| | | | | | |
  当前位置:首页 > 团队科研成果 

Individual identifcation using multi-metric of DTI
作者:数学建模与神经计算 发布日期:2019-2-26
 点击:1418
关键词:-

/Uploads/file/20190226/20190226190783618361.pdf

Individual identifcation using multi-metric of DTI in Alzheimer’s disease and mild cognitive impairment

Ying-Teng Zhang(张应腾) and Shen-Quan Liu(刘深泉)*

School of Mathematics, South China University of Technology, Guangzhou 510640, China


Abstract: Accurate identifcation of the most relevant white matter (WM) tracts linked to Alzheimer’s disease (AD) and mild cognitive impairment (MCI) is crucial so as to improve diagnosis techniques and to better understand the neurodegenerative process. In this work, we aim to apply machine learning method for individual identifcation and identify the discriminate features associated with AD and MCI. DTI scans of 48 patients with AD, 39 patients with late MCI (LMCI), 75 patients with early MCI (EMCI) and 51 agematched healthy controls (HC) are acquired from the ADNI database. In addition to the common fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (DA) and radial diffusivity (RD) metrics, there are two novel metrics, named local diffusion homogeneity used spearman’s rank correlation coefcient (LDHs) and Kendalls coefcient concordance (LDHk), which are taken as classifcation metrics. The recursive feature elimination (RFE) method for support vector machine (SVM) and logistic regression (LR) combined with leave-one-out cross validation (LOOCV) are applied to determine the optimal feature dimensions. Then SVM and LR are implemented the classifcation and compared the classifcation performance each other. The results can be well proved that multi-type and multi-regional features combination received higher accuracy than single metric. In addition, the permutation test, receiver operating characteristic (ROC) curves and area under the curve (AUC) are validated the robustness of the classifers. Finally, the uncinated fasciculus, cingulum, corpus callosum, corona radiate, external capsule and internal capsule have been regard as the most important WM tracts to identify AD, MCI and HC. Our fndings reveal a guidance role for machine-learning based image analysis on clinical diagnosis.

Keywords: Alzheimer’s disease, mild cognitive impairment, magnetic resonance imaging, classification

收 藏 推 荐 打 印 关 闭
上一篇:Dynamical properties of firing patterns in ELL pyr 下一篇:The Effects of Medium Spiny Neuron Morphologcial C
   关于我们
s
s
   推荐产品
   图片文章
   最新资讯
二次整合和放电神经元网络中的跨尺度兴奋性
具有二阶突触的精确和启发式神经质量模型...
一个具有突触延迟的大的峰值神经元系统的...
具有短期突触可塑性的峰值神经元网络的平...
排斥抑制在兴奋网络同步中的协同效应
具有双峰异质性的二次整合-触发神经元网...
 
友情链接: 神经计算   国家自然科学基   华南理工大学   全国大学生数学   美国数学建模竞   MATLAB  
咨询热线:刘教授 13650823684 邮箱:liushenat@sohu.com 备案编号:豫ICP备18005949号
地址:广州市番禺区广州大学城 邮编:510006  本站域名:mashqliu.com
Copyright © 2018-2024 数学建模与神经计算 Inc, All Rights Reserved.
在线客服
刘教授 13650823684
客服代表
点击这里给我发消息