The Clinical Added Value of Imaging: A Perspective From Outcome Prediction

Abstract

Objective measures of psychiatric health would be of benefit in clinical practice. Despite considerable research in the area of psychiatric neuroimaging outcome prediction, translating putative neuroimaging markers (neuromarkers) of a disorder into clinical practice has proven challenging. We reviewed studies that used neuroimaging measures to predict treatment response and disease outcomes in major depressive disorder, substance use, autism spectrum disorder, psychosis, and dementia. The majority of studies sought to predict psychiatric outcomes rather than develop a specific biological index of future disease trajectory. Studies varied widely with respect to sample size and quantification of out-of-sample prediction model performance. Many studies were able to predict psychiatric outcomes with moderate accuracy, with neuroimaging data often augmenting the prediction compared to clinical or psychometric data alone. We make recommendations for future research with respect to methods that can increase the generalizability and reproducibility of predictions. Large sample sizes in conjunction with machine learning methods, such as feature selection, cross-validation, and random label permutation, provide significant improvement to and quantification of generalizability. Further refinement of neuroimaging protocols and analysis methods will likely facilitate the clinical applicability of predictive imaging markers in psychiatry. Such clinically relevant neuromarkers need not necessarily be grounded in the pathophysiology of the disease, but identifying these neuromarkers may suggest targets for future research into disease mechanisms. The ability of imaging prediction models to augment clinical judgments will ultimately depend on the personal and economic costs and benefits to the patient.

Keywords: Machine learning; Neuroimaging; Nosology; Prediction; Psychiatry; Reproducibility.