Predicting individual behavioral traits from brain idiosyncrasies has broad practical implications, yet predictions vary widely. This constraint may be driven by a combination of signal and noise in both brain and behavioral variables. Here, we expand on this idea, highlighting the potential of extended sampling ‘precision’ studies. First, we discuss their relevance to improving the reliability of individualized estimates by minimizing measurement noise. Second, we review how targeted within-subject experiments, when combined with individualized analysis or modeling frameworks, can maximize signal. These improvements in signal-to-noise facilitated by precision designs can help boost prediction studies. We close by discussing the integration of precision approaches with large-sample consortia studies to leverage the advantages of both.

中文翻译：

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使用精确方法改进大脑行为预测


根据大脑特质预测个体行为特征具有广泛的实际意义，但预测差异很大。这种约束可能是由大脑和行为变量中的信号和噪声的组合驱动的。在这里，我们扩展了这一想法，强调了扩展抽样“精度”研究的潜力。首先，我们讨论了它们与通过最小化测量噪声来提高个性化估计的可靠性的相关性。其次，我们回顾了有针对性的受试者内实验在与个体化分析或建模框架相结合时如何最大化信号。精密设计促进了信噪比的这些改进，有助于促进预测研究。最后，我们讨论了精密度方法与大样本联盟研究的整合，以利用两者的优势。