TMS-evoked long-lasting artefacts: A new adaptive algorithm for EEG signal correction

OBJECTIVE: During EEG the discharge of TMS generates a long-lasting decay artefact (DA) that makes the analysis of TMS-evoked potentials (TEPs) difficult. Our aim was twofold: (1) to describe how the DA affects the recorded EEG and (2) to develop a new adaptive detrend algorithm (ADA) able to correct the DA. METHODS: We performed two experiments testing 50 healthy volunteers. In experiment 1, we tested the efficacy of ADA by comparing it with two commonly-used independent component analysis (ICA) algorithms. In experiment 2, we further investigated the efficiency of ADA and the impact of the DA evoked from TMS over frontal, motor and parietal areas. RESULTS: Our results demonstrated that (1) the DA affected the EEG signal in the spatiotemporal domain; (2) ADA was able to completely remove the DA without affecting the TEP waveforms; (3). ICA corrections produced significant changes in peak-to-peak TEP amplitude. CONCLUSIONS: ADA is a reliable solution for the DA correction, especially considering that (1) it does not affect physiological responses; (2) it is completely data-driven and (3) its effectiveness does not depend on the characteristics of the artefact and on the number of recording electrodes. SIGNIFICANCE: We proposed a new reliable algorithm of correction for long-lasting TMS-EEG artifacts

Transcranial magnetic stimulation and EEG in studies of brain function

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a multimodal technique, with a temporal resolution of submilliseconds, for studying cortical excitability and connectivity. When TMS is combined with neuronavigation, resulting in so-called navigated TMS (nTMS), the technique becomes very powerful. However, despite the potential of TMS–EEG, its use for studying lateral areas has been restricted because the TMS pulse induces strong muscle artifacts, making the EEG data useless for further analyses. In this Thesis, methods for analyzing TMS-evoked EEG data from lateral areas are introduced. First, TMS–EEG is used to study Broca's area and dorsal premotor cortex. Due to the fact that those areas are close to cranial muscles, their stimulation evokes large muscle artifacts in EEG recordings. The behavior of the artifacts is described in detail. Two approaches to deal with large artifacts are presented. In the first approach, independent component analysis (ICA) is used. Here, FastICA algorithm is modified to make the search of the components more robust and easier, allowing one to get more stable results. The second approach presents methods for suppressing the artifacts rather than removing them. These methods were combined with source localization showing that the artifact suppression is efficient. The methods were tested with both real and simulated data, suggesting they are useful for artifact correction. For a better understanding of the effects of repetitive nTMS during naming tasks and the cortical organization of speech in general, here another study is introduced to understand the sensitivity of object and action naming tasks to repetitive nTMS. The distributions of cortical sites, where repetitive nTMS produced naming errors during both tasks, are compared. Thus, it is shown how this study can impact on both cognitive neuroscience and clinical practice. In the last part, the beamformer method is improved to study source localization, which makes it a robust method to study time-correlated sources. In this Thesis, I discuss how all these methods together can contribute to study brain connectivity of language and lateral areas with TMS–EEG, opening new possibilities for basic research and clinical applications

Comparative analysis of TMS-EEG signal using different approaches in healthy subjects

openThe integration of transcranial magnetic stimulation with electroencephalography (TMS-EEG) represents a useful non-invasive approach to assess cortical excitability, plasticity and intra-cortical connectivity in humans in physiological and pathological conditions. However, biological and environmental noise sources can contaminate the TMS-evoked potentials (TEPs). Therefore, signal preprocessing represents a fundamental step in the analysis of these potentials and is critical to remove artefactual components while preserving the physiological brain activity. The objective of the present study is to evaluate the effects of different signal processing pipelines, (namely Leodori et al., Rogasch et al., Mutanen et al.) applied on TEPs recorded in five healthy volunteers after TMS stimulation of the primary motor cortex (M1) of the dominant hemisphere. These pipelines were used and compared to remove artifacts and improve the quality of the recorded signals, laying the foundation for subsequent analyses. Various algorithms, such as Independent Component Analysis (ICA), SOUND, and SSP-SIR, were used in each pipeline. Furthermore, after signal preprocessing, current localization was performed to map the TMS-induced neural activation in the cortex. This methodology provided valuable information on the spatial distribution of activity and further validated the effectiveness of the signal cleaning pipelines. Comparing the effects of the different pipelines on the same dataset, we observed considerable variability in how the pipelines affect various signal characteristics. We observed significant differences in the effects on signal amplitude and in the identification and characterisation of peaks of interest, i.e., P30, N45, P60, N100, P180. The identification and characteristics of these peaks showed variability, especially with regard to the early peaks, which reflect the cortical excitability of the stimulated area and are the more affected by biological and stimulation-related artifacts. Despite these differences, the topographies and source localisation, which are the most informative and useful in reconstructing signal dynamics, were consistent and reliable between the different pipelines considered. The results suggest that the existing methodologies for analysing TEPs produce different effects on the data, but are all capable of reproducing the dynamics of the signal and its components. Future studies evaluating different signal preprocessing methods in larger populations are needed to determine an appropriate workflow that can be shared through the scientific community, in order to make the results obtained in different centres comparable

Voimakkaiden TMS-artefaktojen analyysi ja poisto EEG-datasta

Transkraniaalisen magneettistimulaation (TMS) ja elektroenkefalografian (EEG) avulla voidaan stimuloida aivokuorta ei-invasiivisesti ja samalla tarkkailla aiheutetun aivoaktivaation leviämistä. TMS-stimulaatio kuitenkin aiheuttaa EEG-signaaliin artefaktoja, jotka voivat kestää jopa kymmeniä millisekunteja ja kokonaan peittää tutkittavan aivoaktiivisuuden. Lihasten aktivoituminen aiheuttaa artefaktoja erityisesti lateraalisilla aivoalueilla, mm. stimuloitaessa aivojen pääasiallisia kielialueita. Tämän työn on tarkoitus oli määrittää, mistä artefaktat johtuvat, ja kuinka niitä voitaisiin pienentää mittauksen aikana ja sen jälkeisessä data-analyysissä. Artefaktojen todettiin olevan suurimmillaan stimuloitaessa anteriorisia ja lateraalisia aivoalueita. Näillä alueilla artefaktojen poistoa vaikeuttavat niiden voimakkuus ja pitkä kesto. Artefaktojen poistoon analyysivaiheessa kokeiltiin kahta menetelmää. Riippumattomien komponenttien analyysi (independent component analysis, ICA) ja monilähdemalliin perustuva korjaus pystyivät poistamaan osan häiriöpiikeistä, mutta vaikuttivat myös aivoperäiseen signaaliin artefaktan aikana ja jälkeen.Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) provides a means to non-invasively stimulate cortical brain areas and to monitor the evoked neuronal activation and connectivity. However, TMS causes various artifacts that can distort the EEG signal. Especially in the lateral areas of the brain, which include the main areas of language processing, the muscle artifact can mask the neuronal activity for tens of milliseconds. The purpose of this thesis was to characterize the artifacts and nd ways to reduce them during data acquisition and in o²ine analysis. The results show that the artifact is highest in anterior and lateral areas and that the high amplitude and long duration of the artifact in lateral areas makes removing it a challenging task. Two methods of o²ine artifact removal were evaluated for the high-artifact data. Independent component analysis (ICA) and multiple-source modeling were able to reduce the artifact, but also arected the brain signal during or after the artifact

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Resting-state functional connectivity MRI (rs-fcMRI) is a technique that identifies connectivity between different brain regions based on correlations over time in the blood-oxygenation level dependent signal. rs-fcMRI has been applied extensively to identify abnormalities in brain connectivity in different neurologic and psychiatric diseases. However, the relationship among rs-fcMRI connectivity abnormalities, brain electrophysiology and disease state is unknown, in part because the causal significance of alterations in functional connectivity in disease pathophysiology has not been established. Transcranial Magnetic Stimulation (TMS) is a technique that uses electromagnetic induction to noninvasively produce focal changes in cortical activity. When combined with electroencephalography (EEG), TMS can be used to assess the brain's response to external perturbations. Here we provide a protocol for combining rs-fcMRI, TMS and EEG to assess the physiologic significance of alterations in functional connectivity in patients with neuropsychiatric disease. We provide representative results from a previously published study in which rs-fcMRI was used to identify regions with abnormal connectivity in patients with epilepsy due to a malformation of cortical development, periventricular nodular heterotopia (PNH). Stimulation in patients with epilepsy resulted in abnormal TMS-evoked EEG activity relative to stimulation of the same sites in matched healthy control patients, with an abnormal increase in the late component of the TMS-evoked potential, consistent with cortical hyperexcitability. This abnormality was specific to regions with abnormal resting-state functional connectivity. Electrical source analysis in a subject with previously recorded seizures demonstrated that the origin of the abnormal TMS-evoked activity co-localized with the seizure-onset zone, suggesting the presence of an epileptogenic circuit. These results demonstrate how rs-fcMRI, TMS and EEG can be utilized together to identify and understand the physiological significance of abnormal brain connectivity in human diseases

Studying the cortical state with transcranial magnetic stimulation

Cortical excitability and connectivity describe the state of the cerebral cortex. They reflect the ability of neurons to respond to input and the way information flows in the neuronal networks. These properties can be assessed with transcranial magnetic stimulation (TMS), which enables direct and noninvasive modulation of cortical activity. Electrophysiological or hemodynamic recordings of TMS-evoked activity or behavioral measures of the stimulation effect characterize the state of the cortex during and as a result of the stimulation. In the research reported in this Thesis, the ability of TMS to inform us about the cortical state is studied from different points of view. First, we examine the relationships between different measures of cortical excitability to better understand the physiology behind them; we show how cortical background activity is related to motor cortical excitability and how the evoked responses reflect the excitability. Second, this study addresses the questions whether the TMS-evoked responses include stimulation-related artifacts, how these artifacts are generated, and how they can be avoided or removed. Specifically, we present a method to remove the artifacts from TMS-evoked electroencephalographic (EEG) signals arising as a result of cranial muscle stimulation. The use of TMS-EEG has been limited to relatively medial sites because of these artifacts, but the new method enables studying the cortical state even when stimulating areas near the cranial muscles, especially lateral sites. Finally, this work provides new information about brain function. The mechanisms how the brain processes visually guided timed motor actions are elucidated. Moreover, we show that cortical excitability as measured with TMS-evoked EEG increases during the course of wakefulness and decreases during sleep, which contributes to our understanding of what happens in the brain during wakefulness that makes us feel tired and why the brain needs sleep. The study also shows the sensitivity of the TMS-EEG measurement to changes in the state of the cortex. Accordingly, we demonstrate the power of TMS in studying the cortical state

EEG Artifact Removal in TMS Studies of Cortical Speech Areas

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.Peer reviewe

Causal evidence that intrinsic beta frequency is relevant for enhanced signal propagation in the motor system as shown through rhythmic TMS

Correlative evidence provides support for the idea that brain oscillations underpin neural computations. Recent work using rhythmic stimulation techniques in humans provide causal evidence but the interactions of these external signals with intrinsic rhythmicity remain unclear. Here, we show that sensorimotor cortex precisely follows externally applied rhythmic TMS (rTMS) stimulation in the beta-band but that the elicited responses are strongest at the intrinsic individual beta-peak-frequency. While these entrainment effects are of short duration, even subthreshold rTMS pulses propagate through the network and elicit significant cortico-spinal coupling, particularly when stimulated at the individual beta-frequency. Our results show that externally enforced rhythmicity interacts with intrinsic brain rhythms such that the individual peak frequency determines the effect of rTMS. The observed downstream spinal effect at the resonance frequency provides evidence for the causal role of brain rhythms for signal propagation

Changes in Cortical Activation by Transcranial Magnetic Stimulation Due to Coil Rotation Are Not Attributable to Cranial Muscle Activation

Transcranial magnetic stimulation coupled with electroencephalography (TMS-EEG) allows for the study of brain dynamics in health and disease. Cranial muscle activation can decrease the interpretability of TMS-EEG signals by masking genuine EEG responses and increasing the reliance on preprocessing methods but can be at least partly prevented by coil rotation coupled with the online monitoring of signals; however, the extent to which changing coil rotation may affect TMS-EEG signals is not fully understood. Our objective was to compare TMS-EEG data obtained with an optimal coil rotation to induce motor evoked potentials (M1standard) while rotating the coil to minimize cranial muscle activation (M1emg). TMS-evoked potentials (TEPs), TMS-related spectral perturbation (TRSP), and intertrial phase clustering (ITPC) were calculated in both conditions using two different preprocessing pipelines based on independent component analysis (ICA) or signal-space projection with source-informed reconstruction (SSP-SIR). Comparisons were performed with cluster-based correction. The concordance correlation coefficient was computed to measure the similarity between M1standard and M1emg TMS-EEG signals. TEPs, TRSP, and ITPC were significantly larger in M1standard than in M1emg conditions; a lower CCC than expected was also found. These results were similar across the preprocessing pipelines. While rotating the coil may be advantageous to reduce cranial muscle activation, it may result in changes in TMS-EEG signals; therefore, this solution should be tailored to the specific experimental context