%0 Electronic Article
%A Royer, Jessica
%A Rodríguez-Cruces, Raúl
%A Tavakol, Shahin
%A Larivière, Sara
%A Herholz, Peer
%A Li, Qiongling
%A de Wael, Reinder Vos
%A Paquola, Casey
%A Benkarim, Oualid
%A Park, Bo-yong
%A Lowe, Alexander J.
%A Margulies, Daniel
%A Smallwood, Jonathan
%A Bernasconi, Andrea
%A Bernasconi, Neda
%A Frauscher, Birgit
%A Bernhardt, Boris C.
%T An Open MRI Dataset for Multiscale Neuroscience
%M FZJ-2021-05136
%D 2021
%X 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 ).
%F PUB:(DE-HGF)25
%9 Preprint
%R 10.1101/2021.08.04.454795
%U https://juser.fz-juelich.de/record/903463