NDLI: Extracting Information from Cortical Connectivity Patterns Estimated from High Resolution EEG Recordings: A Theoretical Graph Approach

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Extracting Information from Cortical Connectivity Patterns Estimated from High Resolution EEG Recordings: A Theoretical Graph Approach

Content Provider	Springer Nature Link
Author	Vico Fallani, Fabrizio Astolfi, Laura Cincotti, Febo Mattia, Donatella Tocci, Andrea Marciani, Maria Grazia Colosimo, Alfredo Salinari, Serenella Gao, Shangkai Cichocki, Andrzej Babiloni, Fabio
Copyright Year	2007
Abstract	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, WorldWideWeb 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.
Starting Page	125
Ending Page	136
Page Count	12
File Format	PDF
ISSN	08960267
Journal	Brain Topography
Volume Number	19
Issue Number	3
e-ISSN	15736792
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2007-06-21
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Efficiency Degree distributions Small-world Digraph DTF High resolution EEG Spinal cord injured Foot movement Neurology Psychiatry Neurosciences
Content Type	Text
Resource Type	Article
Subject	Neurology Anatomy Radiology, Nuclear Medicine and Imaging Neurology (clinical) Radiological and Ultrasound Technology

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