METHODS article
Front. Neuroimaging
Sec. Neuroimaging Analysis and Protocols
This article is part of the Research TopicMind-Body Networks: Structural, Functional, and Metabolic Processes in Central-Autonomic Regulation in Health and DiseaseView all articles
M-ECG: Extracting Heart Signals with a Novel Computational Analysis of Magnetoencephalography Data
Provisionally accepted
Aqil Izadysadr*
Hamideh Sadat Bagherzadeh
Jennifer R Stapleton-KotloskiGautam PopliCormac O`Donovan
Dwayne W Godwin- Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, United States
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Magnetoencephalography (MEG) captures neural activity with high temporal and spatial resolution, but it typically discards other biopotentials, such as cardiac signals, as noise. Here, we demonstrate the feasibility of extracting cardiac signals from MEG recordings using a novel algorithm to compute heart rate variability (HRV), a key autonomic biomarker. Using the Brainstorm MEG auditory dataset and the Open MEG Archive resting-state sample dataset, we developed an approach that isolates MEG-derived electrocardiogram (M-ECG) using either independent component analysis or MEG reference sensors. This algorithm identifies physiologically valid R-peaks, removes outliers, and corrects aberrant RR intervals to enable accurate HRV computation. We evaluated HRV derived from M-ECG against HRV derived from simultaneously recorded electrocardiogram (ECG) using time-domain and frequency-domain measures, along with non-parametric statistical tests and similarity metrics. Results revealed strong temporal and spectral agreement between M-ECG and simultaneously recorded ECG signals, including alignment across HRV bands and minimal bias in RR intervals. These findings highlight the potential of M-ECG for noninvasively assessing autonomic function using existing MEG data. Incorporating HRV into MEG studies could advance our understanding of brain-heart interactions and provide new diagnostic and prognostic insights, particularly in neurological disorders involving autonomic dysregulation.
Keywords: Magnetoencephalography, MEG-derived electrocardiogram (M-ECG), heart rate variability (HRV), autonomic dysfunction, Multimodal Imaging
Received: 29 Jul 2025; Accepted: 03 Dec 2025.
Copyright: © 2025 Izadysadr, Bagherzadeh, Stapleton-Kotloski, Popli, O`Donovan and Godwin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Aqil Izadysadr
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.