ArticleMolecular psychiatry2020
Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity.
Article in Molecular psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 3 of them syntheses that pooled it.
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Who cites it
77 citing papers in PubMed, 3 syntheses or guidelines pooled it, 153 citations in OpenAlex.
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- fMRI connectivity as a biomarker of antipsychotic treatment response: A systematic review.NeuroImage. Clinical · 2023Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Multimodal workflows optimally predict response to repetitive transcranial magnetic stimulation in patients with schizophrenia: a multisite machine learning analysis.Translational psychiatry · 2024Trial
- 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
- Toward precision medicine: can neuroimaging prospectively predict early treatment outcomes in schizophrenia spectrum disorders? A systematic review and meta-analysis.European archives of psychiatry and clinical neuroscience · 2026Review
- Individualized prediction of heroin cue-induced craving using task-based EEG functional connectivity.NPJ digital medicine · 2026Article
- Clinical deep phenotyping of treatment response in schizophrenia (CDP-STAR): design and methodology of a prospective multimodal observational study.European archives of psychiatry and clinical neuroscience · 2026Observational
- Machine learning-based predictive models and subtypes patterns in peripheral blood of schizophrenia based on a machine learning computational framework.Schizophrenia (Heidelberg, Germany) · 2026Article
- Decoding psychotropic-induced metabolic disturbances: gut-brain axis, multi-omics, and nutrition-lifestyle integration.Frontiers in nutrition · 2026Review
- Functional Coupling and Longitudinal Outcome Prediction in First-Episode Psychosis.Biological psychiatry global open science · 2025Article
- AI and Machine Learning Terminology in Medicine, Psychology, and Social Sciences: Tutorial and Practical Recommendations.Journal of medical Internet research · 2025Review
- Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression.Neuroscience bulletin · 2025Article
- Default mode network-basal ganglia network connectivity predicts the transition to postherpetic neuralgia.IBRO neuroscience reports · 2025Article
- Visualizing functional network connectivity differences using an explainable machine-learning method.Physiological measurement · 2025Article
- Prediction of antipsychotic drug efficacy for schizophrenia treatment based on neural features of the resting-state functional connectome.Translational psychiatry · 2025Article
- Multi-feature fusion RFE random forest for schizophrenia classification and treatment response prediction.Scientific reports · 2025Article
- Can artificial intelligence be the future solution to the enormous challenges and suffering caused by Schizophrenia?Schizophrenia (Heidelberg, Germany) · 2025Review
- A hybrid model for predicting response to risperidone after first-episode psychosis.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025Article
- EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection.PeerJ. Computer science · 2025Article
17 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
Abstract
Identifying biomarkers in schizophrenia during the first episode without the confounding effects of treatment has been challenging. Leveraging these biomarkers to establish diagnosis and make individualized predictions of future treatment responses to antipsychotics would be of great value, but there has been limited progress. In this study, by using machine learning algorithms and the functional connections of the superior temporal cortex, we successfully identified the first-episode drug-naive (FEDN) schizophrenia patients (accuracy 78.6%) and predict their responses to antipsychotic treatment (accuracy 82.5%) at an individual level. The functional connections (FC) were derived using the mutual information and the correlations, between the blood-oxygen-level dependent signals of the superior temporal cortex and other cortical regions acquired with the resting-state functional magnetic resonance imaging. We also found that the mutual information and correlation FC was informative in identifying individual FEDN schizophrenia and prediction of treatment response, respectively. The methods and findings in this paper could provide a critical step toward individualized identification and treatment response prediction in first-episode drug-naive schizophrenia, which could complement other biomarkers in the development of precision medicine approaches for this severe mental disorder.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.