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.
Show 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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