Izadysadr, A. | bioRxiv Science

bioRxiv2025

M-ECG: Extracting Heart Signals with a Novel Computational Analysis of Magnetoencephalography Data

Magnetoencephalography (MEG) measures the magnetic fields generated by neural activity with high temporal and spatial resolution. Because of its focus on brain activity, other biopotentials, including muscle artifacts and heart signals, are typically filtered or rejected. In this study, the feasibility of extracting cardiac signals from MEG data, which is termed magnetoencephalographic electrocardiogram (M-ECG; in contrast to the electrocardiogram or ECG) is explored. Using the publicly available Brainstorm MEG auditory dataset CTF and OMEGA resting-state sample dataset, a novel algorithm is developed that utilizes either independent component analysis (ICA) or MEG reference sensors to extract M-ECG signals and compute heart rate variability (HRV) from MEG data reliably and accurately. Signal processing methods in the time, frequency, and time-frequency domains along with statistical tests such as Spearman correlation, root mean square error, mean absolute error, Bland-Altman mean difference, and Mann-Whitney U Test are employed to assess the similarities across the signals. The results indicate a significant alignment of temporal and frequency spectral power characteristics between M-ECG HRV and ECG HRV signals, suggesting a promising degree of similarity and correspondence. The findings highlight the feasibility of extracting M-ECG and computing HRV directly from raw MEG data. These insights hold the potential to enhance multimodal neuroimaging methodologies and further elucidate the intricate interplay between brain activity and cardiovascular function. The potential of HRV as a biomarker for brain disorders could improve diagnostic accuracy, prognostic assessment, and therapeutic strategies, particularly in neurological disorders with centrally mediated autonomic dysfunction.

neuroscience↗

bioRxiv2022

Imaging cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG beta rhythm

Accurate mapping of cortical engagement during mental imagery or cognitive tasks remains a challenging brain-imaging problem with immediate relevance to the development of brain-computer interfaces (BCI). We analyzed data from fourteen individuals who performed cued motor imagery, mental arithmetic, or silent word generation tasks during MEG recordings. During the motor imagery task, participants imagined the movement of either both hands (HANDS) or both feet (FEET) after the appearance of a static visual cue. During the cognitive task, participants either mentally subtracted two numbers that were displayed on the screen (SUB) or generated words starting with a letter cue that was presented (WORD). The MEG recordings were denoised using a combination of spatiotemporal filtering, the elimination of noisy epochs, and ICA decomposition. Cortical source power in the beta-band (17-25 Hz) was estimated from the selected temporal windows using a frequency-resolved beamforming method applied to the sensor-level MEG signals. The task-related cortical engagement was inferred from beta power decrements within non-overlapping 400 ms temporal windows between 400 and 2800 ms after cue presentation relative to a baseline 400 ms temporal window before cue onset. We estimated the significance of these power changes within each parcel of the Desikan-Killiany atlas using a non-parametric permutation test at the group level. During the HANDS and FEET movement-imagery conditions, beta-power decreased in premotor and motor areas, consistent with a robust engagement of these cortical regions during motor imagery. During WORD and SUB tasks, beta-power decrements signaling cortical engagement were lateralized to left hemispheric brain areas that are expected to engage in language and arithmetic processing within the temporal (superior temporal gyrus), parietal (supramarginal gyrus), and (inferior) frontal regions. A leave-one-subject-out cross-validation using a support vector machine (SVM) applied to beta power decrements across brain parcels yielded accuracy rates of 74% and 68% respectively, for classifying motor-imagery (HANDS-vs-FEET) and cognitive (WORD-vs-SUB) tasks. From the motor-versus-nonmotor contrasts, accuracy rates of 85% and 80% respectively, were observed for HAND-vs-WORD and HAND-vs-SUB. A multivariate Gaussian process classifier (GPC) provided an accuracy rate of 60% for a four-way (HANDS-FEET-WORD-SUB) classification problem. The regions identified by both SVM and GPC classification weight maps were largely consistent with the source modeling findings. Within-subject correlations of beta-decrements during the two task sessions provided insights into the level of engagement by individual subjects and showed moderately high correlations for most subjects. Our results show that it is possible to map the dynamics of cortical engagement during mental processes in the absence of dynamic sensory stimuli or overt behavioral outputs using task-related beta-power decrements. The ability to do so with the high spatiotemporal resolution afforded by MEG could potentially help better characterize the physiological basis of motor or cognitive impairments in neurological disorders and guide strategies for neurorehabilitation.

neuroscience↗