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Erschienen in: Cognitive Neurodynamics 1/2018

16.09.2017 | Research Article

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

verfasst von: Nasibeh Talebi, Ali Motie Nasrabadi, Iman Mohammad-Rezazadeh

Erschienen in: Cognitive Neurodynamics | Ausgabe 1/2018

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Abstract

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.

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Metadaten
Titel
Estimation of effective connectivity using multi-layer perceptron artificial neural network
verfasst von
Nasibeh Talebi
Ali Motie Nasrabadi
Iman Mohammad-Rezazadeh
Publikationsdatum
16.09.2017
Verlag
Springer Netherlands
Erschienen in
Cognitive Neurodynamics / Ausgabe 1/2018
Print ISSN: 1871-4080
Elektronische ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-017-9453-1

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