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All Outputs (7)

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.

Relationships between cortical myeloarchitecture and electrophysiological networks (2016)
Journal Article
Hunt, B. A. E., Tewarie, P. K., Mougin, O. E., Geades, N., Jones, D. K., Singh, K. D., …Brookes, M. J. (2016). Relationships between cortical myeloarchitecture and electrophysiological networks. Proceedings of the National Academy of Sciences, 113(47), 13510-13515. https://doi.org/10.1073/pnas.1608587113

The human brain relies upon the dynamic formation and dissolution of a hierarchy of functional networks to support ongoing cognition. However, how functional connectivities underlying such networks are supported by cortical microstructure remains poo... Read More about Relationships between cortical myeloarchitecture and electrophysiological networks.

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.

Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach (2016)
Journal Article
Van Mieghem, P., Tewarie, P., Hillebrand, A., van Dijk, B. W., Stam, C. J., O'Neill, G. C., …Brookes, M. J. (2016). Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach. NeuroImage, 142, 324-336. https://doi.org/10.1016/j.neuroimage.2016.07.057

Neuronal oscillations exist across a broad frequency spectrum, and are thought to provide a mechanism of interaction between spatially separated brain regions. Since ongoing mental activity necessitates the simultaneous formation of multiple networks... Read More about Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach.

How reliable are MEG resting-state connectivity metrics? (2016)
Journal Article
Colclough, G., Woolrich, M., Tewarie, P., Brookes, M., Quinn, A., & Smith, S. (2016). How reliable are MEG resting-state connectivity metrics?. NeuroImage, 138, 284-293. https://doi.org/10.1016/j.neuroimage.2016.05.070

MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to emplo... Read More about How reliable are MEG resting-state connectivity metrics?.

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.