Four Different Autism Subtypes Identified by Brain Activity

Summary: Using a combination of machine learning and neuroimaging data, researchers report people on the autism spectrum can be classified into four different subtype groups based on brain activity and behavior.

Source: Weill Cornell University

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.

Building on that success, the team set out to determine if similar subgroups exist among individuals with autism, and whether different gene pathways underlie them. She explained that autism is a highly heritable condition associated with hundreds of genes that has diverse presentation and limited therapeutic options.

To investigate this, Dr. Buch pioneered new analyses for integrating neuroimaging data with gene expression data and proteomics, introducing them to the lab and enabling testing and developing hypotheses about how risk variants interact in the autism subgroups.

“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,” Dr. 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.”

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.

“New methods of machine learning that can deal with thousands of genes, brain activity differences and multiple behavioral variations made the study possible,” said co-senior author Dr. Logan Grosenick, an assistant professor of neuroscience in psychiatry at Weill Cornell Medicine, who pioneered machine-learning techniques used for biological subtyping in the autism and depression studies.

Those 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 severe deficits in social communication but less repetitive behaviors, while the other had more repetitive behaviors and less social impairment.

The connections between the parts of the brain that process visual information and help the brain identify the most salient incoming information were hyperactive in the subgroup with more social impairment. These same connections were weak in the group with more repetitive behaviors.

“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. 

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.

The team analyzed gene expression that explained the atypical brain connections present in each subgroup to better understand what was causing the differences and found many were genes previously linked with autism. They also analyzed network interactions between proteins associated with the atypical brain connections, and looked for proteins that might serve as a hub.

Oxytocin, a hormone previously linked with positive social interactions, was a hub protein in the subgroup of individuals with more social impairment but relatively limited repetitive behaviors.

Studies have looked at the use of intranasal oxytocin as a therapy for people with autism with mixed results, Dr. Buch said. She said it would be interesting to test whether oxytocin therapy is more effective in this subgroup.

“You could have treatment that is working in a subgroup of people with autism, but that benefit washes out in the larger trial because you are not paying attention to subgroups,” Dr. Grosenick said.

This shows a brain
Machine learning of brain-behavior dimensions reveals four subtypes of autism spectrum disorder linked to distinct molecular pathways. Image is in the public domain

The team confirmed their results on a second human dataset, finding the same four subgroups. As a final verification of the team’s results, Dr. Buch conducted an unbiased text-mining analysis she developed of biomedical literature that showed other studies had independently connected the autism-linked genes with the same behavioral traits associated with the subgroups.

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

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

In the meantime, Dr. Buch said they’ve received encouraging feedback from individuals with autism about their work. One neuroscientist with autism spoke to Dr. Buch after a presentation and said his diagnosis was confusing because his autism was so different than others but that her data helped explain his experience.

“Being diagnosed with a subtype of autism could have been helpful for him,” Dr. Buch said.  

About this machine learning and autism research news

Author: Press Office
Source: Weill Cornell University
Contact: Press Office – Weill Cornell University
Image: The image is in the public domain

Original Research: Closed access.
Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder” by Conor Liston et al. Nature Neuroscience


Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder

The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood.

Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that predicted individual differences in ASD behaviors and were stable in cross-validation.

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.

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.

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  1. Thank you for sharing this informative article on the subtypes of autism spectrum disorder (ASD) and how machine learning can help with early diagnosis. It’s great to see advancements in technology being used to improve the lives of those with ASD and their families. It’s fascinating how the study found that different subtypes of ASD show different patterns of neural connectivity, which could help personalize treatment plans. I hope to see more research being done in this area to further our understanding of ASD and improve the quality of life for those affected.

  2. With the rate of autism-diagnoses greatly increasing, maybe school teachers should receive mandatory autism [spectrum disorder] training. There could also be an inclusion in standard high school curriculum of child-development science that would also teach students about the often-debilitating condition (without being overly complicated).

    If nothing else, the curriculum would offer students an idea/clue as to whether they themselves are emotionally/mentally compatible with the immense responsibility and strains of regular, non-ASD-child parenthood.

    It would explain to students how, among other aspects of the condition, people with ASD (including those with higher functioning autism) are often deemed willfully ‘difficult’ and socially incongruent, when in fact such behavior is really not a choice. And how “camouflaging” or “masking,” terms used to describe ASD people pretending to naturally fit into a socially ‘normal’ environment, causes their already high anxiety and depression levels to further increase.

    Of course, this exacerbation is reflected in the disproportionately high rate of suicide among ASD people. … There could also be childrearing/parenting instruction in regards to children born with ASD.

    [As for my own autism-spectrum disordered brain, I’m sometimes told, “But you’re so smart!” To this I immediately agitatedly reply: “But for every ‘gift’ I have, there are a corresponding three or four deficits.” It’s crippling, and on multiple levels!]

    Low-functioning autism is already readily recognized and treated, but higher-functioning ASD cases are basically left to fend for themselves. … As a moral rule, a physically and mentally sound future should be EVERY child’s fundamental right, especially considering the very troubled world into which they never asked to enter.

  3. Oxytocin is a hormone, not a protein. In this summary, it is incorrectly categorized as a protein:

    “Oxytocin, a protein previously linked with positive social interactions, was a hub protein in the subgroup of individuals with more social impairment but relatively limited repetitive behaviors.”

  4. Has any research been carried out on any connection between alcohol/drugs and Autism? If as stated that Autism is hereditary could there be a connection between parent/s that drank heavily or not.
    There are many groups of people in the world that do not drink alcohol – do they have the same percentage of Autism within that group?

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