ArticleNeuropsychopharmacology : official publication of the American College of Neuropsychopharmacology2021
Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning.
Article in Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Integrating Neuroimaging and Machine Learning to Predict Mental Disorder Outcomes: A Systematic Review.Advances in experimental medicine and biology · 2026Pooled it
- Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping.Frontiers in psychiatry · 2021Pooled it
- Polygenic risk meets early trauma: toward a neuropsychodevelopmental model of schizophrenia.Journal of neural transmission (Vienna, Austria : 1996) · 2026Article
- Polygenic associations with phenotypic classes across the psychosis-affective spectrum.medRxiv : the preprint server for health sciences · 2026Article
- Beyond DSM Categories: Criteria for Biologically Valid Disease Axes in Psychiatry.Journal of clinical medicine · 2026Review
- Identification of distinct clinical phenotypes and their neurobiological signatures in stress-exposed individuals: A multimodal machine learning approach.European psychiatry : the journal of the Association of European Psychiatrists · 2026Article
- Beyond data and technology: the need for new thinking to enable the era of precision prevention.BMC medicine · 2026Review
- Biochemical and brain heterogeneity characterizes psychiatric and non-psychiatric illness.Nature communications · 2026Article
- Deep learning-based stratification of Schizophrenia Spectrum Disorder from real-world data reveals distinct profiles of common and rare variant genetic signal.medRxiv : the preprint server for health sciences · 2026Article
- A cautionary tale for AI and machine learning in psychiatry.Translational psychiatry · 2026Review
- Transdiagnostic types of formal thought disorder and their association with gray matter brain structure: a model-based cluster analytic approach.Molecular psychiatry · 2025Article
- Review
- Protein associations and protein-metabolite interactions with depressive symptoms and the p-factor.Translational psychiatry · 2025Article
- Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.Schizophrenia bulletin · 2025Review
- Psychiatry in the age of AI: transforming theory, practice, and medical education.Frontiers in public health · 2025Review
- The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research.Biological psychiatry global open science · 2025Review
- Analyzing service descriptors and patients' clinical characteristics may help understand heterogeneity in long-term trajectory of patients with schizophrenia, bipolar and major depressive disorder.PLOS mental health · 2025Article
- Associations between antagonistic SNPs for neuropsychiatric disorders and human brain structure.Translational psychiatry · 2024Article
- The interplay between polygenic score for tumor necrosis factor-α, brain structural connectivity, and processing speed in major depression.Molecular psychiatry · 2024Article
- Individual deviations from normative electroencephalographic connectivity predict antidepressant response.Journal of affective disorders · 2024Article
Corrections and comments
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Authors and funding
30 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.
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