Magnetic Resonance Imaging and Applications

**3**

**Chapter 1**

*Yongxia Zhou*

relational neurocognitive functions.

**1. Introduction**

neurocognitive test, correlation, age, APOE gene

**Abstract**

Longitudinal Changes of

Structural and Functional

Connectivity and Correlations

Revealing brain functional and micro-structural changes over a relatively short period at individual levels are especially important given that many risks associated with age including vascular and neuroinflammation increases and could confound the baseline fMRI parametric images. Cellular-level axonal injury and/or demyelination as well as dispersed mesoscopic level substance abnormal aggregation and structural/functional abnormality could occur in short subacute/acute phases, while literatures related to longitudinal changes with age are limited with only our previous fMRI findings. Longitudinal data were used to characterize these multiparameters including random intercept and interval per individual. No significant age by gender interactions have been found to either DTI fractional anisotropy (FA) or diffusivity metrics. The interval effective regions showed longitudinal change of FA and radial diffusivity (RD)/axial diffusivity (AX) values remained similar to the aging results found with cross-sectional data. Significant correlations between DTI and fMRI metrics as well as between imaging and neurocognitive data including speed and memory were found. Our results indicate significant and consistent age, gender and apolipoprotein E (APOE) genotypic effects on structural and functional connectivity at both short-interval and cross-sectional ranges, together with cor-

**Keywords:** microstructure, function connectivity, DTI, longitudinal change,

Functional connectivity based on MRI (fcMRI) measures simultaneous and synchronous neuronal activities at various regions connected intrinsically in brain with functional MRI time courses. In our recent study, we reported lower functional connectivity in posterior cingulate and temporal regions with aging within the default mode network (DMN) [1]. We also found higher fcMRI in the dorso-attentional network (DAN) (including in the dorso-lateral prefrontal cortex) at age, which could be due to the negative connectivity with DMN. Furthermore longitudinal changes in fcMRI occurred in regions similar to those demonstrating cross-sectional effects of age, with only a few small brain areas showing significant age by interval

with Neurocognitive Metrics

## **Chapter 1**

## Longitudinal Changes of Structural and Functional Connectivity and Correlations with Neurocognitive Metrics

*Yongxia Zhou*

## **Abstract**

Revealing brain functional and micro-structural changes over a relatively short period at individual levels are especially important given that many risks associated with age including vascular and neuroinflammation increases and could confound the baseline fMRI parametric images. Cellular-level axonal injury and/or demyelination as well as dispersed mesoscopic level substance abnormal aggregation and structural/functional abnormality could occur in short subacute/acute phases, while literatures related to longitudinal changes with age are limited with only our previous fMRI findings. Longitudinal data were used to characterize these multiparameters including random intercept and interval per individual. No significant age by gender interactions have been found to either DTI fractional anisotropy (FA) or diffusivity metrics. The interval effective regions showed longitudinal change of FA and radial diffusivity (RD)/axial diffusivity (AX) values remained similar to the aging results found with cross-sectional data. Significant correlations between DTI and fMRI metrics as well as between imaging and neurocognitive data including speed and memory were found. Our results indicate significant and consistent age, gender and apolipoprotein E (APOE) genotypic effects on structural and functional connectivity at both short-interval and cross-sectional ranges, together with correlational neurocognitive functions.

**Keywords:** microstructure, function connectivity, DTI, longitudinal change, neurocognitive test, correlation, age, APOE gene

## **1. Introduction**

Functional connectivity based on MRI (fcMRI) measures simultaneous and synchronous neuronal activities at various regions connected intrinsically in brain with functional MRI time courses. In our recent study, we reported lower functional connectivity in posterior cingulate and temporal regions with aging within the default mode network (DMN) [1]. We also found higher fcMRI in the dorso-attentional network (DAN) (including in the dorso-lateral prefrontal cortex) at age, which could be due to the negative connectivity with DMN. Furthermore longitudinal changes in fcMRI occurred in regions similar to those demonstrating cross-sectional effects of age, with only a few small brain areas showing significant age by interval

or gender by interval effects. The rate of fcMRI longitudinal change, however, was not influenced significantly by baseline age or gender, after adjusting for baseline age and gender modulation effects in majority of brain regions, suggesting moderate linear interval effects of fcMRI longitudinal changes in brain. Our results from this relatively large cohort suggest that fcMRI variability from various networks in different scales might be useful to monitor brain changes in normal aging and preclinical stages of Alzheimer's disease [1]. As for diffusion tensor imaging (DTI), we used both voxel-wise four DTI metrics and tract-specific ROI analysis to investigate myelin and axonal integrity differences with age, gender and APOE genotype with four DTI metrics at baseline [2–5]. One of the main findings was the decreased fractional anisotropy (FA) but increased radial diffusivity (RD) with age based on both voxel-wise and tract-specific analyses indicating both axonal degeneration and demyelination [6, 7]. Dramatic decreases of FA with age, especially in participants over 50 years old, accompanied by increased RD suggest that white matter (WM) integrity declines with age. In contrast, changes in axial diffusivity (AX) and mean diffusivity (MD) with age are in two-way: higher AX and MD in some tracts and cortical regions including bilateral thalamic radiation and cingulum bundles, as well as decreased AX and MD in some long-distance fasciculus [8–11].

Regarding DTI and fMRI correlations, significant gray matter (GM) and WM correspondences based on GM atrophy and WM fractional anisotropy (FA) reductions in several brain regions were found in multiple sclerosis (MS) patients including primary visual cortex/optic radiation as well as somatosensory cortex/ superior longitudinal fasciculus at baseline [12]. However, with disease progresses, these associations might be deteriorated and are not maintained [13–15], although each imaging feature at baseline and longitudinal time points remain consistent and highly correlated [16, 17]. The degree of change (or rate of change) of each metric is dependent on the sensitivity during the disease course [18–20]. For instance, GM atrophy and functional coordination decrement were found at follow-up visit in MS patients, in contrast to the usual observation of significant FA reductions and WM lesions predominantly in corpus callosum, periventricular areas, occipital horns and cingulum areas at baseline in MS compared to controls [12]. In mild traumatic brain injury (MTBI), 1 year after injury, there was measurable global brain atrophy, larger than that in control subjects. The anterior cingulate WM bilaterally and the left cingulate gyrus isthmus WM, as well as the right precuneal GM, showed significant decreases in regional volume in patients with MTBI over the 1st year after injury [21]. However at baseline, after normalization to supratentorial brain volume, there were no significant regional brain volume differences between patients with MTBI at the time of their initial visit and the control group. Our observations complement these findings and indicate that specific brain structure such as the cingulum and precuneus may be more vulnerable to long-term structural changes [22, 23].

It had been reported that baseline imaging findings including micro-structure integrity measured with DTI can predict functional activation and coordination at follow-ups. We had reported that FA measure at baseline predicted follow-up functional coordination score from fMRI data (r = 0.68, P = 0.007), indicating a possible initial WM inflammatory factor to the subsequent neurodegenerative processes in MS patients [12]. We also found that baseline composite imaging metrics can predict cognitive function and neuropsychological scores. For instance, in MTBI, the clinical symptom at follow-up visit could be predicted with high accuracy from baseline imaging features with r = −0.82, P < 0.001 for depression; r = −0.65, P = 0.01 for anxiety; r = −0.71, P = 0.005 for fatigue; and r = −0.67, P = 0.008 for post-concussion syndrome (PCS) [23]. Revealing the brain microstructural changes over a relatively short period at individual levels are especially important given that many risks associated with age including vascular and

**5**

*Longitudinal Changes of Structural and Functional Connectivity and Correlations…*

better illustrate the full spectrum of multiple phenotypic data.

60–69, 186 aged 70–79, and 138 aged 80–89.

neuroinflammation increases and could confound the baseline parametric images of each individual. However, literatures related to longitudinal changes of neuroim-

The goals of this study were to assess baseline and longitudinal age and genderrelated changes with neuroimaging and neurocognitive data from a large sample of healthy older adults, as well as apolipoprotein E (APOE) genotypic effects. The strengths of this study are the extensive and well-characterized large sample of older adults, and multiple imaging metrics including advanced fcMRI, four DTI (FA, MD, RD, AX) to capture extensive properties of functional connectivity and white matter myelination at both baseline and longitudinal follow-up time points. Both conventional whole brain voxel-wise analyses and fiber track-specific ROI quantification measures that are more robust and less prone to registration error were used to increase the white matter myelin detection specificity. Beside previous fcMRI findings [1], we also investigate longitudinal changes of multiple DTI and fMRI metrics as well as neurocognitive tests. The correlations among different imaging metrics as well as between neuroimaging findings and neurocognitive scores were quantified to

We studied 572 cognitively normal participants in the neuroimaging substudy from the Baltimore Longitudinal Study of Aging (BLSA) who had DTI assessments. Exclusion criteria were as follows: subjects with excessive motions and unwanted imaging qualities (N = 17 and 30 scans); subjects with incidental findings of brain lesions or other central nervous diseases, such as, Parkinson's disease (N = 4). Twenty individuals aged 24–39, 39 individuals aged 40–49, 51 aged 50–59, 137 aged

Three hundred and eighty-seven participants (68%) had available APOE genotype information, 107 APOE ε4+ and 280 APOE ε4− participants were further divided into six sub-groups based on the APOE isoforms. These sample characteristics are shown in **Table 1**. Two hundred and forty-five subjects had longitudinal follow-up (interval range 0.9–3.5 years, mean interval of 1.9 ± 0.6 years) and were used to characterize aging effects at short interval. Neurocognitive data from 21 cognitive tests with 59 variables that measure multiple cognitive functionalities including visual perception and attention, learning and memory encoding and recall, language fluency, and executive function for each participant was collected at the same day of the MRI scan [25]. After post-processing with normalization, 52 test scores were used for further analysis including correlation tests.

MRI imaging was obtained with a 3T whole-body scanner (Philips, Achieva) at National Institute of Aging, using an eight-channel head coil. The DTI sequence was evaluated previously and found to have good intra-site reliability and inter-section reproducibility [1]. Specifically, standard echo-planar imaging (EPI)-based DTI protocol was performed during the routine 45-min scan (TR/TE = 6801/75 msec, flip

cover the whole cerebrum). Thirty-two diffusion gradient directions (diffusion gradient

of 3:58 min for each run as well as two identical runs were obtained for each subject.

time Δ = 36.3 ms and pulse duration δ = 16 ms) with b-factor of 700 s/mm2

, spatial resolution = 0.83 × 0.83 × 2.2 mm3

, 65 slices to

and a total

*DOI: http://dx.doi.org/10.5772/intechopen.86641*

aging data with age are still limited [24].

**2. Methods**

**2.1 Participants**

**2.2 Imaging parameters**

angle = 90°, FOV = 212 × 212 mm2

*Longitudinal Changes of Structural and Functional Connectivity and Correlations… DOI: http://dx.doi.org/10.5772/intechopen.86641*

neuroinflammation increases and could confound the baseline parametric images of each individual. However, literatures related to longitudinal changes of neuroimaging data with age are still limited [24].

The goals of this study were to assess baseline and longitudinal age and genderrelated changes with neuroimaging and neurocognitive data from a large sample of healthy older adults, as well as apolipoprotein E (APOE) genotypic effects. The strengths of this study are the extensive and well-characterized large sample of older adults, and multiple imaging metrics including advanced fcMRI, four DTI (FA, MD, RD, AX) to capture extensive properties of functional connectivity and white matter myelination at both baseline and longitudinal follow-up time points. Both conventional whole brain voxel-wise analyses and fiber track-specific ROI quantification measures that are more robust and less prone to registration error were used to increase the white matter myelin detection specificity. Beside previous fcMRI findings [1], we also investigate longitudinal changes of multiple DTI and fMRI metrics as well as neurocognitive tests. The correlations among different imaging metrics as well as between neuroimaging findings and neurocognitive scores were quantified to better illustrate the full spectrum of multiple phenotypic data.

## **2. Methods**

*Medical Imaging - Principles and Applications*

or gender by interval effects. The rate of fcMRI longitudinal change, however, was not influenced significantly by baseline age or gender, after adjusting for baseline age and gender modulation effects in majority of brain regions, suggesting moderate linear interval effects of fcMRI longitudinal changes in brain. Our results from this relatively large cohort suggest that fcMRI variability from various networks in different scales might be useful to monitor brain changes in normal aging and preclinical stages of Alzheimer's disease [1]. As for diffusion tensor imaging (DTI), we used both voxel-wise four DTI metrics and tract-specific ROI analysis to investigate myelin and axonal integrity differences with age, gender and APOE genotype with four DTI metrics at baseline [2–5]. One of the main findings was the decreased fractional anisotropy (FA) but increased radial diffusivity (RD) with age based on both voxel-wise and tract-specific analyses indicating both axonal degeneration and demyelination [6, 7]. Dramatic decreases of FA with age, especially in participants over 50 years old, accompanied by increased RD suggest that white matter (WM) integrity declines with age. In contrast, changes in axial diffusivity (AX) and mean diffusivity (MD) with age are in two-way: higher AX and MD in some tracts and cortical regions including bilateral thalamic radiation and cingulum bundles, as

well as decreased AX and MD in some long-distance fasciculus [8–11].

Regarding DTI and fMRI correlations, significant gray matter (GM) and WM correspondences based on GM atrophy and WM fractional anisotropy (FA) reductions in several brain regions were found in multiple sclerosis (MS) patients including primary visual cortex/optic radiation as well as somatosensory cortex/ superior longitudinal fasciculus at baseline [12]. However, with disease progresses, these associations might be deteriorated and are not maintained [13–15], although each imaging feature at baseline and longitudinal time points remain consistent and highly correlated [16, 17]. The degree of change (or rate of change) of each metric is dependent on the sensitivity during the disease course [18–20]. For instance, GM atrophy and functional coordination decrement were found at follow-up visit in MS patients, in contrast to the usual observation of significant FA reductions and WM lesions predominantly in corpus callosum, periventricular areas, occipital horns and cingulum areas at baseline in MS compared to controls [12]. In mild traumatic brain injury (MTBI), 1 year after injury, there was measurable global brain atrophy, larger than that in control subjects. The anterior cingulate WM bilaterally and the left cingulate gyrus isthmus WM, as well as the right precuneal GM, showed significant decreases in regional volume in patients with MTBI over the 1st year after injury [21]. However at baseline, after normalization to supratentorial brain volume, there were no significant regional brain volume differences between patients with MTBI at the time of their initial visit and the control group. Our observations complement these findings and indicate that specific brain structure such as the cingulum and precuneus may be more vulnerable to long-term structural changes [22, 23].

It had been reported that baseline imaging findings including micro-structure integrity measured with DTI can predict functional activation and coordination at follow-ups. We had reported that FA measure at baseline predicted follow-up functional coordination score from fMRI data (r = 0.68, P = 0.007), indicating a possible initial WM inflammatory factor to the subsequent neurodegenerative processes in MS patients [12]. We also found that baseline composite imaging metrics can predict cognitive function and neuropsychological scores. For instance, in MTBI, the clinical symptom at follow-up visit could be predicted with high accuracy from baseline imaging features with r = −0.82, P < 0.001 for depression; r = −0.65, P = 0.01 for anxiety; r = −0.71, P = 0.005 for fatigue; and r = −0.67, P = 0.008 for post-concussion syndrome (PCS) [23]. Revealing the brain microstructural changes over a relatively short period at individual levels are especially important given that many risks associated with age including vascular and

**4**

#### **2.1 Participants**

We studied 572 cognitively normal participants in the neuroimaging substudy from the Baltimore Longitudinal Study of Aging (BLSA) who had DTI assessments. Exclusion criteria were as follows: subjects with excessive motions and unwanted imaging qualities (N = 17 and 30 scans); subjects with incidental findings of brain lesions or other central nervous diseases, such as, Parkinson's disease (N = 4). Twenty individuals aged 24–39, 39 individuals aged 40–49, 51 aged 50–59, 137 aged 60–69, 186 aged 70–79, and 138 aged 80–89.

Three hundred and eighty-seven participants (68%) had available APOE genotype information, 107 APOE ε4+ and 280 APOE ε4− participants were further divided into six sub-groups based on the APOE isoforms. These sample characteristics are shown in **Table 1**. Two hundred and forty-five subjects had longitudinal follow-up (interval range 0.9–3.5 years, mean interval of 1.9 ± 0.6 years) and were used to characterize aging effects at short interval. Neurocognitive data from 21 cognitive tests with 59 variables that measure multiple cognitive functionalities including visual perception and attention, learning and memory encoding and recall, language fluency, and executive function for each participant was collected at the same day of the MRI scan [25]. After post-processing with normalization, 52 test scores were used for further analysis including correlation tests.

#### **2.2 Imaging parameters**

MRI imaging was obtained with a 3T whole-body scanner (Philips, Achieva) at National Institute of Aging, using an eight-channel head coil. The DTI sequence was evaluated previously and found to have good intra-site reliability and inter-section reproducibility [1]. Specifically, standard echo-planar imaging (EPI)-based DTI protocol was performed during the routine 45-min scan (TR/TE = 6801/75 msec, flip angle = 90°, FOV = 212 × 212 mm2 , spatial resolution = 0.83 × 0.83 × 2.2 mm3 , 65 slices to cover the whole cerebrum). Thirty-two diffusion gradient directions (diffusion gradient time Δ = 36.3 ms and pulse duration δ = 16 ms) with b-factor of 700 s/mm2 and a total of 3:58 min for each run as well as two identical runs were obtained for each subject.



**7**

*Longitudinal Changes of Structural and Functional Connectivity and Correlations…*

sue types and registration/normalization of EPI images to MNI space.

TE = 2000/30 msec, flip angle = 75°, FOV = 240 × 240 mm2

A standard echo-planar imaging (EPI) resting-state (RS)-fMRI protocol (TR/

MRI protocol. A total of 180 volumes were acquired during the 6-min RS-fMRI scan. Participants were instructed to remain still, with eyes open and focused on a cross fixation, and encouraged to relax during the scan. A 3-dimensional T1-weighted MPRAGE (magnetization prepared rapid gradient-echo imaging) sequence (TR/TE/TI = 6.8/3.2/849.2 msec, FA = 8°, FOV = 192 × 256 × 256 mm3

DTI data were first pre-processed with the diffusion toolkit toolbox (http:// trackvis.org) to obtain the FA/RD/AX/RD values in original b0 space. For the FA/ RA/AX/RD quantification, the FMRIB, Software Library (FSL, http://fsl.fmrib. ox.ac.uk/fsl) tract-based spatial statistics toolbox steps 1–2 (i.e., preprocessing, brain mask extraction with FA > 0.2 and normalization) were used for registration of all participants' FA into the FSL 1-mm white matter skeleton template. The transformation of the individual FA data to the FSL Montreal Neurological Institute (MNI) template with 1-mm isotropic voxel size, was implemented with the nonlinear registration tool FNIRT based on a b-spline representation of the registration warp field. After normalization of FA map to the MNI space, tract-specific mean FA values were obtained in 20 regions from the well-defined probabilistic tract template (FSL/JHU ICBM atlas). Quantitative MD/AX/RD values were obtained by applying the same transformation from individual FA to template space and

The anatomic T1-MPRAGE and 4D EPI functional data were preprocessed using

For fcMRI processing, the first four volumes of the RS-fMRI data of each subject were discarded for scanner and image stability. Preprocessing steps for RS-fMRI data included rigid alignment of the time frames using AFNI motion correction algorithms, spatial smoothing using a Gaussian kernel with 6 mm full-width-athalf-maximum (FWHM), and band-pass temporal filtering of 0.005–0.1 Hz to improve signal-to-noise ratio. Removal of nuisance signals was then performed using a Gaussian regression model after co-registration to MPRAGE data. Namely, motion parameters, global signal, and signals derived from CSF and WM based on the tissue masks were modeled in the Gaussian linear mixed model, and residual signal at each voxel was maintained for further analyses. Finally the residual 4D fMRI data after regression were transformed to MNI standard space [26]. A DMN seed including both medial prefrontal cortex (MED) (MNI center: 0, 48, 23 mm) and posterior cingulate cortex (PCC) (MNI center: 26, 248, 39 mm) with a com-

(each seed of 2056 mm3

as the combined core seed. We chose the combined core seed over separate PCC and MED seeds because the latter approach generates different and incomplete DMN

) was used. We refer to this seed

both FSL and Analysis of Functional NeuroImages (AFNI) programs (adapted scripts from http://www.nitrc.org/projects/fcon\_1000 developed based on FSL and AFNI). For structural MPRAGE data used for fMRI data normalization, preprocessing included reorientation to the right-posterior-inferior convention and skull stripping, and segmentation into three tissue types: GM, WM, and cerebrospinal fluid (CSF). The segmental tissue masks were used to derive the nuisance fMRI signals in WM and CSF. Finally, the MPRAGE image was co-registered with the fMRI data and normalized to the Montreal Neurologic Institute (MNI) 152-brain template

, 37 slices) was performed during an approximately 45-min brain

) was acquired in sagittal-view for segmentation of tis-

, voxel

,

*DOI: http://dx.doi.org/10.5772/intechopen.86641*

computed with tract-specific values [1].

with 2-mm isotropic voxel size [1, 12].

bined volume of 4112 mm3

size =3 × 3 × 4 mm3

voxel size = 1.2 × 1 × 1 mm3

**2.3 Image processing**

*Longitudinal Changes of Structural and Functional Connectivity and Correlations… DOI: http://dx.doi.org/10.5772/intechopen.86641*

A standard echo-planar imaging (EPI) resting-state (RS)-fMRI protocol (TR/ TE = 2000/30 msec, flip angle = 75°, FOV = 240 × 240 mm2 , voxel size =3 × 3 × 4 mm3 , 37 slices) was performed during an approximately 45-min brain MRI protocol. A total of 180 volumes were acquired during the 6-min RS-fMRI scan. Participants were instructed to remain still, with eyes open and focused on a cross fixation, and encouraged to relax during the scan. A 3-dimensional T1-weighted MPRAGE (magnetization prepared rapid gradient-echo imaging) sequence (TR/TE/TI = 6.8/3.2/849.2 msec, FA = 8°, FOV = 192 × 256 × 256 mm3 , voxel size = 1.2 × 1 × 1 mm3 ) was acquired in sagittal-view for segmentation of tissue types and registration/normalization of EPI images to MNI space.

#### **2.3 Image processing**

*Medical Imaging - Principles and Applications*

**6**

**Characteristic**

Total n Age, years; mean ± SD

Gender n; women/men

Education, years; mean ± SD

MMSE at visit

**Table 1.**

*Sample characteristics for the whole sample and six APOE genotypic sub-groups.*

**All DTI baseline**

572 69.7 ± 13.4

311/261 17.1 ± 2.7 28.6 ± 1.5

28.3 ± 1.5

28.8 ± 1.4

29.5 ± 0.6

28.4 ± 1.6

28.5 ± 1.4

29.0 ± 1.4

28.8 ± 1.3

17.5 ± 1.0

17.4 ± 2.5

17.1 ± 2.1

16.9 ± 2.6

17.6 ± 2.2

16.0 ± 2.8

3/1

56/39

7/1

115/104

30/29

1/1

68.4 ± 16.8

67.0 ± 12.1

69.4 ± 12.6

70.1 ± 11.3

70.0 ± 10.1

75.9 ± 16.4

72.0 ± 12.2

135/101

17.0 ± 2.4

4

95

8

219

59

2

236

**ε4/ε4**

**ε3/ε4**

**ε2/ε4**

**ε3/ε3**

**ε2/ε3**

**ε2/ε2**

**Both DTI and fMRI**

DTI data were first pre-processed with the diffusion toolkit toolbox (http:// trackvis.org) to obtain the FA/RD/AX/RD values in original b0 space. For the FA/ RA/AX/RD quantification, the FMRIB, Software Library (FSL, http://fsl.fmrib. ox.ac.uk/fsl) tract-based spatial statistics toolbox steps 1–2 (i.e., preprocessing, brain mask extraction with FA > 0.2 and normalization) were used for registration of all participants' FA into the FSL 1-mm white matter skeleton template. The transformation of the individual FA data to the FSL Montreal Neurological Institute (MNI) template with 1-mm isotropic voxel size, was implemented with the nonlinear registration tool FNIRT based on a b-spline representation of the registration warp field. After normalization of FA map to the MNI space, tract-specific mean FA values were obtained in 20 regions from the well-defined probabilistic tract template (FSL/JHU ICBM atlas). Quantitative MD/AX/RD values were obtained by applying the same transformation from individual FA to template space and computed with tract-specific values [1].

The anatomic T1-MPRAGE and 4D EPI functional data were preprocessed using both FSL and Analysis of Functional NeuroImages (AFNI) programs (adapted scripts from http://www.nitrc.org/projects/fcon\_1000 developed based on FSL and AFNI). For structural MPRAGE data used for fMRI data normalization, preprocessing included reorientation to the right-posterior-inferior convention and skull stripping, and segmentation into three tissue types: GM, WM, and cerebrospinal fluid (CSF). The segmental tissue masks were used to derive the nuisance fMRI signals in WM and CSF. Finally, the MPRAGE image was co-registered with the fMRI data and normalized to the Montreal Neurologic Institute (MNI) 152-brain template with 2-mm isotropic voxel size [1, 12].

For fcMRI processing, the first four volumes of the RS-fMRI data of each subject were discarded for scanner and image stability. Preprocessing steps for RS-fMRI data included rigid alignment of the time frames using AFNI motion correction algorithms, spatial smoothing using a Gaussian kernel with 6 mm full-width-athalf-maximum (FWHM), and band-pass temporal filtering of 0.005–0.1 Hz to improve signal-to-noise ratio. Removal of nuisance signals was then performed using a Gaussian regression model after co-registration to MPRAGE data. Namely, motion parameters, global signal, and signals derived from CSF and WM based on the tissue masks were modeled in the Gaussian linear mixed model, and residual signal at each voxel was maintained for further analyses. Finally the residual 4D fMRI data after regression were transformed to MNI standard space [26]. A DMN seed including both medial prefrontal cortex (MED) (MNI center: 0, 48, 23 mm) and posterior cingulate cortex (PCC) (MNI center: 26, 248, 39 mm) with a combined volume of 4112 mm3 (each seed of 2056 mm3 ) was used. We refer to this seed as the combined core seed. We chose the combined core seed over separate PCC and MED seeds because the latter approach generates different and incomplete DMN

connectivity patterns [27], whereas the combined core seed yields consistent and complete depictions of DMN [26]. Whole brain voxel-wise Pearson correlation coefficients were computed between the average time series within the seed and the time course of each fMRI voxel in the brain. Finally Z-statistics were derived voxelwise. 2nd-level Gaussian random field (GRF) and family-wise corrections were applied to derive the functional connectivity (FC) map with FSL toolbox.

In addition to the combined core seed, the fcMRI generated from other seeds were also evaluated to study the systematic-level fcMRI, with a total of 26 seeds [1]. The other 25 seeds included 12 conventional regions of different sub-areas of DMN (e.g., PCC and intra-parietal sulcus), three thalamic (left, right, and whole thalamus) and seven subthalamic seeds [28], and three subcortical seeds (caudate and putamen from the MNI template, and hypothalamus from an in-house developed probability map) [29]. The conventional 12 seeds were derived from the script seed library (http://www.nitrc.org/projects/fcon\_1000), including the hippocampal formation and frontal eye field (FEF) seeds that generated the task-positive networks (i.e., these networks are more active at task-conditions, in contrast to resting state). All seeds were well-evaluated and validated previously [27, 28, 30]. The global mean Z-values were obtained from the fcMRI maps generated from each of 26 seeds to study age and gender effects as well, by averaging the fcMRI Z-maps over the whole brain with a threshold of GRF cluster-corrected P < 0.01.

Meanwhile in the resting state, fractional amplitude of low-frequency fluctuations (fALFF) has been shown to be higher in the DMN regions that are active and it had also been reported that task-related (e.g., working memory, motor visual stimuli and cognitive tasks) alterations of low-frequency oscillations could reflect real-time neuronal activity [28]. The idea of fALFF method was to scale the summary of amplitude at low-frequency band (e.g., 0.01–0.08 Hz) to the summary of amplitude across whole band to remove white and physiological noise. In this study, the resting-state fALFF Z-value at baseline and longitudinal changes of fALFF, as well as correlations with the age and other fcMRI/DTI neuroimaging metrics were performed from 236 participants with available data resource.

#### **2.4 Statistical analyses**

For DTI, effects of age, gender and APOE genotype were studied at both wholebrain voxel-wise level and tract-specific ROI analyses using the four DTI metrics-FA, RD, AX and MD. Linear mixed effects (LME) model was applied to characterize both baseline and longitudinal effects of age, gender and age by gender interactions of the four metrics [1, 31]. We used the MATLAB Statistics toolbox (www.mathworks. com, R2015b) and in-house programs to perform model fitting as listed in Eq. (1). Longitudinal data were incorporated to characterize the longitudinal change of DTI metrics with interval as the prediction parameter as well. In order to account for within-individual correlations stemming from follow-up data, we included random intercept and random interval (i.e., random slope) terms per individual in LME.

 DTI~β<sup>0</sup> + β<sup>1</sup> gender + β<sup>2</sup> baseage + β<sup>3</sup> interval + β<sup>4</sup> interval:baseage + β<sup>5</sup> gender:baseage + β<sup>6</sup> interval:gender (1)

**9**

*Longitudinal Changes of Structural and Functional Connectivity and Correlations…*

fied by genotypic isoforms were performed in MATLAB toolbox.

accounting for baseline age and gender interactions [1].

fcMRI~β0 + β1 baseage + β2 gender + β3 interval

adjusted for age were performed as well.

**3. Results**

**3.1 Age effects on DTI**

For tract-specific ROI-based analyses, the mean value of each regional FA and diffusivity (i.e., RD, AX and MD) analysis were quantified with FSL toolbox and in-house programs developed with MATLAB (www.mathematics.com). Both linear and quadratic fitting were used to examine white matter myelination along aging trajectories. Mean values of all six APOE genotypes were derived from each ROI to form the waveform and multiple-group comparisons of the four DTI metrics strati-

To characterize cross-sectional and longitudinal changes in fcMRI, we used a LME model, similar to DTI longitudinal data model with the same analyses algorithm as listed in Eq. (2). In order to account for cross-sectional differences across individuals, we included baseline age and gender as covariates. Baseline age was centered at group mean of 69.4 years. Men were coded as 0.5 and women as −0.5. Time interval in years between baseline and follow-up was included to capture longitudinal change in fcMRI. We also included interaction terms with interval, random intercept and interval terms to compute longitudinal rates of fcMRI change

+ β4 interval:baseage + β5 gender:baseage + β6 interval:gender (2)

Conventional statistical comparison (with relatively smaller number of participants) using a two-sample t-test at baseline and 3 years follow-ups, adjusting for gender, with the same statistical threshold as used in LME model (P < 0.01 and cluster size ≥10 voxels) was used for longitudinal fALFF data quantification. To validate the age and gender effects observed in LME model, SPM-based conventional regression model including general linear correlational analysis between age and fcMRI adjusted for gender, and comparison between women and men group

Whole brain DTI FA showed prominent aging effects (i.e., reduced FA with aging) in the main projections fibers including cingulum bundle and superior longitudinal fasciculus (P < 0.00001) (**Figure 1a**). The monotonically reduction of FA was observed in relatively older subject with age larger than 50 years old that construe the majority of the sample size. RD, on the other hand, showed only significantly higher RD values with age in bilateral thalamic radiations, bilateral somatosensory cortex, visual cortex, anterior and posterior cingulate gyri, middle temporal cortex including hippocampus, subcallosal cortex and posterior cerebellum (P < 0.01, cluster size = 10). Very small clusters and primarily in the cerebellum was found to have lower RD along the age (**Figure 1b**). Axial diffusivity showed significantly higher AX values with age in some similar regions to RD including bilateral thalamic radiations, bilateral somatosensory cortex, visual cortex, posterior cerebellum and superior corona radiata tract (P < 0.01, cluster size = 10). In contrast to RD and FA, AX was also significantly lower in white matter regions including bilateral cortico-spinal tract, inferior longitudinal fasciculus, optic radiation and cerebellum (**Figure 1c**). MD shows almost a similar aging pattern as of AX (**Figure 1d**) [1].

As expected, all the tract-based ROI showed significant aging effects (i.e., reduced FA with age) after adjustment for multiple comparisons (r = 0.3–0.7, mean r = 0.5, corrected P < 0.0001). Quadratic fitting of FA from 20 track-specific ROIs showed

*DOI: http://dx.doi.org/10.5772/intechopen.86641*

For whole-brain analyses, voxel-wise linear regression with DTI metrics as the dependent variable and age as an independent variable using SPM12 software (Statistical Parametric Mapping, http://fil.ion.ucl.ac.uk/spm/software/spm12) was implemented. Gender term was included as a covariate. Then two-sample t-test comparison using baseline DTI data was also implemented to study gender differences between women and men, and age was used as a covariate [32].

*Longitudinal Changes of Structural and Functional Connectivity and Correlations… DOI: http://dx.doi.org/10.5772/intechopen.86641*

For tract-specific ROI-based analyses, the mean value of each regional FA and diffusivity (i.e., RD, AX and MD) analysis were quantified with FSL toolbox and in-house programs developed with MATLAB (www.mathematics.com). Both linear and quadratic fitting were used to examine white matter myelination along aging trajectories. Mean values of all six APOE genotypes were derived from each ROI to form the waveform and multiple-group comparisons of the four DTI metrics stratified by genotypic isoforms were performed in MATLAB toolbox.

To characterize cross-sectional and longitudinal changes in fcMRI, we used a LME model, similar to DTI longitudinal data model with the same analyses algorithm as listed in Eq. (2). In order to account for cross-sectional differences across individuals, we included baseline age and gender as covariates. Baseline age was centered at group mean of 69.4 years. Men were coded as 0.5 and women as −0.5. Time interval in years between baseline and follow-up was included to capture longitudinal change in fcMRI. We also included interaction terms with interval, random intercept and interval terms to compute longitudinal rates of fcMRI change accounting for baseline age and gender interactions [1].

 fcMRI~β0 + β1 baseage + β2 gender + β3 interval + β4 interval:baseage + β5 gender:baseage + β6 interval:gender (2)

Conventional statistical comparison (with relatively smaller number of participants) using a two-sample t-test at baseline and 3 years follow-ups, adjusting for gender, with the same statistical threshold as used in LME model (P < 0.01 and cluster size ≥10 voxels) was used for longitudinal fALFF data quantification. To validate the age and gender effects observed in LME model, SPM-based conventional regression model including general linear correlational analysis between age and fcMRI adjusted for gender, and comparison between women and men group adjusted for age were performed as well.

### **3. Results**

*Medical Imaging - Principles and Applications*

connectivity patterns [27], whereas the combined core seed yields consistent and complete depictions of DMN [26]. Whole brain voxel-wise Pearson correlation coefficients were computed between the average time series within the seed and the time course of each fMRI voxel in the brain. Finally Z-statistics were derived voxelwise. 2nd-level Gaussian random field (GRF) and family-wise corrections were applied to derive the functional connectivity (FC) map with FSL toolbox.

In addition to the combined core seed, the fcMRI generated from other seeds were also evaluated to study the systematic-level fcMRI, with a total of 26 seeds [1]. The other 25 seeds included 12 conventional regions of different sub-areas of DMN (e.g., PCC and intra-parietal sulcus), three thalamic (left, right, and whole thalamus) and seven subthalamic seeds [28], and three subcortical seeds (caudate and putamen from the MNI template, and hypothalamus from an in-house developed probability map) [29]. The conventional 12 seeds were derived from the script seed library (http://www.nitrc.org/projects/fcon\_1000), including the hippocampal formation and frontal eye field (FEF) seeds that generated the task-positive networks (i.e., these networks are more active at task-conditions, in contrast to resting state). All seeds were well-evaluated and validated previously [27, 28, 30]. The global mean Z-values were obtained from the fcMRI maps generated from each of 26 seeds to study age and gender effects as well, by averaging the fcMRI Z-maps over the whole

Meanwhile in the resting state, fractional amplitude of low-frequency fluctuations (fALFF) has been shown to be higher in the DMN regions that are active and it had also been reported that task-related (e.g., working memory, motor visual stimuli and cognitive tasks) alterations of low-frequency oscillations could reflect real-time neuronal activity [28]. The idea of fALFF method was to scale the summary of amplitude at low-frequency band (e.g., 0.01–0.08 Hz) to the summary of amplitude across whole band to remove white and physiological noise. In this study, the resting-state fALFF Z-value at baseline and longitudinal changes of fALFF, as well as correlations with the age and other fcMRI/DTI neuroimaging metrics were

For DTI, effects of age, gender and APOE genotype were studied at both wholebrain voxel-wise level and tract-specific ROI analyses using the four DTI metrics-FA, RD, AX and MD. Linear mixed effects (LME) model was applied to characterize both baseline and longitudinal effects of age, gender and age by gender interactions of the four metrics [1, 31]. We used the MATLAB Statistics toolbox (www.mathworks. com, R2015b) and in-house programs to perform model fitting as listed in Eq. (1). Longitudinal data were incorporated to characterize the longitudinal change of DTI metrics with interval as the prediction parameter as well. In order to account for within-individual correlations stemming from follow-up data, we included random intercept and random interval (i.e., random slope) terms per individual in LME.

+ β<sup>4</sup> interval:baseage + β<sup>5</sup> gender:baseage + β<sup>6</sup> interval:gender (1)

For whole-brain analyses, voxel-wise linear regression with DTI metrics as the dependent variable and age as an independent variable using SPM12 software (Statistical Parametric Mapping, http://fil.ion.ucl.ac.uk/spm/software/spm12) was implemented. Gender term was included as a covariate. Then two-sample t-test comparison using baseline DTI data was also implemented to study gender differ-

ences between women and men, and age was used as a covariate [32].

brain with a threshold of GRF cluster-corrected P < 0.01.

performed from 236 participants with available data resource.

DTI~β<sup>0</sup> + β<sup>1</sup> gender + β<sup>2</sup> baseage + β<sup>3</sup> interval

**2.4 Statistical analyses**

**8**

#### **3.1 Age effects on DTI**

Whole brain DTI FA showed prominent aging effects (i.e., reduced FA with aging) in the main projections fibers including cingulum bundle and superior longitudinal fasciculus (P < 0.00001) (**Figure 1a**). The monotonically reduction of FA was observed in relatively older subject with age larger than 50 years old that construe the majority of the sample size. RD, on the other hand, showed only significantly higher RD values with age in bilateral thalamic radiations, bilateral somatosensory cortex, visual cortex, anterior and posterior cingulate gyri, middle temporal cortex including hippocampus, subcallosal cortex and posterior cerebellum (P < 0.01, cluster size = 10). Very small clusters and primarily in the cerebellum was found to have lower RD along the age (**Figure 1b**). Axial diffusivity showed significantly higher AX values with age in some similar regions to RD including bilateral thalamic radiations, bilateral somatosensory cortex, visual cortex, posterior cerebellum and superior corona radiata tract (P < 0.01, cluster size = 10). In contrast to RD and FA, AX was also significantly lower in white matter regions including bilateral cortico-spinal tract, inferior longitudinal fasciculus, optic radiation and cerebellum (**Figure 1c**). MD shows almost a similar aging pattern as of AX (**Figure 1d**) [1].

As expected, all the tract-based ROI showed significant aging effects (i.e., reduced FA with age) after adjustment for multiple comparisons (r = 0.3–0.7, mean r = 0.5, corrected P < 0.0001). Quadratic fitting of FA from 20 track-specific ROIs showed

#### **Figure 1.**

*(a) Prominent aging effects were demonstrated with negative correlation between voxel-wise FA and age, i.e., decreased FA along the age (statistical T map, P < 0.01, cluster size = 10) for all brain regions. On the other hand, RD was increased along the age for some of the brain regions including somatosensory cortex and cingulum bundle (b). There are both increases and decreases of AX (c) and MD (d) in different brain regions along the age, and the changes of AX and MD have very similar patterns (all P < 0.01, cluster size = 10). Background image was derived from average of all subjects' FA maps in MNI space.*

aging trajectories with maturation age (i.e., mean FA reaches maximum) falling between [24, 28–41] years. The relatively earlier maturation age was found in the major forceps and bilateral thalamic radiation (28–33 years) and later maturation age from bilateral hippocampal portion of the cingulum and corticospinal tracts (39–42 years) (**Figure 2**). The tract-based ROI that has the earliest maturation age with highest FA at 27.9 years is the major forceps. The left hippocampal portion of the cingulum bundle

**11**

*Longitudinal Changes of Structural and Functional Connectivity and Correlations…*

has the latest maturation age (42.1 years). While the diffusivity measures from tractbased ROIs showed mostly linearly increases of diffusivity along the age with RD, and

*Quadratic aging trajectories of two tract-specific ROIs. Star (cyan color) indicates maturation age when mean FA of the tract each tract-specific ROI reaches maximum. Major forceps has the earliest maturation age, with highest FA at 27.9 years (blue color). While the left hippocampal portion of the cingulum bundle has the latest* 

Using diffusion toolkit (http://www.nitre.org/projects/trackvis/) with an advanced tensorline propagation algorithm for fiber tracking, we found tracts that play important roles in memory and cognitive function also illustrated significant aging effects, including fiber tract numbers of the fornix that connects the hippocampus to the whole brain, and fibro bundles connecting bilateral parahippocampus to the whole brain were decreased significantly with aging (both P < 0.00001).

Based on LME model, no significant age by gender interactions have been found to either FA or diffusivity metrics in the whole-brain voxel-wise analyses indicating aging and gender effects can be studied independently (**Figure 3a**). The interval effective regions estimated from LME model showed longitudinal change of FA and RD/AX values remained similar to the aging results found with cross-sectional data as in **Figure 1**

and gender effective brain regions (P < 0.01, cluster size = 10) (**Figure 3b**).

We found men had significantly higher FA in the hippocampal portion of the cingulum bundle, secondary somatosensory cortex, thalamus, cingulate and cerebellar regions compared to women, based on voxel-wise FA comparisons (P < 0.01). Lower FA in men than women in bilateral inferior longitudinal fasciculus, anterior thalamic radiation, frontal cortex and temporal part of the superior longitudinal fasciculus were also observed (P < 0.01, cluster size = 10). Scattered cortical regions including superior frontal, cerebellum and insular showed higher RD in men than women, and lower RD

some tracts showed no significant aging effects based on AX or MD [1].

**3.2 Longitudinal change of FA**

*maturation age, with highest FA at 42.1 years (red color).*

**Figure 2.**

**3.3 Gender effects on DTI**

*DOI: http://dx.doi.org/10.5772/intechopen.86641*

*Longitudinal Changes of Structural and Functional Connectivity and Correlations… DOI: http://dx.doi.org/10.5772/intechopen.86641*

#### **Figure 2.**

*Medical Imaging - Principles and Applications*

**10**

**Figure 1.**

aging trajectories with maturation age (i.e., mean FA reaches maximum) falling between [24, 28–41] years. The relatively earlier maturation age was found in the major forceps and bilateral thalamic radiation (28–33 years) and later maturation age from bilateral hippocampal portion of the cingulum and corticospinal tracts (39–42 years) (**Figure 2**). The tract-based ROI that has the earliest maturation age with highest FA at 27.9 years is the major forceps. The left hippocampal portion of the cingulum bundle

*Background image was derived from average of all subjects' FA maps in MNI space.*

*(a) Prominent aging effects were demonstrated with negative correlation between voxel-wise FA and age, i.e., decreased FA along the age (statistical T map, P < 0.01, cluster size = 10) for all brain regions. On the other hand, RD was increased along the age for some of the brain regions including somatosensory cortex and cingulum bundle (b). There are both increases and decreases of AX (c) and MD (d) in different brain regions along the age, and the changes of AX and MD have very similar patterns (all P < 0.01, cluster size = 10).* 

*Quadratic aging trajectories of two tract-specific ROIs. Star (cyan color) indicates maturation age when mean FA of the tract each tract-specific ROI reaches maximum. Major forceps has the earliest maturation age, with highest FA at 27.9 years (blue color). While the left hippocampal portion of the cingulum bundle has the latest maturation age, with highest FA at 42.1 years (red color).*

has the latest maturation age (42.1 years). While the diffusivity measures from tractbased ROIs showed mostly linearly increases of diffusivity along the age with RD, and some tracts showed no significant aging effects based on AX or MD [1].

Using diffusion toolkit (http://www.nitre.org/projects/trackvis/) with an advanced tensorline propagation algorithm for fiber tracking, we found tracts that play important roles in memory and cognitive function also illustrated significant aging effects, including fiber tract numbers of the fornix that connects the hippocampus to the whole brain, and fibro bundles connecting bilateral parahippocampus to the whole brain were decreased significantly with aging (both P < 0.00001).

#### **3.2 Longitudinal change of FA**

Based on LME model, no significant age by gender interactions have been found to either FA or diffusivity metrics in the whole-brain voxel-wise analyses indicating aging and gender effects can be studied independently (**Figure 3a**). The interval effective regions estimated from LME model showed longitudinal change of FA and RD/AX values remained similar to the aging results found with cross-sectional data as in **Figure 1** and gender effective brain regions (P < 0.01, cluster size = 10) (**Figure 3b**).

#### **3.3 Gender effects on DTI**

We found men had significantly higher FA in the hippocampal portion of the cingulum bundle, secondary somatosensory cortex, thalamus, cingulate and cerebellar regions compared to women, based on voxel-wise FA comparisons (P < 0.01). Lower FA in men than women in bilateral inferior longitudinal fasciculus, anterior thalamic radiation, frontal cortex and temporal part of the superior longitudinal fasciculus were also observed (P < 0.01, cluster size = 10). Scattered cortical regions including superior frontal, cerebellum and insular showed higher RD in men than women, and lower RD

#### **Figure 3.**

*(a) Based on LME model, no significant age by gender interaction of DTI FA and diffusivity values across whole-brain with only a few outliers (P < 0.01). (b) The interval effect estimated from LME model using longitudinal data showed longitudinal change of FA, RD and AX values (MD is almost the same as AX) in brain regions similar to the baseline aging effects (1–3 years of interval of follow-up time; P < 0.01, cluster size = 10), suggesting an observable longitudinal change within a short time interval.*

in men only with small clusters in cerebellum. Furthermore AX and MD values in most of brain regions were higher in men than women (P < 0.01, cluster size = 10).

### **3.4 APOE genotype effects**

Voxel-wise FA and MD comparisons between different APOE genotypes showed differences in scattered brain clusters (P < 0.01, cluster size = 10). Scattered brain regions showed both higher FA and higher diffusivity in APOE ε2/ε3 compared to APOE ε3/ε3, as well as comparing APOE ε3/ε4 to APOE ε3/ε3. Only RD was decreased in small clusters in APOE ε3/ε4 compared to APOE ε3/ε3 (P < 0.01, cluster size = 10). And AX comparison between APOE ε2/ε3 vs. APOE ε3/ε3 showed more brain regions with higher AX in APOE ε2/ε3 carriers. MD showed similar pattern as of AX.

Furthermore, track-based mean FA stratified by different APOE genotype in majority of fibers demonstrated an incremental consistent pattern (44-24-33-23- 34-22 chain, lowest in APOE44 and highest in APOE22 carriers); especially in right cortico-spinal tract and bilateral uncinate fasciculus (**Figure 4**). RD waveforms of 20 ROIs stratified by APOE genotype showed different waveforms than FA, with highest RD in APOE ε2/ε3 isoforms, and lower in APOE ε4+ carriers in all 20 ROIs. Both MD and AX measures showed very similar waveforms as of RD in 20 ROIs.

In addition, 11 out of 26 seed-based fcMRI strength (mean Z-value, with P < 0.001) stratified by different APOE genotype follow an incremental pattern with the 44-24-33-23-34-22 isoform including mean fcMRI seeding from subcortical thalamus (b–e), hypothalamus (f), and task-positive intra-parietal sulcus (a) as well as ventral medial prefrontal cortex; especially the hypothalamus (f) and from the thalamus segment 3 (d) that projected to visual cortex. On the other hand,

**13**

**Figure 5.**

*L = left; R = right.*

**Figure 4.**

**3.5 fALFF and fcMRI results**

*Longitudinal Changes of Structural and Functional Connectivity and Correlations…*

gradual decrement of fcMRI strength with the APOE 44-24-33-23-34-22 genotypic chain of the DMN connecting from the posterior cingulate cortex (PCC) seed (g)

*APOE genotypic effects on tract-specific DTI mean FA in 20 brain tracts. The mean FA of bilateral corticospinal, left cingulum and right superior longitudinal fasciculus tracts (marked with large \*\*) follow the APOE 44-24-33-23-34 incremental pattern consistently. Inf = inferior; SLF = superior longitudinal fasciculus;* 

*Effects of APOE genotype on global fcMRI strength of task-positive networks seeding from intra-parietal sulcus (a), thalamus (b,c,d,e) and hypothalamus (f) demonstrated increasing patterns of fcMRI along with the 44-24-33-23-34-22 genotype. On the other hand, decreasing genotypic patterns of resting-state default mode networks (DMN) seeding from the posterior cingulum (g) and core seeds of DMN (h) were observed.*

As of fALFF, group mean image demonstrated higher fALFF in cortical gray matter with only temporal cortex largely spared (**Figure 6a**). Baseline aging effects

and DMN core seed (h) had been observed as well (**Figure 5**).

*DOI: http://dx.doi.org/10.5772/intechopen.86641*

*Longitudinal Changes of Structural and Functional Connectivity and Correlations… DOI: http://dx.doi.org/10.5772/intechopen.86641*

#### **Figure 4.**

*Medical Imaging - Principles and Applications*

**3.4 APOE genotype effects**

pattern as of AX.

**Figure 3.**

in men only with small clusters in cerebellum. Furthermore AX and MD values in most

*(a) Based on LME model, no significant age by gender interaction of DTI FA and diffusivity values across whole-brain with only a few outliers (P < 0.01). (b) The interval effect estimated from LME model using longitudinal data showed longitudinal change of FA, RD and AX values (MD is almost the same as AX) in brain regions similar to the baseline aging effects (1–3 years of interval of follow-up time; P < 0.01, cluster* 

Voxel-wise FA and MD comparisons between different APOE genotypes showed differences in scattered brain clusters (P < 0.01, cluster size = 10). Scattered brain regions showed both higher FA and higher diffusivity in APOE ε2/ε3 compared to APOE ε3/ε3, as well as comparing APOE ε3/ε4 to APOE ε3/ε3. Only RD was decreased in small clusters in APOE ε3/ε4 compared to APOE ε3/ε3 (P < 0.01, cluster size = 10). And AX comparison between APOE ε2/ε3 vs. APOE ε3/ε3 showed more brain regions with higher AX in APOE ε2/ε3 carriers. MD showed similar

Furthermore, track-based mean FA stratified by different APOE genotype in majority of fibers demonstrated an incremental consistent pattern (44-24-33-23- 34-22 chain, lowest in APOE44 and highest in APOE22 carriers); especially in right cortico-spinal tract and bilateral uncinate fasciculus (**Figure 4**). RD waveforms of 20 ROIs stratified by APOE genotype showed different waveforms than FA, with highest RD in APOE ε2/ε3 isoforms, and lower in APOE ε4+ carriers in all 20 ROIs. Both MD and AX measures showed very similar waveforms as of RD in 20 ROIs. In addition, 11 out of 26 seed-based fcMRI strength (mean Z-value, with P < 0.001) stratified by different APOE genotype follow an incremental pattern with the 44-24-33-23-34-22 isoform including mean fcMRI seeding from subcortical thalamus (b–e), hypothalamus (f), and task-positive intra-parietal sulcus (a) as well as ventral medial prefrontal cortex; especially the hypothalamus (f) and from the thalamus segment 3 (d) that projected to visual cortex. On the other hand,

of brain regions were higher in men than women (P < 0.01, cluster size = 10).

*size = 10), suggesting an observable longitudinal change within a short time interval.*

**12**

*Effects of APOE genotype on global fcMRI strength of task-positive networks seeding from intra-parietal sulcus (a), thalamus (b,c,d,e) and hypothalamus (f) demonstrated increasing patterns of fcMRI along with the 44-24-33-23-34-22 genotype. On the other hand, decreasing genotypic patterns of resting-state default mode networks (DMN) seeding from the posterior cingulum (g) and core seeds of DMN (h) were observed.*

#### **Figure 5.**

*APOE genotypic effects on tract-specific DTI mean FA in 20 brain tracts. The mean FA of bilateral corticospinal, left cingulum and right superior longitudinal fasciculus tracts (marked with large \*\*) follow the APOE 44-24-33-23-34 incremental pattern consistently. Inf = inferior; SLF = superior longitudinal fasciculus; L = left; R = right.*

gradual decrement of fcMRI strength with the APOE 44-24-33-23-34-22 genotypic chain of the DMN connecting from the posterior cingulate cortex (PCC) seed (g) and DMN core seed (h) had been observed as well (**Figure 5**).

#### **3.5 fALFF and fcMRI results**

As of fALFF, group mean image demonstrated higher fALFF in cortical gray matter with only temporal cortex largely spared (**Figure 6a**). Baseline aging effects

#### **Figure 6.**

*Functional activity measured with fALFF with group mean (a) (corrected P < 0.001) showing higher fALFF in cortical gray matter with only temporal cortex largely spared. Decreased activity with age in the superior middle frontal and precuneus as well as in the cortical caudate region (b), but increased activity in the cerebellum and bilateral frontal white matter (c) (both cluster corrected P < 0.001) were observed. Women group had higher fALFF Z values in frontal white matter area and small regions in caudate (d), while men group had higher fALFF in cerebellum (e) (both corrected P < 0.001).*

**15**

DMN regions (r = 0.19, P = 0.004).

**4. Discussion and conclusion**

*Longitudinal Changes of Structural and Functional Connectivity and Correlations…*

demonstrated decreased activity with age in the caudate, superior middle frontal and precuneus regions, but increased activity in the cerebellum and bilateral frontal white matter (P < 0.001) (**Figure 6b** and **c**). Average fALFF activity strength over the whole brain demonstrated magnificent aging effects (r = −0.28, P = 0.00001). Gender comparison showed slight difference with men had lower fALFF than women in small regions of caudate and frontal white matter clusters, but higher activity of men than women in the cerebellum (P < 0.001) (**Figure 6d** and **e**). Longitudinal comparison of baseline fALFF and 3 years later showed decreased functional activity in the right inferior parietal lobe and right occipital cortex; accompanied by increased activity in the front eye field region, left superior frontal and left temporal cortices (P < 0.01).

LME model performed to available 52 neurocognitive tests at baseline also found significant aging effects (P < 0.00001) in almost all tests with worsening cognitive function at age. And similar longitudinal interval effects were found with a smaller

Significant correlations between average Z-value of fcMRI strength in the DMN and neurocognitive tests were found as following: (1) between DMN Z and digital span test (DST) total score (r = 0.19, P < 0.00001); (2) between average DMN Z and Pegboard dominant (Dom) motor function score (r = 0.22, P < 0.00001); (3) between average DMN Z and Pegboard non-dominant (NonDom) mean

score (r = 0.17, P = 0.00002); and (4) between average DMN Z and category fluency (FluenCat) test mean score (r = 0.12, P = 0.003). Interestingly, significant correlations between average DMN Z and graph-theory based resting-state functional network small-worldness properties were found as well, including: (1) between DMN Z and relative local efficiency (r = 0.15, P = 0.0002); (2) between DMN Z and absolute local efficiency (r = 0.17, P = 0.00002); (3) between DMN Z and relative global efficiency (r = 0.09, P = 0.02); (4) between DMN Z and absolute global efficiency (r = 0.1, P = 0.01); and (5) between DMN Z and small-worldness configuration (r = 0.15, P = 0.0001). Significant correlations between age and average

Significant correlations between four DTI metrics (FA/MD/AX/MD) and neurocognitive functions were found including California verbal learning test (CVLT), Fluencat, Benton visual retention total errors (BVTOT), Dom and NDom tests that measure visual perception and memory dysfunction, language fluency, communication and social function, speed and accuracy, cognitive flexibility, visual attention, spatial orientation, working memory and executive function, as well as movement speed and motor function domains (most P < 0.0001). And correlations were found in all 20 tracts that connect to the whole brain indicating regional and global-wise associations between brain structure connectivity and neurocognitive alterations. Significant correlations among imaging metrics were found as well including: (1) average whole-brain fALFF (a biomarker for functional activity) Z-value based on resting-state fMRI data and age (r = −0.28, P = 0.00002); (2) average fALFF Z and mean FA of whole brain (r = 0.26, P = 0.00007); (3) average DMN Z and mean FA of whole brain (r = 0.18, P = 0.007); and (4) average DMN Z and mean FA of

One of the main findings of DTI was the decreased FA but increased RD along the age based on both voxel-wise and track-specific analyses at both baseline and

significance level for each cognitive test (most P < 0.001).

DMN Z (r = −0.20, P < 0.00001; N = 608) are noted additionally.

*DOI: http://dx.doi.org/10.5772/intechopen.86641*

**3.6 Correlations**

*Longitudinal Changes of Structural and Functional Connectivity and Correlations… DOI: http://dx.doi.org/10.5772/intechopen.86641*

demonstrated decreased activity with age in the caudate, superior middle frontal and precuneus regions, but increased activity in the cerebellum and bilateral frontal white matter (P < 0.001) (**Figure 6b** and **c**). Average fALFF activity strength over the whole brain demonstrated magnificent aging effects (r = −0.28, P = 0.00001). Gender comparison showed slight difference with men had lower fALFF than women in small regions of caudate and frontal white matter clusters, but higher activity of men than women in the cerebellum (P < 0.001) (**Figure 6d** and **e**). Longitudinal comparison of baseline fALFF and 3 years later showed decreased functional activity in the right inferior parietal lobe and right occipital cortex; accompanied by increased activity in the front eye field region, left superior frontal and left temporal cortices (P < 0.01).

#### **3.6 Correlations**

*Medical Imaging - Principles and Applications*

**14**

**Figure 6.**

*Functional activity measured with fALFF with group mean (a) (corrected P < 0.001) showing higher fALFF in cortical gray matter with only temporal cortex largely spared. Decreased activity with age in the superior middle frontal and precuneus as well as in the cortical caudate region (b), but increased activity in the cerebellum and bilateral frontal white matter (c) (both cluster corrected P < 0.001) were observed. Women group had higher fALFF Z values in frontal white matter area and small regions in caudate (d), while men* 

*group had higher fALFF in cerebellum (e) (both corrected P < 0.001).*

LME model performed to available 52 neurocognitive tests at baseline also found significant aging effects (P < 0.00001) in almost all tests with worsening cognitive function at age. And similar longitudinal interval effects were found with a smaller significance level for each cognitive test (most P < 0.001).

Significant correlations between average Z-value of fcMRI strength in the DMN and neurocognitive tests were found as following: (1) between DMN Z and digital span test (DST) total score (r = 0.19, P < 0.00001); (2) between average DMN Z and Pegboard dominant (Dom) motor function score (r = 0.22, P < 0.00001); (3) between average DMN Z and Pegboard non-dominant (NonDom) mean score (r = 0.17, P = 0.00002); and (4) between average DMN Z and category fluency (FluenCat) test mean score (r = 0.12, P = 0.003). Interestingly, significant correlations between average DMN Z and graph-theory based resting-state functional network small-worldness properties were found as well, including: (1) between DMN Z and relative local efficiency (r = 0.15, P = 0.0002); (2) between DMN Z and absolute local efficiency (r = 0.17, P = 0.00002); (3) between DMN Z and relative global efficiency (r = 0.09, P = 0.02); (4) between DMN Z and absolute global efficiency (r = 0.1, P = 0.01); and (5) between DMN Z and small-worldness configuration (r = 0.15, P = 0.0001). Significant correlations between age and average DMN Z (r = −0.20, P < 0.00001; N = 608) are noted additionally.

Significant correlations between four DTI metrics (FA/MD/AX/MD) and neurocognitive functions were found including California verbal learning test (CVLT), Fluencat, Benton visual retention total errors (BVTOT), Dom and NDom tests that measure visual perception and memory dysfunction, language fluency, communication and social function, speed and accuracy, cognitive flexibility, visual attention, spatial orientation, working memory and executive function, as well as movement speed and motor function domains (most P < 0.0001). And correlations were found in all 20 tracts that connect to the whole brain indicating regional and global-wise associations between brain structure connectivity and neurocognitive alterations.

Significant correlations among imaging metrics were found as well including: (1) average whole-brain fALFF (a biomarker for functional activity) Z-value based on resting-state fMRI data and age (r = −0.28, P = 0.00002); (2) average fALFF Z and mean FA of whole brain (r = 0.26, P = 0.00007); (3) average DMN Z and mean FA of whole brain (r = 0.18, P = 0.007); and (4) average DMN Z and mean FA of DMN regions (r = 0.19, P = 0.004).

#### **4. Discussion and conclusion**

One of the main findings of DTI was the decreased FA but increased RD along the age based on both voxel-wise and track-specific analyses at both baseline and

longitudinal follow-up visits. Longitudinal data revealed similar rate of change of DTI metrics associated with age and gender as to cross-sectional results, indicating these changes were observable over a very short period (e.g., longitudinal interval of 1–3 years). Especially the significant decreases of FA along the age in most of brain regions suggest that white matter integrity reduces with age. Radial diffusivity (RD) increased with age in the regions that play important roles in memory, visual and motor function such as bilateral thalamic radiations, bilateral somatosensory cortex, visual cortex, anterior and posterior cingulate gyri, middle temporal cortex and hippocampus (P < 0.01, cluster size = 10). This suggested that demyelination process that resulted in radial space increases occurred in these brain tracts with age, and was also confirmed with significant reduced fiber-bundles from fornix and parahippocampus, as well as the latest maturation age of the cingulum bundle that was more vulnerable to demyelination and retrograde degeneration [1]. While changes in AX and MD are in two-way: increased AX and MD in some tracts and cortical regions including bilateral thalamic radiation and cingulum bundles, together with decreased AX and MD in some long-distance fasciculus including bilateral corticospinal tract, inferior longitudinal fasciculus and optic radiation. Lower FA and higher RD indicating axonal degeneration have been found in a small sample with similar age range [24, 33]. A few long commissure and association fibers including corpus callosum, cortico-spinal tract, cingulum bundle and superior longitudinal fibers might also undergo Wallerian degeneration [34] with increased RD but decreased AX along the age [1].

Consistent with the current view of neuroplasticity, neuroprotective and compensation roles of fMRI connectivity and activation [35–37], mean fcMRI values from DMN core and PCC core were lowest in the least risky APOE22 isoform, but highest in the most risky APOE44 isoform. And the waveforms of global DMN fcMRI strength decreases from the most to least risky genetic isoforms. However, for the subcortical regions including thalamus and hypothalamus seeds, the fcMRI increases from the expected most risky to least risky genetic isoforms. These changes of divergent waveforms of fcMRI from DMN and subcortical regions had also confirmed the opposite directions of resting-state DMN network and taskpositive or attentional-recruitment networks, and might indicate less efficient or over-recruitment of neuronal source usage at most risky APOE44 carriers [38–41]. On the other hand, the DTI metric of FA from majority of fibers demonstrated an incremental consistent pattern from APOE44 to APOE22 carriers, indicating micro-structural integrity was associated positively and tightly with the genotypic functional role of each APOE allele.

Our results of significant imaging quantifications and neurocognitive tests indicate neuronal degeneration, functional disconnectivity as well as white matter deterioration (demyelination, Wallerian degeneration and structural connectivity) at age go parallel with each other, and present together with neurocognitive dysfunction (especially in the domains of memory, cognitive flexibility, visual perception and attention, and executive function). Similar correlation results were found between each tract-specific DTI metric and one-domain neurocognitive test suggest that regional correlations agree with each other, and significant structural connectivity-neurocognitive function correlations remain consistent across the whole brain. Associations between DMN functional connectivity and neurocognitive scores of memory, motor coordination and language social function are expected given the importance of DMN in these domains [42–44]. DMN also represents more global integration function based on the significant correlations between DMN fcMRI and local/global efficiencies of network analysis [45, 46]. Significant correlations were also found between global FA and global functional activity from fALFF; as well as between DMN fcMRI and DMN FA. Scattered longitudinal changes

**17**

*Longitudinal Changes of Structural and Functional Connectivity and Correlations…*

study to reflect neuronal activation under task conditions [28].

and gender differences of fALFF were found with different patterns from fcMRI (largely decreased DMN but increased DAN regions of fcMRI). However, the spatial distribution pattern of fALFF was mainly in cortical gray matter (significantly higher in occipital, parietal and frontal cortices but with relatively lower activation pattern in temporal cortex). Although fALFF is not a good biomarker due to lack of functional and spatial specialization, it might be used in epoch-related task fMRI

While our results are consistent with several published articles and are also in agreement with functional and structural connectivity findings [36, 47–50], current study is still limited to the scope of conventional fMRI and DTI sequence with normal aging samples. Further improvement of the technique with acceleration-based fMRI acquisition and multi-shell and multi-b-value DTI [32] as well as validation of our observations using other molecular imaging findings such as amyloid and tau imaging that provide pathological evidence besides the current neuroimaging findings are expected [23, 32, 51, 52]. It had been reported that task-based fMRI data could reflect specific cognitive function such as executive function, high-level cognitive function and communication skill, we expect more correlations could be found between fMRI data and neurocognitive scores in other cognitive domains [53, 54]. In conclusion, different sensitivities of DTI metrics in various brain regions have been observed of the age, gender and genotypic effects. For instance, FA measures showed age effects on white matter integrity across adulthood, with increases in FA through the 30's and 40's and subsequent decreases in middle-age and older adults. Accompanying the decreases of FA along the age in most of brain regions are the radial diffusivity increases that indicates demyelination process with age. AX and MD showed both lower and higher with age in different brain regions, suggesting possible axonal and Wallerian degenerations in these brain regions. We found longitudinal changes in both DTI and fcMRI in regions were similar to those demonstrating cross-sectional effects of age; for instance decreased fcMRI in DMN but increased fcMRI in anti-correlated DAN networks. The APOE genotypic signatures of FA and functional connectivity suggested possible tight associations between myelin/neuronal activation and APOE gene, indicating different roles of APOE alleles on brain structural conductivity, demyelination and neuroplasticity. Taken together, our neuroimaging and correlational neurocognitive results indicate significant and consistent age, gender and APOE genotypic effects on structural and functional connectivity at both baseline and longitudinal short-interval ranges.

*DOI: http://dx.doi.org/10.5772/intechopen.86641*

#### *Longitudinal Changes of Structural and Functional Connectivity and Correlations… DOI: http://dx.doi.org/10.5772/intechopen.86641*

and gender differences of fALFF were found with different patterns from fcMRI (largely decreased DMN but increased DAN regions of fcMRI). However, the spatial distribution pattern of fALFF was mainly in cortical gray matter (significantly higher in occipital, parietal and frontal cortices but with relatively lower activation pattern in temporal cortex). Although fALFF is not a good biomarker due to lack of functional and spatial specialization, it might be used in epoch-related task fMRI study to reflect neuronal activation under task conditions [28].

While our results are consistent with several published articles and are also in agreement with functional and structural connectivity findings [36, 47–50], current study is still limited to the scope of conventional fMRI and DTI sequence with normal aging samples. Further improvement of the technique with acceleration-based fMRI acquisition and multi-shell and multi-b-value DTI [32] as well as validation of our observations using other molecular imaging findings such as amyloid and tau imaging that provide pathological evidence besides the current neuroimaging findings are expected [23, 32, 51, 52]. It had been reported that task-based fMRI data could reflect specific cognitive function such as executive function, high-level cognitive function and communication skill, we expect more correlations could be found between fMRI data and neurocognitive scores in other cognitive domains [53, 54].

In conclusion, different sensitivities of DTI metrics in various brain regions have been observed of the age, gender and genotypic effects. For instance, FA measures showed age effects on white matter integrity across adulthood, with increases in FA through the 30's and 40's and subsequent decreases in middle-age and older adults. Accompanying the decreases of FA along the age in most of brain regions are the radial diffusivity increases that indicates demyelination process with age. AX and MD showed both lower and higher with age in different brain regions, suggesting possible axonal and Wallerian degenerations in these brain regions. We found longitudinal changes in both DTI and fcMRI in regions were similar to those demonstrating cross-sectional effects of age; for instance decreased fcMRI in DMN but increased fcMRI in anti-correlated DAN networks. The APOE genotypic signatures of FA and functional connectivity suggested possible tight associations between myelin/neuronal activation and APOE gene, indicating different roles of APOE alleles on brain structural conductivity, demyelination and neuroplasticity. Taken together, our neuroimaging and correlational neurocognitive results indicate significant and consistent age, gender and APOE genotypic effects on structural and functional connectivity at both baseline and longitudinal short-interval ranges.

*Medical Imaging - Principles and Applications*

increased RD but decreased AX along the age [1].

functional role of each APOE allele.

longitudinal follow-up visits. Longitudinal data revealed similar rate of change of DTI metrics associated with age and gender as to cross-sectional results, indicating these changes were observable over a very short period (e.g., longitudinal interval of 1–3 years). Especially the significant decreases of FA along the age in most of brain regions suggest that white matter integrity reduces with age. Radial diffusivity (RD) increased with age in the regions that play important roles in memory, visual and motor function such as bilateral thalamic radiations, bilateral somatosensory cortex, visual cortex, anterior and posterior cingulate gyri, middle temporal cortex and hippocampus (P < 0.01, cluster size = 10). This suggested that demyelination process that resulted in radial space increases occurred in these brain tracts with age, and was also confirmed with significant reduced fiber-bundles from fornix and parahippocampus, as well as the latest maturation age of the cingulum bundle that was more vulnerable to demyelination and retrograde degeneration [1]. While changes in AX and MD are in two-way: increased AX and MD in some tracts and cortical regions including bilateral thalamic radiation and cingulum bundles, together with decreased AX and MD in some long-distance fasciculus including bilateral corticospinal tract, inferior longitudinal fasciculus and optic radiation. Lower FA and higher RD indicating axonal degeneration have been found in a small sample with similar age range [24, 33]. A few long commissure and association fibers including corpus callosum, cortico-spinal tract, cingulum bundle and superior longitudinal fibers might also undergo Wallerian degeneration [34] with

Consistent with the current view of neuroplasticity, neuroprotective and compensation roles of fMRI connectivity and activation [35–37], mean fcMRI values from DMN core and PCC core were lowest in the least risky APOE22 isoform, but highest in the most risky APOE44 isoform. And the waveforms of global DMN fcMRI strength decreases from the most to least risky genetic isoforms. However, for the subcortical regions including thalamus and hypothalamus seeds, the fcMRI increases from the expected most risky to least risky genetic isoforms. These changes of divergent waveforms of fcMRI from DMN and subcortical regions had also confirmed the opposite directions of resting-state DMN network and taskpositive or attentional-recruitment networks, and might indicate less efficient or over-recruitment of neuronal source usage at most risky APOE44 carriers [38–41]. On the other hand, the DTI metric of FA from majority of fibers demonstrated an incremental consistent pattern from APOE44 to APOE22 carriers, indicating micro-structural integrity was associated positively and tightly with the genotypic

Our results of significant imaging quantifications and neurocognitive tests indicate neuronal degeneration, functional disconnectivity as well as white matter deterioration (demyelination, Wallerian degeneration and structural connectivity) at age go parallel with each other, and present together with neurocognitive dysfunction (especially in the domains of memory, cognitive flexibility, visual perception and attention, and executive function). Similar correlation results were found between each tract-specific DTI metric and one-domain neurocognitive test suggest that regional correlations agree with each other, and significant structural connectivity-neurocognitive function correlations remain consistent across the whole brain. Associations between DMN functional connectivity and neurocognitive scores of memory, motor coordination and language social function are expected given the importance of DMN in these domains [42–44]. DMN also represents more global integration function based on the significant correlations between DMN fcMRI and local/global efficiencies of network analysis [45, 46]. Significant correlations were also found between global FA and global functional activity from fALFF; as well as between DMN fcMRI and DMN FA. Scattered longitudinal changes

**16**

*Medical Imaging - Principles and Applications*

## **Author details**

Yongxia Zhou1,2

1 Department of Radiology, Columbia University, New York, NY, USA

2 Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA

\*Address all correspondence to: yongxia.zhou@yahoo.com

© 2019 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.

**19**

*Longitudinal Changes of Structural and Functional Connectivity and Correlations…*

[9] Agosta F, Dalla Libera D, Spinelli EG,

[10] Kochunov P, Glahn DC, Lancaster J, et al. Fractional anisotropy of cerebral white matter and thickness of cortical gray matter across the lifespan. NeuroImage. 2011;**58**:41-49

[11] Horch RA, Gore JC, Does MD. Origins of the ultrashort-T2 1H NMR signals in myelinated nerve: A direct measure of myelin content? Magnetic Resonance in

[12] Zhou Y. Neuroimaging in Multiple Sclerosis. New York, USA: Nova

[13] Bendfeldt K, Kuster P, Traud S, Egger H, Winklhofer S, Mueller-Lenke N, et al. Association of regional gray matter volume loss and progression of white matter lesions in multiple sclerosis—A longitudinal voxel-based morphometry study. NeuroImage. 2009;**45**:60-67

[14] Bjartmar C, Wujek JR, Trapp BD. Axonal loss in the pathology of MS: Consequences for understanding the progressive phase of the disease. Journal of the Neurological Sciences.

[15] Bodini B, Khaleeli Z, Cercignani M, Miller DH, Thompson AJ, Ciccarelli O. Exploring the relationship between white matter and gray matter damage in early primary progressive multiple sclerosis: An in vivo study with TBSS and VBM. Human Brain Mapping.

[16] Kolasa M, Hakulinen U, Helminen M, Hagman S, Raunio M, Rossi M, et al. Longitudinal assessment of clinically

et al. Myeloid microvesicles in cerebrospinal fluid are associated with myelin damage and neuronal loss in mild cognitive impairment and Alzheimer disease. Annals of Neurology.

2014;**76**:813-825

Medicine. 2011;**66**:24-31

Publishers; 2017

2003;**206**:165-171

2009;**30**:2852-2861

*DOI: http://dx.doi.org/10.5772/intechopen.86641*

[1] Zhou Y. Functional Neuroimaging with Multiple Modalities. New York,

[2] Huster D, Yao X, Hong M. Membrane protein topology probed by (1) H spin diffusion from lipids using solidstate NMR spectroscopy. Journal of the American Chemical Society.

[3] Basser PJ. Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR in Biomedicine. 1995;**8**:333-344

[4] Zhou XJ. Diffusion tensor imaging: Techniques and clinical applications. In: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference. Vol. 7. 2004.

[5] Thiessen JD, Zhang Y, Zhang H, et al. Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination. NMR in Biomedicine. 2013;**26**:1562-1581

[6] Billiet T, Vandenbulcke M, Madler B, et al. Age-related microstructural differences quantified using myelin water imaging and advanced diffusion

[7] Gazes Y, Bowman FD, Razlighi QR, O'Shea D, Stern Y, Habeck C. White matter tract covariance patterns predict age-declining cognitive abilities. NeuroImage. 2016;**125**:53-60

[8] Sasson E, Doniger GM, Pasternak O,

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*Medical Imaging - Principles and Applications*

**18**

**Author details**

Yongxia Zhou1,2

Los Angeles, CA, USA

1 Department of Radiology, Columbia University, New York, NY, USA

\*Address all correspondence to: yongxia.zhou@yahoo.com

provided the original work is properly cited.

2 Department of Biomedical Engineering, University of Southern California,

© 2019 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,

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*Medical Imaging - Principles and Applications*

isolated syndrome with diffusion tensor imaging and volumetric MRI. Clinical

2 years and individual differences in change. Neurobiology of Aging.

[25] McCarrey AC, An Y, Kitner-Triolo

MH,Ferrucci L, Resnick SM. Gender differences in cognitive trajectories in clinically normal older adults. Psychology and Aging.

[26] Zhou Y, Milham MP, Lui YW, Miles L, Reaume J, Sodickson DK, et al. Default-mode network disruption in mild traumatic brain injury. Radiology.

[27] Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL. Functional-anatomic fractionation of the brain's default network. Neuron.

[28] Zhou Y, Lui YW, Zuo XN, Milham MP, Reaume J, Grossman RI, et al. Characterization of thalamo-cortical association using amplitude and connectivity of functional MRI in mild traumatic brain injury. Journal of Magnetic Resonance Imaging.

[29] Zhou Y. Abnormal structural and functional hypothalamic connectivity in mild traumatic brain injury. Journal of Magnetic Resonance Imaging.

[30] Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews. Neuroscience.

Differential trajectories of age-related changes in components of executive and memory processes. Psychology

[32] Zhou Y. Functional Neuroimaging Methods and Frontiers. New York, USA:

[31] Goh JO, An Y, Resnick SM.

and Aging. 2012;**27**:707-719

Nova Publishers; 2018

2015;**36**:1834-1848

2016;**31**:166-175

2002;**265**:882-892

2010;**65**:550-562

2014;**39**:1558-1568

2017;**45**:1105-1112

2007;**8**:700-711

[17] Rocca MA, Preziosa P, Mesaros S, Pagani E, Dackovic J, Stosic-Opincal T, et al. Clinically isolated syndrome suggestive of multiple sclerosis: Dynamic patterns of gray and white matter changes—A 2-year MR imaging study. Radiology. 2016;**278**:841-853

[18] Forn C, Barros-Loscertales A, Escudero J, Benlloch V, Campos S, Antonia Parcet M, et al. Compensatory activations in patients with multiple sclerosis during preserved performance on the auditory N-back task. Human Brain Mapping. 2007;**28**:424-430

[19] Lowe MJ, Beall EB, Sakaie KE, Koenig KA, Stone L, Marrie RA, et al. Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity. Human Brain Mapping. 2008;**29**:818-827

[20] Lisak RP. Neurodegeneration in multiple sclerosis: Defining the problem. Neurology. 2007;**68**:S5-S12,

[21] Yount R, Raschke KA, Biru M, et al. Traumatic brain injury and atrophy of the cingulate gyrus. The Journal of Neuropsychiatry and Clinical Neurosciences. 2002;**14**(4):416-423

[22] Hudak A, Warner M, Marquez de la Plata C, Moore C, Harper C, Diaz-Arrastia R. Brain morphometry changes and depressive symptoms after traumatic brain injury. Psychiatry

Research. 2011;**191**(3):160-165

Nova Publishers; 2017

[23] Zhou Y. Neuroimaging in Mild Traumatic Brain Injury. New York, USA:

[24] Bender AR, Raz N. Normalappearing cerebral white matter in healthy adults: Mean change over

discussion S43-54

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**20**

[34] Kodiweera C, Alexander AL, Harezlak J, McAllister TW, Wu YC. Age effects and gender differences in human brain white matter of young to middle-aged adults: A DTI, NODDI, and q-space study. NeuroImage. 2016;**128**:180-192

[35] Scheinost D, Finn ES, Tokoglu F, et al. Gender differences in normal age trajectories of functional brain networks. Human Brain Mapping. 2015;**36**:1524-1535

[36] Trachtenberg AJ, Filippini N, Ebmeier KP, Smith SM, Karpe F, Mackay CE. The effects of APOE on the functional architecture of the resting brain. NeuroImage. 2012;**59**:565-572

[37] Shu H et al. Opposite neural trajectories of apolipoprotein E 4 and 2 alleles with aging associated with different risks of Alzheimer's disease. Cerebral Cortex. 2016;**26**:1421-1429

[38] Kennedy KM et al. Effects of beta-amyloid accumulation on neural function during encoding across the adult lifespan. NeuroImage. 2012;**62**:1-8. DOI: 10.1016/j.neuroimage

[39] Buckner RL. Memory and executive function in aging and AD: Multiple factors that cause decline and reserve factors that compensate. Neuron. 2004;**44**:195-208

[40] Sala-Llonch R, Bartres-Faz D, Junque C. Reorganization of brain networks in aging: A review of functional connectivity studies. Frontiers in Psychology. 2015;**6**:663

[41] Legon W, Punzell S, Dowlati E, Adams SE, Stiles AB, Moran RJ. Altered prefrontal excitation/inhibition balance and prefrontal output: Markers of aging in human memory networks. Cerebral Cortex. 2016;**26**(11):4315-4326

[42] Raichle ME, Mac Leod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America. 2001;**98**:676-682

[43] Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, et al. Molecular, structural, and functional characterization of Alzheimer's disease: Evidence for a relationship between default activity, amyloid, and memory. The Journal of Neuroscience. 2005;**25**:7709-7717

[44] Jones DT et al. Age-related changes in the default mode network are more advanced in Alzheimer disease. Neurology. 2011;**77**:1524-1531. DOI: 10.1212/WNL.0b013e318233b33d

[45] Fjell AM, Sneve MH, Storsve AB, Grydeland H, Yendiki A, Walhovd KB. Brain events underlying episodic memory changes in aging: A longitudinal investigation of structural and functional connectivity. Cerebral Cortex. 2016;**26**:1272-1286

[46] Kennedy KM, Rodrigue KM, Bischof GN, Hebrank AC, Reuter-Lorenz PA, Park DC. Age trajectories of functional activation under conditions of low and high processing demands: An adult lifespan fMRI study of the aging brain. NeuroImage. 2015;**104**:21-34

[47] Westlye LT, Reinvang I, Rootwelt H, Espeseth T. Effects of APOE on brain white matter microstructure in healthy adults. Neurology. 2012;**79**:1961-1969

[48] Ward AM, Mormino EC, Huijbers W, Schultz AP, Hedden T, Sperling RA. Relationships between default-mode network connectivity, medial temporal lobe structure, and age-related memory deficits. Neurobiology of Aging. 2015;**36**:265-272

[49] Zhou J, Greicius MD, Gennatas ED, et al. Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer's disease. Brain: A Journal of Neurology. 2010;**133**:1352-1367

[50] Bilgel M, An Y, Zhou Y, et al. Individual estimates of age at detectable amyloid onset for risk factor assessment. Alzheimer's & Dementia: The Journal of the Alzheimer's Association. 2015;**36**(8):2333

[51] Scholl M, Lockhart SN, Schonhaut DR, et al. PET imaging of tau deposition in the aging human brain. Neuron. 2016;**89**(5):971-982

[52] Sheline YI, Raichle ME, Snyder AZ, et al. Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biological Psychiatry. 2010;**67**:584-587

[53] Tsvetanov KA, Henson RN, Tyler LK, Razi A, Geerligs L, Ham TE, et al. Extrinsic and intrinsic brain network connectivity maintains cognition across the lifespan despite accelerated decay of regional brain activation. The Journal of Neuroscience. 2016;**36**:3115-3126

[54] Worthy DA, Davis T, Gorlick MA, Cooper JA, Bakkour A, Mumford JA, et al. Neural correlates of state-based decision-making in younger and older adults. NeuroImage. 2015;**130**:13-23

**23**

**Chapter 2**

**Abstract**

directions.

neuropathic pain

**1. Introduction**

and shallow reflex disappear [3].

The Application of Functional

Neuropathic Pain

*Zhi Dou and Liqiang Yang*

Magnetic Resonance Imaging in

In the past, neuropathic pain has been lacking in ideal imaging research methods, which not only limits our research on the pathogenesis of neuropathic pain but also seriously affects the prognosis of treatments. With the rapid development of fMRI technology, more and more scholars have begun to use fMRI technology in the study of neuropathic pain in recent years. This provides a new idea for revealing the underlining mechanisms of neuropathic pain and improving the clinical treatment concepts. In this chapter, we summarized the recent studies of fMRI in neuropathic pain so that readers can better understand the research status and future research

**Keywords:** functional magnetic resonance imaging, brain region, brain network,

Neuropathic pain (NP) is a common type of pain disease with a prevalence of 1–2% in the total population [1, 2]. Although it is less common than nociceptive pain caused by degeneration of the spine and bones, neuropathic pain is often more severe, accompanied by severe emotional reactions, and the clinical efficacy is not ideal. The problems caused by neuropathic pain in the declined quality of life and the loss of working capacity have caused enormous burdens on patients, families, and our society. The International Association for the Study of Pain (IASP) defines neuropathic pain as "the pain that arises as a direct consequence of a lesion or diseases affecting the somatosensory system." According to the location of the damage to the nervous system, neuropathic pain can be divided into peripheral and central types. Typical symptoms include hyperalgesia, allodynia, spontaneous pain, paresthesia, and other positive signs, as well as negative signs such as sensory loss

It is now widely accepted that neuropathic pain is caused by a common change in the sensitivity of peripheral and central nervous system signaling. Peripheral mechanisms may include ectopic and spontaneous discharges, pseudo synaptic conduction, changes in ion channel expression, sympathetic neuron sprouting into dorsal root ganglia, and sensitization of nociceptors. The central mechanism also plays an important role in the pathogenesis of neuropathic pain, especially the processing and integration of information in the high-level centers such as the cerebral

## **Chapter 2**

*Medical Imaging - Principles and Applications*

[49] Zhou J, Greicius MD, Gennatas ED, et al. Divergent network connectivity changes in behavioural variant frontotemporal dementia and

Alzheimer's disease. Brain: A Journal of

[51] Scholl M, Lockhart SN, Schonhaut DR, et al. PET imaging of tau deposition in the aging human brain. Neuron.

[52] Sheline YI, Raichle ME, Snyder AZ, et al. Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biological

Neurology. 2010;**133**:1352-1367

[50] Bilgel M, An Y, Zhou Y, et al. Individual estimates of age at detectable amyloid onset for risk factor assessment. Alzheimer's & Dementia: The Journal of the Alzheimer's Association.

deficits. Neurobiology of Aging.

2015;**36**:265-272

2015;**36**(8):2333

2016;**89**(5):971-982

2016;**36**:3115-3126

Psychiatry. 2010;**67**:584-587

[53] Tsvetanov KA, Henson RN, Tyler LK, Razi A, Geerligs L, Ham TE, et al. Extrinsic and intrinsic brain network connectivity maintains cognition across the lifespan despite accelerated decay of regional brain activation. The Journal of Neuroscience.

[54] Worthy DA, Davis T, Gorlick MA, Cooper JA, Bakkour A, Mumford JA, et al. Neural correlates of state-based decision-making in younger and older adults. NeuroImage. 2015;**130**:13-23

**22**

## The Application of Functional Magnetic Resonance Imaging in Neuropathic Pain

*Zhi Dou and Liqiang Yang*

## **Abstract**

In the past, neuropathic pain has been lacking in ideal imaging research methods, which not only limits our research on the pathogenesis of neuropathic pain but also seriously affects the prognosis of treatments. With the rapid development of fMRI technology, more and more scholars have begun to use fMRI technology in the study of neuropathic pain in recent years. This provides a new idea for revealing the underlining mechanisms of neuropathic pain and improving the clinical treatment concepts. In this chapter, we summarized the recent studies of fMRI in neuropathic pain so that readers can better understand the research status and future research directions.

**Keywords:** functional magnetic resonance imaging, brain region, brain network, neuropathic pain

## **1. Introduction**

Neuropathic pain (NP) is a common type of pain disease with a prevalence of 1–2% in the total population [1, 2]. Although it is less common than nociceptive pain caused by degeneration of the spine and bones, neuropathic pain is often more severe, accompanied by severe emotional reactions, and the clinical efficacy is not ideal. The problems caused by neuropathic pain in the declined quality of life and the loss of working capacity have caused enormous burdens on patients, families, and our society. The International Association for the Study of Pain (IASP) defines neuropathic pain as "the pain that arises as a direct consequence of a lesion or diseases affecting the somatosensory system." According to the location of the damage to the nervous system, neuropathic pain can be divided into peripheral and central types. Typical symptoms include hyperalgesia, allodynia, spontaneous pain, paresthesia, and other positive signs, as well as negative signs such as sensory loss and shallow reflex disappear [3].

It is now widely accepted that neuropathic pain is caused by a common change in the sensitivity of peripheral and central nervous system signaling. Peripheral mechanisms may include ectopic and spontaneous discharges, pseudo synaptic conduction, changes in ion channel expression, sympathetic neuron sprouting into dorsal root ganglia, and sensitization of nociceptors. The central mechanism also plays an important role in the pathogenesis of neuropathic pain, especially the processing and integration of information in the high-level centers such as the cerebral cortex, brainstem, and cerebellum, which are closely related to the chronic pain and the emergence of many typical symptoms and signs [3]. In the past, studies on the central mechanism of neuropathic pain were mostly limited to clinical observations and animal experiments. However, in recent years, with the development of neuroimaging techniques, especially the maturity of functional magnetic resonance, researchers have been able to explore the structure and function of the brain in multiple levels, providing new ideas for the study of the underlying changes of central nerves system in neuropathic pain.

### **2. Functional magnetic resonance imaging (fMRI)**

Magnetic resonance imaging is an imaging technique based on the principle of nuclear magnetic resonance. According to the difference of research purposes, it can be roughly divided into two categories: structural imaging and functional imaging. The purpose of structure imaging is mainly to study the anatomical structure of brain tissue and the structural fiber connection between different brain regions. The main techniques are conventional brain structure imaging and voxel-based morphological measurement (VBM), diffusion tensor imaging (DTI), etc. VBM can provide regions of interest for brain function changes. DTI can be used to analyze the anatomical basis of functional connectivity in brain regions and therefore belongs to the generalized fMRI. The narrow sense of fMRI mainly refers to a technique for studying brain function activities by monitoring changes in cerebral blood flow. The purpose is to explore the neural activity of each brain region under different physiological or pathological conditions. According to the differences in research methods, it can be divided into task-state fMRI and resting-state fMRI.

#### **2.1 VBM**

VBM is a technique for analyzing brain magnetic resonance images at the voxel level. It can quantitatively calculate the changes in local gray and white matter density and volume, so as to accurately display the morphological changes of brain structure. Neuropathic pain can cause changes in the plasticity of the brain structure, such as variation in the gray matter density of the cortex in the brain region. The degree of gray matter density in some brain regions is also related to various clinical indicators such as the length of disease and pain intensity. These changes can be studied with VBM [4].

By tracking follow-up of patients with herpes zoster (HZ), Cao et al. compared the differences in gray matter volume between patients with acute herpes zoster and postherpetic neuralgia (PHN). They found that the PHN brain showed decreased gray matter volume in the frontal lobe, the parietal lobe, and the occipital lobe but increased in the cerebellum and the temporal lobe. These changes may be correlated with HZ-PHN chronification [5]. In several trigeminal neuralgia studies, the reduction of the volume of the anterior cingulate cortex (ACC) and the increase of the volume of the temporal cortex were found. Li et al. also considered that the increase in the volume of the upper and middle gyrus was proportional to the duration of trigeminal neuralgia [6, 7].

#### **2.2 DTI**

Modern brain science believes that the human brain is a complex and efficient network called the brain network. Each region of the brain is responsible for relatively independent functions and has a large number of structural and functional

**25**

brain in a specific state [13, 14].

*The Application of Functional Magnetic Resonance Imaging in Neuropathic Pain*

connections with each other. The brain network is formed based on this separation and integration principle. White matter fiber bundle is the material basis for connecting the various nodes of the network for information transmission. Any damage to the structure or function of the white matter fiber bundles may affect the information transmission of the brain and cause disease manifestation. DTI is currently the only noninvasive method for effectively observing and tracking white matter fiber bundles. Because of the directionality of the white matter fiber bundles, the internal water molecules are dispersed in a direction-dependent manner, which is called anisotropic. By tracking of the movement direction of water molecules, DTI can reflect the dispersion characteristics of water in white matter fibers and reveal the influence of neuropathic pain on the connection state of brain

In many DTI studies, trigeminal neuralgia is the most studied type of neuropathic pain. In the case of primary trigeminal neuralgia caused by neurovascular compression, the degree of damage of the trigeminal white matter fiber can be estimated by DTI, thereby assessing the prognosis of microvascular decompression [8]. The diffusion of water molecules from the trigeminal root and root entry zone can help to classify the TN in order to select a more appropriate treatment [9]. In the study of postherpetic neuralgia, researchers also found that there exists altered microstructure integrity of white matter in multiple brain regions in patients with PHN, and these changes increase in size as the duration of the pain increases [10].

Blood oxygenation level-dependent (BOLD) fMRI technique is currently the most widely used fMRI imaging method [11]. The normal functional activity of neurons requires a stable supply of oxygen provided by hemoglobin in the blood, wherein oxyhemoglobin exhibits diamagnetism in the magnetic field due to the shielding effect of oxygen atoms, while deoxyhemoglobin exhibits paramagnetism. In this way, differences in oxygenated blood and deoxygenated magnetic susceptibility can be captured by magnetically sensitive weighted MR images using BOLD fMRI technique [12]. When neurons are excited, the oxygen consumption of these neurons and their surrounding tissue increases, causing a transient decrease in blood oxygen content. This change in the ratio of oxygenated and deoxygenated hemoglobin causes a downward initial tilt angle of the BOLD signal. Due to the continued demand for oxygen, the blood flow there will increase rapidly, and excessive compensation will make the proportion of oxygenated hemoglobin increase and the BOLD signal go up. When the neurons stop exciting and the demand for oxygen is reduced, BOLD will have a negative signal after the stimulus and then gradually return to the baseline. Therefore, by measuring the changes of BOLD signal, fMRI can detect the activation of various regions of the brain and can analyze the temporal correlation of activation or inhibition of different brain regions by simultaneously recording the time series, thereby establishing functional connections of the

According to the design type of fMRI research, it can be divided into task state and resting state. Task-state fMRI can detect brain regions closely related to certain functions or stimuli by comparing the fMRI images of subjects with and without tasks. It belongs to the study of functional separation of brain regions. By task-state fMRI, a large amount of data has been obtained about the locations and activations of brain regions in neuropathic pain. Resting-state fMRI refers to the data collection when the subject is lying still, the whole body is relaxed, the external stimuli are excluded, and the awake state is maintained. The obtained information is considered to reflect the spontaneous activities in baseline state of the central nervous

*DOI: http://dx.doi.org/10.5772/intechopen.89200*

network structure.

**2.3 fMRI**

*The Application of Functional Magnetic Resonance Imaging in Neuropathic Pain DOI: http://dx.doi.org/10.5772/intechopen.89200*

connections with each other. The brain network is formed based on this separation and integration principle. White matter fiber bundle is the material basis for connecting the various nodes of the network for information transmission. Any damage to the structure or function of the white matter fiber bundles may affect the information transmission of the brain and cause disease manifestation. DTI is currently the only noninvasive method for effectively observing and tracking white matter fiber bundles. Because of the directionality of the white matter fiber bundles, the internal water molecules are dispersed in a direction-dependent manner, which is called anisotropic. By tracking of the movement direction of water molecules, DTI can reflect the dispersion characteristics of water in white matter fibers and reveal the influence of neuropathic pain on the connection state of brain network structure.

In many DTI studies, trigeminal neuralgia is the most studied type of neuropathic pain. In the case of primary trigeminal neuralgia caused by neurovascular compression, the degree of damage of the trigeminal white matter fiber can be estimated by DTI, thereby assessing the prognosis of microvascular decompression [8]. The diffusion of water molecules from the trigeminal root and root entry zone can help to classify the TN in order to select a more appropriate treatment [9]. In the study of postherpetic neuralgia, researchers also found that there exists altered microstructure integrity of white matter in multiple brain regions in patients with PHN, and these changes increase in size as the duration of the pain increases [10].

#### **2.3 fMRI**

*Medical Imaging - Principles and Applications*

central nerves system in neuropathic pain.

**2. Functional magnetic resonance imaging (fMRI)**

cortex, brainstem, and cerebellum, which are closely related to the chronic pain and the emergence of many typical symptoms and signs [3]. In the past, studies on the central mechanism of neuropathic pain were mostly limited to clinical observations and animal experiments. However, in recent years, with the development of neuroimaging techniques, especially the maturity of functional magnetic resonance, researchers have been able to explore the structure and function of the brain in multiple levels, providing new ideas for the study of the underlying changes of

Magnetic resonance imaging is an imaging technique based on the principle of nuclear magnetic resonance. According to the difference of research purposes, it can be roughly divided into two categories: structural imaging and functional imaging. The purpose of structure imaging is mainly to study the anatomical structure of brain tissue and the structural fiber connection between different brain regions. The main techniques are conventional brain structure imaging and voxel-based morphological measurement (VBM), diffusion tensor imaging (DTI), etc. VBM can provide regions of interest for brain function changes. DTI can be used to analyze the anatomical basis of functional connectivity in brain regions and therefore belongs to the generalized fMRI. The narrow sense of fMRI mainly refers to a technique for studying brain function activities by monitoring changes in cerebral blood flow. The purpose is to explore the neural activity of each brain region under different physiological or pathological conditions. According to the differences in research methods, it can be divided into task-state fMRI and resting-state fMRI.

VBM is a technique for analyzing brain magnetic resonance images at the voxel

By tracking follow-up of patients with herpes zoster (HZ), Cao et al. compared the differences in gray matter volume between patients with acute herpes zoster and postherpetic neuralgia (PHN). They found that the PHN brain showed decreased gray matter volume in the frontal lobe, the parietal lobe, and the occipital lobe but increased in the cerebellum and the temporal lobe. These changes may be correlated with HZ-PHN chronification [5]. In several trigeminal neuralgia studies, the reduction of the volume of the anterior cingulate cortex (ACC) and the increase of the volume of the temporal cortex were found. Li et al. also considered that the increase in the volume of the upper and middle gyrus was proportional to the duration of

Modern brain science believes that the human brain is a complex and efficient network called the brain network. Each region of the brain is responsible for relatively independent functions and has a large number of structural and functional

level. It can quantitatively calculate the changes in local gray and white matter density and volume, so as to accurately display the morphological changes of brain structure. Neuropathic pain can cause changes in the plasticity of the brain structure, such as variation in the gray matter density of the cortex in the brain region. The degree of gray matter density in some brain regions is also related to various clinical indicators such as the length of disease and pain intensity. These changes

**24**

**2.2 DTI**

**2.1 VBM**

can be studied with VBM [4].

trigeminal neuralgia [6, 7].

Blood oxygenation level-dependent (BOLD) fMRI technique is currently the most widely used fMRI imaging method [11]. The normal functional activity of neurons requires a stable supply of oxygen provided by hemoglobin in the blood, wherein oxyhemoglobin exhibits diamagnetism in the magnetic field due to the shielding effect of oxygen atoms, while deoxyhemoglobin exhibits paramagnetism. In this way, differences in oxygenated blood and deoxygenated magnetic susceptibility can be captured by magnetically sensitive weighted MR images using BOLD fMRI technique [12]. When neurons are excited, the oxygen consumption of these neurons and their surrounding tissue increases, causing a transient decrease in blood oxygen content. This change in the ratio of oxygenated and deoxygenated hemoglobin causes a downward initial tilt angle of the BOLD signal. Due to the continued demand for oxygen, the blood flow there will increase rapidly, and excessive compensation will make the proportion of oxygenated hemoglobin increase and the BOLD signal go up. When the neurons stop exciting and the demand for oxygen is reduced, BOLD will have a negative signal after the stimulus and then gradually return to the baseline. Therefore, by measuring the changes of BOLD signal, fMRI can detect the activation of various regions of the brain and can analyze the temporal correlation of activation or inhibition of different brain regions by simultaneously recording the time series, thereby establishing functional connections of the brain in a specific state [13, 14].

According to the design type of fMRI research, it can be divided into task state and resting state. Task-state fMRI can detect brain regions closely related to certain functions or stimuli by comparing the fMRI images of subjects with and without tasks. It belongs to the study of functional separation of brain regions. By task-state fMRI, a large amount of data has been obtained about the locations and activations of brain regions in neuropathic pain. Resting-state fMRI refers to the data collection when the subject is lying still, the whole body is relaxed, the external stimuli are excluded, and the awake state is maintained. The obtained information is considered to reflect the spontaneous activities in baseline state of the central nervous

system. These spontaneous activities not only consume a lot of energy (60–80% of the total energy consumption of the brain) but also have an inherent spatial pattern called the resting brain functional network, which belongs to the study of functional integration between different brain regions of the brain [15, 16]. Studies have shown that many neurologically related diseases, including neuropathic pain, can have a characteristic impact on this resting brain functional network [17, 18], whereas the study of this brain functional network change is more conducive to the clarification of the disease mechanism and the improvement of the diagnosis and treatment level of neuropathic pain [15].

## **3. fMRI study of the central mechanism of neuropathic pain**

## **3.1 Pain perception in the brain and the process of information transmission**

The brain's perception of pain has been one of the most interesting topics in the field of neuroimaging. Since the initial stage of fMRI technology, there have been a large number of related studies. With the in-depth study of the processing and transmission of pain information, the concept of "pain matrix" has gradually formed, which means that pain is achieved through the division of work between multiple regions of the brain, just like a network structure. The pain matrix mainly includes the thalamus, insula, primary somatosensory cortex (S1), secondary somatosensory cortex (S2), anterior cingulate cortex, periaqueductal gray (PAG), amygdala, etc. (**Figure 1**) [20–22]. Significant functional changes will occur in various brain regions within this pain matrix when suffering from acute nociceptive pain [23].

fMRI can be used not only in humans but also in animals such as rats and monkeys. Among these, spared nerve injury (SNI) rats can produce persistent and stable symptoms such as hyperalgesia, allodynia, and spontaneous pain, which are commonly used in the study of neuropathic pain. Komaki et al. studied the restingstate fMRI changes in the SNI rat model and studied the node efficiency of some regions of interest and the functional connections between regions of interest by graph theory. They found that the centrality and node efficiency of the S1 region

**27**

*The Application of Functional Magnetic Resonance Imaging in Neuropathic Pain*

in the opposite side of the injured limb were significantly lower after injury, while the functional connection between the ACC and the posterolateral nucleus of the thalamus was significantly enhanced. This phenomenon may be related to the regulation of secondary nociceptor function by glial cells in the thalamus [24], and

mononuclear/macrophage and T lymphocytes may also be involved in [25]. The results of the above studies indicate that although many brain regions involved in pain perception overlap in their respective functions, their functional connections are regularly changing [26]. The lateral thalamus, S2, and insula may be involved in the perception of pain information, and excessive activation of the posterior parietal and prefrontal cortex (PFC) promotes cognitive attention to nociceptive stimuli. Different parts of the ACC are involved in the adaptation or emotional response to pain. Motion control areas (such as basal ganglia, SMA, cerebellum) are involved in the functional regulation of the pain-suppression system and the avoidance behavior of pain. The amygdala plays an important role in the processing of nociceptive information processing and participates in the regulation of medial prefrontal cortex and spinal cord excitability [27, 28]. Abnormal amygdala function is associated with the formation of neuropathic pain, and the destruction of the amygdala can reduce the incidence of neuropathic pain [29]. Through the study of these regular changes, we can deepen the understanding of pain information processing in the brain, and it also helps to define the neurobiological basis of

the formation of various typical symptoms of neuropathic pain.

The normal perception of pain and avoidance response is an important physiological protection mechanism. The chronic process of pain is the cause of neuropathic pain and the root of refractory disease. In the past, the understanding of the chronic process of neuropathic pain mostly stayed in the sensitization of nociceptors, the axonal buds of sensory neurons, etc. The understanding of advanced central structures and functional changes in the cerebral cortex was still limited. Nevertheless, fMRI is a powerful tool for studying the process of chronic pain. Both clinical observation and animal experiments have found that chronic process of neuropathic pain is associated with large-scale brain function changes and morpho-

Zhang et al. found that the connection strength of the brain's default network in patients with chronic neuropathic pain was significantly different from that of healthy controls, mainly as the weakening connections in characteristic areas of the default network itself (clamping back in the middle, back, inferior parietal lobule) and motor-related areas (superior parietal lobule, auxiliary sports area) [31]. Hubbard et al.'s task-state fMRI study of SNI rats found that when they stimulated the injured limbs in the injury (4 weeks after surgery), the activity of the contralateral somatosensory cortex (S1, S2), the posterolateral nucleus of the thalamus, and the dorsal striatum was enhanced compared with pre-injury and the control groups, whereas the activity of areas associated with painful emotional responses, such as contralateral insula, medial thalamus, and ipsilateral ACC, was inhibited. Restingstate fMRI study showed an enhanced functional connection between the nucleus accumbens (NAc) and the dorsal striatum. At the same time, molecular biology study found that the expression of dopamine 1A receptor and κ opioid receptor in NAc was downregulated. Moreover, the degree of functional compromise is proportional to the degree of downregulation of dopamine receptor gene expression. Inhibition of NAc's functional activity (injection of lidocaine) significantly reduced pain in SNI model animals [32]. At the late stage of injury (20 weeks after surgery), ACC, prefrontal, insula, basal ganglia, and S1 activity was significantly enhanced,

**3.2 Chronic process of neuropathic pain**

logical remodeling [30].

*DOI: http://dx.doi.org/10.5772/intechopen.89200*

**Figure 1.** *Schematic diagram of pain matrix [19].*

#### *The Application of Functional Magnetic Resonance Imaging in Neuropathic Pain DOI: http://dx.doi.org/10.5772/intechopen.89200*

in the opposite side of the injured limb were significantly lower after injury, while the functional connection between the ACC and the posterolateral nucleus of the thalamus was significantly enhanced. This phenomenon may be related to the regulation of secondary nociceptor function by glial cells in the thalamus [24], and mononuclear/macrophage and T lymphocytes may also be involved in [25].

The results of the above studies indicate that although many brain regions involved in pain perception overlap in their respective functions, their functional connections are regularly changing [26]. The lateral thalamus, S2, and insula may be involved in the perception of pain information, and excessive activation of the posterior parietal and prefrontal cortex (PFC) promotes cognitive attention to nociceptive stimuli. Different parts of the ACC are involved in the adaptation or emotional response to pain. Motion control areas (such as basal ganglia, SMA, cerebellum) are involved in the functional regulation of the pain-suppression system and the avoidance behavior of pain. The amygdala plays an important role in the processing of nociceptive information processing and participates in the regulation of medial prefrontal cortex and spinal cord excitability [27, 28]. Abnormal amygdala function is associated with the formation of neuropathic pain, and the destruction of the amygdala can reduce the incidence of neuropathic pain [29]. Through the study of these regular changes, we can deepen the understanding of pain information processing in the brain, and it also helps to define the neurobiological basis of the formation of various typical symptoms of neuropathic pain.

#### **3.2 Chronic process of neuropathic pain**

The normal perception of pain and avoidance response is an important physiological protection mechanism. The chronic process of pain is the cause of neuropathic pain and the root of refractory disease. In the past, the understanding of the chronic process of neuropathic pain mostly stayed in the sensitization of nociceptors, the axonal buds of sensory neurons, etc. The understanding of advanced central structures and functional changes in the cerebral cortex was still limited. Nevertheless, fMRI is a powerful tool for studying the process of chronic pain. Both clinical observation and animal experiments have found that chronic process of neuropathic pain is associated with large-scale brain function changes and morphological remodeling [30].

Zhang et al. found that the connection strength of the brain's default network in patients with chronic neuropathic pain was significantly different from that of healthy controls, mainly as the weakening connections in characteristic areas of the default network itself (clamping back in the middle, back, inferior parietal lobule) and motor-related areas (superior parietal lobule, auxiliary sports area) [31]. Hubbard et al.'s task-state fMRI study of SNI rats found that when they stimulated the injured limbs in the injury (4 weeks after surgery), the activity of the contralateral somatosensory cortex (S1, S2), the posterolateral nucleus of the thalamus, and the dorsal striatum was enhanced compared with pre-injury and the control groups, whereas the activity of areas associated with painful emotional responses, such as contralateral insula, medial thalamus, and ipsilateral ACC, was inhibited. Restingstate fMRI study showed an enhanced functional connection between the nucleus accumbens (NAc) and the dorsal striatum. At the same time, molecular biology study found that the expression of dopamine 1A receptor and κ opioid receptor in NAc was downregulated. Moreover, the degree of functional compromise is proportional to the degree of downregulation of dopamine receptor gene expression. Inhibition of NAc's functional activity (injection of lidocaine) significantly reduced pain in SNI model animals [32]. At the late stage of injury (20 weeks after surgery), ACC, prefrontal, insula, basal ganglia, and S1 activity was significantly enhanced,

*Medical Imaging - Principles and Applications*

treatment level of neuropathic pain [15].

nociceptive pain [23].

system. These spontaneous activities not only consume a lot of energy (60–80% of the total energy consumption of the brain) but also have an inherent spatial pattern called the resting brain functional network, which belongs to the study of functional integration between different brain regions of the brain [15, 16]. Studies have shown that many neurologically related diseases, including neuropathic pain, can have a characteristic impact on this resting brain functional network [17, 18], whereas the study of this brain functional network change is more conducive to the clarification of the disease mechanism and the improvement of the diagnosis and

**3. fMRI study of the central mechanism of neuropathic pain**

**3.1 Pain perception in the brain and the process of information transmission**

fMRI can be used not only in humans but also in animals such as rats and monkeys. Among these, spared nerve injury (SNI) rats can produce persistent and stable symptoms such as hyperalgesia, allodynia, and spontaneous pain, which are commonly used in the study of neuropathic pain. Komaki et al. studied the restingstate fMRI changes in the SNI rat model and studied the node efficiency of some regions of interest and the functional connections between regions of interest by graph theory. They found that the centrality and node efficiency of the S1 region

The brain's perception of pain has been one of the most interesting topics in the field of neuroimaging. Since the initial stage of fMRI technology, there have been a large number of related studies. With the in-depth study of the processing and transmission of pain information, the concept of "pain matrix" has gradually formed, which means that pain is achieved through the division of work between multiple regions of the brain, just like a network structure. The pain matrix mainly includes the thalamus, insula, primary somatosensory cortex (S1), secondary somatosensory cortex (S2), anterior cingulate cortex, periaqueductal gray (PAG), amygdala, etc. (**Figure 1**) [20–22]. Significant functional changes will occur in various brain regions within this pain matrix when suffering from acute

**26**

**Figure 1.**

*Schematic diagram of pain matrix [19].*

while activity in the medial thalamus and PAG areas was inhibited [33]. It can be seen that the formation of neuropathic pain is related to the inhibition of the function of the descending regulation system of pain [34]. ACC activity is inhibited in the early stages of nerve injury, and with the chronic process of pain, ACC activity is gradually enhanced, which may be related to the unpleasant emotional experience of neuropathic pain [29, 35, 36].

A recent resting-state fMRI study found that several major components of the limbic system, hippocampus, amygdala, striatum, and medial prefrontal cortex, are associated with neuropathic pain formation and maintenance [37]. Although SNI rats showed significant changes of activity degree in some brain regions in the early stage of pain, the functional connection between brain regions was not significantly different from that in rats receiving physiological pain stimulation. It can be considered as a normal response of pain matrix to noxious stimuli. However, with the extension of model establishment time, significant changes in the brain functional network occur, and the remodeled brain network has a specific topology. The vast majority of changes in long-term functional connections (97%) occurred within the edge system, and between the edge system and the nociceptive network, while there were no functional connection changes in the nociceptive network [38]. The limbic system neural network adjusts the reward and punishment, appetite, aversion, etc. to generate emotions and behaviors that can adapt to the pain state, so that the brain network gradually adapts to this pain state and the pain tends to be chronic.

#### **3.3 Hyperalgesia and allodynia**

The concept of hyperalgesia and allodynia is often confused, but in fact their formation mechanisms are different. Hyperalgesia refers to a phenomenon in which the pain threshold caused by tissue damage is reduced and the response to noxious stimulation is abnormally enhanced and prolonged. Maihofner et al. found that mechanical pain sensitivity led to abnormal activation of S1, S2, parietal association cortex (PA), insular, superior frontal cortex (SFC), and inferior frontal cortex (IFC) [39], whereas patients with hyperalgesia will have abnormal activation of S1, S2, PA, medial frontal cortex (MFC), ACC, and contralateral SFC and IFC [40]. Zambreanu et al. found that in resting state, patients with no spontaneous pain but hyperalgesia would have abnormal functional activities in multiple regions of the midbrain reticular formation in the brainstem region, namely, nucleus cuneiformis (NCF), rostral superior colliculi (SC), and PAG [41]. The resting-state fMRI of the SNI rat model showed a significant increase in the functional connection between the hippocampus and the striatum, and the intensity was inversely correlated with the mechanical pain threshold of SNI rats.

The clinical manifestation of allodynia is that non-noxious stimuli (such as light touch, mild rubbing, non-noxious cold stimuli) can cause pain, which is a manifestation of "misreading" of somatosensory information. Peripheral sensory nerves are classified into three types: Aβ, Aδ, and C fibers. Normally, the tactile signal is transmitted through the Aβ fiber to the mechanical stimulation zone of the spinal cord. However, in neuropathic pain, Aβ fibers may be abnormally linked to the pain transmission pathway, leading to symptoms such as allodynia [42, 43]. Task-state fMRI is the ideal tool for studying allodynia. Localization and functional connectivity of brain regions associated with allodynia can be achieved by comparing the states of no stimulation, stimulating pain hypersensitivity, and stimulating the same part of the contralateral body. Clinical studies have shown that somatic stimulation signals are amplified by the thalamic and thalamic-parietal circuits, causing excessive activation of the lateral pain sensory system and attention network (posterior parietal lobe). Unlike the response of the thalamic-parietal system,

**29**

*The Application of Functional Magnetic Resonance Imaging in Neuropathic Pain*

the role of ACC and medial prefrontal lobe in allodynia is more complicated. First, different parts of ACC respond differently to nociceptive stimuli. The central part of ACC was significantly activated during peripheral nerve injury-induced allodynia, whereas there was no significant change in the degree of activation in pain hypersensitivity caused by Wallenberg's lateral infarction. This may be due to the fact that the central part of ACC receives signals from the spinal thalamus bundle and Wallenberg's syndrome causes damage to the spinal thalamic bundle; nevertheless peripheral neuropathic pain does not. However, in most neuropathic pains, whether peripheral or central, the axons of ACC are shown to have a reduced degree of activation when allodynia is induced. The brain regions activated by different stimulating components are also different. Cold and mechanical stimulation can cause significant activation of the prefrontal cortex (PFC) and basal ganglia, and the degree of activation is related to the functional status of ACC [33], and insula

A similar phenomenon was also found in fMRI study of the SNI animal model by Komaki et al. According to the physiological characteristics of each nerve fiber, the Aβ fiber can be selectively excited by a current of 2000 Hz, 2.2 mA. When the hind paws of normal rats were administered with 2000 Hz, 2.2 mA DC stimulation, it caused only significant activation of the S1 region; however, when the same intensity of current was used to stimulate the pain-sensing hypersensitivity zone of SNI rats, it was found that not only S1, ACC, and thalamus were also significant

Spontaneous pain is generally difficult to study with fMRI. Because it is difficult

to obtain a comparison of pain and painlessness in the same patient under clinical conditions, this type of clinical study is still rare. A few studies include spinal cordectomy for cancer pain [46], persistent neuropathic pain before and after local anesthesia [47], and central pain before and after motor cortex stimulation [48]. A common finding of these studies is the reduction in local blood flow in the thalamus, that is, the decrease in thalamic activity, which is restored after pain relief. This depression of lateral thalamic function is found both in peripheral and central

In the study of trigeminal neuralgia and sphenopalatine neuralgia with typical spontaneous pain characteristics, it was found that the functional connection between the ipsilateral hypothalamus and the contralateral S1 and the ipsilateral wedge was weakened during the pain attack and remission. However, compared with the pain relief period, the functional connection between the hypothalamus and the S1, anterior wedge, and cerebellum is much less during

**3.5 Characteristic changes of brain function in different types of neuropathic** 

In addition to the common changes in neuropathic pains, different types of neuropathic pain also have characteristic changes. By studying these changes, it is helpful to clarify its pathogenesis, and it is also possible to screen out specific neuro-

Neurovascular compression has long been considered to be the cause of primary trigeminal neuralgia. The 3D TOF MRA and 3D FIESTA sequences can clearly show the positional relationship between the nerves and blood vessels in the root enter zone (REZ) [52–54]. However, nerve compression is not the direct

imaging markers for the diagnosis and evaluation of diseases (**Figure 2**).

*DOI: http://dx.doi.org/10.5772/intechopen.89200*

signal significantly enhances during stimulation [44].

activated [45].

**3.4 Spontaneous pain**

neuropathic pain [49].

the onset of pain [50].

**pain**

*The Application of Functional Magnetic Resonance Imaging in Neuropathic Pain DOI: http://dx.doi.org/10.5772/intechopen.89200*

the role of ACC and medial prefrontal lobe in allodynia is more complicated. First, different parts of ACC respond differently to nociceptive stimuli. The central part of ACC was significantly activated during peripheral nerve injury-induced allodynia, whereas there was no significant change in the degree of activation in pain hypersensitivity caused by Wallenberg's lateral infarction. This may be due to the fact that the central part of ACC receives signals from the spinal thalamus bundle and Wallenberg's syndrome causes damage to the spinal thalamic bundle; nevertheless peripheral neuropathic pain does not. However, in most neuropathic pains, whether peripheral or central, the axons of ACC are shown to have a reduced degree of activation when allodynia is induced. The brain regions activated by different stimulating components are also different. Cold and mechanical stimulation can cause significant activation of the prefrontal cortex (PFC) and basal ganglia, and the degree of activation is related to the functional status of ACC [33], and insula signal significantly enhances during stimulation [44].

A similar phenomenon was also found in fMRI study of the SNI animal model by Komaki et al. According to the physiological characteristics of each nerve fiber, the Aβ fiber can be selectively excited by a current of 2000 Hz, 2.2 mA. When the hind paws of normal rats were administered with 2000 Hz, 2.2 mA DC stimulation, it caused only significant activation of the S1 region; however, when the same intensity of current was used to stimulate the pain-sensing hypersensitivity zone of SNI rats, it was found that not only S1, ACC, and thalamus were also significant activated [45].

#### **3.4 Spontaneous pain**

*Medical Imaging - Principles and Applications*

of neuropathic pain [29, 35, 36].

**3.3 Hyperalgesia and allodynia**

the mechanical pain threshold of SNI rats.

while activity in the medial thalamus and PAG areas was inhibited [33]. It can be seen that the formation of neuropathic pain is related to the inhibition of the function of the descending regulation system of pain [34]. ACC activity is inhibited in the early stages of nerve injury, and with the chronic process of pain, ACC activity is gradually enhanced, which may be related to the unpleasant emotional experience

A recent resting-state fMRI study found that several major components of the limbic system, hippocampus, amygdala, striatum, and medial prefrontal cortex, are associated with neuropathic pain formation and maintenance [37]. Although SNI rats showed significant changes of activity degree in some brain regions in the early stage of pain, the functional connection between brain regions was not significantly different from that in rats receiving physiological pain stimulation. It can be considered as a normal response of pain matrix to noxious stimuli. However, with the extension of model establishment time, significant changes in the brain functional network occur, and the remodeled brain network has a specific topology. The vast majority of changes in long-term functional connections (97%) occurred within the edge system, and between the edge system and the nociceptive network, while there were no functional connection changes in the nociceptive network [38]. The limbic system neural network adjusts the reward and punishment, appetite, aversion, etc. to generate emotions and behaviors that can adapt to the pain state, so that the brain

network gradually adapts to this pain state and the pain tends to be chronic.

The concept of hyperalgesia and allodynia is often confused, but in fact their formation mechanisms are different. Hyperalgesia refers to a phenomenon in which the pain threshold caused by tissue damage is reduced and the response to noxious stimulation is abnormally enhanced and prolonged. Maihofner et al. found that mechanical pain sensitivity led to abnormal activation of S1, S2, parietal association cortex (PA), insular, superior frontal cortex (SFC), and inferior frontal cortex (IFC) [39], whereas patients with hyperalgesia will have abnormal activation of S1, S2, PA, medial frontal cortex (MFC), ACC, and contralateral SFC and IFC [40]. Zambreanu et al. found that in resting state, patients with no spontaneous pain but hyperalgesia would have abnormal functional activities in multiple regions of the midbrain reticular formation in the brainstem region, namely, nucleus cuneiformis (NCF), rostral superior colliculi (SC), and PAG [41]. The resting-state fMRI of the SNI rat model showed a significant increase in the functional connection between the hippocampus and the striatum, and the intensity was inversely correlated with

The clinical manifestation of allodynia is that non-noxious stimuli (such as light touch, mild rubbing, non-noxious cold stimuli) can cause pain, which is a manifestation of "misreading" of somatosensory information. Peripheral sensory nerves are classified into three types: Aβ, Aδ, and C fibers. Normally, the tactile signal is transmitted through the Aβ fiber to the mechanical stimulation zone of the spinal cord. However, in neuropathic pain, Aβ fibers may be abnormally linked to the pain transmission pathway, leading to symptoms such as allodynia [42, 43]. Task-state fMRI is the ideal tool for studying allodynia. Localization and functional connectivity of brain regions associated with allodynia can be achieved by comparing the states of no stimulation, stimulating pain hypersensitivity, and stimulating the same part of the contralateral body. Clinical studies have shown that somatic stimulation signals are amplified by the thalamic and thalamic-parietal circuits, causing excessive activation of the lateral pain sensory system and attention network (posterior parietal lobe). Unlike the response of the thalamic-parietal system,

**28**

Spontaneous pain is generally difficult to study with fMRI. Because it is difficult to obtain a comparison of pain and painlessness in the same patient under clinical conditions, this type of clinical study is still rare. A few studies include spinal cordectomy for cancer pain [46], persistent neuropathic pain before and after local anesthesia [47], and central pain before and after motor cortex stimulation [48]. A common finding of these studies is the reduction in local blood flow in the thalamus, that is, the decrease in thalamic activity, which is restored after pain relief. This depression of lateral thalamic function is found both in peripheral and central neuropathic pain [49].

In the study of trigeminal neuralgia and sphenopalatine neuralgia with typical spontaneous pain characteristics, it was found that the functional connection between the ipsilateral hypothalamus and the contralateral S1 and the ipsilateral wedge was weakened during the pain attack and remission. However, compared with the pain relief period, the functional connection between the hypothalamus and the S1, anterior wedge, and cerebellum is much less during the onset of pain [50].

#### **3.5 Characteristic changes of brain function in different types of neuropathic pain**

In addition to the common changes in neuropathic pains, different types of neuropathic pain also have characteristic changes. By studying these changes, it is helpful to clarify its pathogenesis, and it is also possible to screen out specific neuroimaging markers for the diagnosis and evaluation of diseases (**Figure 2**).

Neurovascular compression has long been considered to be the cause of primary trigeminal neuralgia. The 3D TOF MRA and 3D FIESTA sequences can clearly show the positional relationship between the nerves and blood vessels in the root enter zone (REZ) [52–54]. However, nerve compression is not the direct

**Figure 2.** *Characteristic ROIs of trigeminal neuralgia [51].*

cause of trigeminal neuralgia. There are also many individuals in the population who have neurological compression but no clinical symptoms of trigeminal neuralgia [55]. Lin et al. found that there were no damage and functional changes in the white matter fibrin myelin or axons in patients with nerve compression but asymptomatic in their DTI study [56]. DeSouza found that the fractional anisotropy (FA) of REZ in patients with trigeminal neuralgia was 22% lower than that of the healthy side and 27% lower than that of healthy controls. Other white matter microstructural measurements of patients such as radial diffusivity (RD), axial diffusivity (AD), and mean diffusivity (MD) were higher than those of healthy controls, indicating demyelinating lesions without axonal injury may be an important factor in the pathogenesis of trigeminal neuralgia [57]. They then compared changes in white matter fiber connections in the REZ region before and after microvascular decompression. It is found that FA, MD, RD, and AD all recovered in the normal direction after treatment and the degree of recovery was proportional to the degree of pain relief [58]. It is suggested that nerve compression by blood vessels is only the inducement of trigeminal neuralgia, and the occurrence and maintenance of pain may be related to abnormal white matter fibers at REZ.

The task-state fMRI conducted by Moisset initially explored the effect of the trigger point of patients with trigeminal neuralgia on brain functional activity. They found that when the pain was onset, spinal trigeminal nucleus (SpV), thalamus, S1, S2, ACC, insular, premotor cortex, motor cortex, frontal nucleus, putamen, etc. were clearly activated in patients with trigger points. In patients without trigger points, the brain regions abovementioned were also activated except SpV, brainstem, and ACC. It can be considered that the structural or functional changes at these three regions may be related to the formation of the trigger point [59]. There are still a small number of studies on resting-state fMRI in patients with trigeminal neuralgia. Athinoula et al. found a weaker functional connection between amygdala and insula and S2 in a comparative study of migraine and trigeminal neuralgia [60].

**31**

*The Application of Functional Magnetic Resonance Imaging in Neuropathic Pain*

Wang et al.'s study showed a significant reduction in local consistency activity in the amygdala, hippocampus, and cerebellum in patients with trigeminal neuralgia [61]. The cause of residual limb pain or phantom pain after amputation has been controversial. Some people think that the sudden introduction of nerve afferents from the limbs will cause the expansion or displacement of the sensory cortex, which is called the incompatible remodeling of the cortex [62, 63]. This theory has been proposed for more than 20 years, but it is difficult to be verified before the rise of fMRI technology [64, 65]. Lotze et al. conducted a task-state fMRI study and found that cortical remodeling in patients with phantom limb pain is not limited to S1 but also includes some auxiliary sports areas [66]. For example, when the lips move, the activation degree of the representative region of the broken limb in M1 is also significantly enhanced, indicating that the lip represents region expands and displaces to the cortex of broken limb [67]. Moreover, the degree of displacement of the cortex to the representative area of the isolated limb is positively correlated with the degree of pain [68]. After mirror therapy, cortical displacement can be partially

Cauda et al. found that the functional connections between the ventral posterior

nucleus (VP) and the medial dorsal nucleus (MD) and the cerebral cortex were weakened in patients with diabetic peripheral neuropathy [70]. Cifre observed a weakened connection between resting thalamus and insula in patients with fibromyalgia [71]. In patients with postherpetic neuralgia, the reward circuit consisting of the striatum, prefrontal cortex, amygdala, and hippocampus and the circuits composed of striatum, thalamus, and insular leaves have very close functional connection [72]. With the advancement of fMRI data analysis methods and machine learning techniques, these seemingly cluttered functional connectivity features are highly likely to be used as neuroimaging markers for the diagnosis of neuropathic pain. In other studies of nervous system diseases, some scholars have successfully used fMRI technology to construct a resting brain functional network model for patients with Alzheimer's disease. And with the using of multimode variable analysis method, the sensitivity and specificity of early screening of Alzheimer's disease

**4. The value of fMRI in the clinical treatment of neuropathic pain**

In the past, the examination and evaluation of neuropathic pain relied mainly on medical history, symptoms, and signs and lacked tools for quantitative assessment. Even with the von Frey fiber test, the same stimulation site and strength often result in a lack of consistency [74]. The reason is that the activity of the nerve is disturbed by many factors. Inclusion of peripheral and central sensitization, genetics, cognition, and emotional response during testing will affect the signal transmission of noxious stimuli to the painful sensation area of the brain [75]. In addition, the subjective tendencies of participants and examiners may also cause serious bias. However, fMRI technology may provide us with qualitative and quantitative observations [76]. For example, PoCG's corresponding somatosensory representative area can help clinicians determine the pain site, and the degree of activation can also reflect the intensity of pain [51]. The local activity consistency of rostralanterior part of ACC in patients with postherpetic neuralgia was significantly correlated

fMRI can also be used to observe the effects of treatments on brain function or to assess the therapeutic effects of neuropathic pain. For example, after the application of opioids to achieve pain relief, an increase in ACC activation can be found. Thalamic electrical stimulation can significantly increase the activation of

*DOI: http://dx.doi.org/10.5772/intechopen.89200*

restored, and the pain intensity can be reduced [69].

in the general population is up to 90% [73].

with the anxiety and depression scores [77, 78].

#### *The Application of Functional Magnetic Resonance Imaging in Neuropathic Pain DOI: http://dx.doi.org/10.5772/intechopen.89200*

Wang et al.'s study showed a significant reduction in local consistency activity in the amygdala, hippocampus, and cerebellum in patients with trigeminal neuralgia [61].

The cause of residual limb pain or phantom pain after amputation has been controversial. Some people think that the sudden introduction of nerve afferents from the limbs will cause the expansion or displacement of the sensory cortex, which is called the incompatible remodeling of the cortex [62, 63]. This theory has been proposed for more than 20 years, but it is difficult to be verified before the rise of fMRI technology [64, 65]. Lotze et al. conducted a task-state fMRI study and found that cortical remodeling in patients with phantom limb pain is not limited to S1 but also includes some auxiliary sports areas [66]. For example, when the lips move, the activation degree of the representative region of the broken limb in M1 is also significantly enhanced, indicating that the lip represents region expands and displaces to the cortex of broken limb [67]. Moreover, the degree of displacement of the cortex to the representative area of the isolated limb is positively correlated with the degree of pain [68]. After mirror therapy, cortical displacement can be partially restored, and the pain intensity can be reduced [69].

Cauda et al. found that the functional connections between the ventral posterior nucleus (VP) and the medial dorsal nucleus (MD) and the cerebral cortex were weakened in patients with diabetic peripheral neuropathy [70]. Cifre observed a weakened connection between resting thalamus and insula in patients with fibromyalgia [71]. In patients with postherpetic neuralgia, the reward circuit consisting of the striatum, prefrontal cortex, amygdala, and hippocampus and the circuits composed of striatum, thalamus, and insular leaves have very close functional connection [72]. With the advancement of fMRI data analysis methods and machine learning techniques, these seemingly cluttered functional connectivity features are highly likely to be used as neuroimaging markers for the diagnosis of neuropathic pain. In other studies of nervous system diseases, some scholars have successfully used fMRI technology to construct a resting brain functional network model for patients with Alzheimer's disease. And with the using of multimode variable analysis method, the sensitivity and specificity of early screening of Alzheimer's disease in the general population is up to 90% [73].

## **4. The value of fMRI in the clinical treatment of neuropathic pain**

In the past, the examination and evaluation of neuropathic pain relied mainly on medical history, symptoms, and signs and lacked tools for quantitative assessment. Even with the von Frey fiber test, the same stimulation site and strength often result in a lack of consistency [74]. The reason is that the activity of the nerve is disturbed by many factors. Inclusion of peripheral and central sensitization, genetics, cognition, and emotional response during testing will affect the signal transmission of noxious stimuli to the painful sensation area of the brain [75]. In addition, the subjective tendencies of participants and examiners may also cause serious bias. However, fMRI technology may provide us with qualitative and quantitative observations [76]. For example, PoCG's corresponding somatosensory representative area can help clinicians determine the pain site, and the degree of activation can also reflect the intensity of pain [51]. The local activity consistency of rostralanterior part of ACC in patients with postherpetic neuralgia was significantly correlated with the anxiety and depression scores [77, 78].

fMRI can also be used to observe the effects of treatments on brain function or to assess the therapeutic effects of neuropathic pain. For example, after the application of opioids to achieve pain relief, an increase in ACC activation can be found. Thalamic electrical stimulation can significantly increase the activation of

*Medical Imaging - Principles and Applications*

*Characteristic ROIs of trigeminal neuralgia [51].*

cause of trigeminal neuralgia. There are also many individuals in the population who have neurological compression but no clinical symptoms of trigeminal neuralgia [55]. Lin et al. found that there were no damage and functional changes in the white matter fibrin myelin or axons in patients with nerve compression but asymptomatic in their DTI study [56]. DeSouza found that the fractional anisotropy (FA) of REZ in patients with trigeminal neuralgia was 22% lower than that of the healthy side and 27% lower than that of healthy controls. Other white matter microstructural measurements of patients such as radial diffusivity (RD), axial diffusivity (AD), and mean diffusivity (MD) were higher than those of healthy controls, indicating demyelinating lesions without axonal injury may be an important factor in the pathogenesis of trigeminal neuralgia [57]. They then compared changes in white matter fiber connections in the REZ region before and after microvascular decompression. It is found that FA, MD, RD, and AD all recovered in the normal direction after treatment and the degree of recovery was proportional to the degree of pain relief [58]. It is suggested that nerve compression by blood vessels is only the inducement of trigeminal neuralgia, and the occurrence and maintenance of pain may be related to abnormal white matter

The task-state fMRI conducted by Moisset initially explored the effect of the trigger point of patients with trigeminal neuralgia on brain functional activity. They found that when the pain was onset, spinal trigeminal nucleus (SpV), thalamus, S1, S2, ACC, insular, premotor cortex, motor cortex, frontal nucleus, putamen, etc. were clearly activated in patients with trigger points. In patients without trigger points, the brain regions abovementioned were also activated except SpV, brainstem, and ACC. It can be considered that the structural or functional changes at these three regions may be related to the formation of the trigger point [59]. There are still a small number of studies on resting-state fMRI in patients with trigeminal neuralgia. Athinoula et al. found a weaker functional connection between amygdala and insula and S2 in a comparative study of migraine and trigeminal neuralgia [60].

**30**

fibers at REZ.

**Figure 2.**

the rostralanterior part of ACC and the basal part of frontal cortex. These two sites are generally in a state of functional inhibition in patients with chronic neuropathic pain. A study of drug therapy for trigeminal neuralgia has shown that lamotrigine can reduce the pain level of patients by reducing the excitability of the prefrontal, parietal, and temporal lobe and inhibit allodynia [79].

As a method for treating neuropathic pain, neuromodulation technology has been applied for decades. However, whether it is invasive spinal cord electrical stimulation, deep brain stimulation, or noninvasive transcranial magnetic stimulation, there is a problem of inefficiency. The reason may be due to individual differences in the effects of disease on the brain functional network. fMRI can accurately capture the brain regions with abnormal functions and combine the analysis of functional connections in the brain to determine the regions of interest for neuromodulation, which is used to guide the target area of transcranial magnetic stimulation or the placement of epidural stimulation electrodes [80, 81]. It is helpful to improve the efficacy of neuromodulation techniques.

## **5. Limitations and future direction of fMRI research in NP**

Most of the previous studies were limited by the fMRI data analysis method. They can only analyze for a single factor, or only focus on brain regions or functional connections with significant differences, while ignoring the complexity and synergy of the brain function network as a whole structure. And the conclusions drawn lack clinical utility and provide limited assistance in the diagnosis and prediction of diseases.

In recent years, with the increasing maturity of machine learning technology, machine learning and pattern recognition technology are being used more and more for fMRI data analysis. By using machine learning technology to process the massive characteristic data generated by fMRI, multiple dimensions such as gray matter volume, diffusion of water molecules, and functional connectivity can be used simultaneously [82]. In the future, this research method of overall analysis of brain function networks can realize pattern recognition of different disease states. It can make fMRI technology better serve the clinic and provide assistance for the diagnosis and prognosis analysis of neuropathic pain.

## **6. Conclusion**

A large number of brain regions associated with neuropathic pain have been discovered by fMRI technology, and there has been some data accumulation for changes in functional connectivity between brain regions. However, how to analyze and process these fMRI data and make a reasonable explanation for better understanding the underlying disease mechanism as well as treatment improvement is the key to further expand the value of fMRI application in the future.

**33**

**Author details**

Beijing, China

Zhi Dou\* and Liqiang Yang

\*Address all correspondence to: douzhi@xwhosp.org

provided the original work is properly cited.

Department of Pain Management, Xuanwu Hospital, Capital Medical University,

© 2019 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,

*The Application of Functional Magnetic Resonance Imaging in Neuropathic Pain*

*DOI: http://dx.doi.org/10.5772/intechopen.89200*

*The Application of Functional Magnetic Resonance Imaging in Neuropathic Pain DOI: http://dx.doi.org/10.5772/intechopen.89200*

## **Author details**

*Medical Imaging - Principles and Applications*

parietal, and temporal lobe and inhibit allodynia [79].

improve the efficacy of neuromodulation techniques.

diagnosis and prognosis analysis of neuropathic pain.

key to further expand the value of fMRI application in the future.

prediction of diseases.

**6. Conclusion**

**5. Limitations and future direction of fMRI research in NP**

the rostralanterior part of ACC and the basal part of frontal cortex. These two sites are generally in a state of functional inhibition in patients with chronic neuropathic pain. A study of drug therapy for trigeminal neuralgia has shown that lamotrigine can reduce the pain level of patients by reducing the excitability of the prefrontal,

As a method for treating neuropathic pain, neuromodulation technology has been applied for decades. However, whether it is invasive spinal cord electrical stimulation, deep brain stimulation, or noninvasive transcranial magnetic stimulation, there is a problem of inefficiency. The reason may be due to individual differences in the effects of disease on the brain functional network. fMRI can accurately capture the brain regions with abnormal functions and combine the analysis of functional connections in the brain to determine the regions of interest for neuromodulation, which is used to guide the target area of transcranial magnetic stimulation or the placement of epidural stimulation electrodes [80, 81]. It is helpful to

Most of the previous studies were limited by the fMRI data analysis method. They can only analyze for a single factor, or only focus on brain regions or functional connections with significant differences, while ignoring the complexity and synergy of the brain function network as a whole structure. And the conclusions drawn lack clinical utility and provide limited assistance in the diagnosis and

In recent years, with the increasing maturity of machine learning technology, machine learning and pattern recognition technology are being used more and more for fMRI data analysis. By using machine learning technology to process the massive characteristic data generated by fMRI, multiple dimensions such as gray matter volume, diffusion of water molecules, and functional connectivity can be used simultaneously [82]. In the future, this research method of overall analysis of brain function networks can realize pattern recognition of different disease states. It can make fMRI technology better serve the clinic and provide assistance for the

A large number of brain regions associated with neuropathic pain have been discovered by fMRI technology, and there has been some data accumulation for changes in functional connectivity between brain regions. However, how to analyze and process these fMRI data and make a reasonable explanation for better understanding the underlying disease mechanism as well as treatment improvement is the

**32**

Zhi Dou\* and Liqiang Yang Department of Pain Management, Xuanwu Hospital, Capital Medical University, Beijing, China

\*Address all correspondence to: douzhi@xwhosp.org

© 2019 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.

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Section 2

Nuclear Medicine PET/CT

Imaging and Applications

39

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Section 2
