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Updated: May 22, 2025
Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease
Published on: August 30, 2011
Exploration of working memory retrieval stage for mild cognitive impairment: time-varying causality analysis of
Yi Jiang1,2,3, Zhiwei Guo1,2,3, Xiaobo Zhou4,5
1The National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
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., the 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 the 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 intervention treatment of MCI-WM memory disorders.
Insights
Mild Cognitive Impairment (MCI) patients exhibit distinct working memory (WM) deficits compared to normal controls. Abnormal brain network patterns in MCI during memory retrieval, particularly in the left prefrontal and right occipital lobes, can aid in diagnosis.
Area of Science:
- Neuroscience
- Cognitive Science
- Biomedical Engineering
Background:
- Mild Cognitive Impairment (MCI) represents a transitional stage between normal aging and Alzheimer's disease (AD).
- Understanding MCI mechanisms is crucial for early intervention to slow cognitive decline.
- Working memory (WM) deficits are a common symptom of MCI, necessitating research into its underlying neural processes.
Purpose of the Study:
- To investigate information processing and causality relationships among brain regions during the memory retrieval stage of WM in MCI patients.
- To analyze dynamic brain network patterns using electroencephalogram (EEG) signals under varying cognitive loads.
- To identify potential biomarkers for MCI diagnosis based on EEG and behavioral data.
Main Methods:
- Recruited 21 MCI patients and 20 normal cognitive controls (NC).
- Utilized a delayed matching sample paradigm with low (4 items) and high (6 items) memory loads.
- Constructed time-varying brain networks using Adaptive Transfer Function (ADTF) on EEG data during memory retrieval.
Main Results:
- Significant differences in accuracy and reaction time were observed between MCI and NC groups, especially at low memory load.
- MCI patients showed irregular dynamic brain network patterns during memory retrieval compared to the regular patterns in NC.
- Abnormal brain network activity in MCI was localized to the left prefrontal (FC1) and right occipital (PO8) lobes, particularly under low load conditions.
Conclusions:
- Behavioral performance and abnormal electrophysiological signals in specific brain regions (left prefrontal, right occipital) show potential for MCI diagnosis.
- This study provides novel insights into the neural mechanisms of WM deficits in MCI using large-scale dynamic network analysis.
- Findings offer a reference for clinical interventions targeting WM memory disorders in MCI patients.
