| Project/Area Number |
17300092
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | Institute of Statistical Mathematics |
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Principal Investigator |
OZAKI Tohru ISM, Dept.Statistical Modeling, Professor, モデリング研究系, 教授 (00000208)
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| Co-Investigator(Kenkyū-buntansha) |
SADATOU Norihiro IISP, Division of Cerebral Integration, Professor, 大脳皮質機能研究系, 教授 (00273003)
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| Project Period (FY) |
2005 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥13,600,000 (Direct Cost: ¥13,600,000)
Fiscal Year 2006: ¥6,600,000 (Direct Cost: ¥6,600,000)
Fiscal Year 2005: ¥7,000,000 (Direct Cost: ¥7,000,000)
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| Keywords | 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 |
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| Research Abstract |
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.
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