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Epilepsy: New Leaps for Ancient Disease

By Olivia Stovicek

Epilepsy, or the condition of having recurrent, unprovoked seizures, is among the world’s oldest and most difficult to treat diseases. Documentation of epilepsy’s signs and symptoms goes back nearly 4500 years, and it is the fourth most common neurological disorder in the U.S., with approximately 4% of people in the U.S. and worldwide developing epilepsy in their lifetime., Nevertheless, almost 30% of patients have seizures that cannot be controlled with existing antiseizure medication, and for nearly two-thirds of patients, their epilepsy has no known cause.3, A variety of recent research, however, has highlighted how approaches that focus on networks—both networks of genes within cells and brain networks—may be able to finally achieve the next leaps in the field.

Epilepsy is really many diseases rather than one; there are nearly forty known forms of epilepsy, with distinct symptoms and treatments. Some of these are caused by mutations in a single gene; a classic example is Dravet syndrome, in which almost all cases are caused by mutations in an important sodium channel, a protein that allows sodium ions to cross a cell’s membrane and helps determine the cell’s electrical behavior.4 This is unusual, however. Most epilepsy symptoms are determined by not one but many genes, often in interaction with environmental factors—even epilepsies acquired after stroke or infection often have genetic contributions.4 The first gene associated with epilepsy was identified in 1990, and since then accelerating discovery of epilepsy-related genes has made it increasingly clear that most forms of epilepsy without known cause are likely to have a genetic basis, most individuals’ specific mutations are rare, and many epilepsies have highly complex genetic contributions.4, In order to unravel the details of individual patients’ epilepsies, and to understand how complex gene interactions lead to epilepsy, in silico, i.e. computational, models are needed to figure out how multiple genes with minor contributions can have a significant effect.

One way combinatorial effects of gene mutations have been explored is by sequencing all known ion channel genes at once.6 These genes are the largest one group of genes implicated in causing epilepsy. Modeling has shown that the clinical status of patients with mutations in these genes depends not on how many mutations they have, but the particular pattern of them, how they each contribute to the overall network regulating the electrical properties of the cell; modeling the effects of each mutation can help predict whether that particular combination will lead to epilepsy.6 

Networks of interactions aren’t just important on a gene level in epilepsy, however. Epilepsy is now understood as a “network disease,” with seizures that originate in one part of the brain (the “seizure onset zone”) occurring and spreading because of those neurons’ interactions with neurons in many other parts of the brain. Understanding these networks better may make it possible to predict seizures better or even alter the networks to prevent seizures. For example, it could become possible to implant a device that stimulates a particular brain region that controls many other parts of the epileptic network, thereby regulating seizure activity.8 

This is tricky, but important work. A 2017 study from Geier and Lehnertz sought to investigate which parts of the brain are important in epileptic networks at what times.7 They took records of brain activity for 17 patients for several days straight, and, based on the placement of different electrodes on different regions of the brain and a mathematical analysis, they constructed networks of epileptic brain interactions where electrode positions were the network nodes and were linked based on dependence on each other. The researchers then analyzed which nodes or brain regions were most important, measured by whether a node interacted strongly with many other nodes and whether it connected many other pairs of nodes. They showed that the importance of different brain regions in an epileptic network changes significantly over time, largely in ways directed by the body’s daily rhythms. Part of what this means is that the seizure onset zone—often considered the most important part of the epileptic network—is not always the most important, and that other parts of the brain should be explored in looking to treat seizures.

Other work is exploring how to identify the roles of different important brain regions in regulating seizure networks. Seizures involve abnormal synchronization in the activity of neurons, and a 2016 study from Khambati et al. sought to identify whether “tug of war” or “push-pull control” between brain regions that synchronize the activity of other neurons and those that desynchronize it was a mechanism of seizure regulation.8 To explore this question, they again turned to networks; using similar methods to Geier and Lehnertz, they built models of dynamic epileptic networks right before and during a seizure. Then, they removed different electrodes—and therefore different nodes/brain regions—from their model to see how that affected the network. They were able to show that certain brain regions increased or decreased synchronization of the overall network, using a tug-of-war system, and that parts of the network outside of the seizure-onset zone regulate the synchronization of network activity. Like Geier and Lehnertz, their work partly highlighted how “healthy” regions of the brain not historically considered important in epilepsy can contribute to seizures, but it also provided a way of looking at different mechanisms of epilepsy regulation at the network level.

Though much of the recent work on epileptic networks has focused on figuring out how seizures work, with the hope of that information helping patients in the future, some of it is much closer to affecting patients’ lives. One study released just in the last few months, for example, may help make surgery easier for epilepsy patients. Surgical removal of areas in the brain that cause seizures can be extremely helpful for some patients who do not respond well to epilepsy medication, but in order to identify which areas to remove, patients often have to undergo invasive surgery so that their brain activity can be monitored in detail. They must be monitored until at least one seizure occurs, so patients typically have to spend a week in a hospital before they can have a second operation to remove seizure-causing regions.9 Researchers Park and Madsen, however, were able to find a way to predict which parts of patients’ brains were causing seizures without having to monitor them for a seizure first.9 They adapted an approach called Granger causality analysis, invented by an economist, to identify networks of causal relationships between activity in different parts of a brain not having a seizures. Areas with a high concentration of causal nodes, as identified using this method, predicted quite well the areas that would be chosen for removal using the typical method. Though this technique needs refinement and further study before it can be used, it presents an exciting possibility.

Though these are just a few examples, they highlight the potential of network-based investigations of epilepsy for advancing understanding and treatment of the disease. Keep an eye on this area of research in the future; it just might be a spot for the next few breakthroughs.

1. http://www.epilepsy.com/learn/epilepsy-101/what-epilepsy

2. https://www.ncbi.nlm.nih.gov/pubmed/17261678

3. https://www.nature.com/nrneurol/journal/v10/n5/pdf/nrneurol.2014.63.pdf

4. https://www.nature.com/nrneurol/journal/v10/n5/pdf/nrneurol.2014.62.pdf

5. http://onlinelibrary.wiley.com/doi/10.1111/j.1528-1167.2010.02522.x/abstract

6. Noebels, J. (2015) Pathway-driven discovery of epilepsy genes. Nat. Neurosci. 18(3), 344–350.

7. Geier, C., and Lehnertz, K. (2017) Long-term variability of importance of brain regions in evolving epileptic brain networks. Chaos 27, 043112.

8. Khambhati, A.N., Davis, K.A., Lucas, T.H., Litt, B., and Bassett, D.S. (2016) Virtual Cortical Resection Reveals Push-Pull Network Control Preceding Seizure Evolution. Neuron 91, 1170–1182.

9. Park, E-H., and Madsen, J.R. (2017) Granger causality analysis of interictal iEEG predicts seizure focus and ultimate resection. Neurosurgery 0, 1–11.

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