Detecting correlation changes in electrophysiological data.

Jianhua Wu; Keith Kendrick; Jianfeng Feng

doi:10.1016/j.jneumeth.2006.10.017

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PubMed · 17137633

Detecting correlation changes in electrophysiological data.

Jianhua Wu·Keith Kendrick·Jianfeng Feng

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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.

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Action Potentials Algorithms Animals Brain Brain Mapping Electrophysiology Humans Models, Neurological Multivariate Analysis Neurons Signal Processing, Computer-Assisted Journal Article Research Support, Non-U.S. Gov't

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Jianhua Wu, Keith Kendrick, Jianfeng Feng. 2006-11-29. Detecting correlation changes in electrophysiological data.. https://doi.org/10.1016/j.jneumeth.2006.10.017

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