ReviewAdvances in neurobiology2024
Neurophysiological Models in Individuals at Clinical High Risk for Psychosis: Using Translational EEG Paradigms to Forecast Psychosis Risk and Resilience.
Review in Advances in neurobiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Excitation/inhibition imbalance and conversion to psychosis in the clinical high risk syndrome: Biophysical modeling finds reduced pyramidal cell excitability across EEG paradigms.medRxiv : the preprint server for health sciences · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
Over the last several decades, there have been major research efforts to improve the identification of youth and young adults at clinical high-risk for psychosis (CHR-P). Among individuals identified as CHR-P based on clinical criteria, approximately 20% progress to full-blown psychosis over 2-3 years and 30% achieve remission. In more recent years, neurophysiological measures with established sensitivity to schizophrenia have gained traction in the study of CHR-P and its range of clinical outcomes, with the goal of identifying specific biomarkers that precede psychosis onset that 7 chapter, we review studies examining several translational electroencephalography (EEG) and event-related potential (ERP) measures, which have known sensitivity to schizophrenia and reflect abnormal sensory, perceptual, and cognitive processing of task stimuli, as predictors of future clinical outcomes in CHR-P individuals. We discuss the promise of these EEG/ERP biomarkers of psychosis risk, including their potential to provide (a) translational bridges between human studies and animal models focused on drug development for early psychosis, (b) target engagement measures for clinical trials, and (c) prognostic indicators that could enhance personalized treatment planning.
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