Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (available at: adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns†
Article first published online: 28 AUG 2012
Copyright © 2012 Wiley Periodicals, Inc.
Human Brain Mapping
Volume 34, Issue 12, pages 3411–3425, December 2013
How to Cite
Wee, C.-Y., Yap, P.-T., Shen, D. and for the Alzheimer's Disease Neuroimaging Initiative (2013), Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns. Hum. Brain Mapp., 34: 3411–3425. doi: 10.1002/hbm.22156
- Issue published online: 26 OCT 2013
- Article first published online: 28 AUG 2012
- Manuscript Accepted: 2 JUN 2012
- Manuscript Revised: 25 MAY 2012
- Manuscript Received: 17 JAN 2012
- NIH. Grant Numbers: EB006733, EB008374, EB009634, MH088520
- Alzheimer's disease (AD);
- mild cognitive impairment (MCI);
- magnetic resonance imaging (MRI);
- cortical thickness;
- multi-kernel support vector machine (SVM)
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.