Abstract
In this work we apply multivariate feature selection methods to construct a classifier that is able to differentiate among control subjects 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 discovery 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.
This work was supported by grants Spain CICYT grant TEC2008-02473, Madrid Regional Government grant CCG10- UC3M/TIC-5511 and PASCAL2 Network of Excellence IST-2007-216886, and the Carlos III Health Institute (PI09/01331 and CP10/00604). Dr. Soriano-Mas is funded by a Miguel Servet contract from the Carlos III Health Institute (CP10/00604).
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Parrado-Hernández, E. et al. (2012). Identification of OCD-Relevant Brain Areas through Multivariate Feature Selection. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_8
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DOI: https://doi.org/10.1007/978-3-642-34713-9_8
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