DOI: 10.1101/19006650Sep 18, 2019Paper

Predicting individual clinical trajectories of depression with generative embedding

MedRxiv : the Preprint Server for Health Sciences
S. FrässleKlaas E Stephan


Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift and enduring recovery, others show relapsing-remitting or chronic disease course. Predicting individual clinical trajectories at an early disease stage is a key challenge for psychiatry and might facilitate individually tailored interventions. So far, however, reliable predictors at the single-patient level are absent. Here, we evaluated the utility of a machine learning strategy - generative embedding - which combines an interpretable generative model with a discriminative classifier. Specifically, we used functional magnetic resonance imaging (fMRI) data of emotional face perception in 85 MDD patients from the multi-site longitudinal NEtherlands Study of Depression and Anxiety (NESDA) who had been followed up over two years and classified into three subgroups with distinct clinical trajectories. Combining a generative model of effective (directed) connectivity with support vector machines (SVMs), it was possible to predict whether a given patient will experience chronic depression vs. fast remission with a balanced accuracy of 79%. Gradual improvement vs. fast remi...Continue Reading

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