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

Chong Yaw Wee, Pew Thian Yap, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

178 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)3411-3425
Number of pages15
JournalHuman Brain Mapping
Issue number12
Publication statusPublished - 2013 Dec


  • Alzheimer's disease (AD)
  • Cortical thickness
  • Magnetic resonance imaging (MRI)
  • Mild cognitive impairment (MCI)
  • Multi-kernel support vector machine (SVM)

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology


Dive into the research topics of 'Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns'. Together they form a unique fingerprint.

Cite this