Brain scans have uncovered two biologically distinct forms of autism hidden within the spectrum.
An international team of researchers has identified at least two biologically distinct forms of autism by examining how different regions of the brain communicate with one another. The findings could help advance more precise and personalized approaches to autism care and support.
The study was led by scientists at Istituto Italiano di Tecnologia (IIT-Italian Institute of Technology) in Rovereto (Trento, Italy) and the Child Mind Institute in New York (USA), with contributions from researchers at the University of Trento. Their results were published in Nature Neuroscience.
The researchers found two recurring patterns of brain connectivity. In one group, known as the “hyperconnectivity” subtype, communication between brain regions was stronger than usual. In the other, called the “hypoconnectivity” subtype, communication was reduced.
Brain Connectivity Patterns Reveal Autism Subtypes
The project was coordinated by Alessandro Gozzi, PhD, director of the Center for Neuroscience and Cognitive Systems (CNCS) at the IIT, and Adriana Di Martino, MD, founding director of the Autism Center at the Child Mind Institute.
According to the research team, this work represents the first systematic attempt to interpret human brain imaging patterns (via fMRI) by tracing them back to underlying biological mechanisms identified in mouse models. By connecting specific brain connectivity patterns to particular biological pathways, the study provides a potential framework for future precision medicine strategies.
To investigate these relationships, the researchers analyzed functional connectivity in 20 mouse models and examined brain scans from 940 children and young adults with autism, along with scans from more than 1,000 neurotypical individuals.
The analysis uncovered two reproducible autism subtypes. The hypoconnectivity subtype was associated with synaptic pathways, while the hyperconnectivity subtype was linked to immune-related biological systems. Together, these two groups represented approximately 25% of the people with autism included in the study.
“For decades, we’ve observed tremendous variability in how autism manifests, but we lacked direct evidence that these differences reflected distinct underlying biology,” said Dr. Alessandro Gozzi, at Italian Institute of Technology. “Our approach enabled us to isolate specific genetic and immune factors, then translate those signatures to human brain scans, showing that different connectivity patterns encode different mechanistic pathways underlying autism.”
Mouse Models Provide a Biological Roadmap
To better understand the origins of these brain patterns, the researchers combined imaging data with genetic and biochemical analyses in mouse models. This allowed them to connect specific connectivity signatures with changes in cellular function.
The work revealed how molecular pathways, including synaptic and immune-related mechanisms, can produce distinct patterns of brain connectivity that are detectable with fMRI. These biological signatures identified in mice then served as reference patterns for finding similar subtypes in human brain scans.
“The mouse models gave us a biological ‘Rosetta Stone,'” said Dr. Adriana Di Martino at the Child Mind Institute. “We could see which biological pathways drive which connectivity signatures, then search for those same patterns in humans.”
Human Brain Data Confirm the Findings
The human imaging data came from the Autism Brain Imaging Data Exchange (ABIDE), a pioneering neuroimaging initiative co-founded by Dr. Di Martino that aggregates datasets from research laboratories worldwide, and the Child Mind Institute.
When the researchers examined the human data, they identified matching hyperconnectivity and hypoconnectivity subtypes. Further gene expression analyses supported the findings. Brain regions showing hypoconnectivity were enriched for synaptic genes, while regions with hyperconnectivity showed enrichment for immune-related genes, reflecting the same biological mechanisms observed in the mouse models.
The consistency of the findings across independent datasets provided additional confidence in the results.
“Finding the same subtypes reproducible across dozens of independent research sites was critical validation,” added Dr. Gozzi.
Toward Personalized Autism Care
The two subtypes also differed in their overall functional brain organization and showed modest differences on standardized autism assessments. Individuals in the hyperconnectivity group scored somewhat higher on measures of autism severity.
“Brain-based biological markers reveal distinctions that current behavioral assessments don’t fully capture,” noted Dr. Di Martino.
The researchers emphasize that these two subtypes likely represent only part of autism’s biological complexity. While they identified two dominant connectivity patterns, they believe additional autism subtypes may emerge as larger datasets become available and analytical methods continue to improve.
Reference: “Autism subtypes identified using cross-species functional connectivity analyses” by Marco Pagani, Valerio Zerbi, Silvia Gini, Filomena Grazia Alvino, Abhishek Banerjee, Andrea Barberis, M. Albert Basson, Yuri Bozzi, Alberto Galbusera, Jacob Ellegood, Michela Fagiolini, Jason P. Lerch, Michela Matteoli, Caterina Montani, Davide Pozzi, Giovanni Provenzano, Maria Luisa Scattoni, Nicole Wenderoth, Ting Xu, Michael V. Lombardo, Michael P. Milham, Adriana Di Martino and Alessandro Gozzi, 15 May 2026, Nature Neuroscience.
DOI: 10.1038/s41593-026-02287-z
The study was made possible through an international collaboration coordinated by the Italian Institute of Technology and the Child Mind Institute. Funding was provided by the Simons Foundation Autism Research Initiative, the European Research Council through the #DISCONN and #BRAINAMICS projects, the Brain and Behavior Foundation, the Fondazione Telethon, and the US National Institute of Mental Health.
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1 Comment
This explains a lot about the conflicting reports, especially about hypersensitivity. I look forward to learning more. Good work all!