Korkealaakso, S.; Ahrends, C.; Liljeström, M.; Vidaurre, D.; Renvall, H.; Pauls, K. A. M.

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.