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

Stability of transcranial magnetic stimulation electroencephalogram evoked potentials in pediatric epilepsy

Transcranial magnetic stimulation paired with electroencephalography (TMS-EEG) can measure local excitability and functional connectivity. To address trial-to-trial variability, responses to multiple TMS pulses are recorded to obtain an average TMS evoked potential (TEP). Balancing adequate data acquisition to establish stable TEPs with feasible experimental duration is critical when applying TMS-EEG to clinical populations. Here we aim to investigate the minimum number of pulses (MNP) required to achieve stable TEPs in children with epilepsy. Eighteen children with Self-Limited Epilepsy with Centrotemporal Spikes, a common epilepsy arising from the motor cortices, underwent multiple 100-pulse blocks of TMS to both motor cortices over two days. TMS was applied at 120% of resting motor threshold (rMT) up to a maximum of 100% maximum stimulator output. The average of all 100 pulses was used as a "gold-standard" TEP to which we compared "candidate" TEPs obtained by averaging subsets of pulses. We defined TEP stability as the MNP needed to achieve a concordance correlation coefficient of 80% between the candidate and "gold-standard" TEP. We additionally assessed whether experimental or clinical factors affected TEP stability. Results show that stable TEPs can be derived from fewer than 100 pulses, a number typically used for designing TMS-EEG experiments. The early segment (15-80 ms) of the TEP was less stable than the later segment (80-350 ms). Global mean field amplitude derived from all channels was less stable than local TEP derived from channels overlying the stimulated site. TEP stability did not differ depending on stimulated hemisphere, block order, or antiseizure medication use, but was greater in older children. Stimulation administered with an intensity above the rMT yielded more stable local TEPs. Studies of TMS-EEG in pediatrics have been limited by the complexity of experimental set-up and time course. This study serves as a critical starting point, demonstrating the feasibility of designing efficient TMS-EEG studies that use a relatively small number of pulses to study pediatric epilepsy and potentially other pediatric groups.

Adapted Beamforming: A Robust and Flexible Approach for Removing Various Types of Artifacts from TMS-EEG Data

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.

Neural effects of TMS trains on the human prefrontal cortex

How does a train of TMS pulses modify neural activity in humans? Despite adoption of repetitive TMS (rTMS) for the treatment of neuropsychiatric disorders, we still do not understand how rTMS changes the human brain. This limited understanding stems in part from a lack of methods for noninvasively measuring the neural effects of a single TMS train-a fundamental building block of treatment-as well as the cumulative effects of consecutive TMS trains. Gaining this understanding would provide foundational knowledge to guide the next generation of treatments. Here, to overcome this limitation, we developed methods to noninvasively measure causal and acute changes in cortical excitability and evaluated this neural response to single and sequential TMS trains. In 16 healthy adults, standard 10 Hz trains were applied to the dorsolateral prefrontal cortex in a randomized, sham-controlled, event-related design and changes were assessed based on the TMS-evoked potential (TEP), a measure of cortical excitability. We hypothesized that single TMS trains would induce changes in the local TEP amplitude and that those changes would accumulate across sequential trains, but primary analyses did not indicate evidence in support of either of these hypotheses. Exploratory analyses demonstrated non-local neural changes in sensor and source space and local neural changes in phase and source space. Together these results suggest that single and sequential TMS trains may not be sufficient to modulate local cortical excitability indexed by typical TEP amplitude metrics but may cause neural changes that can be detected outside the stimulation area or using phase or source space metrics. This work should be contextualized as methods development for the monitoring of transient noninvasive neural changes during rTMS and contributes to a growing understanding of the neural effects of rTMS.

Neural effects of TMS trains on the human prefrontal cortex

How does a train of TMS pulses modify neural activity in humans? Despite adoption of repetitive TMS (rTMS) for the treatment of neuropsychiatric disorders, we still do not understand how rTMS changes the human brain. This limited understanding stems in part from a lack of methods for noninvasively measuring the neural effects of a single TMS train - a fundamental building block of treatment - as well as the cumulative effects of consecutive TMS trains. Gaining this understanding would provide foundational knowledge to guide the next generation of treatments. Here, to overcome this limitation, we developed methods to noninvasively measure causal and acute changes in cortical excitability and evaluated this neural response to single and sequential TMS trains. In 16 healthy adults, standard 10 Hz trains were applied to the dorsolateral prefrontal cortex (dlPFC) in a randomized, sham-controlled, event-related design and changes were assessed based on the TMS-evoked potential (TEP), a measure of cortical excitability. We hypothesized that single TMS trains would induce changes in the local TEP amplitude and that those changes would accumulate across sequential trains, but primary analyses did not indicate evidence in support of either of these hypotheses. Exploratory analyses demonstrated non-local neural changes in sensor and source space and local neural changes in phase and source space. Together these results suggest that single and sequential TMS trains may not be sufficient to modulate local cortical excitability indexed by typical TEP amplitude metrics but may cause neural changes that can be detected outside the stimulation area or using phase or source space metrics. This work should be contextualized as methods development for the monitoring of transient noninvasive neural changes during rTMS and contributes to a growing understanding of the neural effects of rTMS.

Causal evidence for a coordinated temporal interplay within the language network

Recent neurobiological models on language suggest that auditory sentence comprehension is supported by a coordinated temporal interplay within a left-dominant brain network, including the posterior inferior frontal gyrus (pIFG), posterior superior temporal gyrus and sulcus (pSTG/STS), and angular gyrus (AG). Here, we probed the timing and causal relevance of the interplay between these regions by means of concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG). Our TMS-EEG experiments reveal region- and time-specific causal evidence for a bidirectional information flow from left pSTG/STS to left pIFG and back during auditory sentence processing. Adapting a condition-and-perturb approach, our findings further suggest that the left pSTG/STS can be supported by the left AG in a state-dependent manner.

Reliability and Validity of Transcranial Magnetic Stimulation-Electroencephalography Biomarkers

Noninvasive brain stimulation and neuroimaging have revolutionized human neuroscience with a multitude of applications, including diagnostic subtyping, treatment optimization, and relapse prediction. It is therefore particularly relevant to identify robust and clinically valuable brain biomarkers linking symptoms to their underlying neural mechanisms. Brain biomarkers must be reproducible (i.e., have internal reliability) across similar experiments within a laboratory and be generalizable (i.e., have external reliability) across experimental setups, laboratories, brain regions, and disease states. However, reliability (internal and external) is not alone sufficient; biomarkers also must have validity. Validity describes closeness to a true measure of the underlying neural signal or disease state. We propose that these metrics, reliability and validity, should be evaluated and optimized before any biomarker is used to inform treatment decisions. Here, we discuss these metrics with respect to causal brain connectivity biomarkers from coupling transcranial magnetic stimulation (TMS) with electroencephalography (EEG). We discuss controversies around TMS-EEG stemming from the multiple large off-target components (noise) and relatively weak genuine brain responses (signal), as is unfortunately often the case in noninvasive human neuroscience. We review the current state of TMS-EEG recordings, which consist of a mix of reliable noise and unreliable signal. We describe methods for evaluating TMS-EEG biomarkers, including how to assess internal and external reliability across facilities, cognitive states, brain networks, and disorders and how to validate these biomarkers using invasive neural recordings or treatment response. We provide recommendations to increase reliability and validity, discuss lessons learned, and suggest future directions for the field.

Natural Oscillatory Frequency Slowing in the Premotor Cortex of Early-Course Schizophrenia Patients: A TMS-EEG Study

Despite the heavy burden of schizophrenia, research on biomarkers associated with its early course is still ongoing. Single-pulse Transcranial Magnetic Stimulation coupled with electroencephalography (TMS-EEG) has revealed that the main oscillatory frequency (or "natural frequency") is reduced in several frontal brain areas, including the premotor cortex, of chronic patients with schizophrenia. However, no study has explored the natural frequency at the beginning of illness. Here, we used TMS-EEG to probe the intrinsic oscillatory properties of the left premotor cortex in early-course schizophrenia patients (<2 years from onset) and age/gender-matched healthy comparison subjects (HCs). State-of-the-art real-time monitoring of EEG responses to TMS and noise-masking procedures were employed to ensure data quality. We found that the natural frequency of the premotor cortex was significantly reduced in early-course schizophrenia compared to HCs. No correlation was found between the natural frequency and age, clinical symptom severity, or dose of antipsychotic medications at the time of TMS-EEG. This finding extends to early-course schizophrenia previous evidence in chronic patients and supports the hypothesis of a deficit in frontal cortical synchronization as a core mechanism underlying this disorder. Future work should further explore the putative role of frontal natural frequencies as early pathophysiological biomarkers for schizophrenia.

Neural bases of motor fatigue in multiple sclerosis: A multimodal approach using neuromuscular assessment and TMS-EEG

Motor fatigue is one of the most common symptoms in multiple sclerosis (MS) patients. Previous studies suggested that increased motor fatigue in MS may arise at the central nervous system level. However, the mechanisms underlying central motor fatigue in MS are still unclear. This paper investigated whether central motor fatigue in MS reflects impaired corticospinal transmission or suboptimal primary motor cortex (M1) output (supraspinal fatigue). Furthermore, we sought to identify whether central motor fatigue is associated with abnormal M1 excitability and connectivity within the sensorimotor network. Twenty-two patients affected by relapsing-remitting MS and 15 healthy controls (HCs) performed repeated blocks of contraction at different percentages of maximal voluntary contraction with the right first dorsal interosseus muscle until exhaustion. Peripheral, central, and supraspinal components of motor fatigue were quantified by a neuromuscular assessment based on the superimposed twitch evoked by peripheral nerve and transcranial magnetic stimulation (TMS). Corticospinal transmission, excitability and inhibition during the task were tested by measurement of motor evoked potential (MEP) latency, amplitude, and cortical silent period (CSP). M1 excitability and connectivity was measured by TMS-evoked electroencephalography (EEG) potentials (TEPs) elicited by M1 stimulation before and after the task. Patients completed fewer blocks of contraction and showed higher values of central and supraspinal fatigue than HCs. We found no MEP or CSP differences between MS patients and HCs. Patients showed a post-fatigue increase in TEPs propagation from M1 to the rest of the cortex and in source-reconstructed activity within the sensorimotor network, in contrast to the reduction observed in HCs. Post-fatigue increase in source-reconstructed TEPs correlated with supraspinal fatigue values. To conclude, MS-related motor fatigue is caused by central mechanisms related explicitly to suboptimal M1 output rather than impaired corticospinal transmission. Furthermore, by adopting a TMS-EEG approach, we proved that suboptimal M1 output in MS patients is associated with abnormal task-related modulation of M1 connectivity within the sensorimotor network. Our findings shed new light on the central mechanisms of motor fatigue in MS by highlighting a possible role of abnormal sensorimotor network dynamics. These novel results may point to new therapeutical targets for fatigue in MS.

Transferability of cathodal tDCS effects from the primary motor to the 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 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.

TMS combined with EEG: Recommendations and open issues for data collection and analysis

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.

Characterizing cortical responses to short-term multidisciplinary intensive rehabilitation treatment in patients with Parkinson’s disease: A transcranial magnetic stimulation and electroencephalography study

Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) is a powerful non-invasive tool for qualifying the neurophysiological effects of interventions by recording TMS-induced cortical activation with high temporal resolution and generates reproducible and reliable waves of activity without participant cooperation. Cortical dysfunction contributes to the pathogenesis of the clinical symptoms of Parkinson’s disease (PD). Here, we examined changes in cortical activity in patients with PD following multidisciplinary intensive rehabilitation treatment (MIRT). Forty-eight patients with PD received 2 weeks of MIRT. The cortical response was examined following single-pulse TMS over the primary motor cortex by 64-channel EEG, and clinical symptoms were assessed before and after MIRT. TMS-evoked potentials were quantified by the global mean field power, as well as oscillatory power in theta, alpha, beta, and gamma bands, and their clinical correlations were calculated. After MIRT, motor and non-motor symptoms improved in 22 responders, and only non-motor function was enhanced in 26 non-responders. Primary motor cortex stimulation reduced global mean field power amplitudes in responders but not significantly in non-responders. Oscillations exhibited attenuated power in the theta, beta, and gamma bands in responders but only reduced gamma power in non-responders. Associations were observed between beta oscillations and motor function and between gamma oscillations and non-motor symptoms. Our results suggest that motor function enhancement by MIRT may be due to beta oscillatory power modulation and that alterations in cortical plasticity in the primary motor cortex contribute to PD recovery.

Investigating Methods for Cognitive Workload Estimation for Assistive Robots

Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing modality might allow robots to derive the best evidence of human workload. In this work, we analyzed and modeled data from a multi-modal simulated driving study specifically designed to evaluate different levels of cognitive workload induced by various secondary tasks such as dialogue interactions and braking events in addition to the primary driving task. Specifically, we performed statistical analyses of various physiological signals including eye gaze, electroencephalography, and arterial blood pressure from the healthy volunteers and utilized several machine learning methodologies including k-nearest neighbor, naive Bayes, random forest, support-vector machines, and neural network-based models to infer human cognitive workload levels. Our analyses provide evidence for eye gaze being the best physiological indicator of human cognitive workload, even when multiple signals are combined. Specifically, the highest accuracy (in %) of binary workload classification based on eye gaze signals is 80.45 ∓ 3.15 achieved by using support-vector machines, while the highest accuracy combining eye gaze and electroencephalography is only 77.08 ∓ 3.22 achieved by a neural network-based model. Our findings are important for future efforts of real-time workload estimation in the multimodal human-robot interactive systems given that eye gaze is easy to collect and process and less susceptible to noise artifacts compared to other physiological signal modalities.

The influence of sensory potentials on transcranial magnetic stimulation - Electroencephalography recordings

OBJECTIVE

It remains unclear to what extent Transcranial Magnetic Stimulation-evoked potentials (TEPs) reflect sensory (auditory and somatosensory) potentials as opposed to cortical excitability. The present study aimed to determine; a) the extent to which sensory potentials contaminate TEPs using a spatially-matched sham condition, and b) whether sensory potentials reflect auditory or somatosensory potentials alone, or a combination of the two.

METHODS

Twenty healthy participants received active or sham stimulation, with the latter consisting a sham coil click combined with scalp electrical stimulation. Two additional conditions i) electrical stimulation and ii) auditory stimulation alone, were included in a subset of 13 participants.

RESULTS

Signals from active and sham stimulation were correlated in spatial and temporal domains > 55 ms post-stimulation. Relative to auditory or electrical stimulation alone, sham stimulation resulted in a) larger potentials, b) stronger correlations with active stimulation and c) a signal that was not a linear sum of electrical and auditory stimulation alone.

CONCLUSIONS

Sensory potentials can confound interpretations of TEPs at timepoints > 55 ms post-stimulation. Furthermore, TEP contamination cannot be explained by auditory or somatosensory potentials alone, but instead reflects a non-linear interaction between both.

SIGNIFICANCE

Future studies may benefit from controlling for sensory contamination using spatially-matched sham conditions, and which consist of combined auditory and somatosensory stimulation.

Identifying novel biomarkers with TMS-EEG - Methodological possibilities and challenges

Biomarkers are essential for understanding the underlying pathologies in brain disorders and for developing effective treatments. Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) is an emerging neurophysiological tool that can be used for biomarker development. This method can identify biomarkers associated with the function and dynamics of the inhibitory and excitatory neurotransmitter systems and effective connectivity between brain areas. In this review, we outline the current state of the TMS-EEG biomarker field by summarizing the existing protocols and the possibilities and challenges associated with this methodology.