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

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.

Paper Details

Date Published: 15 March 2019
PDF: 9 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109490M (15 March 2019); doi: 10.1117/12.2513023
Show Author Affiliations
Anas Z. Abidin, Univ. of Rochester (United States)
Adora M. Dsouza, Univ. of Rochester Medical Ctr. (United States)
Axel Wismüller M.D., Univ. of Rochester Medical Ctr. (United States)
Ludwig Maximilian Univ. (Germany)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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