Widespread associations between behavioral metrics and brain microstructure in ASD suggest age mediates subtypes of ASD.
Brain-behavior links in ASD shift with age, so single composite scores mask distinct developmental subtypes.
01Research in Context
What this study did
The team scanned brains of autistic kids and teens. They looked at tiny details in white matter.
They also gave the same kids standard behavior tests. Then they asked: which brain bits line up with which behaviors?
Last, they checked if age changes the picture.
What they found
Almost every behavior score linked to some microstructural spot.
Age pulled the kids into clear groups. Same score at different ages meant different brain patterns.
So one "autism profile" hides several brain paths.
How this fits with other research
Fitzgerald et al. (2019) first showed wide white-matter damage in ASD. HJ et al. widen the map and add age clusters.
Capio et al. (2013) saw white-matter maturation stall. HJ et al. agree, but show the stall pairs with different behaviors at different ages.
Chien et al. (2026) tracked the same kids for five years and found growing deviation. Their longitudinal view supersedes HJ’s snapshot, yet both point to age as key.
Cai et al. (2021) found ASD networks peak seven years late. HJ et al. extend that idea by linking late peaks to real-life symptoms.
Why it matters
Stop treating "moderate autism" as one thing. Ask the child’s age, then probe the skills tied to that age’s brain cluster. Pair goals with the tracts most linked at that stage.
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02At a glance
03Original abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by differences in social communication and repetitive behaviors. Our laboratory has previously found that g-ratio, the proportion of axon width to myelin diameter, and axonal conduction velocity, which is associated with the capacity of an axon to carry information, are both decreased throughout the adolescent brain in ASD. By associating these differences with performance on cognitive and behavioral tests, this study aims to first associate a broad array of behavioral metrics with neuroimaging markers of ASD, and to explore the prevalence of ASD subtypes using a neuroimaging driven perspective. Analyzing 273 participants (148 with ASD) ages 8 to 17 years through an NIH-sponsored Autism Centers of Excellence network (MH100028), we observe widespread associations between behavioral and cognitive evaluations of autism and between behavioral and microstructural metrics, alongside different directional correlations between different behavioral metrics. Stronger associations with individual subcategories from each test rather than summary scores suggest that different neuronal profiles may be masked by composite test scores. Machine learning cluster analyses applied to neuroimaging data reinforce the association between neuroimaging and behavioral metrics and suggest that age-related maturation of brain metrics may drive changes in ASD behavior. This suggests that if ASD can be definitively subtyped, these subtypes may show different behavioral trajectories across the developmental period. Clustering identified a pattern of restrictive and repetitive behavior in some participants and a second group that was defined by high sensory sensitivity and language performance.
, 2025 · doi:10.1162/imag.a.144