Newswise Jesse A. Pfammatter was awarded the Epilepsia Open Prize 2020 Basic Science Prize for the articleAn automated, machine learning-based detection algorithm for spike-wave discharges (SWDs) in a mouse model of absence epilepsy.

Clinical spike-wave discharges (SWDs) are EEG patterns seen particularly during absence seizures, a nonconvulsive type of seizure. Though they were first identified more than 100 years ago, many aspects of SWDs are still being discovered.

Despite the time and effort, the standard of practice both clinically and in research is to manually identify SWDs, versus using software. Jesse Pfammatter, a post-doc in Mathew V. Jones lab at the University of Wisconsin, didn't subscribe to this standard for a couple of reasons. First, Pfammatter hadnt been an epilepsy researcher for very long. Second, he was self-professedly terrible at manual identification.

Pfammatter also had noticed that others weren't terrifically skilled at it either; in fact, identifying SWDs seemed quite subjective. Every human is different in their scoring of these, said Pfammatter. Not only is their scoring different from other humans scoring, but people change their minds about a discharge depending on its context. If its near other events, people tend to mark it as epileptiform, whereas if its by itself, they dont.

Pfammatter and colleagues also found that expert human scorers could change their minds about a specific EEG waveform upon repeated viewing. Sometimes, a single expert would disagree with themselves quite often, depending on what waveform they were looking at, said Jones. This suggests that human scoring of EEG waveforms is not entirely reliable.

Theres useful information in variability

Rather than give in to the idea that SWDs are merely difficult to categorize, Pfammatter saw the complexity as potentially useful. We put a lot of things in categories that dont necessarily fit in categories, he said. What if the subjective nature of the discharges could be harnessed?

Pfammatter and colleagues developed an automated detection algorithm that uses machine learning to highlight the variability in SWDs, rather than ignore it. The software uses a probabilistic scale, not a categorical scale, explained Pfammatter. Our research assumes theres useful information in variability.

The algorithms probability scores correlated with the variability seen in human scoring. The group also found that higher-probability events more strongly related to the physiology of the mice, particularly in terms of sleep-wake transitions, which are associated with many epilepsies.

Next, the group plans to apply the algorithm to experimental situations in which SWD variability provides information about physiology. For example, said Pfammatter, We know that treatments are effective across a spectrumits isnt just yes, they stop seizures or no, they dont. Were hoping we can test some of these signals to see if they change in the presence of certain therapeutics. That could give us insight into how treatments are working. And maybe we can start to understand why a medication works in one animal but not in another.

The lab also works with models of post-traumatic epilepsy. After a trauma, theres some probability that someone will develop epilepsy, in some unknown amount of time, Pfammatter said. Were finding subtle EEG markers that might indicate which individuals might go on to develop epilepsy. Its a higher-resolution understanding.

From entomology to epilepsy

Back to that first point, that Pfammatter hasnt been an epilepsy researcher for very long. Pfammatter grew up in a northern suburb of Chicago and earned a PhD in entomology at the University of Wisconsin. He studied bark beetle ecology and took extensive graduate coursework in statistics and biometrics.

Toward the end of his graduate career, he met Jones, associate professor of neurology, at a bar frequented by graduate students and faculty members. The two began talking and learning about one anothers research. Though they were studying different topics, both were immersed in multivariate data. His research was really interesting to me, Pfammatter said. There are similarities between community ecology and neuroscience, and the statistical analysis tool set is similar.

Pfammatter and Jones eventually agreed that they might work well as a team. We were working on different subjects, but we had a lot in common; its worked out fabulously, said Jones. There was a learning curve, but its all gone very well. Im really proud of Jesse.

Beyond the lab, Pfammatter loves outdoor adventures and likes to paint mountain scenes, but lately there hasnt been time. He and wife Rachel recently finished home renovations, and in early June they embarked on their next big adventurethe birth of their son.

About the ILAE Journal Prizes

Each year, the ILAE awards one Basic Science and one Clinical Science Prize to the first authors of original research articles published inEpilepsia Open during the preceding year. Awardees are nominated and selected by the journal's editorial board, the editors-in-chief, and the ILAE president.

The prize is intended to stimulate excellence in epilepsy research and reward young researchers for outstanding contributions to the field.

Watch the 2020 Journal Prize Symposium on YouTube, including a talk by Dr. Pfammatter about his research.

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Founded in 1909, the International League Against Epilepsy (ILAE) is a global organization with more than 120 national chapters.

Through promoting research, education and training to improve the diagnosis, treatment and prevention of the disease, ILAE is working toward a world where no persons life is limited by epilepsy.

To learn more, visit ourwebsite(available in multiple languages) or find us onFacebook.

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Harnessing complexity to advance epilepsy research: Learning the language of EEG spike-wave discharges - Newswise