Autism spectrum disorder, conceptual illustration
Credit: KATERYNA KON/SCIENCE PHOTO LIBRARY/Getty Images

A brain study conducted by researchers at Weill Cornell Medicine has found that people with autism can be classified into four distinct subtypes based on their brain activity and behavior. The study, published in the journal Nature Neuroscience, linked patterns of brain connections with behavioral traits in people with autism that included verbal ability, social affect, and repetitive or stereotypic behaviors.

The new research used a machine learning algorithm to analyze neuroimaging data from 299 people with autism and another 907 neurotypical people. The investigators then 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 that explain the brain and behavioral differences.

“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 Conor Liston, MD, PhD, an associate professor of psychiatry and of neuroscience in the Feil Family Brain and Mind Research Institute at Weill Cornell Medicine.

The investigators said that they were able to find three latent dimensions of functional brain network connectivity as a result of their analysis of the data that predicted individual differences in autism spectrum disorder (ASD).

“Clustering along these three dimensions revealed four reproducible ASD subgroups with distinct functional connectivity alterations in ASD-related networks and clinical symptom profiles that were reproducible in an independent sample,” the researchers wrote. “By integrating neuroimaging data with normative gene expression data from two independent transcriptomic atlases, we found that within each subgroup, ASD-related functional connectivity was explained by regional differences in the expression of distinct ASD-related gene sets. These gene sets were differentially associated with distinct molecular signaling pathways involving immune and synapse function, G-protein-coupled receptor signaling, protein synthesis and other processes. Collectively, our findings delineate atypical connectivity patterns underlying different forms of ASD that implicate distinct molecular signaling mechanisms.”

Liston’s lab has previously led research delving into different subtypes of depression using a similar machine learning method. In this research, published in 2016 in Nature Medicine, the team also identified four different subtypes of depression and follow-on studies has demonstrated that the distinct subtypes of depression respond differently to different therapeutic approaches.

“If you put people with depression in the right group, you can assign them the best therapy,” noted lead author Amanda Buch, PhD a postdoctoral associate of neuroscience in psychiatry at Weill Cornell Medicine.

The newest work in ASD looked to use the same approach applied years earlier to better understand the subtypes of autism. To do this, Buch developed a new method for integrating different data sets that included neuroimaging, gene expression, and proteomics data to better understand the complex interactions to more precisely characterize different ASD phenotypes.

“One of the barriers to developing therapies for autism is that the diagnostic criteria are broad, and thus apply to a large and phenotypically diverse group of people with different underlying biological mechanisms,” Buch said. “To personalize therapies for individuals with autism, it will be important to understand and target this biological diversity. It is hard to identify the optimal therapy when everyone is treated as being the same, when they are each unique.”

A key factor hindering prior research has been the lack of an adequate dataset of magnetic resonance imaging (MRI) of people with autism that can be leveraged against genomic and proteomic data to refine the understanding of the different presentations of ASD. But Buch and team were able to tap into a newly shared data set created by Adriana Di Martino, research director of the Autism Center at the Child Mind Institute, and other colleagues across the country.

Of the four subtypes identified in the study, two of the groups had above-average verbal intelligence; one group also had severe deficits in social communication and less repetitive behaviors, while the fourth had more repetitive behaviors and less social impairment. In the group with social impairment, connections between the parts of the brain that process visual information and help the brain identify the most salient incoming information were hyperactive. For those with more repetitive behaviors, these connections were weak.

“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,” noted Buch.

The other two groups had severe social impairments and repetitive behaviors but had verbal abilities at the opposite ends of the spectrum. Despite some behavioral similarities, the investigators discovered completely distinct brain connection patterns in these two subgroups.

Looking ahead, the Weill Cornell team will next conduct a mouse study seeking to identify potential targeted therapeutics for each subgroup. The team is working to further refine its machine learning model and has begun collaborating with other research groups that have large, related data sets.

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