Improving prediction of Alzheimer's disease using patterns of cortical thinning and homogenizing images according to disease stage

Abstract

Predicting Alzheimer's disease (AD) in individuals with some symp-toms of cognitive decline may have great influence on treatment choice and guide subject selection in trials on disease modifying drugs. Structural MRI has the potential of revealing early signs of neurodegeneration in the human brain and may thus aid in predicting and diagnosing AD. Surface-based cortical thickness measurements from T1-weighted MRI have demonstrated high sensi-tivity to cortical gray matter changes. In this study, we investigated the possibil-ity of using patterns of cortical thickness measurements for predicting AD in subjects with mild cognitive impairment (MCI). Specific patterns of atrophy were identified at four time periods before diagnosis of probable AD and fea-tures were selected as regions of interest within these patterns. The selected re-gions were used for cortical thickness measurements and applied in a classifier for testing the ability to predict AD at the four stages. The accuracy of the pre-diction improved as the time to conversion from MCI to AD decreased, from 70% at 3 years before the clinical criteria for AD was met, to 76% at 6 months before AD. These results show that prediction accuracies of conversion from MCI to AD can be improved by learning the atrophy patterns that are specific to the different stages of disease progression. This has the potential to guide the further development of imaging biomarkers in AD.