Detecting connectivity changes in autism spectrum disorder using large-scale Granger causality

We investigated functional MRI connectivity changes in brain networks of subjects with Autism Spectrum Disorder (ASD) using large-scale Granger causality (lsGC), which can provide a truly multivariate representation of directed connectivity. To this end, we investigated the use of lsGC for capturing pair-wise interactions between regional timeseries extracted using ROIs from different resting-state brain networks. We studied these measures in a dataset comprising 59 subjects (34 healthy, 25 autistic; age-matched) from the Autism Brain Imaging Data Exchange (ABIDE) project. A general linear model was used to study the differences between the two groups when controlling for age when comparing: (i) connectivity strength and diversity of each node in the network, (ii) global graph measures, and (iii) regional graph statistics. Clustering coefficient and small-worldness properties were significantly (p<0.05) increased in ASD subjects. Furthermore, we were able to localize differences in connectivity strength within the nodes of the frontoparietal, cingulo-opercular, as well as the sensorimotor network, in line with previously published literature. For comparison, a corresponding analysis using correlation-based connectivity did not reveal any significant differences between groups. Our results indicate that lsGC, in combination with a network analysis framework can serve as an alternative methodology for the analysis of clinical resting-state fMRI data.