TY  - EJOUR
AU  - Royer, Jessica
AU  - Rodríguez-Cruces, Raúl
AU  - Tavakol, Shahin
AU  - Larivière, Sara
AU  - Herholz, Peer
AU  - Li, Qiongling
AU  - de Wael, Reinder Vos
AU  - Paquola, Casey
AU  - Benkarim, Oualid
AU  - Park, Bo-yong
AU  - Lowe, Alexander J.
AU  - Margulies, Daniel
AU  - Smallwood, Jonathan
AU  - Bernasconi, Andrea
AU  - Bernasconi, Neda
AU  - Frauscher, Birgit
AU  - Bernhardt, Boris C.
TI  - An Open MRI Dataset for Multiscale Neuroscience
M1  - FZJ-2021-05136
PY  - 2021
AB  - 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 ).
LB  - PUB:(DE-HGF)25
DO  - DOI:10.1101/2021.08.04.454795
UR  - https://juser.fz-juelich.de/record/903463
ER  -