Independent component analysis-based classification of Alzheimer's disease MRI data

There is an unmet medical need to identify neuroimaging biomarkers that allow us to accurately diagnose and monitor Alzheimer's disease (AD) at its very early stages and to assess the response to AD-modifying therapies. To a certain extent, volumetric and functional magnetic resonance imaging (fMRI) studies can detect changes in structure, cerebral blood flow, and blood oxygenation that distinguish AD and mild cognitive impairment (MCI) subjects from healthy control (HC) subjects. However, it has been challenging to use fully automated MRI analytic methods to identify potential AD neuroimaging biomarkers. We have thus proposed a method based on independent component analysis (ICA) for studying potential AD-related MR image features that can be coupled with the use of support vector machine (SVM) for classifying scans into categories of AD, MCI, and HC subjects. The MRI data were selected from the Open Access Series of Imaging Studies (OASIS) and the Alzheimer's Disease Neuroimaging Initiative databases. The experimental results showed that the ICA method coupled with SVM classifier can differentiate AD and MCI patients from HC subjects, although further methodological improvement in the analytic method and inclusion of additional variables may be required for optimal classification.