A spatiotemporal dynamic distributed solution to the MEG inverse problem
MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution. However, estimation of brain source currents from surface recordings requires solving an ill-conditioned inverse problem. Converging lines of evidence in neuroscience, from neuronal network models t...
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| Format: | Article |
| Language: | en_US |
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Elsevier
2016
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| Online Access: | http://hdl.handle.net/1721.1/102244 https://orcid.org/0000-0001-5651-5060 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0001-6841-112X https://orcid.org/0000-0002-6777-7979 |
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| author | Lamus, Camilo Temereanca, Simona Brown, Emery N. Hamalainen, Matti S. Purdon, Patrick Lee |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Lamus, Camilo Temereanca, Simona Brown, Emery N. Hamalainen, Matti S. Purdon, Patrick Lee |
| author_sort | Lamus, Camilo |
| collection | MIT |
| description | MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution. However, estimation of brain source currents from surface recordings requires solving an ill-conditioned inverse problem. Converging lines of evidence in neuroscience, from neuronal network models to resting-state imaging and neurophysiology, suggest that cortical activation is a distributed spatiotemporal dynamic process, supported by both local and long-distance neuroanatomic connections. Because spatiotemporal dynamics of this kind are central to brain physiology, inverse solutions could be improved by incorporating models of these dynamics. In this article, we present a model for cortical activity based on nearest-neighbor autoregression that incorporates local spatiotemporal interactions between distributed sources in a manner consistent with neurophysiology and neuroanatomy. We develop a dynamic Maximum a Posteriori Expectation-Maximization (dMAP-EM) source localization algorithm for estimation of cortical sources and model parameters based on the Kalman Filter, the Fixed Interval Smoother, and the EM algorithms. We apply the dMAP-EM algorithm to simulated experiments as well as to human experimental data. Furthermore, we derive expressions to relate our dynamic estimation formulas to those of standard static models, and show how dynamic methods optimally assimilate past and future data. Our results establish the feasibility of spatiotemporal dynamic estimation in large-scale distributed source spaces with several thousand source locations and hundreds of sensors, with resulting inverse solutions that provide substantial performance improvements over static methods. |
| first_indexed | 2024-09-23T13:11:01Z |
| format | Article |
| id | mit-1721.1/102244 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:11:01Z |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1022442022-10-01T13:35:44Z A spatiotemporal dynamic distributed solution to the MEG inverse problem Lamus, Camilo Temereanca, Simona Brown, Emery N. Hamalainen, Matti S. Purdon, Patrick Lee Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences McGovern Institute for Brain Research at MIT Picower Institute for Learning and Memory Lamus, Camilo Hamalainen, Matti S. Brown, Emery N. Temereanca, Simona Purdon, Patrick Lee MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution. However, estimation of brain source currents from surface recordings requires solving an ill-conditioned inverse problem. Converging lines of evidence in neuroscience, from neuronal network models to resting-state imaging and neurophysiology, suggest that cortical activation is a distributed spatiotemporal dynamic process, supported by both local and long-distance neuroanatomic connections. Because spatiotemporal dynamics of this kind are central to brain physiology, inverse solutions could be improved by incorporating models of these dynamics. In this article, we present a model for cortical activity based on nearest-neighbor autoregression that incorporates local spatiotemporal interactions between distributed sources in a manner consistent with neurophysiology and neuroanatomy. We develop a dynamic Maximum a Posteriori Expectation-Maximization (dMAP-EM) source localization algorithm for estimation of cortical sources and model parameters based on the Kalman Filter, the Fixed Interval Smoother, and the EM algorithms. We apply the dMAP-EM algorithm to simulated experiments as well as to human experimental data. Furthermore, we derive expressions to relate our dynamic estimation formulas to those of standard static models, and show how dynamic methods optimally assimilate past and future data. Our results establish the feasibility of spatiotemporal dynamic estimation in large-scale distributed source spaces with several thousand source locations and hundreds of sensors, with resulting inverse solutions that provide substantial performance improvements over static methods. 2016-04-15T15:38:44Z 2016-04-15T15:38:44Z 2011-11 2011-11 Article http://purl.org/eprint/type/JournalArticle 10538119 http://hdl.handle.net/1721.1/102244 Lamus, Camilo, Matti S. Hamalainen, Simona Temereanca, Emery N. Brown, and Patrick L. Purdon. “A Spatiotemporal Dynamic Distributed Solution to the MEG Inverse Problem.” NeuroImage 63, no. 2 (November 2012): 894–909. https://orcid.org/0000-0001-5651-5060 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0001-6841-112X https://orcid.org/0000-0002-6777-7979 en_US http://dx.doi.org/10.1016/j.neuroimage.2011.11.020 NeuroImage Creative Commons Attribution-Noncommercial-NoDerivatives http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier PMC |
| spellingShingle | Lamus, Camilo Temereanca, Simona Brown, Emery N. Hamalainen, Matti S. Purdon, Patrick Lee A spatiotemporal dynamic distributed solution to the MEG inverse problem |
| title | A spatiotemporal dynamic distributed solution to the MEG inverse problem |
| title_full | A spatiotemporal dynamic distributed solution to the MEG inverse problem |
| title_fullStr | A spatiotemporal dynamic distributed solution to the MEG inverse problem |
| title_full_unstemmed | A spatiotemporal dynamic distributed solution to the MEG inverse problem |
| title_short | A spatiotemporal dynamic distributed solution to the MEG inverse problem |
| title_sort | spatiotemporal dynamic distributed solution to the meg inverse problem |
| url | http://hdl.handle.net/1721.1/102244 https://orcid.org/0000-0001-5651-5060 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0001-6841-112X https://orcid.org/0000-0002-6777-7979 |
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