Approximately 65 million people worldwide live with epilepsy, 3 million of those in the United States.
Statistics from the Epilepsy Foundation show that there are 150,000 new cases of epilepsy each year. Fully one third of epilepsy patients live with uncontrollable seizures because none of the medications or treatments currently available will work for them.
Researchers at the University of Pennsylvania’s School of Engineering and Applied Science, along with colleagues at the Perelman School of Medicine are hoping to change that by looking specifically at where in the brain a seizure begins.
An alternative treatment to medication is the removal of the small group of neurons in the brain where the electrical misfires of an epileptic seizure begin. The problem is that finding these tiny neuronal networks is difficult. Each patient has a unique network in the brain, so finding one specific path to the affected area has proven challenging.
The team in Pennsylvania went about the task by crunching the numbers. Using brain data crowdsourced from 22 epilepsy patients with implanted electrodes, the researchers have developed a series of algorithms. These complex mathematical formulas seem to be able to predict where in the brain a seizure will originate and which groups of neurons it will likely spread to as it grows.
Danielle Bassett, the Skirkanich Assistant Professor of Innovation in Penn Engineering led the research along with Brian Litt, a professor of Neurology in Penn’s Perelman School of Medicine and of Bioengineering in Penn Engineering; and Ankit Khambhati, a graduate student in the Litt Lab.
The most significant part of this new research is the creation of the International Epilepsy Electrophysiology Portal, founded by Litt and Zachary Ives, a professor and Markowitz Faculty Fellow in Penn Engineering’s Department of Computer & Information Science; and Gregory Worrell, a neurologist at the Mayo Clinic.
22 patients involved in the study had between 80 and 100 electrodes placed in their brains. These sensors collected the data from direct brain recordings during seizures. The data was then used to map the network of brain activity so that overtime, trends might develop, as well as to define specific stages of seizures, where they begin and how they progress.
In this study, the researchers were able to analyze 88 seizures from these patients. The team wanted to see if there was any commonality in the patterns of electrical activity during the seizures and the math involved helped them parse the data into information that hadn’t been seen before.
In a press release from the University Professor Bassett said, “These new computational techniques allow us to see how different parts of the brain are communicating with one another as we go about our daily lives. Critically, we can see how these communication patterns change as the brain changes its activity. This new ability offers a fundamental understanding of the functional relationships that drive a seizure.” ()
The algorithms developed also helped in predicting where a seizure might begin by looking at the data of pre-seizure activity.
Ankit Khambhati, from the Litt Lab said, “We show there is a high correspondence between certain topological features that are predictive of brain regions that initiate seizures. Specifically, we can use pre-seizure brain activity to determine the regions that are mostly densely interconnected, as they’re most likely to be where the seizure starts.”
Data hosted on the International Epilepsy Electrophysiology Portal is freely available to the public. Information on how to access or contribute data is available
here. Check out the video below to learn more about the different kind of seizures and the electrical patterns in the brain that can cause them.
