Multiple discriminant analysis (MDA) is a general ization 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 technetium99methylcysteinatedimer e9mTc-ECD) SPECT datasets of 10 Alzheimer's disease (AD) patients, 11 frontotemporal dementia (FID) 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 prin cipal 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 discrimination of another dataset on the same plane indicates that the model is well suited to account for disease-specific characteristics within the classes, even for patients with early-onset AD and FTD.

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