Large-scale analysis of interindividual variability in theta-burst stimulation data: Results from the ‘Big TMS Data Collaboration’
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. This study brought together over 60 TMS researchers to form the ‘Big TMS Da...
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| Published in | Brain stimulation Vol. 13; no. 5; pp. 1476 - 1488 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.09.2020
Elsevier |
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| Online Access | Get full text |
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| Abstract | 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.
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.
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.
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.
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.
•430 healthy participants’ theta-burst stimulation data was pooled across 22 studies.•Individual participant data was analysed using mixed-effects regression.•Baseline MEP amplitude, age, muscle, and time of day, predicted iTBS response.•Baseline MEP amplitude and timepoint predicted cTBS response.•Specific recommendations are proposed to reduce TBS variability. |
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| AbstractList | 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.
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.
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.
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.
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.
•430 healthy participants’ theta-burst stimulation data was pooled across 22 studies.•Individual participant data was analysed using mixed-effects regression.•Baseline MEP amplitude, age, muscle, and time of day, predicted iTBS response.•Baseline MEP amplitude and timepoint predicted cTBS response.•Specific recommendations are proposed to reduce TBS variability. 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. 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. 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. 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. 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. 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. 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.BACKGROUNDMany 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.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.OBJECTIVE/HYPOTHESISThis 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.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.METHODS118 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.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.RESULTS430 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.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.CONCLUSIONSThis 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. |
| Author | Rogasch, Nigel C. Youssef, George J. Enticott, Peter G. Fitzgerald, Paul B. Fried, Peter J. Gomes-Osman, Joyce Pascual-Leone, Alvaro Jannati, Ali Stamm, Julie Davies, Charlotte B. Koch, Giacomo Bowe, Steven J. Chung, Sung Wook Bereznicki, Hannah G.K. Clark, Gillian M. Di Lazzaro, Vincenzo Corp, Daniel T. |
| AuthorAffiliation | 7 Monash Alfred Psychiatry Research Centre, Central Clinical School, The Alfred and Monash University, Melbourne, Australia 17 Department of Neurology, Harvard Medical School, Boston, MA, USA 5 Department of Physical Therapy, University of Miami Miller School of Medicine, Miami, FL 6 Department of Kinesiology, University of Wisconsin-Madison, Madison, WI 8 Deakin Biostatistics Unit Faculty of Health Deakin University, Geelong, Australia 10 Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute, Adelaide, Australia 15 Unit of Neurology, Neurophysiology and Neurobiology, Università Campus Bio-Medico, Rome, Italy 2 Berenson-Allen Center for Non-Invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA 3 Centre for Adolescent Health, Murdoch Children’s Research Institute, Parkville, Australia 12 Epworth Centre for Innovation in Mental Health, Epworth HealthCare and Central Clinical School, Melbou |
| AuthorAffiliation_xml | – name: 13 Non-invasive Brain Stimulation Unit, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy – name: 1 Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia – name: 8 Deakin Biostatistics Unit Faculty of Health Deakin University, Geelong, Australia – name: 7 Monash Alfred Psychiatry Research Centre, Central Clinical School, The Alfred and Monash University, Melbourne, Australia – name: 16 Hinda and Arthur Marcus Institute for Aging Research. Hebrew SeniorLife, Boston, MA, USA – name: 6 Department of Kinesiology, University of Wisconsin-Madison, Madison, WI – name: 12 Epworth Centre for Innovation in Mental Health, Epworth HealthCare and Central Clinical School, Melbourne, Australia – name: 18 Guttmann Brain Health Institute, Institut Guttmann de Neurorehabilitació, Universitat Autonoma de Barcelona, Barcelona, Spain – name: 17 Department of Neurology, Harvard Medical School, Boston, MA, USA – name: 14 Department of Biomedical and Specialty Surgical Sciences, Section of Human Physiology, University of Ferrara, Italy – name: 10 Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute, Adelaide, Australia – name: 2 Berenson-Allen Center for Non-Invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA – name: 15 Unit of Neurology, Neurophysiology and Neurobiology, Università Campus Bio-Medico, Rome, Italy – name: 5 Department of Physical Therapy, University of Miami Miller School of Medicine, Miami, FL – name: 11 The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia – name: 3 Centre for Adolescent Health, Murdoch Children’s Research Institute, Parkville, Australia – name: 9 Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, Australia – name: 4 Neuromodulation Program and Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA |
| Author_xml | – sequence: 1 givenname: Daniel T. orcidid: 0000-0003-2435-077X surname: Corp fullname: Corp, Daniel T. email: daniel.corp@deakin.edu.au organization: Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia – sequence: 2 givenname: Hannah G.K. surname: Bereznicki fullname: Bereznicki, Hannah G.K. organization: Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia – sequence: 3 givenname: Gillian M. orcidid: 0000-0002-6541-5130 surname: Clark fullname: Clark, Gillian M. organization: Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia – sequence: 4 givenname: George J. surname: Youssef fullname: Youssef, George J. organization: Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia – sequence: 5 givenname: Peter J. orcidid: 0000-0001-6041-2996 surname: Fried fullname: Fried, Peter J. organization: Berenson-Allen Center for Non-Invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA – sequence: 6 givenname: Ali orcidid: 0000-0003-0826-1275 surname: Jannati fullname: Jannati, Ali organization: Berenson-Allen Center for Non-Invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA – sequence: 7 givenname: Charlotte B. surname: Davies fullname: Davies, Charlotte B. organization: Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia – sequence: 8 givenname: Joyce surname: Gomes-Osman fullname: Gomes-Osman, Joyce organization: Berenson-Allen Center for Non-Invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA – sequence: 9 givenname: Julie surname: Stamm fullname: Stamm, Julie organization: Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, USA – sequence: 10 givenname: Sung Wook surname: Chung fullname: Chung, Sung Wook organization: Monash Alfred Psychiatry Research Centre, Central Clinical School, The Alfred and Monash University, Melbourne, Australia – sequence: 11 givenname: Steven J. surname: Bowe fullname: Bowe, Steven J. organization: Deakin Biostatistics Unit Faculty of Health Deakin University, Geelong, Australia – sequence: 12 givenname: Nigel C. surname: Rogasch fullname: Rogasch, Nigel C. organization: Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, Australia – sequence: 13 givenname: Paul B. surname: Fitzgerald fullname: Fitzgerald, Paul B. organization: Monash Alfred Psychiatry Research Centre, Central Clinical School, The Alfred and Monash University, Melbourne, Australia – sequence: 14 givenname: Giacomo orcidid: 0000-0001-6155-9439 surname: Koch fullname: Koch, Giacomo organization: Non-invasive Brain Stimulation Unit, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy – sequence: 15 givenname: Vincenzo orcidid: 0000-0002-9113-5925 surname: Di Lazzaro fullname: Di Lazzaro, Vincenzo organization: Unit of Neurology, Neurophysiology and Neurobiology, Università Campus Bio-Medico, Rome, Italy – sequence: 16 givenname: Alvaro surname: Pascual-Leone fullname: Pascual-Leone, Alvaro organization: Hinda and Arthur Marcus Institute for Aging Research. Hebrew SeniorLife, Boston, MA, USA – sequence: 17 givenname: Peter G. orcidid: 0000-0002-6638-951X surname: Enticott fullname: Enticott, Peter G. organization: Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32758665$$D View this record in MEDLINE/PubMed |
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| Copyright | 2020 The Authors Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved. |
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| Keywords | Transcranial, and magnetic stimulation Big data Theta-burst stimulation Variability |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Julie Stamm: Methodology, Writing - Review & Editing Sung Wook Chung: Methodology, Data Curation, Writing - Review & Editing Steven J. Bowe: Methodology, Data Curation, Writing - Review & Editing Peter G. Enticott: Conceptualisation, Methodology, Data Curation, Writing - Original Draft, Writing - Review & Editing CRediT author statement Charlotte B. Davies: Conceptualisation, Methodology, Writing - Original Draft, Writing - Review & Editing Paul B. Fitzgerald: Methodology, Data Curation, Writing - Review & Editing Ali Jannati: Conceptualisation, Methodology, Data Curation, Writing - Review & Editing Gillian M. Clark: Conceptualisation, Methodology, Data Curation, Writing - Original Draft, Writing - Review & Editing Alvaro Pascual-Leone: Conceptualisation, Methodology, Data Curation, Writing - Review & Editing George J. Youssef: Conceptualisation, Methodology, Software, Data Curation, Writing - Review & Editing Giacomo Koch: Methodology, Data Curation, Writing - Review & Editing Daniel T. Corp: Conceptualisation, Methodology, Software, Data Curation, Writing - Original Draft, Writing - Review & Editing, Project administration Hannah G. K. Bereznicki: Conceptualisation, Methodology, Data Curation, Writing - Original Draft, Writing - Review & Editing. Peter J. Fried: Conceptualisation, Methodology, Data Curation, Writing - Review & Editing Joyce Gomes-Osman: Conceptualisation, Methodology, Writing - Original Draft, Writing - Review & Editing Vincenzo Di Lazzaro: Conceptualisation, Methodology, Data Curation, Writing - Review & Editing Nigel C. Rogasch: Methodology, Data Curation, Writing - Review & Editing |
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| Snippet | Many studies have attempted to identify the sources of interindividual variability in response to theta-burst stimulation (TBS). However, these studies have... Background: Many studies have attempted to identify the sources of interindividual variability in response to theta-burst stimulation (TBS). However, these... |
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| Title | Large-scale analysis of interindividual variability in theta-burst stimulation data: Results from the ‘Big TMS Data Collaboration’ |
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