{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T06:14:43Z","timestamp":1788502483859,"version":"build-2803163510"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001782","name":"University of Melbourne","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001782","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Struct Funct"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The processing of neuroimaging data typically involves a complicated set of operations, which often require different software packages and have intensive computational and storage demands. Although there are many options available for the neuroimaging researcher to establish their preferred set of processing operations, there is relatively little guidance on how to orchestrate such operations within a coherent data processing workflow. Here, by describing the construction of a simplified neuroimaging processing workflow, we demonstrate how the established general-purpose workflow manager \u2018Snakemake\u2019 can be used to facilitate neuroimaging data processing. Snakemake allows researchers to use a Python-based markup language to describe how input files are transformed into output files. Snakemake then uses these rules to schedule and execute the computational jobs that are required to generate the output files, without unnecessary recreation of existing output files. This allows the complete processing workflow to be executed with a single command and creates workflows that are interpretable, efficient, and reproducible.<\/jats:p>","DOI":"10.1007\/s00429-026-03189-3","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T06:11:32Z","timestamp":1788502292000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Orchestrating neuroimaging data processing using the \u2018Snakemake\u2019 workflow manager"],"prefix":"10.1007","volume":"231","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5520-632X","authenticated-orcid":false,"given":"Damien J.","family":"Mannion","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8943-2808","authenticated-orcid":false,"given":"Maria del Mar","family":"Quiroga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5711-6408","authenticated-orcid":false,"given":"Jacob M.","family":"Paul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0679-4959","authenticated-orcid":false,"given":"Marta I.","family":"Garrido","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,4]]},"reference":[{"key":"3189_CR1","unstructured":"Bilder R, Poldrack R, Cannon T, London E, Freimer N, Congdon E, Sabb F (2020) UCLA Consortium for Neuropsychiatric Phenomics LA5c study. 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