An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex

• Published in: Nature communications Vol. 16; no. 1; pp. 3361 - 15
• Main Authors: Weis, Marissa A., Papadopoulos, Stelios, Hansel, Laura, Lüddecke, Timo, Celii, Brendan, Fahey, Paul G., Wang, Eric Y., Bae, J. Alexander, Bodor, Agnes L., Brittain, Derrick, Buchanan, JoAnn, Bumbarger, Daniel J., Castro, Manuel A., Collman, Forrest, da Costa, Nuno Maçarico, Dorkenwald, Sven, Elabbady, Leila, Halageri, Akhilesh, Jia, Zhen, Jordan, Chris, Kapner, Dan, Kemnitz, Nico, Kinn, Sam, Lee, Kisuk, Li, Kai, Lu, Ran, Macrina, Thomas, Mahalingam, Gayathri, Mitchell, Eric, Mondal, Shanka Subhra, Mu, Shang, Nehoran, Barak, Popovych, Sergiy, Reid, R. Clay, Schneider-Mizell, Casey M., Seung, H. Sebastian, Silversmith, William, Takeno, Marc, Torres, Russel, Turner, Nicholas L., Wong, William, Wu, Jingpeng, Yin, Wenjing, Yu, Szi-chieh, Reimer, Jacob, Berens, Philipp, Tolias, Andreas S., Ecker, Alexander S.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.04.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378; 631/378/116; 631/378/2613; Animals; Cerebral cortex; Dendrites - physiology; Dendrites - ultrastructure; Dendritic structure; Electron microscopy; Female; Humanities and Social Sciences; Machine learning; Male; Mice; Mice, Inbred C57BL; Morphology; multidisciplinary; Neocortex; Neural networks; Neurons; Neurons - cytology; Neurons - physiology; Neurons - ultrastructure; Science; Science (multidisciplinary); Unsupervised learning; Visual cortex; Visual Cortex - cytology; Visual Cortex - physiology; Visual pathways
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-025-58763-w

Neurons in the neocortex exhibit astonishing morphological diversity, which is critical for properly wiring neural circuits and giving neurons their functional properties. However, the organizational principles underlying this morphological diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological “bar code” describing more than 30,000 excitatory neurons in mouse visual areas V1, AL, and RL that were reconstructed from the millimeter scale MICrONS serial-section electron microscopy volume. Contrary to previous classifications into discrete morphological types (m-types), our data-driven approach suggests that the morphological landscape of cortical excitatory neurons is better described as a continuum, with a few notable exceptions in layers 5 and 6. Dendritic morphologies in layers 2–3 exhibited a trend towards a decreasing width of the dendritic arbor and a smaller tuft with increasing cortical depth. Inter-area differences were most evident in layer 4, where V1 contained more atufted neurons than higher visual areas. Moreover, we discovered neurons in V1 on the border to layer 5, which avoided deeper layers with their dendrites. In summary, we suggest that excitatory neurons’ morphological diversity is better understood by considering axes of variation than using distinct m-types. Excitatory neurons in the neocortex exhibit considerable morphological diversity, yet their organizational principles remain a subject of ongoing research. Here, the authors use unsupervised learning to show that most excitatory neuron morphologies in the mouse visual cortex form a continuum, with notable exceptions in deeper layers.