权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Time series modeling of non-invasive measurement of brain functioning

脑功能无创测量的时间序列建模

基本信息

批准号：
17300092
负责人：
OZAKI Tohru
金额：
$ 8.7万
依托单位：
Institute of Statistical Mathematics
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2005
资助国家：
日本
起止时间：
2005 至 2006
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17300092/
关键词：
fMRI modeling spatial time series model hemodyanmics innovation approach EEG modeling inverse problem dynamic inverse problem space-time Kalman filter イノヴェーションアプローイノヴェーションアプローチ

项目摘要

In the present project we have developed a ToolBox for fMRI Connectivity Analysis. Here we employ NN-ARX model which we developed in our previous JSPS project. We use the model to whiten the 140,000 dimensional fMRI time series into 140,000 dimensional innovation series. Here all the obvious correlations between the neighboring voxels and the activated voxels by the stimulus are removed by the whitening. We proved that it can be effectively used for discovering dynamic connectivity between remaining remote voxels. The test version of the toolbox has been already used by our collaborators in National Institute of Physiological Science and Tohoku University Medical School for a few experimental data of fMRI. A method for estimating the dynamic causality between a few voxels is also implemented in the toolbox.We also developed a statistical method for decomposing the EEG/MEG data into component signals by using state space modeling techniques. Advantage over non-dynamic signal decomposition method such as ICA is confirmed.

在目前的项目中，我们已经开发了一个工具箱用于fMRI连接分析。在这里，我们使用我们在以前的jsp项目中开发的NN-ARX模型。我们使用该模型将14万维的fMRI时间序列白化为14万维的创新序列。在这里，所有邻近体素和被刺激激活的体素之间的明显相关性都被白化去除。我们证明了它可以有效地用于发现剩余远程体素之间的动态连接。工具箱的测试版本已经被我们在国立生理科学研究所和东北大学医学院的合作者用于fMRI的一些实验数据。在工具箱中还实现了一种估计几个体素之间动态因果关系的方法。我们还开发了一种利用状态空间建模技术将EEG/MEG数据分解成分量信号的统计方法。验证了该方法相对于ICA等非动态信号分解方法的优越性。