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

Area of Science:

• Neuroscience
• Psychiatry
• Medical Imaging

Background:

• Clinical response to brain stimulation for depression varies significantly.
• Predicting individual patient response remains a major challenge in the field.

Purpose of the Study:

• To synthesize recent advancements in neural predictors for depression treatment response.
• To propose a framework for evaluating the clinical utility of potential biomarkers.

Main Methods:

• Review of data-driven approaches (e.g., machine learning on neuroimaging data).
• Review of theory-driven approaches (e.g., task-based neuroimaging).
• Mechanistic insights from cognitive processes altered by interventions.

Main Results:

• Developments in predictors stem from data-driven and theory-driven methods.
• Machine learning on resting-state or structural neuroimaging data is a key data-driven approach.
• Task-based neuroimaging offers mechanistic insights.

Conclusions:

• A pragmatic framework is proposed for biomarker discovery and testing.
• The framework involves identifying cognitive-neural phenotypes and validating them.
• It includes phased randomized controlled trials (RCTs) to confirm biomarker utility and superiority.