on the multiple deficit model, (2) use neuroimaging to test a further refined set of brain areas (including areas hypothesised by other dyslexia theories) in longitudinal designs, (3) control the effects of co-occurring neurodevelopmental disorders, (4) use high-field MRI (including diffusion techniques), multiband fMRI and MEG with optically pumped magnetometers, (5) progress imaging genetics and

(6) pursue neuroimaging intergenerational transmission of brain circuity.

**Keywords:** dyslexia, MRI, fMRI, neuroimaging, individual differences, a multiple case study, co-occurring neurodevelopmental disorders, reading disorder, imaging

**1.1 A brief summary of neuroimaging methods and neuroimaging research on the biomarkers of neurological, neuropsychiatric and neurodevelopmental** 

There are six main neuroimaging methods: magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG) and positron emission tomography (PET). MRI and DTI enable investigation of brain structure,

### **Chapter 5**

*Neuroimaging - Structure, Function and Mind*

[59] Tonoike M. Odor perception: The mechanism of how odor is perceived, human olfactory displays and interfaces: Odor sensing and presentation. In: IGI Global Disseminator and Knowledge.

[60] Zhou W, Jiang Y, He S, Chen D. Olfaction modulates visual perception in binocular rivalry. Current Biology.

[61] Calvert GA, Campbell R, Braer MJ. Evidence from functional magnetic resonance imaging of crossmodal binding in the human heteromodal cortex. Current Biology: CB.

[62] Calvert GA. Crossmodal processing in the human brain: Insights from functional neuroimaging studies. Cerebral Cortex. 2011;**11**:1110-1123

[63] Davis MH. Measuring indivisual differences in emphathy: Evidence for a multidimensional approach. Journal of Personality & Social Psychology.

[64] Simpson JR, Snyder AZ, Gusnard DA, Raichle ME. Emotional-induced changes in human medial prefrontal cortex: 1. During cognitive task performance. Proceedings of the National Academy of Sciences of the United States of America.

[65] Buccino G, Binkofski F, Fink GR, Fadiga L, Fogassi L, Gallese V. Action observation activates premotor and parietal areas in a somatotopic manner: An fMRI study. European Journal of Neuroscience. 2001;**13**:400-404

[66] Cherkassky VL, Kana RK, Keller TA, Just MA. Functional connectivity in a baseline resting-state network in autism. Neuroreport.

2006;**17**(16):1787-1690

2013. pp. 44-59

2010;**20**:1356-1358

2000;**10**:649-657

1983;**44**:113-126

2001;**98**:683-687

[51] Gallese V, Goldman A. Mirror neurons and the simulation theory of mindreading. Trends in Cognitive

[52] McClure SM, Li J, Tomlin D, et al. Neural correlates of behavioral preference for culturally familiar drinks.

[53] Mukamel R, Ekstrom AD, Kaplan J, Iacoboni M, Fried I. Singleneuron responses in humans during execution and observation of actions. Current

[54] Hari R, Forss N, Avikainen S, et al. Activation of human primary motor cortex during action observation: A Neuromagnetic study. Proceedings of the National Academy of Sciences

[55] Yarita H, Iino M, Tanabe T, Kogure S, Takagi SF. A transthalamic olfactory pathway to orbitofrontal cortex in the monkey. Journal of Neurophysiology.

[56] Tanabe T, Yarita H, Iino M, Ooshima Y, Takagi SF. An olfactory projection area in orbitofrontal cortex of the monkey. Journal of Neurophysiology.

Sciences. 1998;**2**:493-501

Neuron. 2004;**44**:379-387

Biology. 2010;**20**(8):750-756

of United States of America. 1998;**95**(25):15061-15065

1980;**45**:69-85

1975;**38**:1269-1283

[57] Seubert J, Gregory KK, Chamberland J, Dessirier JM,

journal.pone.0098347

2013;**12**(3):369-384

Lundstrom JN. Odor valence linearly modulates attractiveness, but not age assessment, of invariant facial features in a memory-based rating task. PLoS One. 2014;**9**:e98347. DOI: 10.1371/

[58] Tonoike M, Yoshida T, Sakuma H, Wang L-Q. fMRI measurement of integrative effects of visual and chemical senses stimuli in humans. Journal of Integrative Neuroscience.

**100**

## Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals with Dyslexia Consistent with a Multiple Deficit Model

*Agnieszka A. Reid*

### **Abstract**

Neuroimaging has become a powerful way of studying in vivo brain function and structure. The aim here is to comprehensively review Reid's fMRI study which is the first to use a multiple case approach to investigate individual differences among 18 participants with dyslexia (DPs) and 16 control participants (CPs) and to directly test the predictions of the main dyslexia theories on reading deficit. The results show that the neural correlates of reading deficit for all DPs (except one) are consistent with more than one theory, supporting a multiple deficit model. Striking individual differences between DPs were found; even if the neural correlates of reading deficit in two DPs were consistent with the same theory, the affected brain areas could differ. To make progress, research on causes of reading deficit in dyslexia would need to (1) focus on the multiple deficit model, (2) use neuroimaging to test a further refined set of brain areas (including areas hypothesised by other dyslexia theories) in longitudinal designs, (3) control the effects of co-occurring neurodevelopmental disorders, (4) use high-field MRI (including diffusion techniques), multiband fMRI and MEG with optically pumped magnetometers, (5) progress imaging genetics and (6) pursue neuroimaging intergenerational transmission of brain circuity.

**Keywords:** dyslexia, MRI, fMRI, neuroimaging, individual differences, a multiple case study, co-occurring neurodevelopmental disorders, reading disorder, imaging genetics, multiple deficit model

### **1. Introduction**

#### **1.1 A brief summary of neuroimaging methods and neuroimaging research on the biomarkers of neurological, neuropsychiatric and neurodevelopmental disorders**

There are six main neuroimaging methods: magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG) and positron emission tomography (PET). MRI and DTI enable investigation of brain structure,

whereas fMRI, EEG, MEG and PET enable research into brain function. MRI produces high-resolution images of the brain, with clearly distinguishable grey and white matter, ventricles and fibre tracts. DTI is a method which is mainly used to investigate the anatomical structure of the axon tracts and can provide information on the between-regional anatomical connectivity in the brain. An MRI scanner is used to perform DTI which measures the motion and density of the water in the axons. fMRI uses magnetic resonance imaging to measure brain activity by measuring the ratio of oxygenated to deoxygenated haemoglobin, and this value is referred to as the blood oxygen level-dependent (BOLD) effect; brain activity is usually measured in an experimental task, relative to a control task. EEG is an electrophysiological method for recording global electrical activity of the brain. In order to ask questions on how brain activity is modulated in response to a particular task, an event-related potential (ERP) needs to be extracted from the global EEG signal. MEG is a technique which allows the mapping of brain activity by recording the magnetic fields created by the electrical currents of the brain, using very sensitive magnetometers. Finally, PET measures metabolic activity in the brain by monitoring the distribution of a radioactive tracer. As with fMRI, PET relies on the fact that local blood flow increases in active brain areas. Unlike MEG and EEG, fMRI and PET do not directly measure neural events but metabolic changes which are correlated with neural activity. The neuroimaging techniques differ with respect to critical variables in brain mapping, such as spatial and temporal resolution. Spatial resolution is the ability to distinguish two separate objects that are situated close to one another, whereas temporal resolution is the ability to detect two events that happen in close temporal proximity [1]. ERP and MEG have relatively good temporal resolution of milliseconds (0.01 s) but relatively poorer spatial resolution (10 mm). Structural MRI has relatively good spatial resolution; brain structures much smaller than 1 mm can be resolved with this method, including subcortical structures, such as the superior colliculus. DTI's spatial resolution has been improving, and highspatial-resolution DTI imaging has been reported with a resolution of 1 mm [2]. fMRI is characterised by relatively good temporal resolution of seconds to hundreds of milliseconds and spatial resolution of 4–5 mm. PET has relatively lower spatial (5–10 mm) and temporal (60–1000 s) resolutions [1]. It should be emphasised here that the neuroimaging methods introduced above are subject to steady improvement, with regard to their spatial and temporal resolution and other characteristics; furthermore new neuroimaging methods are being developed. For instance, three more recent neuroimaging methods need to be mentioned here: diffusion kurtosis imaging (DKI) [3], a neuroimaging method that provides independent and additional information (to that acquired with DTI) which indicates the complexity of the microstructural environment of the imaged tissue, neurite orientation dispersion and density imaging (NODDI) [4] (see Section 3.4) and magnetic field correlation imaging (MFC) [5], a neuroimaging technique used for the quantitative assessment of iron within the brain. For more details on neuroimaging methods, see [6–9].

Neuroimaging has become a popular and powerful way of studying in vivo brain function and structure in health and disease. One important branch of neuroimaging is the search for a biomarker in neurological, neuropsychiatric and neurodevelopmental disorders (including dyslexia). For instance, promising strides here have been made using various neuroimaging techniques in Alzheimer's disease (MRI [10], fMRI [11], PET [12] and MEG [13]), schizophrenia (PET [14], EEG [15] and MEG [16]), attention deficit hyperactivity disorder (ADHD) (MEG and structural MRI [17], DKI [18], MRI and MFC [19]) and dyslexia (MEG and structural MRI [17], structural MRI [20], ERPs [21, 22], MEG [23] and fMRI [24]). It should be noted that some of the above cited papers explicitly claim the search for neuroimaging biomarkers, while others do not,

**103**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals…*

but the results reported can be considered as potential candidates for neuroimaging biomarkers. However, an obstacle to the development of neuroimaging biomarkers in neurodevelopmental disorders, such as dyslexia and ADHD, is sample heterogeneity, due to the phenotypic and aetiological complexity and cooccurrence of other disorders. Therefore, it is likely that no single neuroimaging biomarker (or even multiple biomarkers from the same domain) may be sufficient for reliable and accurate diagnosis of these disorders and there needs to be a shift towards identifying sets of biomarkers, possibly from different domains. The serious problem of sample heterogeneity which is associated with neurodevelopmental disorders was the main reason behind adopting a different approach in

**1.2. Dyslexia and the most researched causal theories of this disorder**

have no doubt it is due to some congenital defect' [26, p.1378].

reasonable level of reading ability, becoming compensated DPs.

example, 63 and 60% in samples in [36] and [38], respectively.

There are three main, most researched causal theories of dyslexia, and each theory postulates a different and single underlying cause of literacy difficulties in dyslexia. A short description of each theory is included below, but the detailed review of these theories is beyond the scope of this chapter; interested readers are referred to the references and to Reid's publication [25]. According to the

'Percy F ... has always been a bright and intelligent boy, quick at games, and in no way inferior to others of his age. His great difficulty has been – and is now – his inability to learn to read. This inability is so remarkable, and so pronounced, that I

Dyslexia is one of the most prevalent neurodevelopmental disorders—it affects from 5 to 17.5% of the English-speaking population [27]. DPs exhibit difficulties in learning to read, despite sociocultural opportunities, a scholarly education, adequate conventional instruction and intelligence, as well as intact sensory abilities [28]. It has been demonstrated [29] that the rates of reading disability are higher in boys than in girls. Untreated dyslexia is likely to have a serious impact on the life of an individual, including learning ability, self-esteem, mental health, relationships, social participation, employment and economic status. The vast majority of research on dyslexia has been conducted in English (an unrepresentative language in terms of grapheme-to-phoneme correspondence). More recent research across different languages indicates that dyslexia also occurs in other languages, including languages with an orthographic transparency higher than English [30–32]. Dyslexia is characterised by a strong heritable component [33]. Most research on dyslexia has focused on deficits; however, some publications have explored positive aspects of dyslexia [34]. There is now considerable evidence that dyslexia co-occurs more frequently than by chance with other neurodevelopmental disorders, such as ADHD and developmental coordination disorder (DCD). About 20–42% of reading disabled children also meets the criteria for ADHD [35, 36]. Furthermore, there is growing evidence that some reading impaired individuals exhibit motor difficulties [37, 38]. The prevalence of dyslexia and DCD co-occurrence are relatively high, for

This chapter reviews the first fMRI study [25] which used a multiple case approach to investigate reading deficit in participants with similar difficulties to Percy F's struggles described 122 years ago. Such difficulties are nowadays defined as developmental dyslexia (henceforth dyslexia). The above example is only given to illustrate the profound and puzzling literacy difficulties experienced by individuals with dyslexia and not to discuss Morgan's [26] interpretation of reading difficulties as congenital word blindness. It should also be emphasised that despite such profound difficulties when learning to read, most individuals with dyslexia reach a

*DOI: http://dx.doi.org/10.5772/intechopen.80677*

Reid's [25] fMRI study reviewed in this chapter.

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals… DOI: http://dx.doi.org/10.5772/intechopen.80677*

but the results reported can be considered as potential candidates for neuroimaging biomarkers. However, an obstacle to the development of neuroimaging biomarkers in neurodevelopmental disorders, such as dyslexia and ADHD, is sample heterogeneity, due to the phenotypic and aetiological complexity and cooccurrence of other disorders. Therefore, it is likely that no single neuroimaging biomarker (or even multiple biomarkers from the same domain) may be sufficient for reliable and accurate diagnosis of these disorders and there needs to be a shift towards identifying sets of biomarkers, possibly from different domains. The serious problem of sample heterogeneity which is associated with neurodevelopmental disorders was the main reason behind adopting a different approach in Reid's [25] fMRI study reviewed in this chapter.

#### **1.2. Dyslexia and the most researched causal theories of this disorder**

'Percy F ... has always been a bright and intelligent boy, quick at games, and in no way inferior to others of his age. His great difficulty has been – and is now – his inability to learn to read. This inability is so remarkable, and so pronounced, that I have no doubt it is due to some congenital defect' [26, p.1378].

This chapter reviews the first fMRI study [25] which used a multiple case approach to investigate reading deficit in participants with similar difficulties to Percy F's struggles described 122 years ago. Such difficulties are nowadays defined as developmental dyslexia (henceforth dyslexia). The above example is only given to illustrate the profound and puzzling literacy difficulties experienced by individuals with dyslexia and not to discuss Morgan's [26] interpretation of reading difficulties as congenital word blindness. It should also be emphasised that despite such profound difficulties when learning to read, most individuals with dyslexia reach a reasonable level of reading ability, becoming compensated DPs.

Dyslexia is one of the most prevalent neurodevelopmental disorders—it affects from 5 to 17.5% of the English-speaking population [27]. DPs exhibit difficulties in learning to read, despite sociocultural opportunities, a scholarly education, adequate conventional instruction and intelligence, as well as intact sensory abilities [28]. It has been demonstrated [29] that the rates of reading disability are higher in boys than in girls. Untreated dyslexia is likely to have a serious impact on the life of an individual, including learning ability, self-esteem, mental health, relationships, social participation, employment and economic status. The vast majority of research on dyslexia has been conducted in English (an unrepresentative language in terms of grapheme-to-phoneme correspondence). More recent research across different languages indicates that dyslexia also occurs in other languages, including languages with an orthographic transparency higher than English [30–32]. Dyslexia is characterised by a strong heritable component [33]. Most research on dyslexia has focused on deficits; however, some publications have explored positive aspects of dyslexia [34]. There is now considerable evidence that dyslexia co-occurs more frequently than by chance with other neurodevelopmental disorders, such as ADHD and developmental coordination disorder (DCD). About 20–42% of reading disabled children also meets the criteria for ADHD [35, 36]. Furthermore, there is growing evidence that some reading impaired individuals exhibit motor difficulties [37, 38]. The prevalence of dyslexia and DCD co-occurrence are relatively high, for example, 63 and 60% in samples in [36] and [38], respectively.

There are three main, most researched causal theories of dyslexia, and each theory postulates a different and single underlying cause of literacy difficulties in dyslexia. A short description of each theory is included below, but the detailed review of these theories is beyond the scope of this chapter; interested readers are referred to the references and to Reid's publication [25]. According to the

*Neuroimaging - Structure, Function and Mind*

whereas fMRI, EEG, MEG and PET enable research into brain function. MRI produces high-resolution images of the brain, with clearly distinguishable grey and white matter, ventricles and fibre tracts. DTI is a method which is mainly used to investigate the anatomical structure of the axon tracts and can provide information on the between-regional anatomical connectivity in the brain. An MRI scanner is used to perform DTI which measures the motion and density of the water in the axons. fMRI uses magnetic resonance imaging to measure brain activity by measuring the ratio of oxygenated to deoxygenated haemoglobin, and this value is referred to as the blood oxygen level-dependent (BOLD) effect; brain activity is usually measured in an experimental task, relative to a control task. EEG is an electrophysiological method for recording global electrical activity of the brain. In order to ask questions on how brain activity is modulated in response to a particular task, an event-related potential (ERP) needs to be extracted from the global EEG signal. MEG is a technique which allows the mapping of brain activity by recording the magnetic fields created by the electrical currents of the brain, using very sensitive magnetometers. Finally, PET measures metabolic activity in the brain by monitoring the distribution of a radioactive tracer. As with fMRI, PET relies on the fact that local blood flow increases in active brain areas. Unlike MEG and EEG, fMRI and PET do not directly measure neural events but metabolic changes which are correlated with neural activity. The neuroimaging techniques differ with respect to critical variables in brain mapping, such as spatial and temporal resolution. Spatial resolution is the ability to distinguish two separate objects that are situated close to one another, whereas temporal resolution is the ability to detect two events that happen in close temporal proximity [1]. ERP and MEG have relatively good temporal resolution of milliseconds (0.01 s) but relatively poorer spatial resolution (10 mm). Structural MRI has relatively good spatial resolution; brain structures much smaller than 1 mm can be resolved with this method, including subcortical structures, such as the superior colliculus. DTI's spatial resolution has been improving, and highspatial-resolution DTI imaging has been reported with a resolution of 1 mm [2]. fMRI is characterised by relatively good temporal resolution of seconds to hundreds of milliseconds and spatial resolution of 4–5 mm. PET has relatively lower spatial (5–10 mm) and temporal (60–1000 s) resolutions [1]. It should be emphasised here that the neuroimaging methods introduced above are subject to steady improvement, with regard to their spatial and temporal resolution and other characteristics; furthermore new neuroimaging methods are being developed. For instance, three more recent neuroimaging methods need to be mentioned here: diffusion kurtosis imaging (DKI) [3], a neuroimaging method that provides independent and additional information (to that acquired with DTI) which indicates the complexity of the microstructural environment of the imaged tissue, neurite orientation dispersion and density imaging (NODDI) [4] (see Section 3.4) and magnetic field correlation imaging (MFC) [5], a neuroimaging technique used for the quantitative assessment of iron within the brain. For more details on neuroimaging methods, see [6–9]. Neuroimaging has become a popular and powerful way of studying in vivo brain function and structure in health and disease. One important branch of neuroimaging is the search for a biomarker in neurological, neuropsychiatric and neurodevelopmental disorders (including dyslexia). For instance, promising strides here have been made using various neuroimaging techniques in Alzheimer's disease (MRI [10], fMRI [11], PET [12] and MEG [13]), schizophrenia (PET [14], EEG [15] and MEG [16]), attention deficit hyperactivity disorder (ADHD) (MEG and structural MRI [17], DKI [18], MRI and MFC [19]) and dyslexia (MEG and structural MRI [17], structural MRI [20], ERPs [21, 22], MEG [23] and fMRI [24]). It should be noted that some of the above cited papers explicitly claim the search for neuroimaging biomarkers, while others do not,

**102**

phonological deficit theory (PDT) [39–41], phonological deficit is the underlying cause of dyslexia. This means that DPs have a specific impairment in the representation and processing of speech sounds (phonemes) [41] or a deficit in accessing intact phonological representations [42]. According to the PDT, the phonological deficit leads to poor grapheme-to-phoneme conversion and this in turn leads to poor reading. It is claimed that the phonological deficit also manifests itself on the behavioural level by difficulties in phonological fluency [32, 40], phonological awareness [40, 43] and verbal short-term memory [44, 45]. The deficit postulated by the PDT was specified on the biological level as the left (L) perisylvian region abnormality [46] and recently as the L temporoparietal abnormality and L frontal abnormality [47].

The visual magnocellular deficit theory (MDT) [48–50] claims that the underlying cause of literacy problems in dyslexia is not language specific but a more general impairment of the visual magnocellular system with spared parvocellular system. Magnocellular neurons are defined at the level of the retinal ganglion cell which have specific projections to the lateral geniculate nucleus (LGN) in the thalamus. The results in support of the MDT include reduced contrast sensitivity [51], unsteady binocular fixation [48] and a significantly higher threshold for the perception of coherent movement in random-dot kinematograms in DPs than in CPs [52]. The MDT claims that the visual magnocellular system impairment in dyslexia has a genetic origin. According to Stein [48], the clearest genetic result is for linkage to the region on the short arm of chromosome 6 which helps to control the production of antibodies (see also [53, 54] for recent studies showing association between motion deficit and the *DCDC2* gene). The magnocellular system is hypothesised to play an important role in reading and orthographic and phonological representations [48]. First, it subserves the process of image stabilisation and/or letter localisation in words during reading [55]. Second, it affects orthographic knowledge, through reading skill. Third, it affects phonological representations through orthographic representations [48]. For the most recent version of the MDT, see [56].

According to the cerebellar deficit theory (CDT), the underlying cause of dyslexia is a cerebellar impairment. Cerebellar dysfunction has been linked to problems in (1) motor skills, (2) perception and production in timing tasks, (3) automatisation of motor skill and (4) classical conditioning of the eye-blink response. Dyslexia research has shown that DPs indeed exhibit deficits over a range of functions which rely on cerebellar processing, such as motor skills, including balancing [57], eye-blink conditioning [58] and time estimation [59]. Nicolson et al. [60] put forward a hypothetical ontogenetic causal chain according to which cerebellar deficit could lead to reading difficulties in dyslexia by two routes. The major route claims that cerebellar impairment leads to mild articulatory problems, which lead to an impoverished representation of the phonological characteristics of speech. In turn, this causes difficulties in phonological awareness and subsequently results in difficulties with learning to read. Furthermore, reduced articulation speed leads to reduced working memory. The second route claims that difficulties in reading acquisition stem from a cerebellar deficit which causes problems with automatising skills and knowledge, leading to problems with (1) automatic grapheme-to-phoneme conversion, (2) automatic word recognition, (3) automatic verbal working memory and (4) automatic awareness of the orthographic regularities. Motor problems (also caused by cerebellar impairment) lead to dysgraphia (writing impairment). Additionally, balance deficits are also caused by cerebellar deficit. However, these motor difficulties (except for the articulatory difficulties) and problems with balance do not lead to reading difficulties, but the underlying cerebellar deficit [60].

**105**

**2.1 Hypotheses**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals…*

**2. The first neuroimaging study to use a multiple case approach to** 

Most neuroimaging (and behavioural) studies which have been formulated within the main theories of dyslexia have shortcomings (for a review of studies, see [25]). First, they have used group comparisons which can cloud the less frequent differences between DPs and controls (CPs). Second, they mostly investigated a single underlying cause, hypothesised by one theory. Third, the majority of them concentrated on finding a deficit without empirically showing its relationship with reading deficit, which defines dyslexia. For instance, significantly lower BOLD signal in DPs (vs. CPs) was reported [61] in the R cerebellar cortex when learning a new sequence of finger presses and interpreted as support for the CDT. Another study [62] revealed lack of fMRI activation in V5/MT in DPs in contrast to CPs (while participants viewed a coherently moving, low-contrast, random-dot stimulus), and the results were interpreted as being in agreement with the

MDT. However, a demonstration of a significant between-group difference on these variables does not show that there is a relationship with reading, even if DPs had a documented reading deficit, and their reading scores significantly differed from the CPs. This is because a given variable may be a correlate or biological marker of

The goal of Reid's study [25] was to shed more light on the neural correlates of reading deficit in dyslexia and address the above criticisms: First, by choosing a multiple case study to investigate individual differences among DPs. Second, by contrasting the hypotheses based on each of the main theories, on the neural correlates of the reading impairment, in individual DP (vs. CP), thereby detecting differences which otherwise would have been obscured in the between-group comparison, due to heterogeneity among DPs. The behavioural studies suggest that there are subtypes of dyslexia [32, 40, 64–68], but they cannot be investigated by focusing on one theory. Third, by focusing on a reading task using fMRI - which provides an opportunity to more directly investigate the relationship between the predictions of a given

First, if, as hypothesised by the PDT, the neural correlates of reading deficit in DPs lie within the phonological network, then DPs should show abnormal activation in all or some areas within this network. As the descriptive terms for phonological deficit on the biological level (L perisylvian, L temporoparietal and L frontal regions) were not detailed enough to thoroughly test the PDT on the neural level, a literature review was undertaken [25] and showed that phonological processing (operationalised as phonological awareness, naming and short-term memory) involves many brain areas but it is still unclear what role each area plays in phonological processing. Broadly speaking, the phonological processing network (also validated with the broader literature review presented in [25]) included the following L hemisphere areas: the inferior frontal gyrus (BA44/45)—Broca's area, Wernicke's area (BA22), the middle temporal gyrus (BA21), the insula, inferior parietal lobule (including the angular gyrus (BA39) and the supramarginal gyrus (BA40)), the precentral gyrus PMC (premotor cortex) (BA6), the fusiform gyrus (BA19/37) and the posterior fusiform gyrus. The role of the L posterior fusiform gyrus is unclear, with some researchers advocating its involvement exclusively in orthographic processing [69] and other investigators [70] in mapping orthography onto phonology. The above listed areas were used to test the PDT. To detect abnormality in the neural correlates of the reading impairment of a given DP, not

*DOI: http://dx.doi.org/10.5772/intechopen.80677*

**investigate individual differences among DPs**

dyslexia, which is independent of any reading deficit [63].

theory and the neural correlates of reading impairment in dyslexia.

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals… DOI: http://dx.doi.org/10.5772/intechopen.80677*

#### **2. The first neuroimaging study to use a multiple case approach to investigate individual differences among DPs**

Most neuroimaging (and behavioural) studies which have been formulated within the main theories of dyslexia have shortcomings (for a review of studies, see [25]). First, they have used group comparisons which can cloud the less frequent differences between DPs and controls (CPs). Second, they mostly investigated a single underlying cause, hypothesised by one theory. Third, the majority of them concentrated on finding a deficit without empirically showing its relationship with reading deficit, which defines dyslexia. For instance, significantly lower BOLD signal in DPs (vs. CPs) was reported [61] in the R cerebellar cortex when learning a new sequence of finger presses and interpreted as support for the CDT. Another study [62] revealed lack of fMRI activation in V5/MT in DPs in contrast to CPs (while participants viewed a coherently moving, low-contrast, random-dot stimulus), and the results were interpreted as being in agreement with the MDT. However, a demonstration of a significant between-group difference on these variables does not show that there is a relationship with reading, even if DPs had a documented reading deficit, and their reading scores significantly differed from the CPs. This is because a given variable may be a correlate or biological marker of dyslexia, which is independent of any reading deficit [63].

The goal of Reid's study [25] was to shed more light on the neural correlates of reading deficit in dyslexia and address the above criticisms: First, by choosing a multiple case study to investigate individual differences among DPs. Second, by contrasting the hypotheses based on each of the main theories, on the neural correlates of the reading impairment, in individual DP (vs. CP), thereby detecting differences which otherwise would have been obscured in the between-group comparison, due to heterogeneity among DPs. The behavioural studies suggest that there are subtypes of dyslexia [32, 40, 64–68], but they cannot be investigated by focusing on one theory. Third, by focusing on a reading task using fMRI - which provides an opportunity to more directly investigate the relationship between the predictions of a given theory and the neural correlates of reading impairment in dyslexia.

#### **2.1 Hypotheses**

*Neuroimaging - Structure, Function and Mind*

abnormality [47].

version of the MDT, see [56].

difficulties, but the underlying cerebellar deficit [60].

phonological deficit theory (PDT) [39–41], phonological deficit is the underlying cause of dyslexia. This means that DPs have a specific impairment in the representation and processing of speech sounds (phonemes) [41] or a deficit in accessing intact phonological representations [42]. According to the PDT, the phonological deficit leads to poor grapheme-to-phoneme conversion and this in turn leads to poor reading. It is claimed that the phonological deficit also manifests itself on the behavioural level by difficulties in phonological fluency [32, 40], phonological awareness [40, 43] and verbal short-term memory [44, 45]. The deficit postulated by the PDT was specified on the biological level as the left (L) perisylvian region abnormality [46] and recently as the L temporoparietal abnormality and L frontal

The visual magnocellular deficit theory (MDT) [48–50] claims that the underlying cause of literacy problems in dyslexia is not language specific but a more general impairment of the visual magnocellular system with spared parvocellular system. Magnocellular neurons are defined at the level of the retinal ganglion cell which have specific projections to the lateral geniculate nucleus (LGN) in the thalamus. The results in support of the MDT include reduced contrast sensitivity [51], unsteady binocular fixation [48] and a significantly higher threshold for the perception of coherent movement in random-dot kinematograms in DPs than in CPs [52]. The MDT claims that the visual magnocellular system impairment in dyslexia has a genetic origin. According to Stein [48], the clearest genetic result is for linkage to the region on the short arm of chromosome 6 which helps to control the production of antibodies (see also [53, 54] for recent studies showing association between motion deficit and the *DCDC2* gene). The magnocellular system is hypothesised to play an important role in reading and orthographic and phonological representations [48]. First, it subserves the process of image stabilisation and/or letter localisation in words during reading [55]. Second, it affects orthographic knowledge, through reading skill. Third, it affects phonological representations through orthographic representations [48]. For the most recent

According to the cerebellar deficit theory (CDT), the underlying cause of dyslexia is a cerebellar impairment. Cerebellar dysfunction has been linked to problems in (1) motor skills, (2) perception and production in timing tasks, (3) automatisation of motor skill and (4) classical conditioning of the eye-blink response. Dyslexia research has shown that DPs indeed exhibit deficits over a range of functions which rely on cerebellar processing, such as motor skills, including balancing [57], eye-blink conditioning [58] and time estimation [59]. Nicolson et al. [60] put forward a hypothetical ontogenetic causal chain according to which cerebellar deficit could lead to reading difficulties in dyslexia by two routes. The major route claims that cerebellar impairment leads to mild articulatory problems, which lead to an impoverished representation of the phonological characteristics of speech. In turn, this causes difficulties in phonological awareness and subsequently results in difficulties with learning to read. Furthermore, reduced articulation speed leads to reduced working memory. The second route claims that difficulties in reading acquisition stem from a cerebellar deficit which causes problems with automatising skills and knowledge, leading to problems with (1) automatic grapheme-to-phoneme conversion, (2) automatic word recognition, (3) automatic verbal working memory and (4) automatic awareness of the orthographic regularities. Motor problems (also caused by cerebellar impairment) lead to dysgraphia (writing impairment). Additionally, balance deficits are also caused by cerebellar deficit. However, these motor difficulties (except for the articulatory difficulties) and problems with balance do not lead to reading

**104**

First, if, as hypothesised by the PDT, the neural correlates of reading deficit in DPs lie within the phonological network, then DPs should show abnormal activation in all or some areas within this network. As the descriptive terms for phonological deficit on the biological level (L perisylvian, L temporoparietal and L frontal regions) were not detailed enough to thoroughly test the PDT on the neural level, a literature review was undertaken [25] and showed that phonological processing (operationalised as phonological awareness, naming and short-term memory) involves many brain areas but it is still unclear what role each area plays in phonological processing. Broadly speaking, the phonological processing network (also validated with the broader literature review presented in [25]) included the following L hemisphere areas: the inferior frontal gyrus (BA44/45)—Broca's area, Wernicke's area (BA22), the middle temporal gyrus (BA21), the insula, inferior parietal lobule (including the angular gyrus (BA39) and the supramarginal gyrus (BA40)), the precentral gyrus PMC (premotor cortex) (BA6), the fusiform gyrus (BA19/37) and the posterior fusiform gyrus. The role of the L posterior fusiform gyrus is unclear, with some researchers advocating its involvement exclusively in orthographic processing [69] and other investigators [70] in mapping orthography onto phonology. The above listed areas were used to test the PDT. To detect abnormality in the neural correlates of the reading impairment of a given DP, not

all the areas involved in phonological processing needed to exhibit atypical activation, because individuals might have differed in the neural implementation of the phonological network and/or in the presence of areas with atypical activation. The PDT also predicts that DPs should not show abnormal activations in the magnocellular system and the cerebellum, as predicted by the MDT and CDT, respectively.

Second, if, as predicted by the MDT, reading impairment in dyslexia is due to magnocellular abnormality, then DPs should show significantly lower activation in the V5/MT. The neuroimaging research on the MDT [62, 71] focused on the V5/MT area because it receives the input predominantly from the magnocellular stream [72]. The involvement of V5/MT in reading was demonstrated in a study by Liederman et al. [55] which showed that a virtual lesion of V5/MT, created by repetitive transcranial magnetic stimulation (rTMS) during reading in CPs, resulted in visual but not phonological errors. Furthermore, there may also be differences between CPs and DPs in other areas within the magnocellular system. In the study reported in [25], three areas in both hemispheres were investigated: the V5/MT, V1 and V2. This is because of (1) significant correlations between fMRI activation in these areas (under low mean luminance moving grating conditions), and reading performance were reported [73] and (2) V1 and V2 could be more reliably localised than the remaining motion-sensitive areas, using available cytoarchitectonic maps [74, 75]. Hypoactivation in L and right (R) V1 and/or in V2 was interpreted as supporting the MDT only if discovered jointly with underactivation in the V5/MT. The V5/MT receives input predominantly from the magnocellular stream [72], but V1 and V2 consist of partially separated magno and parvo cell inputs. Therefore, the underactivation of V1 and V2 may reflect underactivation of either parvo cells or magno cells or a combination of these. Hypoactivation in V1 and/or V2, with no underactivation in the V5/MT, was interpreted as a visual but not a magnocellular deficit. A hypothetical visual deficit theory (VDT) was put forward, and it was argued that in DPs who exhibited underactivation in V1 and/or V2, without hypoactivation of V5/MT, hypoactivation is in agreement with the VDT but not with the MDT.

Third, given, that according to the CDT, the underlying cause of dyslexia is a cerebellar impairment, one would predict that the neural correlates of reading problems in DPs are localised within the cerebellum and therefore DPs should show atypical activation during reading in some regions of the cerebellum. However, the CDT does not specify which cerebellar areas should be affected. As the research reported in [25] investigated reading, the focus there was mainly on the cerebellar language areas. Probably the most reliable results regarding the language areas in the cerebellum come from the meta-analysis by Stoodley and Schmahmann [76]. The areas include the R lobule VI (Hem), R and L Crus I (Hem), R Crus II (Hem), R Vermal lobule VIIAt (R Vermal lobule VI) and L lobule VI (Hem). These areas were selected to test the CDT in DPs' reading. Additionally, some areas were also included, either because they were shown to significantly differ in DPs and CPs (R Vermal lobule VI [20], the L and R Crus II and the paramedian R and L lobule (VIIB) [77]) or because they were activated during silent reading in CPs (L and R Crus I, L and R Crus II, L and R lobule VI and L and R lobule VIIB [78]). Most of these areas overlapped with Stoodley and Schmahmann's [76] regions.

Finally, it needs to be stated that the MDT and CDT also make additional predictions. The MDT postulates that the magnocellular system is important in the acquisition of accurate visual representations of the written, orthographic forms of words and that this is essential to grasp their structure at the phonemic level. Therefore, it has been hypothesised [49] that a deficient magnocellular system could be the underlying cause of deficient phonological representations and therefore of a phonological deficit. Hence it is possible that the hypoactivation in phonological areas (coupled with the hypoactivation in the V5/MT) in DPs during reading is also consistent with

**107**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals…*

the MDT (and with the PDT, as discussed above). However, the methods used in [25] do not allow for teasing apart whether the hypoactivation in phonological areas (co-occurring with hypoactivation in magnocellular areas) is 'purely phonological' or has been influenced by magnocellular malfunctioning. The hypoactivation in DPs in phonological areas in the presence (but not in the absence) of the hypoactivation of magnocellular areas is interpreted here as being consistent with the MDT (and with the PDT, as specified above). Moving to the CDT, it predicts that a phonological deficit (in phonological awareness and in reading) can be caused by a cerebellar impairment. Therefore it is possible that the hypoactivation in phonological areas (coupled with the hypoactivation in cerebellar areas) in DPs during reading, in Reid's study, may also be consistent with the CDT. However, the methodology used in [25] does not allow for teasing apart these effects. The hypoactivation in DPs in phonological areas in the presence (but not in the absence) of the hypoactivation of cerebellar areas was interpreted in [25] as being consistent with the CDT (and with the PDT, as specified above). It is important to keep in mind, however, that interpreting hypoactivation within the phonological areas as being also consistent with the MDT and CDT holds only if one takes the perspective of the MDT or CDT, respectively. In contrast, from

the theoretical perspective of the PDT, such interpretations do not hold.

more details on participants and other aspects of the study, see [25].

The participants were tested using a broad battery of behavioural measures (see [25] for details). The fMRI reading task reported in Reid [25] had three conditions.

**2.3 Materials, stimuli and fMRI task**

Thirty-eight adult native English speakers from three UK universities took part in Reid's study [25]. They were all right handed, with normal hearing, normal or corrected to normal vision, without clinical ADHD (defined as a score < 70 on the ADHD D index on Conners' scales [79]), without clinical DCD (as defined in DSM-IV [80]) or any other known sensory, neurological, psychiatric or neurodevelopmental disorders. There were indications that DP8 and DP15 may be 'at risk' of clinical DCD (They were the only DPs who responded 'yes' to the question on whether their DCD difficulties significantly interfered with their everyday life). DP8 and DP15 were included in Reid's study [25], but a DCD measure obtained from a questionnaire (based on DSM-IV, Adult DCD Checklist (DANDA—Developmental Adult Neuro-Diversity Association) and questions devised by A. Reid (see [25] for details) was used as a covariate in the fMRI analysis. Furthermore, DP8's and DP15's fMRI data were additionally analysed for possible DCD effects. Four participants were excluded from the analysis (1 CP did not provide a dyslexia diagnosis and 3 DPs because their fMRI data could not be salvaged by the recommended techniques [8]). Eighteen individual DPs and 16 CPs (treated as a control group) were entered into an fMRI multiple case analysis. All DPs (6 males and 12 females; mean age 21.28 years (SD = 3.3)) reported a history of persistent literacy difficulties (mainly with reading) and had a formal diagnosis of dyslexia. Twelve DPs (66.7%) disclosed that literacy problems occurred in one or more of their first-degree relatives. CPs (5 males and 11 females; mean age 21.38 years (SD = 6.03)) had no literacy problems or any other known sensory, neurological, psychiatric or neurodevelopmental disorders. Although the DP and CP groups were matched on years of education, age, handedness, verbal IQ, performance IQ and full scale IQ, this was not always the case in the multiple fMRI case analyses which compared every individual DP to CPs. Hence additionally, age, handedness and FSIQ were used as covariates in these analyses. For

*DOI: http://dx.doi.org/10.5772/intechopen.80677*

**2.2 Participants**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals… DOI: http://dx.doi.org/10.5772/intechopen.80677*

the MDT (and with the PDT, as discussed above). However, the methods used in [25] do not allow for teasing apart whether the hypoactivation in phonological areas (co-occurring with hypoactivation in magnocellular areas) is 'purely phonological' or has been influenced by magnocellular malfunctioning. The hypoactivation in DPs in phonological areas in the presence (but not in the absence) of the hypoactivation of magnocellular areas is interpreted here as being consistent with the MDT (and with the PDT, as specified above). Moving to the CDT, it predicts that a phonological deficit (in phonological awareness and in reading) can be caused by a cerebellar impairment. Therefore it is possible that the hypoactivation in phonological areas (coupled with the hypoactivation in cerebellar areas) in DPs during reading, in Reid's study, may also be consistent with the CDT. However, the methodology used in [25] does not allow for teasing apart these effects. The hypoactivation in DPs in phonological areas in the presence (but not in the absence) of the hypoactivation of cerebellar areas was interpreted in [25] as being consistent with the CDT (and with the PDT, as specified above). It is important to keep in mind, however, that interpreting hypoactivation within the phonological areas as being also consistent with the MDT and CDT holds only if one takes the perspective of the MDT or CDT, respectively. In contrast, from the theoretical perspective of the PDT, such interpretations do not hold.

#### **2.2 Participants**

*Neuroimaging - Structure, Function and Mind*

all the areas involved in phonological processing needed to exhibit atypical activation, because individuals might have differed in the neural implementation of the phonological network and/or in the presence of areas with atypical activation. The PDT also predicts that DPs should not show abnormal activations in the magnocellular system and the cerebellum, as predicted by the MDT and CDT, respectively. Second, if, as predicted by the MDT, reading impairment in dyslexia is due to magnocellular abnormality, then DPs should show significantly lower activation in the V5/MT. The neuroimaging research on the MDT [62, 71] focused on the V5/MT area because it receives the input predominantly from the magnocellular stream [72]. The involvement of V5/MT in reading was demonstrated in a study by Liederman et al. [55] which showed that a virtual lesion of V5/MT, created by repetitive transcranial magnetic stimulation (rTMS) during reading in CPs, resulted in visual but not phonological errors. Furthermore, there may also be differences between CPs and DPs in other areas within the magnocellular system. In the study reported in [25], three areas in both hemispheres were investigated: the V5/MT, V1 and V2. This is because of (1) significant correlations between fMRI activation in these areas (under low mean luminance moving grating conditions), and reading performance were reported [73] and (2) V1 and V2 could be more reliably localised than the remaining motion-sensitive areas, using available cytoarchitectonic maps [74, 75]. Hypoactivation in L and right (R) V1 and/or in V2 was interpreted as supporting the MDT only if discovered jointly with underactivation in the V5/MT. The V5/MT receives input predominantly from the magnocellular stream [72], but V1 and V2 consist of partially separated magno and parvo cell inputs. Therefore, the underactivation of V1 and V2 may reflect underactivation of either parvo cells or magno cells or a combination of these. Hypoactivation in V1 and/or V2, with no underactivation in the V5/MT, was interpreted as a visual but not a magnocellular deficit. A hypothetical visual deficit theory (VDT) was put forward, and it was argued that in DPs who exhibited underactivation in V1 and/or V2, without hypoactivation of V5/MT,

hypoactivation is in agreement with the VDT but not with the MDT.

these areas overlapped with Stoodley and Schmahmann's [76] regions.

Finally, it needs to be stated that the MDT and CDT also make additional predictions. The MDT postulates that the magnocellular system is important in the acquisition of accurate visual representations of the written, orthographic forms of words and that this is essential to grasp their structure at the phonemic level. Therefore, it has been hypothesised [49] that a deficient magnocellular system could be the underlying cause of deficient phonological representations and therefore of a phonological deficit. Hence it is possible that the hypoactivation in phonological areas (coupled with the hypoactivation in the V5/MT) in DPs during reading is also consistent with

Third, given, that according to the CDT, the underlying cause of dyslexia is a cerebellar impairment, one would predict that the neural correlates of reading problems in DPs are localised within the cerebellum and therefore DPs should show atypical activation during reading in some regions of the cerebellum. However, the CDT does not specify which cerebellar areas should be affected. As the research reported in [25] investigated reading, the focus there was mainly on the cerebellar language areas. Probably the most reliable results regarding the language areas in the cerebellum come from the meta-analysis by Stoodley and Schmahmann [76]. The areas include the R lobule VI (Hem), R and L Crus I (Hem), R Crus II (Hem), R Vermal lobule VIIAt (R Vermal lobule VI) and L lobule VI (Hem). These areas were selected to test the CDT in DPs' reading. Additionally, some areas were also included, either because they were shown to significantly differ in DPs and CPs (R Vermal lobule VI [20], the L and R Crus II and the paramedian R and L lobule (VIIB) [77]) or because they were activated during silent reading in CPs (L and R Crus I, L and R Crus II, L and R lobule VI and L and R lobule VIIB [78]). Most of

**106**

Thirty-eight adult native English speakers from three UK universities took part in Reid's study [25]. They were all right handed, with normal hearing, normal or corrected to normal vision, without clinical ADHD (defined as a score < 70 on the ADHD D index on Conners' scales [79]), without clinical DCD (as defined in DSM-IV [80]) or any other known sensory, neurological, psychiatric or neurodevelopmental disorders. There were indications that DP8 and DP15 may be 'at risk' of clinical DCD (They were the only DPs who responded 'yes' to the question on whether their DCD difficulties significantly interfered with their everyday life). DP8 and DP15 were included in Reid's study [25], but a DCD measure obtained from a questionnaire (based on DSM-IV, Adult DCD Checklist (DANDA—Developmental Adult Neuro-Diversity Association) and questions devised by A. Reid (see [25] for details) was used as a covariate in the fMRI analysis. Furthermore, DP8's and DP15's fMRI data were additionally analysed for possible DCD effects. Four participants were excluded from the analysis (1 CP did not provide a dyslexia diagnosis and 3 DPs because their fMRI data could not be salvaged by the recommended techniques [8]). Eighteen individual DPs and 16 CPs (treated as a control group) were entered into an fMRI multiple case analysis. All DPs (6 males and 12 females; mean age 21.28 years (SD = 3.3)) reported a history of persistent literacy difficulties (mainly with reading) and had a formal diagnosis of dyslexia. Twelve DPs (66.7%) disclosed that literacy problems occurred in one or more of their first-degree relatives. CPs (5 males and 11 females; mean age 21.38 years (SD = 6.03)) had no literacy problems or any other known sensory, neurological, psychiatric or neurodevelopmental disorders. Although the DP and CP groups were matched on years of education, age, handedness, verbal IQ, performance IQ and full scale IQ, this was not always the case in the multiple fMRI case analyses which compared every individual DP to CPs. Hence additionally, age, handedness and FSIQ were used as covariates in these analyses. For more details on participants and other aspects of the study, see [25].

#### **2.3 Materials, stimuli and fMRI task**

The participants were tested using a broad battery of behavioural measures (see [25] for details). The fMRI reading task reported in Reid [25] had three conditions.

Condition 1 consisted of 100 English words (high familiarity, imageability and concreteness, two-syllable, five to seven letters, with regular spelling selected from the MRC psycholinguistic database [81]); Condition 2 contained 100 pseudowords created by the substitution of consonants in the onset or middle of words from Condition 1. Condition 3 (the control condition) consisted of a fixation cross. The fMRI experiment had an event-related design [82] with stimuli from all conditions randomly intermixed. Each stimulus was displayed for 1000 milliseconds, with an interstimulus interval (ISI) of 3000 milliseconds and a stimulus onset asynchrony (SOA) of 4000 milliseconds. The focus in Reid's [25] communication was on word reading which involved the contrast of Conditions 1 and 3.

#### **2.4 fMRI data acquisition**

The MRI and fMRI data were acquired at the Aston University MRI Research Centre using a 3 T Trio Siemens scanner equipped with echo planar imaging and a standard eight-channel head coil. A high-resolution structural MRI image was acquired first, followed by fMRI data acquisition during the reading task. For fMRI data, 44 (3 × 3 × 3 mm) slices, covering the whole brain, were acquired every 3 sec (TR = 3000 ms, TE = 30, flip angle = 90, FOVread = 192, FOVphase = 100) for a total of 404 volumes. In the scanner the participants were asked to silently read words and to keep their gaze fixed on the '+' sign shown in the centre of the field of view on the screen. They were asked to read every item carefully because there would be a posttest after the fMRI experiment. The posttest scores were summarised in *d Prime* and entered as covariates into the second-level neuroimaging analysis. To monitor participants' vigilance, they were required to press a response button (with their left index finger) when a black star (displayed during ISI) became red. This occurred on 10% of trials.

#### **2.5 Data preprocessing**

SPM5 was used to analyse (and preprocess) the fMRI data. The preprocessing involved realignment, slice timing correction, coregistration, segmentation, normalisation and smoothing [83]. Usually, realignment is run first and slice timing correction second; however, because each volume was acquired in slices in an interleaved fashion, starting from the bottom slice, the order of these two steps was swapped (John Ashburner, email communication, June 4, 2007). The slice timing correction was applied to correct the differences in slice acquisition times. The 'realign' function was used to remove confounds which can arise in the fMRI data from changes in signal intensity over time due to head motion. Realignment parameters were saved for each participant for each session and entered into the design matrix as covariates. A coregistration function was used to coregister the functional (MRI) and the structural (MRI) data so as to maximise their mutual information. A segmentation function was used to segment the structural image according to tissue probability, using default maps, creating grey and white matter images and a bias-field corrected structural image. The data were pooled into the same anatomical space using a spatial normalisation function to put the MRI images into a standard space defined by template images (corresponding to the space defined by the International Consortium for Brain Mapping (ICBM), NIH P-20 project). The data were smoothed with an 8-mm Gaussian kernel.

#### **2.6 Data analysis**

In the first-level analysis, the word condition was explicitly modelled. The control condition was implicitly modelled [84]. To avoid confounding the BOLD response

**109**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals…*

due to the 'Star' stimulus and 'Button Press', they were included in the design matrix as regressors. The shape of the canonical haemodynamic response function (HRF) (SPM5) was used to model the experimental haemodynamic response. Further inclusion of the dispersion and time derivatives was necessary to account for variations in the voxel-to-voxel and subject-to-subject responses, especially in the experiment that involved the DPs characterised by heterogeneity with respect to behavioural and neuroimaging findings. The time derivative allows for the variation in the peak response of plus or minus 1 second, whereas the dispersion derivative allows for the variation in the width of the response by a similar amount [83]. A t-contrast (Word>Fixation Cross) was tested in the first-level analysis. The secondlevel analysis focused on comparison of a given individual DP and the CPs (treated as a group). Data analysis in the second level involved a two-sample t-test. Two contrasts were tested: CPs > DP (hypoactivation) and DP > CPs (hyperactivation). A number of DPs showed elevated (but non-'clinical') scores on the ADHD and DCD measures in comparison to the CPs; hence these scores were entered into the secondlevel analysis as covariates. Participants' age, handedness, FSIQ and d Prime scores were also entered into the second-level analysis as covariates, as discussed above.

There is growing evidence that different brain regions, such as BA44 and BA45 are characterised by high inter-participant structural variability [85]. Bearing this in mind, a mask for the ROI analysis was prepared mainly using cytoarchitectonic areas (see note for **Table 1**). The ROI mask consisted of 31 areas. Twenty-nine areas were created as individual ROIs in the AT (V.1.8) [86], and two areas (not available in AT (V.1.8)) were created as individual ROIs in MarsBar (version 0.43) [87]. The ROIs created in MarsBar were coregistered to the ROIs created in the AT (V.1.8). All ROIs were combined (and binarised) into one mask using SPM5. The 31 ROI mask was coregistered in SPM5 (using the resliced option) to the fMRI data before running the ROI analysis. As DPs are usually characterised by considerable heterogeneity, activation in a brain area was considered as supporting a given hypothesis when the

probability that a given voxel belonged to that area was 10% or higher [88].

The multiple case analysis of DPs' performance on psychometric tests revealed marked heterogeneity among DPs, and this was in line with the previous findings [32, 40, 64–68] (see [25] for details). The neuroimaging results for underactivation in each individual DP, as compared to CPs (CPs > DPs) during word reading relative to the control condition, are shown in **Table 1** and **Figure 1** (see also Appendix B **Table 1** to **18** for MNI coordinates of the BOLD [25]). Hypoactivation is usually assumed to reflect a functional disruption in a system [89]. In the context of the dyslexia theories, hypoactivation in the hypothesised brain areas was interpreted as lending support for these theories. The contrast DP > CPs revealed brain areas which were hyperactivated by a given individual DP (vs. CPs) during word reading, relative to the control condition. Hyperactivation is usually interpreted as a correlate of a compensatory mechanism [89]. Because the dyslexia theories are concerned with a deficit and not compensatory mechanisms, hyperactivation of the brain areas associated with these main theories was not interpreted as evidence of support for them. An inspection of **Table 1** and **Figure 1** reveals that all individuals with dyslexia exhibited heterogeneous and complex patterns of hypoactivation which involved the areas predicted by the dyslexia theories. Five DPs showed

*DOI: http://dx.doi.org/10.5772/intechopen.80677*

**2.7 ROI analysis (mask)**

**2.8 Results and discussion**

overactivation; see text below.

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals… DOI: http://dx.doi.org/10.5772/intechopen.80677*

due to the 'Star' stimulus and 'Button Press', they were included in the design matrix as regressors. The shape of the canonical haemodynamic response function (HRF) (SPM5) was used to model the experimental haemodynamic response. Further inclusion of the dispersion and time derivatives was necessary to account for variations in the voxel-to-voxel and subject-to-subject responses, especially in the experiment that involved the DPs characterised by heterogeneity with respect to behavioural and neuroimaging findings. The time derivative allows for the variation in the peak response of plus or minus 1 second, whereas the dispersion derivative allows for the variation in the width of the response by a similar amount [83]. A t-contrast (Word>Fixation Cross) was tested in the first-level analysis. The secondlevel analysis focused on comparison of a given individual DP and the CPs (treated as a group). Data analysis in the second level involved a two-sample t-test. Two contrasts were tested: CPs > DP (hypoactivation) and DP > CPs (hyperactivation). A number of DPs showed elevated (but non-'clinical') scores on the ADHD and DCD measures in comparison to the CPs; hence these scores were entered into the secondlevel analysis as covariates. Participants' age, handedness, FSIQ and d Prime scores were also entered into the second-level analysis as covariates, as discussed above.

#### **2.7 ROI analysis (mask)**

*Neuroimaging - Structure, Function and Mind*

**2.4 fMRI data acquisition**

**2.5 Data preprocessing**

Condition 1 consisted of 100 English words (high familiarity, imageability and concreteness, two-syllable, five to seven letters, with regular spelling selected from the MRC psycholinguistic database [81]); Condition 2 contained 100 pseudowords created by the substitution of consonants in the onset or middle of words from Condition 1. Condition 3 (the control condition) consisted of a fixation cross. The fMRI experiment had an event-related design [82] with stimuli from all conditions randomly intermixed. Each stimulus was displayed for 1000 milliseconds, with an interstimulus interval (ISI) of 3000 milliseconds and a stimulus onset asynchrony (SOA) of 4000 milliseconds. The focus in Reid's [25] communication was on word

The MRI and fMRI data were acquired at the Aston University MRI Research Centre using a 3 T Trio Siemens scanner equipped with echo planar imaging and a standard eight-channel head coil. A high-resolution structural MRI image was acquired first, followed by fMRI data acquisition during the reading task. For fMRI data, 44

(3 × 3 × 3 mm) slices, covering the whole brain, were acquired every 3 sec (TR = 3000 ms, TE = 30, flip angle = 90, FOVread = 192, FOVphase = 100) for a total of 404 volumes. In the scanner the participants were asked to silently read words and to keep their gaze fixed on the '+' sign shown in the centre of the field of view on the screen. They were asked to read every item carefully because there would be a posttest after the fMRI experiment. The posttest scores were summarised in *d Prime* and entered as covariates into the second-level neuroimaging analysis. To monitor participants' vigilance, they were required to press a response button (with their left index finger) when a black star

SPM5 was used to analyse (and preprocess) the fMRI data. The preprocessing involved realignment, slice timing correction, coregistration, segmentation, normalisation and smoothing [83]. Usually, realignment is run first and slice timing correction second; however, because each volume was acquired in slices in an interleaved fashion, starting from the bottom slice, the order of these two steps was swapped (John Ashburner, email communication, June 4, 2007). The slice timing correction was applied to correct the differences in slice acquisition times. The 'realign' function was used to remove confounds which can arise in the fMRI data from changes in signal intensity over time due to head motion. Realignment parameters were saved for each participant for each session and entered into the design matrix as covariates. A coregistration function was used to coregister the functional (MRI) and the structural (MRI) data so as to maximise their mutual information. A segmentation function was used to segment the structural image according to tissue probability, using default maps, creating grey and white matter images and a bias-field corrected structural image. The data were pooled into the same anatomical space using a spatial normalisation function to put the MRI images into a standard space defined by template images (corresponding to the space defined by the International Consortium for Brain Mapping (ICBM), NIH P-20 project). The

In the first-level analysis, the word condition was explicitly modelled. The control condition was implicitly modelled [84]. To avoid confounding the BOLD response

reading which involved the contrast of Conditions 1 and 3.

(displayed during ISI) became red. This occurred on 10% of trials.

data were smoothed with an 8-mm Gaussian kernel.

**108**

**2.6 Data analysis**

There is growing evidence that different brain regions, such as BA44 and BA45 are characterised by high inter-participant structural variability [85]. Bearing this in mind, a mask for the ROI analysis was prepared mainly using cytoarchitectonic areas (see note for **Table 1**). The ROI mask consisted of 31 areas. Twenty-nine areas were created as individual ROIs in the AT (V.1.8) [86], and two areas (not available in AT (V.1.8)) were created as individual ROIs in MarsBar (version 0.43) [87]. The ROIs created in MarsBar were coregistered to the ROIs created in the AT (V.1.8). All ROIs were combined (and binarised) into one mask using SPM5. The 31 ROI mask was coregistered in SPM5 (using the resliced option) to the fMRI data before running the ROI analysis. As DPs are usually characterised by considerable heterogeneity, activation in a brain area was considered as supporting a given hypothesis when the probability that a given voxel belonged to that area was 10% or higher [88].

#### **2.8 Results and discussion**

The multiple case analysis of DPs' performance on psychometric tests revealed marked heterogeneity among DPs, and this was in line with the previous findings [32, 40, 64–68] (see [25] for details). The neuroimaging results for underactivation in each individual DP, as compared to CPs (CPs > DPs) during word reading relative to the control condition, are shown in **Table 1** and **Figure 1** (see also Appendix B **Table 1** to **18** for MNI coordinates of the BOLD [25]). Hypoactivation is usually assumed to reflect a functional disruption in a system [89]. In the context of the dyslexia theories, hypoactivation in the hypothesised brain areas was interpreted as lending support for these theories. The contrast DP > CPs revealed brain areas which were hyperactivated by a given individual DP (vs. CPs) during word reading, relative to the control condition. Hyperactivation is usually interpreted as a correlate of a compensatory mechanism [89]. Because the dyslexia theories are concerned with a deficit and not compensatory mechanisms, hyperactivation of the brain areas associated with these main theories was not interpreted as evidence of support for them. An inspection of **Table 1** and **Figure 1** reveals that all individuals with dyslexia exhibited heterogeneous and complex patterns of hypoactivation which involved the areas predicted by the dyslexia theories. Five DPs showed overactivation; see text below.


**111**

**ROI/DP** L Lob. VIIa Cr.II

R Lob. VIIa Cr.II

L Lob. VI R Lob. VI R Lob. VI (Ver.)

L Lob. VIIb

R Lob. VIIb

+

+

+

+ +

✓

*Note: 1–18, a unique number for every participant with dyslexia; the presence of underactivation within ROI in the individual DP is denoted by '+' (p < .05 FDR, corrected for multiple comparisons) and* '✓*'* 

*(p < .001, uncorrected for multiple comparisons); 31 ROI included in the mask, L area 44 and L area 45 [90] (equivalent to Broca's area L BA44 and L BA45), L areas Ig1, Ig2, Id1 (the posterior insula) [91],* 

*L area 6 (the premotor cortex, equivalent to L BA6) [92], L IPC PFop, L IPC L PFt, L IPC PF, L IPC PFm and L IPC PFcm (inferior parietal lobule, these areas approximately cover the region of L BA40 on* 

*the supramarginal gyrus with extension into the depth of the Sylvian fissure [93]); L IPC PGa and IPC PGp [94] (inferior parietal cortex, these areas are located approximately at the position of the angular* 

*gyrus (BA39), L area TE3 in the lateral part of the superior temporal gyrus, perhaps homologous to BA22 (Wernicke's area) [95], the L middle temporal gyrus (L MTGL) and the L fusiform gyrus (L FG)* 

*[96], the L hOC5 and R hOC5 [75] (equivalent to L and R V5/MT), L and R area 17 and area 18 [74] (equivalent to L and R V1 and V2) and nine cerebellar regions (L Lobule VIIa Crus I (Hem), R Lobule* 

*VIIa Crus I (Hem), L Lobule VIIa Crus II (Hem), R Lobule VIIa Crus II (Hem), L Lobule VI (Hem), R Lobule VI (Hem), R Lobule VI (Vermis), L Lobule VIIb (Hem) and R Lobule VIIb (Hem) [97]; Lob.* 

*denotes lobule; all lobules are hemispheric, except one (R Lob. VI (Ver.)) which is vermal.*

**Table 1.**

*Brain areas (ROI) underactivated in individual DPs (CPs > DPs).*

✓

✓

+

+

+

+

+ +

+

+

✓

+

+

+

+

+

✓

+

+ +

+

✓

+ +

+

**1**

**2**

**3**

**4**

**5**

**6**

**7** ✓

**8**

**9**

**10**

**11**

+

+

**12**

**13**

**14**

**15**

**16**

**17**

+

**18**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals…*

*DOI: http://dx.doi.org/10.5772/intechopen.80677*



*[96], the L hOC5 and R hOC5 [75] (equivalent to L and R V5/MT), L and R area 17 and area 18 [74] (equivalent to L and R V1 and V2) and nine cerebellar regions (L Lobule VIIa Crus I (Hem), R Lobule VIIa Crus I (Hem), L Lobule VIIa Crus II (Hem), R Lobule VIIa Crus II (Hem), L Lobule VI (Hem), R Lobule VI (Hem), R Lobule VI (Vermis), L Lobule VIIb (Hem) and R Lobule VIIb (Hem) [97]; Lob. denotes lobule; all lobules are hemispheric, except one (R Lob. VI (Ver.)) which is vermal.*

**Table 1.** *Brain areas (ROI) underactivated in individual DPs (CPs > DPs).*

*Neuroimaging - Structure, Function and Mind*

**110**

**ROI/DP** L area 44 L area 45 L area Ig1

L area Ig2

L area Id1

L area 6 L IPC PFop

L IPC L PFt

L IPC PF L IPC PFm L IPC PFcm

L IPC PGa L IPC PGp L area TE3

L MTG

L FG L hOC5

R hOC5 L area 17 R area 17 L area 18 R area 18 L Lob. VIIa Cr.I

R Lob. VIIa Cr.I

+

+

+

+

+

✓

+

+

+

+

+

✓

+

+

+

+

+

+

+

+

+

✓

+

+

+

+

+

✓

+

+

+

+

+

+ + +

✓

✓

✓

+

+

+

+ + + +

+

✓ ✓

+

✓ ✓

+

✓

+

+

+

✓

+ +

✓

✓

+

✓

+

+

✓

✓

+

+ + + +

+ + +

> +

> > +

✓

+ +

+

+ +

+

+

+

+

+

+ +

+

+

+

✓

+

+

+

+

+

+

+

**1** +

+ +

✓

✓

+

**2**

**3**

**4**

**5**

**6**

**7**

**8**

**9** +

+

**10**

**11**

**12**

**13**

**14**

+

+

+

+

**15**

**16**

**17**

**18**

**Figure 1.**

*Clusters of underactivation (CPs > DP) for individual DPs. Underactivation is superimposed on a volumerendered brain (a spatially normalised anatomical image for an individual DP). Cluster size threshold k* ≥ *6. An ROI mask was applied; see Section 2.7 and note for* **Table 1***.*

The main goal of the research reported in [25] was to shed more light on the underlying reading impairment (which defines dyslexia) in adult DPs, as hypothesised by the PDT, MDT and CDT, with special focus on individual differences among DPs. When the hypotheses based on the three main theories of dyslexia were contrasted in the same DPs, the neural correlates of word reading deficit were consistent with the PDT in 17 cases (94.4%), with the CDT in 18 cases (100%) and in 1 case (5.5%) with the MDT. Furthermore, the reading deficit of 10 cases (56%) was consistent with the VDT but not with the MDT.

A more detailed inspection of the neuroimaging results for reading revealed that when hypotheses based on the three main theories are tested in individual DPs, DPs showed complex and heterogeneous patterns of underactivation in the brain regions predicted by the dyslexia theories. For instance, DP1 showed hypoactivation in eight areas predicted by the PDT (L area 6 (BA6), L area 44 (BA44), L middle temporal gyrus (BA21), L fusiform gyrus (BA19/37), L TE 3 (part of BA22), L IPC (PF) (BA40), L IPC (PFcm) (BA40) and L IPC (PGp) (BA39)), one area hypothesised by the MDT (R hOC5 (V5/MT)) and three areas predicted by the CDT (R Lobule VIIa Crus I (Hem), L Lobule VIIa Crus II (Hem) and R Lobule VIIa Crus II (Hem). DP10 exhibited hypoactivation in one area hypothesised by the PDT (L IPC

**113**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals…*

(PFm) (BA40)) and four areas predicted by the CDT (L Lobule VIIa Crus I (Hem), R Lobule VIIa Crus I (Hem), R Lobule VIIa Crus II (Hem) and R Lobule VI (Hem)). In contrast, DP13 hypoactivated only areas predicted by the CDT (L Lobule VIIa Crus I (Hem), R Lobule VIIa Crus I (Hem) and R Lobule VIIa Crus II (Hem))

Moreover, the neuroimaging data exhibited a high degree of individual differences. Even if the neural correlates of reading disorder in two DPs were consistent with the same theory, the neural correlates in those DPs could differ. For instance, within the framework of the PDT, DP6 showed hypoactivation in the L area 44 (BA44) and L IPC (PGp) (BA39); DP10 exhibited hypoactivation in L IPC (PFm) (BA40), whereas DP12 hypoactivated L FG (fusiform gyrus). This is also the case for the neural correlates of reading deficit hypothesised by the CDT. For instance, DP1 showed hypoactivation in R Lobule VIIa Crus I (Hem), L Lobule VIIa Crus II (Hem) and R Lobule VIIa Crus II (Hem); DP14 hypoactivated L Lobule VIIa Crus I (Hem), R Lobule VIIa Crus I (Hem), R Lobule VIIa Crus II (Hem), R Lobule VI (Hem) and R Lobule VI (Vermis), whereas DP4 showed hypoactivation only in L Lobule VIIb (Hem). The traditional approach, based on group comparison where only betweengroup differences (DPs vs. CPs) were tested, could not reveal the individual differ-

The results revealed considerable individual differences in patterns of hypoactivation within the reading network among DPs, which are unexpected in the context of the between-group comparison studies, which have dominated neuroimaging research on dyslexia. Nevertheless, they are perhaps less surprising if one considers the fact that reading is a relatively new (less than 6000 years old) cultural invention in human evolutionary history. It requires areas which evolved for vision, language and associative learning. Reading acquisition is an exercise in brain plasticity; the goal of which is to create an efficient reading network which enables the unimpaired reader to get from visual precept to meaning in approximately 250 milliseconds [98]. As in the ontogenetic development of an individual, a number of brain regions need to be 'adapted' for reading; it is perhaps not surprising that in different

Five (28%) DPs in the study [25] exhibited hyperactivation. Similar to the patterns of underactivation, overactivation differed in different DPs. DP4 exhibited overactivation in L area 6, L insula (Ig2), L IPC (PFm), L IPC (PGa), L area 17, R area 17, L area 18 and R area 18, R Lobule VIIa Crus I (Hem) and L Lobule VI (Hem). DP5 hyperactivated L area 17 and R area 18. DP8 overactivated L insula (Id1) and L area 18. DP13 show hyperactivation in L area 6, L middle temporal gyrus and L area 17. Finally, DP17 exhibited overactivation in L fusiform gyrus, insula (Id1) and L Lobule VIIb (Hem). All results for ROI analyses at p < 0.001 (uncorrected for multiple comparisons), except for DP4's results at p < 0.05 (FDR). Overactivation in some DPs in the areas hypothesised to show underactivation in DPs by the PDT indicates that a compensatory network is not limited to the frontal regions, as suggested by a number of studies based on group comparisons (for instance, see [89]), but involves brain regions distributed across the phonological reading network. Cerebellar and secondary and/or primary visual areas were overactivated in two and four DPs, respectively, suggesting the existence of a potential

An important common characteristic of the dyslexia theories (the PDT, MDT and CDT) investigated in [25] is the assumption that a single underlying deficit is necessary and sufficient to cause symptoms of dyslexia: phonological, or visual magnocellular, or cerebellar, respectively. As mentioned above, one of the limitations of research on dyslexia is that it has mostly investigated one theory in a given sample of DPs. The findings reported in [25] reveal that if one investigates

*DOI: http://dx.doi.org/10.5772/intechopen.80677*

(see **Table 1** and **Figure 1** for the other cases).

ences among DPs as shown in [25].

DPs, different components may be deficient.

compensatory network within these brain regions.

#### *Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals… DOI: http://dx.doi.org/10.5772/intechopen.80677*

(PFm) (BA40)) and four areas predicted by the CDT (L Lobule VIIa Crus I (Hem), R Lobule VIIa Crus I (Hem), R Lobule VIIa Crus II (Hem) and R Lobule VI (Hem)). In contrast, DP13 hypoactivated only areas predicted by the CDT (L Lobule VIIa Crus I (Hem), R Lobule VIIa Crus I (Hem) and R Lobule VIIa Crus II (Hem)) (see **Table 1** and **Figure 1** for the other cases).

Moreover, the neuroimaging data exhibited a high degree of individual differences. Even if the neural correlates of reading disorder in two DPs were consistent with the same theory, the neural correlates in those DPs could differ. For instance, within the framework of the PDT, DP6 showed hypoactivation in the L area 44 (BA44) and L IPC (PGp) (BA39); DP10 exhibited hypoactivation in L IPC (PFm) (BA40), whereas DP12 hypoactivated L FG (fusiform gyrus). This is also the case for the neural correlates of reading deficit hypothesised by the CDT. For instance, DP1 showed hypoactivation in R Lobule VIIa Crus I (Hem), L Lobule VIIa Crus II (Hem) and R Lobule VIIa Crus II (Hem); DP14 hypoactivated L Lobule VIIa Crus I (Hem), R Lobule VIIa Crus I (Hem), R Lobule VIIa Crus II (Hem), R Lobule VI (Hem) and R Lobule VI (Vermis), whereas DP4 showed hypoactivation only in L Lobule VIIb (Hem). The traditional approach, based on group comparison where only betweengroup differences (DPs vs. CPs) were tested, could not reveal the individual differences among DPs as shown in [25].

The results revealed considerable individual differences in patterns of hypoactivation within the reading network among DPs, which are unexpected in the context of the between-group comparison studies, which have dominated neuroimaging research on dyslexia. Nevertheless, they are perhaps less surprising if one considers the fact that reading is a relatively new (less than 6000 years old) cultural invention in human evolutionary history. It requires areas which evolved for vision, language and associative learning. Reading acquisition is an exercise in brain plasticity; the goal of which is to create an efficient reading network which enables the unimpaired reader to get from visual precept to meaning in approximately 250 milliseconds [98]. As in the ontogenetic development of an individual, a number of brain regions need to be 'adapted' for reading; it is perhaps not surprising that in different DPs, different components may be deficient.

Five (28%) DPs in the study [25] exhibited hyperactivation. Similar to the patterns of underactivation, overactivation differed in different DPs. DP4 exhibited overactivation in L area 6, L insula (Ig2), L IPC (PFm), L IPC (PGa), L area 17, R area 17, L area 18 and R area 18, R Lobule VIIa Crus I (Hem) and L Lobule VI (Hem). DP5 hyperactivated L area 17 and R area 18. DP8 overactivated L insula (Id1) and L area 18. DP13 show hyperactivation in L area 6, L middle temporal gyrus and L area 17. Finally, DP17 exhibited overactivation in L fusiform gyrus, insula (Id1) and L Lobule VIIb (Hem). All results for ROI analyses at p < 0.001 (uncorrected for multiple comparisons), except for DP4's results at p < 0.05 (FDR). Overactivation in some DPs in the areas hypothesised to show underactivation in DPs by the PDT indicates that a compensatory network is not limited to the frontal regions, as suggested by a number of studies based on group comparisons (for instance, see [89]), but involves brain regions distributed across the phonological reading network. Cerebellar and secondary and/or primary visual areas were overactivated in two and four DPs, respectively, suggesting the existence of a potential compensatory network within these brain regions.

An important common characteristic of the dyslexia theories (the PDT, MDT and CDT) investigated in [25] is the assumption that a single underlying deficit is necessary and sufficient to cause symptoms of dyslexia: phonological, or visual magnocellular, or cerebellar, respectively. As mentioned above, one of the limitations of research on dyslexia is that it has mostly investigated one theory in a given sample of DPs. The findings reported in [25] reveal that if one investigates

*Neuroimaging - Structure, Function and Mind*

The main goal of the research reported in [25] was to shed more light on the underlying reading impairment (which defines dyslexia) in adult DPs, as hypothesised by the PDT, MDT and CDT, with special focus on individual differences among DPs. When the hypotheses based on the three main theories of dyslexia were contrasted in the same DPs, the neural correlates of word reading deficit were consistent with the PDT in 17 cases (94.4%), with the CDT in 18 cases (100%) and in 1 case (5.5%) with the MDT. Furthermore, the reading deficit of 10 cases (56%)

*Clusters of underactivation (CPs > DP) for individual DPs. Underactivation is superimposed on a volumerendered brain (a spatially normalised anatomical image for an individual DP). Cluster size threshold k* ≥ *6.* 

A more detailed inspection of the neuroimaging results for reading revealed that when hypotheses based on the three main theories are tested in individual DPs, DPs showed complex and heterogeneous patterns of underactivation in the brain regions predicted by the dyslexia theories. For instance, DP1 showed hypoactivation in eight areas predicted by the PDT (L area 6 (BA6), L area 44 (BA44), L middle temporal gyrus (BA21), L fusiform gyrus (BA19/37), L TE 3 (part of BA22), L IPC (PF) (BA40), L IPC (PFcm) (BA40) and L IPC (PGp) (BA39)), one area hypothesised by the MDT (R hOC5 (V5/MT)) and three areas predicted by the CDT (R Lobule VIIa Crus I (Hem), L Lobule VIIa Crus II (Hem) and R Lobule VIIa Crus II (Hem). DP10 exhibited hypoactivation in one area hypothesised by the PDT (L IPC

was consistent with the VDT but not with the MDT.

*An ROI mask was applied; see Section 2.7 and note for* **Table 1***.*

**112**

**Figure 1.**

individual DPs, comparing the predictions of all the main dyslexia theories, the neural correlates of reading for all DPs (except one DP) were in agreement with the hypotheses based on more than one theory. In the sample reported in [25], the neuroimaging results for one case (5.6%) were in agreement with the PDT, MDT and CDT and for another case with only the CDT. The results for six cases (33.3%) were in agreement with the PDT and CDT and the findings for 10 cases (55.6%) with the PDT, CDT and VDT. The results for all, but one DP, supported a multiple deficit model.

Supporters of the PDT may argue that the neural correlates of reading in all cases (except for DP13) are in agreement with the core deficit, as hypothesised by the PDT and that the hypoactivation in the cerebellum and/or magnocellular areas in these DPs just co-occurs with dyslexia. As highlighted above, contrary to previous studies, Reid's study [25] investigated the more direct link between reading deficit in DPs and the predictions of the main dyslexia theories on the neural level by using an fMRI reading task. Hence it seems reasonable to interpret the findings of hypoactivation in the areas hypothesised by the PDT and the CDT, in the same DP, as lending support to the claim that reading in a given DP is consistent with the predictions of both theories and therefore both phonological areas and cerebellar areas contribute to the reading impairment in a given DP and the CDT deficit is not just co-occurring with no causal effect on reading deficit (as argued by the protagonists of the PDT). The same reasoning also applies to DPs who exhibited underactivation in both phonological and visual/magnocellular areas.

Taking into consideration the additional predictions of the CDT (discussed above), it might be the case that the underactivation in phonological areas in all DPs (except DP13) is also consistent with the CDT (and with the PDT), but this holds only from the perspective of the CDT and not the perspective of the PDT. Finally, it is also possible that the underactivation in phonological areas in DP1 is also in line with the additional predictions of the MDT (discussed earlier); however, this is true only from the perspective of the MDT and not from the perspective of the PDT (see Section 3.1 for further discussion).

A single deficit model has been dominant for many years in the research on dyslexia and other neurodevelopmental disorders. Each dyslexia theory postulates a different and single underlying cause of dyslexia. However, a single deficit model, although parsimonious and straightforward to test, has limitations. For instance, it cannot explain cases which exhibit a single deficit but do not have a reading disorder. Such cases have been reported in longitudinal studies involving children 'at risk' of dyslexia [99]. Reid et al. [32] also reported cases of adult CPs, who, although exhibiting a phonological deficit, did not have a reading impairment. Furthermore, the single deficit model cannot account for the more frequent than chance co-occurrence of other neurodevelopmental disorders with dyslexia (see below for a further discussion). Therefore, Pennington [100] formulated a multiple deficit model (MMD). The MMD recognises the fact that there are multitudes of environmental and genetic risk factors and that they do not operate independently. It is possible that they are correlated with each other or that they share effects of gene-by-environment interaction, or genes may interact with each other as they are part of the genetic system. The model does not specify the causal connections between the levels of analyses, including feedback loops from the behavioural level to the neural system level (or even to the aetiology level). The strength and existence of causal connections need to be resolved empirically [100]. Multidisciplinary research on the underlying causes of reading disorder in dyslexia within the MDM holds significant promise.

**115**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals…*

**3.1 Neuroimaging studies testing a further refined set of brain areas (including areas hypothesised by other dyslexia theories) in longitudinal designs**

Research on the brain areas involved in language processing and reading, including those areas hypothesised by the main theories of dyslexia, is active. For instance, there is now growing evidence of the involvement of subcortical brain areas in reading and language skills [101, 102]. Also new research has been reported for the MDT. For instance, a high-resolution proton density-weighted MRI study [103] revealed that L LGN (but not R LGN) was significantly smaller in volume and differed in shape in vivo in DPs (vs. CPs). These results are consistent with the MDT, and future neuroimaging research testing the MDT needs to include LGN as an ROI in a neuroimaging study on reading deficit in DPs. Furthermore, there are other theories of dyslexia, for instance, the auditory MDT [48] and the lowfrequency phase-locking mechanism deficit theory [104]. Further research on the underlying reading deficits in dyslexia, using a refined set of brain areas (including also areas hypothesised by the other theories of dyslexia), is warranted, and it is

The study presented in [25] investigated reading in adult DPs in an fMRI task. Although such studies are valuable as they provide insight into the neural correlates of reading in a mature system, it is possible that the adult neural system may have been significantly or partially altered due to compensatory mechanisms. Given that reading is a learned skill that is acquired through instruction and practice over a relatively long period of time, it is likely that brain-based findings are going to be dynamic, and therefore longitudinal neuroimaging studies, starting with newborns with familial risk of dyslexia, are indispensable in tracking the developmental trajectory of reading deficits in dyslexia. Longitudinal studies may also be successful in testing the additional predictions of the CDT and MDT which could not be resolved in Reid's study [25].

**3.2 Controlling the effects of co-occurring neurodevelopmental disorders**

The current research indicates that co-occurrence of neurodevelopmental disorders is most likely more common than cases of 'pure' disorders [36]. A detailed history was taken in Reid's study [25] from participants regarding different disorders, and measures were collected for ADHD and DCD and entered into the fMRI analyses as covariates. This procedure ensured that the results were not confounded by these variables. Furthermore, the supplementary fMRI analyses showed that two DPs (11%) who were identified as possibly being at risk of clinical DCD exhibited underactivation in the areas consistent with DCD, but the underactivated areas for DP8 and DP15 differed (see [25] for details). These findings underscore the co-occurrence of these neurodevelopmental disorders and heterogeneity among

There is growing evidence that dyslexia may co-occur with other disorders, such as specific language impairment (SLI), speech sound disorder (SSD), autism spectrum disorder (ASD), dyscalculia, conduct disorder, oppositional defiant disorder, anxiety, depression and disruptive, impulse-control and conduct disorders (CDs). Currently the relationship between these disorders and dyslexia is unclear [105]. It should be emphasised here that, although some efforts, especially more recently, are made to control the effects of some co-occurring disorders, the effects of some other co-occurring disorders are not controlled for in dyslexia studies. Therefore there is an urgent need for future research on the underlying causes of reading

argued below that longitudinal designs are indispensable here.

participants who are at risk of clinical DCD.

*DOI: http://dx.doi.org/10.5772/intechopen.80677*

**3. Future directions**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals… DOI: http://dx.doi.org/10.5772/intechopen.80677*

#### **3. Future directions**

*Neuroimaging - Structure, Function and Mind*

deficit model.

lular areas.

Section 3.1 for further discussion).

individual DPs, comparing the predictions of all the main dyslexia theories, the neural correlates of reading for all DPs (except one DP) were in agreement with the hypotheses based on more than one theory. In the sample reported in [25], the neuroimaging results for one case (5.6%) were in agreement with the PDT, MDT and CDT and for another case with only the CDT. The results for six cases (33.3%) were in agreement with the PDT and CDT and the findings for 10 cases (55.6%) with the PDT, CDT and VDT. The results for all, but one DP, supported a multiple

Supporters of the PDT may argue that the neural correlates of reading in all cases (except for DP13) are in agreement with the core deficit, as hypothesised by the PDT and that the hypoactivation in the cerebellum and/or magnocellular areas in these DPs just co-occurs with dyslexia. As highlighted above, contrary to previous studies, Reid's study [25] investigated the more direct link between reading deficit in DPs and the predictions of the main dyslexia theories on the neural level by using an fMRI reading task. Hence it seems reasonable to interpret the findings of hypoactivation in the areas hypothesised by the PDT and the CDT, in the same DP, as lending support to the claim that reading in a given DP is consistent with the predictions of both theories and therefore both phonological areas and cerebellar areas contribute to the reading impairment in a given DP and the CDT deficit is not just co-occurring with no causal effect on reading deficit (as argued by the protagonists of the PDT). The same reasoning also applies to DPs who exhibited underactivation in both phonological and visual/magnocel-

Taking into consideration the additional predictions of the CDT (discussed above), it might be the case that the underactivation in phonological areas in all DPs (except DP13) is also consistent with the CDT (and with the PDT), but this holds only from the perspective of the CDT and not the perspective of the PDT. Finally, it is also possible that the underactivation in phonological areas in DP1 is also in line with the additional predictions of the MDT (discussed earlier); however, this is true only from the perspective of the MDT and not from the perspective of the PDT (see

A single deficit model has been dominant for many years in the research on dyslexia and other neurodevelopmental disorders. Each dyslexia theory postulates a different and single underlying cause of dyslexia. However, a single deficit model, although parsimonious and straightforward to test, has limitations. For instance, it cannot explain cases which exhibit a single deficit but do not have a reading disorder. Such cases have been reported in longitudinal studies involving children 'at risk' of dyslexia [99]. Reid et al. [32] also reported cases of adult CPs, who, although exhibiting a phonological deficit, did not have a reading impairment. Furthermore, the single deficit model cannot account for the more frequent than chance co-occurrence of other neurodevelopmental disorders with dyslexia (see below for a further discussion). Therefore, Pennington [100] formulated a multiple deficit model (MMD). The MMD recognises the fact that there are multitudes of environmental and genetic risk factors and that they do not operate independently. It is possible that they are correlated with each other or that they share effects of gene-by-environment interaction, or genes may interact with each other as they are part of the genetic system. The model does not specify the causal connections between the levels of analyses, including feedback loops from the behavioural level to the neural system level (or even to the aetiology level). The strength and existence of causal connections need to be resolved empirically [100]. Multidisciplinary research on the underlying causes of reading disorder in dyslexia within the MDM

**114**

holds significant promise.

#### **3.1 Neuroimaging studies testing a further refined set of brain areas (including areas hypothesised by other dyslexia theories) in longitudinal designs**

Research on the brain areas involved in language processing and reading, including those areas hypothesised by the main theories of dyslexia, is active. For instance, there is now growing evidence of the involvement of subcortical brain areas in reading and language skills [101, 102]. Also new research has been reported for the MDT. For instance, a high-resolution proton density-weighted MRI study [103] revealed that L LGN (but not R LGN) was significantly smaller in volume and differed in shape in vivo in DPs (vs. CPs). These results are consistent with the MDT, and future neuroimaging research testing the MDT needs to include LGN as an ROI in a neuroimaging study on reading deficit in DPs. Furthermore, there are other theories of dyslexia, for instance, the auditory MDT [48] and the lowfrequency phase-locking mechanism deficit theory [104]. Further research on the underlying reading deficits in dyslexia, using a refined set of brain areas (including also areas hypothesised by the other theories of dyslexia), is warranted, and it is argued below that longitudinal designs are indispensable here.

The study presented in [25] investigated reading in adult DPs in an fMRI task. Although such studies are valuable as they provide insight into the neural correlates of reading in a mature system, it is possible that the adult neural system may have been significantly or partially altered due to compensatory mechanisms. Given that reading is a learned skill that is acquired through instruction and practice over a relatively long period of time, it is likely that brain-based findings are going to be dynamic, and therefore longitudinal neuroimaging studies, starting with newborns with familial risk of dyslexia, are indispensable in tracking the developmental trajectory of reading deficits in dyslexia. Longitudinal studies may also be successful in testing the additional predictions of the CDT and MDT which could not be resolved in Reid's study [25].

#### **3.2 Controlling the effects of co-occurring neurodevelopmental disorders**

The current research indicates that co-occurrence of neurodevelopmental disorders is most likely more common than cases of 'pure' disorders [36]. A detailed history was taken in Reid's study [25] from participants regarding different disorders, and measures were collected for ADHD and DCD and entered into the fMRI analyses as covariates. This procedure ensured that the results were not confounded by these variables. Furthermore, the supplementary fMRI analyses showed that two DPs (11%) who were identified as possibly being at risk of clinical DCD exhibited underactivation in the areas consistent with DCD, but the underactivated areas for DP8 and DP15 differed (see [25] for details). These findings underscore the co-occurrence of these neurodevelopmental disorders and heterogeneity among participants who are at risk of clinical DCD.

There is growing evidence that dyslexia may co-occur with other disorders, such as specific language impairment (SLI), speech sound disorder (SSD), autism spectrum disorder (ASD), dyscalculia, conduct disorder, oppositional defiant disorder, anxiety, depression and disruptive, impulse-control and conduct disorders (CDs). Currently the relationship between these disorders and dyslexia is unclear [105]. It should be emphasised here that, although some efforts, especially more recently, are made to control the effects of some co-occurring disorders, the effects of some other co-occurring disorders are not controlled for in dyslexia studies. Therefore there is an urgent need for future research on the underlying causes of reading

deficit in dyslexia to control for the effects of the co-occurring disorders either by the exclusion of cases with such disorders or by collecting appropriate data (including genetic data, where available) to be used as covariates in the analyses.

The issue of co-occurring disorders is complex and can be further underscored by an observation that a person with a given neurodevelopmental disorder (e.g. dyslexia) may have first-degree relatives diagnosed with different neurodevelopmental disorders, for example, one with ADHD and another with DCD (Deborah Dewey, personal communication, July 2, 2015). It is currently unclear why this is the case, but it may suggest that genes that affect one neurodevelopmental disorder are also likely to affect other neurodevelopmental disorders [106]. The field of molecular genetics of co-occurring neurodevelopmental disorders is young. However, some findings have already suggested that common single-nucleotide polymorphisms on a number of chromosomes increase susceptibility to both dyslexia and SLI [107]. Research investigating generalist gene hypothesis [106], de novo gene mutations [107] and pleiotropic effects [108], using state-of-the-art molecular technologies, such as high-throughput genotyping and next-generation sequencing of whole genomes, holds the promise of providing important answers here. In summary, molecular genetics of co-occurring neurodevelopmental disorders makes progress in identifying genetic components which increase the susceptibility to more than one neurodevelopmental disorder. The more is known here, the easier it would be to also control the genetic component in experimental work. It must be emphasised that co-occurrence of neurodevelopmental disorders cannot be ignored in the future research on dyslexia because it is a potentially serious confound which is likely to distort results. See, for instance [109, 110], for findings which show that ADHD symptoms mediate deficits in developmental dyslexia.

#### **3.3 Using a variety of imaging tools in dyslexia research**

A promising way forward in dyslexia research would be to test individual DPs (or samples of DPs as homogenous, as possible, with respect to behavioural and genetic profiles) using various neuroimaging techniques, in addition to fMRI, which would allow for a fuller characterisation of DPs' neural profiles, including the neural correlates of reading deficit. Some attempts have already been made; for instance, a recent study [111] used structural MRI, diffusion MRI and probabilistic tractography to investigate the structural connections of the visual sensory pathway in dyslexia in vivo. The results revealed altered structural connectivity in DPs in the direct pathway between the L LGN and L V5/MT but not between the L LGN and L V1. Another study [112] combined fMRI with multi-voxel pattern analysis and functional and structural connectivity analysis of DTI data in adult DPs. The results revealed that phonetic representations in the L and R auditory cortex were intact, whereas anatomical and functional connections found between these areas and the L inferior frontal gyrus were disrupted, suggesting an access deficit.

Another fruitful way forward would be to ask novel questions using neuroimaging. Pugh et al. magnetic resonance spectroscopy (MRS) study [113] was the first to test the role of multiple metabolites in developing readers. The authors reported an inverse relationship between both glutamate and choline and reading ability, such that higher concentrations of these metabolites were associated with lower reading scores. Given that heightened levels of glutamate can reflect hyperexcitability [114], whereas heightened levels of choline are associated with abnormal white matter organisation [115], the results reported in [113] suggest potential links between abnormal white matter organisation and reading deficit and hyperexcitability and reading deficit in atypical brain development and reading acquisition. The findings reported in [113] are cited (among others) in support of a recently formulated neural noise hypothesis (NNH) of dyslexia (see [116] for details).

**117**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals…*

disorder in dyslexia, as well as reliable biomarkers for dyslexia.

endophenotypes reported in [25] is due in part to dyslexia risk genes.

The neuroimaging data undoubtedly provide a description of endophenotypes in dyslexia, but they do not offer an explanation of what causes such endophenotypes. As discussed above, Reid's study [25] contrasted, on the neural level, the explanatory frameworks of the main dyslexia theories, but an explanation at the genetic level was not investigated (as genetic data were not available for the studied DPs). Given findings on dyslexia within the fields of molecular genetics and imaging genetics, it is likely that the heterogeneity among DP's phenotypes and

Imaging genetics offers a bridge between behavioural measures and the brain. Relatively direct connections have been made between (1) brain function and dyslexia risk genes and (2) brain anatomy and dyslexia risk genes [120]. As a full summary of studies on imaging genetics in DPs (and in CPs) is beyond the scope of this chapter, interested readers are referred to the relevant reviews [102, 121]. Findings on brain function and genes associated with dyslexia are briefly summarised first. Cope et al.'s study [122] reported the strongest association between an fMRI activation for a reading task in the L anterior inferior parietal lobe and tandem repeat BV677278 in *DCDC2*. Another fMRI study [123], involving CPs and a reading task, reported that (1) single-nucleotide polymorphisms (SNPs) rs6980093 and rs7799109 (in *FOXP2*) were associated with variations of activation in the L frontal cortex and (2) SNP rs17243157 in the *KIAA0319/TTRAP/THEM2* locus was associated with asymmetry in the functional activation of the superior temporal sulcus. Wilcke et al.'s fMRI study [124] revealed a significant main effect for 'genetic risk' of *FOXP2* variant (rs12533005-G) in a temporoparietal area (significantly lower activation in the 'at risk of dyslexia' group than in the 'non-at-risk' group in the angular and supramarginal gyri). A MEG study [125] reported that DPs with a weakly expressing haplotype of *ROBO1* exhibited defective interaural interaction and the extent of the deficit correlated with the *ROBO1* expression level. Finally, another MEG study [126] reported that about half of DPs exhibited significantly higher levels of variability in their cortical responses to auditory and visual stimuli

**3.4 Imaging genetics**

Finally, recent MRI advances, such as multiband fMRI [117] and high-field MRI [118], promise to increase the spatial and/or temporal resolution of MRI and fMRI. Also, recent developments of more sophisticated diffusion MRI techniques, such as neurite orientation dispersion and density imaging (NODDI), hold promise of new insights into white matter structure and organisation in DPs (see Section 3.4 for further discussion of this). Furthermore, new developments in MEG also look promising. For instance, advanced preprocessing techniques which enable decomposition of the signal into components with origin inside and outside the head increase the signal-to-noise ratio by approximately 100%, enabling therefore even one-trial measurements with the standard MEG systems (e.g. whole head 306 Elekta or 275 CTF channel systems). Furthermore, optically pumped magnetometers (which allow MEG sensors to get closer to the head) should considerably increase the signal-to-noise ratio of MEG [119]. As the defining characteristic of dyslexia is impaired reading—a skill characterised by extremely rapid and interlocked processing events—it is likely that MEG (with its relatively high temporal resolution) would play a particularly important role in providing valuable insights into the underlying causes of reading deficit in this neurodevelopmental disorder. In summary, the advances discussed above offer new possibilities in dyslexia research, so that dyslexia endophenotypes can be investigated with higher spatial and temporal resolution, increasing the chance of elucidating the underlying causes of reading

*DOI: http://dx.doi.org/10.5772/intechopen.80677*

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals… DOI: http://dx.doi.org/10.5772/intechopen.80677*

Finally, recent MRI advances, such as multiband fMRI [117] and high-field MRI [118], promise to increase the spatial and/or temporal resolution of MRI and fMRI. Also, recent developments of more sophisticated diffusion MRI techniques, such as neurite orientation dispersion and density imaging (NODDI), hold promise of new insights into white matter structure and organisation in DPs (see Section 3.4 for further discussion of this). Furthermore, new developments in MEG also look promising. For instance, advanced preprocessing techniques which enable decomposition of the signal into components with origin inside and outside the head increase the signal-to-noise ratio by approximately 100%, enabling therefore even one-trial measurements with the standard MEG systems (e.g. whole head 306 Elekta or 275 CTF channel systems). Furthermore, optically pumped magnetometers (which allow MEG sensors to get closer to the head) should considerably increase the signal-to-noise ratio of MEG [119]. As the defining characteristic of dyslexia is impaired reading—a skill characterised by extremely rapid and interlocked processing events—it is likely that MEG (with its relatively high temporal resolution) would play a particularly important role in providing valuable insights into the underlying causes of reading deficit in this neurodevelopmental disorder. In summary, the advances discussed above offer new possibilities in dyslexia research, so that dyslexia endophenotypes can be investigated with higher spatial and temporal resolution, increasing the chance of elucidating the underlying causes of reading disorder in dyslexia, as well as reliable biomarkers for dyslexia.

#### **3.4 Imaging genetics**

*Neuroimaging - Structure, Function and Mind*

deficit in dyslexia to control for the effects of the co-occurring disorders either by the exclusion of cases with such disorders or by collecting appropriate data (includ-

The issue of co-occurring disorders is complex and can be further underscored by

A promising way forward in dyslexia research would be to test individual DPs (or samples of DPs as homogenous, as possible, with respect to behavioural and genetic profiles) using various neuroimaging techniques, in addition to fMRI, which would allow for a fuller characterisation of DPs' neural profiles, including the neural correlates of reading deficit. Some attempts have already been made; for instance, a recent study [111] used structural MRI, diffusion MRI and probabilistic tractography to investigate the structural connections of the visual sensory pathway in dyslexia in vivo. The results revealed altered structural connectivity in DPs in the direct pathway between the L LGN and L V5/MT but not between the L LGN and L V1. Another study [112] combined fMRI with multi-voxel pattern analysis and functional and structural connectivity analysis of DTI data in adult DPs. The results revealed that phonetic representations in the L and R auditory cortex were intact, whereas anatomical and functional connections found between these areas and the

Another fruitful way forward would be to ask novel questions using neuroimaging. Pugh et al. magnetic resonance spectroscopy (MRS) study [113] was the first to test the role of multiple metabolites in developing readers. The authors reported an inverse relationship between both glutamate and choline and reading ability, such that higher concentrations of these metabolites were associated with lower reading scores. Given that heightened levels of glutamate can reflect hyperexcitability [114], whereas heightened levels of choline are associated with abnormal white matter organisation [115], the results reported in [113] suggest potential links between abnormal white matter organisation and reading deficit and hyperexcitability and reading deficit in atypical brain development and reading acquisition. The findings reported in [113] are cited (among others) in support of a recently formulated

ing genetic data, where available) to be used as covariates in the analyses.

**3.3 Using a variety of imaging tools in dyslexia research**

L inferior frontal gyrus were disrupted, suggesting an access deficit.

neural noise hypothesis (NNH) of dyslexia (see [116] for details).

an observation that a person with a given neurodevelopmental disorder (e.g. dyslexia) may have first-degree relatives diagnosed with different neurodevelopmental disorders, for example, one with ADHD and another with DCD (Deborah Dewey, personal communication, July 2, 2015). It is currently unclear why this is the case, but it may suggest that genes that affect one neurodevelopmental disorder are also likely to affect other neurodevelopmental disorders [106]. The field of molecular genetics of co-occurring neurodevelopmental disorders is young. However, some findings have already suggested that common single-nucleotide polymorphisms on a number of chromosomes increase susceptibility to both dyslexia and SLI [107]. Research investigating generalist gene hypothesis [106], de novo gene mutations [107] and pleiotropic effects [108], using state-of-the-art molecular technologies, such as high-throughput genotyping and next-generation sequencing of whole genomes, holds the promise of providing important answers here. In summary, molecular genetics of co-occurring neurodevelopmental disorders makes progress in identifying genetic components which increase the susceptibility to more than one neurodevelopmental disorder. The more is known here, the easier it would be to also control the genetic component in experimental work. It must be emphasised that co-occurrence of neurodevelopmental disorders cannot be ignored in the future research on dyslexia because it is a potentially serious confound which is likely to distort results. See, for instance [109, 110], for findings which show that ADHD symptoms mediate deficits in developmental dyslexia.

**116**

The neuroimaging data undoubtedly provide a description of endophenotypes in dyslexia, but they do not offer an explanation of what causes such endophenotypes. As discussed above, Reid's study [25] contrasted, on the neural level, the explanatory frameworks of the main dyslexia theories, but an explanation at the genetic level was not investigated (as genetic data were not available for the studied DPs). Given findings on dyslexia within the fields of molecular genetics and imaging genetics, it is likely that the heterogeneity among DP's phenotypes and endophenotypes reported in [25] is due in part to dyslexia risk genes.

Imaging genetics offers a bridge between behavioural measures and the brain. Relatively direct connections have been made between (1) brain function and dyslexia risk genes and (2) brain anatomy and dyslexia risk genes [120]. As a full summary of studies on imaging genetics in DPs (and in CPs) is beyond the scope of this chapter, interested readers are referred to the relevant reviews [102, 121]. Findings on brain function and genes associated with dyslexia are briefly summarised first. Cope et al.'s study [122] reported the strongest association between an fMRI activation for a reading task in the L anterior inferior parietal lobe and tandem repeat BV677278 in *DCDC2*. Another fMRI study [123], involving CPs and a reading task, reported that (1) single-nucleotide polymorphisms (SNPs) rs6980093 and rs7799109 (in *FOXP2*) were associated with variations of activation in the L frontal cortex and (2) SNP rs17243157 in the *KIAA0319/TTRAP/THEM2* locus was associated with asymmetry in the functional activation of the superior temporal sulcus. Wilcke et al.'s fMRI study [124] revealed a significant main effect for 'genetic risk' of *FOXP2* variant (rs12533005-G) in a temporoparietal area (significantly lower activation in the 'at risk of dyslexia' group than in the 'non-at-risk' group in the angular and supramarginal gyri). A MEG study [125] reported that DPs with a weakly expressing haplotype of *ROBO1* exhibited defective interaural interaction and the extent of the deficit correlated with the *ROBO1* expression level. Finally, another MEG study [126] reported that about half of DPs exhibited significantly higher levels of variability in their cortical responses to auditory and visual stimuli

in several brain areas of the reading network. A positive and significant relationship between the degree of neural variability in the primary auditory cortex across both DPs and CPs and the number of risk alleles at rs6935076 in *KIAA0319* was found, supporting the link between *KIAA0319* and neural variability.

Moving to studies which focused on brain structure and dyslexia risk genes, four publications need to be mentioned. A voxel-based morphometry (VBM) study [127] showed that participants with high genetic risk variants in *TNFRSF1B* exhibited significantly lower grey matter (GM) probability in Heschl's gyrus/posterior superior temporal sulcus (HG/pSTS) but significantly higher GM probability in pSTS and the converse was true for participants with low genetic risk variants in *TNFRSF1B*. A structural MRI study [128] reported that *DYX1C1*, *DCDC2* and *KIAA0319* contained SNPs that significantly correlated with white matter volume in the L temporoparietal area and that white matter volume influenced reading ability in a general population sample. Finally, two studies need to be briefly discussed here—both using DTI. It should be noted that DTI (and a more sophisticated diffusion MRI techniques, such as NODDI, mentioned above, which provides more specific markers of brain tissue microstructure than standard indices from DTI) could become particularly important neuroimaging techniques in dyslexia research when combined with genetic measures because there is evidence that suggests that some dyslexia risk candidate genes (e.g. *DCDC2, KIAA0319, DYX1C1, FOXP2* and *CNTNAP2*) are involved in neuronal migration (a period in brain development during which young neurons 'look' for their final destination in the brain; this process requires stringent controls that are genetically governed) and/or neurite outgrowth [102]. Such genes (together with the environment and gene-by-environment interaction) may contribute to shaping the brain's white matter structure which can be inferred from the results obtained from MRI diffusion techniques. One of the first studies [129], which combined genetic, DTI (and behavioural) measures, reported that *MRPL19/C2ORF3* was associated with general cognitive ability in DPs and participants with SLI. Also associations between white matter structure measured using DTI and genotypes at the *MRPL19/ C2ORF3* (in an independent sample) were found in the posterior corpus callosum and cingulum connecting the temporal, parietal and occipital areas. More recently, a voxel-based DTI study [130] revealed that DPs with a deletion in *DCDC2*/intron 2 compared to CPs exhibited significantly lower fractional anisotropy (FA) in a number of L hemisphere areas (including superior longitudinal fasciculus, arcuate fasciculus, inferior longitudinal fasciculus, optic radiation, corpus callosum, inferior cerebellar pedunculus and two R hemisphere areas (superior longitudinal fasciculus and corpus callosum)), indicating anatomical abnormalities of these white matter structures.

Although imaging genetics is a relatively young field and most findings need to be replicated, endophenotypes uncovered by imaging genetics hold promise for building a link between the behavioural and genetic characteristics of DPs [131]. Currently, however, the imaging genetics results are insufficient to obtain a full picture of the underlying causes of reading deficit in dyslexia. Advancement of imaging genetics in dyslexia needs to proceed in three major ways. First, new hypothesis-driven imaging genetic studies must be designed to investigate the function of neuronal migration (and other) genes and their relationships with wellcharacterised cognitive and sensory vulnerability and to find connections between such susceptibility variants and neuroanatomical endophenotypes [102]. The integration of specific behavioural, imaging and genetic data may result in the identification of brain areas with gene and behavioural specific effects or with widespread effects [102]. Second, although valuable results have emerged from known dyslexia risk genes, they cannot test other genetic impacts on the overall reading deficits in dyslexia. Therefore, sequencing studies and genome-wide association studies (GWAS) are needed, so that new genes associated with risk of dyslexia can

**119**

**4. Conclusion**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals…*

**3.5 Neuroimaging intergenerational transmission of brain circuity**

brain circuits in individuals who haven't yet learnt to read [135].

Intergenerational transmission is defined as 'the transfer of traits from parents to offspring, including genetic and non-genetic influences. For example, the impact of prenatal effects (e.g. parent nutrition and in utero environment) as well as postnatal rearing effects and other environmental factors could lead to epigenetic or behavioural changes in the offspring, which are thereby intergenerationally transmitted' [134, p. 644]. Intergenerational neuroimaging is a new approach which uses neuroimaging to investigate the relationship of cognitive and neural phenotypes between children and their parents. It holds the promise of shedding light on the ontogeny of complex neurodevelopmental disorders, including dyslexia. One of the major goals of neuroimaging intergenerational transmission of brain circuity in such disorders is to dissociate the different sources of intergenerational effects on the brain circuity on dyslexia by contrasting parent–child pairs from natural conception, adoptive families and in vitro fertilisation (IVF). Such designs have a potential in addressing many important questions in dyslexia research, including (1) intergenerational effects on the brain structure and function (including those supporting reading ability) and (2) the impact of gender-specific effects at the prenatal stage (especially important as dyslexia is more prevalent in males [29]), including the effects of prenatal testosterone levels on brain development, epigenetic effects of estrogen on dyslexia risk genes and gender-specific transmission patterns in reading-related

The results from the first neuroimaging study to use a multiple case approach to investigate individual differences among DPs [25], reviewed here, revealed that DPs are characterised by marked heterogeneity and complexity in the neural correlates of their reading deficit; even if the reading deficit of two DPs was consistent with the same theory, their affected brain areas could differ. The results further show that the neural correlates of reading deficit for all (except one) DPs were consistent with more than one theory, supporting a multiple deficit model. It is suggested that future research on causes of reading deficit in dyslexia, to make significant progress, would need to (1) focus on the multiple deficit model [100], (2) use neuroimaging to test a further refined set of brain areas (including areas hypothesised by other dyslexia theories) in longitudinal designs, (3) control the effects of cooccurring neurodevelopmental disorders, (4) use different imaging tools

be discovered and their role tested in the neuroimaging studies, providing a fuller picture of phenotypes and endophenotypes in dyslexia [121]. Such attempts have already started; for instance, a GWAS [132] reported that mismatch negativity (MMN) (which reflects automatic speech deviance processing and is abnormal in DPs) was significantly associated with an intergenic SNP on chromosome 4q32.1. This SNP is hypothesised to have a potential effect on the expression of *SLC2A3*—a gene that encodes a neuronal glucose transporter. The results suggest a possible trans-regulation effect on *SLC2A3*, which might cause glucose deficits in DPs and this in turn may account for DPs' attenuated MMN response. Third, as behavioural deficits overlap across neurodevelopmental disorders, it is of importance to include in the imaging genetics genes associated with different co-occurring disorders, including dyslexia. Such attempts have already been reported in dyslexia with respect to, for instance, *FOXP2* [124]—a gene originally associated with developmental verbal dyspraxia and included in imaging genetics in this disorder [133].

*DOI: http://dx.doi.org/10.5772/intechopen.80677*

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals… DOI: http://dx.doi.org/10.5772/intechopen.80677*

be discovered and their role tested in the neuroimaging studies, providing a fuller picture of phenotypes and endophenotypes in dyslexia [121]. Such attempts have already started; for instance, a GWAS [132] reported that mismatch negativity (MMN) (which reflects automatic speech deviance processing and is abnormal in DPs) was significantly associated with an intergenic SNP on chromosome 4q32.1. This SNP is hypothesised to have a potential effect on the expression of *SLC2A3*—a gene that encodes a neuronal glucose transporter. The results suggest a possible trans-regulation effect on *SLC2A3*, which might cause glucose deficits in DPs and this in turn may account for DPs' attenuated MMN response. Third, as behavioural deficits overlap across neurodevelopmental disorders, it is of importance to include in the imaging genetics genes associated with different co-occurring disorders, including dyslexia. Such attempts have already been reported in dyslexia with respect to, for instance, *FOXP2* [124]—a gene originally associated with developmental verbal dyspraxia and included in imaging genetics in this disorder [133].

#### **3.5 Neuroimaging intergenerational transmission of brain circuity**

Intergenerational transmission is defined as 'the transfer of traits from parents to offspring, including genetic and non-genetic influences. For example, the impact of prenatal effects (e.g. parent nutrition and in utero environment) as well as postnatal rearing effects and other environmental factors could lead to epigenetic or behavioural changes in the offspring, which are thereby intergenerationally transmitted' [134, p. 644]. Intergenerational neuroimaging is a new approach which uses neuroimaging to investigate the relationship of cognitive and neural phenotypes between children and their parents. It holds the promise of shedding light on the ontogeny of complex neurodevelopmental disorders, including dyslexia. One of the major goals of neuroimaging intergenerational transmission of brain circuity in such disorders is to dissociate the different sources of intergenerational effects on the brain circuity on dyslexia by contrasting parent–child pairs from natural conception, adoptive families and in vitro fertilisation (IVF). Such designs have a potential in addressing many important questions in dyslexia research, including (1) intergenerational effects on the brain structure and function (including those supporting reading ability) and (2) the impact of gender-specific effects at the prenatal stage (especially important as dyslexia is more prevalent in males [29]), including the effects of prenatal testosterone levels on brain development, epigenetic effects of estrogen on dyslexia risk genes and gender-specific transmission patterns in reading-related brain circuits in individuals who haven't yet learnt to read [135].

#### **4. Conclusion**

*Neuroimaging - Structure, Function and Mind*

in several brain areas of the reading network. A positive and significant relationship between the degree of neural variability in the primary auditory cortex across both DPs and CPs and the number of risk alleles at rs6935076 in *KIAA0319* was found,

Moving to studies which focused on brain structure and dyslexia risk genes, four publications need to be mentioned. A voxel-based morphometry (VBM) study [127] showed that participants with high genetic risk variants in *TNFRSF1B* exhibited significantly lower grey matter (GM) probability in Heschl's gyrus/posterior superior temporal sulcus (HG/pSTS) but significantly higher GM probability in pSTS and the converse was true for participants with low genetic risk variants in *TNFRSF1B*. A structural MRI study [128] reported that *DYX1C1*, *DCDC2* and *KIAA0319* contained SNPs that significantly correlated with white matter volume in the L temporoparietal area and that white matter volume influenced reading ability in a general population sample. Finally, two studies need to be briefly discussed here—both using DTI. It should be noted that DTI (and a more sophisticated diffusion MRI techniques, such as NODDI, mentioned above, which provides more specific markers of brain tissue microstructure than standard indices from DTI) could become particularly important neuroimaging techniques in dyslexia research when combined with genetic measures because there is evidence that suggests that some dyslexia risk candidate genes (e.g. *DCDC2, KIAA0319, DYX1C1, FOXP2* and *CNTNAP2*) are involved in neuronal migration (a period in brain development during which young neurons 'look' for their final destination in the brain; this process requires stringent controls that are genetically governed) and/or neurite outgrowth [102]. Such genes (together with the environment and gene-by-environment interaction) may contribute to shaping the brain's white matter structure which can be inferred from the results obtained from MRI diffusion techniques. One of the first studies [129], which combined genetic, DTI (and behavioural) measures, reported that *MRPL19/C2ORF3* was associated with general cognitive ability in DPs and participants with SLI. Also associations between white matter structure measured using DTI and genotypes at the *MRPL19/ C2ORF3* (in an independent sample) were found in the posterior corpus callosum and cingulum connecting the temporal, parietal and occipital areas. More recently, a voxel-based DTI study [130] revealed that DPs with a deletion in *DCDC2*/intron 2 compared to CPs exhibited significantly lower fractional anisotropy (FA) in a number of L hemisphere areas (including superior longitudinal fasciculus, arcuate fasciculus, inferior longitudinal fasciculus, optic radiation, corpus callosum, inferior cerebellar pedunculus and two R hemisphere areas (superior longitudinal fasciculus and corpus callosum)), indicating anatomical abnormalities of these white matter structures. Although imaging genetics is a relatively young field and most findings need to be replicated, endophenotypes uncovered by imaging genetics hold promise for building a link between the behavioural and genetic characteristics of DPs [131]. Currently, however, the imaging genetics results are insufficient to obtain a full picture of the underlying causes of reading deficit in dyslexia. Advancement of imaging genetics in dyslexia needs to proceed in three major ways. First, new hypothesis-driven imaging genetic studies must be designed to investigate the function of neuronal migration (and other) genes and their relationships with wellcharacterised cognitive and sensory vulnerability and to find connections between such susceptibility variants and neuroanatomical endophenotypes [102]. The integration of specific behavioural, imaging and genetic data may result in the identification of brain areas with gene and behavioural specific effects or with widespread effects [102]. Second, although valuable results have emerged from known dyslexia risk genes, they cannot test other genetic impacts on the overall reading deficits in dyslexia. Therefore, sequencing studies and genome-wide association studies (GWAS) are needed, so that new genes associated with risk of dyslexia can

supporting the link between *KIAA0319* and neural variability.

**118**

The results from the first neuroimaging study to use a multiple case approach to investigate individual differences among DPs [25], reviewed here, revealed that DPs are characterised by marked heterogeneity and complexity in the neural correlates of their reading deficit; even if the reading deficit of two DPs was consistent with the same theory, their affected brain areas could differ. The results further show that the neural correlates of reading deficit for all (except one) DPs were consistent with more than one theory, supporting a multiple deficit model. It is suggested that future research on causes of reading deficit in dyslexia, to make significant progress, would need to (1) focus on the multiple deficit model [100], (2) use neuroimaging to test a further refined set of brain areas (including areas hypothesised by other dyslexia theories) in longitudinal designs, (3) control the effects of cooccurring neurodevelopmental disorders, (4) use different imaging tools

(high-field MRI (including diffusion techniques), multiband fMRI and MEG with optically pumped magnetometers), (5) progress imaging genetics and (6) pursue the neuroimaging intergenerational transmission of brain circuity.

#### **Acknowledgements**

**Figure 1** is republished with permission of Nova Science Publishers, Inc., from Reid, A.A., An fMRI multiple case study of the neural correlates of reading deficit in individuals with developmental dyslexia: Theoretical implications, in Advances in Neuroimaging Research, 2014; permission conveyed through Copyright Clearance Center, Inc..

The Lord Dowding Fund financed the MRI scanning for Reid's study [25]. Thanks are due to the participants and to Joel Talcott, Liz Wilkinson, Simon Eickhoff, the Aston Neuroimaging Group and the FIL Methods Group for help and advice with various aspects of the study [25].

### **Conflict of interest**

The author declares no conflict of interest.

### **Author details**

Agnieszka A. Reid Independent Researcher, Cambridge, UK

\*Address all correspondence to: agnieszka.reid@virginmedia.com

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**121**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals…*

diagnosis: Report of 2 cases. Alzheimer Disease and Associated Disorders.

[11] Wagner AD. Early detection of Alzheimer's disease: An fMRI marker for people at risk? Nature Neuroscience.

[12] Nobili F et al. Unawareness of memory deficit in amnestic MCI: FDG-PET findings. Journal of Alzheimer's Disease. 2010;**22**(3):

[13] Josef Golubic S et al. MEG biomarker of Alzheimer's disease: Absence of a prefrontal generator during auditory sensory gating. Human Brain Mapping.

[14] Bose SK et al. Classification of schizophrenic patients and healthy controls using [18F] fluorodopa PET imaging. Schizophrenia Research.

[15] Taylor JA et al. Auditory prediction errors as individual biomarkers of schizophrenia. Neuroimage: Clinical.

biomarkers of schizophrenia from MEG resting-state functional connectivity networks: Preliminary data. Journal of Behavioral and Brain Science.

[17] Serrallach B et al. Neural biomarkers for dyslexia, ADHD, and ADD in the auditory cortex of children. Frontiers in

development. Human Brain Mapping.

[16] Bowyer SM et al. Potential

Neuroscience. 2016;**10**:324

2014;**35**(5):2148-2162

[18] Adisetiyo V et al. Attentiondeficit/hyperactivity disorder without comorbidity is associated with distinct atypical patterns of cerebral microstructural

2017;**38**(10):5180-5194

2008;**106**(2-3):148-155

2017;**15**:264-273

2015;**5**(01):1-11

2010;**24**(1):108-114

2000;**3**(10):973-974

993-1003

*DOI: http://dx.doi.org/10.5772/intechopen.80677*

[1] Mazziotta JC. Time and space. In: Toga AW, Mazziotta JC, editors. Brain Mapping: The Methods. San Diego, USA: Academic Press; 2002. pp. 33-46

[2] Kamali A et al. Diffusion tensor tractography of the mammillothalamic tract in the human brain using a high spatial resolution DTI technique. Scientific Reports. 2018;**8**(1):5229

[3] Steven AJ, Zhuo J, Melhem ER. Diffusion kurtosis imaging: An emerging technique for evaluating the microstructural environment of the brain. American Journal of Roentgenology. 2014;**202**(1):W26-W33

[4] Zhang H et al. NODDI: Practical

in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage.

[5] Jensen JH et al. Magnetic field correlation imaging.

Magnetic Resonance in Medicine.

[6] Hanson SJ, Bunzl M, editors. Foundational Issues in Human Brain Mapping. Kindle Edition. Cambridge,

[7] Jones DK, editor. Diffusion MRI. Theory, Methods and

Applications. Oxford: Oxford University

[8] Poldrack RA, Mumford JA, Nichols TE. Handbook of Functional MRI Data Analysis. Cambridge: Cambridge

[9] Toga AW, Mazziotta JC, editors. Brain Mapping: The Methods. 2nd ed. San Diego, USA: Academic Press; 2002

[10] Frisoni GB et al. Preliminary evidence of validity of the revised criteria for Alzheimer disease

2012;**61**(4):1000-1016

2006;**55**(6):1350-1361

MA: MIT Press; 2010

University Press; 2011

Press; 2011

**References**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals… DOI: http://dx.doi.org/10.5772/intechopen.80677*

#### **References**

*Neuroimaging - Structure, Function and Mind*

advice with various aspects of the study [25].

The author declares no conflict of interest.

**Acknowledgements**

Clearance Center, Inc..

**Conflict of interest**

(high-field MRI (including diffusion techniques), multiband fMRI and MEG with optically pumped magnetometers), (5) progress imaging genetics and (6) pursue

**Figure 1** is republished with permission of Nova Science Publishers, Inc., from Reid, A.A., An fMRI multiple case study of the neural correlates of reading deficit in individuals with developmental dyslexia: Theoretical implications, in Advances

in Neuroimaging Research, 2014; permission conveyed through Copyright

The Lord Dowding Fund financed the MRI scanning for Reid's study [25]. Thanks are due to the participants and to Joel Talcott, Liz Wilkinson, Simon Eickhoff, the Aston Neuroimaging Group and the FIL Methods Group for help and

the neuroimaging intergenerational transmission of brain circuity.

**120**

**Author details**

Agnieszka A. Reid

provided the original work is properly cited.

Independent Researcher, Cambridge, UK

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: agnieszka.reid@virginmedia.com

[1] Mazziotta JC. Time and space. In: Toga AW, Mazziotta JC, editors. Brain Mapping: The Methods. San Diego, USA: Academic Press; 2002. pp. 33-46

[2] Kamali A et al. Diffusion tensor tractography of the mammillothalamic tract in the human brain using a high spatial resolution DTI technique. Scientific Reports. 2018;**8**(1):5229

[3] Steven AJ, Zhuo J, Melhem ER. Diffusion kurtosis imaging: An emerging technique for evaluating the microstructural environment of the brain. American Journal of Roentgenology. 2014;**202**(1):W26-W33

[4] Zhang H et al. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage. 2012;**61**(4):1000-1016

[5] Jensen JH et al. Magnetic field correlation imaging. Magnetic Resonance in Medicine. 2006;**55**(6):1350-1361

[6] Hanson SJ, Bunzl M, editors. Foundational Issues in Human Brain Mapping. Kindle Edition. Cambridge, MA: MIT Press; 2010

[7] Jones DK, editor. Diffusion MRI. Theory, Methods and Applications. Oxford: Oxford University Press; 2011

[8] Poldrack RA, Mumford JA, Nichols TE. Handbook of Functional MRI Data Analysis. Cambridge: Cambridge University Press; 2011

[9] Toga AW, Mazziotta JC, editors. Brain Mapping: The Methods. 2nd ed. San Diego, USA: Academic Press; 2002

[10] Frisoni GB et al. Preliminary evidence of validity of the revised criteria for Alzheimer disease

diagnosis: Report of 2 cases. Alzheimer Disease and Associated Disorders. 2010;**24**(1):108-114

[11] Wagner AD. Early detection of Alzheimer's disease: An fMRI marker for people at risk? Nature Neuroscience. 2000;**3**(10):973-974

[12] Nobili F et al. Unawareness of memory deficit in amnestic MCI: FDG-PET findings. Journal of Alzheimer's Disease. 2010;**22**(3): 993-1003

[13] Josef Golubic S et al. MEG biomarker of Alzheimer's disease: Absence of a prefrontal generator during auditory sensory gating. Human Brain Mapping. 2017;**38**(10):5180-5194

[14] Bose SK et al. Classification of schizophrenic patients and healthy controls using [18F] fluorodopa PET imaging. Schizophrenia Research. 2008;**106**(2-3):148-155

[15] Taylor JA et al. Auditory prediction errors as individual biomarkers of schizophrenia. Neuroimage: Clinical. 2017;**15**:264-273

[16] Bowyer SM et al. Potential biomarkers of schizophrenia from MEG resting-state functional connectivity networks: Preliminary data. Journal of Behavioral and Brain Science. 2015;**5**(01):1-11

[17] Serrallach B et al. Neural biomarkers for dyslexia, ADHD, and ADD in the auditory cortex of children. Frontiers in Neuroscience. 2016;**10**:324

[18] Adisetiyo V et al. Attentiondeficit/hyperactivity disorder without comorbidity is associated with distinct atypical patterns of cerebral microstructural development. Human Brain Mapping. 2014;**35**(5):2148-2162

[19] Adisetiyo V et al. Multimodal MR imaging of brain iron in attention deficit hyperactivity disorder: A noninvasive biomarker that responds to psychostimulant treatment? Radiology. 2014;**272**(2):524-532

[20] Pernet CR et al. Brain classification reveals the right cerebellum as the best biomarker of dyslexia. BMC Neuroscience. 2009;**10**:67

[21] Guttorm TK et al. Newborn event-related potentials predict poorer pre-reading skills in children at risk for dyslexia. Journal of Learning Disabilities. 2010;**43**(5):391-401

[22] Maurer U et al. Impaired tuning of a fast occipito-temporal response for print in dyslexic children learning to read. Brain. 2007;**130**(Pt 12):3200-3210

[23] Salmelin R, Helenius P, Service E. Neurophysiology of fluent and impaired reading: A magnetoencephalographic approach. Journal of Clinical Neurophysiology. 2000;**17**(2):163-174

[24] Perrachione TK et al. Dysfunction of rapid neural adaptation in dyslexia. Neuron. 2016;**92**(6):1383-1397

[25] Reid AA. An fMRI multiple case study of the neural correlates of reading deficit in individuals with developmental dyslexia: Theoretical implications. In: Asher-Hansley V, editor. Advances in Neuroimaging Research. New York: Nova Science Publishers; 2014. pp. 1-119

[26] Morgan P. A case study of congenital word blindness. British Medical Journal. 1896;**7**:1378-1379

[27] Shaywitz SE. Current concepts: Dyslexia. The New England Journal of Medicine. 1998;**338**:307-312

[28] World Health Organization. The International Classification of Diseases. Classification of Mental

and Behavioural Disorders. Vol. 10. Geneva: World Health Organization Publications; 1993

[29] Rutter M et al. Sex differences in developmental reading disability: New findings from 4 epidemiological studies. Journal of the American Medical Association. 2004;**291**(16):2007-2012

[30] Goulandris N, editor. Dyslexia in Different Languages. Cross-Linguistic Comparisons. London: Whurr; 2003

[31] Reid AA. Developmental dyslexia: Evidence from Polish. In: Joshi RM, Aaron PG, editors. Handbook of Orthography and Literacy. Mahwah: LEA; 2006. pp. 249-274

[32] Reid AA et al. Cognitive profiles of adult developmental dyslexics: Theoretical implications. Dyslexia. 2007;**13**:1-24

[33] Poelmans G et al. A theoretical molecular network for dyslexia: Integrating available genetic findings. Molecular Psychiatry. 2011;**16**(4):365-382

[34] Nicolson R. Positive Dyslexia. Sheffield, UK: Rodin Books; 2015

[35] Germano E, Gagliano A, Curatolo P. Comorbidity of ADHD and dyslexia. Developmental Neuropsychology. 2010;**35**(5):475-493

[36] Kaplan BJ et al. DCD may not be a discrete disorder. Human Movement Science. 1998;**17**:471-490

[37] Fawcett AJ, Nicholson RI. Persistent deficits in motor skill of children with dyslexia. Journal of Motor Behaviour. 1995;**27**:235-240

[38] Iversen S et al. Motor coordination difficulties in a municipality group and in a clinical sample of poor readers. Dyslexia. 2005;**11**(3):217-231

[39] Frith U. Paradoxes in the definition of dyslexia. Dyslexia. An International Journal of Research and Practice. 1999;**5**(4):192-214

**123**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals…*

dyslexia. Trends in Neurosciences.

[52] Hansen PC et al. Are dyslexics' visual deficits limited to measures of dorsal stream function? Neuroreport.

[53] Cicchini GM et al. Strong motion deficits in dyslexia associated with DCDC2 gene alteration. The Journal of Neuroscience. 2015;**35**(21):8059-8064

[54] Gori S et al. The DCDC2 intron 2 deletion impairs illusory motion perception unveiling the selective role of magnocellular-dorsal stream in reading (dis)ability. Cerebral Cortex.

[55] Liederman J et al. The role of motion direction selective extrastriate regions in reading: A transcranial magnetic stimulation study. Brain and Language.

[56] Stein J. The current status of the magnocellular theory of developmental dyslexia. Neuropsychologia. 2018

Performance of dyslexic children on cerebellar and cognitive tests. Journal of Motor Behavior. 1999;**31**(1):68-78

[59] Nicolson RI, Fawcett AJ, Dean P. Time estimation deficits in developmental dyslexia: Evidence of cerebellar involvement. Proceedings of the

Biological Sciences. 1995;**259**(1354):43-47

[60] Nicolson RI, Fawcett AJ, Dean P.

cerebellar deficit hypothesis. Trends in Neurosciences. 2001;**24**(9):508-511

Developmental dyslexia: The

[57] Fawcett AJ, Nicolson RI.

[58] Nicolson RI et al. Eyeblink conditioning indicates cerebellar abnormality in dyslexia. Experimental Brain Research. 2002;**143**(1):42-50

[51] Lovegrove WJ et al. Contrast sensitivity functions and specific reading disability. Neuropsychologia. 1982;**20**:309-315

1997;**20**(4):147-152

2001;**12**(7):1527-1530

2015;**25**(6):1685-1695

2003;**85**(1):140-155

*DOI: http://dx.doi.org/10.5772/intechopen.80677*

developmental dyslexia: Insights from a multiple case study of dyslexic adults.

[40] Ramus F et al. Theories of

Brain. 2003;**126**(Pt 4):841-865

[41] Snowling MJ, Caravolas M. Developmental dyslexia. In: Gaskell MG, editor. The Oxford Handbook of Psycholinguistics. Oxford: Oxford University Press; 2007. pp. 667-683

[42] Ramus F, Szenkovits G. What phonological deficit? The Quarterly Journal of Experimental Psychology.

[43] Olson R et al. Specific deficits in component reading and language skills: Genetic and environmental influences. Journal of Learning Disabilities.

[44] Paulesu E et al. A cultural effect on brain function. Nature Neuroscience.

[45] Wang S, Gathercole SE. Working memory deficits in children with reading difficulties: Memory span and dual task coordination. Journal of Experimental Child Psychology.

[46] Frith U. Brain, mind and behaviour in dyslexia. In: Hulme C, Snowling M, editors. Dyslexia: Biology, Cognition and Intervention. London: Whurr Pulbishers Ltd; 1997. pp. 1-19

Developmental Disorders of Language, Learning and Cognition. Chichester,

[48] Stein J. The magnocellular theory of developmental dyslexia. Dyslexia.

[49] Stein J. Visual motion sensitivity and reading. Neuropsychologia.

[50] Stein J, Walsh V. To see but not to read: The magnocellular theory of

2008;**61**:129-141

1989;**22**:339-349

2000;**3**(1):91-96

2013;**115**(1):188-197

[47] Hulme C, Snowling MJ.

UK: Wiley-Blackwell; 2009

2001;**7**(1):12-36

2003;**41**:1785-1793

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals… DOI: http://dx.doi.org/10.5772/intechopen.80677*

[40] Ramus F et al. Theories of developmental dyslexia: Insights from a multiple case study of dyslexic adults. Brain. 2003;**126**(Pt 4):841-865

*Neuroimaging - Structure, Function and Mind*

[19] Adisetiyo V et al. Multimodal MR imaging of brain iron in attention deficit hyperactivity disorder: A

and Behavioural Disorders. Vol. 10. Geneva: World Health Organization

[29] Rutter M et al. Sex differences in developmental reading disability: New findings from 4 epidemiological studies.

[30] Goulandris N, editor. Dyslexia in Different Languages. Cross-Linguistic Comparisons. London: Whurr; 2003

[31] Reid AA. Developmental dyslexia: Evidence from Polish. In: Joshi RM, Aaron PG, editors. Handbook of Orthography and Literacy. Mahwah: LEA; 2006. pp. 249-274

[32] Reid AA et al. Cognitive profiles of adult developmental dyslexics: Theoretical implications. Dyslexia. 2007;**13**:1-24

[33] Poelmans G et al. A theoretical molecular network for dyslexia: Integrating available genetic findings. Molecular Psychiatry. 2011;**16**(4):365-382

[34] Nicolson R. Positive Dyslexia. Sheffield, UK: Rodin Books; 2015

2010;**35**(5):475-493

1995;**27**:235-240

1999;**5**(4):192-214

Science. 1998;**17**:471-490

[35] Germano E, Gagliano A, Curatolo P. Comorbidity of ADHD and dyslexia. Developmental Neuropsychology.

[36] Kaplan BJ et al. DCD may not be a discrete disorder. Human Movement

[37] Fawcett AJ, Nicholson RI. Persistent deficits in motor skill of children with dyslexia. Journal of Motor Behaviour.

[38] Iversen S et al. Motor coordination difficulties in a municipality group and in a clinical sample of poor readers. Dyslexia. 2005;**11**(3):217-231

[39] Frith U. Paradoxes in the definition of dyslexia. Dyslexia. An International Journal of Research and Practice.

Journal of the American Medical Association. 2004;**291**(16):2007-2012

Publications; 1993

noninvasive biomarker that responds to psychostimulant treatment? Radiology.

[20] Pernet CR et al. Brain classification reveals the right cerebellum as the best biomarker of dyslexia. BMC

[22] Maurer U et al. Impaired tuning of a fast occipito-temporal response for print in dyslexic children learning to read. Brain. 2007;**130**(Pt 12):3200-3210

[24] Perrachione TK et al. Dysfunction of rapid neural adaptation in dyslexia.

[25] Reid AA. An fMRI multiple case study of the neural correlates of reading deficit in individuals with developmental dyslexia: Theoretical implications. In: Asher-Hansley V, editor. Advances in Neuroimaging Research. New York: Nova Science

Neuron. 2016;**92**(6):1383-1397

Publishers; 2014. pp. 1-119

[26] Morgan P. A case study of congenital word blindness. British Medical Journal. 1896;**7**:1378-1379

Medicine. 1998;**338**:307-312

[28] World Health Organization. The International Classification of Diseases. Classification of Mental

[27] Shaywitz SE. Current concepts: Dyslexia. The New England Journal of

2014;**272**(2):524-532

Neuroscience. 2009;**10**:67

[23] Salmelin R, Helenius P, Service E. Neurophysiology of fluent and impaired reading: A magnetoencephalographic approach. Journal of Clinical Neurophysiology.

2000;**17**(2):163-174

[21] Guttorm TK et al. Newborn event-related potentials predict poorer pre-reading skills in children at risk for dyslexia. Journal of Learning Disabilities. 2010;**43**(5):391-401

**122**

[41] Snowling MJ, Caravolas M. Developmental dyslexia. In: Gaskell MG, editor. The Oxford Handbook of Psycholinguistics. Oxford: Oxford University Press; 2007. pp. 667-683

[42] Ramus F, Szenkovits G. What phonological deficit? The Quarterly Journal of Experimental Psychology. 2008;**61**:129-141

[43] Olson R et al. Specific deficits in component reading and language skills: Genetic and environmental influences. Journal of Learning Disabilities. 1989;**22**:339-349

[44] Paulesu E et al. A cultural effect on brain function. Nature Neuroscience. 2000;**3**(1):91-96

[45] Wang S, Gathercole SE. Working memory deficits in children with reading difficulties: Memory span and dual task coordination. Journal of Experimental Child Psychology. 2013;**115**(1):188-197

[46] Frith U. Brain, mind and behaviour in dyslexia. In: Hulme C, Snowling M, editors. Dyslexia: Biology, Cognition and Intervention. London: Whurr Pulbishers Ltd; 1997. pp. 1-19

[47] Hulme C, Snowling MJ. Developmental Disorders of Language, Learning and Cognition. Chichester, UK: Wiley-Blackwell; 2009

[48] Stein J. The magnocellular theory of developmental dyslexia. Dyslexia. 2001;**7**(1):12-36

[49] Stein J. Visual motion sensitivity and reading. Neuropsychologia. 2003;**41**:1785-1793

[50] Stein J, Walsh V. To see but not to read: The magnocellular theory of dyslexia. Trends in Neurosciences. 1997;**20**(4):147-152

[51] Lovegrove WJ et al. Contrast sensitivity functions and specific reading disability. Neuropsychologia. 1982;**20**:309-315

[52] Hansen PC et al. Are dyslexics' visual deficits limited to measures of dorsal stream function? Neuroreport. 2001;**12**(7):1527-1530

[53] Cicchini GM et al. Strong motion deficits in dyslexia associated with DCDC2 gene alteration. The Journal of Neuroscience. 2015;**35**(21):8059-8064

[54] Gori S et al. The DCDC2 intron 2 deletion impairs illusory motion perception unveiling the selective role of magnocellular-dorsal stream in reading (dis)ability. Cerebral Cortex. 2015;**25**(6):1685-1695

[55] Liederman J et al. The role of motion direction selective extrastriate regions in reading: A transcranial magnetic stimulation study. Brain and Language. 2003;**85**(1):140-155

[56] Stein J. The current status of the magnocellular theory of developmental dyslexia. Neuropsychologia. 2018

[57] Fawcett AJ, Nicolson RI. Performance of dyslexic children on cerebellar and cognitive tests. Journal of Motor Behavior. 1999;**31**(1):68-78

[58] Nicolson RI et al. Eyeblink conditioning indicates cerebellar abnormality in dyslexia. Experimental Brain Research. 2002;**143**(1):42-50

[59] Nicolson RI, Fawcett AJ, Dean P. Time estimation deficits in developmental dyslexia: Evidence of cerebellar involvement. Proceedings of the Biological Sciences. 1995;**259**(1354):43-47

[60] Nicolson RI, Fawcett AJ, Dean P. Developmental dyslexia: The cerebellar deficit hypothesis. Trends in Neurosciences. 2001;**24**(9):508-511

[61] Nicolson RI et al. Association of abnormal cerebellar activation with motor learning difficulties in dyslexic adults. Lancet. 1999;**353**(9165):1662-1667

[62] Eden GF et al. Abnormal processing of visual motion in dyslexia revealed by functional brain imaging. Nature. 1996;**382**(6586):66-69

[63] Frith C, Frith U. A biological marker for dyslexia. Nature. 1996;**382**:19-20

[64] Heim S et al. Cognitive subtypes of dyslexia. Acta Neurobiologiae Experimentalis (Wars). 2008;**68**(1):73-82

[65] Menghini D et al. Different underlying neurocognitive deficits in developmental dyslexia: A comparative study. Neuropsychologia. 2010;**48**(4):863-872

[66] Snowling MJ. Specific disorders and broader phenotypes: The case of dyslexia. The Quarterly Journal of Experimental Psychology. 2008;**61**(1):142-156

[67] Zoubrinetzky R, Bielle F, Valdois S. New insights on developmental dyslexia subtypes: Heterogeneity of mixed reading profiles. PLoS One. 2014;**9**(6):e99337

[68] Reid AA. Cognitive profiles of individuals with dyslexia: Insights from a large sample study. Paper presented at the 57th Annual Conference of the IDA. Indianapolis, USA; 2006

[69] Cohen L, Dehaene S. Specialization within the ventral stream: The case for the visual word form area. NeuroImage. 2004;**22**(1):466-476

[70] Price CJ, Devlin JT. The myth of the visual word form area. NeuroImage. 2003;**19**(3):473-481

[71] Vanni S et al. Visual motion activates V5 in dyslexics. Neuroreport. 1997;**8**(8):1939-1942

[72] Watson JDG et al. Area V5 of the human brain: Evidence from a combined study using positron emission tomography and magnetic resonance imaging. Cerebral Cortex. 1993;**3**:79-94

[73] Demb JB, Boynton GM, Heeger DJ. Functional magnetic resonance imaging of early visual pathways in dyslexia. The Journal of Neuroscience. 1998;**18**(17):6939-6951

[74] Amunts K et al. Brodmann's areas 17 and 18 brought into stereotaxic spacewhere and how variable? NeuroImage. 2000;**11**(1):66-84

[75] Malikovic A et al. Cytoarchitectonic analysis of the human extrastriate cortex in the region of V5/MT+: A probabilistic, stereotaxic map of area hOc5. Cerebral Cortex. 2007;**17**(3):562-574

[76] Stoodley CJ, Schmahmann JD. Functional topography in the human cerebellum: A meta-analysis of neuroimaging studies. NeuroImage. 2009;**44**:489-501

[77] Finch AJ, Nicolson RI, Fawcett AJ. Evidence for a neuroanatomical difference within the olivo-cerebellar pathway of adults with dyslexia. Cortex. 2002;**38**(4):529-539

[78] Fulbright RK et al. The cerebellum's role in reading: A functional MR imaging study. American Journal of Neuroradiology. 1999;**20**(10):1925-1930

[79] Conners CK, Erhardt D, Sparrow EP. Conners' Adult ADHD Rating Scales (CAARS). New York: MHS; 1999

[80] American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). Washington, DC: American Psychiatric Association; 1994

[81] Coltheart M. The MRC psycholinguistic database. Quarterly Journal of Experimental Psychology. 1981;**33A**:497-505

**125**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals…*

[90] Amunts K et al. Broca's region revisited: Cytoarchitecture and intersubject variability. The Journal of Comparative Neurology.

[91] Kurth F et al. Cytoarchitecture and probabilistic maps of the human posterior insular cortex. Cerebral

[92] Geyer S. The Microstructural Border Between the Motor and the Cognitive Domain in the Human Cerebral Cortex.

[94] Caspers S et al. The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual

1999;**412**(2):319-341

Cortex. 2009;**20**: 1448-61

Wien: Springer; 2003

2008;**212**(6):481-495

variability. NeuroImage. 2006;**33**(2):430-448

[95] Morosan P et al. Multimodal architectonic mapping of human superior temporal gyrus. Anatomy and

Embryology. 2005;**210**:401-406

[96] Tzourio-Mazoyer N et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage.

[97] Diedrichsen J et al. A probabilistic MR atlas of the human cerebellum. NeuroImage. 2009;**46**(1):39-46

[98] Pugh KR. Neuroimaging studies of skilled reading and reading disability. Paper presented at the 57th Annual Conference of the IDA. Indianapolis,

[99] Pennington BF. Using genetics and neuropsychology to understand dyslexia and its comorbidities. Paper presented at the 8th BDA International Conference. Harrogate, UK; 2011

2002;**15**:273-289

USA; 2006

[93] Caspers S et al. The human inferior parietal lobule in stereotaxic space. Brain Structure and Function.

*DOI: http://dx.doi.org/10.5772/intechopen.80677*

[82] Donaldson DI, Buckner RL. Effective paradigm design. In: Jezzard P, Matthews PM, Smith SM, editors. Functional MRI. An Introduction to Methods. Oxford, UK: Oxford University Press;

[83] Friston KJ, Statistics I. Experimental

[84] Glaser DE. Experimental Design. Talk Given at the Statistical Parametric Mapping Course. London: Wellcome Department of Imaging Neuroscience;

[85] Amunts K et al. Analysis of neural mechanisms underlying verbal fluency in cytoarchitectonically defined

stereotaxic space–the roles of Brodmann

areas 44 and 45. NeuroImage.

cytoarchitectonic maps and

[88] Heim S et al. Interaction of phonological awareness and 'magnocellular' processing during normal and dyslexic reading:

Dyslexia. 2010;**16**(3):258-282

[89] Shaywitz SE et al. Functional disruption in the organization of the brain for reading in dyslexia. Proceedings of the National Academy of Sciences of the United States of America. 1998;**95**(5):2636-2641

Behavioural and fMRI investigations.

2005;**25**(4):1325-1335

Sendai, Japan

[86] Eickhoff SB et al. A new SPM toolbox for combining probabilistic

functional imaging data. NeuroImage.

[87] Brett M, et al. Region of interest analysis using an SPM toolbox. In: Proceedings of the 8th International Conference on Functional Mapping of the Human Brain; 2002. Available on CD-ROM in NeuroImage; Vol 16 No 2:

2004;**22**(1):42-56

design and statistical parametric mapping. In: Toga AW, Mazziotta JC, editors. Brain Mapping: The Methods. San Diego, USA: Academic Press; 2002.

2001. pp. 177-195

pp. 605-631

2006

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals… DOI: http://dx.doi.org/10.5772/intechopen.80677*

[82] Donaldson DI, Buckner RL. Effective paradigm design. In: Jezzard P, Matthews PM, Smith SM, editors. Functional MRI. An Introduction to Methods. Oxford, UK: Oxford University Press; 2001. pp. 177-195

*Neuroimaging - Structure, Function and Mind*

[72] Watson JDG et al. Area V5 of the human brain: Evidence from a combined study using positron emission tomography and magnetic resonance imaging. Cerebral Cortex. 1993;**3**:79-94

[73] Demb JB, Boynton GM, Heeger DJ.

[74] Amunts K et al. Brodmann's areas 17 and 18 brought into stereotaxic spacewhere and how variable? NeuroImage.

[75] Malikovic A et al. Cytoarchitectonic analysis of the human extrastriate cortex in the region of V5/MT+: A probabilistic, stereotaxic map of area hOc5. Cerebral

Functional magnetic resonance imaging of early visual pathways in dyslexia. The Journal of Neuroscience.

1998;**18**(17):6939-6951

2000;**11**(1):66-84

2009;**44**:489-501

2002;**38**(4):529-539

Association; 1994

1981;**33A**:497-505

[81] Coltheart M. The MRC

Cortex. 2007;**17**(3):562-574

[76] Stoodley CJ, Schmahmann JD. Functional topography in the human cerebellum: A meta-analysis of neuroimaging studies. NeuroImage.

[77] Finch AJ, Nicolson RI, Fawcett AJ. Evidence for a neuroanatomical difference within the olivo-cerebellar pathway of adults with dyslexia. Cortex.

[78] Fulbright RK et al. The cerebellum's role in reading: A functional MR imaging study. American Journal of Neuroradiology. 1999;**20**(10):1925-1930

[79] Conners CK, Erhardt D, Sparrow EP. Conners' Adult ADHD Rating Scales (CAARS). New York: MHS; 1999

[80] American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-IV).

Washington, DC: American Psychiatric

psycholinguistic database. Quarterly Journal of Experimental Psychology.

[62] Eden GF et al. Abnormal processing of visual motion in dyslexia revealed by functional brain imaging. Nature.

[63] Frith C, Frith U. A biological marker for dyslexia. Nature. 1996;**382**:19-20

Experimentalis (Wars). 2008;**68**(1):73-82

comparative study. Neuropsychologia.

[66] Snowling MJ. Specific disorders and broader phenotypes: The case of dyslexia. The Quarterly Journal of Experimental Psychology.

[67] Zoubrinetzky R, Bielle F, Valdois S. New insights on developmental dyslexia subtypes: Heterogeneity of mixed reading profiles. PLoS One.

[68] Reid AA. Cognitive profiles of individuals with dyslexia: Insights from a large sample study. Paper presented at the 57th Annual Conference of the

[69] Cohen L, Dehaene S. Specialization within the ventral stream: The case for the visual word form area. NeuroImage.

[70] Price CJ, Devlin JT. The myth of the visual word form area. NeuroImage.

[71] Vanni S et al. Visual motion activates V5 in dyslexics. Neuroreport.

IDA. Indianapolis, USA; 2006

[64] Heim S et al. Cognitive subtypes of dyslexia. Acta Neurobiologiae

[65] Menghini D et al. Different underlying neurocognitive deficits in developmental dyslexia: A

2010;**48**(4):863-872

2008;**61**(1):142-156

2014;**9**(6):e99337

2004;**22**(1):466-476

2003;**19**(3):473-481

1997;**8**(8):1939-1942

[61] Nicolson RI et al. Association of abnormal cerebellar activation with motor learning difficulties in dyslexic adults. Lancet. 1999;**353**(9165):1662-1667

1996;**382**(6586):66-69

**124**

[83] Friston KJ, Statistics I. Experimental design and statistical parametric mapping. In: Toga AW, Mazziotta JC, editors. Brain Mapping: The Methods. San Diego, USA: Academic Press; 2002. pp. 605-631

[84] Glaser DE. Experimental Design. Talk Given at the Statistical Parametric Mapping Course. London: Wellcome Department of Imaging Neuroscience; 2006

[85] Amunts K et al. Analysis of neural mechanisms underlying verbal fluency in cytoarchitectonically defined stereotaxic space–the roles of Brodmann areas 44 and 45. NeuroImage. 2004;**22**(1):42-56

[86] Eickhoff SB et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage. 2005;**25**(4):1325-1335

[87] Brett M, et al. Region of interest analysis using an SPM toolbox. In: Proceedings of the 8th International Conference on Functional Mapping of the Human Brain; 2002. Available on CD-ROM in NeuroImage; Vol 16 No 2: Sendai, Japan

[88] Heim S et al. Interaction of phonological awareness and 'magnocellular' processing during normal and dyslexic reading: Behavioural and fMRI investigations. Dyslexia. 2010;**16**(3):258-282

[89] Shaywitz SE et al. Functional disruption in the organization of the brain for reading in dyslexia. Proceedings of the National Academy of Sciences of the United States of America. 1998;**95**(5):2636-2641

[90] Amunts K et al. Broca's region revisited: Cytoarchitecture and intersubject variability. The Journal of Comparative Neurology. 1999;**412**(2):319-341

[91] Kurth F et al. Cytoarchitecture and probabilistic maps of the human posterior insular cortex. Cerebral Cortex. 2009;**20**: 1448-61

[92] Geyer S. The Microstructural Border Between the Motor and the Cognitive Domain in the Human Cerebral Cortex. Wien: Springer; 2003

[93] Caspers S et al. The human inferior parietal lobule in stereotaxic space. Brain Structure and Function. 2008;**212**(6):481-495

[94] Caspers S et al. The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual variability. NeuroImage. 2006;**33**(2):430-448

[95] Morosan P et al. Multimodal architectonic mapping of human superior temporal gyrus. Anatomy and Embryology. 2005;**210**:401-406

[96] Tzourio-Mazoyer N et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage. 2002;**15**:273-289

[97] Diedrichsen J et al. A probabilistic MR atlas of the human cerebellum. NeuroImage. 2009;**46**(1):39-46

[98] Pugh KR. Neuroimaging studies of skilled reading and reading disability. Paper presented at the 57th Annual Conference of the IDA. Indianapolis, USA; 2006

[99] Pennington BF. Using genetics and neuropsychology to understand dyslexia and its comorbidities. Paper presented at the 8th BDA International Conference. Harrogate, UK; 2011

[100] Pennington BF. From single to multiple deficit models of developmental disorders. Cognition. 2006;**101**:385-413

[101] Krishnan S, Watkins KE, Bishop DV. Neurobiological basis of language learning difficulties. Trends in Cognitive Sciences. 2016;**20**(9):701-714

[102] Mascheretti S et al. Neurogenetics of developmental dyslexia: From genes to behavior through brain neuroimaging and cognitive and sensorial mechanisms. Translational Psychiatry. 2017;**7**(1):e987

[103] Giraldo-Chica M, Hegarty JP, Schneider KA. Morphological differences in the lateral geniculate nucleus associated with dyslexia. Neuroimage: Clinical. 2015;**7**:830-836

[104] Goswami U. A temporal sampling framework for developmental dyslexia. Trends in Cognitive Sciences. 2011;**15**(1):3-10

[105] Hendren RL et al. Recognizing psychiatric comorbidity with reading disorders. Frontiers in Psychiatry. 2018;**9**:101

[106] Plomin R, Kovas Y. Generalist genes and learning disabilities. Psychological Bulletin. 2005;**131**(4):592-617

[107] Deriziotis P, Fisher SE. Speech and language: Translating the genome. Trends in Genetics. 2017;**33**(9):642-656

[108] Mascheretti S et al. Complex effects of dyslexia risk factors account for ADHD traits: Evidence from two independent samples. Journal of Child Psychology and Psychiatry. 2017;**58**(1):75-82

[109] Gooch D, Snowling M, Hulme C. Time perception, phonological skills and executive function in children with dyslexia and/or ADHD symptoms. Journal of Child Psychology and Psychiatry. 2011;**52**(2):195-203

[110] Wimmer H, Mayringer H, Raberger T. Reading and dual-task balancing: Evidence against the automatization deficit explanation of developmental dyslexia. Journal of Learning Disabilities. 1999;**32**(5):473-478

[111] Muller-Axt C, Anwander A, von Kriegstein K. Altered structural connectivity of the left visual thalamus in developmental dyslexia. Current Biology. 2017;**27**(23):3692-3698 e4

[112] Boets B et al. Intact but less accessible phonetic representations in adults with dyslexia. Science. 2013;**342**(6163):1251-1254

[113] Pugh KR et al. Glutamate and choline levels predict individual differences in reading ability in emergent readers. The Journal of Neuroscience. 2014;**34**(11):4082-4089

[114] Carrey N et al. Glutamatergic changes with treatment in attention deficit hyperactivity disorder: A preliminary case series. Journal of Child and Adolescent Psychopharmacology. 2002;**12**(4):331-336

[115] Gass A, Richards TL. Serial proton magnetic resonance spectroscopy of normal-appearing gray and white matter in MS. Neurology. 2013;**80**(1):17-18

[116] Hancock R, Pugh KR, Hoeft F. Neural noise hypothesis of developmental dyslexia. Trends in Cognitive Sciences. 2017;**21**(6):434-448

[117] Todd N et al. Evaluation of 2D multiband EPI imaging for highresolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts. NeuroImage. 2016;**124**(Pt A):32-42

[118] De Martino F et al. Frequency preference and attention effects across cortical depths in the human primary auditory cortex. Proceedings of the National Academy of Sciences

**127**

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals…*

genetic variant predict dyslexia phenotype. Cortex. 2015;**71**:291-305

[128] Darki F et al. Three dyslexia susceptibility genes, DYX1C1, DCDC2, and KIAA0319, affect temporo-parietal white matter structure. Biological Psychiatry. 2012;**72**(8):671-676

[129] Scerri TS et al. The dyslexia candidate locus on 2p12 is associated with general cognitive ability and white matter structure. PLoS One.

[130] Marino C et al. The DCDC2/ intron 2 deletion and white matter disorganization: Focus on developmental dyslexia. Cortex.

[131] Carrion-Castillo A, Franke B, Fisher SE. Molecular genetics of dyslexia: An overview. Dyslexia.

[132] Roeske D et al. First genome-wide association scan on neurophysiological endophenotypes points to transregulation effects on SLC2A3 in

dyslexic children. Molecular Psychiatry.

[133] Liegeois F et al. Language fMRI abnormalities associated with FOXP2 gene mutation. Nature Neuroscience.

[134] Ho TC et al. Intergenerational neuroimaging of human brain circuitry. Trends in Neurosciences.

Intergenerational transmission of reading and reading brain networks. In: Galaburda A, editor. Dyslexia and Neuroscience. Brookes Publishing;

2012;**7**(11):e50321

2014;**57**:227-243

2013;**19**(4):214-240

2011;**16**(1):97-107

2003;**6**(11):1230-1237

2016;**39**(10):644-648

Baltimore, USA. 2018

[135] Hoeft F, Hancock R.

*DOI: http://dx.doi.org/10.5772/intechopen.80677*

of the United States of America. 2015;**112**(52):16036-16041

[119] Boto E et al. A new generation of magnetoencephalography: Room temperature measurements using optically-pumped magnetometers. NeuroImage. 2017;**149**:404-414

[120] Reid AA. Developmental dyslexia–a critical review of imaginggenetics studies. Poster presented at the 22th Annual Meeting of the OHBM. Geneva, Switzerland; 2016

[121] Eicher JD, Gruen JR. Imaginggenetics in dyslexia: Connecting risk genetic variants to brain neuroimaging and ultimately to reading impairments. Molecular Genetics and Metabolism.

[122] Cope N et al. Variants in the DYX2 locus are associated with altered brain activation in readingrelated brain regions in subjects with reading disability. NeuroImage.

[123] Pinel P et al. Genetic variants of FOXP2 and KIAA0319/TTRAP/THEM2 locus are associated with altered brain activation in distinct language-related regions. The Journal of Neuroscience.

[124] Wilcke A et al. Imaging genetics of FOXP2 in dyslexia. European Journal of Human Genetics. 2012;**20**(2):224-229

[125] Lamminmaki S et al. Human ROBO1 regulates interaural interaction in auditory pathways. The Journal of Neuroscience. 2012;**32**(3):966-971

[126] Centanni TM et al. Increased variability of stimulus-driven cortical responses is associated with genetic variability in children with and without dyslexia. Developmental Cognitive

[127] Mannel C et al. Working-memory endophenotype and dyslexia-associated

Neuroscience. 2018;**34**:7-17

2013;**110**(3):201-212

2012;**63**(1):148-156

2012;**32**(3):817-825

*Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals… DOI: http://dx.doi.org/10.5772/intechopen.80677*

of the United States of America. 2015;**112**(52):16036-16041

*Neuroimaging - Structure, Function and Mind*

[110] Wimmer H, Mayringer H, Raberger T. Reading and dual-task balancing: Evidence against the automatization deficit explanation of developmental

dyslexia. Journal of Learning Disabilities. 1999;**32**(5):473-478

[111] Muller-Axt C, Anwander A, von Kriegstein K. Altered structural connectivity of the left visual thalamus in developmental dyslexia. Current Biology. 2017;**27**(23):3692-3698 e4

[112] Boets B et al. Intact but less accessible phonetic representations in adults with dyslexia. Science. 2013;**342**(6163):1251-1254

[113] Pugh KR et al. Glutamate and choline levels predict individual differences in reading ability in emergent readers. The Journal of Neuroscience. 2014;**34**(11):4082-4089

[114] Carrey N et al. Glutamatergic changes with treatment in attention deficit hyperactivity disorder: A

2002;**12**(4):331-336

2013;**80**(1):17-18

preliminary case series. Journal of Child and Adolescent Psychopharmacology.

[115] Gass A, Richards TL. Serial proton magnetic resonance spectroscopy of normal-appearing gray and white matter in MS. Neurology.

[116] Hancock R, Pugh KR, Hoeft F.

developmental dyslexia. Trends in Cognitive Sciences. 2017;**21**(6):434-448

[117] Todd N et al. Evaluation of 2D multiband EPI imaging for highresolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts. NeuroImage.

[118] De Martino F et al. Frequency preference and attention effects across cortical depths in the human primary auditory cortex. Proceedings of the National Academy of Sciences

Neural noise hypothesis of

2016;**124**(Pt A):32-42

[100] Pennington BF. From single to multiple deficit models of

2006;**101**:385-413

developmental disorders. Cognition.

[101] Krishnan S, Watkins KE, Bishop DV. Neurobiological basis of language learning difficulties. Trends in Cognitive

[102] Mascheretti S et al. Neurogenetics of developmental dyslexia: From genes to behavior through brain neuroimaging

mechanisms. Translational Psychiatry.

[104] Goswami U. A temporal sampling

dyslexia. Trends in Cognitive Sciences.

[105] Hendren RL et al. Recognizing psychiatric comorbidity with reading disorders. Frontiers in Psychiatry.

[106] Plomin R, Kovas Y. Generalist genes and learning disabilities.

[107] Deriziotis P, Fisher SE. Speech and language: Translating the genome. Trends in Genetics. 2017;**33**(9):642-656

[108] Mascheretti S et al. Complex effects of dyslexia risk factors account for ADHD traits: Evidence from two independent samples. Journal of Child Psychology and Psychiatry.

[109] Gooch D, Snowling M, Hulme C. Time perception, phonological skills and executive function in children with dyslexia and/or ADHD symptoms. Journal of Child Psychology and Psychiatry. 2011;**52**(2):195-203

Psychological Bulletin. 2005;**131**(4):592-617

2017;**58**(1):75-82

Sciences. 2016;**20**(9):701-714

and cognitive and sensorial

[103] Giraldo-Chica M, Hegarty JP, Schneider KA. Morphological differences in the lateral geniculate nucleus associated with dyslexia. Neuroimage: Clinical.

framework for developmental

2017;**7**(1):e987

2015;**7**:830-836

2011;**15**(1):3-10

2018;**9**:101

**126**

[119] Boto E et al. A new generation of magnetoencephalography: Room temperature measurements using optically-pumped magnetometers. NeuroImage. 2017;**149**:404-414

[120] Reid AA. Developmental dyslexia–a critical review of imaginggenetics studies. Poster presented at the 22th Annual Meeting of the OHBM. Geneva, Switzerland; 2016

[121] Eicher JD, Gruen JR. Imaginggenetics in dyslexia: Connecting risk genetic variants to brain neuroimaging and ultimately to reading impairments. Molecular Genetics and Metabolism. 2013;**110**(3):201-212

[122] Cope N et al. Variants in the DYX2 locus are associated with altered brain activation in readingrelated brain regions in subjects with reading disability. NeuroImage. 2012;**63**(1):148-156

[123] Pinel P et al. Genetic variants of FOXP2 and KIAA0319/TTRAP/THEM2 locus are associated with altered brain activation in distinct language-related regions. The Journal of Neuroscience. 2012;**32**(3):817-825

[124] Wilcke A et al. Imaging genetics of FOXP2 in dyslexia. European Journal of Human Genetics. 2012;**20**(2):224-229

[125] Lamminmaki S et al. Human ROBO1 regulates interaural interaction in auditory pathways. The Journal of Neuroscience. 2012;**32**(3):966-971

[126] Centanni TM et al. Increased variability of stimulus-driven cortical responses is associated with genetic variability in children with and without dyslexia. Developmental Cognitive Neuroscience. 2018;**34**:7-17

[127] Mannel C et al. Working-memory endophenotype and dyslexia-associated

**127**

genetic variant predict dyslexia phenotype. Cortex. 2015;**71**:291-305

[128] Darki F et al. Three dyslexia susceptibility genes, DYX1C1, DCDC2, and KIAA0319, affect temporo-parietal white matter structure. Biological Psychiatry. 2012;**72**(8):671-676

[129] Scerri TS et al. The dyslexia candidate locus on 2p12 is associated with general cognitive ability and white matter structure. PLoS One. 2012;**7**(11):e50321

[130] Marino C et al. The DCDC2/ intron 2 deletion and white matter disorganization: Focus on developmental dyslexia. Cortex. 2014;**57**:227-243

[131] Carrion-Castillo A, Franke B, Fisher SE. Molecular genetics of dyslexia: An overview. Dyslexia. 2013;**19**(4):214-240

[132] Roeske D et al. First genome-wide association scan on neurophysiological endophenotypes points to transregulation effects on SLC2A3 in dyslexic children. Molecular Psychiatry. 2011;**16**(1):97-107

[133] Liegeois F et al. Language fMRI abnormalities associated with FOXP2 gene mutation. Nature Neuroscience. 2003;**6**(11):1230-1237

[134] Ho TC et al. Intergenerational neuroimaging of human brain circuitry. Trends in Neurosciences. 2016;**39**(10):644-648

[135] Hoeft F, Hancock R. Intergenerational transmission of reading and reading brain networks. In: Galaburda A, editor. Dyslexia and Neuroscience. Brookes Publishing; Baltimore, USA. 2018

**129**

Section 3

Structural Imaging
