B1334
Title: Dynamic functional connectivity MEG features of Alzheimer's disease
Authors: Fei Jiang - The University of California, San Francisco (United States) [presenting]
Abstract: Dynamic resting state functional connectivity (RSFC) characterizes time-varying functional brain network activity fluctuations. A novel and robust time-varying dynamic network (TVDN) approach is used to extract the dynamic RSFC features from high-resolution magnetoencephalography (MEG) data of participants with Alzheimer's disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. It is shown that the dynamic manifold features are the most predictive of AD among all the functional features investigated. These include the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have high sensitivity and specificity in distinguishing AD from healthy subjects. Intriguingly, it is found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. Graph theoretic metrics of the dynamic component of TVDN are significantly different in AD versus controls. These results indicate that dynamic RSFC features are impacted in neurodegenerative diseases like Alzheimer's disease and may be crucial to understanding the pathophysiological trajectory of these diseases.
