Ten simple rules for predictive modeling of individual differences in neuroimaging

Dustin Scheinost, Stephanie Noble, Corey Horien, Abigail S. Greene, Evelyn M. R. Lake, Mehraveh Salehi, Siyuan Gao, Xilin Shen, David O'Connor, Daniel S. Barron, Sarah W. Yip, Monica D. Rosenberg, R. Todd Constable

Abstract: Highlights • 10 simple rules to help researchers apply predictive modeling to connectivity data. • Rules are general to methodological approach with practical examples. • 4 rules for validating predictive models through independent data. • 3 rules for assessing model performance. • 3 rules for removing confounds and increasing interpretability of models. Abstract Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches. We hope these ten rules will increase the use of predictive models with neuroimaging data. Previous article in issue Next article in issue