Spatiotemporal Analysis of Brain Functional Connectivity

Ahmad Mheich 1, 2 Mahmoud Hassan 1 Olivier Dufor 3, 4 Mohamad Khalil 2 Claude Berrou 3, 4 Fabrice Wendling 1, *
* Auteur correspondant
3 Lab-STICC_TB_CACS_IAS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Brain functions are based on interactions between neural assemblies distributed within and across distinct cerebral regions. During cognitive tasks, these interactions are dynamic and take place at the millisecond time scale. In this context, the excellent temporal resolution (<1 ms) of the Electroencephalographic -EEG- signals allows for detection of very short-duration events and therefore, offers the unique opportunity to follow, over time, the dynamic properties of cognitive processes. In this paper we propose a new algorithm to track the functional brain connectivity dynamics. During picture recognition and naming task, this algorithm aims at segmenting high resolution (hr) EEG functional connectivity microstates. The proposed algorithm is based on the K-means clustering of the connectivity graphs obtained from the Phase Locking Values (PLV). Results show that the algorithm is able to track the brain functional connectivity dynamics during picture naming task.
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Communication dans un congrès
MBEC 2014 : 6th European Conference of the International Federation for Medical and Biological Engineering, Sep 2014, Dubrovnik, Croatia. Springer International Publishing, 45, pp.934-937, 2014, IFMBE Proceedings. 〈10.1007/978-3-319-11128-5_232〉
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Ahmad Mheich, Mahmoud Hassan, Olivier Dufor, Mohamad Khalil, Claude Berrou, et al.. Spatiotemporal Analysis of Brain Functional Connectivity. MBEC 2014 : 6th European Conference of the International Federation for Medical and Biological Engineering, Sep 2014, Dubrovnik, Croatia. Springer International Publishing, 45, pp.934-937, 2014, IFMBE Proceedings. 〈10.1007/978-3-319-11128-5_232〉. 〈inserm-01071395〉

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