在全脑关联研究中，更长时间的扫描提高预测能力并降低成本，这一成果由新加坡国立大学B. T. Thomas Yeo小组经过不懈努力而取得。相关论文发表在2025年7月16日出版的《自然》杂志上。

课题组研究人员推导了一个理论模型，表明个体水平的表型预测精度随着样本量和总扫描时间（样本量×每人扫描时间）的增加而增加。每个参与者的扫描时间)。该模型很好地解释了来自9个静息功能磁共振成像和任务功能磁共振成像数据集的76种表型的经验预测准确性（R² = 0.89），涵盖了不同的扫描仪、获取、种族群体、疾病和年龄。对于扫描≤20最小，精度随总扫描时间的对数线性增加，表明样本大小和扫描时间最初是可互换的。

然而，样本大小最终更为重要。然而，当考虑到每个参与者的间接成本（例如招募费用）时，对于提高预测性能而言，较长的扫描时间比较大的样本量要便宜得多。要实现高预测性能，10分钟扫描方案性价比最低——多数情况下最优扫描时长至少需20分钟，30分钟方案平均最具成本效益，较10分钟方案可节省22%成本。超过最佳扫描时间比低于最佳扫描时间便宜，因此研究人员建议扫描时间至少为30分钟。与静息状态全脑BWAS相比，任务- fmri的扫描时间更短，而皮层下-全脑BWAS的扫描时间更长。与标准功率计算相比，他们的结果表明，联合优化样本量和扫描时间可以提高预测精度，同时降低成本。他们的经验参考可以在网上获得，以供未来的研究设计（https://thomasyeolab.github.io/OptimalScanTimeCalculator/index.html）。

研究人员表示，在全脑关联研究（BWAS）中，一个普遍存在的难题是是否优先考虑功能性磁共振成像（fMRI）扫描时间或样本量。

附：英文原文

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

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

Issue&Volume: 2025-07-16

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 ≤20min, 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, 10min scans are cost inefficient. In most scenarios, the optimal scan time is at least 20min. On average, 30min scans are the most cost-effective, yielding 22% savings over 10min scans. Overshooting the optimal scan time is cheaper than undershooting it, so we recommend a scan time of at least 30min. 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).

DOI: 10.1038/s41586-025-09250-1

Source: https://www.nature.com/articles/s41586-025-09250-1