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Ten simple rules for predictive modeling of individual differences in neuroimaging.

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.

SUBMITTER: Scheinost D

PROVIDER: S-EPMC6521850 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Ten simple rules for predictive modeling of individual differences in neuroimaging.

Scheinost Dustin D Noble Stephanie S Horien Corey C Greene Abigail S AS Lake Evelyn Mr EM Salehi Mehraveh M Gao Siyuan S Shen Xilin X O'Connor David D Barron Daniel S DS Yip Sarah W SW Rosenberg Monica D MD Constable R Todd RT

NeuroImage 20190301

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 examp ...[more]

PMID: 30831310

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