Education, and the balance between dynamic and stationary functional connectivity jointly support executive functions in relapsing–remitting multiple sclerosis

• Published in: Human brain mapping Vol. 39; no. 12; pp. 5039 - 5049
• Main Authors: Lin, Sue‐Jin, Vavasour, Irene, Kosaka, Brenda, Li, David K. B., Traboulsee, Anthony, MacKay, Alex, McKeown, Martin J.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.12.2018
• Subjects: Adult; Anxiety; Biomarkers; Cerebral Cortex - diagnostic imaging; Cerebral Cortex - physiopathology; Cerebral hemispheres; Cognition; Cognitive ability; Cognitive tasks; Connectome - methods; Coordination; Correlation analysis; Demographics; Density; Education; Educational Status; Executive function; Executive Function - physiology; Fatigue; Female; Functional magnetic resonance imaging; Functionals; Graph theory; Humans; Intelligence; Magnetic Resonance Imaging; Male; Mental depression; Middle Aged; Mood; Multiple sclerosis; Multiple Sclerosis, Relapsing-Remitting - diagnostic imaging; Multiple Sclerosis, Relapsing-Remitting - physiopathology; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; Severity of Illness Index; Short term memory; cognitive function; dynamic functional connectivity; graph theory; sliding window approach; multiset canonical correlation analysis; resting-state fMRI; network characteristics
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.24343

Graphical network characteristics and nonstationary functional connectivity features, both derived from resting‐state functional magnetic resonance imaging (rsfMRI) data, have been associated with cognitive performance in healthy subjects. How these features jointly relate to cognition in diseased states has not been investigated. In this study, 46 relapsing–remitting multiple sclerosis subjects underwent rsfMRI scans and a focused cognitive battery. With a sliding window approach, we examined six dynamic network features that indicated how connectivity changed over time as well as six measures derived from graph theory to reflect static network characteristics. Multiset canonical correlation analysis (MCCA) was then carried out to investigate the relations between dynamic network features, stationary network characteristics, cognitive testing, demographic, disease severity, and mood. Multiple sclerosis (MS) subjects demonstrated weaker connectivity strength, decreased network density, reduced global changes, but increased changes in interhemispheric connectivity compared to controls. The MCCA model determined that executive functions and processing speed ability measured by Wechsler Adult Intelligence Scale IV (WAIS‐IV) Working Memory Index, WAIS‐IV Processing Speed Index, and the Verbal Fluency Test were positively correlated with education, dynamic connectivity, and static connectivity strength; while poor task switching was correlated with disease severity, psychiatric comorbidities such as depression, anxiety, and fatigue, and static network density. Taken together, our results suggest that better executive functioning in MS requires maintenance of a continued coordination between stationary and dynamic functional connectivity as well as the support of education, and dynamic functional connectivity may provide an additional cognitive biomarker of disease severity in the MS population.