Seizure prognosis. Photo credit: Melanie Proix

Patterns of brain activity can be used to predict seizure risk in epilepsy patients several days in advance. This emerges from a new analysis of data obtained by neuroscientists at UC San Francisco, the University of Bern and the University of Geneva from clinically approved brain implants.

“For forty years, seizure prediction efforts have focused on developing early warning systems that can alert patients, at best, a few seconds or minutes of a seizure. This is the first time anyone can reliably predict multiple seizures days in advance of what it could really allow people to plan their lives when they are at high or low risk, “said Dr. med. Vikram Rao, neurologist at the UCSF Epilepsy Center, part of the UCSF Helen Diller Medical Center in Parnassus Heights. Rao was a co-senior author on the new study, which was published in The Lancet Neurology on December 17, 2020.

Epilepsy is a chronic disease characterized by recurrent seizures – brief storms of electrical activity in the brain that can cause convulsions, hallucinations, or loss of consciousness. For decades, epilepsy researchers around the world have worked to identify patterns of electrical activity in the brain that may indicate an impending seizure, but with limited success. In part, according to the study’s authors, this is because technology has limited the field to recording brain activity for days to days at most, and in artificial stationary environments.

At the UCSF Epilepsy Center, a major patient referral center in the western United States, Rao pioneered the use of an implanted brain stimulation device that can quickly stop seizures by precisely stimulating a patient’s brain at the first sign of an impending seizure. This device, known as the NeuroPace RNA system, has also enabled Rao’s team to study seizure-related brain activity that has been recorded over many months or even years in patients while they lead their normal lives – usually in neuroscience unknown.

In analyzing this data, Dr. Rao and Dr. Maxime Baud, a former UCSF neurologist who is now an epileptologist at the University of Bern and the Wyss Center for Bio- and Neurotechnology in Geneva, recently found that the seizures are less random than they appear, identifying weekly to monthly cycles of the ” Brain irritability “, which predict a higher likelihood of a seizure.

In their new study, Rao and Baud wanted to test whether these regular patterns could be used to make clinically reliable predictions of seizure risk.

“Currently, the perceived risk of seizures for people with epilepsy is constant as there are no methods to identify periods of high or low risk,” said Baud, co-senior author of the new study. “This has far-reaching implications for daily activities, including avoiding potentially dangerous situations like bathing, cooking on a hot stove, and exercising.”

Under the direction of Timothée Proix, Ph.D., of the University of Geneva, researchers created statistical models that matched patterns of recorded brain activity with subsequent seizures in 18 epilepsy patients with NeuroPace implanted devices at UCSF and California Pacific Medical Center were observed in San Francisco. They then tested these prediction algorithms using data from 157 participants who had participated in the multicenter long-term treatment study of the RNS system between 2004 and 2018.

Looking back on the study data, the researchers were able to identify periods of time when patients were almost ten times more likely to have a seizure than when they started the study, and in some patients, signs of those periods of increased risk could be seen several days later.

Of course, an increased risk of seizures does not necessarily mean that a seizure will occur. Epileptologists still do not fully understand what leads to a seizure at any given time, although many people report reliable triggers such as stress, alcohol, missed drug doses, or lack of sleep. He compares the system with the forecast models of weather forecasters, with which we often decide what clothes to wear and whether to bring an umbrella when going out.

“I don’t think I can ever tell a patient that she’s going to have a seizure at exactly 3:17 p.m. tomorrow – it’s like predicting when lightning will strike,” said Rao, Ernest Gallo, Endowed Professor of Neurology at UCSF Weill Institute for Neuroscience. “But our results in this study give me hope that one day I can tell her that her brain activity has a 90 percent chance of having a seizure tomorrow because of her brain activity. So she should consider avoiding triggers like alcohol and stick to high levels dispense.” -Hazard activities such as driving. “

Accurately predicting seizure risk in advance could also potentially allow neurologists to adjust patient medication doses accordingly, the researchers said, keeping doses low most of the time to minimize side effects and only increasing dosages during times of higher seizure risk.

The researchers found significant differences in predicting future seizure risk based on study participants’ brain activity. While 40 percent of the participants in the RNA systems study could predict the risk several days in advance, other participants’ brain data only predicted the risk of the following day, and still others did not show at all the cycles of activity needed to make reliable predictions.

More research is needed to interpret this variability, says Rao. The RNA system itself is designed to detect and avert impending seizures, not to provide predictive seizure prediction. Therefore, it is possible that specially designed devices can detect predictive fluctuations in brain activity in a wider range of patients. Or it could be that, as in many ways, epilepsy patients simply vary in the predictability of their risk cycles.

‘It’s worth noting that patients right now have absolutely no information about the future – which looks like they have no idea what the weather might be tomorrow – and we believe our results could add to this uncertainty significantly for many people to decrease, “said Rao. “To truly determine the usefulness of these prognoses and determine which patients will benefit most from them, a prospective study is needed. This is the next step.”

Monthly brain cycles predict seizures in patients with epilepsy. Provided by the University of California, San Francisco

Quote: Seizure risk was predicted days in advance using brain implant data (2020, December 17) retrieved from https://medicalxpress.com/news/2020-12-seizure-days-advance-brain-implant.html on December 18, 2020 were retrieved

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