Effective Brain Connectivity from Intracranial EEG Recordings: Identification of Epileptogenic Zone in Human Focal Epilepsies

In the context of focal and drug-resistant epilepsy, surgical resection of the epileptogenic zone (EZ) may be the only therapeutic option for reducing or suppressing seizures. The aim of epilepsy surgery is the exeresis of the EZ, which is assumed to be the cortical region responsible for the onset, early organization, and propagation of seizures. EZ represents the minimum amount of cortex that must be resected in order to achieve seizure freedom; therefore, the correct identification of its extent and organization is a crucial objective. Nevertheless, the rather high rate of failure in epilepsy surgery in extra-temporal epilepsies highlights that the precise identification of the EZ is still an unsolved problem and that more sophisticated methods of investigation are required. In many patients, intracranial stereo-EEG recordings still represent the gold standard for the epilepsy surgery work-up, and, over the last 10 years, considerable efforts have been made to develop advanced signal analysis techniques able to improve the identification of the EZ. Since it is widely assumed that epileptic phenomena are associated with abnormal changes in brain synchronization mechanisms, particular attention has been paid to those methods aimed at quantifying and characterizing the interactions and causal relationships of neuronal populations, and initial evidence has shown that this can be a suitable approach to localizing the EZ. The aim of this review is to provide an overview of the different intracranial EEG signal processing methods used to identify the EZ, with particular attention being given to the methods aimed at characterizing effective brain connectivity using intracranial EEG recordings. Then, we briefly present our studies of the connectivity pattern associated with a particular form of focal epilepsy (type II focal cortical dysplasia), based on multivariate autoregressive parametric models and measures derived from graph theory.