The Publication Database hosted by SPL
|
All Publications
Upload
Advanced Search
Gallery View
Download Statistics
Help
Import
Log in
Multimodal Functional Imaging using fMRI-informed Regional EEG/MEG Source Estimation
|
|
Institution: |
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. wanmei@mit.edu |
Publisher: |
Elsevier Science |
Publication Date: |
Aug-2010 |
Journal: |
Neuroimage |
Volume Number: |
52 |
Issue Number: |
1 |
Pages: |
97-108 |
Citation: |
Neuroimage. 2010 Aug 1;52(1):97-108. |
PubMed ID: |
20211266 |
PMCID: |
PMC2884199 |
Keywords: |
EEG, MEG, Inverse solver, fMRI, Expectation, maximization, Re-weighted minimum-norm estimate |
Appears in Collections: |
NA-MIC, NAC |
Sponsors: |
5R01-EB006385-03 (EB) funded by NIBIB NIH HHS DA022759-03 (DA) funded by NIDA NIH HHS P41 RR014075-075760 (RR) funded by NCRR NIH HHS P41 RR13218 (RR) funded by NCRR NIH HHS P41 RR14075 (RR) funded by NCRR NIH HHS R01 EB006385-01A1 (EB) funded by NIBIB NIH HHS R01 HD040712-01A2 (HD) funded by NICHD NIH HHS R01 NS037462-08 (NS) funded by NINDS NIH HHS R01 NS048279-01A2 (NS) funded by NINDS NIH HHS R01 HD040712 (HD) funded by NICHD NIH HHS R01 NS037462 (NS) funded by NINDS NIH HHS R01 NS048279 (NS) funded by NINDS NIH HHS U54 EB005149 (EB) funded by NIBIB NIH HHS |
Generated Citation: |
Ou W., Nummenmaa A., Ahveninen J., Belliveau J.W., Hämäläinen M.S., Golland P. Multimodal Functional Imaging using fMRI-informed Regional EEG/MEG Source Estimation. Neuroimage. 2010 Aug 1;52(1):97-108. PMID: 20211266. PMCID: PMC2884199. |
| Downloaded: | 580 times. [view map] |
| Paper: | Download, View online |
| Export citation: |
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 a region-based approach, FIRE estimates the model parameters for each region independently. Hence, it can be efficiently applied on a dense grid of source locations. The optimization procedure at the core of FIRE is related to the re-weighted minimum-norm algorithms. The weights in the proposed approach are computed from both the current source estimates and fMRI data, leading to robust estimates in the presence of silent sources in either fMRI or E/MEG measurements. We employ a Monte Carlo evaluation procedure to compare the proposed method to several other joint E/MEG-fMRI algorithms. Our results show that FIRE provides the best trade-off in estimation accuracy between the spatial and the temporal accuracy. Analysis using human E/MEG-fMRI data reveals that FIRE significantly reduces the ambiguities in source localization present in the minimum-norm estimates, and that it accurately captures activation timing in adjacent functional regions.
Additional Material
1 File (174.503kB)
Ou-Neuroimage2010-fig1.jpg (174.503kB)