Correlations of frontal resting-state EEG markers with MMSE scores in patients with Alzheimer’s disease

Abstract

Background A previous study suggests that resting-state EEG biomarkers measured at prefrontal region (Fp1, and Fp2) are moderately correlated with Mini-Mental State Examination (MMSE) scores of elderly people with Alzheimer’s disease. In this study, our objective was to investigate whether resting-state EEG biomarkers recorded from frontal region are correlated with each MMSE sub-scores. 20 elderly patients diagnosed as Alzheimer’s disease entered to the study. After completion of MMSE, subjects underwent EEG for 5 min with closed eyes condition. We measured median frequency, theta/alpha power ratio, and relative powers. To examine the relationship between these features and MMSE sub-scores first, Pearson correlation coefficients were computed for each feature and MMSE sub-scores. Then, p values were computed for each correlation. Finally, a Bonferroni correction was done. Results Nine correlations have been found for markers recorded from F3, F7, and Fz. Alpha and beta relative powers were the markers which shows correlations. We found that MMSE overall, attention, and calculation scores are significantly correlated with beta relative powers recorded from F3, and Fz, and alpha relative power from F7. Orientation to time scores were correlated with F3, and Fz beta relative powers. The only correlation found for orientation to place was beta relative power of F3. Conclusions Our results indicate that there are correlations between frontal EEG markers and MMSE sub-scores of patients with Alzheimer’s disease. The results show that alpha and beta relative powers are markers correlated with MMSE scores. It seems that if we want to develop predicting models for Alzheimer’s disease, using data recorded from other frontal electrodes, especially what we have introduced should be considered.

Oscillatory characteristics of resting-state magnetoencephalography reflect pathological and symptomatic conditions of cognitive impairment

“…The relationship between oscillatory parameters measured using EEG and NPAs has been described previously. For example, previous studies have shown negative correlations between parieto-occipital delta sources and MMSE scores ( Babiloni et al, 2006a ; Lizio et al, 2016 ), positive correlations between RPs in the alpha and beta bands in frontal electrodes and MMSE scores ( Torabinikjeh et al, 2022 ), and positive correlations between the prefrontal MF, IAF, alpha-to-theta ratio, and MMSE scores after adjusting for age and education level ( Choi et al, 2019 ). Moreover, other studies examined the relationships between EEG oscillatory parameters and MMSE scores in patients with probable AD using a coefficient of determination ( R 2 ), which quantifies the amount of data variation explained by MMSE; these studies revealed that the changes in RPs in the theta, alpha, and beta bands and SSE corresponded to changes in MMSE scores ( Garn et al, 2014 , 2015 ; Coronel et al, 2017 ).…”

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

“…Four main frequency bands, namely delta (δ) [0.1-4] Hz, theta (θ) [4][5][6][7][8] Hz, alpha (α) [8][9][10][11][12][13] Hz and beta (β) [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz were extracted from each EEG signal using designed bandpass filters, and each related dataset was created. Furthermore, in order to assess which frequency band was the most distinctive in the classification of HC, SCD and MCI, we also filtered the signals in the entire range [0.1-30] Hz, and an additional dataset (all-band) was generated.…”

“…Several studies proposed resting-state EEG (rsEEG) rhythms as candidate biomarkers of AD [28][29][30][31]. A more comprehensive review of research in this field can be found in the work by Babiloni et al [32].…”

Quantification of the robustness of functional neural networks: application to the characterization of Alzheimer’s disease continuum

“…Zhang et al (2021) found that α oscillation spectral power and β oscillation phase synchronization index correlated well with the MMSE test scores in AD groups. Finally, (Torabinikjeh et al 2022) indicated that α and β relative powers were markers correlated with MMSE scores. We can also see the replicability between databases, showing the potential of the new methodology to characterize the alterations on the functional network.…”

“…In fact, the cognitive evaluation with the MMSE and the functional neural network robustness alterations may reflect the same underlying phenomena. Different studies have assessed the correlation of the MMSE (or other neurophysiological tests) with M/EEG measures (Choi et al 2019, Zorick et al 2020, Torabinikjeh et al 2022. Choi et al (2019) suggested that restingstate EEG slowing measured in the prefrontal regions may be useful for the screening of AD patients as it was able to predict MMSE values.…”