Using Large-Scale Granger Causality to Study Changes in Brain Network Properties in the Clinically Isolated Syndrome (CIS) Stage of Multiple Sclerosis

• Published in: Proceedings of SPIE, the international society for optical engineering Vol. 10137
• Main Authors: Abidin, Anas Z, Chockanathan, Udaysankar, DSouza, Adora M, Inglese, Matilde, Wismüller, Axel
• Format: Journal Article
• Language: English
• Published: United States 01.03.2017
• Subjects: time series analysis; multiple sclerosis; cognitive impairment; graph measures; network topology; resting state fMRI; Granger causality; multi-variate analysis
• ISSN: 0277-786X; 1996-756X
• DOI: 10.1117/12.2254395

Clinically Isolated Syndrome (CIS) is often considered to be the first neurological episode associated with Multiple sclerosis (MS). At an early stage the inflammatory demyelination occurring in the CNS can manifest as a change in neuronal metabolism, with multiple asymptomatic white matter lesions detected in clinical MRI. Such damage may induce topological changes of brain networks, which can be captured by advanced functional MRI (fMRI) analysis techniques. We test this hypothesis by capturing the effective relationships of 90 brain regions, defined in the Automated Anatomic Labeling (AAL) atlas, using a large-scale Granger Causality (lsGC) framework. The resulting networks are then characterized using graph-theoretic measures that quantify various network topology properties at a global as well as at a local level. We study for differences in these properties in network graphs obtained for 18 subjects (10 male and 8 female, 9 with CIS and 9 healthy controls). Global network properties captured trending differences with modularity and clustering coefficient ( <0.1). Additionally, local network properties, such as local efficiency and the strength of connections, captured statistically significant ( <0.01) differences in some regions of the inferior frontal and parietal lobe. We conclude that multivariate analysis of fMRI time-series can reveal interesting information about changes occurring in the brain in early stages of MS.