Measurement of dynamic task related functional networks using MEG
The characterisation of dynamic electrophysiological brain networks, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method for measuring such networks in the human brain using magnetoencephalography (ME...
| Main Authors: | , , , , , , , |
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/39029/ |
| _version_ | 1848795746103459840 |
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| author | O’Neill, George C. Tewarie, Prejaas K. Colclough, Giles L. Gascoyne, Lauren E. Hunt, Benjamin A.E. Morris, Peter G. Woolrich, Mark W. Brookes, Matthew J. |
| author_facet | O’Neill, George C. Tewarie, Prejaas K. Colclough, Giles L. Gascoyne, Lauren E. Hunt, Benjamin A.E. Morris, Peter G. Woolrich, Mark W. Brookes, Matthew J. |
| author_sort | O’Neill, George C. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The characterisation of dynamic electrophysiological brain networks, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method for measuring such networks in the human brain using magnetoencephalography (MEG). Previous network analyses look for brain regions that share a common temporal profile of activity. Here distinctly, we exploit the high spatio-temporal resolution of MEG to measure the temporal evolution of connectivity between pairs of parcellated brain regions. We then use an ICA based procedure to identify networks of connections whose temporal dynamics covary. We validate our method using MEG data recorded during a finger movement task, identifying a transient network of connections linking somatosensory and primary motor regions, which modulates during the task. Next, we use our method to image the networks which support cognition during a Sternberg working memory task. We generate a novel neuroscientific picture of cognitive processing, showing the formation and dissolution of multiple networks which relate to semantic processing, pattern recognition and language as well as vision and movement. Our method tracks the dynamics of functional connectivity in the brain on a timescale commensurate to the task they are undertaking. |
| first_indexed | 2025-11-14T19:36:59Z |
| format | Article |
| id | nottingham-39029 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:36:59Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-390292020-05-04T18:05:30Z https://eprints.nottingham.ac.uk/39029/ Measurement of dynamic task related functional networks using MEG O’Neill, George C. Tewarie, Prejaas K. Colclough, Giles L. Gascoyne, Lauren E. Hunt, Benjamin A.E. Morris, Peter G. Woolrich, Mark W. Brookes, Matthew J. The characterisation of dynamic electrophysiological brain networks, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method for measuring such networks in the human brain using magnetoencephalography (MEG). Previous network analyses look for brain regions that share a common temporal profile of activity. Here distinctly, we exploit the high spatio-temporal resolution of MEG to measure the temporal evolution of connectivity between pairs of parcellated brain regions. We then use an ICA based procedure to identify networks of connections whose temporal dynamics covary. We validate our method using MEG data recorded during a finger movement task, identifying a transient network of connections linking somatosensory and primary motor regions, which modulates during the task. Next, we use our method to image the networks which support cognition during a Sternberg working memory task. We generate a novel neuroscientific picture of cognitive processing, showing the formation and dissolution of multiple networks which relate to semantic processing, pattern recognition and language as well as vision and movement. Our method tracks the dynamics of functional connectivity in the brain on a timescale commensurate to the task they are undertaking. Elsevier 2016-08-29 Article PeerReviewed O’Neill, George C., Tewarie, Prejaas K., Colclough, Giles L., Gascoyne, Lauren E., Hunt, Benjamin A.E., Morris, Peter G., Woolrich, Mark W. and Brookes, Matthew J. (2016) Measurement of dynamic task related functional networks using MEG. NeuroImage . ISSN 1095-9572 (In Press) Network Dynamics Magnetoencephalography MEG Sternberg Task http://www.sciencedirect.com/science/article/pii/S1053811916304530 doi:10.1016/j.neuroimage.2016.08.061 doi:10.1016/j.neuroimage.2016.08.061 |
| spellingShingle | Network Dynamics Magnetoencephalography MEG Sternberg Task O’Neill, George C. Tewarie, Prejaas K. Colclough, Giles L. Gascoyne, Lauren E. Hunt, Benjamin A.E. Morris, Peter G. Woolrich, Mark W. Brookes, Matthew J. Measurement of dynamic task related functional networks using MEG |
| title | Measurement of dynamic task related functional networks using MEG |
| title_full | Measurement of dynamic task related functional networks using MEG |
| title_fullStr | Measurement of dynamic task related functional networks using MEG |
| title_full_unstemmed | Measurement of dynamic task related functional networks using MEG |
| title_short | Measurement of dynamic task related functional networks using MEG |
| title_sort | measurement of dynamic task related functional networks using meg |
| topic | Network Dynamics Magnetoencephalography MEG Sternberg Task |
| url | https://eprints.nottingham.ac.uk/39029/ https://eprints.nottingham.ac.uk/39029/ https://eprints.nottingham.ac.uk/39029/ |