Presentation + Paper
15 March 2019 Detecting connectivity changes in autism spectrum disorder using large-scale Granger causality
Author Affiliations +
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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Anas Z. Abidin, Adora M. Dsouza, and Axel Wismüller M.D. "Detecting connectivity changes in autism spectrum disorder using large-scale Granger causality", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109490M (15 March 2019); https://doi.org/10.1117/12.2513023
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KEYWORDS
Brain

Functional magnetic resonance imaging

Statistical analysis

Magnetic resonance imaging

Neuroimaging

Principal component analysis

Time series analysis

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