Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation
Name
nihms202466.pdf
Description
Accepted version
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3.44 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Ou, Wanmei
Nummenmaa, Aapo
Ahveninen, Jyrki
Belliveau, John W
Hämäläinen, Matti S
Golland, Polina
Date Issued
2010
Journal
NeuroImage
Publisher
Elsevier BV
Version
Author's final manuscript
Abstract
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. © 2010 Elsevier Inc.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.neuroimage.2010.03.001
