%0 Journal Article %1 Wendling2010 %A Wendling, Fabrice %A Chauvel, Patrick %A Biraben5, Arnaud %A Bartolomei, Fabrice %D 2010 %J Front Syst Neurosci. %K EEG, connectivity, effective, epilepsy functional, intracerebral model, network, neuronal non-linear population regression, %P Article 154 %T From intracerebral EEG signals to brain connectivity- identification of epileptogenic networks in partial epilepsy %V 4 %X Epilepsy is a complex neurological disorder characterized by recurring seizures. In 30% of patients, seizures are insufficiently reduced by anti-epileptic drugs. In the case where seizures originate from a relatively circumscribed region of the brain, epilepsy is said to be partial and surgery can be indicated. The success of epilepsy surgery depends on the accurate localization and delineation of the epileptogenic zone (which often involves several structures), responsible for seizures. It requires a comprehensive pre-surgical evaluation of patients that includes not only imaging data but also long-term monitoring of electrophysiological signals (scalp and intracerebral EEG). During the past decades, considerable effort has been devoted to the development of signal analysis techniques aimed at characterizing the functional connectivity among spatially distributed regions over interictal (outside seizures) or ictal (during seizures) periods from EEG data. Most of these methods rely on the measurement of statistical couplings among signals recorded from distinct brain sites. However, methods differ with respect to underlying theoretical principles (mostly coming from the field of statistics or the field of non-linear physics). The objectives of this paper are: (i) to provide an brief overview of methods aimed at characterizing functional brain connectivity from electrophysiological data, (ii) to provide concrete application examples in the context of drug-refractory partial epilepsies, and iii) to highlight some key points emerging from results obtained both on real intracerebral EEG signals and on signals simulated from physiologically plausible models in which the underlying connectivity patterns are known a priori (ground truth).

@article{Wendling2010, abstract = {Epilepsy is a complex neurological disorder characterized by recurring seizures. In 30% of patients, seizures are insufficiently reduced by anti-epileptic drugs. In the case where seizures originate from a relatively circumscribed region of the brain, epilepsy is said to be partial and surgery can be indicated. The success of epilepsy surgery depends on the accurate localization and delineation of the epileptogenic zone (which often involves several structures), responsible for seizures. It requires a comprehensive pre-surgical evaluation of patients that includes not only imaging data but also long-term monitoring of electrophysiological signals (scalp and intracerebral EEG). During the past decades, considerable effort has been devoted to the development of signal analysis techniques aimed at characterizing the functional connectivity among spatially distributed regions over interictal (outside seizures) or ictal (during seizures) periods from EEG data. Most of these methods rely on the measurement of statistical couplings among signals recorded from distinct brain sites. However, methods differ with respect to underlying theoretical principles (mostly coming from the field of statistics or the field of non-linear physics). The objectives of this paper are: (i) to provide an brief overview of methods aimed at characterizing functional brain connectivity from electrophysiological data, (ii) to provide concrete application examples in the context of drug-refractory partial epilepsies, and iii) to highlight some key points emerging from results obtained both on real intracerebral EEG signals and on signals simulated from physiologically plausible models in which the underlying connectivity patterns are known a priori (ground truth).}, added-at = {2012-01-27T14:10:42.000+0100}, author = {Wendling, Fabrice and Chauvel, Patrick and Biraben5, Arnaud and Bartolomei, Fabrice}, biburl = {https://www.bibsonomy.org/bibtex/2f935970c480ac2ef01983678e65e35d0/muhe}, file = {From intracerebral EEG signals to brain connectivity- identification of epileptogenic networks in partial epilepsy.pdf:2010\\From intracerebral EEG signals to brain connectivity- identification of epileptogenic networks in partial epilepsy.pdf:PDF}, interhash = {191dd305ea8644fb1acb36657c8acac1}, intrahash = {f935970c480ac2ef01983678e65e35d0}, journal = {Front Syst Neurosci.}, keywords = {EEG, connectivity, effective, epilepsy functional, intracerebral model, network, neuronal non-linear population regression,}, owner = {Mu}, pages = {Article 154}, pdf = {LFN-Datenbank\Brain\From intracerebral EEG signals to brain connectivity- identification of epileptogenic networks in partial epilepsy.pdf}, timestamp = {2012-01-27T14:11:16.000+0100}, title = {From intracerebral EEG signals to brain connectivity- identification of epileptogenic networks in partial epilepsy}, volume = 4, year = 2010 }