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Estimation of effective connectivity using multi-layer perceptron artificial neural network.

Nasibeh Talebi1, Ali Motie Nasrabadi1, Iman Mohammad-Rezazadeh2

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Summary

This study introduces CREANN, an artificial neural network method to estimate brain connectivity. It accurately models causal relationships in brain signals, even with noisy data.

Keywords:
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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Effective connectivity estimation typically relies on temporal precedence for causality.
  • Existing methods may lack robustness or require detailed system knowledge.

Purpose of the Study:

  • To develop a novel method for estimating causal relationships and effective connectivity between brain regions.
  • To introduce the Causality coefficient measure for quantifying interaction strength.

Main Methods:

  • Utilizing a multi-layer perceptron feed-forward artificial neural network (ANN) for input-output mapping.
  • Defining a "Causality coefficient" based on ANN structure, weights, and activation function parameters.
  • Implementing the Causal Relationship Estimation by Artificial Neural Network (CREANN) method.

Main Results:

  • CREANN accurately estimates time-invariant and time-varying effective connectivity (MVAR coefficients).
  • The method demonstrates robustness to noise, model order, and initial network conditions.
  • Application to EEG data during memory tasks revealed changes in information flow during episodic memory retrieval.

Conclusions:

  • CREANN provides a robust and accurate approach for estimating causal relationships among brain signals.
  • The method is suitable for analyzing dynamic changes in brain network interactions.
  • CREANN offers a valuable tool for neuroscience research, particularly in understanding cognitive processes.