Releases: data-openneuro/brain2
DESIGNER Preprocessed Diffusion Dataset (Early Reading)
DESIGNER Preprocessed Diffusion Dataset (Early Reading)
Author: Moramay Ramos-Flores
Instituto de Neurobiología, Universidad Nacional Autónoma de México (UNAM), Querétaro, México.
Description
This dataset contains diffusion-weighted MRI (DWI) data from Spanish-speaking children at the early stages of reading acquisition. All DWI data were preprocessed using the DESIGNER pipeline and include the corresponding .nii.gz, .bval, and .bvec files. The dataset supports the study of white‑matter pathways involved in phonological awareness and early reading development.
Participants
The dataset includes 61 Mexican Spanish-speaking children between 6.0 and 8.5 years old, classified as early readers.
All children were monolingual, without neurological, developmental, or psychiatric diagnoses, and had no contraindications for MRI.
Imaging Modalities
- DWI: Multishell diffusion (b = 800 and b = 2500)
De-identification
All data were fully de-identified prior to release. Diffusion images do not contain facial features, and sensitive metadata fields were removed.
Study Design
This is a cross-sectional observational study. No tasks were performed during MRI acquisition. Reading and phonological assessments were conducted outside the scanner.
Funding
Funding Sources: DGAPA-PAPIIT Universidad Nacional Autónoma de México IN206825, Google Gift 2021, Secretaría de Ciencias, Humanidades, Tecnología e Innovación CVU 1061192 (MRF).
Notes
- Dataset follows the BIDS specification.
- Compatible with DIPY, FSL, MRtrix, and pyAFQ.
- All diffusion images were preprocessed using the DESIGNER pipeline.
hyperface: a naturalistic fMRI dataset to characterize human face processing
This dataset contains the raw data for
hyperface: a naturalistic fMRI dataset to characterize human face processing, by
Matteo Visconti di Oleggio Castello, Guo Jiahui, Ma Feilong, Manon de
Villemejane, James V. Haxby, and M. Ida Gobbini.
Sample code for the QA analyses is available at
https://github.com/mvdoc/hyperface-data-paper.
The hyperface fMRIPrep derivatives are available at
https://openneuro.org/datasets/ds007384.
The hyperface FreeSurfer derivatives are available at
https://openneuro.org/datasets/ds007378.
The Grand Budapest Hotel raw data is available at
https://openneuro.org/datasets/ds003017. See also the associated paper:
Visconti di Oleggio Castello, M., Chauhan, V., Jiahui, G., & Gobbini, M.
I. (2020). An fMRI dataset in response to "The Grand Budapest Hotel", a
socially-rich, naturalistic movie. Scientific Data, 7(1), 1-9.
https://doi.org/10.1038/s41597-020-00735-4
The identity decoding raw data is available at
https://openneuro.org/datasets/ds003834. See also the associated paper:
Visconti di Oleggio Castello, M., Haxby, J.V., & Gobbini, M.I. (2021).
Shared neural codes for visual and semantic information about familiar
faces in a common representational space. Proceedings of the National
Academy of Sciences. https://doi.org/10.1073/pnas.2110474118
See also the associated paper:
Jiahui, G., Feilong, M., Visconti di Oleggio Castello, M., Nastase, S.A.,
Haxby, J.V., & Gobbini, M.I. (2023). Modeling naturalistic face processing
in humans with deep convolutional neural networks. Proceedings of the National
Academy of Sciences. https://doi.org/10.1073/pnas.2304085120
If you use this dataset or the code, please cite the corresponding
paper: TODO
Notes
The dataset includes three sessions per participant:
- ses-1: Contains functional, fieldmap, DWI, and T2w anatomical data
- ses-2: Contains functional, fieldmap, and DWI data
- ses-budapest: Contains only T1w anatomical scans
The original budapest functional and fieldmap data have been removed from this
dataset. Users who need the complete dataset including budapest functional data can
use the merge script located under the code/ directory to combine this dataset with
the original budapest data from https://openneuro.org/datasets/ds003017
if needed.
All anatomicals (T1w, T2w) were defaced with pydeface.
Contact information
For questions on this dataset, please contact Matteo Visconti di Oleggio
Castello (matteo.visconti@berkeley.edu).
Dataset: T1-Weighted Structural MRI and fMRI of Participants Viewing Self-Avatar Exoskeleton Walking (11 SWS Cycles)
Neuroimaging Data Collected During Kinesthetic Motor Imagery of Walking vs. Rest
This dataset includes multimodal neuroimaging recordings from five participants performing kinesthetic motor imagery (KI) while viewing themselves walking in an exoskeleton. The dataset includes synchronized MRI (structural and functional) and EEG recordings organized according to the BIDS specification. Functional MRI data were acquired in two runs while participants viewed a 10-minute video, along with a separate baseline scan during which participants simulated a resting state for approximately 5 minutes.
MRI sessions were conducted after participants completed nine sessions of EEG‑controlled exoskeleton walking and standing experiments.
Dataset link: https://openneuro.org/datasets/ds006940
MRI Acquisition:
- Scanner: Philips Ingenia 3.0T (Koninklijke Philips N.V., The Netherlands)
- Structural scans: T1‑weighted anatomical images
- Functional scans (fMRI): Participants viewed a 10‑minute video of themselves walking in the exoskeleton, filmed from a first‑person perspective. The video contained 11 Stop‑Walk‑Stop (SWS) cycles. During viewing, participants were instructed to evoke KI in synchrony with the exoskeleton movements.
- Baseline condition: Participants mentally simulated resting state for approximately 5 minutes while fMRI data was recorded.
EEG Acquisition:
- MR‑compatible EEG cap (Brain Products GmbH, Gilching, Germany)
- Electrode locations are provided in EEGLAB format.
- 59 scalp channels + 4 EOG channels + 1 ECG channel
Stimuli:
- A video stimulus (
stimuli/walking_exoskeleton_S1.mp4) was presented during walking tasks.
Participants:
Five healthy adults out of seven participated in the EEG‑controlled exoskeleton experiments.
Participants S6 and S7 did not undergo MRI scanning due to a pause in data collection during the COVID‑19 pandemic.
Folder Structure (Example: Participant S1)
├── dataset_description.json ├── README ├── derivatives │ └── sub-01 │ └── ses-01 │ ├── anat │ │ └── sub-01_ses-01_T1w.nii │ ├── dwi │ │ ├── sub-01_ses-01_run-001_dwi.json │ │ ├── sub-01_ses-01_run-001_dwi.bval │ │ ├── sub-01_ses-01_run-001_dwi.bvec │ │ └── sub-01_ses-01_run-001_dwi.nii.gz │ │ │ └── spm │ ├── sub-01_ses-01_beta_0001.nii │ ├── ... │ ├── sub-01_ses-01_beta_0008.nii │ ├── sub-01_ses-01_con_0001.nii │ ├── ... │ ├── sub-01_ses-01_con_0004.nii │ ├── sub-01_ses-01_smpt_0001.nii │ ├── ... │ ├── sub-01_ses-01_smpt_0004.nii │ ├── sub-01_ses-01_mask.mat │ ├── sub-01_ses-01_resms.mat │ ├── sub-01_ses-01_rpv.mat │ └── sub-01_ses-01_spm.mat │ ├── stimuli │ └── walking_exoskeleton_S1.mp4 │ ├── sub-01 │ └── ses-01 │ ├── anat │ │ ├── sub-01_ses-01_T1w.json │ │ └── sub-01_ses-01_T1w.nii │ ├── eeg │ │ ├── sub-01_ses-01_coordsystem.json │ │ ├── sub-01_ses-01_electrodes.json │ │ ├── sub-01_ses-01_electrodes.tsv │ │ ├── sub-01_ses-01_task-baseline_eeg.eeg │ │ ├── sub-01_ses-01_task-baseline_eeg.json │ │ ├── sub-01_ses-01_task-baseline_eeg.vhdr │ │ ├── sub-01_ses-01_task-baseline_eeg.vmrk │ │ ├── sub-01_ses-01_task-walking1_eeg.eeg │ │ ├── ... │ │ └── sub-01_ses-01_task-walking2_eeg.vmrk │ │ │ └── func │ ├── sub-01_ses-01_task-baseline_run-001_bold.json │ ├── sub-01_ses-01_task-baseline_run-001_bold.nii.gz │ ├── sub-01_ses-01_task-walking1_run-001_bold.json │ ├── sub-01_ses-01_task-walking1_run-001_bold.nii.gz │ ├── sub-01_ses-01_task-walking2_run-001_bold.json │ └── sub-01_ses-01_task-walking2_run-001_bold.nii.gz
Validation Data
A validation file (derivatives/MRI_DataValidation.xls) is provided to summarize dataset completeness and quality checks.
-
Sheet: Files
Lists presence/absence of EEG, MRI, and SPM outputs across subjects (S1–S5).
Includes counts for beta, con, spmT maps, and DTI volumes. -
Sheet: VMRK-R128
Reports event marker counts (R128 triggers) for baseline, walking1, and walking2 tasks. -
Sheet: EEG-Duration
Provides task durations (minutes) for 'baseline', 'walking1', and 'walking2' EEG recordings.
Notes on Organization
-
Raw data (anat, func, eeg) are stored under each subject directory (sub-XX/ses-YY).
-
Derivatives: Preprocessed outputs are stored separately under derivatives/sub-XX/ses-YY, including:
- Statistical Parametric Mapping (SPM) outputs
- SPM-normalized (warped) anatomical scans
- Diffusion Tensor Imaging (DTI) derivatives
- Validation Excel file -
The video stimulus is stored in the top-level stimuli/ folder.
-
Naming conventions follow BIDS entities:
- sub-<label> : subject identifier
- ses-<label> : session identifier
- task-<label> : task name (baseline, walking1, walking2)
- run-<index> : run number
Citation
If you use this dataset, please cite the associated study and acknowledge the contributors.
https://openneuro.org/datasets/ds006945
TRAMFIX: TRavelling Across Melbourne for FIXel-based analysis
TRAMFIX Dataset
This is the TRAMFIX (TRavelling Across Melbourne for FIXel-based analysis) dataset.
The /derivatives directory contains preprocessed DWI data for 10 travelling heads subjects.
This includes for each participant four sessions (corresponding to four different MRI scanners).
ds006688_2
Automated release for ds006688_2
ds006688_1
Automated release for ds006688_1
Penn LEAD: Penn Longitudinal Executive functioning in Adolescent Development
Penn LEAD: Penn Longitudinal Executive functioning in Adolescent Development
Penn LEAD is a longitudinal dataset consisting of clinical, cognitive, and multimodal neuroimaging data from 132 adolescents, designed to investigate transdiagnostic executive function during development.
Note that in some cases, there may be more runs than expected or noted in dataset contents below if a run was repeated. If a run was repeated, there might be an extra run in the dataset. Additionally, in cases where the data from one of the runs was poor quality, there might be a second and third run but no first run.
Any variant to the metadata that might differ from what is described below will be flagged in the file name for that scan in the acquisition entity (i.e., sub-\*_ses-\*_acq-VARIANT{variant_description}_run-\*_\*.*)
Dataset contents
Structural MRI
Each participant has one T1-weighted normalized scan (176 slices, TR = 2500 ms, TE = 2.9 ms), one T2-weighted (not normalized) scan (176 slices, TR = 3200 ms, TE = 565 ms), and one T2-weighted normalized scan, all with the AP phase encoding direction. T1 scans are refaced and T2 scans are defaced. The processed sMRI data derivatives can be found here.
Functional MRI
Each participant has two resting-state fMRI runs (522 volumes and 383 volumes, TR = 800 ms, TE = 30 ms, 60 slices) and one n-back task fMRI run with fractal images (522 volumes, TR = 800 ms, TE = 30 ms, 60 slices). The processed fMRI data derivatives can be found here and here.
Note that for one subject, they had to re-run the resting-state fMRI, since in the first run, a movie was accidentally playing in the first run. Hence, the task entity was renamed to task-restfilm.
Diffusion MRI
One 103-volume multi-shell (MB = 3), single spin-echo DWI run was acquired for all participants using a monopolar sampling scheme (TR = 4200 ms, TE = 89 ms, voxel size = 1.7 mm isotropic). The processed DWI data derivatives can be found here and here.
Perfusion MRI
One background-suppressed, unbalanced, 3D stack of spirals PCASL scan was acquired for all participants (labeling duration = 1500 ms, TR = 4298 ms, TE = 10 ms). The processed ASL data derivatives can be found here.
A reference scan was also acquired to assist in ASL calibration, consisting of an M0 scan that is retained in the perfusion folder with the m0scan suffix.
MEGRE
We acquired a multi-echo gradient-echo sequence with four echos, for use in quantitative susceptibility mapping (QSM), at 1.5 mm isotropic resolution (TR = 35 ms, voxel size = 1.5 * 1.5 * 1.5 mm).
Note that we did not curate or preprocess the MEGRE data, but raw data can be found in the dataset.
Field maps
For distortion correction, pairs of field maps with opposite phase-encoding polarity (anterior to
posterior and posterior to
anterior) were acquired for both diffusion-weighted and fMRI scans.
Phenotypic Data
Clinical diagnostic data can be found in the participants.tsv file.
Cognitive Data
Participants completed a number of cognitive tasks from the Penn CNB. These included:
- Penn Abstraction, Inhibition and Working Memory Task
- Penn Face Memory Test
- Children's Penn Word Memory Test
- Short Computerized Finger-Tapping Task
- Penn Emotion Recognition Task for Children
- Digit Symbol Test
- Short Letter N-Back (2 back)
- Measured Emotion Differentiation Test
- Motor Praxis Test
- Penn Conditional Exclusion Task for Children
- Short Penn Continuous Performance Test - Number and Letter Version
- Penn Matrix Reasoning Test
- Variable Short Penn Line Orientation Test
- Children's Verbal Reasoning Test
- Short Visual Object Learning Test
- Trailmaking A
- Trailmaking B
- Effort Discounting Task
- Delayed Discounting Task
- Risk Discounting Task
- Age Differentiation Test
The following subjects/sessions are missing cognitive data:
- sub-21161, ses-1
- sub-22510, ses-2
- sub-22617, ses-2
- sub-22618, ses-2
- sub-23582, ses-1
- sub-20812, ses-2 had a session 1 that was started, but incomplete, and a session 2 that began a couple of days later. As such, we only have CNB data from ses-1, and ses-3, but ses-2 did not have a separate CNB administered.
Other data
The acquisition date and time for each session was rounded to the 15th of the month and the nearest hour, and stored in the *_sessions.tsv files.
Additional notes
Note that sub-20812 has two sessions (ses-1 and ses-2) that are only a few days apart. Ses-1 contains incomplete data and thus the scans were repeated a few days later for ses-2.