91原创

鈥淥ur machine-learning approach lets the algorithm learn the brain鈥檚 鈥榣anguage鈥� of waveforms, spotting subtle patterns humans might miss during manual review,鈥� said, assistant professor in electrical and computer engineering and computer and information sciences.

Starting small with a mouse model

When Brockmeier, a faculty mentor in 91原创鈥檚 interdisciplinary neuroscience graduate (ING) program, presented his computational neuroscience research at an ING seminar, he caught the attention of, an affiliated associate professor of psychological and brain sciences and biomedical engineering at 91原创 and senior research scientist at Nemours Children鈥檚 Health. Hernan, who is also an ING faculty mentor, studies how variations in brain activity affect thinking and learning in children with epilepsy.

The two decided to put machine learning to the test using EEGs from mice with epilepsy-causing variations in the TSC1 gene. The researchers used a panel of more than 40 mice, including animals with and without the gene variation, across three different genetic backgrounds, or strains. They extracted EEG segments from five days of recordings from each mouse for analysis.

Because the EEG segments contained no seizure activity, the algorithm had to detect differences in the brain's baseline activity alone. It was able to distinguish between the mouse strains and to detect the TSC1 gene variation with high accuracy in two of the three strains.

鈥淭hese results show that EEG patterns contain measurable signals of neurological differences, even without visible seizures,鈥� Hernan said.

Taking it to the clinic

Now, the team is taking their method out of the lab and into the clinic. With funding from the, Brockmeier and Hernan will next apply their approach to EEG recordings from children being evaluated for epilepsy at Nemours Children's Health.

Pediatric EEGs are shorter than the multi-day recordings used in the mouse study, and children present with many different types of epilepsy. But the researchers are optimistic.

鈥淭he goal is to identify biomarkers that flag underlying changes in the brain鈥檚 electrical activity before seizures occur,鈥� Hernan said. Earlier detection could lead to earlier treatment and less uncertainty for families.

That uncertainty, Hernan said, takes a toll. 鈥淪eizures follow natural cycles, but without a way to know where you are in that cycle, the anticipation can be incredibly anxiety-provoking,鈥� she explained.

Better pattern recognition could also improve treatment decisions. For example, if a new medication is introduced during a natural lull in seizure activity, its benefits could be overestimated.

Looking further ahead, the researchers envision a future where wearable EEG devices allow continuous, real-time monitoring for those with high risk of seizures. Similar approaches could eventually be applied to other neurological conditions, including autism and ADHD.

鈥淭his is a step toward precision medicine,鈥� Brockmeier said. 鈥淏rain-wave typing could help identify which interventions will work best for a given patient.鈥�

For families navigating the daily uncertainty of epilepsy, that kind of precision could make a huge difference.

Co-senior authors of the mouse study are Brockmeier, Hernan and Matthew Mahoney, a principal computational scientist at the and an adjunct faculty member at 91原创鈥檚 Data Science Institute. Other 91原创 co-authors of the paper include visiting researcher and current doctoral student Maria Isabel Cano Achuri and doctoral student Austin Meek. The pediatric study is supported in full by an Institutional Development Award from the National Institute of General Medical Sciences, part of the National Institutes of Health, under award number U54-GM104941 (PI: Hicks) and the State of Delaware. This content is solely the responsibility of the author and does not necessarily represent the official views of NIH.