Investigation of intracranial pharmacotherapy in Genetic Absence Epilepsy Rats from Strasbourg (GAERS): a potential strategy to overcome the limitations of the standard treatment in epilepsy

Epilepsy is a chronic disorder of the brain and affects approximately 50 million people worldwide. It is, thus, one of the most common neurological diseases (World Health Organization, 2006). The main approach of epilepsy treatment is systemic drug application. The central nervous system (CNS), however, is a particularly challenging target for drug delivery. Systemic drug therapy is limited by the blood-brain-barrier (BBB), restricting the distribution of pharmaceuticals into the CNS. One approach to by-pass the BBB is intrathecal (IT) administration of anti-epileptic drugs with direct application of substances into the cerebrospinal fluid (CSF). This study aimed to investigate whether IT application of anticonvulsant substances is a reasonable approach to treat epilepsy. The in-depth evaluation of IT drug application was performed in the genetic absence epilepsy rats from Strasbourg (GAERS). Seizures in GAERS rats closely resemble human absence seizures and can be detected as characteristic spike-and-wave discharges (SWDs) in the electroencephalogram (EEG). To quantify seizure occurrence in GAERS and assess the efficacy of anti-epileptic therapy, automated seizure detection based on EEG recordings was implemented. With this method, seizure detection was performed with an F-score of 96 %. The analysis of 12h and 24h recordings in untreated animals revealed circadian undulations of SWD activity with a peak of seizures between 2am and 4am. This observation was in agreement with earlier studies that showed that SWD activity depends on the vigilance level in rats (Drinkenburg et al., 1991). By intracerebroventricular (i.c.v.) injections, drug application into the CSF was archived. To this end, a guide cannula was implanted into the right lateral ventricle. Initially, the standard anti-absence drugs, ethosuximide (ETX), and valproate (VPA) (Manning et al., 2003), were tested with this IT application approach. The treatment caused a substantial and dose-dependent reduction in SWD for both drugs and revealed that localized therapy with ETX is significantly more effective than with VPA. Additionally, the i.c.v. administration of ETX was dramatically more efficient than systemic ETX application of the same dose. The subsequent analysis of substance distributions in the brain after intracranial application suggested that the therapeutic effect was not caused by the indirect entry of ETX into brain parenchyma via the bloodstream but rather mediated by the direct entry from the CSF. Additional experiments to explore the therapeutic efficacy of the neuropeptides Neuropeptide Y (NPY) and Somatostatin (SST) as potential alternative to standard drugs, did not reveal a robust anti-absence effect. Consequently, these neuropeptides were not considered potent substances for localized drug therapy in absence epilepsy

Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network

The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly attractive option. For such implantable medical devices, efficient and low energy consumption, small size, and efficient processing architectures are essential. To meet these requirements, epileptic seizure detection by analysis and classification of brain signals with a convolutional neural network (CNN) is an attractive approach. This work presents a CNN for epileptic seizure detection capable of running on an ultra-low-power microprocessor. The CNN is implemented and optimized in MATLAB. In addition, the CNN is also implemented on a GAP8 microprocessor with RISC-V architecture. The training, optimization, and evaluation of the proposed CNN are based on the CHB-MIT dataset. The CNN reaches a median sensitivity of 90% and a very high specificity over 99% corresponding to a median false positive rate of 6.8 s per hour. After implementation of the CNN on the microcontroller, a sensitivity of 85% is reached. The classification of 1 s of EEG data takes t=35 ms and consumes an average power of P≈140 μW. The proposed detector outperforms related approaches in terms of power consumption by a factor of 6. The universal applicability of the proposed CNN based detector is verified with recording of epileptic rats. This results enable the design of future medical devices for epilepsy treatment

Task-specific oscillatory synchronization of prefrontal cortex, nucleus reuniens, and hippocampus during working memory

Summary: Working memory requires maintenance of and executive control over task-relevant information on a timescale of seconds. Spatial working memory depends on interactions between hippocampus, for the representation of space, and prefrontal cortex, for executive control. A monosynaptic hippocampal projection to the prefrontal cortex has been proposed to serve this interaction. However, connectivity and inactivation experiments indicate a critical role of the nucleus reuniens in hippocampal-prefrontal communication. We have investigated the dynamics of oscillatory coherence throughout the prefrontal-hippocampal-reuniens network in a touchscreen-based working memory task. We found that coherence at distinct frequencies evolved depending on phase and difficulty of the task. During choice, the reuniens did not participate in enhanced prefrontal-hippocampal theta but in gamma coherence. Strikingly, the reuniens was strongly embedded in performance-related increases in beta coherence, suggesting the execution of top-down control. In addition, we show that during working memory maintenance the prefrontal-hippocampal-reuniens network displays performance-related delay activity