Four different autism subtypes identified in brain studies

Machine Learning

A study by researchers at Weill Cornell Medicine found that people with autism spectrum disorders can be classified into four different subtypes based on their brain activity and behavior.

The study, published March 9 in Nature Neuroscience, leveraged machine learning to analyze newly available neuroimaging data from 299 autistic and 907 neurotypical patients. They found patterns of brain connectivity associated with behavioral traits in people with autism, such as language ability, social influence, and repetitive or stereotypic behavior. They confirmed that her four subgroups of autism could be replicated in another dataset, and showed that differences in regional gene expression and protein-protein interactions explain differences in brain and behavior. .

“As with many neuropsychiatric diagnoses, individuals with autism spectrum disorders experience different kinds of difficulties with social interactions, communication, and repetitive behaviors. We believe there are probably many different types of autism spectrum disorders that require “Our research sheds light on new approaches to discover subtypes of autism, which may one day lead to new approaches for diagnosis and treatment.”

A previous study published by Liston and colleagues in Nature Medicine in 2017 used similar machine learning techniques to identify four biologically distinct subtypes of depression.

“By sorting people with depression into appropriate groups, we can assign them the most appropriate treatments,” says lead author Amanda Buch, Ph.D., postdoctoral fellow in neuroscience in psychiatry at Weill Cornell Medicine. said.

Based on that success, the team set out to determine whether similar subgroups exist among individuals with autism and whether different genetic pathways underlie them. , explained that autism is a highly heritable condition, linked to hundreds of genes, which present with diverse symptoms and limited treatment options. To do so, Buch pioneered new analyzes to integrate neuroimaging data with gene expression data and proteomics, introduced them into the lab, and explored how risk variants interact in subgroups of autism. It allowed us to test and develop hypotheses about

“One of the obstacles to developing therapeutics for autism is that the diagnostic criteria are broad and therefore not applicable to large, phenotypically diverse groups of people with different underlying biological mechanisms. to be done,” said Buch. “It is important to understand and target this biodiversity in order to individualize treatment for people with autism. It is difficult to identify an effective treatment.”

Until recently, Buch pointed out, there was not a large enough collection of functional magnetic resonance imaging data for autistic patients to conduct large-scale machine learning studies. However, the large datasets created and shared by Adrianadi, Dr. Martino, and other colleagues across the country, research director of the Child’s Mind Her Institute’s Center for Autism, are not sufficient for research. provided a large dataset.

“New methods in machine learning that can process thousands of genes, differences in brain activity, and multiple behavioral variations made this study possible,” said co-lead author, Weill Cornell Medicine, Department of Psychiatry. said Dr. Logan Grosenick, assistant professor of neuroscience. Pioneering machine learning techniques used for biological subtyping in autism and depression research.

These advances allowed the team to identify four clinically distinct groups of people with autism. Two of the groups had above average verbal intelligence. One group also had significant deficits in social communication, but less repetitive behavior, while the other group had more repetitive behavior and less social impairment. Connections between parts of the brain that help identify incoming information were highly active in the subgroup with more social impairment. These same connections were weaker in the group with more repetitive behaviors.

“It was interesting at the brain circuit level that there were similar brain networks associated with both of these subtypes, but the connectivity of these same networks was atypical in the opposite direction,” says Weil. Buch, who holds a Ph.D. from Cornell University School of Medicine, said. He studied science in Liston’s lab and now works in Grossenick’s lab.

Two other groups had severe social impairments and repetitive behaviors, but language abilities on opposite ends of the spectrum. We found completely different brain connectivity patterns in these two subgroups.

To better understand what is driving the differences, the team analyzed gene expression that accounts for the atypical brain connections present in each subgroup, many of which were previously associated with autism. I discovered that They also analyzed network interactions between proteins associated with atypical brain connections and looked for proteins that might act as hubs. One oxytocin was the hub protein for a subgroup of individuals with high social impairment but relatively limited repetitive behaviors. She said it would be interesting to test whether oxytocin therapy is more effective in this subgroup.

“Although subgroups of people with autism can receive effective treatments, the lack of attention to subgroups undermines their effectiveness in larger trials,” Grosenick said. increase.

The team checked the results on a second human dataset and found the same four subgroups. As a final validation of the team’s results, Buch reviewed the biomedical literature she developed that showed other studies independently linked genes associated with autism with the same behavioral traits associated with subgroups. We conducted an unbiased text mining analysis.

The team will next study these subgroups and potential subgroup-targeted therapies in mice. Collaborations with several other research teams with large human datasets are also underway. The team is also working to further refine their machine learning techniques.

“We are trying to make machine learning more cluster-aware,” says Grosenick.

In the meantime, Buch said he received encouraging feedback from people with autism about their work. He said his diagnosis was confusing because his autism was so different from others, but that her data helped explain his experience.

“Having been diagnosed with a subtype of autism may have helped him,” Buch said.

Bridget Kuehn is a freelance writer for Weill Cornell Medicine.

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