Multivariate searchlight classification of structural magnetic resonance imaging in children and adolescents with autism

Abstract

Background: Autism spectrum disorders (ASD) are neurodevelopmental disorders with a prevalence of nearly 1:100. Structural imaging studies point to disruptions in multiple brain areas, yet the precise neuroanatomical nature of these disruptions remains unclear. Characterization of brain structural differences in children with ASD is critical for development of biomarkers that may eventually be used to improve diagnosis and monitor response to treatment.

Methods: We use voxel-based morphometry along with a novel multivariate pattern analysis approach and searchlight algorithm to classify structural magnetic resonance imaging data acquired from 24 children and adolescents with autism and 24 age-, gender-, and IQ-matched neurotypical participants.

Results: Despite modest voxel-based morphometry differences, multivariate pattern analysis revealed that the groups could be distinguished with accuracies of approximately 90% based on gray matter in the posterior cingulate cortex, medial prefrontal cortex, and bilateral medial temporal lobes-regions within the default mode network. Abnormalities in the posterior cingulate cortex were associated with impaired Autism Diagnostic Interview communication scores. Gray matter in additional prefrontal, lateral temporal, and subcortical structures also discriminated between groups with accuracies between 81% and 90%. White matter in the inferior fronto-occipital and superior longitudinal fasciculi, and the genu and splenium of the corpus callosum, achieved up to 85% classification accuracy.

Conclusions: Multiple brain regions, including those belonging to the default mode network, exhibit aberrant structural organization in children with autism. Brain-based biomarkers derived from structural magnetic resonance imaging data may contribute to identification of the neuroanatomical basis of symptom heterogeneity and to the development of targeted early interventions.

Figure 1

A. Results from searchlight classification of gray matter. Regions discriminating between participants groups include prefrontal, posterior parietal, temporal, default mode network, medial temporal, and subcortical areas. The highest classification accuracies were obtained from gray matter in the posterior cingulate cortex and parahippocampal gyrus (92%), medial prefrontal cortex (88%) and posterior parietal cortices (85%), all regions within the default mode network. B. Results from searchlight classification of white matter. Regions discriminating between participant groups include inferior fronto-occipital fasciculus, superior longitudinal fasciculus, and the genu and splenium of the corpus callosum.

Figure 2

Results from searchlight classification of gray matter (blue) and group differences in gray matter revealed by univariate VBM analyses (red and yellow). Areas where VBM of gray matter showed AD > TD are in red, areas where VBM of gray matter showed TD > AD are in yellow.

Figure 3

Relationship between SVM and symptom severity. Children with the most severe autism as indexed by ADI-R communication (r = .536, p < .01) subscale are better discriminators between groups on the basis of gray matter (GM) in the posterior cingulate cortex than those with less severe symptomatology.