| Literature DB >> 29883736 |
Lionel Barnett1, Adam B Barrett1, Anil K Seth2.
Abstract
Granger-Geweke causality (GGC) is a powerful and popular method for identifying directed functional ('causal') connectivity in neuroscience. In a recent paper, Stokes and Purdon (2017b) raise several concerns about its use. They make two primary claims: (1) that GGC estimates may be severely biased or of high variance, and (2) that GGC fails to reveal the full structural/causal mechanisms of a system. However, these claims rest, respectively, on an incomplete evaluation of the literature, and a misconception about what GGC can be said to measure. Here we explain how existing approaches resolve the first issue, and discuss the frequently-misunderstood distinction between functional and effective neural connectivity which underlies Stokes and Purdon's second claim.Keywords: Effective connectivity; Functional connectivity; Granger causality; Statistical inference
Mesh:
Year: 2018 PMID: 29883736 DOI: 10.1016/j.neuroimage.2018.05.067
Source DB: PubMed Journal: Neuroimage ISSN: 1053-8119 Impact factor: 6.556