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Epileptic seizures maybe predictable with the help of brain patterns

ANI  |  Washington D.C. [U.S.A] 

might not be random, but rather follow a cycle in the brain and a new research has found that cracking that pattern might help doctors to predict and treat better.

The findings suggest that researchers may soon be possible for clinicians to identify when patients are at highest risk for seizures, allowing patients to plan around these brief but potentially dangerous events.

Study's Vikram Rao, MD, PhD, an assistant professor of at UCSF, said, "One of the most disabling aspects of having is the seeming randomness of If your can't tell you if your next is a minute from now or a year from now, you live your life in a state of constant uncertainty, like walking on eggshells. The exciting thing here is that we may soon be able to empower patients by letting them know when they are at high risk and when they can worry less."

During a seizure, abnormal electrical activity in the brain can cause numerous symptoms, including a loss of awareness or consciousness, changes in the senses, hallucinations, numbness, feelings of electric shocks, drooling, convulsions and difficulty in breathing.

The new study, based on recordings from the brains of 37 patients fitted with NeuroPace implants, a that can quickly halt by precisely stimulating a patient's brain as a begins, confirmed previous clinical and research observations of daily cycles in patients' risk, explaining why many patients tend to experience at the same time of day.

But the study also revealed that brain irritability rises and falls in much longer cycles lasting weeks or even months, and that are more likely to occur during the rising phase of these longer cycles, just before the peak.

The lengths of these long cycles differ from person to person but are highly stable over many years in individual patients, the researchers found.

The researchers show in the paper that when the highest-risk parts of a patient's daily and long-term cycles of brain irritability overlap, are nearly seven times more likely to occur than when the two cycles are mismatched.

The team is now using data to develop a new approach to forecasting patients' risk, which could allow patients to avoid potentially dangerous activities such as swimming or driving when their risk is highest, and to potentially take steps (such as additional medication doses) to reduce their risk.

Rao concluded by saying, "I like to compare it to a weather forecast. In the past, the field has focused on predicting the exact moment a will occur, which is like predicting when lightning will strike.

That's pretty hard. It may be more useful to be able tell people there is a 5 percent chance of a thunderstorm this week, but a 90 percent chance next week. That kind of information lets you prepare."

The study was published in journal Nature Communications.

(This story has not been edited by Business Standard staff and is auto-generated from a syndicated feed.)

First Published: Tue, January 09 2018. 12:50 IST