001044428 001__ 1044428
001044428 005__ 20250821202242.0
001044428 0247_ $$2doi$$a10.1038/s41586-025-09250-1
001044428 0247_ $$2ISSN$$a0028-0836
001044428 0247_ $$2ISSN$$a1476-4687
001044428 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-03189
001044428 037__ $$aFZJ-2025-03189
001044428 082__ $$a500
001044428 1001_ $$0P:(DE-HGF)0$$aOoi, Leon Qi Rong$$b0
001044428 245__ $$aLonger scans boost prediction and cut costs in brain-wide association studies
001044428 260__ $$aLondon [u.a.]$$bNature Publ. Group$$c2025
001044428 3367_ $$2DRIVER$$aarticle
001044428 3367_ $$2DataCite$$aOutput Types/Journal article
001044428 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1755780075_2068
001044428 3367_ $$2BibTeX$$aARTICLE
001044428 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001044428 3367_ $$00$$2EndNote$$aJournal Article
001044428 520__ $$aA pervasive dilemma in brain-wide association studies1 (BWAS) is whether to prioritize functional magnetic resonance imaging (fMRI) scan time or sample size. We derive a theoretical model showing that individual-level phenotypic prediction accuracy increases with sample size and total scan duration (sample size × scan time per participant). The model explains empirical prediction accuracies well across 76 phenotypes from nine resting-fMRI and task-fMRI datasets (R2 = 0.89), spanning diverse scanners, acquisitions, racial groups, disorders and ages. For scans of ≤20 min, accuracy increases linearly with the logarithm of the total scan duration, suggesting that sample size and scan time are initially interchangeable. However, sample size is ultimately more important. Nevertheless, when accounting for the overhead costs of each participant (such as recruitment), longer scans can be substantially cheaper than larger sample size for improving prediction performance. To achieve high prediction performance, 10 min scans are cost inefficient. In most scenarios, the optimal scan time is at least 20 min. On average, 30 min scans are the most cost-effective, yielding 22% savings over 10 min scans. Overshooting the optimal scan time is cheaper than undershooting it, so we recommend a scan time of at least 30 min. Compared with resting-state whole-brain BWAS, the most cost-effective scan time is shorter for task-fMRI and longer for subcortical-to-whole-brain BWAS. In contrast to standard power calculations, our results suggest that jointly optimizing sample size and scan time can boost prediction accuracy while cutting costs. Our empirical reference is available online for future study design (https://thomasyeolab.github.io/OptimalScanTimeCalculator/index.html).
001044428 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001044428 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001044428 7001_ $$0P:(DE-HGF)0$$aOrban, Csaba$$b1
001044428 7001_ $$0P:(DE-HGF)0$$aZhang, Shaoshi$$b2
001044428 7001_ $$0P:(DE-HGF)0$$aNichols, Thomas E.$$b3
001044428 7001_ $$0P:(DE-HGF)0$$aTan, Trevor Wei Kiat$$b4
001044428 7001_ $$0P:(DE-HGF)0$$aKong, Ru$$b5
001044428 7001_ $$0P:(DE-HGF)0$$aMarek, Scott$$b6
001044428 7001_ $$0P:(DE-HGF)0$$aDosenbach, Nico U. F.$$b7
001044428 7001_ $$0P:(DE-HGF)0$$aLaumann, Timothy O.$$b8
001044428 7001_ $$0P:(DE-HGF)0$$aGordon, Evan M.$$b9
001044428 7001_ $$0P:(DE-HGF)0$$aYap, Kwong Hsia$$b10
001044428 7001_ $$0P:(DE-HGF)0$$aJi, Fang$$b11
001044428 7001_ $$0P:(DE-HGF)0$$aChong, Joanna Su Xian$$b12
001044428 7001_ $$0P:(DE-HGF)0$$aChen, Christopher$$b13
001044428 7001_ $$0P:(DE-HGF)0$$aAn, Lijun$$b14
001044428 7001_ $$0P:(DE-HGF)0$$aFranzmeier, Nicolai$$b15
001044428 7001_ $$0P:(DE-HGF)0$$aRoemer-Cassiano, Sebastian N.$$b16
001044428 7001_ $$0P:(DE-HGF)0$$aHu, Qingyu$$b17
001044428 7001_ $$0P:(DE-HGF)0$$aRen, Jianxun$$b18
001044428 7001_ $$0P:(DE-HGF)0$$aLiu, Hesheng$$b19
001044428 7001_ $$0P:(DE-HGF)0$$aChopra, Sidhant$$b20
001044428 7001_ $$0P:(DE-HGF)0$$aCocuzza, Carrisa V.$$b21
001044428 7001_ $$0P:(DE-HGF)0$$aBaker, Justin T.$$b22
001044428 7001_ $$0P:(DE-HGF)0$$aZhou, Juan Helen$$b23
001044428 7001_ $$0P:(DE-HGF)0$$aBzdok, Danilo$$b24
001044428 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b25$$ufzj
001044428 7001_ $$0P:(DE-HGF)0$$aHolmes, Avram J.$$b26
001044428 7001_ $$0P:(DE-HGF)0$$aYeo, B. T. Thomas$$b27$$eCorresponding author
001044428 7001_ $$0P:(DE-HGF)0$$aJack, Clifford R.$$b28
001044428 773__ $$0PERI:(DE-600)1413423-8$$a10.1038/s41586-025-09250-1$$p731–740$$tNature$$v644$$x0028-0836$$y2025
001044428 8564_ $$uhttps://juser.fz-juelich.de/record/1044428/files/s41586-025-09250-1.pdf$$yOpenAccess
001044428 909CO $$ooai:juser.fz-juelich.de:1044428$$popenaire$$popen_access$$pVDB$$pdriver$$pdnbdelivery
001044428 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b25$$kFZJ
001044428 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$a HHU Düsseldorf$$b25
001044428 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a     Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore     Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, 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     Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore     N.1 Institute for Health, National University of Singapore, Singapore, Singapore     Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA$$b27
001044428 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a thomas.yeo@nus.edu.sg$$b27
001044428 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001044428 9141_ $$y2025
001044428 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)1040$$2StatID$$aDBCoverage$$bZoological Record$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)1060$$2StatID$$aDBCoverage$$bCurrent Contents - Agriculture, Biology and Environmental Sciences$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)3003$$2StatID$$aDEAL Nature$$d2025-01-06$$wger
001044428 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)9960$$2StatID$$aIF >= 60$$bNATURE : 2022$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)1210$$2StatID$$aDBCoverage$$bIndex Chemicus$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)1200$$2StatID$$aDBCoverage$$bChemical Reactions$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001044428 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bNATURE : 2022$$d2025-01-06
001044428 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2025-01-06
001044428 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001044428 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2025-01-06
001044428 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
001044428 980__ $$ajournal
001044428 980__ $$aVDB
001044428 980__ $$aUNRESTRICTED
001044428 980__ $$aI:(DE-Juel1)INM-7-20090406
001044428 9801_ $$aFullTexts