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 H. O’Connor,Daniel S. Barron,Sarah W. Yip,Monica D. Rosenberg,R. Todd Constable +12 more
TLDR
Ten simple rules to help researchers apply predictive modeling to connectivity data are offered and it is hoped these ten rules will increase the use of predictive models with neuroimaging data.About:
This article is published in NeuroImage.The article was published on 2019-06-01 and is currently open access. It has received 250 citations till now. The article focuses on the topics: Connectome & Neuroimaging.read more
Citations
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Journal ArticleDOI
Establishment of Best Practices for Evidence for Prediction: A Review.
TL;DR: Various measures of predictive performance and the limitations of some commonly used measures are discussed, with a focus on the importance of using multiple measures when assessing performance.
Journal ArticleDOI
Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises.
TL;DR: An overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade is provided and connectome-based predictive modeling, which has grown in popularity in recent years is highlighted.
Journal ArticleDOI
Combining multiple connectomes improves predictive modeling of phenotypic measures.
TL;DR: Two methods for combining multiple connectomes from different task conditions in one predictive model are demonstrated and it is shown that these models outperform a previously validated single connectome-based predictive model approach.
Journal ArticleDOI
A neuroimaging biomarker for sustained experimental and clinical pain
Jae-Joong Lee,Hong Ji Kim,Marta Ceko,Bo-yong Park,Soo Ahn Lee,Hyunjin Park,Mathieu Roy,Seong-Gi Kim,Tor D. Wager,Choong-Wan Woo +9 more
TL;DR: In this paper, the authors developed a functional magnetic resonance imaging signature based on whole-brain functional connectivity that tracks experimentally induced tonic pain intensity and tested its sensitivity, specificity and generalizability to clinical pain across six studies (total n = 334).
Journal ArticleDOI
Toward a unified framework for interpreting machine-learning models in neuroimaging.
TL;DR: This protocol describes how to assess the interpretability of models based on fMRI and introduces a unified framework that consists of model-, feature- and biology-level assessments to provide complementary results that support the understanding of how and why a model works.
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