Exploration of Working Memory Retrieval Stage for Mild Cognitive Impairment: Time-Varying Causality Analysis of Electroencephalogram Based on Dynamic Brain Networks

Abstract Background Mild Cognitive Impairment (MCI) is an intermediate stage between the expected cognitive decline of normal aging and Alzheimer's disease (AD). Its management is crucial for it helps intervene and slow the progression of cognitive decline to AD. However, the understanding of the MCI mechanism is not completely clear. As working memory (WM) damage is a common symptom of MCI, this study focused on the core stage of WM, i.e., memory retrieval stage, to investigate information processing and the causality relationships among brain regions based on electroencephalogram (EEG) signals. Method 21 MCI and 20 normal cognitive control (NC) participants were recruited. The delayed matching sample paradigm with two different loads was employed to evaluate their WM functions. A time-varying network based on the Adaptive transfer function (ADTF) was constructed on the EEG of the memory retrieval trials.to perform the dynamic brain network analysis. Results Our results showed that: (a) Behavioral data analysis: there were significant differences in accuracy and accuracy / reaction time between MCI and NC in tasks with memory load capacity of low load-four and high load-six, especially in tasks with memory load capacity of four. (b) Dynamic brain network analysis: there were significant differences in the dynamic changes of brain network patterns between the two groups during the memory retrieval stage of WM task. Specifically, in low load WM tasks, the dynamic brain network changes of NC were more regular to accommodate for efficient information processing, with important core nodes showing a transition from bottom to up, while MCI did not display a regular dynamic brain network pattern. Further, the brain functional areas associated with low load WM disorders were mainly located in the left prefrontal lobe (FC1) and right occipital lobe (PO8). Compared with low load WM task, during the high load WM task, the dynamic brain network changes of NC during the memory retrieval stage were regular, and the core nodes exhibited a consistent transition phenomenon from up to bottom to up, which were not observed in MCI. Conclusions Behavioral data in the low load WM task paradigm and abnormal electrophysiological signals in the left prefrontal (FC1) and right occipital lobes (PO8) could be used for MCI diagnosis. This is the first time based on large-scale dynamic network methods to investigate the dynamic network patterns of MCI memory retrieval stages under different load WM tasks, providing a new perspective on the neural mechanisms of WM deficits in MCI patients and providing some reference for the clinical treatment of MCI-WM memory disorders.