MICA-MICs: a dataset for Microstructure-Informed ConnectomicsThe MICA-MICs dataset provides raw and fully processed multimodal neuroimaging data acquired in 50 healthy control participants at a filed 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. Creators: 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 J. Lowe; Daniel Margulies; Jonathan Smallwood; Andrea Bernasconi; Neda Bernasconi; Birgit Frauscher; Boris C. Bernhardt Licenses: CC0 Version: 1.1 Modalities: Multimodal imaging, Structural imaging, Diffusion-weighted Imaging, Functional imaging, Connectome Formats: NIfTI, JSON, TXT Size: 70.0 GB Number of files: 11284 Number of Subjects: 50 Github Sources: https://portal.conp.ca/dataset?id=projects/mica-mics...Access is restricted by the data custodian. The collection's description and structure are public; the data is not available for querying through this network.MICA-MICs: a dataset for Microstructure-Informed Connectomics is published on Viral AI.