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Abidin, Anas Z.; Dsouza, Adora M. and Wismüller, Axel (2019): Detecting Connectivity Changes in Autism Spectrum Disorder Using Large-Scale Granger Causality. In: Medical Imaging 2019: Image Processing, Vol. 10949, 109490M

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We investigated functional MRI connectivity changes in brain networks of subjects with Autism Spectrum Disorder (ASD) using large-scale Granger causality (1sGC), which can provide a truly multivariate representation of directed connectivity. To this end, we investigated the use of 1sGC for capturing pair-wise interactions between regional time-series 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 fronto-parietal, 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 1sGC, in combination with a network analysis framework can serve as an alternative methodology for the analysis of clinical resting-state fMRI data.

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