Feasibility Study of the Time-variant Functional Connectivity Pattern during an Epileptic Seizure

Epilepsy is a neurological disorder characterized by seizures, i.e. abnormal synchronous activity of neurons in the brain. Intracranial ElectroEncephaloGraphy (iEEG) is the recording of brain activity at a high temporal resolution through electrodes placed within different brain regions. Intracranial electrodes are used to access structures deep within the brain and to reveal brain activity which is not displayed in scalp EEG recordings. In order to identify pattern of propagation across brain areas, a connectivity measure named the Adapted Directed Transfer Function (ADTF) has been developed. This measure reveals connections between different regions by exploiting statistical dependencies within multichannel recordings. The ADTF can be derived from the coefficients of a time-variant multivariate autoregressive (TVAR) model fitted to the data. We applied the ADTF to 26 iEEG signals recorded during a subclinical seizure to identify the propagation of electrical activity specific to epilepsy. We showed the feasibility of detecting the propagation pattern during the epileptic seizure. The leading region seen in the pattern was consistent with post-operative results. We proved that connectivity patterns derived from iEEG recordings can provide useful information about seizure propagation and may improve the accuracy of the pre-surgical evaluation in patients affected by refractory epilepsy.