Atypical sensory traits and changes in white matter microstructures connected to the amygdala and hippocampus of the autistic brain

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Understanding whether these traits of individuals with ASD are associated with white matter fiber tracts connected to these subcortical structures may be important; however, this relationship remains unexplored. Therefore, we examined associations between sensory traits and abnormalities in white matter fiber tracts in individuals with ASD. Methods Overall, 40 adults with ASD and 83 typically developing (TD) adults were included, and all participants were aged over 18 years. The participants completed the Adolescent/Adult Sensory Profile (AASP), a self-reported questionnaire, and underwent diffusion tensor imaging. We computed four diffusion tensor metrics (fractional anisotropy [FA], mean diffusivity, axonal diffusivity, and radial diffusivity [RD]) for the bilateral amygdala-connected white matter (AWM) and hippocampus-connected white matter (HWM). Results The ASD group exhibited lower FA and higher RD in both the AWM and HWM than the TD group. Significant group differences were observed in correlations between sensation seeking and right white matter microstructures: for the FA of the AWM, the ASD group exhibited a significant positive correlation, whereas the TD group tended toward a negative correlation, and for the FA of the HWM, the ASD group tended toward a positive correlation, whereas the TD group exhibited a significant negative correlation. Conclusion The abnormal sensory traits in ASD pathogenesis may result from demyelination or axonal damage in the AWM and HWM, indicating that the right hemispheric dominance of ASD is present in the white matter structures associated with abnormal sensory processing. autism spectrum disorder sensory traits white matter amygdala hippocampus Figures Figure 1 Figure 2 Introduction Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a prevalence of 27.6 per 1000 children (Maenner et al 2023 ), which is increasing in children and adults (Grosvenor et al 2024 ). In addition to classically defined behavioral traits such as impairments in social communication, restricted interests, and repetitive behaviors, the disorder has recently been characterized by atypical behavioral responses to the sensory environment (American Psychiatric Association 2013 ). According to the revised Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) (APA 2013), behaviors involved in abnormal sensory processing have been suggested as diagnostic criteria for ASD. The Adolescent/Adult Sensory Profile (AASP) is a self-report questionnaire used to evaluate atypical behavioral responses to sensory stimuli (Brown and Dunn 2002 ). Significant associations have been reported between sensory profile scores and classical ASD diagnostic tools, including the Autism-Spectrum Quotient (AQ) (Horder et al 2014 ) and the Social Responsiveness Scale (SRS) (Hilton et al 2010 ). Advances in neuroimaging techniques have enabled researchers to identify abnormal development in subcortical regions, particularly the amygdala and hippocampus, in individuals with ASD. The amygdala is part of the "social brain" network, which includes the orbitofrontal cortex and the superior temporal sulcus (Brothers 1990 ). Structurally, the amygdala volumes of young individuals with ASD are larger than those of their neurotypical peers (Sparks et al 2002 ; Schumann et al 2004 ; Mosconi et al 2009 ; Zhu et al 2018 ). Considering that individuals with amygdalar lesions exhibit impaired facial recognition, amygdalar dysfunction has been suggested in ASD (Ishitobi et al 2011 ). Consistent with this finding, a neuroimaging study provided evidence of an association between impaired amygdala function and difficulties in facial expression recognition in individuals with ASD. Accordingly, researchers hypothesized that the amygdala underlies social dysfunctions in ASD (Baron-Cohen et al 2000 ). Accumulating evidence suggests that the traits of ASD cannot be explained solely by the atypical function of the amygdala; rather, connections between the amygdala and other brain regions better account for ASD symptoms. Therefore, the focus of this study was on the anomalous structural connections of the amygdala rather than its volume. In addition to changes in the amygdala, the autistic brain exhibits alterations in the hippocampus. Social communication impairments in ASD may be attributed to changes in hippocampus-dependent functions (Banker et al 2021 ). Green et al . posited that increased hippocampal activation is related to sensory sensitivity in ASD (2013). Similar to amygdalar enlargement, individuals with ASD exhibit hippocampal hypertrophy during childhood (Sparks et al 2002 ; Schumann et al 2004 ). We previously demonstrated that sensory traits are subcortically associated with hippocampal volume, as well as structural changes in several cortical regions (e.g., the thickness of the lingual cortex and the lateral orbitofrontal cortex) in ASD (Habata et al 2021 ). Therefore, the white matter fiber tracts connected to the hippocampus may subserve sensory information delivery to cortical regions. However, whether sensory traits in ASD are associated with the white matter microstructural features of the amygdala and hippocampus in the autistic brain remains largely unknown. Diffusion tensor imaging (DTI), which captures the diffusive motion of water molecules within the brain, enables the evaluation of neural fiber orientation, tissue integrity, and structural connectivity (Mori and Zhang 2006 ). In ASD, numerous reports show abnormalities in white matter structure across various brain regions, including the cerebral white matter, cerebellum, corpus callosum, and corticospinal tract (Walker et al 2012 ). Specifically, affected individuals exhibited elevated mean diffusivity (MD) values in the white matter fibers connecting the right amygdala and multiple cortical regions, which were significantly associated with SRS social impairment scores (Gibbard et al 2018 ). However, to the best of our knowledge, the relationship between structural anomalies in the white matter connected to the hippocampus and sensory traits in ASD remains unexplored. Using AASP and DTI, we aimed to examine the associations between sensory traits and abnormalities in white matter fiber tracts in individuals with ASD. We hypothesized that differences in structural connectivity, particularly those connected to the amygdala and hippocampus, between the ASD and typically developing (TD) groups are associated with sensory traits. Methods Participants Forty adults with ASD (26 males, 14 females; mean age, 27.2 [SD = 5.5] years) and 83 TD adults (43 males, 40 females; mean age, 28.4 [SD = 8.0] years) participated in the study. We assessed sensory traits using the AASP. Based on the DSM-5 (APA 2013), experienced clinicians (HK and IO) made clinical diagnoses of ASD. These participants were diagnosed with ASD based on DSM-5 classifications (APA 2013) by an experienced clinician (HK) and standardized criteria using the Diagnostic Interview for Social and Communication Disorders (Wing et al 2002 ). Most participants in this group had their ASD diagnosis confirmed using the Autism Diagnostic Observation Schedule, Second Edition (Lord et al 1989 ). We assessed autism characteristics using the AQ (Baron-Cohen et al 2001 ) and SRS (Constantino et al 2003 ). Intelligence quotient (IQ) was measured using the Wechsler Adult Intelligence Scale, 3rd ed. Exclusion criteria for all the participants included any history of brain injury, head trauma, excessive alcohol consumption, drug toxicity, or major physical illness. All the participants had a full-scale IQ above 80. Several participants with ASD had comorbidities: nine, two, one, one, and one participant had depressive disorders, obsessive-compulsive disorder, avoidant/restrictive food intake disorder, panic disorder, and specific learning disorder, respectively. The purpose and content of this study were explained to the participants, and written consent was obtained. This study was approved by the Ethics Committee of the University of Fukui (approval number: 20170182) and conducted in accordance with the Declaration of Helsinki. The AASP The AASP (Brown and Dunn 2002 ) allows us to assess behavioral response patterns to sensory stimuli and symptoms on a five-point Likert scale. The validity of the AASP has been verified in Japan (Brown et al 2015 ). All 60 items of the AASP comprised elements of the sensory processing quadrant and six modalities. The quadrant scores include low registration, sensation seeking, sensory sensitivity, and sensation avoidance. Each quadrant score consists of 15 items. The six modalities are visual (10 items), auditory (11 items), touch (13 items), smell/taste (8 items), movement (8 items), and activity (10 items). A summary of the participants’ AASP scores is provided in Table 1 . Table 1 Adolescent/Adult Sensory Profile data of the participants ASD TD p Quadrant scores Low registration 37.48 (10.19) 26.41 (6.22) < 0.001 Sensation seeking 32.30 (8.01) 39.73 (6.97) < 0.001 Sensory sensitivity 43.28 (11.20) 33.45 (7.27) < 0.001 Sensation avoiding 45.50 (11.26) 33.61 (7.31) < 0.001 Modality-specific subscale Taste/smell 17.98 (3.89) 16.63 (3.56) 0.059 Movement 17.15 (4.19) 17.23 (3.56) 0.914 Visual 25.85 (6.13) 22.95 (4.73) 0.005 Touch 34.18 (7.52) 28.49 (6.23) < 0.001 Activity level 32.10 (5.62) 25.72 (4.63) < 0.001 Auditory 31.43 (7.21) 22.18 (5.42) < 0.001 ASD, autism spectrum disorder; TD, typically developing MRI Acquisition MRI was performed at the University of Fukui Hospital using a 3-Tesla SIGNA PET/MR scanner with an 8-channel head coil (GE Medical Systems, Milwaukee, WI, USA). The acquired high-resolution T1-weighted anatomical MRI had the following parameters: repetition time (TR) of 6.38 ms, echo time (TE) of 1.99 ms, flip angle of 11°, field of view (FOV) of 256 mm, matrix of 256 × 256 with 172 slices, and a voxel size of 1.0 × 1.0 × 1.0 mm 3 . Diffusion-weighted images were captured using single-shot echo-planar imaging. The specific parameters were as follows: acquisition matrix of 128 × 128, minimum TE, TR of 9327 ms, FOV of 240 mm, matrix of 240 × 240, pixels measuring 1.9 × 1.9 mm², encompassing 45 axial slices, and a slice thickness of 3.0 mm without gap. These images were oriented in 30 isotropic directions with b-values of 1000 s/mm 2 and 0. DTI Preprocessing Preprocessing and analysis of diffusion-weighted images were conducted using the DSI Studio software ( http://dsi-studio.labsolver.org/ ). We followed the conventional procedure to reconstruct diffusion photographs via q-sampling imaging and traced the fibers using a deterministic method (Yeh et al 2013 ). Using the Runge–Kutta method, fiber paths were consistently mapped for all the selected scans after quality control (Yoldemir et al 2012 ). The parameters for the standard analysis procedures included a fractional anisotropy (FA) limit of 0.2, an angle limit of 60°, a step size of 0.85 voxels, a smoothing parameter of 0, a total of 150,000 seed points, and fiber lengths between 10 and 400 mm. We separately identified the amygdala-connected white matter (AWM) and hippocampus-connected white matter (HWM) using the AAL2 model. From these structures, the following diffusion tensor metrics were derived: FA, axonal diffusivity (AD), radial diffusivity (RD), and MD. As an example, the right AWM delineated using DSI Studio is shown in Fig. 1 . The collected DTI data were subjected to quality assessment (MJ, TK, or HO). Statistical Analysis We used t -tests to assess group differences in demographic data (sex, age, IQ, AQ score, SRS score, and AASP score) between the ASD and TD groups. We evaluated group differences in the diffusion tensor metrics (FA, MD, AD, and RD) of AWM and HWM separately using analysis of covariance, with age, body mass index (BMI), sex, full-scale IQ, and volume of each white matter tract as covariates. Additionally, we performed partial correlation analyses to examine the associations between AASP scores (quadrant scores and six modality-specific scores) and four diffusion measures of AWM and HWM, with age, BMI, sex, full-scale IQ, and volume of each white matter tract as covariates. The threshold for statistical significance was set at p < 0.05. Statistical analyses were performed using SPSS version 27 software (IBM Corp., Armonk, NY, USA). Furthermore, to compare correlations between the ASD and TD groups, we performed a Fisher r -to- z value transformation using VassarStats ( http://vassarstats.net/ ; accessed on October 12, 2023). Results Behavioral Data for Sensory Traits No significant differences were observed in sex, BMI, age, or IQ between the ASD and TD groups (Table 2 ). AQ (Baron-Cohen et al 2001 ) and SRS (Constantino et al 2003 ) scores were significantly higher in the ASD group (Table 2 ). Regarding the AASP quadrant scores, the ASD group had higher scores for low registration (mean ± standard deviation: 37.48 ± 10.19), sensory sensitivity (43.28 ± 11.20), and sensation avoidance (45.50 ± 11.26) than the TD group (26.41 ± 6.22 for low registration, 33.45 ± 7.27 for sensory sensitivity, and 33.61 ± 7.31 for sensation avoiding). By contrast, the ASD group showed lower scores only for sensation seeking (ASD: 32.30 ± 8.01; TD: 39.73 ± 6.97). In terms of the modality-specific subscales, the ASD group scored higher in the visual (25.85 ± 6.13), touch (34.18 ± 7.52), activity level (32.10 ± 5.62), and auditory (31.43 ± 7.21) domains than the TD group (22.95 ± 4.73 for visual, 28.49 ± 6.23 for touch, 25.72 ± 4.63 for activity level, and 22.18 ± 5.42 for auditory). No significant differences were observed in taste, smell, or movement scores (Table 1 ). Table 2 Demographic data of the participants ASD TD p Subjects 40 83 - Sex (n, male/female) 26/14 43/40 0.170 BMI (kg/m 2 ) 22.4 (4.1) 21.9 (2.7) 0.398 Age (years) 27.2 (5.5) 28.4 (8.0) 0.369 Range 18–41 19–53 - Full-scale IQ 109.5 (12.0) 109.9 (11.5) 0.857 Verbal IQ 111.5 (13.5) 109.7 (12.8) 0.464 Performance IQ 104.8 (12.9) 108.2 (11.9) 0.147 AQ (total) 32.3 (5.3) 15.8 (6.0) < 0.001 SRS (total) 112.4 (25.9) 48.9 (18.0) < 0.001 ASD, autism spectrum disorder; TD, typically developing; IQ, intelligence quotient; BMI, body mass index; AQ, Autism-Spectrum Quotient; SRS, Social Responsiveness Scale Group Differences in Diffusion Measures between the ASD and TD Groups A significant group effect was found for the average FA of AWM (F = 17.88, p < 0.001, effect size = 0.134 for the right; F = 22.65, p < 0.001, effect size = 0.163 for the left) and HWM (F = 17.88, p < 0.001, effect size = 0.134 for the right; F = 14.80, p < 0.001, effect size = 0.113 for the left), as well as for the average RD of AWM (F = 5.83, p = 0.017, effect size = 0.048 for the right; F = 5.37, p = 0.022, effect size = 0.044 for the left) and HWM (F = 15.80, p < 0.001, effect size = 0.120 for the right; F = 10.20, p = 0.002, effect size = 0.081 for the left). Furthermore, we found that, compared with that in the TD group (0.366 ± 0.030 for left AWM; 0.372 ± 0.026 for right AWM; 0.477 ± 0.022 for left HWM; 0.443 ± 0.022 for right HWM), the ASD group had lower average FA values for the bilateral AWM (0.364 ± 0.022 for left; 0.365 ± 0.016 for right) and bilateral HWM (0.476 ± 0.020 for left; 0.442 ± 0.016 for right). The ASD group showed higher average RD values for the bilateral AWM (0.765 ± 0.061 for left; 0.734 ± 0.035 for right) and bilateral HWM (0.647 ± 0.039 for left; 0.665 ± 0.034 for right) than the TD group (0.763 ± 0.053 for left AWM; 0.721 ± 0.041 for right AWM; 0.639 ± 0.030 for left HWM; 0.658 ± 0.034 for right HWM). Additionally, the ASD group showed a higher average MD of the right HWM (0.893 ± 0.036) than the TD group (0.887 ± 0.035) (Table 3 ). No group differences were observed with respect to the other diffusion measures (Table 3 ). Table 3 Differences in diffusion tensor measures between the ASD and TD groups White matter pathway Diffusion tensor index ASD TD F p L. Amygdala FA 0.364 (0.022) 0.366 (0.030) 22.65 < 0.001 MD 0.960 (0.067) 0.960 (0.055) 1.45 0.231 AD 1.350 (0.084) 1.354 (0.071) 0.18 0.677 RD 0.765 (0.061) 0.763 (0.053) 5.37 0.022 R. Amygdala FA 0.365 (0.016) 0.372 (0.026) 17.88 < 0.001 MD 0.922 (0.039) 0.911 (0.043) 1.82 0.180 AD 1.300 (0.053) 1.293 (0.063) 0.03 0.858 RD 0.734 (0.035) 0.721 (0.041) 5.83 0.017 L. Hippocampus FA 0.476 (0.020) 0.477 (0.022) 14.80 < 0.001 MD 0.894 (0.040) 0.886 (0.030) 2.64 0.107 AD 1.389 (0.054) 1.381 (0.052) 0.41 0.524 RD 0.647 (0.039) 0.639 (0.030) 10.20 0.002 R. Hippocampus FA 0.442 (0.016) 0.443 (0.022) 17.88 < 0.001 MD 0.893 (0.036) 0.887 (0.035) 6.04 0.015 AD 1.349 (0.049) 1.344 (0.056) 0.08 0.776 RD 0.665 (0.034) 0.658 (0.034) 15.80 < 0.001 ASD, autism spectrum disorder; TD, typically developing; FA, fractional anisotropy; MD, mean diffusivity; AD, axonal diffusivity; RD, radial diffusivity; L, left; R, right Correlation Analysis between Diffusion Tensor Measures and Sensory Scores In the ASD group, sensation-seeking scores were positively correlated not only with the average FA of the right AWM (r = 0.371, p = 0.028) but also with the average AD of the right AWM (r = 0.449, p = 0.007) (Table 4 ). The TD group showed significant positive correlations between the average FA of the right AWM and low registration scores (r = 0.357, p = 0.001) and between the average RD of the left HWM and sensation-seeking scores (r = 0.231, p = 0.042). Additionally, the TD group showed a significant negative correlation between the average FA of the right HWM and sensation-seeking scores (r = -0.309, p = 0.006) (Table 4 ). Table 4 Relationship between diffusion measures and AASP scores in the ASD and TD groups White matter pathway Diffusion tensor index The adolescent/adult sensory profile Correlation coefficient p ASD R. Amygdala FA Sensation seeking 0.371 0.028 R. Amygdala AD Sensation seeking 0.440 0.024 TD R. Amygdala FA Low registration 0.357 0.001 L. Hippocampus RD Sensation seeking 0.231 0.042 R. Hippocampus FA Sensation seeking -0.309 0.006 ASD, autism spectrum disorder; TD, typically developing; FA, fractional anisotropy; AD, axonal diffusivity; RD, radial diffusivity; L, left; R, right; Analysis of covariance; with age, body mass index; sex, full-scale intelligence quotient and volume of each white matter tract as covariates. Correlation Differences between the ASD and TD Groups There were significant group differences in the correlations between the average FA of the right AWM and sensation-seeking scores (z = 2.91, p = 0.004, Fig. 2 a) and between the average FA of the right HWM and sensation-seeking scores (z = 2.93, p = 0.003, Fig. 2 b). Discussion The present study investigated sensory traits and structural changes in white matter fibers connected to the amygdala and hippocampus and their relationship with ASD. Corroborating existing reports on atypical sensory traits in individuals with ASD, significant group differences were observed in all quadrant scores (low registration, sensation seeking, sensory sensitivity, and sensation avoidance) and four modality-specific subscales (visual, touch, activity level, and auditory) between the ASD and TD groups. The ASD group exhibited lower FA and higher RD in the bilateral AWM and HWM and higher MD in the right HWM than the TD group. These images show cerebral white matter microstructural abnormalities, such as demyelination and axonal damage, in the bilateral AWM and HWM of patients with ASD. Differences between the ASD and TD groups in the bilateral AWM and HWM were observed with respect to sensory traits. Specifically, the ASD group showed a positive correlation between the FA of the right AWM and sensation-seeking scores. Conversely, the TD group exhibited a negative association between the FA of the right HWM and sensation-seeking scores. In the quadrant scores, sensation-seeking refers to the tendency to have a high neurological threshold, prompting the search for specific sensory stimuli to meet this threshold. These results may be attributed to differing relationships between sensory traits, particularly sensation seeking, and the microstructures of the right AWM and HWM in individuals with ASD and TD adults. Furthermore, we observed a group difference in the correlations between sensation seeking and the FA of the right AWM and HWM, which may reflect altered structural asymmetry in the autistic brain (Postema et al 2019 ). Our findings suggest potential associations between sensory properties and the white matter microstructure connected to the two subcortical structures in patients with ASD. These results may be useful in the clinical assessment of ASD severity. DTI Previous studies have found abnormalities in the AWM of individuals with ASD and proposed that these anomalies are associated with ASD-related social and cognitive impairments (Walker et al 2012 ; Gibbard et al 2018 ; Conturo et al 2008 ; Jou et al 2011 ). Supporting this, we observed a decrease in FA values in the AWM and HWM of patients with ASD. Considering that the FA value indicates white matter fiber coherence (Engelhorn et al 2012 ) and that a low FA value reflects damage from various pathological conditions (Alexander et al 2007 ), our finding of decreased FA in ASD indicates compromised white matter fiber integrity. Although increased RD is not pathologically specific, it suggests demyelination, axonal damage, and other factors, alluding to white matter damage (Klawiter et al 2011 ). Aging is associated with decreased FA and increased RD in various brain regions, including the parahippocampal white matter (Madden et al 2012 ). Considering this, we speculated that the altered white matter structure connected to the amygdala and hippocampus may manifest as axonal loss or reduced myelin formation in the autistic brain. Amygdala and Sensory Processing The amygdala, an integral component of the limbic system, forms numerous neural connections with various brain regions, including the entorhinal cortex, prefrontal cortex, hippocampus, and thalamus (Sah et al 2003 ; Hennessey et al 2018 ; Meisner et al 2022 ). In humans, the amygdala receives input across multiple modalities, contributing significantly to facial recognition and reactions to aversion and fear (Baron-Cohen et al 2000 ). Animal studies have shown that the amygdala plays a pivotal role in processing sensory information. For instance, the brains of macaque monkeys show extensive cortical projections from the amygdala to visual cortical areas that subserve sensory perception (Freese and Amaral 2005 ). In murine models, the amygdala plays an instrumental role in decision-making in response to olfactory stimuli (Mori and Sakano 2021 ). Green et al . highlighted augmented activity in response to aversive sensory stimuli, not only in primary sensory regions but also in the amygdala in ASD (2013), and this activity was correlated with sensory sensitivity scores. In line with this, the current study showed alterations in AWM in ASD, although it was associated not with sensory sensitivity but with sensory seeking. Our results showed lower sensation-seeking scores in the ASD group than in the TD group, which is consistent with the results of previous studies in adults with ASD (Mayer 2017 ; Ohta et al 2020 ). Conversely, some studies in patients with ASD younger than 18 years reported higher sensation-seeking scores in the ASD groups (Surgent et al 2022 ; He et al 2021 ). Because neural responses to sensory stimuli in individuals with ASD change with age (Cakar et al 2023 ), the contribution of the amygdala to sensory traits may differ with different age groups. Hippocampus and Sensory Processing The hippocampus is connected to cortical regions, including the posterior cingulate gyrus, medial prefrontal cortex, and angular gyrus, forming an expansive intracerebral network. In terms of sensory processing, the hippocampus is known for its role in olfaction: it receives substantial afferent inputs from the entorhinal cortex, an integral station for olfactory processing. A study showed significant correlations between olfactory thresholds and hippocampal volumes in TD individuals: a volumetric increase in the right hippocampus was associated with a decline in olfactory thresholds (Smitka et al 2012 ). Atrophy of the hippocampus is a well-known feature in patients with Alzheimer's disease and is accompanied by changes in olfaction (Marigliano et al 2014 ). Individuals with ASD exhibit hippocampal hypertrophy (Sparks et al 2002 ; Schumann et al 2004 ). Groen et al . found a volumetric increase in the hippocampus of patients with ASD, indicative of heightened activity in these structures and hypersensitivity to sensory stimuli (2010). Given the role of the hippocampus in sensory processing, hippocampal enlargement in ASD reflects an adaptive response to hypersensitivity to sensory inputs. This is corroborated by our finding of a negative correlation between hippocampal volume and the sensory traits of taste and olfaction in ASD (Habata et al 2021 ). The present study provides evidence for changes in HWM with respect to sensory properties in ASD. Hemisphere Asymmetry Left–right asymmetry has been associated with various psychiatric disorders, including ASD. We found that sensory traits and white matter microstructures were differentially correlated between the ASD and TD groups in the right hemisphere. Using a large brain image dataset, Postema et al . reported reduced asymmetry in cortical thickness, including in the fusiform, cingulate, orbitofrontal cortex, and orbitofrontal surface area, in individuals with ASD compared with that in TD individuals (2019). In a study focusing on structural connectivity, the leftward lateralization of networks involving the fusiform, rostral middle frontal, and medial orbitofrontal cortices was decreased in ASD (Sha et al 2022 ). Each study focused on different aspects, such as cortical thickness, surface area, and connectivity, which may contribute to variability in hemispheric asymmetry findings. However, these reports share the common finding that left–right symmetry is reduced in ASD. In sensory regions, rightward lateralization is decreased in ASD (Yoo et al 2024 ). This indicates that the right hemispheric dominance of the autistic brain is related to sensory traits in ASD. Similarly, previous DTI studies have shown reduced rightward asymmetry of FA in the whole brain in ASD individuals compared with that in TD individuals (Carper et al 2016 ). In the present study, our results also showed different correlations between sensory traits and FA in the AWM and HWM only in the right hemisphere, which may confirm the right hemispheric dominance of the autistic brain. Limitations This study had certain limitations. First, challenges related to sex and age variations were encountered. Although we controlled for sex and age in our analysis to mitigate confounding effects, the potential influence of sex- and age-related changes on brain morphology and sensory variations cannot be overlooked. Second, the current study explored sensory traits not through experimental tasks using stimuli of various sensory modalities but through self-assessment. Therefore, it cannot assess neuronal responses of the amygdala or hippocampus to sensory inputs. Finally, our study participants in the ASD group were able to complete the AASP and demonstrated high verbal and cognitive abilities. This implies that our study participants did not include individuals with severe ASD traits. Conclusions Findings from this study showed that atypical sensory traits, particularly sensation seeking, in ASD are associated with changes in the white matter fiber tracts connected to the amygdala and hippocampus. These findings suggest that abnormal sensory traits in patients with ASD may be due to demyelination and axonal damage in the white matter connected to the two subcortical structures. These results are valuable for elucidating the neural basis of sensory traits in ASD. Future large-scale studies should be conducted to validate the results of the present study; moreover, the relationship between sensory traits and white matter microstructures should be examined via more objective methods, such as experimental tasks using various sensory stimuli. Declarations Competing interests The authors declare no conflict of interest. Ethics approval This study was approved by the Ethics Committee of the University of Fukui (approval number: 20170182) and conducted in accordance with the Declaration of Helsinki. Consent to participate The purpose and content of this study were explained to the participants, and written consent was obtained. Funding This research was supported by the KBRI Basic Research Program through the Korea Brain Research Institute and funded by the Ministry of Science and ICT (24-BR-05-01) as well as Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (24H00622 and 20H04272). Author Contribution Conceptualization: Taku Kamiya, Minyoung Jung, and Hirotaka Kosaka; Methodology: Minyoung Jung, Hirotaka Kosaka, Ichiro M. Omori; Data curation: Taku Kamiya, Kaie Habata, Daichi Shiotsu, Takuya Makino, Kotaro Kowada, Riku Sanada, Ichiro M. Omori, Hidehiko Okazawa, Minyoung Jung, and Hirotaka Kosaka; Formal analysis: Taku Kamiya, Kaie Habata, Yongjeon Cheong, Daichi Shiotsu, Takuya Makino, Kotaro Kowada, Riku Sanada, Ichiro M. Omori, Hidehiko Okazawa, Minyoung Jung, and Hirotaka Kosaka; Writing – original draft: Taku Kamiya, Kaie Habata, Yongjeon Cheong, Minyoung Jung, and Hirotaka Kosaka. All authors read and approved the final manuscript. Acknowledgments This research was supported by the KBRI Basic Research Program through the Korea Brain Research Institute and funded by the Ministry of Science and ICT (24-BR-05-01) as well as Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (24H00622 and 20H04272). Data Availability The study data can be made available upon request from the corresponding authors. References Alexander AL, Lee JE, Lazar M, Field AS (2007) Diffusion tensor imaging of the brain. Neurotherapeutics 4:316–329. https://doi.org/10.1016/j.nurt.2007.05.011 American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders: DSM-5. https://www.academia.edu/download/38718268/csl6820_21.pdf . Accessed 5 Oct 2023 Banker SM, Gu X, Schiller D, Foss-Feig JH (2021) Hippocampal contributions to social and cognitive deficits in autism spectrum disorder. Trends Neurosci 44:793–807. https://doi.org/10.1016/j.tins.2021.08.005 Baron-Cohen S, Ring HA, Bullmore ET, Wheelwright S, Ashwin C, Williams SC (2000) The amygdala theory of autism. Neurosci Biobehav Rev 24:355–364. https://doi.org/10.1016/s0149-7634(00)00011-7 Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E (2001) The Autism-Spectrum Quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J Autism Dev Disord 31:5–17. https://doi.org/10.1023/a:1005653411471 Brothers L (1990) The social brain: a project for integrating primate behavior and neuropsychology in a new domain. Concepts Neurosci 1:27–51 Brown C, Dunn W (2002) Adolescent/adult sensory profile. San Antonio, TX Psychological Corporation. https://www.scirp.org/reference/referencespapers?referenceid=380137 . Accessed 1 Apr 2025 Brown C, Dunn W, Tsujii M, Hagiwara T, Iwanaga R, Ito H, Tani I (2015) The Japanese Version of Adolescent/Adult Sensory Profile. https://ci.nii.ac.jp/ncid/BB19724379 . Accessed 1 Apr 2025 Cakar ME, Cummings KK, Bookheimer SY, Dapretto M, Green SA (2023) Age-related changes in neural responses to sensory stimulation in autism: a cross-sectional study. Mol Autism 14:38. https://doi.org/10.1186/s13229-023-00571-4 Carper RA, Treiber JM, DeJesus SY, Müller RA (2016) Reduced Hemispheric Asymmetry of White Matter Microstructure in Autism Spectrum Disorder. J Am Acad Child Adolesc Psychiatry 55:1073–1080. http://doi.org/10.1016/j.jaac.2016.09.491 Constantino JN, Davis SA, Todd RD, Schindler MK, Gross MM, Brophy SL, Metzger LM, Shoushtari CS, Splinter R, Reich W (2003) Validation of a brief quantitative measure of autistic traits: comparison of the social responsiveness scale with the autism diagnostic interview-revised. J Autism Dev Disord 33:427–433. https://doi.org/10.1023/a:1025014929212 Conturo TE, Williams DL, Smith CD, Gultepe E, Akbudak E, Minshew NJ (2008) Neuronal fiber pathway abnormalities in autism: an initial MRI diffusion tensor tracking study of hippocampo-fusiform and amygdalo-fusiform pathways. J Int Neuropsychol Soc 14:933–946. https://doi.org/10.1017/S1355617708081381 Engelhorn T, Michelson G, Waerntges S, Otto M, El-Rafei A, Struffert T, Doerfler A (2012) Changes of radial diffusivity and fractional anisotropy in the optic nerve and optic radiation of glaucoma patients. ScientificWorldJournal 2012:849632. https://doi.org/10.1100/2012/849632 Freese JL, Amaral DG (2005) The organization of projections from the amygdala to visual cortical areas TE and V1 in the macaque monkey. J Comp Neurol 486:295–317. https://doi.org/10.1002/cne.20520 Gibbard CR, Ren J, Skuse DH, Clayden JD, Clark CA (2018) Structural connectivity of the amygdala in young adults with autism spectrum disorder. Hum Brain Mapp 39:1270–1282. https://doi.org/10.1002/hbm.23915 Green SA, Rudie JD, Colich NL, Wood JJ, Shirinyan D, Hernandez L, Tottenham N, Dapretto M, Bookheimer SY (2013) Overreactive brain responses to sensory stimuli in youth with autism spectrum disorders. J Am Acad Child Adolesc Psychiatry 52:1158–1172. https://doi.org/10.1016/j.jaac.2013.08.004 Groen W, Teluij M, Buitelaar J, Tendolkar I (2010) Amygdala and hippocampus enlargement during adolescence in autism. J Am Acad Child Adolesc Psychiatry 49:552–560. https://doi.org/10.1016/j.jaac.2009.12.023 Grosvenor LP, Croen LA, Lynch FL, Marafino BJ, Maye M, Penfold RB, Simon GE, Ames JL (2024) Autism diagnosis among US children and adults, 2011–2022. JAMA Netw Open 7:e2442218. https://doi.org/10.1001/jamanetworkopen.2024.42218 Habata K, Cheong Y, Kamiya T, Shiotsu D, Omori IM, Okazawa H, Jung M, Kosaka H (2021) Relationship between sensory characteristics and cortical thickness/volume in autism spectrum disorders. Transl Psychiatry 11:616. https://doi.org/10.1038/s41398-021-01743-7 He JL, Oeltzschner G, Mikkelsen M, Deronda A, Harris AD, Crocetti D, Wodka EL, Mostofsky SH, Edden RAE, Puts NAJ (2021) Region-specific elevations of glutamate + glutamine correlate with the sensory symptoms of autism spectrum disorders. Transl Psychiatry 11:411. https://doi.org/10.1038/s41398-021-01525-1 Hennessey T, Andari E, Rainnie DG (2018) RDoC-based categorization of amygdala functions and its implications in autism. Neurosci Biobehav Rev 90:115–129. https://doi.org/10.1016/j.neubiorev.2018.04.007 Hilton CL, Harper JD, Kueker RH, Lang AR, Abbacchi AM, Todorov A, LaVesser PD (2010) Sensory responsiveness as a predictor of social severity in children with high functioning autism spectrum disorders. J Autism Dev Disord 40:937–945. https://doi.org/10.1007/s10803-010-0944-8 Horder J, Wilson CE, Mendez MA, Murphy DG (2014) Autistic traits and abnormal sensory experiences in adults. J Autism Dev Disord 44:1461–1469. https://doi.org/10.1007/s10803-013-2012-7 Ishitobi M, Kosaka H, Omori M, Matsumura Y (2011) Differential amygdala response to lower face in patients with autistic spectrum disorders: an fMRI study. Res Autism Spectr Disord 5:910–919. https://doi.org/10.1016/j.rasd.2010.10.005 Jou RJ, Jackowski AP, Papademetris X, Rajeevan N, Staib LH, Volkmar FR (2011) Diffusion tensor imaging in autism spectrum disorders: preliminary evidence of abnormal neural connectivity. Aust N Z J Psychiatry 45:153–162. https://doi.org/10.3109/00048674.2010.534069 Klawiter EC, Schmidt RE, Trinkaus K, Liang HF, Budde MD, Naismith RT, Song SK, Cross AH, Benzinger TL (2011) Radial diffusivity predicts demyelination in ex vivo multiple sclerosis spinal cords. NeuroImage 55:1454–1460. https://doi.org/10.1016/j.neuroimage.2011.01.007 Lord C, Rutter M, Goode S, Heemsbergen J, Jordan H, Mawhood L, Schopler E (1989) Autism diagnostic observation schedule: a standardized observation of communicative and social behavior. J Autism Dev Disord 19:185–212. https://doi.org/10.1007/BF02211841 Madden DJ, Bennett IJ, Burzynska A, Potter GG, Chen NK, Song AW (2012) Diffusion tensor imaging of cerebral white matter integrity in cognitive aging. Biochim Biophys Acta 1822:386–400. https://doi.org/10.1016/j.bbadis.2011.08.003 Maenner MJ, Warren Z, Williams AR, Amoakohene E, Bakian AV, Bilder DA, Durkin MS, Fitzgerald RT, Furnier SM, Hughes MM, Ladd-Acosta CM, McArthur D, Pas ET, Salinas A, Vehorn A, Williams S, Esler A, Grzybowski A, Hall-Lande J, Nguyen RHN, Pierce K, Zahorodny W, Hudson A, Hallas L, Mancilla KC, Patrick M, Shenouda J, Sidwell K, DiRienzo M, Gutierrez J, Spivey MH, Lopez M, Pettygrove S, Schwenk YD, Washington A, Shaw KA (2023) Prevalence and characteristics of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2020. MMWR Surveill Summ 72:1–14. https://doi.org/10.15585/mmwr.ss7202a1 Marigliano V, Gualdi G, Servello A, Marigliano B, Volpe LD, Fioretti A, Pagliarella M, Valenti M, Masedu F, Di Biasi C, Ettorre E, Fusetti M (2014) Olfactory deficit and hippocampal volume loss for early diagnosis of Alzheimer disease: a pilot study. Alzheimer Dis Assoc Disord 28:194–197. https://doi.org/10.1097/WAD.0b013e31827bdb9f Mayer JL (2017) The relationship between autistic traits and atypical sensory functioning in neurotypical and ASD adults: a spectrum approach. J Autism Dev Disord 47:316–327. https://doi.org/10.1007/s10803-016-2948-5 Meisner OC, Nair A, Chang SWC (2022) Amygdala connectivity and implications for social cognition and disorders. Handb Clin Neurol 187:381–403. https://doi.org/10.1016/B978-0-12-823493-8.00017-1 Mori K, Sakano H (2021) Olfactory circuitry and behavioral decisions. Annu Rev Physiol 83:231–256. https://doi.org/10.1146/annurev-physiol-031820-092824 Mori S, Zhang J (2006) Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron 51:527–539. https://doi.org/10.1016/j.neuron.2006.08.012 Mosconi MW, Cody-Hazlett H, Poe MD, Gerig G, Smith RG, Piven J (2009) Longitudinal study of amygdala volume and joint attention in 2- to 4-year-old children with autism. Arch Gen Psychiatry 66:509–516. https://doi.org/10.1001/archgenpsychiatry.2009.19 Ohta H, Aoki YY, Itahashi T, Kanai C, Fujino J, Nakamura M, Kato N, Hashimoto RI (2020) White matter alterations in autism spectrum disorder and attention-deficit/hyperactivity disorder in relation to sensory profile. Mol Autism 11:77. https://doi.org/10.1186/s13229-020-00379-6 Postema MC, van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Filho GB, Calderoni S, Calvo R, Daly E, Deruelle C, Duan M, Duran FLS, Durston S, Ecker C, Ehrlich S, Fair D, Fedor J, Fitzgerald J, Floris DL, Franke B, Freitag CM, Gallagher L, Glahn DC, Haar S, Hoekstra L, Jahanshad N, Jalbrzikowski M, Janssen J, King JA, Lazaro L, Luna B, McGrath J, Medland SE, Muratori F, Murphy DGM, Neufeld J, O'Hearn K, Oranje B, Parellada M, Pariente JC, Postema MC, Remnelius KL, Retico A, Rosa PGP, Rubia K, Shook D, Tammimies K, Taylor MJ, Tosetti M, Wallace GL, Zhou F, Thompson PM, Fisher SE, Buitelaar JK, Francks C (2019) Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets. Nat Commun 10:4958. https://doi.org/10.1038/s41467-019-13005-8 Sah P, Faber ESL, De Armentia ML, Power J (2003) The amygdaloid complex: anatomy and physiology. Physiol Rev 83:803–834. https://doi.org/10.1152/physrev.00002.2003 Schumann CM, Hamstra J, Goodlin-Jones BL, Lotspeich LJ, Kwon H, Buonocore MH, Lammers CR, Reiss AL, Amaral DG (2004) The amygdala is enlarged in children but not adolescents with autism; the hippocampus is enlarged at all ages. J Neurosci 24:6392–6401. https://doi.org/10.1523/JNEUROSCI.1297-04.2004 Sha Z, van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Bernhardt B, Bolte S, Busatto GF, Calderoni S, Calvo R, Daly E, Deruelle C, Duan M, Duran FLS, Durston S, Ecker C, Ehrlich S, Fair D, Fedor J, Fitzgerald J, Floris DL, Franke B, Freitag CM, Gallagher L, Glahn DC, Haar S, Hoekstra L, Jahanshad N, Jalbrzikowski M, Janssen J, King JA, Lazaro L, Luna B, McGrath J, Medland SE, Muratori F, Murphy DGM, Neufeld J, O'Hearn K, Oranje B, Parellada M, Pariente JC, Postema MC, Remnelius KL, Retico A, Rosa PGP, Rubia K, Shook D, Tammimies K, Taylor MJ, Tosetti M, Wallace GL, Zhou F, Thompson PM, Fisher SE, Buitelaar JK, Francks C (2022) Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium. Mol Psychiatry 27:2114–2125. https://doi.org/10.1038/s41380-022-01452-7 Smitka M, Puschmann S, Buschhueter D, Gerber JC, Witt M, Honeycutt N, Abolmaali N, Hummel T (2012) Is there a correlation between hippocampus and amygdala volume and olfactory function in healthy subjects? NeuroImage 59:1052–1057. https://doi.org/10.1016/j.neuroimage.2011.09.024 Sparks BF, Friedman SD, Shaw DW, Aylward EH, Echelard D, Artru AA, Maravilla KR, Giedd JN, Munson J, Dawson G, Dager SR (2002) Brain structural abnormalities in young children with autism spectrum disorder. Neurology 59: 184–192. https://doi.org/ Surgent O, Riaz A, Ausderau KK, Adluru N, Kirk GR, Guerrero-Gonzalez J, Skaletski EC, Kecskemeti SR, Iii DCD, Weismer SE, Alexander AL, Travers BG (2022) Brainstem white matter microstructure is associated with hyporesponsiveness and overall sensory features in autistic children. Mol Autism 13:48. https://doi.org/10.1186/s13229-022-00524-3 Walker L, Gozzi M, Lenroot R, Thurm A, Behseta B, Swedo S, Pierpaoli C (2012) Diffusion tensor imaging in young children with autism: biological effects and potential confounds. Biol Psychiatry 72:1043–1051. https://doi.org/10.1016/j.biopsych.2012.08.001 Wing L, Leekam SR, Libby SJ, Gould J, Larcombe M (2002) The Diagnostic Interview for Social and Communication Disorders: background, inter-rater reliability and clinical use. J Child Psychol Psychiatry 43:307–325. https://doi.org/10.1111/1469-7610.00023 Yeh F-C, Verstynen TD, Wang Y, Fernández-Miranda JC, Tseng WYI (2013) Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS ONE 8:e80713. https://doi.org/10.1371/journal.pone.0080713 Yoldemir B, Acar B, Firat Z, Kiliçkesmez Ö (2012) SMT: a reliability based interactive DTI tractography algorithm. IEEE Trans Med Imaging 31:1929–1940. https://doi.org/10.1109/TMI.2012.2210052 Yoo S, Jang Y, Hong S-J, Park H, Valk SL, Bernhardt BC, Park BY (2024) Whole-brain structural connectome asymmetry in autism. NeuroImage 288:120534. https://doi.org/10.1016/j.neuroimage.2024.120534 Zhu Z, Fang X, Chen H, Zhu X, Zhang L, Zhai X, Cui Z, Gao Q (2018) Alterations in volumes and MRI features of amygdala in Chinese autistic preschoolers associated with social and behavioral deficits. Brain Imaging Behav 12:1814–1821. https://doi.org/10.1007/s11682-018-9853-9 Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":142131,"visible":true,"origin":"","legend":"\u003cp\u003eWhite matter fiber tracks in the amygdala and hippocampus. (a) All seed regions include the bilateral amygdala and hippocampus. (b) White matter fiber tracks in the amygdala. (c) White matter fiber tracks in the hippocampus. Amyg, amygdala; Hip, hippocampus\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8023186/v1/7b8bc1fda305f347716341e0.jpg"},{"id":96206154,"identity":"59fb0783-2eec-478d-bcd8-735ac254f7dd","added_by":"auto","created_at":"2025-11-18 17:21:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":231267,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences between diffusion tensor measure and AASP score correlations in the ASD and TD groups.\u003c/p\u003e\n\u003cp\u003e(a) A correlation exists between the average FA of the white matter in the right amygdala and sensory-seeking scores on AASP in the ASD group (r=0.371, \u003cem\u003ep\u003c/em\u003e=0.028). The correlations between average FA in the right amygdala and sensory seeking were significantly different between both groups (\u003cem\u003ep\u003c/em\u003e=0.004). (b) A correlation exists between the average FA of the white matter in the right hippocampus and sensory-seeking scores on AASP in the TD group (\u003cem\u003er\u003c/em\u003e =−0.309, \u003cem\u003ep\u003c/em\u003e=0.006). The correlations between average FA in the right hippocampus and sensory seeking differed significantly between both the groups (\u003cem\u003ep\u003c/em\u003e=0.003)\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8023186/v1/5f9fc2c7f0c0789816ac9d64.jpg"},{"id":96257195,"identity":"64c9883e-1fda-4c45-875d-2932136f2e2e","added_by":"auto","created_at":"2025-11-19 07:51:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1296109,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8023186/v1/e905de73-671f-496d-9f46-edb523fcc7a0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Atypical sensory traits and changes in white matter microstructures connected to the amygdala and hippocampus of the autistic brain","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAutism spectrum disorder (ASD) is a neurodevelopmental disorder with a prevalence of 27.6 per 1000 children (Maenner et al \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which is increasing in children and adults (Grosvenor et al \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition to classically defined behavioral traits such as impairments in social communication, restricted interests, and repetitive behaviors, the disorder has recently been characterized by atypical behavioral responses to the sensory environment (American Psychiatric Association \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). According to the revised Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) (APA 2013), behaviors involved in abnormal sensory processing have been suggested as diagnostic criteria for ASD. The Adolescent/Adult Sensory Profile (AASP) is a self-report questionnaire used to evaluate atypical behavioral responses to sensory stimuli (Brown and Dunn \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Significant associations have been reported between sensory profile scores and classical ASD diagnostic tools, including the Autism-Spectrum Quotient (AQ) (Horder et al \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and the Social Responsiveness Scale (SRS) (Hilton et al \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdvances in neuroimaging techniques have enabled researchers to identify abnormal development in subcortical regions, particularly the amygdala and hippocampus, in individuals with ASD. The amygdala is part of the \"social brain\" network, which includes the orbitofrontal cortex and the superior temporal sulcus (Brothers \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Structurally, the amygdala volumes of young individuals with ASD are larger than those of their neurotypical peers (Sparks et al \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Schumann et al \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Mosconi et al \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zhu et al \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Considering that individuals with amygdalar lesions exhibit impaired facial recognition, amygdalar dysfunction has been suggested in ASD (Ishitobi et al \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Consistent with this finding, a neuroimaging study provided evidence of an association between impaired amygdala function and difficulties in facial expression recognition in individuals with ASD. Accordingly, researchers hypothesized that the amygdala underlies social dysfunctions in ASD (Baron-Cohen et al \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Accumulating evidence suggests that the traits of ASD cannot be explained solely by the atypical function of the amygdala; rather, connections between the amygdala and other brain regions better account for ASD symptoms. Therefore, the focus of this study was on the anomalous structural connections of the amygdala rather than its volume.\u003c/p\u003e\u003cp\u003eIn addition to changes in the amygdala, the autistic brain exhibits alterations in the hippocampus. Social communication impairments in ASD may be attributed to changes in hippocampus-dependent functions (Banker et al \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Green \u003cem\u003eet al\u003c/em\u003e. posited that increased hippocampal activation is related to sensory sensitivity in ASD (2013). Similar to amygdalar enlargement, individuals with ASD exhibit hippocampal hypertrophy during childhood (Sparks et al \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Schumann et al \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). We previously demonstrated that sensory traits are subcortically associated with hippocampal volume, as well as structural changes in several cortical regions (e.g., the thickness of the lingual cortex and the lateral orbitofrontal cortex) in ASD (Habata et al \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, the white matter fiber tracts connected to the hippocampus may subserve sensory information delivery to cortical regions. However, whether sensory traits in ASD are associated with the white matter microstructural features of the amygdala and hippocampus in the autistic brain remains largely unknown.\u003c/p\u003e\u003cp\u003eDiffusion tensor imaging (DTI), which captures the diffusive motion of water molecules within the brain, enables the evaluation of neural fiber orientation, tissue integrity, and structural connectivity (Mori and Zhang \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In ASD, numerous reports show abnormalities in white matter structure across various brain regions, including the cerebral white matter, cerebellum, corpus callosum, and corticospinal tract (Walker et al \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Specifically, affected individuals exhibited elevated mean diffusivity (MD) values in the white matter fibers connecting the right amygdala and multiple cortical regions, which were significantly associated with SRS social impairment scores (Gibbard et al \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, to the best of our knowledge, the relationship between structural anomalies in the white matter connected to the hippocampus and sensory traits in ASD remains unexplored.\u003c/p\u003e\u003cp\u003eUsing AASP and DTI, we aimed to examine the associations between sensory traits and abnormalities in white matter fiber tracts in individuals with ASD. We hypothesized that differences in structural connectivity, particularly those connected to the amygdala and hippocampus, between the ASD and typically developing (TD) groups are associated with sensory traits.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eForty adults with ASD (26 males, 14 females; mean age, 27.2 [SD\u0026thinsp;=\u0026thinsp;5.5] years) and 83 TD adults (43 males, 40 females; mean age, 28.4 [SD\u0026thinsp;=\u0026thinsp;8.0] years) participated in the study. We assessed sensory traits using the AASP. Based on the DSM-5 (APA 2013), experienced clinicians (HK and IO) made clinical diagnoses of ASD. These participants were diagnosed with ASD based on DSM-5 classifications (APA 2013) by an experienced clinician (HK) and standardized criteria using the Diagnostic Interview for Social and Communication Disorders (Wing et al \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Most participants in this group had their ASD diagnosis confirmed using the Autism Diagnostic Observation Schedule, Second Edition (Lord et al \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). We assessed autism characteristics using the AQ (Baron-Cohen et al \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and SRS (Constantino et al \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Intelligence quotient (IQ) was measured using the Wechsler Adult Intelligence Scale, 3rd ed. Exclusion criteria for all the participants included any history of brain injury, head trauma, excessive alcohol consumption, drug toxicity, or major physical illness. All the participants had a full-scale IQ above 80. Several participants with ASD had comorbidities: nine, two, one, one, and one participant had depressive disorders, obsessive-compulsive disorder, avoidant/restrictive food intake disorder, panic disorder, and specific learning disorder, respectively. The purpose and content of this study were explained to the participants, and written consent was obtained. This study was approved by the Ethics Committee of the University of Fukui (approval number: 20170182) and conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eThe AASP\u003c/h3\u003e\n\u003cp\u003eThe AASP (Brown and Dunn \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) allows us to assess behavioral response patterns to sensory stimuli and symptoms on a five-point Likert scale. The validity of the AASP has been verified in Japan (Brown et al \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). All 60 items of the AASP comprised elements of the sensory processing quadrant and six modalities. The quadrant scores include low registration, sensation seeking, sensory sensitivity, and sensation avoidance. Each quadrant score consists of 15 items. The six modalities are visual (10 items), auditory (11 items), touch (13 items), smell/taste (8 items), movement (8 items), and activity (10 items). A summary of the participants\u0026rsquo; AASP scores is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAdolescent/Adult Sensory Profile data of the participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eASD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eQuadrant scores\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow registration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.48 (10.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.41 (6.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensation seeking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.30 (8.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.73 (6.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensory sensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.28 (11.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.45 (7.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensation avoiding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.50 (11.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.61 (7.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eModality-specific subscale\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTaste/smell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.98 (3.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.63 (3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMovement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.15 (4.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.23 (3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.85 (6.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.95 (4.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTouch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.18 (7.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.49 (6.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActivity level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.10 (5.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.72 (4.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuditory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.43 (7.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.18 (5.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eASD, autism spectrum disorder; TD, typically developing\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eMRI Acquisition\u003c/h3\u003e\n\u003cp\u003eMRI was performed at the University of Fukui Hospital using a 3-Tesla SIGNA PET/MR scanner with an 8-channel head coil (GE Medical Systems, Milwaukee, WI, USA). The acquired high-resolution T1-weighted anatomical MRI had the following parameters: repetition time (TR) of 6.38 ms, echo time (TE) of 1.99 ms, flip angle of 11\u0026deg;, field of view (FOV) of 256 mm, matrix of 256 \u0026times; 256 with 172 slices, and a voxel size of 1.0 \u0026times; 1.0 \u0026times; 1.0 mm\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDiffusion-weighted images were captured using single-shot echo-planar imaging. The specific parameters were as follows: acquisition matrix of 128 \u0026times; 128, minimum TE, TR of 9327 ms, FOV of 240 mm, matrix of 240 \u0026times; 240, pixels measuring 1.9 \u0026times; 1.9 mm\u0026sup2;, encompassing 45 axial slices, and a slice thickness of 3.0 mm without gap. These images were oriented in 30 isotropic directions with b-values of 1000 s/mm\u003csup\u003e2\u003c/sup\u003e and 0.\u003c/p\u003e\n\u003ch3\u003eDTI Preprocessing\u003c/h3\u003e\n\u003cp\u003ePreprocessing and analysis of diffusion-weighted images were conducted using the DSI Studio software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dsi-studio.labsolver.org/\u003c/span\u003e\u003cspan address=\"http://dsi-studio.labsolver.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We followed the conventional procedure to reconstruct diffusion photographs via q-sampling imaging and traced the fibers using a deterministic method (Yeh et al \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Using the Runge\u0026ndash;Kutta method, fiber paths were consistently mapped for all the selected scans after quality control (Yoldemir et al \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The parameters for the standard analysis procedures included a fractional anisotropy (FA) limit of 0.2, an angle limit of 60\u0026deg;, a step size of 0.85 voxels, a smoothing parameter of 0, a total of 150,000 seed points, and fiber lengths between 10 and 400 mm. We separately identified the amygdala-connected white matter (AWM) and hippocampus-connected white matter (HWM) using the AAL2 model. From these structures, the following diffusion tensor metrics were derived: FA, axonal diffusivity (AD), radial diffusivity (RD), and MD. As an example, the right AWM delineated using DSI Studio is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The collected DTI data were subjected to quality assessment (MJ, TK, or HO).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eWe used \u003cem\u003et\u003c/em\u003e-tests to assess group differences in demographic data (sex, age, IQ, AQ score, SRS score, and AASP score) between the ASD and TD groups. We evaluated group differences in the diffusion tensor metrics (FA, MD, AD, and RD) of AWM and HWM separately using analysis of covariance, with age, body mass index (BMI), sex, full-scale IQ, and volume of each white matter tract as covariates. Additionally, we performed partial correlation analyses to examine the associations between AASP scores (quadrant scores and six modality-specific scores) and four diffusion measures of AWM and HWM, with age, BMI, sex, full-scale IQ, and volume of each white matter tract as covariates. The threshold for statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Statistical analyses were performed using SPSS version 27 software (IBM Corp., Armonk, NY, USA). Furthermore, to compare correlations between the ASD and TD groups, we performed a Fisher \u003cem\u003er\u003c/em\u003e-to-\u003cem\u003ez\u003c/em\u003e value transformation using VassarStats (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://vassarstats.net/\u003c/span\u003e\u003cspan address=\"http://vassarstats.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on October 12, 2023).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eBehavioral Data for Sensory Traits\u003c/h2\u003e\u003cp\u003eNo significant differences were observed in sex, BMI, age, or IQ between the ASD and TD groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). AQ (Baron-Cohen et al \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and SRS (Constantino et al \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) scores were significantly higher in the ASD group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Regarding the AASP quadrant scores, the ASD group had higher scores for low registration (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation: 37.48\u0026thinsp;\u0026plusmn;\u0026thinsp;10.19), sensory sensitivity (43.28\u0026thinsp;\u0026plusmn;\u0026thinsp;11.20), and sensation avoidance (45.50\u0026thinsp;\u0026plusmn;\u0026thinsp;11.26) than the TD group (26.41\u0026thinsp;\u0026plusmn;\u0026thinsp;6.22 for low registration, 33.45\u0026thinsp;\u0026plusmn;\u0026thinsp;7.27 for sensory sensitivity, and 33.61\u0026thinsp;\u0026plusmn;\u0026thinsp;7.31 for sensation avoiding). By contrast, the ASD group showed lower scores only for sensation seeking (ASD: 32.30\u0026thinsp;\u0026plusmn;\u0026thinsp;8.01; TD: 39.73\u0026thinsp;\u0026plusmn;\u0026thinsp;6.97). In terms of the modality-specific subscales, the ASD group scored higher in the visual (25.85\u0026thinsp;\u0026plusmn;\u0026thinsp;6.13), touch (34.18\u0026thinsp;\u0026plusmn;\u0026thinsp;7.52), activity level (32.10\u0026thinsp;\u0026plusmn;\u0026thinsp;5.62), and auditory (31.43\u0026thinsp;\u0026plusmn;\u0026thinsp;7.21) domains than the TD group (22.95\u0026thinsp;\u0026plusmn;\u0026thinsp;4.73 for visual, 28.49\u0026thinsp;\u0026plusmn;\u0026thinsp;6.23 for touch, 25.72\u0026thinsp;\u0026plusmn;\u0026thinsp;4.63 for activity level, and 22.18\u0026thinsp;\u0026plusmn;\u0026thinsp;5.42 for auditory). No significant differences were observed in taste, smell, or movement scores (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic data of the participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eASD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubjects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (n, male/female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26/14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43/40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.4 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.9 (2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.2 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.4 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19\u0026ndash;53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFull-scale IQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109.5 (12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109.9 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVerbal IQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111.5 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109.7 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.464\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerformance IQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104.8 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108.2 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ (total)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.3 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.8 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSRS (total)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112.4 (25.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.9 (18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eASD, autism spectrum disorder; TD, typically developing; IQ, intelligence quotient; BMI, body mass index; AQ, Autism-Spectrum Quotient; SRS, Social Responsiveness Scale\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGroup Differences in Diffusion Measures between the ASD and TD Groups\u003c/h3\u003e\n\u003cp\u003eA significant group effect was found for the average FA of AWM (F\u0026thinsp;=\u0026thinsp;17.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, effect size\u0026thinsp;=\u0026thinsp;0.134 for the right; F\u0026thinsp;=\u0026thinsp;22.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, effect size\u0026thinsp;=\u0026thinsp;0.163 for the left) and HWM (F\u0026thinsp;=\u0026thinsp;17.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, effect size\u0026thinsp;=\u0026thinsp;0.134 for the right; F\u0026thinsp;=\u0026thinsp;14.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, effect size\u0026thinsp;=\u0026thinsp;0.113 for the left), as well as for the average RD of AWM (F\u0026thinsp;=\u0026thinsp;5.83, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017, effect size\u0026thinsp;=\u0026thinsp;0.048 for the right; F\u0026thinsp;=\u0026thinsp;5.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022, effect size\u0026thinsp;=\u0026thinsp;0.044 for the left) and HWM (F\u0026thinsp;=\u0026thinsp;15.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, effect size\u0026thinsp;=\u0026thinsp;0.120 for the right; F\u0026thinsp;=\u0026thinsp;10.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, effect size\u0026thinsp;=\u0026thinsp;0.081 for the left). Furthermore, we found that, compared with that in the TD group (0.366\u0026thinsp;\u0026plusmn;\u0026thinsp;0.030 for left AWM; 0.372\u0026thinsp;\u0026plusmn;\u0026thinsp;0.026 for right AWM; 0.477\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022 for left HWM; 0.443\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022 for right HWM), the ASD group had lower average FA values for the bilateral AWM (0.364\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022 for left; 0.365\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016 for right) and bilateral HWM (0.476\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020 for left; 0.442\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016 for right). The ASD group showed higher average RD values for the bilateral AWM (0.765\u0026thinsp;\u0026plusmn;\u0026thinsp;0.061 for left; 0.734\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035 for right) and bilateral HWM (0.647\u0026thinsp;\u0026plusmn;\u0026thinsp;0.039 for left; 0.665\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034 for right) than the TD group (0.763\u0026thinsp;\u0026plusmn;\u0026thinsp;0.053 for left AWM; 0.721\u0026thinsp;\u0026plusmn;\u0026thinsp;0.041 for right AWM; 0.639\u0026thinsp;\u0026plusmn;\u0026thinsp;0.030 for left HWM; 0.658\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034 for right HWM). Additionally, the ASD group showed a higher average MD of the right HWM (0.893\u0026thinsp;\u0026plusmn;\u0026thinsp;0.036) than the TD group (0.887\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). No group differences were observed with respect to the other diffusion measures (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferences in diffusion tensor measures between the ASD and TD groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite matter pathway\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiffusion tensor index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eASD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL. Amygdala\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.364 (0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.366 (0.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.960 (0.067)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.960 (0.055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.350 (0.084)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.354 (0.071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.677\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.765 (0.061)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.763 (0.053)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR. Amygdala\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.365 (0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.372 (0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.922 (0.039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.911 (0.043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.180\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.300 (0.053)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.293 (0.063)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.858\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.734 (0.035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.721 (0.041)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL. Hippocampus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.476 (0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.477 (0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.894 (0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.886 (0.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.389 (0.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.381 (0.052)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.524\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.647 (0.039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.639 (0.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR. Hippocampus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.442 (0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.443 (0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.893 (0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.887 (0.035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.349 (0.049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.344 (0.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.776\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.665 (0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.658 (0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eASD, autism spectrum disorder; TD, typically developing; FA, fractional anisotropy; MD, mean diffusivity; AD, axonal diffusivity; RD, radial diffusivity; L, left; R, right\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation Analysis between Diffusion Tensor Measures and Sensory Scores\u003c/h2\u003e\u003cp\u003eIn the ASD group, sensation-seeking scores were positively correlated not only with the average FA of the right AWM (r\u0026thinsp;=\u0026thinsp;0.371, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) but also with the average AD of the right AWM (r\u0026thinsp;=\u0026thinsp;0.449, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The TD group showed significant positive correlations between the average FA of the right AWM and low registration scores (r\u0026thinsp;=\u0026thinsp;0.357, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and between the average RD of the left HWM and sensation-seeking scores (r\u0026thinsp;=\u0026thinsp;0.231, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042). Additionally, the TD group showed a significant negative correlation between the average FA of the right HWM and sensation-seeking scores (r = -0.309, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between diffusion measures and AASP scores in the ASD and TD groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite matter pathway\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiffusion tensor index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe adolescent/adult sensory profile\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCorrelation coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eASD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR. Amygdala\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSensation seeking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR. Amygdala\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSensation seeking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR. Amygdala\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow registration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL. Hippocampus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSensation seeking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR. Hippocampus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSensation seeking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eASD, autism spectrum disorder; TD, typically developing; FA, fractional anisotropy; AD, axonal diffusivity; RD, radial diffusivity; L, left; R, right; Analysis of covariance; with age, body mass index; sex, full-scale intelligence quotient and volume of each white matter tract as covariates.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation Differences between the ASD and TD Groups\u003c/h2\u003e\u003cp\u003eThere were significant group differences in the correlations between the average FA of the right AWM and sensation-seeking scores (z\u0026thinsp;=\u0026thinsp;2.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) and between the average FA of the right HWM and sensation-seeking scores (z\u0026thinsp;=\u0026thinsp;2.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study investigated sensory traits and structural changes in white matter fibers connected to the amygdala and hippocampus and their relationship with ASD. Corroborating existing reports on atypical sensory traits in individuals with ASD, significant group differences were observed in all quadrant scores (low registration, sensation seeking, sensory sensitivity, and sensation avoidance) and four modality-specific subscales (visual, touch, activity level, and auditory) between the ASD and TD groups. The ASD group exhibited lower FA and higher RD in the bilateral AWM and HWM and higher MD in the right HWM than the TD group. These images show cerebral white matter microstructural abnormalities, such as demyelination and axonal damage, in the bilateral AWM and HWM of patients with ASD. Differences between the ASD and TD groups in the bilateral AWM and HWM were observed with respect to sensory traits. Specifically, the ASD group showed a positive correlation between the FA of the right AWM and sensation-seeking scores. Conversely, the TD group exhibited a negative association between the FA of the right HWM and sensation-seeking scores. In the quadrant scores, sensation-seeking refers to the tendency to have a high neurological threshold, prompting the search for specific sensory stimuli to meet this threshold. These results may be attributed to differing relationships between sensory traits, particularly sensation seeking, and the microstructures of the right AWM and HWM in individuals with ASD and TD adults. Furthermore, we observed a group difference in the correlations between sensation seeking and the FA of the right AWM and HWM, which may reflect altered structural asymmetry in the autistic brain (Postema et al \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our findings suggest potential associations between sensory properties and the white matter microstructure connected to the two subcortical structures in patients with ASD. These results may be useful in the clinical assessment of ASD severity.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDTI\u003c/h2\u003e\u003cp\u003ePrevious studies have found abnormalities in the AWM of individuals with ASD and proposed that these anomalies are associated with ASD-related social and cognitive impairments (Walker et al \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gibbard et al \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Conturo et al \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Jou et al \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Supporting this, we observed a decrease in FA values in the AWM and HWM of patients with ASD. Considering that the FA value indicates white matter fiber coherence (Engelhorn et al \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and that a low FA value reflects damage from various pathological conditions (Alexander et al \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), our finding of decreased FA in ASD indicates compromised white matter fiber integrity. Although increased RD is not pathologically specific, it suggests demyelination, axonal damage, and other factors, alluding to white matter damage (Klawiter et al \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Aging is associated with decreased FA and increased RD in various brain regions, including the parahippocampal white matter (Madden et al \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Considering this, we speculated that the altered white matter structure connected to the amygdala and hippocampus may manifest as axonal loss or reduced myelin formation in the autistic brain.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eAmygdala and Sensory Processing\u003c/h2\u003e\u003cp\u003eThe amygdala, an integral component of the limbic system, forms numerous neural connections with various brain regions, including the entorhinal cortex, prefrontal cortex, hippocampus, and thalamus (Sah et al \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Hennessey et al \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Meisner et al \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In humans, the amygdala receives input across multiple modalities, contributing significantly to facial recognition and reactions to aversion and fear (Baron-Cohen et al \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Animal studies have shown that the amygdala plays a pivotal role in processing sensory information. For instance, the brains of macaque monkeys show extensive cortical projections from the amygdala to visual cortical areas that subserve sensory perception (Freese and Amaral \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In murine models, the amygdala plays an instrumental role in decision-making in response to olfactory stimuli (Mori and Sakano \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGreen \u003cem\u003eet al\u003c/em\u003e. highlighted augmented activity in response to aversive sensory stimuli, not only in primary sensory regions but also in the amygdala in ASD (2013), and this activity was correlated with sensory sensitivity scores. In line with this, the current study showed alterations in AWM in ASD, although it was associated not with sensory sensitivity but with sensory seeking. Our results showed lower sensation-seeking scores in the ASD group than in the TD group, which is consistent with the results of previous studies in adults with ASD (Mayer \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ohta et al \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, some studies in patients with ASD younger than 18 years reported higher sensation-seeking scores in the ASD groups (Surgent et al \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; He et al \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Because neural responses to sensory stimuli in individuals with ASD change with age (Cakar et al \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the contribution of the amygdala to sensory traits may differ with different age groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eHippocampus and Sensory Processing\u003c/h2\u003e\u003cp\u003eThe hippocampus is connected to cortical regions, including the posterior cingulate gyrus, medial prefrontal cortex, and angular gyrus, forming an expansive intracerebral network. In terms of sensory processing, the hippocampus is known for its role in olfaction: it receives substantial afferent inputs from the entorhinal cortex, an integral station for olfactory processing. A study showed significant correlations between olfactory thresholds and hippocampal volumes in TD individuals: a volumetric increase in the right hippocampus was associated with a decline in olfactory thresholds (Smitka et al \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Atrophy of the hippocampus is a well-known feature in patients with Alzheimer's disease and is accompanied by changes in olfaction (Marigliano et al \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Individuals with ASD exhibit hippocampal hypertrophy (Sparks et al \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Schumann et al \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Groen \u003cem\u003eet al\u003c/em\u003e. found a volumetric increase in the hippocampus of patients with ASD, indicative of heightened activity in these structures and hypersensitivity to sensory stimuli (2010). Given the role of the hippocampus in sensory processing, hippocampal enlargement in ASD reflects an adaptive response to hypersensitivity to sensory inputs. This is corroborated by our finding of a negative correlation between hippocampal volume and the sensory traits of taste and olfaction in ASD (Habata et al \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The present study provides evidence for changes in HWM with respect to sensory properties in ASD.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eHemisphere Asymmetry\u003c/h2\u003e\u003cp\u003eLeft\u0026ndash;right asymmetry has been associated with various psychiatric disorders, including ASD. We found that sensory traits and white matter microstructures were differentially correlated between the ASD and TD groups in the right hemisphere. Using a large brain image dataset, Postema \u003cem\u003eet al\u003c/em\u003e. reported reduced asymmetry in cortical thickness, including in the fusiform, cingulate, orbitofrontal cortex, and orbitofrontal surface area, in individuals with ASD compared with that in TD individuals (2019). In a study focusing on structural connectivity, the leftward lateralization of networks involving the fusiform, rostral middle frontal, and medial orbitofrontal cortices was decreased in ASD (Sha et al \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Each study focused on different aspects, such as cortical thickness, surface area, and connectivity, which may contribute to variability in hemispheric asymmetry findings. However, these reports share the common finding that left\u0026ndash;right symmetry is reduced in ASD. In sensory regions, rightward lateralization is decreased in ASD (Yoo et al \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This indicates that the right hemispheric dominance of the autistic brain is related to sensory traits in ASD.\u003c/p\u003e\u003cp\u003eSimilarly, previous DTI studies have shown reduced rightward asymmetry of FA in the whole brain in ASD individuals compared with that in TD individuals (Carper et al \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the present study, our results also showed different correlations between sensory traits and FA in the AWM and HWM only in the right hemisphere, which may confirm the right hemispheric dominance of the autistic brain.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study had certain limitations. First, challenges related to sex and age variations were encountered. Although we controlled for sex and age in our analysis to mitigate confounding effects, the potential influence of sex- and age-related changes on brain morphology and sensory variations cannot be overlooked. Second, the current study explored sensory traits not through experimental tasks using stimuli of various sensory modalities but through self-assessment. Therefore, it cannot assess neuronal responses of the amygdala or hippocampus to sensory inputs. Finally, our study participants in the ASD group were able to complete the AASP and demonstrated high verbal and cognitive abilities. This implies that our study participants did not include individuals with severe ASD traits.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eFindings from this study showed that atypical sensory traits, particularly sensation seeking, in ASD are associated with changes in the white matter fiber tracts connected to the amygdala and hippocampus. These findings suggest that abnormal sensory traits in patients with ASD may be due to demyelination and axonal damage in the white matter connected to the two subcortical structures. These results are valuable for elucidating the neural basis of sensory traits in ASD. Future large-scale studies should be conducted to validate the results of the present study; moreover, the relationship between sensory traits and white matter microstructures should be examined via more objective methods, such as experimental tasks using various sensory stimuli.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics approval\u003c/h2\u003e\u003cp\u003eThis study was approved by the Ethics Committee of the University of Fukui (approval number: 20170182) and conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cp\u003eThe purpose and content of this study were explained to the participants, and written consent was obtained.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported by the KBRI Basic Research Program through the Korea Brain Research Institute and funded by the Ministry of Science and ICT (24-BR-05-01) as well as Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (24H00622 and 20H04272).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: Taku Kamiya, Minyoung Jung, and Hirotaka Kosaka; Methodology: Minyoung Jung, Hirotaka Kosaka, Ichiro M. Omori; Data curation: Taku Kamiya, Kaie Habata, Daichi Shiotsu, Takuya Makino, Kotaro Kowada, Riku Sanada, Ichiro M. Omori, Hidehiko Okazawa, Minyoung Jung, and Hirotaka Kosaka; Formal analysis: Taku Kamiya, Kaie Habata, Yongjeon Cheong, Daichi Shiotsu, Takuya Makino, Kotaro Kowada, Riku Sanada, Ichiro M. Omori, Hidehiko Okazawa, Minyoung Jung, and Hirotaka Kosaka; Writing \u0026ndash; original draft: Taku Kamiya, Kaie Habata, Yongjeon Cheong, Minyoung Jung, and Hirotaka Kosaka. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThis research was supported by the KBRI Basic Research Program through the Korea Brain Research Institute and funded by the Ministry of Science and ICT (24-BR-05-01) as well as Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (24H00622 and 20H04272).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe study data can be made available upon request from the corresponding authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlexander AL, Lee JE, Lazar M, Field AS (2007) Diffusion tensor imaging of the brain. Neurotherapeutics 4:316\u0026ndash;329. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nurt.2007.05.011\u003c/span\u003e\u003cspan address=\"10.1016/j.nurt.2007.05.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmerican Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders: DSM-5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.academia.edu/download/38718268/csl6820_21.pdf\u003c/span\u003e\u003cspan address=\"https://www.academia.edu/download/38718268/csl6820_21.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 5 Oct 2023\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBanker SM, Gu X, Schiller D, Foss-Feig JH (2021) Hippocampal contributions to social and cognitive deficits in autism spectrum disorder. Trends Neurosci 44:793\u0026ndash;807. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tins.2021.08.005\u003c/span\u003e\u003cspan address=\"10.1016/j.tins.2021.08.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaron-Cohen S, Ring HA, Bullmore ET, Wheelwright S, Ashwin C, Williams SC (2000) The amygdala theory of autism. Neurosci Biobehav Rev 24:355\u0026ndash;364. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0149-7634(00)00011-7\u003c/span\u003e\u003cspan address=\"10.1016/s0149-7634(00)00011-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E (2001) The Autism-Spectrum Quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J Autism Dev Disord 31:5\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/a:1005653411471\u003c/span\u003e\u003cspan address=\"10.1023/a:1005653411471\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrothers L (1990) The social brain: a project for integrating primate behavior and neuropsychology in a new domain. Concepts Neurosci 1:27\u0026ndash;51\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrown C, Dunn W (2002) Adolescent/adult sensory profile. San Antonio, TX Psychological Corporation. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.scirp.org/reference/referencespapers?referenceid=380137\u003c/span\u003e\u003cspan address=\"https://www.scirp.org/reference/referencespapers?referenceid=380137\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 1 Apr 2025\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrown C, Dunn W, Tsujii M, Hagiwara T, Iwanaga R, Ito H, Tani I (2015) The Japanese Version of Adolescent/Adult Sensory Profile. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ci.nii.ac.jp/ncid/BB19724379\u003c/span\u003e\u003cspan address=\"https://ci.nii.ac.jp/ncid/BB19724379\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 1 Apr 2025\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCakar ME, Cummings KK, Bookheimer SY, Dapretto M, Green SA (2023) Age-related changes in neural responses to sensory stimulation in autism: a cross-sectional study. Mol Autism 14:38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13229-023-00571-4\u003c/span\u003e\u003cspan address=\"10.1186/s13229-023-00571-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarper RA, Treiber JM, DeJesus SY, M\u0026uuml;ller RA (2016) Reduced Hemispheric Asymmetry of White Matter Microstructure in Autism Spectrum Disorder. J Am Acad Child Adolesc Psychiatry 55:1073\u0026ndash;1080. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.jaac.2016.09.491\u003c/span\u003e\u003cspan address=\"10.1016/j.jaac.2016.09.491\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConstantino JN, Davis SA, Todd RD, Schindler MK, Gross MM, Brophy SL, Metzger LM, Shoushtari CS, Splinter R, Reich W (2003) Validation of a brief quantitative measure of autistic traits: comparison of the social responsiveness scale with the autism diagnostic interview-revised. J Autism Dev Disord 33:427\u0026ndash;433. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/a:1025014929212\u003c/span\u003e\u003cspan address=\"10.1023/a:1025014929212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConturo TE, Williams DL, Smith CD, Gultepe E, Akbudak E, Minshew NJ (2008) Neuronal fiber pathway abnormalities in autism: an initial MRI diffusion tensor tracking study of hippocampo-fusiform and amygdalo-fusiform pathways. J Int Neuropsychol Soc 14:933\u0026ndash;946. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1355617708081381\u003c/span\u003e\u003cspan address=\"10.1017/S1355617708081381\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEngelhorn T, Michelson G, Waerntges S, Otto M, El-Rafei A, Struffert T, Doerfler A (2012) Changes of radial diffusivity and fractional anisotropy in the optic nerve and optic radiation of glaucoma patients. ScientificWorldJournal 2012:849632. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1100/2012/849632\u003c/span\u003e\u003cspan address=\"10.1100/2012/849632\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFreese JL, Amaral DG (2005) The organization of projections from the amygdala to visual cortical areas TE and V1 in the macaque monkey. J Comp Neurol 486:295\u0026ndash;317. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cne.20520\u003c/span\u003e\u003cspan address=\"10.1002/cne.20520\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGibbard CR, Ren J, Skuse DH, Clayden JD, Clark CA (2018) Structural connectivity of the amygdala in young adults with autism spectrum disorder. Hum Brain Mapp 39:1270\u0026ndash;1282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hbm.23915\u003c/span\u003e\u003cspan address=\"10.1002/hbm.23915\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreen SA, Rudie JD, Colich NL, Wood JJ, Shirinyan D, Hernandez L, Tottenham N, Dapretto M, Bookheimer SY (2013) Overreactive brain responses to sensory stimuli in youth with autism spectrum disorders. J Am Acad Child Adolesc Psychiatry 52:1158\u0026ndash;1172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jaac.2013.08.004\u003c/span\u003e\u003cspan address=\"10.1016/j.jaac.2013.08.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGroen W, Teluij M, Buitelaar J, Tendolkar I (2010) Amygdala and hippocampus enlargement during adolescence in autism. J Am Acad Child Adolesc Psychiatry 49:552\u0026ndash;560. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jaac.2009.12.023\u003c/span\u003e\u003cspan address=\"10.1016/j.jaac.2009.12.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrosvenor LP, Croen LA, Lynch FL, Marafino BJ, Maye M, Penfold RB, Simon GE, Ames JL (2024) Autism diagnosis among US children and adults, 2011\u0026ndash;2022. JAMA Netw Open 7:e2442218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamanetworkopen.2024.42218\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2024.42218\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHabata K, Cheong Y, Kamiya T, Shiotsu D, Omori IM, Okazawa H, Jung M, Kosaka H (2021) Relationship between sensory characteristics and cortical thickness/volume in autism spectrum disorders. Transl Psychiatry 11:616. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41398-021-01743-7\u003c/span\u003e\u003cspan address=\"10.1038/s41398-021-01743-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe JL, Oeltzschner G, Mikkelsen M, Deronda A, Harris AD, Crocetti D, Wodka EL, Mostofsky SH, Edden RAE, Puts NAJ (2021) Region-specific elevations of glutamate\u0026thinsp;+\u0026thinsp;glutamine correlate with the sensory symptoms of autism spectrum disorders. Transl Psychiatry 11:411. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41398-021-01525-1\u003c/span\u003e\u003cspan address=\"10.1038/s41398-021-01525-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHennessey T, Andari E, Rainnie DG (2018) RDoC-based categorization of amygdala functions and its implications in autism. Neurosci Biobehav Rev 90:115\u0026ndash;129. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neubiorev.2018.04.007\u003c/span\u003e\u003cspan address=\"10.1016/j.neubiorev.2018.04.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHilton CL, Harper JD, Kueker RH, Lang AR, Abbacchi AM, Todorov A, LaVesser PD (2010) Sensory responsiveness as a predictor of social severity in children with high functioning autism spectrum disorders. J Autism Dev Disord 40:937\u0026ndash;945. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10803-010-0944-8\u003c/span\u003e\u003cspan address=\"10.1007/s10803-010-0944-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHorder J, Wilson CE, Mendez MA, Murphy DG (2014) Autistic traits and abnormal sensory experiences in adults. J Autism Dev Disord 44:1461\u0026ndash;1469. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10803-013-2012-7\u003c/span\u003e\u003cspan address=\"10.1007/s10803-013-2012-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIshitobi M, Kosaka H, Omori M, Matsumura Y (2011) Differential amygdala response to lower face in patients with autistic spectrum disorders: an fMRI study. Res Autism Spectr Disord 5:910\u0026ndash;919. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rasd.2010.10.005\u003c/span\u003e\u003cspan address=\"10.1016/j.rasd.2010.10.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJou RJ, Jackowski AP, Papademetris X, Rajeevan N, Staib LH, Volkmar FR (2011) Diffusion tensor imaging in autism spectrum disorders: preliminary evidence of abnormal neural connectivity. Aust N Z J Psychiatry 45:153\u0026ndash;162. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3109/00048674.2010.534069\u003c/span\u003e\u003cspan address=\"10.3109/00048674.2010.534069\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlawiter EC, Schmidt RE, Trinkaus K, Liang HF, Budde MD, Naismith RT, Song SK, Cross AH, Benzinger TL (2011) Radial diffusivity predicts demyelination in ex vivo multiple sclerosis spinal cords. NeuroImage 55:1454\u0026ndash;1460. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2011.01.007\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2011.01.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLord C, Rutter M, Goode S, Heemsbergen J, Jordan H, Mawhood L, Schopler E (1989) Autism diagnostic observation schedule: a standardized observation of communicative and social behavior. J Autism Dev Disord 19:185\u0026ndash;212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF02211841\u003c/span\u003e\u003cspan address=\"10.1007/BF02211841\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMadden DJ, Bennett IJ, Burzynska A, Potter GG, Chen NK, Song AW (2012) Diffusion tensor imaging of cerebral white matter integrity in cognitive aging. Biochim Biophys Acta 1822:386\u0026ndash;400. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbadis.2011.08.003\u003c/span\u003e\u003cspan address=\"10.1016/j.bbadis.2011.08.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaenner MJ, Warren Z, Williams AR, Amoakohene E, Bakian AV, Bilder DA, Durkin MS, Fitzgerald RT, Furnier SM, Hughes MM, Ladd-Acosta CM, McArthur D, Pas ET, Salinas A, Vehorn A, Williams S, Esler A, Grzybowski A, Hall-Lande J, Nguyen RHN, Pierce K, Zahorodny W, Hudson A, Hallas L, Mancilla KC, Patrick M, Shenouda J, Sidwell K, DiRienzo M, Gutierrez J, Spivey MH, Lopez M, Pettygrove S, Schwenk YD, Washington A, Shaw KA (2023) Prevalence and characteristics of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2020. MMWR Surveill Summ 72:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15585/mmwr.ss7202a1\u003c/span\u003e\u003cspan address=\"10.15585/mmwr.ss7202a1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarigliano V, Gualdi G, Servello A, Marigliano B, Volpe LD, Fioretti A, Pagliarella M, Valenti M, Masedu F, Di Biasi C, Ettorre E, Fusetti M (2014) Olfactory deficit and hippocampal volume loss for early diagnosis of Alzheimer disease: a pilot study. Alzheimer Dis Assoc Disord 28:194\u0026ndash;197. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/WAD.0b013e31827bdb9f\u003c/span\u003e\u003cspan address=\"10.1097/WAD.0b013e31827bdb9f\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMayer JL (2017) The relationship between autistic traits and atypical sensory functioning in neurotypical and ASD adults: a spectrum approach. J Autism Dev Disord 47:316\u0026ndash;327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10803-016-2948-5\u003c/span\u003e\u003cspan address=\"10.1007/s10803-016-2948-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeisner OC, Nair A, Chang SWC (2022) Amygdala connectivity and implications for social cognition and disorders. Handb Clin Neurol 187:381\u0026ndash;403. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-12-823493-8.00017-1\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-823493-8.00017-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMori K, Sakano H (2021) Olfactory circuitry and behavioral decisions. Annu Rev Physiol 83:231\u0026ndash;256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-physiol-031820-092824\u003c/span\u003e\u003cspan address=\"10.1146/annurev-physiol-031820-092824\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMori S, Zhang J (2006) Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron 51:527\u0026ndash;539. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuron.2006.08.012\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2006.08.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMosconi MW, Cody-Hazlett H, Poe MD, Gerig G, Smith RG, Piven J (2009) Longitudinal study of amygdala volume and joint attention in 2- to 4-year-old children with autism. Arch Gen Psychiatry 66:509\u0026ndash;516. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/archgenpsychiatry.2009.19\u003c/span\u003e\u003cspan address=\"10.1001/archgenpsychiatry.2009.19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhta H, Aoki YY, Itahashi T, Kanai C, Fujino J, Nakamura M, Kato N, Hashimoto RI (2020) White matter alterations in autism spectrum disorder and attention-deficit/hyperactivity disorder in relation to sensory profile. Mol Autism 11:77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13229-020-00379-6\u003c/span\u003e\u003cspan address=\"10.1186/s13229-020-00379-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePostema MC, van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Filho GB, Calderoni S, Calvo R, Daly E, Deruelle C, Duan M, Duran FLS, Durston S, Ecker C, Ehrlich S, Fair D, Fedor J, Fitzgerald J, Floris DL, Franke B, Freitag CM, Gallagher L, Glahn DC, Haar S, Hoekstra L, Jahanshad N, Jalbrzikowski M, Janssen J, King JA, Lazaro L, Luna B, McGrath J, Medland SE, Muratori F, Murphy DGM, Neufeld J, O'Hearn K, Oranje B, Parellada M, Pariente JC, Postema MC, Remnelius KL, Retico A, Rosa PGP, Rubia K, Shook D, Tammimies K, Taylor MJ, Tosetti M, Wallace GL, Zhou F, Thompson PM, Fisher SE, Buitelaar JK, Francks C (2019) Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets. Nat Commun 10:4958. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-019-13005-8\u003c/span\u003e\u003cspan address=\"10.1038/s41467-019-13005-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSah P, Faber ESL, De Armentia ML, Power J (2003) The amygdaloid complex: anatomy and physiology. Physiol Rev 83:803\u0026ndash;834. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/physrev.00002.2003\u003c/span\u003e\u003cspan address=\"10.1152/physrev.00002.2003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchumann CM, Hamstra J, Goodlin-Jones BL, Lotspeich LJ, Kwon H, Buonocore MH, Lammers CR, Reiss AL, Amaral DG (2004) The amygdala is enlarged in children but not adolescents with autism; the hippocampus is enlarged at all ages. J Neurosci 24:6392\u0026ndash;6401. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1523/JNEUROSCI.1297-04.2004\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.1297-04.2004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSha Z, van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Bernhardt B, Bolte S, Busatto GF, Calderoni S, Calvo R, Daly E, Deruelle C, Duan M, Duran FLS, Durston S, Ecker C, Ehrlich S, Fair D, Fedor J, Fitzgerald J, Floris DL, Franke B, Freitag CM, Gallagher L, Glahn DC, Haar S, Hoekstra L, Jahanshad N, Jalbrzikowski M, Janssen J, King JA, Lazaro L, Luna B, McGrath J, Medland SE, Muratori F, Murphy DGM, Neufeld J, O'Hearn K, Oranje B, Parellada M, Pariente JC, Postema MC, Remnelius KL, Retico A, Rosa PGP, Rubia K, Shook D, Tammimies K, Taylor MJ, Tosetti M, Wallace GL, Zhou F, Thompson PM, Fisher SE, Buitelaar JK, Francks C (2022) Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium. Mol Psychiatry 27:2114\u0026ndash;2125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41380-022-01452-7\u003c/span\u003e\u003cspan address=\"10.1038/s41380-022-01452-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmitka M, Puschmann S, Buschhueter D, Gerber JC, Witt M, Honeycutt N, Abolmaali N, Hummel T (2012) Is there a correlation between hippocampus and amygdala volume and olfactory function in healthy subjects? NeuroImage 59:1052\u0026ndash;1057. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2011.09.024\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2011.09.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSparks BF, Friedman SD, Shaw DW, Aylward EH, Echelard D, Artru AA, Maravilla KR, Giedd JN, Munson J, Dawson G, Dager SR (2002) Brain structural abnormalities in young children with autism spectrum disorder. Neurology 59: 184\u0026ndash;192. https://doi.org/\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSurgent O, Riaz A, Ausderau KK, Adluru N, Kirk GR, Guerrero-Gonzalez J, Skaletski EC, Kecskemeti SR, Iii DCD, Weismer SE, Alexander AL, Travers BG (2022) Brainstem white matter microstructure is associated with hyporesponsiveness and overall sensory features in autistic children. Mol Autism 13:48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13229-022-00524-3\u003c/span\u003e\u003cspan address=\"10.1186/s13229-022-00524-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalker L, Gozzi M, Lenroot R, Thurm A, Behseta B, Swedo S, Pierpaoli C (2012) Diffusion tensor imaging in young children with autism: biological effects and potential confounds. Biol Psychiatry 72:1043\u0026ndash;1051. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biopsych.2012.08.001\u003c/span\u003e\u003cspan address=\"10.1016/j.biopsych.2012.08.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWing L, Leekam SR, Libby SJ, Gould J, Larcombe M (2002) The Diagnostic Interview for Social and Communication Disorders: background, inter-rater reliability and clinical use. J Child Psychol Psychiatry 43:307\u0026ndash;325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1469-7610.00023\u003c/span\u003e\u003cspan address=\"10.1111/1469-7610.00023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYeh F-C, Verstynen TD, Wang Y, Fern\u0026aacute;ndez-Miranda JC, Tseng WYI (2013) Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS ONE 8:e80713. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0080713\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0080713\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoldemir B, Acar B, Firat Z, Kili\u0026ccedil;kesmez \u0026Ouml; (2012) SMT: a reliability based interactive DTI tractography algorithm. IEEE Trans Med Imaging 31:1929\u0026ndash;1940. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/TMI.2012.2210052\u003c/span\u003e\u003cspan address=\"10.1109/TMI.2012.2210052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoo S, Jang Y, Hong S-J, Park H, Valk SL, Bernhardt BC, Park BY (2024) Whole-brain structural connectome asymmetry in autism. NeuroImage 288:120534. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2024.120534\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2024.120534\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu Z, Fang X, Chen H, Zhu X, Zhang L, Zhai X, Cui Z, Gao Q (2018) Alterations in volumes and MRI features of amygdala in Chinese autistic preschoolers associated with social and behavioral deficits. Brain Imaging Behav 12:1814\u0026ndash;1821. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11682-018-9853-9\u003c/span\u003e\u003cspan address=\"10.1007/s11682-018-9853-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"journal-of-neural-transmission","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Journal of Neural Transmission","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"autism spectrum disorder, sensory traits, white matter, amygdala, hippocampus","lastPublishedDoi":"10.21203/rs.3.rs-8023186/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8023186/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAim\u003c/h2\u003e\u003cp\u003eAutism spectrum disorder (ASD) is characterized by atypical sensory traits. Understanding whether these traits of individuals with ASD are associated with white matter fiber tracts connected to these subcortical structures may be important; however, this relationship remains unexplored. Therefore, we examined associations between sensory traits and abnormalities in white matter fiber tracts in individuals with ASD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eOverall, 40 adults with ASD and 83 typically developing (TD) adults were included, and all participants were aged over 18 years. The participants completed the Adolescent/Adult Sensory Profile (AASP), a self-reported questionnaire, and underwent diffusion tensor imaging. We computed four diffusion tensor metrics (fractional anisotropy [FA], mean diffusivity, axonal diffusivity, and radial diffusivity [RD]) for the bilateral amygdala-connected white matter (AWM) and hippocampus-connected white matter (HWM).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe ASD group exhibited lower FA and higher RD in both the AWM and HWM than the TD group. Significant group differences were observed in correlations between sensation seeking and right white matter microstructures: for the FA of the AWM, the ASD group exhibited a significant positive correlation, whereas the TD group tended toward a negative correlation, and for the FA of the HWM, the ASD group tended toward a positive correlation, whereas the TD group exhibited a significant negative correlation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe abnormal sensory traits in ASD pathogenesis may result from demyelination or axonal damage in the AWM and HWM, indicating that the right hemispheric dominance of ASD is present in the white matter structures associated with abnormal sensory processing.\u003c/p\u003e","manuscriptTitle":"Atypical sensory traits and changes in white matter microstructures connected to the amygdala and hippocampus of the autistic brain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 17:21:42","doi":"10.21203/rs.3.rs-8023186/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-06T13:37:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T11:57:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T16:23:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34389194786072141431677851674847636005","date":"2025-11-16T15:53:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69590336731166792275917222396594288752","date":"2025-11-06T14:09:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-06T13:20:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-05T07:43:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-04T19:55:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neural Transmission","date":"2025-11-04T01:10:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"journal-of-neural-transmission","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Journal of Neural Transmission","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"75fe2681-83f7-4bfb-93b6-fc898fd241d6","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-22T05:53:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 17:21:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8023186","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8023186","identity":"rs-8023186","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}