A characterization of epileptogenesis presented in hippocampal neural activity in a rat tetanus toxin model

We built a regression model to describe the progress of epileptogenesis in a rat intrahippocampal tetanus toxin (TeNT) epilepsy model by identifying informative neural features from hippocampal local field potentials (LFPs). The LFPs were recorded from the awake and freely behaving animals during the latent period and the active-seizure period. Frequency domain neural features including power spectral density, coherence and phase coherence were calculated from the hippocampal LFPs. A least angle regression with elastic net regularization (LARS-ENR) model successfully predicted a relative day from the first seizure in multiple rats (R²_test = 0.724±0.025). By leveraging a characteristic of LARS-ENR which reduces unnecessary features, we identified the neural features related to epileptogenesis in a TeNT model.