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NIH BRAIN CONNECTS - LINC UM1 Macaque Data

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@data-hcp data-hcp released this 30 Jun 01:38
001372
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Learn about vigilant mode.

This repository provides macaque diffusion MRI data from the NIH BRAIN CONNECTS - LINC UM1 project, released through the DANDI Archive. The dataset is part of the Large-scale Imaging of Neural Circuits (LINC) effort, which aims to develop multimodal imaging resources for studying brain connections across scales.

The dataset focuses on high-resolution macaque diffusion MRI and derived tractography/microstructure products relevant to cortico-subcortical circuitry, including the internal capsule, hyperdirect pathway, and basal ganglia.

  • Source: https://dandiarchive.org/dandiset/001372
  • DANDI ID: 001372
  • Status: Draft
  • Data standard: Brain Imaging Data Structure (BIDS)
  • Access: Open access
  • License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Dataset Overview

  • Dataset name: NIH BRAIN CONNECTS - LINC UM1 - Macaque Data
  • Species: Macaque
  • Subjects: 4
  • File count: 192
  • Dataset size: ~67.1 GB
  • Primary modality: Diffusion MRI
  • Data type: Derived BIDS-compatible diffusion imaging data

Keywords

  • Multimodal imaging
  • Diffusion MRI
  • Tractography

Subject Matter

  • Internal capsule
  • Hyperdirect pathway
  • Basal ganglia

Project Background

The Center for Large-scale Imaging of Neural Circuits (LINC) is part of the NIH BRAIN Initiative CONNECTS program. The project aims to develop imaging technologies that bridge microscopic and macroscopic scales of brain connectivity.

This macaque dataset supports research on:

  • Whole-brain diffusion MRI tractography
  • Cortico-subcortical projection mapping
  • Microstructural modeling
  • Cross-scale validation of brain connectivity
  • Deep-brain stimulation-related pathways

Data Contents

The current DANDI draft release contains diffusion MRI derivatives for:

sub-M1
sub-M2
sub-M3
sub-M4

The data are organized primarily under:

derivatives/<subject>/dwi/

Acquisition / Derivative Types

The asset list includes several diffusion MRI acquisition and derivative categories.

High-resolution multishell diffusion MRI

Example files:

sub-M*_acq-HighRes+MultiShell_dwi.nii.gz
sub-M*_acq-HighRes+MultiShell_dwi.bvals
sub-M*_acq-HighRes+MultiShell_dwi.bvecs

These files provide high-resolution multishell diffusion data and associated gradient tables.

High-resolution undersampled grid diffusion MRI

Example files:

sub-M*_acq-HighRes+UndersampledGrid_dwi.nii.gz
sub-M*_acq-HighRes+UndersampledGrid_dwi.bvals
sub-M*_acq-HighRes+UndersampledGrid_dwi.bvecs

These files provide an additional high-resolution diffusion sampling scheme.

Multi-dimensional diffusion MRI

Example files:

sub-M*_acq-MultiDim_run-*_dwi.nii.gz
sub-M*_acq-MultiDim_run-*_dwi.bval
sub-M*_acq-MultiDim_run-*_dwi.bvec

The multidimensional diffusion data include multiple runs and support modeling across diffusion weighting and acquisition dimensions.

Derived Maps

The repository includes several diffusion-derived scalar and vector maps.

Examples include:

desc-MultiTE+diravg.nii.gz
desc-SingleTE+diravg.nii.gz
desc-MultiTE+Rsoma.nii.gz
desc-MultiTE+fsoma.nii.gz
desc-MultiTE+T2soma.nii.gz
desc-MultiTE+fneurite.nii.gz
desc-MultiTE+T2neurite.nii.gz

These files may support microstructural modeling of soma- and neurite-related signal components.

Tractography / CSD Products

The dataset also includes constrained spherical deconvolution (CSD) and tractography-related derivatives.

Example files:

desc-CSD+fodf+l0.nii.gz
desc-CSD+dec+scalar.nii.gz
desc-CSD+dec+univec.nii.gz
desc-CSD+tdi+scalar.nii.gz
desc-CSD+tdi+univec.nii.gz

These derivatives can support:

  • Fiber orientation analysis
  • Tract density imaging
  • Tractography visualization
  • White matter pathway reconstruction

Suggested Applications

This dataset is useful for:

  • Macaque tractography benchmarking
  • Microstructure model development
  • High-resolution diffusion MRI reconstruction
  • Cortico-subcortical pathway mapping
  • Internal capsule and hyperdirect pathway analysis
  • Cross-scale connectomics research
  • Fiber Data Hub tractography visualization and distribution

Data Access

The dataset can be accessed from DANDI:

https://dandiarchive.org/dandiset/001372

Using the DANDI CLI:

dandi download DANDI:001372/draft

Because this Dandiset is currently in draft status, users should check the DANDI page for updates or future published versions before citing or redistributing derived products.

License

This dataset is released under:

Creative Commons Attribution 4.0 International (CC BY 4.0)

License text:

https://creativecommons.org/licenses/by/4.0/

This license allows sharing and adaptation of the data, including redistribution and derived works, provided that appropriate attribution is given.

Funding

This dataset is associated with the NIH BRAIN Initiative Connectivity Across Scales program.

  • Funding source: National Institutes of Health (NIH)
  • Award number: 1 UM1 NS132358

Citation

Please cite the DANDI dataset when using this repository:

NIH BRAIN CONNECTS - LINC UM1 - Macaque Data.
DANDI Archive.
https://dandiarchive.org/dandiset/001372

Please also cite any related publications or preprints listed on the DANDI page, including the LINC diffusion MRI work associated with this dataset.

Contact

DANDI contact:

Kabilar Gunalan

Listed contributors include members of the LINC / BRAIN CONNECTS team, including Ting Gong, Chiara Maffei, Kabilar Gunalan, Satrajit Ghosh, Suzanne Haber, and Anastasia Yendiki.

Assets 89

IronTract Challenge – Public dMRI Data

IronTract Challenge – Public dMRI Data

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@data-hcp data-hcp released this 29 Jun 20:37
001289
6feecc4
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Verified
Learn about vigilant mode.

This dataset provides public diffusion MRI data from the IronTract Challenge, a community challenge designed to evaluate methods for reconstructing white matter pathways from diffusion MRI tractography.

The challenge uses macaque brain data with tracer-based anatomical reference information, enabling quantitative assessment of tractography accuracy. The dataset is intended as a validation and benchmarking resource for diffusion MRI reconstruction, tractography, and connectome analysis methods.

  • Source: https://dandiarchive.org/dandiset/001289
  • DANDI ID: 001289
  • Status: Draft
  • Data standard: Brain Imaging Data Structure (BIDS)
  • Access: Open access
  • License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Dataset Overview

  • Dataset name: IronTract Challenge - Public dMRI data
  • Subjects: 2
  • Samples: 1
  • File count: 17
  • Dataset size: ~991.6 MB
  • Primary modality: Diffusion MRI
  • Additional data: Tracer injection masks registered to diffusion MRI space

Background

The IronTract Challenge was developed to study how different diffusion MRI analysis methods recover known anatomical pathways.

The challenge is based on macaque brains with:

  • High-resolution ex vivo diffusion MRI
  • Multi-shell diffusion acquisitions
  • Cartesian-grid diffusion spectrum imaging (DSI)
  • Tracer injection information used as anatomical reference data

This design allows users to compare tractography results against independent anatomical tracing information.

Data Contents

The current DANDI release includes diffusion MRI derivatives and associated gradient information for two subjects:

  • sub-MR243
  • sub-MR256

Diffusion MRI files

For each subject, the dataset includes:

  • Multi-shell diffusion MRI data
  • DSI diffusion MRI data
  • Corresponding bvals
  • Corresponding bvecs

Example file types:

*_acq-MulShell_desc-DWImasked.nii.gz
*_acq-MulShell.bvals
*_acq-MulShell.bvecs

*_acq-DSI_desc-DWImasked.nii.gz
*_acq-DSI.bvals
*_acq-DSI.bvecs

Tracer injection masks

The dataset also includes tracer injection masks in diffusion MRI space:

derivatives/micr/sub-MR243/*_stain-FS_space-dwi_desc-injection.nii.gz
derivatives/micr/sub-MR256/*_stain-LY_space-dwi_desc-injection.nii.gz

These files provide anatomical reference information for tractography validation.

Suggested Uses

This dataset is useful for:

  • Validating tractography methods
  • Comparing multi-shell and DSI reconstruction strategies
  • Testing diffusion MRI preprocessing pipelines
  • Evaluating false positive and false negative tractography results
  • Developing post-processing methods for streamline filtering
  • Benchmarking connectome reconstruction workflows

Data Access

The dataset can be accessed from DANDI:

https://dandiarchive.org/dandiset/001289

Using the DANDI CLI:

dandi download DANDI:001289/draft

License

This dataset is released under:

Creative Commons Attribution 4.0 International (CC BY 4.0)

This license permits sharing, reuse, and adaptation, including commercial use, provided that appropriate attribution is given.

License text:

https://creativecommons.org/licenses/by/4.0/

Citation

Please cite the original IronTract Challenge publication when using this dataset:

Maffei C. et al.
Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI.
NeuroImage 257:119327, 2022.
https://doi.org/10.1016/j.neuroimage.2022.119327

Please also cite the DANDI source:

IronTract Challenge - Public dMRI data.
DANDI Archive.
https://dandiarchive.org/dandiset/001289

Contact

DANDI contact listed for this dataset:

  • Kabilar Gunalan

Contributor listed on DANDI:

  • Anastasia Yendiki

Notes

This Dandiset is currently listed as a draft release on DANDI. Users should check the DANDI page for updates, publication status, and any future versioned releases before citing or redistributing derived data.

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