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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Distinct pattern of brain structural deficits in subsyndromes of schizophrenia delineated by psychopathology
Igor Nenadic1, Heinrich Sauer, Christian Gaser
1Department of Psychiatry, Friedrich-Schiller-University of Jena, Philosophenweg 3, D-07743 Jena, Germany. igor.nenadic@uni-jena.de
Abstract:
Brain morphological changes are among the best-studied potential endophenotypes in schizophrenia and linked to genetic liability and expression of disease phenotype. Yet, there is considerable heterogeneity across individual subjects making its use as a disease-specific marker difficult. In this study we consider psychopathological variability of disease phenotype to delineate subsyndromes of schizophrenia, link them to distinct brain morphological patterns, and use a classification approach to test specificity of achieved discrimination. We first applied voxel-based morphometry (VBM) to compare 99 patients with DSM-IV schizophrenia (stable psychopathology and antipsychotic medication) with 113 matched healthy controls, then delineated three subgroups within the patient cohort based on psychopathology pattern and compared differential patterns of grey matter abnormalities. Finally, we tested accuracy of assigning any individual MRI scan to either the control group or any of the three patient subgroups. While VBM analysis showed overlap of brain structural deficits mostly in prefrontal areas, the disorganised subsyndrome showed stronger deficits in medial temporal and cerebellar regions, the paranoid/hallucinatory subsyndrome showed additional effects in the superior temporal cortex, and the negative subsyndrome showed stronger deficits in the thalamus. Using an automated algorithm, we achieved 95.8% accuracy classifying any given scan to one of the subgroups. Patterns of psychopathology are meaningful parameters in reducing heterogeneity of brain morphological endophenotypes in schizophrenia.
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