From Bench to Bedside: Computational and Biomarker-Driven Approaches for Psychosis Management
A special issue of Neurology International (ISSN 2035-8377).
Deadline for manuscript submissions: 31 March 2026 | Viewed by 40
Special Issue Editors
2. Department of Psychiatry, New Hampshire Hospital, Concord, NH, USA
3. Department of Biomedical Engineering, SUNY Downstate Health Sciences University School of Graduate Studies, Brooklyn, NY, USA
4. Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY, USA
Interests: psychosis biomarkers; event-related potential (ERP); mismatch negativity; translational psychiatry; biophysical modeling; machine learning; deep neural networks
2. Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
Interests: computational neuroscience; biophysical modeling; computational psychiatry; neuro-inspired artificial intelligence (neuro-AI)
Special Issue Information
Dear Colleagues,
Psychosis treatment faces critical challenges in terms of personalization, efficacy, and translational gaps between research and clinical practice. Thus, we welcome original research and reviews addressing innovative strategies to advance precision psychiatry, with a focus on the following:
- Bench-to-Bedside Biomarkers: Translating molecular, electrophysiological, neuroimaging, or digital biomarkers into clinically actionable tools for diagnosis, prognosis, or treatment monitoring in psychosis.
- AI-Driven Treatment Selection: Applications of machine learning (e.g., predictive modeling) to optimize interventions (pharmacological, psychosocial) for individual patients.
- Computational Psychiatry in Action: Leveraging computational techniques (e.g., dynamical systems modeling, reinforcement learning) to optimize medication management and/or decode the underpinnings of psychotic phenomena (e.g., delusions, hallucinations, cognitive deficits).
We welcome studies bridging neuroscience, clinical psychiatry, and data science, including the following:
- Translational biomarker studies (from basic science to clinical trials);
- Explainable AI for clinical decision support in real-world settings;
- Theoretical or mechanistic models of psychosis (e.g., Bayesian inference, network dysfunction);
- Digital health tools integrating computational psychiatry.
Dr. Mohamed ElSayed
Dr. Samuel Neymotin
Guest Editors
Manuscript Submission Information
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Keywords
- precision psychiatry
- psychosis biomarkers
- machine learning
- computational modeling
- antipsychotics
- translational neuroscience
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