title: Analyzing coherent brain networks with Granger causality.
abstract: Multielectrode neurophysiological recording and functional brain imaging produce massive quantities of data. Multivariate time series analysis provides the basic framework for analyzing the patterns of neural interactions in these data. Neural interactions are directional. Being able to assess the directionality of neuronal interactions is thus a highly desired capability for understanding the cooperative nature of neural computation. Research over the last few years has identified Granger causality as a promising technique to furnish this capability. In this paper, we first introduce the concept of Granger causality and then present results from the application of this technique to multichannel local field potential data from an awake-behaving monkey.
authors.affiliation: Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Fl 32611, USA. mding@bme.ufl.edu
authors.forename: Mingzhou
authors.initials: M
authors.lastname: Ding
authors.forename: Jue
authors.initials: J
authors.lastname: Mo
authors.forename: Charles E
authors.initials: CE
authors.lastname: Schroeder
authors.forename: Xiaotong
authors.initials: X
authors.lastname: Wen
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001931:Brain Mapping / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D008251:Macaca; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014793:Visual Cortex / Q000502:physiology*