Literature DB >> 32103250

Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.

Ganesh B Chand1,2, Dominic B Dwyer3, Guray Erus1,2, Aristeidis Sotiras1,2,4, Erdem Varol1,2,5, Dhivya Srinivasan1,2, Jimit Doshi1,2, Raymond Pomponio1,2, Alessandro Pigoni3,6, Paola Dazzan7, Rene S Kahn8, Hugo G Schnack9, Marcus V Zanetti10,11, Eva Meisenzahl12, Geraldo F Busatto10, Benedicto Crespo-Facorro13,14, Christos Pantelis15, Stephen J Wood16,17,18, Chuanjun Zhuo19,20, Russell T Shinohara2,21, Haochang Shou2,21, Yong Fan1,2, Ruben C Gur1,22, Raquel E Gur1,22, Theodore D Satterthwaite2,22, Nikolaos Koutsouleris3, Daniel H Wolf2,22, Christos Davatzikos1,2.   

Abstract

Neurobiological 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.
© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  neuroanatomical heterogeneity; schizophrenia; semi-supervised machine learning; structural MRI; voxel-wise analysis

Mesh:

Year:  2020        PMID: 32103250      PMCID: PMC7089665          DOI: 10.1093/brain/awaa025

Source DB:  PubMed          Journal:  Brain        ISSN: 0006-8950            Impact factor:   13.501


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