Working memory load-related electroencephalographic parameters can differentiate progressive from stable mild...

Area of Science:

• Neuroscience
• Cognitive Neurology
• Biomedical Engineering

Background:

• Alzheimer's disease (AD) and mild cognitive impairment (MCI) are associated with changes in brain activity during memory tasks.
• Event-related potentials (ERPs) and brain rhythm synchronization are altered in AD patients.

Purpose of the Study:

• To investigate if electroencephalography (EEG) parameters, specifically P200 and N200 latencies and beta event-related synchronization (ERS), can predict cognitive decline in MCI.
• To differentiate between stable MCI (SMCI) and progressive MCI (PMCI) using EEG markers.

Main Methods:

• EEG data, including P200/N200 latencies and beta ERS, were recorded during attentional and working memory tasks (two-back) in elderly controls (EC), MCI cases, and AD patients.
• MCI patients were categorized as SMCI or PMCI based on cognitive decline after one year.
• Statistical models and receiver operating characteristic (ROC) curves were used to assess the predictive value of EEG parameters.

Main Results:

• Progressive MCI (PMCI) and AD patients exhibited longer P200/N200 latencies and lower beta ERS during the two-back task compared to EC and SMCI.
• P200 latency, N200 latency, and beta ERS values significantly differentiated between SMCI and PMCI.
• A combination of these EEG markers achieved 90% accuracy in classifying MCI progression, with P200 latency showing a high area under the ROC curve (0.93).

Conclusions:

• Progressive MCI and AD share similar patterns of working memory-related EEG activation, characterized by prolonged latencies and reduced beta ERS.
• P200 latency during a working memory task is a potential simple and effective EEG biomarker for predicting rapid cognitive decline in MCI.