Multiple Discriminant Analysis of SPECT Data for Alzheimer’s Disease, Frontotemporal Dementia and Asymptomatic Controls

Abstract

Multiple discriminant analysis (MDA) is a generalization of the Fisher discriminant analysis (FDA) and makes it possible to discriminate more than two classes by projecting the data onto a subspace. In this work, it was applied to technetium- 99methylcysteinatedimer (99mTc-ECD) SPECT datasets of 10 Alzheimer’s disease (AD) patients, 11 frontotemporal dementia (FTD) patients and 11 asymptomatic controls (CTR). Principal component analysis (PCA) was used for dimensionality reduction, followed by projection of the data onto a discrimination plane via MDA. In order to separate the different groups, linear boundaries were calculated by applying FDA to two classes at a time (linear machine). By executing the F-test for different numbers of principal components and examining the corresponding classification accuracy, an optimal discrimination plane based on the first three principal components was determined. In order to further assess the method, another dataset comprising patients with early-onset AD and FTD (beginning or suspected disease) was projected by the same method onto this discrimination plane, resulting in a correct classification for most cases. The successful iscrimination of another dataset on the same plane indicates that the model is well suited to account fordisease-specific characteristics within the classes, even for patients with early-onset AD and FTD.