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| Home > Publications database > Deep learning networks reflect cytoarchitectonic features used in brain mapping > print |
| 001 | 888898 | ||
| 005 | 20231123201912.0 | ||
| 024 | 7 | _ | |a 10.1038/s41598-020-78638-y |2 doi |
| 024 | 7 | _ | |a 2128/26562 |2 Handle |
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| 024 | 7 | _ | |a WOS:000603657800005 |2 WOS |
| 037 | _ | _ | |a FZJ-2020-05303 |
| 082 | _ | _ | |a 530 |
| 100 | 1 | _ | |a Kiwitz, Kai |0 P:(DE-Juel1)171890 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Deep learning networks reflect cytoarchitectonic features used in brain mapping |
| 260 | _ | _ | |a Darmstadt |c 2020 |b GSI |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1700723389_27668 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 571 - Connectivity and Activity (POF3-571) |0 G:(DE-HGF)POF3-571 |c POF3-571 |f POF III |x 0 |
| 536 | _ | _ | |a HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) |0 G:(EU-Grant)720270 |c 720270 |f H2020-Adhoc-2014-20 |x 1 |
| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 2 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 3 |
| 536 | _ | _ | |a Automatic classification of cytoarchitectonic human cortical brain regions using Deep Learning (jinm16_20200501) |0 G:(DE-Juel1)jinm16_20200501 |c jinm16_20200501 |f Automatic classification of cytoarchitectonic human cortical brain regions using Deep Learning |x 4 |
| 536 | _ | _ | |a SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017) |0 G:(DE-Juel1)HGF-SMHB-2013-2017 |c HGF-SMHB-2013-2017 |f SMHB |x 5 |
| 536 | _ | _ | |a Helmholtz AI - Helmholtz Artificial Intelligence Coordination Unit – Local Unit FZJ (E.40401.62) |0 G:(DE-Juel-1)E.40401.62 |c E.40401.62 |x 6 |
| 700 | 1 | _ | |a Schiffer, Christian |0 P:(DE-Juel1)170068 |b 1 |u fzj |
| 700 | 1 | _ | |a Spitzer, Hannah |0 P:(DE-Juel1)167110 |b 2 |
| 700 | 1 | _ | |a Dickscheid, Timo |0 P:(DE-Juel1)165746 |b 3 |u fzj |
| 700 | 1 | _ | |a Amunts, Katrin |0 P:(DE-Juel1)131631 |b 4 |u fzj |
| 773 | _ | _ | |a 10.1038/s41598-020-78638-y |0 PERI:(DE-600)2104902-6 |p 22039 |t Scientific Report |v 10 |y 2020 |x 0174-0814 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/888898/files/Kiwitz_et_al_Scientific_Reports_2020.pdf |y OpenAccess |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-571 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-500 |4 G:(DE-HGF)POF |v Connectivity and Activity |x 0 |
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