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Hippocampal shape analysis of Alzheimer disease based on machine learning methods
Li, S.1; Shi, F.1; Pu, F.1; Li, X.1; Jiang, T.1; Xie, S.1; Wang, Y.1
刊名AMERICAN JOURNAL OF NEURORADIOLOGY
2007-08-01
DOI10.3174/ajnr.AO620
28期:7页:1339-1345
收录类别SCI
文章类型Article
WOS标题词Science & Technology
类目[WOS]Clinical Neurology ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
研究领域[WOS]Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
关键词[WOS]VOXEL-BASED MORPHOMETRY ; GRAY-MATTER LOSS ; ENTORHINAL CORTEX ; NEUROFIBRILLARY TANGLES ; MEMORY IMPAIRMENT ; SENILE PLAQUES ; DEMENTIA ; MRI ; CLASSIFICATION ; SEVERITY
英文摘要

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.

语种英语
WOS记录号WOS:000249278700026
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被引频次:55[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.bjmu.edu.cn/handle/400002259/52584
专题北京大学第一临床医学院_放射治疗科
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
2.Peking Univ, Dept Radiol, Hosp 1, Beijing 100871, Peoples R China
3.Peking Univ, Dept Neuropsychol, Hosp 1, Beijing 100871, Peoples R China
4.Beijing Univ Aeronaut & Astronaut, Dept B oengn, Beijing 100083, Peoples R China
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GB/T 7714
Li, S.,Shi, F.,Pu, F.,et al. Hippocampal shape analysis of Alzheimer disease based on machine learning methods[J]. AMERICAN JOURNAL OF NEURORADIOLOGY,2007,28(7):1339-1345.
APA Li, S..,Shi, F..,Pu, F..,Li, X..,Jiang, T..,...&Wang, Y..(2007).Hippocampal shape analysis of Alzheimer disease based on machine learning methods.AMERICAN JOURNAL OF NEURORADIOLOGY,28(7),1339-1345.
MLA Li, S.,et al."Hippocampal shape analysis of Alzheimer disease based on machine learning methods".AMERICAN JOURNAL OF NEURORADIOLOGY 28.7(2007):1339-1345.
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