干旱区生态环境知识资源中心(Arid): 面向阿尔茨海默病的脑网络多频融合图核

Arid

	面向阿尔茨海默病的脑网络多频融合图核
其他题名	Multi-Frequency Fused Graph Kernel of Brain Network for Alzheimers Disease
	汪新蕾; 王之琼; 王中阳; 信俊昌; 谷峪
来源期刊	计算机学报
ISSN	0254-4164
出版年	2020
卷号	43 期号:1
中文摘要	复杂网络分析与机器学习方法相结合的阿尔茨海默病辅助诊断研究受到了越来越多的关注,其通常采用脑功能网络的方法来描述大脑活动的信息.然而,现有的成果大多基于时域信号匹配构建脑功能网络,忽略了脑活动信息在各个频段下的差异.因此,本文提出了脑网络多频融合图核的阿尔茨海默病诊断方法.首先,将功能磁共振成像产生的图像通过小波变换的方法进行分频段处理;其次,分别计算得到的各频段图像中任意两个脑区间的互信息,并设定阈值与互信息值进行比较进而构造出多频脑网络模型;然后,基于此提出面向多频脑网络模型的融合图核;最后,基于多频融合图核、采用核极限学习机在ADNI(Alzheimers Disease Neuroimaging Initiative)公开数据库中获取的一组数据以及在OASIS(Open Access Series of Imaging Studies)公开数据库上获取的一组数据进行阿尔茨海默病的诊断.同时,还通过实验验证了不同参数设置对诊断结果的影响.两组数据集的实验结果表明, 提出的多频融合图核的辅助诊断方法能够取得最佳性能,且该方法的辅助诊断准确率在两种数据集上比对比方法的最好结果分别提高了13.79%和15.29% .
英文摘要	In recent years,Alzheimers disease aided diagnosis research combining complex network analysis and machine learning method has received more and more attention.Usually,brain functional networks are used to describe the information of brain activity,and they are becoming one of the most important ways to diagnose some mental diseases.However,in the existing solutions,brain networks are mostly constructed based on signal matching in the time domain,which ignores the differences of brain activity information at various frequency bands.Furthermore,machine learning method cannot be applied to the data in the form of graphs.Based on it,we define the similarity between two graphs by the method of graph kernel,and then realize the classification by using the graph kernel for machine learning.Aiming at dealing with the problems,we propose a diagnosis method for multi-frequency fused graph kernel of brain network,which is used to classify the patients with Alzheimers disease and the normal people combined with the machine learning method.This method not only retains the multi-band information of brain activity,but also considers the unique topological characteristics of the brain network itself.Specifically,the resting state functional magnetic resonance imaging is first processed to different frequency bands by the wavelet transform technique,in order to obtain multiple frequency bands and construct the brain networks and calculate the graph kernels in different frequency bands of the image.In this way,it can express the activity information reflected by the brain at different frequencies.Secondly,the mutual information values between any two brain regions in each frequency band are calculated and the multi-frequency brain networks are obtained by comparing mutual information values and thresholds in different frequency bands.Then,the brain networks in all frequency bands together form a multi-frequency brain functional network model.After constructing the multi-frequency brain network model,the multi-frequency fused graph kernel of brain network is proposed.According to the obtained multi-frequency brain network model,the graph kernel between any two brain networks in the corresponding frequency band is calculated,and then the calculated graph kernels of different frequency bands are linearly combined according to the multiple kernel learning method.Consequently,all the graph kernels of different frequency bands are fused into one kernel to form a multi-frequency fused graph kernel of brain network.Finally,a multi-frequency fused graph kernel and kernel-based extreme learning machine are combined to use for diagnosis of Alzheimers disease on real data sets acquired from the ADNI database and OASIS database.At the same time,the influence of different parameter settings on the diagnostic results is also tested by the experiments.The experimental results show that the proposed multi-frequency fused graph kernel of brain network can get the best performance and improve the diagnostic accuracy by 13.79%and 15.29%compared to the best results of the comparison method on ADNI data sets and OASIS data sets respectively.These results reflect that the multi-frequency fused graph kernel can better describe the similarity between brain networks through the structural information and multi-frequency information of the brain functional networks.
中文关键词	阿尔茨海默病 ; 功能磁共振成像 ; 脑功能网络 ; 多频融合图核 ; 核极限学习机
英文关键词	Alzheimers disease functional magnetic resonance imaging brain functional network multi-frequency fused graph kernel kernel-based extreme learning machine
类型	Article
语种	中文
收录类别	CSCD
WOS类目	Computer Science
CSCD记录号	CSCD:6655705
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/353633
作者单位	汪新蕾, 东北大学计算机科学与工程学院;;东北大学医学与生物信息工程学院, ;;, 沈阳;;沈阳, ;; 110169;;110169. 王之琼, 东北大学医学与生物信息工程学院;;沈阳东软智能医疗科技研究院有限公司, ;;, 沈阳;;沈阳, ;; 110169;;110000. 王中阳, 东北大学医学与生物信息工程学院, 沈阳, 辽宁 110169, 中国. 信俊昌, 东北大学计算机科学与工程学院, 沈阳, 辽宁 110169, 中国. 谷峪, 东北大学计算机科学与工程学院, 沈阳, 辽宁 110169, 中国.
推荐引用方式 GB/T 7714	汪新蕾,王之琼,王中阳,等. 面向阿尔茨海默病的脑网络多频融合图核[J],2020,43(1).
APA	汪新蕾,王之琼,王中阳,信俊昌,&谷峪.(2020).面向阿尔茨海默病的脑网络多频融合图核.计算机学报,43(1).
MLA	汪新蕾,et al."面向阿尔茨海默病的脑网络多频融合图核".计算机学报 43.1(2020).