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Imaging functional brain connectivity patterns from high‐resolution EEG and fMRI via graph theory
Author(s) -
Astolfi L.,
De Vico Fallani F.,
Cincotti F.,
Mattia D.,
Marciani M. G.,
Bufalari S.,
Salinari S.,
Colosimo A.,
Ding L.,
Edgar J. C.,
Heller W.,
Miller G. A.,
He B.,
Babiloni F.
Publication year - 2007
Publication title -
psychophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.661
H-Index - 156
eISSN - 1469-8986
pISSN - 0048-5772
DOI - 10.1111/j.1469-8986.2007.00556.x
Subject(s) - stroop effect , electroencephalography , functional connectivity , psychology , neuroscience , coherence (philosophical gambling strategy) , computational model , pattern recognition (psychology) , set (abstract data type) , functional magnetic resonance imaging , artificial intelligence , graph theory , power graph analysis , graph , computer science , cognitive psychology , cognition , mathematics , theoretical computer science , statistics , combinatorics , programming language
We describe a set of computational tools able to estimate cortical activity and connectivity from high‐resolution EEG and fMRI recordings in humans. These methods comprise the estimation of cortical activity using realistic geometry head volume conductor models and distributed cortical source models, followed by the evaluation of cortical connectivity between regions of interest coincident with the Brodmann areas via the use of Partial Directed Coherence. Connectivity patterns estimated on the cortical surface in different frequency bands are then imaged and interpreted with measures based on graph theory. These computational tools were applied on a set of EEG and fMRI data from a Stroop task to demonstrate the potential of the proposed approach. The present findings suggest that the methodology is able to identify differences in functional connectivity patterns elicited by different experimental tasks or conditions.