Cognitive Neurodynamics

, Volume 12, Issue 1, pp 21–42 | Cite as

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

  • Nasibeh Talebi
  • Ali Motie Nasrabadi
  • Iman Mohammad-Rezazadeh
Research Article


Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN’s ability to generate appropriate input–output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of “Causality coefficient” is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called “CREANN” (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals.


Effective connectivity Multi-layer perceptron artificial neural network Multivariate autoregressive Causality Memory recognition 



We are thankful to Professor Tim Curran and his co-authors for providing the EEG data. This research is supported by Cognitive Sciences and technologies Council of Iran, under the Grant Number 2688.


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© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Nasibeh Talebi
    • 1
  • Ali Motie Nasrabadi
    • 1
  • Iman Mohammad-Rezazadeh
    • 2
  1. 1.Department of Biomedical Engineering, Faculty of EngineeringShahed UniversityTehranIran
  2. 2.HRL Laboratories, LLCMalibuUSA

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