Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns

• Published in: Human brain mapping Vol. 34; no. 12; pp. 3411 - 3425
• Main Authors: Wee, Chong-Yaw, Yap, Pew-Thian, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: New York, NY Blackwell Publishing Ltd 01.12.2013; Wiley-Liss; John Wiley & Sons, Inc; John Wiley and Sons Inc
• Subjects: Aged; Aged, 80 and over; Alzheimer Disease - diagnosis; Alzheimer's disease (AD); Biological and medical sciences; Brain Mapping; Cerebral Cortex - pathology; Cognitive Dysfunction - diagnosis; cortical thickness; Databases, Factual - statistics & numerical data; Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases; Female; Humans; Investigative techniques, diagnostic techniques (general aspects); Longitudinal Studies; Magnetic Resonance Imaging; magnetic resonance imaging (MRI); Male; Medical sciences; Middle Aged; mild cognitive impairment (MCI); multi-kernel support vector machine (SVM); Nervous system; Neurology; Predictive Value of Tests; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Support Vector Machine; Nervous system diseases; Radiodiagnosis; Alzheimer disease; cortical thickness; Prediction; mild cognitive impairment (MCI); Alzheimer's disease (AD); Nuclear magnetic resonance imaging; Cerebral disorder; magnetic resonance imaging (MRI); Central nervous system disease; multi-kernel support vector machine (SVM); Degenerative disease; Vector
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.22156

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. Hum Brain Mapp 34:3411–3425, 2013. © 2012 Wiley Periodicals, Inc.