Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation
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. Furthe...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV
2021
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| Online Access: | https://hdl.handle.net/1721.1/134473 |
| _version_ | 1826192936976515072 |
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| author | Ou, Wanmei Nummenmaa, Aapo Ahveninen, Jyrki Belliveau, John W Hämäläinen, Matti S Golland, Polina |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Ou, Wanmei Nummenmaa, Aapo Ahveninen, Jyrki Belliveau, John W Hämäläinen, Matti S Golland, Polina |
| author_sort | Ou, Wanmei |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T09:31:26Z |
| format | Article |
| id | mit-1721.1/134473 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:31:26Z |
| publishDate | 2021 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1344732023-10-05T20:01:34Z Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation Ou, Wanmei Nummenmaa, Aapo Ahveninen, Jyrki Belliveau, John W Hämäläinen, Matti S Golland, Polina Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory 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. 2021-10-27T20:05:10Z 2021-10-27T20:05:10Z 2010 2019-05-29T16:55:06Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/134473 en 10.1016/j.neuroimage.2010.03.001 NeuroImage Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV PMC |
| spellingShingle | Ou, Wanmei Nummenmaa, Aapo Ahveninen, Jyrki Belliveau, John W Hämäläinen, Matti S Golland, Polina Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation |
| title | Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation |
| title_full | Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation |
| title_fullStr | Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation |
| title_full_unstemmed | Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation |
| title_short | Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation |
| title_sort | multimodal functional imaging using fmri informed regional eeg meg source estimation |
| url | https://hdl.handle.net/1721.1/134473 |
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