BIDS Phenotype External Dataset Example COPY OF "The NIMH Healthy Research Volunteer Dataset" (ds003982)

Modality-agnostic files were copied over and the CHANGES file was updated.

THE ORIGINAL DATASET ds003982 README FOLLOWS

A comprehensive clinical, MRI, and MEG collection characterizing healthy research volunteers collected at the National Institute of Mental Health (NIMH) Intramural Research Program (IRP) in Bethesda, Maryland using medical and mental health assessments, diagnostic and dimensional measures of mental health, cognitive and neuropsychological functioning, structural and functional magnetic resonance imaging (MRI), along with diffusion tensor imaging (DTI), and a comprehensive magnetoencephalography battery (MEG).

In addition, blood samples are currently banked for future genetic analysis. All data collected in this protocol are broadly shared in the OpenNeuro repository, in the Brain Imaging Data Structure (BIDS) format. In addition, blood samples of healthy volunteers are banked for future analyses. All data collected in this protocol are broadly shared here, in the Brain Imaging Data Structure (BIDS) format. In addition, task paradigms and basic pre-processing scripts are shared on GitHub. This dataset is unique in its depth of characterization of a healthy population in terms of brain health and will contribute to a wide array of secondary investigations of non-clinical and clinical research questions.

This dataset is licensed under the Creative Commons Zero (CC0) v1.0 License.

Recruitment

Inclusion criteria for the study require that participants are adults at or over 18 years of age in good health with the ability to read, speak, understand, and provide consent in English. All participants provided electronic informed consent for online screening and written informed consent for all other procedures. Exclusion criteria include:

• A history of significant or unstable medical or mental health condition requiring treatment
• Current self-injury, suicidal thoughts or behavior
• Current illicit drug use by history or urine drug screen
• Abnormal physical exam or laboratory result at the time of in-person assessment
• Less than an 8th grade education or IQ below 70
• Current employees, or first-degree relatives of NIMH employees

Study participants are recruited through direct mailings, bulletin boards and listservs, outreach exhibits, print advertisements, and electronic media.

Clinical Measures

All potential volunteers first visit the study website (https://nimhresearchvolunteer.ctss.nih.gov), check a box indicating consent, and complete preliminary self-report screening questionnaires. The study website is HIPAA compliant and therefore does not collect PII ; instead, participants are instructed to contact the study team to provide their identity and contact information. The questionnaires include demographics, clinical history including medications, disability status (WHODAS 2.0), mental health symptoms (modified DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure), substance use survey (DSM-5 Level 2), alcohol use (AUDIT), handedness (Edinburgh Handedness Inventory), and perceived health ratings. At the conclusion of the questionnaires, participants are again prompted to send an email to the study team. Survey results, supplemented by NIH medical records review (if present), are reviewed by the study team, who determine if the participant is likely eligible for the protocol. These participants are then scheduled for an in-person assessment. Follow-up phone screenings were also used to determine if participants were eligible for in-person screening.

In-person Assessments

At this visit, participants undergo a comprehensive clinical evaluation to determine final eligibility to be included as a healthy research volunteer. The mental health evaluation consists of a psychiatric diagnostic interview (Structured Clinical Interview for DSM-5 Disorders (SCID-5), along with self-report surveys of mood (Beck Depression Inventory-II (BD-II) and anxiety (Beck Anxiety Inventory, BAI) symptoms. An intelligence quotient (IQ) estimation is determined with the Kaufman Brief Intelligence Test, Second Edition (KBIT-2). The KBIT-2 is a brief (20-30 minute) assessment of intellectual functioning administered by a trained examiner. There are three subtests, including verbal knowledge, riddles, and matrices.

Medical Evaluation

Medical evaluation includes medical history elicitation and systematic review of systems. Biological and physiological measures include vital signs (blood pressure, pulse), as well as weight, height, and BMI. Blood and urine samples are taken and a complete blood count, acute care panel, hepatic panel, thyroid stimulating hormone, viral markers (HCV, HBV, HIV), C-reactive protein, creatine kinase, urine drug screen and urine pregnancy tests are performed. In addition, blood samples that can be used for future genomic analysis, development of lymphoblastic cell lines or other biomarker measures are collected and banked with the NIMH Repository and Genomics Resource (Infinity BiologiX). The Family Interview for Genetic Studies (FIGS) was later added to the assessment in order to provide better pedigree information; the Adverse Childhood Events (ACEs) survey was also added to better characterize potential risk factors for psychopathology. The entirety of the in-person assessment not only collects information relevant for eligibility determination, but it also provides a comprehensive set of standardized clinical measures of volunteer health that can be used for secondary research.

MRI Scan

Participants are given the option to consent for a magnetic resonance imaging (MRI) scan, which can serve as a baseline clinical scan to determine normative brain structure, and also as a research scan with the addition of functional sequences (resting state and diffusion tensor imaging). The MR protocol used was initially based on the ADNI-3 basic protocol, but was later modified to include portions of the ABCD protocol in the following manner:

1. The T1 scan from ADNI3 was replaced by the T1 scan from the ABCD protocol.
2. The Axial T2 2D FLAIR acquisition from ADNI2 was added, and fat saturation turned on.
3. Fat saturation was turned on for the pCASL acquisition.
4. The high-resolution in-plane hippocampal 2D T2 scan was removed and replaced with the whole brain 3D T2 scan from the ABCD protocol (which is resolution and bandwidth matched to the T1 scan).
5. The slice-select gradient reversal method was turned on for DTI acquisition, and reconstruction interpolation turned off.
6. Scans for distortion correction were added (reversed-blip scans for DTI and resting state scans).
7. The 3D FLAIR sequence was made optional and replaced by one where the prescription and other acquisition parameters provide resolution and geometric correspondence between the T1 and T2 scans.

At the time of the MRI scan, volunteers are administered a subset of tasks from the NIH Toolbox Cognition Battery. The four tasks include:

1. Flanker inhibitory control and attention task assesses the constructs of attention and executive functioning.
2. Executive functioning is also assessed using a dimensional change card sort test.
3. Episodic memory is evaluated using a picture sequence memory test.
4. Working memory is evaluated using a list sorting test.

MEG

An optional MEG study was added to the protocol approximately one year after the study was initiated, thus there are relatively fewer MEG recordings in comparison to the MRI dataset. MEG studies are performed on a 275 channel CTF MEG system (CTF MEG, Coquiltam BC, Canada). The position of the head was localized at the beginning and end of each recording using three fiducial coils. These coils were placed 1.5 cm above the nasion, and at each ear, 1.5 cm from the tragus on a line between the tragus and the outer canthus of the eye. For 48 participants (as of 2/1/2022), photographs were taken of the three coils and used to mark the points on the T1 weighted structural MRI scan for co-registration. For the remainder of the participants (n=16 as of 2/1/2022), a Brainsight neuronavigation system (Rogue Research, Montréal, Québec, Canada) was used to coregister the MRI and fiducial localizer coils in realtime prior to MEG data acquisition.

Specific Measures within Dataset

Online and In-person behavioral and clinical measures, along with the corresponding phenotype file name, sorted first by measurement location and then by file name.

BIDS Phenotype Aggregation Example COPY OF "The NIMH Healthy Research Volunteer Dataset" (ds003982)

Modality-agnostic files were copied over and the CHANGES file was updated. Data was aggregated using:

python phenotype.py aggregate subject -i segregated_subject -o aggregated_subject

phenotype.py came from the GitHub repository: https://github.com/ericearl/bids-phenotype

THE ORIGINAL DATASET ds003982 README FOLLOWS

A comprehensive clinical, MRI, and MEG collection characterizing healthy research volunteers collected at the National Institute of Mental Health (NIMH) Intramural Research Program (IRP) in Bethesda, Maryland using medical and mental health assessments, diagnostic and dimensional measures of mental health, cognitive and neuropsychological functioning, structural and functional magnetic resonance imaging (MRI), along with diffusion tensor imaging (DTI), and a comprehensive magnetoencephalography battery (MEG).

In addition, blood samples are currently banked for future genetic analysis. All data collected in this protocol are broadly shared in the OpenNeuro repository, in the Brain Imaging Data Structure (BIDS) format. In addition, blood samples of healthy volunteers are banked for future analyses. All data collected in this protocol are broadly shared here, in the Brain Imaging Data Structure (BIDS) format. In addition, task paradigms and basic pre-processing scripts are shared on GitHub. This dataset is unique in its depth of characterization of a healthy population in terms of brain health and will contribute to a wide array of secondary investigations of non-clinical and clinical research questions.

This dataset is licensed under the Creative Commons Zero (CC0) v1.0 License.

Recruitment

Inclusion criteria for the study require that participants are adults at or over 18 years of age in good health with the ability to read, speak, understand, and provide consent in English. All participants provided electronic informed consent for online screening and written informed consent for all other procedures. Exclusion criteria include:

• A history of significant or unstable medical or mental health condition requiring treatment
• Current self-injury, suicidal thoughts or behavior
• Current illicit drug use by history or urine drug screen
• Abnormal physical exam or laboratory result at the time of in-person assessment
• Less than an 8th grade education or IQ below 70
• Current employees, or first-degree relatives of NIMH employees

Study participants are recruited through direct mailings, bulletin boards and listservs, outreach exhibits, print advertisements, and electronic media.

Clinical Measures

All potential volunteers first visit the study website (https://nimhresearchvolunteer.ctss.nih.gov), check a box indicating consent, and complete preliminary self-report screening questionnaires. The study website is HIPAA compliant and therefore does not collect PII ; instead, participants are instructed to contact the study team to provide their identity and contact information. The questionnaires include demographics, clinical history including medications, disability status (WHODAS 2.0), mental health symptoms (modified DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure), substance use survey (DSM-5 Level 2), alcohol use (AUDIT), handedness (Edinburgh Handedness Inventory), and perceived health ratings. At the conclusion of the questionnaires, participants are again prompted to send an email to the study team. Survey results, supplemented by NIH medical records review (if present), are reviewed by the study team, who determine if the participant is likely eligible for the protocol. These participants are then scheduled for an in-person assessment. Follow-up phone screenings were also used to determine if participants were eligible for in-person screening.

In-person Assessments

At this visit, participants undergo a comprehensive clinical evaluation to determine final eligibility to be included as a healthy research volunteer. The mental health evaluation consists of a psychiatric diagnostic interview (Structured Clinical Interview for DSM-5 Disorders (SCID-5), along with self-report surveys of mood (Beck Depression Inventory-II (BD-II) and anxiety (Beck Anxiety Inventory, BAI) symptoms. An intelligence quotient (IQ) estimation is determined with the Kaufman Brief Intelligence Test, Second Edition (KBIT-2). The KBIT-2 is a brief (20-30 minute) assessment of intellectual functioning administered by a trained examiner. There are three subtests, including verbal knowledge, riddles, and matrices.

Medical Evaluation

Medical evaluation includes medical history elicitation and systematic review of systems. Biological and physiological measures include vital signs (blood pressure, pulse), as well as weight, height, and BMI. Blood and urine samples are taken and a complete blood count, acute care panel, hepatic panel, thyroid stimulating hormone, viral markers (HCV, HBV, HIV), C-reactive protein, creatine kinase, urine drug screen and urine pregnancy tests are performed. In addition, blood samples that can be used for future genomic analysis, development of lymphoblastic cell lines or other biomarker measures are collected and banked with the NIMH Repository and Genomics Resource (Infinity BiologiX). The Family Interview for Genetic Studies (FIGS) was later added to the assessment in order to provide better pedigree information; the Adverse Childhood Events (ACEs) survey was also added to better characterize potential risk factors for psychopathology. The entirety of the in-person assessment not only collects information relevant for eligibility determination, but it also provides a comprehensive set of standardized clinical measures of volunteer health that can be used for secondary research.

MRI Scan

Participants are given the option to consent for a magnetic resonance imaging (MRI) scan, which can serve as a baseline clinical scan to determine normative brain structure, and also as a research scan with the addition of functional sequences (resting state and diffusion tensor imaging). The MR protocol used was initially based on the ADNI-3 basic protocol, but was later modified to include portions of the ABCD protocol in the following manner:

1. The T1 scan from ADNI3 was replaced by the T1 scan from the ABCD protocol.
2. The Axial T2 2D FLAIR acquisition from ADNI2 was added, and fat saturation turned on.
3. Fat saturation was turned on for the pCASL acquisition.
4. The high-resolution in-plane hippocampal 2D T2 scan was removed and replaced with the whole brain 3D T2 scan from the ABCD protocol (which is resolution and bandwidth matched to the T1 scan).
5. The slice-select gradient reversal method was turned on for DTI acquisition, and reconstruction interpolation turned off.
6. Scans for distortion correction were added (reversed-blip scans for DTI and resting state scans).
7. The 3D FLAIR sequence was made optional and replaced by one where the prescription and other acquisition parameters provide resolution and geometric correspondence between the T1 and T2 scans.

At the time of the MRI scan, volunteers are administered a subset of tasks from the NIH Toolbox Cognition Battery. The four tasks include:

1. Flanker inhibitory control and attention task assesses the constructs of attention and executive functioning.
2. Executive functioning is also assessed using a dimensional change card sort test.
3. Episodic memory is evaluated using a picture sequence memory test.
4. Working memory is evaluated using a list sorting test.

MEG

An optional MEG study was added to the protocol approximately one year after the study was initiated, thus there are relatively fewer MEG recordings in comparison to the MRI dataset. MEG studies are performed on a 275 channel CTF MEG system (CTF MEG, Coquiltam BC, Canada). The position of the head was localized at the beginning and end of each recording using three fiducial coils. These coils were placed 1.5 cm above the nasion, and at each ear, 1.5 cm from the tragus on a line between the tragus and the outer canthus of the eye. For 48 participants (as of 2/1/2022), photographs were taken of the three coils and used to mark the points on the T1 weighted structural MRI scan for co-registration. For the remainder of the participants (n=16 as of 2/1/2022), a Brainsight neuronavigation system (Rogue Research, Montréal, Québec, Canada) was used to coregister the MRI and fiducial localizer coils in realtime prior to MEG data acquisition.

Specific Measures within Dataset

Online and In-person behavioral and clinical measures, along with the corresponding phenotype file name, sorted first by measurement location and then by file name.

This folder contains data organised in BIDS format to test BIDS Manager-Pipeline (https://github.com/Dynamap/BIDS_Manager/tree/dev).

MEG raw dataset in BIDS format.

Part of THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in brain and behavior.

See related materials in Collection at: https://doi.org/10.25452/figshare.plus.c.6161151

Longitudinal study of pre-symptomatic Alzheimer's Disease

Converted dataset PROJECT_DAYS_P3_NUMBERS in BIDS standard. The data were converted from BrainVision format to BIDS format using a tool created during the master thesis in ZČU.

This data was part of the study of:

M.P. Weisend, F.M. Hanlon, R. Montaño, S.P. Ahlfors, A.C. Leuthold, D. Pantazis, J.C. Mosher, A.P. Georgopoulos, M.S. Hämäläinen, C.J. Aine,, V. (2007). Paving the way for cross-site pooling of magnetoencephalography (MEG) data. International Congress Series, Volume 1300, Pages 615-618,.

It was converted to BIDS with MNE-BIDS:

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Following the MEG-BIDS format:

Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. https://doi.org/10.1038/sdata.2018.110

Includes minimally preprocessed and co-registered dataset for example subject containing both diffusion weighted and T1 weighted MR images, both in BIDS format.

The dataset in the root directory (i.e. starting with /sub-) should be used as input to many end-to-end pipelines.

The dataset in the preprocessed directory (i.e. starting with /derivatives/preproc/) should be used as input to modelling pipelines such as tractometry or connectivity analysis.

MNE-somato-data-bids

This dataset contains the MNE-somato-data in BIDS format.

The conversion can be reproduced through the Python script stored in the /code directory of this dataset. See the README in that directory.

The /derivatives directory contains the outputs of running the FreeSurfer pipeline recon-all on the MRI data with no additional commandline options (only defaults were used):

$ recon-all -i sub-01_T1w.nii.gz -s 01 -all

After the recon-all call, there were further FreeSurfer calls from the MNE API:

$ mne make_scalp_surfaces -s 01 --force $ mne watershed_bem -s 01

The derivatives also contain the forward model *-fwd.fif, which was produced using the source space definition, a *-trans.fif file, and the boundary element model (=conductor model) that lives in freesurfer/subjects/01/bem/*-bem-sol.fif.

The *-trans.fif file is not saved, but can be recovered from the anatomical landmarks in the sub-01/anat/T1w.json file and MNE-BIDS' function get_head_mri_transform.

See: https://github.com/mne-tools/mne-bids for more information.

Notes on FreeSurfer

the FreeSurfer pipeline recon-all was run new for the sake of converting the somato data to BIDS format. This needed to be done to change the "somato" subject name to the BIDS subject label "01". Note, that this is NOT "sub-01", because in BIDS, the "sub-" is just a prefix, whereas the "01" is the subject label.

The main folder contains the raw EEG data in standard bids format. See references.

Code and figures: https://doi.org/10.17605/OSF.IO/PWC4K Manuscript: https://doi.org/10.1101/2023.06.06.543985

References:

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. https://doi.org/10.1038/sdata.2018.110

Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.

This is a eye-tracking dataset in the BIDS format (http://bids.neuroimaging.io/).

Please find the details on the study here:

Gagl B. (2016) Blue hypertext is a good design decision: no perceptual disadvantage in reading and successful highlighting of relevant information. PeerJ 4:e2467 https://doi.org/10.7717/peerj.2467

We disseminate a dataset comprising paired 3T and 7T MRI scans from 20 healthy volunteers, with manual hippocampal subfield annotations on 7T T2-weighted images. This dataset is designed to support the development and evaluation of both 3T-to-7T MR image synthesis models and automated hippocampal segmentation algorithms on 3T images. We assessed the image quality using MRIQC. The dataset is freely accessible on IEEE DataPort, a data repository created by IEEE and can be found at the following URL: https://ieeexplore.ieee.org/document/10218394/algorithms?tabFilter=dataset. The shared dataset comprises four principal directories.The first directory contains raw MRI data in .ima format within rawdata_DICOM. Additionally, the acquired MRI scans were converted from DICOM to the Neuroimaging Informatics Technology Initiative (NIfTI) format and organized in accordance with the Brain Imaging Data Structure (BIDS) format by employing the BIDScoin Python application (version 4.3.0) and stored in rawdata_BIDS directory.The third directory pertains to hippocampal subfield segmentation and includes two subdirectories: 'hippo_subfield\7T_T2w_0.7_for_subfield_delineation', featuring 7T T2w MRI data downsampled to a 0.7 mm slice thickness through B-spline interpolation, post Gaussian smoothing denoising and N4 bias field correction using Advanced Normalization Tools (ANTs); and 'hippo_subfield\hippo_label', which contains the manual segmentation labels for hippocampal subregions for each subject. The fourth directory, \MRIQC, designated for the results of quality control assessments. For each participant, the \MRIQC directories contain \anat and \func subdirectories, which hold image quality metric reports for T1w, T2w, and resting-state functional scans. These quality metrics, available in both .html and .json formats, aid in evaluating data quality and provide estimates of motion, signal-to-noise ratios, and intensity non-uniformities, supplemented with visual reports.It is noteworthy that, due to detectable head motion during the original scans, the 3T T2w images for two participants were subject to rescanning. Subsequently, only the datasets from these supplementary sessions have been preserved within the rawdata\BIDS directory for further quality evaluation. Additionally, Diffusion Weighted Imaging (DWI) sequences included in the rawdata\DICOM directory for 3T MRI were not matched with 7T MRI sequences and, thus, are excluded from the BIDS-formatted shared dataset.

To view the dataset please select the data tab above or use the following link: https://data.texas.gov/dataset/2025-SRWDBTSSBF-Bid-Fair-Submission-Form/fugc-cz79/data_preview

This resource defines the Hierarchical Event Descriptor (HED) specification, including the core specification with detailed rules about the handling of the vocabulary, tool behavior, and errors. This specification lays out the rules that HED-compliant tools must follow to correctly handle HED annotations. It is meant for tool developers and for users who need to understand the details of the behavior. If you are new to HED, please visit the HED homepage or the HED resources site. The current, officially released specification can also be browsed in HTML format. The HED specification is maintained in the GitHub hed-specification repository which is part of the GitHub HED Standard organization. HED is the annotation standard for events and other tabular metadata in the Brain Imaging Data Structure (BIDS) standard. Release 3.1.1 added additional minor corrections and clarifications in the specification document and does not include any enhancements from version 3.0.0.

We present simulation results from a study with The Virtual Brain (TVB). Structural, functional and simulated data have been prepared in accordance with Brain Imaging Data Structure (BIDS) standards and annotated according to the openMINDS metadata framework. This simultaneous electroencephalography (EEG) - functional magnetic resonance imaging (fMRI) resting-state data, diffusion-weighted MRI (dwMRI), and structural MRI were acquired for 50 healthy adult subjects (18 - 80 years of age, mean 41.24±18.33; 31 females, 19 males) at the Berlin Center for Advanced Imaging, Charité University Medicine, Berlin, Germany. We constructed personalized models from this multimodal data of 50 healthy individuals with TVB. We calculated the optimal parameters on an individual basis that predict multiple empirical features in fMRI and EEG, e.g. dynamic functional connectivity and bimodality in the alpha band power, and analyzed inter-individual differences with respect to optimized parameters and structural as well as functional connectivity in a previous study (Triebkorn et al. 2024). We present this large comprehensive empirical and simulated data set in an annotated and structured format following the BIDS Extension Proposal for computational modeling data. We describe how we processed and converted the diverse data sources to make it reusable. In its current form, this dataset can be reused for further research and provides ready-to-use data at various levels of processing including the thereof inferred brain simulation results for a large data set of healthy subjects with a wide age range.

The original MEG dataset was acquired from all 306 sensors (204 gradiometers and 102 magnetometers) during two different sessions for each participant and recorded as .fif files. As each session consists of two data files due to the session break, for better handling of the data, we have merged these files to create one single '.fif' file for each session. Thus, there are two raw '.fif' data files for each participant (i.e. one for each session). It is worth to be noted the data is available in two data formats i.e. MEG-BIDS format '.fif' and MATLAB compatible '.mat' file at the repository. The data directory for MEG-BIDS is defined, where only one subject data structure is illustrated to avoid repetition. The folder named 'MEG_BIDS' contains two files named 'dataset_description.json' and participant.tsv'. Further, there are 17 sub-folders (one for each participant data), each having scan file_scan.tsv' and a sub-folder named meg'. Eachmeg' folder contains five files i.e. _coordsystem.json',_channels.tsv',_events.tsv',_meg.fif', and `_meg.json'.

This dataset contains unprocessed functional MRI (fMRI) data acquired in common marmosets (Callithrix jacchus), The data were obtained during a continuous infusion of the sedative medetomidine, supplemented with a low concentration of isoflurane. All experiments were carried out in accordance with the guidelines from Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes.

Related paper

This dataset supplements the following manuscript.

Preserving functional network structure under anesthesia in the marmoset monkey brain

M Ortiz-Rios, N Sirmpilatze, J Koenig, S Boretius - bioRxiv, 2023

doi: https://doi.org/10.1101/2023.11.21.568138

Data structure

The main data files are organized into eight zipped folders - sub-02.zip, .... sub-09.zip - each constituting a dataset formatted according to the Brain Imaging Data Structure specifications (BIDS v1.6.0).

• Each BIDS-formatted dataset contains subfolders for individual sessions (e.g. ses-0074, etc.).Additionally, a text file, participants.tsv, with some essential information about the subjects (e.g. age, weight, sex).
• Each subject-specific folder contains subfolders named func and anat, storing fMRI and structural MRI data respectively. The (f)MRI data are provided in NIfTI format (suffixed with .nii.gz).
• The func files are named as {sub-id}_{ses-id}_{run-id}_{task-id}_BOLD.nii.gz. The task id is based on runs acquired for resting-state or during visual stimulation.

BIDS-formatted datasets

The basic characteristics of the datasets are given below. More details can be found in the preprint.

1. Marmoset
   • Institution: German Primate Center (Deutsches Primatenzentrum GmbH - Leibniz-Institut für Primatenforschung), Göttingen, Germany
   • MR system: Bruker BioSpec 9.4 T, equpped with B-GA 20S gradient
   • Anatomical MRI scan: Proton density-weighted (PDw) with magnetization transfer (MT) pulse, 1 per subject
   • fMRI scan: GE-EPI, several runs per subject (between 6 and 9 runs), duration 330 s for visual runs and 600 s for resting-state runs, all with a TR of 2 s and a resolution of 0.4 mm isotropic.
   • Subjects: 8 Callithrix jacchus
   • Age range: 3 - 10 years
   • Weight range: 382 - 505 g
   • Sex: 5 males and 3 females
   • Ethics oversight: Lower Saxony State Office for Consumer Protection and Food Safety, Hannover, Germany (approval numbers 33.19-42502-04-17/2496 and 33.19-42502-04-17/2535)

A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking

Description

This dataset was collected in 2020, which combines high-density Electroencephalography (HD-EEG, 128 channels) and mouse-tracking intended as a resource for examining the dynamic decision process of semantics and preference choices in the human brain. The dataset includes high-density resting-state and task-related (food preference choices and semantic judgments) EEG acquired from 31 individuals (ages: 18-33).

EEG acquisition

The EEG data were acquired using a 128-channel cap based on the standard 10/20 System with Electrical Geodesics Inc (EGI, Eugene, Oregon) system. During recording, sampling rate was 1000Hz, and the E129 (Cz) electrode was used as reference. Electrode impedances were kept below 50kohm for each electrode during the experiment.

Main files

sub-*: EEG (.set) and behavior data with BIDS format.

sourcedata/rawdata: Raw .mff EGI data and behavior data with subject information desensitization.

sourcedata/psychopy: Stimuli and PsychoPy scripts for presentation.

derivatives/eeglab-preproc: Preprocessed continuous EEG data with EEGLAB (Easy to set different epoch time windows for further analysis).

Others

Please refer to the corresponding paper and GitHub code to get more details.

References

Chen, K., Wang, R., Huang, J., Gao, F., Yuan, Z., Qi, Y., & Wu, H. (2022). A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking. Scientific Data, 9(1), 416. https://doi.org/10.1038/s41597-022-01538-5

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8

———————————————————————————————— ORIGINAL PAPERS ———————————————————————————————— 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 (DOI: 10.18112/openneuro.ds002338.v1.0.0). 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 by the following subfields: ID : Subject ID, for example sub-xp101 lapC3_ERD : a 1x1280 vector of neurofeedback scores. 4 scores per secondes, for the whole session. eeg : a 64x80200 matrix, with the pre-processed EEG signals with the step described above, filtered between 8 and 30 Hz. lapC3_bandpower_8Hz_30Hz : 1x1280 vector. Bandpower of the filtered signal with a laplacian centred on C3, used to estimate the lapC3_ERD. lapC3_filter : 1x64 vector. Laplacian filter centred on C3 channel.

———————————————————————————————— 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/

In view of BOLD-NF scores computation, fMRI data were preprocessed using AutoMRI, a software based on 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 template For each session, a first level general linear model analysis modeling 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 ipsilesional primary motor area (M1) and supplementary motor area (SMA) respectively.

The BOLD-NF scores were calculated as the difference between percentage signal change in the two ROIs (SMA and M1) and a large deep background region (slice 3 out of 16) whose activity is not correlated with the NF task. A smoothed version of the NF scores over the precedent three volumes was also computed.

The NF_boldi structure has the following structure

NF_bold → .m1 → .nf → .smoothnf
→ .roimean (averaged BOLD signal in the ROI) → .bgmean (averaged BOLD signal in the background slice) → .method
NFscores.fmri → .sma→ .nf → .smoothnf
→ .roimean (averaged BOLD signal in the 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 mask (.roimask).

More details about signal processing and NF calculation can be found in Perronnet et al. 2017 and Perronnet et al. 2018.

———————————————————————————————— ANATOMICAL MRI DATA ———————————————————————————————— As a structural reference for the fMRI analysis, a high resolution 3D T1 MPRAGE sequence was acquired with the following parameters

3T Siemens Verio 3D T1 MPRAGE TR=1.9 s TE=22.6