Frontiers in Neuroinformatics
Research Topic: Information-based methods for neuroimaging: analyzing structure, function and dynamics
The aim of this Research Topic is to discuss the state of the art on the use of Information-based methods in the analysis of neuroimaging data.
Information-based methods, typically built as extensions of the Shannon Entropy, are at the basis of model-free approaches which, being based on probability distributions rather than on specific expectations, can account for all possible non-linearities present in the data in a model-independent fashion.
Thus, for instance, to compute the statistical dependence between two random variables, the Mutual Information accounts for the information bits that the two variables are sharing (if it is zero, the two variables are statistically independent).
Mutual Information-like methods can also be applied on interacting dynamical variables described by time-series, thus addressing the uncertainty reduction (or information) in one variable by conditioning on another set of variables. This is the spirit of the growing-in-popularity Transfer Entropy (Schreiber 2000), an Information-based method to estimate directed influence.
Daniele Marinazzo, University of Gent, Belgium
Jesus M. Cortes, Ikerbasque. Biocruces Health Research Institute, Spain
Miguel Angel Muñoz, UNIVERSIDAD DE GRANADA, Spain
Deadline for abstract submission: 01 Jan 2013
Deadline for full article submission: 01 Jun 2013