EEG & AI to Identify Glutamatergic Responders: A Path to Overcoming Schizophrenia’s Etiologic Heterogeneity
- Highlight the limitations of DSM 5 diagnostic categories in schizophrenia and illustrate how etiologic heterogeneity has contributed to repeated failures of mechanistically novel drug candidates
- Demonstrate how EEG derived cortical activity patterns, paired with AI driven analysis, can provide a mechanistic basis for prospectively identifying patients whose glutamatergic pathology aligns with specific therapeutic mechanisms
- Present evidence from pomaglumetad Phase 3 re analysis showing that baseline EEG markers can identify a ~20% subgroup with substantial cognitive and negative symptom improvement, revealing responders concealed within a non significant overall trial
- Outline how ongoing work in schizophrenia and autism aims to validate EEG based responder identification across disorders with shared cortical circuit disruption, supporting a broader precision therapy framework