There’s a new scientific journal article out by Buch et al.!Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder (Buch et al., 2023) In this study, researchers used pre-existing databases of brain imaging data in combination with common autistic behavioural traits to create four distinct clusters of autism presentations.
What did researchers conducting this study do?
First, they collected fMRI brain scan and behavioural data from existing online databases. Then, they looked at how the activity patterns of autistic brains at rest differed from the activity patterns of neurotypical brains at rest.
They next used statistics to identify specific brain connectivity patterns that were associated with characteristics based on verbal IQ measures and the ADOS-2 clinical diagnostic tool for autism.
They isolated 3 brain-behaviour dimensions:Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder (Buch et al., 2023)
- Verbal intelligence (IQ): found to be associated with connectivity between brain regions related to language processing and reading ability
- Includes connectivity between the corticothalamic, visual network, and striatum
- While Verbal IQ is not a diagnostic trait of autism, it measures problem solving, abstract reasoning, and working memory and is useful for generating behavioural profiles since there is a wide variability of scores (among both neurotypicals and autistics)
- Social affect: linked to connectivity between brain regions known to regulate social and emotional processing.
- Includes connectivity between the salience network, visual network, and striatal areas
- Social affect measures autistic traits related to social communication and social interaction
- Repetitive behaviours: associated with connectivity between brain regions related to executive functions such as cognitive control, response inhibition and action selection.
- Includes corticostriatal connectivity with primary motor areas and the frontoparietal task control network
- Repetitive behaviours measures autistic traits like special interests, sensory sensitivities, preference for routines, etc.
Researchers then assigned a score to individuals based on each of these dimensions. These scores allowed them to cluster the patterns of results into 4 distinct subgroups.
Brain-behaviour-based autism phenotypes
Using a combination of brain connectivity and behaviour to define autism phenotypes is an interesting premise since we already know that when looking at behavioural traits alone, autistic individuals are incredibly diverse. Previous attempts to define different autism subtypes haven’t made much sense given how heterogenous these traits are.
These are the 4 phenotypes researchers in the study generated along with the dimensional characteristics of each subgroup:Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder (Buch et al., 2023)
|Subgroup 1||Subgroup 2||Subgroup 3||Subgroup 4|
|Verbal IQ||Higher than average||Lower than average||Average||Average|
|Social affect autistic traits||Higher than average||Higher than average||Higher than average||Lower than average|
|Repetitive behaviours autistic traits||Higher than average||Higher than average||Lower than average||Higher than average|
|Brain connectivity differences (compared to neurotypicals)||Lower language processing area connectivity||Higher language processing area connectivity||Higher social affect area connectivity||Lower social affect area connectivity|
Genetics & autism brain-behaviour phenotypes
The study team also found that each of these autism phenotypes was correlated with a specific pattern of gene expression. For example, individuals in subgroup 4 had brain activity patterns that were associated with decreased expression of the HTR1A gene—a serotonin-related gene. Serotonin is a chemical in our brain that regulates many aspects of behaviour, including cognition.
These findings suggest that specific gene-expression patterns may act as a biomarker for classifying autistic individuals into one of these groups without having to perform any brain scans. For example, if an autistic individual presents with decreased HTR1A gene expression, a clinician can deduce that they match the subgroup 4 phenotype.
Finding patterns amid complex neurological data
Something I find cool about this study is that the researchers were able to validate a lot of the preexisting data on brain connectivity in autistics. The general consensus has traditionally been that autistic brain connectivity patterns are different than neurotypical patterns and are also different across each autistic individual. There have not been many attempts to amalgamate all the variable findings into a single model.
Here, they were able to show that across many findings in the literature, there are indeed patterns of brain activity that sync with behavioural traits. They were able to classify all these brain activity patterns into the three dimensions of verbal intelligence, social affect, and repetitive behavioural traits.
How are these phenotypes useful for the autistic community?
As a neuroscientist, I’m always excited to learn about the innovative ways researchers study brain and behaviour to advance our understanding of how and why we are the way that we are. At the same time, something that’s always at the back of my mind is how these findings can be useful for the autistic community.
Outside of exploring brain functioning and genetic variation among neurodivergent populations, I personally don’t think these phenotypes are super helpful for autistic individuals looking to understand themselves or looking to figure out what kind of support needs may benefit them.
For example, in order to figure out which subgroup you belong to, you would need to know how you score on behavioural traits in combination with an fMRI scan of your brain at rest. Or, you would need to know your gene expression patterns. Perhaps down the line, clinicians can use specific genetic profiles to identify which subgroup an autistic fits into. But even then, what is the point?
The authors of the paper elude to how defining these subgroups may help with autism treatments. They hypothesize that the reason existing treatments don’t work to “reduce autism symptoms” might be because they only apply for autistics belonging to a specific subgroup. However, treatments aimed at “reducing/curing autism symptoms” don’t necessarily serve the majority of the autistic community who are advocating for more neurodiversity inclusion and acceptance. In contrast, if the treatments are about reducing distress and identifying a set of optimal support needs, then this could be great!
What are your thoughts? Outside of a scientific discovery setting, do you find these brain-behaviour phenotypes useful?
Let us know what you think!