Multi-study evaluation of neuroimaging-based prediction of medication class in mood disorders

Abstract

Clinicians often face a dilemma in diagnosing bipolar disorder patients with complex symptoms who spend more time in a depressive state than a manic state. The current gold standard for such diagnosis, the Diagnostic and Statistical Manual (DSM), is not objectively grounded in pathophysiology. In such complex cases, relying solely on the DSM may result in misdiagnosis as major depressive disorder (MDD). A biologically-based classification algorithm that can accurately predict treatment response may help patients suffering from mood disorders. Here we used an algorithm to do so using neuroimaging data. We used the neuromark framework to learn a kernel function for support vector machine (SVM) on multiple feature subspaces. The neuromark framework achieves up to 95.45% accuracy, 0.90 sensitivity, and 0.92 specificity in predicting antidepressant (AD) vs. mood stabilizer (MS) response in patients. We incorporated two additional datasets to evaluate the generalizability of our approach. The trained algorithm achieved up to 89% accuracy, 0.88 sensitivity, and 0.89 specificity in predicting the DSM-based diagnosis on these datasets. We also translated the model to distinguish responders to treatment from nonresponders with up to 70% accuracy. This approach reveals multiple salient biomarkers of medication-class of response within mood disorders.

Keywords: Bipolar disorder; Kernel SVM; Major depressive disorder; Neuromark; Treatment response.

Figure 1:

Flowchart of our classification scheme. A. Resting-state fMRI data are put through the Neuromark ICA pipeline for feature extraction (spatial maps, time courses, and FNC). B. Classification is performed using kernel SVM algorithm & 10fold cross-validation. Known medication-class of treatment response (mood stabilizers (MS)/antidepressants (AD)) is used as the targets to train the models. C. Experiments are run using spatial maps (SMs), functional network connectivity (FNC), and their combination as features. D. Trained models are tested on independent data.

Figure 2:

The neuromark SM templates. These are obtained using group ICA analysis on HCP, GSP controls data, and a greedy algorithm to identify the most replicable components. 53 spatial maps are divided into 7 functional domains. These templates can be used as references to estimate subject spatial maps and time courses from new and unseen data.

Figure 3:

Best component(s) for treatment response prediction in each functional domain across different experiments