OpenNeuro is a database of openly-available brain imaging data. The data are shared according to a Creative Commons CC0 license, providing a broad range of brain imaging data to researchers and citizen scientists alike. The database primarily focuses on functional magnetic resonance imaging (fMRI) data, but also includes other imaging modalities including structural and diffusion MRI, electroencephalography (EEG), and magnetoencephalograpy (MEG). OpenfMRI is a project of the Center for Reproducible Neuroscience at Stanford University. Development of the OpenNeuro resource has been funded by the National Science Foundation, National Institute of Mental Health, National Institute on Drug Abuse, and the Laura and John Arnold Foundation.

Neurocognitive aging data release with behavioral, structural, and multi-echo functional MRI measures (https://doi.org/10.1038/s41597-022-01231-7)

Data included as part of: Age differences in the functional architecture of the human brain (https://doi.org/10.1093/cercor/bhac056)

Two 10-minute multi-echo resting-state runs were collected from 301 healthy adults (181 younger, 120 older adults) across 2 sites. Acquisition parameters are summarized below. A subset of 246 participants also have T2-FLAIR images. Pulse and respiration data are available for 233 participants. Full detail can be found in the data descriptor and empirical paper linked above. Accompanying behavioral data can be found on OSF: https://osf.io/yhzxe/ (doi: 10.17605/OSF.IO/YHZXE).

NOTE Some participants were included as part of another openneuro dataset (DuPre et al, 2018; OpenNeuro Accession Number: ds000210), as noted by 'openneuro' in the participants file.

Site 1 T1w: TR = 2530ms TE = 3.4ms Flip angle = 7 degrees Voxel size = 1mm isotropic Time = 5m25s 176 slices

FLAIR: TR = 12000ms TE = 95ms TI = 2712ms Flip angle = 160 degrees Voxel size = 1x1x3mm Time = 2m36s 42 slices (11 participants have 46, 1 has 43; see participants.tsv)

Rest: TR = 3000ms TE = 13.7ms, 30ms, 47ms Flip angle = 83 degrees Voxel size = 3mm isotropic Time = 10m06s Sessions = 2 204 volumes/session (1 participant has 206; see participants.tsv)

Site 2 T1w: TR = 1900 ms TE = 2.52 ms Flip angle = 9 degrees Voxel size = 1mm isotropic Time = 4m26s 192 slices

FLAIR: TR = 12000ms TE = 95ms TI = 2759.4ms Flip angle = 160 degrees Voxel size = .8x.8x3mm Time = 3m38s 44 slices

Rest: TR = 3000ms TE = 14ms, 29.96ms, 45.92ms Flip angle = 83 degrees Voxel size = 3.4mm x 3.4mm x 3mm Time = 10m06s Sessions = 2 200 volumes/session

UCLA Consortium for Neuropsychiatric Phenomics LA5c Study

Preprocessed data described in

Gorgolewski KJ, Durnez J and Poldrack RA. Preprocessed Consortium for Neuropsychiatric Phenomics dataset. F1000Research 2017, 6:1262 https://doi.org/10.12688/f1000research.11964.2

are available at https://legacy.openfmri.org/dataset/ds000030/ and via Amazon Web Services S3 protocol at: s3://openneuro/ds000030/ds000030_R1.0.5/uncompressed/derivatives/

Subjects / Participants

The participants.tsv file contains subject IDs with demographic informations as well as an inventory of the scans that are included for each subject.

Dataset Derivatives (/derivatives)

The /derivaties folder contains summary information that reflects the data and its contents:

1. Final_Scan_Count.pdf - Plot showing the over all scan inclusion, for quick reference.
2. parameter_plots/ - Folder contains many scan parameters plotted over time. Plot symbols are color coded by imaging site. Intended to provide a general sense of protocol adherence throughout the study. Individual parameters scan be found in the scan .json sidecar file. A single file containing the combined data from all of the imaging .json sidecars if provided in parameter_plots/MR_Scan_Parameters.tsv file.
3. physio_plots/ - Folder contains a plot of the physiological recording trace for the Breath Hold and Resting State functional scans. For the BHT, the instructional cue timings are represented by shaded background.
4. event_plots/ - Simple plots of the function task events files. The x-axis is always time (onset), and the y-axis can be task-specific. Also intended as a quick reference or summary.
5. mriqcp/ - Output of the current version (as of 27 Jan 2016) of MRIQCP (MRI Quality Control Protocol: https://github.com/poldracklab/mriqc). Included are numeric results of anatomical and functional protocols as well as single subject results plotted against group distribution.
6. data_browser/ - a rudimentary data visualization for MRIQP (see: http://wtriplett.github.io/ds030/)

Scan-specific Notes

All scan files were converted from scanner DICOM files using dcm2niix (0c9e5c8 from https://github.com/neurolabusc/dcm2niix.git). Extra DICOM metadata elements were extracted using GDCM (http://gdcm.sourceforge.net/wiki/index.php/Main_Page) and combined to form each scan's .json sidecar.

Note regarding scan and task timing: In most cases, the trigger time was provided in the task data file and has been transferred into the TaskParameter section of each scans *_bold.json file. If the trigger time is available, a correction was performed to the onset times to account for trigger delay. The uncompensated onset times are included in the onset_NoTriggerAdjust column. There will be an 8 second discrepancy between the compensated and uncompensated that accounts for pre-scans (4 TRs) performed by the scanner. In the cases where the trigger time is not available, the output of (TotalScanTime - nVols*RepetitionTime) may provide an estimate of pre-scan time.

T1w Anatomical

Defacing was performed using freesurfer mri_deface (https://surfer.nmr.mgh.harvard.edu/fswiki/mri_deface)

Bischoff-Grethe, Amanda et al. "A Technique for the Deidentification of Structural Brain MR Images." Human brain mapping 28.9 (2007): 892–903. PMC. Web. 27 Jan. 2016.

PAMenc / PAMret

The larger amount of missing PAM scans is due to a task design change early in the study. It was decided that data collected before the design change would be excluded.

Stop Signal

The Stop Signal task consisted of both a training task (no MRI) and the in-scanner fMRI task. The data from the training run is included in each subject's beh folder with the task name "stopsignaltraining".

Known Issues:

Some of the T1-weighted images included within this dataset (around 20%) show an aliasing artifact potentially generated by a headset. The artifact renders as a ghost that may overlap the cortex through one or both temporal lobes. A list of participants showing the artifact has been added to the dataset.

Grant proposal for NIH R24MH117179 (funded 9/1/2018)

Open platform for analyzing and sharing neuroimaging data from human brain imaging research studies. Brain Imaging Data Structure ( BIDS) compliant database. Formerly known as OpenfMRI. Data archives to hold magnetic resonance imaging data. Platform for sharing MRI, MEG, EEG, iEEG, and ECoG data.

fMIR dataset from openneuro.org

Brain Tumor Connectomics Data

Post-operative data of 7 glioma patients, 12 meningioma patients (1 [sub-PAT11] with bad data quality) and 10 control subjects. Out of the 11 glioma patients, 14 meningioma patients, and 11 healthy controls that were included pre-surgically ("BTC_preop, ds001226" on OpenNeuro), 7 glioma patients (1 drop-out, 1 no resection, 2 end of study), 12 meningioma patients (1 drop-out, 1 MRI not possible because of cochlear implant), and 10 healthy controls (1 drop-out) agreed to participate post-operatively.

Data used in the papers

Aerts H, Schirner M, Jeurissen B, Van Roost D, Achten E, Ritter P, Marinazzo D. Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain. eNeuro. 2018 Jun 4;5(3):ENEURO.0083-18.2018. doi: 10.1523/ENEURO.0083-18.2018. PMID: 29911173; PMCID: PMC6001263.

and

Aerts H, Schirner M, Dhollander T, Jeurissen B, Achten E, Van Roost D, Ritter P, Marinazzo D. Modeling brain dynamics after tumor resection using The Virtual Brain. Neuroimage. 2020 Jun;213:116738. doi: 10.1016/j.neuroimage.2020.116738. Epub 2020 Mar 16. PMID: 32194282.

Contact information: Name: Hannelore Aerts & Daniele Marinazzo Email: daniele.marinazzo@ugent.be

Compared to the initial database, 6 patients were excluded: 2 because of glioma grade 4, 3 because of subtentorial tumor, 1 because of absence of MRI data (subdural grid).

Of all subjects the following data were acquired: - T1w MPRAGE anatomical scan (anat) - resting-state fMRI (func) - multi-shell HARDI diffusion-weighted MRI (dwi, acq=AP) - short DWI with reverse phase encoding directions (dwi, acq=PA) - cognitive assessment using the Cambridge Neuropsychological Test Automated Battery (CANTAB): MOT, RVP, RTI, SSP & SOC - questionnaires assessing demographic information, lifestyle habits & emotional functioning

The "derivatives" folder contains

• tumor masks, obtained with a combination of manual delineation and disconnectome
• time series, structural and functional connectivity matrices, and resting state HRF for each ROI.
• rsHRF parameters obtained with the rsHRF toolbox described here Wu GR, Colenbier N, Van Den Bossche S, Clauw K, Johri A, Tandon M, Marinazzo D. rsHRF: A toolbox for resting-state HRF estimation and deconvolution. Neuroimage. 2021 Dec 1;244:118591. doi: 10.1016/j.neuroimage.2021.118591. Epub 2021 Sep 21. PMID: 34560269.
• fMRI quality control: Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ; MRIQC: Advancing the Automatic Prediction of Image Quality in MRI from Unseen Sites; PLOS ONE 12(9):e0184661; doi:10.1371/journal.pone.0184661.
• diffusion quality control Bastiani M, Cottaar M, Fitzgibbon SP, Suri S, Alfaro-Almagro F, Sotiropoulos SN, Jbabdi S, Andersson JLR. Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. Neuroimage. 2019 Jan 1;184:801-812. doi: 10.1016/j.neuroimage.2018.09.073. Epub 2018 Sep 26. PMID: 30267859; PMCID: PMC6264528. The structural connectivity (SC) is derived with the TVB pipeline (https://github.com/BrainModes/TVB-empirical-data-pipeline) with manual segmentation when necessary. The regions are according to the Desikan-Killiany atlas.

A companion dataset, containing the preoperatory data, is accessible at https://openneuro.org/datasets/ds001226

This FAIRsharing record describes: The OpenNeuro project (formerly known as the OpenfMRI project) was established in 2010 to provide a resource for researchers interested in making their neuroimaging data openly available to the research community. It is managed by Russ Poldrack and Chris Gorgolewski of the Center for Reproducible Neuroscience at Stanford University. The project has been developed with funding from the National Science Foundation, National Institute of Drug Abuse, and the Laura and John Arnold Foundation.

The model cross-validated on the ABIDE dataset performs with AUC = 0.707 and ACC = 76% on DS030. The recall column shows the insensitivity of the classifier to the true “exclude” cases. The predicted group summarizes the confusion matrix corresponding to the prediction experiment.

The cross-validated area under the curve (AUC) and accuracy (ACC) scores calculated on the ABIDE dataset (train set) are less biased when LoSo is used to create the outer folds, as compares to the evaluation scores obtained in DS030 (held-out set).

Dataset obtained from Open Neuro. 15 cases with segmentation of side ventricles on brain MRI T1W scans. Cases obtained from open neuro: https://openneuro.org/ Segmentations done by the MedSeg team Our site: MedSeg Our tool: MedSeg Segmentation More data here

Context

A test-retest fMRI dataset for motor, language and spatial attention functions.

Content

Single session, single subject from ds000114 dataset. Full dataset can be found at https://openneuro.org/datasets/ds000114. More info at https://dx.doi.org/10.1186%2F2047-217X-2-6

• BOLD timeseries in standard space (MNI)
• brain mask in standard space (MNI)
• timing of in scanner stimuli
• scanning parameters

Acknowledgements

Banner Image by Ken Treloar on Unsplash

Full dataset can be found at https://openneuro.org/datasets/ds000114. More info at https://dx.doi.org/10.1186%2F2047-217X-2-6

Experiment Details Electroencephalography recordings from 16 subjects to fast streams of gabor-like stimuli. Images were presented in rapid serial visual presentation streams at 6.67Hz and 20Hz rates. Participants performed an orthogonal fixation colour change detection task.

Experiment length: 1 hour Raw and preprocessed data are available online through openneuro: https://openneuro.org/datasets/ds004357. Supplementary Material and analysis scripts are available on github: https://github.com/Tijl/features-eeg

Brain Tumor Connectomics Data

Pre-operative data of 11 glioma patients, 14 meningioma patients and 11 control subjects that were used in the papers

Aerts H, Schirner M, Jeurissen B, Van Roost D, Achten E, Ritter P, Marinazzo D. Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain. eNeuro. 2018 Jun 4;5(3):ENEURO.0083-18.2018. doi: 10.1523/ENEURO.0083-18.2018. PMID: 29911173; PMCID: PMC6001263.

and

Aerts H, Schirner M, Dhollander T, Jeurissen B, Achten E, Van Roost D, Ritter P, Marinazzo D. Modeling brain dynamics after tumor resection using The Virtual Brain. Neuroimage. 2020 Jun;213:116738. doi: 10.1016/j.neuroimage.2020.116738. Epub 2020 Mar 16. PMID: 32194282.

Contact information: Name: Hannelore Aerts & Daniele Marinazzo Email: daniele.marinazzo@ugent.be

Compared to the initial database, 6 patients were excluded: 2 because of glioma grade 4, 3 because of subtentorial tumor, 1 because of absence of MRI data (subdural grid).

Of all subjects the following data were acquired: - T1w MPRAGE anatomical scan (anat) - resting-state fMRI (func) - multi-shell HARDI diffusion-weighted MRI (dwi, acq=AP) - short DWI with reverse phase encoding directions (dwi, acq=PA) - cognitive assessment using the Cambridge Neuropsychological Test Automated Battery (CANTAB): MOT, RVP, RTI, SSP & SOC - questionnaires assessing demographic information, lifestyle habits & emotional functioning

The "derivatives" folder contains

• tumor masks, obtained with a combination of manual delineation and disconnectome
• time series, structural and functional connectivity matrices, and resting state HRF for each ROI. The structural connectivity (SC) is derived with the TVB pipeline (https://github.com/BrainModes/TVB-empirical-data-pipeline) with manual segmentation when necessary. The regions are according to the Desikan-Killiany atlas. The resting state HRF is obtained with the toolbox described here Wu GR, Colenbier N, Van Den Bossche S, Clauw K, Johri A, Tandon M, Marinazzo D. rsHRF: A toolbox for resting-state HRF estimation and deconvolution. Neuroimage. 2021 Dec 1;244:118591. doi: 10.1016/j.neuroimage.2021.118591. Epub 2021 Sep 21. PMID: 34560269.

A companion dataset, containing the postoperatory data, is accessible at https://openneuro.org/datasets/ds002080

This dataset contains MRI associated with the following publication, currently submitted to Stroke.

Title: Head down tilt 15° increases cerebral perfusion before recanalization in acute ischemic stroke due to large vessel occlusion. A pre-clinical MRI study Authors: Simone Beretta, Davide Carone, Tae-Hee Cho, Martina Viganò, Susanna Diamanti, Jacopo Mariani, Francesco Pedrazzini, Elisa Bianchi, Cristiano Pini, Radu Bolbos, Marlene Wiart, Carlo Ferrarese, Fabien Chauveau Preregistration: preclinicaltrials.eu, identifier PCTE0000198

Raw data:

MRI for 28 adult male Wistar rats (ILAR code Crl:WI(Han); RRID:RGD_2308816)
Magnetic field: 7T
Anesthesia: isoflurane
Time points (in hours, following occlusion): H0 (during MCA occlusion); H24 (after reperfusion)
Ethics statement: agreement numbers APAFIS#15529-2018061512184831v2 and 32924-2021091015062327v4 from local committee CELYNE-CNREEA (C2EA-42, Lyon, France)

Derivatives:

Time-to-Peak (TTP) maps calculated from DSC-PWI 
Matlab code used for TTP calculation
Excel files describing the experimental series and the main results

Notes:

No specification is available for Dynamic-Susceptibility-Contrast Perfusion-Weighted-Imaging (DSC-PWI), so the corresponding files are labelled in .bidsignore 
R10 had no anatomical T2w on H0
H24 session could not be performed for 8 animals (R03-R10-R26-R28-R29-R31-R33-R34)
R28 was not positioned properly during the first run of DSC-PWI, positioning was corrected for the second run

This dataset has been converted using BrkRaw (v0.3.7) on 2024-01-23.

How to cite?

• https://doi.org/10.5281/zenodo.3818615

This dataset is referred to as "Study 2" in the RPN-signature paper (see below) and was used to validate the predictive signature.

Paper: T. Spisak et al., Pain-free resting-state functional brain connectivity predicts individual pain sensitivity, accepted in Nature Communications, 2019.

Webpage: https://spisakt.github.io/RPN-signature

This dataset was obtained from the OpenfMRI project (http://www.openfmri.org). Accession #: ds102 Description: Flanker task (event-related)

The "NYU Slow Flanker" dataset comprises data collected from 26 healthy adults (age and sex included in Slow_Flanker_age_sex.txt) while they performed a slow event-related Eriksen Flanker task.

**Please note that all data have been uploaded regardless of quality it is up to the user to check for data quality (movement etc).

On each trial (inter-trial interval (ITI) varied between 8 s and 14 s; mean ITI=12 s),participants used one of two buttons on a response pad to indicate the direction of a central arrow in an array of 5 arrows. In congruent trials the flanking arrows pointed in the same direction as the central arrow (e.g., < < < < <), while in more demanding incongruent trials the flanking arrows pointed in the opposite direction (e.g., < < > < <).

Subjects performed two 5-minute blocks, each containing 12 congruent and 12 incongruent trials, presented in a pseudorandom order.

Functional imaging data were acquired using a research dedicated Siemens Allegra 3.0 T scanner, with a standard Siemens head coil, located at the NYU Center for Brain Imaging.

We obtained 146 contiguous echo planar imaging (EPI) whole-brainfunctional volumes (TR=2000 ms; TE=30 ms; flip angle=80, 40 slices, matrix=64x64; FOV=192 mm; acquisition voxel size=3x3x4mm) during each of the two flanker task blocks. A high-resolution T1-weighted anatomical image was also acquired using a magnetization prepared gradient echo sequence (MPRAGE, TR=2500 ms; TE= 3.93 ms; TI=900 ms; flip angle=8; 176 slices, FOV=256 mm).

Please cite one of the following references if you use these data:

Kelly, A.M., Uddin, L.Q., Biswal, B.B., Castellanos, F.X., Milham, M.P. (2008). Competition between functional brain networks mediates behavioral variability. Neuroimage, 39(1):527-37

Mennes, M., Kelly, C., Zuo, X.N., Di Martino, A., Biswal, B.B., Castellanos, F.X., Milham, M.P. (2010). Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity. Neuroimage, 50(4):1690-701. doi: 10.1016/j.neuroimage.2010.01.002. Epub 2010 Jan 15. Erratum in: Neuroimage. 2011 Mar 1;55(1):434

Mennes, M., Zuo, X.N., Kelly, C., Di Martino, A., Zang, Y.F., Biswal, B., Castellanos, F.X., Milham, M.P. (2011). Linking inter-individual differences in neural activation and behavior to intrinsic brain dynamics. Neuroimage, 54(4):2950-9. doi: 10.1016/j.neuroimage.2010.10.046

This dataset is made available under the Public Domain Dedication and License v1.0, whose full text can be found at http://www.opendatacommons.org/licenses/pddl/1.0/. We hope that all users will follow the ODC Attribution/Share-Alike Community Norms (http://www.opendatacommons.org/norms/odc-by-sa/); in particular, while not legally required, we hope that all users of the data will acknowledge the OpenfMRI project and NSF Grant OCI-1131441 (R. Poldrack, PI) in any publications.

This dataset included 33 healthy participants collected at Pennsylvania State University with informed consent. Simultaneously collected EEG and BOLD signals for each participant were recorded and organized at each folder.

Each scanning section consisted of an anatomical session, two 10-min resting-state sessions, and several 15-min sleep sessions. The first resting-state session was conducted before a visual-motor adaptation task (Albouy et al, Journal of Sleep Research, 2013) and the second resting-state session was conducted after a visual-motor adaptation task.

The scored sleep stages for these 33 subjects were organized under 'sourcedata' folder. Each TSV file contained the sleep stages for each 30-sec epoch across different sessions for each subject. In the TSV file, “w” represents wakefulness and “1, 2, 3” represents NREM1, NREM2, and NREM3, respectively. Some epochs scoring with uncertainty are noted as “uncertain” and some epochs with too large artifacts to score reasonably are noted as “unscorable”.

MR imaging data were collected on a 3 Tesla Prisma Siemens Fit scanner using a Siemens 20-channel receive-array coil. Anatomical images were acquired using a MPRAGE sequence (TR: 2300 milliseconds, TE: 2.28 milliseconds, 1mm isotropic spatial resolution, FOV: 256 millimeters, flip angle: 8 degrees, matrix size: 256×256×192, acceleration factor: 2). Blood oxygenation level-dependent (BOLD) fMRI data were acquired using an EPI sequence (TR: 2100 milliseconds, TE: 25 milliseconds, slice thickness: 4mm, slices: 35, FOV: 240mm, in-plane resolution: 3mm×3mm).

EEG data were collected using a 32-channel MR-compatible EEG system from Brain Products, Germany. Electrodes were placed based on the 10-20 international system. EOG and ECG recorded eye movement and cardiac signal, respectively. EEG data were collected at a sampling rate of 5000 Hz with a band-pass filter of 0-250 Hz. R128 in the EEG signals corresponds to the BOLD fMRI volume trigger. S1 markers in the EEG during sleep sessions correspond to participants hitting buttons indicating wakefulness state. S2 and S3 markers during sleep sessions represent no button hitting and can be ignored.

For more information or any questions about this dataset, please see the two papers listed in the References and Links section or contact Dr. Yameng Gu (ymgu95@gmail.com)

———————————————————————————————— ORIGINAL PAPERS ————————————————————————————————

Lioi, G., Cury, C., Perronnet, L., Mano, M., Bannier, E., Lécuyer, A., & Barillot, C. (2019). Simultaneous MRI-EEG during a motor imagery neurofeedback task: an open access brain imaging dataset for multi-modal data integration Authors. BioRxiv. https://doi.org/https://doi.org/10.1101/862375

Mano, Marsel, Anatole Lécuyer, Elise Bannier, Lorraine Perronnet, Saman Noorzadeh, and Christian Barillot. 2017. “How to Build a Hybrid Neurofeedback Platform Combining EEG and FMRI.” Frontiers in Neuroscience 11 (140). https://doi.org/10.3389/fnins.2017.00140 Perronnet, Lorraine, L Anatole, Marsel Mano, Elise Bannier, Maureen Clerc, Christian Barillot, Lorraine Perronnet, et al. 2017. “Unimodal Versus Bimodal EEG-FMRI Neurofeedback of a Motor Imagery Task.” Frontiers in Human Neuroscience 11 (193). https://doi.org/10.3389/fnhum.2017.00193.

This dataset named XP1 can be pull together with the dataset XP2, available here : https://openneuro.org/datasets/ds002338. Data acquisition methods have been described in Perronnet et al. (2017, Frontiers in Human Neuroscience). Simultaneous 64 channels EEG and fMRI during right-hand motor imagery and neurofeedback (NF) were acquired in this study (as well as in XP2). For this study, 10 subjects performed three types of NF runs (bimodal EEG-fMRI NF, unimodal EEG-NF and fMRI-NF).

———————————————————————————————— EXPERIMENTAL PARADIGM ————————————————————————————————
Subjects were instructed to perform a kinaesthetic motor imagery of the right hand and to find their own strategy to control and bring the ball to the target. The experimental protocol consisted of 6 EEG-fMRI runs with a 20s block design alternating rest and task motor localizer run (task-motorloc) - 8 blocks X (20s rest+20 s task) motor imagery run without NF (task-MIpre) -5 blocks X (20s rest+20 s task) three NF runs with different NF conditions (task-eegNF, task-fmriNF, task-eegfmriNF) occurring in random order- 10 blocks X (20s rest+20 s task) motor imagery run without NF (task-MIpost) - 5 blocks X (20s rest+20 s task)

———————————————————————————————— EEG DATA ———————————————————————————————— EEG data was recorded using a 64-channel MR compatible solution from Brain Products (Brain Products GmbH, Gilching, Germany).

RAW EEG DATA

EEG was sampled at 5kHz with FCz as the reference electrode and AFz as the ground electrode, and a resolution of 0.5 microV. Following the BIDs arborescence, raw eeg data for each task can be found for each subject in

XP1/sub-xp1*/eeg

in Brain Vision Recorder format (File Version 1.0). Each raw EEG recording includes three files: the data file (.eeg), the header file (.vhdr) and the marker file (*.vmrk). The header file contains information about acquisition parameters and amplifier setup. For each electrode, the impedance at the beginning of the recording is also specified. For all subjects, channel 32 is the ECG channel. The 63 other channels are EEG channels.

The marker file contains the list of markers assigned to the EEG recordings and their properties (marker type, marker ID and position in data points). Three type of markers are relevant for the EEG processing: R128 (Response): is the fMRI volume marker to correct for the gradient artifact S 99 (Stimulus): is the protocol marker indicating the start of the Rest block S 2 (Stimulus): is the protocol marker indicating the start of the Task (Motor Execution Motor Imagery or Neurofeedback)
Warning : in few EEG data, the first S99 marker might be missing, but can be easily “added” 20 s before the first S 2.

PREPROCESSED EEG DATA

Following the BIDs arborescence, processed eeg data for each task and subject in the pre-processed data folder :

XP1/derivatives/sub-xp1*/eeg_pp/*eeg_pp.*

and following the Brain Analyzer format. Each processed EEG recording includes three files: the data file (.dat), the header file (.vhdr) and the marker file (*.vmrk), containing information similar to those described for raw data. In the header file of preprocessed data channels location are also specified. In the marker file the location in data points of the identified heart pulse (R marker) are specified as well.

EEG data were pre-processed using BrainVision Analyzer II Software, with the following steps: Automatic gradient artifact correction using the artifact template subtraction method (Sliding average calculation with 21 intervals for sliding average and all channels enabled for correction. Downsampling with factor: 25 (200 Hz) Low Pass FIR Filter:Cut-off Frequency: 50 Hz. Ballistocardiogram (pulse) artifact correction using a semiautomatic procedure (Pulse Template searched between 40 s and 240 s in the ECG channel with the following parameters:Coherence Trigger = 0.5, Minimal Amplitude = 0.5, Maximal Amplitude = 1.3. The identified pulses were marked with R. Segmentation relative to the first block marker (S 99) for all the length of the training protocol (las S 2 + 20 s).

EEG NF SCORES

Neurofeedback scores can be found in the .mat structures in

XP1/derivatives/sub-xp1*/NF_eeg/d_sub*NFeeg_scores.mat

Structures names NF_eeg are composed of the following subfields:

NF_eeg → .nf_laterality (NF score computed as for real-time calculation - equation (1))
→ .filteegpow_left (Bandpower of the filtered eeg signal in C1) → .filteegpow_right (Bandpower of the filtered eeg signal in C2) → .nf (vector of NF scores -4 per s- computed as in eq 3) for comparison with XP2 → .smoothed → .eegdata (64 X 200 X 400 matrix, with the pre-processed EEG signals according to the steps described above) → .method

Where the subfield method contains information about the laplacian filtered used and the frequency band of interest.

———————————————————————————————— BOLD fMRI DATA ———————————————————————————————— All DICOM files were converted to Nifti-1 and then in BIDs format (version 2.1.4) using the software dcm2niix (version v1.0.20190720 GVV7.4.0)

fMRI acquisitions were performed using echo- planar imaging (EPI) and covering the entire brain with the following parameters

3T Siemens Verio EPI sequence TR=2 s TE=23 ms Resolution 2x2x4 mm3 FOV = 210×210mm2 N of slices: 32 No slice gap

As specified in the relative task event files in XP1\ *events.tsv files onset, the scanner began the EPI pulse sequence two seconds prior to the start of the protocol (first rest block), so the the first two TRs should be discarded. The useful TRs for the runs are therefore

task-motorloc: 320 s (2 to 322) task-MIpre and task-MIpost: 200 s (2 to 202) task-eegNF, task-fmriNF, task-eegfmriNF: 400 s (2 to 402)

In task events files for the different tasks, each column represents:

• 'onset': onset time (sec) of an event
• 'duration': duration (sec) of the event
• 'trial_type': trial (block) type: rest or task (Rest, Task-ME, Task-MI, Task-NF)
• ''stim_file’: image presented in a stimulus block: during Rest, Motor Imagery (Task-MI) or Motor execution (Task-ME) instructions were presented. On the other hand, during Neurofeedback blocks (Task-NF) the image presented was a ball moving in a square that the subject could control self-regulating his EEG and/or fMRI brain activity.

Following the BIDs arborescence, the functional data and relative metadata are found for each subject in the following directory

XP1/sub-xp1*/func

BOLD-NF SCORES

For each subject and NF session, a matlab structure with BOLD-NF features can be found in

XP1/derivatives/sub-xp1*/NF_bold/

For each subject and NF session, a Matlab structure with BOLD-NF features can be found in

XP1/derivatives/sub-xp1*/NF_bold/

In view of BOLD-NF scores computation, fMRI data were preprocessed using SPM8 and with the following steps: slice-time correction, spatial realignment and coregistration with the anatomical scan, spatial smoothing with a 6 mm Gaussian kernel and normalization to the Montreal Neurological Institute (MNI) template. For each session, a first level general linear model analysis was then performed. The resulting activation maps (voxel-wise Family-Wise error corrected at p < 0.05) were used to define two ROIs (9x9x3 voxels) around the maximum of activation in the left and right motor cortex. The BOLD-NF scores (fMRI laterality index) were calculated as the difference between percentage signal change in the left and right motor ROIs as for the online NF calculation. A smoothed and normalized version of the NF scores over the precedent three volumes was also computed. To allow for comparison and aggregation of the two datasets XP1 and XP2 we also computed NF scores considering the left motor cortex and a background as for online NF calculation in XP2.

In the NF_bold folder, the Matlab files sub-xp1*_task-*_NFbold_scores.mat have therefore the following structure :

NF_bold → .nf_laterality (calculated as for online NF calculation) → .smoothnf_laterality → .normnf_laterality → .nf (calculated as for online NF calculation in XP2) → .roimean_left (averaged BOLD signal in the left motor ROI) → .roimean_right (averaged BOLD signal in the right motor ROI) → .bgmean (averaged BOLD signal in the background slice) → .method

Where the subfield ".method" contains information about the ROI size (.roisize), the background mask (.bgmask) and ROI masks (.roimask_left,.roimask_right ). More details about signal processing and NF calculation can be