Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: A support vector machine learning approach
Gould, Ian C., Shepherd, Alana M., Laurens, Kristin R., Cairns, Murray J., Carr, Vaughn J. and Green, Melissa J.. (2014) Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: A support vector machine learning approach. NeuroImage: Clinical. 6, pp. 229 - 236. https://doi.org/10.1016/j.nicl.2014.09.009
|Authors||Gould, Ian C., Shepherd, Alana M., Laurens, Kristin R., Cairns, Murray J., Carr, Vaughn J. and Green, Melissa J.|
Heterogeneity in the structural brain abnormalities associated with schizophrenia has made identification of reliable neuroanatomical markers of the disease difficult. The use of more homogenous clinical phenotypes may improve the accuracy of predicting psychotic disorder/s on the basis of observable brain disturbances. Here we investigate the utility of cognitive subtypes of schizophrenia – ‘cognitive deficit’ and ‘cognitively spared’ – in determining whether multivariate patterns of volumetric brain differences can accurately discriminate these clinical subtypes from healthy controls, and from each other. We applied support vector machine classification to grey- and white-matter volume data from 126 schizophrenia patients previously allocated to the cognitive spared subtype, 74 cognitive deficit schizophrenia patients, and 134 healthy controls. Using this method, cognitive subtypes were distinguished from healthy controls with up to 72% accuracy. Cross-validation analyses between subtypes achieved an accuracy of 71%, suggesting that some common neuroanatomical patterns distinguish both subtypes from healthy controls. Notably, cognitive subtypes were best distinguished from one another when the sample was stratified by sex prior to classification analysis: cognitive subtype classification accuracy was relatively low ( < 60% ) without stratification, and increased to 83% for females with sex stratification. Distinct neuroanatomical patterns predicted cognitive subtype status in each sex: sex-specific multivariate patterns did not predict cognitive subtype status in the other sex above chance, and weight map analyses demonstrated negative correlations between the spatial patterns of weights underlying classification for each sex. These results suggest that in typical mixed-sex samples of schizophrenia patients, the volumetric brain differences between cognitive subtypes are relatively minor in contrast to the large common disease-associated changes. Volumetric differences that distinguish between cognitive subtypes on a case-by-case basis appear to occur in a sex-specific manner that is consistent with previous evidence of disrupted relationships between brain structure and cognition in male, but not female, schizophrenia patients. Consideration of sex-specific differences in brain organization is thus likely to assist future attempts to distinguish subgroups of schizophrenia patients on the basis of neuroanatomical features.
|Keywords||cognition; cognitive deficit; subtypes; voxel based morphometry; magnetic resonance imaging; sex differences|
|Journal citation||6, pp. 229 - 236|
|Digital Object Identifier (DOI)||https://doi.org/10.1016/j.nicl.2014.09.009|
|Open access||Open access|
|Page range||229 - 236|
|Research Group||School of Philosophy|
© 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-SA license (http://creativecommons.org/licenses/by-nc-sa/3.0/).
|Place of publication||United States|
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