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A 3D visualization of the 13 major regions in the mouse brain. Black dots mark the centers of the 213 subdivisions used by SPERRFY to analyze relationships between brain connectivity and gene activity patterns.
Image Credit: Koike et al., PNAS, 2026.
(CC BY 4.0)
Scientific Frontline: Extended "At a Glance" Summary: Genetic Neural Wiring and SPERRFY
The Core Concept: A newly decoded, gene-encoded blueprint functions as a spatial "wiring map" that guides growing nerve fibers (axons) to connect with the precise target regions in the developing brain.
Key Distinction/Mechanism: Unlike previous models that relied heavily on physical distance or isolated sensory circuits, researchers utilized SPERRFY—a machine learning method—to analyze the overlapping activity patterns of 763 genes across 213 brain regions. This approach demonstrated that gene expression gradients act as a "GPS," pairing source and target regions to predict whole-brain connectivity with high accuracy.
Major Frameworks/Components:
- SPERRFY Algorithm: A machine learning tool designed to decode unique molecular identities by matching the gene activity profiles of neuronal source and target regions.
- Gene Expression Gradients: Chemical signals that vary in strength and genetic activity, providing spatial coordinates for growing neurons.
- Dual-Level Map Operation: Broad genetic activity patterns outline the general organization between brain regions, while highly detailed patterns manage specific, localized connections.
Branch of Science: Neuroscience, Genetics, Computational Biology, and Developmental Biology.
Future Application: The SPERRFY framework can be adapted to map the neuroanatomy of other species (such as humans, marmosets, and fruit flies) to study brain evolution. It also establishes a baseline for investigating how genetic disruptions in neural wiring contribute to neurodevelopmental disorders.
Why It Matters: Answering a fundamental question in neuroscience, this research provides the first whole-brain, data-driven evidence that complex neural circuits are guided by deterministic genetic designs, offering unprecedented insights into brain development and function.
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| Molecular gradient maps underlying brain wiring. These maps are created from overlapping activity patterns of many genes across the mouse brain. The upper row shows source maps for sending brain regions, and the lower row shows target maps for receiving brain regions. Relationships between source and target maps predict which brain regions are likely to be connected. Red and blue indicate high and low map values, respectively. Image Credit: Koike et al., PNAS, 2026. (CC BY 4.0) |
How complex neural circuits are genetically designed and wired is a fundamental question in neuroscience. Scientists have shown for the first time that genes encode a “wiring map” that guides neurons to connect with the correct brain regions. The findings, based on a machine learning analysis of mouse brain data, were published in Proceedings of the National Academy of Sciences and offer new avenues for research into brain development and disease.
Mapping Connections Between Brain Regions with Data
The research team, led by scientists from Nagoya University in Japan, aimed to understand the wiring rules that guide nerve fibers during brain development. These long, thin fibers, called axons, extend from neurons and send signals to other neurons.
The researchers developed an analysis method called SPERRFY that combines two datasets. One dataset maps which brain regions are connected to each other, and the other tracks the activity levels of 763 genes in all 213 brain regions in mice.
“Some genes are highly active in certain brain regions and less active in others. These differences create distinct patterns of gene activity throughout the brain,” said Naoki Honda, senior author and professor from Nagoya University’s Graduate School of Medicine. “When hundreds of patterns overlap, they give each brain region a unique molecular identity. These identities are what SPERRFY was designed to decode.”
When the researchers fed both datasets into a machine learning algorithm, SPERRFY identified these patterns of gene activity, called gene expression gradients, which predict which brain regions are likely to connect. For each pair of connected brain regions, SPERRFY paired the gene activity profile of the source region (where the nerve fiber originates) with the profile of the target region to which it connects.
From these gene expression gradients, the researchers produced a brain wiring map that tells each brain region where it is relative to every other region. Overlapping patterns of gene activity reconstructed the brain’s connection patterns with a prediction performance score of 0.88 on a scale of 0 to 1, where 1.0 indicates perfect prediction. By comparison, predictions based only on the physical distance between brain regions scored approximately 0.70.
Molecular gradient maps underlying brain wiring. These maps are created from overlapping activity patterns of many genes across the mouse brain. The upper row shows source maps for sending brain regions, and the lower row shows target maps for receiving brain regions. Relationships between source and target maps predict which brain regions are likely to be connected. Red and blue indicate high and low map values, respectively.
Additionally, the researchers discovered that the brain’s wiring map operates on two levels. Broad gene activity patterns determine the overall organization between brain regions, while more detailed patterns regulate the specific connections within them.
Testing a 60-Year-Old Theory on the Whole Brain
The findings build on the chemoaffinity theory proposed by Nobel laureate Roger Sperry in 1963. He suggested that neurons find their connection partners by following molecular concentration gradients—chemical signals that vary in strength throughout the brain. These gradients act like a GPS system for growing nerve fibers.
“The chemoaffinity theory was well established for simple circuits such as the visual and olfactory systems. But until now, the complexity of whole-brain connectivity made it difficult to test whether the same principle operates across the brain,” said Jigen Koike, first author and former PhD student at Hiroshima University, who also conducted research as a special research student at Nagoya University’s Graduate School of Medicine.
This complexity made it extremely difficult to test Sperry’s theory across the entire brain without computational tools. Using machine learning, the researchers developed the tools to do this for the first time. Their findings support the idea that this long-standing principle is not limited to simple sensory circuits but also helps explain how connections are organized across the whole brain.
Future Research
When the researchers compared the activity of 763 genes against the wiring map, SPERRFY also identified specific genes with closely matching activity patterns, including genes known to guide nerve growth. This supports the validity of the method and provides a starting point for research on the molecular mechanisms of brain wiring.
The researchers note that their method can be applied to any species for which maps of the brain’s neural circuits and gene expression data are available, such as humans, marmosets, and fruit flies. As these datasets expand, the method could help determine if the same molecular wiring principles are shared across species and how they have evolved. SPERRFY could also assist scientists in understanding how disruptions in brain wiring contribute to neurodevelopmental disorders.
Funding: This work was supported by JST, the establishment of university fellowships toward the creation of science technology innovation (grant number JPMJFS2129); JST SPRING (grant number JPMJSP2132); JSPS KAKENHI (grant number JP22H05163); the Moonshot R&D—MILLENNIA Program (grant number JPMJMS2024-9); the Agency for Medical Research and Development (AMED) Multidisciplinary Frontier Brain and Neuroscience Discoveries (Brain/MINDS 2.0) (grant numbers JP25wm0625322 and JP25wm0625210); and the Cooperative Study Program of Exploratory Research Center on Life and Living Systems (ExCELLS: program number 19–102).
Published in journal: Proceedings of the National Academy of Sciences
Authors: Jigen Koike, Ken Nakae, Riichiro Hira, Yuichiro Yada, and Honda Naoki
Source/Credit: Nagoya University
Reference Number: ns051426_01
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