Cocaine dependent classification using brain magnetic resonance imaging

Authors:
M. Termenon;Manuel Graña;A. Barrós-Loscertales;J. C. Bustamante;C. Ávila
Affiliations:
Grupo de Inteligencia Computacional (GIC), UPV/EHU, Spain;Grupo de Inteligencia Computacional (GIC), UPV/EHU, Spain;Dpto. Psicología Básica, Clínica y Psicobiología, Universitat Jaume I, Spain;Dpto. Psicología Básica, Clínica y Psicobiología, Universitat Jaume I, Spain;Dpto. Psicología Básica, Clínica y Psicobiología, Universitat Jaume I, Spain
Venue:
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Year:
2012

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Abstract

The purpose of this study is to elucidate if it is possible to discriminate between cocaine dependent patients and healthy controls applying computer aided diagnosis tools to brain magnetic resonance imaging. Feature extraction was done computing Pearson's correlation using subjects class as indicative variable. Linear support vector machines classifiers were trained and tested on the most significative voxels using leave one out cross-validation process. Results show that classifier achieve on average almost perfect accuracy, sensitivity and specificity in a group of 30 cocaine-dependent and 35 controls, supporting the usefulness of this process to discriminate between these subjects.