Discovering brain regions relevant to obsessive-compulsive disorder identification through bagging and transduction

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Publication Details

Author list: Soriano-Mas C
Publisher: Elsevier
Publication year: 2014
Journal: Medical Image Analysis (1361-8415)
Volume number: 18
Issue number: 3
Start page: 435
End page: 448
Number of pages: 14
ISSN: 1361-8415
Languages: English-Great Britain (EN-GB)


Abstract

In the present study we applied a multivariate feature selection method
based on the analysis of the sign consistency of voxel weights across
bagged linear Support Vector Machines (SVMs) with the aim of detecting
brain regions relevant for the discrimination of subjects with
obsessive-compulsive disorder (OCD, n=86) from healthy controls (n=86).
Each participant underwent a structural magnetic resonance imaging
(sMRI) examination that was pre-processed in Statistical Parametric
Mapping (SPM8) using the standard pipeline of voxel-based morphometry
(VBM) studies. Subsequently, we applied our multivariate feature
selection algorithm, which also included an L2 norm regularization to
account for the clustering nature of MRI data, and a transduction-based
refinement to further control overfitting. Our approach proved to be
superior to two state-of-the-art feature selection methods (i.e.,
mass-univariate t-Test selection and recursive feature elimination),
since, following the application of transductive refinement, we obtained
a lower test error rate of the final classifier. Importantly, the
regions identified by our method have been previously reported to be
altered in OCD patients in studies using traditional brain morphometry
methods. By contrast, the discrimination patterns obtained with the
t-Test and the recursive feature elimination approaches extended across
fewer brain regions and included fewer voxels per cluster. These
findings suggest that the feature selection method presented here
provides a more comprehensive characterization of the disorder, thus
yielding not only a superior identification of OCD patients on the basis
of their brain anatomy, but also a discrimination map that incorporates
most of the alterations previously described to be associated with the
disorder.


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Last updated on 2019-13-08 at 00:45