Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2026 Jan;11(1):80-90.
doi: 10.1016/j.bpsc.2025.07.001. Epub 2025 Jul 10.

Parsing Autism Heterogeneity: Transcriptomic Subgrouping of Imaging-Derived Phenotypes in Autism

Johanna Leyhausen  1 , Caroline Gurr  2 , Lisa M Berg  3 , Hanna Seelemeyer  2 , Bassem Hermila  2 , Tim Schäfer  2 , Andreas G Chiocchetti  4 , Charlotte M Pretzsch  5 , Eva Loth  5 , Bethany Oakley  5 , Jan K Buitelaar  6 , Christian F Beckmann  6 , Tony Charman  7 , Thomas Bourgeron  8 , Eli Barthome  8 , Tobias Banaschewski  9 , Emily J H Jones  10 ; EU-AIMS LEAP Group; Declan Murphy  5 , Christine Ecker  11
Affiliations
Free article

Parsing Autism Heterogeneity: Transcriptomic Subgrouping of Imaging-Derived Phenotypes in Autism

Johanna Leyhausen et al. Biol Psychiatry Cogn Neurosci Neuroimaging. 2026 Jan.
Free article

Abstract

Background: Neurodevelopmental conditions, such as autism, are highly heterogeneous at both the mechanistic and phenotypic levels. Therefore, parsing heterogeneity is vital for uncovering underlying processes that could inform the development of targeted, personalized support. We aimed to parse heterogeneity in autism by identifying subgroups that converge at both the phenotypic and molecular levels.

Methods: An imaging transcriptomics approach was used to link neuroanatomical imaging-derived phenotypes in autism to whole-brain gene expression signatures provided by the Allen Human Brain Atlas. Neuroimaging and clinical data of 359 autistic participants ages 6 to 30 years were provided by EU-AIMS (European Autism Interventions) LEAP (Longitudinal European Autism Project). Individuals were stratified using data-driven clustering techniques based on the correlation between brain phenotypes and transcriptomic profiles. The resulting subgroups were characterized on the clinical, neuroanatomical, and molecular levels.

Results: We identified 3 subgroups of autistic individuals based on the correlation between imaging-derived phenotypes and transcriptomic profiles that showed different clinical phenotypes. The individuals with the strongest transcriptomic associations with imaging-derived phenotypes showed the lowest level of symptom severity. The gene sets most characteristic for each subgroup were significantly enriched for genes previously implicated in autism etiology, including processes such as synaptic transmission and neuronal communication, and mapped onto different gene ontology categories.

Conclusions: Autistic individuals can be subgrouped based on the transcriptomic signatures associated with their neuroanatomical fingerprints, which reveal subgroups that show differences in clinical measures. The study presents an analytical framework for linking neurodevelopmental and clinical diversity in autism to underlying molecular mechanisms, thus highlighting the need for personalized support strategies.

Keywords: Autism spectrum disorder; Brain structure; Imaging transcriptomics; Neuroimaging; Stratification; Structural MRI1.

PubMed Disclaimer