From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care

Biomedicines, Journal Year: 2025, Volume and Issue: 13(1), P. 167 - 167

Published: Jan. 12, 2025

Background/Objectives: The dual forces of structured inquiry and serendipitous discovery have long shaped neuropsychiatric research, with groundbreaking treatments such as lithium ketamine resulting from unexpected discoveries. However, relying on chance is becoming increasingly insufficient to address the rising prevalence mental health disorders like depression schizophrenia, which necessitate precise, innovative approaches. Emerging technologies artificial intelligence, induced pluripotent stem cells, multi-omics potential transform this field by allowing for predictive, patient-specific interventions. Despite these advancements, traditional methodologies animal models single-variable analyses continue be used, frequently failing capture complexities human conditions. Summary: This review critically evaluates transition serendipity precision-based in research. It focuses key innovations dynamic systems modeling network-based approaches that use genetic, molecular, environmental data identify new therapeutic targets. Furthermore, it emphasizes importance interdisciplinary collaboration human-specific overcoming limitations Conclusions: We highlight precision psychiatry’s transformative revolutionizing care. paradigm shift, combines cutting-edge systematic frameworks, promises increased diagnostic accuracy, reproducibility, efficiency, paving way tailored better patient outcomes

Language: Английский

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Characterizing multivariate regional hubs for schizophrenia classification, sex differences, and brain age estimation using explainable AI

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: March 4, 2025

Abstract Purpose To investigate multivariate regional patterns for schizophrenia (SZ) classification, sex differences, and brain age by utilizing structural MRI, demographics, explainable artificial intelligence (AI). Methods Various AI models were employed, the outperforming model was identified SZ predictions. For classification tasks, support vector classifier (SVC), k-nearest neighbor (KNN), deep learning neural network (DL) compared. In case of regression-based prediction, Lasso regression (LR), Ridge (RR), (SVR), DL each or task, optimal further integrated with Shapley additive explanations (SHAP) significant identified. Results Our results demonstrated that outperformed other in We then SHAP, this DL-SHAP used to identify individualized associated prediction. Using approach, we found individuals had anatomical changes particularly left pallidum, posterior insula, hippocampus, putamen regions, such different between female male patients. Finally, applied method prediction suggested important regions related aging health controls (HC) processes. Conclusion This study systematically utilized predictive modeling novel approaches complex involved built a deeper understanding neurobiological mechanisms disease, offering new insights future diagnosis treatments laying foundation development precision medicine.

Language: Английский

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Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application

BMC Psychiatry, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 14, 2025

Language: Английский

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Functional connectivity of the striatum in psychosis: meta-analysis of functional Magnetic Resonance Imaging studies and replication on an independent sample

Neuroscience & Biobehavioral Reviews, Journal Year: 2025, Volume and Issue: unknown, P. 106179 - 106179

Published: April 1, 2025

Language: Английский

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On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 30, 2024

Language: Английский

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