Integration and segregation in Autism Spectrum Disorders modulated by age, disease, and interaction: A graph theoretic study of intrinsic functional connectivity

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting 1 in 50 children between the ages of 6 and 17 years. Brain connectivity and graph theoretic methods have been particularly very useful in shedding light on the differences between high functioning autistic children compared to typically developing (TD) ones. However, very recent developments in network measures raise a cautionary note by highlighting gross under- and over-connectivity in ASD may be an oversimplified hypothesis. Thus the primary aim of our study is to investigate these notions in functional connectomics of ASD versus TD by subjecting the data to reproducibility experiments using two independent datasets. Further, we tested the hypothesis of alteration in network segregation and integration in the ASD subjects. We have analyzed the resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) data from the University of California Los Angeles (UCLA) multimodal connectivity database (n=42 ASD, n=37 TD) and rs-fMRI data from the Autism Brain Imaging Data Exchange (ABIDE) (n=187 ASD, n=176 TD) dataset. We assessed the differences in connection strength between TD and ASD subjects. We also performed graph theoretical analysis to analyze the effect of disease on various network measures. Further, using the larger ABIDE dataset, we performed two-factor ANOVA test, to study the effect of age, disease and their interaction by classifying the TD and ASD participants into two cohorts: children (9-12 years, n=73 TD and n=87 ASD) and adolescents (13-16 years, n=103 TD and n=100 ASD). In ASD, we show the existence of atypical connectivity within and between functional networks as compared to TD. We also found in ASD both hypo-and hyper-connectivity within functional networks such as the default mode network (DMN). Further, graph theoretic analysis showed that there is significant effect of age and disease on modularity, clustering coefficient, and local efficiency. We also identified specific areas within the DMN, sensorimotor, visual and attention networks that are affected by age, disease and their interaction. Overall, our findings suggest that maturation, disease and their interaction are critical for unraveling the biological basis and developmental trajectory in ASD and other neuropsychiatric disorders.