title: Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns.
abstract: This article describes a novel approach to extract cortical morphological abnormality patterns from structural magnetic resonance imaging (MRI) data to improve the prediction accuracy of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Conventional approaches extract cortical morphological information, such as regional mean cortical thickness and regional cortical volumes, independently at different regions of interest (ROIs) without considering the relationship between these regions. Our approach involves constructing a similarity map where every element in the map represents the correlation of regional mean cortical thickness between a pair of ROIs. We will demonstrate in this article that this correlative morphological information gives significant improvement in classification performance when compared with ROI-based morphological information. Classification performance is further improved by integrating the correlative information with ROI-based information via multi-kernel support vector machines. This integrated framework achieves an accuracy of 92.35% for AD classification with an area of 0.9744 under the receiver operating characteristic (ROC) curve, and an accuracy of 83.75% for MCI classification with an area of 0.9233. In differentiating MCI subjects who converted to AD within 36 months from non-converters, an accuracy of 75.05% with an area of 0.8426 under ROC curve was achieved, indicating excellent diagnostic power and generalizability. The current work provides an alternative approach to extraction of high-order cortical information from structural MRI data for prediction of neurodegenerative diseases such as AD.
authors.affiliation: Image Display, Enhancement, and Analysis (IDEA) Laboratory, Biomedical Research Imaging Center (BRIC), Department of Radiology, University of North Carolina at Chapel Hill, North Carolina, USA.
authors.forename: Chong-Yaw
authors.initials: CY
authors.lastname: Wee
authors.forename: Pew-Thian
authors.initials: PT
authors.lastname: Yap
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis*; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000473:pathology*; D060825:Cognitive Dysfunction / Q000175:diagnosis*; D016208:Databases, Factual / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D060388:Support Vector Machine