Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm.
Humans
Schizophrenia
/ diagnostic imaging
Male
Female
Adult
Algorithms
Magnetic Resonance Imaging
Gray Matter
/ diagnostic imaging
Machine Learning
Middle Aged
Brain
/ diagnostic imaging
Cross-Sectional Studies
Europe
Neuroimaging
Reproducibility of Results
North America
Hippocampus
/ diagnostic imaging
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
17 Jul 2024
17 Jul 2024
Historique:
received:
17
10
2023
accepted:
03
07
2024
medline:
17
7
2024
pubmed:
17
7
2024
entrez:
16
7
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 39013848
doi: 10.1038/s41467-024-50267-3
pii: 10.1038/s41467-024-50267-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5996Investigateurs
Andrea de Bartolomeis
(A)
Tamsyn E Van Rheenen
(TE)
Informations de copyright
© 2024. The Author(s).
Références
Organization W. H. The Global Burden Of Disease: 2004 Update. (World Health Organization, 2008).
Howes, O. D. & Onwordi, E. C. The synaptic hypothesis of schizophrenia version III: a master mechanism. Mol. Psychiatry 28, 1843–1856 (2023).
pubmed: 37041418
pmcid: 10575788
doi: 10.1038/s41380-023-02043-w
McCutcheon, R. A., Krystal, J. H. & Howes, O. D. Dopamine and glutamate in schizophrenia: biology, symptoms and treatment. World Psychiatry 19, 15–33 (2020).
pubmed: 31922684
pmcid: 6953551
doi: 10.1002/wps.20693
Wolfers, T. et al. Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA Psychiatry 75, 1146–1155 (2018).
pubmed: 30304337
pmcid: 6248110
doi: 10.1001/jamapsychiatry.2018.2467
Fusar-Poli, P. et al. Heterogeneity of psychosis risk within individuals at clinical high risk: a meta-analytical stratification. JAMA Psychiatry 73, 113–120 (2016).
pubmed: 26719911
doi: 10.1001/jamapsychiatry.2015.2324
McCutcheon, R. A. et al. The efficacy and heterogeneity of antipsychotic response in schizophrenia: A meta-analysis. Mol. Psychiatry 26, 1310–1320 (2021).
pubmed: 31471576
doi: 10.1038/s41380-019-0502-5
Collado-Torres, L. et al. Regional heterogeneity in gene expression, regulation, and coherence in the frontal cortex and hippocampus across development and schizophrenia. Neuron 103, 203–216 e208 (2019).
pubmed: 31174959
pmcid: 7000204
doi: 10.1016/j.neuron.2019.05.013
Brugger, S. P. & Howes, O. D. Heterogeneity and homogeneity of regional brain structure in schizophrenia: a meta-analysis. JAMA Psychiatry 74, 1104–1111 (2017).
pubmed: 28973084
pmcid: 5669456
doi: 10.1001/jamapsychiatry.2017.2663
Braff, D. L., Ryan, J., Rissling, A. J. & Carpenter, W. T. Lack of use in the literature from the last 20 years supports dropping traditional schizophrenia subtypes from DSM-5 and ICD-11. Schizophr. Bull. 39, 751–753 (2013).
pubmed: 23674819
pmcid: 3686462
doi: 10.1093/schbul/sbt068
The, L. ICD-11: a brave attempt at classifying a new world. Lancet 391, 2476 (2018).
doi: 10.1016/S0140-6736(18)31370-9
Oren, O., Gersh, B. J. & Bhatt, D. L. Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints. Lancet Digit Health 2, e486–e488 (2020).
pubmed: 33328116
doi: 10.1016/S2589-7500(20)30160-6
Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31–38 (2022).
pubmed: 35058619
doi: 10.1038/s41591-021-01614-0
Wen, J. et al. Multi-scale semi-supervised clustering of brain images: deriving disease subtypes. Med Image Anal. 75, 102304 (2022).
pubmed: 34818611
doi: 10.1016/j.media.2021.102304
Lalousis, P. A. et al. Heterogeneity and classification of recent onset psychosis and depression: a multimodal machine learning approach. Schizophr. Bull. 47, 1130–1140 (2021).
pubmed: 33543752
pmcid: 8266654
doi: 10.1093/schbul/sbaa185
Chand, G. B. et al. Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain 143, 1027–1038 (2020).
pubmed: 32103250
pmcid: 7089665
doi: 10.1093/brain/awaa025
Yang, Z. et al. A deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure. Nat. Commun. 12, 7065 (2021).
pubmed: 34862382
pmcid: 8642554
doi: 10.1038/s41467-021-26703-z
Dwyer, D. B. et al. Brain subtyping enhances the neuroanatomical discrimination of schizophrenia. Schizophr. Bull. 44, 1060–1069 (2018).
pubmed: 29529270
pmcid: 6101481
doi: 10.1093/schbul/sby008
Luo, C. et al. Subtypes of schizophrenia identified by multi-omic measures associated with dysregulated immune function. Mol. Psychiatry 26, 6926–6936 (2021).
pubmed: 34588622
doi: 10.1038/s41380-021-01308-6
Young, A. L. et al. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat. Commun. 9, 4273 (2018).
pubmed: 30323170
pmcid: 6189176
doi: 10.1038/s41467-018-05892-0
Vogel, J. W. et al. Four distinct trajectories of tau deposition identified in Alzheimer’s disease. Nat. Med. 27, 871–881 (2021).
pubmed: 33927414
pmcid: 8686688
doi: 10.1038/s41591-021-01309-6
Young, A. L. et al. Characterizing the clinical features and atrophy patterns of MAPT-related frontotemporal dementia with disease progression modeling. Neurology 97, e941–e952 (2021).
pubmed: 34158384
pmcid: 8408507
doi: 10.1212/WNL.0000000000012410
Jiang, Y. et al. Neuroimaging biomarkers define neurophysiological subtypes with distinct trajectories in schizophrenia. Nat. Ment. Health 1, 186–199 (2023).
doi: 10.1038/s44220-023-00024-0
Jiang, Y. et al. Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images. Nat. Commun. 15, 2221 (2024).
pubmed: 38472252
pmcid: 10933450
doi: 10.1038/s41467-024-46629-6
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 84, 644–654 (2018).
pubmed: 29960671
pmcid: 6177304
doi: 10.1016/j.biopsych.2018.04.023
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 21, 585 (2016).
pubmed: 26283641
doi: 10.1038/mp.2015.118
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 28, 5206–5216 (2023).
pubmed: 37537281
pmcid: 11041797
doi: 10.1038/s41380-023-02141-9
Koshiyama, D. et al. White matter microstructural alterations across four major psychiatric disorders: mega-analysis study in 2937 individuals. Mol. Psychiatry 25, 883–895 (2020).
pubmed: 31780770
doi: 10.1038/s41380-019-0553-7
Howes, O. D., Cummings, C., Chapman, G. E. & Shatalina, E. Neuroimaging in schizophrenia: an overview of findings and their implications for synaptic changes. Neuropsychopharmacology 48, 151–167 (2023).
pubmed: 36056106
doi: 10.1038/s41386-022-01426-x
Alnaes, D. et al. Brain heterogeneity in schizophrenia and its association with polygenic risk. JAMA Psychiatry 76, 739–748 (2019).
pubmed: 30969333
pmcid: 6583664
doi: 10.1001/jamapsychiatry.2019.0257
Howes, O. D. & Kapur, S. A neurobiological hypothesis for the classification of schizophrenia: type A (hyperdopaminergic) and type B (normodopaminergic). Br. J. Psychiatry 205, 1–3 (2014).
pubmed: 24986384
doi: 10.1192/bjp.bp.113.138578
Jiang, Y. et al. Progressive reduction in gray matter in patients with schizophrenia assessed with mr imaging by using causal network analysis. Radiology 287, 729 (2018).
pubmed: 29668409
doi: 10.1148/radiol.2018184005
Kirschner, M. et al. Orbitofrontal-striatal structural alterations linked to negative symptoms at different stages of the schizophrenia spectrum. Schizophr. Bull. 47, 849–863 (2021).
pubmed: 33257954
doi: 10.1093/schbul/sbaa169
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. USA 98, 11650–11655 (2001).
pubmed: 11573002
pmcid: 58784
doi: 10.1073/pnas.201243998
Thompson, P. M. et al. Time-lapse mapping of cortical changes in schizophrenia with different treatments. Cereb. Cortex 19, 1107–1123 (2009).
pubmed: 18842668
doi: 10.1093/cercor/bhn152
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 21, 1090–1098 (2016).
pubmed: 26194183
doi: 10.1038/mp.2015.90
Crow, T. J. Is schizophrenia the price that Homo sapiens pays for language? Schizophr. Res. 28, 127–141 (1997).
pubmed: 9468348
doi: 10.1016/S0920-9964(97)00110-2
Palaniyappan, L. & Liddle, P. F. Does the salience network play a cardinal role in psychosis? An emerging hypothesis of insular dysfunction. J. Psychiatry Neurosci. 37, 17–27 (2012).
pubmed: 21693094
pmcid: 3244495
doi: 10.1503/jpn.100176
McGuire, P. K., Murray, R. & Shah, G. Increased blood flow in Broca’s area during auditory hallucinations in schizophrenia. Lancet 342, 703–706 (1993).
pubmed: 8103821
doi: 10.1016/0140-6736(93)91707-S
Vercammen, A., Knegtering, H., den Boer, J. A., Liemburg, E. J. & Aleman, A. Auditory hallucinations in schizophrenia are associated with reduced functional connectivity of the temporo-parietal area. Biol. Psychiatry 67, 912–918 (2010).
pubmed: 20060103
doi: 10.1016/j.biopsych.2009.11.017
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. 47, 562–574 (2021).
pubmed: 32926141
doi: 10.1093/schbul/sbaa127
Pantelis, C. et al. Neuroanatomical abnormalities before and after onset of psychosis: a cross-sectional and longitudinal MRI comparison. Lancet 361, 281–288 (2003).
pubmed: 12559861
doi: 10.1016/S0140-6736(03)12323-9
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 72, 316–324 (2015).
pubmed: 25651194
pmcid: 4768742
doi: 10.1001/jamapsychiatry.2014.2414
Steen, R. G., Mull, C., McClure, R., Hamer, R. M. & Lieberman, J. A. Brain volume in first-episode schizophrenia: systematic review and meta-analysis of magnetic resonance imaging studies. Br. J. Psychiatry 188, 510–518 (2006).
pubmed: 16738340
doi: 10.1192/bjp.188.6.510
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. USA 110, E2400–E2409 (2013).
pubmed: 23729812
pmcid: 3696825
doi: 10.1073/pnas.1304308110
Kahn, R. S. & Sommer, I. E. The neurobiology and treatment of first-episode schizophrenia. Mol. Psychiatry 20, 84–97 (2015).
pubmed: 25048005
doi: 10.1038/mp.2014.66
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 78, 403–412 (2015).
pubmed: 25802081
doi: 10.1016/j.biopsych.2015.02.008
McCutcheon, R. A., Reis Marques, T. & Howes, O. D. Schizophrenia-an overview. JAMA Psychiatry 77, 201–210 (2020).
pubmed: 31664453
doi: 10.1001/jamapsychiatry.2019.3360
Brugger, S. P. et al. Heterogeneity of Striatal Dopamine Function in Schizophrenia: Meta-analysis of Variance. Biol. Psychiatry 87, 215–224 (2020).
pubmed: 31561858
doi: 10.1016/j.biopsych.2019.07.008
Chase, H. W., Loriemi, P., Wensing, T., Eickhoff, S. B. & Nickl-Jockschat, T. Meta-analytic evidence for altered mesolimbic responses to reward in schizophrenia. Hum. Brain Mapp. 39, 2917–2928 (2018).
pubmed: 29573046
pmcid: 6866586
doi: 10.1002/hbm.24049
Koch, K. et al. Functional connectivity and grey matter volume of the striatum in schizophrenia. Br. J. Psychiatry 205, 204–213 (2014).
pubmed: 25012683
doi: 10.1192/bjp.bp.113.138099
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 28, 4363–4373 (2023).
pubmed: 37644174
pmcid: 10827665
doi: 10.1038/s41380-023-02221-w
Chand, G. B. et al. Schizophrenia imaging signatures and their associations with cognition, psychopathology, and genetics in the general population. Am. J. Psychiatry 179, 650–660 (2022).
pubmed: 35410495
pmcid: 9444886
doi: 10.1176/appi.ajp.21070686
Mouchlianitis, E., McCutcheon, R. & Howes, O. D. Brain-imaging studies of treatment-resistant schizophrenia: a systematic review. Lancet Psychiatry 3, 451–463 (2016).
pubmed: 26948188
pmcid: 5796640
doi: 10.1016/S2215-0366(15)00540-4
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. 20, 1241–1252 (2022).
pubmed: 34370638
pmcid: 9886826
doi: 10.2174/1570159X19666210809101248
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. 12, 319–328 (2019).
pubmed: 30473477
doi: 10.1016/j.brs.2018.11.006
Jiang, Y. et al. Insular changes induced by electroconvulsive therapy response to symptom improvements in schizophrenia. Prog. Neuropsychopharmacol. Biol. Psychiatry 89, 254–262 (2019).
pubmed: 30248379
doi: 10.1016/j.pnpbp.2018.09.009
Ho, B. C., Andreasen, N. C., Ziebell, S., Pierson, R. & Magnotta, V. Long-term antipsychotic treatment and brain volumes: a longitudinal study of first-episode schizophrenia. Arch. Gen. Psychiatry 68, 128–137 (2011).
pubmed: 21300943
pmcid: 3476840
doi: 10.1001/archgenpsychiatry.2010.199
Lewandowski K. E., Bouix S., Ongur D., Shenton M. E. Neuroprogression across the Early Course of Psychosis. J Psychiatr Brain Sci 5, e200002 (2020).
Tanaka, S. C. et al. A multi-site, multi-disorder resting-state magnetic resonance image database. Sci. Data 8, 227 (2021).
pubmed: 34462444
pmcid: 8405782
doi: 10.1038/s41597-021-01004-8
Keator, D. B. et al. The function biomedical informatics research network data repository. Neuroimage 124, 1074–1079 (2016).
pubmed: 26364863
doi: 10.1016/j.neuroimage.2015.09.003
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 11, 367–388 (2013).
pubmed: 23760817
doi: 10.1007/s12021-013-9184-3
Alpert, K., Kogan, A., Parrish, T., Marcus, D. & Wang, L. The northwestern university neuroimaging data archive (NUNDA). Neuroimage 124, 1131–1136 (2016).
pubmed: 26032888
doi: 10.1016/j.neuroimage.2015.05.060
Kogan, A., Alpert, K., Ambite, J. L., Marcus, D. S. & Wang, L. Northwestern University schizophrenia data sharing for SchizConnect: A longitudinal dataset for large-scale integration. Neuroimage 124, 1196–1201 (2016).
pubmed: 26087378
doi: 10.1016/j.neuroimage.2015.06.030
Poldrack, R. A. et al. A phenome-wide examination of neural and cognitive function. Sci. Data 3, 160110 (2016).
pubmed: 27922632
pmcid: 5139672
doi: 10.1038/sdata.2016.110
Repovs, G. & Barch, D. M. Working memory related brain network connectivity in individuals with schizophrenia and their siblings. Front Hum. Neurosci. 6, 137 (2012).
pubmed: 22654746
pmcid: 3358772
doi: 10.3389/fnhum.2012.00137
Soler-Vidal, J. et al. Brain correlates of speech perception in schizophrenia patients with and without auditory hallucinations. PLOS ONE 17, e0276975 (2022).
pubmed: 36525414
pmcid: 9757556
doi: 10.1371/journal.pone.0276975
Kay, S. R., Fiszbein, A. & Opler, L. A. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr. Bull. 13, 261–276 (1987).
pubmed: 3616518
doi: 10.1093/schbul/13.2.261
Lindenmayer, J. P., Bernstein-Hyman, R. & Grochowski, S. Five-factor model of schizophrenia. Initial validation. J. Nerv. Ment. Dis. 182, 631–638 (1994).
pubmed: 7964671
doi: 10.1097/00005053-199411000-00006
Rolls, E. T., Huang, C.-C., Lin, C.-P., Feng, J. & Joliot, M. Automated anatomical labelling atlas 3. Neuroimage 206, 116189 (2020).
pubmed: 31521825
doi: 10.1016/j.neuroimage.2019.116189
Pomponio, R. et al. Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. Neuroimage 208, 116450 (2020).
pubmed: 31821869
doi: 10.1016/j.neuroimage.2019.116450
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 31, 968–980 (2006).
pubmed: 16530430
doi: 10.1016/j.neuroimage.2006.01.021
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 115, 117–137 (2015).
pubmed: 25936807
doi: 10.1016/j.neuroimage.2015.04.042
Saygin, Z. M. et al. High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas. Neuroimage 155, 370–382 (2017).
pubmed: 28479476
doi: 10.1016/j.neuroimage.2017.04.046
Iglesias, J. E. et al. A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology. Neuroimage 183, 314–326 (2018).
pubmed: 30121337
doi: 10.1016/j.neuroimage.2018.08.012
Iglesias, J. E. et al. Bayesian segmentation of brainstem structures in MRI. Neuroimage 113, 184–195 (2015).
pubmed: 25776214
doi: 10.1016/j.neuroimage.2015.02.065