001031455 001__ 1031455
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001031455 037__ $$aFZJ-2024-05674
001031455 041__ $$aEnglish
001031455 1001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b0$$eCorresponding author$$ufzj
001031455 1112_ $$a8th BigBrain Workshop$$cPadua$$d2024-09-09 - 2024-09-11$$wItaly
001031455 245__ $$aCytoNet: A Deep Neural Network for Whole-brain Characterization of Human Cytoarchitecture
001031455 260__ $$c2024
001031455 3367_ $$033$$2EndNote$$aConference Paper
001031455 3367_ $$2DataCite$$aOther
001031455 3367_ $$2BibTeX$$aINPROCEEDINGS
001031455 3367_ $$2DRIVER$$aconferenceObject
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001031455 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1730972546_30533$$xAfter Call
001031455 502__ $$cHeinrich-Heine-University Düsseldorf
001031455 520__ $$aThe characterization of cytoarchitecture in the human brain provides an essential building block for the creation of a high-resolution multi-modal brain atlas. Cytoarchitecture is defined by the spatial organization of neuronal cells, including their shape, density, size, cell type, as well as their columnar and laminar arrangement, which differ between brain regions. High-throughput light-microscopic scanning of large, cell-body stained histological sections obtained by sectioning postmortem human brains enables detailed examination of cytoarchitectonic organizational principles across multiple brain samples, which is mandatory to capture the highly variable cytoarchitectonic organization. The limited scalability of existing methods to image and analyze datasets in the terabyte to petabyte range motivates current developments of AI methods for data-driven characterization and classification of human cytoarchitecture at large scale.In this work, we present CytoNet, a deep neural network model that enables data-driven characterization of cytoarchitecture in the human brain. CytoNet is a convolutional neural network that is trained on 200 000 image patches (2048px@2μm/px) extracted from 4115 histological sections of 9 postmortem brains. The model is trained using a novel contrastive learning objective that derives the similarity relationship between image samples from their spatial distance in a common reference brain space. Using this loss, CytoNet is trained to map spatially close image samples, which likely show similar cytoarchitectonic structures, to similar feature representations.We demonstrate that feature representations extracted by CytoNet allow classifying cytoarchitectonic areas, predicting spatial and morphological features, studying inter-individual variations, and enabling data-driven quantification and query-based exploration of microstructural principles at whole-brain level. Moreover, we show that the latent space learned by CytoNet exhibits an anatomically highly plausible structure that facilitates intuitive exploration of brain organization. CytoNet significantly extends existing methods for cytoarchitecture analysis and thus provides the foundation for novel analysis workflows that have the potential to facilitate studies relating the brain’s microstructure to connectivity and function.
001031455 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001031455 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x1
001031455 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x2
001031455 536__ $$0G:(EU-Grant)101147319$$aEBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319)$$c101147319$$fHORIZON-INFRA-2022-SERV-B-01$$x3
001031455 536__ $$0G:(DE-Juel-1)E.40401.62$$aHelmholtz AI - Helmholtz Artificial Intelligence  Coordination Unit – Local Unit FZJ (E.40401.62)$$cE.40401.62$$x4
001031455 536__ $$0G:(DE-HGF)ZT-I-PF-4-061$$aX-BRAIN (ZT-I-PF-4-061)$$cZT-I-PF-4-061$$x5
001031455 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b1$$ufzj
001031455 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b2$$ufzj
001031455 8564_ $$uhttps://events.hifis.net/event/1416/contributions/11261/
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001031455 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)170068$$aForschungszentrum Jülich$$b0$$kFZJ
001031455 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b1$$kFZJ
001031455 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165746$$aForschungszentrum Jülich$$b2$$kFZJ
001031455 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5254$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001031455 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x1
001031455 9141_ $$y2024
001031455 920__ $$lyes
001031455 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
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001031455 980__ $$aVDB
001031455 980__ $$aI:(DE-Juel1)INM-1-20090406
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