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Granger Causality relationships between SNMF components of LFPs during SD rats working memory task
Liu Xu 1 #,Bai Wenwen 2,Liu Tiaotiao 2,Yi Hu 2,Tian Xin 2 *
1.School of Biomedical Engineering, Research Center of Basic Medicine,Tianjin Medical University, TianJin 300070
2.School of Biomedical Engineering, Research Center of Basic Medicine,Tianjin Medical University
*Correspondence author
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Funding: National Natural Science Foundation of China （No.61074131 and 91132722）, the Doctoral Fund of the Ministry of Education of China（No.20101202110007）
Opened online:16 May 2013
Accepted by: none
Citation: Liu Xu,Bai Wenwen,Liu Tiaotiao.Granger Causality relationships between SNMF components of LFPs during SD rats working memory task[OL]. [16 May 2013] http://en.paper.edu.cn/en_releasepaper/content/4539143
 
 
Recent studies have applied Granger causality to multivariate population recordings such as local field potentials (LFPs) or electroencephalography (EEG), which were usually observed signals. In this study, we used Granger causality to analyze relationships between source components of LFPs recorded from microelectrode arrays targeting the rats' prefrontal cortex when rats were at rest (rest group) and during Y-maze working memory task (working memory group). LFPs signals are assumed to be linear mixtures of source components. Furthermore, evidence indicates sparse coding of neural systems. Therefore, we used sparse non-negative matrix factorization (SNMF) to separate blind source components for LFPs. Then we used Granger causality to determine the strength and direction of relationships between source components.Our results showed that causal connectivity indices 'causal density' of the new networks consisting of source components strengthened than LFPs networks. By studying the new networks of rest group and working memory group, we found that the causal connectivity of working memory group was more enhanced than rest group.This paper presents a thought of applying Granger causality to source components, which may be especially useful to reveal essential causal interactions of neural networks.
Keywords:Granger causality; LFPs; Networks; Source components; Working memory
 
 
 

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