title: Estimation of the cortical connectivity by high-resolution EEG and structural equation modeling: simulations and application to finger tapping data.
abstract: Today, the concept of brain connectivity plays a central role in the neuroscience. While functional connectivity is defined as the temporal coherence between the activities of different brain areas, the effective connectivity is defined as the simplest brain circuit that would produce the same temporal relationship as observed experimentally between cortical sites. The most used method to estimate effective connectivity in neuroscience is the structural equation modeling (SEM), typically used on data related to the brain hemodynamic behavior. However, the use of hemodynamic measures limits the temporal resolution on which the brain process can be followed. The present research proposes the use of the SEM approach on the cortical waveforms estimated from the high-resolution EEG data, which exhibits a good spatial resolution and a higher temporal resolution than hemodynamic measures. We performed a simulation study, in which different main factors were systematically manipulated in the generation of test signals, and the errors in the estimated connectivity were evaluated by the analysis of variance (ANOVA). Such factors were the signal-to-noise ratio and the duration of the simulated cortical activity. Since SEM technique is based on the use of a model formulated on the basis of anatomical and physiological constraints, different experimental conditions were analyzed, in order to evaluate the effect of errors made in the a priori model formulation on its performances. The feasibility of the proposed approach has been shown in a human study using high-resolution EEG recordings related to finger tapping movements.
authors.affiliation: Department of Human Physiology and Pharmacology, University of Rome "La Sapienza," 00185 Rome, Italy. laura.astolfi@uniroma1.it
authors.forename: Laura
authors.initials: L
authors.lastname: Astolfi
authors.forename: Febo
authors.initials: F
authors.lastname: Cincotti
authors.forename: Claudio
authors.initials: C
authors.lastname: Babiloni
authors.forename: Filippo
authors.initials: F
authors.lastname: Carducci
authors.forename: Alessandra
authors.initials: A
authors.lastname: Basilisco
authors.forename: Paolo M
authors.initials: PM
authors.lastname: Rossini
authors.forename: Serenella
authors.initials: S
authors.lastname: Salinari
authors.forename: Donatella
authors.initials: D
authors.lastname: Mattia
authors.forename: Sergio
authors.initials: S
authors.lastname: Cerutti
authors.forename: D Ben
authors.initials: DB
authors.lastname: Dayan
authors.forename: Lei
authors.initials: L
authors.lastname: Ding
authors.forename: Ying
authors.initials: Y
authors.lastname: Ni
authors.forename: Bin
authors.initials: B
authors.lastname: He
authors.forename: Fabio
authors.initials: F
authors.lastname: Babiloni
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D005240:Feasibility Studies; D005385:Fingers / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D009068:Movement / Q000502:physiology*