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

A pervasive dilemma in brain-wide association studies<sup>1</sup> (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 (R<sup>2</sup> = 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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Publication Details

Journal
Nature
Published
2025-07-16
DOI
https://doi.org/10.1038/s41586-025-09250-1
Citations
36
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
51.52

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article

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

Lijun An, Simon Bodo Eickhoff, Kwong Hsia Yap, B.T. Thomas Yeo et al.
36 citations
Nature
Functional Brain Connectivity Studies
51.52
article

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

Lijun An, Simon Bodo Eickhoff, Kwong Hsia Yap, B.T. Thomas Yeo, Carrisa V. Cocuzza, Christopher Chen, Fang Ji, Nico U.F. Dosenbach, Shaoshi Zhang, Trevor Wei Kiat Tan, Joanna Su Xian Chong, Csaba Orbán, Juan Helen Zhou, Qingyu Hu, Leon Qi Rong Ooi, Scott Marek, Sebastian Niclas Roemer, Danilo Bzdok, Evan M. Gordon, Thomas E. Nichols, Hesheng Liu, Ru Kong, Sidhant Chopra, Justin T. Baker, Timothy O. Laumann, Nicolai Franzmeier, Jianxun Ren, Avram J. Holmes
article en
36 citations

Abstract

A pervasive dilemma in brain-wide association studies<sup>1</sup> (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 (R<sup>2</sup> = 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 ).

NatureVol. 644(8077)
Rutgers, The State University of New Jersey (US), Montreal Neurological Institute and Hospital (CA), Harvard University (US), National University of Singapore (SG), Forschungszentrum Jülich (DE), Lund University (SE), Peking University (CN), Washington University in St. Louis (US), Science for Life Laboratory (SE), McLean Hospital (US), LMU Klinikum (DE), Yale University (US), University of Oxford (GB), Orygen Youth Health (AU), Wellcome Centre for Integrative Neuroimaging (GB), Mallinckrodt (United States) (US), National University Cancer Institute, Singapore (SG), Mental Health Australia (AU), Orygen (AU), Munich Cluster for Systems Neurology (DE), Mayo Clinic in Arizona (US), Athinoula A. Martinos Center for Biomedical Imaging (US), National University Health System (SG), Mila - Quebec Artificial Intelligence Institute (CA), Clinical Nutrition Research Centre (SG), Heinrich Heine University Düsseldorf (DE), McGill University (CA), Ludwig-Maximilians-Universität München (DE), University of Gothenburg (SE)
Foundation for the National Institutes of Health, Alzheimer's Association, Eli Lilly and Company, Pfizer, Biogen, F. Hoffmann-La Roche, Meso Scale Diagnostics, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Northern California Institute for Research and Education, Hope Center for Neurological Disorders, Eisai, Servier, H. Lundbeck A/S, IXICO, Temasek Foundation, National Institutes of Health, Genentech, Intellectual and Developmental Disabilities Research Center, McDonnell Center for Systems Neuroscience, Canadian Institutes of Health Research, Medical Research Council, National Medical Research Council, National Institute of Mental Health
Openalex Percentile: Top 0%
Functional Brain Connectivity Studies
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