The Use of Time-Variant EEG Granger Causality for Inspecting Directed Interdependencies of Neural Assemblies

J Neurosci Methods. 2003 Mar 30;124(1):27-44. doi: 10.1016/s0165-0270(02)00366-7.

Abstract

Understanding of brain functioning requires the investigation of activated cortical networks, in particular the detection of interactions between different cortical sites. Commonly, coherence and correlation are used to describe interrelations between EEG signals. However, on this basis, no statements on causality or the direction of their interrelations are possible. Causality between two signals may be expressed in terms of upgrading the predictability of one signal by the knowledge of the immediate past of the other signal. The best-established approach in this context is the so-called Granger causality. The classical estimation of Granger causality requires the stationarity of the signals. In this way, transient pathways of information transfer stay hidden. The study presents an adaptive estimation of Granger causality. Simulations demonstrate the usefulness of the time-variant Granger causality for detecting dynamic causal relations within time intervals of less than 100 ms. The time-variant Granger causality is applied to EEG data from the Stroop task. It was shown that conflict situations generate dense webs of interactions directed from posterior to anterior cortical sites. The web of directed interactions occurs mainly 400 ms after the stimulus onset and lasts up to the end of the task.

Publication types

  • Clinical Trial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms
  • Attention / physiology
  • Brain Mapping / methods*
  • Causality*
  • Cerebral Cortex / physiology*
  • Conflict, Psychological
  • Electroencephalography / methods*
  • Electrooculography / methods
  • Evoked Potentials, Visual / physiology
  • Humans
  • Male
  • Models, Neurological*
  • Models, Statistical
  • Nerve Net / physiology*
  • Psychomotor Performance / physiology
  • Reaction Time / physiology
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Statistics as Topic