Assessment of effective connectivity among cortical regions based on a neural mass model

M. Zavaglia, L. Astolfi, F. Babiloni, M. Ursino

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Assessment of brain connectivity among different brain areas during cognitive or motor tasks is a crucial problem in neuroscience today. Aim of this work is to use a neural mass model to assess the effect of various connectivity patterns in the power spectral density (PSD) of cortical EEG, and investigate the possibility to derive connectivity circuits from real EEG data. To this end, a model of an individual region of interest (ROI) has been built as the parallel arrangement of three populations, each described as in [1]. The present study suggests that the model can be used as a simulation tool, able to produce reliable intracortical EEG signals. Moreover, it can be used to look for simple connectivity circuits, able to explain the main features of observed cortical PSD. These results may open new prospective in the use of neurophysiological models, instead of empirical models, to assess effective connectivity from neuroimaging information.

Original languageEnglish
Title of host publication28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Pages590-594
Number of pages5
DOIs
StatePublished - 2006
Externally publishedYes
Event28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States
Duration: 30 Aug 20063 Sep 2006

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (Print)0589-1019

Conference

Conference28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Country/TerritoryUnited States
CityNew York, NY
Period30/08/063/09/06

Fingerprint

Dive into the research topics of 'Assessment of effective connectivity among cortical regions based on a neural mass model'. Together they form a unique fingerprint.

Cite this