Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm
Language English Country England, Great Britain Media electronic
Document type Journal Article
PubMed
39013848
PubMed Central
PMC11252381
DOI
10.1038/s41467-024-50267-3
PII: 10.1038/s41467-024-50267-3
Knihovny.cz E-resources
- MeSH
- Algorithms * MeSH
- Adult MeSH
- Hippocampus diagnostic imaging pathology MeSH
- Middle Aged MeSH
- Humans MeSH
- Magnetic Resonance Imaging * MeSH
- Brain diagnostic imaging pathology MeSH
- Neuroimaging MeSH
- Cross-Sectional Studies MeSH
- Reproducibility of Results MeSH
- Schizophrenia * diagnostic imaging pathology MeSH
- Gray Matter * diagnostic imaging pathology MeSH
- Machine Learning MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Europe MeSH
- North America MeSH
Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.
Centre for Addiction and Mental Health TO Canada
Centro de Investigación Biomédica en Red de Salud Mental Instituto de Salud Carlos 3 Madrid Spain
Chinese Institute for Brain Research Beijing PR China
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 UK
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 Pediatrics University of California Irvine Irvine CA USA
Department of Psychiatry and Behavioral Sciences University of Minnesota Minneapolis MN USA
Department of Psychiatry and Human Behavior University of California Irvine Irvine CA USA
Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany
Department of Psychiatry Boston Children's Hospital Harvard Medical School Boston MA USA
Department of Psychiatry Jeonbuk National University Hospital Jeonju Korea
Department of Psychiatry Jeonbuk National University Medical School Jeonju Korea
Department of Psychiatry Taipei Veterans General Hospital Taipei Taiwan
Department of Psychiatry Temerty Faculty of Medicine University of Toronto TO Canada
Department of Psychology University of Minnesota Minneapolis MN USA
Department of Psychology University of Oslo Oslo Norway
Ege University Institute of Health Sciences Department of Neuroscience Izmir Turkey
Ege University School of Medicine Department of Psychiatry SoCAT Lab Izmir Turkey
Faculty of Electrical Engineering Czech Technical University Prague Prague Czech Republic
FIDMAG Germanes Hospitalàries Research Foundation Barcelona Spain
German Center for Mental Health Site Jena Magdeburg Halle Magdeburg Germany
Institute for Translational Neuroscience University of Münster Münster Germany
Institute for Translational Psychiatry 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
Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore
Melbourne Neuropsychiatry Centre Department of Psychiatry University of Melbourne MEL Australia
Minneapolis VA Medical Center University of Minnesota Minneapolis MN USA
MOE Frontiers Center for Brain Science Fudan University Shanghai China
National Institute of Mental Health Klecany Czech Republic
Olin Neuropsychiatry Research Center Institute of Living Hartford CT USA
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 Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu China
School of Clinical Medicine University of New South Wales SYD Australia
School of Data Science Fudan University Shanghai China
School of Psychology University of New South Wales SYD Australia
Section of Psychiatry Department of Neuroscience University Federico 2 Naples Italy
West Region Institute of Mental Health Singapore Singapore
Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore
Zhangjiang Fudan International Innovation Center Shanghai China
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Organization W. H. The Global Burden Of Disease: 2004 Update. (World Health Organization, 2008).
Howes OD, Onwordi EC. The synaptic hypothesis of schizophrenia version III: a master mechanism. Mol. Psychiatry. 2023;28:1843–1856. doi: 10.1038/s41380-023-02043-w. PubMed DOI PMC
McCutcheon RA, Krystal JH, Howes OD. Dopamine and glutamate in schizophrenia: biology, symptoms and treatment. World Psychiatry. 2020;19:15–33. doi: 10.1002/wps.20693. PubMed DOI PMC
Wolfers T, et al. Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA Psychiatry. 2018;75:1146–1155. doi: 10.1001/jamapsychiatry.2018.2467. PubMed DOI PMC
Fusar-Poli P, et al. Heterogeneity of psychosis risk within individuals at clinical high risk: a meta-analytical stratification. JAMA Psychiatry. 2016;73:113–120. doi: 10.1001/jamapsychiatry.2015.2324. PubMed DOI
McCutcheon RA, et al. The efficacy and heterogeneity of antipsychotic response in schizophrenia: A meta-analysis. Mol. Psychiatry. 2021;26:1310–1320. doi: 10.1038/s41380-019-0502-5. PubMed DOI 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;103:203–216 e208. doi: 10.1016/j.neuron.2019.05.013. PubMed DOI PMC
Brugger SP, Howes OD. Heterogeneity and homogeneity of regional brain structure in schizophrenia: a meta-analysis. JAMA Psychiatry. 2017;74:1104–1111. doi: 10.1001/jamapsychiatry.2017.2663. PubMed DOI PMC
Braff DL, Ryan J, Rissling AJ, Carpenter WT. 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;39:751–753. doi: 10.1093/schbul/sbt068. PubMed DOI PMC
The L. ICD-11: a brave attempt at classifying a new world. Lancet. 2018;391:2476. doi: 10.1016/S0140-6736(18)31370-9. PubMed DOI
Oren O, Gersh BJ, Bhatt DL. Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints. Lancet Digit Health. 2020;2:e486–e488. doi: 10.1016/S2589-7500(20)30160-6. PubMed DOI
Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat. Med. 2022;28:31–38. doi: 10.1038/s41591-021-01614-0. PubMed DOI
Wen J, et al. Multi-scale semi-supervised clustering of brain images: deriving disease subtypes. Med Image Anal. 2022;75:102304. doi: 10.1016/j.media.2021.102304. PubMed DOI PMC
Lalousis PA, et al. Heterogeneity and classification of recent onset psychosis and depression: a multimodal machine learning approach. Schizophr. Bull. 2021;47:1130–1140. doi: 10.1093/schbul/sbaa185. PubMed DOI PMC
Chand GB, et al. Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain. 2020;143:1027–1038. doi: 10.1093/brain/awaa025. PubMed DOI PMC
Yang Z, et al. A deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure. Nat. Commun. 2021;12:7065. doi: 10.1038/s41467-021-26703-z. PubMed DOI PMC
Dwyer DB, et al. Brain subtyping enhances the neuroanatomical discrimination of schizophrenia. Schizophr. Bull. 2018;44:1060–1069. doi: 10.1093/schbul/sby008. PubMed DOI PMC
Luo C, et al. Subtypes of schizophrenia identified by multi-omic measures associated with dysregulated immune function. Mol. Psychiatry. 2021;26:6926–6936. doi: 10.1038/s41380-021-01308-6. PubMed DOI
Young AL, et al. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat. Commun. 2018;9:4273. doi: 10.1038/s41467-018-05892-0. PubMed DOI PMC
Vogel JW, et al. Four distinct trajectories of tau deposition identified in Alzheimer’s disease. Nat. Med. 2021;27:871–881. doi: 10.1038/s41591-021-01309-6. PubMed DOI PMC
Young AL, et al. Characterizing the clinical features and atrophy patterns of MAPT-related frontotemporal dementia with disease progression modeling. Neurology. 2021;97:e941–e952. doi: 10.1212/WNL.0000000000012410. PubMed DOI PMC
Jiang Y, et al. Neuroimaging biomarkers define neurophysiological subtypes with distinct trajectories in schizophrenia. Nat. Ment. Health. 2023;1:186–199. doi: 10.1038/s44220-023-00024-0. DOI
Jiang Y, et al. Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images. Nat. Commun. 2024;15:2221. doi: 10.1038/s41467-024-46629-6. PubMed DOI PMC
van Erp TGM, 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;84:644–654. doi: 10.1016/j.biopsych.2018.04.023. PubMed DOI PMC
van Erp TG, et al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol. Psychiatry. 2016;21:585. doi: 10.1038/mp.2015.118. PubMed DOI 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;28:5206–5216. doi: 10.1038/s41380-023-02141-9. PubMed DOI PMC
Koshiyama D, et al. White matter microstructural alterations across four major psychiatric disorders: mega-analysis study in 2937 individuals. Mol. Psychiatry. 2020;25:883–895. doi: 10.1038/s41380-019-0553-7. PubMed DOI PMC
Howes OD, Cummings C, Chapman GE, Shatalina E. Neuroimaging in schizophrenia: an overview of findings and their implications for synaptic changes. Neuropsychopharmacology. 2023;48:151–167. doi: 10.1038/s41386-022-01426-x. PubMed DOI PMC
Alnaes D, et al. Brain heterogeneity in schizophrenia and its association with polygenic risk. JAMA Psychiatry. 2019;76:739–748. doi: 10.1001/jamapsychiatry.2019.0257. PubMed DOI PMC
Howes OD, Kapur S. A neurobiological hypothesis for the classification of schizophrenia: type A (hyperdopaminergic) and type B (normodopaminergic) Br. J. Psychiatry. 2014;205:1–3. doi: 10.1192/bjp.bp.113.138578. PubMed DOI
Jiang Y, et al. Progressive reduction in gray matter in patients with schizophrenia assessed with mr imaging by using causal network analysis. Radiology. 2018;287:729. doi: 10.1148/radiol.2018184005. PubMed DOI
Kirschner M, et al. Orbitofrontal-striatal structural alterations linked to negative symptoms at different stages of the schizophrenia spectrum. Schizophr. Bull. 2021;47:849–863. doi: 10.1093/schbul/sbaa169. PubMed DOI PMC
Thompson PM, et al. Mapping adolescent brain change reveals dynamic wave of accelerated gray matter loss in very early-onset schizophrenia. Proc. Natl Acad. Sci. USA. 2001;98:11650–11655. doi: 10.1073/pnas.201243998. PubMed DOI PMC
Thompson PM, et al. Time-lapse mapping of cortical changes in schizophrenia with different treatments. Cereb. Cortex. 2009;19:1107–1123. doi: 10.1093/cercor/bhn152. PubMed DOI PMC
Fillman SG, 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;21:1090–1098. doi: 10.1038/mp.2015.90. PubMed DOI PMC
Crow TJ. Is schizophrenia the price that Homo sapiens pays for language? Schizophr. Res. 1997;28:127–141. doi: 10.1016/S0920-9964(97)00110-2. PubMed DOI
Palaniyappan L, Liddle PF. Does the salience network play a cardinal role in psychosis? An emerging hypothesis of insular dysfunction. J. Psychiatry Neurosci. 2012;37:17–27. doi: 10.1503/jpn.100176. PubMed DOI PMC
McGuire PK, Murray R, Shah G. Increased blood flow in Broca’s area during auditory hallucinations in schizophrenia. Lancet. 1993;342:703–706. doi: 10.1016/0140-6736(93)91707-S. PubMed DOI
Vercammen A, Knegtering H, den Boer JA, Liemburg EJ, Aleman A. Auditory hallucinations in schizophrenia are associated with reduced functional connectivity of the temporo-parietal area. Biol. Psychiatry. 2010;67:912–918. doi: 10.1016/j.biopsych.2009.11.017. PubMed DOI
Del Re EC, 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;47:562–574. doi: 10.1093/schbul/sbaa127. PubMed DOI PMC
Pantelis C, et al. Neuroanatomical abnormalities before and after onset of psychosis: a cross-sectional and longitudinal MRI comparison. Lancet. 2003;361:281–288. doi: 10.1016/S0140-6736(03)12323-9. PubMed DOI
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;72:316–324. doi: 10.1001/jamapsychiatry.2014.2414. PubMed DOI PMC
Steen RG, Mull C, McClure R, Hamer RM, Lieberman JA. Brain volume in first-episode schizophrenia: systematic review and meta-analysis of magnetic resonance imaging studies. Br. J. Psychiatry. 2006;188:510–518. doi: 10.1192/bjp.188.6.510. PubMed DOI
Balu DT, et al. Multiple risk pathways for schizophrenia converge in serine racemase knockout mice, a mouse model of NMDA receptor hypofunction. Proc. Natl Acad. Sci. USA. 2013;110:E2400–E2409. doi: 10.1073/pnas.1304308110. PubMed DOI PMC
Kahn RS, Sommer IE. The neurobiology and treatment of first-episode schizophrenia. Mol. Psychiatry. 2015;20:84–97. doi: 10.1038/mp.2014.66. PubMed DOI PMC
Vita A, De Peri L, Deste G, Barlati S, Sacchetti E. The effect of antipsychotic treatment on cortical gray matter changes in schizophrenia: does the class matter? a meta-analysis and meta-regression of longitudinal magnetic resonance imaging studies. Biol. Psychiatry. 2015;78:403–412. doi: 10.1016/j.biopsych.2015.02.008. PubMed DOI
McCutcheon RA, Reis Marques T, Howes OD. Schizophrenia-an overview. JAMA Psychiatry. 2020;77:201–210. doi: 10.1001/jamapsychiatry.2019.3360. PubMed DOI
Brugger SP, et al. Heterogeneity of Striatal Dopamine Function in Schizophrenia: Meta-analysis of Variance. Biol. Psychiatry. 2020;87:215–224. doi: 10.1016/j.biopsych.2019.07.008. PubMed DOI
Chase HW, Loriemi P, Wensing T, Eickhoff SB, Nickl-Jockschat T. Meta-analytic evidence for altered mesolimbic responses to reward in schizophrenia. Hum. Brain Mapp. 2018;39:2917–2928. doi: 10.1002/hbm.24049. PubMed DOI PMC
Koch K, et al. Functional connectivity and grey matter volume of the striatum in schizophrenia. Br. J. Psychiatry. 2014;205:204–213. doi: 10.1192/bjp.bp.113.138099. PubMed DOI
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;28:4363–4373. doi: 10.1038/s41380-023-02221-w. PubMed DOI PMC
Chand GB, et al. Schizophrenia imaging signatures and their associations with cognition, psychopathology, and genetics in the general population. Am. J. Psychiatry. 2022;179:650–660. doi: 10.1176/appi.ajp.21070686. PubMed DOI PMC
Mouchlianitis E, McCutcheon R, Howes OD. Brain-imaging studies of treatment-resistant schizophrenia: a systematic review. Lancet Psychiatry. 2016;3:451–463. doi: 10.1016/S2215-0366(15)00540-4. PubMed DOI PMC
Jiang Y, Duan M, He H, Yao D, Luo C. Structural and functional MRI brain changes in patients with schizophrenia following electroconvulsive therapy: a systematic review. Curr. Neuropharmacol. 2022;20:1241–1252. doi: 10.2174/1570159X19666210809101248. PubMed DOI 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;12:319–328. doi: 10.1016/j.brs.2018.11.006. PubMed DOI
Jiang Y, et al. Insular changes induced by electroconvulsive therapy response to symptom improvements in schizophrenia. Prog. Neuropsychopharmacol. Biol. Psychiatry. 2019;89:254–262. doi: 10.1016/j.pnpbp.2018.09.009. PubMed DOI
Ho BC, Andreasen NC, Ziebell S, Pierson R, Magnotta V. Long-term antipsychotic treatment and brain volumes: a longitudinal study of first-episode schizophrenia. Arch. Gen. Psychiatry. 2011;68:128–137. doi: 10.1001/archgenpsychiatry.2010.199. PubMed DOI PMC
Lewandowski K. E., Bouix S., Ongur D., Shenton M. E. Neuroprogression across the Early Course of Psychosis. J Psychiatr Brain Sci 5, e200002 (2020). PubMed PMC
Tanaka SC, et al. A multi-site, multi-disorder resting-state magnetic resonance image database. Sci. Data. 2021;8:227. doi: 10.1038/s41597-021-01004-8. PubMed DOI PMC
Keator DB, et al. The function biomedical informatics research network data repository. Neuroimage. 2016;124:1074–1079. doi: 10.1016/j.neuroimage.2015.09.003. PubMed DOI PMC
Gollub RL, et al. The MCIC collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia. Neuroinformatics. 2013;11:367–388. doi: 10.1007/s12021-013-9184-3. PubMed DOI PMC
Alpert K, Kogan A, Parrish T, Marcus D, Wang L. The northwestern university neuroimaging data archive (NUNDA) Neuroimage. 2016;124:1131–1136. doi: 10.1016/j.neuroimage.2015.05.060. PubMed DOI PMC
Kogan A, Alpert K, Ambite JL, Marcus DS, Wang L. Northwestern University schizophrenia data sharing for SchizConnect: A longitudinal dataset for large-scale integration. Neuroimage. 2016;124:1196–1201. doi: 10.1016/j.neuroimage.2015.06.030. PubMed DOI PMC
Poldrack RA, et al. A phenome-wide examination of neural and cognitive function. Sci. Data. 2016;3:160110. doi: 10.1038/sdata.2016.110. PubMed DOI PMC
Repovs G, Barch DM. Working memory related brain network connectivity in individuals with schizophrenia and their siblings. Front Hum. Neurosci. 2012;6:137. doi: 10.3389/fnhum.2012.00137. PubMed DOI PMC
Soler-Vidal J, et al. Brain correlates of speech perception in schizophrenia patients with and without auditory hallucinations. PLOS ONE. 2022;17:e0276975. doi: 10.1371/journal.pone.0276975. PubMed DOI PMC
Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr. Bull. 1987;13:261–276. doi: 10.1093/schbul/13.2.261. PubMed DOI
Lindenmayer JP, Bernstein-Hyman R, Grochowski S. Five-factor model of schizophrenia. Initial validation. J. Nerv. Ment. Dis. 1994;182:631–638. doi: 10.1097/00005053-199411000-00006. PubMed DOI
Rolls ET, Huang C-C, Lin C-P, Feng J, Joliot M. Automated anatomical labelling atlas 3. Neuroimage. 2020;206:116189. doi: 10.1016/j.neuroimage.2019.116189. PubMed DOI
Pomponio R, et al. Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. Neuroimage. 2020;208:116450. doi: 10.1016/j.neuroimage.2019.116450. PubMed DOI PMC
Desikan RS, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968–980. doi: 10.1016/j.neuroimage.2006.01.021. PubMed DOI
Iglesias JE, 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;115:117–137. doi: 10.1016/j.neuroimage.2015.04.042. PubMed DOI PMC
Saygin ZM, et al. High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas. Neuroimage. 2017;155:370–382. doi: 10.1016/j.neuroimage.2017.04.046. PubMed DOI PMC
Iglesias JE, et al. A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology. Neuroimage. 2018;183:314–326. doi: 10.1016/j.neuroimage.2018.08.012. PubMed DOI PMC
Iglesias JE, et al. Bayesian segmentation of brainstem structures in MRI. Neuroimage. 2015;113:184–195. doi: 10.1016/j.neuroimage.2015.02.065. PubMed DOI PMC