2011 Fiscal Year Final Research Report
Fifth-dimensional bio-electromagnetic brain imaging
| Project/Area Number |
20500394
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Biomedical engineering/Biological material science
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| Research Institution | Tokyo Metropolitan University |
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Principal Investigator |
SEKIHARA Kensuke 首都大学東京, システムデザイン研究科, 教授 (40326020)
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| Project Period (FY) |
2008 – 2011
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| Keywords | 生体情報・計測 / 脳機能計測 / 脳画像イメージング |
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| Research Abstract |
The spatiotemporal dynamics of cortical oscillations across human brain regions remain poorly understood because of a lack of adequately validated methods for reconstructing such activity from noninvasive electrophysiological data. In this research project, we have proposed a novel algorithms optimized for robust source time. frequency reconstruction from magnetoencephalography(MEG) data. The efficacy of the method is demonstrated with simulated sources and is also applied to real MEG data. Source-space coherence analysis involves solving the inverse problem, estimating the time courses of specific brain regions, and then examining the coherence between activities at different brain regions. However, such an analysis is confounded by spurious coherence caused due to the leakage properties of the inverse algorithm employed. Such spurious coherence is typically manifested as an artifactual large peak around the seed voxel, called seed blur, in the resulting coherence images. This seed bl
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ur often obscures important details of brain interactions. We propose the use of the imaginary part of the coherence to remove the spurious coherence caused by the leakage of an imaging algorithm. We have performed a theoretical analysis that explains how the use of imaginary part can remove this spurious coherence, and this analysis has been validated by both computer simulations and experiments using resting-state MEG data. We investigate the possibility of estimating causal influences among brain activities. One promising measures are Granger-causality-based measures. However, the MVAR modeling used for estimating Granger-causality-based measures does not take interferences into account, and the background interferences may cause significant amount of errors in the estimated MVAR coefficients, leading to completely wrong causality relationships. In this research we have tested the effectiveness of imposing the sparsity on AR solutions for reducing the influence of the background brain noise in the source-space causality analysis. Less
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[Journal Article] Five-dimensional neuroimaging : Localization of the time-frequency dynamics of cortical activity2008
Author(s)
Dalal SS, Guggisberg AG, Edwards E, Sekihara K, Findlay AM, Canolty RT, Berger MS, Knight RT, Barbaro NM, Kirsch HE, Nagarajan SS
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Journal Title
NeuroImage
Volume: 40
Pages: 1686-1700
Peer Reviewed
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