Dynamic functional connectivity MEG features of Alzheimer’s disease

• Published in: NeuroImage (Orlando, Fla.) Vol. 281; p. 120358
• Main Authors: Jin, Huaqing, Ranasinghe, Kamalini G., Prabhu, Pooja, Dale, Corby, Gao, Yijing, Kudo, Kiwamu, Vossel, Keith, Raj, Ashish, Nagarajan, Srikantan S., Jiang, Fei
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2023; Elsevier Limited; Elsevier
• Subjects: Alzheimer Disease; Alzheimer's disease; Brain; Brain state switch; Dementia; Disease; Dynamic resting state; Functional connectivity; Functional magnetic resonance; Humans; Magnetic Resonance Imaging - methods; Magnetoencephalography; Magnetoencephalography - methods; Multi-modality imaging; Neural networks; Neurodegenerative Diseases; Functional connectivity; Multi-modality imaging; Alzheimer’s disease; Magnetoencephalography; Functional magnetic resonance; Dynamic resting state; Brain state switch; Dementia
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2023.120358

Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated 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 that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach 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. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. 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 higher sensitivity and specificity in distinguishing AD from healthy subjects than the existing benchmarks do. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer’s disease, and may be crucial to understanding the pathophysiological trajectory of these diseases. •The work employs a novel time-varying dynamic network (TVDN) approach to extract the dynamic features from MEG data, circumventing the limitations of conventional sliding-window approaches.•Based on the analysis, we find that AD patients generally have higher spatial complexity, but lower temporal complexity compared with healthy controls.•We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different.