“I was looking for students who had computer programming experience or not, biology experience or not, psychology experience or not,” says Rachel. “I was looking for a variety of students.”
She wanted a well-rounded group of thinkers who could problem solve from various angles. Her BIOL 373 course was an experiment in every sense of the word, with a mixture of students and a class-created syllabus to boot. The course was a follow-up to her graduate school research on detecting seizures in mice, but this time she wanted to study seizures in humans to more accurately diagnose seizure locations, making the class all about translational research—shifting research from animals to humans.
For patients with uncontrolled epilepsy, surgery may provide relief by removing the piece of the brain where seizures occur, but it isn’t always a guaranteed solution. Surgeons use electroencephalography, commonly known as an EEG, to record brain activity and determine which part of the brain would be removed during surgery.
But Rachel says the process is still advancing and isn’t perfect.
“To be frank the EEG is still pretty crude. It’s come a long way, but we’re reading electrical activity from millions of neurons at once, and it’s not super detailed or fine scaled. There are a lot of challenges interpreting the EEG,” she says. “If you give two neurologists who have been reading EEGs for decades the same [EEG to analyze], they aren’t agreeing perfectly.”
Using a computer program that Rachel wrote in graduate school to detect seizures and other brain waves in mice, the class used the software to input human data using public research available from the National Institute of Health’s database. Students were then tasked with scoring the EEG’s manually against the computer software for comparison.
Since the class had varying levels of knowledge about neuroscience, the first half of the semester was focused on student presentations about the process of research and common neuroscience terms, while the second semester broke the class into two groups: literature review and writing an article while the second group analysed EEGs.
“I’ve been in classes where the syllabuses are started and the learning community of students help shape it but never anything like this,” Brian Dahlberg’18 says of the class-created syllabus. “It was guided research, it was doing real research with training wheels on. Rachel was there to give feedback and help us adjust,” says Brian. “It was a lot of learning by example.”
A cognitive science and environmental chemistry major, Brian was looking for ways to link his love of cognitive science and biology when he heard of Rachel’s course. Brian manually scored the results of EEG, something he had no experience with coming into the class. He was responsible for visually scoring seizures in order to compare results to the computer program which could take as long as four-to-six hours.
“The code development has to happen hand-in-hand with visual scoring because you’re trying to create an algorithm that can do better than visual scoring and to do that you have to compare them,” says Brian.
Yihe Xue’18 used the skills she learned about seizure analysis in Rachel’s class to complete a summer internship at the University of Wisconsin-Madison, where she analyzed the EEGs of rats in response to different drugs. She then returned to Beloit to do additional work with Rachel and others students on the project in the fall.
Since the class, Rachel presented two posters on the course and research outcomes at the Society for Neuroscience Annual Meeting. She is working on a manuscript regarding the class and plans to teach the class again in the fall 2018 focusing on seizure prediction.