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Deep learning networks reflect cytoarchitectonic features used in brain mapping
The distribution of neurons in the cortex (cytoarchitecture) differs between cortical areas and constitutes the basis for structural maps of the human brain. Deep learning approaches provide a promising alternative to overcome throughput limitations of currently used cytoarchitectonic mapping method...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744572/ https://www.ncbi.nlm.nih.gov/pubmed/33328511 http://dx.doi.org/10.1038/s41598-020-78638-y |
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author | Kiwitz, Kai Schiffer, Christian Spitzer, Hannah Dickscheid, Timo Amunts, Katrin |
author_facet | Kiwitz, Kai Schiffer, Christian Spitzer, Hannah Dickscheid, Timo Amunts, Katrin |
author_sort | Kiwitz, Kai |
collection | PubMed |
description | The distribution of neurons in the cortex (cytoarchitecture) differs between cortical areas and constitutes the basis for structural maps of the human brain. Deep learning approaches provide a promising alternative to overcome throughput limitations of currently used cytoarchitectonic mapping methods, but typically lack insight as to what extent they follow cytoarchitectonic principles. We therefore investigated in how far the internal structure of deep convolutional neural networks trained for cytoarchitectonic brain mapping reflect traditional cytoarchitectonic features, and compared them to features of the current grey level index (GLI) profile approach. The networks consisted of a 10-block deep convolutional architecture trained to segment the primary and secondary visual cortex. Filter activations of the networks served to analyse resemblances to traditional cytoarchitectonic features and comparisons to the GLI profile approach. Our analysis revealed resemblances to cellular, laminar- as well as cortical area related cytoarchitectonic features. The networks learned filter activations that reflect the distinct cytoarchitecture of the segmented cortical areas with special regard to their laminar organization and compared well to statistical criteria of the GLI profile approach. These results confirm an incorporation of relevant cytoarchitectonic features in the deep convolutional neural networks and mark them as a valid support for high-throughput cytoarchitectonic mapping workflows. |
format | Online Article Text |
id | pubmed-7744572 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77445722020-12-17 Deep learning networks reflect cytoarchitectonic features used in brain mapping Kiwitz, Kai Schiffer, Christian Spitzer, Hannah Dickscheid, Timo Amunts, Katrin Sci Rep Article The distribution of neurons in the cortex (cytoarchitecture) differs between cortical areas and constitutes the basis for structural maps of the human brain. Deep learning approaches provide a promising alternative to overcome throughput limitations of currently used cytoarchitectonic mapping methods, but typically lack insight as to what extent they follow cytoarchitectonic principles. We therefore investigated in how far the internal structure of deep convolutional neural networks trained for cytoarchitectonic brain mapping reflect traditional cytoarchitectonic features, and compared them to features of the current grey level index (GLI) profile approach. The networks consisted of a 10-block deep convolutional architecture trained to segment the primary and secondary visual cortex. Filter activations of the networks served to analyse resemblances to traditional cytoarchitectonic features and comparisons to the GLI profile approach. Our analysis revealed resemblances to cellular, laminar- as well as cortical area related cytoarchitectonic features. The networks learned filter activations that reflect the distinct cytoarchitecture of the segmented cortical areas with special regard to their laminar organization and compared well to statistical criteria of the GLI profile approach. These results confirm an incorporation of relevant cytoarchitectonic features in the deep convolutional neural networks and mark them as a valid support for high-throughput cytoarchitectonic mapping workflows. Nature Publishing Group UK 2020-12-16 /pmc/articles/PMC7744572/ /pubmed/33328511 http://dx.doi.org/10.1038/s41598-020-78638-y Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Kiwitz, Kai Schiffer, Christian Spitzer, Hannah Dickscheid, Timo Amunts, Katrin Deep learning networks reflect cytoarchitectonic features used in brain mapping |
title | Deep learning networks reflect cytoarchitectonic features used in brain mapping |
title_full | Deep learning networks reflect cytoarchitectonic features used in brain mapping |
title_fullStr | Deep learning networks reflect cytoarchitectonic features used in brain mapping |
title_full_unstemmed | Deep learning networks reflect cytoarchitectonic features used in brain mapping |
title_short | Deep learning networks reflect cytoarchitectonic features used in brain mapping |
title_sort | deep learning networks reflect cytoarchitectonic features used in brain mapping |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744572/ https://www.ncbi.nlm.nih.gov/pubmed/33328511 http://dx.doi.org/10.1038/s41598-020-78638-y |
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