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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Artículo científico |
| Langue: | en |
| Publié: |
eLife
2025
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| Sujets: | |
| Accès en ligne: | https://pubmed.ncbi.nlm.nih.gov/41118245/ |
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| _version_ | 1868266138817789952 |
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| author | Koonce, Noelle L Emerson, Sarah E Bhaskar, Dhananjay Kuchroo, Manik Moyle, Mark W Arroyo-Morales, Pura Vázquez-Martínez, Nabor Emerson, Jamie I Krishnaswamy, Smita Mohler, William A Colón-Ramos, Daniel A |
| author_facet | Koonce, Noelle L Emerson, Sarah E Bhaskar, Dhananjay Kuchroo, Manik Moyle, Mark W Arroyo-Morales, Pura Vázquez-Martínez, Nabor Emerson, Jamie I Krishnaswamy, Smita Mohler, William A Colón-Ramos, Daniel A Koonce, Noelle L Emerson, Sarah E Bhaskar, Dhananjay Kuchroo, Manik Moyle, Mark W Arroyo-Morales, Pura Vázquez-Martínez, Nabor Emerson, Jamie I Krishnaswamy, Smita Mohler, William A Colón-Ramos, Daniel A |
| collection | PubMed - marine biology |
| contents | Exploring neurodevelopment via spatiotemporal collation of anatomical networks with NeuroSC. Koonce, Noelle L Emerson, Sarah E Bhaskar, Dhananjay Kuchroo, Manik Moyle, Mark W Arroyo-Morales, Pura Vázquez-Martínez, Nabor Emerson, Jamie I Krishnaswamy, Smita Mohler, William A Colón-Ramos, Daniel A Connectome Animals Caenorhabditis elegans Brain Microscopy, Electron Neuropil Nerve Net Software Volume electron microscopy (vEM) datasets such as those generated for connectome studies allow nanoscale quantifications and comparisons of the cell biological features underpinning circuit architectures. Quantifying cell biological relationships in the connectome yields rich, multidimensional datasets that benefit from data science approaches, including dimensionality reduction and integrated graphical representations of neuronal relationships. We developed NeuroSC ( https://neurosc.net/) an open source online platform that bridges sophisticated graph analytics from data science approaches with the underlying cell biological features in the connectome. We analyze a series of published brain neuropils and demonstrate how these integrated representations of neuronal relationships facilitate comparisons across connectomes, catalyzing new insights into the structure-function relationships of the circuits and their changes during development. NeuroSC is designed for intuitive examination and comparisons across connectomes, enabling synthesis of knowledge from high-level abstractions of neuronal relationships derived from data science techniques to the detailed identification of the cell biological features underpinning these abstractions. |
| format | Artículo científico |
| id | pubmed_41118245 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | eLife |
| record_format | pubmed |
| spellingShingle | Exploring neurodevelopment via spatiotemporal collation of anatomical networks with NeuroSC. Koonce, Noelle L Emerson, Sarah E Bhaskar, Dhananjay Kuchroo, Manik Moyle, Mark W Arroyo-Morales, Pura Vázquez-Martínez, Nabor Emerson, Jamie I Krishnaswamy, Smita Mohler, William A Colón-Ramos, Daniel A Connectome Animals Caenorhabditis elegans Brain Microscopy, Electron Neuropil Nerve Net Software Exploring neurodevelopment via spatiotemporal collation of anatomical networks with NeuroSC. Koonce, Noelle L Emerson, Sarah E Bhaskar, Dhananjay Kuchroo, Manik Moyle, Mark W Arroyo-Morales, Pura Vázquez-Martínez, Nabor Emerson, Jamie I Krishnaswamy, Smita Mohler, William A Colón-Ramos, Daniel A Connectome Animals Caenorhabditis elegans Brain Microscopy, Electron Neuropil Nerve Net Software Volume electron microscopy (vEM) datasets such as those generated for connectome studies allow nanoscale quantifications and comparisons of the cell biological features underpinning circuit architectures. Quantifying cell biological relationships in the connectome yields rich, multidimensional datasets that benefit from data science approaches, including dimensionality reduction and integrated graphical representations of neuronal relationships. We developed NeuroSC ( https://neurosc.net/) an open source online platform that bridges sophisticated graph analytics from data science approaches with the underlying cell biological features in the connectome. We analyze a series of published brain neuropils and demonstrate how these integrated representations of neuronal relationships facilitate comparisons across connectomes, catalyzing new insights into the structure-function relationships of the circuits and their changes during development. NeuroSC is designed for intuitive examination and comparisons across connectomes, enabling synthesis of knowledge from high-level abstractions of neuronal relationships derived from data science techniques to the detailed identification of the cell biological features underpinning these abstractions. |
| title | Exploring neurodevelopment via spatiotemporal collation of anatomical networks with NeuroSC. |
| topic | Connectome Animals Caenorhabditis elegans Brain Microscopy, Electron Neuropil Nerve Net Software |
| url | https://pubmed.ncbi.nlm.nih.gov/41118245/ |