Biology subjects

Roine, T.

Publications and source records attributed to Roine, T..

8 recordsLinked to original sources

bioRxiv2026

Distinct cortical excitability and connectivity profiles within the human SMA complex

Introduction and aimUnderstanding brain functions increasingly relies on a network-based perspective, emphasizing interactions across distributed regions. High-order cognitive functions, like language and executive processes, engage such networks rather than isolated cortical areas, yet mapping their dynamics in humans remains challenging. The supplementary motor area (SMA) complex serves as a crucial hub, functioning as an interface between cognitive and sensorimotor processing. Navigated transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) enables causal, time-resolved assessment of cortical excitability and connectivity with high temporal and spatial precision. Extending prior proof-of-concept work, this study aims to provide normative neurophysiological signatures of the SMA complex (pre-SMA and SMA). MethodsTwenty-one healthy subjects underwent a TMS-EEG session where six stimulation targets over the SMA complex (three in pre-SMA, one in the border between pre-SMA and SMA, two in SMA) were recorded. Global cortical excitability via global mean field power (GMFP) was computed over three time-windows (10-50, 50-100, and 100-200 ms) and compared across targets. Normalized power in different frequency bands (, {beta}1, {beta}2, {gamma}) and natural frequency (NF) were also computed and compared across stimulation sites. Then, we calculated tractography-derived connectivity metrics to examine corticothalamic projections from each stimulation site. Apparent fiber density (AFD)-derived connection strength quantified intra-axonal fibre volume along pathways to the whole thalamus, whereas fibre bundle capacity (FBC) assessed the aggregate intra-axonal cross-sectional area at pathway endpoints, providing an estimate of information transmission capacity to each of the 23 thalamic nuclei. ResultsCortical excitability and oscillatory properties varied significantly across TMS targets. A gradient in cortical excitability was observed, where anterior and middle portions of pre-SMA exhibited significantly lower GMFP in the early post-stimulus phase (i.e., 10-50 ms) than SMA portions; interestingly, these differences were clearly dampened in a later time-window (50-100 ms) and disappeared after this period. Furthermore, higher and lower {beta}2 power were observed in anterior pre-SMA than posterior pre-SMA portions. No differences were observed in NF. Crucially, we also observed significant differences in whole- and single-nuclei thalamic structural connectivity. DiscussionThese findings provide direct human in vivo evidence for distinct neurophysiological and structural connectivity profiles of the pre-SMA and SMA, possibly reflecting their different cytoarchitectonic, myeloarchitectonic and connectivity properties. Variations in cortical excitability, oscillatory dynamics, and FBC along the rostro-caudal axis may mirror functional specialization, with pre-SMA supporting cognitive control processes and SMA engaging motor-related thalamocortical networks. Together, the results demonstrate the sensitivity of TMS-EEG and diffusion-weighted connectomics to measure fine-grained neurophysiological differences of corticothalamic projections, which can lead to individualized neuromodulation targeting.

neuroscience↗

bioRxiv2025

Fast and standardized motor-hotspot determination with automated TMS mapping

Determining the optimal stimulation target for motor responses (motor hotspot) and the required intensity for reliably eliciting said responses (motor threshold) are common procedures in transcranial magnetic stimulation (TMS) research and treatments. However, the procedures for determining them are user-dependent, slow, and lack standardization, leading to long stimulation sessions with potentially inadequate outcomes. Partially automated algorithms for determining the motor threshold have been developed, but the motor hotspot is still largely mapped by hand. Automating the hotspot mapping will accelerate the process and improve standardization and accuracy. We developed a fully automated algorithm for finding the motor hotspot with multi-locus TMS and Bayesian optimization. Tested online in five healthy participants, the algorithm located motor hotspots with (mean {+/-} 95% CI) 2.1 {+/-} 0.7 mm and 6 {+/-} 2{degrees} difference from the global best target with only (mean) 47 stimuli. This is a significant improvement from previous motor-mapping algorithms, which do not optimize for stimulation location and orientation simultaneously. This accurate, fast, and user-independent procedure paves the way for faster experimental processes and more streamlined clinical applications.

neuroscience↗

bioRxiv2025

Predictive modeling of TMS-evoked responses: Unraveling instantaneous excitability states

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) and electromyography (EMG) provides a unique window into instantaneous cortical and corticospinal excitability states. We investigated 50 healthy participants to determine how fluctuations in pre-stimulus brain activity influence single-trial TMS-evoked potentials (TEPs) and motor-evoked potentials (MEPs). We developed a novel automated source-level TEP extraction method using individualized spatiotemporal priors that is robust against poor single-trial signal-to-noise ratios (SNRs) and ongoing oscillations. TEP and MEP amplitudes were predicted with linear mixed-effects models based on pre-stimulation EEG band-powers (theta to gamma), while accounting for temporal drifts (within-session trends), coil control, and inter-subject differences. We found that higher pre-stimulus sensorimotor alpha, beta, and gamma power were each associated with larger TEPs, indicating a more excitable cortical state. Increases in alpha and gamma power immediately before stimulation specifically predicted larger MEPs, reflecting increased corticospinal excitability. These results reveal relationships between ongoing oscillatory brain states and TMS response amplitudes, identifying EEG biomarkers of high- and low-excitability states. In conclusion, our study demonstrates the feasibility of single-trial source-level TMS-EEG analysis and shows that spontaneous alpha-, beta-, and gamma-band oscillations modulate motor cortical and corticospinal responsiveness. These findings pave the way for EEG-informed, brain-state-dependent TMS protocols to optimize neuromodulatory interventions in clinical and research applications.

neuroscience↗

bioRxiv2025

A High-Precision Timing Method and Digital Interface for Closed-Loop TMS

ObjectiveCurrent transcranial magnetic stimulation (TMS) protocols exhibit high inter-subject variability in treatment outcomes, highlighting the need for personalized, brain-state-dependent closed-loop stimulation protocols. To enable such protocols, we aim to provide robust, precisely timed external control of TMS, with stimulation timed relative to feedback signals such as the electroencephalogram (EEG). ApproachCommercial TMS devices typically rely on trigger signals for precise external pulse timing, while adjusting stimulation parameters, such as intensity, is better handled via serial digital communication, which supports robust error detection and feedback. However, combining these communication methods is inherently complex and prone to timing issues, such as race conditions. Furthermore, trigger signals lack capabilities essential for real-time systems, such as preventing late pulse delivery. We present a method for precise and accurate pulse timing, implemented through a digital interface that uses exclusively serial digital messaging, eliminating the need for trigger signals. This interface enables external control of pulse timing, intensity, and other parameters. The TMS device maintains its own internal clock and delivers pulses at pre-scheduled times, decoupling timing precision from the control device. Additionally, we propose a method for synchronizing such time-tracking TMS devices with commercial EEG systems, enabling precisely timed EEG-TMS. Main resultsUsing these methods, our custom TMS device delivered pulses precisely aligned to the EEG signal, with timing errors consistently below 0.3 ms. These errors were constrained by the experimental setup, including the sampling rate of our EEG device and the signal-to-noise ratio affecting pulse detection. SignificanceOur timing method achieves sub-millisecond precision in brain-state-dependent closed-loop EEG-TMS, providing a foundation for robust TMS timing that supports adaptive, personalized stimulation protocols. The digital control interface, co-designed with our TMS device, integrates pulse timing and parameters, setting a precedent for future advancements in computer-controlled TMS.

bioengineering↗

bioRxiv2025

NeuroSimo: an open-source software for closed-loop EEG- or EMG-guided TMS

ObjectiveOur goal was to create open-source software for closed-loop EEG-TMS that allows researchers to rapidly prototype and develop novel stimulation paradigms in a high-level programming language. This addresses the limitations of current solutions, which often rely on proprietary hardware and software, limiting their accessibility and customizability, or comprise ad-hoc pipelines tailored to specific use cases. ApproachWe developed NeuroSimo, a software platform that enables arbitrary EEG-TMS stimulation protocols written in Python, leveraging Pythons ecosystem of scientific, neuroimaging, and machine learning libraries. The core software is written in C++ with an embedded Python interpreter and employs the Robot Operating System (ROS 2) for inter-process communication. NeuroSimo runs on real-time-enabled Linux Ubuntu, using LabJack T4 for pulse triggering, and supports two EEG device models (Bittium NeurOne, BrainProducts actiCHamp) and TMS devices that deliver pulses via trigger signals. The software includes a graphical user interface for configuration and performance monitoring, and supports GPU processing for neural network computations. Main resultsIn brain-state-dependent stimulation using the Phastimate algorithm, which targets TMS pulses to the trough of sensorimotor -rhythm, NeuroSimo achieved a median timing error of 0.2 ms (95% CI: 0.2-0.2 ms), a 99th-percentile of 0.6 ms (0.6-0.6 ms), and a maximum of 1.4 ms. SignificanceAs an open-source platform combining Pythons flexibility with real-time closed-loop EEG-TMS, NeuroSimo enables researchers to develop and implement novel therapeutic approaches, marking a significant advance in personalized brain stimulation.

bioengineering↗

bioRxiv2024

Optimization of TMS target engagement: novel evidence based on combined TMS-EEG and dMRI tractography of brain circuitry

Neuromodulation is based on the principle that brain stimulation produces plastic changes in cerebral circuitry. Given the intersubject structural and functional variability, neuromodulation has a personalized effect in the brain. Moreover, because of cerebral dominance and interhemispheric functional and structural differences in the same individual, the characterization of specific brain circuitries involved is currently not feasible. This notion is extremely important for neuromodulation treatments applied in neuropsychiatry. Specifically, the efficacy of the neuromodulation treatments is critically dependent on the anatomical precision of the brain target and the circuitry which has been affected. However, a complete understanding of how the brain behaves under stimulation needs a combined characterization of its neurophysiological response. This can be achieved by TMS-EEG guided by current multimodal neuroimaging techniques in real time. Herein, we present novel data based on dMRI tractography-guided TMS-EEG on one healthy young adult volunteer.

neuroscience↗

bioRxiv2024

Forecasting EEG time series with WaveNet

Forecasting electroencephalography (EEG) signals, i.e., estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and closed-loop brain stimulation. As these applications are becoming more and more common, the importance of a good prediction model has increased. Previously, the autoregressive model (AR) has been employed for this task -- however, its prediction accuracy tends to fade quickly as multiple steps are predicted. We aim to improve on this by applying probabilistic deep learning to make robust longer-range forecasts. For this, we applied the probabilistic deep neural network model WaveNet to forecast resting-state EEG in theta- (4-7.5 Hz) and alpha-frequency (8-13 Hz) bands and compared it to the AR model. WaveNet reliably predicted EEG signals in both theta and alpha frequencies over 100 ms ahead, with mean errors of 0.8{+/-}0.6 {micro}V (theta) and 0.7{+/-}0.5 {micro}V (alpha), and outperformed the AR model in estimating the signal amplitude and phase. Furthermore, we found that the probabilistic approach offers a way of forecasting even more accurately while effectively discarding uncertain predictions. We demonstrate for the first time that probabilistic deep learning can be utilised to forecast resting-state EEG time series. In the future, the developed model can enhance the real-time estimation of brain states in brain-computer interfaces and brain stimulation protocols. It may also be useful for answering neuroscientific questions and for diagnostic purposes.

neuroscience↗

bioRxiv2023

Test-retest reliability of MEG functional brain connectivity related to language processing

The number of studies examining changes in functional connectivity of the human brain is increasing rapidly. In this magnetoencephalography (MEG) study, we examined the reliability of dynamic connectivity related to language processing in a picture naming test-retest paradigm, using data collected from the same participants on two separate days. We determined the connections that were reliable across both days and also examined the behavioral, functional, and structural properties underlying this reliability. A particularly salient finding among a rich set of results was a reliable pattern of beta connectivity increase in the left motor and frontal regions (0-400 ms and 400-800 ms after stimulus onset) and gamma connectivity decrease in the bilateral motor regions (800-1200 ms) which we suggest to represent the motor preparation of speech production. Furthermore, the reliable connections tended to be more frequently associated with the behavioral performance than the non-reliable ones. Finally, the reliable connections were also linked to stronger functional connectivity, as well as to stronger structural connectivity and shorter structural path length, as determined through diffusion MRI (magnetic resonance imaging). Overall, this study defines reliable language-related functional connectivity and introduces practices that may increase reliability. AUTHOR SUMMARYResearch applying connectivity metrics in neuroimaging has increased rapidly during recent years. Hence, the focus has also been to define the best methods for increasing the reliability of connectivity estimation. This study determined reliable functional connectivity from MEG data related to language processing. Moreover, we defined what makes a connection reliable by studying the behavioral, functional, and structural properties underlying the reliable connections.

neuroscience↗