Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods

• Published in: Schizophrenia research Vol. 214; pp. 43 - 50
• Main Authors: Honnorat, Nicolas, Dong, Aoyan, Meisenzahl-Lechner, Eva, Koutsouleris, Nikolaos, Davatzikos, Christos
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.12.2019
• Subjects: Adult; Brain - diagnostic imaging; Cohort Studies; Female; Gray matter; Humans; Image Processing, Computer-Assisted - methods; Machine learning; Magnetic Resonance Imaging - methods; Male; Multivariate Analysis; Multivariate pattern analysis; Pattern Recognition, Automated - methods; Psychiatric/Mental Health; Schizophrenia; Schizophrenia - diagnostic imaging; Supervised Machine Learning; VBM; White matter; Young Adult; VBM; Schizophrenia; Multivariate pattern analysis; Gray matter; White matter; Machine learning
• ISSN: 0920-9964; 1573-2509; 1573-2509
• DOI: 10.1016/j.schres.2017.12.008

Schizophrenia is associated with heterogeneous clinical symptoms and neuroanatomical alterations. In this work, we aim to disentangle the patterns of neuroanatomical alterations underlying a heterogeneous population of patients using a semi-supervised clustering method. We apply this strategy to a cohort of patients with schizophrenia of varying extends of disease duration, and we describe the neuroanatomical, demographic and clinical characteristics of the subtypes discovered. We analyze the neuroanatomical heterogeneity of 157 patients diagnosed with Schizophrenia, relative to a control population of 169 subjects, using a machine learning method called CHIMERA. CHIMERA clusters the differences between patients and a demographically-matched population of healthy subjects, rather than clustering patients themselves, thereby specifically assessing disease-related neuroanatomical alterations. Voxel-Based Morphometry was conducted to visualize the neuroanatomical patterns associated with each group. The clinical presentation and the demographics of the groups were then investigated. Three subgroups were identified. The first two differed substantially, in that one involved predominantly temporal-thalamic-peri-Sylvian regions, whereas the other involved predominantly frontal regions and the thalamus. Both subtypes included primarily male patients. The third pattern was a mix of these two and presented milder neuroanatomic alterations and comprised a comparable number of men and women. VBM and statistical analyses suggest that these groups could correspond to different neuroanatomical dimensions of schizophrenia. Our analysis suggests that schizophrenia presents distinct neuroanatomical variants. This variability points to the need for a dimensional neuroanatomical approach using data-driven, mathematically principled multivariate pattern analysis methods, and should be taken into account in clinical studies.