Pauls, K. A. M. | Search bioRxiv

bioRxiv2026

Test-retest reliable and site-robust Hidden Markov Model framework for discovering whole-brain beta activity

Sensorimotor beta activity (13-30 Hz) is a key neuronal signature in the human sensorimotor system, and its features can be effectively measured using functional brain imaging methods such as magnetoencephalography (MEG). In addition to its importance in healthy brain processing, beta activity has been shown to be altered in several neurological diseases, underscoring its potential as a biomarker. To serve as biomarkers, features must be reliably defined, stable across measurements and, ideally, amenable to automated analysis, yet current approaches to beta characterization require subjective decisions and manual work. We here describe a hidden Markov model (HMM) based approach to automatically segment beta events from source level MEG beta band activity into discrete high- and low-beta states. We demonstrate the differences between the proposed HMM based approach and a commonly used amplitude-envelope based approach to analyse high- and low-beta modulation. We show that the methods complement each other both when applied to resting data and task related passive movement data. Furthermore, we assess the test-retest reliability of the proposed pipeline within individuals using intraclass correlation coefficients (ICC), and test if HMM constructed at one measurement site can be applied to data acquired at another site, thereby evaluating its multisite transferability. We show that the proposed approach produces stable results within subjects and across sites for many of the features. The ICC values were excellent for high-beta state (86-100% of brain areas), while low-beta state test-retest reliability was more modest. Most of the features showed statistically significant differences between sites only in a few brain areas, indicating very good multisite stability. The proposed approach can serve as an automated, reproducible analysis pipeline for, e.g., clinical applications, and appears suitable for multi-site datasets.

neuroscience↗

bioRxiv2023

Human sensorimotor beta event characteristics and aperiodic signal are highly heritable

Individuals phenotypes, including the brains structure and function, are largely determined by genes and their interplay. The resting brain generates salient rhythmic patterns which can be characterized non-invasively using functional neuroimaging such as magnetoencephalography (MEG). One of these rhythms, the somatomotor ( rolandic) beta rhythm, shows intermittent high amplitude events which predict behavior across tasks and species. Beta rhythm is altered in neurological disease. The aperiodic ( 1/f) signal present in electrophysiological recordings is also modulated by some neurological conditions and aging. Both sensorimotor beta and aperiodic signal could thus serve as biomarkers of sensorimotor function. Knowledge about the extent to which these brain functional measures are heritable could shed light on the mechanisms underlying their generation. We investigated the heritability and variability of human spontaneous sensorimotor beta rhythm and aperiodic activity in 210 healthy adult siblings spontaneous MEG activity. Both the overall beta spectral power as well as time-resolved beta event amplitude parameters were highly heritable, whereas the heritabilities for peak frequency and measures of event duration remained nonsignificant. Interestingly, the most heritable trait was the aperiodic 1/f signal, with a heritability of 0.94 in the right hemisphere. Human sensorimotor neural activity can thus be dissected into different components with variable heritability. We postulate that differences in heritability in part reflect different underlying signal generating mechanisms. The 1/f signal and beta event amplitude measures may depend more on fixed, anatomical parameters, whereas beta event duration and its modulation reflect dynamic characteristics, guiding their use as potential disease biomarkers.

neuroscience↗