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

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

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

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

The role of neuronavigation in TMS-EEG studies : Current applications and future perspectives

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) allows measuring noninvasively the electrical response of the human cerebral cortex to a direct perturbation. Complementing TMSEEG with a structural neuronavigation tool (nTMS-EEG) is key for accurately selecting cortical areas, targeting them, and adjusting the stimulation parameters based on some relevant anatomical priors. This step, together with the employment of visualization tools designed to perform a quality check of TMS-evoked potentials (TEPs) in real-time during TMS-EEG data acquisition, is pivotal for maximizing the impact of the TMS pulse on the cortex and in ensuring highly reproducible measurements within sessions and across subjects. Moreover, storing stimulation parameters in the neumnavigation system can help in replicating the stimulation parameters within and across experimental sessions and sharing them across research centers. Finally, the systematic employment of neumnavigation in TMS-EEG studies is also critical to standardize measurements in clinical populations in search for reliable diagnostic and prognostic TMS-EEG-based biomarkers for neurological and psychiatric disorders.Peer reviewe

Transcranial Magnetic Stimulation and Neuroimaging Coregistration

The development of neuroimaging techniques is one of the most impressive advancements in neuroscience. The main reason for the widespread use of these instruments lies in their capacity to provide an accurate description of neural activity during a cognitive process or during rest. This important advancement is related to the possibility to selectively detect changes of neuronal activity in space and time by means of different biological markers. Specifically, functional magnetic resonance imaging (fMRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), and nearinfrared spectroscopy (NIRS) use metabolic markers of ongoing neuronal activity to provide an accurate description of the activation of specific brain areas with high spatial resolution. Similarly, electroencephalography (EEG) is able to detect electric markers of neuronal activity, providing an accurate description of brain activation with high temporal resolution. The application of these techniques during a cognitive task allows important inferences regarding the relation between the detected neural activity, the cognitive process involved in an ongoing task, and behaviour: this is known as a \u201ccorrelational approach\u201d

Phase Dependency of the Human Primary Motor Cortex and Cholinergic Inhibition Cancelation during Beta tACS

The human motor cortex has a tendency to resonant activity at about 20 Hz so stimulation should more readily entrain neuronal populations at this frequency. We investigated whether and how different interneuronal circuits contribute to such resonance by using transcranial magnetic stimulation (TMS) during transcranial alternating current stimulation (tACS) at motor (20 Hz) and a nonmotor resonance frequency (7 Hz). We tested different TMS interneuronal protocols and triggered TMS pulses at different tACS phases. The effect of cholinergic short-latency afferent inhibition (SAI) was abolished by 20 Hz tACS, linking cortical beta activity to sensorimotor integration. However, this effect occurred regardless of the tACS phase. In contrast, 20 Hz tACS selectively modulated MEP size according to the phase of tACS during single pulse, GABAAergic short-interval intracortical inhibition (SICI) and glutamatergic intracortical facilitation (ICF). For SICI this phase effect was more marked during 20 Hz stimulation. Phase modulation of SICI also depended on whether or not spontaneous beta activity occurred at ~20 Hz, supporting an interaction effect between tACS and underlying circuit resonances. The present study provides in vivo evidence linking cortical beta activity to sensorimotor integration, and for beta oscillations in motor cortex being promoted by resonance in GABAAergic interneuronal circuits

TAAC - TMS Adaptable Auditory Control: A universal tool to mask TMS clicks

Background: Coupling transcranial magnetic stimulation with electroencephalography (TMS-EEG) allows recording the EEG response to a direct, non-invasive cortical perturbation. However, obtaining a genuine TMS- evoked EEG potential requires controlling for several confounds, among which a main source is represented by the auditory evoked potentials (AEPs) associated to the TMS discharge noise (TMS click). This contaminating factor can be in principle prevented by playing a masking noise through earphones. New method: Here we release TMS Adaptable Auditory Control (TAAC), a highly flexible, open-source, Matlab®- based interface that generates in real-time customized masking noises. TAAC creates noises starting from the stimulator-specific TMS click and tailors them to fit the individual, subject-specific click perception by mixing and manipulating the standard noises in both time and frequency domains. Results: We showed that TAAC allows us to provide standard as well as customized noises able to effectively and safely mask the TMS click. Comparison with existing methods: Here, we showcased two customized noises by comparing them to two standard noises previously used in the TMS literature (i.e., a white noise and a noise generated from the stimulator-specific TMS click only). For each, we quantified the Sound Pressure Level (SPL; measured by a Head and Torso Simulator - HATS) required to mask the TMS click in a population of 20 healthy subjects. Both customized noises were effective at safe (according to OSHA and NIOSH safety guidelines) and lower SPLs with respect to standard noises. Conclusions: At odds with previous methods, TAAC allows creating effective and safe masking noises specifically tailored on each TMS device and subject. The combination of TAAC with tools for the real-time visualization of TEPs can help control the influence of auditory confounds also in non-compliant patients. Finally, TAAC is a highly flexible and open-source tool, so it can be further extended to meet different experimental requirements