Quantification of changes in language-related brain areas in autism spectrum disorders using large-scale network analysis

Abstract

Diagnosis of autism spectrum disorders (ASD) is difficult, as symptoms vary greatly and are difficult to quantify objectively. Recent work has focused on the assessment of non-invasive diffusion tensor imaging based biomarkers of the disease that reflect the microstructural characteristics of neuronal pathways in the brain. While tractography-based approaches typically analyse specific structures of interest, a graph-based large-scale network analysis of the connectome can yield comprehensive measures of the global architecture of the brain. Aim of this work was to assess the concept of network centrality as a tool to perform structure specific analysis within the global network architecture. Our approach was evaluated on 18 children suffering from ASD and 18 typically developed controls using magnetic resonance imaging based cortical parcellations in combination with diffusion tensor imaging tractography. We show that the reduced capacity for comprehension of language in ASD is reflected in the significantly (p < 0.001) reduced network centrality of Wernicke’s area while the motor cortex, that was used as a control region, did not show any significant alterations. Our results demonstrate the applicability of large-scale network analysis tools in the domain of region-specific analysis and may be an important contribution to future diagnostic tools in the clinical context of ASD diagnosis.