Neuroimaging Biomarkers for Diagnosing Depression Subtypes and Predicting Treatment Response

Background & Unmet Need

• Depression is a heterogeneous syndrome that encompasses varied, co-occurring clinical symptoms and divergent responses to treatment
• However, the relationship between dysfunction and abnormal connectivity in the brain and clinical phenotypes is poorly understood
• The association between clinical subtypes and their biological substrates is inconsistent and variable at the individual level, and to-date have not proven useful for differentiating individual patients or informing treatment decisions
• Unmet Need: Objective and clinically actionable biomarkers to diagnose subtypes of depression and guide treatment selection

Technology Overview

• The Technology: Diagnostic biomarkers for depression biotypes based on whole-brain patterns of dysfunctional connectivity evaluated by functional magnetic resonance imaging (fMRI)
• The Discovery: Using fMRI in a large multisite sample (n = 1,188), the inventors demonstrated that patients may be divided into four distinct subtypes defined by patterns of dysfunctional connectivity in limbic and frontostriatal networks
• PoC Data: Clustering patients on this basis generated diagnostic biomarkers with high sensitivity and specificity (>80%) for depression subtypes
• Depression biotypes were stable over time and were replicated in an independent cohort
• Biotypes predicted response to targeted neurostimulation therapy for medication-resistant depression more effectively than relying on clinical symptoms

Technology Applications

• Diagnosis of depression subtypes
• Treatment selection based on depression subtype
• Monitoring treatment response over time

Technology Advantages

• Connectivity-based biotypes are clinically meaningful, measurable, and replicable across patient cohorts
• Biomarker-based classifiers detect biotypes with high sensitivity and specificity
• More accurate prediction of treatment response to rTMS than based on clinical features