Epilepsy is a neurological disorder characterized by seizures, i.e. abnormal synchronous activity of neurons in the brain. During a focal seizure, the abnormal synchronous activity starts in a specific brain region and rapidly propagates to neighboring regions. 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 that cannot be observed with scalp EEG recordings. In order to identify the 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. In this paper the applicability to locate the epileptogenic focus by time-variant connectivity analysis of seizure onsets based on the ADTF is shown. Furthermore, different normalizations of the ADTF (the integrated ADTF, the masked ADTF and the full frequency ADTF) are compared to investigate whether one is more suitable to describe the spreading of epileptic activity during an epileptic seizure. We quantified the performance of different connectivity measures during simulations of an epileptic seizure onset. The full frequency ADTF outperforms the integrated ADTF and masked ADTF. Accordingly, we applied this full frequency ADTF to 4 seizure onset and 29 subclinical seizure IEEG recordings of a patient with refractory epilepsy. Hereby, we showed 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 with refractory epilepsy.
Algorithms Causality