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

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.

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Publication Details

Journal
Nature Communications
Published
2025-04-09
DOI
https://doi.org/10.1038/s41467-025-58763-w
Citations
11
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
14.31

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article

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

R. Clay Reid, Sergiy Popovych, JoAnn Buchanan, Szi-chieh Yu et al.
11 citations
Nature Communications
Neural dynamics and brain function
14.31
article

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

R. Clay Reid, Sergiy Popovych, JoAnn Buchanan, Szi-chieh Yu, Stelios Papadopoulos, Nuno Maçarico da Costa, Thomas Macrina, Paul G. Fahey, Russel Torres, Sven Dorkenwald, Shanka Subhra Mondal, Laura Hansel, Manuel Castro, Leila Elabbady, Nicholas L. Turner, Philipp Berens, H. Sebastian Seung, William Silversmith, Nico Kemnitz, Jingpeng Wu, Alexander S. Ecker, Sam Kinn, Marc Takeno, Daniel J. Bumbarger, Timo Lüddecke, Andreas S. Tolias, Shang Mu, Akhilesh Halageri, Brendan Celii, Jacob Reimer, Casey M Schneider-Mizell, Eric Mitchell, Gayathri Mahalingam, Dan Kapner, Forrest Collman, Barak Nehoran, Derrick Brittain, Kai Li, Eric Wang, J. Alexander Bae, Kisuk Lee, Marissa A. Weis, Zhen Jia, William Wong, A. Bodor, Ran Lu, Wenjing Yin, Chris Jordan
article en
11 citations

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.

Nature CommunicationsVol. 16(1)
Allen Institute for Brain Science (US), Baylor College of Medicine (US), Neurosciences Institute (US), Princeton University (US), Max Planck Institute for Dynamics and Self-Organization (DE), European Neuroscience Institute Göttingen (DE), Bernstein Center for Computational Neuroscience Tübingen (DE), Stanford Medicine (US), Allen Institute (US), Hertie Institute for Clinical Brain Research (DE), Tübingen AI Center, Massachusetts Institute of Technology (US), University of Göttingen (DE), Rice University (US), University of Tübingen (DE), Stanford University (US)
National Science Foundation, International Max Planck Research School for Environmental, Cellular and Molecular Microbiology, European Commission, International Max Planck Research School for Advanced Methods in Process and Systems Engineering, Intelligence Advanced Research Projects Activity, Interior Business Center, HORIZON EUROPE Framework Programme, National Institute of Mental Health
Openalex Percentile: Top 1%
Neural dynamics and brain function
14.31
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