ArticleThe American journal of psychiatry2023
A Functional Connectome-Based Neural Signature for Individualized Prediction of Antipsychotic Response in First-Episode Psychosis.
Article in The American journal of psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Mapping Cerebellar Connectivity to Cognition in Psychosis: Convergent Evidence From Functional Magnetic Resonance Imaging and Transcranial Magnetic Stimulation.Biological psychiatry · 2026Trial
- Functional Connectivity Predicting Transdiagnostic Treatment Outcomes in Internalizing Psychopathologies.JAMA network open · 2025Trial
- Baseline symptom-related white matter tracts predict individualized treatment response to 12-week antipsychotic monotherapies in first-episode schizophrenia.Translational psychiatry · 2024Trial
- Connectomic signatures advance the understandings of transdiagnostic heterogeneity in psychosis.Communications medicine · 2026Review
- Connectome-based substrates of emotional N-back performance in the adolescent brain cognitive development study.Developmental cognitive neuroscience · 2026Article
- Personalized prediction of treatment response during the acute phase of schizophrenia based on the connectome-based predictive model.European archives of psychiatry and clinical neuroscience · 2026Article
- Personality Heterogeneity Shapes the Neural Organization of Conditioned Threat Learning.Research square · 2026Article
- Microstructural Alterations of the Cerebellum-Ventral Tegmental Area Pathways in First-Episode Psychosis.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Article
- Explainable artificial intelligence (XAI) in schizophrenia: enhancing transparency in clinical decision-making.Frontiers in systems biology · 2026Review
- Cerebellar-Cortical Connectivity and Prediction of Antipsychotic Treatment Response in First-Episode Psychosis.Schizophrenia bulletin · 2025Article
- Functional Coupling and Longitudinal Outcome Prediction in First-Episode Psychosis.Biological psychiatry global open science · 2025Article
- Effects on Multimodal Connectivity Patterns in Female Schizophrenia During 8 Weeks of Antipsychotic Treatment.Schizophrenia bulletin · 2025Article
- Connectome-based predictive modeling of early and chronic psychosis symptoms.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Article
- Prediction of antipsychotic drug efficacy for schizophrenia treatment based on neural features of the resting-state functional connectome.Translational psychiatry · 2025Article
- Abnormalities in cognitive-related functional connectivity can be used to identify patients with schizophrenia and individuals in clinical high-risk.BMC psychiatry · 2025Article
- An artificial intelligence-derived metabolic network predicts psychosis in Alzheimer's disease.Brain communications · 2025Article
- Biomarker discovery using machine learning in the psychosis spectrum.Biomarkers in neuropsychiatry · 2024Article
- Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Higher-Order Intrinsic Brain Network Trajectories After Antipsychotic Treatment in Medication-Naïve Patients With First-Episode Psychosis.Biological psychiatry · 2024Article
- Neuroimaging in schizophrenia: From group-average abnormalities to individualised circuit models.Science progressReview
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Abstract
objectiveIdentification of robust biomarkers that predict individualized response to antipsychotic treatment at the early stage of psychotic disorders remains a challenge in precision psychiatry. The aim of this study was to investigate whether any functional connectome-based neural traits could serve as such a biomarker.
methodsIn a discovery sample, 49 patients with first-episode psychosis received multi-paradigm fMRI scans at baseline and were clinically followed up for 12 weeks under antipsychotic monotherapies. Treatment response was evaluated at the individual level based on the psychosis score of the Brief Psychiatric Rating Scale. Cross-paradigm connectivity and connectome-based predictive modeling were employed to train a predictive model that uses baseline connectomic measures to predict individualized change rates of psychosis scores, with model performance evaluated as the Pearson correlations between the predicted change rates and the observed change rates, based on cross-validation. The model generalizability was further examined in an independent validation sample of 24 patients in a similar design.
resultsThe results revealed a paradigm-independent connectomic trait that significantly predicted individualized treatment outcome in both the discovery sample (predicted-versus-observed r=0.41) and the validation sample (predicted-versus-observed r=0.47, mean squared error=0.019). Features that positively predicted psychosis change rates primarily involved connections related to the cerebellar-cortical circuitry, and features that negatively predicted psychosis change rates were chiefly connections within the cortical cognitive systems.
conclusionsThis study discovers and validates a connectome-based functional signature as a promising early predictor for individualized response to antipsychotic treatment in first-episode psychosis, thus highlighting the potential clinical value of this biomarker in precision psychiatry.
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