Research Paper Volume 12, Issue 21 pp 21613—21637

Age- and gender-specific characteristics of the resting-state brain activity: a magnetoencephalography study

Hideyuki Hoshi1, , Yoshihito Shigihara1, ,

  • 1 Precision Medicine Centre, Hokuto Hospital, Obihiro-shi, Hokkaido, Japan

Received: January 29, 2020       Accepted: August 1, 2020       Published: November 4, 2020
How to Cite

Copyright: © 2020 Hoshi and Shigihara. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Aging and gender influence regional brain activities. Although these biases should be considered during the clinical examinations using magnetoencephalography, they have yet to be standardized. In the present study, resting-state magnetoencephalography data were recorded from 54 healthy females and 48 males aged 22 to 75 years, who were controlled for cognitive performance. The regional oscillatory power was estimated for each frequency band (delta, theta, alpha, beta, low-gamma, and high-gamma) using the sLORETA-like algorithm and the biases of age and gender were evaluated, respectively. The results showed that faster oscillatory powers increased with age in the rostral regions and decreased in the caudal regions, while few slower oscillatory powers changed with age. Gender differences in oscillatory powers were found in a broad frequency range, mostly in the caudal brain regions. The present study characterized the effects of healthy aging and gender asymmetricity on the regional resting-state brain activity, with the aim to facilitate the accurate and efficient use of magnetoencephalography in clinical practice.


EEG: electroencephalography; MEG: magneto encephalography; MRI: magnetic resonance imaging; fMRI: functional magnetic resonance imaging; MMSE-J: mini-mental state examination Japanese version; EC: eyes-closed; EO: eyes-open; ROI: region of interest; PCA: principal component analysis; ICA: independent component analysis; FWE: family-wise-error.