New research uses AI to unravel the complex wiring of the motor system

The nervous system is a marvel of biological engineering, composed of intricate networks that control every aspect of an animal's movement and behavior. A fundamental question in neuroscience is how these vast, complex circuits are assembled during development. A recent study by a group of researchers including Erdem Varol, Assistant Professor of Computer Science and Engineering and a member of the Visualization, Imaging and Data Analysis Center, has provided new insights into this problem by studying how the neurons responsible for leg movement in fruit flies (Drosophila melanogaster) establish their connections.

The researchers developed ConnectionMiner, a novel computational tool that integrates gene expression data with electron microscopy-derived connectomes. This tool enabled them to infer neuronal identities and predict synaptic connectivity with remarkable accuracy. Their findings, published on bioRxiv [PDF], offer a blueprint for understanding how neurons wire themselves into functional circuits.

Neurons form connections based on genetic and molecular cues, but identifying the precise mechanisms behind this process has been difficult. In the fruit fly, roughly 69 motor neurons (MNs) in each leg are responsible for controlling movement. These neurons receive input from more than 1,500 premotor neurons (preMNs) through over 200,000 synapses. The challenge lies in understanding how each MN finds the right preMN partners and how these connections are established at the molecular level.

By applying single-cell RNA sequencing (scRNAseq) at multiple developmental stages, the researchers tracked how different gene families, particularly transcription factors (TFs) and cell adhesion molecules (CAMs), shape the unique identities of MNs. They discovered that these molecular signals not only define neuronal types but also correlate with the strength of their synaptic connections.

Traditional methods of studying neuronal circuits rely on either gene expression data (which tells us what molecules neurons produce) or connectomics (which maps how neurons are wired together). However, integrating these two datasets has been a major challenge. ConnectionMiner bridges this gap by using machine learning to refine ambiguous neuronal annotations, effectively reconstructing the genetic and synaptic landscape of the nervous system.

The researchers tested their tool on the Drosophila leg motor system, identifying combinatorial gene signatures that likely orchestrate the assembly of circuits from preMNs to MNs and ultimately to muscles. By leveraging both transcriptomic (gene expression) and connectomic (wiring) data, ConnectionMiner successfully resolved previously uncharacterized neuronal identities and predicted the molecular interactions driving connectivity.

By mapping these relationships, ConnectionMiner provides a predictive framework for understanding how the nervous system assembles itself.

“The nervous system is one of the most complex networks that we know of, and deciphering its molecular building blocks is key to understanding much about our health, our behavior and our lives in general,” says Varol. “Tools like ConnectionMiner are a major stepping stone towards unlocking the brain’s molecular blueprint — enabling us to identify the genes that build neural circuits, revolutionize the diagnosis and treatment of neurological disorders, and fundamentally enhance our understanding of how brain wiring drives behavior.”

This research has far-reaching implications. Understanding the molecular rules that govern neural connectivity in fruit flies could inform studies of more complex nervous systems, including our own. The principles uncovered here might help explain how neural circuits form during development, how they recover from injury, and even how neurodevelopmental disorders arise when connectivity goes awry.

Furthermore, computational tools like ConnectionMiner represent a paradigm shift in neuroscience. By integrating artificial intelligence with biological data, researchers can now tackle questions that were previously too complex to analyze. The approach outlined in this study could be applied to other model organisms, potentially unlocking new insights into brain development, neural repair, and artificial intelligence itself.

Gupta, H.P., Azevedo, A.W., Chen, Y.C., Xing, K., Sims, P.A., Varol, E., & Mann, R.S. (2025). Decoding neuronal wiring by joint inference of cell identity and synaptic connectivity. bioRxiv. https://doi.org/10.1101/2025.03.04.640006