Mapping neural correlates of biological motion perception in autistic children using high-density diffuse optical tomography

Abstract

Background: Autism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits.

Methods: We used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models.

Results: We found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits.

Limitations: Optical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism.

Conclusions: This study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits.

Keywords: Autism spectrum disorder; Biological motion; High-density diffuse optical tomography; Neuroimaging; Social perception.

Fig. 1

HD-DOT system and study Design. A. HD-DOT array on a school-age participant. B. The field of view of the HD-DOT system on the cortical surface. C. The DOT field of view, broken into parcels based on the Gordon parcellation, included 141 unique parcels and sampled auditory, cingulo-opercular (CinguloOperc), default mode, dorsal attention (DorsalAttn), frontal parietal (FrontoParietal), somatosensory hand (SMhand), somatosensory mouth (SMmouth), ventral attention (VentralAttn), visual, and no-assignment (None) functional networks. D. The study design of the biological motion perception experiment, participants passively viewed point-light displays of coherent biological and scrambled motion separately through a 20-inch (diagonal) LCD monitor. E. Distribution of the Social Responsiveness Scale-2 t scores for ASD, and NAI, and PS participants

Fig. 2

Data quality assessment. A. Distribution of mean signal-to-noise ratio in the pulse frequency band (i.e., 0.5–2 Hz) from (i) full samples (participant number = 112, 321 biological motion runs) and (ii) final samples (i.e., participant number = 44, NAI n = 21, and ASD n = 23). B. Distribution of good measurement percentage of all measurements within 4 cm source-detector separation for (i) full and (ii) final sample. NAI and ASD groups did not significantly differ from each other (p = 0.469, z = 0.724). C. Distribution of post-censoring median GVTD (i) of all runs of all participants and (ii) final sample. In the final sample, there is no significant difference in median GVTD (p = 0.07, z = 1.8) between NAI and ASD cohorts

Fig. 3

Within and between group contrast (coherent > scrambled biological motion) for HbO. A(i). NAI children (n = 21) unthresholded t-map. Red regions indicate a greater response to coherent biological motion, while blue regions indicate a greater response to scrambled motion. Cluster correction revealed six regions of significant activity, visualized on the cortical A(ii) surface and within the A(iii) volume. B(i). Autistic children (n = 23) unthresholded t-map. Cluster correction results in four significant regions of contrast activity, shown on the B(ii) cortical surface and B(iii) volumetric maps. C(i). Welch-Satterthwaite corrected t-map showing NAI contrast greater than ASD. Red regions indicate greater NAI contrast activity ASD. Cluster correction revealed six significant regions visualized on the C(ii) surface and within the C(iii) volume. Cluster-corrected maps are thresholded at voxel-wise p < 0.0075 and FDR-corrected at a cluster significance of p < 0.0125)

Fig. 4

Parcel based correlation map between SRS t-score and beta contrast values. The maps depict brain-wide correlations across A 21 NAI and B 23 ASD participants. Positive correlations are represented in red, while negative correlations are shown in blue. Parcels with nominally significant correlations (p < 0.025) are highlighted for both C NAI and D ASD groups

Fig. 5

Parcels exhibiting correlation between SRS t-score and mean parcel beta contrast (coherent > scrambled). A. Left fusiform contrast positively correlated with SRS t-score in NAI group. B. Beta contrast in right dorsolateral prefrontal cortex positively correlated with SRS t-score in the ASD group. C. Negative correlation between SRS t-score and parcel-based brain activity in the angular gyrus in the ASD group. D-H. Positive correlations between brain contrast and SRS t-score in the ASD group