Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease

Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). ; Detection of Alzheimer's disease (AD) at the first stages of the pathology is an important task to accelerate the development of new therapies and improve treatment. Compared to AD detection, the prediction of AD using structural MRI at the mild cognitive impairment (MCI) or pre-MCI stage is more complex because the associated anatomical changes are more subtle. In this study, we analyzed the capability of a recently proposed method, SNIPE (Scoring by Nonlocal Image Patch Estimator), to predict AD by analyzing entorhinal cortex (EC) and hippocampus (HC) scoring over the entire ADNI database (834 scans). Detection (AD vs. CN) and prediction (progressive - pMCI vs. stable - sMCI) efficiency of SNIPE were studied using volumetric and grading biomarkers. First, our results indicate that grading-based biomarkers are more relevant for prediction than volume-based biomarkers. Second, we show that HC-based biomarkers are more important than EC-based biomarkers for prediction. Third, we demonstrate that the results obtained by SNIPE are similar to or better than results obtained in an independent study using HC volume, cortical thickness, and tensor-based morphometry, individually and in combination. Fourth, a comparison of new patch-based methods shows that the nonlocal redundancy strategy involved in SNIPE obtained similar results to a new local sparse-based approach. Finally, we present the first results of patch-based morphometry to illustrate the progression of the pathology. ; We wish to thank Dr. Robin Wolz for providing the list of ADNI subjects used in his study, which allowed us to perform the presented method comparison. We also want to thank the Canadian Institutes of Health Research (MOP-111169) and the Fonds de la recherche en sante du Quebec. Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative ...