001     903463
005     20211219012041.0
024 7 _ |2 doi
|a 10.1101/2021.08.04.454795
024 7 _ |2 Handle
|a 2128/29435
024 7 _ |a altmetric:111230025
|2 altmetric
037 _ _ |a FZJ-2021-05136
100 1 _ |0 0000-0002-4448-8998
|a Royer, Jessica
|b 0
|e Corresponding author
245 _ _ |a An Open MRI Dataset for Multiscale Neuroscience
260 _ _ |c 2021
336 7 _ |0 PUB:(DE-HGF)25
|2 PUB:(DE-HGF)
|a Preprint
|b preprint
|m preprint
|s 1639131807_20223
336 7 _ |2 ORCID
|a WORKING_PAPER
336 7 _ |0 28
|2 EndNote
|a Electronic Article
336 7 _ |2 DRIVER
|a preprint
336 7 _ |2 BibTeX
|a ARTICLE
336 7 _ |2 DataCite
|a Output Types/Working Paper
520 _ _ |a Multimodal neuroimaging grants a powerful window into the structure and function of the human brain at multiple scales. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends (also referred to as gradients) in brain microstructure and connectivity, offering an integrative framework to study multiscale brain organization. Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29.54±5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted MRI, and resting-state functional MRI at 3 Tesla. In addition to raw anonymized MRI data, this release includes brain-wide connectomes derived from i) resting-state functional imaging, ii) diffusion tractography, iii) microstructure covariance analysis, and iv) geodesic cortical distance, gathered across multiple parcellation scales. Alongside, we share large-scale gradients estimated from each modality and parcellation scale. Our dataset will facilitate future research examining the coupling between brain microstructure, connectivity, and macroscale function. MICA-MICs is available on the Canadian Open Neuroscience Platform’s data portal ( https://portal.conp.ca ).
536 _ _ |0 G:(DE-HGF)POF4-5254
|a 5254 - Neuroscientific Data Analytics and AI (POF4-525)
|c POF4-525
|f POF IV
|x 0
536 _ _ |0 G:(DE-HGF)InterLabs-0015
|a HIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
|c InterLabs-0015
|x 1
588 _ _ |a Dataset connected to CrossRef
700 1 _ |0 0000-0002-2917-1212
|a Rodríguez-Cruces, Raúl
|b 1
700 1 _ |0 0000-0002-7585-8963
|a Tavakol, Shahin
|b 2
700 1 _ |a Larivière, Sara
|b 3
700 1 _ |0 0000-0002-9840-6257
|a Herholz, Peer
|b 4
700 1 _ |0 0000-0001-6989-2981
|a Li, Qiongling
|b 5
700 1 _ |0 0000-0003-0574-6576
|a de Wael, Reinder Vos
|b 6
700 1 _ |0 P:(DE-Juel1)187055
|a Paquola, Casey
|b 7
|u fzj
700 1 _ |0 0000-0003-3922-7643
|a Benkarim, Oualid
|b 8
700 1 _ |0 0000-0001-7096-337X
|a Park, Bo-yong
|b 9
700 1 _ |a Lowe, Alexander J.
|b 10
700 1 _ |0 0000-0002-8880-9204
|a Margulies, Daniel
|b 11
700 1 _ |0 0000-0002-7298-2459
|a Smallwood, Jonathan
|b 12
700 1 _ |a Bernasconi, Andrea
|b 13
700 1 _ |a Bernasconi, Neda
|b 14
700 1 _ |a Frauscher, Birgit
|b 15
700 1 _ |0 0000-0001-9256-6041
|a Bernhardt, Boris C.
|b 16
|e Corresponding author
773 _ _ |a 10.1101/2021.08.04.454795
856 4 _ |u https://juser.fz-juelich.de/record/903463/files/Royer_etal_2021.08.04.454795v1.full.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:903463
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910 1 _ |0 I:(DE-588b)5008462-8
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|a Forschungszentrum Jülich
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|l Natural, Artificial and Cognitive Information Processing
|v Decoding Brain Organization and Dysfunction
|x 0
914 1 _ |y 2021
915 _ _ |0 StatID:(DE-HGF)0510
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|a OpenAccess
915 _ _ |0 LIC:(DE-HGF)CCBY4
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920 1 _ |0 I:(DE-Juel1)INM-1-20090406
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|l Strukturelle und funktionelle Organisation des Gehirns
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