Robustness of multivariate image analysis assessed by resampling techniques and applied to FDG-PET scans of patients with Alzheimer's disease

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Abstract

For finite and noisy samples extraction of robust features or patterns which are representative of the population is a formidable task in which over-interpretation is not uncommon. In this work, resampling techniques have been applied to a sample of 42 FDG PET brain images of 19 healthy volunteers (HVs) and 23 Alzheimer's disease (AD) patients to assess the robustness of image features extracted through principal component analysis (PCA) and Fisher discriminant analysis (FDA). The objective of this work is to: 1) determine the relative variance described by the PCA to the population variance; 2) assess the robustness of the PCA to the population sample using the largest principal angle between PCA subspaces; 3) assess the robustness and accuracy of the FDA. Since the sample does not have histopathological data the impact of possible clinical misdiagnosis on the discrimination analysis is investigated.

The PCA can describe up to 40% of the total population variability. Not more than the first three or four PCs can be regarded as robust on which a robust FDA can be build. Standard error images showed that regions close to the falx and around ventricles are less stable. Using the first three PCs, sensitivity and specificity were 90.5% and 96.9% respectively.

The use of resampling techniques in the evaluation of the robustness of many multivariate image analysis methods enables researchers to avoid over-analysis when using these methods applied to many different neuroimaging studies often with small sample sizes.

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