Extracting information from cortical connectivity patterns estimated from high resolution EEG recordings: a theoretical graph approach.

Extracting information from cortical connectivity patterns estimated from high resolution EEG recordings: a theoretical graph approach. Research Abstract Details 

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Extracting information from cortical connectivity patterns estimated from high resolution EEG recordings: a theoretical graph approach. Abstract Text:

Fabrizio De Vico Fallani,Laura Astolfi,Febo Cincotti,Donatella Mattia,Andrea Tocci,Maria Grazia Marciani,Alfredo Colosimo,Serenella Salinari,Shangkai Gao,Andrzej Cichocki,Fabio Babiloni,

Over the last 20 years, a body of techniques known as high resolution EEG has allowed precise estimation of cortical activity from non-invasive EEG measurements. The availability of cortical waveforms from non-invasive EEG recordings allows to have not only the level of activation within a single region of interest (ROI) during a particular task, but also to estimate the causal relationships among activities of several cortical regions. However, interpreting resulting connectivity patterns is still an open issue, due to the difficulty to provide an objective measure of their properties across different subjects or groups. A novel approach addressed to solve this difficulty consists in manipulating these functional brain networks as graph objects for which a large body of indexes and tools are available in literature and already tested for complex networks at different levels of scale (Social, WorldWide-Web and Proteomics). In the present work, we would like to show the suitability of such approach, showing results obtained comparing separately two groups of subjects during the same motor task and two different motor tasks performed by the same group. In the first experiment two groups of subjects (healthy and spinal cord injured patients) were compared when they moved and attempted to move simultaneously their right foot and lips, respectively. The contrast between the foot-lips movement and the simple foot movement was addressed in the second experiment for the population of the healthy subjects. For both the experiments, the main question is whether the "architecture" of the functional connectivity networks obtained could show properties that are different in the two groups or in the two tasks. All the functional connectivity networks gathered in the two experiments showed ordered properties and significant differences from "random" networks having the same characteristic sizes. The proposed approach, based on the use of indexes derived from graph theory, can apply to cerebral connectivity patterns estimated not only from the EEG signals but also from different brain imaging methods.

Extracting information from cortical connectivity patterns estimated from high resolution EEG recordings: a theoretical graph approach. Publishing Authors By Initials

F De Vico Fallani,L Astolfi,F Cincotti,D Mattia,A Tocci,MG Marciani,A Colosimo,S Salinari,S Gao,A Cichocki,F Babiloni,

For similar nervous system: neural pathways research abstracts see: nervous system: neural pathways research

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Extracting information from cortical connectivity patterns estimated from high resolution EEG recordings: a theoretical graph approach. Journal Published:

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: Brain topography

VOLUME: 19

Page Numbers: 125-36

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ISSN: 0896-0267

DAY: 21

MONTH: 06

YEAR: 2007

Extracting information from cortical connectivity patterns estimated from high resolution EEG recordings: a theoretical graph approach. Information

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LANGUAGE: eng

NlmUniqueID: 8903034

Extracting information from cortical connectivity patterns estimated from high resolution EEG recordings: a theoretical graph approach. Keywords Mesh Terms:

KEYWORDS: Neural Pathways

MESH TERMS: physiology

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Grant and Affiliation Information for Extracting information from cortical connectivity patterns estimated from high resolution EEG recordings: a theoretical graph approach.

AFFILIATION: Interdep. Research Centre for Models and Information Analysis in Biomedical Systems, University La Sapienza, Rome, Italy.

Country: United States

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MEDLINETA: Brain Topogr

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