Memory processes are based on large cortical networks characterized by non-stationary properties and time scales which represent a limitation to the traditional connectivity estimation methods. The recent development of connectivity approaches able to consistently describe the temporal evolution of large dimension connectivity networks, in a fully multivariate way, represents a tool that can be used to extract novel information about the processes at the basis of memory functions. In this paper, we applied such advanced approach in combination with the use of state-of-the-art graph theory indexes, computed on the connectivity networks estimated from high density electroencephalographic (EEG) data recorded in a group of healthy adults during the Sternberg Task. The results show how this approach is able to return a characterization of the main phases of the investigated memory task which is also sensitive to the increased length of the numerical string to be memorized. © 2013 IEEE.

Advanced methods for time-varying effective connectivity estimation in memory processes / Astolfi, Laura; Toppi, Jlenia; G., Wood; S., Kober; M., Risetti; L., Macchiusi; Salinari, Serenella; Babiloni, Fabio; D., Mattia. - 2013:(2013), pp. 2936-2939. ((Intervento presentato al convegno 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 tenutosi a Osaka nel 3 July 2013 through 7 July 2013 [10.1109/embc.2013.6610155].

Advanced methods for time-varying effective connectivity estimation in memory processes.

ASTOLFI, LAURA;TOPPI, JLENIA;SALINARI, Serenella;BABILONI, Fabio;
2013

Abstract

Memory processes are based on large cortical networks characterized by non-stationary properties and time scales which represent a limitation to the traditional connectivity estimation methods. The recent development of connectivity approaches able to consistently describe the temporal evolution of large dimension connectivity networks, in a fully multivariate way, represents a tool that can be used to extract novel information about the processes at the basis of memory functions. In this paper, we applied such advanced approach in combination with the use of state-of-the-art graph theory indexes, computed on the connectivity networks estimated from high density electroencephalographic (EEG) data recorded in a group of healthy adults during the Sternberg Task. The results show how this approach is able to return a characterization of the main phases of the investigated memory task which is also sensitive to the increased length of the numerical string to be memorized. © 2013 IEEE.
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Advanced methods for time-varying effective connectivity estimation in memory processes / Astolfi, Laura; Toppi, Jlenia; G., Wood; S., Kober; M., Risetti; L., Macchiusi; Salinari, Serenella; Babiloni, Fabio; D., Mattia. - 2013:(2013), pp. 2936-2939. ((Intervento presentato al convegno 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 tenutosi a Osaka nel 3 July 2013 through 7 July 2013 [10.1109/embc.2013.6610155].
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