Journal
JOURNAL OF NEUROSCIENCE METHODS
Volume 201, Issue 2, Pages 404-419Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jneumeth.2011.08.010
Keywords
Granger causality; Digital filtering; Vector autoregressive modelling; Time series analysis
Categories
Funding
- Dr. Mortimer and Theresa Sackler Foundation
- EPSRC [EP/GOO7543/1]
- Engineering and Physical Sciences Research Council [EP/G007543/1] Funding Source: researchfish
- EPSRC [EP/G007543/1] Funding Source: UKRI
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Granger causality (G-causality) is increasingly employed as a method for identifying directed functional connectivity in neural time series data. However, little attention has been paid to the influence of common preprocessing methods such as filtering on G-causality inference. Filtering is often used to remove artifacts from data and/or to isolate frequency bands of interest. Here, we show [following Geweke (1982)] that G-causality for a stationary vector autoregressive (VAR) process is fully invariant under the application of an arbitrary invertible filter; therefore filtering cannot and does not isolate frequency-specific G-causal inferences. We describe and illustrate a simple alternative: integration of frequency domain (spectral) G-causality over the appropriate frequencies (band limited G-causality). We then show, using an analytically solvable minimal model, that in practice G-causality inferences often do change after filtering, as a consequence of large increases in empirical model order induced by filtering. Finally, we demonstrate a valid application of filtering in removing a nonstationary (line noise) component from data. In summary, when applied carefully, filtering can be a useful preprocessing step for removing artifacts and for furnishing or improving stationarity; however filtering is inappropriate for isolating causal influences within specific frequency bands. (C) 2011 Elsevier B.V. All rights reserved.
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