Scientists Create ChatGPT-like AI Model for Neuroscience to Build One of the Most Detailed Mouse Brain Maps to Date
AI-produced rendering of mouse brain regionalization overlaid with network motifs, symbolizing the fusion of artificial intelligence and neuroanatomical discovery.
Three-dimensional representation of region/subregion in mouse brain map created by CellTransformer. Fewer regions are generated for visual clarity/simplicity
Examples from 1300 regions/subregion in mouse brain created by CellTransformer
Study authors Reza Abbasi-Asl, Ph.D., associate professor of neurology, bioengineering and therapeutic sciences at University of California, San Francisco with Alex Lee, Ph.D. candidate at University of California, San Francisco
Artificial intelligence reveals undiscovered regions of the brain from large-scale spatial transcriptomics data
It’s like going from a map showing only continents and countries to one showing states and cities. Based on decades of neuroscience, new regions correspond to specialized brain functions.”
SEATTLE, WA, UNITED STATES, October 7, 2025 /EINPresswire.com/ -- In a powerful fusion of AI and neuroscience, researchers at the University of California, San Francisco (UCSF) and Allen Institute designed an AI model that has created one of the most detailed maps of the mouse brain to date, featuring 1,300 regions/subregions. This new map includes previously uncharted subregions of the brain, opening new avenues for neuroscience exploration. The findings were published today in Nature Communications. They offer an unprecedented level of detail and advance our understanding of the brain by allowing researchers to link specific functions, behaviors, and disease states to smaller, more precise cellular regions—providing a roadmap for new hypotheses and experiments about the roles these areas play. — Bosiljka Tasic, Ph.D.
“It’s like going from a map showing only continents and countries to one showing states and cities,” said Bosiljka Tasic, Ph.D., director of molecular genetics at the Allen Institute and one of the study authors. “This new, detailed brain parcellation solely based on data, and not human expert annotation, reveals previously uncharted subregions of the mouse brain. And based on decades of neuroscience, new regions correspond to specialized brain functions to be discovered.”
At the heart of this breakthrough is CellTransformer, a powerful AI model that can automatically identify important subregions of the brain from massive spatial transcriptomics datasets. Spatial transcriptomics reveals where certain brain cell types are positioned in the brain but does not reveal regions of the brain based on their composition. Now, CellTransformer allows scientists to define brain regions and subdivisions based on calculations of shared cellular neighborhoods, much like sketching a city’s borders based on the types of buildings within it.
“Our model is built on the same powerful technology as AI tools like ChatGPT. Both are built on a ‘transformer’ framework which excels at understanding context,” said Reza Abbasi-Asl, Ph.D., associate professor of neurology and bioengineering at UCSF and senior author of the study. “While transformers are often applied to analyze the relationship between words in a sentence, we use CellTransformer to analyze the relationship between cells that are nearby in space. It learns to predict a cell's molecular features based on its local neighborhood, allowing it to build up a detailed map of the overall tissue organization.”
This model successfully replicates known regions of the brain, such as the hippocampus; but more importantly, it can also discover previously uncatalogued, finer-grained subregions in poorly understood brain regions, such as the midbrain reticular nucleus, which plays a complex role in movement initiation and release.
[What Makes this Brain Map Distinct from Others]
This new brain map depicts brain regions, versus cell types; and unlike previous brain maps, CellTransformer’s is entirely data-driven, meaning its boundaries are defined by cellular and molecular data rather than human interpretation. With 1,300 regions and subregions, it also represents one of the most granular and complex data-driven brain maps of any animal to date.
[Role of the Allen Institute’s Common Coordinates Framework (CCF]
The Allen Institute’s Common Coordinate Framework (CCF) served as the essential gold standard for validating CellTransformer’s accuracy. “By comparing the brain regions automatically identified by CellTransformer to the CCF, we were able to show that our data-driven method was identifying areas aligned with known expert-defined anatomical structures,” said Alex Lee, a PhD candidate at UCSF and first author of the study. “Seeing that our model produces results so similar to CCF, which is such a well-characterized and high-quality resource for the field, was reassuring. The high level of agreement with the CCF provided a critical benchmark, giving confidence that the new subregions discovered by CellTransformer may also be biologically meaningful. We are hoping to explore and validate the results with further computational and experimental studies."
The potential of this research to unlock critical insights reaches beyond neuroscience. CellTransformer’s powerful AI capabilities are tissue agnostic: They can be used on other organ systems and tissues, including cancerous tissue, where large-scale spatial transcriptomics data is available to better understand the biology of health and disease and fuel the discovery of new treatments and therapies.
Liz Dueweke
Allen Institute
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