Abstract
Schizo-obsessive disorder is characterized by the clinical syndrome in which comorbid obsessive–compulsive disorder accompanies schizophrenia. A substantial number of studies have investigated the neuropsychological and clinical differences between schizophrenia and schizo-obsessive disorder. However, the neurostructural differences between these two groups have not been adequately investigated. The aim of this study was to explore gray matter differences between schizophrenia and schizo-obsessive patients using voxel-based morphometry and support vector machines combined with feature selection algorithm. Twenty-three schizophrenia and 23 schizo-obsessive patients matched by age, gender and handedness were recruited. Clinical assessments were completed in addition to high-resolution structural MRI scanning. Group differences were investigated using contrast maps, and significant regions were subjected to a feature selection and support vector machine hybrid model. In addition, voxel-of-interest values for the commonly shared brain areas between schizophrenia and OCD reported in previous meta-analyses were also used as inputs in this step. The results showed that schizo-obsessive patients had greater gray matter densities in paracentral areas (including supplementary motor area) and middle cingulate gyrus than schizophrenia patients. These brain areas together with the fronto-subcortical areas could successfully discriminate two groups with an accuracy of 78.26 %. Our results provide the first neuroanatomical evidence that schizo-obsessive disorder and schizophrenia may be two distinct clinical entities. Based on these findings, considering schizo-obsessive disorder as a subtype of schizophrenia is discernible.
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Uskudar University Research Council and NPIstanbul Neuropsychiatry Hospital funded the sample collection and MRI scans (No. NPI-120814).
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Tas, C., Mogulkoc, H., Eryilmaz, G. et al. Discriminating schizophrenia and schizo-obsessive disorder: a structural MRI study combining VBM and machine learning methods. Neural Comput & Applic 29, 377–387 (2018). https://doi.org/10.1007/s00521-016-2451-0
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DOI: https://doi.org/10.1007/s00521-016-2451-0