Biomedical Engineering Trainee Spotlight: Billy Carson, MS

Scientists have shown that the brain of an autistic person works differently. Understanding exactly how functions differ — and how they affect social and language skills — is still a big question. Biomedical engineering student Billy Carson, MS, is learning new ways to analyze brain wave electroencephalogram (EEG) activity to better understand how autism affects brain function. His mentor is David Carlson, PhD, assistant professor in civil and environmental engineering and computer science.
As part of the National Institutes of Health (NIH) Autism Center of Excellence research program, under Carlson’s supervision, Carson’s current research project will examine how brain signals map onto different behavior patterns, such as whether a child is able to sustain their attention or gets distracted easily.

“Essentially, Billy and I hope to identify which brain networks are associated with specific characteristics of autism and how these networks are different in autistic children,” explains Carlson. “We expect to get a clearer picture of how brain-wave activity directly affects learning, and social and language development skills. The research could lead to new brain-based biomarkers that could help identify autism or track progress in clinical trials.”

Already, research has shown that machine learning algorithms can identify brain patterns related to differences in brain processing. The team has published research in multiple scientific journals, including Neuron and Advances in Neural Information Processing Systems. To map the brain networks, Carson will bring sophisticated computational tools to the current project, including machine learning tools that reliably and automatically catch patterns and structure in data.

“The signals we pick up on while capturing brain-wave activity represent billions of neurons ‘talking’ to one another to process sensory information and respond to stimuli. Machine learning methods are well-suited for the task of picking out the important parts of these recorded brain signals and sifting out unhelpful information that we call noise,” said Carlson. “We can gather and summarize the important information and create a visual of the neurons ‘talking’ to each other, too.”

Uncovering ways to detect autism, which are non-subjective, biological markers using tools like EEGs, could transform diagnosis and lead to children getting an earlier start with proven therapies that promote social and communication skills.

Carson, who “gets excited when accessing and exploring new brain data,” sums up the long-term payoff and bigger picture of his mentor’s research lab.
“Determining how the brains of autistic children function could lead us to new interventions and supports for those who need them, and could help us evaluate the benefits of those supports. We may also be able to provide doctors a better way to find autism, opening the door to earlier, more reliable diagnosis for kids who may be missed using today’s diagnostic tools.”