Abstracts

Rationale: Prolonged video-EEG monitoring in chronic epilepsy rodent models has become an important tool in preclinical drug development of new therapies, in particular for anti-epileptogenesis, disease modification and drug resistant epilepsy. We have developed a convenient, easy to use, interactive software tool for reliable detection of electroencephalographic seizures in rodent models of acquired and genetic epilepsy. Methods: We applied a novel method based on an advanced time-frequency analysis which detects the episodes of EEG with excessive activity in certain frequency bands. The method uses an original technique of short term spectral analysis that calculates the Fourier transform within a specific frequency band with arbitrary frequency resolution. We utilised our novel method to evaluated for seizures in long-term EEG recordings of post-status epilepticus (post-SE) rats (n=119) and mice (n=7), fluid percussion injury rats (FPI, n=9); Genetic Absence Epilepsy Rats from Strasbourg (GAERS, n=41), Wistar Albino Glaxo rats of Rijswijk (WAG/Rij, n=14), four mutant mouse models on a variant of strain backgrounds in the genes Gabrg2 (n=4), Gria4 (n=8), Scn8a (n=4), Gnb1 (n=2) which all exhibit spontaneous spike-wave discharges (SWD) and a Kcnt1 gene knockin mouse model (n=8) that presents with spontaneous tonic and generalized tonic-clonic seizures. The results of our automated seizure detection software were compared against manual analysis of EEG recordings performed by two experienced observers. Results: Our computer program had a high sensitivity, detecting 100% of seizures detected in the manual analysis in all of the tested animals (216 animals, 19471 seizures or SWDs). We found that the seizures in post-SE rats (993 seizures), FPI rats (49 seizures), GAERS (8733 seizures) and WAG/Rij (825 seizures) showed high values of power spectrum in the 17-25 Hz frequency band. This specificity, that comes from the frequency composition of individual spike-wave complexes within the seizures in these animals, was used for seizure detection. Interestingly, the seizures found in the Kcnt1 mouse model (n=90) were reliably detected using the same frequency band as for rats. The bands used for SWD detection in mutant mouse models Gabrg2 (55 SWDs), Gria4 (601 SWDs), Scn8a (453 SWDs), Gnb1 (7367 SWDs) were slightly wider (17-27 Hz or 14-27 Hz). The seizures in post-SE mouse model of temporal lobe epilepsy (n=305) were smoother than those in post-SE rats, so for their detection a lower frequency band of 7-13 Hz was applied. Electrode artefacts may also significantly contribute to the corresponding frequency band, so they were also selected by the program. This selection, however, generated a very low rate of false positives. For their elimination, a quick user inspection was needed. The overall processing time for 12 day-long recordings varied from few minutes (5-10) to an hour, depending on the number of artefacts. Conclusions: Our seizure detection tool provides high sensitivity, with acceptable specificity, for long and short-term EEG recordings from a wide variety of rat and mouse, genetic and acquired, epilepsy models. This has the potential to improve the efficiency and rigor of preclinical research and therapy development using these models. Funding: Funding to assist this research program was provided by Kaoskey Pty Ltd, Sydney, Australia.