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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\u2010level 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\u2010fMRI 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\u2010fMRI with stimulation equipment certified as safe to use inside the MR scanner, and of correcting for image artifacts caused by tDCS. tDCS\u2010fMRI 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.<\/jats:p>","DOI":"10.1002\/hbm.24908","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T08:07:20Z","timestamp":1577174840000},"page":"1950-1967","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":102,"title":["Methodology for tDCS integration with fMRI"],"prefix":"10.1002","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9295-6733","authenticated-orcid":false,"given":"Zeinab","family":"Esmaeilpour","sequence":"first","affiliation":[{"name":"Neural Engineering Laboratory, Department of Biomedical Engineering The City College of the City University of New York, City College Center for Discovery and Innovation  New York New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. 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