Brain networks and scalp electroencephalogram

0497963 - ÚI 2019 CH eng A - Abstrakt
Paluš, Milan
Brain networks and scalp electroencephalogram.
NDES 2017. Conference Programme. Zürich, 2017. s. 6-6.
[NDES 2017: Nonlinear Dynamics of Electronic Systems /25./. 05.06.2017-07.06.2017, Zernez]
Grant CEP: GA MZd(CZ) NV15-33250A
Institucionální podpora: RVO:67985807
https://www.ini.uzh.ch/~lorimert/NDES2017/assets/NDES2017_programme_booklet.pdf

Understanding how neurons and neuronal assemblies communicate is one of the greatest challenges of modern science. Adequate description and quantification of brain connectivity (i.e., communication between neuronal assemblies) is not only important for understanding the structure and function of brain networks, but also for diagnosis and treatment of neuropsychiatric diseases, since brain disorders – from schizophrenia to depression to post-traumatic stress disorder – are considered as disorders of connectivity. Functional brain networks are derived from multivariate time series of a quantity reflecting time evolution of brain activity. Modern neuroimaging methods became popular for the inference of the functional networks, however, the scalp electroencephalogram (EEG) is probably the most available and least expensive, non-invasive method to record the brain electrical activity. We will discuss measures of synchronization and coherence which can be used to infer connectivity patterns from scalp EEG, with a special emphasis on measures designed to cope with the effects of conductivity and reference electrode. Another challenging topic is the detection of cross-frequency interactions, namely the phase-amplitude coupling. We will ask whether we can detect cross-frequency interactions from scalp EEG and whether we can identify them as causal, e.g. in the sense "the phase of slow oscillations determines the amplitude of fast oscillations."
Trvalý link: http://hdl.handle.net/11104/0290403