Encoding Cortical Dynamics in Sparse Features
Distributed cortical solutions of magnetoencephalography (MEG) and electroencephalography (EEG) exhibit complex spatial and temporal dynamics. The extraction of patterns of interest and dynamic features from these cortical signals has so far relied on the expertise of investigators. There is a defin...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | en_US |
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Frontiers Research Foundation
2014
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| Online Access: | http://hdl.handle.net/1721.1/88051 https://orcid.org/0000-0003-1967-7436 |
| _version_ | 1811086537083846656 |
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| author | Khan, Sheraz Lefevre, Julien Baillet, Sylvain Michmizos, Konstantinos Ganesan, Santosh Kitzbichler, Manfred G. Zetino, Manuel Hamalainen, Matti S. Papadelis, Christos Kenet, Tal |
| author2 | McGovern Institute for Brain Research at MIT |
| author_facet | McGovern Institute for Brain Research at MIT Khan, Sheraz Lefevre, Julien Baillet, Sylvain Michmizos, Konstantinos Ganesan, Santosh Kitzbichler, Manfred G. Zetino, Manuel Hamalainen, Matti S. Papadelis, Christos Kenet, Tal |
| author_sort | Khan, Sheraz |
| collection | MIT |
| description | Distributed cortical solutions of magnetoencephalography (MEG) and electroencephalography (EEG) exhibit complex spatial and temporal dynamics. The extraction of patterns of interest and dynamic features from these cortical signals has so far relied on the expertise of investigators. There is a definite need in both clinical and neuroscience research for a method that will extract critical features from high-dimensional neuroimaging data in an automatic fashion. We have previously demonstrated the use of optical flow techniques for evaluating the kinematic properties of motion field projected on non-flat manifolds like in a cortical surface. We have further extended this framework to automatically detect features in the optical flow vector field by using the modified and extended 2-Riemannian Helmholtz–Hodge decomposition (HHD). Here, we applied these mathematical models on simulation and MEG data recorded from a healthy individual during a somatosensory experiment and an epilepsy pediatric patient during sleep. We tested whether our technique can automatically extract salient dynamical features of cortical activity. Simulation results indicated that we can precisely reproduce the simulated cortical dynamics with HHD; encode them in sparse features and represent the propagation of brain activity between distinct cortical areas. Using HHD, we decoded the somatosensory N20 component into two HHD features and represented the dynamics of brain activity as a traveling source between two primary somatosensory regions. In the epilepsy patient, we displayed the propagation of the epileptic activity around the margins of a brain lesion. Our findings indicate that HHD measures computed from cortical dynamics can: (i) quantitatively access the cortical dynamics in both healthy and disease brain in terms of sparse features and dynamic brain activity propagation between distinct cortical areas, and (ii) facilitate a reproducible, automated analysis of experimental and clinical MEG/EEG source imaging data. |
| first_indexed | 2024-09-23T13:27:31Z |
| format | Article |
| id | mit-1721.1/88051 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:27:31Z |
| publishDate | 2014 |
| publisher | Frontiers Research Foundation |
| record_format | dspace |
| spelling | mit-1721.1/880512022-10-01T15:25:53Z Encoding Cortical Dynamics in Sparse Features Khan, Sheraz Lefevre, Julien Baillet, Sylvain Michmizos, Konstantinos Ganesan, Santosh Kitzbichler, Manfred G. Zetino, Manuel Hamalainen, Matti S. Papadelis, Christos Kenet, Tal McGovern Institute for Brain Research at MIT Khan, Sheraz Michmizos, Konstantinos Distributed cortical solutions of magnetoencephalography (MEG) and electroencephalography (EEG) exhibit complex spatial and temporal dynamics. The extraction of patterns of interest and dynamic features from these cortical signals has so far relied on the expertise of investigators. There is a definite need in both clinical and neuroscience research for a method that will extract critical features from high-dimensional neuroimaging data in an automatic fashion. We have previously demonstrated the use of optical flow techniques for evaluating the kinematic properties of motion field projected on non-flat manifolds like in a cortical surface. We have further extended this framework to automatically detect features in the optical flow vector field by using the modified and extended 2-Riemannian Helmholtz–Hodge decomposition (HHD). Here, we applied these mathematical models on simulation and MEG data recorded from a healthy individual during a somatosensory experiment and an epilepsy pediatric patient during sleep. We tested whether our technique can automatically extract salient dynamical features of cortical activity. Simulation results indicated that we can precisely reproduce the simulated cortical dynamics with HHD; encode them in sparse features and represent the propagation of brain activity between distinct cortical areas. Using HHD, we decoded the somatosensory N20 component into two HHD features and represented the dynamics of brain activity as a traveling source between two primary somatosensory regions. In the epilepsy patient, we displayed the propagation of the epileptic activity around the margins of a brain lesion. Our findings indicate that HHD measures computed from cortical dynamics can: (i) quantitatively access the cortical dynamics in both healthy and disease brain in terms of sparse features and dynamic brain activity propagation between distinct cortical areas, and (ii) facilitate a reproducible, automated analysis of experimental and clinical MEG/EEG source imaging data. Nancy Lurie Marks Family Foundation Simons Foundation National Institute for Biomedical Imaging and Bioengineering (U.S.) (NIBIB:5R01EB009048) National Institute for Biomedical Imaging and Bioengineering (U.S.) (NIBIB:P41RR014075) Fonds de la recherche en santé du Québec (Senior-Scientist Salary Award, Quebec Fund for Health Research) National Institutes of Health (U.S.) (NIH 2R01EB009048-05) Natural Sciences and Engineering Research Council of Canada (Discovery Grant) 2014-06-20T16:58:57Z 2014-06-20T16:58:57Z 2014-05 2014-03 Article http://purl.org/eprint/type/JournalArticle 1662-5161 http://hdl.handle.net/1721.1/88051 Khan, Sheraz, Julien Lefevre, Sylvain Baillet, Konstantinos P. Michmizos, Santosh Ganesan, Manfred G. Kitzbichler, Manuel Zetino, Matti S. Hamalainen, Christos Papadelis, and Tal Kenet. “Encoding Cortical Dynamics in Sparse Features.” Frontiers in Human Neuroscience 8 (May 23, 2014). https://orcid.org/0000-0003-1967-7436 en_US http://dx.doi.org/10.3389/fnhum.2014.00338 Frontiers in Human Neuroscience Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Frontiers Research Foundation Frontiers Research Foundation |
| spellingShingle | Khan, Sheraz Lefevre, Julien Baillet, Sylvain Michmizos, Konstantinos Ganesan, Santosh Kitzbichler, Manfred G. Zetino, Manuel Hamalainen, Matti S. Papadelis, Christos Kenet, Tal Encoding Cortical Dynamics in Sparse Features |
| title | Encoding Cortical Dynamics in Sparse Features |
| title_full | Encoding Cortical Dynamics in Sparse Features |
| title_fullStr | Encoding Cortical Dynamics in Sparse Features |
| title_full_unstemmed | Encoding Cortical Dynamics in Sparse Features |
| title_short | Encoding Cortical Dynamics in Sparse Features |
| title_sort | encoding cortical dynamics in sparse features |
| url | http://hdl.handle.net/1721.1/88051 https://orcid.org/0000-0003-1967-7436 |
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