{"id":509506,"date":"2026-06-20T11:30:06","date_gmt":"2026-06-20T11:30:06","guid":{"rendered":"https:\/\/www.newsbeep.com\/ie\/509506\/"},"modified":"2026-06-20T11:30:06","modified_gmt":"2026-06-20T11:30:06","slug":"scientists-identify-two-distinct-biological-subtypes-of-autism-using-brain-scans","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ie\/509506\/","title":{"rendered":"Scientists identify two distinct biological subtypes of autism using brain scans"},"content":{"rendered":"

A recent study published in Nature Neuroscience<\/a> provides evidence that the biological diversity of autism can be categorized into distinct brain connectivity subtypes. By analyzing brain scans from both mouse models and human participants, scientists found that individuals on the autism spectrum tend to exhibit either unusually high or unusually low levels of communication between different brain regions. These two patterns appear to be driven by entirely different biological mechanisms, which suggests a new way to understand and potentially support autistic individuals.<\/p>\n

Autism spectrum disorder is known for its wide variety of traits. Some autistic people experience significant challenges with language and motor skills, while others do not. This outward variety is often assumed to reflect a variety of underlying biological causes. <\/p>\n

Finding direct evidence to link specific behaviors to specific biological causes is quite difficult. Only a small percentage of autistic individuals possess known genetic mutations that scientists can easily identify and study. This makes it hard to group people biologically using genetics alone.<\/p>\n

\u201cThe study started from a very simple but long-standing question: why is autism so heterogeneous?\u201d said Alessandro Gozzi, director of the Functional Neuroimaging Laboratory<\/a> and senior scientist at the Center for Neuroscience and Cognitive Systems at the Italian Institute of Technology in Rovereto. \u201cWe know that autistic individuals can differ enormously in their symptoms, abilities, and support needs, but it has been much harder to understand whether this diversity also reflects different underlying biological mechanisms.\u201d<\/p>\n

To bridge this gap, the authors turned to functional magnetic resonance imaging. This technology, often referred to as fMRI, measures brain activity by detecting changes in blood flow. When different areas of the brain show synchronized changes in blood flow while a person or animal is at rest, those areas are considered functionally connected.<\/p>\n

The rationale for the research was to see if different genetic and environmental factors associated with autism produce recognizable patterns of functional connectivity. By starting with genetically modified mice, the team aimed to map out specific brain patterns and then look for those exact same patterns in human brain scans.<\/p>\n

Past findings using fMRI have been largely inconsistent. \u201cIn brain imaging studies, autism has often been associated with very mixed findings: some studies reported reduced connectivity between brain regions, while others reported increased connectivity,\u201d Gozzi said. \u201cRather than treating this variability as noise, we wanted to test the idea that it might contain biological information. In other words, different patterns of brain connectivity might reflect different biological subtypes of autism.\u201d<\/p>\n

The researchers first examined fMRI data from 20 different mouse models of autism. These included 17 models with specific genetic alterations, two models involving immune system changes, and one specially bred mouse line. Each model was compared to a control group of typical mice to see how the specific biological change affected the functional connectivity of their brains.<\/p>\n

When the scientists grouped the whole brain fMRI results from these 20 models, they noticed two dominant patterns. \u201cWhat surprised us most was how clearly the two opposite connectivity patterns emerged across species,\u201d Gozzi said. \u201cWe saw related hypo- and hyperconnectivity patterns in mouse models and in human autism datasets, and these patterns were linked to different biological pathways.\u201d<\/p>\n

Eleven of the mouse models showed hypoconnectivity, meaning their brain regions communicated with each other much less than expected. The other nine models showed hyperconnectivity, meaning their brain regions communicated much more than expected.<\/p>\n

Next, the team used a computational technique to see which biological pathways were linked to these two patterns. They looked at the genes associated with each mouse model and mapped out how those genes interact with other proteins. This network of interacting genes is known as an interactome.<\/p>\n

\u201cIn our study, we identified two major connectivity-defined subtypes,\u201d Gozzi said. \u201cOne was characterized by reduced communication between brain regions and was linked to synaptic mechanisms, which are central to how neurons communicate. The other was characterized by increased communication between brain regions and was associated with immune-related mechanisms and alterations in gene regulation.\u201d<\/p>\n

Synapses are the tiny gaps where nerve cells send chemical signals to one another to facilitate brain communication. The hyperconnectivity pattern, on the other hand, was linked to the immune system and to the ways cells translate genetic instructions into proteins.<\/p>\n

Guided by these findings in mice, the researchers then examined a massive collection of human fMRI data. This dataset included resting state brain scans from 940 individuals diagnosed with autism and 1,036 neurotypical individuals. The participants ranged in age from 5 to 30 years old and had their scans collected across 38 different research centers.<\/p>\n

The team focused on specific evolutionarily conserved brain regions. These are brain areas that are anatomically and functionally similar in both mice and humans. By looking at these specific areas, they successfully identified the same two functional connectivity subtypes in the human participants.<\/p>\n

To ensure their findings were reliable, the researchers split the human data into two separate groups. The first group served as a discovery dataset containing exactly 78.5 percent of the participants. The remaining 21.5 percent served as a replication dataset to verify the initial results.<\/p>\n

The two subtypes appeared consistently in both datasets. Together, the hypoconnectivity and hyperconnectivity groups accounted for 25.1 percent of the human autism scans analyzed. The remaining scans did not neatly fit into either of these two extreme categories.<\/p>\n

These findings help contextualize previous mixed results in human studies. \u201cThis was important because it suggests that apparently conflicting findings in previous autism imaging studies may not simply reflect inconsistency,\u201d Gozzi said. \u201cSome of them may reflect real biological differences between subgroups of individuals.\u201d<\/p>\n

These two human subtypes displayed very different brain network architectures. Individuals in the hyperconnectivity group showed massively increased connections between deeper, subcortical brain areas and the outer cerebral cortex. Those in the hypoconnectivity group showed decreased connections between the brain regions responsible for processing sensory and motor information.<\/p>\n

The human subtypes also showed different behavioral profiles. The researchers looked at standardized symptom severity scores for a subset of the participants. Individuals in the hyperconnectivity group tended to have slightly higher scores related to social communication and interaction.<\/p>\n

Finally, the scientists mapped human gene expression data onto the fMRI patterns to see if the biological causes matched the mouse models. They found a very similar match across both species. The brain areas that were under connected in humans were highly enriched for genes related to synaptic function.<\/p>\n

At the same time, the human brain areas that were over connected were enriched for genes related to the immune system. \u201cThe main takeaway is that the diversity of autism is not only a diversity of symptoms,\u201d Gozzi said. \u201cAt least in part, it also reflects biologically distinct patterns in the way brain circuits communicate.\u201d<\/p>\n

Readers should not interpret these results to mean that autistic individuals can all be easily sorted into these categories. \u201cI think the broader message is that we should not assume that all autistic individuals share the same underlying biology simply because they fall under the same diagnostic label,\u201d Gozzi said. \u201cTwo people may look similar clinically, but the brain and molecular mechanisms contributing to their condition may be different.\u201d<\/p>\n

\u201cAt the same time, I would emphasize that our goal is not to create simplistic new labels,\u201d Gozzi added. \u201cThe goal is to understand the biological structure underneath the autism spectrum, so that future research \u2014 and eventually clinical trials \u2014 can be better matched to the mechanisms involved.\u201d<\/p>\n

The authors also note some limitations to the current findings. \u201cThe most important limitation is that this is not a clinical diagnostic tool,\u201d Gozzi said. \u201cWe cannot yet scan an individual person and use this information to guide clinical decisions.\u201d<\/p>\n

The two identified subtypes only accounted for roughly one quarter of the autistic individuals in the study. \u201cAnother important point is that the two subtypes we identified account for only part of autism heterogeneity,\u201d Gozzi noted. \u201cThis is not surprising, because autism is highly diverse, but it means that additional biological subtypes almost certainly remain to be discovered.\u201d<\/p>\n

\u201cThis does not mean that there are only two types of autism,\u201d Gozzi continued. \u201cRather, it suggests that the autism spectrum may contain biologically distinct subgroups, and that understanding these differences could eventually help research move toward more personalized approaches.\u201d<\/p>\n

In the future, the team hopes to uncover additional patterns within the wider spectrum. \u201cWe also want to refine the biological map,\u201d Gozzi said. \u201cIn this study we identified two dominant signatures, but autism is unlikely to be explained by only two categories. With richer mouse and human datasets, we hope to identify more granular biologically defined subtypes and understand what hypo- and hyperconnectivity mean at the physiological level.\u201d<\/p>\n

Human data will also need to expand to fully comprehend the impact of these subtypes on daily life. \u201cA major next step is to understand what these brain-based subtypes mean in people,\u201d Gozzi said. \u201cFor that, we need larger human datasets with much deeper clinical and behavioral information, including cognition, sensory symptoms, development, adaptive functioning, genetics, and clinical history.\u201d<\/p>\n

The study, \u201cAutism subtypes identified using cross-species functional connectivity analyses<\/a>,\u201d was authored 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.<\/p>\n","protected":false},"excerpt":{"rendered":"A recent study published in Nature Neuroscience provides evidence that the biological diversity of autism can be categorized…\n","protected":false},"author":2,"featured_media":509507,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[61,60,82],"class_list":["post-509506","post","type-post","status-publish","format-standard","has-post-thumbnail","category-science","tag-ie","tag-ireland","tag-science"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/509506","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/comments?post=509506"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/509506\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media\/509507"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media?parent=509506"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/categories?post=509506"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/tags?post=509506"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}