Hierarchical MMN subcomponents in schizophrenia: Predictive coding biomarkers and clinical translation

Area of Science:

• Neuroscience
• Psychiatry
• Biomarkers

Background:

• Mismatch negativity (MMN) is a key neurophysiological marker for auditory predictive coding.
• Schizophrenia exhibits significant mechanistic heterogeneity, impacting auditory processing.
• Understanding MMN's role is crucial for deciphering schizophrenia's diverse neurobiology.

Purpose of the Study:

• To review 25 years of research on MMN and schizophrenia.
• To propose that early and late MMN subcomponents reflect distinct hierarchical disruptions in schizophrenia.
• To advance MMN as a translational tool for personalized, biomarker-guided interventions.

Main Methods:

• Narrative review of literature from PubMed, Web of Science, and Scopus (2000-2025).
• Keywords: mismatch negativity, schizophrenia, predictive coding, EEG biomarkers.
• Analysis of MMN subcomponent timing (early ~150 ms, late ~250 ms) and associated neurobiological correlates.

Main Results:

• Early MMN deficits are linked to NMDA receptor hypofunction, theta abnormalities, cognitive impairment, and functional decline.
• Late MMN impairments correlate with prefrontal predictive hierarchy failures, positive symptoms, and dopaminergic dysregulation.
• MMN subcomponents can stratify schizophrenia into neurophysiologically defined subtypes.

Conclusions:

• A dual-pathway model suggests early MMN as a trait marker and late MMN as a state marker.
• Personalized interventions (e.g., NMDA modulators, neurofeedback) can target specific MMN deficits.
• MMN offers a framework for advancing biomarker-guided care in schizophrenia, moving beyond a "one-MMN-fits-all" approach.