An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex
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...
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2025-04-01
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| Online Access: | https://doi.org/10.1038/s41467-025-58763-w |
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| author | 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 da 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 Alexander S. Ecker |
| author_facet | 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 da 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 Alexander S. Ecker |
| author_sort | Marissa A. Weis |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6e418b6d64d24ea68bf02f0530c90285 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-6e418b6d64d24ea68bf02f0530c902852025-08-20T03:06:57ZengNature PortfolioNature Communications2041-17232025-04-0116111510.1038/s41467-025-58763-wAn unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortexMarissa A. Weis0Stelios Papadopoulos1Laura Hansel2Timo Lüddecke3Brendan Celii4Paul G. Fahey5Eric Y. Wang6J. Alexander Bae7Agnes L. Bodor8Derrick Brittain9JoAnn Buchanan10Daniel J. Bumbarger11Manuel A. Castro12Forrest Collman13Nuno Maçarico da Costa14Sven Dorkenwald15Leila Elabbady16Akhilesh Halageri17Zhen Jia18Chris Jordan19Dan Kapner20Nico Kemnitz21Sam Kinn22Kisuk Lee23Kai Li24Ran Lu25Thomas Macrina26Gayathri Mahalingam27Eric Mitchell28Shanka Subhra Mondal29Shang Mu30Barak Nehoran31Sergiy Popovych32R. Clay Reid33Casey M. Schneider-Mizell34H. Sebastian Seung35William Silversmith36Marc Takeno37Russel Torres38Nicholas L. Turner39William Wong40Jingpeng Wu41Wenjing Yin42Szi-chieh Yu43Jacob Reimer44Philipp Berens45Andreas S. Tolias46Alexander S. Ecker47Institute of Computer Science and Campus Institute Data Science, University of GöttingenCenter for Neuroscience and AI, Baylor College of MedicineInstitute of Computer Science and Campus Institute Data Science, University of GöttingenInstitute of Computer Science and Campus Institute Data Science, University of GöttingenCenter for Neuroscience and AI, Baylor College of MedicineCenter for Neuroscience and AI, Baylor College of MedicineCenter for Neuroscience and AI, Baylor College of MedicinePrinceton Neuroscience Institute, Princeton UniversityAllen Institute for Brain ScienceAllen Institute for Brain ScienceAllen Institute for Brain ScienceAllen Institute for Brain SciencePrinceton Neuroscience Institute, Princeton UniversityAllen Institute for Brain ScienceAllen Institute for Brain SciencePrinceton Neuroscience Institute, Princeton UniversityAllen Institute for Brain SciencePrinceton Neuroscience Institute, Princeton UniversityPrinceton Neuroscience Institute, Princeton UniversityPrinceton Neuroscience Institute, Princeton UniversityAllen Institute for Brain SciencePrinceton Neuroscience Institute, Princeton UniversityAllen Institute for Brain SciencePrinceton Neuroscience Institute, Princeton UniversityPrinceton Neuroscience Institute, Princeton UniversityPrinceton Neuroscience Institute, Princeton UniversityPrinceton Neuroscience Institute, Princeton UniversityAllen Institute for Brain SciencePrinceton Neuroscience Institute, Princeton UniversityPrinceton Neuroscience Institute, Princeton UniversityPrinceton Neuroscience Institute, Princeton UniversityPrinceton Neuroscience Institute, Princeton UniversityPrinceton Neuroscience Institute, Princeton UniversityAllen Institute for Brain ScienceAllen Institute for Brain SciencePrinceton Neuroscience Institute, Princeton UniversityPrinceton Neuroscience Institute, Princeton UniversityAllen Institute for Brain ScienceAllen Institute for Brain SciencePrinceton Neuroscience Institute, Princeton UniversityPrinceton Neuroscience Institute, Princeton UniversityPrinceton Neuroscience Institute, Princeton UniversityAllen Institute for Brain SciencePrinceton Neuroscience Institute, Princeton UniversityCenter for Neuroscience and AI, Baylor College of MedicineHertie Institute for AI in Brain Health, University of TübingenCenter for Neuroscience and AI, Baylor College of MedicineInstitute of Computer Science and Campus Institute Data Science, University of GöttingenAbstract 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.https://doi.org/10.1038/s41467-025-58763-w |
| spellingShingle | 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 da 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 Alexander S. Ecker An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex Nature Communications |
| title | An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex |
| title_full | An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex |
| title_fullStr | An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex |
| title_full_unstemmed | An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex |
| title_short | An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex |
| title_sort | unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex |
| url | https://doi.org/10.1038/s41467-025-58763-w |
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