MATTHEW BROOKES MATTHEW.BROOKES@NOTTINGHAM.AC.UK
Professor of Physics
Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity
Brookes, Matthew Jon; O'Neill, George C.; Hall, Emma L.; Woolrich, Mark W.; Baker, Adam; Palazzo Corner, Sofia; Robson, Sian E.; Morris, Peter G.; Barnes, Gareth R.
Authors
George C. O'Neill
Emma L. Hall
Mark W. Woolrich
Adam Baker
Sofia Palazzo Corner
Sian E. Robson
Peter G. Morris
Gareth R. Barnes
Abstract
The topic of functional connectivity in neuroimaging is expanding rapidly and many studies now focus on coupling between spatially separate brain regions. These studies show that a relatively small number of large scale networks exist within the brain, and that healthy function of these networks is disrupted in many clinical populations. To date, the vast majority of studies probing connectivity employ techniques that compute time averaged correlation over several minutes, and between specific pre-defined brain locations. However, increasing evidence suggests that functional connectivity is non-stationary in time. Further, electrophysiological measurements show that connectivity is dependent on the frequency band of neural oscillations. It is also conceivable that networks exhibit a degree of spatial inhomogeneity, i.e. the large scale networks that we observe may result from the time average of multiple transiently synchronised sub-networks, each with their own spatial signature. This means that the next generation of neuroimaging tools to compute functional connectivity must account for spatial inhomogeneity, spectral non-uniformity and temporal non-stationarity. Here, we present a means to achieve this via application of windowed canonical correlation analysis (CCA) to source space projected MEG data. We describe the generation of time–frequency connectivity plots, showing the temporal and spectral distribution of coupling between brain regions. Moreover, CCA over voxels provides a means to assess spatial non-uniformity within short time–frequency windows. The feasibility of this technique is demonstrated in simulation and in a resting state MEG experiment where we elucidate multiple distinct spatio-temporal-spectral modes of covariation between the left and right sensorimotor areas.
Citation
Brookes, M. J., O'Neill, G. C., Hall, E. L., Woolrich, M. W., Baker, A., Palazzo Corner, S., …Barnes, G. R. (2014). Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity. NeuroImage, 91, 282-299. https://doi.org/10.1016/j.neuroimage.2013.12.066
Journal Article Type | Article |
---|---|
Acceptance Date | Dec 31, 2013 |
Online Publication Date | Jan 10, 2014 |
Publication Date | May 1, 2014 |
Deposit Date | Jul 10, 2015 |
Publicly Available Date | Jul 10, 2015 |
Journal | NeuroImage |
Print ISSN | 1053-8119 |
Electronic ISSN | 1095-9572 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 91 |
Pages | 282-299 |
DOI | https://doi.org/10.1016/j.neuroimage.2013.12.066 |
Keywords | MEG; Functional Connectivity; Neural Oscillations; non-stationarity; brain networks; Canonical correlation; multi-variate; leakage reduction |
Public URL | https://nottingham-repository.worktribe.com/output/726005 |
Publisher URL | http://www.sciencedirect.com/science/article/pii/S1053811914000123 |
Contract Date | Jul 10, 2015 |
Files
Transient_sync_REVISED3.pdf
(2.1 Mb)
PDF
Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0
You might also like
Functional connectivity in MRI is driven by spontaneous BOLD events
(2015)
Journal Article
Fast transient networks in spontaneous human brain activity
(2014)
Journal Article
Abnormal visuomotor processing in schizophrenia
(2015)
Journal Article
A multi-layer network approach to MEG connectivity analysis
(2016)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search