Edited by:
Reviewed by:
*Correspondence:
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Sensory processing disorders (SPDs) affect up to 16% of school-aged children, and contribute to cognitive and behavioral deficits impacting affected individuals and their families. While sensory processing differences are now widely recognized in children with autism, children with sensory-based dysfunction who do not meet autism criteria based on social communication deficits remain virtually unstudied. In a previous pilot diffusion tensor imaging (DTI) study, we demonstrated that boys with SPD have altered white matter microstructure primarily affecting the posterior cerebral tracts, which subserve sensory processing and integration. This disrupted microstructural integrity, measured as reduced white matter fractional anisotropy (FA), correlated with parent report measures of atypical sensory behavior. In this present study, we investigate white matter microstructure as it relates to tactile and auditory function in depth with a larger, mixed-gender cohort of children 8–12 years of age. We continue to find robust alterations of posterior white matter microstructure in children with SPD relative to typically developing children (TDC), along with more spatially distributed alterations. We find strong correlations of FA with both parent report and direct measures of tactile and auditory processing across children, with the direct assessment measures of tactile and auditory processing showing a stronger and more continuous mapping to the underlying white matter integrity than the corresponding parent report measures. Based on these findings of microstructure as a neural correlate of sensory processing ability, diffusion MRI merits further investigation as a tool to find biomarkers for diagnosis, prognosis and treatment response in children with SPD. To our knowledge, this work is the first to demonstrate associations of directly measured tactile and non-linguistic auditory function with white matter microstructural integrity – not just in children with SPD, but also in TDC.
Hypo-and/or hyper responsiveness to sensory stimulation is estimated to occur in 5–16% of children within the general population, and 40–80% of children with neurodevelopmental disorders (
The present literature on SPD primarily utilizes parent/caregiver report measures that describe sensory-related behaviors and physiological measures that provide information about arousal and sensory reactivity. Recently, our group has published two studies using diffusion tensor imaging (DTI) to better define the neural correlates of these sensory processing deficits. Our first study took a whole-brain, data-driven approach to demonstrate decreased fractional anisotropy (FA) and increased mean diffusivity (MD) and radial diffusivity (RD), reflecting reduced microstructural integrity, in the posterior white matter tracts of 16 boys with SPD compared to 24 neurotypically developing boys (
As our previous imaging findings were limited to a small cohort of affected boys, we seek to investigate these results in a larger mixed-gender cohort sample. We hypothesize that boys and girls with SPD will show impaired white matter microstructural integrity, with a posterior predominance, relative to typically developing children (TDC). We further hypothesize that this microstructural integrity will correlate with parent report as well as with direct measurements of sensory processing, but that the direct measurements will show stronger correlation with the underlying microstructure.
Children ages 8–12 years were enrolled under an institutional review board approved protocol. SPD subjects were recruited from the UCSF Sensory Neurodevelopment and Autism Program (SNAP) and from local online parent board listings. TDC were recruited from online parent group listings as well as referrals from affiliated sensory neurodevelopment and autism research groups. Informed consent was obtained from the parents or legal guardians, with the assent of all participants. Exclusion criteria were brain malformation or injury, movement disorder, bipolar disorder, psychotic disorder, hearing impairment, full-scale IQ (FSIQ) score <70 on the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV;
Autism spectrum disorders was intially screened for using the Social Communication Questionnaire (SCQ;
All TDC and SPD subjects were assessed with the Sensory Profile (
The Acoustic Index of the Differential Screening Test for Processing (DSTP) was used to assess auditory processing (
Summary demographic information is included in
Demographic information and sensory scores.
#TDC/#SPD | TDC (mean ± standard deviation) | SPD (mean ± standard deviation) | ||
---|---|---|---|---|
Age (years) | 41/40 | 10.1 ± 1.1 | 9.6 ± 1.2 | 0.066 |
FSIQ | 41/40 | 116 ± 10 | 112 ± 13 | 0.077 |
SP – Auditory | 41/39 | 33.8 ± 3.5 | 23.2 ± 5.00 | |
SP – Tactile | 41/39 | 83.5 ± 5.8 | 62.4 ± 12.2 | |
DSTP | 34/35 | 36.1 ± 3.53 | 32.2 ± 5.6 | |
Graphesthesia | 33/34 | 21.5 ± 3.91 | 19.1 ± 4.8 |
MR imaging was performed on a 3T Tim Trio scanner (Siemens, Erlangen, Germany) using a 12 channel head coil. Structural MR imaging of the brain was performed with an axial 3D magnetization prepared rapid acquisition gradient-echo T1-weighted sequence (TE = 2.98 ms, TR = 2300 ms, TI = 900 ms, flip angle of 90°) with in-plane resolution of 1 × 1 mm on a 256 × 256 matrix and 160 1.0 mm contiguous partitions. Whole-brain diffusion imaging was performed with a multislice 2D single-shot twice-refocused spin echo echo-planar sequence with 64 diffusion-encoding directions, diffusion-weighting strength of
The diffusion-weighted images were corrected for motion and eddy currents using FMRIB’s Linear Image Registration Tool (FLIRT
Tract-Based Spatial Statistics (TBSS) in FSL (
For each subject, mean FA values were obtained within the significant voxels of each of the four white matter regions implicated in the group difference analyses – the left and right posterior thalamic radiations (PTR), splenium of the corpus callosum (SCC), and retrolenticular limb of the right internal capsule (RLIC), as defined by the JHU white matter atlas. Then, general linear models (GLMs) of FA as a function of group (TDC or SPD), age, and gender were created for the each of these ROIs:
where group is 1 for TDC and 0 for SPD, and gender is 1 for females and 0 for males. The coefficient values (β1-β3) and their significance levels were assessed for each ROI.
In order to limit the number of statistical comparisons, FA alone was examined for correlations with our sensory variables. FA was tested for correlations with the Sensory Profile auditory score and DSTP separately to assess the relationship between white matter microstructure and auditory processing. FA was tested for correlations with the Sensory Profile tactile score and Graphesthesia separately to assess the relationship between white matter microstructure and tactile processing. The correlation analyses were performed on a voxel-wise basis along the white matter skeleton.
As a
To further investigate the contributions of different types of auditory processing to the correlational results found with DSTP, an additional
Significantly lower FA, and higher MD and RD values, were found in the SPD cohort relative to TDC (
The coefficient estimates and
Coefficient estimates and
Group (TDC = 1, SPD = 0) |
Age |
Gender ( |
||||
---|---|---|---|---|---|---|
b1 | b2 | b3 | ||||
PTR-L | 0.028 | 0.0066 | 0.012 | 0.14 | ||
PTR-R | 0.028 | 0.0080 | 0.016 | 0.063 | ||
RLIC-R | 0.014 | 0.0033 | 0.16 | 0.010 | 0.12 | |
SCC | 0.019 | 0.0049 | 0.0039 | 0.49 |
There were widespread, significant positive associations of FA across groups with the Sensory Profile tactile score and with graphesthesia, after regression of motion parameters (
Number of significantly correlated voxels in several ROIs, along with results of the GLMs of Sensory Profile tactile score and Graphesthesia as functions of group, FA, age, and gender.
SP Tactile |
Graphesthesia |
|||||
---|---|---|---|---|---|---|
# sig vox | p_FA | p_TDCvSPD | # sig vox | p_FA | p_TDCvSPD | |
ACR-L | 659 | 0.074 | 361 | 0.11 | ||
ACR-R | 372 | 0.14 | 938 | |||
SCR-L | 179 | 181 | ||||
SCR-R | 24 | 0.85 | 170 | |||
PCR-L | 221 | 232 | ||||
PCR-R | 229 | 0.11 | 398 | 0.056 | ||
ALIC-L | 267 | 0.29 | – | – | – | |
ALIC-R | 308 | 0.10 | 234 | |||
PLIC-L | 590 | 49 | 0.20 | |||
PLIC-R | 506 | 322 | ||||
RLIC-L | 393 | 375 | 0.721 | |||
RLIC-R | 333 | 0.091 | 386 | 0.301 | ||
PTR-L | 720 | 910 | 0.056 | |||
PTR-R | 644 | 425 | 0.057 | 0.062 | ||
GCC | 19 | 0.069 | 1039 | |||
BCC | 398 | 0.21 | 1803 | |||
SCC | 1010 | 1654 | 0.055 | |||
CGC-L | 172 | 0.052 | 73 | |||
CGC-R | 2 | 0.41 | – | – | – | |
EC-L | 602 | 60 | ||||
EC-R | 278 | 370 | ||||
SLF-L | 98 | 549 | ||||
SLF-R | 102 | 0.44 | 161 | |||
SS-L | 156 | 186 | 0.099 | |||
SS-R | 187 | 0.17 | 169 |
The significance of the group and FA predictor variables in the GLMs for prediction of the Sensory Profile tactile score and Graphesthesia are included in
Representative plots of the Sensory Profile tactile score model and Graphesthesia vs. FA in significant voxels of the bilateral PTR and SCC are displayed in
There were widespread, significant positive associations of FA across groups with the Sensory Profile auditory score and with DSTP, after regression of motion parameters (
Number of significantly correlated voxels in several ROIs, along with results of the GLMs of Sensory Profile auditory score and DSTP as functions of group, FA, age, and gender.
SP – Auditory |
DSTP |
|||||
---|---|---|---|---|---|---|
Num vox | p_FA | p_TDCvSPD | Num vox | p_FA | p_TDCvSPD | |
ACR-L | 562 | 0.13 | 1155 | 0.077 | ||
ACR-R | 573 | 0.26 | 1371 | 0.12 | ||
SCR-L | 79 | 0.17 | 148 | |||
SCR-R | 61 | 0.77 | 464 | |||
PCR-L | 48 | 0.26 | 236 | |||
PCR-R | 298 | 0.11 | 518 | 0.068 | ||
ALIC-L | 276 | 0.06 | 398 | 0.18 | ||
ALIC-R | 374 | 0.20 | 576 | 0.19 | ||
PLIC-L | 339 | 0.28 | 491 | 0.058 | ||
PLIC-R | 173 | 0.64 | 553 | 0.082 | ||
RLIC-L | – | – | – | 539 | 0.075 | |
RLIC-R | 5 | 0.69 | 475 | 0.11 | ||
PTR-L | 396 | 647 | 0.23 | |||
PTR-R | 429 | 768 | 0.29 | |||
GCC | 551 | 0.25 | 1234 | 0.086 | ||
BCC | 1718 | 0.055 | 2173 | |||
SCC | 1293 | 0.096 | 1032 | 0.18 | ||
CGC-L | 102 | 255 | 0.067 | |||
CGC-R | 7 | 10 | 0.26 | |||
EC-L | 399 | 383 | 0.091 | |||
EC-R | 289 | 1104 | 0.063 | |||
SLF-L | – | – | – | 560 | 0.068 | |
SLF-R | 141 | 0.062 | 852 | 0.060 | ||
SS-L | – | – | – | 246 | ||
SS-R | – | – | – | 308 | 0.36 |
The significance of the group and FA predictor variables in the GLMs for prediction of the Sensory Profile auditory score and DSTP are included in
Representative plots of the Sensory Profile tactile score model and Graphesthesia versus FA in significant voxels of the bilateral PTR and SCC are displayed in
Given the extensive and robust correlations of FA with the DSTP acoustic subscore, additional post-hoc tests for correlations were performed between FA and the three subscores of the DSTP acoustic test – dichotic digits, temporal patterning, and auditory discrimination. The number of significantly correlated voxels in several ROIs is included in
Number of significantly correlated voxels in several ROIs, along with results of the three DSTP subscores as functions of group, FA, age, and gender.
DSTPdd |
DSTPtp |
DSTPad |
|||||||
---|---|---|---|---|---|---|---|---|---|
# sig vox | p_FA | p_TDCvSPD | # sig vox | p_FA | p_TDCvSPD | # sig vox | p_FA | p_TDCvSPD | |
ACR-L | 734 | 0.32 | 582 | 0.10 | – | – | – | ||
ACR-R | 1190 | 0.34 | 918 | 1176 | 0.52 | ||||
SCR-L | 334 | 0.15 | 7 | 0.23 | – | – | – | ||
SCR-R | 460 | 0.18 | 326 | 83 | 0.19 | ||||
PCR-L | 255 | 0.21 | 52 | 0.27 | – | – | – | ||
PCR-R | 499 | 0.27 | 341 | 40 | 0.25 | ||||
ALIC-L | 268 | 0.36 | 316 | – | – | – | |||
ALIC-R | 521 | 0.50 | 475 | 326 | 0.64 | ||||
PLIC-L | 584 | 0.21 | 306 | 0.077 | – | – | – | ||
PLIC-R | 602 | 0.30 | 338 | 0.29 | 18 | 0.55 | |||
RLIC-L | 455 | 0.28 | 440 | – | – | – | |||
RLIC-R | 453 | 0.43 | 363 | 0.14 | 111 | 0.62 | |||
PTR-L | 596 | 0.67 | 463 | 0.32 | – | – | – | ||
PTR-R | 894 | 0.99 | 583 | 0.12 | 305 | 0.81 | |||
GCC | 1116 | 0.33 | 928 | 0.095 | 426 | 0.44 | |||
BCC | 2048 | 0.18 | 1788 | 0.085 | 529 | 0.23 | |||
SCC | 939 | 0.43 | 871 | 0.074 | 129 | 0.45 | |||
CGC-L | 268 | 0.28 | 153 | – | – | – | |||
CGC-R | 8 | 0.20 | 69 | 0.080 | – | – | – | ||
EC-L | 223 | 0.18 | 439 | – | – | – | |||
EC-R | 911 | 0.23 | 598 | 752 | 0.44 | ||||
SLF-L | 702 | 0.24 | 9 | 0.14 | – | – | – | ||
SLF-R | 981 | 0.18 | 715 | 206 | 0.45 | ||||
SS-L | 177 | 0.20 | 235 | 5 | 0.18 | ||||
SS-R | 259 | 0.56 | 284 | 212 | 0.98 |
The significance of the group and FA predictor variables in the GLMs for prediction of each DSTP subscore are included in
Representative plots of each DSTP subscore versus FA in significant voxels of the bilateral PTR and SCC are displayed in
These findings corroborate and generalize our previous work demonstrating the role of disrupted posterior white matter microstructure in SPD. Furthermore, the larger, mixed-gender cohort unmasks a more extensive distribution of white matter differences which includes anterior white matter. More importantly, to our knowledge, this work is the first to demonstrate a relationship between direct measurements of tactile function and non-linguistic auditory function with white matter microstructural integrity not just in SPD, but also in TDC.
As in our prior work (
We find that correlations of white matter microstructure with direct measurements of tactile and auditory processing are stronger than the correlations of microstructure with parent report measures, likely due to the more objective nature of the direct measurements. Furthermore, the stronger concordance between the TDC and SPD regression lines of FA with the direct sensory measurements of Graphesthesia and DSTP, as compared with the sensory profile tactile and auditory scores, suggest that these direct measurements map more closely to the underlying biology. The offset between the TDC and SPD regression lines of the sensory profile metrics with FA may reflect biased parent reporting as a function of the presence or absence of an SPD diagnosis. More explicitly, if a child without an SPD label and a child with an SPD label exhibit the same level of function for a given sensory processing domain, parents of the child who has not been attributed an SPD label may be less likely to report abnormalities than the parents of the child who has been clinically assigned an SPD label.
The relative lack of anatomic specificity of these correlations of DTI with sensory processing measures may be due, in part, to the high degree of microstructural covariance between different white matter tracts (
Diffusion tensor imaging has previously been used to link degree of periventricular white matter injury in the PTR, as assessed by size reduction of white matter tracts on visual inspection, with contralateral touch threshold, proprioception, and motor severity in children with cerebral palsy (
Our results are the first to demonstrate associations of white matter microstructure with tactile processing, both among children with SPD as well as among TDC. Both the Sensory Profile tactile score and Graphesthesia are associated with FA in regions subserving primary sensory processing, including the posterior projection tracts of the superior and posterior corona radiata, the posterior limbs of the internal capsule, and the PTR. Both are also associated with the SCC, which connects homologous sensory areas. However, they are also associated with microstructure in associational tracts such as the external capsules, superior longitudinal fasciculus, sagittal stratum. Unlike the Sensory Profile tactile score, Graphesthesia demonstrates associations with the frontal regions of the right anterior corona radiata, anterior limb of the RLIC, and the genu of the corpus callosum. The widespread nature of these correlations may in part reflect the non-specificity of the assessments used, in addition to the previously mentioned contribution of high microstructural covariance of white matter (from the beginning of the Discussion). For example, in addition to primary tactile processing, Graphesthesia engages secondary modalities requiring synthesis and interpretation of the primary tactile inputs, including the spatio-temporal analysis of these inputs to form a visual representation of what was drawn on the back of the hand, and the motor planning and coordination to re-create this image.
The tactile sense develops the earliest among all sensory systems, with somatosensory responses detected
One prior study reported associations between FA and performance on auditory processing tasks in TDC (
To our knowledge, our study is the first to demonstrate associations of white matter microstructure with non-linguistic auditory processing, both among children with SPD as well as among TDC. Both the Sensory Profile auditory score and DSTP are associated with FA in the PTR, which contains the primary auditory projection pathway. However, they are also associated with microstructure in associational tracts such as the external capsules and the cingulate portion of the cingulum bundles. Unlike the Sensory Profile auditory score, DSTP demonstrates widespread associations with both frontal and posterior projection and commissural pathways, along with the associational tracts of the superior longitudinal fasciculus and sagittal stratum. Similar to tactile processing, one contributor to the extensive regions of significant correlations with the DSTP task may lie in the test’s additional recruitment of attentional processes. For example, dichotic listening tasks have long been used to test different neural models of attention (
Auditory processing is of primary importance for language acquisition, with speech perception requiring the ability to determine spectral shape, to discriminate modulation of amplitude and spectral frequencies, and to do this at varying temporal resolutions (
Despite the larger number of subjects in this study, we are still limited by sample size in our ability to harness DTI as a clinically utilizable tool for the diagnosis, prognosis, and treatment of SPD. Going forward, larger sample sizes and multimodal imaging biomarkers from DTI, fMRI, and MEG may aid in better definition and diagnosis of SPD. This could be of particular use if these biomarkers can identify individuals at risk for SPD at early ages before clinical symptoms are apparent, allowing for early intervention and recruitment of support services. In addition to diagnosis, larger scale longitudinal studies will allow us to evaluate the utility of quantitative imaging biomarkers, as compared with clinical metrics, neuropsychological testing, or direct sensory testing, for the prognostication of behavioral and cognitive development of individuals with SPD. Finally, interventional studies will allow us to evaluate the utility of quantitative imaging biomarkers for the monitoring of behavioral and psychopharmacological interventions, as well as for the prediction of interventional outcome. These biomarkers may further aid in the design of interventions if they can be used to stratify the SPD population into subgroups that will better respond to particular interventions. Overall, future studies will aim to shift SPD from a clinical diagnosis to a “biomarker diagnosis,” with imaging, and in particular DTI, metrics among the most promising of these biomarkers.
Conception and design: JO, EM, PM. Acquisition: AB-A, SD, SH, ABA, JH. Analysis: Y-SC, MG, JO, EM, PM. Interpretation: YSC, MG, JPO, EJM, PM.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This work was funded by grants from the Wallace Research Foundation to EJM and to PM and a gift from Toby Mickelson and Donald Brody to EJM. EJM has also received neuroimaging support that contributed to this work from NIH K23 MH083890. We also received generous support from the SPD community of family and friends through gifts large and small to our UCSF Sensory Neurodevelopment and Autism Program (SNAP).