Title: Time-varying functional connectivity features are strong predictors of Alzheimers disease
Authors: Fei Jiang - The University of California, San Francisco (United States) [presenting]
Abstract: Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. Considered superior to static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations preventing them from extracting reliable features to address this question. We use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high-resolution magnetoencephalography data. This algorithm automatically and adaptively learns the low-dimensional manifold of dynamic RSFC and detects dynamic state transitions in data. We show that both the number of transitions, dwell times, and the number of brain states are strong predictors of Alzheimer's disease (AD). Furthermore, these dynamic features from TVDN have high sensitivity and specificity in distinguishing AD and healthy subjects. These results indicate that robust dynamic resting-state functional connectivity features are impacted in dementias like Alzheimer's disease, and may be crucial to understanding the neuropathological disease impact and trajectory.