{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T08:25:21Z","timestamp":1790756721489,"version":"4.1.0"},"reference-count":57,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:00:00Z","timestamp":1636761600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["20319\/Z\/16\/Z"],"award-info":[{"award-number":["20319\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["NIH R01MH096906"],"award-info":[{"award-number":["NIH R01MH096906"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Human Brain Mapping"],"published-print":{"date-parts":[[2022,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Task\u2010fMRI researchers have great flexibility as to how they analyze their data, with multiple methodological options to choose from at each stage of the analysis workflow. While the development of tools and techniques has broadened our horizons for comprehending the complexities of the human brain, a growing body of research has highlighted the pitfalls of such methodological plurality. In a recent study, we found that the choice of software package used to run the analysis pipeline can have a considerable impact on the final group\u2010level results of a task\u2010fMRI investigation (Bowring et al., 2019,\n                    <jats:italic>BMN<\/jats:italic>\n                    ). Here we revisit our work, seeking to identify the stages of the pipeline where the greatest variation between analysis software is induced. We carry out further analyses on the three datasets evaluated in\n                    <jats:italic>BMN<\/jats:italic>\n                    , employing a common processing strategy across parts of the analysis workflow and then utilizing procedures from three software packages (AFNI, FSL, and SPM) across the remaining steps of the pipeline. We use quantitative methods to compare the statistical maps and isolate the main stages of the workflow where the three packages diverge. Across all datasets, we find that variation between the packages' results is largely attributable to a handful of individual analysis stages, and that these sources of variability were heterogeneous across the datasets (e.g., choice of first\u2010level signal model had the most impact for the balloon analog risk task dataset, while first\u2010level noise model and group\u2010level model were more influential for the false belief and antisaccade task datasets, respectively). We also observe areas of the analysis workflow where changing the software package causes minimal differences in the final results, finding that the group\u2010level results were largely unaffected by which software package was used to model the low\u2010frequency fMRI drifts.\n                  <\/jats:p>","DOI":"10.1002\/hbm.25713","type":"journal-article","created":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T06:25:18Z","timestamp":1636784718000},"page":"1112-1128","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Isolating the sources of\n                    <scp>pipeline\u2010variability<\/scp>\n                    in\n                    <scp>group\u2010level task\u2010fMRI<\/scp>\n                    results"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5553-7249","authenticated-orcid":false,"given":"Alexander","family":"Bowring","sequence":"first","affiliation":[{"name":"Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health Big Data Institute, University of Oxford  Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4516-5103","authenticated-orcid":false,"given":"Thomas E.","family":"Nichols","sequence":"additional","affiliation":[{"name":"Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health Big Data Institute, University of Oxford  Oxford UK"},{"name":"Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences University of Oxford  Oxford UK"},{"name":"Department of Statistics University of Warwick  Coventry UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Camille","family":"Maumet","sequence":"additional","affiliation":[{"name":"Inria, Univ Rennes, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228  Rennes France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,13]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2007.06.004"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2007.04.042"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2314-9"},{"key":"e_1_2_7_5_1","unstructured":"Bowring A. 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