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About human epilepsy

This is the portrait of Fedor Dostoevsky who suffered from epilepsy. Source:

Finally, we have submitted our paper to the journal after 3 years of hard work. It was a long journey with ups and downs, so now it is a good occasion to share the main points of our research.

Epilepsy is very common neurological disorder, it affects nearly 1% of the human population. Greeks gave it the name ἐπίληπτος, aka “epileptos”, which means captured. Both ancient Greeks and Romans explained epilepsy by divine intervention. Since that time the humanity has substantially improved our understanding of this disease. In this post I would like to talk about what we learned in our research. The main story is on bioarxiv of course, but it is it is not always easy to comprehend it from there.

In the nervous system, as well as in the other parts of the body there are a lot of processes related to homeostasis. They are necessary to keep the system near the operating point, where it could function the most efficient way. One example of this homeostatic process is the balance between excitation and inhibition in biological neural network. Epilepsy is usually the result of disruption of such balance towards more excitation. In the network there are two processes balancing each other. Excitation makes neurons more excitable and helps them to transmit the information, while inhibition allows the network to keep the excitation under the control. When inhibition entirely wins over excitation, the brain is usually in the deep non-rem phase of sleep or in coma. When excitation wins, the whole network moves towards the epileptic seizure, which often leads to convulsions.

To better understand what exactly is happening during the epilepsy, we studied pieces of the brain that were removed from the seizure focus of epileptic patients. In certain cases, when seizures are happening in the specific part of the brain, it is possible to remove the focus and reduce or completely eliminate the amount of seizures. In the majority of cases it leads to remission. The tissue we studied comes from human epileptic hippocampus, which is also responsible for new memory formation, therefore removing this part of the brain often leads to memory problems.

As it often happens in case of pathologies of the nervous system, it is rare occasion when one particular gene is broken. Usually pathologic changes in neurons are the result of activity of several genes and it is often really hard to understand the causation. Moreover, it is almost impossible to compare these results to control. Because of clear ethical reasons, we could not take the control healthy tissue and compared with the diseased one. Since we do not have a control, we followed an alternative strategy. The neural tissue that comes from the hippocampus often also have sclerosis. This is another pathological process, when the normal tissue is replaced by the connective tissue. In the brain the neurons are replaced by various types of glial cells. Since we had our tissue which comes from different patients in different stages of sclerosis, we made the comparisons between less and more sclerotic neurons. It allowed us to see more clear picture of what is happening.

It turned out that neurons of epileptic patients from later stage of sclerosis have more excitable neurons. In particular, they start responding to the injected current faster than neurons from the early stage of sclerosis. Moreover, we found that these neurons are becoming physically larger and receive higher amount of the synaptic input from the other neurons. These discoveries allowed us to make the mathematical model of biological neurons from late and early stages of sclerosis and analyze the dynamics of the biological neural networks with these cells. Using the mathematical model we evaluated the role of different factors which contribute to the increase of excitability.

It is important to mention that mathematical modeling is especially important for understanding of the biological processes. Biological systems such as nervous system as a whole or even a single neuron consist of different parts. Often it is also not clear which factors provide the largest contribution and which factors seem big, but their effect is negligible. In the process of making the model, we learned which properties of neurons we could affect in order to reduce the excitability. In the future it would allow us to find better drug targets or viral therapy to reduce the excitability in epilepsy without removing the pathologic brain parts.

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