Longer scans boost prediction and cut costs in brain-wide association studies

Leon Qi Rong Ooi, Csaba Orban, Shaoshi Zhang, Thomas E. Nichols, Trevor Wei Kiat Tan, Ru Kong, Scott Marek, Nico U. F. Dosenbach, Timothy O. Laumann, Evan M. Gordon, Kwong Hsia Yap, Fang Ji, Joanna Su Xian Chong, Christopher Chen, Lijun An, Nicolai Franzmeier, Sebastian N. Roemer-Cassiano, Qingyu Hu, Jianxun Ren, Hesheng Liu, Sidhant Chopra, Carrisa V. Cocuzza, Justin T. Baker, Juan Helen Zhou, Danilo Bzdok, Simon B. Eickhoff, Avram J. Holmes and B. T. Thomas Yeo ()
Additional contact information
Leon Qi Rong Ooi: National University of Singapore
Csaba Orban: National University of Singapore
Shaoshi Zhang: National University of Singapore
Thomas E. Nichols: University of Oxford
Trevor Wei Kiat Tan: National University of Singapore
Ru Kong: National University of Singapore
Scott Marek: Washington University School of Medicine
Nico U. F. Dosenbach: Washington University School of Medicine
Timothy O. Laumann: Washington University School of Medicine
Evan M. Gordon: Washington University School of Medicine
Kwong Hsia Yap: National University Health System
Fang Ji: National University of Singapore
Joanna Su Xian Chong: National University of Singapore
Christopher Chen: National University Health System
Lijun An: Lund University
Nicolai Franzmeier: LMU Munich
Sebastian N. Roemer-Cassiano: LMU Munich
Qingyu Hu: Changping Laboratory
Jianxun Ren: Changping Laboratory
Hesheng Liu: Changping Laboratory
Sidhant Chopra: Orygen
Carrisa V. Cocuzza: Yale University
Justin T. Baker: Harvard Medical School
Juan Helen Zhou: National University of Singapore
Danilo Bzdok: Department of Biomedical Engineering
Simon B. Eickhoff: Research Center Jülich
Avram J. Holmes: Rutgers University
B. T. Thomas Yeo: National University of Singapore

Nature, 2025, vol. 644, issue 8077, 731-740

Abstract: Abstract 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 ).

Date: 2025
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DOI: 10.1038/s41586-025-09250-1

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