RT Journal Article
T1 Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.
A1 Chand, Ganesh B
A1 Dwyer, Dominic B
A1 Erus, Guray
A1 Sotiras, Aristeidis
A1 Varol, Erdem
A1 Srinivasan, Dhivya
A1 Doshi, Jimit
A1 Pomponio, Raymond
A1 Pigoni, Alessandro
A1 Dazzan, Paola
A1 Kahn, Rene S
A1 Schnack, Hugo G
A1 Zanetti, Marcus V
A1 Meisenzahl, Eva
A1 Busatto, Geraldo F
A1 Crespo-Facorro, Benedicto
A1 Pantelis, Christos
A1 Wood, Stephen J
A1 Zhuo, Chuanjun
A1 Shinohara, Russell T
A1 Shou, Haochang
A1 Fan, Yong
A1 Gur, Ruben C
A1 Gur, Raquel E
A1 Satterthwaite, Theodore D
A1 Koutsouleris, Nikolaos
A1 Wolf, Daniel H
A1 Davatzikos, Christos
K1 neuroanatomical heterogeneity
K1 schizophrenia
K1 semi-supervised machine learning
K1 structural MRI
K1 voxel-wise analysis
AB 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.
YR 2020
FD 2020
LK http://hdl.handle.net/10668/15170
UL http://hdl.handle.net/10668/15170
LA en
DS RISalud
RD Apr 6, 2025