{ "abstract": "Head motion systematically biases functional connectivity (FC) estimates in resting-state functional MRI (rs-fMRI). A common mitigation strategy is to censor high-motion volumes and discard high-motion runs. However, overly stringent censoring risks discarding signal alongside noise, potentially degrading FC estimates. Here, we test the efficacy of various censoring strategies on individual-specific cortical parcellations and personalized transcranial magnetic stimulation (TMS) target selection. Using precision-fMRI datasets comprising 50 individuals, we define individualized “ground-truth” references from ≥1 hour of low-motion data per participant. We then simulate 10-minute or 20-minute rs-fMRI sessions with varying motion levels from the remaining data, yielding final samples of 22 and 19 participants, respectively. Higher motion produces parcellations and TMS targets that deviate further from the ground-truth references. However, at any given motion level, lenient censoring produces higher quality parcellations and personalized TMS targets than strict censoring. The improvement is comparable to doubling scan duration from 20 to 40 minutes under strict censoring. With personalized connectome-guided TMS, a common dilemma is whether to re-scan patients with only high-motion runs. A mixed-motion session with one low-motion run and one high-motion run may often be considered usable after discarding the high-motion run and strict censoring. We find that lenient censoring of high-motion-only sessions yields TMS targets comparable to—or even better than—those derived from strictly censored mixed-motion sessions. Therefore, within the motion range and parcellation/TMS targeting frameworks evaluated here, patients may not need to be re-scanned solely because all runs exceed strict censoring criteria.", "abstract_shown": true, "affiliations": [ "Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore", "Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore", "Department of Medicine, Healthy Longevity Translational Research Programme, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore", "N.1 Institute for Health, National University of Singapore, Singapore, Singapore", "Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore", "Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia", "School of Biomedical Sciences, Faculty of Health, Medicine and Behavioural Sciences, University of Queensland, Brisbane, Queensland, Australia", "Department of Psychiatry, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, United States", "Center for Brain Circuit Therapeutics, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, United States", "Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom", "Centre for Integrative Neuroimaging (OxCIN), FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom", "Department of Statistics, Indiana University, Bloomington, IN, United States", "Department of Psychological Medicine, National University Hospital, Singapore, Singapore", "Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States" ], "article_type": "research article", "arxiv": null, "author_total": 11, "authors": [ { "affs": [ 0, 1, 2, 3, 4 ], "name": "Trevor Wei Kiat Tan", "orcid": "0000-0002-3732-4768" }, { "affs": [ 0, 1, 2, 3 ], "name": "Ru Kong", "orcid": "0000-0001-7842-0329" }, { "affs": [ 0, 1, 2, 3 ], "name": "Aihuiping Xue", "orcid": "0009-0008-7907-8594" }, { "affs": [ 0 ], "name": "Jingwen Cheng", "orcid": "0009-0000-4376-5765" }, { "affs": [ 5 ], "name": "Bjorn Burgher", "orcid": "0000-0002-0115-0268" }, { "affs": [ 5, 6 ], "name": "Luca Cocchi", "orcid": "0000-0003-3651-2676" }, { "affs": [ 7, 8 ], "name": "Shan H. Siddiqi", "orcid": "0000-0002-0967-9289" }, { "affs": [ 9, 10 ], "name": "Thomas E. Nichols", "orcid": "0000-0002-4516-5103" }, { "affs": [ 11 ], "name": "Amanda F. Mejia", "orcid": "0000-0002-4312-8974" }, { "affs": [ 12 ], "name": "Phern-Chern Tor", "orcid": "0000-0001-7972-3030" }, { "affs": [ 0, 1, 2, 3, 4, 13 ], "name": "B.T. Thomas Yeo", "orcid": "0000-0002-0119-3276" } ], "bibtex": "https://research.draconitic.com/paper/fmkjzeexf6bc5.bib", "cited_by": 0, "doi": "10.1162/imag.a.1422", "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13728936/", "id": "fmkjzeexf6bc5", "issns": [ "2837-6056" ], "landing_url": "https://doi.org/10.1162/imag.a.1422", "language": "en", "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "heading", "Research Article" ], [ "openalex", "2805|Functional Brain Connectivity Studies" ] ], "openalex": "W7220408106", "pdf_url": "https://direct.mit.edu/imag/article-pdf/doi/10.1162/IMAG.a.1422/2631423/imag.a.1422.pdf", "pmcid": "PMC13728936", "pmid": "42978984", "published": "2026-11-02", "publisher": "MIT Press", "ris": "https://research.draconitic.com/paper/fmkjzeexf6bc5.ris", "sources": [ { "first_seen": "2026-11-07T12:21:48Z", "native_id": "PMC13728936", "source": "pmc", "url": "https://doi.org/10.1162/imag.a.1422" } ], "subjects": [ { "field": "biology", "origin": "openalex:2805|Functional Brain Connectivity Studies", "subfield": "neuroscience" } ], "title": "Excessive censoring degrades individual-specific cortical parcellations and personalized TMS targets", "title_text": "Excessive censoring degrades individual-specific cortical parcellations and personalized TMS targets", "type": "peer-reviewed", "updated_at": "2026-11-07T12:21:48Z", "url": "https://research.draconitic.com/paper/fmkjzeexf6bc5", "venue": "Imaging Neuroscience", "versions": [] }