Releases: data-others/brain2
Ex vivo Mesoscale Human Temporal Lobe Dataset
This dataset provides mesoscale-resolution diffusion MRI and tractography data of the human temporal lobe, focusing on the hippocampus and fimbria connectivity at an unprecedented level of anatomical detail.
The high-resolution acquisition enables visualization of intra-hippocampal lamellae, interlamellar and translamellar fibers, and connectivity patterns between hippocampal subfields and major white matter bundles.
License
Public domain (Dryad)
Citation
Modo, M. (2025). Ex vivo Mesoscale human temporal lobe dataset [Data set]. Dryad.
https://doi.org/10.5061/dryad.jh9w0vtnq
Primary article: NeuroImage — https://doi.org/10.1016/j.neuroimage.2025.121125
Source
https://doi.org/10.5061/dryad.jh9w0vtnq
Publisher: Dryad (Published Mar 25, 2025)
Funding: National Institute of Neurological Disorders and Stroke (R21NS088167)
Dataset Overview
| Category | Details |
|---|---|
| Specimen | Human temporal lobe (ex vivo) |
| Resolution | 250 µm isotropic (acquired), upsampled to 125 µm |
| Sequence | Diffusion MRI (GQI-based reconstruction) |
| Main Regions | Hippocampus, fimbria, amygdala, temporal pole |
| Substructures | Subregions (head, body, tail) and subfields (CA1–3, dentate gyrus) |
| Identified Pathways | 50+ pathways between hippocampal head and body; intra-lamellar and translamellar fibers |
| Special Findings | 12 lamellae defined by morphology and fiber connectivity; topographic segregation within the fimbria |
| Total Data Size | ~15.1 GB |
Methods Summary
- Acquisition: Ex vivo mesoscale diffusion MRI at 250 µm isotropic resolution (upsampled to 125 µm).
- Reconstruction: Generalized Q-Sampling Imaging (GQI).
- Tractography: Dissection of intra-hippocampal and fimbria pathways using DSI Studio.
- ROI Atlases: Multiple region-of-interest (ROI) sets are included for fimbria, hippocampal subfields, subregions, and the full hippocampus.
- Connectivity Analysis: 50 dissection-defined pathways mapped; analysis revealed translamellar and interlamellar connections across CA and DG lamellae.
Files Included
MRI_GQI_Files.zip— Raw and reconstructed mesoscale diffusion MRI dataROIs_Hippocampus.zip,ROIs_HC_subfields.zip,ROIs_HC_regional_subfields.zip,ROIs_Fimbria.zip— ROI templatesREADME.md— Methods and usage details
Research Context
This dataset provides the first mesoscale mapping of the human hippocampal connectome, revealing the fine-scale fiber organization between hippocampal subfields and white matter tracts.
It contributes to understanding:
- Structural substrates of memory and emotion circuits
- Detailed anterior–posterior hippocampal connectivity
- Anatomical basis for neurological and psychiatric interventions
Keywords
Hippocampus • Diffusion MRI • Tractography • Connectome • Fimbria • Mesoscale • Subfields • Ex vivo • Temporal lobe • Neuroanatomy
Brain and Spinal Cord Multi-Shell Diffusion MRI Dataset
This dataset provides high-quality structural and diffusion-weighted MRI data of both the brain and cervical spinal cord from 11 healthy participants, with test–retest sessions in 6 of them.
It is organized in compliance with the Brain Imaging Data Structure (BIDS v1.9.0) and supports integrated analyses of the brain and spinal cord within the same imaging framework.
The data include multi-shell diffusion MRI (dMRI), structural (T1w, T2w, FLAIR), and spinal cord–specific contrasts (T2w, T2* mFFE), alongside a rich set of derivatives such as model fits (DTI, NODDI, SMT, SANDI), tissue and spinal cord segmentations, template registrations, and FreeSurfer reconstructions.
The dataset serves as a comprehensive multimodal resource for studying white matter microstructure, neuroanatomy, and cross-region connectivity throughout the central nervous system.
License
Creative Commons Attribution 4.0 International (CC BY 4.0)
Citation
Schilling, K. (2025).
Brain and Spinal Cord Multi-Shell Diffusion MRI Dataset [Data set].
In Characterization of neurite and soma organization in the brain and spinal cord with diffusion MRI (v1.0). Zenodo.
https://doi.org/10.5281/zenodo.15512428
Source
https://doi.org/10.5281/zenodo.15512428
Contact: [email protected]
Institution: Vanderbilt University Medical Center
Project title: Characterization of neurite and soma organization in the brain and spinal cord with diffusion MRI
Funding: Supported by multiple NIH grants (K01EB030039, R03TR004434, K01EB032898, R01NS109114, R01NS117816, R01NS104149, R01EB017230)
Dataset Information
| Category | Details |
|---|---|
| Subjects | 11 healthy volunteers (6 with test–retest) |
| Study Type | Multi-shell diffusion MRI of brain and cervical spinal cord |
| Organization | BIDS v1.9.0 (brain: acq-brain; cord: acq-cord) |
| Imaging Sites | Brain and cervical spinal cord |
| Data Modalities | T1w, T2w, FLAIR, multi-shell DWI, mFFE (T2*) |
| Reverse PE Scans | Included for both brain and spinal cord |
| Spinal Cord DWI Parts | Provided as part-1, part-2, and part-3 |
| Derived Data | Preprocessed DWI, model fits (NODDI, SMT, SANDI, DTI, DKI), tissue/cord segmentations, FreeSurfer outputs |
| File Structure | /rawdata and /derivatives directories |
| Data Standard | Fully BIDS-compliant |
Purpose
This dataset aims to support the development, validation, and benchmarking of imaging and analysis tools that unify brain and spinal cord studies.
It is especially useful for research on neurite and soma density modeling, multi-compartment diffusion imaging, and comparative neuroanatomy across central nervous system regions.
By including both structural and diffusion data for the brain and cord in the same cohort, this dataset enables cross-modality, cross-region, and test–retest investigations of tissue microstructure and connectivity.
File Information
| File | Description | Size | Checksum |
|---|---|---|---|
| Brain_Cord_MultiShell.zip | Raw and derivative MRI data for all 11 subjects, organized in BIDS format | 34.4 GB | md5:00589b8cbf1ed17f4f6e84a62dbf886f |
Keywords
Diffusion MRI • Multi-shell • Brain • Spinal Cord • SANDI • SMT • NODDI • Microstructure • BIDS • White Matter • Test–Retest
EDEN2020 Human Brain MRI Datasets for Healthy Volunteers
This dataset provides high-resolution multimodal MRI data from 15 healthy adult volunteers acquired on a 3T scanner at the Neuroradiology Unit and CERMAC (Center of Excellence for High Field Magnetic Resonance), Vita-Salute San Raffaele University, and IRCCS San Raffaele Scientific Institute in Milan, Italy.
The dataset was collected as part of the EDEN2020 (Enhanced Delivery Ecosystem for Neurosurgery on 2020) project, funded by the European Commission (Grant 688279), to advance imaging-guided neurosurgical research.
Each subject’s dataset includes structural, angiographic, and diffusion MRI data for morphological, vascular, and white matter microstructure analysis.
The collection supports research in HARDI, DTI, NODDI, and probabilistic tractography, with derived maps and reconstructions provided in NIfTI-1 format.
License
Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Citation
Castellano, A., Pieri, V., Galvan, S., Rodriguez y Baena, F., & Falini, A. (2019).
EDEN2020 Human Brain MRI Datasets for Healthy Volunteers (1.0) [Data set]. Zenodo.
https://doi.org/10.5281/zenodo.3994749
Source
https://doi.org/10.5281/zenodo.3994749
Contact: [email protected]
Institution: Vita-Salute San Raffaele University / IRCCS San Raffaele Scientific Institute
Project: EDEN2020 — Enhanced Delivery Ecosystem for Neurosurgery on 2020
Funding: European Commission, Horizon 2020 (Grant No. 688279)
Dataset Information
| Category | Details |
|---|---|
| Subjects | 15 healthy adult volunteers |
| Scanner | 3T MRI scanner (CERMAC, Milan, Italy) |
| Sequences Included | T1_3D_PROSET_Sag, 3D_FLAIR_Tra, SWIp_axial, s3DI_MC_HR, MIP_s3DI_MC_HR, raw_data_DTI_32, raw_data_NODDI, B0_reverse |
| Derived Data | DTI maps, HARDI maps, NODDI maps, probabilistic tractography |
| File Format | NIfTI-1 (.nii, .nii.gz) |
| Data Conversion | DICOM converted using dcm2niix v1.0.20200331 |
| Institutions | Vita-Salute San Raffaele University, IRCCS San Raffaele Scientific Institute |
| Location | Milan, Italy |
Purpose
The EDEN2020 Human Brain MRI Dataset was created to establish a multimodal imaging reference for neurosurgical navigation and tractography validation.
By integrating high-resolution anatomical, angiographic, and diffusion imaging, the dataset enables quantitative modeling of gray–white matter boundaries, vascular structures, and fiber connectivity.
It provides essential test data for developing and benchmarking tools in neuro-navigation, white matter modeling, and advanced diffusion reconstruction.
File Information
| File | Description | Size | Checksum |
|---|---|---|---|
| CTRL_0866.zip | MRI data of subject 0866 | 491.5 MB | md5:df3d0c1861c71fb7830044e2170e4f91 |
| CTRL_0933.zip | MRI data of subject 0933 | 431.7 MB | md5:7aa43fd122fa8fd3a216b01ceca05e24 |
| CTRL_1277.zip | MRI data of subject 1277 | 480.5 MB | md5:82a3858df7ff6dbafa34f053b515983c |
| CTRL_1686.zip | MRI data of subject 1686 | 446.3 MB | md5:6b6d8e21596ec1286e4f6dbe2f6f823f |
| CTRL_2020.zip | MRI data of subject 2020 | 451.1 MB | md5:eced02b3490f1aa88013d1a306a0bb33 |
| CTRL_2476.zip | MRI data of subject 2476 | 440.5 MB | md5:4d5c95bb775e436beceb0a096e4c608f |
| CTRL_3352.zip | MRI data of subject 3352 | 456.0 MB | md5:f160d356f3a10247bd6976105ca6ebd4 |
| CTRL_4871.zip | MRI data of subject 4871 | 509.1 MB | md5:47bf407fc8eff932b320f4925c999ccc |
| CTRL_5069.zip | MRI data of subject 5069 | 545.4 MB | md5:e49e6e22bc06f80cb28ac547736dbf84 |
| CTRL_5361.zip | MRI data of subject 5361 | 404.6 MB | md5:53c7e8690d1536c2e1f2cd98608dac2a |
| CTRL_5900.zip | MRI data of subject 5900 | 436.2 MB | md5:8f16a811a387159862b596bd1304c09c |
| CTRL_5960.zip | MRI data of subject 5960 | 436.6 MB | md5:de6226940f7ce947cdda06c400990e22 |
| CTRL_6685.zip | MRI data of subject 6685 | 443.7 MB | md5:379dc0a1cf20c57a3f2b502b7abfede8 |
| CTRL_8275.zip | MRI data of subject 8275 | 481.1 MB | md5:84f06997887050241175b3a5489f9f77 |
| CTRL_9647.zip | MRI data of subject 9647 | 467.7 MB | md5:b0956d2f7cf93528e04d7901f31eeb55 |
| Readme.md | Original dataset documentation | 4.6 KB | md5:7681547ae40a72b7d43301dd31c6a371 |
Keywords
MRI • HARDI • DTI • NODDI • Tractography • Brain • Neurosurgery • White Matter • High-Resolution MRI • EDEN2020
Structural and Functional Basis of Neuronal Plasticity — Healthy Control (HC) Group
License
Creative Commons Attribution 4.0 International (CC BY 4.0)
Source
https://zenodo.org/records/15149088
Contact: [email protected]
Data from Healthy Controls (HC) group
Overview
This dataset includes imaging data from the Healthy Control (HC) group of the project “Structural and Functional Basis of Neuronal Plasticity.”
It was designed to investigate cerebral blood flow (CBF) and metabolic responses to motor learning using multimodal MRI techniques.
We developed an MRI protocol combining Diffusion-Weighted Imaging (DWI) and pseudo-continuous arterial spin labeling (pCASL) with pre-saturation, background suppression, and a dual-excitation (DEXI) readout.
This protocol enables mapping of CBF and absolute CMRO₂ during resting states and after motor task learning within the MRI scanner.
Dataset Information
| Category | Details |
|---|---|
| Subjects | 40 healthy controls |
| Scanner | Siemens Prisma 3T (Siemens Healthineers, Forchheim, Germany) |
| Head Coil | 32-channel receive-only |
| Anatomical Scan | T1-weighted MP2RAGE |
| Functional / Perfusion Scans | 3 DEXI pCASL acquisitions |
| Diffusion Scans | 2 DWI acquisitions |
| Metabolic Scans | TRUST and IR-EPI acquisitions for brain metabolic metrics |
Task Description
Participants performed a visuomotor matching task inside the MRI scanner:
- A white circle expanded and contracted on a black screen.
- Participants controlled a green circle using an MRI-compatible data glove (Fifth Dimension Technologies, Pretoria, South Africa) to match its size to the white circle.
- The task included 15 blocks (7 fixed-sequence, 8 random), each lasting 35.2 s, followed by 26.4 s of rest.
- Stimuli were presented using PsychoPy.
Additional Collected Data
Neuropsychological assessments, end-tidal CO₂ and O₂ traces, and task performance metrics were also recorded.
These datasets are currently being converted and analyzed and are not yet included in this release.
Known Missing or Partial Data
- sub-001–010: No SWI images (SWI introduced starting from sub-011)
- sub-011, sub-013: Fewer DWI directions in second run due to scanner issue
- sub-020: No SWI images
- sub-025: Presence of an arachnoid cyst
- sub-027: Did not complete protocol
- sub-030, sub-032, sub-033, sub-039: Dropouts
In Vivo Human Whole-Brain Connectom Diffusion MRI Dataset at 760 µm Isotropic Resolution
This dataset provides a whole-brain in vivo diffusion MRI (dMRI) acquisition at 760 µm isotropic resolution, sampled at 1,260 q-space points across nine 2-hour sessions on a single healthy adult subject.
It demonstrates the limits of ultra-high-resolution diffusion imaging achievable with advanced Connectom-class hardware, optimized acquisition protocols, and reconstruction pipelines.
The dataset enables exploration of fine-scale in vivo brain structures, fiber microarchitecture, and serves as a benchmark for developing high-resolution modeling, sub-sampling, denoising, and reconstruction algorithms.
License
Creative Commons Zero v1.0 Universal (CC0-1.0) — public domain dedication
Citation
Wang, F., Dong, Z., Tian, Q., Liao, C., Fan, Q., Hoge, W. S., Keil, B., Polimeni, J. R., Wald, L. L., Huang, S. Y., & Setsompop, K. (2021).
In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution) [Data set]. Dryad.
https://doi.org/10.5061/dryad.rjdfn2z8g
https://datadryad.org/dataset/doi:10.5061/dryad.nzs7h44q2
Source
https://doi.org/10.5061/dryad.rjdfn2z8g
Contact: [email protected]
Institutions: Massachusetts General Hospital (MGH) & Harvard Medical School
Supported by: NIH BRAIN Initiative & MGH A.A. Martinos Center for Biomedical Imaging
Related work: 10.5061/dryad.nzs7h44q2
Dataset Information
| Category | Details |
|---|---|
| Subject | Single healthy adult volunteer |
| Scanner | 3 T Connectom MRI system (300 mT/m gradient strength) |
| Coil | Custom-built 64-channel phased-array head coil |
| Resolution | 0.76 mm × 0.76 mm × 0.76 mm isotropic |
| Sessions | 9 sessions × 2 hours = 18 hours total acquisition |
| q-space Sampling | 1,260 directions total |
| Sequence | SNR-efficient diffusion-weighted spin-echo EPI |
| Reconstruction | Parallel imaging with ghost-artifact reduction and SENSE acceleration |
| Stabilization | Personalized motion-robust head stabilizer |
| Additional Data | Submillimeter T1w and T2w anatomical MRI, field maps, and preprocessing code |
| Data Format | NIfTI (.nii.gz) and supporting metadata (.bval, .bvec, JSON) |
Experimental Setup
This dataset was acquired using state-of-the-art Connectom hardware and optimized dMRI sequences to push spatial resolution while maintaining high SNR.
Key elements:
- High gradient strength (300 mT/m) allowing short diffusion times and strong diffusion weighting.
- Custom 64-channel phased-array coil for increased parallel imaging acceleration and sensitivity.
- Advanced ghost reduction algorithms during reconstruction.
- SNR-efficient acquisition design minimizing echo-train blurring.
- Nine separate scanning sessions ensuring full q-space coverage while minimizing motion contamination.
Preprocessing and Code
The dataset includes accompanying preprocessing scripts and documentation for:
- Denoising
- Eddy-current and motion correction
- Gradient-nonlinearity correction
- Signal normalization and registration to T1/T2 anatomy
All reconstruction and preprocessing steps are described in detail within the included README and supplementary code repository.
Purpose
This dataset represents one of the highest-resolution in vivo human dMRI datasets publicly available.
It is intended to support:
- Development and benchmarking of microstructural modeling and tractography algorithms
- Validation of denoising and sub-sampling frameworks
- Studies of fine-scale cortical and subcortical connectivity
Keywords
Diffusion MRI • High Resolution • Connectom Scanner • 760 µm • Human Brain • Microstructure • q-space Sampling • Parallel Imaging • Ghost Reduction • Tractography • Martinos Center • BRAIN Initiative
Imaging Chinese Young Brains (I See Your Brain)
This dataset is a part of the CHIMGEN study, which is the largest prospective neuroimaging genetic cohort for Chinese Han adults with lifespan natural and socioeconomic measurements obtained from remote sensing. All participants were recruited by advertisements posted in colleges and communities. Participants were excluded if they met any of the following criteria: regular smoker, pregnancy, abnormal color discrimination, a history of alcohol or drug abuse, currently any medication, MRI contraindications, neuropsychiatric or severe somatic disorder and sedative-hypnotic medication within a month or any medication for major neuropsychiatric disorders.
This dataset contained multimodal neuroimaging data from 215 right-handed Chinese healthy volunteers (156 females; 18-30 years old, mean = 22.55, SD = 2.68). The brain images were acquired by a 3.0-Tesla scanner (GE MR 750) at the MRI Research Center in the Institute of Psychology, Chinese Academy of Sciences. The multimodal imaging protocol implemented a set of MRI sequences of the high-resolution T1-weighted structural, resting-state functional, diffusion tensor, and arterial spin labeling MRI.
We are releasing these data for both research and education training in human brain sciences. Specifically, to protect personal information, we applied the face masking toolkit to achieve privacy anonymization by blurring facial information while all the participants agreed to share their anonymized data to the public. To monitor the quality of all structural and functional images for each participant, we also employed mri_qc toolkit for generating reports on the data quality, which are included in the sharing. All data structures conform to the specification of BIDS data structure.
License
CC BY 4.0
https://www.scidb.cn/detail?dataSetId=826407529641672704&version=V2
Citation
Peng Gao, Hao-Ming Dong, Yin-Shan Wang, et al. Imaging Chinese Young Brains (I See Your Brain)[DS/OL]. V2. Science Data Bank, 2021[2025-05-08]. https://cstr.cn/31253.11.sciencedb.00740. CSTR:31253.11.sciencedb.00740.
Peng Gao, Hao-Ming Dong, Yin-Shan Wang, et al. Imaging Chinese Young Brains (I See Your Brain)[DS/OL]. V2. Science Data Bank, 2021[2025-05-08]. https://doi.org/10.11922/sciencedb.00740. DOI:10.11922/sciencedb.00740.
Functional and Structural MRI Data Following Transcranial Static Magnetic Stimulation Over the Motor Cortex
This dataset provides multiecho resting-state fMRI, diffusion-weighted imaging (DWI), and high-resolution structural T1-weighted MRI data from right-handed healthy participants.
Each subject participated in two experimental sessions involving real and sham transcranial static-magnetic-field stimulation (tSMS) over the right motor cortex (hand area).
The study employs a double-blind randomized crossover design, enabling direct comparison between stimulation conditions.
This resource facilitates investigation into tSMS-induced changes in functional connectivity, microstructural organization, and motor network dynamics, combining structural, diffusion, and multiecho functional MRI.
License
Creative Commons Attribution 4.0 International (CC BY 4.0)
Citation
Caballero-Insaurriaga, J., Pineda-Pardo, J. Á., & Foffani, G. (2025).
Functional and Structural Magnetic Resonance Imaging Data Following Transcranial Static Magnetic Stimulation Over the Motor Cortex (1.0.1) [Data set]. Zenodo.
https://doi.org/10.5281/zenodo.15224957
Source
https://doi.org/10.5281/zenodo.15224957
Contact: [email protected]
Institutions: Universidad de Cantabria, Hospital Nacional de Parapléjicos, Universidad Autónoma de Madrid
Funding: Supported by institutional research programs in non-invasive brain stimulation (NIBS)
Dataset Information
| Category | Details |
|---|---|
| Subjects | Right-handed healthy adults |
| Design | Randomized double-blind crossover (real vs sham tSMS) |
| Target Site | Right motor cortex (hand area) |
| Modalities | Multiecho resting-state fMRI, diffusion-weighted imaging (DWI), high-resolution T1-weighted MRI |
| Conditions | 30-minute real tSMS and sham tSMS sessions |
| Sessions | Two sessions per subject, separated by one week, same weekday and time |
| Data Format | NIfTI (.nii/.nii.gz) |
| Correction Notice (v1.0.1) | Fixed scaling of phase images (now in −π..π range) |
Experimental Details
This dataset includes multiecho resting-state fMRI, Diffusion-Weighted Imaging, and high-resolution structural T1-weighted MRI data acquired from right-handed healthy participants, immediately following 30-minute real and sham tSMS over the right motor cortex (hand region).
Each participant underwent two separate sessions, one with real and one with sham stimulation, performed on separate days but at the same time of day and weekday.
The order of stimulation was randomized and double-blind, ensuring balanced and unbiased comparisons.
Demographics and Experimental Information
Additional participant details, including demographics (e.g., exact age) and condition-specific experimental notes, are available upon reasonable request to the authors.
Conversion to NIfTI
All data were converted from native DICOM to NIfTI format using dcm2niix.
File naming corrections and supplemental JSON metadata fields were added using a custom Python script to ensure consistency and completeness.
Defacing
High-resolution T1-weighted anatomical scans were defaced using FSL’s fsl_deface command.
While this tool reliably removes identifiable facial features, it can slightly reduce the apparent size of the frontal pole (see [Bischoff-Grethe et al., NeuroImage, 2021, doi:10.1016/j.neuroimage.2021.117845]).
To mitigate this, the defacing mask was expanded prior to defacing by combining (binary union) the default mask with a dilated brain mask (7×7×7 voxel kernel).
The brain mask was extracted using FreeSurfer’s mri_synthstrip, and dilation and combination were performed using fslmaths, ensuring full brain coverage for all subjects.
Scaling of Phase Images
Both multiecho fMRI and DWI were acquired using the CMRR multiband-accelerated EPI pulse sequences (no multiband factor applied due to coil constraints).
Magnitude and phase reconstructions were exported for each dataset.
Native phase images were scaled in the −4096..4094 integer range (as discussed in CMRR Issue #238) and verified by the authors.
Using FSL tools, these were losslessly rescaled to radians (−π..π) by adjusting scl_slope and scl_inter fields in the NIfTI header without altering data type or stored voxel values.
Rescaling formula:
[
\text{phase (radians)} = \frac{x}{4096} \times \pi
]
Software Versions
| Component | Version / Build |
|---|---|
| CMRR Multiband EPI | R016–R017 |
| dcm2niix | v1.0.20230411 (GCC 12.2.0, Linux x86-64) |
| Python | 3.8.17 |
| FSL | 6.0.6.4 |
| FreeSurfer | 7.3.2 |
File Information
| File | Description | Size | Checksum |
|---|---|---|---|
| tSMS_M1R_MRI.zip | MRI data (multi-echo fMRI, DWI, and T1w) for both real and sham stimulation sessions | 35.2 GB | md5:2d9f8b9c1ca647dac96619442cffa321 |
Keywords
MRI • fMRI • Multi-Echo • Resting-State • Diffusion MRI • DWI • Motor Cortex • tSMS • Transcranial Static Magnetic Field • Non-Invasive Brain Stimulation • NIBS • CMRR • Phase Correction
Test-Retest Cross-Scanner Multi-Modal Brain MRI of Healthy Subjects — B-Q Minded Brain Study
The B-Q Minded Brain Study (TRCSMM) dataset provides test–retest cross-scanner multi-modal MRI data from healthy volunteers.
It was created as part of the B-Q MINDED project — an international research effort on quantitative MRI (qMRI) coordinated by Prof. Dr. Jan Sijbers at the University of Antwerp.
The study aims to evaluate and harmonize qMRI metrics across scanners to improve data comparability and robustness in multi-center neuroimaging research.
Funded by the EU Horizon 2020 program (Marie Skłodowska-Curie grant agreement No. 764513), B-Q MINDED integrates research, innovation, and training to pave the way for clinical translation of qMRI.
License
Creative Commons Attribution 4.0 International (CC BY 4.0)
Citation
Pinto, M., Paolella, R., Smekens, C., Vanhevel, F., Billiet, T., Sijbers, J., & Van Dyck, P. (2025).
Test–Retest Cross-Scanner Multi-Modal Brain MRI of Healthy Subjects: B-Q Minded Brain Study [Data set]. Zenodo.
https://doi.org/10.5281/zenodo.6473268Pinto M.S., Anania V., Paolella R., Smekens C., Billiet T., Janssens T., den Dekker A.J., Sijbers J., Guns P-J., & Van Dyck P. (2025).
Harmonization of diffusion MRI on healthy subjects using NeuroCombat and LongCombat: a B-Q Minded brain intra- and inter-scanner study.
Frontiers in Neuroscience, 19:1591169. https://doi.org/10.3389/fnins.2025.1591169
Source
https://doi.org/10.5281/zenodo.6473268
Contact: [email protected]
Project: B-Q MINDED
Funding: EU Horizon 2020, Marie Skłodowska-Curie grant agreement No. 764513
Dataset Information
| Category | Details |
|---|---|
| Subjects | Healthy volunteers |
| Study Type | Test-retest, cross-scanner, multi-modal MRI |
| Institutions | University of Antwerp (UA), Antwerp University Hospital (UZA), icometrix, Siemens |
| Scanners | Siemens Skyra 3T and Siemens PrismaFit 3T |
| Location | UZA Radiology Department, Belgium |
| Sequences | Anatomical MRI and Diffusion MRI |
| Ethics Approval | UZA Ethics Committee No. 19/50/620 (Belgian registration B300202042715) |
| Approval Date | January 6, 2020 |
Purpose
The TRCSMM Brain Study evaluates intra- and inter-scanner reproducibility of MRI metrics and provides a foundation for cross-platform diffusion MRI harmonization.
It supports reproducibility research and algorithm development in quantitative neuroimaging.
File Information
| File | Size | Checksum |
|---|---|---|
| BQMinded_data.zip | 8.1 GB | md5:646d1f867bf88aa57084959478d276c8 |
Keywords
MRI • Diffusion MRI • Harmonization • Inter-scanner • Intra-scanner • Cross-scanner • Test–retest • Healthy Volunteers