MEANOVA

Applications of multi-variate analysis of variance (MANOVA) to multi-electrode array electrophysiology dataP.M. Hortona, Corresponding Author Contact Information, E-mail The Corresponding Author, L. Bonnya, A.U. Nicolb, K.M. Kendrickb and J.F.Fengc, d, Corresponding Author Contact Information, E-mail The Corresponding AuthoraDepartment of Informatics, Sussex University, Falmer, Brighton BN1 9QH, UKbLaboratory of Cognitive and Behavioural Neuroscience, The Babraham Institute, Cambridge CB2 4AT, UKcDepartment of Mathematics, Hunan Normal University, 410081, Changsha, PR ChinadDepartment of Computer Science, Warwick University, Coventry CV4 7AL, UKReceived 11 August 2004; revised 22 November 2004; accepted 13 January 2005. Available online 9 March 2005.AbstractWe have developed an adaptation of multi-variate analysis of variance (MANOVA) to analyze statistically both local and global patterns of multi-electrode array (MEA) electrophysiology data where the activities of many (typically ¿100) neurons have been recorded simultaneously. Whereas simple application of standard MANOVA techniques prohibits extraction of useful information in this kind of data, our new approach, MEANOVA (=MEA+MANOVA), allows a more useful and powerful approach to analyze such complex neurophysiological data. The MEANOVA test enables the detection of the “hot-spots” in the MEA data and has been validated using recordings from the rat olfactory bulb. To further validate the power of this approach, we have also applied the MEANOVA test to data obtained from a simple computational networknext term model. This MEANOVA software and other useful statistical methods for MEA data can be downloaded from http://www.sussex.ac.uk/Users/pmh20.Keywords: Multi-electrode array; Multi-variate statistical analysis; Olfactory bulb; Odour; Olfactory bulb modelling

ScienceDirect - Journal of Neuroscience Methods : Applications of multi-variate analysis of variance (MANOVA) to multi-electrode array electrophysiology data