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RESEARCH PAPER / 2025-01-17

Healthy aging changes in conventional frequency bands of neuroelectric brain activity reconstructed from resting-state MEG.

Mikhail Ustinin, Anna Boyko, Stanislav Rykunov

Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Moscow, 125047, Russia.Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Moscow, 125047, Russia. · pubmed

Abstract

Age-related dependencies of electric and spectral powers in conventional frequency bands were studied by the newly proposed method of detailed spectral analysis. The magnetic encephalograms (MEG) and magnetic resonance images (MRI) of the head were obtained from the open archive Cam-CAN. The spatial distributions of elementary spectral components (MEG-based functional tomograms) were reconstructed from MEG for 501 subjects (248 males and 253 females, ages 18-88 years, mean age 54.8 ±18.4). Physiological noise was eliminated by joint analysis of MEG-based functional tomogram and magnetic resonance image for each subject. Spectral and electric powers were calculated in six conventional frequency bands (1-4 Hz - delta; 4-8 Hz - theta; 8-13 Hz - alpha; 13-21 Hz - beta-1; 21-30 Hz - beta-2; 30-48 Hz - gamma), and age-related changes were examined. It was found that the spectral power of the delta band is significantly decreasing (p-value 0.002) and beta-1 and gamma are significantly increasing (p-values 0.001, 0.003). Electric power in the delta band is significantly decreasing (p-value 0.033), while electric power in the beta-1 band is significantly increasing (p-value 0.001). Also, the summary electric power of theta, alpha, beta-1, beta-2, and gamma bands is significantly increasing (p-value 0.024). Results represent general time dependence of the aging brain's electrical sources. The proposed method of joint MEG and MRI analysis can be used for a detailed study of sources location and connectivity.

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Research summary

The paper claims that age-related changes in spectral and electric powers of neuroelectric brain activity can be quantified through a detailed analysis of MEG and MRI data. This research is relevant as it explores the physiological changes associated with aging, contributing to the understanding of the aging process and potentially informing interventions aimed at promoting healthy aging.

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