ArticleBiological psychiatry2020
Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization: An International Machine Learning Study.
Article in Biological psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 4 of them syntheses that pooled it.
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Who cites it
61 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Advances in Schizophrenia Subtyping: A Systematic Review of Machine Learning Applications in Neuroimaging.Advances in experimental medicine and biology · 2026Pooled it
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- Shared and distinct brain fMRI response during performance of working memory tasks in adult patients with schizophrenia and major depressive disorder.Human brain mapping · 2021Pooled it
- Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping.Frontiers in psychiatry · 2021Pooled it
- Neurophysiological subtyping of schizophrenia reveals a reproducible phenotype with temporoparietal circuit disruptions.General psychiatry · 2026Article
- Altered cortical-subcortical structural connectivity underlying symptom dimensions in schizophrenia.Brain imaging and behavior · 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
- Model-based semantic distance reveals adaptive coordination of distinct cognitive systems in flexible knowledge retrieval.Nature communications · 2026Article
- Article
- A Framework for Advancing Mechanistic Neurobehavioral Biomarkers in Psychiatry.Biological psychiatry · 2026Review
- Machine-Learning Decomposition Identifies a Big Two Structure in Human Personality with Distinct Neurocognitive Profiles.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Widespread cortical thinning and subcortical alterations in mood and psychotic disorders across first-episode and chronic stages.Journal of affective disorders · 2026Article
- Intrinsic connectivity patterns of striatal subfields predict individual dimensions of psychopathology and are associated with cholinergic and serotonergic neurotransmission in schizophrenia.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Age-Dependent Corpus Callosum Thickness Abnormalities and Clinical Implications in Treatment-Naïve First-Episode Schizophrenia.Alpha psychiatry · 2026Article
- Functional heterogeneity in non-suicidal self-injury across psychiatric disorders: neural and psychosocial correlates.Translational psychiatry · 2026Article
- Investigating biotypes and neural characteristics among anxiety, depression, and comorbidity via a novel noisy label learning method.Psychoradiology · 2026Article
- Revealing multiple biological subtypes of schizophrenia through a data-driven approach.Journal of translational medicine · 2025Article
- Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application.BMC psychiatry · 2025Article
- COSMIN systematic review and meta-analysis of the measurement properties of the Positive and Negative Syndrome Scale (PANSS).EClinicalMedicine · 2025Article
- COSMIN review of the PANSS Marder factor solution and other factor models in people with schizophrenia.Schizophrenia (Heidelberg, Germany) · 2025Article
1 more citing papers are in PubMed but not listed here.
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25 authors.
Funding
Abstract
backgroundDisentangling psychopathological heterogeneity in schizophrenia is challenging, and previous results remain inconclusive. We employed advanced machine learning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scale and used it to identify psychopathological subtypes as well as their neurobiological differentiations.
methodsPositive and Negative Syndrome Scale data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients; 586 followed up after 1.35 ± 0.70 years) were used for learning the factor structure by an orthonormal projective non-negative factorization. An international sample, pooled from 9 medical centers across Europe, the United States, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional magnetic resonance imaging connectivity patterns.
resultsA 4-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original Positive and Negative Syndrome Scale subscales and previously proposed factor models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventromedial frontal cortex, temporoparietal junction, and precuneus.
conclusionsMachine learning applied to multisite data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia.
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