Almost 200,000 new cases of epilepsy are diagnosed every year in the United States. Many of these are caused by an initial precipitating injury (IPI) (e.g status epilepticus, febrile seizures or traumatic brain injury). There is a need to develop interventions that could prevent the occurrence of epilepsy in these patients. The clinical challenge for testing and applying antiepileptogenic therapy is in identifying the subset of those who eventually became epileptic out of approximately 2 million individuals experiencing an IPI each year. The NINDS, in association with the AES, recently published a report identifying the most important research directions that should be undertaken to ultimately find cures for epilepsy. One of the 3 benchmarks considered as a top priority for the near future is the identification of biomarkers for epileptogenesis. At the present time, no biomarkers predictive of the likelihood of developing epilepsy after an IPI are available and this is an important reason why no clinical trials have identified an intervention during the latent period that clearly prevents the occurrence of epilepsy. The main goal of this proposal is to determine whether a new noninvasive electrographic putative biomarker of epileptogenesis in an animal model of chronic epilepsy can be consistently recorded during the latent period, and whether it can be used to reliably identify which animals later develop recurrent spontaneous seizures. The results of the proposed research could be used to facilitate assessment of antiepileptogenic interventions in patients. The putative biomarker we wish to study is an abnormality of the UP-DOWN State (UDS) EEG pattern. The features of the normal UDS pattern consist of an UP-phase and a Down-phase as a slow oscillation with a frequency of less than 1 Hz. The UP-phase is associated with prominent beta-gamma oscillations. The features of the pathological UDS pattern, which are seen only in epileptic animals include: 1- the occurrence of epileptiform events we have termed """"""""UPspikes"""""""" during the UP-phase, and 2 - a prolongation of the UP-phase duration. We hypothesize that this pathological UDS pattern could be a valuable predictor of future seizure occurrence. We also propose to evaluate mechanisms of pathological change in the UDS pattern by analyzing the activity of principal cells and interneurons identified by juxtacellular labeling. We will focus our efforts on the analysis of the UDS electrographic pattern prior to pilocarpine induced status epilepticus and compare it to activity recorded during the latent period before spontaneous seizures occur. We anticipate that the identification of pathological features in the UDS EEG pattern will make them a valuable early diagnostic biomarker of epileptogenesis and predictor of later seizure occurrence. An understanding of the neuronal mechanisms underlying the pathological UDS EEG patterns will provide novel targets for future approaches to the treatment of epilepsy in its earlier stages, and help pave new ways to prevent epilepsy.
At the present time, no biomarkers predictive of the likelihood of developing epilepsy after a traumatic brain injury are available and this is an important reason why no clinical trials have identified an intervention during the latent period that clearly prevents the occurrence of epilepsy. Early diagnosis of progressive epileptogenesis with early intervention is important for more effective treatment options and disease management strategies. The goal of this proposal is to identify cellular mechanisms responsible for generation of UPspikes, which is a new biomarker of epileptogenesis.
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|Bragin, A; Benassi, S K; Engel Jr, J (2012) Patterns of the UP-Down state in normal and epileptic mice. Neuroscience 225:76-87|
|Kheiri, Farshad; Bragin, Anatol; Engel Jr, Jerome et al. (2012) Non-linear classification of heart rate parameters as a biomarker for epileptogenesis. Epilepsy Res 100:59-66|
|Bragin, Anatol; Benassi, Simone K; Kheiri, Farshad et al. (2011) Further evidence that pathologic high-frequency oscillations are bursts of population spikes derived from recordings of identified cells in dentate gyrus. Epilepsia 52:45-52|
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