PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Identification of OCD-relevant brain areas through Multivariate Feature Selection
Emilio Parrado-Hernandez, Vanessa Gomez-Verdejo, Manel Martinez-Ramon, P Alonso, J Pujol, J.M. Menchon, N. Cardoner and Carles Soriano-Mas
In: NIPS Workshop on Machine Learning and Interpretation in Neuroimaging, 16-17 Dec 2012, Sierra Nevada, Spain.

Abstract

In this work we apply multivariate feature selection methods to construct a classifier that is able to differentiate among control sub- jects and OCD patients, with the purpose of bringing out regions of the brain that are relevant for the detection of the disease. Results show a dis- covery of regions that present great agreement with traditional methods used in OCD problems, but with the advantage of showing which ones are representative of control subjects or patients and providing cleaner and more accurate region maps.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:9094
Deposited By:Emilio Parrado-Hernandez
Deposited On:21 February 2012