Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer's disease.

Background: The choice of biomarkers for early detection of Alzheimer's disease (AD) is important for improving the accuracy of imaging-based prediction of conversion from mild cognitive impairment (MCI) to AD. The primary goal of this study was to assess the effects of imaging modalities and brain atlases on prediction. We also investigated the influence of support vector machine recursive feature elimination (SVM-RFE) on predictive performance.

Methods: Eighty individuals with amnestic MCI [40 developed AD within 3 years] underwent structural magnetic resonance imaging (MRI) and (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans at baseline. Using Automated Anatomical Labeling (AAL) and LONI Probabilistic Brain Atlas (LPBA40), we extracted features representing gray matter density and relative cerebral metabolic rate for glucose in each region of interest from the baseline MRI and FDG-PET data, respectively. We used linear SVM ensemble with bagging and computed the area under the receiver operating characteristic curve (AUC) as a measure of classification performance. We performed multiple SVM-RFE to compute feature ranking. We performed analysis of variance on the mean AUCs for eight feature sets.

Results: The interactions between atlas and modality choices were significant. The main effect of SVM-RFE was significant, but the interactions with the other factors were not significant.

Comparison With Existing Method: Multimodal features were found to be better than unimodal features to predict AD. FDG-PET was found to be better than MRI.

Conclusions: Imaging modalities and brain atlases interact with each other and affect prediction. SVM-RFE can improve the predictive accuracy when using atlas-based features.