Key to linking brain activity to consciousness – or to any brain function – is to be able to decipher causal interactions among different brain regions from neuroimaging data. One useful tool for doing this is Granger causality, a measure of ‘directed functional connectivity’ which is based on precedence and predictability. Put simply, one signal A is said to ‘Granger cause’ a different signal B if (and only if) A contains information that helps predict the future of B, over and above information already in the past of B. At the Sackler Centre we have been pioneering the theory and application of Granger causality to data from neuroscience. We have written the standard analysis software in the field, which is freely available as a fully-documented MATLAB toolbox, and we are examining how the method can applied in common neuroimaging contexts like functional MRI (fMRI), EEG, and intracranially recorded brain signals.

People:

Lionel Barnett, Adam Barrett, Anil Seth, Michael Schartner

Reference:

Barnett, L.C., and Seth, A.K. (2014). The MVGC multivariate Granger causality analysis toolbox: A new approach to Granger causal inference. Journal of Neuroscience Methods, 223:50-68

Seth, A.K., Chorley, P., and Barnett, L.C. (2013). Granger causality analysis of fMRI BOLD signals is invariant to hemodynamic convolution but not downsampling. Neuroimage 65:540-555