Biology subjects

Korkealaakso, S.

Publications and source records attributed to Korkealaakso, S..

3 recordsLinked to original sources

bioRxiv2026

MEG-informed navigated TMS for individualized speech cortical mapping

Speech cortical mapping by means of navigated repetitive transcranial magnetic stimulation (SCM nrTMS) provides neurosurgeons with noninvasive prior information about individuals cortical speech network. Individualized mapping is required, since the exact locations and activation patterns of speech production show high variability between individuals. We hypothesized that magnetoencephalography (MEG) data of an individuals speech production could guide the SCM TMS process temporally and spatially, leading to higher error rates at MEG-defined locations with TMS pulse timings coinciding with MEG activity. 13 healthy subjects participated in MEG and TMS measurements, where the timing of the TMS pulse (PTI; picture-to-TMS interval) was adjusted based on the individuals MEG activation in a picture naming task. At the group level, significant correlations were observed between the latency of the peak MEG activation and the PTI that produced the highest speech error rate. The MEG peak preceded the best PTI by 132 ms (R=0.713, p=0.006) across the entire stimulation area in the lateral left hemisphere, and by 103 ms (R=0.673, p=0.012) in the left frontal regions. We found 17 combinations of PTI and stimulation area in which the subjects speech error rate increased significantly compared to their average error rate. Our findings suggest that optimal PTIs are highly individual, and that individualizing the PTI according to MEG activation provides a straightforward method for accounting individual variability in speech function and may increase the sensitivity and utility of SCM TMS.

neuroscience↗

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↗

bioRxiv2025

Time- and frequency-resolved decomposition of beta brain activity reveals two functionally distinct beta bands

Oscillatory brain activity in the beta (13-30 Hz) range plays a central role in sensorimotor and other healthy brain processes and is a potential disease biomarker. However, even in the well-studied sensorimotor system, inconsistencies remain concerning betas differential role in maintaining and terminating movement. Emerging evidence suggests that beta comprises two frequency ranges, a lower (<20 Hz) and a higher (>20 Hz) subrange. Moreover, beta occurs in transient events which correlate with perception and action. We here use human whole-brain magnetoencephalography (MEG) recordings and a Hidden Markov Model based analysis approach to discover whole-brain beta-band activity during rest and a naturalistic motor task in a data-driven manner. We successfully delineate two anatomically and functionally distinct beta band components which coexist throughout the neocortex. Low-beta (<20 Hz) events are very rare, high amplitude events, whereas high-beta (>20 Hz) is common but lower in amplitude. Both bands show state-specific anatomical distribution and modulation during a motor task: Low-beta is entirely absent during movement, whereas high-beta is only partially suppressed and reoccurs during postural maintenance. During post-movement beta rebound, low-beta event probability increases threefold, accompanied by strong low-beta event synchronization. A third state, probably corresponding to gamma activity, is active during task execution. Beta rebound occurs via a directed, sequential shift from gamma/active state via high-beta state to low-beta state. Our results provide strong experimental evidence for the coexistence of two functionally and anatomically distinct beta bands, and provide insights into their role in movement initiation, maintenance, and termination. Short abstractThe brains beta band activity is modulated by sensorimotor processing, but inconsistencies remain concerning betas role in maintaining and terminating movement. Mounting evidence points towards two beta bands with distinct functional roles. We use human whole-brain magnetoencephalography and a data-driven Hidden Markov Model based analysis approach to detect whole-brain beta-band activity. We delineate two anatomically and functionally distinct beta band states: a rarely occurring low-beta (<20 Hz) and a common high-beta (>20 Hz) state. During movement, low-beta is completely absent, whereas high-beta is only partially suppressed. During post-movement beta rebound, only low-beta increases significantly, accompanied by pronounced low-beta synchronization. Activity in gamma state increases during movement. Beta rebound occurs via a sequential shift from gamma via high-beta to low-beta state. Our results provide strong experimental evidence for the coexistence of two functionally and anatomically distinct beta bands and provide insights into their role in movement initiation, maintenance and termination.

neuroscience↗