Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

The brain produces its cognition by recruiting multiple brain areas across networks, and so we are developing novel methods for inferring brain networks from M/EEG data.

First, a parcellation is defined. These parcels are then treated as nodes in a network, and the relationships between them can be analysed leading to graphs of the functional connections between brain regions (i.e. a connectome). 

We are developing tools that can handle the spatial leakage and false connections induced by the uncertainties in M/EEG source reconstruction. These approaches allow multi-region network analysis to be performed, providing more accurate estimation of M/EEG connectomes. 

These techniques are being applied to clinical research datasets, and to the Human Connectome Project MEG data to assess the heritability of functional connectivity.

 

The tool for doing this kind of analysis (MEG ROI nets) is available to download and use at the OHBA Analysis Group Software Page.


References

Colclough, G. L., Brookes, M., Smith, S. M. and Woolrich, M. W., "A symmetric multivariate leakage correction for MEG connectomes" NeuroImage 117, pp. 439-448 (2015).

Colclough, G. L., Woolrich, M. W., Tewarie, P. K., Brookes, M. J., Quinn, A. J., & Smith, S. M. (2016). How reliable are MEG resting-state connectivity metrics? NeuroImage, 1–10.