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Estimating causal interaction between prefrontal cortex and striatum by transfer entropy.

Chaofei Ma1, Xiaochuan Pan2, Rubin Wang2

  • 1Institute for Cognitive Neurodynamics, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237 People's Republic of China.

Cognitive Neurodynamics
|January 16, 2014
PubMed
Summary
This summary is machine-generated.

Transfer entropy (TE) quantifies causal interactions between systems. This study presents an efficient algorithm for TE calculation, successfully estimating coupling strength and information flow in both simulated and real brain data.

Keywords:
Causal interactionLocal field potentialMutual informationTransfer entropy

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Area of Science:

  • Neuroscience
  • Information Theory
  • Computational Biology

Background:

  • Causal interactions in complex systems are challenging to model.
  • Transfer entropy (TE) offers a model-free approach to assess information flow.
  • Efficient algorithms are needed for real-time analysis of neurophysiological data.

Purpose of the Study:

  • To develop and validate an efficient algorithm for calculating transfer entropy (TE).
  • To assess the capability of TE in estimating coupling strength and directionality.
  • To investigate functional connectivity dynamics between brain regions using TE.

Main Methods:

  • Developed an efficient algorithm for computing TE from time-series data.
  • Validated the algorithm using simulated nonlinearly coupled systems.
  • Applied the algorithm to local field potentials (LFPs) from the monkey prefrontal cortex and striatum.

Main Results:

  • The algorithm accurately estimated coupling strength and information transmission direction in simulations.
  • TE analysis of LFPs revealed stronger information flow from LPFC to striatum, aligning with known anatomy.
  • Dynamic variations in TE values correlated with behavioral states in the monkey.

Conclusions:

  • The developed TE algorithm is effective for estimating functional connectivity between brain regions.
  • TE can characterize the dynamical properties of neural interactions.
  • This method provides insights into information processing in the brain.