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only differs from the feed-forward-loop motif for the functional link between the
two areas activated, we can claim the privileged scheme of communication
within the functional networks estimated consists in a parallel activation from a
particular ROI of two other distinct areas, whose communication seems to
increase significantly only during the proper movement execution.
10.5. Conclusions
One of the interesting characteristics of the brain networks presented in this
Chapter is that such networks have no precise anatomical support, i.e. there is no
particular cerebral structure that implements the network itself. Thus, those brain
networks represent functional networks, which could change in topology and
properties according to the specific subject's behavior. Another attractive
characteristic is that these functional networks are estimated from high-resolution
EEG signals. This allows the representation of the graph nodes as particular
regions of interest on the cortex. This approach gives to the researcher a
“window” to access the brain functions in a different perspective than the usual
techniques encountered in the neuroscience literature.
In fact, the development of brain imaging devices (such as the functional
Magnetic Resonance Imaging (fMRI), but also the high-resolution EEG
technology) often give to the scientist a series of colored hot-spots in the brain
that sub-serve the functions performed by the subject during a particular task.
Actually, if we look at thousands of fMRI studies a possible impression is that a
specific cortical area gets “activated” during the performance of whatever
cognitive or motor operation. In this scenario of modern “color phrenology”, the
study of functional cortical connectivity suggests an image of the brain as a
system of objects that rapidly changes the way in which they are interconnected,
according to the complexity and to the dynamic of the task proposed to the
subject. It is opinion of the Authors that the perspective offered by the use of
graph theory to the functional cortical connectivity networks estimated from
high-resolution EEG recordings could be a promising way to approach the brain
functioning from a modern point of view.
References
Achard S., Salvador R., Whitcher B., Suckling J. and Bullmore, Ed. A Resilient, Low-Frequency,
Small-World Human Brain Functional Network with Highly Connected Association Cortical
Hubs. The Journal of Neuroscience, 26(1):63-72, 2006.
Akaike H. (1974) A new look at statistical model identification. IEEE Trans Automat Control AC-
19:716-723.
