Responsive electrical stimulation suppresses epileptic seizures in rats

Activated protein C in epilepsy pathophysiology

Epilepsy is one of the most common neurologic disorders that is characterized by recurrent seizures, and depending on the type of seizure, it could lead to a severe outcome. Epilepsy's mechanism of development is not fully understood yet, but some of the common features of the disease are blood-brain barrier disruption, microglia activation, and neuroinflammation. Those are also targets of activated protein C (APC). In fact, by downregulating thrombin, known as a pro-inflammatory, APC acts as an anti-inflammatory. APC is also an anti-apoptotic protein, instance by blocking p53-mediated apoptosis. APC's neuroprotective effect could prevent blood-brain barrier dysfunction by acting on endothelial cells. Furthermore, through the downregulation of proapoptotic, and proinflammatory genes, APC's neuroprotection could reduce the effect or prevent epilepsy pathogenesis. APC's activity acts on blood-brain barrier disruption, inflammation, and apoptosis and causes neurogenesis, all hallmarks that could potentially treat or prevent epilepsy. Here we review both Activated Protein C and epilepsy mechanism, function, and the possible association between them.

Computerized application for epilepsy in China: Does the era of artificial intelligence comes?

Epilepsy, one of the most common neurological diseases in China, is notorious for its spontaneous, unprovoked and recurrent seizures. The etiology of epilepsy varies among individual patients, including congenital gene mutation, traumatic injury, infections, etc. This heterogeneity partly hampered the accurate diagnosis and choice of appropriate treatments. Encouragingly, great achievements have been achieved in computational science, making it become a key player in medical fields gradually and bringing new hope for rapid and accurate diagnosis as well as targeted therapies in epilepsy. Here, we historically review the advances of computerized applications in epilepsy-especially those tremendous findings achieved in China-for different purposes including seizure prediction, localization of epileptogenic zone, post-surgical prognosis, etc. Special attentions are paid to the great progress based on artificial intelligence (AI), which is more "sensitive", "smart" and "in-depth" than human capacities. At last, we give a comprehensive discussion about the disadvantages and limitations of current computerized applications for epilepsy and propose some future directions as further stepping stones to embrace "the era of AI" in epilepsy.

Advances in electrical impedance tomography-based brain imaging

Novel advances in the field of brain imaging have enabled the unprecedented clinical application of various imaging modalities to facilitate disease diagnosis and treatment. Electrical impedance tomography (EIT) is a functional imaging technique that measures the transfer impedances between electrodes on the body surface to estimate the spatial distribution of electrical properties of tissues. EIT offers many advantages over other neuroimaging technologies, which has led to its potential clinical use. This qualitative review provides an overview of the basic principles, algorithms, and system composition of EIT. Recent advances in the field of EIT are discussed in the context of epilepsy, stroke, brain injuries and edema, and other brain diseases. Further, we summarize factors limiting the development of brain EIT and highlight prospects for the field. In epilepsy imaging, there have been advances in EIT imaging depth, from cortical to subcortical regions. In stroke research, a bedside EIT stroke monitoring system has been developed for clinical practice, and data support the role of EIT in multi-modal imaging for diagnosing stroke. Additionally, EIT has been applied to monitor the changes in brain water content associated with cerebral edema, enabling the early identification of brain edema and the evaluation of mannitol dehydration. However, anatomically realistic geometry, inhomogeneity, cranium completeness, anisotropy and skull type, etc., must be considered to improve the accuracy of EIT modeling. Thus, the further establishment of EIT as a mature and routine diagnostic technique will necessitate the accumulation of more supporting evidence.

Multi-disease Deep Brain Stimulation

Current closed-loop deep brain stimulation (DBS) devices can generally tackle one disorder. This paper presents the design and evaluation of a multi-disease closed-loop DBS device that can sense multiple brain biomarkers, detect a disorder, and adaptively deliver electrical stimulation pulses based on the disease state. The device consists of: (i) a neural sensor, (ii) a controller involving a feature extractor, a disease classifier, and a control strategy, and (iii) neural stimulator. The neural sensor records and processes local field potentials and spikes from within the brain using two low-frequency and high-frequency channels. The feature extractor digitally processes the output of the neural sensor, and extracts five potential biomarkers: alpha, beta, slow gamma, high-frequency oscillations, and spikes. The disease classifier identifies the type of the neurological disorder through an analysis of the biomarkers' amplitude features. The control strategy considers the disease state and supplies the stimulation settings to the neural stimulator. Both the disease classifier and control strategy are based on fuzzy algorithms. The neural stimulator generates electrical stimulation pulses according to the control commands, and delivers them to the target area of the brain. The device can generate current stimulation pulses with specific amplitude, frequency, and duration. The fabricated device has the maximum radius of 15 mm. Its total weight including the circuit board, battery and battery holder is 5.1 g. The performance of the integrated device has been evaluated through six bench and in-vitro experiments. The experimental results are presented, analyzed, and discussed. Six bench and in-vitro experiments were conducted using sinusoidal, normal pre-recorded, and diseased neural signals representing normal, epilepsy, depression and PD conditions. The results obtained through these tests indicate the successful neural sensing, classification, control, and neural stimulating performance.

Exploring human epileptic activity at the single-neuron level

Today, localization of the seizure focus heavily relies on EEG monitoring (scalp or intracranial). However, current technology enables much finer resolutions. The activity of hundreds of single neurons in the human brain can now be simultaneously explored before, during, and after a seizure or in association with an interictal discharge. This technology opens up new horizons to understanding epilepsy at a completely new level. This review therefore begins with a brief description of the basis of the technology, the microelectrodes, and the setup for their implantation in patients with epilepsy. Using these electrodes, recent studies provide novel insights into both the time domain and firing patterns of epileptic activity of single neurons. In the time domain, seizure-related activity may occur even minutes before seizure onset (in its current, EEG-based definition). Seizure-related neuronal interactions exhibit complex heterogeneous dynamics. In the seizure-onset zone, changes in firing patterns correlate with cell loss; in the penumbra, neurons maintain their spike stereotypy during a seizure. Hence, investigation of the extracellular electrical activity is expected to provide a better understanding of the mechanisms underlying the disease; it may, in the future, serve for a more accurate localization of the seizure focus; and it may also be employed to predict the occurrence of seizures prior to their behavioral manifestation in order to administer automatic therapeutic interventions.

THE EFFECT OF HIGH AND LOW FREQUENCY CORTICAL STIMULATION WITH A FIXED OR A POISSON DISTRIBUTED INTERPULSE INTERVAL ON CORTICAL EXCITABILITY IN RATS

Neurostimulation is a promising treatment for refractory epilepsy. We studied the effect of cortical stimulation with different parameters in the rat motor cortex stimulation model. High intensity simulation (threshold for motor response - 100 μA), high frequency (130 Hz) stimulation during 1 h decreased cortical excitability, irrespective of the interpulse interval used (fixed or Poisson distributed). Low intensity (10 μA) and/or low frequency (5 Hz) stimulation had no effect. Cortical stimulation appears promising for the treatment of neocortical epilepsy if frequency and intensity are high enough.

Creating the feedback loop: closed-loop neurostimulation

Current DBS therapy delivers a train of electrical pulses at set stimulation parameters. This open-loop design is effective for movement disorders, but therapy may be further optimized by a closed loop design. The technology to record biosignals has outpaced our understanding of their relationship to the clinical state of the whole person. Neuronal oscillations may represent or facilitate the cooperative functioning of brain ensembles, and may provide critical information to customize neuromodulation therapy. This review addresses advances to date, not of the technology per se, but of the strategies to apply neuronal signals to trigger or modulate stimulation systems.

The effects of responsive and scheduled subicular high frequency stimulation in the intra-hippocampal kainic acid seizure model

BACKGROUND

Responsive stimulation is a promising and newly emerging treatment for refractory temporal lobe epilepsy in which current is delivered to target areas following seizure occurrence.

OBJECTIVE

We compared responsive and scheduled subicular high frequency stimulation (HFS) with a sham control group on acute seizures and seizure sensitivity two weeks later. We also investigated the role of status epilepticus (SE) on efficacy of both types of stimulation.

METHOD

Adult Wistar rats received kainic acid (KA) injections intrahippocampally until they reached Stage V (Racine scale) on Day 1. Responsive, scheduled or sham HFS (125 Hz, 100 μs) was delivered in three groups while EEG was recorded. All rats received KA injections again on Day 15 to measure the excitability of animals to KA, again with EEG monitoring.

RESULTS

All rats reached Stage V and 60% reached SE on Day 1. Focal seizures were suppressed in both stimulated groups (the scheduled group was slightly more effective) on both days in only non-SE rats. Similar stimulation effects were found on generalized seizures but mainly on Day 15.

CONCLUSION

Both types of subicular HFS suppressed focal and generalized seizures, albeit differently. Scheduled stimulation seemed a bit more effective, and the amount of stimulation might be a factor that influences the differences between the stimulated groups. Beneficial effects of HFS were restricted to non-SE rats and HFS did not suppress or even worsen seizures in SE rats.