Home > Publications database > Using the multilevel human brain atlas in reproducible workflows with siibra-python > print

001     1031528
005     20241107210039.0
037 _ _ |a FZJ-2024-05723
041 _ _ |a English
100 1 _ |a Dickscheid, Timo
|0 P:(DE-Juel1)165746
|b 0
|u fzj
111 2 _ |a The Julich-Brain Atlas at EBRAINS - Introduction, Concepts and Hands-on Sessions
|c online
|d 2024-06-19 - 2024-06-19
|w Germany
245 _ _ |a Using the multilevel human brain atlas in reproducible workflows with siibra-python
260 _ _ |c 2024
336 7 _ |a lecture
|2 DRIVER
336 7 _ |a Generic
|0 31
|2 EndNote
336 7 _ |a MISC
|2 BibTeX
336 7 _ |a Lecture
|b lecture
|m lecture
|0 PUB:(DE-HGF)17
|s 1730963230_18998
|2 PUB:(DE-HGF)
|x Outreach
336 7 _ |a LECTURE_SPEECH
|2 ORCID
336 7 _ |a Text
|2 DataCite
520 _ _ |a Using the multilevel human brain atlas in reproducible workflows with siibra-python
Timo Dickscheid

siibra is a software tool suite that allows to access the multilevel human brain atlas by providing access to reference templates at different spatial scales, complementary brain parcellations maps, and multimodal regional data from different sources which is linked to brain anatomy at different spatial scales. Besides interactive exploration in the 3D web viewer siibra-explorer, the framework can be leveraged for scripting, reproducible workflows and application development using the siibra-python programming library.This session will introduce the core concepts of siibra-python and demonstrate a range of typical programming patterns to use the atlas. It will cover practical coding exercises demonstrating how to fetch brain region maps, access high-resolution microscopy data including the BigBrain dataset, and extract multimodal regional features such as cortical thicknesses, cell and neurotransmitter densities, gene expressions, and connectivity data. Participants will gain first insight of the features of siibra-python to enhance their ability to perform advanced neuroimaging analyses with data coming from different modalities and resolutions.
536 _ _ |a 5251 - Multilevel Brain Organization and Variability (POF4-525)
|0 G:(DE-HGF)POF4-5251
|c POF4-525
|f POF IV
|x 0
536 _ _ |a 5254 - Neuroscientific Data Analytics and AI (POF4-525)
|0 G:(DE-HGF)POF4-5254
|c POF4-525
|f POF IV
|x 1
536 _ _ |a EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319)
|0 G:(EU-Grant)101147319
|c 101147319
|f HORIZON-INFRA-2022-SERV-B-01
|x 2
536 _ _ |a HIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
|0 G:(DE-HGF)InterLabs-0015
|c InterLabs-0015
|x 3
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 4
856 4 _ |u https://go.fzj.de/Julich-Brain-EBRAINS-School
909 C O |o oai:juser.fz-juelich.de:1031528
|p openaire
|p VDB
|p ec_fundedresources
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)165746
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5251
|x 0
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5254
|x 1
914 1 _ |y 2024
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-1-20090406
|k INM-1
|l Strukturelle und funktionelle Organisation des Gehirns
|x 0
980 _ _ |a lecture
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-1-20090406
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21