Browsing by Author "Mutanen, Tuomas P."

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Adapted Beamforming : A Robust and Flexible Approach for Removing Various Types of Artifacts from TMS–EEG Data
(2024-09) Metsomaa, Johanna; Song, Yufei; Mutanen, Tuomas P.; Gordon, Pedro C.; Ziemann, Ulf; Zrenner, Christoph; Hernandez-Pavon, Julio C.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Electroencephalogram (EEG) recorded as response to transcranial magnetic stimulation (TMS) can be highly informative of cortical reactivity and connectivity. Reliable EEG interpretation requires artifact removal as the TMS-evoked EEG can contain high-amplitude artifacts. Several methods have been proposed to uncover clean neuronal EEG responses. In practice, determining which method to select for different types of artifacts is often difficult. Here, we used a unified data cleaning framework based on beamforming to improve the algorithm selection and adaptation to the recorded signals. Beamforming properties are well understood, so they can be used to yield customized methods for EEG cleaning based on prior knowledge of the artifacts and the data. The beamforming implementations also cover, but are not limited to, the popular TMS–EEG cleaning methods: independent component analysis (ICA), signal-space projection (SSP), signal-space-projection-source-informed-reconstruction method (SSP–SIR), the source-estimate-utilizing noise-discarding algorithm (SOUND), data-driven Wiener filter (DDWiener), and the multiple-source approach. In addition to these established methods, beamforming provides a flexible way to derive novel artifact suppression algorithms by considering the properties of the recorded data. With simulated and measured TMS–EEG data, we show how to adapt the beamforming-based cleaning to different data and artifact types, namely TMS-evoked muscle artifacts, ocular artifacts, TMS-related peripheral responses, and channel noise. Importantly, beamforming implementations are fast to execute: We demonstrate how the SOUND algorithm becomes orders of magnitudes faster via beamforming. Overall, the beamforming-based spatial filtering framework can greatly enhance the selection, adaptability, and speed of EEG artifact removal.
An analytical approach to identify indirect multisensory cortical activations elicited by TMS?
(2021-03-01) Niessen, Eva; Bracco, Martina; Mutanen, Tuomas P.; Robertson, Edwin M.
Letter
Automatic and robust noise suppression in EEG and MEG: The SOUND algorithm
(2018-02-01) Mutanen, Tuomas P.; Metsomaa, Johanna; Liljander, Sara; Ilmoniemi, Risto J.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Electroencephalography (EEG) and magnetoencephalography (MEG) often suffer from noise- and artifact-contaminated channels and trials. Conventionally, EEG and MEG data are inspected visually and cleaned accordingly, e.g., by identifying and rejecting the so-called ”bad” channels. This approach has several shortcomings: data inspection is laborious, the rejection criteria are subjective, and the process does not fully utilize all the information in the collected data. Here, we present noise-cleaning methods based on modeling the multi-sensor and multi-trial data. These approaches offer objective, automatic, and robust removal of noise and disturbances by taking into account the sensor- or trial-specific signal-to-noise ratios. We introduce a method called the source-estimate-utilizing noise-discarding algorithm (the SOUND algorithm). SOUND employs anatomical information of the head to cross-validate the data between the sensors. As a result, we are able to identify and suppress noise and artifacts in EEG and MEG. Furthermore, we discuss the theoretical background of SOUND and show that it is a special case of the well-known Wiener estimators. We explain how a completely data-driven Wiener estimator (DDWiener) can be used when no anatomical information is available. DDWiener is easily applicable to any linear multivariate problem; as a demonstrative example, we show how DDWiener can be utilized when estimating event-related EEG/MEG responses. We validated the performance of SOUND with simulations and by applying SOUND to multiple EEG and MEG datasets. SOUND considerably improved the data quality, exceeding the performance of the widely used channel-rejection and interpolation scheme. SOUND also helped in localizing the underlying neural activity by preventing noise from contaminating the source estimates. SOUND can be used to detect and reject noise in functional brain data, enabling improved identification of active brain areas.
A Common Task Structure Links Together the Fate of Different Types of Memories
(2020-06-08) Mutanen, Tuomas P.; Bracco, Martina; Robertson, Edwin M.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Our memories frequently have features in common. For example, a learned sequence of words or actions can follow a common rule, which determines their serial order, despite being composed of very different events [1, 2]. This common abstract structure might link the fates of memories together. We tested this idea by creating different types of memory task: a sequence of words or actions that either did or did not have a common structure. Participants learned one of these memory tasks and then they learned another type of memory task 6 h later, either with or without the same structure. We then tested the newly formed memory's susceptibility to interference. We found that the newly formed memory was protected from interference when it shared a common structure with the earlier memory. Specifically, learning a sequence of words protected a subsequent sequence of actions learned hours later from interference, and conversely, learning a sequence of actions protected a subsequent sequence of words learned hours later from interference provided the sequences shared a common structure. Yet this protection of the newly formed memory came at a cost. The earlier memory had disrupted recall when it had the same rather than a different structure to the newly formed and protected memory. Thus, a common structure can determine what is retained (i.e., protected) and what is modified (i.e., disrupted). Our work reveals that a shared common structure links the fate of otherwise different types of memories together and identifies a novel mechanism for memory modification.
Distinct frequencies balance segregation with interaction between different memory types within a prefrontal circuit
(2023-06-19) Bracco, Martina; Mutanen, Tuomas P.; Veniero, Domenica; Thut, Gregor; Robertson, Edwin M.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Once formed, the fate of memory is uncertain. Subsequent offline interactions between even different memory types (actions versus words) modify retention.1 2 3 4 5 6 These interactions may occur due to different oscillations functionally linking together different memory types within a circuit.7 8 9 10 11 12 13 With memory processing driving the circuit, it may become less susceptible to external influences.14 We tested this prediction by perturbing the human brain with single pulses of transcranial magnetic stimulation (TMS) and simultaneously measuring the brain activity changes with electroencephalography (EEG15 16 17). Stimulation was applied over brain areas that contribute to memory processing (dorsolateral prefrontal cortex, DLPFC; primary motor cortex, M1) at baseline and offline, after memory formation, when memory interactions are known to occur.1 4 6 10 18 The EEG response decreased offline (compared with baseline) within the alpha/beta frequency bands when stimulation was applied to the DLPFC, but not to M1. This decrease exclusively followed memory tasks that interact, revealing that it was due specifically to the interaction, not task performance. It remained even when the order of the memory tasks was changed and so was present, regardless of how the memory interaction was produced. Finally, the decrease within alpha power (but not beta) was correlated with impairment in motor memory, whereas the decrease in beta power (but not alpha) was correlated with impairment in word-list memory. Thus, different memory types are linked to different frequency bands within a DLPFC circuit, and the power of these bands shapes the balance between interaction and segregation between these memories.
EEG Artifact Removal in TMS Studies of Cortical Speech Areas
(2020-01-01) Salo, Karita S.T.; Mutanen, Tuomas P.; Vaalto, Selja M.I.; Ilmoniemi, Risto J.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) is commonly applied for studying the effective connectivity of neuronal circuits. The stimulation excites neurons, and the resulting TMS-evoked potentials (TEPs) are recorded with EEG. A serious obstacle in this method is the generation of large muscle artifacts from scalp muscles, especially when frontolateral and temporoparietal, such as speech, areas are stimulated. Here, TMS–EEG data were processed with the signal-space projection and source-informed reconstruction (SSP–SIR) artifact-removal methods to suppress these artifacts. SSP–SIR suppressed muscle artifacts according to the difference in frequency contents of neuronal signals and muscle activity. The effectiveness of SSP–SIR in rejecting muscle artifacts and the degree of excessive attenuation of brain EEG signals were investigated by comparing the processed versions of the recorded TMS–EEG data with simulated data. The calculated individual lead-field matrix describing how the brain signals spread on the cortex were used as simulated data. We conclude that SSP–SIR was effective in suppressing artifacts also when frontolateral and temporoparietal cortical sites were stimulated, but it may have suppressed also the brain signals near the stimulation site. Effective connectivity originating from the speech-related areas may be studied even when speech areas are stimulated at least on the contralateral hemisphere where the signals were not suppressed that much.
Effective Intracerebral Connectivity in Acute Stroke: A TMS–EEG Study
(2023-02) Tecchio, Franca; Giambattistelli, Federica; Porcaro, Camillo; Cottone, Carlo; Mutanen, Tuomas P.; Pizzella, Vittorio; Marzetti, Laura; Ilmoniemi, Risto J.; Vernieri, Fabrizio; Rossini, Paolo Maria
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Stroke is a major cause of disability because of its motor and cognitive sequelae even when the acute phase of stabilization of vital parameters is overcome. The most important improvements occur in the first 8–12 weeks after stroke, indicating that it is crucial to improve our understanding of the dynamics of phenomena occurring in this time window to prospectively target rehabilitation procedures from the earliest stages after the event. Here, we studied the intracortical excitability properties of delivering transcranial magnetic stimulation (TMS) to the primary motor cortex (M1) of left and right hemispheres in 17 stroke patients who suffered a mono-lateral left hemispheric stroke, excluding pure cortical damage. All patients were studied within 10 days of symptom onset. TMS-evoked potentials (TEPs) were collected via a TMS-compatible electroencephalogram system (TMS–EEG) concurrently with motor-evoked responses (MEPs) induced in the contralateral first dorsal interosseous muscle. Comparison with age-matched healthy volunteers was made by collecting the same bilateral-stimulation data in nine healthy volunteers as controls. Excitability in the acute phase revealed relevant changes in the relationship between left lesioned and contralesionally right hemispheric homologous areas both for TEPs and MEPs. While the paretic hand displayed reduced MEPs compared to the non-paretic hand and to healthy volunteers, TEPs revealed an overexcitable lesioned hemisphere with respect to both healthy volunteers and the contra-lesion side. Our quantitative results advance the understanding of the impairment of intracortical inhibitory networks. The neuronal dysfunction most probably changes the excitatory/inhibitory on-center off-surround organization that supports already acquired learning and reorganization phenomena that support recovery from stroke sequelae.
The impact of artifact removal approaches on TMS–EEG signal
(2021-10-01) Bertazzoli, Giacomo; Esposito, Romina; Mutanen, Tuomas P.; Ferrari, Clarissa; Ilmoniemi, Risto J.; Miniussi, Carlo; Bortoletto, Marta
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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 and global mean field power (GMFP) were found between the preprocessing pipelines. Topographies of TEPs from the different pipelines were all highly correlated (ρ>0.8) at latencies over 100 ms. By contrast, waveforms at latencies under 100 ms showed a variable level of correlation, with ρ 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 further studies are needed to understand advantages and disadvantages of the different approaches.
Local brain-state dependency of effective connectivity: A pilot TMS-EEG study
(2022) Granö, Ida; Mutanen, Tuomas P.; Tervo, Aino; Nieminen, Jaakko O.; Souza, Victor H.; Fecchio, Matteo; Rosanova, Mario; Lioumis, Pantelis; Ilmoniemi, Risto J.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Background: Spontaneous cortical oscillations have been shown to modulate cortical responses to transcranial magnetic stimulation (TMS). However, whether these oscillations influence cortical effective connectivity is largely unknown. We conducted a pilot study to set the basis for addressing how spontaneous oscillations affect cortical effective connectivity measured through TMS-evoked potentials (TEPs). Methods: We 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. Results: Phase affected the GMFA in four out of 12 datasets (3 subjects × 2 stimulation sites × 2 frequency bands). Two of the observed significant intervals were before 50 ms, two between 50 and 100 ms, and one after 100 ms post-stimulus. Source estimates showed complex spatial differences between the classes in the cortical spreading of the TMS-evoked activity. Conclusions: TMS-evoked effective connectivity seems to depend on the phase of local cortical oscillations at the stimulated site. This work paves the way to design future closed-loop stimulation paradigms.
Noninvasive extraction of microsecond-scale dynamics from human motor cortex
(2018) Koponen, Lari M.; Nieminen, Jaakko O.; Mutanen, Tuomas P.; Ilmoniemi, Risto J.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
State-of-the-art noninvasive electromagnetic recording techniques allow observing neuronal dynamics down to the millisecond scale. Direct measurement of faster events has been limited to in vitro or invasive recordings. To overcome this limitation, we introduce a new paradigm for transcranial magnetic stimulation. We adjusted the stimulation waveform on the microsecond scale, by varying the duration between the positive and negative phase of the induced electric field, and studied corresponding changes in the elicited motor responses. The magnitude of the electric field needed for given motor-evoked potential amplitude decreased exponentially as a function of this duration with a time constant of 17 μs. Our indirect noninvasive measurement paradigm allows studying neuronal kinetics on the microsecond scale in vivo.
Protocol to assess changes in brain network resistance to perturbation during offline processing using TMS-EEG
(2025-03-21) Bracco, Martina; Mutanen, Tuomas P.; Veniero, Domenica; Thut, Gregor; Robertson, Edwin M.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Transcranial magnetic stimulation (TMS) perturbs specific brain regions and, combined with electroencephalography (EEG), enables the assessment of activity within their connected networks. We present a resting-state TMS-EEG protocol, combined with a controlled experimental design, to assess changes in brain network activity during offline processing, following a behavioral task. We describe steps for experimental design planning, setup preparation, data collection, and analysis. This approach minimizes biases inherent to TMS-EEG, ensuring an accurate assessment of changes within the network. For complete details of the use and execution of this protocol, please refer to Bracco et al.1
Signal-Space Projection Suppresses the tACS Artifact in EEG Recordings
(2020-12-18) Vosskuhl, Johannes; Mutanen, Tuomas P.; Neuling, Toralf; Ilmoniemi, Risto J.; Herrmann, Christoph S.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Background: To probe the functional role of brain oscillations, transcranial alternating current stimulation (tACS) has proven to be a useful neuroscientific tool. Because of the excessive tACS-caused artifact at the stimulation frequency 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 Method: In 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 one human subject to demonstrate the best-performing data-correction approach in a proof of principle. Results: The 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 Methods: The best results were achieved with SSP, which outperformed sine subtraction and template subtraction. Conclusion: Our results demonstrate the feasibility of SSP by applying it to a phantom measurement with pre-recorded signal and one human tACS + EEG dataset. For a full validation of SSP, more data are needed.
Source-based artifact-rejection techniques available in TESA, an open-source TMS–EEG toolbox
(2020-09-01) Mutanen, Tuomas P.; Biabani, Mana; Sarvas, Jukka; Ilmoniemi, Risto J.; Rogasch, Nigel C.
Letter
Source-based artifact-rejection techniques for TMS–EEG
(2022-12-01) Mutanen, Tuomas P.; Metsomaa, Johanna; Makkonen, Matilda; Varone, Giuseppe; Marzetti, Laura; Ilmoniemi, Risto J.
A2 Katsausartikkeli tieteellisessä aikakauslehdessä
Neuronal electroencephalography (EEG) signals arise from the cortical postsynaptic currents. Due to the conductive properties of the head, these neuronal sources produce relatively smeared spatial patterns in EEG. We can model these topographies to deduce which signals reflect genuine TMS-evoked cortical activity and which data components are merely noise and artifacts. This review will concentrate on two source-based artifact-rejection techniques developed for TMS–EEG data analysis, signal-space-projection–source-informed reconstruction (SSP–SIR), and the source-estimate-utilizing noise-discarding algorithm (SOUND). The former method was designed for rejecting TMS-evoked muscle artifacts, while the latter was developed to suppress noise signals from EEG and magnetoencephalography (MEG) in general. We shall cover the theoretical background for both methods, but most importantly, we will describe some essential practical perspectives for using these techniques effectively. We demonstrate and explain what approaches produce the most reliable inverse estimates after cleaning the data or how to perform non-biased comparisons between cleaned datasets. All noise-cleaning algorithms compromise the signals of interest to a degree. We elaborate on how the source-based methods allow objective quantification of the overcorrection. Finally, we consider possible future directions. While this article concentrates on TMS–EEG data analysis, many theoretical and practical aspects, presented here, can be readily applied in other EEG/MEG applications. Overall, the source-based cleaning methods provide a valuable set of TMS–EEG preprocessing tools. We can objectively evaluate their performance regarding possible overcorrection. Furthermore, the overcorrection can always be taken into account to compare cleaned datasets reliably. The described methods are based on current electrophysiological and anatomical understanding of the head and the EEG generators; strong assumptions of the statistical properties of the noise and artifact signals, such as independence, are not needed.
T4TE: Team for TMS−EEG to improve reproducibility through an open collaborative initiative
(2023-01-01) Bortoletto, Marta; Veniero, Domenica; Julkunen, Petro; Hernandez-Pavon, Julio C.; Mutanen, Tuomas P.; Zazio, Agnese; Bagattini, Chiara
Letter
TMS combined with EEG: Recommendations and open issues for data collection and analysis
(2023-03-01) Hernandez-Pavon, Julio C.; Veniero, Domenica; Bergmann, Til Ole; Belardinelli, Paolo; Bortoletto, Marta; Casarotto, Silvia; Casula, Elias P.; Farzan, Faranak; Fecchio, Matteo; Julkunen, Petro; Kallioniemi, Elisa; Lioumis, Pantelis; Metsomaa, Johanna; Miniussi, Carlo; Mutanen, Tuomas P.; Rocchi, Lorenzo; Rogasch, Nigel C.; Shafi, Mouhsin M.; Siebner, Hartwig R.; Thut, Gregor; Zrenner, Christoph; Ziemann, Ulf; Ilmoniemi, Risto J.
A2 Katsausartikkeli tieteellisessä aikakauslehdessä
Transcranial magnetic stimulation (TMS) evokes neuronal activity in the targeted cortex and connected brain regions. The evoked brain response can be measured with electroencephalography (EEG). TMS combined with simultaneous EEG (TMS−EEG) is widely used for studying cortical reactivity and connectivity at high spatiotemporal resolution. Methodologically, the combination of TMS with EEG is challenging, and there are many open questions in the field. Different TMS−EEG equipment and approaches for data collection and analysis are used. The lack of standardization may affect reproducibility and limit the comparability of results produced in different research laboratories. In addition, there is controversy about the extent to which auditory and somatosensory inputs contribute to transcranially evoked EEG. This review provides a guide for researchers who wish to use TMS−EEG to study the reactivity of the human cortex. A worldwide panel of experts working on TMS−EEG covered all aspects that should be considered in TMS−EEG experiments, providing methodological recommendations (when possible) for effective TMS−EEG recordings and analysis. The panel identified and discussed the challenges of the technique, particularly regarding recording procedures, artifact correction, analysis, and interpretation of the transcranial evoked potentials (TEPs). Therefore, this work offers an extensive overview of TMS−EEG methodology and thus may promote standardization of experimental and computational procedures across groups.
Transferability of cathodal tDCS effects from the primary motor to the prefrontal cortex: A multimodal TMS-EEG study
(2023-03-01) Mosayebi-Samani, Mohsen; Agboada, Desmond; Mutanen, Tuomas P.; Haueisen, Jens; Kuo, Min Fang; Nitsche, Michael A.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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 per participant, in which four cathodal tDCS dosages, low, medium, and high, as well as sham stimulation, were applied over the left M1 and left 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). 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.