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

Marissa A. Weis, Stelios Papadopoulos, Laura Hansel, Timo Lüddecke, Brendan Celii, Paul G. Fahey, Eric Y. Wang, J. Alexander Bae, Agnes L. Bodor, Derrick Brittain, JoAnn Buchanan, Daniel J. Bumbarger, Manuel A. Castro, Forrest Collman, Nuno Maçarico Costa, Sven Dorkenwald, Leila Elabbady, Akhilesh Halageri, Zhen Jia, Chris Jordan, Dan Kapner, Nico Kemnitz, Sam Kinn, Kisuk Lee, Kai Li, Ran Lu, Thomas Macrina, Gayathri Mahalingam, Eric Mitchell, Shanka Subhra Mondal, Shang Mu, Barak Nehoran, Sergiy Popovych, R. Clay Reid, Casey M. Schneider-Mizell, H. Sebastian Seung, William Silversmith, Marc Takeno, Russel Torres, Nicholas L. Turner, William Wong, Jingpeng Wu, Wenjing Yin, Szi-chieh Yu, Jacob Reimer, Philipp Berens, Andreas S. Tolias and Alexander S. Ecker ()
Additional contact information
Marissa A. Weis: University of Göttingen
Stelios Papadopoulos: Baylor College of Medicine
Laura Hansel: University of Göttingen
Timo Lüddecke: University of Göttingen
Brendan Celii: Baylor College of Medicine
Paul G. Fahey: Baylor College of Medicine
Eric Y. Wang: Baylor College of Medicine
J. Alexander Bae: Princeton University
Agnes L. Bodor: Allen Institute for Brain Science
Derrick Brittain: Allen Institute for Brain Science
JoAnn Buchanan: Allen Institute for Brain Science
Daniel J. Bumbarger: Allen Institute for Brain Science
Manuel A. Castro: Princeton University
Forrest Collman: Allen Institute for Brain Science
Nuno Maçarico Costa: Allen Institute for Brain Science
Sven Dorkenwald: Princeton University
Leila Elabbady: Allen Institute for Brain Science
Akhilesh Halageri: Princeton University
Zhen Jia: Princeton University
Chris Jordan: Princeton University
Dan Kapner: Allen Institute for Brain Science
Nico Kemnitz: Princeton University
Sam Kinn: Allen Institute for Brain Science
Kisuk Lee: Princeton University
Kai Li: Princeton University
Ran Lu: Princeton University
Thomas Macrina: Princeton University
Gayathri Mahalingam: Allen Institute for Brain Science
Eric Mitchell: Princeton University
Shanka Subhra Mondal: Princeton University
Shang Mu: Princeton University
Barak Nehoran: Princeton University
Sergiy Popovych: Princeton University
R. Clay Reid: Allen Institute for Brain Science
Casey M. Schneider-Mizell: Allen Institute for Brain Science
H. Sebastian Seung: Princeton University
William Silversmith: Princeton University
Marc Takeno: Allen Institute for Brain Science
Russel Torres: Allen Institute for Brain Science
Nicholas L. Turner: Princeton University
William Wong: Princeton University
Jingpeng Wu: Princeton University
Wenjing Yin: Allen Institute for Brain Science
Szi-chieh Yu: Princeton University
Jacob Reimer: Baylor College of Medicine
Philipp Berens: University of Tübingen
Andreas S. Tolias: Baylor College of Medicine
Alexander S. Ecker: University of Göttingen

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-58763-w

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