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Schoisswohl S, Kanig C, Osnabruegge M, Agboada D, Langguth B, Rethwilm R, Hebel T, Abdelnaim MA, Mack W, Seiberl W, Kuder M, Schecklmann M. Monitoring Changes in TMS-Evoked EEG and EMG Activity During 1 Hz rTMS of the Healthy Motor Cortex. eNeuro 2024; 11:ENEURO.0309-23.2024. [PMID: 38565296 PMCID: PMC11015949 DOI: 10.1523/eneuro.0309-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 12/13/2023] [Accepted: 01/08/2024] [Indexed: 04/04/2024] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique capable of inducing neuroplasticity as measured by changes in peripheral muscle electromyography (EMG) or electroencephalography (EEG) from pre-to-post stimulation. However, temporal courses of neuromodulation during ongoing rTMS are unclear. Monitoring cortical dynamics via TMS-evoked responses using EMG (motor-evoked potentials; MEPs) and EEG (transcranial-evoked potentials; TEPs) during rTMS might provide further essential insights into its mode of action - temporal course of potential modulations. The objective of this study was to first evaluate the validity of online rTMS-EEG and rTMS-EMG analyses, and second to scrutinize the temporal changes of TEPs and MEPs during rTMS. As rTMS is subject to high inter-individual effect variability, we aimed for single-subject analyses of EEG changes during rTMS. Ten healthy human participants were stimulated with 1,000 pulses of 1 Hz rTMS over the motor cortex, while EEG and EMG were recorded continuously. Validity of MEPs and TEPs measured during rTMS was assessed in sensor and source space. Electrophysiological changes during rTMS were evaluated with model fitting approaches on a group- and single-subject level. TEPs and MEPs appearance during rTMS was consistent with past findings of single pulse experiments. Heterogeneous temporal progressions, fluctuations or saturation effects of brain activity were observed during rTMS depending on the TEP component. Overall, global brain activity increased over the course of stimulation. Single-subject analysis revealed inter-individual temporal courses of global brain activity. The present findings are in favor of dose-response considerations and attempts in personalization of rTMS protocols.
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Affiliation(s)
- Stefan Schoisswohl
- Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany
- Department of Human Sciences, Institute of Psychology, Universität der Bundeswehr München, 85579 Neubiberg, Germany
| | - Carolina Kanig
- Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany
- Department of Human Sciences, Institute of Psychology, Universität der Bundeswehr München, 85579 Neubiberg, Germany
| | - Mirja Osnabruegge
- Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany
- Department of Human Sciences, Institute of Psychology, Universität der Bundeswehr München, 85579 Neubiberg, Germany
| | - Desmond Agboada
- Department of Human Sciences, Institute of Psychology, Universität der Bundeswehr München, 85579 Neubiberg, Germany
| | - Berthold Langguth
- Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany
| | - Roman Rethwilm
- Department of Human Sciences, Institute of Sport Science, Universität der Bundeswehr München, 85579 Neubiberg, Germany
| | - Tobias Hebel
- Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany
| | - Mohamed A Abdelnaim
- Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany
| | - Wolfgang Mack
- Department of Human Sciences, Institute of Psychology, Universität der Bundeswehr München, 85579 Neubiberg, Germany
| | - Wolfgang Seiberl
- Department of Human Sciences, Institute of Sport Science, Universität der Bundeswehr München, 85579 Neubiberg, Germany
| | - Manuel Kuder
- Department of Electrical Engineering, Universität der Bundeswehr München, 85579 Neubiberg, Germany
| | - Martin Schecklmann
- Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany
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Alavi SMM, Vila-Rodriguez F, Mahdi A, Goetz SM. Closed-loop optimal and automatic tuning of pulse amplitude and width in EMG-guided controllable transcranial magnetic stimulation. Biomed Eng Lett 2023; 13:119-127. [PMID: 37124104 PMCID: PMC10130260 DOI: 10.1007/s13534-022-00259-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 11/26/2022] [Accepted: 12/20/2022] [Indexed: 01/01/2023] Open
Abstract
This paper proposes an efficient algorithm for automatic and optimal tuning of pulse amplitude and width for sequential parameter estimation (SPE) of the neural membrane time constant and input-output (IO) curve parameters in closed-loop electromyography-guided (EMG-guided) controllable transcranial magnetic stimulation (cTMS). The proposed SPE is performed by administering a train of optimally tuned TMS pulses and updating the estimations until a stopping rule is satisfied or the maximum number of pulses is reached. The pulse amplitude is computed by the Fisher information maximization. The pulse width is chosen by maximizing a normalized depolarization factor, which is defined to separate the optimization and tuning of the pulse amplitude and width. The normalized depolarization factor maximization identifies the critical pulse width, which is an important parameter in the identifiability analysis, without any prior neurophysiological or anatomical knowledge of the neural membrane. The effectiveness of the proposed algorithm is evaluated through simulation. The results confirm satisfactory estimation of the membrane time constant and IO curve parameters for the simulation case. By defining the stopping rule based on the satisfaction of the convergence criterion with tolerance of 0.01 for 5 consecutive times for all parameters, the IO curve parameters are estimated with 52 TMS pulses, with absolute relative estimation errors (AREs) of less than 7%. The membrane time constant is estimated with 0.67% ARE, and the pulse width value tends to the critical pulse width with 0.16% ARE with 52 TMS pulses. The results confirm that the pulse width and amplitude can be tuned optimally and automatically to estimate the membrane time constant and IO curve parameters in real-time with closed-loop EMG-guided cTMS.
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Affiliation(s)
- S. M. Mahdi Alavi
- The Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Fidel Vila-Rodriguez
- The Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Adam Mahdi
- Surrey Institute for People-Centred AI, University of Surrey, Surrey, UK
- Oxford Internet Institute, University of Oxford, Oxford, UK
| | - Stefan M. Goetz
- Department of Engineering, University of Cambridge, Cambridge, UK
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC USA
- Department of Neurosurgery, Duke University, Durham, NC USA
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Hernandez-Pavon JC, Veniero D, Bergmann TO, Belardinelli P, Bortoletto M, Casarotto S, Casula EP, Farzan F, Fecchio M, Julkunen P, Kallioniemi E, Lioumis P, Metsomaa J, Miniussi C, Mutanen TP, Rocchi L, Rogasch NC, Shafi MM, Siebner HR, Thut G, Zrenner C, Ziemann U, Ilmoniemi RJ. TMS combined with EEG: Recommendations and open issues for data collection and analysis. Brain Stimul 2023; 16:567-593. [PMID: 36828303 DOI: 10.1016/j.brs.2023.02.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 02/10/2023] [Accepted: 02/19/2023] [Indexed: 02/25/2023] Open
Abstract
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.
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Affiliation(s)
- Julio C Hernandez-Pavon
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Legs + Walking Lab, Shirley Ryan AbilityLab, Chicago, IL, USA; Center for Brain Stimulation, Shirley Ryan AbilityLab, Chicago, IL, USA.
| | | | - Til Ole Bergmann
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Germany; Leibniz Institute for Resilience Research (LIR), Mainz, Germany
| | - Paolo Belardinelli
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, TN, Italy; Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany
| | - Marta Bortoletto
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Silvia Casarotto
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy; IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Elias P Casula
- Department of Systems Medicine, University of Tor Vergata, Rome, Italy
| | - Faranak Farzan
- Simon Fraser University, School of Mechatronic Systems Engineering, Surrey, British Columbia, Canada
| | - Matteo Fecchio
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Petro Julkunen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland; Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland
| | - Elisa Kallioniemi
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Pantelis Lioumis
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Johanna Metsomaa
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Carlo Miniussi
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, TN, Italy
| | - Tuomas P Mutanen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Lorenzo Rocchi
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
| | - Nigel C Rogasch
- University of Adelaide, Adelaide, Australia; South Australian Health and Medical Research Institute, Adelaide, Australia; Monash University, Melbourne, Australia
| | - Mouhsin M Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Gregor Thut
- School of Psychology and Neuroscience, University of Glasgow, United Kingdom
| | - Christoph Zrenner
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Canada; Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Ulf Ziemann
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
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Hassan U, Feld GB, Bergmann TO. Automated real-time EEG sleep spindle detection for brain-state-dependent brain stimulation. J Sleep Res 2022; 31:e13733. [PMID: 36130730 DOI: 10.1111/jsr.13733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/31/2022] [Accepted: 09/02/2022] [Indexed: 10/14/2022]
Abstract
Sleep spindles are a hallmark electroencephalographic feature of non-rapid eye movement sleep, and are believed to be instrumental for sleep-dependent memory reactivation and consolidation. However, direct proof of their causal relevance is hard to obtain, and our understanding of their immediate neurophysiological consequences is limited. To investigate their causal role, spindles need to be targeted in real-time with sensory or non-invasive brain-stimulation techniques. While fully automated offline detection algorithms are well established, spindle detection in real-time is highly challenging due to their spontaneous and transient nature. Here, we present the real-time spindle detector, a robust multi-channel electroencephalographic signal-processing algorithm that enables the automated triggering of stimulation during sleep spindles in a phase-specific manner. We validated the real-time spindle detection method by streaming pre-recorded sleep electroencephalographic datasets to a real-time computer system running a Simulink® Real-Time™ implementation of the algorithm. Sleep spindles were detected with high levels of Sensitivity (~83%), Precision (~78%) and a convincing F1-Score (~81%) in reference to state-of-the-art offline algorithms (which reached similar or lower levels when compared with each other), for both naps and full nights, and largely independent of sleep scoring information. Detected spindles were comparable in frequency, duration, amplitude and symmetry, and showed the typical time-frequency characteristics as well as a centroparietal topography. Spindles were detected close to their centre and reliably at the predefined target phase. The real-time spindle detection algorithm therefore empowers researchers to target spindles during human sleep, and apply the stimulation method and experimental paradigm of their choice.
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Affiliation(s)
- Umair Hassan
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Mainz, Germany.,Leibniz Institute for Resilience Research, Mainz, Germany
| | - Gordon B Feld
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Til Ole Bergmann
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Mainz, Germany.,Leibniz Institute for Resilience Research, Mainz, Germany.,Department of Neurology & Stroke, Hertie Institute for Clinical Brain Research, Eberhard Karls University of Tübingen, Tübingen, Germany
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Gao B, Wang Y, Zhang D, Wang Z, Wang Z. Intermittent theta-burst stimulation with physical exercise improves poststroke motor function: A systemic review and meta-analysis. Front Neurol 2022; 13:964627. [PMID: 36110393 PMCID: PMC9468864 DOI: 10.3389/fneur.2022.964627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Abstract
Background Intermittent theta-burst stimulation (iTBS) is an optimized rTMS modality that could modulate the excitability of neural structures. Several studies have been conducted to investigate the efficacy of iTBS in improving the motor function of stroke patients. However, the specific role of iTBS in motor function recovery after stroke is unclear. Hence, in our study, we performed a meta-analysis to investigate the efficacy of iTBS for the motor function improvement of stroke patients. Methods MEDLINE, Embase, and Cochrane Library were searched until May 2022 for randomized controlled trials (RCTs). Results Thirteen RCTs with 334 patients were finally included in our study. The primary endpoints were the Fugl-Meyer assessment scale (FMA) and Motor Assessment Scale (MAS) change from baseline. We found that iTBS led to a significant reduction in FMA score (P = 0.002) but not in MAS score (P = 0.24) compared with the sham group. Moreover, standard 600-pulse stimulation showed a better effect on motor function improvement than the sham group (P = 0.004), however, 1200-pulse iTBS showed no effect on motor function improvement after stroke (P = 0.23). The effect of iTBS for improving motor function only exists in chronic stroke patients (P = 0.02) but not in subacute patients (P = 0.27). Conclusion This study supports that iTBS has good efficacy for improving motor function in stroke patients. Therefore, standard 600-pulse stimulation iTBS therapy is proper management and treatment for chronic stroke.
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Affiliation(s)
- Bixi Gao
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Stroke Research, Soochow University, Suzhou, China
| | - Yunjiang Wang
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Stroke Research, Soochow University, Suzhou, China
- Department of Neurosurgery, Yancheng Third People's Hospital, Yancheng, China
| | - Dingding Zhang
- Department of Anesthesia, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zongqi Wang
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Stroke Research, Soochow University, Suzhou, China
- *Correspondence: Zongqi Wang
| | - Zhong Wang
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Stroke Research, Soochow University, Suzhou, China
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Ni R, Yuan Y, Yang L, Meng Q, Zhu Y, Zhong Y, Cao Z, Zhang S, Yao W, Lv D, Chen X, Chen X, Bu J. Novel Non-invasive Transcranial Electrical Stimulation for Parkinson's Disease. Front Aging Neurosci 2022; 14:880897. [PMID: 35493922 PMCID: PMC9039727 DOI: 10.3389/fnagi.2022.880897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 03/15/2022] [Indexed: 11/25/2022] Open
Abstract
Conventional transcranial electrical stimulation (tES) is a non-invasive method to modulate brain activity and has been extensively used in the treatment of Parkinson's disease (PD). Despite promising prospects, the efficacy of conventional tES in PD treatment is highly variable across different studies. Therefore, many have tried to optimize tES for an improved therapeutic efficacy by developing novel tES intervention strategies. Until now, these novel clinical interventions have not been discussed or reviewed in the context of PD therapy. In this review, we focused on the efficacy of these novel strategies in PD mitigation, classified them into three categories based on their distinct technical approach to circumvent conventional tES problems. The first category has novel stimulation modes to target different modulating mechanisms, expanding the rang of stimulation choices hence enabling the ability to modulate complex brain circuit or functional networks. The second category applies tES as a supplementary intervention for PD hence amplifies neurological or behavioral improvements. Lastly, the closed loop tES stimulation can provide self-adaptive individualized stimulation, which enables a more specialized intervention. In summary, these novel tES have validated potential in both alleviating PD symptoms and improving understanding of the pathophysiological mechanisms of PD. However, to assure wide clinical used of tES therapy for PD patients, further large-scale trials are required.
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Affiliation(s)
- Rui Ni
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Ye Yuan
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Li Yang
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Qiujian Meng
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Ying Zhu
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Yiya Zhong
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Zhenqian Cao
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Shengzhao Zhang
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Wenjun Yao
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Daping Lv
- Department of Neurology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xin Chen
- Department of Neurology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xianwen Chen
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Junjie Bu
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
- Department of Neurosurgery, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
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Removing artifacts from TMS-evoked EEG: A methods review and a unifying theoretical framework. J Neurosci Methods 2022; 376:109591. [PMID: 35421514 DOI: 10.1016/j.jneumeth.2022.109591] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 02/15/2022] [Accepted: 03/26/2022] [Indexed: 11/24/2022]
Abstract
Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a technique for studying cortical excitability and connectivity in health and disease, allowing basic research and potential clinical applications. A major methodological issue, severely limiting the applicability of TMS-EEG, relates to the contamination of EEG signals by artifacts of biologic or non-biologic origin. To solve this problem, several methods, based on independent component analysis (ICA), principal component analysis (PCA), signal space projection (SSP), and other approaches, have been developed over the last decade. This article is divided into two parts. In the first part, we review the theoretical background of the currently available TMS-EEG artifact removal methods. In the second part, we formally introduce the mathematics underpinnings of the cleaning methods. We classify them into spatial and temporal filters based on their properties. Since the most frequently used TMS-EEG cleaning approach are spatial filter methods, we focus on them and introduce beamforming as a unified framework of the most popular spatial filtering techniques. This unifying approach enables the comparative assessment of these methods by highlighting their differences in terms of assumptions, challenges, and applicability for different types of artifacts and data. The different properties and challenges of the methods discussed are illustrated with both simulated and recorded data. This article targets non-mathematical and mathematical audiences. Accordingly, those readers interested in essential background information on these methods can focus on Section 2. Whereas theory-oriented readers may find Section 3 helpful for making informed decisions between existing methods and developing the methodology further.
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Rogasch NC, Biabani M, Mutanen TP. Designing and comparing cleaning pipelines for TMS-EEG data: a theoretical overview and practical example. J Neurosci Methods 2022; 371:109494. [PMID: 35143852 DOI: 10.1016/j.jneumeth.2022.109494] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 02/01/2022] [Accepted: 02/04/2022] [Indexed: 10/19/2022]
Abstract
Combining transcranial magnetic stimulation (TMS) with electroencephalography (EEG) is growing in popularity as a method for probing the reactivity and connectivity of neural circuits in basic and clinical research. However, using EEG to measure the neural responses to TMS is challenging due to the unique artifacts introduced by combining the two techniques. In this paper, we overview the artifacts present in TMS-EEG data and the offline cleaning methods used to suppress these unwanted signals. We then describe how open science practices, including the development of open-source toolboxes designed for TMS-EEG analysis (e.g., TESA - the TMS-EEG signal analyser), have improved the availability and reproducibility of TMS-EEG cleaning methods. We provide theoretical and practical considerations for designing TMS-EEG cleaning pipelines and then give an example of how to compare different pipelines using TESA. We show that changing even a single step in a pipeline designed to suppress decay artifacts results in TMS-evoked potentials (TEPs) with small differences in amplitude and spatial topography. The variability in TEPs resulting from the choice of cleaning pipeline has important implications for comparing TMS-EEG findings between research groups which use different online and offline approaches. Finally, we discuss the challenges of validating cleaning pipelines and recommend that researchers compare outcomes from TMS-EEG experiments using multiple pipelines to ensure findings are not related to the choice of cleaning methods. We conclude that the continued improvement, availability, and validation of cleaning pipelines is essential to ensure TMS-EEG reaches its full potential as a method for studying human neurophysiology.
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Affiliation(s)
- Nigel C Rogasch
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide; Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University.
| | - Mana Biabani
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University
| | - Tuomas P Mutanen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Finland
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