Dynamic functional connectivity MEG features of Alzheimer’s disease

Abstract

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 high sensitivity and specificity in distinguishing AD from healthy subjects. 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.

Semantic Strength in Dyslexia: neurocognitive correlates of semantic fluency

“…Thus, the pre-processed rs-fMRI data were analyzed using a time-varying dynamic approach (Jiang et al, 2022;Jin et al, 2023), a technique that measures changes in brain network The brain regions were anatomically localized using the Brainnetome Atlas (https://atlas.brainnetome.org; Fan et el., 2016). In our study, we followed the method of Jin et al…”

“…It has two components: a temporal component, which describes how brain states change over time (dynamic), and a spatial component, which represents the static connections between brain regions. The model separates these components and uses piece-wise constant multivariate signal generation described in detail inJiang et al, 2022 andJin et al 2023.…”