Detecting correlation changes in electrophysiological data

被引：3
|
作者：
Wu, Jianhua
Kendrick, Keith
Feng, Jianfeng [1 ]
机构：
[1] Univ Warwick, Dept Comp Sci, Coventry CV4 7AL, W Midlands, England
[2] Babraham Inst, Lab Cognit & Behav Neurosci, Cambridge CB2 4AT, England
[3] Hunan Normal Univ, Dept Math, Changsha 410081, Peoples R China
来源：
JOURNAL OF NEUROSCIENCE METHODS | 2007年 / 161卷 / 01期
基金：
英国工程与自然科学研究理事会;
关键词：
multielectrode data; hierarchical multi-variate analysis of variance; mean; variance;
D O I：
10.1016/j.jneumeth.2006.10.017
中图分类号：
Q5 [生物化学];
学科分类号：
071010 ; 081704 ;
摘要：
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. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码：155 / 165
页数：11
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