title: Detecting correlation changes in electrophysiological data.
abstract: A correlation multi-variate analysis of variance (MANOVA) test to statistically analyze changing patterns of multi-electrode array (MEA) electrophysiology data is developed. The approach enables us not only to detect significant mean changes, but also significant correlation changes in response to external stimuli. Furthermore, a method to single out hot-spot variables in the MEA data both for the mean and correlation is provided. Our methods have been validated using both simulated spike data and recordings from sheep inferotemporal cortex.
authors.affiliation: Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Wu
authors.forename: Keith
authors.initials: K
authors.lastname: Kendrick
authors.forename: Jianfeng
authors.initials: J
authors.lastname: Feng
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D001921:Brain / Q000166:cytology; D001931:Brain Mapping; D004594:Electrophysiology; D006801:Humans; D008959:Models, Neurological; D015999:Multivariate Analysis; D009474:Neurons / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted