Accurate detection of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) is critical as therapeutic strategies increasingly emphasize early intervention. While the Mini-Mental State Examination (MMSE) remains a widely used cognitive screening tool, its sensitivity to subtle neurophysiological changes is limited. Emerging evidence suggests that EEG-based biomarkers may provide more objective and biologically grounded indicators of cognitive decline.
A retrospective study conducted at Hawaii Pacific Neuroscience evaluated the effectiveness of BEAM™ (Biomarker-Based Electrophysiology for Advanced Brain Monitoring) in detecting MCI and Alzheimer’s disease, comparing EEG-derived biomarkers with MMSE performance and age-related normative values .
Understanding the Limitations of MMSE in Cognitive Decline
MMSE is commonly used in clinical practice to assess global cognitive function. However, MMSE scores can be influenced by education level, language, and cultural factors, and may not capture early neurophysiological changes associated with neurodegenerative disease.
Electrophysiological tools such as EEG and event-related potentials (ERPs) directly measure brain function, offering a complementary approach that reflects neural processing rather than behavioral performance alone.
What Is BEAM™?
BEAM™ is an EEG-based diagnostic platform developed using machine learning algorithms. It integrates:
- Resting-state EEG analysis
- Neurocognitive task-based ERP measurements
- Automated extraction of multiple electrophysiological biomarkers
BEAM™ is designed to detect patterns associated with cognitive impairment and neurodegeneration, potentially improving early identification of MCI and Alzheimer’s disease .
Study Aims and Design
The study aimed to evaluate the relationship between BEAM™ EEG biomarkers, MMSE scores, and age, and to assess whether BEAM™ measures may serve as useful indicators of cognitive decline.
Methods Overview
A retrospective chart review was conducted on 104 patients diagnosed with MCI or Alzheimer’s disease between March and June 2024. All participants underwent BEAM™ testing, which included:
EEG Conditions
- Resting state (eyes open and eyes closed, 5 minutes each)
Neurocognitive Tasks
- Auditory Oddball (AO)
- 3-Choice Vigilance Test (3CVT)
- Standard Image Recognition (SIR)
A total of 30 EEG biomarkers were analyzed, including:
- Spectral power and frequency-based measures
- Event-related potentials (ERP)
- Heart rate variability (HRV) measures .
Key Results: BEAM™ Biomarkers vs MMSE
The study identified several significant relationships between BEAM™ biomarkers, MMSE scores, and age:
- Mean MMSE score: 24.47
- Weak inverse correlation with age (r = −0.31, p < 0.01)
- Auditory Oddball (AO) N1 peak latency:
- Direct correlation with age (r = 0.34, p < 0.01)
- 3CVT P2 peak latency:
- Moderate correlation with age (r = 0.40, p < 0.01)
- Resting-state peak alpha frequency:
- Weak inverse correlation with age (r = −0.20, p < 0.05)
- AO P300 maximum latency:
- Weak direct correlation with age (r = 0.23, p < 0.05)
- Task accuracy:
- Inversely correlated with age in both 3CVT (r = −0.24) and SIR (r = −0.33), p < 0.05
Clinical Implications of BEAM™ EEG Biomarkers
The findings suggest that BEAM™ parameters, particularly:
- 3CVT P2 peak latency
- AO N1 peak latency
may be sensitive biomarkers of age-related cognitive decline. These electrophysiological markers demonstrated stronger or complementary associations with age compared to MMSE, supporting their potential role in enhancing cognitive assessment.
Advancing Objective Detection of Cognitive Decline
By directly measuring neural activity, BEAM™ offers several advantages:
- Objective, physiology-based assessment
- Reduced reliance on subjective cognitive testing
- Potential for earlier detection of neurodegenerative changes
- Scalable application in clinical and research settings
These features position BEAM™ as a promising tool for improving diagnosis and monitoring of MCI and Alzheimer’s disease.
Conclusion
This retrospective study demonstrates that BEAM™ EEG biomarkers, particularly ERP latency measures, may provide meaningful insights into cognitive decline beyond traditional MMSE scores. The findings support further investigation of BEAM™ as a non-invasive, objective approach to detecting and monitoring MCI and Alzheimer’s disease, with potential implications for both clinical care and research.
