Researchers combine brain imaging and molecular data to uncover shared and distinct biological signatures across major neurodevelopmental disorders

Differences in brain connectivity are linked to several neurodevelopmental disorders, yet scientists still struggle to determine what changes are shared or unique to each condition. A new study analyzing brain scans from more than 2,100 individuals reveals a common pattern of disrupted neural connectivity across autism, ADHD, and schizophrenia. The research also identifies distinct molecular and cellular signatures for each disorder, offering insight into how complex psychiatric conditions arise from overlapping yet different biological mechanisms.

Neurodevelopmental and psychiatric conditions such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and schizophrenia are typically studied as separate disorders. Each disorder has its own diagnostic criteria, symptoms, and treatment strategies. Yet research over the past decade has revealed that they may share genetic influences and biological pathways. Understanding these shared mechanisms could help scientists move beyond traditional diagnostic boundaries and uncover the underlying brain systems that contribute to multiple disorders.

Addressing this challenge, a team of researchers led by Professor Fengchun Wu from the Department of Psychiatry at The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China, along with Professor Kai Wu from the School of Biomedical Sciences and Engineering at the South China University of Technology, Guangzhou International Campus, China, conducted a large-scale analysis of brain imaging data to explore shared and disorder-specific neural mechanisms. By combining advanced computational analysis with molecular data, the team aimed to uncover patterns of brain connectivity that link these conditions. Their findings were published on 4 February, 2026 and published in Volume 9 of the journal Research.

One promising way to examine these disorders is by studying how different parts of the brain communicate with each other. Functional connectivity—the coordinated activity between brain regions—plays a central role in cognition, emotion, and behavior. However, identifying consistent patterns across individuals with complex psychiatric conditions is difficult because brain signals vary widely from person to person.

To overcome this challenge, the researchers analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from 2,176 participants, including individuals diagnosed with ASD, ADHD, and schizophrenia, as well as healthy controls. Using a computational approach called heterogeneous matrix factorization, they extracted shared patterns of brain activity across individuals. These shared signals were then used to construct functional connectivity networks that reveal how different brain regions interact.

The analysis uncovered a shared abnormal connectivity pattern linking deep regulatory systems—such as the cerebellum and subcortical networks—with higher-order cortical regions involved in perception, attention, and decision-making. This finding suggests that disruptions in communication between fundamental regulatory structures and complex cognitive networks may represent a common neural feature underlying several neurodevelopmental disorders.

At the same time, the analysis revealed clear differences between disorders. Autism and ADHD displayed similar connectivity structures but in opposite directions: connectivity was reduced in ASD but increased in ADHD. Schizophrenia, by contrast, showed more widespread and heterogeneous disruptions across brain networks. These disorder-specific deviations were also associated with differences in symptom severity, suggesting that the connectivity patterns reflect meaningful biological variations rather than random changes.

To further understand the biological basis of these patterns, the researchers linked connectivity changes to molecular and cellular characteristics of the brain. The shared connectivity pattern was associated with genes involved in synaptic organization, lipid metabolism, and cellular structural processes. Meanwhile, disorder-specific connectivity deviations were connected to distinct neurotransmitter systems and cellular pathways, indicating that different molecular mechanisms may shape the unique features of each condition.

“Understanding both the shared and distinct neural signatures of these disorders can help us better interpret how complex psychiatric conditions develop,” said Prof. Wu. “Our findings suggest that common disruptions in brain networks may form a biological foundation across diagnostic categories.”

The study also demonstrates the power of integrating large-scale brain imaging data with molecular information. According to Prof. Wu, such approaches may help bridge the gap between brain-level observations and the underlying biological mechanisms. “Combining neuroimaging with molecular and cellular data allows us to build a multiscale understanding of brain disorders,” he said.

While further studies are needed, the findings could help researchers identify biological markers that improve early detection of neurodevelopmental conditions. Over time, such work may also reshape how scientists classify psychiatric disorders—shifting the focus from symptom-based categories toward shared biological systems that influence brain health across the lifespan.

The complete study is accessible via DOI:10.34133/research.1115

About the Research journal
Launched in 2018, Research is the first journal in the Science Partner Journal (SPJ) program. Research is published by the American Association for the Advancement of Science (AAAS) in association with Science and Technology Review Publishing House. Research publishes fundamental research in the life and physical sciences as well as important findings or issues in engineering and applied science. The journal publishes original research articles, reviews, perspectives, and editorials. IF=10.7, Citescore=13.3.

Website: https://spj.science.org/journal/research

Funding Information
This work was supported by the National Key Research and Development Program of China (2023YFC2414500 and 2023YFC2414504), the Guangdong Basic and Applied Basic Research Foundation Outstanding Youth Project (2021B1515020064), the National Natural Science Foundation of China (81971585, 72174082, 82271953, and 82301688), the Key Research and Development Program of Guangdong (2023B0303020001 and 2023B0303010003), the Guangdong Basic and Applied Basic Research Foundation (2022A1515140142), the Natural Science Foundation of Guangdong Province (2024A1515013058), and the Science and Technology Program of Guangzhou (202206060005, 202206080005, 202206010077, 202206010034, 202201010093, 2023A03J0856, and 2023A03J0839).