**3.1 Inputs to auditory cortex**

Auditory processing is essential for a wide range of our sensory experiences, including the identification of and attention to environmental sounds, verbal communication, and the enjoyment of music. The intricate sounds in our daily environments are encoded by our auditory system as the intensity of their individual component frequencies, comparable to a Fourier analysis [98]. This spectral sound information is thus one fundamental aspect of the auditory feature space (**Figure 7A, C**). The basilar membrane of the inner ear responds topographically to incoming sound waves with higher frequencies transduced to neural signals near the entrance to the cochlea and progressively lower frequencies transduced further along the membrane. This organized gradient of frequencies (i.e., tones) is referred to as tonotopy (i.e., a map of tones); this topography may also be termed cochleotopy, referring to a map of the cochlea. Tonotopic organization is maintained as auditory information is processed and passed on from the inner ear through the brainstem, to the thalamus, and into PAC along Heschl's gyrus (HG; **Figure 1**; for additional discussion, see [2, 5, 6, 12, 99, 100]). The preservation of such topographical organization from the basilar membrane of the inner ear to auditory cortex allows for a common reference frame across this hierarchically organized sensory system [6, 7, 12, 13, 22, 23].

A second fundamental aspect of the auditory feature space is temporal sound information, termed periodicity (**Figure 7B, D**) [10, 101, 102]. Human psychoacoustic studies indicate that there are separable filter banks (i.e., neurons with distinct receptive fields) for not only frequency spectra—as expected given tonotopy, but also temporal information [103–105]. The auditory nerve likely encodes such temporal information through activity time-locked to the periodicity of the amplitude modulation (i.e., the length of time from peak-to-peak of the temporal envelope) [101, 106]. Temporally varying aspects of sound are thought to preferentially active neurons selective for the onset and offset of sounds and for sounds of certain durations. Organized representations of periodicity in primates have been

**13**

**Figure 7.**

dimensions to identify complete AFMs.

*amplitudes are again depicted as 'warmer' colors (see dB legend on bottom).*

*Attention and Working Memory in Human Auditory Cortex*

measured to date in the thalamus and PAC of macaque and human, respectively, and are termed periodotopy, a map of neurons that respond differentially to sounds of different temporal envelope modulation rates [5, 10, 107]. Repeating periodotopic gradients exist in the same cortical locations as, but are orthogonal to, tonotopic gradients, which allows researchers to use measurements of these two acoustic

*Example tonotopic and periodotopic stimuli for auditory field mapping. (A) Three stimulus values for one dimension of auditory feature space (e.g., tonotopy) are depicted in the graph: 1—low (L, red); 2—medium (M, green); 3—high (H, blue). (B) Three stimulus values for a second dimension of auditory feature space (e.g., periodotopy) are depicted in the second graph: 1—low (L, orange); 2—medium (M, aqua); 3—high (H, purple). (C) Tonotopic representations can be measured using narrowband noise stimuli, which hold periodicity constant and vary frequency. (i) Sound amplitude (arbitrary units) for this stimulus set as a function of time in seconds. (ii) Sound spectrograms for two example narrowband noise stimuli with center frequencies (CF) of 1600 Hz (top) and 6400 Hz (bottom). Higher amplitudes in decibels (dB) are represented as 'warmer' colors (see dB legend below). (D) Periodotopic representations can be measured using broadband noise stimuli, which maintain constant frequency information and vary periodicity. (i) Sound amplitude (arbitrary units) for this stimulus set as a function of time in seconds. (ii) Sound spectrograms for two example broadband noise stimuli with amplitude modulation (AM) rates of 2 Hz (top) and 8 Hz (bottom). Higher* 

*DOI: http://dx.doi.org/10.5772/intechopen.85537*

*Attention and Working Memory in Human Auditory Cortex DOI: http://dx.doi.org/10.5772/intechopen.85537*

#### **Figure 7.**

*The Human Auditory System - Basic Features and Updates on Audiological Diagnosis and Therapy*

objects can be held in visual working memory on average, areas that increase their activity with arrays 1, 2, 3 objects and remaining constant with arrays of 4 or more objects should be areas controlling visual working memory. Such areas were found bilaterally in parietal cortex by multiple laboratories [57, 62, 91, 93], but activity related to visual working memory has also been measured in early visual cortex (e.g., V1 and hV4) [55, 65, 94], prefrontal cortex [95], and possibly in object-processing regions in lateral occipital cortex [62], indicating that working-memory tasks recruit areas throughout the visual-processing hierarchy. (We note that the report of objectprocessing regions is controversial, as the cortical coordinates reported in that study are more closely consistent with the human motion-processing complex, hMT+, than the lateral occipital complex [15, 17, 96, 97]). However, little has been done to measure visual-working-memory activity in visual field maps, and so these studies should be considered preliminary rather than definitive. Measurements within

Auditory-working-memory localization with fMRI has been quite limited compared to its visual counterpart, and largely concentrated on speech stimuli rather than fundamental auditory stimuli [30, 68]. As noted above with attention localization with fMRI, too many variables exist with highly complex stimuli, and as such, a different approach is necessary. Furthermore, even low-level auditory sensory areas

Auditory processing is essential for a wide range of our sensory experiences, including the identification of and attention to environmental sounds, verbal communication, and the enjoyment of music. The intricate sounds in our daily environments are encoded by our auditory system as the intensity of their individual component frequencies, comparable to a Fourier analysis [98]. This spectral sound information is thus one fundamental aspect of the auditory feature space (**Figure 7A, C**). The basilar membrane of the inner ear responds topographically to incoming sound waves with higher frequencies transduced to neural signals near the entrance to the cochlea and progressively lower frequencies transduced further along the membrane. This organized gradient of frequencies (i.e., tones) is referred to as tonotopy (i.e., a map of tones); this topography may also be termed cochleotopy, referring to a map of the cochlea. Tonotopic organization is maintained as auditory information is processed and passed on from the inner ear through the brainstem, to the thalamus, and into PAC along Heschl's gyrus (HG; **Figure 1**; for additional discussion, see [2, 5, 6, 12, 99, 100]). The preservation of such topographical organization from the basilar membrane of the inner ear to auditory cortex allows for a common reference frame across this hierarchi-

A second fundamental aspect of the auditory feature space is temporal sound information, termed periodicity (**Figure 7B, D**) [10, 101, 102]. Human psychoacoustic studies indicate that there are separable filter banks (i.e., neurons with distinct receptive fields) for not only frequency spectra—as expected given tonotopy, but also temporal information [103–105]. The auditory nerve likely encodes such temporal information through activity time-locked to the periodicity of the amplitude modulation (i.e., the length of time from peak-to-peak of the temporal envelope) [101, 106]. Temporally varying aspects of sound are thought to preferentially active neurons selective for the onset and offset of sounds and for sounds of certain durations. Organized representations of periodicity in primates have been

CFMs would, in fact, help to clear up such controversies.

have only very recently been properly identified [5, 10].

**3. Auditory processing in human cortex**

cally organized sensory system [6, 7, 12, 13, 22, 23].

**3.1 Inputs to auditory cortex**

**12**

*Example tonotopic and periodotopic stimuli for auditory field mapping. (A) Three stimulus values for one dimension of auditory feature space (e.g., tonotopy) are depicted in the graph: 1—low (L, red); 2—medium (M, green); 3—high (H, blue). (B) Three stimulus values for a second dimension of auditory feature space (e.g., periodotopy) are depicted in the second graph: 1—low (L, orange); 2—medium (M, aqua); 3—high (H, purple). (C) Tonotopic representations can be measured using narrowband noise stimuli, which hold periodicity constant and vary frequency. (i) Sound amplitude (arbitrary units) for this stimulus set as a function of time in seconds. (ii) Sound spectrograms for two example narrowband noise stimuli with center frequencies (CF) of 1600 Hz (top) and 6400 Hz (bottom). Higher amplitudes in decibels (dB) are represented as 'warmer' colors (see dB legend below). (D) Periodotopic representations can be measured using broadband noise stimuli, which maintain constant frequency information and vary periodicity. (i) Sound amplitude (arbitrary units) for this stimulus set as a function of time in seconds. (ii) Sound spectrograms for two example broadband noise stimuli with amplitude modulation (AM) rates of 2 Hz (top) and 8 Hz (bottom). Higher amplitudes are again depicted as 'warmer' colors (see dB legend on bottom).*

measured to date in the thalamus and PAC of macaque and human, respectively, and are termed periodotopy, a map of neurons that respond differentially to sounds of different temporal envelope modulation rates [5, 10, 107]. Repeating periodotopic gradients exist in the same cortical locations as, but are orthogonal to, tonotopic gradients, which allows researchers to use measurements of these two acoustic dimensions to identify complete AFMs.

## **3.2 fMRI measurements of auditory field maps**

Measurements of the structure and function of human PAC and lower-level auditory cortex have been relatively few to date, with many studies hampered by methodological issues (for reviews, see [5, 23]. Precise measurements of AFMs across primary and lower-level auditory cortex are vital, however, for studying the neural underpinnings of such prominent auditory behaviors as attention and working memory. Recent research has now successfully applied fMRI methods commonly used to measure visual field maps to the study of AFMs in human auditory cortex.

### *3.2.1 Phase-encoded fMRI*

The phase-encoded fMRI paradigm provides highly detailed *in vivo* measurements of CFMs in individual subjects [9, 10, 15, 108–111]. This technique measures topographical representations using stimuli that periodically repeat a set of values in an orderly sequence (**Figure 7**). The phase-encoded methods are specialized for AFM measurements by combining this periodic stimulus with a sparse-sampling paradigm (**Figure 8**) [10, 112–115]. Sparse-sampling separates the auditory stimulus presentation from the noise of the MR scanner during data acquisition to avoid contamination of the data by nonstimulus sounds [116–118].

The periodic stimulus allows for the use of a Fourier analysis to determine the value of the stimulus (e.g., 800 Hz frequency for tonotopy) that most effectively drives each cortical location [110]. The cortical response at a specific location is said to be 'in phase' throughout the scan with the stimulus value that most effectively activates it, hence the term 'phase-encoded' mapping. The alternate term 'travelingwave' mapping arises from the consecutive activation of one neighboring cortical location after the other to create a wave-like pattern of activity across the CFM during the stimulus presentation. The phase-encoded paradigm only captures cortical activity that is at the stimulus frequency, thus excluding unrelated cortical activity and other sources of noise. Similarly, cortical regions that are not organized topographically will not be significantly activated by phase-encoded stimuli, as there would be no differential activation across the cortical representation [8, 15, 16]. The statistical threshold for phase-encoded cortical activity is commonly determined by coherence, which is a measure of the amplitude of the BOLD signal modulation at the frequency of the stimulus presentation (e.g., six stimulus cycles per scan), divided by the square root of the power over all other frequencies except the first and second harmonic (e.g., 12 and 18 cycles per scan) [15, 17, 110].

Measurement and analysis of phase-encoded CFM data must be performed within individual subjects rather than across group averages to avoid problematically blurring together discrete CFMs and their associated computations (for extended discussions, see [5, 15, 17]). CFMs may differ radically in size and anatomical position among individual subjects independent of brain size; this variation is reflected in associated shifts in cytoarchitectural and topographic boundaries [119–124]. In the visual system, for example, V1 can differ in size by at least a factor of three despite its location on the relatively stable calcarine sulcus [120]. Accordingly, when such data are group-averaged across subjects, especially through such approaches as aligning data from individual brains to an average brain with atlases such as Talairach space [125] or Montreal Neurological Institute (MNI) coordinates [126], the measurements will be blurred to such a degree that the measured topography of the CFMs is inaccurate or even lost. Blurring from such whole-brain anatomical co-alignment will thus cause different CFMs to be incorrectly averaged together into a single measurement, mixing data together from adjacent CFMs within each subject and preventing the analysis of the distinct computations of each CFM.

**15**

**Figure 8.**

*3.2.2 Criteria for auditory field map identification*

*comprising six cycles. (D) Legend denotes color-coding for diagrams above.*

In order to avoid the imprecise application of the term 'map' to topographical gradients or other similar patterns of cortical organization, the designation of an AFM—and CFMs in general—should be established according to several key

*Schematic of phase-encoded fMRI paradigm for auditory field mapping experiments. (A) Diagram of a single stimulus phase shows the components of a single block of one auditory stimulus presentation (striped green) followed by an fMRI data acquisition period (solid green). This sparse-sampling paradigm separates the auditory stimulus presentation from the noisy environment of the MR scanner acquisition. The timing of the acquisition (2 s delay) is set to collect the approximate peak response of auditory cortex to the stimulus, in accordance with the estimated hemodynamic delay. (B) Each phase (block) of an example tonotopic stimulus is displayed within the gray box above the colored blocks; one block thus represents one stimulus position in the 'phase-encoded' sequence. The diagram of an example stimulus cycle below this depicts six presentation blocks (striped green+ solid green) grouped together into one stimulus cycle (blue). Each block, or stimulus phase, in each cycle represents a specific frequency; e.g., for tonotopic measurements, the stimulus that is presented sequentially changes to each of the Hz listed in the gray box. The term 'traveling-wave' is also used to describe this type of phase-encoded stimulus presentation, as the stimuli produce a sequential activation of representations across a topographically organized cortical region. (C) Diagram shows a full, single scan* 

*Attention and Working Memory in Human Auditory Cortex*

*DOI: http://dx.doi.org/10.5772/intechopen.85537*

*Attention and Working Memory in Human Auditory Cortex DOI: http://dx.doi.org/10.5772/intechopen.85537*

#### **Figure 8.**

*The Human Auditory System - Basic Features and Updates on Audiological Diagnosis and Therapy*

Measurements of the structure and function of human PAC and lower-level auditory cortex have been relatively few to date, with many studies hampered by methodological issues (for reviews, see [5, 23]. Precise measurements of AFMs across primary and lower-level auditory cortex are vital, however, for studying the neural underpinnings of such prominent auditory behaviors as attention and working memory. Recent research has now successfully applied fMRI methods commonly used to measure visual field maps to the study of AFMs in human auditory cortex.

The phase-encoded fMRI paradigm provides highly detailed *in vivo* measurements of CFMs in individual subjects [9, 10, 15, 108–111]. This technique measures topographical representations using stimuli that periodically repeat a set of values in an orderly sequence (**Figure 7**). The phase-encoded methods are specialized for AFM measurements by combining this periodic stimulus with a sparse-sampling paradigm (**Figure 8**) [10, 112–115]. Sparse-sampling separates the auditory stimulus presentation from the noise of the MR scanner during data acquisition to avoid

The periodic stimulus allows for the use of a Fourier analysis to determine the value of the stimulus (e.g., 800 Hz frequency for tonotopy) that most effectively drives each cortical location [110]. The cortical response at a specific location is said to be 'in phase' throughout the scan with the stimulus value that most effectively activates it, hence the term 'phase-encoded' mapping. The alternate term 'travelingwave' mapping arises from the consecutive activation of one neighboring cortical location after the other to create a wave-like pattern of activity across the CFM during the stimulus presentation. The phase-encoded paradigm only captures cortical activity that is at the stimulus frequency, thus excluding unrelated cortical activity and other sources of noise. Similarly, cortical regions that are not organized topographically will not be significantly activated by phase-encoded stimuli, as there would be no differential activation across the cortical representation [8, 15, 16]. The statistical threshold for phase-encoded cortical activity is commonly determined by coherence, which is a measure of the amplitude of the BOLD signal modulation at the frequency of the stimulus presentation (e.g., six stimulus cycles per scan), divided by the square root of the power over all other frequencies except the first

contamination of the data by nonstimulus sounds [116–118].

and second harmonic (e.g., 12 and 18 cycles per scan) [15, 17, 110].

and preventing the analysis of the distinct computations of each CFM.

Measurement and analysis of phase-encoded CFM data must be performed within individual subjects rather than across group averages to avoid problematically blurring together discrete CFMs and their associated computations (for extended discussions, see [5, 15, 17]). CFMs may differ radically in size and anatomical position among individual subjects independent of brain size; this variation is reflected in associated shifts in cytoarchitectural and topographic boundaries [119–124]. In the visual system, for example, V1 can differ in size by at least a factor of three despite its location on the relatively stable calcarine sulcus [120]. Accordingly, when such data are group-averaged across subjects, especially through such approaches as aligning data from individual brains to an average brain with atlases such as Talairach space [125] or Montreal Neurological Institute (MNI) coordinates [126], the measurements will be blurred to such a degree that the measured topography of the CFMs is inaccurate or even lost. Blurring from such whole-brain anatomical co-alignment will thus cause different CFMs to be incorrectly averaged together into a single measurement, mixing data together from adjacent CFMs within each subject

**3.2 fMRI measurements of auditory field maps**

*3.2.1 Phase-encoded fMRI*

**14**

*Schematic of phase-encoded fMRI paradigm for auditory field mapping experiments. (A) Diagram of a single stimulus phase shows the components of a single block of one auditory stimulus presentation (striped green) followed by an fMRI data acquisition period (solid green). This sparse-sampling paradigm separates the auditory stimulus presentation from the noisy environment of the MR scanner acquisition. The timing of the acquisition (2 s delay) is set to collect the approximate peak response of auditory cortex to the stimulus, in accordance with the estimated hemodynamic delay. (B) Each phase (block) of an example tonotopic stimulus is displayed within the gray box above the colored blocks; one block thus represents one stimulus position in the 'phase-encoded' sequence. The diagram of an example stimulus cycle below this depicts six presentation blocks (striped green+ solid green) grouped together into one stimulus cycle (blue). Each block, or stimulus phase, in each cycle represents a specific frequency; e.g., for tonotopic measurements, the stimulus that is presented sequentially changes to each of the Hz listed in the gray box. The term 'traveling-wave' is also used to describe this type of phase-encoded stimulus presentation, as the stimuli produce a sequential activation of representations across a topographically organized cortical region. (C) Diagram shows a full, single scan comprising six cycles. (D) Legend denotes color-coding for diagrams above.*

#### *3.2.2 Criteria for auditory field map identification*

In order to avoid the imprecise application of the term 'map' to topographical gradients or other similar patterns of cortical organization, the designation of an AFM—and CFMs in general—should be established according to several key

criteria (**Figure 9**) (for reviews, see [5, 8, 15]). First, by definition, each AFM must contain at least the two orthogonal, nonrepeating topographical representations of fundamental acoustic feature space described above: tonotopy and periodotopy (**Figure 9A**) [10, 17, 21, 108, 110, 111]. When this criterion is ignored and the measurement of only one topographical representation is acquired (e.g., tonotopy), it is impossible to correctly identify boundaries among cortical regions. Measurements of the organization and function of specific regions of early auditory cortex in human long have mostly relied on tonotopic measurements alone, which has resulted in variable, conflicting, and ultimately unusable interpretations of the organization of human PAC and surrounding regions (for detailed reviews, see [5, 23]).

The representation of one dimension of sensory space—one topographical gradient along cortex like tonotopy—is not adequate to delineate an AFM, or CFMs in any sensory system. The measurement of a singular topographical dimension merely demonstrates that this particular aspect of sensory feature space is represented along that cortical region. The CFMs within that cortical region cannot be identified without measuring an orthogonal second dimension: a region of cortex with a large, confluent gradient for one dimension could denote a single CFM (**Figure 9Ai, ii**) or many CFMs (**Figure 9Ai, iii**), depending upon the organization of the overlapping second topography. Similarly, the two overlapping gradients must be approximately orthogonal, as they will otherwise not represent all the points in sensory space uniquely (**Figure 9B**) [15, 16, 127, 128]. As the complexity of adjacent gradients increases, the determination of the emergent CFM organization grows increasingly complicated.

Due to the relatively recent measurements of periodotopic representations in human auditory cortex and monkey midbrain, AFMs in core and belt regions can now be identified [10, 102]. The identification of periodotopy as the second key dimension of auditory feature space is strengthened by psychoacoustic studies, which show that separable filter banks occur not only for frequency spectra, but also temporal information, indicating the presence of neurons with receptive fields tuned to ranges of frequencies and periods [14, 103–105]. Additionally, representations of temporal acoustic information (i.e., periodicity) have been measured in the auditory system of other model organisms, including PAC in domestic cat and inferior colliculus in chinchilla [129, 130].

A second AFM criterion is that each of its topographical representations must be organized as a generally contiguous and orderly gradient [16, 128]. For such a gradient to develop, the representation must be organized such that it covers a full range of sensory space, in order from one boundary to the other (e.g., from lower to upper frequencies for tonotopy; **Figure 9C**). A topographical gradient is thus one of the most highly structured features of the cortical surface that can be measured using fMRI. The odds of two orderly, orthogonal gradients arising as a spurious pattern from noise in an overlapping section of cortex is extraordinarily low (for a calculation of the probability of spurious gradients arising from noise, see [19]).

Third, each CFM should contain representations of a considerable amount of sensory space. Differences in cortical magnification are likely among CFMs with different computational needs, but a large portion of sensory space is still expected to be represented (e.g., [15, 16, 19, 21, 97, 127, 131]). A high-quality fMRI measurement of the topography is necessary to adequately capture the sensory range and magnification. The quality of the measurement is dependent upon choosing an appropriate set of phase-encoded stimuli. The sampling density and range of values in the stimulus set both affect the accuracy and precision of the measurement. For example, the intensity (i.e., loudness) of the tonotopic stimulus alone can alter the width of the receptive fields of neurons in PAC and consequently increase the

**17**

**Figure 9.**

careful consideration.

*regions outside the diagram. (for review, see [23]).*

*Attention and Working Memory in Human Auditory Cortex*

lateral spread of the BOLD signal measured in neuroimaging [132]. In addition, some degree of blurring in the measurements of the topography is expected due to such factors as the overlapping broad receptive fields, the inherent spatial spread of the fMRI signal, and measurement noise [64, 109, 133, 134]. The stimulus parameters and how they may affect the cortical responses should therefore be given

*Definition of auditory field maps (AFMs). (A) (i) Schematic of a single gradient of dimension 1 (e.g., tonotopy). Black arrow shows the low-to-high gradient for this tonotopic gradient. With only measurements of the single dimension of tonotopy, it cannot be determined whether the region within dimension 1 contains one or more cortical field maps without measuring a second, orthogonal gradient. (ii) Schematic of a single gradient of dimension 2 (e.g., periodotopy) overlapping the tonotopic gradient in (i) to form a single AFM like hA1. Black arrow shows the low-to-high gradient for this periodotopic gradient. Note the orthogonal orientation of the two gradients (i vs. ii) composing this AFM. (iii) schematic of an alternative gradient organization for periodotopy overlapping the same tonotopic gradient in (i). Black arrows now show two low-to-high gradients (G1: gradient 1, G2: gradient 2) of this second dimension within the same territory as the orthogonal low-tohigh gradient in (i). The gray dotted line marks the boundary dividing this region into two AFMs. (B) (i) In a properly defined AFM, measurements along the cortical representation of a single value of tonotopy (e.g., green) span all values of periodotopy (e.g., orange to cyan to purple), and vice versa. (ii) Schematic of vectors drawn along a single CFM from centers of low-stimulus-value regions of interest (ROIs) to high-stimulus-value ROIs for dimensions 1 (e.g., red to blue) and 2 (e.g., orange to purple). The offset measured between the lowto-high vectors for each dimension should be approximately 90° to be considered orthogonal and thus allow for each voxel/portion of the map to represent a unique combination of dimension 1 and dimension 2 values. (C) The diagram demonstrates how gradient boundaries for one dimension of an AFM are determined. Black dots denote hypothetical measurement points along the cortical surface shown in (A, iii). Black arrows note gradient directions (low, L, to medium, M, to high, H). Dashed gray lines mark gradient reversals. Two gradients that span the full range of dimension 2 measurements can be divided into G1 and G2, with the representations of stimulus values increasing from low to high across the cortical surface in one gradient to the boundary where the representations in the next map then reverse back from high to low along the cortical surface in the next gradient. G3 and G4 (gradients 3 and 4, respectively) denote additional gradients continuing at reversal to* 

Fourth, the general features of the topographies composing the CFMs and the pattern of CFMs across cortex should both be consistent among individuals. It is essential to remember, nevertheless, that cytoarchitectural and topographic boundaries in PAC

*DOI: http://dx.doi.org/10.5772/intechopen.85537*

*Attention and Working Memory in Human Auditory Cortex DOI: http://dx.doi.org/10.5772/intechopen.85537*

#### **Figure 9.**

*The Human Auditory System - Basic Features and Updates on Audiological Diagnosis and Therapy*

criteria (**Figure 9**) (for reviews, see [5, 8, 15]). First, by definition, each AFM must contain at least the two orthogonal, nonrepeating topographical representations of fundamental acoustic feature space described above: tonotopy and periodotopy (**Figure 9A**) [10, 17, 21, 108, 110, 111]. When this criterion is ignored and the measurement of only one topographical representation is acquired (e.g., tonotopy), it is impossible to correctly identify boundaries among cortical regions. Measurements of the organization and function of specific regions of early auditory cortex in human long have mostly relied on tonotopic measurements alone, which has resulted in variable, conflicting, and ultimately unusable interpretations of the organization of human PAC and surrounding regions (for detailed reviews,

The representation of one dimension of sensory space—one topographical gradient along cortex like tonotopy—is not adequate to delineate an AFM, or CFMs in any sensory system. The measurement of a singular topographical dimension merely demonstrates that this particular aspect of sensory feature space is represented along that cortical region. The CFMs within that cortical region cannot be identified without measuring an orthogonal second dimension: a region of cortex with a large, confluent gradient for one dimension could denote a single CFM (**Figure 9Ai, ii**) or many CFMs (**Figure 9Ai, iii**), depending upon the organization of the overlapping second topography. Similarly, the two overlapping gradients must be approximately orthogonal, as they will otherwise not represent all the points in sensory space uniquely (**Figure 9B**) [15, 16, 127, 128]. As the complexity of adjacent gradients increases, the determination of the emergent CFM organization

Due to the relatively recent measurements of periodotopic representations in human auditory cortex and monkey midbrain, AFMs in core and belt regions can now be identified [10, 102]. The identification of periodotopy as the second key dimension of auditory feature space is strengthened by psychoacoustic studies, which show that separable filter banks occur not only for frequency spectra, but also temporal information, indicating the presence of neurons with receptive fields tuned to ranges of frequencies and periods [14, 103–105]. Additionally, representations of temporal acoustic information (i.e., periodicity) have been measured in the auditory system of other model organisms, including PAC in domestic cat and

A second AFM criterion is that each of its topographical representations must be organized as a generally contiguous and orderly gradient [16, 128]. For such a gradient to develop, the representation must be organized such that it covers a full range of sensory space, in order from one boundary to the other (e.g., from lower to upper frequencies for tonotopy; **Figure 9C**). A topographical gradient is thus one of the most highly structured features of the cortical surface that can be measured using fMRI. The odds of two orderly, orthogonal gradients arising as a spurious pattern from noise in an overlapping section of cortex is extraordinarily low (for a calcula-

tion of the probability of spurious gradients arising from noise, see [19]).

Third, each CFM should contain representations of a considerable amount of sensory space. Differences in cortical magnification are likely among CFMs with different computational needs, but a large portion of sensory space is still expected to be represented (e.g., [15, 16, 19, 21, 97, 127, 131]). A high-quality fMRI measurement of the topography is necessary to adequately capture the sensory range and magnification. The quality of the measurement is dependent upon choosing an appropriate set of phase-encoded stimuli. The sampling density and range of values in the stimulus set both affect the accuracy and precision of the measurement. For example, the intensity (i.e., loudness) of the tonotopic stimulus alone can alter the width of the receptive fields of neurons in PAC and consequently increase the

**16**

see [5, 23]).

grows increasingly complicated.

inferior colliculus in chinchilla [129, 130].

*Definition of auditory field maps (AFMs). (A) (i) Schematic of a single gradient of dimension 1 (e.g., tonotopy). Black arrow shows the low-to-high gradient for this tonotopic gradient. With only measurements of the single dimension of tonotopy, it cannot be determined whether the region within dimension 1 contains one or more cortical field maps without measuring a second, orthogonal gradient. (ii) Schematic of a single gradient of dimension 2 (e.g., periodotopy) overlapping the tonotopic gradient in (i) to form a single AFM like hA1. Black arrow shows the low-to-high gradient for this periodotopic gradient. Note the orthogonal orientation of the two gradients (i vs. ii) composing this AFM. (iii) schematic of an alternative gradient organization for periodotopy overlapping the same tonotopic gradient in (i). Black arrows now show two low-to-high gradients (G1: gradient 1, G2: gradient 2) of this second dimension within the same territory as the orthogonal low-tohigh gradient in (i). The gray dotted line marks the boundary dividing this region into two AFMs. (B) (i) In a properly defined AFM, measurements along the cortical representation of a single value of tonotopy (e.g., green) span all values of periodotopy (e.g., orange to cyan to purple), and vice versa. (ii) Schematic of vectors drawn along a single CFM from centers of low-stimulus-value regions of interest (ROIs) to high-stimulus-value ROIs for dimensions 1 (e.g., red to blue) and 2 (e.g., orange to purple). The offset measured between the lowto-high vectors for each dimension should be approximately 90° to be considered orthogonal and thus allow for each voxel/portion of the map to represent a unique combination of dimension 1 and dimension 2 values. (C) The diagram demonstrates how gradient boundaries for one dimension of an AFM are determined. Black dots denote hypothetical measurement points along the cortical surface shown in (A, iii). Black arrows note gradient directions (low, L, to medium, M, to high, H). Dashed gray lines mark gradient reversals. Two gradients that span the full range of dimension 2 measurements can be divided into G1 and G2, with the representations of stimulus values increasing from low to high across the cortical surface in one gradient to the boundary where the representations in the next map then reverse back from high to low along the cortical surface in the next gradient. G3 and G4 (gradients 3 and 4, respectively) denote additional gradients continuing at reversal to regions outside the diagram. (for review, see [23]).*

lateral spread of the BOLD signal measured in neuroimaging [132]. In addition, some degree of blurring in the measurements of the topography is expected due to such factors as the overlapping broad receptive fields, the inherent spatial spread of the fMRI signal, and measurement noise [64, 109, 133, 134]. The stimulus parameters and how they may affect the cortical responses should therefore be given careful consideration.

Fourth, the general features of the topographies composing the CFMs and the pattern of CFMs across cortex should both be consistent among individuals. It is essential to remember, nevertheless, that cytoarchitectural and topographic boundaries in PAC

vary dramatically in size and anatomical location independent of overall brain size [119, 121–124, 135], as do CFMs across visual cortex [16, 17, 120, 136]. Regardless of these variations, the overall organization among specific CFMs and cloverleaf clusters will be maintained across individuals.

### *3.2.3 Definition of auditory field map boundaries*

The measurement of AFMs is one of the few reliable *in vivo* methods to localize the distinct borders of the auditory core and belt regions in individual subjects [5, 10, 12, 23]. The boundaries of an AFM—and of CFMs in general—are determined by carefully defining the edges of overlapping sections of tonotopic and periodotopic gradients within a specific cortical region in an individual hemisphere (**Figure 9**). If a set of overlapping representations of the two dimensions is present in isolation, the boundary of the AFM can be estimated to be where the gradient responses end, although there will likely be some spatial blurring or spreading of the representation along these edges (**Figure 9Ai, ii**) [16, 17, 110, 137]. For multiple, adjacent representations that each span the full range of one dimension (e.g., low-to-high frequencies of tonotopy) can be divided into two sections at the point at which the gradients reverse (**Figure 9Ai, iii**). At the gradient reversals, the representations of stimulus values increase from low to high (or vice versa) across the cortical surface in one section to the boundary where the representations in the next AFM then reverse back from high to low (or vice versa) along the cortical surface in the next section (**Figure 9C**). Such phase-encoded fMRI measurements of the boundaries of the AFMs in human auditory cortex have been shown to be closely related to those determined by invasive human cytoarchitectural studies and nonhuman primate cytoarchitectural, connectivity, and tonotopic measurements [2, 5, 10, 121, 138–144].

At a scale of several centimeters, groups of adjacent CFMs are organized within both auditory and visual cortex into a macrostructural pattern called the cloverleaf cluster, named for the similarity of the organization of the individual CFMs composing a cluster to the leaves of a clover plant [8, 10, 15–20]. Within a cluster, one dimension of sensory topography is represented in concentric, circular bands from center to periphery of the cluster, and the second, orthogonal dimension separates this confluent representation into multiple CFMs with radial bands spanning the cluster center to periphery. In AFM clusters, a confluent, concentric tonotopic representation is divided into specific AFMs by reversal in the orthogonal periodotopic gradients. Neighboring cloverleaf clusters are then divided along the tonotopic reversals at the cluster boundaries.

While CFM clusters have consistent positions relative to one another across the cortical surface, CFMs within each cluster may be oriented differently among individuals as if rotating about a cluster's central representation. This inter-subject is consistent with the variability in molecular gradient expression that gives rise to the development of cortical topographical gradients [145–149]. This unpredictability of cluster anatomical location and rotation emphasizes the need for careful data analysis to be performed in individual subjects, in which common CFMs can be identified by analyzing the pattern of CFMs and cloverleaf clusters within that sensory system.

#### **3.3 Organization of human auditory field maps**

#### *3.3.1 Auditory cortex organization in macaque monkey vs. human*

Auditory processing in human cortex and in nonhuman primates occurs bilaterally along the temporal lobes near the lateral sulcus (**Figure 1**; e.g., [5, 10, 115, 121, 139–142, 144, 150–153]). In the macaque monkey model system upon which

**19**

*gyrus*

*Attention and Working Memory in Human Auditory Cortex*

homology to macaque, but adds an 'h' to signify human [10].

*3.3.2 Eleven human AFMs compose three cloverleaf clusters overlapping Heschl's* 

in its representations of broad ranges of tonotopy and periodotopy [2].

A reversal in the tonotopic gradient along the anteromedial edge of the HG cluster divides it from the CM/CL cluster just past the tip of HG (**Figure 10B, C**). A high-periodicity gradient reversal splits this tonotopic gradient into hCM, and hCL, two regions associated with early language and speech processing as well as audiovisual integration (**Figure 10D, E**) [164]. Finally, the reversal in the tonotopic gradient along the posteriolateral edge of the HG cluster separates it from the RT cluster positioned where HG meets STG (**Figure 10B, C**). Two reversals in

With our new understanding of periodotopic representations overlapping the previously identified tonotopic gradients, *in vivo* fMRI measurements can now identify the 11 AFMs that compose the core and belt regions of human auditory cortex (**Figure 10**) [5, 10, 12, 23]. Running from STG to the circular sulcus (CiS) along HG are three distinct, concentrically organized, tonotopic representations. The primary circular tonotopic gradient is one dimension of the HG cloverleaf cluster, with a confluent low-tone representation located centrally and expanding smoothly to high-tone representations at the outer edge (**Figure 10B, C**) [5]. The HG cluster is divided along the orthogonal periodotopic reversals into two AFMs each of core, medial belt, and lateral belt: hA1, hR, hMM, hRM, hML, and hAL (**Figure 10D, E**). Positioned at the tip of HG, hA1 is the largest of these core and belt AFMs, with the posterior/lateral region representing low tones and the anterior/medial region representing high ones. HA1 is involved in the most basic of cortical auditory computations, which is reflected

much of our understanding of human audition is based, converging evidence from cytoarchitectural, connectivity, electrophysiological, and neuroimaging studies have generally identified 13 auditory cortical areas grouped into core, medial and lateral belt, and parabelt regions that are associated with primary, secondary, and tertiary levels of processing, respectively (for extended discussions, see [2, 5, 154]). Auditory processing in macaque cortex begins along the superior temporal gyrus (STG) within three primary auditory areas: A1, R, and RT [140]. In contrast to early visual processing in which primary visual cortex is composed of V1 alone, primary auditory cortex is considered to be a core region composed of these three AFMs; all three areas contain the expanded layer IV arising from dense thalamic inputs and the high expression of cytochrome oxidase, acetylcholinesterase, and parvalbumin distinctive to primary sensory cortices [2, 142, 143, 150, 152, 154–157]. The eight belt regions are divided into four areas along both the lateral (CL, ML, AL, RTL) and medial (CM, RM, MM, RTM) sides of the core [158–160]. Along the lateral belt, two additional areas create the parabelt, which allocates auditory information to neighboring auditory cortex as well as to multimodal cortical regions [2, 161]. Based on cytoarchitectural, connectivity, and neuroimaging measurements, early auditory processing in human cortex has been shown to resemble the organization of lower-level macaque auditory processing [10, 23, 121, 144, 151–153, 162]. Over the ~25 million years of evolutionary separation between the species, the core, belt, and parabelt areas have rotated from the STG to Heschl's gyrus (HG), an anatomical feature unique to humans [11, 163]. The specific structure of HG differs across individuals, variably existing as a single or double gyrus. PAC is then either mostly centered on the single HG or overlapping both gyri in the case of two (**Figure 1B, C**) [122, 135, 136]. Core, belt, and parabelt areas have thus shifted in orientation from a strictly rostralcaudal axis for A1 to R to RT along macaque STG to a medial-lateral axis along human HG for hA1, hR, and hRT. The naming of the AFMs in human is based on the likely

*DOI: http://dx.doi.org/10.5772/intechopen.85537*

#### *Attention and Working Memory in Human Auditory Cortex DOI: http://dx.doi.org/10.5772/intechopen.85537*

*The Human Auditory System - Basic Features and Updates on Audiological Diagnosis and Therapy*

vary dramatically in size and anatomical location independent of overall brain size [119, 121–124, 135], as do CFMs across visual cortex [16, 17, 120, 136]. Regardless of these variations, the overall organization among specific CFMs and cloverleaf clusters

The measurement of AFMs is one of the few reliable *in vivo* methods to localize the distinct borders of the auditory core and belt regions in individual subjects [5, 10, 12, 23]. The boundaries of an AFM—and of CFMs in general—are determined by carefully defining the edges of overlapping sections of tonotopic and periodotopic gradients within a specific cortical region in an individual hemisphere (**Figure 9**). If a set of overlapping representations of the two dimensions is present in isolation, the boundary of the AFM can be estimated to be where the gradient responses end, although there will likely be some spatial blurring or spreading of the representation along these edges (**Figure 9Ai, ii**) [16, 17, 110, 137]. For multiple, adjacent representations that each span the full range of one dimension (e.g., low-to-high frequencies of tonotopy) can be divided into two sections at the point at which the gradients reverse (**Figure 9Ai, iii**). At the gradient reversals, the representations of stimulus values increase from low to high (or vice versa) across the cortical surface in one section to the boundary where the representations in the next AFM then reverse back from high to low (or vice versa) along the cortical surface in the next section (**Figure 9C**). Such phase-encoded fMRI measurements of the boundaries of the AFMs in human auditory cortex have been shown to be closely related to those determined by invasive human cytoarchitectural studies and nonhuman primate cytoarchitectural, connectivity, and tonotopic measurements [2, 5, 10, 121, 138–144].

At a scale of several centimeters, groups of adjacent CFMs are organized within both auditory and visual cortex into a macrostructural pattern called the cloverleaf cluster, named for the similarity of the organization of the individual CFMs composing a cluster to the leaves of a clover plant [8, 10, 15–20]. Within a cluster, one dimension of sensory topography is represented in concentric, circular bands from center to periphery of the cluster, and the second, orthogonal dimension separates this confluent representation into multiple CFMs with radial bands spanning the cluster center to periphery. In AFM clusters, a confluent, concentric tonotopic representation is divided into specific AFMs by reversal in the orthogonal periodotopic gradients. Neighboring cloverleaf clusters are

While CFM clusters have consistent positions relative to one another across the cortical surface, CFMs within each cluster may be oriented differently among individuals as if rotating about a cluster's central representation. This inter-subject is consistent with the variability in molecular gradient expression that gives rise to the development of cortical topographical gradients [145–149]. This unpredictability of cluster anatomical location and rotation emphasizes the need for careful data analysis to be performed in individual subjects, in which common CFMs can be identified by analyzing the pattern of CFMs and cloverleaf clusters within that

Auditory processing in human cortex and in nonhuman primates occurs bilaterally along the temporal lobes near the lateral sulcus (**Figure 1**; e.g., [5, 10, 115, 121, 139–142, 144, 150–153]). In the macaque monkey model system upon which

then divided along the tonotopic reversals at the cluster boundaries.

**3.3 Organization of human auditory field maps**

*3.3.1 Auditory cortex organization in macaque monkey vs. human*

will be maintained across individuals.

*3.2.3 Definition of auditory field map boundaries*

**18**

sensory system.

much of our understanding of human audition is based, converging evidence from cytoarchitectural, connectivity, electrophysiological, and neuroimaging studies have generally identified 13 auditory cortical areas grouped into core, medial and lateral belt, and parabelt regions that are associated with primary, secondary, and tertiary levels of processing, respectively (for extended discussions, see [2, 5, 154]). Auditory processing in macaque cortex begins along the superior temporal gyrus (STG) within three primary auditory areas: A1, R, and RT [140]. In contrast to early visual processing in which primary visual cortex is composed of V1 alone, primary auditory cortex is considered to be a core region composed of these three AFMs; all three areas contain the expanded layer IV arising from dense thalamic inputs and the high expression of cytochrome oxidase, acetylcholinesterase, and parvalbumin distinctive to primary sensory cortices [2, 142, 143, 150, 152, 154–157]. The eight belt regions are divided into four areas along both the lateral (CL, ML, AL, RTL) and medial (CM, RM, MM, RTM) sides of the core [158–160]. Along the lateral belt, two additional areas create the parabelt, which allocates auditory information to neighboring auditory cortex as well as to multimodal cortical regions [2, 161].

Based on cytoarchitectural, connectivity, and neuroimaging measurements, early auditory processing in human cortex has been shown to resemble the organization of lower-level macaque auditory processing [10, 23, 121, 144, 151–153, 162]. Over the ~25 million years of evolutionary separation between the species, the core, belt, and parabelt areas have rotated from the STG to Heschl's gyrus (HG), an anatomical feature unique to humans [11, 163]. The specific structure of HG differs across individuals, variably existing as a single or double gyrus. PAC is then either mostly centered on the single HG or overlapping both gyri in the case of two (**Figure 1B, C**) [122, 135, 136]. Core, belt, and parabelt areas have thus shifted in orientation from a strictly rostralcaudal axis for A1 to R to RT along macaque STG to a medial-lateral axis along human HG for hA1, hR, and hRT. The naming of the AFMs in human is based on the likely homology to macaque, but adds an 'h' to signify human [10].
