Cortical alpha rhythms in mild Alzheimer's disease. A multicentric EEG study

Resting-state EEG alpha/theta power ratio discriminates early-onset Alzheimer's disease from healthy controls

OBJECTIVES

The present study aims to compare early-onset Alzheimer's disease (EOAD) patients with healthy controls (HC), and late-onset Alzheimer's disease (LOAD) patients using resting-state delta, theta, alpha, and beta oscillations and provide a cut-off score of alpha/theta ratio to discriminate individuals with EOAD and young HC.

METHODS

Forty-seven individuals with EOAD, 51 individuals with LOAD, and demographically-matched 49 young and 51 older controls were included in the study. Spectral-power analysis using Fast-Fourier Transformation (FFT) is performed on resting-state electroencephalography (EEG) data. Delta, theta, alpha, and beta oscillations compared between groups and Receiver Operating Characteristic (ROC) curve analysis was conducted.

RESULTS

Compared to healthy controls individuals with EOAD showed an increase in slow frequency bands and a decrease in fast frequency bands. Frontal alpha/theta power ratio is the best discriminating value between EOAD and young HC with the sensitivity and specificity greater than 80% with area under the curve (AUC) 0.881.

CONCLUSIONS

EOAD display more widespread and severe electrophysiological abnormalities than LOAD and HC which may reflect more pronounced pathological burden and cholinergic deficits in EOAD. Additionally, the alpha/theta ratio can discriminate EOAD and young HC successfully.

SIGNIFICANCE

This study is the first to report that resting-state EEG power can be a promising marker for diagnostic accuracy between EOAD and healthy controls.

EEG beta 2 power as surrogate marker for memory impairment: a pilot study

BACKGROUND

Memory deficits are dominant in dementia and are positively correlated with electroencephalographic (EEG) beta power. EEG beta power can predict the progression of Alzheimer´s (AD) as early as at the stage of mild cognitive impairment (MCI) and could possibly be used as surrogate marker for memory impairment. The objective of this study is to analyze the relationship between frontal and parietal EEG beta power and memory-test outcome. Frontal and parietal beta power is analyzed for a resting state and an eyes-closed backward counting condition and related to memory impairment parameters.

METHODS

A total of 28 right-handed female geriatric patients (mean age = 80.6) participated voluntarily in this study. Beta 1 (12.9-19.2 Hz) and beta 2 (19.2-32.4 Hz) EEG power at F3, F4, Fz, P3, P4, and Pz are correlated with immediate wordlist recall, delayed wordlist recall, recognition of learned words, and delayed figure recall. For classification between impaired and intact memory, we calculated a binary logistic regression model with memory impairment as a dependent variable and beta 2 power as an independent variable.

RESULTS

We found significant positive correlations between frontal and parietal beta power and delayed memory recall. A significant correlation (Bonferroni correction, p < 0.05) was found at F4 beta 2 during backward counting. The binary logistic regression model with F4 beta 2 power during the counting condition as a predictor yielded a sensitivity of 76.9% (95% CI) and a specificity of 73.3% (95% CI) for classifying patients into "verbal-memory impaired" and "intact."

CONCLUSIONS

EEG beta 2 power recorded during a backward counting condition with eyes closed can be used as surrogate marker for verbal memory impairment in geriatric patients. Antidepressant treatment was correlated with EEG data in resting state but not in counting condition. Further studies are necessary to verify the results of this pilot study.