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| 001 | 1044428 | ||
| 005 | 20250821202242.0 | ||
| 024 | 7 | _ | |a 10.1038/s41586-025-09250-1 |2 doi |
| 024 | 7 | _ | |a 0028-0836 |2 ISSN |
| 024 | 7 | _ | |a 1476-4687 |2 ISSN |
| 024 | 7 | _ | |a 10.34734/FZJ-2025-03189 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2025-03189 |
| 082 | _ | _ | |a 500 |
| 100 | 1 | _ | |a Ooi, Leon Qi Rong |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Longer scans boost prediction and cut costs in brain-wide association studies |
| 260 | _ | _ | |a London [u.a.] |c 2025 |b Nature Publ. Group |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1755780075_2068 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a A 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). |
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| 700 | 1 | _ | |a Orban, Csaba |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Zhang, Shaoshi |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Nichols, Thomas E. |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Tan, Trevor Wei Kiat |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Kong, Ru |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Marek, Scott |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Dosenbach, Nico U. F. |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Laumann, Timothy O. |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Gordon, Evan M. |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Yap, Kwong Hsia |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Ji, Fang |0 P:(DE-HGF)0 |b 11 |
| 700 | 1 | _ | |a Chong, Joanna Su Xian |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Chen, Christopher |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a An, Lijun |0 P:(DE-HGF)0 |b 14 |
| 700 | 1 | _ | |a Franzmeier, Nicolai |0 P:(DE-HGF)0 |b 15 |
| 700 | 1 | _ | |a Roemer-Cassiano, Sebastian N. |0 P:(DE-HGF)0 |b 16 |
| 700 | 1 | _ | |a Hu, Qingyu |0 P:(DE-HGF)0 |b 17 |
| 700 | 1 | _ | |a Ren, Jianxun |0 P:(DE-HGF)0 |b 18 |
| 700 | 1 | _ | |a Liu, Hesheng |0 P:(DE-HGF)0 |b 19 |
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| 700 | 1 | _ | |a Baker, Justin T. |0 P:(DE-HGF)0 |b 22 |
| 700 | 1 | _ | |a Zhou, Juan Helen |0 P:(DE-HGF)0 |b 23 |
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| 700 | 1 | _ | |a Holmes, Avram J. |0 P:(DE-HGF)0 |b 26 |
| 700 | 1 | _ | |a Yeo, B. T. Thomas |0 P:(DE-HGF)0 |b 27 |e Corresponding author |
| 700 | 1 | _ | |a Jack, Clifford R. |0 P:(DE-HGF)0 |b 28 |
| 773 | _ | _ | |a 10.1038/s41586-025-09250-1 |0 PERI:(DE-600)1413423-8 |p 731–740 |t Nature |v 644 |y 2025 |x 0028-0836 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1044428/files/s41586-025-09250-1.pdf |y OpenAccess |
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| 910 | 1 | _ | |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 |0 I:(DE-HGF)0 |b 27 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a thomas.yeo@nus.edu.sg |0 I:(DE-HGF)0 |b 27 |6 P:(DE-HGF)0 |
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