001031456 001__ 1031456
001031456 005__ 20241107210038.0
001031456 037__ $$aFZJ-2024-05675
001031456 041__ $$aEnglish
001031456 1001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b0$$ufzj
001031456 1112_ $$a8th BigBrain Workshop$$cPadua$$d2024-09-09 - 2024-09-11$$wItaly
001031456 245__ $$aMaking the multiscale organization of the human brain accessible to reproducible workflows using siibra-python
001031456 260__ $$c2024
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001031456 520__ $$aUnderstanding the human brain requires access to experimental data that capture relevant aspects of brain organization across a broad range of scales and modalities, and typically originate from a plethora of resources. To make multimodal and multidimensional measures of brain organization accessible, they need to be integrated into a common reference framework and exposed via suitable software interfaces. This tutorial will introduce participants to siibra toolsuite, which provides access to a multilevel atlas of the human brain built from “big data”. The atlas integrates brain reference templates at different spatial scales, complementary parcellation maps, and a wide range of multimodal data features. It links macroanatomical concepts and their inter-subject variability with measurements of the microstructural composition and intrinsic variance of brain regions, using cytoarchitectonic maps as a reference, and integrating the BigBrain model as microscopic reference template. The tool suite includes a web-based 3D viewer (siibra-explorer) and a Python library (siibra-python) to support a broad range of neuroscientific use cases. It makes use of EBRAINS as a data sharing platform and cloud infrastructure and implements interfaces to other neuroscience resources. The focus of this tutorial will be on building reproducible workflows with BigBrain data using the siibra-python library.
001031456 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001031456 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x1
001031456 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x2
001031456 536__ $$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
001031456 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
001031456 7001_ $$0P:(DE-Juel1)131636$$aBludau, Sebastian$$b1$$ufzj
001031456 8564_ $$uhttps://events.hifis.net/event/1416/contributions/11503/
001031456 909CO $$ooai:juser.fz-juelich.de:1031456$$popenaire$$pVDB$$pec_fundedresources
001031456 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165746$$aForschungszentrum Jülich$$b0$$kFZJ
001031456 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131636$$aForschungszentrum Jülich$$b1$$kFZJ
001031456 9131_ $$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$$vDecoding Brain Organization and Dysfunction$$x0
001031456 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$$x1
001031456 9141_ $$y2024
001031456 920__ $$lyes
001031456 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001031456 980__ $$alecture
001031456 980__ $$aVDB
001031456 980__ $$aI:(DE-Juel1)INM-1-20090406
001031456 980__ $$aUNRESTRICTED