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@ARTICLE{Royer:903463,
      author       = {Royer, Jessica and Rodríguez-Cruces, Raúl and Tavakol,
                      Shahin and Larivière, Sara and Herholz, Peer and Li,
                      Qiongling and de Wael, Reinder Vos and Paquola, Casey and
                      Benkarim, Oualid and Park, Bo-yong and Lowe, Alexander J.
                      and Margulies, Daniel and Smallwood, Jonathan and
                      Bernasconi, Andrea and Bernasconi, Neda and Frauscher,
                      Birgit and Bernhardt, Boris C.},
      title        = {{A}n {O}pen {MRI} {D}ataset for {M}ultiscale
                      {N}euroscience},
      reportid     = {FZJ-2021-05136},
      year         = {2021},
      abstract     = {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 ).},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      HIBALL - Helmholtz International BigBrain Analytics and
                      Learning Laboratory (HIBALL) (InterLabs-0015)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)InterLabs-0015},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2021.08.04.454795},
      url          = {https://juser.fz-juelich.de/record/903463},
}