ArticleMolecular psychiatry2023
Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium.
Article in Molecular psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Estimating Multimodal Structural Brain Variability in Schizophrenia Spectrum Disorders: A Worldwide ENIGMA Study.The American journal of psychiatry · 2025Pooled it
- Connectomic signatures advance the understandings of transdiagnostic heterogeneity in psychosis.Communications medicine · 2026Review
- Mapping the psychosis spectrum - imaging neurosubtypes from multiscale functional network connectivity.Molecular psychiatry · 2026Article
- Machine learning-derived Alzheimer's disease dimensions: neuroanatomical, cognitive, clinical, functional, and genetic risk heterogeneity signatures.medRxiv : the preprint server for health sciences · 2026Article
- Neuroimaging-Based Subgroups in Schizophrenia: A Critical Appraisal of Clustering Studies.Biological psychiatry global open science · 2026Review
- Subtyping schizophrenia via machine learning by using structural neuroimaging.Translational psychiatry · 2025Review
- Reduced brain structural similarity is associated with maturation, neurobiological features, and clinical status in schizophrenia.Nature communications · 2025Article
- Practical AI application in psychiatry: historical review and future directions.Molecular psychiatry · 2025Review
- Longitudinal Development of Neurocognitive Functioning and Gray Matter Volume in Youths With Recurrent Psychosis Spectrum Symptoms.Schizophrenia bulletin · 2025Article
- Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learning.Npj mental health research · 2025Article
- Neurobiology-based cognitive biotypes using multi-scale intrinsic connectivity networks in psychotic disorders.Schizophrenia (Heidelberg, Germany) · 2025Article
- Characterizing multivariate regional hubs for schizophrenia classification, sex differences, and brain age estimation using explainable AI.medRxiv : the preprint server for health sciences · 2025Article
- Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity.Schizophrenia (Heidelberg, Germany) · 2025Article
- Corticolimbic circuitry as a druggable target in schizophrenia spectrum disorders: a narrative review.Translational psychiatry · 2025Review
- Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.Biological psychiatry · 2024Review
- Brain aging patterns in a large and diverse cohort of 49,482 individuals.Nature medicine · 2024Article
- Neurobiology-based Cognitive Biotypes Using Multi-scale Intrinsic Connectivity Networks in Psychotic Disorders.medRxiv : the preprint server for health sciences · 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
- Dysregulated Salience Network Control over Default-Mode and Central-Executive Networks in Schizophrenia Revealed Using Stochastic Dynamical Causal Modeling.Brain connectivity · 2024Article
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42 authors.
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Abstract
Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.
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