Uncovering short-time correlations between multichannel recordings of brain activity : A phase-space approach

Abstract

Short-time correlations in multivariate time series are notoriously difficult to detect. Extending the classical concept of correlation, we present a method to tackle this problem with little computational cost. In essence, the method uncovers multidimensional phase-locking and is especially useful for revealing changes of the dynamical coupling between different brain areas. The approach aL.,o permits us to estimate the shortest time-window in which the coupling occurs. Furthermore, the coupling can be quantified by a measure that defines a metric space. Therefore it may be used to identify task-dependent coupling hierarchies between two brain areas. We illustrate the analysis technique by studying the dynamical coupling between two brain regions involved in olfactory processing in the honeybee. We show that the neural activity in both areas is coupled with each other and that the coupling increases significantly during odor processing.