Tuomas P. Mutanen

The Effect of Stimulus Parameters on TMS-EEG Muscle Artifacts

BACKGROUND When transcranial magnetic stimulation (TMS) is delivered close to the lateral aspects of the head, large-amplitude (~10-1000 μV) biphasic electroencephalographic (EEG) deflections, peaking at around 4-10 and 8-20 ms, appear. OBJECTIVE To characterize the spatiotemporal features of these artifacts, to quantify the effect of stimulus parameters on them, and thus, to study the feasibility of different measurement procedures to decrease the artifacts online. Furthermore, to show that these deflections, when measured with a sample-and-hold system, mainly result from excitation of cranial muscles. METHODS Three subjects received TMS to 16 sites over the left hemisphere. TMS-compatible EEG was recorded simultaneously. Four other subjects received TMS to M1 with different coil rotation and tilt angles and stimulation intensities. We also stimulated a conductive phantom and recorded simultaneous EEG to exclude the possibility of residual electromagnetic artifacts. RESULTS The artifacts were largest when the stimulator was placed above cranial muscles, whereas stimulation of relatively central sites far from the muscles produced muscle artifact-free data. The laterally situated EEG channels were most severely contaminated. The artifacts were significantly reduced when reducing the intensity or when tilting or rotating the coil so that coil wings moved further away from the temporal muscle, while brain responses remained visible. Stimulation of the phantom did not produce such large-amplitude biphasic artifacts. CONCLUSION Altering the stimulation parameters can reduce the described artifact, while brain responses can still be recorded. The early, laterally appearing, large biphasic TMS-evoked EEG deflections recorded with a sample-and-hold system are caused by cranial muscle activation.

Uncovering neural independent components from highly artifactual TMS-evoked EEG data

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful tool for studying cortical excitability and connectivity. To enhance the EEG interpretation, independent component analysis (ICA) has been used to separate the data into independent components (ICs). However, TMS can evoke large artifacts in EEG, which may greatly distort the ICA separation. The removal of such artifactual EEG from the data is a difficult task. In this paper we study how badly the large artifacts distort the ICA separation, and whether the distortions could be avoided without removing the artifacts. We first show that, in the ICA separation, the time courses of the ICs are not affected by the large artifacts, but their topographies could be greatly distorted. Next, we show how this distortion can be circumvented. We introduce a novel technique of suppression, by which the EEG data are modified so that the ICA separation of the suppressed data becomes reliable. The suppression, instead of removing the artifactual EEG, rescales all the data to about the same magnitude as the neural EEG. For the suppressed data, ICA returns the original time courses, but instead of the original topographies, it returns modified ones, which can be used, e.g., for the source localization. We present three suppression methods based on principal component analysis, wavelet analysis, and whitening of the data matrix, respectively. We test the methods with numerical simulations. The results show that the suppression improves the source localization.

TMS-evoked changes in brain-state dynamics quantified by using EEG data

To improve our understanding of the combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) method in general, it is important to study how the dynamics of the TMS-modulated brain activity differs from the dynamics of spontaneous activity. In this paper, we introduce two quantitative measures based on EEG data, called mean state shift (MSS) and state variance (SV), for evaluating the TMS-evoked changes in the brain-state dynamics. MSS quantifies the immediate TMS-elicited change in the brain state, whereas SV shows whether the rate at which the brain state changes is modulated by TMS. We report a statistically significant increase for a period of 100–200 ms after the TMS pulse in both MSS and SV at the group level. This indicates that the TMS-modulated brain state differs from the spontaneous one. Moreover, the TMS-modulated activity is more vigorous than the natural activity.

Recovering TMS-evoked EEG responses masked by muscle artifacts

Combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) often suffers from large muscle artifacts. Muscle artifacts can be removed using signal-space projection (SSP), but this can make the visual interpretation of the remaining EEG data difficult. We suggest to use an additional step after SSP that we call source-informed reconstruction (SIR). SSP-SIR improves substantially the signal quality of artifactual TMS-EEG data, causing minimal distortion in the neuronal signal components. In the SSP-SIR approach, we first project out the muscle artifact using SSP. Utilizing an anatomical model and the remaining signal, we estimate an equivalent source distribution in the brain. Finally, we map the obtained source estimate onto the original signal space, again using anatomical information. This approach restores the neuronal signals in the sensor space and interpolates EEG traces onto the completely rejected channels. The introduced algorithm efficiently suppresses TMS-related muscle artifacts in EEG while retaining well the neuronal EEG topographies and signals. With the presented method, we can remove muscle artifacts from TMS-EEG data and recover the underlying brain responses without compromising the readability of the signals of interest.

Dealing with artifacts in TMS-evoked EEG

The artifact problem in TMS-evoked EEG is analyzed in an attempt to clarify the nature of the problem and to present solutions. The best way to deal with artifacts is to avoid them; the removal or suppression of the unavoidable artifacts should be based on accurate information about their characteristics and the properties of the signal of interest.

Automatic and robust noise suppression in EEG and MEG: The SOUND algorithm

&NA; 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. HighlightsWe present the SOUND algorithm that is based on the optimal Wiener‐filtering.SOUND automatically identifies and suppresses noise in multichannel MEG/EEG data.SOUND surpasses the common channel‐rejection and interpolation scheme.Running SOUND takes significantly less time compared to visual data inspection.The MATLAB implementation of SOUND is provided in a freely downloadable demo package.

Noninvasive extraction of microsecond-scale dynamics from human motor cortex

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.

Individual Activation Patterns After the Stimulation of Different Motor Areas: A Transcranial Magnetic Stimulation–Electroencephalography Study

BACKGROUND The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) enables one to study effective connectivity and activation order in neuronal networks. OBJECTIVE To characterize effective connectivity originating from the primary motor cortex (M1), dorsal premotor area (PMd), and supplementary motor area (SMA). METHODS Three right-handed volunteers (2 males, aged 25-30) participated in a navigated TMS-EEG experiment. M1, PMd, and SMA over the non-dominant hemisphere were stimulated with 150 TMS pulses. Minimum-norm estimates (MNE) were derived from the EEG data to estimate the spatial spreading of TMS-elicited neuronal activation on an individual level. RESULTS The activation order of the cortical areas varied depending on the stimulated area. There were similarities and differences in the spatial distribution of the TMS-evoked potentials between subjects. The similarities in cortical activation patterns were seen at short post-stimulus latencies and the differences at long latencies. CONCLUSIONS This pilot study suggests that cortical activation patterns and the activation order of motor areas differ inter-individually and depend on the stimulated motor area. It further indicates that TMS-activated effective connections or underlying structural connections vary between subjects. The spatial patterns of TMS-evoked potentials differ between subjects especially at long latencies, when probably more complex neuronal networks are active.Abstract The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) enables one to study effective connectivity and activation order in neuronal networks. To charac...

O152 Investigating effective connectivity in the motor network with TMS-evoked cortical potentials

Objectives Our purpose was to learn what the spatial distribution of transcranial magnetic stimulation (TMS)-evoked potentials can reveal about connectivity originating from premotor, supplementary, and primary motor cortices. Methods The data were collected with the combination of navigated TMS (nTMS) and EEG from four subjects (one subject reported here). The primary, pre-, and supplementary motor cortices in both hemispheres were stimulated, each area receiving 150 pulses at stimulation intensity of 90% of the electric field of ipsilateral APB motor threshold. The EEG datasets were preprocessed with novel artifact-removal algorithms (Mutanen et al., NeuroImage 2016). The first four peaks and their latencies were determined from the global mean field amplitudes (GMFA). At these peak latencies, minimum-norm estimates (MNE) indicated sites of most prevalent cortical activity. Results The new artifact-removal method proved to be useful as the first step in the data analysis. The spreading of neuronal activity depends on the stimulation target; the order of the activated cortical areas varies when different motor-related areas are stimulated. Discussion Our combination of experimental settings, data processing tools, and data-analysis methods can be used to evaluate effective connections from motor areas. Cortical activation patterns differ depending on the stimulated motor area. Conclusions nTMS–EEG can be used to investigate the connectivity originating from motor areas. Significance nTMS–EEG may be used to select stimulation sites on the cortex when specific neuronal connections should be strengthened by TMS, for example, in stroke patients.

P 222. Studying the dynamics of the TMS-EEG signal

Introduction Combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) has proven to be a useful tool when probing the effective connectivity. However, the TMS-evoked responses seem to vary significantly from trial to another. This is partially due to the constantly changing underlying brain state, which is likely to affect the evoked response. Thus, it is important to understand the relationship between the current brain state dynamics and the evoked TMS-EEG responses. This work concentrates on the non-linear dynamic behaviour of EEG signal before and after TMS stimuli. Objectives Our objective is to study the effects of TMS on the dynamics of the brain state as well as the effects of the current brain state dynamics on the TMS-evoked responses. Materials and methods In the analysis, 16 different TMS-EEG data sets, where the stimuli had been delivered on the left primary motor cortex with 100% motor threshold intensity. The Nexstim eXimia magnetic stimulator and TMS-compatible EEG system were used to collect the data. It is known that the trajectory of a system in the state space can reveal some vital properties of the underlying dynamics. In this work, we studied the projection of the state-space trajectory onto the EEG signal space using 16 EEG channels confining the stimulation area. The data analysis consisted of computation of three commonly known non-linear measures for trial-level data: quantitative recurrence analysis, Lyapunov stability, and correlation dimension. In short, the recurrence analysis measures the distances between the state vectors defining the state of the system as a function of time. Lyapunov stability is used to describe the chaotic properties of the system, whereas correlation dimension measures the complexity of the underlying system. In the figure one can see a schematic picture of the hypothesis behind the present work. The post-synaptic currents (A) define accurately the electric state of the brain. The brain state advances spontaneously in the state space (red line). However, TMS might shift the system rapidly to a certain subset and also affect the later motion of the trajectory (green line). We can observe the projection of the trajectory on the EEG signal space (dotted lines) spanned by channels ch i and ch j . Results The mean distance between the state vectors right before and right after the stimulus was greater than the mean distance separating state vectors during spontaneous activity. Furthermore, the results indicated that the state vector was moving faster during approximately 300-ms long period after the stimulus than during spontaneous activity. The results also showed some evidence that the correlation dimension might decrease as an effect of TMS. With the present data available the Lyapunov-stability analysis did not show clear results. Conclusion The results indicate that the “artificial” activity created by TMS is propagating faster in the system than the spontaneous activity. This might be due to higher local free energy close to the stimulation site, which the system tries to minimize as fast as possible. Furthermore, it seems that TMS actually does shift the brain state to a slightly different subset in the brain state space.