Researchers at Weill Cornell Medicine identified four distinct subtypes of autism spectrum disorder through machine learning analysis of neuroimaging data, potentially paving the way for more personalized treatments. People with autism spectrum disorder can be classified into four distinct subtyp
Machine learning of brain-behavior dimensions reveals four subtypes of autism spectrum disorder linked to distinct molecular pathways. Here, the 3D prism cube represents the machine learning of the three brain-behavior dimensions, etched onto the prism’s glass. White light or “data” passes into the prism or “machine learning algorithm,” splitting into four colored light paths that represent the spectrum of autistic people in the four autism subtypes.
“If you put people with depression in the right group, you can assign them the best therapy,” said lead author Dr. Amanda Buch, a postdoctoral associate of neuroscience in psychiatry at Weill Cornell Medicine. Until recently, there were not large enough collections of functional magnetic resonance imaging data of people with autism to conduct large-scale machine learning studies, Dr. Buch noted. But a large dataset created and shared by Dr. Adriana Di Martino, research director of the Autism Center at the Child Mind Institute, as well as other colleagues across the country, provided the large dataset needed for the study.
“It was interesting on a brain circuit level that there were similar brain networks implicated in both of these subtypes, but the connections in these same networks were atypical in opposite directions,” said Dr. Buch, who completed her doctorate from Weill Cornell Graduate School of Medical Sciences in Dr. Liston’s lab and is now working in Dr. Grosenick’s lab.
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