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Multivariate Searchlight Classification of Structural Magnetic Resonance Imaging in Children and Adolescents with Autism

Published:September 05, 2011DOI:https://doi.org/10.1016/j.biopsych.2011.07.014

      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.

      Key Words

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      Linked Article

      • Erratum
        Biological PsychiatryVol. 70Issue 11
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          Errors have been discovered in “Multivariate Searchlight Classification of Structural Magnetic Resonance Imaging in Children and Adolescents with Autism ” by Uddin et al., published in Biological Psychiatry (2011;70:833–841). These errors largely relate to the reported statistics of the support vector machine relationship with symptom severity, and require correction to Figure 3, Table 3, and the related results and discussion text. These are secondary results that do not otherwise affect the main focus of the paper.
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