Picard版本分析功能磁共振成像数据的比较

Yulong Xiong, Qin Yu, Shuang He, Haitong Tang, Kaiyue Liu, Ni-zhuan Wang
{"title":"Picard版本分析功能磁共振成像数据的比较","authors":"Yulong Xiong, Qin Yu, Shuang He, Haitong Tang, Kaiyue Liu, Ni-zhuan Wang","doi":"10.1145/3448748.3448818","DOIUrl":null,"url":null,"abstract":"Independent Component Analysis (ICA) is a popular method that uses statistical principles to separate the mixture into statistically independent non-Gaussian sources. It has been well used in functional Magnetic Resonance Imaging (fMRI) data. However, real fMRI data can rarely be accurately modeled as mixtures of independent components, the convergence of ICA may be impaired. This paper is based on the idea of preconditioned ICA for real data (Picard), which involves a preprocessing L-BFGS strategy based on orthogonal matrix sets. In this study, we designed an experiment to validate the idea that Picard can improve ICA algorithms such as Infomax, Extended-Infomax, and FastICA, respectively named Picard 1, Picard 2, and Picard 3, for fMRI data analysis. Three Picard versions were performed on the simulated and noisy fMRI mixtures to verify the ability to separate independent sources. Experimental results showed that Picard 3 outperformed Picard 1 and Picard 2 on both noiseless and noisy simulated fMRI data, which implied the priority of Picard 3 in fMRI data analysis.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Comparison of Picard Versions for Analyzing functional Magnetic Resonance Imaging Data\",\"authors\":\"Yulong Xiong, Qin Yu, Shuang He, Haitong Tang, Kaiyue Liu, Ni-zhuan Wang\",\"doi\":\"10.1145/3448748.3448818\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Independent Component Analysis (ICA) is a popular method that uses statistical principles to separate the mixture into statistically independent non-Gaussian sources. It has been well used in functional Magnetic Resonance Imaging (fMRI) data. However, real fMRI data can rarely be accurately modeled as mixtures of independent components, the convergence of ICA may be impaired. This paper is based on the idea of preconditioned ICA for real data (Picard), which involves a preprocessing L-BFGS strategy based on orthogonal matrix sets. In this study, we designed an experiment to validate the idea that Picard can improve ICA algorithms such as Infomax, Extended-Infomax, and FastICA, respectively named Picard 1, Picard 2, and Picard 3, for fMRI data analysis. Three Picard versions were performed on the simulated and noisy fMRI mixtures to verify the ability to separate independent sources. Experimental results showed that Picard 3 outperformed Picard 1 and Picard 2 on both noiseless and noisy simulated fMRI data, which implied the priority of Picard 3 in fMRI data analysis.\",\"PeriodicalId\":115821,\"journal\":{\"name\":\"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing\",\"volume\":\"101 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3448748.3448818\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3448748.3448818","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

独立分量分析(ICA)是一种常用的方法,它利用统计原理将混合数据分离成统计独立的非高斯源。它在功能磁共振成像(fMRI)数据中得到了很好的应用。然而,真实的fMRI数据很少能被准确地建模为独立成分的混合物,ICA的收敛性可能会受到损害。本文以实际数据预处理ICA (Picard)思想为基础,提出了一种基于正交矩阵集的预处理L-BFGS策略。在本研究中,我们设计了一个实验来验证Picard可以改进用于fMRI数据分析的ICA算法,如Infomax、Extended-Infomax和FastICA,分别命名为Picard 1、Picard 2和Picard 3。在模拟和噪声fMRI混合物上执行了三个Picard版本,以验证分离独立源的能力。实验结果表明,在无噪声和有噪声的模拟fMRI数据上,Picard 3都优于Picard 1和Picard 2,这表明Picard 3在fMRI数据分析中具有优先性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Comparison of Picard Versions for Analyzing functional Magnetic Resonance Imaging Data
Independent Component Analysis (ICA) is a popular method that uses statistical principles to separate the mixture into statistically independent non-Gaussian sources. It has been well used in functional Magnetic Resonance Imaging (fMRI) data. However, real fMRI data can rarely be accurately modeled as mixtures of independent components, the convergence of ICA may be impaired. This paper is based on the idea of preconditioned ICA for real data (Picard), which involves a preprocessing L-BFGS strategy based on orthogonal matrix sets. In this study, we designed an experiment to validate the idea that Picard can improve ICA algorithms such as Infomax, Extended-Infomax, and FastICA, respectively named Picard 1, Picard 2, and Picard 3, for fMRI data analysis. Three Picard versions were performed on the simulated and noisy fMRI mixtures to verify the ability to separate independent sources. Experimental results showed that Picard 3 outperformed Picard 1 and Picard 2 on both noiseless and noisy simulated fMRI data, which implied the priority of Picard 3 in fMRI data analysis.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Comparison of Picard Versions for Analyzing functional Magnetic Resonance Imaging Data Study on Dispatching Model of Block Economy Based-Data Mining A Modified HOG Algorithm based on the Prewitt Operator RNA-seq Reveals the Increased Risk of Heart and Cardiovascular Disease by SARS-CoV-2 Infection Curative Effect of Tongyu Decoction on Neurological Deficit and Rehabilitation Effect of Patients with Cerebral Hemorrhage in Recovery Period
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1