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. The painted background of a sequencing array represents the molecular associations of the autism subtypes. Credit: Dr. Amanda Buch
People with autism spectrum disorder can be classified into four distinct subtypes based on their brain activity and behavior, according to a study from Weill Cornell Medicine investigators.
The study, published March 9 in Nature Neuroscience, leveraged machine learning to analyze newly available neuroimaging data from 299 people with autism and 907 neurotypical people. They found patterns of brain connections linked with behavioral traits in people with autism, such as verbal ability, social affect, and repetitive or stereotypic behaviors. They confirmed that the four autism subgroups could also be replicated in a separate dataset and showed that differences in regional gene expression and protein-protein interactions explain the brain and behavioral differences.
“Like many neuropsychiatric diagnoses, individuals with autism spectrum disorder experience many different types of difficulties with social interaction, communication and repetitive behaviors. Scientists believe there are probably many different types of autism spectrum disorder that might require different treatments, but there is no consensus on how to define them,” said co-senior author Dr. Conor Liston, an associate professor of psychiatry and of neuroscience in the Feil Family Brain and Mind Research Institute at Weill Cornell Medicine. “Our work highlights a new approach to discovering subtypes of autism that might one day lead to new approaches for diagnosis and treatment.”
A previous study published by Dr. Liston and colleagues in Nature Medicine in 2017 used similar machine-learning methods to identify four biologically distinct subtypes of depression, and subsequent work has shown that those subgroups respond differently to various depression therapies.
“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.
Source: Weill Cornell