Using precision approaches to improve brain-behavior prediction

Recent work has highlighted that a large number of participants are needed to reproducibly predict individual behavior traits based on characteristics in brain structure or function. However, having enough data per participant is also critical for prediction.Here, we review recent evidence that the limited performance of current brain-behavior prediction models is driven by two major causes, which are noisy data and small effects. We offer a framework to tackle these challenges through ‘precision’ brain and behavioral approaches that collect more per-participant data and implement within-subject experimental designs.We discuss how integrating precision approaches with consortium studies may provide improved brain-behavior predictions necessary to achieve more powerful clinical applications. 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. 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.