A multi-layer network approach to MEG connectivity analysis
Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture chal...
| 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/42020/ |
| _version_ | 1848796401469751296 |
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| author | Brookes, Matthew J. Tewarie, Prejaas K. Hunt, Benjamin A. E. Robson, Siân E. Gascoyne, Lauren E. Liddle, Elizabeth B. Liddle, Peter F. Morris, Peter G. |
| author_facet | Brookes, Matthew J. Tewarie, Prejaas K. Hunt, Benjamin A. E. Robson, Siân E. Gascoyne, Lauren E. Liddle, Elizabeth B. Liddle, Peter F. Morris, Peter G. |
| author_sort | Brookes, Matthew J. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia. |
| first_indexed | 2025-11-14T19:47:24Z |
| format | Article |
| id | nottingham-42020 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:47:24Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-420202020-05-04T17:51:48Z https://eprints.nottingham.ac.uk/42020/ A multi-layer network approach to MEG connectivity analysis Brookes, Matthew J. Tewarie, Prejaas K. Hunt, Benjamin A. E. Robson, Siân E. Gascoyne, Lauren E. Liddle, Elizabeth B. Liddle, Peter F. Morris, Peter G. Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia. Elsevier 2016-05-15 Article PeerReviewed Brookes, Matthew J., Tewarie, Prejaas K., Hunt, Benjamin A. E., Robson, Siân E., Gascoyne, Lauren E., Liddle, Elizabeth B., Liddle, Peter F. and Morris, Peter G. (2016) A multi-layer network approach to MEG connectivity analysis. NeuroImage, 132 . pp. 425-438. ISSN 1053-8119 Multi-layer networks; Magnetoencephalography; MEG; Functional connectivity; Neural oscillations; Schizophrenia; Visual cortex; Motor cortex http://www.sciencedirect.com/science/article/pii/S1053811916001543 doi:10.1016/j.neuroimage.2016.02.045 doi:10.1016/j.neuroimage.2016.02.045 |
| spellingShingle | Multi-layer networks; Magnetoencephalography; MEG; Functional connectivity; Neural oscillations; Schizophrenia; Visual cortex; Motor cortex Brookes, Matthew J. Tewarie, Prejaas K. Hunt, Benjamin A. E. Robson, Siân E. Gascoyne, Lauren E. Liddle, Elizabeth B. Liddle, Peter F. Morris, Peter G. A multi-layer network approach to MEG connectivity analysis |
| title | A multi-layer network approach to MEG connectivity analysis |
| title_full | A multi-layer network approach to MEG connectivity analysis |
| title_fullStr | A multi-layer network approach to MEG connectivity analysis |
| title_full_unstemmed | A multi-layer network approach to MEG connectivity analysis |
| title_short | A multi-layer network approach to MEG connectivity analysis |
| title_sort | multi-layer network approach to meg connectivity analysis |
| topic | Multi-layer networks; Magnetoencephalography; MEG; Functional connectivity; Neural oscillations; Schizophrenia; Visual cortex; Motor cortex |
| url | https://eprints.nottingham.ac.uk/42020/ https://eprints.nottingham.ac.uk/42020/ https://eprints.nottingham.ac.uk/42020/ |