It may soon be possible to diagnose autism spectrum disorder (ASD) based on a simple brain scan. Researchers from the Center for Cognitive Brain Imaging at Carnegie Mellon University discovered a brain pattern that allowed them to identify autistic brains with near total accuracy. This method of diagnosing autism could lead to a faster evaluation process. Currently, diagnosis of ASD requires time-consuming interviews and behavioral observation.
The researchers performed brain scans on 34 young adults: 17 with high-functioning ASD and 17 without ASD. While the researchers scanned the participants’ brains with an fMRI, they asked the participants to think about different social interactions like hug, humiliate, kick, and adore. Using machine learning techniques, the researchers measured activity in 135 small, peppercorn-sized parts of the brain. Analyzing the activation levels revealed a pattern of brain activity that, they discovered, is highly consistent from person to person.
The brain pattern was significantly different in the participants with ASD compared to the participants without ASD. Using only the patterns generated from the fMRI scans, the researchers were able to correctly identify whether or not a person had ASD in 33 out of the 34 participants. The brain scanning technique yielded 97 percent accuracy in identifying ASD.
Lead study author, professor of psychology, and director of the Center for Cognitive Brain Imaging Marcel Just, PhD, explained that the results suggest that people with ASD have an altered sense of self compared to people without ASD. “There was an area associated with the representation of self that did not activate in people with autism. When they thought about hugging or adoring or persuading or hating, they thought about it like somebody watching a play or ready a dictionary definition. They didn’t think of it as it applied to them.”
This study, the first of its kind to take advantage of this type of brain activity, could lead to an objective method for diagnosing ASD.
This research is published in the journal PLOS One.
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