Quantitative profiling of whole-brain connectomes at single-axon resolution using deep learning and high-resolution light sheet microscopy

Attarpour, A.; Raffiee, M.; Xu, T.; Osmann, J.; Patel, S.; Yu, F.; Au, B.; Clappison, A.; Biparva, M.; Zhu, R.; Crow, A.; Eshaghi Gharagoz, B.; Rozak, M. W.; Aubert, I.; McLaurin, J.; Deisseroth, K.; Stefanovic, B.; Goubran, M.

doi:10.1101/2025.11.14.688340

bioRxiv · 10.1101/2025.11.14.688340

Quantitative profiling of whole-brain connectomes at single-axon resolution using deep learning and high-resolution light sheet microscopy

Attarpour, A.·Raffiee, M.·Xu, T.·Osmann, J.·Patel, S.·Yu, F.·Au, B.·Clappison, A.·Biparva, M.·Zhu, R.·Crow, A.·Eshaghi Gharagoz, B.·Rozak, M. W.·Aubert, I.·McLaurin, J.·Deisseroth, K.·Stefanovic, B.·Goubran, M.

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Abstract

Revealing how individual axons create a brain-wide connectome would be indispensable for understanding brain function and behavior, yet remains technically challenging. We introduce MAPL3, an end-to-end pipeline that integrates self-supervised learning with an innovative deep architecture to capture local and global brain-wide axonal projections. MAPL3 enables subject- and population-level quantitative laminar analysis, generalizes across experiments, and outperforms state-of-the-art methods. We showcase its ability to map the circuitry of the orbitofrontal cortex from single axons to whole-brain projectome.

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Attarpour, A., Raffiee, M., Xu, T., Osmann, J., Patel, S., Yu, F., Au, B., Clappison, A., Biparva, M., Zhu, R., Crow, A., Eshaghi Gharagoz, B., Rozak, M. W., Aubert, I., McLaurin, J., Deisseroth, K., Stefanovic, B., Goubran, M.. 2025-11-16. Quantitative profiling of whole-brain connectomes at single-axon resolution using deep learning and high-resolution light sheet microscopy. https://doi.org/10.1101/2025.11.14.688340

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