title: Quantifying the causal interactions in the brain using a measure of directed transinformation.
abstract: Most of the brain's cognitive functions rely on the coordinated interactions of neuronal sources that are distributed within and across specialized brain areas. It is important to quantify these temporal interactions directly from neuroimaging data such as the electroencephalogram (EEG). A variety of measures have been proposed to quantify the neural interactions including linear correlation measures and nonlinear information theoretic measures. An important aspect of neural interactions is the direction of the information flow, i.e., the causal interaction between the different regions. In this paper, we propose using a directed transinformation measure (T measure) to quantify these causal interactions. This measure is a generalization of Granger causality and quantifies both the linear and nonlinear interactions between the signals. The proposed measure is applied to both simulated and real EEG signals and is shown to be sensitive to the dependencies between signals.
authors.affiliation: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA.
authors.forename: Rana Hammad
authors.initials: RH
authors.lastname: Raza
authors.forename: Selin
authors.initials: S
authors.lastname: Aviyente
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D001921:Brain / Q000473:pathology / Q000503:physiopathology*; D001927:Brain Diseases / Q000175:diagnosis / Q000503:physiopathology*; D015984:Causality; D003071:Cognition; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D015999:Multivariate Analysis; D009991:Oscillometry; D011336:Probability; D012815:Signal Processing, Computer-Assisted