Decreased centrality of cortical volume covariance networks in autism spectrum disorders.

Autism spectrum disorders (ASD) are a group of neurodevelopmental conditions characterized by atypical structural and functional brain connectivity. Complex network analysis has been mainly used to describe altered network-level organization for functional systems and white matter tracts in ASD. However, atypical functional and structural connectivity are likely to be also linked to abnormal development of the correlated structure of cortical gray matter. Such covariations of gray matter are particularly well suited to the investigation of the complex cortical pathology of ASD, which is not confined to isolated brain regions but instead acts at the systems level. In this study, we examined network centrality properties of gray matter networks in adults with ASD (n = 84) and neurotypical controls (n = 84) using graph theoretical analysis. We derived a structural covariance network for each group using interregional correlation matrices of cortical volumes extracted from a surface-based parcellation scheme containing 68 cortical regions. Differences between groups in closeness network centrality measures were evaluated using permutation testing. We identified several brain regions in the medial frontal, parietal and temporo-occipital cortices with reductions in closeness centrality in ASD compared to controls. We also found an association between an increased number of autistic traits and reduced centrality of visual nodes in neurotypicals. Our study shows that ASD are accompanied by atypical organization of structural covariance networks by means of a decreased centrality of regions relevant for social and sensorimotor processing. These findings provide further evidence for the altered network-level connectivity model of ASD.

Relationship Between Surface-Based Brain Morphometric Measures and Intelligence in Autism Spectrum Disorders: Influence of History of Language Delay.

Autism spectrum disorders (ASD) are a group of conditions that show abnormalities in the neuroanatomy of multiple brain regions. The variability in the development of intelligence and language among individuals on the autism spectrum has long been acknowledged, but it remains unknown whether these differences impact on the neuropathology of ASD. In this study, we aimed to compare associations between surface-based regional brain measures and general intelligence (IQ) scores in ASD individuals with and without a history of language delay. We included 64 ASD adults of normal intelligence (37 without a history of language delay and 27 with a history of language delay and 80 neurotypicals). Regions with a significant association between verbal and nonverbal IQ and measures of cortical thickness (CT), surface area, and cortical volume were first identified in the combined sample of individuals with ASD and controls. Thicker dorsal frontal and temporal cortices, and thinner lateral orbital frontal and parieto-occipital cortices were associated with greater and lower verbal IQ scores, respectively. Correlations between cortical volume and verbal IQ were observed in similar regions as revealed by the CT analysis. A significant difference between ASD individuals with and without a history of language delay in the association between CT and verbal IQ was evident in the parieto-occipital region. These results indicate that ASD subgroups defined on the basis of differential language trajectories in childhood can have different associations between verbal IQ and brain measures in adulthood despite achieving similar levels of cognitive performance.

Inter-regional cortical thickness correlations are associated with autistic symptoms: a machine-learning approach.

The investigation of neural substrates of autism spectrum disorder using neuroimaging has been the focus of recent literature. In addition, machine-learning approaches have also been used to extract relevant information from neuroimaging data. There are only few studies directly exploring the inter-regional structural relationships to identify and characterize neuropsychiatric disorders. In this study, we concentrate on addressing two issues: (i) a novel approach to extract individual subject features from inter-regional thickness correlations based on structural magnetic resonance imaging (MRI); (ii) using these features in a machine-learning framework to obtain individual subject prediction of a severity scores based on neurobiological criteria rather than behavioral information. In a sample of 82 autistic patients, we have shown that structural covariances among several brain regions are associated with the presence of the autistic symptoms. In addition, we also demonstrated that structural relationships from the left hemisphere are more relevant than the ones from the right. Finally, we identified several brain areas containing relevant information, such as frontal and temporal regions. This study provides evidence for the usefulness of this new tool to characterize neuropsychiatric disorders.