MEP-ART: A system for real-time feedback and analysis of transcranial magnetic stimulation motor evoked potentials

Transcranial magnetic stimulation (TMS) of primary motor cortex (M1) is commonly used to evaluate functional integrity of the corticospinal tract (CST). TMS-elicited motor-evoked potentials (MEPs) recorded with electromyography (EMG) have been used to measure excitability of the corticospinal motor pathway [[1]McDonnell M.N. Stinear C.M. TMS measures of motor cortex function after stroke: a meta-analysis.Brain Stimulation. 2017; 10: 721-734https://doi.org/10.1016/j.brs.2017.03.008Abstract Full Text Full Text PDF PubMed Scopus (110) Google Scholar]. The quality of recorded MEP data can be enhanced by real-time feedback of baseline EMG activity, visualization of MEPs, and online analysis during the TMS session [[2]Kaelin-Lang A. Cohen L.G. Enhancing the quality of studies using transcranial magnetic and electrical stimulation with a new computer-controlled system.J Neurosci Methods. 2000; 102 (https://doi.org/S0165-0270(00)00284-3 [pii]): 81-89Crossref PubMed Scopus (65) Google Scholar]. Several open source TMS analysis toolboxes exist [3Jackson N. Greenhouse I. VETA: an open-source matlab-based toolbox for the collection and analysis of electromyography combined with transcranial magnetic stimulation.Front Neurosci. 2019; 13https://doi.org/10.3389/fnins.2019.00975Crossref Scopus (3) Google Scholar, 4Mullins C.R. Hanlon C.A. MAVIN: an open-source tool for interactive analysis and visualization of EMG data.Brain Stimul. 2016; 9: 305-306https://doi.org/10.1016/j.brs.2015.11.009Abstract Full Text Full Text PDF PubMed Scopus (3) Google Scholar, 5Harquel S. Beynel L. Guyader N. Marendaz C. David O. Chauvin A. CortExTool: a toolbox for processing motor cortical excitability measurements by transcranial magnetic stimulation.2016Google Scholar, 6van de Ruit M. Pearson T. Grey M.J. Novel tools for rapid online data acquisition of the TMS stimulus-response curve.Brain Stimul. 2019; 12: 192-194https://doi.org/10.1016/j.brs.2018.09.015Abstract Full Text Full Text PDF PubMed Scopus (4) Google Scholar]. Although the existing tools are very useful, limitations in their functionality remain. For example, several toolboxes are limited to analyzing data after the TMS recording session, when it is too late to address the factors that lead to poor quality of the recorded data or insufficient collection of MEP data [3Jackson N. Greenhouse I. VETA: an open-source matlab-based toolbox for the collection and analysis of electromyography combined with transcranial magnetic stimulation.Front Neurosci. 2019; 13https://doi.org/10.3389/fnins.2019.00975Crossref Scopus (3) Google Scholar, 4Mullins C.R. Hanlon C.A. MAVIN: an open-source tool for interactive analysis and visualization of EMG data.Brain Stimul. 2016; 9: 305-306https://doi.org/10.1016/j.brs.2015.11.009Abstract Full Text Full Text PDF PubMed Scopus (3) Google Scholar, 5Harquel S. Beynel L. Guyader N. Marendaz C. David O. Chauvin A. CortExTool: a toolbox for processing motor cortical excitability measurements by transcranial magnetic stimulation.2016Google Scholar]. While one toolbox provides online data processing, the analysis is limited to the MEP recruitment curve (MEP-rc) only [[6]van de Ruit M. Pearson T. Grey M.J. Novel tools for rapid online data acquisition of the TMS stimulus-response curve.Brain Stimul. 2019; 12: 192-194https://doi.org/10.1016/j.brs.2018.09.015Abstract Full Text Full Text PDF PubMed Scopus (4) Google Scholar]. Therefore, to address these limitations we have developed a comprehensive, modular, open-source toolbox to record and analyze MEP data in real time during the TMS recording session (Fig. 1A). MEP-ART (MEP Analysis in Real Time) is an open source MATLAB-based toolbox available at https://github.com/Pundik-BPNR-Lab/MEP-ART. MEP-ART is designed to perform real-time data analysis by: 1) providing continuous visual feedback of EMG activity; 2) monitoring stimulator output (SO); 3) displaying and recording EMG at the time of stimulation; and, 4) extracting and processing MEP amplitude in an analysis module. The analysis modules include: 1) MEP-rc fitting; 2) short interval intracortical inhibition (SICI) and intracortical facilitation (ICF) display; and, 3) EMG averaging. Visualization and recording of EMG and MEPs with immediate analysis provides real-time quality assurance. Live analysis of data allows for assessment of data quality and informs the user whether collection of additional data is warranted. •MEP-ART consists of three apps run in three separate instances of MATLAB: 1) Activity Monitor (ActMon) (Fig. 1B); 2) Display Data (DispData) (Fig. 1C); and 3) Magnetic Stimulator Monitor (MagSpy) (Fig. 1D). The analysis modules (Fig. 1E) are run within DispData. Communication among the apps is achieved using memory-mapped files, a mechanism where apps can map part of their memory to a location in a shared file.•Magnetic stimulator: The code currently operates with Magstim 2002 (and BiStim2) (Magstim Inc., Eden Prairie MN). Brainsight neuronavigation (Rogue Research Inc., Montreal, Canada) is connected via a serial cable to the Magstim. Brainsight continuously queries Magstim’s status. Magstim’s response is returned to Brainsight and sent to MagSpy (Fig. 1D) via a custom-made serial splitter and serial-to-USB cable. MagSpy continuously reads the SO and relays it to other modules. Magstim generates a trigger pulse at the time of stimulation that is sent to the EMG recording system.•EMG system: BrainAmp ExG (Brain Vision LLC, Morrisville, NC) is used for collecting EMG with up to 8 channels. The system and accompanying software amplify and display EMG; the data along with the trigger pulse from the stimulator are then streamed to the Activity Monitor (ActMon) (Fig. 1B).•EMG visualization: ActMon provides continuous feedback of EMG activity to assist the subject with maintaining a desired EMG level. When ActMon detects a trigger, it relays EMG data to the MEP Display (DispData) (Fig. 1C). DispData shows EMG data within a specified (customizable) window. It displays the baseline EMG level, whether it is in the target range, the MEP amplitude (measured as the difference between the maximum and minimum signal within the adjustable timeframe after the stimulus), and a checkbox indicating if the data is to be included in analysis.•Analysis module selection (Fig. 1E): Information from DispData and MagSpy are relayed to a selected analysis module (Fig. 1F–H). There are three analysis modules.1Recruitment curve fitting module: MEP amplitude from DispData is plotted against SO from MagSpy. A Boltzmann sigmoid function is fitted to the recorded MEPs:MEP(S)=MEPmin+MEPmax−MEPmin1+em(S50−S)where S is SO, MEPmax and MEPmin are the maximum and the minimum asymptotes, m is the maximum slope which occurs at S50, the inflection point between MEPmax and MEPmin. The display includes the curve’s parameter estimates, their confidence intervals, curve fit R2, and the area under the curve. The display of datapoints and the estimated curve assist in determining what additional SO levels may improve curve fit.2SICI/ICF analysis module: MEP amplitude from DispData is plotted in 3 columns depending upon the stimulus being delivered: test stimulus, SICI, or ICF. The 98% confidence intervals surrounding the mean value of each stimulus type facilitate the operator in determining if enough data have been recorded.3EMG averaging module: DispData window contains a checkbox to indicate if the displayed trial of data should be included in analysis. The operator can scroll through trials and select/unselect trials. Selected trials are shown (light lines) with their average (bold line) in the averaging module.•Data saving and offline re-analysis: EMG data for each stimulus is saved in csv files along with the SO and whether it was selected or unselected to include in the analysis. The datapoints used in each analysis module and analysis results are saved in csv and text files. The software includes an offline application to read, display, and analyze the data using the same real-time analysis modules. During analysis, whether offline or online, individual datapoints can be unselected and excluded from analysis as needed. In summary, we have developed a real-time MEP recording and analysis system. The continuous EMG activity indicator, MEP data display, and three analysis modules provide real-time feedback during the data recording session. This feedback helps identify EMG artifacts, ensure appropriate SO levels are employed, and verify if enough data are recorded. The result is a more efficient and higher quality TMS/MEP data collection experiment. This work was supported by grants awarded to investigators of the Brain Plasticity and NeuroRecovery Laboratory at the Louis Stokes Cleveland VA Medical Center, Cleveland, OH .

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