Functional connectivity is a popular technique used to study the brain’s functional architecture. The method that is typically used to study connectivity in fMRI data involves averaging signals (voxels) from regions of interest and computing Pearson correlations to determine the connectivity between different regions. In a recent study in Neuroimage, Linda Geerligs and Richard Henson introduced a new way to study connectivity between brain regions, using a multivariate statistical association measure called distance correlation. Because of its multivariate nature, distance correlation can be computed based on all the signals from a region of interest without the need to average over voxels first. Using data from the Cambridge Centre for Aging and Neuroscience project, they showed that measuring functional connectivity with distance correlation led to more reliable and robust connectivity estimates than the Pearson correlation method. Moreover, functional connectivity measured with distance correlation was more similar to structural connectivity, suggesting that it may better represent the true underlying functional architecture of the brain. Because distance correlation is easy to implement and fast to compute, it is a promising alternative to Pearson correlations for investigating functional connectivity. In addition, it is able to detect dependencies between multi-voxel patterns within different brain regions, potentially offering a new window on representational transformations between regions.
The paper can be accessed here
Geerligs L, Cam-CAN, Henson R (2016) Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation. Neuroimage. 135, 16–31. doi:10.1016/j.neuroimage.2016.04.047