All Outputs (4)

Optimising experimental design for MEG resting state functional connectivity measurement (2016)
Journal Article
Liuzzi, L., Gascoyne, L. E., Tewarie, P. K., Barratt, E. L., Boto, E., & Brookes, M. J. (2017). Optimising experimental design for MEG resting state functional connectivity measurement. NeuroImage, 155, 565-576. https://doi.org/10.1016/j.neuroimage.2016.11.064

The study of functional connectivity using magnetoencephalography (MEG) is an expanding area of neuroimaging, and adds an extra dimension to the more common assessments made using fMRI. The importance of such metrics is growing, with recent demonstra... Read More about Optimising experimental design for MEG resting state functional connectivity measurement.

Measurement of dynamic task related functional networks using MEG (2016)
Journal Article
O’Neill, G. C., Tewarie, P. K., Colclough, G. L., Gascoyne, L. E., Hunt, B. A., Morris, P. G., …Brookes, M. J. (2017). Measurement of dynamic task related functional networks using MEG. NeuroImage, 146, 667-678. https://doi.org/10.1016/j.neuroimage.2016.08.061

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 i... Read More about Measurement of dynamic task related functional networks using MEG.

A multi-layer network approach to MEG connectivity analysis (2016)
Journal Article
Brookes, M. J., Tewarie, P. K., Hunt, B. A. E., Robson, S. E., Gascoyne, L. E., Liddle, E. B., …Morris, P. G. (2016). A multi-layer network approach to MEG connectivity analysis. NeuroImage, 132, 425-438. https://doi.org/10.1016/j.neuroimage.2016.02.045

© 2016 The Authors. 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... Read More about A multi-layer network approach to MEG connectivity analysis.

Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions (2016)
Journal Article
Tewarie, P. K., Bright, M. G., Hillebrand, A., Robson, S. E., Gascoyne, L. E., Morris, P. G., …Brookes, M. J. (2016). Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions. NeuroImage, 130, https://doi.org/10.1016/j.neuroimage.2016.01.053

Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typi... Read More about Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions.