Treating Epilepsy with Physics

In this article, Dr. Louis Nemzer describes how novel approaches to predicting and treating seizures are being developed thanks to advances in our understanding of the physics of the brain

There are millions of people who live with unpredictable seizures that are – for reasons we don’t completely understand – not well controlled by medication or even surgery.

So, what can physics say about this challenge?

The brain is an incredibly complex network consisting of tens of billions of neurons forming hundreds of trillions of connections, which makes the task of prediction seem daunting. However, each neuron is still a physical object that obeys the laws of physics.

As part of our research at Nova Southeastern University in Fort Lauderdale, US, we have carried out computer simulations of the behaviour of small networks of neurons using the Hodgkin–Huxley equations. This work has let us monitor the effects of slight changes to individual parameters, such as the threshold for each neuron to fire, or the “stickiness” of ion gates, which makes them stay open longer than they should. All of these may have a significant impact on the excitability of each neuron.

To classify the risk of seizures, our group in Florida is adapting machine-learning algorithms that are already used in medicine to diagnose radiology images. We are currently training the machine-learning models – and the simulations we created before are helping by generating simulated data that the algorithms can learn from. Next, as long as we have enough labelled ECoG data from patients, we hope to be able to build a highly accurate warning program even if we do not know which features in the signal the algorithm is using. While this “black-box” approach may seem disconcerting at first, the primary test will be the usefulness, if not the explainability, of the resulting system.

Why Do Some People Stop Breathing After Seizures?

Could a chemical produced by the brain that regulates mood, sleep and breathing also be protective in people with epilepsy? New research has found that higher levels of serotonin in the blood after a seizure are linked to a lower incidence of seizure-related breathing problems called apneas, when a person temporarily stops breathing. The study is published in the September 4, 2019, online issue of Neurology®, the medical journal of the American Academy of Neurology.

“Serotonin, a hormone that transmits signals between nerve cells in the brain, is known to regulate breathing and waking from sleep, but what is unknown is how it may influence breathing before, during and after seizures,” said study author Samden D. Lhatoo, MD, FRCP, of McGovern Medical School at University of Texas Health Science Center in Houston, Texas, who conducted the research at Case Western Reserve University in Cleveland, Ohio. “Our findings show that higher levels of serotonin after a seizure are associated with less breathing dysfunction, and while we cannot make any links between serotonin levels and a risk of sudden unexplained death in epilepsy (SUDEP), our research may provide some important clues, since SUDEP has been linked in previous research to profound breathing dysfunction after generalized convulsive seizures.”

Researchers found that serotonin levels after a seizure were higher than before a seizure in people who did not temporarily stop breathing during a seizure. For 32 people who did not temporarily stop breathing during a seizure, serotonin levels were an average of 140 nanograms per milliliter (ng/ml) higher than an average of 110 ng/ml before seizure. For 17 people who did temporarily stop breathing, their serotonin levels were not significantly higher compared to before seizure.

“Our results give new insight into a possible link between serotonin levels and breathing during and after seizure,” said Lhatoo. “This may give hope that perhaps someday new therapies could be developed that may help prevent SUDEP. However, our study was small and much more research is needed to confirm our findings in larger groups before any treatment decisions can be made. It is also important to note that excess serotonin can be harmful, so we strongly recommend against anyone trying to find ways to increase their serotonin levels in response to our study findings.”

In addition to the small study size, a limitation of the study was that the timing of blood draws was not consistent.

Testing New Treatment for Epilepsy Patients

University of Houston associate professor of biomedical engineering Nuri Ince, who pioneered a dramatic decrease in the time it takes to detect the seizure onset zone (SOZ) in the brain, has been awarded $2.3 million by the National Institutes of Health to expand his testing in a large number of adult and pediatric epilepsy cases.

Current treatment protocols for detecting the actual part of the brain that causes seizures, the SOZ, require prolonged monitoring of intracranial EEGs (iEEG) for days or weeks following surgical insertion of electrodes. The prolonged monitoring adds to the risk of complications that can include intracranial bleeding and potentially death. Using his newly-created machine learning algorithms, Ince observed that high frequency oscillations (HFO) in the seizure onset zone form repetitive waveform patterns that identify their location.

Using these stereotyped HFOs, Ince knows he can find the zone in an hour. He thinks he can do it in about 10-to-20 minutes.

“We believe that accurate detection of high frequency oscillations in brief iEEG recordings can identify the SOZ, eliminating the necessity of prolonged monitoring and reducing the associated risks,” said Ince. “A patient could be operated on at the same time he is having the electrodes attached to his brain, eliminating the patient being sent to an epilepsy monitoring unit for days or weeks to be observed. This would mark a totally new treatment and dramatically reduce risks and burdens to families.”

Each year 150,000 people are diagnosed with epilepsy and 30% of them will suffer from a drug resistant form of the disease. When medication fails, the next course of treatment is surgical resection, or removal, of the SOZ.

Patient and Family Perspectives of Pediatric Psychogenic Non-Epileptic Seizures: A Systematic Review

Exploring the perspectives of those affected by psychogenic non-epileptic seizures (PNES) may be essential in learning more about the nature of this condition.

The aim of this systematic review is to synthesize the evidence regarding the perspectives of children and adolescents with PNES, and the perspectives of their parents, caregivers and families. Studies were included if they (1) explored PNES in a paediatric population, (2) explored the perspectives of the child or adolescent with PNES, or the perspectives of their parents, caregivers or families, (3) were original research, and (4) were written in the English language.

Eight studies were identified for inclusion following searching of CINAHL Complete, Medline (Ovid), PsycINFO, PubMed and Web of Science databases, along with additional hand searching of reference lists. Quality assessment of articles was conducted using the Critical Appraisal Skills Programme (CASP) qualitative checklist.

Seven articles were deemed high quality, and one article was deemed moderate quality. Common threads across studies included: “legitimacy and the importance of understanding”, “distress and the social and emotional impact of PNES” and “moving forward”.

Clinicians must take care in the delivery of the diagnosis; including the use of an appropriate name for this condition, and providing an explanation of PNES that is acceptable to the patient, as well as ensuring that follow-up support is provided. Further reviews are required that utilize more well-established quality appraisal scoring systems and with the inclusion of grey literature, which refers to evidence not published by commercial academic publishers.

Review of Machine Learning Applications in Epilepsy: Harnessing Statistical and Computer Science for Epilepsy Research

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions.

Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre-surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources.

A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.

(Re)Defining Success in Epilepsy Surgery: The Importance of Relative Seizure Reduction in Patient-Reported Quality of Life

Objective: Previous work has suggested that seizure outcome is the most important predictor of quality of life (QoL) after epilepsy surgery, but it is unknown which specific seizure outcome measure should be used in judging surgical success. These researchers assess three different seizure outcome measures (relative seizure reduction, absolute seizure reduction, and seizure freedom [yes/no]) to investigate which measure best predicts postoperative QoL.

Methods: The study prospectively surveyed patients at outpatient visits before and after epilepsy surgery (n = 550). The QoL measure was the Quality of Life in Epilepsy (QOLIE-10) score at the patient’s most recent office visit. The team created multivariate regression models to predict postoperative QOLIE-10, with a different seizure outcome measure in each model. They then compared models using adjusted R2 values and Akaike information criteria (AIC).

Results: The cohort had a high level of disease severity and complexity (17% repeat surgery, 39% extratemporal, and 18% nonlesional). For the cohort as a whole, mean absolute seizure frequency decreased from 1 per day to 0.1 per day (P < .001), and mean reduction was 73% (95% confidence interval [CI] 66%-81%). Average improvement in QoL score was 5.3 (95% CI 4.1-6.5) points. Of patients who reported an improvement in QoL, 27% had persistent seizures. Comparison of regression models to predict QoL showed that the worst model was provided when using “absolute seizure reduction,” but that models using “relative seizure reduction” and “seizure freedom (yes/no)” were equally strong.

Significance: In the high severity and complexity cohort, a substantial subset of patients (27%) reported improved quality of life despite persistent seizures. Relative seizure reduction was at least as good a predictor of quality of life as seizure freedom. A yes/no seizure freedom variable may be a suboptimal measure of surgical success, especially in high complexity cohorts.

Physical Activity Levels are Lower Among People Living with Epilepsy

How physically active and sedentary people with epilepsy are is unclear.

This research team conducted a meta-analysis to investigate physical activity and sedentary behavior levels compared with the general population in people with epilepsy across the lifespan. Embase, PubMed, PsycARTICLES, and CINAHL Plus were searched from inception until 1/3/2019. A random effects meta-analysis was conducted.

Adults with epilepsy (mean age range = 30-47 years) were significantly less likely to comply with physical activity recommendations [odds ratio (OR) = 0.68; 95% confidence interval (CI) = 0.53-0.87; P < 0.001; N analyses = 10; n epilepsy = 1599; n controls = 137,800] and more likely to be inactive (as defined by individual study criteria) (OR = 1.57; 95% CI = 1.34-1.84; P < 0.001; N analyses = 6; n epilepsy = 6032; n controls = 928,184). Data in children (mean age range = 10-12 years) were limited (N = 4; n = 170) and inconsistent while there were no data available for middle-aged and old age (>65 years) people with epilepsy.

These data demonstrate that adults with epilepsy are less physically active than the general population. Public health campaigns specifically targeting the prevention of physical inactivity in adults with epilepsy are warranted. More research on physical activity and sedentary levels in children, adolescents, middle-aged, and old age but also adult people with epilepsy is needed before specific recommendations can be formulated.

Mechanism of Epilepsy Causing Membrane Protein Discovered

The Korea Brain Research Institute announced that a team led by principal researcher Lim Hyun-Ho discovered a new 3-D structure and membrane protein mechanism which causes epilepsy and muscle problems. The study results were published in the August issue of Proceedings of the National Academy of Sciences (PNAS).

Neurons control physiological phenomena such as delivery of electrical signals and secretion of signal transduction materials by exchanging chloride (Cl-) ions and hydrogen ions (H+) in the cell membrane. If there is a problem with the CLC transporter protein that is involved in this process, muscle problems, epilepsy, hearing loss and blindness can develop.

The research team led by Dr. Lim Hyun-Ho, succeeded in identifying a new structure of external glutamate residue, which plays a critical role in the ion exchange of single CLC transporter proteins, for the first time in the world.

Epilepsy and the Family: Caregiver Stress and Sibling Experiences

Seizures and their consequences affect every aspect of a caregiver’s world: their physical health, emotional health, psychological health, social relationships, education, employment, finances and futures. These multiple effects often result in stress and anxiety.

A 2019 study in Epilepsia Open examined seizure burden in young children and the effects on parents. The study concluded:

1. Seizure unpredictability is an important contributor to seizure burden.

2. Seizure unpredictability creates a constant state of impending or actual crisis.

3. Parents experience the long?term effects of their children’s seizures as a type of chronic traumatic stress disorder.

And yet, it’s still largely unacceptable for parents to acknowledge how stressful and exhausting it can be to care for, love and live with someone who has epilepsy.

“It’s hard for many parents to recognize or admit, because it can feel as though they are betraying their child by doing so,” said Devine. “If we can realize that we can love our children immensely and also recognize that parenting can be a traumatic experience due to their illness, that is the first step towards recovery and reclaiming health of body, mind, and spirit.”

CURE Discovery: A New Model to Understand Focal Cortical Dysplasias

Dr. Eid Tore


  • CURE Taking Flight Grantee Dr. Yu Wang and his team created a new, better animal model of focal cortical dysplasias (FCDs) to understand how they can lead to focal epilepsy.
  • The team found that 100% of the animals developed spontaneous seizures, had hyperactivation of the mTOR pathway, and displayed brain malformations as seen in human FCDs.
  • The team treated the animals with a drug called everolimus, which reversed the abnormal increase in neuronal size seen in some types of FCDs.

Deep Dive

DefinitionsDr. Yu Wang and his colleagues at the University of Michigan are one step closer to understanding the process of epileptogenesis in FCDs and developing therapies for it. FCDs (small groupings of neurons that fail to develop correctly) are a common cause of difficult-to-treat focal epilepsies. Surgery is often the only treatment option for those impacted by FCDs, and up to 50% of pediatric epilepsy surgery patients have FCDs that are detectible by MRI.1

Dr. Wang’s team sought to generate a novel animal model of FCD with the same features as humans with mutations in a gene called DEPDC5. This gene is part of the mTOR signaling pathway and is associated with development of FCD-related. Other animal models used to study DEPDC5 are limited in usefulness as they fail to either develop spontaneous seizures or show the same electroclinical features as human FCDs.

To create their model, Dr. Wang and his team used a gene editing technique called CRISPR-IUE in rats to delete DEPDC5 only in the brain’s cortex and not the entire body, thereby more accurately replicating human FCD.2 The team found that 100% of the animals developed spontaneous seizures, had hyperactivation of the mTOR pathway, and displayed brain malformations as seen in human FCDs. Importantly, these animals displayed seizure activity highly similar to those recorded in humans with FCDs.

Key OutcomesThis new model had another feature seen in some types of FCDs: enlarged cell size. Dr. Wang and his team observed that neurons with DEPDC5 deletion were almost twice the size of neurons with a normal level of DEPDC5. The team treated the animals with a drug called everolimus and reversed the increase in neuronal size in their model.

Dr. Wang’s work has led to a model that represents the genetics, pathology, and EEG features of human FCDs. Critically, their work demonstrates that a known drug may be valuable in treating these epilepsies. The team is currently using this model to understand how DEPDC5 mutations cause malformations leading to epilepsy, and to develop a novel gene therapy strategy for mTOR-related cortical malformation and epilepsies. While Dr. Wang’s career is just starting to take flight, we can’t wait to see how his insights and innovations will drive science forward!

1 Bast T. Focal cortical dysplasia: prevalence, clinical presentation and epilepsy in children and adults. Acta Neurol Scand. 2006 Feb;113(2):72-81
2 Hu S, Knowlton RC Somatic Depdc5 Deletion Recapitulates Electroclinical Features of Human Focal Cortical Dysplasia Type IIA. Ann Neurol. 2018 Jul;84(1):140-146