{"entity": "researcher", "timestamp": "2026-09-27T17:06:55.424Z", "family": "Yap", "given": "Kwong Hsia", "initials": "KH", "orcid": "0000-0001-9617-9970", "affiliations": ["Memory, Ageing and Cognition Centre, National University Health System, Singapore.", "Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/e681e7be593f4da1af5f054cf9c11946.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/e681e7be593f4da1af5f054cf9c11946"}}, "publications": [{"entity": "publication", "iuid": "fed777ef692d4f6291d96bdd9aa3ffbc", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/fed777ef692d4f6291d96bdd9aa3ffbc.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/fed777ef692d4f6291d96bdd9aa3ffbc"}}, "title": "Longer scans boost prediction and cut costs in brain-wide association studies.", "authors": [{"family": "Ooi", "given": "Leon Qi Rong", "initials": "LQR", "orcid": "0000-0002-3546-4580", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ebe0d31183d441339365d04b82de818e.json"}}, {"family": "Orban", "given": "Csaba", "initials": "C", "orcid": "0000-0001-9133-3561", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/71c09fa5d88e4647ada303902bc77a56.json"}}, {"family": "Zhang", "given": "Shaoshi", "initials": "S", "orcid": "0000-0002-6352-9150", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f736d5d1c9094b7daf5e97df2ab159d8.json"}}, {"family": "Nichols", "given": "Thomas E", "initials": "TE", "orcid": "0000-0002-4516-5103", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/849f3024103b4d0da2669e0fe0f9146a.json"}}, {"family": "Tan", "given": "Trevor Wei Kiat", "initials": "TWK"}, {"family": "Kong", "given": "Ru", 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"initials": "D", "orcid": "0000-0003-3466-6620", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/27ee0d69626c43929e03d75fcaa92c12.json"}}, {"family": "Eickhoff", "given": "Simon B", "initials": "SB", "orcid": "0000-0001-6363-2759", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/adade102f594420dba34773923828c24.json"}}, {"family": "Holmes", "given": "Avram J", "initials": "AJ", "orcid": "0000-0001-6583-803X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5b2ffbe9d0584d48a71cdfdaf45f181f.json"}}, {"family": "Yeo", "given": "B T Thomas", "initials": "BTT", "orcid": "0000-0002-0119-3276", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/115d5e2815cb478da50216d86075afad.json"}}, {"family": "Alzheimer\u2019s Disease Neuroimaging Initiative", "given": "", "initials": ""}], "type": "journal article", "published": "2025-03-11", "journal": {"title": "bioRxiv", "issn": "2692-8205", "issn-l": null}, "abstract": "A pervasive dilemma in brain-wide association studies (BWAS) is whether to prioritize functional MRI (fMRI) scan time or sample size. We derive a theoretical model showing that individual-level phenotypic prediction accuracy increases with sample size and total scan duration (sample size \u00d7 scan time per participant). The model explains empirical prediction accuracies extremely well across 76 phenotypes from nine resting-fMRI and task-fMRI datasets (R2 = 0.89), spanning a wide range of scanners, acquisitions, racial groups, disorders and ages. For scans \u226420 mins, prediction accuracy increases linearly with the logarithm of total scan duration, suggesting interchangeability of sample size and scan time. However, sample size is ultimately more important than scan time in determining prediction accuracy. Nevertheless, when accounting for overhead costs associated with each participant (e.g., recruitment costs), to boost prediction accuracy, longer scans can yield substantial cost savings over larger sample size. To achieve high prediction performance, 10-min scans are highly cost inefficient. In most scenarios, the optimal scan time is \u226520 mins. On average, 30-min scans are the most cost-effective, yielding 22% cost savings over 10-min scans. Overshooting is cheaper than undershooting the optimal scan time, so we recommend aiming for \u226530 mins. Compared with resting-state whole-brain BWAS, the most cost-effective scan time is shorter for task-fMRI and longer for subcortical-cortical BWAS. Standard power calculations maximize sample size at the expense of scan time. Our study demonstrates that optimizing both sample size and scan time can boost prediction power while cutting costs. Our empirically informed reference is available for future study planning: WEB_APPLICATION_LINK.", "doi": "10.1101/2024.02.16.580448", "pmid": "38405815", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC10889017"}, {"db": "pii", "key": "2024.02.16.580448"}], "notes": [], "created": "2026-09-23T13:44:10.636Z", "modified": "2026-09-23T13:44:11.138Z"}]}