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

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 � 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 diverse scanners, acquisitions, racial groups, disorders and ages. For scans ?20 mins, accuracy increases linearly with the logarithm of total scan duration, suggesting sample size and scan time are initially interchangeable. However, sample size is ultimately more important. Nevertheless, when accounting for overhead costs associated with each participant (e.g., 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, optimal scan time is ?20 mins. On average, 30-min scans are the most cost-effective, yielding 22% savings over 10-min scans. Overshooting optimal scan time is cheaper than undershooting, so we recommend ?30 mins. 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 for future study design: https://thomasyeolab.github.io/OptimalScanTimeCalculator/index.html