ArticleSchizophrenia bulletin2020
Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence.
Article in Schizophrenia bulletin, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 4 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
49 citing papers in PubMed, 4 syntheses or guidelines pooled it, 97 citations in OpenAlex.
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis.NeuroImage. Clinical · 2021Pooled it
- Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders.Translational psychiatry · 2020Pooled it
- From voxels to models: a multi-center voxel-based morphometry study identifying robust biomarkers for deep learning classification of schizophrenia.Brain imaging and behavior · 2026Article
- Graph theory reveals functional connectome disruptions in adolescent major depressive disorder with childhood trauma.Communications medicine · 2026Article
- Identifying transdiagnostic neurobiomarkers for precision major psychiatric disordersJournal of translational internal medicine · 2026Article
- Review
- A novel strategy for enhanced schizophrenia detection using established CNN architectures.BMC medical imaging · 2025Article
- Mapping Neurochemical Signatures onto Brain Structure for Neurotransmitter-Informed Discrimination of Schizophrenia Patients from Healthy Controls.medRxiv : the preprint server for health sciences · 2025Article
- Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application.BMC 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
- External validation of a prognostic model to improve prediction of psychosis: a retrospective cohort study in primary care.The British journal of general practice : the journal of the Royal College of General Practitioners · 2024Article
- Predictive and Explainable Artificial Intelligence for Neuroimaging Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Multivariable prediction of functional outcome after first-episode psychosis: a crossover validation approach in EUFEST and PSYSCAN.Schizophrenia (Heidelberg, Germany) · 2024Article
- Cognitive subgroups of affective and non-affective psychosis show differences in medication and cortico-subcortical brain networks.Scientific reports · 2024Article
- Longitudinal MicroRNA Signature of Conversion to Psychosis.Schizophrenia bulletin · 2024Article
- Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2024Article
- Article
- Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning.Neuroscience bulletin · 2023Review
Corrections and comments
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Authors and funding
17 authors at 6 institutions in 6 countries.
Funding
Abstract
Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study aimed to elucidate the extent to which the application of ML to neuroanatomical data allows detection of first episode psychosis (FEP), while putting in place methodological precautions to avoid overoptimistic results. We tested both traditional ML and an emerging approach known as deep learning (DL) using 3 feature sets of interest: (1) surface-based regional volumes and cortical thickness, (2) voxel-based gray matter volume (GMV) and (3) voxel-based cortical thickness (VBCT). To assess the reliability of the findings, we repeated all analyses in 5 independent datasets, totaling 956 participants (514 FEP and 444 within-site matched controls). The performance was assessed via nested cross-validation (CV) and cross-site CV. Accuracies ranged from 50% to 70% for surfaced-based features; from 50% to 63% for GMV; and from 51% to 68% for VBCT. The best accuracies (70%) were achieved when DL was applied to surface-based features; however, these models generalized poorly to other sites. Findings from this study suggest that, when methodological precautions are adopted to avoid overoptimistic results, detection of individuals in the early stages of psychosis is more challenging than originally thought. In light of this, we argue that the current evidence for the diagnostic value of ML and structural neuroimaging should be reconsidered toward a more cautious interpretation.
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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.