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Methodology for tDCS integration with fMRI

Understanding and reducing variability of response to transcranial direct current stimulation (tDCS) requires measuring what factors predetermine sensitivity to tDCS and tracking individual response to tDCS. Human trials, animal models, and computational models suggest structural traits and function...

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Autores principales: Esmaeilpour, Zeinab, Shereen, A. Duke, Ghobadi‐Azbari, Peyman, Datta, Abhishek, Woods, Adam J., Ironside, Maria, O'Shea, Jacinta, Kirk, Ulrich, Bikson, Marom, Ekhtiari, Hamed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267907/
https://www.ncbi.nlm.nih.gov/pubmed/31872943
http://dx.doi.org/10.1002/hbm.24908
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author Esmaeilpour, Zeinab
Shereen, A. Duke
Ghobadi‐Azbari, Peyman
Datta, Abhishek
Woods, Adam J.
Ironside, Maria
O'Shea, Jacinta
Kirk, Ulrich
Bikson, Marom
Ekhtiari, Hamed
author_facet Esmaeilpour, Zeinab
Shereen, A. Duke
Ghobadi‐Azbari, Peyman
Datta, Abhishek
Woods, Adam J.
Ironside, Maria
O'Shea, Jacinta
Kirk, Ulrich
Bikson, Marom
Ekhtiari, Hamed
author_sort Esmaeilpour, Zeinab
collection PubMed
description Understanding and reducing variability of response to transcranial direct current stimulation (tDCS) requires measuring what factors predetermine sensitivity to tDCS and tracking individual response to tDCS. Human trials, animal models, and computational models suggest structural traits and functional states of neural systems are the major sources of this variance. There are 118 published tDCS studies (up to October 1, 2018) that used fMRI as a proxy measure of neural activation to answer mechanistic, predictive, and localization questions about how brain activity is modulated by tDCS. FMRI can potentially contribute as: a measure of cognitive state‐level variance in baseline brain activation before tDCS; inform the design of stimulation montages that aim to target functional networks during specific tasks; and act as an outcome measure of functional response to tDCS. In this systematic review, we explore methodological parameter space of tDCS integration with fMRI spanning: (a) fMRI timing relative to tDCS (pre, post, concurrent); (b) study design (parallel, crossover); (c) control condition (sham, active control); (d) number of tDCS sessions; (e) number of follow up scans; (f) stimulation dose and combination with task; (g) functional imaging sequence (BOLD, ASL, resting); and (h) additional behavioral (cognitive, clinical) or quantitative (neurophysiological, biomarker) measurements. Existing tDCS‐fMRI literature shows little replication across these permutations; few studies used comparable study designs. Here, we use a representative sample study with both task and resting state fMRI before and after tDCS in a crossover design to discuss methodological confounds. We further outline how computational models of current flow should be combined with imaging data to understand sources of variability. Through the representative sample study, we demonstrate how modeling and imaging methodology can be integrated for individualized analysis. Finally, we discuss the importance of conducting tDCS‐fMRI with stimulation equipment certified as safe to use inside the MR scanner, and of correcting for image artifacts caused by tDCS. tDCS‐fMRI can address important questions on the functional mechanisms of tDCS action (e.g., target engagement) and has the potential to support enhancement of behavioral interventions, provided studies are designed rationally.
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spelling pubmed-72679072020-06-12 Methodology for tDCS integration with fMRI Esmaeilpour, Zeinab Shereen, A. Duke Ghobadi‐Azbari, Peyman Datta, Abhishek Woods, Adam J. Ironside, Maria O'Shea, Jacinta Kirk, Ulrich Bikson, Marom Ekhtiari, Hamed Hum Brain Mapp Review Article Understanding and reducing variability of response to transcranial direct current stimulation (tDCS) requires measuring what factors predetermine sensitivity to tDCS and tracking individual response to tDCS. Human trials, animal models, and computational models suggest structural traits and functional states of neural systems are the major sources of this variance. There are 118 published tDCS studies (up to October 1, 2018) that used fMRI as a proxy measure of neural activation to answer mechanistic, predictive, and localization questions about how brain activity is modulated by tDCS. FMRI can potentially contribute as: a measure of cognitive state‐level variance in baseline brain activation before tDCS; inform the design of stimulation montages that aim to target functional networks during specific tasks; and act as an outcome measure of functional response to tDCS. In this systematic review, we explore methodological parameter space of tDCS integration with fMRI spanning: (a) fMRI timing relative to tDCS (pre, post, concurrent); (b) study design (parallel, crossover); (c) control condition (sham, active control); (d) number of tDCS sessions; (e) number of follow up scans; (f) stimulation dose and combination with task; (g) functional imaging sequence (BOLD, ASL, resting); and (h) additional behavioral (cognitive, clinical) or quantitative (neurophysiological, biomarker) measurements. Existing tDCS‐fMRI literature shows little replication across these permutations; few studies used comparable study designs. Here, we use a representative sample study with both task and resting state fMRI before and after tDCS in a crossover design to discuss methodological confounds. We further outline how computational models of current flow should be combined with imaging data to understand sources of variability. Through the representative sample study, we demonstrate how modeling and imaging methodology can be integrated for individualized analysis. Finally, we discuss the importance of conducting tDCS‐fMRI with stimulation equipment certified as safe to use inside the MR scanner, and of correcting for image artifacts caused by tDCS. tDCS‐fMRI can address important questions on the functional mechanisms of tDCS action (e.g., target engagement) and has the potential to support enhancement of behavioral interventions, provided studies are designed rationally. John Wiley & Sons, Inc. 2019-12-24 /pmc/articles/PMC7267907/ /pubmed/31872943 http://dx.doi.org/10.1002/hbm.24908 Text en © 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Esmaeilpour, Zeinab
Shereen, A. Duke
Ghobadi‐Azbari, Peyman
Datta, Abhishek
Woods, Adam J.
Ironside, Maria
O'Shea, Jacinta
Kirk, Ulrich
Bikson, Marom
Ekhtiari, Hamed
Methodology for tDCS integration with fMRI
title Methodology for tDCS integration with fMRI
title_full Methodology for tDCS integration with fMRI
title_fullStr Methodology for tDCS integration with fMRI
title_full_unstemmed Methodology for tDCS integration with fMRI
title_short Methodology for tDCS integration with fMRI
title_sort methodology for tdcs integration with fmri
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267907/
https://www.ncbi.nlm.nih.gov/pubmed/31872943
http://dx.doi.org/10.1002/hbm.24908
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