Finding regional models of the Alzheimer disease by fusing information from neuropsychological tests and structural MR images

Diana L. Giraldo; Juan D. García-Arteaga; Eduardo Romero M.D.

doi:10.1117/12.2217021

24 March 2016 Finding regional models of the Alzheimer disease by fusing information from neuropsychological tests and structural MR images

Diana L. Giraldo, Juan D. García-Arteaga, Eduardo Romero M.D.

Author Affiliations +

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97852H (2016) https://doi.org/10.1117/12.2217021
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Initial diagnosis of Alzheimer's disease (AD) is based on the patient's clinical history and a battery of neuropsy-chological tests. This work presents an automatic strategy that uses Structural Magnetic Resonance Imaging (MRI) to learn brain models for different stages of the disease using information from clinical assessments. Then, a comparison of the discriminant power of the models in different anatomical areas is made by using the brain region of the models as a reference frame for the classification problem, by using the projection into the AD model a Receiver Operating Characteristic (ROC) curve is constructed. Validation was performed using a leave- one-out scheme with 86 subjects (20 AD and 60 NC) from the Open Access Series of Imaging Studies (OASIS) database. The region with the best classification performance was the left amygdala where it is possible to achieve a sensibility and specificity of 85% at the same time. The regions with the best performance, in terms of the AUC, are in strong agreement with those described as important for the diagnosis of AD in clinical practice.

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Diana L. Giraldo, Juan D. García-Arteaga, and Eduardo Romero M.D. "Finding regional models of the Alzheimer disease by fusing information from neuropsychological tests and structural MR images", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97852H (24 March 2016); https://doi.org/10.1117/12.2217021

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KEYWORDS

Brain

Magnetic resonance imaging

Neuroimaging

Alzheimer's disease

Data modeling

Databases

Amygdala

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