TheVirtualBrain Macaque MRI

uploaded by Kelly Shen on 2019-04-26 - over 2 years ago
last modified on 2019-06-05 - over 2 years ago
authored by Kelly Shen, Joseph S. Gati, Ravi S. Menon, Stefan Everling, Anthony R. McIntosh
825111
We found 2 Warnings in your dataset. You are not required to fix warnings, but doing so will make your dataset more BIDS compliant.

/sub-01/anat/sub-01_T1w.nii.gz

The most common set of dimensions is: 200,128,256 (voxels), This file has the dimensions: 202,128,256 (voxels).

/sub-01/dwi/sub-01_run-01_dwi.nii.gz

The most common set of dimensions is: 104,104,46,65 (voxels), This file has the dimensions: 128,128,46,65 (voxels). The most common resolution is: 1.00mm x 1.00mm x 1.10mm x 7.50s, This file has the resolution: 1.00mm x 1.00mm x 1.10mm x 6.00s.

/sub-01/dwi/sub-01_run-02_dwi.nii.gz

The most common set of dimensions is: 104,104,46,65 (voxels), This file has the dimensions: 128,128,46,65 (voxels). The most common resolution is: 1.00mm x 1.00mm x 1.10mm x 7.50s, This file has the resolution: 1.00mm x 1.00mm x 1.10mm x 6.00s.

/sub-02/anat/sub-02_T1w.nii.gz

The most common set of dimensions is: 200,128,256 (voxels), This file has the dimensions: 202,128,256 (voxels).

/sub-02/dwi/sub-02_run-01_dwi.nii.gz

The most common set of dimensions is: 104,104,46,65 (voxels), This file has the dimensions: 128,128,46,65 (voxels). The most common resolution is: 1.00mm x 1.00mm x 1.10mm x 7.50s, This file has the resolution: 1.00mm x 1.00mm x 1.10mm x 6.00s.

/sub-02/dwi/sub-02_run-02_dwi.nii.gz

The most common set of dimensions is: 104,104,46,65 (voxels), This file has the dimensions: 128,128,46,65 (voxels). The most common resolution is: 1.00mm x 1.00mm x 1.10mm x 7.50s, This file has the resolution: 1.00mm x 1.00mm x 1.10mm x 6.00s.

/sub-02/func/sub-02_task-rest_run-01_bold.nii.gz

The most common set of dimensions is: 96,96,42,600 (voxels), This file has the dimensions: 96,96,40,600 (voxels).

/sub-02/func/sub-02_task-rest_run-02_bold.nii.gz

The most common set of dimensions is: 96,96,42,600 (voxels), This file has the dimensions: 96,96,40,600 (voxels).

/sub-02/func/sub-02_task-rest_run-03_bold.nii.gz

The most common set of dimensions is: 96,96,42,600 (voxels), This file has the dimensions: 96,96,40,600 (voxels).

/sub-02/func/sub-02_task-rest_run-04_bold.nii.gz

The most common set of dimensions is: 96,96,42,600 (voxels), This file has the dimensions: 96,96,40,600 (voxels).

and 2 more files

/sub-02/func/sub-02_task-rest_run-01_bold.nii.gz

The number of elements in the SliceTiming array should match the 'k' dimension of the corresponding NIfTI volume.

SliceTiming array is of length 42 and the value of the 'k' dimension is 40 for the corresponding nifti header.

/sub-02/func/sub-02_task-rest_run-02_bold.nii.gz

The number of elements in the SliceTiming array should match the 'k' dimension of the corresponding NIfTI volume.

SliceTiming array is of length 42 and the value of the 'k' dimension is 40 for the corresponding nifti header.

/sub-02/func/sub-02_task-rest_run-03_bold.nii.gz

The number of elements in the SliceTiming array should match the 'k' dimension of the corresponding NIfTI volume.

SliceTiming array is of length 42 and the value of the 'k' dimension is 40 for the corresponding nifti header.

/sub-02/func/sub-02_task-rest_run-04_bold.nii.gz

The number of elements in the SliceTiming array should match the 'k' dimension of the corresponding NIfTI volume.

SliceTiming array is of length 42 and the value of the 'k' dimension is 40 for the corresponding nifti header.


OpenNeuro Accession Number: ds001875
Files: 130, Size: 10.75GB, Subjects: 9, Session: 1
Available Tasks: rest
Available Modalities: MRI

README

This dataset contains the raw MRI data used to build and validate the macaque connectome in TheVirtualBrain (thevirtualbrain.org), a software platform for large-scale network modelling. It includes T1, DWI and resting-state fMRI scans from 9 macaques. In addition to the raw data, we also provide preprocessed derivatives.

Other derivatives such as the macaque connectome, associated ROI parcellation and other data useful for large-scale modelling in TheVirtualBrain can be found at Zenodo.org (https://doi.org/10.5281/zenodo.1471588).

Details of this dataset can be found in Shen K, Bezgin G, Schirner M, Ritter P, Everling S, McIntosh AR (2018) A macaque connectome for large-scale network simulations in TheVirtualBrain. bioRxiv 480905, doi: https://doi.org/10.1101/480905, which should be cited when publishing work using this dataset. Additional details on fMRI acquisition and preprocessing can be found in Ghahremani M, Hutchison RM, Menon RS, Everling S (2017) Frontoparietal functional connectivity in the common marmoset. Cerebral Cortex 27 (8): 3890-3905. Additional details on DWI acquisition, preprocessing and tractography can be found in Shen K, Goulas A, Grayson DS, Eusebio J, Gati JS, Menon RS, McIntosh AR, Everling S (2019) Exploring the limits of network topology estimation using diffusion-based tractography and tracer studies in the macaque cortex. Neuroimage 191: 81-92.

Authors

  • Kelly Shen
  • Joseph S. Gati
  • Ravi S. Menon
  • Stefan Everling
  • Anthony R. McIntosh

Dataset DOI

10.18112/openneuro.ds001875.v1.0.3

License

CC0

Acknowledgements

How to Acknowledge

The following publication should be cited when using this dataset: Shen K, Bezgin G, Schirner M, Ritter P, Everling S, McIntosh AR (2018) A macaque connective for large-scale network simulations in TheVirtualBrain. bioRxiv 480905, doi: https://doi.org/10.1101/48905.

Funding

  • Canadian Institutes of Health Research (FRN 148365)
  • Canada First Research Excellence Fund (to BrainsCAN)
  • J.S. McDonnell Foundation (Collaborative Research Grant 220020255)

References and Links

  • Additional information on this dataset can be found in the following papers: 1) Ghahremani M, Hutchison RM, Menon RS, Everling S (2017) Frontoparietal functional connectivity in the common marmoset. Cerebral Cortex 27 (8): 3890-3905, and 2) Shen K, Goulas A, Grayson DS, Eusebio J, Gati JS, Menon RS, McIntosh AR, Everling S (2019) Exploring the limits of network topology estimation using diffusion-based tractography and tracer studies in the macaque cortex. Neuroimage 191: 81-92.

Ethics Approvals

How To Cite

Copy
Kelly Shen and Joseph S. Gati and Ravi S. Menon and Stefan Everling and Anthony R. McIntosh (2019). TheVirtualBrain Macaque MRI. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds001875.v1.0.3
More citation info

TheVirtualBrain Macaque MRI

uploaded by Kelly Shen on 2019-04-26 - over 2 years ago
last modified on 2019-06-05 - over 2 years ago
authored by Kelly Shen, Joseph S. Gati, Ravi S. Menon, Stefan Everling, Anthony R. McIntosh
825111

OpenNeuro Accession Number: ds001875
Files: 130, Size: 10.75GB, Subjects: 9, Session: 1
Available Tasks: rest
Available Modalities: MRI

README

This dataset contains the raw MRI data used to build and validate the macaque connectome in TheVirtualBrain (thevirtualbrain.org), a software platform for large-scale network modelling. It includes T1, DWI and resting-state fMRI scans from 9 macaques. In addition to the raw data, we also provide preprocessed derivatives.

Other derivatives such as the macaque connectome, associated ROI parcellation and other data useful for large-scale modelling in TheVirtualBrain can be found at Zenodo.org (https://doi.org/10.5281/zenodo.1471588).

Details of this dataset can be found in Shen K, Bezgin G, Schirner M, Ritter P, Everling S, McIntosh AR (2018) A macaque connectome for large-scale network simulations in TheVirtualBrain. bioRxiv 480905, doi: https://doi.org/10.1101/480905, which should be cited when publishing work using this dataset. Additional details on fMRI acquisition and preprocessing can be found in Ghahremani M, Hutchison RM, Menon RS, Everling S (2017) Frontoparietal functional connectivity in the common marmoset. Cerebral Cortex 27 (8): 3890-3905. Additional details on DWI acquisition, preprocessing and tractography can be found in Shen K, Goulas A, Grayson DS, Eusebio J, Gati JS, Menon RS, McIntosh AR, Everling S (2019) Exploring the limits of network topology estimation using diffusion-based tractography and tracer studies in the macaque cortex. Neuroimage 191: 81-92.

Authors

  • Kelly Shen
  • Joseph S. Gati
  • Ravi S. Menon
  • Stefan Everling
  • Anthony R. McIntosh

Dataset DOI

10.18112/openneuro.ds001875.v1.0.3

License

CC0

Acknowledgements

How to Acknowledge

The following publication should be cited when using this dataset: Shen K, Bezgin G, Schirner M, Ritter P, Everling S, McIntosh AR (2018) A macaque connective for large-scale network simulations in TheVirtualBrain. bioRxiv 480905, doi: https://doi.org/10.1101/48905.

Funding

  • Canadian Institutes of Health Research (FRN 148365)
  • Canada First Research Excellence Fund (to BrainsCAN)
  • J.S. McDonnell Foundation (Collaborative Research Grant 220020255)

References and Links

  • Additional information on this dataset can be found in the following papers: 1) Ghahremani M, Hutchison RM, Menon RS, Everling S (2017) Frontoparietal functional connectivity in the common marmoset. Cerebral Cortex 27 (8): 3890-3905, and 2) Shen K, Goulas A, Grayson DS, Eusebio J, Gati JS, Menon RS, McIntosh AR, Everling S (2019) Exploring the limits of network topology estimation using diffusion-based tractography and tracer studies in the macaque cortex. Neuroimage 191: 81-92.

Ethics Approvals

How To Cite

Copy
Kelly Shen and Joseph S. Gati and Ravi S. Menon and Stefan Everling and Anthony R. McIntosh (2019). TheVirtualBrain Macaque MRI. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds001875.v1.0.3
More citation info

Dataset File Tree

Git Hash: ee439c6 

BIDS Validation

We found 2 Warnings in your dataset. You are not required to fix warnings, but doing so will make your dataset more BIDS compliant.

/sub-01/anat/sub-01_T1w.nii.gz

The most common set of dimensions is: 200,128,256 (voxels), This file has the dimensions: 202,128,256 (voxels).

/sub-01/dwi/sub-01_run-01_dwi.nii.gz

The most common set of dimensions is: 104,104,46,65 (voxels), This file has the dimensions: 128,128,46,65 (voxels). The most common resolution is: 1.00mm x 1.00mm x 1.10mm x 7.50s, This file has the resolution: 1.00mm x 1.00mm x 1.10mm x 6.00s.

/sub-01/dwi/sub-01_run-02_dwi.nii.gz

The most common set of dimensions is: 104,104,46,65 (voxels), This file has the dimensions: 128,128,46,65 (voxels). The most common resolution is: 1.00mm x 1.00mm x 1.10mm x 7.50s, This file has the resolution: 1.00mm x 1.00mm x 1.10mm x 6.00s.

/sub-02/anat/sub-02_T1w.nii.gz

The most common set of dimensions is: 200,128,256 (voxels), This file has the dimensions: 202,128,256 (voxels).

/sub-02/dwi/sub-02_run-01_dwi.nii.gz

The most common set of dimensions is: 104,104,46,65 (voxels), This file has the dimensions: 128,128,46,65 (voxels). The most common resolution is: 1.00mm x 1.00mm x 1.10mm x 7.50s, This file has the resolution: 1.00mm x 1.00mm x 1.10mm x 6.00s.

/sub-02/dwi/sub-02_run-02_dwi.nii.gz

The most common set of dimensions is: 104,104,46,65 (voxels), This file has the dimensions: 128,128,46,65 (voxels). The most common resolution is: 1.00mm x 1.00mm x 1.10mm x 7.50s, This file has the resolution: 1.00mm x 1.00mm x 1.10mm x 6.00s.

/sub-02/func/sub-02_task-rest_run-01_bold.nii.gz

The most common set of dimensions is: 96,96,42,600 (voxels), This file has the dimensions: 96,96,40,600 (voxels).

/sub-02/func/sub-02_task-rest_run-02_bold.nii.gz

The most common set of dimensions is: 96,96,42,600 (voxels), This file has the dimensions: 96,96,40,600 (voxels).

/sub-02/func/sub-02_task-rest_run-03_bold.nii.gz

The most common set of dimensions is: 96,96,42,600 (voxels), This file has the dimensions: 96,96,40,600 (voxels).

/sub-02/func/sub-02_task-rest_run-04_bold.nii.gz

The most common set of dimensions is: 96,96,42,600 (voxels), This file has the dimensions: 96,96,40,600 (voxels).

and 2 more files

/sub-02/func/sub-02_task-rest_run-01_bold.nii.gz

The number of elements in the SliceTiming array should match the 'k' dimension of the corresponding NIfTI volume.

SliceTiming array is of length 42 and the value of the 'k' dimension is 40 for the corresponding nifti header.

/sub-02/func/sub-02_task-rest_run-02_bold.nii.gz

The number of elements in the SliceTiming array should match the 'k' dimension of the corresponding NIfTI volume.

SliceTiming array is of length 42 and the value of the 'k' dimension is 40 for the corresponding nifti header.

/sub-02/func/sub-02_task-rest_run-03_bold.nii.gz

The number of elements in the SliceTiming array should match the 'k' dimension of the corresponding NIfTI volume.

SliceTiming array is of length 42 and the value of the 'k' dimension is 40 for the corresponding nifti header.

/sub-02/func/sub-02_task-rest_run-04_bold.nii.gz

The number of elements in the SliceTiming array should match the 'k' dimension of the corresponding NIfTI volume.

SliceTiming array is of length 42 and the value of the 'k' dimension is 40 for the corresponding nifti header.

Dataset File Tree

Git Hash: ee439c6 

Comments

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By ben.fulcher@sydney.edu.au - over 2 years ago
Typo in DOI to bioRxiv paper, should be: https://doi.org/10.1101/480905
By kshen@research.baycrest.org - over 2 years ago
Thanks Ben -- this has been corrected now.