Skip to main content

Brainstem white matter microstructure is associated with hyporesponsiveness and overall sensory features in autistic children

Abstract

Background

Elevated or reduced responses to sensory stimuli, known as sensory features, are common in autistic individuals and often impact quality of life. Little is known about the neurobiological basis of sensory features in autistic children. However, the brainstem may offer critical insights as it has been associated with both basic sensory processing and core features of autism.

Methods

Diffusion-weighted imaging (DWI) and parent-report of sensory features were acquired from 133 children (61 autistic children with and 72 non-autistic children, 6–11 years-old). Leveraging novel DWI processing techniques, we investigated the relationship between sensory features and white matter microstructure properties (free-water-elimination-corrected fractional anisotropy [FA] and mean diffusivity [MD]) in precisely delineated brainstem white matter tracts. Follow-up analyses assessed relationships between microstructure and sensory response patterns/modalities and analyzed whole brain white matter using voxel-based analysis.

Results

Results revealed distinct relationships between brainstem microstructure and sensory features in autistic children compared to non-autistic children. In autistic children, more prominent sensory features were generally associated with lower MD. Further, in autistic children, sensory hyporesponsiveness and tactile responsivity were strongly associated with white matter microstructure in nearly all brainstem tracts. Follow-up voxel-based analyses confirmed that these relationships were more prominent in the brainstem/cerebellum, with additional sensory-brain findings in the autistic group in the white matter of the primary motor and somatosensory cortices, the occipital lobe, the inferior parietal lobe, and the thalamic projections.

Limitations

All participants communicated via spoken language and acclimated to the sensory environment of an MRI session, which should be considered when assessing the generalizability of this work to the whole of the autism spectrum.

Conclusions

These findings suggest unique brainstem white matter contributions to sensory features in autistic children compared to non-autistic children. The brainstem correlates of sensory features underscore the potential reflex-like nature of behavioral responses to sensory stimuli in autism and have implications for how we conceptualize and address sensory features in autistic populations.

Background

Approximately 90% of autistic children [1, 2] and 5–15% of non-autistic children exhibit elevated sensory features, characterized by enhanced or reduced reactivity to or interest in sensory stimuli [3,4,5]. (Please note, identity-first language is used in alignment with the majority preference of the autistic community [6, 7].) These elevated sensory features are associated with decreased motor performance [8], increased core autism traits [9, 10], increased anxiety [11, 12], decreased adaptive behaviors [13], and decreased quality of life [14, 15]. Currently, the neurobiological mechanisms of sensory features in autistic and non-autistic populations are unclear [16]. However, the underexplored brainstem may offer critical insights into the neural basis of sensory features due to its established role in sensory processing [17, 18] and associations with core autism traits [19, 20]. Therefore, the purpose of the present study was to examine the relationship between brainstem microstructure and sensory features in autistic and non-autistic children.

The brainstem is an early developing and highly conserved [21] structure that is comprised of tightly intertwined white matter tracts, many of which have been linked to sensory processing. Brainstem white matter fibers serve as initial conduits of sensory information, relaying signals from primary sensory organs to nuclei within the brainstem, cerebrum, and cerebellum [17, 22,23,24]. Brainstem white matter tracts further support basic sensory information processing by transmitting sensory signals among nuclei with demonstrated roles in sensory gating [25, 26], visual salience [27], multisensory integration [28], and sensory responsivity [29, 30]. While much of this work has been done in animal models, similar associations in humans have been established between brainstem white matter and the early stages of sensory processing [26, 31,32,33]. Moreover, early developing brainstem pathways are known to subserve early-in-life auditory, visual, gustatory, olfactory processing as well as tactile-motor integration (as reviewed by [18]). However, it remains unclear how brainstem white matter is related to sensory responses in autism. Despite the brainstem’s demonstrated role in the fundamental elements of sensory processing, previous work looking at the neural contributions to sensory response patterns has largely focused on telencephalic structures as key regions of interest [34,35,36,37,38,39]. Therefore, we still do not know whether brainstem white matter contributions are limited to relaying and processing basic sensory information or extend into producing heightened or reduced sensory responses.

Evaluating sensory features and their relationships to brainstem microstructure in autistic populations is critical as evidence indicates that brainstem white matter may uniquely contribute to autism [19] and elevated sensory features are highly prevalent in the autistic population. Epidemiological, molecular, and behavioral evidence suggests that brainstem organization may be closely tied to the etiology of autism [18,19,20, 40]. Indeed, one of the earliest hypotheses regarding the neural basis of autism centered upon the brainstem’s reticular formation [41]. More recently, several articles have reviewed the evidence of the brainstem’s role in autism and have put forth theories about how the structure, function, and development of brainstem white matter tracts and gray matter nuclei may be involved in autistic traits [18,19,20]. Additionally, an exploratory analysis from Wolff and colleagues [42] linked sensory features to brainstem-cerebellar white matter, finding that infants who later received an autism spectrum diagnosis showed inverse sensory-microstructure correlations compared to infants who did not receive a diagnosis. These diagnosis-dependent neural correlates of sensory features in autism are supported by evidence suggesting the presentation of sensory features and their neurobiological bases may be unique in autism compared to non-autism and/or other psychiatric conditions [43]. For example, evidence suggests that sensory hyporesponsiveness in autistic populations may be unique in both its behavioral presentation and neural basis. Hyporesponsiveness is more prevalent in autistic individuals than in other populations [36, 44,45,46,47] and has been associated with altered patterns of neural activity in infants with and without a predisposition for autism [48]. This evidence coupled with the distinct contributions of the brainstem to autism traits [19] highlights the need for a direct comparison of brainstem neural correlates in autistic and non-autistic youth. This direct comparison will determine not only how the brainstem is involved in sensory processing but also if its involvement is similar or distinct in autistic and non-autistic populations. Previously, methodological constraints limited the feasibility of collecting high resolution diffusion-weighted imaging (DWI) data (traditionally a time consuming and sensory intensive process) in pediatric populations with sensory features. However, recent advancements in our DWI protocol have allowed us to overcome these limitations, providing high apparent resolution and improved gray-white matter contrast without requiring long acquisition times [49]. These innovations offer the opportunity to investigate white matter microstructure of brainstem tracts in children with elevated sensory features with a higher degree of precision than ever before.

Using our optimized DWI, the aim of this study was to determine the extent to which brainstem white matter tracts are associated with individual differences in the sensory features of autistic and non-autistic children (6–11 years of age). Even though the brainstem begins to form in the first trimester of pregnancy [50], there is evidence that the brainstem tracts subserving vision undergo activity-dependent myelination based on sensory stimulation in the first year of life [51]. Auditory, olfactory, tactile, and gustatory brainstem tracts are likely to similarly undergo post-natal tuning based on sensorimotor experiences [18, 20]. This experience-based tuning may lead to cascading white matter differences in school-aged autistic children and beyond. Therefore, this age range was selected for feasibility of collecting the MRI parameters and with the idea that differences in early-maturing brainstem circuits may continue to subserve the sensory features commonly reported in autistic children [18]. Based on literature from animal models and humans involving brainstem white matter in basic sensory processing, we hypothesized that brainstem microstructure as measured by diffusion MRI (free-water-eliminated fractional anisotropy [FWE-FA] and mean diffusivity [FWE-MD]) would be related to the presence of elevated sensory features in both autistic and non-autistic children. While other DTI measures are possible to calculate, we chose FWE-FA and FWE-MD based on FA and MD findings of previous literature [42] and evidence of reduced artifacts in brain areas surrounded by cerebrospinal fluid (CSF) when using FWE [52, 53]. While FWE-DTI measures do not directly measure microstructure, they are commonly used as markers sensitive to changes in white matter microstructural features, including axonal morphology and myelination, axon bundle density and fiber orientation distribution, and other intra- and extra- cellular processes. Based on the evidence suggesting unique brainstem involvement in autism [19] and a diagnosis-dependent relationship between sensory features and white matter microstructure [42], we further examined the possibility that sensory-brainstem relationships would be unique within each diagnostic group. To test these hypotheses, we performed region of interest (ROI) linear regression predicting FWE-FA and FWE-MD of brainstem tracts from sensory caregiver report and diagnostic group status, while controlling for key variables such as age, sex, and head motion during the DWI scan. A significant main effect for sensory features would support that the FWE-FA and FWE-MD of the brainstem white matter tracts are significantly associated with caregiver-reported sensory features across groups. A significant group-by-sensory interaction would support our hypothesis of unique brainstem-sensory relations in autistic compared to non-autistic children. Follow-up analyses explored these effects within the autistic group as a function of sensory pattern and sensory modality. To contextualize brainstem findings, follow-up, whole-brain voxel-based correlates of sensory features were assessed across both groups and within just the autistic group.

Methods

Participants

156 participants were enrolled and participated in this study. However, as can be seen in Additional file 1: Fig. 1, due to a scanner upgrade malfunction that affected scans (n = 8), incomplete DWI data (n = 10), incomplete T1-weighted [T1w] structural data (n = 1), DWI scans not meeting our quality control standards (n = 3), and an extreme outlier in the SEQ behavioral data (n = 1), the final sample was 133 participants (ages 6.0–10.9, 37 female), with 61 in the autistic diagnostic group (6.14–10.90 years, 12 female) and 72 in the non-autistic group (6.02–10.97 years, 25 female). A very conservative a priori power analysis was conducted to determine our ability to detect voxel-based findings (Additional File 1). Due to COVID-19, the autistic group's sample size was below the intended sample size of the power analysis. All participants were required to communicate verbally and have an IQ score greater than 60 using the Wechsler Abbreviated Scale of Intelligence, 2nd Edition (WASI-2) [54] or greater than 70 on the Kaufman Brief Intelligence Test-Second Edition (KBIT-2) [55]. None of the participants had a previous diagnosis of tuberous sclerosis, Down syndrome, fragile X, hypoxia–ischemia, notable and uncorrected hearing or vision loss, or a history of severe head injury. The institutional review board at the University of Wisconsin–Madison approved all procedures. In each case, the child participant provided assent and a parent or guardian provided informed consent.

To confirm previous community diagnoses of autism spectrum disorder (ASD), participants in the autistic group were comprehensively evaluated for ASD by meeting cutoffs on either (1) the Autism Diagnostic Observation Schedule, 2nd edition (ADOS-2; cutoff = 8) [56] and the Autism Diagnostic Interview-Revised (ADI-R) [57] or (2) the Social Responsiveness Scale, second edition (SRS-2; cutoff = 60) [58] and the Social Communication Questionnaire (SCQ; cutoff = 15) [59].

Non-autistic participants were required to score less than 8 on the SCQ [59]. Additionally, participants were excluded from the non-autistic group if they had a previous diagnosis of any neurodevelopmental disorder including attention deficit/hyperactivity disorder, bipolar disorder, major depressive disorder, or if they had a first-degree relative with ASD. Table 1 contains group contrasts in the demographic information.

Table 1 Demographic information for participant sample

Sensory experience questionnaire version 3.0 (SEQ 3.0)

The SEQ 3.0 is a 105-item caregiver report instrument developed to characterize sensory features in both non-autistic individuals and in those with certain developmental disorders, including ASD. The assessment is designed for use in children aged 2–12 years-old and measures sensory responses to experiences using a 5-point scale, with higher scores representing more prominent sensory features [61]. The questionnaire contains 97 items which specifically measure the occurrence of behaviors across sensory response patterns (hyperresponsiveness, hyporesponsiveness, enhanced perception, sensory seeking) and across sensory modalities (visual, auditory, gustatory, tactile, and vestibular). By combining the results from items, a composite score of overall sensory features can be calculated. A subset of these SEQ 3.0 data have been previously used to examine associations among sensory features, motor skills, and IQ [8].

Brain imaging acquisition and processing

Imaging data were acquired on a 3T GE Discovery MR750 scanner (Waukesha, WI) in the Waisman Center at the University of Wisconsin–Madison. Diffusion-weighted images (DWIs) were obtained using a 32-channel phased array head coil (Nova Medical, Wilmington, MA) and a multi-shell spin-echo echo-planar imaging (EPI) pulse sequence (9 directions at b = 350 s/mm2, 18 directions at 800 s/mm2, and 36 directions at b = 2000s/mm2, and 6 non-diffusion-weighted [b = 0 s/mm2] volumes; TR/TE = 9000/74.4 ms; FOV = 230 mm × 230 mm, in-plane resolution 2.4 mm × 2.4 mm, interpolated to 1.8 mm × 1.8 mm; 76 overlapping slices, slice thickness 3.6 mm, spacing between slice centers 1.8 mm – to achieve 1.8 mm isotropic sampling). An additional 6 non-diffusion-weighted volumes with reverse phase-encoded direction were collected for use in correcting susceptibility-induced artifacts [62], which may be severe around the brainstem in EPI acquisitions and affect interpretability of data in these regions. The approximate duration of the DWI scan was 10 min. Whole-brain structural imaging was done using a 3D T1w MPnRAGE sequence with 1 mm isotropic resolution (approximately 8 min). The MPnRAGE pulse sequence is a novel imaging method that combines magnetization preparation using inversion recovery with a rapid 3D radial k-space readout [63]. The MPnRAGE reconstruction enables retrospective head-motion correction, tissue-specific segmentation, and reliable quantitative T1 mapping [64].

DWI data were processed to minimize noise [65, 66], Gibbs ringing [67], artifacts caused by motion, eddy current [68,69,70], EPI distortion [62], as well as B0 field inhomogeneities [71, 72]. To enhance the apparent spatial resolution, DWI data were then processed in accordance to TiDi-Fused protocol [49]. The mean DWI b = 0 volume was spatially aligned to the T1 weighted image derived from the MPnRAGE using rigid transformations (6 degrees of freedom) implemented with the boundary-based registration (BBR) [73] routine in the FreeSurfer image analysis suite [74]. The estimated transformation that resulted from the optimal alignment was then applied to the entire DWI series with cubic B-spline interpolation up-sampled to the T1w resolution (1 mm isotropic) using ANTs [75]. The rotational component of the rigid body transformation was then applied to the DWI encoding directions.

Free water eliminated (FWE) diffusion tensor imaging (DTI), which has been shown to produce more complete, anatomically plausible tract reconstructions in regions with suspected CSF partial volume artifacts [76], was used during diffusion tensor estimation. FWE fractional anisotropy (FWE-FA) and FWE mean diffusivity (FWE-MD) maps were generated from the resulting tensor maps [74, 77]. FWE-DTI metrics are sensitive to changes in in vivo tissue microstructural properties, particularly the density and organization of axons in white matter. Increased FWE-FA and decreased FWE-MD are commonly associated with more dense and more organized white matter tracts. The average relative voxel displacement between volumes acquired during the DWI scan was estimated using eddy_qc and utilized to quantify participant head motion [78]. All FWE-DTI images passed a visual inspection for processing artifacts prior to statistical analyses.

Statistical analysis

Brainstem white matter region of interest analysis

Since the regions were based on probabilistic tractography visitation counts (normalized to values between 0 and 1 at each voxel), we computed summary diffusion measures in each bundle using the weighted median. Weighted median [79] values of the FWE-DTI measures were extracted from 23 bilateral brainstem fiber bundles (Additional file 1: Fig. 2) defined on a probabilistic brainstem connectome atlas [80]. Using ‘antsRegistration’ [75] with affine and diffeomorphic transformations, tracts were warped to a T1w study specific template that was aligned with the MNI152 T1w image. The tracts were then mapped to subject specific native space by applying the inverse transformations estimated during the template construction. The tract representations were inspected visually to ensure a faithful representation of the spatial pattern and anatomical placement were obtained. Inspection of the tract representations showed that the superior cerebellar peduncle-cerebellorubral (SCPCR) tract was fully encompassed by the superior cerebellar peduncle-cerebellothalamic (SCPCT) tract. Therefore, the final analyses included 11 regions of interest (ROIs; 10 bilateral bundles and the middle cerebellar peduncle [MCP] bundle) and excluded the SCPCR. Bundles were warped to the native subject space, with cubic interpolation, using the transforms generated during population template estimation. While some evidence from functional neuroimaging studies suggests cerebellar asymmetry and asymmetry in brainstem based auditory processing [81, 82], there is little current evidence to suggest global structural asymmetry in brainstem or cerebellar white matter tracts [83]. Therefore, primary analyses utilized the bilateral average of the weighted median from each bundle. However, follow-up analyses were conducted to evaluate potential laterality effects. All bundles were quality assessed by outlier analysis of the summary measures. Tracts that demonstrated significant relationships between FWE-DTI and sensory features after multiple comparison correction additionally passed a visual inspection performed in each DWI scan’s native space. FWE-DTI metrics did not significantly differ between the autistic and non-autistic groups in any of the white matter bundles (Additional file 1: Table 1).

Using multiple linear regression, FWE-DTI metrics (FA and MD) in each of the 11 brainstem ROIs were predicted as a function of diagnostic group (autistic vs non-autistic), overall sensory features, and their interaction, while controlling for age, sex, and head motion. Using partial Pearson correlations (controlling for age, sex, and head motion), follow-up ROI analyses were conducted within the autistic group examining FWE-DTI metrics of each bilateral bundle in relation to each sensory response pattern and modality [84]. FDR multiple comparison correction was employed across the 11 bundles for each FWE-DTI metric (FDR-adjusted p < 0.05) [85].

Follow-up analyses investigated the relationship between sensory features and brainstem white matter metrics (FA and MD) estimated using traditional tensor fitting algorithms [86] without FWE correction. Additional follow-up analyses were performed using a linear mixed effects models to examine the effects of white matter tract laterality on group-by-overall sensory feature interaction effects. These analyses predicted FWE-DTI of each tract from diagnostic group, overall sensory features, tract laterality (right vs left), each of their two-way interactions, and their three-way interaction while controlling for age, sex, and head motion and including a random effect for participant.

Follow-up voxel-based analysis with tissue-specific, smoothing-compensated (T-SPOON)

To investigate whether the diagnosis-dependent relationships found between FWE-MD and sensory features were specific to brainstem white matter or reflective of an altered whole-brain white-matter system, we performed tissue-specific, smoothing compensated (T-SPOON) method for voxel-based analysis (VBA). T-SPOON was implemented to account for common pitfalls of traditional VBA and to enhance the interpretability of VBA results [87]. T-SPOON-corrected FWE-MD maps were generated in accordance with previous work [88]. T-SPOON VBA was utilized rather than other whole brain analysis techniques, such as tract based spatial statistics (TBSS [89]), as it allows for a more accurate representation of brainstem white matter tracts. In fact, an in-house test suggested that the TBSS skeleton only represented 21% of brainstem white matter. Permutation Analysis of Linear Models (PALM) was used to perform voxel-wise statistical parametric mapping [90,91,92]. Using linear regression, FWE-MD was predicted as a function of overall sensory features, diagnosis (autistic/non-autistic), and their interaction while accounting for age, sex, and average head motion. A follow-up VBA was performed to identify areas where FWE-MD was associated with overall sensory features within the autistic group. For all VBAs, multiple comparisons correction was performed using FDR-correction (FDR adjusted p < 0.05) [85, 90].

Results

ROI brainstem results for sensory main effects and sensory-by-diagnosis interactions

We examined FWE-FA and FWE-MD in each brainstem white matter tract as a function of total sensory features and their interaction with diagnosis (autistic vs non-autistic). No significant main effects were found in models predicting FWE-MD or FWE-FA. Significant sensory features-by-diagnosis interaction effects were found for FWE-MD in the following tracts: corticospinal tract (CST), medial lemniscus (ML), lateral lemniscus (LL), parieto-occipito-temporo-pontine tract (POTPT), spinothalamic tract (STT), superior cerebellar peduncle cerebellothalamic tract (SCPCT), inferior cerebellar peduncle vestibulocerebellar tract (ICPVC), inferior cerebellar peduncle medulla-cerebellar tract (ICPMC), and middle cerebellar peduncle (MCP). Elevated total sensory features were associated with decreased FWE-MD in the autistic group and increased FWE-MD in the non-autistic group (p < 0.05, FDR-corrected) (Fig. 1, Table 2). No significant interaction effects were found for FWE-FA after FDR correction (Table 3). Follow-up analyses sought to determine if effects were lateralized to right or left brainstem white matter pathways but found no significant effects after FDR correction (Additional file 1: Table 2). Together, these findings indicate a diagnosis-dependent relationship between total sensory features and brainstem white matter microstructure, specifically FWE-MD.

Fig. 1
figure 1

Diagnosis-dependent relationships between brainstem white matter microstructure and total sensory features. Brainstem white matter tracts that exhibit total sensory feature-by-diagnosis interaction effects for the free-water-elimination mean diffusivity (FWE-MD). Analyses account for age, sex, and average head motion and apply an FDR correction for multiple comparisons. Correlations within the autistic (red circles) and non-autistic (blue triangles) groups are shown in the A corticospinal tract (CST), B medial lemniscus (ML), C lateral lemniscus (LL), D parieto-occipito-temporo-pontine tract (POTPT), E superior cerebellar peduncle cerebellothalamic tract (SCPCT), F spinothalamic tract (STT), G inferior cerebellar peduncle vestibulocerebellar tract (ICPVC), H inferior cerebellar peduncle medulla-cerebellar tract (ICPMC), and I middle cerebellar peduncle (MCP)

Table 2 Effects of total sensory features on brainstem FWE-MD in autistic and non-autistic children
Table 3 Effects of total sensory features on brainstem FWE-FA in autistic and non-autistic children

Follow-Up ROI brainstem results for sensory response patterns within the autistic group

Within the autistic group, follow-up analyses were conducted to assess the relationship between FWE-DTI measures and specific sensory response patterns (Table 4). Hyporesponsiveness was negatively associated with FWE-MD in nine of 11 brainstem tracts and was positively associated with FWE-FA in the MCP (p < 0.05, FDR-corrected) (Fig. 2). There were no significant associations for hyperresponsiveness, sensory seeking, nor enhanced perception after FDR correction (Table 4).

Table 4 Brainstem white matter regions of interest and sensory response patterns in the autistic group
Fig. 2
figure 2

Correlations between hyporesponsiveness and brainstem white matter microstructure in autistic children. Brainstem white matter tracts showed significant relationships between hyporesponsiveness and microstructural properties after accounting for age, sex, and average head motion and applying an FDR correction for multiple comparisons. Significant correlations were found with free-water-elimination mean diffusivity (FWE-MD) in the A medial lemniscus (ML), B lateral lemniscus (LL), C parieto-occipito-temporo-pontine tract (POTPT), D spinothalamic tract (STT), E superior cerebellar peduncle cerebellothalamic tract (SCPCT), F superior cerebellar peduncle spinocerebellar tract (SCPSC), G inferior cerebellar peduncle medulla-cerebellar tract (ICPMC), H inferior cerebellar peduncle vestibulocerebellar tract (ICPVC), I middle cerebellar peduncle (MCP), and J FWE-FA in the MCP

Follow-Up ROI brainstem results for sensory modalities within the autistic group

Within the autistic group, additional follow-up analyses were conducted to assess the relationship between FWE-DTI measures and sensory modalities. Tactile sensitivities were associated with FWE-MD in eight of 11 tracts and FWE-FA in the MCP (Fig. 3, Table 5). Further, visual sensitivities were associated with FWE-MD in the LL and POTPT, gustatory sensitivities were associated with FWE-FA in the MCP, and vestibular sensitivities were associated with FWE-MD in the LL. No FWE-DTI correlations were found with auditory sensitivities (Table 5).

Fig. 3
figure 3

Correlations between tactile sensitivity and brainstem white matter microstructure in autistic children. Brainstem white matter tracts showed significant relationships between tactile sensitivity and microstructural properties after accounting for age, sex, and average head motion and applying an FDR correction for multiple comparisons. Significant correlations were found with free-water-elimination mean diffusivity (FWE-MD) in the A medial lemniscus (ML), B lateral lemniscus (LL), C parieto-occipito-temporo-pontine tract (POTPT), D spinothalamic tract (STT), E superior cerebellar peduncle cerebellothalamic tract (SCPCT), F inferior cerebellar peduncle medulla-cerebellar tract (ICPMC), G inferior cerebellar peduncle vestibulocerebellar tract (ICPVC), H middle cerebellar peduncle (MCP), and I FWE-FA in the MCP

Table 5 Brainstem white matter regions of interest and sensory modalities in the autistic group

Follow-Up ROI analyses with traditional (non-FWE) FA and MD

Additional follow-up analyses investigated the relationship between sensory features and brainstem white matter metrics (FA and MD) estimated using traditional tensor fitting algorithms without FWE correction. No significant relationships were found between traditionally calculated tensor metrics and sensory features after FDR correction (Additional file Table 3).

Follow-up whole-brain VBA FWE-MD results across groups and within the autistic group

While numerous relationships between sensory features and brainstem FWE-MD were detected, it was unclear whether these relationships were specific to the brainstem or representative of a global white matter relationship. To investigate this possibility, we conducted follow-up whole brain white matter voxel-based analyses that examined FWE-MD as function of overall sensory features (main effects) and diagnostic group-by-sensory interactions (p < 0.05, FDR-corrected). There were no significant main effects (i.e., cross-diagnostic sensory relations), but there were numerous, large-sized interaction clusters in the white matter of the brainstem pons, cerebellum, occipital lobe, postcentral gyrus, putamen, thalamus, and posterior cingulum (Fig. 4A, Additional file 1: Table 3). Although brainstem and cerebellar white matter comprised only 7% of the total white matter examined, 21% of the total FWE-MD voxels (3,637 mm3) with significant diagnostic group-by-sensory features interaction effects were in the brainstem/cerebellar white matter (Fig. 4B, Additional file 1: Table 4). When brainstem and cerebrum findings were normalized for search space (i.e., the number of possible voxels that could be found to be significantly associated with sensory features within each area), we found that 19% of the brainstem was significant, whereas only 4% of the cerebrum was significant (Fig. 4C). In all cases, FWE-MD was negatively associated with sensory features in the autistic group and positively associated with sensory features in the non-autistic group. Within the autistic group, there were numerous, large-sized main-effect clusters in the white matter of the brainstem midbrain, brainstem pons, cerebellum, occipital lobe, superior longitudinal fasciculus in the inferior parietal lobe, superior frontal lobe, precentral and postcentral gyri, posterior limb of the internal capsule, posterior thalamic radiation, corpus callosum, and cingulum (Fig. 4D, Additional file 1: Table 5). Of these, 12% of voxels that showed a significant negative relationship between sensory features and FWE-MD (4,574 mm3) were found in clusters within the brainstem (Fig. 4E). When brainstem and cerebrum findings were normalized for search space, 24% of the brainstem was significant, whereas only 11% of the cerebrum was significant (Fig. 4F). Taken together, these results indicate that in autistic individuals, brainstem white matter is associated with sensory features to a greater extent than would be expected based on search space alone, making it a key area of interest in understanding sensory-brain relationships in autism.

Fig. 4
figure 4

Regions with distinct sensory-microstructure relationships from whole-brain voxel-based analyses with autistic and non-autistic children. Brainstem + cerebellar white matter (red) and cerebral white matter (blue) voxels indicating a significant total sensory feature by diagnostic group interaction effects represented A spatially, B as a total count, and C normalized for search space (the number of possible voxels that could be found to be significantly associated with sensory features within each area). Voxels indicating a significant total sensory feature man effects within the autistic group represented D spatially, E as a total count, and F normalized for search space

Discussion

This study set out to identify the relationships between sensory features and white matter microstructure of the underexplored brainstem in autistic and non-autistic children. Using a novel DWI protocol that improved the apparent resolution of the brainstem and cerebellum [93], we precisely delineated brainstem and brainstem-cerebellar white matter tracts and examined their associations with total sensory features and specific sensory responses. Consistent with our hypotheses, results revealed that the microstructural properties of brainstem white matter tracts were associated with sensory features, particularly in autistic children. Together, with previous animal literature [23,24,25,26,27,28], this finding suggests that brainstem white matter contributions are not limited to relaying and processing basic sensory information, but that they extend into producing heightened or reduced sensory responses in autistic children. A follow-up whole-brain analysis demonstrated proportionally more of the sensory-brain relationships in autism occurred in the brainstem and cerebellar white matter than what we would have expected based on the size of the search area. These brainstem/cerebellar findings were contextualized by additional brain-sensory findings in white matter areas of the visual cortex, inferior parietal cortex, primary motor and sensory cortices, and thalamic radiations, all areas known to be associated with sensorimotor processing. Further, in autistic children, sensory hyporesponsiveness and tactile sensitivities were associated with white matter microstructure in nearly all brainstem tracts. These findings and their implications are discussed below.

Our study findings suggest that the brainstem plays a role in autistic children's behavioral responses to sensory stimuli. These relationships between brainstem white matter microstructure and sensory features were diagnosis-dependent and extend previous exploratory findings [42] of inverse relationships between sensory features and brain microstructure in autistic children compared to non-autistic children. These results offer intriguing insights into the potential biology underlying microstructural development of the brainstem in autism. In both the current and previous [42] studies, lower MD in the MCP and SCP were associated with more severe sensory features in autistic children but not in non-autistic children. Yet, developmental trajectories of the MCP and SCP from previous work appear to be similar in autistic and non-autistic children, with both diagnostic groups showing similar decreases in MD with age [94]. Together, this information suggests a potentially altered mechanism for sensory responsiveness in autism that heavily depends on brainstem white matter. Specifically, while lower MD is commonly interpreted as indicative of more developed (i.e., more dense and more organized) white matter tracts, present findings suggest that lower MD of the brainstem, cerebellum, and other cerebral areas, may relate to more prevalent sensory features in autistic children. This autism-specific relationship may be indicative of increased brainstem involvement in sensory responsiveness in autistic youth. It may also suggest that higher efficiency information transfer among brainstem sensory processing nuclei can lead to more prominent sensory features in autistic youth. However, MD is an indirect measure of microstructural organization and can be influenced by multiple biological factors [95]. Therefore, further research is needed to determine the precise cytoarchitectural basis of these brainstem-based relationships, using innovative and complementary quantitative MRI strategies [96, 97] that provide additional information about cellular properties of white matter.

The moderate-sized relationships between hyporesponsiveness, defined as a reduced behavioral response to stimuli in the environment, and multiple brainstem structures have implications for how we conceptualize and support diverse sensory features in autistic children. The distinct brainstem-hyporesponsiveness relationships in the autistic compared to the non-autistic groups suggest that: 1) hyporesponsiveness in non-autistic children may be neurobiologically distinct from hyporesponsiveness in autistic children in ways that current behavioral measures may not distinguish, or 2) hyporesponsiveness in autistic and non-autistic children may be an example of multifinality, in which differing neurobiological etiologies lead to similar behavioral symptoms. In either scenario, the associations among brainstem microstructural features and hyporesponsiveness in autism underscore the reflex-like orienting of hyporesponsiveness [44] and help to recontextualize the self-reports of autistic individuals [98, 99] where behavioral responses to sensory stimuli are reported to feel outside of volitional control. Therefore, therapies that use external reward or punishment to target sensory features may be unlikely to be successful as they assume volitional control and are unlikely to target the brainstem-based neural circuitry that may underlie sensory hyporesponsiveness in autistic individuals. Previous research demonstrated that a six-week biofeedback-based training in autistic and non-autistic adolescents induced treatment-specific changes to the superior cerebellar peduncle [88], a region found to be associated with sensory features in both the present study and Wolff et al. [42] Therefore, there is preliminary evidence of brainstem microstructural changes in response to a multi-week intervention. Used in the context of sensory interventions, future studies should track brainstem changes in relation to intervention-related decreases in sensory features.

The present findings also suggest that brainstem white matter may be particularly related to tactile responsivity in autistic individuals, with eight of the 11 brainstem tracts moderately related to responses to touch. Tactile sensitivity has been commonly reported in autistic individuals [100,101,102,103], and reduced tactile responsivity at 12 months was found to be an early predictor of a later autism diagnosis [104]. Furthermore, the inferior olivary nucleus (ION) in the upper medulla aspect of the brainstem is associated with integration of tactile sensations with motor responses and has been previously found to have atypical structure in postmortem brain analysis of autistic individuals [105,106,107]. The ION receives numerous brainstem and cerebellar inputs (as reviewed in [18]) and outputs to the cerebellum via portions of the inferior cerebellar peduncle. Therefore, it is possible that the early-developing brainstem is implicated in tactile experiences of autistic school-aged children in ways that involve the ION. However, future research will be needed to confirm and further examine this relationship, particularly given that the present sensory measure cannot disentangle pain, pressure, and vibration. Fortunately, enhanced imaging of the brainstem may enable elucidation of the size, shape, and microstructural properties of specific brainstem nuclei, like the ION, in future in vivo studies of autistic children and adults.

The follow-up whole-brain analyses further contextualized the present sensory-brainstem findings, by showing that sensory features in autistic children were also related to cerebral white matter in brain areas frequently associated with sensory processing, including the occipital cortex (vision), inferior parietal cortex (audition), primary motor and somatosensory cortices (touch and proprioception), and thalamic projections (multisensory relay). One interpretation of these results is that the brainstem findings are reflective of a whole-brain sensory phenomenon, whereby decreased mean diffusivity is related to more sensory features in autistic children. However, our results also suggested that brainstem and cerebellum findings are overrepresented with respect to the size of the search space, suggesting that the brainstem and cerebellar white matter tracts may play a strong role in the sensory experiences of autistic individuals. These findings are compatible with the brainstem’s involvement in prenatal development of the cortex ([108,109,110]) and the cascading effects on the brain that prenatal brainstem differences combined with ongoing sensorimotor tuning may have [18, 20]. However, longitudinal studies, ideally from early prenatal development into the first few years of postnatal development, will be needed to determine the exact role of the brainstem and cerebellum in sensory processing and overall brain development. In all, the present findings, combined with theoretical work and studies implicating the brainstem in autism [18,19,20], suggest that the brainstem and cerebellum may be integral contributors to the sensory experiences of autistic individuals. Therefore, even though the imaging of the brainstem may require special acquisition and processing procedures [49], including free water elimination, EPI distortion correction, and careful consideration of brainstem masking, these steps are worth taking, as the brainstem and cerebellum are likely key areas to study to better understand the neurobiological basis of the autistic experience.

Limitations

The present findings should be interpreted considering study strengths and limitations. Due to COVID-19 restrictions on in-person research, our sample size in the group of autistic participants was below that which we had intended by a conservative a priori power analysis. However, the present sample size is still one of the largest in the literature. Future research will be needed to replicate these findings. While consistent with the 5–15% of non-autistic children in the general population who exhibit elevated sensory features, a notable limitation was the proportionally small number of participants in the non-autistic group with elevated sensory symptoms, which may have constrained detection of the neural correlates of sensory responsivity in the non-autistic group. Future studies contrasting a sensory processing disorder cohort with autistic individuals in this age range are warranted. Further, our measure of sensory features was limited to caregiver report. Based on evidence suggesting neurobiological relations with observed sensory measures [34], it is possible that even clearer relationships may emerge in combination with observed measures, which will be a key avenue for future research. Moreover, our analyses only analyzed one sensory pattern at a time even though sensory patterns often co-occur [111]. Future research should examine combinations of sensory patterns. Finally, all participants in this study communicated with our study team verbally and were able to acclimate to the sensory environment of an MRI session, and it is possible that children requiring higher cognitive support or sensory responsivity may have opted out of participating which should be considered when assessing the generalizability of our findings to the whole of the autism spectrum.

Conclusions

In summary, the present study evaluated the relationships between brainstem white matter microstructure and sensory features in autistic and non-autistic children. The findings revealed distinct white matter underpinnings of elevated sensory features in autistic children compared to non-autistic children that were prominent in the brainstem and suggestive of a distinct etiology of sensory features in autism. Hyporesponsiveness and tactile responsivity were associated with numerous brainstem tracts in autistic children, suggesting the early-developing and reflex-like nature of sensory orienting and tactile responses in autism. These findings are among the first to suggest that sensory features are aligned with white matter microstructure of the brainstem and support the theory of unique brainstem contributions to behavior in autistic individuals.

Availability of data and materials

A portion of these data are openly available in National Institute of Mental Health Data Archive at http://doi.org/10.15154/1523353, reference number 3088. The remaining data that support the findings of this study are available from the corresponding author, upon reasonable request.

Abbreviations

DWI:

Diffusion-weighted imaging

DTI:

Diffusion tensor imaging

FWE:

Free water eliminated

FA:

Fractional anisotropy

MD:

Mean diffusivity

WASI-2:

Wechsler abbreviated scale of intelligence, 2nd edition

KBIT-2:

Kaufman brief intelligence test-second edition

ADOS-2:

Autism diagnostic observation schedule, 2nd edition

ADI-R:

Autism diagnostic interview-revised

SCQ:

Social communication questionnaire

AVD:

Average volume displacement

SEQ 3.0:

Sensory experience questionnaire

EPI:

Echo planar imaging

SCPCR:

Superior cerebellar peduncle-cerebellorubral

SCPCT:

Superior cerebellar peduncle-cerebellothalamic

ROI:

Region of interest

MCP:

Middle cerebellar peduncle

FDR:

False discovery rate

T-SPOON:

Tissue-specific, smoothing-compensated

VBA:

Voxel-based analysis

PALM:

Permutation analysis of linear models

CST:

Corticospinal tract

ML:

Medial lemniscus

FPT:

Frontopontine tract

POTPT:

Parieto-occipito-temporo-pontine tract

ASD :

Autism spectrum disorder

References

  1. Baranek GT, David FJ, Poe MD, Stone WL. Sensory experiences questionnaire: discriminating sensory features in young children with autism developmental delays and typical development: SEQ. J Child Psychol Psychiatry. 2018;47:591–601. https://doi.org/10.1111/j.1469-7610.2005.01546.x.

    Article  Google Scholar 

  2. Green SA, Hernandez L, Tottenham N, Krasileva K, Bookheimer SY, Dapretto M. Neurobiology of sensory overresponsivity in youth with autism spectrum disorders. JAMA Psychiat. 2015;72:778. https://doi.org/10.1001/jamapsychiatry.2015.0737.

    Article  Google Scholar 

  3. Ahn RR, Miller LJ, Milberger S, McIntosh DN. Prevalence of parents’ perceptions of sensory processing disorders among kindergarten children. Am J Occup Ther. 2004;58:287–93. https://doi.org/10.5014/ajot.58.3.287.

    Article  Google Scholar 

  4. Ben-Sasson A, Carter AS, Briggs-Gowan MJ. Sensory over-responsivity in elementary school: prevalence and social-emotional correlates. J Abnormal Child Psychol. 2009;37:705–16. https://doi.org/10.1007/s10802-008-9295-8.

    Article  CAS  Google Scholar 

  5. Tomchek SD, Dunn W. Sensory processing in children with and without autism: a comparative study using the short sensory profile. Am J Occupat Ther [Internet]. 2007;61:190–200. https://doi.org/10.5014/ajot.61.2.190.

    Article  Google Scholar 

  6. Bottema-Beutel K, Kapp SK, Lester JN, Sasson NJ, Hand BN. Avoiding ableist language: suggestions for autism researchers. Autism in Adulthood. 2020. https://doi.org/10.1089/aut.2020.0014.

    Article  Google Scholar 

  7. Kenny L, Hattersley C, Molins B, Buckley C, Povey C, Pellicano E. Which terms should be used to describe autism? Perspectives from the UK autism community. Autism [Internet]. 2016;20:442–62. https://doi.org/10.1177/1362361315588200.

    Article  Google Scholar 

  8. Surgent OJ, Walczak M, Zarzycki O, Ausderau K, Travers BG. IQ and sensory symptom severity best predict motor ability in children with and without autism spectrum disorder. J Autism Dev Disord [Internet] 2020; https://doi.org/10.1007/s10803-020-04536-x

  9. Baranek GT, Carlson M, Sideris J, Kirby AV, Watson LR, Williams KL, et al. Longitudinal assessment of stability of sensory features in children with autism spectrum disorder or other developmental disabilities: stability of sensory features in ASD. Autism Res. 2019;12:100–11.

    Article  Google Scholar 

  10. Robertson AE, Simmons DR. The relationship between sensory sensitivity and autistic traits in the general population. J Autism Develop Dis. 2013;43:775–84. https://doi.org/10.1007/s10803-012-1608-7.

    Article  Google Scholar 

  11. Green SA, Ben-Sasson A, Soto TW, Carter AS. Anxiety and sensory over-responsivity in toddlers with autism spectrum disorders: bidirectional effects across time. J Autism Dev Disord [Internet]. 2012;42:1112–9. https://doi.org/10.1007/s10803-011-1361-3.

    Article  Google Scholar 

  12. Carpenter KLH, Baranek GT, Copeland WE, Compton S, Zucker N, Dawson G, et al. Sensory over-responsivity: an early risk factor for anxiety and behavioral challenges in young children. J Abnorm Child Psychol [Internet]. 2019;47:1075–88. https://doi.org/10.1007/s10802-018-0502-y.

    Article  Google Scholar 

  13. Jasmin E, Couture M, McKinley P, Reid G, Fombonne E, Gisel E. Sensori-motor and daily living skills of preschool children with autism spectrum disorders. J Autism Develop Dis [Internet]. 2009;39:231–41. https://doi.org/10.1007/s10803-008-0617-z.

    Article  Google Scholar 

  14. Ismael N, Lawson LM, Hartwell J. Relationship between sensory processing and participation in daily occupations for children with autism spectrum disorder: a systematic review of studies that used dunn’s sensory processing framework. Am J Occup Ther [Internet]. 2018;72:720. https://doi.org/10.5014/ajot.2018.024075.

    Article  Google Scholar 

  15. Dellapiazza F, Michelon C, Oreve M-J, Robel L, Schoenberger M, Chatel C, et al. The impact of atypical sensory processing on adaptive functioning and maladaptive behaviors in autism spectrum disorder during childhood: results from the ELENA cohort. J Autism Dev Disord. 2020;50:2142–52.

    Article  Google Scholar 

  16. Uljarević M, Baranek G, Vivanti G, Hedley D, Hudry K, Lane A. Heterogeneity of sensory features in autism spectrum disorder: challenges and perspectives for future research: sensory features in autism. Autism Res [Internet]. 2017;10:703–10.

    Article  Google Scholar 

  17. Ángeles Fernández-Gil M, Palacios-Bote R, Leo-Barahona M, Mora-Encinas JP. Anatomy of the brainstem: A gaze into the stem of life. seminars in ultrasound, CT and MRI [Internet]. 2010 [cited 2021 Feb 24];31:196–219. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0887217110000260

  18. Burstein O, Geva R. The brainstem-informed autism framework: early life neurobehavioral markers. Front Integr Neurosci [Internet]. 2021;15:759614. https://doi.org/10.3389/fnint.2021.759614/full.

    Article  Google Scholar 

  19. Dadalko OI, Travers BG. Evidence for brainstem contributions to autism spectrum disorders. Front Integr Neurosci. 2018;12:47. https://doi.org/10.3389/fnint.2018.00047/full.

    Article  Google Scholar 

  20. Jonathan Delafield‐Butt, Colwyn Trevarthen. On the brainstem origin of autism: disruption to movements of the primary self. Autism: the movement-sensing perspective. CRC Press/Routledge/Taylor & Francis Group; p. 119–37.

  21. Gilland E, Baker R. Evolutionary patterns of cranial nerve efferent nuclei in vertebrates. Brain Behav Evol. 2005;66:234–54.

    Article  Google Scholar 

  22. Ghazni NF, Cahill CM, Stroman PW. Tactile Sensory and pain networks in the human spinal cord and brain stem mapped by means of functional MR imaging. AJNR Am J Neuroradiol [Internet]. 2010;31:661–7. https://doi.org/10.3174/ajnr.A1909.

    Article  CAS  Google Scholar 

  23. Pierrot-Deseilligny C, Tilikete C. New insights into the upward vestibulo-oculomotor pathways in the human brainstem. Progress in Brain Research [Internet]. Elsevier; 2008. p. 509–18. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0079612308006730

  24. Párraga RG, Possatti LL, Alves RV, Ribas GC, Türe U, de Oliveira E. Microsurgical anatomy and internal architecture of the brainstem in 3D images: surgical considerations. JNS [Internet]. 2016;124:1377–95.

    Article  Google Scholar 

  25. Bickford ME Thalamic circuit diversity: modulation of the driver/modulator framework. Front Neural Circuits [Internet] 2016;https://doi.org/10.3389/fncir.2015.00086

  26. Kobayashi Y, Isa T. Sensory-motor gating and cognitive control by the brainstem cholinergic system. Neural Net. 2002;15:731–41.

    Article  Google Scholar 

  27. McFadyen J, Dolan RJ, Garrido MI. The influence of subcortical shortcuts on disordered sensory and cognitive processing. Nat Rev Neurosci [Internet]. 2020;21:264–76.

    Article  CAS  Google Scholar 

  28. Basura GJ, Koehler SD, Shore SE. Multi-sensory integration in brainstem and auditory cortex. Brain Res [Internet]. 2012;1485:95–107.

    Article  CAS  Google Scholar 

  29. Weldon DA, Best PJ. Changes in sensory responsivity in deep layer neurons of the superior colliculus of behaving rats. Behav Brain Res [Internet]. 1992;47:97–101.

    Article  CAS  Google Scholar 

  30. Ganmor E, Katz Y, Lampl I. Intensity-dependent adaptation of cortical and thalamic neurons is controlled by brainstem circuits of the sensory pathway. Neuron [Internet]. 2010;66:273–86.

    Article  CAS  Google Scholar 

  31. Jou RJ, Frazier TW, Keshavan MS, Minshew NJ, Hardan AY. A two-year longitudinal pilot MRI study of the brainstem in autism. Behav Brain Res [Internet]. 2013;251:163–7.

    Article  Google Scholar 

  32. Cauzzo S, Singh K, Stauder M, García-Gomar MG, Vanello N, Passino C, et al. Functional connectome of brainstem nuclei involved in autonomic, limbic, pain and sensory processing in living humans from 7 Tesla resting state fMRI. NeuroImage [Internet]. 2022;118925.

  33. Narayan A, Rowe MA, Palacios EM, Wren-Jarvis J, Bourla I, Gerdes M, et al. Altered cerebellar white matter in sensory processing dysfunction is associated with impaired multisensory integration and attention. Front Psychol. 2020;11: 618436.

    Article  Google Scholar 

  34. Chang Y-S, Gratiot M, Owen JP, Brandes-Aitken A, Desai SS, Hill SS, et al. White matter microstructure is associated with auditory and tactile processing in children with and without sensory processing disorder. Front Neuroanat [Internet]. 2016;https://doi.org/10.3389/fnana.2015.00169/abstract

  35. Owen JP, Marco EJ, Desai S, Fourie E, Harris J, Hill SS, et al. Abnormal white matter microstructure in children with sensory processing disorders. NeuroImage: Clinical [Internet]. 2013;2:844–53.

  36. Tavassoli T, Brandes-Aitken A, Chu R, Porter L, Schoen S, Miller LJ, et al. Sensory over-responsivity: parent report, direct assessment measures, and neural architecture. Molecul Autism [Internet]. 2019;10:4. https://doi.org/10.1186/s13229-019-0255-7.

    Article  Google Scholar 

  37. Brandes-Aitken A, Anguera JA, Chang Y-S, Demopoulos C, Owen JP, Gazzaley A, et al. White matter microstructure associations of cognitive and visuomotor control in children: a sensory processing perspective. Front Integr Neurosci [Internet]. 2019;12:65. https://doi.org/10.3389/fnint.2018.00065/full.

    Article  Google Scholar 

  38. Shiotsu D, Jung M, Habata K, Kamiya T, Omori IM, Okazawa H, et al. Elucidation of the relationship between sensory processing and white matter using diffusion tensor imaging tractography in young adults. Sci Rep [Internet]. 2021;11:12088.

    Article  CAS  Google Scholar 

  39. Ohta H, Aoki YY, Itahashi T, Kanai C, Fujino J, Nakamura M, et al. White matter alterations in autism spectrum disorder and attention-deficit/hyperactivity disorder in relation to sensory profile. Molecul Autism [Internet]. 2020;11:77. https://doi.org/10.1186/s13229-020-00379-6.

    Article  CAS  Google Scholar 

  40. Courchesne E. Brainstem, cerebellar and limbic neuroanatomical abnormalities in autism. Curr Opin Neurobiol. 1997;7:269–78.

    Article  CAS  Google Scholar 

  41. Rimland B. Infantile autism: The syndrome and its implications for a neural theory of behavior. East Norwalk, CT, US: Appleton-Century-Crofts; 1964. p. x, 282.

  42. Wolff JJ, Swanson MR, Elison JT, Gerig G, Pruett JR, Styner MA, et al. Neural circuitry at age 6 months associated with later repetitive behavior and sensory responsiveness in autism. Molecul Autism [Internet]. 2017;8:8. https://doi.org/10.1186/s13229-017-0126-z.

    Article  Google Scholar 

  43. Acevedo B, Aron E, Pospos S, Jessen D. The functional highly sensitive brain: a review of the brain circuits underlying sensory processing sensitivity and seemingly related disorders. Philosoph Trans Royal Soc B: Biol Sci [Internet]. 2018;373:20170161. https://doi.org/10.1098/rstb.2017.0161.

    Article  Google Scholar 

  44. Baranek GT, Watson LR, Boyd BA, Poe MD, David FJ, McGuire L. Hyporesponsiveness to social and nonsocial sensory stimuli in children with autism, children with developmental delays, and typically developing children. Dev Psychopathol [Internet]. 2013;25:307–20.

    Article  Google Scholar 

  45. Schoen SA. Physiological and behavioral differences in sensory processing: a comparison of children with autism spectrum disorder and sensory processing disorder. Front Integr Neurosci [Internet]. 2009;https://doi.org/10.3389/neuro.07.029.2009/abstract

  46. Hannant P, Cassidy S, Van de Weyer R, Mooncey S. Sensory and motor differences in autism spectrum conditions and developmental coordination disorder in children: a cross-syndrome study. Human Move Sci [Internet]. 2018;58:108–18.

    Article  Google Scholar 

  47. Crasta JE, Salzinger E, Lin M-H, Gavin WJ, Davies PL. Sensory processing and attention profiles among children with sensory processing disorders and autism spectrum disorders. Front Integr Neurosci. 2020;14:22. https://doi.org/10.3389/fnint.2020.00022/full.

    Article  Google Scholar 

  48. Simon DM, Damiano CR, Woynaroski TG, Ibañez LV, Murias M, Stone WL, et al. Neural correlates of sensory hyporesponsiveness in toddlers at high risk for autism spectrum disorder. J Autism Develop Dis [Internet]. 2017;47:2710–22. https://doi.org/10.1007/s10803-017-3191-4.

    Article  Google Scholar 

  49. Guerrero-Gonzalez J, Surgent O, Adluru N, Kirk GR, Dean DC III, Kecskemeti SR, et al. Improving imaging of the brainstem and cerebellum in autistic children: transformation-based high-resolution diffusion MRI (TiDi-Fused) in the human brainstem. Front Integr Neurosci [Internet]. 2022;16:804743. https://doi.org/10.3389/fnint.2022.804743/full.

    Article  Google Scholar 

  50. ten Donkelaar HJ, Cruysberg JRM, Pennings R, Lammens M. Development and Developmental Disorders of the Brain Stem. Clinical Neuroembryology [Internet] 2014; https://doi.org/10.1007/978-3-642-54687-7_7

  51. Graven SN. Early neurosensory visual development of the fetus and newborn. Clinics Perinatol. 2004;31:199–216.

    Article  Google Scholar 

  52. Hoy AR, Koay CG, Kecskemeti SR, Alexander AL. Optimization of a free water elimination two-compartment model for diffusion tensor imaging. NeuroImage [Internet]. 2014;103:323–33.

    Article  Google Scholar 

  53. Planetta PJ, Ofori E, Pasternak O, Burciu RG, Shukla P, DeSimone JC, et al. Free-water imaging in Parkinson’s disease and atypical parkinsonism. Brain [Internet]. 2016;139:495–508. https://doi.org/10.1093/brain/awv361.

    Article  Google Scholar 

  54. Wechsler D, Hsiao-pin C. Wechsler abbreviated scale of intelligence. San Antonio, TX: Pearson; 2011.

  55. Kaufman AS, Kaufman NL. Kaufman brief intelligence test KBIT 2 ; manual. 2004.

  56. Lord C, Rutter M, Risi S, Gotham K, Bishop S. Autism diagnostic observation schedule–2nd edition (ADOS-2). Los Angeles, CA: Western Psychological Corporation; 2012.

    Google Scholar 

  57. Lord C, Rutter M, Le Couteur A. Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Develop Dis [Internet]. 1994;24:659–85. https://doi.org/10.1007/BF02172145.

    Article  CAS  Google Scholar 

  58. Constantino J, Gruber C. Social responsiveness scale-second edition (SRS-2). Torrance, CA: Western Psychological Services; 2012.

  59. Rutter M, Bailey AJ, Lord C. The social communication questionnaire: manual. Western Psychol Serv; 2003.

  60. Bastiani M, Cottaar M, Fitzgibbon SP, Suri S, Alfaro-Almagro F, Sotiropoulos SN, et al. Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. Neuroimage. 2019;184:801–12.

    Article  Google Scholar 

  61. Baranek GT. Sensory experiences questionnaire version 3.0. Unpublished manuscript. 2009

  62. Andersson JLR, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage. 2003;20:870–88.

    Article  Google Scholar 

  63. Kecskemeti S, Samsonov A, Velikina J, Field AS, Turski P, Rowley H, et al. Robust motion correction strategy for structural MRI in unsedated children demonstrated with three-dimensional radial MPnRAGE. Radiology. 2018;289:509–16.

    Article  Google Scholar 

  64. Kecskemeti S, Freeman A, Travers BG, Alexander AL. FreeSurfer based cortical mapping and T1-relaxometry with MPnRAGE: test-retest reliability with and without retrospective motion correction. Neuroimage. 2021;242:118447.

    Article  Google Scholar 

  65. Veraart J, Fieremans E, Novikov DS. Diffusion MRI noise mapping using random matrix theory: diffusion MRI noise mapping. Magn Reson Med [Internet]. 2016;76:1582–93.

    Article  CAS  Google Scholar 

  66. Veraart J, Novikov DS, Christiaens D, Ades-aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. NeuroImage [Internet]. 2016;142:394–406.

    Article  Google Scholar 

  67. Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing artifact removal based on local subvoxel-shifts: gibbs-ringing artifact removal. Magn Reson Med [Internet]. 2016;76:1574–81.

    Article  Google Scholar 

  68. Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016;125:1063–78.

    Article  Google Scholar 

  69. Andersson JLR, Graham MS, Zsoldos E, Sotiropoulos SN. Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. Neuro Image [Internet]. 2016;141:556–72.

    Google Scholar 

  70. Andersson JLR, Graham MS, Drobnjak I, Zhang H, Filippini N, Bastiani M. Towards a comprehensive framework for movement and distortion correction of diffusion MR images: within volume movement. NeuroImage [Internet]. 2017;152:450–66.

    Article  Google Scholar 

  71. Tournier J-D, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, et al. MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. NeuroImage [Internet]. 2019;202:116137.

    Article  Google Scholar 

  72. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010;29:1310–20.

    Article  Google Scholar 

  73. Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. NeuroImage [Internet]. 2009;48:63–72.

    Article  Google Scholar 

  74. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. NeuroImage [Internet]. 1999;9:179–94.

    Article  CAS  Google Scholar 

  75. Avants BB, Tustison NJ, Wu J, Cook PA, Gee JC. An open source multivariate framework for n-tissue segmentation with evaluation on public data. Neuroinform. 2011;9:381–400. https://doi.org/10.1007/s12021-011-9109-y.

    Article  Google Scholar 

  76. Hoy AR, Kecskemeti SR, Alexander AL. Free water elimination diffusion tractography: a comparison with conventional and fluid-attenuated inversion recovery, diffusion tensor imaging acquisitions: FWE-DTI Tractography Comparison. J Magn Reson Imaging. 2015;42:1572–81. https://doi.org/10.1002/jmri.24925.

    Article  Google Scholar 

  77. Henriques RN, Rokem A, Garyfallidis E, St-Jean S, Peterson ET, Correia MM. [Re] Optimization of a free water elimination two-compartment model for diffusion tensor imaging [Internet]. Neuroscience; 2017; https://doi.org/10.1101/108795

  78. Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage [Internet]. 2016;125:1063–78.

    Article  Google Scholar 

  79. Cormen TH, Leiserson CE, Rivest RL. Introduction to algorithms. Massachusetts Institute of Technology: MIT Press; 1989.

    Google Scholar 

  80. Tang Y, Sun W, Toga AW, Ringman JM, Shi Y. A probabilistic atlas of human brainstem pathways based on connectome imaging data. Neuroimage. 2018;169:227–39.

    Article  Google Scholar 

  81. Wang D, Buckner RL, Liu H. Cerebellar asymmetry and its relation to cerebral asymmetry estimated by intrinsic functional connectivity. J Neurophys [Internet]. 2013;109:46–57. https://doi.org/10.1152/jn.00598.2012.

    Article  CAS  Google Scholar 

  82. Cardinale RC, Shih P, Fishman I, Ford LM, Müller R-A. Pervasive rightward asymmetry shifts of functional networks in autism spectrum disorder. JAMA Psychiatry [Internet]. 2013;70:975. https://doi.org/10.1001/jamapsychiatry.2013.382.

    Article  Google Scholar 

  83. Kavaklioglu T, Guadalupe T, Zwiers M, Marquand AF, Onnink M, Shumskaya E, et al. Structural asymmetries of the human cerebellum in relation to cerebral cortical asymmetries and handedness. Brain Struct Funct [Internet]. 2017;222:1611–23. https://doi.org/10.1007/s00429-016-1295-9.

    Article  Google Scholar 

  84. Kim S. ppcor: Partial and semi-partial (Part) correlation [Internet]. 2015. Available from: https://CRAN.R-project.org/package=ppcor

  85. Yekutieli D, Benjamini Y. Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics. J Stat Plann Inference [Internet]. 1999;82:171–96.

    Article  Google Scholar 

  86. Chung S, Lu Y, Henry RG. Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters. NeuroImage [Internet]. 2006;33:531–41.

    Article  Google Scholar 

  87. Lee JE, Chung MK, Lazar M, DuBray MB, Kim J, Bigler ED, et al. A study of diffusion tensor imaging by tissue-specific, smoothing-compensated voxel-based analysis. Neuroimage. 2009;44:870–83.

    Article  Google Scholar 

  88. Surgent O, Dean DC, Alexander AL, Dadalko OI, Guerrero-Gonzalez J, Taylor D, et al. Neurobiological and behavioural outcomes of biofeedback-based training in autism: a randomized controlled trial. Brain Commun [Internet]. 2021;https://doi.org/10.1093/braincomms/fcab112/6286947

  89. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage [Internet]. 2006;31:1487–505.

    Article  Google Scholar 

  90. Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. NeuroImage [Internet]. 2014;92:381–97.

    Article  Google Scholar 

  91. Winkler AM, Ridgway GR, Douaud G, Nichols TE, Smith SM. Faster permutation inference in brain imaging. NeuroImage [Internet]. 2016;141:502–16.

    Article  Google Scholar 

  92. Alberton BAV, Nichols TE, Gamba HR, Winkler AM. Multiple testing correction over contrasts for brain imaging. NeuroImage [Internet]. 2020;216:116760.

    Article  Google Scholar 

  93. Guerreo-Gonzalez J, Surgent OJ, Adluru N, Kirk G, Dean DC, Kecskemeti SR, et al. Improving imaging of the brainstem and cerebellum in autistic children: Transformation-based high-resolution diffusion MRI (TiDi-Fused) in the human brainstem. Frontiers in Integrative Neuroscience. in presss

  94. Andrews DS, Lee JK, Harvey DJ, Waizbard-Bartov E, Solomon M, Rogers SJ, et al. A longitudinal study of white matter development in relation to changes in autism severity across early childhood. Biol Psychiatry [Internet]. 2021;89:424–32.

    Article  Google Scholar 

  95. Tardif CL, Gauthier CJ, Steele CJ, Bazin P-L, Schäfer A, Schaefer A, et al. Advanced MRI techniques to improve our understanding of experience-induced neuroplasticity. NeuroImage [Internet]. 2016;131:55–72.

    Article  Google Scholar 

  96. Alexander DC, Dyrby TB, Nilsson M, Zhang H. Imaging brain microstructure with diffusion MRI: practicality and applications. NMR Biomed. 2019;32: e3841.

    Article  Google Scholar 

  97. Deoni SCL. Quantitative relaxometry of the brain. Top Magn Reson Imaging. 2010;21:101–13.

    Article  Google Scholar 

  98. Raymaker DM, Teo AR, Steckler NA, Lentz B, Scharer M, Delos Santos A, et al. “Having all of your internal resources exhausted beyond measure and being left with no clean-up crew”: defining autistic burnout. Autism Adulthood [Internet]. 2020;2:132–43. https://doi.org/10.1089/aut.2019.0079.

    Article  Google Scholar 

  99. Gray S, Kirby AV, Graham Holmes L. Autistic narratives of sensory features, sexuality, and relationships. Autism in Adulthood [Internet]. 2021 ;https://doi.org/10.1089/aut.2020.0049

  100. Foss-Feig JH, Heacock JL, Cascio CJ. Tactile responsiveness patterns and their association with core features in autism spectrum disorders. Res Autism Spect Dis [Internet]. 2012;6:337–44.

    Article  Google Scholar 

  101. Cascio CJ, Moana-Filho EJ, Guest S, Nebel MB, Weisner J, Baranek GT, et al. Perceptual and neural response to affective tactile texture stimulation in adults with autism spectrum disorders: neurobehavioral response to textures in ASD. Autism Res [Internet]. 2012;5:231–44.

    Article  Google Scholar 

  102. Foss-Feig JH, Heacock JL, Cascio CJ. Tactile responsiveness patterns and their association with core features in autism spectrum disorders. Res Autism Spect Dis [Internet]. 2012;6:337–44.

    Article  Google Scholar 

  103. Cascio C, McGlone F, Folger S, Tannan V, Baranek G, Pelphrey KA, et al. Tactile perception in adults with autism: a multidimensional psychophysical study. J Autism Dev Disord [Internet]. 2008;38:127–37. https://doi.org/10.1007/s10803-007-0370-8.

    Article  Google Scholar 

  104. Baranek GT. Autism during infancy: a retrospective video analysis of sensory-motor and social behaviors at 9–12 months of age. J Autism Develop Dis [Internet]. 1999;29:213–24. https://doi.org/10.1023/A:1023080005650.

    Article  CAS  Google Scholar 

  105. Rodier PM, Ingram JL, Tisdale B, Nelson S, Romano J. Embryological origin for autism: developmental anomalies of the cranial nerve motor nuclei. J Comp Neurol [Internet]. 1996;370:247–61.

    Article  CAS  Google Scholar 

  106. Kemper TL, Bauman ML. Neuropathology of infantile autism. Mol Psychiatry [Internet]. 2002;7:S12–3.

    Article  Google Scholar 

  107. Bailey A. A clinicopathological study of autism. Brain [Internet]. 1998;121:889–905. https://doi.org/10.1093/brain/121.5.889.

    Article  Google Scholar 

  108. Stiles J, Jernigan TL. The basics of brain development. Neuropsychol Rev [Internet]. 2010;20:327–48. https://doi.org/10.1007/s11065-010-9148-4.

    Article  Google Scholar 

  109. Rodier PM. Converging evidence for brain stem injury in autism. Dev Psychopathol [Internet]. 2002;14:537–57.

    Article  Google Scholar 

  110. Inui T, Kumagaya S, Myowa-Yamakoshi M. Neurodevelopmental hypothesis about the etiology of autism spectrum disorders. Front Hum Neurosci [Internet]. 2017;11:354. https://doi.org/10.3389/fnhum.2017.00354/full.

    Article  Google Scholar 

  111. Ausderau KK, Furlong M, Sideris J, Bulluck J, Little LM, Watson LR, et al. Sensory subtypes in children with autism spectrum disorder: latent profile transition analysis using a national survey of sensory features. J Child Psychol Psychiatr [Internet]. 2014;55:935–44.

    Article  Google Scholar 

Download references

Acknowledgements

We sincerely thank all the families who spent their time participating in this study. We thank all the team members of Motor & Brain Development Lab for their incredible work on this project.

Funding

This work was supported by the Hartwell Foundation’s Individual Biomedical Award [to BGT] and the National Institutes of Health [P50 HD105353 and U54 HD090256 to the Waisman Center, R01 HD094715 to BGT, KA, ALA, and SEW, T32 NS105602 to University of Wisconsin Neuroscience Training Program for support of OS, and T32 CA009206 to the University of Wisconsin Radiological Sciences Training Program for support of JG-G]. NA was partially supported by NIH grants R01 NS111022, R01 NS117568, P01 AI132132, R01 AI138647, and R01 AG037639. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Child Health & Development or the National Institutes of Health.

Author information

Authors and Affiliations

Authors

Contributions

OS, AR, KA, NA, GRK, JG-G, SK, ES, DD, SEW, ALA, and BGT contributed to the conception and design of the study. OS, NA, GRK, JG-G, SK, DD, and ALA made contributions to image processing. OS and AR performed statistical analysis. OS wrote the first draft of the manuscript. NA, JG-G, and BGT wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Brittany G. Travers.

Ethics declarations

Ethics approval and consent to participate

The institutional review board at the University of Wisconsin–Madison approved all procedures. In each case, the child participant provided assent and a parent or guardian provided informed consent.

Consent for publication

Not applicable.

Competing interests

ALA is part owner of ImgGyd, LLC and inseRT MRI, Inc. (also listed as TherVoyant). While both companies are involved in developing MRI-based surgery techniques, neither are associated with any current areas of his research, including the present publication. All other authors report no biomedical financial interests of potential conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1.

Supplementary information and data analyses.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Surgent, O., Riaz, A., Ausderau, K.K. et al. Brainstem white matter microstructure is associated with hyporesponsiveness and overall sensory features in autistic children. Molecular Autism 13, 48 (2022). https://doi.org/10.1186/s13229-022-00524-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13229-022-00524-3

Keywords

  • Brainstem
  • Sensory features
  • DTI
  • Autism
  • White matter
  • Voxel-based analysis