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Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises

Jing Sui a,b,c,d, Rongtao Jiang a,b, Juan Bustillo e, Vince Calhoun d 

a Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

b School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

c Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China

d Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia

e Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico

JS and RJ contributed equally as joint first authors. 


The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move toward a translational neuroscience era. Regression-based multivariate models (hereafter “predictive modeling”) provide a powerful and widely used approach to predict human behavior with neuroimaging features. These studies maintain a focus on decoding individual differences in a continuously behavioral phenotype from neuroimaging data, opening up an exciting opportunity to describe the human brain at the single-subject level. In this survey, we provide an overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade. We first review regression-based approaches and highlight connectome-based predictive modeling, which has grown in popularity in recent years. Next, we systematically describe recent representative studies using these tools in the context of cognitive function, symptom severity, personality traits, and emotion processing. Finally, we highlight a few challenges related to combining multimodal data, longitudinal prediction, external validations, and the employment of deep learning methods that have emerged from our review of the existing literature, as well as present some promising and challenging future directions. 

Key words: Biomarker; Cognition; Individualized prediction; Mental disorder; Multivariate analyses; Regression

  Figure. Visual summary of studies using regression-based machine learning approaches to predict continuous variables. (A) There is an obviously increasing trend in the number of papers published each year since 2010. (B) The overall prediction accuracy against the corresponding sample size used in the studies. (C) The type of behaviors of interest that are used as the target measures among all surveyed studies. (D) Cognitive metrics adopted in our surveyed studies. (E) Distribution of prediction accuracy for healthy subjects and patients with brain disorders shown as boxplot plot (left) and kernel density (right). (F) Distribution of prediction accuracy from studies using multimodal or unimodal data.