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Multimodal Functional Imaging Using fMRI-Informed Regional EEG/MEG Source Estimation
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Institution: |
1Computer Science and Artificial Intelligence Laboratory, MIT, USA. wanmei@csail.mit.edu 2Athinoula A. Martinos Center for Biomedical Imaging, MGH, USA. |
Publisher: |
Inf Process Med Imaging IPMI 2009 |
Publication Date: |
Jul-2009 |
Citation: |
Inf Process Med Imaging. 2009;21:88-100. |
PubMed ID: |
19694255 |
Appears in Collections: |
NAC, NA-MIC |
Sponsors: |
NIH NIBIB NAMIC U54 EB005149 NIH NCRR NAC P41 RR13218 NIH NCRR P41 RR14075 NSF CAREER Award 0642971 PHS training grant DA022759-03 |
Generated Citation: |
Ou W, Nummenmaa A, Hamalainen M, Golland P. Multimodal Functional Imaging Using fMRI-Informed Regional EEG/MEG Source Estimation. Inf Process Med Imaging. 2009;21:88-100. PMID: 19694255. |
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| Paper: | Download, View online |
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We propose a novel method, fMRI-Informed Regional Estimation (FIRE), which utilizes information from fMRI in E/MEG source reconstruction. FIRE takes advantage of the spatial alignment between the neural and the vascular activities, while allowing for substantial differences in their dynamics. Furthermore, with the regional approach, FIRE can be efficiently applied to a dense grid of sources. Inspection of our optimization procedure reveals that FIRE is related to the re-weighted minimum-norm algorithms, the difference being that the weights in the proposed approach are computed from both the current estimates and fMRI data. Analysis of both simulated and human fMRI-MEG data shows that FIRE reduces the ambiguities in source localization present in the minimum-norm estimates. Comparisons with several joint fMRI-E/MEG algorithms demonstrate robustness of FIRE in the presence of sources silent to either fMRI or E/MEG measurements.
Additional Material
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