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Quantifying connectivity in a physiology based model using adaptive dynamic causal modelling

W. Xiang 1 C. Yang 2 Ahmad Karfoul 1 R Le Bouquin Jeannès 3
1 LTSI - Laboratoire Traitement du Signal et de l'Image
2 LIST - Laboratory of Image Science and Technology [Nanjing]
3 LTSI - Laboratoire Traitement du Signal et de l'Image
Abstract : This paper proposes an Adaptive Dynamic Causal Modelling based approach to detect and quantify effective connectivity in human brain structures injured by epileptic activities. The identification of the parameters in the physiology based model subtended the Electroencephalographic observations is performed by improving the optimization step in the Expectation Maximization algorithm. Considering unidirectional flow propagation, we show the efficiency of our proposed approach compared to the conventional technique. © 2016 IEEE.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01484702
Contributor : Laurent Jonchère <>
Submitted on : Tuesday, March 7, 2017 - 3:50:42 PM
Last modification on : Friday, January 15, 2021 - 3:33:45 AM

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UNIV-RENNES1 | LTSI | UNIV-RENNES

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W. Xiang, C. Yang, Ahmad Karfoul, R Le Bouquin Jeannès. Quantifying connectivity in a physiology based model using adaptive dynamic causal modelling. 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Aug 2016, Orlando, United States. pp.2818--2821, ⟨10.1109/EMBC.2016.7591316⟩. ⟨hal-01484702⟩

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