title: Application of Parallel Factor Analysis (PARAFAC) to electrophysiological data.
abstract: The identification of important features in multi-electrode recordings requires the decomposition of data in order to disclose relevant features and to offer a clear graphical representation. This can be a demanding task. Parallel Factor Analysis (PARAFAC; Hitchcock, 1927; Carrol and Chang, 1970; Harshman, 1970) is a method to decompose multi-dimensional arrays in order to focus on the features of interest, and provides a distinct illustration of the results. We applied PARAFAC to analyse spatio-temporal patterns in the functional connectivity between neurons, as revealed in their spike trains recorded in cat primary visual cortex (area 18). During these recordings we reversibly deactivated feedback connections from higher visual areas in the pMS (posterior middle suprasylvian) cortex in order to study the impact of these top-down signals. Cross correlation was computed for every possible pair of the 16 electrodes in the electrode array. PARAFAC was then used to reveal the effects of time, stimulus, and deactivation condition on the correlation patterns. Our results show that PARAFAC is able to reliably extract changes in correlation strength for different experimental conditions and display the relevant features. Thus, PARAFAC proves to be well-suited for the use in the context of electrophysiological (action potential) recordings.
authors: Systems Neurophysiology, Department of Biology, Technische Universität Darmstadt Darmstadt, Germany ; Department of Neurophysiology, Max Planck Institute for Brain Research Frankfurt, Germany ; Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe University Frankfurt, Germany.
authors: S Katharina
authors: SK
authors: Schmitz
authors: Communications Engineering Lab, Technische Universität Darmstadt Darmstadt, Germany.
authors: Philipp P
authors: PP
authors: Hasselbach
authors: Systems Neurophysiology, Department of Biology, Technische Universität Darmstadt Darmstadt, Germany ; Department of Neurophysiology, Max Planck Institute for Brain Research Frankfurt, Germany.
authors: Boris
authors: B
authors: Ebisch
authors: Communications Engineering Lab, Technische Universität Darmstadt Darmstadt, Germany.
authors: Anja
authors: A
authors: Klein
authors: Department of Neuroinformatics, Institute of Cognitive Science, Universität Osnabrück Osnabrück, Germany.
authors: Gordon
authors: G
authors: Pipa
authors: Systems Neurophysiology, Department of Biology, Technische Universität Darmstadt Darmstadt, Germany ; Department of Neurophysiology, Max Planck Institute for Brain Research Frankfurt, Germany.
authors: Ralf A W
authors: RA
authors: Galuske
keywords: cat primary visual cortex; cortical deactivation; cross correlation; parallel factor analysis; principal component analysis