Biology subjects

Renvall, H.

Publications and source records attributed to Renvall, H..

11 recordsLinked to original sources

bioRxiv2026

Matching Corticospinal and Cortical Excitability along the motor cortex: TMS-EMG-EEG study

There is still no consensus on how to choose intensities for transcranial magnetic stimulation (TMS). The resting motor threshold (RMT), the intensity needed to evoke muscle contractions in the contralateral limb muscles, is a robust muscle-specific index of corticospinal excitability. RMT from one of the hand muscles is commonly used as a reference for TMS neuromodulation interventions and TMS-electroencephalography (EEG) studies. However, it only provides an indirect measure of cortical excitability, and even for the primary motor cortex (M1), we do not understand how cortical and corticospinal excitability correspond to each other. Indeed, it remains unclear whether cortical responses to TMS follow similar location-dependent patterns along the M1, and whether subthreshold stimulation can reliably engage cortical circuits relevant to motor output. Combining TMS with electromyography (EMG) and EEG, TMS-EMG-EEG, enables simultaneous assessment of corticospinal and cortical excitability to answer these questions. We investigated how cortical excitability thresholds (CETs, measured with EEG), relate to corticospinal excitability thresholds (RMTs, measured with EMG) at the hand, arm, and leg hotspots along the M1 in 24 subjects. RMTs and CETs were strongly correlated, and both increased from hand to arm to leg. CETs were generally lower than RMTs, indicating that subthreshold stimulation was enough to evoke robust early TMS evoked potentials (TEPs). Within subjects, CETs differed from the corresponding RMTs at all three M1 hotspots, and this difference increased from hand to leg representations. The findings of the present study argue against using a default hand-muscle RMT to set cortical excitability dosage along the M1 and beyond. Online TMS-EEG can enable real-time dosing and could improve stimulation precision, efficiency, and generalizability, but the site-specific variability calls for adaptive control.

neuroscience↗

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

Magnetoencephalography reveals adaptive neural reorganization maintaining lexical-semantic proficiency in healthy aging

Although semantic cognition remains behaviorally stable with age, neuroimaging studies report age-related alterations in response to semantic context. We aimed to reconcile these inconsistent findings by combining behavioral and magnetoencephalography (MEG) measures and examined the neural mechanisms underlying lexical-semantic processing in healthy aging. Forty-six Finnish-speaking adults (25 female) across younger (22- 32 years) and older (62 - 69 years) cohorts performed a semantic congruency judgment task on word pairs. Both age groups performed the task with high accuracy, with older adults responding faster than young. While advancing age was not associated with impaired task performance, MEG revealed neural reorganization in the old compared to young adults. Young participants exhibited the canonical N400 effect in the left temporal cortex 300 - 400 ms post-stimulus, followed by a Late Positive Complex (LPC), whereas older adults showed less pronounced congruency effects, and the activation distribution was shifted toward frontal and bilateral regions. Despite the absence of the canonical N400 effect in older participants, multivariate time-resolved decoding demonstrated that the congruency effect was decodable from the MEG responses in both groups from [~]300 ms post-stimulus, indicating preserved sensitivity to congruency with age. Decoding performance correlated with response time, suggesting that increased neural separation between semantic concepts facilitates rapid decision-making. Critically, age groups were distinguishable based on their neural responses 300 - 800 ms post-stimulus, reflecting functional reorganization in older adults. Overall, our findings suggest that healthy aging involves adaptive reconfiguration of neural systems, supported by experience-driven strategies that preserve semantic proficiency despite broader age-related cognitive changes.

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↗

bioRxiv2025

Low-dimensional brain-symptom associations delineate depression phenotypes with distinct connectivity biomarkers and symptom profiles

Depression is neurobiologically and clinically heterogeneous. New approaches using resting-state functional MRI (rs-fMRI) functional connectivity (FC) data have modeled the neural basis of depression heterogeneity and revealed unique neural phenotypes. Yet, no studies have identified depression phenotypes from electrophysiological magnetoencephalography (MEG) data although MEG measures human brain dynamics at millisecond precision. We demonstrate here unique depression phenotypes based on MEG-oscillation FC. We collected resting-state MEG, MRI, and clinical symptom data from 263 patients with unipolar depression and 75 healthy controls. We assessed MEG-FC with two oscillatory coupling-mode measures that are fundamental for information processing. To define normative phenotypes, we computed their latent-space low-dimensional brain-symptom associations, and used these components to identify phenotypes using unsupervised machine learning. We identified five stable depression phenotypes that were characterized by unique symptom profiles and distinct spectral patterns. Our results demonstrate new neural underpinnings of depression heterogeneity and reveal unique neural phenotypes with potential personalized diagnostic value.

neuroscience↗

bioRxiv2025

Capturing individual variation in children's oscillatory brain activity during nREM sleep

Human brain dynamics are highly unique between individuals: functional neuroimaging studies have recently described functional features that can be used as neural fingerprints. As such studies have generally been performed on adults, it is unclear if the results are applicable during brain development. Here we applied Bayesian reduced-rank regression (BRRR), to extract low-dimensional representations of electroencephalography (EEG) power spectra measured from normally developing children at different ages. The clinical EEG recordings represented different sleep stages in a pediatric population (N=782) of ages varying from 6 weeks to 19 years. The representations learned within specific sleep stages successfully separated between the subjects. Furthermore, a model trained with data across sleep stages generalized to unseen data, suggesting that informative neural fingerprints could be extracted. Fingerprint stability increased with the age of the subjects. In comparison to correlation-based measures in differentiating individuals from each other, the BRRR model performed better, highlighting the usefulness of the dimensionality reduction for data obtained in demanding clinical settings. While further studies are needed to address the possible non-linear maturation effects over developmental periods, our results demonstrate, for the first time, the existence of stable within-session neurofunctional fingerprints in pediatric populations.

neuroscience↗

bioRxiv2024

Automated speech artefact removal from MEG data utilizing facial gestures and mutual information

The ability to speak is one of the most crucial human skills, motivating neuroscientific studies of speech production and speech-related neural dynamics. Increased knowledge in this area, allows e.g., for development of rehabilitation protocols for language-related disorders. While our understanding of speech-related neural processes has greatly enhanced owing to non-invasive neuroimaging techniques, the interpretations have been limited by speech artefacts caused by the activation of facial muscles that mask important languagerelated information. Despite earlier approaches applying independent component analysis (ICA), the artefact removal process continues to be time-consuming, poorly replicable and affected by inconsistencies between different observers, typically requiring manual selection of artefactual components. The artefact component selection criteria have been variable, leading to non-standardized speech artefact removal processes. To address these issues, we propose here a pipeline for automated speech artefact removal from MEG data. We developed an ICA-based speech artefact removal routine by utilizing EMG data measured from facial muscles during a facial gesture task for isolating the speech-induced artefacts. Additionally, we used mutual information (MI) as a similarity measure between the EMG signals and the ICA-decomposed MEG to provide a feasible way to identify the artefactual components. Our approach efficiently and in an automated manner removed speech artefacts from MEG data. The method can be feasibly applied to improve the understanding of speech-related cortical dynamics, while transparently evaluating the removed and preserved MEG activation.

neuroscience↗

bioRxiv2023

Human sensorimotor resting state beta events and 1/f response show good test-retest reliability

Neurological conditions affecting the sensorimotor system have a profound impact on individuals physical independence and are associated with a considerable socioeconomic burden. Reliable functional biomarkers allowing early diagnosis of these conditions or targeting treatment and rehabilitation can reduce this burden. Magnetoencephalography (MEG) can non-invasively measure the brains salient rhythmic patterns such as the somatomotor ( rolandic) rhythm. This rhythm shows intermittent high amplitude events in the beta (14-30 Hz) frequency range which predict behavior across tasks and species and are altered by neurological diseases affecting the sensorimotor system. Thus, the sensorimotor resting beta phenotype is a promising candidate biomarker of sensorimotor function. A prerequisite for use as a biomarker is that it can be quantified reliably across different measurement sessions. Here, using MEG, we assessed the test-retest stability of spontaneously occurring sensorimotor power spectral characteristics, including both aperiodic (1/f) as well as beta band fluctuations ( beta events) in a cohort of 50 healthy human controls. Test-retest reliability across two separate measurement sessions was assessed using the intraclass correlation coefficient (ICC). Beta events were determined using a thresholding-based approach on a narrow-band filtered amplitude envelope obtained using Morlet wavelet decomposition across a range of parameters (recording length, amplitude threshold and filtering bandwidth). We find that both aperiodic power spectral features as well as several beta event characteristics show good to excellent testretest stability. Especially aperiodic component power spectral features (ICC 0.77-0.88), but also measures of beta event amplitude (ICC 0.74-0.82) were found to be very stable, while measures of individual beta event duration were less reliable, especially for the left hemisphere (ICC right [~]0.7, left [~]0.55). Recordings of 2-3 minutes were sufficient to obtain stable results for most parameters. Important for potential clinical applications, automatization of beta event extraction was successful in 86 % of cases. Beta event rate and duration measures were more sensitive to analysis parameters than the measures of event amplitude. The results suggest the sensorimotor beta phenotype is a stable feature of an individuals resting brain activity even for short, 2-3 minute recordings which can be easily measured in patient populations, facilitating its use as a potential clinical biomarker.

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↗

bioRxiv2019

Dynamic time-locking mechanism in the cortical representation of spoken words

Human speech has a unique capacity to carry and communicate rich meanings. However, it is not known how the highly dynamic and variable perceptual signal is mapped to existing linguistic and semantic representations. In this novel approach, we utilized the natural acoustic variability of sounds and mapped them to magnetoencephalography (MEG) data using physiologically-inspired machine-learning models. We aimed at determining how well the models, differing in their representation of temporal information, serve to decode and reconstruct spoken words from MEG recordings in 16 healthy volunteers. We discovered that time-locking of the cortical activation to the unfolding speech input is crucial for the encoding of the acoustic-phonetic features. In contrast, time-locking was not highlighted in cortical processing of non-speech environmental sounds that conveyed the same meanings as the spoken words, including human-made sounds with temporal modulation content similar to speech. The amplitude envelope of the spoken words was particularly well reconstructed based on cortical evoked responses. Our results indicate that speech is encoded cortically with especially high temporal fidelity. This mechanism may contribute to the frequently reported entrainment of the cortical oscillations to the amplitude envelope of speech. Furthermore, the phoneme content was reflected in cortical evoked responses simultaneously with the spectrotemporal features, pointing to an instantaneous transformation of the unfolding acoustic features into linguistic representations during speech processing.\n\nSummary: Computational modeling of cortical responses to spoken words highlights the relevance of temporal tracking of spectrotemporal features, which is likely pivotal for transforming the acoustic-phonetic features into linguistic representations.

neuroscience↗