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Frontiers Media, Frontiers in Computational Neuroscience, (8), 2014

DOI: 10.3389/fncom.2014.00156

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Spatial component analysis of MRI data for Alzheimer's disease diagnosis: a Bayesian network approach

Journal article published in 2014 by Javier Ramírez, Ignacio A. Illan, Juan Manuel G??rriz S??ez, Juan M. Górriz, Juan Manuel Gorriz, Javier Ramírez, Javier Ram??rez P??rez de Inestrosa, Anke Meyer-Base ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

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.