TY - JOUR
T1 - Large-scale analysis of interindividual variability in theta-burst stimulation data
T2 - Results from the ‘Big TMS Data Collaboration’
AU - the ‘Big TMS Data Collaboration’
AU - Corp, Daniel T.
AU - Bereznicki, Hannah G.K.
AU - Clark, Gillian M.
AU - Youssef, George J.
AU - Fried, Peter J.
AU - Jannati, Ali
AU - Davies, Charlotte B.
AU - Gomes-Osman, Joyce
AU - Stamm, Julie
AU - Chung, Sung Wook
AU - Bowe, Steven J.
AU - Rogasch, Nigel C.
AU - Fitzgerald, Paul B.
AU - Koch, Giacomo
AU - Di Lazzaro, Vincenzo
AU - Pascual-Leone, Alvaro
AU - Enticott, Peter G.
N1 - Funding Information:
A.J. was supported by postdoctoral fellowships from the Natural Sciences and Engineering Research Council of Canada ( NSERC 454617) and the Canadian Institutes of Health Research ( CIHR 41791). A.P.-L. was partly supported by the Sidney R. Baer Jr. Foundation, the National Institutes of Health , the National Science Foundation , and DARPA . A.P.-L. serves on the scientific advisory boards for Starlab Neuroscience, Neuroelectrics, Magstim Inc., Nexstim, and Cognito; and is listed as an inventor on several issued and pending patents on the real-time integration of transcranial magnetic stimulation with electroencephalography and magnetic resonance imaging. J.G.O. was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number KL2TR002737. P.B.F. is supported by a NHMRC Practitioner Fellowship (1078567). P.B.F. has received equipment for research from MagVenture A/S, Medtronic Ltd, Neuronetics and Brainsway Ltd . and funding for research from Neuronetics. He is on scientific advisory boards for Bionomics Ltd and LivaNova and is a founder of TMS Clinics Australia. P.G.E. is supported by a Future Fellowship from the Australian Research Council (FT160100077). N.C.R. was supported by a Discovery Early Career Researcher Award from the Australian Research Council (DE180100741).
Publisher Copyright:
© 2020 The Authors
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Background: Many studies have attempted to identify the sources of interindividual variability in response to theta-burst stimulation (TBS). However, these studies have been limited by small sample sizes, leading to conflicting results. Objective/Hypothesis: This study brought together over 60 TMS researchers to form the ‘Big TMS Data Collaboration’, and create the largest known sample of individual participant TBS data to date. The goal was to enable a more comprehensive evaluation of factors driving TBS response variability. Methods: 118 corresponding authors of TMS studies were emailed and asked to provide deidentified individual TMS data. Mixed-effects regression investigated a range of individual and study level variables for their contribution to iTBS and cTBS response variability. Results: 430 healthy participants’ TBS data was pooled across 22 studies (mean age = 41.9; range = 17–82; females = 217). Baseline MEP amplitude, age, target muscle, and time of day significantly predicted iTBS-induced plasticity. Baseline MEP amplitude and timepoint after TBS significantly predicted cTBS-induced plasticity. Conclusions: This is the largest known study of interindividual variability in TBS. Our findings indicate that a significant portion of variability can be attributed to the methods used to measure the modulatory effects of TBS. We provide specific methodological recommendations in order to control and mitigate these sources of variability.
AB - Background: Many studies have attempted to identify the sources of interindividual variability in response to theta-burst stimulation (TBS). However, these studies have been limited by small sample sizes, leading to conflicting results. Objective/Hypothesis: This study brought together over 60 TMS researchers to form the ‘Big TMS Data Collaboration’, and create the largest known sample of individual participant TBS data to date. The goal was to enable a more comprehensive evaluation of factors driving TBS response variability. Methods: 118 corresponding authors of TMS studies were emailed and asked to provide deidentified individual TMS data. Mixed-effects regression investigated a range of individual and study level variables for their contribution to iTBS and cTBS response variability. Results: 430 healthy participants’ TBS data was pooled across 22 studies (mean age = 41.9; range = 17–82; females = 217). Baseline MEP amplitude, age, target muscle, and time of day significantly predicted iTBS-induced plasticity. Baseline MEP amplitude and timepoint after TBS significantly predicted cTBS-induced plasticity. Conclusions: This is the largest known study of interindividual variability in TBS. Our findings indicate that a significant portion of variability can be attributed to the methods used to measure the modulatory effects of TBS. We provide specific methodological recommendations in order to control and mitigate these sources of variability.
KW - Big data
KW - Theta-burst stimulation
KW - Transcranial, and magnetic stimulation
KW - Variability
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U2 - 10.1016/j.brs.2020.07.018
DO - 10.1016/j.brs.2020.07.018
M3 - Article
C2 - 32758665
AN - SCOPUS:85089684857
VL - 13
SP - 1476
EP - 1488
JO - Brain Stimulation
JF - Brain Stimulation
SN - 1935-861X
IS - 5
ER -