NEW YORK — New research has discovered distinct subtypes of autism spectrum disorder (ASD) based on brain activity and behavior, according to a study by Weill Cornell Medicine investigators. By using machine learning to analyze neuroimaging data, the researchers identified patterns of brain connections associated with different behavioral traits in individuals with ASD. These findings offer new insights into the condition and could lead to improved diagnosis and personalized treatments for ASD.
ASD is a complex neurodevelopmental disorder characterized by difficulties in social interaction, communication, and repetitive behaviors. The study aimed to determine if there are different subgroups within ASD and to understand the underlying genetic pathways. By integrating neuroimaging data with gene expression and proteomics, the researchers identified four clinically distinct groups of individuals with ASD, each exhibiting unique brain connection patterns and behavioral characteristics.
“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,” says 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,” Liston adds in a media release.
The study’s lead author, Dr. Amanda Buch, explains that the diagnostic criteria for ASD are broad, making it challenging to develop targeted therapies. Personalizing treatments for individuals with ASD requires understanding and addressing the biological diversity within the condition.
The identified subgroups exhibited variations in verbal ability, social communication, and repetitive behaviors. The researchers observed differences in brain circuitry between the subgroups, with some brain networks showing atypical connections in opposite directions. The study also identified specific genes linked to autism that explained the unique brain connections observed in each subgroup.
The findings have significant implications for developing more effective treatments for ASD. By identifying subgroups within ASD, it may be possible to assign individuals to therapies that are best suited to their specific needs. The study’s results underscore the importance of considering subgroups in clinical trials and tailoring treatments to address the diverse biological mechanisms underlying ASD.
The research team plans to further investigate the subgroups and potential subgroup-targeted treatments in mice. Collaborations with other research teams and the refinement of machine learning techniques are also underway. The ultimate goal is to advance our understanding of ASD and improve the lives of individuals with the condition.
Dr. Buch shared that they have received positive feedback from individuals with autism, emphasizing the potential impact of their work. A neuroscientist with autism expressed that being diagnosed with a specific subtype of autism could have been helpful for him, as it explained his unique experience within the spectrum. The study’s findings offer hope for a better understanding of ASD and the development of personalized interventions that cater to the diverse needs of individuals with autism.
The study is published in the journal Nature Neuroscience.
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