Template-driven Dynamic Functional Network Connectivity Predicts Medication Class Response for Major Depression and...

The process of finding reliable treatment for major depression and bipolar disorder can be arduous. The myriad behavioral symptoms presented by patients and resistance to treatment from particular medication classes complicate standard diagnostic and prescription methodologies, often requiring multiple attempted treatments during which symptoms may still be present. Physiological information such as neuroimaging scans may help to alleviate some of the uncertainty surrounding diagnosis and treatment when incorporated into a clinical setting. Changes in functional magnetic resonance imaging show particular promise, as the incorporation of dynamical information may provide insights into physiological changes prior to static, structural changes. In this work, we present a novel method for generating robust and replicable dynamic functional network connectivity (dFNC) features from neuroimaging data using a template of dynamic states derived from a large, non-affected data set. We demonstrate that this template-driven dFNC approach expands on standard dFNC approaches by allowing for the derivation of a continuous state-contribution time series. We demonstrate that the derived biomarkers can support high predictive performance for the identification of medication class while also expanding the set of biomarkers available for studying differences in mood disorder medication response.Clinical relevance-This paper introduces a novel method for deriving template-driven dynamic functional network connectivity biomarkers and demonstrates the efficacy of utilizing these biomarkers for the prediction of medication class response for major depression and bipolar disorders. The classification models and derived biomarkers utilized in this work have the potential to play the role of clinical decision support in the prescription of particular medication classes for mood disorders.