Spatial component analysis of MRI data for Alzheimer's disease diagnosis: a Bayesian network approach Álvarez Illán, Ignacio Gorriz Sáez, Juan Manuel Ramírez Pérez De Inestrosa, Javier Meyer-Base, Anke Bayesian networks AD diagnosis Spatial component analysis Magnetic resonance imaging CAD systems This work presents a spatial-component (SC) based approach to aid the diagnosis of Alzheimer's disease (AD) using magnetic resonance images. In this approach, the whole brain image is subdivided in regions or spatial components, and a Bayesian network is used to model the dependencies between affected regions of AD. The structure of relations between affected regions allows to detect neurodegeneration with an estimated performance of 88% on more than 400 subjects and predict neurodegeneration with 80% accuracy, supporting the conclusion that modeling the dependencies between components increases the recognition of different patterns of brain degeneration in AD. 2015-02-16T13:21:26Z 2015-02-16T13:21:26Z 2014 info:eu-repo/semantics/article Illan, I.A.; et al. Spatial component analysis of MRI data for Alzheimer's disease diagnosis: a Bayesian network approach. Frontiers in Computational Neuroscience, 26: 156 (2014). [http://hdl.handle.net/10481/34826] 1662-5188 http://hdl.handle.net/10481/34826 10.3389/fncom.2014.00156 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Frontiers Foundation