Dataset Information

MRI economics: Balancing sample size and scan duration in brain wide association studies.

ABSTRACT: A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan duration given fixed resources. Here, we systematically investigate this trade-off in the context of brain-wide association studies (BWAS) using resting-state functional magnetic resonance imaging (fMRI). We find that total scan duration (sample size × scan duration per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting that sample size and scan duration are broadly interchangeable. The returns of scan duration eventually diminish relative to sample size, which we explain with principled theoretical derivations. When accounting for fixed costs associated with each participant (e.g., recruitment, non-imaging measures), we find that prediction accuracy in small-scale BWAS might benefit from much longer scan durations (>50 min) than typically assumed. Most existing large-scale studies might also have benefited from smaller sample sizes with longer scan durations. Both logarithmic and theoretical models of the relationships among sample size, scan duration and prediction accuracy explain well-predicted phenotypes better than poorly-predicted phenotypes. The logarithmic and theoretical models are also undermined by individual differences in brain states. These results replicate across phenotypic domains (e.g., cognition and mental health) from two large-scale datasets with different algorithms and metrics. Overall, our study emphasizes the importance of scan time, which is ignored in standard power calculations. Standard power calculations inevitably maximize sample size at the expense of scan duration. The resulting prediction accuracies are likely lower than would be produced with alternate designs, thus impeding scientific discovery. Our empirically informed reference is available for future study design: WEB_APPLICATION_LINK.

SUBMITTER: Ooi LQR

PROVIDER: S-EPMC10889017 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Publications

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

Ooi Leon Qi Rong LQR Orban Csaba C Zhang Shaoshi S Nichols Thomas E TE Tan Trevor Wei Kiat TWK Kong Ru R Marek Scott S Dosenbach Nico U F NUF Laumann Timothy T Gordon Evan M EM Yap Kwong Hsia KH Ji Fang F Chong Joanna Su Xian JSX Chen Christopher C An Lijun L Franzmeier Nicolai N Roemer Sebastian Niclas SN Hu Qingyu Q Ren Jianxun J Liu Hesheng H Chopra Sidhant S Cocuzza Carrisa V CV Baker Justin T JT Zhou Juan Helen JH Bzdok Danilo D Eickhoff Simon B SB Holmes Avram J AJ Yeo B T Thomas BTT

bioRxiv : the preprint server for biology 20250311

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 (R<sup>2</sup> = 0.89), spanning a wide range of ...[more]

PMID: 38405815

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Dataset Information

MRI economics: Balancing sample size and scan duration in brain wide association studies.

Publications

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

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