Publication:
Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.

dc.contributor.authorChand, Ganesh B
dc.contributor.authorDwyer, Dominic B
dc.contributor.authorErus, Guray
dc.contributor.authorSotiras, Aristeidis
dc.contributor.authorVarol, Erdem
dc.contributor.authorSrinivasan, Dhivya
dc.contributor.authorDoshi, Jimit
dc.contributor.authorPomponio, Raymond
dc.contributor.authorPigoni, Alessandro
dc.contributor.authorDazzan, Paola
dc.contributor.authorKahn, Rene S
dc.contributor.authorSchnack, Hugo G
dc.contributor.authorZanetti, Marcus V
dc.contributor.authorMeisenzahl, Eva
dc.contributor.authorBusatto, Geraldo F
dc.contributor.authorCrespo-Facorro, Benedicto
dc.contributor.authorPantelis, Christos
dc.contributor.authorWood, Stephen J
dc.contributor.authorZhuo, Chuanjun
dc.contributor.authorShinohara, Russell T
dc.contributor.authorShou, Haochang
dc.contributor.authorFan, Yong
dc.contributor.authorGur, Ruben C
dc.contributor.authorGur, Raquel E
dc.contributor.authorSatterthwaite, Theodore D
dc.contributor.authorKoutsouleris, Nikolaos
dc.contributor.authorWolf, Daniel H
dc.contributor.authorDavatzikos, Christos
dc.date.accessioned2023-02-08T14:42:11Z
dc.date.available2023-02-08T14:42:11Z
dc.date.issued2020
dc.description.abstractNeurobiological heterogeneity in schizophrenia is poorly understood and confounds current analyses. We investigated neuroanatomical subtypes in a multi-institutional multi-ethnic cohort, using novel semi-supervised machine learning methods designed to discover patterns associated with disease rather than normal anatomical variation. Structural MRI and clinical measures in established schizophrenia (n = 307) and healthy controls (n = 364) were analysed across three sites of PHENOM (Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging) consortium. Regional volumetric measures of grey matter, white matter, and CSF were used to identify distinct and reproducible neuroanatomical subtypes of schizophrenia. Two distinct neuroanatomical subtypes were found. Subtype 1 showed widespread lower grey matter volumes, most prominent in thalamus, nucleus accumbens, medial temporal, medial prefrontal/frontal and insular cortices. Subtype 2 showed increased volume in the basal ganglia and internal capsule, and otherwise normal brain volumes. Grey matter volume correlated negatively with illness duration in Subtype 1 (r = -0.201, P = 0.016) but not in Subtype 2 (r = -0.045, P = 0.652), potentially indicating different underlying neuropathological processes. The subtypes did not differ in age (t = -1.603, df = 305, P = 0.109), sex (chi-square = 0.013, df = 1, P = 0.910), illness duration (t = -0.167, df = 277, P = 0.868), antipsychotic dose (t = -0.439, df = 210, P = 0.521), age of illness onset (t = -1.355, df = 277, P = 0.177), positive symptoms (t = 0.249, df = 289, P = 0.803), negative symptoms (t = 0.151, df = 289, P = 0.879), or antipsychotic type (chi-square = 6.670, df = 3, P = 0.083). Subtype 1 had lower educational attainment than Subtype 2 (chi-square = 6.389, df = 2, P = 0.041). In conclusion, we discovered two distinct and highly reproducible neuroanatomical subtypes. Subtype 1 displayed widespread volume reduction correlating with illness duration, and worse premorbid functioning. Subtype 2 had normal and stable anatomy, except for larger basal ganglia and internal capsule, not explained by antipsychotic dose. These subtypes challenge the notion that brain volume loss is a general feature of schizophrenia and suggest differential aetiologies. They can facilitate strategies for clinical trial enrichment and stratification, and precision diagnostics.
dc.identifier.doi10.1093/brain/awaa025
dc.identifier.essn1460-2156
dc.identifier.pmcPMC7089665
dc.identifier.pmid32103250
dc.identifier.pubmedURLhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7089665/pdf
dc.identifier.unpaywallURLhttps://academic.oup.com/brain/article-pdf/143/3/1027/32963562/awaa025.pdf
dc.identifier.urihttp://hdl.handle.net/10668/15170
dc.issue.number3
dc.journal.titleBrain : a journal of neurology
dc.journal.titleabbreviationBrain
dc.language.isoen
dc.organizationHospital Universitario Virgen del Rocío
dc.page.number1027-1038
dc.pubmedtypeJournal Article
dc.pubmedtypeMulticenter Study
dc.pubmedtypeResearch Support, N.I.H., Extramural
dc.pubmedtypeResearch Support, Non-U.S. Gov't
dc.rights.accessRightsopen access
dc.subjectneuroanatomical heterogeneity
dc.subjectschizophrenia
dc.subjectsemi-supervised machine learning
dc.subjectstructural MRI
dc.subjectvoxel-wise analysis
dc.subject.meshAdult
dc.subject.meshAtrophy
dc.subject.meshBrain
dc.subject.meshCase-Control Studies
dc.subject.meshEducational Status
dc.subject.meshFemale
dc.subject.meshGray Matter
dc.subject.meshHumans
dc.subject.meshHypertrophy
dc.subject.meshMachine Learning
dc.subject.meshMagnetic Resonance Imaging
dc.subject.meshMale
dc.subject.meshNeuroimaging
dc.subject.meshSchizophrenia
dc.subject.meshWhite Matter
dc.subject.meshYoung Adult
dc.titleTwo distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.
dc.typeresearch article
dc.type.hasVersionVoR
dc.volume.number143
dspace.entity.typePublication

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