Predicting Response to Brain Stimulation in Depression: a Roadmap for Biomarker Discovery.

Abstract

Purpose of review: Clinical response to brain stimulation treatments for depression is highly variable. A major challenge for the field is predicting an individual patient's likelihood of response. This review synthesises recent developments in neural predictors of response to targeted brain stimulation in depression. It then proposes a framework to evaluate the clinical potential of putative 'biomarkers'.

Recent findings: Largely, developments in identifying putative predictors emerge from two approaches: data-driven, including machine learning algorithms applied to resting state or structural neuroimaging data, and theory-driven, including task-based neuroimaging. Theory-driven approaches can also yield mechanistic insight into the cognitive processes altered by the intervention.

Summary: A pragmatic framework for discovery and testing of biomarkers of brain stimulation response in depression is proposed, involving (1) identification of a cognitive-neural phenotype; (2) confirming its validity as putative biomarker, including out-of-sample replicability and within-subject reliability; (3) establishing the association between this phenotype and treatment response and/or its modifiability with particular brain stimulation interventions via an early-phase randomised controlled trial RCT; and (4) multi-site RCTs of one or more treatment types measuring the generalisability of the biomarker and confirming the superiority of biomarker-selected patients over randomly allocated groups.