Shared Differences Across Cortical Morphometry Features Associated with Autism Spectrum Disorder

Background: Aspects of cortical structure such as cortical thickness, surface area, gyrification, and grey-white matter boundary integrity may represent different biological pathways underlying Autism Spectrum Disorder (ASD). Often these anatomical features are investigated in isolation. However, the identification of between-group differences that are shared across modalities could aid in identifying underlying neural mechanisms common to different anatomical features in ASD.

Objectives: We aimed to (1) identify multimodal components representing the inter-relationship between different in vivo MRI morphometric measures of cortical anatomy and to (2) relate these components to the ASD phenotype.

Methods: 98 adults with ASD (49 males and 49 females; diagnosed using the ADI-R and ADOS) and 98 matched typically developing controls (51 males and 47 females) aged 18-42 years received structural MRI scans at the Institute of Psychiatry, Psychology and Neuroscience, London, and the Autism Research Centre, Cambridge. Freesurfer software (http://surfer.nmr.mgh.harvard.edu/) was used to derive a set of eight morphometric features describing cortical surface anatomy for each participant (i.e. cortical volume, thickness, surface area, sulcal depth, mean radial curvature, metric distortion, local gyrification index, and grey to white matter signal intensity ratios). A multimodal fusion technique, linked independent components analysis (linked ICA) (Groves et al. 2011, 2012), was used to identify components comprised of shared inter-subject variation between the different cortical measures. Relationships between individual multi-modal components and ASD diagnosis and Autism Spectrum Quotient (AQ) scores were assessed through correlation analysis.

Results: We found one component representing increased cortical thickness and grey-white matter signal intensity ratio (GWR) in temporal and parietal regions as well as decreased gyrification in the cingulate gyrus, which was significantly negatively correlated with a diagnosis of ASD (p=6.6^e-5) and AQ scores (p=4.2^e-4). A second component representing (1) increased GWR across the entire cortex, (2) increased cortical thickness and surface area in frontal-temporal regions and (3) decreased cortical folding and gyrification in temporal regions also had a significant negative correlation with AQ measures (p=3.3^e-2).

Conclusions: We found significant correlations with ASD diagnoses and components comprising of reductions in grey-white matter boundary integrity and cortical thickness as well as increased gyrification. Our findings enrich understanding of the relationship between cortical features, and may aid in identifying neurobiological pathways that contribute to the cross-modal pattern of atypical cortical structure observed in ASD.