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AJNR - American Journal of Neuroradiology

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American Journal of Neuroradiology 28:1339-1345, August 2007
DOI 10.3174/ajnr.A0620
© 2007 American Society of Neuroradiology

BRAIN

Hippocampal Shape Analysis of Alzheimer Disease Based on Machine Learning Methods

S. Li^a, F. Shi^b, F. Pu^a, X. Li^b, T. Jiang^b, S. Xie^c and Y. Wang^d

^a Department of Bioengineering, Beijing University of Aeronautics and Astronautics, Beijing, People's Republic of China
^b National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
^c Department of Radiology, Peking University First Hospital, Beijing, People's Republic of China
^d Department of Neuropsycholog, Peking University First Hospital, Beijing, People's Republic of China

Address correspondence to Tianzi Jiang, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, No. 95, Zhongguancun E.RD., Beijing 100080, People's Republic of China; e-mail: jiangtz{at}nlpr.ia.ac.cn

BACKGROUND AND PURPOSE: Alzheimer disease (AD) is a neurodegenerative disease characterized by progressive dementia. The hippocampus is particularly vulnerable to damage at the very earliest stages of AD. This article seeks to evaluate critical AD-associated regional changes in the hippocampus using machine learning methods.

MATERIALS AND METHODS: High-resolution MR images were acquired from 19 patients with AD and 20 age- and sex-matched healthy control subjects. Regional changes of bilateral hippocampi were characterized using computational anatomic mapping methods. A feature selection method for support vector machine and leave-1-out cross-validation was introduced to determine regional shape differences that minimized the error rate in the datasets.

RESULTS: Patients with AD showed significant deformations in the CA1 region of bilateral hippocampi, as well as the subiculum of the left hippocampus. There were also some changes in the CA2–4 subregions of the left hippocampus among patients with AD. Moreover, the left hippocampal surface showed greater variations than the right compared with those in healthy control subjects. The accuracies of leave-1-out cross-validation and 3-fold cross-validation experiments for assessing the reliability of these subregions were more than 80% in bilateral hippocampi.

CONCLUSION: Subtle and spatially complex deformation patterns of hippocampus between patients with AD and healthy control subjects can be detected by machine learning methods.

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