• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

时空脑磁图协方差矩阵建模为克罗内克积之和。

The spatiotemporal MEG covariance matrix modeled as a sum of Kronecker products.

作者信息

Bijma Fetsje, de Munck Jan C, Heethaar Rob M

机构信息

Department PMT, Vrije Universiteit Medical Center, MEG Center, De Boelelaan 1118, Amsterdam, The Netherlands.

出版信息

Neuroimage. 2005 Aug 15;27(2):402-15. doi: 10.1016/j.neuroimage.2005.04.015.

DOI:10.1016/j.neuroimage.2005.04.015
PMID:16019231
Abstract

The single Kronecker product (KP) model for the spatiotemporal covariance of MEG residuals is extended to a sum of Kronecker products. This sum of KP is estimated such that it approximates the spatiotemporal sample covariance best in matrix norm. Contrary to the single KP, this extension allows for describing multiple, independent phenomena in the ongoing background activity. Whereas the single KP model can be interpreted by assuming that background activity is generated by randomly distributed dipoles with certain spatial and temporal characteristics, the sum model can be physiologically interpreted by assuming a composite of such processes. Taking enough terms into account, the spatiotemporal sample covariance matrix can be described exactly by this extended model. In the estimation of the sum of KP model, it appears that the sum of the first 2 KP describes between 67% and 93%. Moreover, these first two terms describe two physiological processes in the background activity: focal, frequency-specific alpha activity, and more widespread non-frequency-specific activity. Furthermore, temporal nonstationarities due to trial-to-trial variations are not clearly visible in the first two terms, and, hence, play only a minor role in the sample covariance matrix in terms of matrix power. Considering the dipole localization, the single KP model appears to describe around 80% of the noise and seems therefore adequate. The emphasis of further improvement of localization accuracy should be on improving the source model rather than the covariance model.

摘要

用于脑磁图(MEG)残差时空协方差的单克罗内克积(KP)模型被扩展为克罗内克积之和。对该KP之和进行估计,使其在矩阵范数下能最佳逼近时空样本协方差。与单KP模型不同,这种扩展允许描述正在进行的背景活动中的多种独立现象。单KP模型可通过假设背景活动由具有特定时空特征的随机分布偶极子产生来解释,而和模型可通过假设此类过程的组合从生理学角度进行解释。考虑足够多的项,该扩展模型可精确描述时空样本协方差矩阵。在估计KP和模型时,前两个KP之和似乎占67%至93%。此外,前两项描述了背景活动中的两个生理过程:局灶性、频率特异性的阿尔法活动,以及更广泛的非频率特异性活动。而且,由于逐次试验变化导致的时间非平稳性在前两项中并不明显,因此在样本协方差矩阵的矩阵幂方面仅起次要作用。考虑到偶极子定位,单KP模型似乎能描述约80%的噪声,因此看起来是足够的。进一步提高定位精度的重点应放在改进源模型而非协方差模型上。

相似文献

1
The spatiotemporal MEG covariance matrix modeled as a sum of Kronecker products.时空脑磁图协方差矩阵建模为克罗内克积之和。
Neuroimage. 2005 Aug 15;27(2):402-15. doi: 10.1016/j.neuroimage.2005.04.015.
2
Spatiotemporal noise covariance estimation from limited empirical magnetoencephalographic data.从有限的经验性脑磁图数据估计时空噪声协方差
Phys Med Biol. 2006 Nov 7;51(21):5549-64. doi: 10.1088/0031-9155/51/21/011. Epub 2006 Oct 9.
3
A space-frequency analysis of MEG background processes.
Neuroimage. 2008 Nov 15;43(3):478-88. doi: 10.1016/j.neuroimage.2008.07.061. Epub 2008 Aug 13.
4
Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data.用于脑磁图神经成像数据的时空贝叶斯推理偶极子分析
Neuroimage. 2005 Oct 15;28(1):84-98. doi: 10.1016/j.neuroimage.2005.06.003. Epub 2005 Jul 15.
5
Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model.基于参数噪声协方差模型的时空脑电图/脑磁图源分析
IEEE Trans Biomed Eng. 2002 Jun;49(6):533-9. doi: 10.1109/TBME.2002.1001967.
6
A mathematical approach to the temporal stationarity of background noise in MEG/EEG measurements.一种用于脑磁图/脑电图测量中背景噪声时间平稳性的数学方法。
Neuroimage. 2003 Sep;20(1):233-43. doi: 10.1016/s1053-8119(03)00215-5.
7
A random dipole model for spontaneous brain activity.
IEEE Trans Biomed Eng. 1992 Aug;39(8):791-804. doi: 10.1109/10.148387.
8
Localization of realistic cortical activity in MEG using current multipoles.使用电流多极子在脑磁图中定位真实的皮质活动。
Neuroimage. 2004 Jun;22(2):779-93. doi: 10.1016/j.neuroimage.2004.02.010.
9
Modeling spatiotemporal covariance for magnetoencephalography or electroencephalography source analysis.用于脑磁图或脑电图源分析的时空协方差建模。
Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Jan;75(1 Pt 1):011928. doi: 10.1103/PhysRevE.75.011928. Epub 2007 Jan 30.
10
Maximum-likelihood estimation of low-rank signals for multiepoch MEG/EEG analysis.用于多时段脑磁图/脑电图分析的低秩信号的最大似然估计
IEEE Trans Biomed Eng. 2004 Nov;51(11):1981-93. doi: 10.1109/TBME.2004.834285.

引用本文的文献

1
TENSOR REGRESSION FOR INCOMPLETE OBSERVATIONS WITH APPLICATION TO LONGITUDINAL STUDIES.用于不完全观测的张量回归及其在纵向研究中的应用
Ann Appl Stat. 2024 Jun;18(2):1195-1212. doi: 10.1214/23-aoas1830. Epub 2024 Apr 5.
2
Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis.在 fMRI 数据分析中结合结构化假设和概率图形模型。
Neuropsychologia. 2020 Jul;144:107500. doi: 10.1016/j.neuropsychologia.2020.107500. Epub 2020 May 17.
3
Detecting Spatio-Temporal Modes in Multivariate Data by Entropy Field Decomposition.
通过熵场分解检测多元数据中的时空模式
J Phys A Math Theor. 2016 Sep 30;49(39). doi: 10.1088/1751-8113/49/39/395001. Epub 2016 Sep 6.
4
Dynamic Multiscale Modes of Resting State Brain Activity Detected by Entropy Field Decomposition.基于熵分解的静息态脑活动的动态多尺度模式。
Neural Comput. 2016 Sep;28(9):1769-811. doi: 10.1162/NECO_a_00871. Epub 2016 Jul 8.
5
Maximum-likelihood estimation of channel-dependent trial-to-trial variability of auditory evoked brain responses in MEG.脑磁图中听觉诱发脑反应的通道依赖性逐次试验变异性的最大似然估计
Biomed Eng Online. 2014 Jun 16;13:75. doi: 10.1186/1475-925X-13-75.
6
A distributed spatio-temporal EEG/MEG inverse solver.一种分布式时空脑电图/脑磁图逆解算器。
Med Image Comput Comput Assist Interv. 2008;11(Pt 1):26-34. doi: 10.1007/978-3-540-85988-8_4.
7
A distributed spatio-temporal EEG/MEG inverse solver.一种分布式时空脑电图/脑磁图逆解算器。
Neuroimage. 2009 Feb 1;44(3):932-46. doi: 10.1016/j.neuroimage.2008.05.063. Epub 2008 Jun 14.