A local adjustment strategy for the initialization of dynamic causal modelling to infer effective connectivity in brain epileptic structures

Abstract : This paper addresses the question of effective connectivity in the human cerebral cortex in the context of epilepsy. Among model based approaches to infer brain connectivity, spectral Dynamic Causal Modelling is a conventional technique for which we propose an alternative to estimate cross spectral density. The proposed strategy we investigated tackles the sub-estimation of the free energy using the well-known variational Expectation-Maximization algorithm highly sensitive to the initialization of the parameters vector by a permanent local adjustment of the initialization process. The performance of the proposed strategy in terms of effective connectivity identification is assessed using simulated data generated by a neuronal mass model (simulating unidirectional and bidirectional flows) and real epileptic intracerebral Electroencephalographic signals. Results show the efficiency of proposed approach compared to the conventional Dynamic Causal Modelling and the one wherein a deterministic annealing scheme is employed.
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Contributeur : Régine Le Bouquin Jeannès <>
Soumis le : mercredi 29 mars 2017 - 16:06:43
Dernière modification le : mercredi 16 mai 2018 - 11:23:42
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Wentao Xiang, Ahmad Karfoul, Huazhong Shu, Régine Le Bouquin Jeannès. A local adjustment strategy for the initialization of dynamic causal modelling to infer effective connectivity in brain epileptic structures. Computers in Biology and Medicine, Elsevier, 2017, 84, pp.30 - 44. 〈10.1016/j.compbiomed.2017.03.006〉. 〈inserm-01498128〉

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