A 3D approach to understanding heterogeneity in early developing autisms.
Veronica Mandelli, Ines Severino, Lisa Eyler and 3 others
PMID 39350293WHAT IT FOUND
Data-driven clustering of early development scores splits autism into two distinct types.
Type I shows profound delays and slower growth in language and motor skills. Type II shows relative strengths and faster development. This distinction holds across independent datasets and predicts different brain biology.
Key findings
01Unsupervised clustering of MSEL and VABS scores identified two stable autism subtypes (Type I and Type II) with 98% accuracy in generalizing to new data.
02Type I autism is characterized by slower developmental trajectories in language, fine motor, and non-verbal cognitive skills compared to Type II over the first decade of life.
03Type I individuals show significantly reduced activation in left hemisphere superior temporal cortex during speech processing compared to Type II and typically developing groups.
STILL TO COME
How it was doneWhat they foundWhat it means for PTsWhat it means for OTsWhat it means for SLPs
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What it does not show
The neuroimaging and gene expression analyses were conducted on relatively small sample sizes. Treatment history data was largely unavailable, so the impact of early intervention on subtype labels is unclear. The model requires both MSEL and VABS data, which may not be routinely collected in all clinical settings. The fMRI analysis focused on speech responses, which is only one aspect of the LIMA features.
Declared interests
The authors declare no conflicts of interest. Funding was provided by the European Research Council (H2020 and Horizon Europe).
The easy way to misread this
Do not assume these subtypes are fixed diagnostic labels that cannot change. The study shows statistical clusters based on early development scores, but it does not prove that a child cannot move between types with intervention or maturation. Additionally, the neurobiological findings are based on small samples and should not be used to make individual clinical predictions about brain structure.