Abstracts

The presence of localized synchronization of neuronal activity in epilepsy has been well documented in microelectrode and clinical intracranial EEG (ICEEG) recordings. An intriguing hypothesis about the development of epilepsy is that a cortical region, when potentiated by repeated seizures, becomes chronically biased, leading to hypersynchrony. Thus, brain regions most involved in epileptogenesis are marked by chronic local hypersynchrony (LH). In this study, we discuss and illustrate the use of the mean phase synchrony measure to detect and map patterns of locally hypersynchronous neuronal activity in long term ICEEG recordings obtained during the epilepsy presurgical evaluation. Seven patients with different epilepsy syndromes were studied. Three 5 minute interictal samples from different monitoring days were tested in each case. Two seizures from each patient were studied, with 30 second samples taken from the preictal, ictal, and postictal periods. Interelectrode synchrony was measured using the mean phase coherence algorithm, which calculates the degree of phase locking independent of amplitude. Pairwise synchrony over one-second epochs was calculated for all adjacent channels of a subdural grid located over or near the clinically determined seizure onset zone. LH regions were defined as a set of contiguous channel pairs with average synchrony greater than one standard deviation above the mean for the entire grid. Chronic LH regions were found in all seven patients, with these characteristics: 1) LH persisted over long recording periods and was state-independent. 2) Each patient had a unique pattern of LH regions with different anatomical distributions. 3) In each case, an LH area was associated with the clinically identified the seizure onset zone. 4) In peri-ictal recordings, there was a relative focal desynchronization preictally, a prominent finding in two patients but present to some degree in all seven, and enhanced hypersynchrony postictally. These findings suggest that local hypersynchrony in the EEG is related to abnormalities that underly partial epilepsy. This identification may prove useful in several areas. First, clinical EEG interpretation may be enhanced by using synchrony measurements to help define epileptogenic networks. Second, the described properties may be investigated to shed light on epileptogenic mechanisms. Third, knowledge of network structure and function may lead to improved techniques for disrupting the network to prevent seizures through resection, on-site neurostimulation, or local drug delivery. Fourth, these observations may lead to improved techniques for automated seizure prediction and detection. FACES