Comparative Efficacy of BEAM EEG and MMSE in Early Detection of Mild Cognitive Impairment: A Pilot Study
Janette Bow-Keola1, Daniel Vodak2, Kai Moriyama3, Yun Pine4, Kylie Yamauchi1, Michael Read1, Shay Nakahira1, Kirra Borrello1, D-Dré Wright1, Anita Cheung1, Ryan Nakamura1, Chris Deng1, Enrique Carrazana5, Amir Meghdadi6, Kore Liow1
1University of Hawaii John A. Burns School of Medicine, 2Masaryk University, 3University of Southern California, 4University of California Santa Barbara, 5Hawaii Pacific Neuroscience, 6Advanced Brain Monitoring LLC
Objective:
To evaluate the effectiveness of BEAMTM in predicting MCI by analyzing the correlation between BEAMTM biomarkers and age, compared to MMSE scores and expected values for MCI patients.
Background:
Alzheimer’s disease (AD) is a progressive neurodegenerative disease. AD and its precursor state, mild cognitive impairment (MCI), is often screened for using tools such as the Mini-Mental State Examination (MMSE). However, its efficacy at identifying early stages of MCI is inconsistent. BEAMTM (Biomarker-based Electrophysiology for Advanced Monitoring) is a novel platform utilizing neurotechnology to evaluate electroencephalograms (EEGs) administered under neurocognitive testing to identify event-related potential (ERPs) that are early biomarkers of MCI.
Design/Methods:
A retrospective chart review was conducted at Hawaii Pacific Neuroscience for patients who underwent BEAMTM testing from March to June 2024. We identified 104 patients diagnosed with MCI based on current MMSE scores who completed EEG testing under resting state (eyes-open and eyes-closed, 5-minutes each) and three scenarios: Auditory Oddball (AO), 3-Choice Vigilance Test (3CVT), and Standard Image Recognition (SIR).
Results:
Mean MMSE was 24.47 and negatively correlated with age (r = -0.31, p < 0.05). Resting state peak alpha scores were weakly indirectly correlated with age (r = -0.20, p < 0.05). AO N1 peak latency exhibited a stronger direct correlation with age ( r = 0.34, p < 0.05). AO P300 max latency was weakly directly correlated with age (r = 0.23, p < 0.05). 3CVT P2 peak latency was positively correlated with age (r = 0.40, p < 0.05). Accuracy was indirectly correlated with age in 3CVT (r = -0.24, p < 0.05) and SIR (r = -0.33, p < 0.05).
Conclusions:
BEAMTM parameters, particularly AO N1 peak latency and 3CVT P2 peak latency, can be useful biomarkers for cognitive decline. The significant correlations between BEAMTM biomarkers and age highlight its potential in clinical settings for diagnosing MCI.
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