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An Open MRI Dataset For Multiscale Neuroscience.

Jessica Royer1,2, Raúl Rodríguez-Cruces3, Shahin Tavakol3

  • 1Multimodal Imaging and Connectome Analysis (MICA) Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec, Canada. jessica.royer@mail.mcgill.ca.

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|September 15, 2022
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Summary

This study introduces the Microstructure-Informed Connectomics (MICA-MICs) multimodal MRI dataset. It enables research into brain microstructure, connectivity, and function across multiple scales.

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Connectomics

Background:

  • Multimodal neuroimaging offers insights into brain structure and function.
  • Investigating the interplay between brain microstructure and connectivity is crucial for understanding brain organization.

Purpose of the Study:

  • To introduce the Microstructure-Informed Connectomics (MICA-MICs) multimodal MRI dataset.
  • To facilitate research on the coupling between brain microstructure, connectivity, and function.

Main Methods:

  • Acquired multimodal MRI data (T1-weighted, quantitative T1, diffusion-weighted, resting-state fMRI) in 50 healthy adults.
  • Generated brain-wide connectomes from functional imaging, diffusion tractography, microstructure covariance, and geodesic distance.
  • Estimated large-scale gradients from each modality and parcellation scale.

Main Results:

  • The MICA-MICs dataset includes raw anonymized MRI data and derived connectomes.
  • Large-scale gradients were estimated across multiple parcellation scales for each modality.
  • The dataset provides a comprehensive resource for studying multiscale brain organization.

Conclusions:

  • The MICA-MICs dataset is a valuable resource for neuroscience research.
  • It will advance the understanding of the relationship between brain microstructure, connectivity, and function.
  • The data is publicly available on the Canadian Open Neuroscience Platform and Open Science Framework.