Large-scale analysis of interindividual variability in single and paired-pulse TMS data: results from the ‘Big TMS Data Collaboration’

Abstract

ObjectiveInterindividual variability of single and paired-pulse TMS data has limited the clinical and experimental applicability of these methods. This study brought together over 60 TMS researchers to create the largest known sample of individual participant single and paired-pulse TMS data to date, enabling a more comprehensive evaluation of factors driving response variability.Methods118 corresponding authors provided deidentified individual TMS data. Mixed-effects regression investigated a range of individual and study level variables for their contribution to variability in response to single and pp TMS data.Results687 healthy participant’s TMS data was pooled across 35 studies. Target muscle, pulse waveform, neuronavigation use, and TMS machine significantly predicted an individual’s single pulse TMS amplitude. Baseline MEP amplitude, M1 hemisphere, and biphasic AMT significantly predicted SICI response. Baseline MEP amplitude, test stimulus intensity, interstimulus interval, monophasic RMT, monophasic AMT, and biphasic RMT significantly predicted ICF response. Age, M1 hemisphere, and TMS machine significantly predicted motor threshold.ConclusionsThis large-scale analysis has identified a number of factors influencing participants’ responses to single and paired pulse TMS. We provide specific recommendations to increase the standardisation of TMS methods within and across laboratories, thereby minimising interindividual variability in single and pp TMS data.Highlights687 healthy participant’s TMS data was pooled across 35 studiesSignificant relationships between age and resting motor thresholdSignificant relationships between baseline MEP amplitude and SICI/ICF

Large-scale analysis of interindividual variability in theta-burst stimulation data: Results from the ‘Big TMS Data Collaboration’

“…While there were moderate effect sizes for these IVs, neither were significant (pulse waveform p ¼ 0.174; TS intensity p ¼ 0.186). Interestingly, our companion paper, investigating single and paired-pulse TMS, showed reduced MEP amplitudes and increased MEP variability for the 120% RMT TS intensity method in comparison to the 1 mV method [42]. There were insufficient data to evaluate whether these two IVs also showed the same trends in iTBS data.…”

“…Lastly, we investigated whether 70% RMT and 80% AMT TS intensity methods might deliver TBS at different machine output intensities, given these methods have previously shown to result in differences in TBS-induced plasticity [41]. To do this, we used the marginal means of biphasic RMT and biphasic AMT as computed in our companion paper [42], and then multiplied these values by 0.7 (i.e., 70% of biphasic RMT) and 0.8 (i.e., 80% of biphasic AMT), respectively.…”

Biomarkers Obtained by Transcranial Magnetic Stimulation in Neurodevelopmental Disorders

“…These composite scores can assist clinical decision-making by placing more precisely an individual patient's baseline and post-intervention neurophysiological responses within his/her clinical cohort. Depending on the TMS protocol, obtaining a reliable TMS measure for optimal clinical use may involve considering age, 36,193,194 sex, [195][196][197] genetic polymorphisms, [198][199][200][201][202] the TMS device, pulse waveform and induced current direction, 33,34,194 stimulation intensity and baseline neurophysiological measuresde.g., rMT, aMT, and baseline MEP amplitude, 83,85,194,203 the target muscle, 194,204 the time of day, 84,194,205 use of neuronavigation 206 and robotic arms, 207 amount and quality of sleep the night before the TMS visit, 208,209 blood glucose level and caffeine intake before and during the TMS visit, [210][211][212] intensity and duration of physical activity before each visit, 213,214 phase of the menstrual cycle, 215,216 and the use of closed-loop systems that deliver TMS pulses timed to real-time, EEG indices of brain states. 217,218 The current state-of-the-art individualized methods for TMS target localization or prediction of response to rTMS treatment rely on measuring the baseline or induced changes in resting-state functional connectivity between relevant brain regions at the level of the Human Connectome [219]…”