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. 2017 Sep 20;38(12):6206–6217. doi: 10.1002/hbm.23822

Gray matter volumes of early sensory regions are associated with individual differences in sensory processing

Sayaka Yoshimura 1,✉, Wataru Sato 1, Takanori Kochiyama 2, Shota Uono 1, Reiko Sawada 1,3, Yasutaka Kubota 4, Motomi Toichi 3,5
PMCID: PMC6867006  PMID: 28940867

Abstract

Sensory processing (i.e., the manner in which the nervous system receives, modulates, integrates, and organizes sensory stimuli) is critical when humans are deciding how to react to environmental demands. Although behavioral studies have shown that there are stable individual differences in sensory processing, the neural substrates that implement such differences remain unknown. To investigate this issue, structural magnetic resonance imaging scans were acquired from 51 healthy adults and individual differences in sensory processing were assessed using the Sensory Profile questionnaire (Brown et al.: Am J Occup Ther 55 (2001) 75–82). There were positive relationships between the Sensory Profile modality‐specific subscales and gray matter volumes in the primary or secondary sensory areas for the visual, auditory, touch, and taste/smell modalities. Thus, the present results suggest that individual differences in sensory processing are implemented by the early sensory regions. Hum Brain Mapp 38:6206–6217, 2017. © 2017 Wiley Periodicals, Inc.

Keywords: sensation, sensory processing, early sensory area, voxel‐based morphometry (VBM), structural magnetic resonance imaging (MRI)

INTRODUCTION

Sensory processing refers to the manner in which the nervous system receives, modulates, integrates, and organizes sensory stimuli, including behavioral responses to sensory input [Miller and Lane, 2000]. This process enables humans to decide how to react when faced with environmental demands. Therefore, sensory processing is an indispensable foundation for a wide variety of activities that humans engage in, including perception, learning, and a variety of behaviors.

Behavioral studies have shown that there are measurable and stable individual differences in sensory processing. Dunn [1997] proposed one of the most noteworthy theories explaining individual differences in sensory processing. Dunn's model suggests that each individual has different thresholds for sensory events, as well as different strategies to self‐regulate sensory inputs; that is, tendencies for behavioral responses to sensory inputs that range from passive to active. Moreover, this model suggests that the individual differences in sensory processing arise from the interaction of these two functions [Dunn, 1997]. Subsequently, Dunn and Brown [1997] developed a questionnaire known as the Sensory Profile, which characterizes sensory experiences, such as visual and auditory behaviors, and evaluates their impact on functional abilities and daily life. This questionnaire produces two types of scores: (1) quadrant scores (i.e., four composite scores), which are indicative of thresholds for sensory events (i.e., low or high thresholds) and assess strategies for the self‐regulation of sensory inputs (i.e., active or passive responses) and (2) modality‐specific scores, which assess atypical tendencies according to each sensory modality, such as vision and audition. The construct validity of the Sensory Profile has been demonstrated by studies showing that the sensory processing patterns measured by the quadrant scores correspond to response patterns in skin conductance [Brown et al., 2001; McIntosh et al., 1999; Miller et al., 1999). Dunn and colleagues reported that the individual differences measured by the Sensory Profile form a normally distributed continuum in the general population [Dunn, 2007]. Furthermore, these differences are relatively stable throughout one's lifetime [Dunn, 2001].

Despite accumulating behavioral evidence, the neural substrates that implement the individual differences in sensory processing remain largely unknown. More specifically, no studies have used structural magnetic resonance imaging (MRI) to investigate the gray matter correlates associated with individual differences in sensory processing. Several MRI studies investigating individuals with atypical sensory processing, such as those with autism spectrum disorder and/or a sensory processing disorder, examined the relationship between modality‐specific sensory scores and brain morphology [Chang et al., 2014, 2016; Owen et al., 2013; Pryweller et al., 2014]; however, these studies only analyzed white matter integrity. Several related functional MRI studies assessed the relationship between individual differences in sensory processing and brain activity and identified the involvement of early (i.e., primary and secondary) sensory regions [Green et al., 2013; Shen et al., 2016]. For example, Green et al. [2013] observed higher activation in primary sensory regions that correspond to visual and auditory stimuli and showed that it was positively correlated with the severity of hypersensitivity symptoms. Based on these data, it was hypothesized that gray matter volumes in early sensory regions would be associated with individual differences in sensory processing.

To test this hypothesis, structural MRI data were acquired from healthy adults and then assessed using the Sensory Profile questionnaire. Previous studies have shown that older individuals exhibit a general decline in sensory abilities [Campbell et al., 1999; Michikawa, 2016; Schubert et al., 2011] and have lower scores on the Sensory Profile relative to younger subjects [Brown and Dunn, 2002]. Additionally, the gray matter volumes of early sensory regions in healthy older adults decrease with age [Lemaître et al., 2005; Potvin et al., 2017; Resnick et al., 2003]. Thus, it was predicted that greater gray matter volumes of the early sensory regions, specifically the primary and secondary sensory areas, would be correlated with higher scores on the modality‐specific Sensory Profile subscales (i.e., atypical sensory processing in each modality). Furthermore, although information regarding the neural substrates of the quadrant scores was lacking, the association between gray matter volumes and quadrant scores was explored.

MATERIALS AND METHODS

Participants

The present study included 51 healthy volunteers (26 females; mean ± standard deviation [SD] age: 22.5 ± 4.5 years; range: 19–43 years) who were confirmed as right handed by the Edinburgh Handedness Inventory [Oldfield, 1971] (the left–right laterality quotients [−100–100] were > 0; mean ± SD: 81.1 ± 23.3). Either a psychiatrist or psychologist administered a short‐structured diagnostic interview using the Mini‐International Neuropsychiatric Interview (MINI) [Sheehan et al., 1998]; no neuropsychiatric problems, including substance use disorders, were detected in any of the participants. The full‐scale intelligence quotient (IQ) scores of the participants were measured with the Wechsler Adult Intelligence Scale, Third Edition (WAIS‐III) and all fell within the normal range (full‐scale IQ: mean = 121.4, SD = 8.5; verbal IQ: mean = 121.5, SD = 9.2; and performance IQ: M = 116.7, SD = 10.3). After the experimental procedures were fully explained, all participants provided written informed consent prior to participation in the study. This study was approved by the local ethics committee of the Primate Research Institute of Kyoto University and was conducted in accordance with the guidelines of the Declaration of Helsinki.

Psychological Questionnaire

The Sensory Profile questionnaire is a standardized assessment tool that measures responses to sensory events in everyday life. There are three versions of the Sensory Profile that are applied based on age; the present study utilized the Adolescent/Adult version of the Sensory Profile [Brown and Dunn, 2002]. For this study, the questionnaire was translated into Japanese because no validated Japanese version of the Adolescent/Adult Sensory Profile had been developed when the study was conducted. All content and all phrases of the translation were confirmed to be equivalent to that of a recently validated Japanese version [Brown and Dunn, 2015] and a back‐translation by a native English speaker was almost identical to the original English version.

The Adolescent/Adult Sensory Profile is a self‐report questionnaire that is rated using a five‐point Likert scale (1 = “almost never” to 5 = “almost always”) for 60 items that assess six sensory processing modalities: visual, auditory, touch, taste/smell, movement, and activity level. These modalities contain 10, 11, 13, 8, 8, and 10 items, respectively. Previous studies have demonstrated relationships between brain morphology and modality‐specific sensory behaviors measured by caregiver report questionnaires or structured assessments [Chang et al., 2014, 2016; Owen et al., 2013; Pryweller et al., 2014] as well as how to evaluate modality‐specific sensory processing using the Adolescent/Adult Sensory Profile [Demopoulos et al., 2015]. Based on these findings, the present study summed the items within each modality to generate six modality‐specific subscales for the visual, auditory, touch, taste/smell, movement, and activity level modalities. Higher modality‐specific scores indicated a greater degree of atypical tendencies in sensory processing (e.g., higher sensitivity or increased rates of sensation‐avoiding behaviors). Additionally, these items were incorporated into four quadrants (i.e., low registration, sensation seeking, sensory sensitivity, and sensation avoiding) according to the manual [Brown and Dunn, 2002]. These scores characterize the neurological thresholds and behavioral responses of individuals to sensory stimuli as follows: Low registration refers to the tendency to show high thresholds to sensory inputs in conjunction with passive responding strategies (i.e., a delayed or absent response to sensory stimuli) across sensory domains; sensation seeking refers to the tendency to show high thresholds in conjunction with active responding strategies (i.e., create or seek out sensory stimulation); sensory sensitivity refers to the tendency to show low thresholds in conjunction with passive responding strategies (i.e., highly sensitive to sensory stimuli but often at the risk of distractibility and discomfort); and sensation avoiding refers to the tendency to show low thresholds in conjunction with active responding strategies (i.e., deliberate acts to reduce or prevent exposure to bothersome sensory stimuli). Higher quadrant scores are indicative of showing the tendency more prominently. Each quadrant contains 15 items and the possible ranges for each of the modality‐specific and quadrant scores are shown in Table 1.

Table 1.

Mean score, standard deviation, range, and possible score for the Sensory Profile modality‐specific subscales and quadrant scores

Mean Standard deviation Range Possible score
Modality‐specific subscales
Visual 25.86 4.72 17–37 10–50
Auditory 32.35 6.11 15–44 11–55
Touch 35.92 5.12 25–55 13–65
Taste/smell 20.61 3.01 13–28 8–40
Movement 21.35 3.54 15–29 8–40
Activity level 32.51 3.89 25–43 10–50
Quadrant scores
Low registration 36.55 5.59 24–49 15–75
Sensation seeking 46.92 6.81 30–61 15–75
Sensory sensitivity 42.86 6.76 28–57 15–75
Sensation avoiding 42.27 6.49 29–60 15–75
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MRI Acquisition

Image scanning was performed on a 3T scanning system (MAGNETOM Trio, A Tim System; Siemens, Malvern, PA) with a 12‐channel head coil and small elastic pads placed on both sides of the head to minimize head motion. A T1‐weighted high‐resolution anatomical image was obtained using a magnetization‐prepared rapid‐acquisition gradient‐echo (MP‐RAGE) sequence with the following characteristics: repetition time = 2250 ms; echo time = 3.06 ms; inversion time = 1000; GRAPPA acceleration factor = 2; bandwidth = 230 Hz/pixel; prescan normalization filter was used; 208 sagittal slices; slice thickness = 1 mm; field of view = 256 × 256 mm; and voxel size = 1 × 1 × 1 mm.

Image Analysis

All image and statistical analyses were performed using a statistical parametric mapping package (SPM8; http://www.fil.ion.ucl.ac.uk/spm) and the VBM8 toolbox (http://dbm.neuro.uni-jena.de) implemented in MATLAB R2012b (Mathworks Inc., Natick, MA). First, two authors of the present study independently conducted visual inspections of the T1 images and confirmed the absence of observable gross abnormalities and artifacts in the images. Then, image preprocessing was performed with the VBM8 toolbox using the recommended procedures according to the manuals [Ashburner, 2010; Kurth and Lüders, 2010]. All structural T1 images were segmented into gray matter, white matter, and cerebrospinal fluid using an adaptive Maximum A Posterior (AMAP) approach [Rajapakse et al., 1997]. The intensity inhomogeneity of each MR image was modeled as slowly varying spatial functions and then corrected in the AMAP estimation. The segmented images were used for partial volume estimation with a simple mixed model of different tissue classes to improve segmentation [Tohka et al., 2004]. Additionally, a spatially adaptive nonlocal means (SANLM) denoising filter was applied to account for spatially varying noise levels [Manjón et al., 2010], and a Markov Random Field (MRF) cleanup was used to improve image quality.

Subsequently, the gray and white matter images in the native space were normalized into a standard stereotactic space defined by the Montreal Neurological Institute (MNI) using the diffeomorphic anatomical registration through the exponentiated lie algebra (DARTEL) approach [Ashburner, 2007]. Predefined templates provided by the VBM8 toolbox, which were derived from 550 healthy brains in the IXI‐database (http://www.brain-development.org), were used. The resulting normalized gray matter images were modulated by the Jacobian determinants with nonlinear warping only (i.e., m0 images in the VBM8 output) to control the effect of total gray matter volume [Good et al., 2001]. Finally, the normalized modulated gray matter images were resampled to a resolution of 1.5 × 1.5 × 1.5 mm and smoothed with an isotropic Gaussian kernel of 12 mm full width at half‐maximum (FWHM) to compensate for anatomical variability among the participants.

Multiple regression analyses were conducted to identify the brain regions related to the Sensory Profile subscales using the normalized gray matter images and scores on the Sensory Profile subscales as the independent variables and sex, age, and full IQ as covariates (effects of no‐interest). For the regions of interest (ROIs) that had prior anatomical hypotheses, anatomical masks were determined by tracing strict anatomical borders with PickAtlas [Maldjian et al., 2003]. More specifically, the ROIs were defined as Brodmann's areas (BAs) 17 and 18 (i.e., the primary and secondary visual cortices) [Gillbert, 2013a] for the visual subscales; BAs 22, 41, and 42 (i.e., the primary and secondary auditory cortices) [Bizley and Cohen, 2013; Oertel and Doupe, 2013] for the auditory subscales; BAs 1, 2, 3, and 40 (i.e., the primary and secondary somatosensory cortices) [Eickhoff et al., 2006; Gardner and Johnson, 2013] for the touch subscales; BAs 11, 12, 13, 14, 16, 27, 28, 34, and 38 and the amygdala for the taste/smell subscales (i.e., covering the orbitofrontal cortex, insular, piriform cortex, entorhinal cortex, temporal pole, and amygdala as taste/smell cortices because no consensus opinion regarding the early sensory regions of the taste/smell modality has been achieved) [Buck and Bargmann, 2013; Gottfried, 2006; Olson et al., 2007; Wong and Gallate 2012]; and BAs 4 and 6 (i.e., the primary and secondary motor cortices) [Kalaska and Rizzolatti, 2013] for the movement subscales. ROIs were not set for the activity level subscales due to a lack of theoretical evidence. Other regions from the whole brain without predictions were also searched. The ROIs for the visual, auditory, touch, taste/smell, and movement subscale analyses and the whole brain analyses included 10,039, 5,870, 12,341, 16,078, 18,516, and 559,774 voxels, respectively.

The associations between scores on the Sensory Profile subscales and the volumes of the gray matter were assessed using T‐statistics. Voxels were considered to show a significant relationship between local gray matter volume and the Sensory Profile subscales if they reached the extent threshold of P < 0.05 as a family‐wise error (FWE) corrected for multiple comparisons. These were determined using Monte Carlo simulations with the AlpahSim function in DPABI version 2.0 (http://rfmri.org/dpabi) at a height threshold of P < 0.001 (uncorrected); that is, 33, 19, 60, 89, 66, and 1219 contiguous voxels for the visual, auditory, touch, taste/smell, and movement subscales analyses and the whole brain analysis, respectively. The brain structures were labeled anatomically and identified according to BAs using PickAtlas [Maldjian et al., 2003].

To illustrate the relationships between gray matter volumes and the Sensory Profile subscales, the gray matter values of the peak voxel were extracted and plotted against the Sensory Profile subscales after adjusting for the effects of no interest by regressing out variance related to sex, age, and full IQ.

Furthermore, the associations between the Sensory Profile modality subscales and large‐scale gray matter volumes were investigated. The mean adjusted values of the gray matter volumes for the ROIs of the visual, auditory, touch, taste/smell, and movement modalities were extracted to covary out the effects of other modalities and the effects of interest (age, sex, and full IQ). Next, the correlations between the values and the Sensory Profile subscales were calculated; P‐values < 0.05 were considered to indicate statistical significance.

Finally, the relationships between the Sensory Profile quadrant subscales and regional gray matter volumes were investigated. The same analyses described above for the Sensory Profile modality subscales were conducted except that the whole brain, rather than the ROIs, was analyzed due to the lack of specific hypotheses.

RESULTS

Psychological Data

In the present study, the scores for almost all items on the Sensory Profile were in good agreement with those of a previous standardization study [Brown and Dunn, 2015]. The mean ± SD, ranges, and possible scores of the Sensory Profile modality‐specific and quadrant scores are shown in Table 1. Kolmogorov–Smirnov tests revealed that these scores had normal distributions (P > 0.05).

The correlations between the Sensory Profile scores and demographic factors are shown in Table 2.

Table 2.

The correlations between the Sensory Profile scores and demographic data

Correlation
Variable 1 2 3 4 5 6 7 8 9 10 11 12a 13
1 Visual
2 Auditory 0.48*
3 Touch 0.39* 0.35*
4 Taste/smell 0.29* 0.39* 0.19
5 Movement 0.67* 0.43* 0.32* 0.23
6 Activity level 0.38* 0.36* 0.39* 0.30* 0.11
7 Low registration 0.55* 0.69* 0.56* 0.33* 0.39* 0.60*
8 Sensation seeking 0.38* 0.21 0.28* 0.47* 0.45* 0.19 0.13
9 Sensory sensitivity 0.67* 0.68* 0.58* 0.17 0.57* 0.52* 0.68* −0.01
10 Sensation avoiding 0.64* 0.67* 0.51* 0.53* 0.47* 0.46* 0.52* 0.13 0.67*
11 Age −0.31* −0.18 −0.24 0.16 −0.20 −0.34* −0.25 0.07 −0.41* −0.27
12 Sexa 0.25 0.03 −0.07 0.12 0.21 −0.04 0.13 0.03 0.07 0.08 −0.04
13 Full‐scale IQ −0.21 −0.09 −0.05 −0.17 −0.03 −0.08 −0.12 −0.10 −0.03 −0.18 −0.03 −0.06
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a

Sex is dummy‐coded as female (1) or male (0).

Correlations represent Pearson's product‐moment correlations.

*P < 0.05.

Relationships Between the Sensory Profile Modality‐Specific Subscales and Regional Gray Matter Volumes

To investigate the primary hypothesis that the gray matter volumes of early sensory regions would be associated with individual differences in sensory processing, a general linear model analysis was performed. Scores on the Sensory Profile subscales were included as the effect‐of‐interest factors while age, sex, and full IQ were included as the effect‐of‐no‐interest covariates for analyses of the ROIs and the whole brain. The ROI analyses revealed significant positive relationships between the Sensory Profile modality‐specific subscales and gray matter volumes in the early sensory regions for all modalities, except for the movement and activity level subscales (Table 3 and Fig. 1). More specifically, the visual, auditory, touch, and taste/smell subscales showed positive relationships with gray matter volumes in the left lingual gyrus (BA 18), left superior temporal gyrus (BA 22), left postcentral gyrus (BA 2), and right temporal pole (BA 38), respectively. Additionally, the whole brain analysis revealed a significant positive association between the touch subscale and gray matter volume in the right rolandic operculum (BA 48). Although a positive relationship between the touch subscale and the left postcentral gyrus (BA 2) was observed, this finding was not statistically significant. No other clusters exhibited significant associations with any Sensory Profile subscales.

Table 3.

Brain regions that exhibited significant relationships with Sensory Profile modality subscales

Modality Brain region BA Coordinates Height T‐value Cluster size (voxels) Cluster P‐value ROI
x y z
Visual L. lingual gyrus 18 −18 −76 −8 4.32 63 0.027 *
Auditory L. superior temporal gyrus 22 −69 −22 12 3.51 89 0.016 *
Touch L. postcentral gyrus 2 −65 −18 34 3.89 57 0.001 *
R. rolandic operculum 48 51 −6 19 4.91 2163 0.006
Taste/smell R. temporal pole 38 44 12 −32 4.00 166 0.001 *
Movement None
Activity level None
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L = left; R = right; BA = Brodmann's area; ROIs = regions of interest which we had prior hypotheses. Cluster P‐values are family‐wise error corrected for multiple comparisons.

Figure 1.

Figure 1

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Brain regions showing significant associations between Sensory Profile (SP) modality‐specific subscales and gray matter volumes. (Left) Statistical parametric maps (P < 0.001, peak‐level uncorrected). The areas are overlaid on the T1 image templates. Blue crosses indicate the locations of peak voxels. (Right) Scatter plots of adjusted gray matter volumes (in arbitrary units) as a function of SP modality‐specific subscales (the sum of 10, 11, 13, and 8 items for visual, auditory, touch, and taste/smell modalities, respectively, scored using five‐point scales) in the peak voxels. The effects of no interest (age, sex, and full IQ) were covaried out. [Color figure can be viewed at http://wileyonlinelibrary.com]

Relationships Between the Sensory Profile Modality Subscales and Large‐Scale Gray Matter Volumes

The present study also investigated whether the total gray matter volumes of early sensory regions were associated with sensory processing. The mean adjusted values of the gray matter volumes of the ROIs for the visual, auditory, touch, taste/smell, and movement modalities were extracted to covary out the effects of the other modalities and the effects of no interest (age, sex, and full IQ). Then, the correlations between the values and Sensory Profile subscales were calculated; there were no significant correlations (r < 0.15, P > 0.1; Supporting Information Fig. 1).

Relationships Between the Sensory Profile Quadrant Subscales and Regional Gray Matter Volumes

Finally, the relationships between the Sensory Profile quadrant subscales and regional gray matter volumes were investigated. The whole brain analyses revealed a significant positive association between the Sensory sensitivity scores and gray matter volumes in the left dorsolateral prefrontal cortex, including the middle and inferior frontal gyri (Table 4 and Fig. 2). There were no other clusters that exhibited significant associations with any of the Sensory Profile quadrant scores.

Table 4.

Brain regions that exhibited significant relationships with Sensory Profile quadrant scores

Quadrant Brain region BA Coordinates Height T‐value Cluster size (voxels) Cluster P‐value
x y z
Low registration None
Sensation seeking None
Sensory sensitivity L. inferior frontal gyrus 45 −44 29 28 4.53 1508 0.023
L. middle frontal gyrus 9 −45 29 43 4.01
Sensation avoiding None
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L = left; R = right; BA = Brodmann's area. Cluster P‐values are family‐wise error corrected for multiple comparisons.

Figure 2.

Figure 2

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Brain regions showing significant associations between Sensory Profile (SP) quadrant scores of sensory sensitivity and gray matter volumes. (Left) A statistical parametric map (P < 0.001, peak‐level uncorrected). The area is overlaid on the T1 image template. Blue cross indicates the location of the peak voxel. (Right) A scatter plot of adjusted gray matter volume (in an arbitrary unit) as a function of sensory sensitivity score in the peak voxel. The effects of no interest (age, sex, and full IQ) were covaried out. [Color figure can be viewed at http://wileyonlinelibrary.com]

DISCUSSION

The primary goal of the present study was to determine whether gray matter volumes in early sensory regions would be associated with individual differences in sensory processing in healthy adults. As predicted, the present results showed positive associations between the gray matter volumes of the early sensory regions and scores on the modality‐specific Sensory Profile subscales for the visual, auditory, touch, and taste/smell modalities. However, significant relationships were not found when large‐scale regions of the entire early sensory cortices were analyzed. These results suggest that larger gray matter volumes in specific regions of the early sensory cortices were related to atypical sensory processing in each sensory modality.

There was a positive relationship between the visual subscale and gray matter volume in the lingual gyrus (BA 18), which suggests that this region is involved in individual differences during visual processing. In humans, visual information is first processed in the calcarine fissure, which is referred to as the primary visual cortex (BA 17), and then in the lingual gyrus, which is referred to as the secondary visual cortex (BA 18) [Zeki et al., 1991]. Subsequently, static visual information for object recognition is processed in the ventral pathway that extends into the temporal lobe while dynamic information for directed movements and spatial attention is processed in the dorsal pathway that extends into the parietal lobe [Gilbert, 2013a, 2013b]. Previous functional neuroimaging findings support the involvement of the lingual gyrus in the individual differences associated with atypical visual processing. For example, patients with “visual snow,” which involves visual symptoms that include seeing snow or television‐like static across the visual field and which is often comorbid with additional symptoms such as palinopsia and photophobia, have a greater degree of metabolic activity in the lingual gyrus than healthy controls [Schankin et al., 2014]. Additionally, the lingual gyrus is thought to be the neural substrate underlying visual synesthesia between graphemes and color [Rich et al., 2006]. Taken together, these data suggest that the lingual gyrus is involved in individual differences, especially atypicality in static and dynamic visual processing.

The auditory subscale was positively correlated to gray matter volume in the superior temporal gyrus (BA 22). This result suggests that this region is involved in individual differences during auditory processing. Although information flow in humans has yet to be precisely defined, auditory information is thought to be first processed in Heschl's gyrus, which is referred to as the primary auditory cortex (BA 41; core region), and then in the planum temporale (BA 42) and superior temporal gyrus (BA 22), which are referred to as the secondary auditory cortex [Bizley and Cohen, 2013; Hackett, 2011; Oertel and Doupe, 2013]. Of the two types of auditory information (i.e., information regarding the identity or location of objects), the superior temporal gyrus processes the former, which includes harmony, pitch, and rhythm, and interprets what this auditory information means [Bizley and Cohen, 2013]. A previous structural MRI study reported that patients with hyperarcusis have an increased volume of the superior temporal gyrus [Mahoney et al. 2011], which supports the present hypotheses. Taken together with the present data, these findings suggest that the superior temporal gyrus is involved in atypical auditory processing, specifically at the level of object identification.

The touch subscale was positively correlated to the gray matter volumes of the postcentral gyrus (BA 2) and rolandic operculum (BA 48), which refers to the lower part of the second frontal gyrus and the anterior part of the insula. This result indicates that these regions are related to individual differences during touch processing. Touch information is first processed in the postcentral gyrus, that is, the primary somatosensory area (BAs 3a, 3b, 1, and 2), and then in the parietal operculum, that is, the secondary somatosensory area (BA 40) [Eickhoff et al., 2006; Gardner and Johnson, 2013]. In the primary somatosensory area, touch information flows from BA 3a through BA 3b and BA 1 to BA 2 and is gradually elaborated upon. For example, whereas the neurons in BA 3a and BA 3b only process the stretching of a muscle and the touch signal, respectively, BA 2 integrates the inputs from these three regions. BA 2 also integrates information from large receptive fields, such as the tips of several adjacent fingers or both the fingers and the palm; this type of integrated information allows humans to recognize an object [Gardner and Johnson, 2013]. An electrophysiological study revealed that focal pathophysiologies in BA 2 in the postcentral gyrus are related to astereognosis, which is the inability to identify an object by active touch with the hands in the absence of other sensory input [Tomberg and Desmedt, 1999]. Taken together, these data suggest that the postcentral gyrus is involved in atypicality during touch processing, specifically at the level of integrative processing. The present study did not propose a hypothesis regarding the rolandic operculum but this region is a primary projection area for lingual somatosensory information and functions as an oral somatosensory region [Cerf‐Ducastel et al., 2001; Hari et al., 1993]. The touch subscales contain multiple items assessing oral somatosensory processing and, therefore, the present findings suggest that the rolandic operculum is also involved in individual differences in touch (containing oral somatosensory) processing.

The taste/smell subscale had a positive relationship with the temporal pole (BA 38). This finding suggests that the temporal pole, which is thought to function as a secondary olfactory area [Olson et al., 2007; Wong and Gallate, 2012], is involved in the individual differences during gustatory/olfactory processing. Whereas most of the Taste items on the Sensory Profile inquire about flavor (e.g., “I don't like strong tasting mints or candies”), previous studies have shown that a significant portion of the sensations that humans perceive as flavor is derived from information provided by the olfactory system [e.g., Small, 2006]. Smell information is first processed in the primary olfactory areas (mainly the piriform cortex) before this information is sent to secondary olfactory areas such as the orbitofrontal cortex [Buck and Bargmann, 2013; Gottfried, 2006]. The functions of the temporal pole vary in different topographical subregions, due to connections with a number of various regions [for a review, see Wong and Gallate, 2012]. Because the temporal pole receives projections from the piriform cortex [Olson et al., 2007], it is thought to function as the secondary olfactory area. Consistent with these findings, Gallo et al. [2013] reported that a patient with a lesion of the temporal pole exhibited characteristics of Gourmand syndrome, which is the tendency to have an obsessive focus on eating, thinking, and writing about fine foods. This patient also showed various differences in taste and olfaction compared with healthy controls, including hyposmia and an unusual liking for low concentrations of salt and monosodium glutamate. Additionally, a computed tomography study found that temporal pole atrophy is frequently identified in individuals with olfactory and gustatory perception disorder [Schellinger et al., 1983]. Taken together with the present data, these findings suggest that the temporal pole is involved in atypical tendencies related to gustatory/olfactory processing.

There were no significant correlations between the movement or activity level subscales and any gray matter volumes. This may be due to the diverse contents of these subscales and the multimodal sensations and/or higher‐order cognition associated with these modalities. For example, the movement subscale contains items referring to proprioceptive processing as well as cognitive processing, such as “I am afraid of heights” and “I chose to engage in physical activities.” The activity level subscale also contains items that may reflect cognitive factors, such as “I don't get jokes as quickly others.”

The present results also revealed positive associations between the gray matter volume of the dorsolateral prefrontal cortex and Sensory sensitivity, which is a quadrant score. This finding might be related to the functions performed by the dorsolateral prefrontal cortex, which include the adjustment of sensory input and regulating negative emotions [Barbas et al., 2011; Etkin et al., 2015; Koenigs and Grafman, 2009; Miller and Cohen 2001; Ochsner et al., 2002]. The dorsolateral prefrontal cortex receives input from a wide variety of brain areas, including visual, auditory, and somatosensory association cortices, and acts to converge multimodal sensory information. These anatomical characteristics allow the dorsolateral prefrontal cortex to accomplish specific tasks by processing information selectively [for a review, see Barbas et al., 2011; Miller and Cohen 2001]. Furthermore, it has been proposed that the dorsolateral prefrontal cortex plays important roles in regulating negative emotions through reappraisal/distraction and suppression strategies [Etkin et al., 2015]. Individuals with high scores for sensory sensitivity tend to be easily bothered by sensory stimuli but continue engaging in uncomfortable experiences due to a lower sensory threshold and the use of passive responding strategies [Dunn, 2001]. Thus, these individuals must frequently engage in emotional regulation strategies to reduce negative sensory experiences and this may be reflected by increased gray matter volume in the dorsolateral prefrontal cortex.

The present results indicated that the early sensory regions, particularly the visual, auditory, touch, and taste/smell areas, are involved in the individual differences associated with sensory processing. This will have several implications for clinical research. For example, people with developmental disorders such as autism spectrum disorder and attention‐deficit hyperactivity disorder often show atypical sensory processing [e.g., Ghanizadeh, 2011; Marco et al., 2011]. In fact, the diagnostic criteria for autism spectrum disorder now include atypical sensory processing [American Psychiatric Association, 2013]. While several brain regions are thought to be the neural bases for these developmental disorders [for a review, see Cauda et al., 2011], the early sensory regions may be associated with atypical processing in these disorders. If this is the case, it may explain why individuals with milder forms of a developmental disorder may exhibit serious sensory dysregulations [e.g., Lai et al., 2011]. Future studies investigating this issue, particularly for autism spectrum disorder, will offer novel insights into the etiologies and diagnoses of such disorders. Currently, the present findings might provide benefits for adolescents or adults whose self‐insight is not well established. When people with low intelligence levels are evaluated with the Adolescent/Adult Sensory Profile, their sensory processing levels cannot be adequately measured because they have difficulty self‐monitoring [Hirashima et al., 2014]. A lack of self‐monitoring has also been observed in individuals with normal intelligence [Yoshimura and Toichi, 2014]. While interviews with caregivers can be useful tools for understanding sensory processing in individuals with low self‐insight, the present results show a distinct relationship between gray matter volumes of the early sensory regions and sensory processing for each modality, and suggest that sensory information processed by a particular brain region might complement self‐ and caregiver reports.

The present study has several limitations that should be noted. First, a self‐report questionnaire was used to assess individual differences in sensory processing. One might argue that individuals cannot be aware of all aspects of sensory processing due to the complexity of these processes. However, previous studies have shown that sensory processing patterns measured by the Sensory Profile correspond to objective measures, such as patterns in the skin conductance response [Brown et al., 2001; McIntosh et al., 1999; Miller et al., 1999], and other studies have reported that the scores of objective reports from participants' parents are correlated with those of the Adolescent/Adult Sensory Profile [Hirashima et al., 2014]. Therefore, it is likely that the present results of the Sensory Profile have a certain level of objectivity. Additionally, subjective sensory experiences cannot necessarily be measured only by objective data because such experiences are often associated with affective reactions. Self‐report methodologies can be useful for measuring affective contents, and the Adolescent/Adult Sensory Profile contains items that assess sensory experiences accompanied by affective reactions. Thus, it has a possible advantage in the sense that a relationship between objective imaging data and subjective sensory experiences was observed in the present study.

Second, several quantitative and qualitative issues related to the participants may limit the findings of this study. As a quantitative issue, the relatively small sample size of the present study decreased its statistical power and might have resulted in nonsignificant findings of other brain regions that were actually associated with individual differences in sensory processing. As one of qualitative issues, the accuracies of the self‐reported statuses of habitual consumption of substances that could possibly influence sensory processing were not checked; however, no substance use problems were detected in any of the participants during the structured interview. The other qualitative issue is that only data from young healthy adults were included. Previous studies of individuals with sensory dysregulation [e.g., Owen et al., 2013] showed that white matter microstructures that connect lower‐order and higher‐order sensory areas may be associated with individual differences in sensory processing. This suggests that the present findings obtained from young healthy adults cannot be directly applied to other cohorts. Future large‐scale research studies assessing various populations are needed to address these limitations.

Furthermore, the participants in this study were all right handed. Although the present results showed that most of the modality‐specific Sensory Profile scores were correlated with the left early sensory areas, the smell and touch scores were correlated with the right temporal pole and rolandic operculum, respectively. Because, unlike other modalities, olfactory and lingual somatosensory information is processed ipsilaterally [Gottfried 2006] and bilaterally [Cerf‐Ducastel et al., 2001], respectively, these lateralized results may reflect, at least in part, the handedness of the participants. Many studies have reported that the patterns of anatomical brain asymmetry differ between right‐ and left‐handed individuals [e.g., Hervé et al., 2006]. Thus, an investigation of the structural differences between right‐handed and left‐handed individuals during sensory processing would likely provide interesting results.

In conclusion, the present study observed positive relationships between the modality‐specific Sensory Profile subscales and gray matter volumes in primary or secondary sensory areas for the visual, auditory, touch, and taste/smell modalities. These results suggest that the gray matter volumes of early sensory areas mediate individual differences in sensory processing. Although additional research will be necessary to determine whether these findings can be applied to individuals with atypical sensory processing, the present study has provided novel information regarding the neural bases of individual differences in sensory processing.

Supporting information

Supporting Information 1

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Supporting Information 2

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ACKNOWLEDGMENTS

This study was technically supported by an incorporated nonprofit organization, the Organization for Promoting Neurodevelopmental Disorder Research. The authors thank all participants for their participation in the experiment and Ms. Yokoyama and Mr. Tanner for their technical assistance.

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