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Estimating Coupling Direction Between Neuronal Populations

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Abstract

To further understand functional connectivity in the brain, we need to identify the coupling direction between neuronal signals recorded from different brain areas. A novel methodology based on permutation analysis and conditional mutual information, called PCMI, is proposed to analyze the coupling direction of bivariate neuronal populations. The coupled neural mass model is used to test the performance of PCMI. Simulations suggest that the method is adequate to estimate coupling direction under a large range of signal-to-noise ratio (SNRs) and coupling efficiency and is better than the traditional CMI method. The method is also applied to investigate the coupling direction between neuronal populations in CA1 and CA3 in the rat hippocampal tetanus toxin model of focal epilepsy; the propagation direction of the seizure events could be elucidated through this coupling direction estimation method. An important next goal is to investigate whether the method proposed herein is suited to analyze interactions in real EEG data.

Keywords

Coupling direction Neuronal populations Permutation Conditional mutual information Epileptic seizures 

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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
  2. 2.Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijingChina

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