Machine learning in major depression: From classification to treatment outcome prediction

Abstract

Aims: Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders.

Discussions: In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression.

Conclusions: We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.

Keywords: classification; machine learning; magnetic resonance imaging; major depressive disorder; review.

Figure 1

Visual summary of the selected MDD studies. A, Total number of papers before screening. B, Number of publications per group classification. C, Proportion of machine‐learning methods used. D, Boxplot of accuracy based on five methods. E, Proportion of MRI modalities used. F, Accuracy based on modalities. G, Boxplot of sample size based on different cross‐validation method. H, Scatter plot of overall reported accuracy vs the total sample size. I, Literature search results for each screening steps.21 J, Summary of steps in MRI machine learning

Figure 2

Brain network studies in MDD classification. A100: Brain network construction with MRI and connectome architecture represented by a connectivity matrix. B86: Region weights and distribution of 442 consensus functional connections identified by classification of MDD and HC demonstrated in sagittal and axial view (left) and in a circle graph (middle). Top 100 most discriminating consensus functional connections in sagittal and axial view (right). [A reproduced from ref. 100; B reproduced from ref. 86]

Figure 3

Predication studies in MDD. A20: Positive association between predicted and true change in the Hamilton Depression Rating Scale (HDRS) score. Positive association between change in HDRS score and subgenual anterior cingulate volume before electroconvulsive therapy (ECT). Gray matter volume (GMV) increasing in the ECT group. Spatial map of correlated anterior cingulate volume. B19: Scatter plot of the predicted ΔHDRS (Hamilton Depression Rating Scale) with respect to their true values for three sites, extracting six identified pre‐electroconvulsive therapy (ECT) gray matter (GM) regions in University of New Mexico (UNM) and using them as regressors for two independent cohorts: Long Island Jewish Health System (LIJ) and University of California at Los Angeles (UCLA). Six identified pre‐electroconvulsive therapy (ECT) GM regions of interest (ROIs) as predictors of ΔHDRS in axial view. Longitudinal GM changes among remitters, nonremitters, and healthy controls of left supplementary motor area (SMA) and superior frontal gyrus (SFG). [A reproduced from ref. 20; B reproduced from ref. 19]