Single participant rawdata and derivatives link to the code: https://github.com/CarolineLndl/Landelle_spinebrain_aging

Data contains:

• rawdata: sub-A006/
• preprocessed data: derivatives/preprocessing
• denoised fmri data: derivatives/denoising
  https://openneuro.org/datasets/ds007313

This repository contains in vivo diffusion MRI data acquired on the Connectome 2.0 scanner (Siemens MAGNETOM Connectom.X, Erlangen, Germany) at the Athinoula A. Martinos Center for Biomedical Imaging. The Connectome 2.0 system features ultra high gradient strength (up to 500 mT/m) and advanced slew rates (600 mT/m/s) for high fidelity microstructural imaging.

All diffusion data were acquired with:

• Pulse width: 17 ms
• Diffusion times (D): 22 ms
• b values = 4, 7.4, 11.8, 17.3, 23.7, 31.3, 39.8, 49.4, 60
  • At D = 30 ms, b values = 200, 950, 2300, 4250, 6750, 9850, 13500, 17800 s/mm2
• Number of directions: 32 for b < 10000 s/mm2; 64 for b > 10000 s/mm2
• TR / TE = 500 / 64 ms
• Voxel size: 0.8 mm isotropic

The dataset includes:

• Raw diffusion weighted images (DWIs) and corresponding b=0 images
• Metadata (b values and b vectors)
  *GRE field map with the same FOV as DWIs

https://openneuro.org/datasets/ds006187

https://openneuro.org/datasets/ds006187

https://openneuro.org/datasets/ds006187

Consciousness is comprised of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that modulate awareness in the human brain, but knowledge about the subcortical networks that sustain arousal is lacking. We integrated data from ex vivo diffusion MRI, immunohistochemistry, and in vivo 7 Tesla functional MRI to map the connectivity of a subcortical arousal network that we postulate sustains wakefulness in the resting, conscious human brain, analogous to the cortical default mode network (DMN) that is believed to sustain self-awareness. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain by correlating ex vivo diffusion MRI with immunohistochemistry in three human brain specimens from neurologically normal individuals scanned at 600-750 µm resolution. We performed deterministic and probabilistic tractography analyses of the diffusion MRI data to map dAAN intra-network connections and dAAN-DMN internetwork connections. Using a newly developed network-based autopsy of the human brain that integrates ex vivo MRI and histopathology, we identified projection, association, and commissural pathways linking dAAN nodes with one another and with cortical DMN nodes, providing a structural architecture for the integration of arousal and awareness in human consciousness. We release the ex vivo diffusion MRI data, corresponding immunohistochemistry data, network-based autopsy methods, and a new brainstem dAAN atlas to support efforts to map the connectivity of human consciousness.
https://openneuro.org/datasets/ds004640

Here, we present an extension to our previously published structural ultrahigh resolution T1-weighted magnetic resonance imaging (MRI) dataset with an isotropic resolution of 250 µm (https://www.nature.com/articles/sdata201732), consisting of multiple additional ultrahigh resolution contrasts. Included are up to 150 µm Time-of-Flight angiography, an updated 250 µm structural T1-weighted reconstruc-tion, 330 µm quantitative susceptibility mapping, up to 450 µm structural T2-weighted imag-ing, 700 µm T1-weighted back-to-back scans, 800 µm diffusion tensor imaging, one hour continuous resting-state functional MRI with an isotropic spatial resolution of 1.8 mm as well as more than 120 other structural T1-weighted volumes together with multiple corresponding proton density weighted acquisitions collected over ten years. All data are from the same participant and were acquired on the same 7 T scanner.
The repository contains the unprocessed data as well as (pre-)processing results. The data were acquired in multiple studies with individual goals. This is a unique and comprehensive collection comprising a “human phantom” dataset. Therefore, we compiled, processed, and structured the data, making them publicly available for further investigation.
https://openneuro.org/datasets/ds003563

Spinal Cord MRI Public Database

Context

For many years, spinal cord MRI has been a nightmare for most neuro-imagers, mostly due to the technical difficulties related to the requirement for high resolution and the presence of motion and susceptibility artifacts. Thankfully though, researchers have been developing methods to overcome these challenges, including more sensitive coil arrays and advanced pulse sequences to mitigate motion and susceptibility artifacts, allowing researchers to acquire high quality spinal cord data with strong potential for becoming relevant biomarkers. The downside of these improvements however, is that it can be overwhelming for researchers with insufficient expertise in MR physics to choose the sequence and parameters adapted to their needs. To address this issue, an initiative has recently been launched by Dr. Cohen-Adad at the 4th Spinal Cord MRI workshop, whose goal was to bring together all spinal cord imaging experts and come up with a consensus acquisition protocol. What remains to be done is finalize this protocol and evaluate its reproducibility across different vendors to provide researchers with normative values and variability indices, allowing them to conduct power analyses before engaging into SC studies.

Goal

The main objectives of this proposal are:

1. Optimize the current state-of-the art protocol,
2. Quantify the intra/inter-site and intra/inter-vendor variability of relevant qMRI metrics across the three main vendors (GE, Philips, Siemens) and
3. Disseminate these protocols/SOPs and publish a white paper.

Protocol

The recommended protocol uses product sequences. However, some old software might not have all up-to-date product sequences, and there could exist research sequences which are equivalent. When applicable, this information will be mentioned in the present document. To harmonize the naming of sequences we followed the BIDS recommendation, i.e.:

• T1w: IR-FSPGR/BRAVO (GE), 3D TFE (Philips), MPRAGE (Siemens)
• T2w: CUBE (GE), VISTA (Philips), SPACE (Siemens)
• DWI: FOCUS (GE), Zoom Diffusion (Philips), ep2d_diff ZOOMit (Siemens)
• GRE-MT1: SPGR (GE), FFE (Philips), GRE (Siemens)
• GRE-MT0: SPGR (GE), FFE (Philips), GRE (Siemens)
• GRE-T1w: SPGR (GE), FFE (Philips), GRE (Siemens)
• GRE-ME: MERGE (GE), mFFE (Philips), GRE (Siemens)

N.B. All importable files are already available for the three vendors (not just the pdf), and if cross-compatibility is broken between models, each file should be there (e.g., VB: .edx vs. VD/VE: .exar for Siemens). Parameters should not be manually copied, to avoid human mistakes. If you cannot import the protocol from the already-available files, please let me know.

Data

Two open access multi-site datasets acquired with the proposed protocol are available:

• Single-subject, male, healthy, 38 y.o., scanned at multiple sites. link
• Multi-subjects, 20-40 y.o., balanced male/female. Subjects are different across sites. link

Ethical consideration and licensing

Each contributor has any necessary ethics / permissions to share the data publicly.
The dataset does not include any identifiable personal health information (including names, zip codes, dates of birth, acquisition dates, facial features on structural scans etc.).
Each contributor agrees that the dataset will become publicly available under an MIT license.

Would you like to contribute?

If you would like to participate in this repository database, please contact Julien Cohen-Adad.

Processing scripts

We provide a processing pipeline to analyze the dataset. For more information, please go to the Github page of the project.
https://openneuro.org/datasets/ds002393

3AM reproducibility phantoms

This dataset consists of a single scan of a set of four straight 3AM phantoms produced with identical (nominal) print parameters. The intention is to use this data to assess the consistency of dMRI model parameters across the different phantoms.

https://openneuro.org/datasets/ds002374

This is the diffusion MRI data acquired with and without deliberate head movements and used to investigate the correction of signal dropout as described in the following publication:

Koch A, Zhukov A, Stöcker T, Groeschel S, Schultz T. SHORE‐based detection and imputation of dropout in diffusion MRI. Magn Reson Med. 2019;00:1–13. https ://doi.org/10.1002/mrm.27893

The folder /sub-01/dwi contains diffusion-weighted MRI data with (run-2) and without (run-1) deliberate head movements, the corresponding bvec and bval files.
Additional b=0 scans acquired with reverse phase encoding (revpe) polarity are located in the folder /bidsignore/sub-01.

https://openneuro.org/datasets/ds002087

Spinal Cord MRI Public Database

Context

For many years, spinal cord MRI has been a nightmare for most neuro-imagers, mostly due to the technical difficulties related to the requirement for high resolution and the presence of motion and susceptibility artifacts. Thankfully though, researchers have been developing methods to overcome these challenges, including more sensitive coil arrays and advanced pulse sequences to mitigate motion and susceptibility artifacts, allowing researchers to acquire high quality spinal cord data with strong potential for becoming relevant biomarkers. The downside of these improvements however, is that it can be overwhelming for researchers with insufficient expertise in MR physics to choose the sequence and parameters adapted to their needs. To address this issue, an initiative has recently been launched by Dr. Cohen-Adad at the 4th Spinal Cord MRI workshop, whose goal was to bring together all spinal cord imaging experts and come up with a consensus acquisition protocol. What remains to be done is finalize this protocol and evaluate its reproducibility across different vendors to provide researchers with normative values and variability indices, allowing them to conduct power analyses before engaging into SC studies.

Goal

The main objectives of this proposal are:

1. Optimize the current state-of-the art protocol,
2. Quantify the intra/inter-site and intra/inter-vendor variability of relevant qMRI metrics across the three main vendors (GE, Philips, Siemens) and
3. Disseminate these protocols/SOPs and publish a white paper.

Protocol

The recommended protocol uses product sequences. However, some old software might not have all up-to-date product sequences, and there could exist research sequences which are equivalent. When applicable, this information will be mentioned in the present document. To harmonize the naming of sequences we followed the BIDS recommendation, i.e.:

• T1w: IR-FSPGR/BRAVO (GE), 3D TFE (Philips), MPRAGE (Siemens)
• T2w: CUBE (GE), VISTA (Philips), SPACE (Siemens)
• DWI: FOCUS (GE), Zoom Diffusion (Philips), ep2d_diff ZOOMit (Siemens)
• GRE-MT1: SPGR (GE), FFE (Philips), GRE (Siemens)
• GRE-MT0: SPGR (GE), FFE (Philips), GRE (Siemens)
• GRE-T1w: SPGR (GE), FFE (Philips), GRE (Siemens)
• GRE-ME: MERGE (GE), mFFE (Philips), GRE (Siemens)

N.B. All importable files are already available for the three vendors (not just the pdf), and if cross-compatibility is broken between models, each file should be there (e.g., VB: .edx vs. VD/VE: .exar for Siemens). Parameters should not be manually copied, to avoid human mistakes. If you cannot import the protocol from the already-available files, please let me know.

Data

Two open access multi-site datasets acquired with the proposed protocol are available:

• Single-subject, male, healthy, 38 y.o., scanned at multiple sites.
• Multi-subjects, 20-40 y.o., balanced male/female. Subjects are different across sites.

Ethical consideration and licensing

Each contributor has any necessary ethics / permissions to share the data publicly.
The dataset does not include any identifiable personal health information (including names, zip codes, dates of birth, acquisition dates, facial features on structural scans etc.).
Each contributor agrees that the dataset will become publicly available under an MIT license.

Would you like to contribute?

If you would like to participate in this repository database, please contact Julien Cohen-Adad.

Processing scripts

We provide a processing pipeline to analyze the dataset. For more information, please go to the Github page of the project.

Maintaining this repository

This section is for internal use and describes the procedure for creating new
releases of the database and making sure it is synced with the local server at
NeuroPoly Lab.

To push changes:

• Make sure local repository is synced with the latest version online.
• Modify/Add/Delete files in the local repository.
• Update file CHANGES
• Upload to openneuro
• At this stage, the status is "draft". Examine the log to assess that only the
  files that were supposed to change (or added) have indeed changed. Archive the log
  file.
• Once validated, create a snapshot.
• Update local repository to latest version online.

https://openneuro.org/datasets/ds001919