CONP Portal | Dataset


MICA-MICs: a dataset for Microstructure-Informed Connectomics
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
Contact: Jessica Royer, jessica.royer@mail.mcgill.ca
Licenses: CC BY-NC
Version: 1.0
Formats: NIfTI JSON TXT
Size: 70.0 GB
No of Files: 11284
No of Subjects: 50
Primary Publication: An open MRI dataset for multiscale neuroscience. Jessica Royer et al. bioRxiv.
Acknowledges: Canadian Institute of Health Research , Canadian Open Neuroscience Platform, Canada Research Chairs program, National Sciences and Engineering Research Council of Canada
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.

Dataset README information

README.md

MICA-MICs: a dataset for Microstructure-Informed Connectomics

The human brain is a highly interconnected network which can be described at multiple spatial and temporal scales. Neuroimaging, in particular magnetic resonance imaging (MRI), has provided a window into brain structure and function, offering versatile contrasts to assess its multiscale organization. Here, we leverage the rich descriptions of brain structure and function offered by MRI to further our understanding of human brain organization across modalities and spatial scales. MICA-MICs is a dataset for microstructure-informed connectomics providing raw and fully processed multimodal neuroimaging data acquired in 50 healthy control participants at an MRI field strength of 3T. Modalities include high-resolution anatomical (T1-weighted), microstructurally-sensitive (quantitative T1), diffusion-weighted, and resting-state functional imaging. We additionally provide users with ready-to-use connectomes built across multiple parcellation schemes based on histology (e.g. Von Economo), sulco-gyral landmarks (e.g. Desikan-Killiany), and function (e.g. Schaefer atlases), for a total of 18 different parcellations of varying spatial scale. All connectome matrices were generated using micapipe (https://micapipe.readthedocs.io/), a robust pre-processing pipeline for multimodal neuroimaging data. By granting access to data modalities at different pre-processing stages, this dataset will streamline the development of novel approaches to study complex interactions between regional and networks-level properties of the human brain.

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., 2021. An open MRI dataset for multiscale neuroscience. bioRxiv 2021.08.04.454795. https://doi.org/10.1101/2021.08.04.454795

https://www.biorxiv.org/content/10.1101/2021.08.04.454795v1

Direct Download

Download Using DataLad

CircleCI status

The following instructions require a basic understanding of UNIX/LINUX command lines. Future portal functionality may include downloads directly from the web browser. Dataset download is currently enabled through DataLad.

Note: The conp-dataset requires version >=0.12.5 of DataLad and version >=8.20200309 of git-annex.

To install DataLad on your system, please refer to the install section of the DataLad Handbook (installation via miniconda is recommended in order to obtain the latest version of DataLad).

1) Initiate the CONP dataset

To initiate the CONP dataset (conp-dataset), run the following command in the directory where you want CONP datasets to be installed:

datalad install https://github.com/CONP-PCNO/conp-dataset.git

2) Install the mica-mics dataset

To install the dataset, go into the created conp-dataset directory and run datalad install on the dataset mica-mics:

cd conp-dataset
datalad install projects/mica-mics

3) Download the mica-mics dataset

Now that the DataLad dataset has been installed, go into the dataset directory under projects/mica-mics.

cd projects/mica-mics

Note that files visible in the dataset are symlinks and will need to be downloaded manually using the datalad get command in the dataset directory:

datalad get <filepath>

Note, if you run datalad get * command, all the files present in the dataset directory will be downloaded.

For more information on how DataLad works, please visit the DataLad Handbook documentation .