Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Center for the Neurobiology of Learning and Memory University of California Irvine 309 Qureshey Research Lab Irvine CA 92697 USA

Centre for Addiction and Mental Health Toronto Canada

Centre for Mental Health and Brain Sciences School of Health Sciences Swinburne University Melbourne Australia

Centro de Investigación Biomédica en Red de Salud Mental Instituto de Salud Carlos 3 Spain

Chinese Institute for Brain Research Beijing PR China

Clinical Translational Neuroscience Laboratory Department of Psychiatry and Human Behavior University of California Irvine Irvine Hall room 109 Irvine CA 92697 3950 USA

Department of Advanced Biomedical Sciences University Federico 2 Naples Italy

Department of Clinical Psychology 4th Military Medical University Xi'an PR China

Department of Computer Science University of Warwick Coventry CV4 7AL UK

Department of Human Genetics and South Texas Diabetes and Obesity Institute School of Medicine University of Texas of the Rio Grande Valley Brownsville TX USA

Department of MRI The 1st Affiliated Hospital of Zhengzhou University Zhengzhou China

Department of Neurology Huashan Hospital Fudan University Shanghai China

Department of Neurology Jena University Hospital Jena Germany

Department of Pathology of Mental Diseases National Institute of Mental Health National Center of Neurology and Psychiatry Kodaira 187 8553 Japan

Department of Pediatrics University of California Irvine Irvine California USA

Department of Psychiatry and Behavioral Sciences University of Minnesota Minneapolis MN USA

Department of Psychiatry and Human Behavior University of California Irvine Irvine California USA

Department of Psychiatry and Psychotherapie and Center for Brain Behavior and Metabolism Lübeck University Lübeck Germany

Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany

Department of Psychiatry and Psychotherapy Philipps Universität Marburg Rudolf Bultmann Str 8 35039 Marburg Germany

Department of Psychiatry Boston Children's Hospital Harvard Medical School Boston MA USA

Department of Psychiatry Division of Clinical Medicine Institute of Medicine University of Tsukuba Tsukuba 305 8575 Japan

Department of Psychiatry Jeonbuk National University Hospital Jeonju Korea

Department of Psychiatry Jeonbuk National University Medical School Jeonju Korea

Department of Psychiatry Psychotherapy and Psychosomatics Psychiatric Hospital University of Zurich Switzerland

Department of Psychiatry Psychotherapy and Psychosomatics Psychiatric University Hospital Zurich Switzerland

Department of Psychiatry Taipei Veterans General Hospital Taipei Taiwan

Department of Psychiatry Temerty Faculty of Medicine University of Toronto Toronto Canada

Department of Psychology University of Minnesota Minneapolis MN USA

Department of Psychology University of Oslo Oslo Norway

Division of Adult Psychiatry Department of Psychiatry University Hospitals of Geneva Switzerland

Douglas Mental Health University Institute Department of Psychiatry McGill University Montréal Canada

Ege University Institute of Health Sciences Department of Neuroscience Izmir Turkey

Ege University School of Medicine Department of Psychiatry SoCAT Lab Izmir Turkey

Experimental Psychopathology and Psychotherapy Department of Psychology University of Zurich Switzerland

Faculty of Electrical Engineering Czech Technical University Prague Prague Czech Republic

FIDMAG Germanes Hospitalàries Research Foundation Barcelona 08035 Spain

Fudan ISTBI ZJNU Algorithm Centre for Brain Inspired Intelligence Zhejiang Normal University Jinhua China

German Center for Mental Health Site Jena Magdeburg Halle Germany

High Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province Center for Information in Medicine University of Electronic Science and Technology of China Chengdu China

Imaging Genetics Center Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Los Angeles CA USA

Institute for Translational Psychiatry University of Münster Münster Germany

Institute for Transnational Psychiatry and Otto Creutzfeldt Center for Behavioral and Cognitive Neuroscience University of Münster Münster Germany

Institute of Computer Science Czech Academy of Sciences Prague Czech Republic

Institute of Neuroscience National Yang Ming Chiao Tung University Taipei Taiwan

Institute of Science and Technology for Brain Inspired Intelligence Fudan University Shanghai China

Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence Ministry of Education Shanghai China

KG Jebsen Centre for Neurodevelopmental Disorders University of Oslo and Oslo University Hospital Oslo Norway

Lee Kong Chian School of Medicine Nanyang Technological University Singapore

Melbourne Neuropsychiatry Centre Department of Psychiatry University of Melbourne Melbourne Australia

Minneapolis VA Medical Center University of Minnesota Minneapolis MN USA

MOE Frontiers Center for Brain Science Fudan University Shanghai China

MR Unit Department of Diagnostic and Interventional Radiology Institute for Clinical and Experimental Medicine Prague Czech Republic

National Clinical Research Center for Mental Disorders Department of Psychiatry The 2nd Xiangya Hospital of Central South University Changsha Hunan PR China

National Institute of Mental Health Klecany Czech Republic

Neuropsychiatry Laboratory Department of Clinical Neuroscience and Neurorehabilitation IRCCS Santa Lucia Foundation Rome Italy

Neuroscience Center Zurich University of Zurich and Swiss Federal Institute of Technology Zurich Zurich Switzerland

NORMENT Centre Division of Mental Health and Addiction Oslo University Hospital and Institute of Clinical Medicine University of Oslo Oslo Norway

Olin Neuropsychiatry Research Center Institute of Living Hartford CT USA

Peking University Sixth Hospital Peking University Institute of Mental Health NHC Key Laboratory of Mental Health Beijing PR China

PKU IDG McGovern Institute for Brain Research Peking University Beijing PR China

Psychiatric Hospital University of Zurich Zurich Switzerland

Psychiatry and Behavioral Health Ohio State Wexner Medical Center Columbus OH USA

Research Institute of Clinical Medicine of Jeonbuk National University Biomedical Research Institute of Jeonbuk National University Hospital Jeonju Korea

Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu China

School of Clinical Medicine University of New South Wales Sydney Australia

School of Data Science Fudan University Shanghai China

School of Psychology University of New South Wales Sydney Australia

Section of Psychiatry Department of Neuroscience University Federico 2 Naples Italy

Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine Shanghai China

Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center Shanghai China

The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of life Science and technology University of Electronic Science and Technology of China Chengdu China

Tri institutional Center for Translational Research in Neuroimaging and Data Science [Georgia State University Georgia Institute of Technology Emory University] Atlanta GA USA

West Region Institute of Mental Health Singapore

Yong Loo Lin School of Medicine National University of Singapore Singapore

Zhangjiang Fudan International Innovation Center Shanghai China

Zobrazit více v PubMed

The L., ICD-11: a brave attempt at classifying a new world. The Lancet, 2018. 391(10139): p. 2476. PubMed

Oren O., Gersh B.J., and Bhatt D.L., Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints. Lancet Digit Health, 2020. 2(9): p. e486–e488. PubMed

Rajpurkar P., et al., AI in health and medicine. Nat Med, 2022. 28(1): p. 31–38. PubMed

Organization, W.H., The global burden of disease: 2004 update. 2008: World Health Organization.

Howes O.D. and Onwordi E.C., The synaptic hypothesis of schizophrenia version III: a master mechanism. Mol Psychiatry, 2023. PubMed PMC

McCutcheon R.A., Krystal J.H., and Howes O.D., Dopamine and glutamate in schizophrenia: biology, symptoms and treatment. World Psychiatry, 2020. 19(1): p. 15–33. PubMed PMC

Wolfers T., et al., Mapping the Heterogeneous Phenotype of Schizophrenia and Bipolar Disorder Using Normative Models. JAMA Psychiatry, 2018. 75(11): p. 1146–1155. PubMed PMC

Fusar-Poli P., et al., Heterogeneity of Psychosis Risk Within Individuals at Clinical High Risk: A Meta-analytical Stratification. JAMA Psychiatry, 2016. 73(2): p. 113–20. PubMed

McCutcheon R.A., et al., The efficacy and heterogeneity of antipsychotic response in schizophrenia: A meta-analysis. Mol Psychiatry, 2021. 26(4): p. 1310–1320. PubMed PMC

Collado-Torres L., et al., Regional Heterogeneity in Gene Expression, Regulation, and Coherence in the Frontal Cortex and Hippocampus across Development and Schizophrenia. Neuron, 2019. 103(2): p. 203–216 e8. PubMed PMC

Brugger S.P. and Howes O.D., Heterogeneity and Homogeneity of Regional Brain Structure in Schizophrenia: A Meta-analysis. JAMA Psychiatry, 2017. 74(11): p. 1104–1111. PubMed PMC

Braff D.L., et al., Lack of use in the literature from the last 20 years supports dropping traditional schizophrenia subtypes from DSM-5 and ICD-11. Schizophr Bull, 2013. 39(4): p. 751–3. PubMed PMC

Wen J., et al., Multi-scale semi-supervised clustering of brain images: Deriving disease subtypes. Med Image Anal, 2022. 75: p. 102304. PubMed PMC

Lalousis P.A., et al., Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach. Schizophr Bull, 2021. 47(4): p. 1130–1140. PubMed PMC

Chand G.B., et al., Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain, 2020. 143(3): p. 1027–1038. PubMed PMC

Yang Z., et al., A deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure. Nat Commun, 2021. 12(1): p. 7065. PubMed PMC

Dwyer D.B., et al., Brain Subtyping Enhances The Neuroanatomical Discrimination of Schizophrenia. Schizophr Bull, 2018. 44(5): p. 1060–1069. PubMed PMC

Luo C., et al., Subtypes of schizophrenia identified by multi-omic measures associated with dysregulated immune function. Mol Psychiatry, 2021. 26(11): p. 6926–6936. PubMed

Young A.L., et al., Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat Commun, 2018. 9(1): p. 4273. PubMed PMC

Vogel J.W., et al., Four distinct trajectories of tau deposition identified in Alzheimer’s disease. Nat Med, 2021. 27(5): p. 871–881. PubMed PMC

Young A.L., et al., Characterizing the Clinical Features and Atrophy Patterns of MAPT-Related Frontotemporal Dementia With Disease Progression Modeling. Neurology, 2021. 97(9): p. e941–e952. PubMed PMC

Jiang Y., et al., Neuroimaging biomarkers define neurophysiological subtypes with distinct trajectories in schizophrenia. Nature Mental Health, 2023. 1(3): p. 186–199.

van Erp T.G.M., et al., Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium. Biol Psychiatry, 2018. 84(9): p. 644–654. PubMed PMC

van Erp T.G., et al., Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol Psychiatry, 2016. 21(4): p. 585. PubMed PMC

Okada N., et al., Subcortical volumetric alterations in four major psychiatric disorders: a mega-analysis study of 5604 subjects and a volumetric data-driven approach for classification. Mol Psychiatry, 2023. PubMed PMC

Koshiyama D., et al., White matter microstructural alterations across four major psychiatric disorders: mega-analysis study in 2937 individuals. Mol Psychiatry, 2020. 25(4): p. 883–895. PubMed PMC

Howes O.D., et al., Neuroimaging in schizophrenia: an overview of findings and their implications for synaptic changes. Neuropsychopharmacology, 2023. 48(1): p. 151–167. PubMed PMC

Alnaes D., et al., Brain Heterogeneity in Schizophrenia and Its Association With Polygenic Risk. JAMA Psychiatry, 2019. 76(7): p. 739–748. PubMed PMC

Howes O.D. and Kapur S., A neurobiological hypothesis for the classification of schizophrenia: type A (hyperdopaminergic) and type B (normodopaminergic). Br J Psychiatry, 2014. 205(1): p. 1–3. PubMed

Jiang Y., et al., Progressive Reduction in Gray Matter in Patients with Schizophrenia Assessed with MR Imaging by Using Causal Network Analysis. Radiology, 2018. 287(2): p. 729. PubMed

Kirschner M., et al., Orbitofrontal-Striatal Structural Alterations Linked to Negative Symptoms at Different Stages of the Schizophrenia Spectrum. Schizophr Bull, 2021. 47(3): p. 849–863. PubMed PMC

Thompson P.M., et al., Mapping adolescent brain change reveals dynamic wave of accelerated gray matter loss in very early-onset schizophrenia. Proc Natl Acad Sci U S A, 2001. 98(20): p. 11650–5. PubMed PMC

Thompson P.M., et al., Time-lapse mapping of cortical changes in schizophrenia with different treatments. Cereb Cortex, 2009. 19(5): p. 1107–23. PubMed PMC

Fillman S.G., et al., Elevated peripheral cytokines characterize a subgroup of people with schizophrenia displaying poor verbal fluency and reduced Broca’s area volume. Mol Psychiatry, 2016. 21(8): p. 1090–8. PubMed PMC

Crow T.J., Is schizophrenia the price that Homo sapiens pays for language? Schizophr Res, 1997. 28(2–3): p. 127–41. PubMed

Palaniyappan L. and Liddle P.F., Does the salience network play a cardinal role in psychosis? An emerging hypothesis of insular dysfunction. J Psychiatry Neurosci, 2012. 37(1): p. 17–27. PubMed PMC

Del Re E.C., et al., Baseline Cortical Thickness Reductions in Clinical High Risk for Psychosis: Brain Regions Associated with Conversion to Psychosis Versus Non-Conversion as Assessed at One-Year Follow-Up in the Shanghai-At-Risk-for-Psychosis (SHARP) Study. Schizophr Bull, 2021. 47(2): p. 562–574. PubMed PMC

Pantelis C., et al., Neuroanatomical abnormalities before and after onset of psychosis: a cross-sectional and longitudinal MRI comparison. Lancet, 2003. 361(9354): p. 281–8. PubMed

Slifstein M., et al., Deficits in prefrontal cortical and extrastriatal dopamine release in schizophrenia: a positron emission tomographic functional magnetic resonance imaging study. JAMA Psychiatry, 2015. 72(4): p. 316–24. PubMed PMC

Steen R.G., et al., Brain volume in first-episode schizophrenia: systematic review and meta-analysis of magnetic resonance imaging studies. Br J Psychiatry, 2006. 188: p. 510–8. PubMed

van Erp T.G., et al., Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol Psychiatry, 2016. 21(4): p. 547–53. PubMed PMC

Balu D.T., et al., Multiple risk pathways for schizophrenia converge in serine racemase knockout mice, a mouse model of NMDA receptor hypofunction. Proc Natl Acad Sci U S A, 2013. 110(26): p. E2400–9. PubMed PMC

McCutcheon R.A., Reis Marques T., and Howes O.D., Schizophrenia-An Overview. JAMA Psychiatry, 2020. 77(2): p. 201–210. PubMed

Brugger S.P., et al., Heterogeneity of Striatal Dopamine Function in Schizophrenia: Meta-analysis of Variance. Biol Psychiatry, 2020. 87(3): p. 215–224. PubMed

Banaj N., et al., Cortical morphology in patients with the deficit and non-deficit syndrome of schizophrenia: a worldwide meta- and mega-analyses. Mol Psychiatry, 2023. PubMed PMC

Mouchlianitis E., McCutcheon R., and Howes O.D., Brain-imaging studies of treatment-resistant schizophrenia: a systematic review. Lancet Psychiatry, 2016. 3(5): p. 451–63. PubMed PMC

Jiang Y., et al., Structural and Functional MRI Brain Changes in Patients with Schizophrenia Following Electroconvulsive Therapy: A Systematic Review. Curr Neuropharmacol, 2022. 20(6): p. 1241–1252. PubMed PMC

Wang J., et al., ECT-induced brain plasticity correlates with positive symptom improvement in schizophrenia by voxel-based morphometry analysis of grey matter. Brain Stimul, 2019. 12(2): p. 319–328. PubMed

Jiang Y., et al., Insular changes induced by electroconvulsive therapy response to symptom improvements in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry, 2019. 89: p. 254–262. PubMed

Ho B.C., et al., Long-term antipsychotic treatment and brain volumes: a longitudinal study of first-episode schizophrenia. Arch Gen Psychiatry, 2011. 68(2): p. 128–37. PubMed PMC

Lewandowski K.E., et al., Neuroprogression across the Early Course of Psychosis. J Psychiatr Brain Sci, 2020. 5. PubMed PMC

Tanaka S.C., et al., A multi-site, multi-disorder resting-state magnetic resonance image database. Sci Data, 2021. 8(1): p. 227. PubMed PMC

Keator D.B., et al., The Function Biomedical Informatics Research Network Data Repository. Neuroimage, 2016. 124(Pt B): p. 1074–1079. PubMed PMC

Gollub R.L., et al., The MCIC collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia. Neuroinformatics, 2013. 11(3): p. 367–88. PubMed PMC

Alpert K., et al., The Northwestern University Neuroimaging Data Archive (NUNDA). Neuroimage, 2016. 124(Pt B): p. 1131–1136. PubMed PMC

Kogan A., et al., Northwestern University schizophrenia data sharing for SchizConnect: A longitudinal dataset for large-scale integration. Neuroimage, 2016. 124(Pt B): p. 1196–1201. PubMed PMC

Poldrack R.A., et al., A phenome-wide examination of neural and cognitive function. Sci Data, 2016. 3: p. 160110. PubMed PMC

Repovs G. and Barch D.M., Working memory related brain network connectivity in individuals with schizophrenia and their siblings. Front Hum Neurosci, 2012. 6: p. 137. PubMed PMC

Soler-Vidal J., et al., Brain correlates of speech perception in schizophrenia patients with and without auditory hallucinations. PLOS ONE, 2022. 17(12): p. e0276975. PubMed PMC

Kay S.R., Fiszbein A., and Opler L.A., The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull, 1987. 13(2): p. 261–76. PubMed

Lindenmayer J.P., Bernstein-Hyman R., and Grochowski S., Five-factor model of schizophrenia. Initial validation. J Nerv Ment Dis, 1994. 182(11): p. 631–8. PubMed

Rolls E.T., et al., Automated anatomical labelling atlas 3. Neuroimage, 2020. 206: p. 116189. PubMed

Pomponio R., et al., Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. Neuroimage, 2020. 208: p. 116450. PubMed PMC

Desikan R.S., et al., An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 2006. 31(3): p. 968–80. PubMed

Iglesias J.E., et al., A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI. Neuroimage, 2015. 115: p. 117–37. PubMed PMC

Saygin Z.M., et al., High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas. Neuroimage, 2017. 155: p. 370–382. PubMed PMC

Iglesias J.E., et al., A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology. Neuroimage, 2018. 183: p. 314–326. PubMed PMC

Iglesias J.E., et al., Bayesian segmentation of brainstem structures in MRI. Neuroimage, 2015. 113: p. 184–95. PubMed PMC