From Discoveries to Therapies: The Race to Cure Epilepsy
- Le Nguyen
- Jan 24
- 17 min read
A new race is underway, not toward the moon, but toward the cure for epilepsy. For the millions of children and adults living with seizures, Drs. William Gaillard, Kareem Zaghloul, and Sridevi Sarma, along with many other researchers, are working to turn that possibility into reality. Their research reveals a future where seizures can finally be predicted, understood, and controlled.
Nearly three millennia ago, around 700 BCE, a Babylonian medical tablet documented a peculiar ailment called miqtu, the Babylonian term for “the falling disease.” Said to cause symptoms ranging from facial twitching to full-body convulsions, miqtu became known among the Babylonians as a manifestation of evil spirits, and the only treatment available was believed to be divine intervention. Among many gods the Babylonians worshipped, one reigned supreme as the most powerful in curing miqtu: Šamaš, the sun god. According to ancient texts, Šamaš could be invoked only by a spiritual healer who acted as his intermediary. Some would chant prayers asking Šamaš to “illuminate” the darkness clouding a patient’s mind. Others might burn juniper and sulfur in a small clay bowl, letting the smoke billow over the patient’s face in an attempt to drive out the malevolent spirit thought to trigger the sudden “falling.”1,2
Over centuries, as scientists and clinicians studied the mystery surrounding miqtu, its medical nature gradually came to light. Today, we know the symptoms of miqtu by another name: seizures.
A seizure is the result of uncontrolled, abnormal electrical activity of the brain that may cause a wide range of symptoms, from the briefest lapses of attention or muscle jerks to severe and prolonged convulsions. Seizures can also vary in frequency, with some people experiencing less than one per year and others having several per day. Some recover immediately after a seizure, while others may take a few minutes to hours before feeling like themselves again. It is usually difficult to pinpoint the specific cause of a seizure, as they can be provoked by a variety of factors, including sleep deprivation, stress, illness, flashing lights, and hormonal changes, or they may occur for no apparent reason.3 Needless to say, seizures can get quite scary, both to witness and to experience. Anyone who experiences recurrent unprovoked seizures, typically at least two occurring more than 24 hours apart, can be considered to have a disorder called epilepsy.
Epilepsy remains a major contributor to the global burden of disease, affecting roughly 50 million people worldwide. Each year, an estimated 5 million people are newly diagnosed. In high-income countries, an estimated 49 per 100,000 people are diagnosed with epilepsy each year. In low- and middle-income countries, this figure can be as high as 139 per 100,000 people.4 This discrepancy reflects differences in exposure to preventable risk factors, such as perinatal brain injury, central nervous system infections, and traumatic brain injury, as well as gaps in early diagnosis and access to consistent treatment.
Epilepsy carries substantial economic and health burdens, from increased health-care needs and lost productivity to higher rates of premature death. Yet for many, the stigma surrounding the condition can be even more devastating than the seizures themselves. People living with epilepsy, along with their families, often face prejudice fueled by persistent myths that the disease is incurable, contagious, or tied to moral wrongdoing. These misconceptions can lead to isolation and deter individuals from seeking care.5 The impact of this stigma extends into daily life, shaping access to educational opportunities, restricting the ability to obtain or retain a driver’s license, narrowing career options, and reducing access to health and life insurance.
“Worldwide, but also [in the U.S.], there's a lot of stigma,” said Dr. William Gaillard, chief of the Divisions of Child Neurology and Epilepsy at Children’s National Hospital. “There's still a sense that the devil may tempt people with epilepsy, that they may not be normal people, and that they may have intellectual disability. And some populations culturally view it as a moral weakness rather than a biological disease. Epilepsy is a biological disorder, just like diabetes or heart disease. It's no different. And it can be evaluated and treated.”
Childhood Epilepsy: Dr. Gaillard’s Quest to Understand and Transform Epileptic Brains
Dr. Gaillard’s path into child neurology was shaped early by his fascination with the brain and the influence of a close family friend, an innovative neurosurgeon who pioneered surgical treatments for Parkinson’s disease. Drawn to the neurosciences, he attended Yale Medical School, initially torn between neurology and neurosurgery. Thanks to the influential mentorship of Dr. Richard Mattson, a renowned epilepsy pharmacologist at Yale, Dr. Gaillard eventually committed to neurology. Discovering that pediatrics was the most enjoyable and fulfilling area for him, he chose to pursue child neurology—a path that, at the time, required separate training programs in pediatrics and neurology. He completed his pediatrics training at the University of Pittsburgh to build a strong clinical foundation, then continued to a neurology residency at Johns Hopkins, one of the nation’s top programs. Afterward, he joined his wife at the National Institutes of Health (NIH), where he completed a fellowship in epilepsy and imaging, an opportunity that aligned with both his scientific interests. Dr. Roger Packer later recruited him to join the faculty at Children’s National Hospital, where he continued his research on the brains of epileptics. Throughout his career at Children’s National Hospital, Dr. Gaillard and his colleagues have published multiple papers in high-impact journals demonstrating that epilepsy is not a mysterious, untouchable disorder, but rather a disease that can be evaluated, mapped, and ultimately treated.
At the most fundamental level, epilepsy occurs when groups of nerve cells, or neurons, in the brain send the wrong electrical signals that cause seizures. In a typical seizure, a large group of neurons fires at the wrong time, disrupting the carefully maintained balance of sodium, potassium, calcium, and chloride ions that neurons rely on to generate and reset electrical signals, a state known as ionic homeostasis. When this balance collapses, extracellular potassium surges, pushing nearby neurons into a more depolarized (closer to their firing threshold) and easily excitable state. These changes occur not only at the membrane but also at the synaptic cleft, where communication between two neurons happens. Glutamate—the brain’s main excitatory neurotransmitter—plays a central role in this synaptic exchange. During a seizure, extracellular glutamate increases sharply, overstimulating excitatory neurotransmitter receptors and driving excessive calcium into the receiving (postsynaptic) neuron. This surge in calcium inside the cell sets off a chain reaction that changes how genes and proteins are made, strengthening excitatory connections while weakening inhibitory ones. As these molecular changes accumulate, the brain’s circuits become increasingly biased toward excitation, lowering the threshold for future seizures and helping explain why epilepsy tends to be a self-reinforcing, chronic disorder rather than a one-time event.
Epilepsy is not a single disease but a family of disorders that vary widely in their causes, symptoms, and the parts of the brain they affect. In some children, seizures arise from the temporal lobe and disrupt memory or language; in others, they begin in the frontal lobe and alter attention, behavior, or motor control. Still, others originate in deeper or more diffuse network, producing subtler cognitive effects that can be difficult to localize. In his lab, Dr. Gaillard is driven by a deceptively simple question: If seizures begin with molecular changes at the synapse, how do these ripples spread across the entire brain, and how can we use that knowledge to help children? His work focuses especially on the language networks, which span the left temporal and left frontal lobes and are home to Wernicke’s area and Broca’s area, the hubs responsible for understanding and producing language. Using some of the most advanced imaging tools available—fMRI, EEG-fMRI, MEG—his team was able to watch the brain in motion and see, in real time, how epileptic activity affects normal neural communication pathways.
What they’ve found is striking. In children with temporal or frontal lobe epilepsy, the normal connections between Broca’s and Wernicke’s areas weaken. The language network completely reorganizes during a seizure, often rerouting activity to nearby regions or even to the right hemisphere in an attempt to compensate for the seizure focus.6 These changes create a visible signature in advanced images, revealing patterns of disrupted connectivity that traditional MRI might entirely miss. And these signatures matter: they help clinicians pinpoint epileptogenic zones arising from small patches of brain cortex where neurons didn’t develop normally, guiding decisions about surgery, neuromodulation, or targeted therapies.
“There are some circumstances where epilepsy causes abnormalities, declines, or impairs function. And that's a little bit difficult to identify, but it is targetable. The plasticity that occurs is really a function of focal, ongoing insults to the brain during a period when the brain is as malleable to change positively.”
Dr. Gaillard’s work doesn’t stop at mapping. His team is building tools to detect seizures with greater precision, combining physiological markers such as abnormal EEG patterns or subtle cardiac signals with automated algorithms. These systems are being trained to recognize subtle shifts in normal brain waves or body signals that don’t cause obvious symptoms but can represent an ongoing or impending seizure. Together, these approaches translate the basic biology of ionic imbalance and synaptic plasticity into tools that can pinpoint where seizures start, predict their impact on a child’s cognition, and personalize treatment to protect developing brains from recurrent seizures.
While the incidence and prevalence of epilepsy are higher for young children than adults, epilepsy can develop in anyone and at any age.7 In adults, however, seizures tend to cause more lasting disruptions. Since the adult brain is already mostly developed, it has far less plasticity and therefore cannot easily adapt its functions around damaged networks. As a result, cognitive and memory-related deficits are more likely to persist in adults than children.8 Paradoxically, this vulnerability, especially within the memory circuits, creates a unique window into how the adult brain works. By observing how seizures affect memories, researchers can decode the complex processes underlying how memories are created, stored, and retrieved, and where their links are most fragile. This is the frontier where neurosurgeon Dr. Kareem Zaghloul and his research team at the National Institute of Neurological Disorders and Stroke are pushing boundaries. By using surgically placed brain recordings in adults with epilepsy to track memory at the level of individual neurons, his team can observe, in real time, how seizures interfere with the brain’s coding machinery.
“In some patients, we have the opportunity to record the activity of individual neurons,” said Dr. Kareem Zaghloul, Senior Investigator and Branch Chief of the Functional Neurosurgery Section at the National Institute of Neurological Disorders and Stroke. “So single-unit spiking activity and so many of the things that have been driving our interest in the last couple of years have been how these patterns of individual neuronal activity actually allow us to encode information about items that you're trying to remember and retrieve.”
Memory, Epilepsy, and Plot Holes: Dr. Zaghloul’s Mission to Fix the Brain’s Glitches
Before he became one of the nation’s leading neurosurgeon-scientists at the NIH, Dr. Zaghloul began his career with a simple fascination: how does the brain take in information, turn it into meaning, and store it as memory? That curiosity first took shape at the Massachusetts Institute of Technology (MIT), where he earned his undergraduate degree in 1995. At MIT, he studied neural computation, using complicated mathematical algorithms to understand how biological circuits might be modeled, predicted, or even reconstructed on computers. That interest deepened at the Perelman School of Medicine at the University of Pennsylvania, where he earned his M.D.-Ph.D. in 2003. His doctoral research focused on computational models of the retina, using silicon and biologically inspired circuitry to understand how early vision encodes the world.
Yet, as he moved through his medical training, Dr. Zaghloul found himself increasingly drawn to the clinical realm. Neurological disease, especially conditions affecting memory and cognition, presented the very puzzles he had been trying to solve in his theoretical work. During his neurosurgery residency at UPenn, he encountered the subspecialty that would ultimately define his career: functional neurosurgery, the branch of the field dedicated to treating intrinsic neurological conditions such as epilepsy and movement disorders. He treated patients whose surgeries were guided not by tumors or strokes, but by the dynamics of the brain’s own circuits, precisely the territory he had been trained to explore. During residency, Dr. Zaghloul joined the lab of Dr. Michael Kahana, a pioneer in human electrophysiology. Dr. Kahana’s group studied how the human brain generates and retrieves memories using recordings from epilepsy patients undergoing intracranial monitoring. For Dr. Zaghloul, this was a pivotal moment in his career. He realized that the clinical care of epilepsy patients gave researchers a rare window into human cognition: electrodes placed to locate seizures could also reveal the activity of single neurons as patients remembered words, imagined images, or navigated tasks.
After completing his residency and post-doctoral work, Dr. Zaghloul joined the National Institute of Neurological Disorders and Stroke at the NIH, first as a staff clinician and ultimately as a Senior Investigator and an attending neurosurgeon. There, he developed a research program dedicated to using intracranial EEG recordings from adult patients with drug-resistant epilepsy to understand how memory is encoded, retrieved, and disrupted.
Intracranial EEG has been a central component of Dr. Zaghloul’s work. Unlike scalp EEG, which records electrical activity from outside the skull, intracranial EEG places electrodes directly into the brain’s surface or within deep structures. It provides millisecond-level precision and millimeter-scale resolution of neural activity. This level of detail allows neurosurgeons to pinpoint the exact region where seizures begin—something scalp EEG and MRI often cannot reveal. For drug-resistant patients with epilepsy for whom surgery may offer a cure, intracranial EEG is a crucial diagnostic step. For Dr. Zaghloul, it also creates a research opportunity to study how memories are encoded and retrieved.
When a person studies words or images, particular neurons in their temporal lobe begin to fire more strongly and frequently to specific items. When they later try to recall that information, their brain replays a similar pattern of activity. In neuroscience, this phenomenon is known as reinstatement. From their research, Dr. Zaghloul’s team found that this replay is supported by fast electrical events called ripples, which are brief bursts (80-250 Hz) of synchronized firing that enable distant brain regions to act in unison.9 These findings paint a picture of memory that is both elegant and dynamic: circuits that fire together at the moment of learning are likely to coordinate again to bring memory back into consciousness.
Dr. Zaghloul’s work is uniquely valuable because he is not studying memory in isolation; he is studying it in the midst of epilepsy, in a brain whose circuits are being challenged by real pathological events. Since his research is conducted in patients undergoing evaluation for surgery, he can observe what happens when seizures invade these very circuits. His recordings have shown that during a seizure, the normal codes that represent memories collapse. Neurons that once fired selectively begin firing chaotically. Ripples become disorganized or vanish. And as a result, a person after an acute seizure may temporarily lose their ability to name objects, understand speech, or access encoded information, sometimes even their autobiographical memories. This helps explain why many adults with epilepsy experience cognitive difficulties both in the period immediately following seizures and, in some cases, chronically between seizures. Their circuits responsible for memory and language are repeatedly destabilized by abnormal electrical activity, leading to persistent challenges with attention, word retrieval, and the consolidation of new memories.
As exciting as Dr. Zaghloul’s research is, it is essential to acknowledge its limitations. His work isn’t a tell-all, nor is it meant to. Every electrode he implants is placed strictly for clinical reasons, designed to locate where a patient’s seizures originate. As a result, his recordings capture only a small sampling of brain activity. He cannot explore regions that are irrelevant to a patient’s epilepsy, nor can he cover the whole brain simply to satisfy scientific curiosity; as a physician, his primary responsibility is always to treat his patients.
But what if you didn’t need to place electrodes in every corner of the brain to understand how the whole system works? What if there were a safer, more scalable way to model the brain’s hidden dynamics and fill in the gaps left by limited sampling? Increasingly, scientists such as Dr. Sridevi Sarma believe the answer lies in machine learning.
“The lesson I learned from controls is I don’t need to understand all the details of how the brain works and its dynamics in order to control it, in order to understand and model it,” said Dr. Sridevi Sarma, Professor of Biomedical Engineering and Associate Director of the Institute for Computational Medicine at Johns Hopkins University. “We’re using feedback to control a very complex system without knowing all its equations.”
When Engineering Meets Epilepsy: Dr. Sarma’s Campaign to Rethink the Brain as a Dynamical System
An engineer by training and a neuroscientist by passion, Dr. Sridevi Sarma has built a career at the intersection of mathematics, medicine, and machine learning, yet her path into neuroscience was far from traditional. Trained exclusively as an electrical engineer, Dr. Sarma earned her bachelor’s, master’s, and PhD degrees in electrical engineering, specializing in the mathematically demanding field of control theory—a discipline devoted to modeling and regulating complex dynamical systems. Yet, everything changed when she decided to take an introductory neuroscience course during graduate school at MIT. Fascinated by the parallels between engineered systems and neural circuits, she completed a postdoctoral fellowship with a clinical research lab specializing in neurophysiology. Here, she first encountered real neural data from patients with Parkinson’s disease and from non-human primate models. The experience confirmed what her engineering intuition had been suggesting: one does not need to understand every aspect of the brain to extract meaningful conclusions about it. “I don’t need to understand all the details of how the brain works and its dynamics in order to control it,” she said, which has always been a guiding principle of her work. A bicycle, she notes, is governed by pages of complex equations, yet humans ride one by relying on feedback—vision, balance, small course corrections—not explicit physics.
This perspective became foundational to her scientific approach. If the brain is a complex, partially observable system that we cannot measure everywhere at once, then control theory and feedback principles offer a way to model its behavior from incomplete data. When Dr. Sarma joined the Johns Hopkins faculty in 2009 and founded the Neuromedical Control Systems Lab, she began applying these engineering concepts to epilepsy. Although anti-epileptic drugs are the first line of therapy, roughly 30 percent of patients are drug-resistant, leaving surgical removal of the epileptogenic zone as their best chance for seizure freedom. Yet surgical success rates hover between 30 and 70 percent, largely because clinicians must rely on time-consuming visual inspection of hundreds of intracranial EEG channels to assess where seizures originate.
Realizing the ineffectiveness of this method, Dr. Sarma came up with a novel idea. Instead of treating each electrode as an isolated channel of data, she began modeling the brain as a networked dynamical system, one in which fragile or unstable nodes may reveal themselves even when seizures are not occurring. Her central question was: if a seizure begins in a particular region of the brain, can we detect that instability from the way the rest of the network interacts with it? Her lab hypothesized that in focal epilepsy (recurring seizures starting in one specific area), the source behaves like a constantly ticking fault in the system, one that the surrounding brain regions must continuously suppress. Even in the absence of a seizure, this ongoing push-and-pull leaves a measurable signature in intracranial EEG data. Dr. Sarma’s computational models capture this exact dynamic, identifying which nodes of the network are most likely to destabilize the system and thus trigger a seizure.
This insight led to her most influential innovation: EZTrack, a patented software tool that identifies the epileptogenic zone using concepts from dynamic systems and network fragility. Instead of waiting for seizures to occur, EZTrack analyzes minutes of intracranial EEG to compute which nodes of the brain are most unstable and therefore most likely to initiate abnormal activity. Large retrospective studies across five epilepsy centers across the nation have shown that EZTrack predicts surgical outcomes with significantly greater accuracy than clinicians, improving outcome prediction by 25% and identifying all surgical failures with 100% accuracy.10 What clinicians struggle to see with the naked eye, Dr. Sarma’s models infer from hidden patterns within the data. The tool is now being commercialized through Neurologic Solutions, Inc., with the hope of making surgical decisions faster, safer, and more effective.
Yet Dr. Sarma’s ambition extends far beyond intracranial monitoring. One of the greatest bottlenecks in epilepsy care occurs at the very beginning: diagnosis. A routine scalp EEG lasts just 30 minutes, and half of all epileptic patients show no abnormalities during that time. Many are misdiagnosed or left untreated for years. But Dr. Sarma believes the epileptic brain reveals its vulnerabilities even at rest. Her lab’s work has shown that subtle changes in network dynamics can be extracted with just minutes of scalp EEG. This effort led to her invention of EpiScalp, a machine-learning tool that produces an accurate epilepsy diagnosis from a single EEG recording. Trained on large datasets of clinically confirmed EEGs, the model learns to recognize subtle network patterns that distinguish epileptic from non-epileptic brains, even when seizures are not captured. In validation studies, EpiScalp consistently outperformed routine clinical interpretation, dramatically reducing misdiagnosis and accelerating access to effective treatment.11
Beyond epilepsy, Dr. Sarma’s team develops computational models of Parkinson’s disease, chronic pain, insomnia, depression, schizophrenia, and general brain health, with the larger goal of shifting neurology from a reactive discipline to a predictive one. By extracting biomarkers from EEG and MRI using machine learning, her lab seeks to identify disease processes long before symptoms emerge. Instead of waiting for memory lapses, tremors, or seizures to declare themselves, Dr. Sarma envisions a future where a ten-minute EEG during an annual checkup could flag risk for neurological disorders years in advance.
The Finish Lines for Epilepsy Research
If the scientists shaping today’s epilepsy research agree on one thing, it’s that the field is on the brink of transformation. And each of them—Dr. Kareem Zaghloul, Dr. Sridevi Sarma, and Dr. William Gaillard—envisions a future of better care with the potential of their research and innovations.
For Dr. Gaillard, the future lies in building a more complete portrait of how epilepsy alters the developing brain. His next steps center on integrating advanced imaging with molecular and genetic data to predict developmental outcomes before they unfold. By coupling functional connectivity maps with biological signatures, Gaillard hopes to identify which children are at risk for language or memory disruption long before those deficits appear clinically. The ultimate goal is anticipatory care, guided not only by where seizures originate, but by how a child’s brain is likely to grow in the presence of epilepsy.
Dr. Zaghloul’s vision complements this, working toward a mechanistic understanding of how seizures interrupt the brain’s internal codes for memory, language, and cognition. Having shown that seizures destabilize the neural patterns that support recall, he now aims to determine whether this knowledge can be used to protect those circuits. His future work focuses on measuring how much disruption is required to impair cognition, identifying which neural signatures predict recovery, and exploring whether electrical brain stimulation or targeted interventions can stabilize vulnerable networks. While his main goal is effective patient care, he also sees an opportunity to use what he has learned about neural coding to forecast when a brain is nearing a tipping point. His next steps are transforming basic insights about memory into practical strategies to preserve it.
For Dr. Sarma, the future is computational, scalable, and clinically immediate. Her team is expanding diagnostic tools like EZTrack and EpiScalp into a unified ecosystem of biomarkers that span the entire clinical workflow. One of her most ambitious goals is developing EEG-based measures of anti-seizure medication efficacy, allowing clinicians to know within hours whether a drug is helping rather than waiting months in a trial-and-error cycle. She is also pushing machine learning toward predicting who will benefit from implanted neurostimulation devices and how stimulation parameters should adapt over time. As she puts it, the challenge is not understanding every equation of the brain, but learning “what can be captured from the signals we have and how to design tools that act on it.”
Taken together, their work points toward a future in which epilepsy care is no longer reactive but predictive and personalized, where seizures are anticipated, brain networks are protected as they develop, and insights from imaging, physiology, and computation converge to move the field closer to a true cure.
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