Extracting Information from Cortical Connectivity Patterns Estimated from High Resolution EEG Recordings: A Theoretical Graph Approach |
| |
Authors: | 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 |
| |
Institution: | (1) Interdep. Research Centre for Models and Information Analysis in Biomedical Systems, University “La Sapienza”, Rome, Italy;(2) IRCCS “Fondazione Santa Lucia”, Rome, Italy;(3) Department of Human Physiology and Pharmacology, University of Rome “La Sapienza”, P.le A. Moro 5, Rome, 00185, Italy;(4) Dep. Informatica e Sistemistica, University “La Sapienza”, Rome, Italy;(5) Department of Neural Engineering, Tsinghua University, Beijng, China;(6) RIKEN Institute, Wako, Saitama, Japan |
| |
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. |
| |
Keywords: | Efficiency Degree distributions Small-world Digraph DTF High resolution EEG Spinal cord injured Foot movement |
本文献已被 PubMed SpringerLink 等数据库收录! |
|