Ten simple rules for predictive modeling of individual differences in neuroimaging

By Dustin Scheinost, S. Noble, C. Horien, A. Greene, E. Lake, Mehraveh Salehi, Siyuan Gao, X. Shen, David O’Connor, D. Barron and 3 more. NeuroImage, 2019

346 citations recorded by the source corpus.

&NA; 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. Highlights10 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.

Subfields: Cognitive Neuroscience, Health, Toxicology and Mutagenesis, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7684523746ed6cd746f2f77e64b38e36cfa4b710

Citations in the corpus

• Review of Multimodal Data Acquisition Approaches for Brain–Computer Interfaces (2024, unknown): “This interplay of technological prowess and ethical stewardship underscores the need for a conscientious balance [52].”