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Mapping the topological organisation of beta oscillations in motor cortex using MEG
The spatial topology of the human motor cortex has been well studied, particularly using functional Magnetic Resonance Imaging (fMRI) which allows spatial separation of haemodynamic responses arising from stimulation of different body parts, individual digits and even spatially separate areas of the...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Academic Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6150950/ https://www.ncbi.nlm.nih.gov/pubmed/29960087 http://dx.doi.org/10.1016/j.neuroimage.2018.06.041 |
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author | Barratt, Eleanor L. Francis, Susan T. Morris, Peter G. Brookes, Matthew J. |
author_facet | Barratt, Eleanor L. Francis, Susan T. Morris, Peter G. Brookes, Matthew J. |
author_sort | Barratt, Eleanor L. |
collection | PubMed |
description | The spatial topology of the human motor cortex has been well studied, particularly using functional Magnetic Resonance Imaging (fMRI) which allows spatial separation of haemodynamic responses arising from stimulation of different body parts, individual digits and even spatially separate areas of the same digit. However, the spatial organisation of electrophysiological responses, particularly neural oscillations (rhythmic changes in electrical potential across cellular assemblies) has been less well studied. Mapping the spatial signature of neural oscillations is possible using magnetoencephalography (MEG), however spatial differentiation of responses induced by movement of separate digits is a challenge, because the brain regions involved are separated by only a few millimetres. In this paper we first show, in simulation, how to optimise experimental design and beamformer spatial filtering techniques to increase the spatial specificity of MEG derived functional images. Combining this result with experimental data, we then capture the organisation of the post-movement beta band (13–30 Hz) oscillatory response to movement of digits 2 and 5 of the dominant hand, in individual subjects. By comparing these MEG results to ultra-high field (7T) fMRI, we also show significant spatial agreement between beta modulation and the blood oxygenation level dependent (BOLD) response. Our results show that, when using an optimised inverse solution and controlling subject movement (using custom fitted foam padding) the spatial resolution of MEG can be of order 3–5 mm. The method described offers exciting potential to understand better the cortical organisation of oscillations, and to probe such organisation in patient populations where those oscillations are known to be abnormal. |
format | Online Article Text |
id | pubmed-6150950 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Academic Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-61509502018-11-01 Mapping the topological organisation of beta oscillations in motor cortex using MEG Barratt, Eleanor L. Francis, Susan T. Morris, Peter G. Brookes, Matthew J. Neuroimage Article The spatial topology of the human motor cortex has been well studied, particularly using functional Magnetic Resonance Imaging (fMRI) which allows spatial separation of haemodynamic responses arising from stimulation of different body parts, individual digits and even spatially separate areas of the same digit. However, the spatial organisation of electrophysiological responses, particularly neural oscillations (rhythmic changes in electrical potential across cellular assemblies) has been less well studied. Mapping the spatial signature of neural oscillations is possible using magnetoencephalography (MEG), however spatial differentiation of responses induced by movement of separate digits is a challenge, because the brain regions involved are separated by only a few millimetres. In this paper we first show, in simulation, how to optimise experimental design and beamformer spatial filtering techniques to increase the spatial specificity of MEG derived functional images. Combining this result with experimental data, we then capture the organisation of the post-movement beta band (13–30 Hz) oscillatory response to movement of digits 2 and 5 of the dominant hand, in individual subjects. By comparing these MEG results to ultra-high field (7T) fMRI, we also show significant spatial agreement between beta modulation and the blood oxygenation level dependent (BOLD) response. Our results show that, when using an optimised inverse solution and controlling subject movement (using custom fitted foam padding) the spatial resolution of MEG can be of order 3–5 mm. The method described offers exciting potential to understand better the cortical organisation of oscillations, and to probe such organisation in patient populations where those oscillations are known to be abnormal. Academic Press 2018-11-01 /pmc/articles/PMC6150950/ /pubmed/29960087 http://dx.doi.org/10.1016/j.neuroimage.2018.06.041 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Barratt, Eleanor L. Francis, Susan T. Morris, Peter G. Brookes, Matthew J. Mapping the topological organisation of beta oscillations in motor cortex using MEG |
title | Mapping the topological organisation of beta oscillations in motor cortex using MEG |
title_full | Mapping the topological organisation of beta oscillations in motor cortex using MEG |
title_fullStr | Mapping the topological organisation of beta oscillations in motor cortex using MEG |
title_full_unstemmed | Mapping the topological organisation of beta oscillations in motor cortex using MEG |
title_short | Mapping the topological organisation of beta oscillations in motor cortex using MEG |
title_sort | mapping the topological organisation of beta oscillations in motor cortex using meg |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6150950/ https://www.ncbi.nlm.nih.gov/pubmed/29960087 http://dx.doi.org/10.1016/j.neuroimage.2018.06.041 |
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