Biology subjects

Mutanen, T. P.

Publications and source records attributed to Mutanen, T. P..

14 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

Real-time noise cancellation for EEG monitoring and brain-computer interfaces

Raw electroencephalography (EEG) data suffer from a low signal-to-noise ratio (SNR). Effective real-time cleaning of EEG would improve the robustness of EEG monitoring, brain-computer interfaces, and brain-state-dependent stimulation applications in both laboratory and real-life environments. Here, we present a real-time source-estimate-utilizing noise-discarding algorithm (rtSOUND) for EEG noise suppression. We adapted the established offline SOUND algorithm for real-time use and implemented it into an open-source EEG-processing software. Instead of using conventional temporal filtering, SOUND operates the data with a spatial filter. This makes it particularly suitable for real-time applications, since each streamed data sample can be cleaned separately in near real-time. Unlike temporal filtering methods, SOUND does not make assumptions about the frequency content of noise and brain signals but assumes that the noise topographies differ from those generated by brain activity. With an EEG forward model, rtSOUND cross-validates the EEG time series and estimates the spatial distribution of noise, resulting in a data-cleaning linear spatial filter. Each streamed EEG sample is processed synchronously, and the filter is periodically updated in an asynchronous parallel process. The algorithm rapidly updates the spatial filter in response to changes in the data, ensuring that noise reduction is both immediate and accurately adapted to the current EEG state. rtSOUND was validated with transcranial magnetic stimulation (TMS)-EEG and resting-state EEG datasets measured from 25 healthy subjects. SNR of both TMS-evoked potentials and resting-state data consistently improved across various metrics. rtSOUND particularly benefitted channels with poor SNR, while retaining the waveforms in high-quality channels. The algorithm was fast enough to process data streamed at 5 kHz. rtSOUND improves the SNR of EEG in real time, providing a robust method to improve signal quality for brain-state-monitoring applications.

neuroscience↗

bioRxiv2026

Adaptive Bayesian localization of motor representation areas

Transcranial magnetic stimulation (TMS) enables non-invasive localization of cortical motor representations, with important clinical applications in presurgical planning. Existing methods either disregard spatial information about the TMS-induced electric field (E-field) or use acquisition schemes that do not leverage previously elicited motor responses to guide subsequent stimulation. We present an adaptive Bayesian localization method that combines real-time E-field optimization with per-trial probabilistic inference. The cortical origin of the motor responses is represented as a spatial probability distribution that is updated after each stimulus. Subsequent stimuli are then optimized to maximize the expected localization improvement given previous responses. We validated the method experimentally, using multi-locus TMS for adaptive localization and single-coil TMS as a non-adaptive reference with randomized coil placements. Across eight subjects, the adaptive protocol at least halved the number of stimuli required for stable localization compared to the randomized protocol, converging in 60 stimuli on average, with 95% highest-density regions often below 10 mm2 by 150 stimuli.

neuroscience↗

bioRxiv2026

TMS timed to interictal epileptiform discharges

Interictal epileptiform discharges (IEDs) are pathological hypersynchronous bursts of electrical brain activity that occur between seizures in patients with epilepsy. IEDs are caused by transient brain states that are difficult to predict, making them a challenging neurophysiological and technological case for brain-state-dependent stimulation. Administering stimulation at IED onset may provide insight into the epileptic network and optimize neurostimulation therapies. Here, we assessed the feasibility of IED-triggered transcranial magnetic stimulation (TMS) in two children with self-limited epilepsy with centrotemporal spikes (SeLECTS), a common pediatric epilepsy in which IEDs emerge from the motor cortex. A convolutional neural network (CNN) was trained on the participants pre-recorded electroencephalography (EEG) data with IEDs annotated by an epileptologist. The CNN was integrated into an EEG-processing pipeline that classified EEG segments as "IED" or "non-IED" in real time. With this pipeline, TMS pulses were administered during IED or non-IED periods in an interleaved, randomized design. We stimulated both the motor cortex generating the IEDs and the contralateral motor cortex and tested the impact of IEDs on TMS-evoked potentials (TEPs). Our study demonstrated that TMS can be timed to IEDs and that there is a site-specific increase in TEP amplitude when stimulating during IEDs. Out of the TMS pulses aimed at an IED, 39% and 19% were successfully delivered during an IED for the two participants, respectively. For future research, we propose ways to address the methodological challenges of IED-timed TMS, enabling brain-state-dependent TMS for epilepsy research and treatment.

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

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↗

bioRxiv2025

The dependency of TMS-evoked potentials on electric-field orientation in the cortex

Transcranial magnetic stimulation (TMS) stimulates the brain by electromagnetic induction. The outcome depends on multiple stimulation parameters such as the induced electric-field pattern (in particular, the location of the peak field and its orientation), intensity and timing. However, it is not clear how the TMS-evoked responses are affected by all the stimulation parameters. This study elucidates the dependency of the TMS-evoked electroencephalography (EEG) responses on the orientation of the stimulating electric field. To achieve this, we analysed a dataset from six subjects who were given pulses with 36 stimulus orientations to the pre-supplementary motor area (pre-SMA). The TMS-evoked potentials (TEPs) and induced oscillations were analysed with cluster-based statistics. Source estimation was performed to assess the effects of stimulus orientation on the TMS-evoked signal propagation. The amplitudes of the early peaks (20 and 40 ms after the TMS pulse) strongly depended on the electric field orientation. Our analysis suggested orientation dependency up to 100 ms post-stimulus in most subjects, indicating changes in stimulation efficacy and potential changes in signal propagation from the stimulated site. These results suggest that different orientations may perturb different networks. Thus, the orientation is a crucial parameter for the stimulation outcome and should be adjusted according to the cortical network under investigation.

neuroscience↗

bioRxiv2022

Transferability of cathodal tDCS effects from the primary motor to the dorsolateral prefrontal cortex: a multimodal TMS-EEG study

Neurophysiological effects of transcranial direct current stimulation (tDCS) have been extensively studied over the primary motor cortex (M1). Much less is however known about its effects over non-motor areas, such as the prefrontal cortex (PFC), which is the neuronal foundation for many high-level cognitive functions and involved in neuropsychiatric disorders. In this study, we, therefore, explored the transferability of cathodal tDCS effects over M1 to the PFC. Eighteen healthy human participants (11 males and 8 females) were involved in eight randomized sessions, in which four cathodal tDCS dosages, low, medium, and high, as well as sham stimulation, were applied over the M1 and PFC. After-effects of tDCS were evaluated via transcranial magnetic stimulation (TMS)-electroencephalography (EEG), and TMS-elicited motor evoked potentials (MEP), for the outcome parameters TMS-evoked potentials (TEP), TMS-evoked oscillations, and MEP amplitude alterations. TEPs were studied both at the regional and global scalp levels. The results indicate a regional dosage-dependent nonlinear neurophysiological effect of M1 tDCS, which is not one-to-one transferable to PFC tDCS. Low and high dosages of M1 tDCS reduced early positive TEP peaks (P30, P60), and MEP amplitudes, while an enhancement was observed for medium dosage M1 tDCS (P30 and MEP amplitudes). In contrast, prefrontal low, medium and high dosage tDCS uniformly reduced the early positive TEP peak amplitudes. Furthermore, for both cortical areas, regional tDCS-induced modulatory effects were not observed for late TEP peaks, nor TMS-evoked oscillations. However, at the global scalp level, widespread effects of tDCS were observed for both, TMS-evoked potentials and oscillations. This study provides the first direct physiological comparison of tDCS effects applied over different brain areas and therefore delivers crucial information for future tDCS applications. SIGNIFICANCE STATEMENTModulatory effects of tDCS over the M1 were largely taken as a template so far for the use of this intervention over non-motor regions. However, the neurophysiological effects of tDCS over non-motor regions, such as the prefrontal cortex (PFC), have been much less explored. In the current study, we, using concurrent transcranial magnetic stimulation- electroencephalography, systematically explored the transferability of cathodal tDCS effects on cortical excitability from M1 to the PFC. The results indicate a dosage-dependent nonlinear neurophysiological effect of motor cortex tDCS, which is not one-to-one transferable to prefrontal tDCS. This study provides the first direct physiological comparison of tDCS effects applied over different brain areas, which will further consolidate the rationale for the extension of tDCS applications at both, basic and clinical levels.

neuroscience↗

bioRxiv2021

Designing and comparing cleaning pipelines for TMS-EEG data: a theoretical overview and practical example

Combining transcranial magnetic stimulation (TMS) with electroencephalography (EEG) is growing in popularity as a method for probing the reactivity and connectivity of neural circuits in basic and clinical research. However, using EEG to measure the neural responses to TMS is challenging due to the unique artifacts introduced by combining the two techniques. In this paper, we overview the artifacts present in TMS-EEG data and the offline cleaning methods used to suppress these unwanted signals. We then describe how open science practices, including the development of open-source toolboxes designed for TMS-EEG analysis (e.g., TESA - the TMS-EEG signal analyser), have improved the availability and reproducibility of TMS-EEG cleaning methods. We provide theoretical and practical considerations for designing TMS-EEG cleaning pipelines and then give an example of how to compare different pipelines using TESA. We show that changing even a single step in a pipeline designed to suppress decay artifacts results in TMS-evoked potentials (TEPs) with small differences in amplitude and spatial topography. The variability in TEPs resulting from the choice of cleaning pipeline has important implications for comparing TMS-EEG findings between research groups which use different online and offline approaches. Finally, we discuss the challenges of validating cleaning pipelines and recommend that researchers compare outcomes from TMS-EEG experiments using multiple pipelines to ensure findings are not related to the choice of cleaning methods. We conclude that the continued improvement, availability, and validation of cleaning pipelines is essential to ensure TMS-EEG reaches its full potential as a method for studying human neurophysiology. HighlightsO_LIConcurrent TMS-EEG is challenging due to artifacts in the recorded signals. C_LIO_LIWe overview offline methods for cleaning TEPs and provide tips on pipeline design. C_LIO_LIWe use TESA to compare pipelines and show changing a single step alters TEPs. C_LIO_LIWe discuss the challenges in validating pipelines for TMS-EEG analysis. C_LIO_LIWe suggest using multiple pipelines to minimise the impact of method choice on TEPs. C_LI

neuroscience↗

bioRxiv2021

Local brain-state dependency of effective connectivity: evidence from TMS-EEG

BackgroundSpontaneous cortical oscillations have been shown to modulate cortical responses to transcranial magnetic stimulation (TMS). If not controlled for, they might increase variability in responses and mask meaningful changes in the signals of interest when studying the brain with TMS combined with electroencephalography (TMS-EEG). To address this challenge in future closed-loop stimulation paradigms, we need to understand how spontaneous oscillations affect TMS-evoked responses. ObjectiveTo describe the effect of the pre-stimulus phase of cortical mu (8-13 Hz) and beta (13-30 Hz) oscillations on TMS-induced effective connectivity patterns. MethodsWe applied TMS to the left primary motor cortex and right pre-supplementary motor area of three subjects while recording EEG. We classified trials off-line into positive- and negative-phase classes according to the mu and beta rhythms. We calculated differences in the global mean-field amplitude (GMFA) and compared the cortical spreading of the TMS-evoked activity between the two classes. ResultsPhase had significant effects on the GMFA in 11 out of 12 datasets (3 subjects x 2 stimulation sites x 2 frequency bands). Seven of the datasets showed significant differences in the time range 15-50 ms, nine in 50-150 ms, and eight after 150 ms post-stimulus. Source estimates showed complex spatial differences between the classes in the cortical spreading of the TMS-evoked activity. ConclusionsTMS-evoked effective connectivity appears to depend on the phase of local cortical oscillations at the stimulated site. This may be crucial for efficient design of future brain-state-dependent and closed-loop stimulation paradigms.

neuroscience↗

bioRxiv2021

The impact of artifact removal approaches on TMS-EEG signal

Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs) allow one to assess cortical excitability and effective connectivity in clinical and basic research. However, obtaining clean TEPs is challenging due to the various TMS-related artifacts that contaminate the electroencephalographic (EEG) signal when the TMS pulse is delivered. Different preprocessing approaches have been employed to remove the artifacts, but the degree of artifact reduction or signal distortion introduced in this phase of analysis is still unknown. Knowing and controlling this potential source of uncertainty will increase the inter-rater reliability of TEPs and improve the comparability between TMS-EEG studies. The goal of this study was to assess the variability in TEP waveforms due to of the use of different preprocessing pipelines. To accomplish this aim, we preprocessed the same TMS-EEG data with four different pipelines and compared the results. The dataset was obtained from 16 subjects in two identical recording sessions, each session consisting of both left dorsolateral prefrontal cortex and left inferior parietal lobule stimulation at 100% of the resting motor threshold. Considerable differences in TEP amplitudes were found between the preprocessing pipelines. Topographies of TEPs from the different pipelines were all highly correlated ({rho}>0.8) at latencies over 100 ms. By contrast, waveforms at latencies under 100 ms showed a variable level of correlation, with {rho} ranging between 0.2 and 0.9. Moreover, the test-retest reliability of TEPs depended on the preprocessing pipeline. Taken together, these results take us to suggest that the choice of the preprocessing approach has a marked impact on the final TEP, and that caution should be taken when comparing TMS-EEG studies that used different approaches. Finally, we propose strategies to control this source of variability.

neuroscience↗

bioRxiv2019

Signal-space projection suppresses the tACS artifact in EEG recordings

1.BackgroundTo probe the functional role of brain oscillations, transcranial alternating current stimulation (tACS) has proven to be a useful neuroscientific tool. Because of the huge tACS-caused artifact in electroencephalography (EEG) signals, tACS-EEG studies have been mostly limited to compare brain activity between recordings before and after concurrent tACS. Critically, attempts to suppress the artifact in the data cannot assure that the entire artifact is removed while brain activity is preserved. The current study aims to evaluate the feasibility of specific artifact correction techniques to clean tACS-contaminated EEG data. New MethodIn the first experiment, we used a phantom head to have full control over the signal to be analyzed. Driving pre-recorded human brain-oscillation signals through a dipolar current source within the phantom, we simultaneously applied tACS and compared the performance of different artifact-correction techniques: sine subtraction, template subtraction, and signal-space projection (SSP). In the second experiment, we combined tACS and EEG on a human subject to validate the best-performing data-correction approach. ResultsThe tACS artifact was highly attenuated by SSP in the phantom and the human EEG; thus, we were able to recover the amplitude and phase of the oscillatory activity. In the human experiment, event-related desynchronization could be restored after correcting the artifact. Comparison with existing methodsThe best results were achieved with SSP, which outperformed sine subtraction and template subtraction. ConclusionsOur results demonstrate the feasibility of SSP by applying it to human tACS-EEG data.

neuroscience↗

bioRxiv2018

Characterizing and minimizing the contribution of sensory inputs to TMS-evoked potentials

BackgroundTranscranial magnetic stimulation (TMS) evokes voltage deflections in electroencephalographic (EEG) recordings, known as TMS-evoked potentials (TEPs), which are increasingly used to study brain dynamics. However, the extent to which TEPs reflect activity directly evoked by magnetic rather than sensory stimulation is unclear.\n\nObjectiveTo characterize and minimize the contribution of sensory inputs to TEPs.\n\nMethodsTwenty-four healthy participants received TMS over the motor cortex using two different intensities (below and above cortical motor threshold) and waveforms (monophasic, biphasic). TMS was also applied over the shoulder as a multisensory control condition. Common sensory attenuation measures, including coil padding and noise masking, were adopted. We examined spatiotemporal relationships between the EEG responses to the scalp and shoulder stimulations at sensor and source levels. Furthermore, we compared three different filters (independent component analysis, signal-space projection with source informed reconstruction (SSP-SIR) and linear regression) designed to attenuate the impact of sensory inputs on TEPs.\n\nResultsThe responses to the scalp and shoulder stimulations were correlated in both temporal and spatial domains, especially after [~]60 ms, regardless of the intensity and stimuli waveform. Among the three filters, SSP-SIR showed the best trade-off between removing sensory-related signals while preserving data not related to the control condition.\n\nConclusionsThe findings demonstrate that TEPs elicited by motor cortex TMS reflect a combination of transcranially and peripherally evoked brain responses despite adopting sensory attenuation methods during experiments, thereby highlighting the importance of adopting sensory control conditions in TMS-EEG studies. Offline filters may help to isolate the transcranial component of the TEP from its peripheral component, but only if these components express different spatiotemporal patterns. More realistic control conditions may help to improve the characterization and attenuation of sensory inputs to TEPs, especially in early responses.

neuroscience↗

bioRxiv2018

TMS-evoked EEG potentials from prefrontal and parietal cortex: reliability, site specificity, and effects of dextromethorphan

Measuring the brains response to transcranial magnetic stimulation (TMS) with electroencephalography (EEG) offers a unique insight into the local cortical circuits and networks activated following stimulation, particularly in non-motor regions where less is known about TMS physiology. However, the mechanisms underlying TMS-evoked EEG potentials (TEPs) remain largely unknown. We assessed TEP reliability, site-specificity, and sensitivity to changes in excitatory neurotransmission mediated by n-methyl-d-aspartate (NMDA) receptors following stimulation of non-motor regions. In fourteen male volunteers, resting EEG and TEPs from prefrontal (PFC) and parietal (PAR) cortex were measured before and after administration of either dextromethorphan (an NMDA receptor antagonist) or placebo across two sessions separated by at least a week in a double-blinded pseudo-randomised crossover design. At baseline, TEPs showed lower within-than between-subject variability for both stimulation sites across sessions, demonstrating the reliability of non-motor TEPs within individuals. There were differences in amplitude between PFC and PAR TEPs across a wide time range (15-250 ms), however the signals were correlated after ~80 ms, suggesting that early peaks reflect site-specific activity, whereas late peaks reflect activity patterns less dependent on the stimulated sites. TEPs were not altered following dextromethorphan compared to placebo, however low frequency resting oscillations were reduced in power. Our findings suggest that TEPs from PFC and PAR: 1) are reliable within and variable between individuals; 2) reflect stimulation site specific activity across early time periods (<80 ms); and 3) are not sensitive to changes in NMDA receptor-mediated neurotransmission.

neuroscience↗