For many who suffer from epilepsy, seizures happen like clockwork. But everyone has a different clock.
A new study co-led by researchers at Rice University and the University of California, San Francisco (UCSF) sought to formalize how these clocks work to give people with epilepsy a better sense of how and why their seizures occur, and perhaps better recognize the early warnings.
In the process, the project led by Rice alumnus Sharon Chiang, a clinical teacher and researcher at UCSF, and her mentor Marina Vannucci, a Noah Harding professor of statistics at Rice’s George R. Brown School of Engineering, have established that aging itself, as well as common triggers, may contribute to how disease affects seizure-prone individuals.
Their study appears in Proceedings of the National Academy of Sciences.
Epilepsy is a disorder in which surges of electrical activity in the brain cause seizures. Often these crises are cyclical and can be triggered by many events. But why different patients experience seizure cycles of different lengths is unknown.
“We developed a new statistical model to explicitly capture the effect of factors that can drive transitions in seizure risk,” Chiang said. “We looked at anti-epileptic drugs and different triggers like illness and menstrual cycles. These are some of the factors commonly thought to increase or decrease the risk of a seizure.
“The relationship between cycle length and age was an interesting finding,” she said. “We are able to see that there are shorter cycles in older age groups and then longer cycles in younger age groups. A shortening of cycle length with age may have potential ramifications in future clinical practice.
In previous studies, the group analyzed patients’ seizure diaries to assess their risk of seizures and looked at brain scans to find markers for epilepsy patients most likely to benefit from brain surgery.
The new work by Chiang, Vannucci and lead author Emily Wang, who earned her Ph.D. at Rice this year, seeks to underscore the importance of forest seizures – and lack thereof – on a day-to-day and long-term basis. This will help establish individual chronotypes or rhythms for patients who experience cyclical seizures and want to understand why seizures occur when they occur, what may trigger them, and how best to treat them.
An online diary that tracks seizure activity both simplifies the process for patients and provides researchers with a wealth of data to mine for their dynamic statistical systems models, with the “dynamic” part capturing changes over time. Seizure Tracker, founded by co-author Robert Moss, provides tools to help patients, physicians, and researchers understand the relationships between seizure activity and therapies that will improve patient care.
For the current study, diaries of more than 1,000 patients aged 2 months to 80 years helped the team model the relationship between “attractor states”, internal and/or external events such as the onset of a new drug or disease, and the peaks and valleys of seizure activity in an individual patient.
“The point of this model is to try to guide the patient and the doctor, in particular,” Vannucci said. “We want to help doctors say, ‘OK, this medicine is really important for this patient with this type of seizure’ and better control their seizures. »
The relevance of a patient’s age also emerged from Seizure Tracker data. “Quiet periods between attacks seem to shorten for patients as they age,” Vannucci noted.
“Triggers and age are the two factors that we found to be important for the state change of attractors,” she said. “The data shows that with age, patients have shorter cycles. This seems obvious, but the link had not been formally established.
The data is accurate for patients who can recognize and record every seizure every day, but researchers know people aren’t always able to do this. This is where the team’s experience in Bayesian modeling helps fill in the gaps.
Vannucci said the new study is the product of research that began in 2017. “Doctors and patients sit down and see when their seizure frequency is increasing or decreasing or stable,” she said. . “We realized that we could formalize in a statistical model the attractors and the latent states and deduce the changes of states.”
This culminated in a 2018 Epilepsia Open study that suggested a Bayesian model could better define seizure risk for patients. From there, she says, the team incorporated attractors and other covariates like drugs to build the model detailed in the current study.
“We hope this study is the best of all and the most useful for patients and doctors,” Vannucci said.
Reference: Wang ET, Vannucci M, Haneef Z, Moss R, Rao VR, Chiang S. A Bayesian Switched Linear Dynamical System for Estimating Seizure Chronotypes. Proceedings of the National Academy of Sciences. 2022;119(46):e2200822119. doi:10.1073/pnas.2200822119
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