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Combining neuropsychological and neuroimaging data to assist the early diagnosis of dementia

Alzheimer's disease (AD) is one of the most common neurodegenerative disorders in elderly and it is expected that its prevalence increases in the near future, mainly due to the aging population in developed nations. In recent years, many computer-aided diagnosis (CAD) systems for AD have been presented. Some of these systems analyze neurological brain images by means of machine learning algorithms in order to find the patterns that characterize the disorder, and a few combine several imaging modalities to improve the diagnostic accuracy. However, they usually do not use neuropsychological testing data in that analysis. In this work, we estimated the advantages of combining neuroimaging and neuropsychological testing data in the development of CAD systems based on machine learning.