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MICA-MICs: a dataset for Microstructure-Informed Connectomics
- Jessica Royer
- Raul Rodriguez-Cruces
- Shahin Tavakol
- Sara Lariviere
- Peer Herholz
- Qiongling Li
- Reinder Vos de Wael
- Casey Paquola
- Oualid Benkarim
- Bo-yong Park
- Alexander Lowe
- Daniel S. Margulies
- Jonathan Smallwood
- Andrea Bernasconi
- Neda Bernasconi
- Birgit Frauscher
- Boris C. Bernhardt
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Description: The MICA-MICs dataset provides raw and fully processed multimodal neuroimaging data acquired in 50 healthy control participants at a field strength of 3T. Modalities include high-resolution anatomical (T1-weighted), microstructurally-sensitive (quantitative T1), diffusion-weighted and resting-state functional imaging. In addition, MICA-MICs provides ready-to-use connectomes built across multiple parcellation schemes based on brain anatomy, function, and histology (18 parcellations in total). Processed matrices are available for each imaging modality across a range of parcellation scales. MICA-MICs can also be accessed from the Canadian Open Neuroscience Platform's data portal: https://portal.conp.ca/dataset?id=projects/mica-mics Please cite the following reference if you use this dataset: Royer, J., Rodriguez-Cruces, R., Tavakol, S., Lariviere, S., Herholz, P., Li, Q., Vos de Wael, R., Paquola, C., Benkarim, O., Park, B., Lowe, A.J., Margulies, D.S., Smallwood, J., Bernasconi, A., Bernasconi, N., Frauscher, B., Bernhardt, B.C. (2022). An open MRI dataset for multiscale neuroscience. Scientific Data, 9(1), 569.