OMEGA - Resting State Sample Dataset

License

• This dataset was obtained from The Open MEG Archive (OMEGA, https://omega.bic.mni.mcgill.ca).

• You are free to use all data in OMEGA for research purposes; please acknowledge its authors and cite the following reference in your publications if you have used data from OMEGA:

• Niso G., Rogers C., Moreau J.T., Chen L.Y., Madjar C., Das S., Bock E., Tadel F., Evans A.C., Jolicoeur P., Baillet S. (2016). OMEGA: The Open MEG Archive. NeuroImage 124, 1182-1187. doi: https://doi.org/10.1016/j.neuroimage.2015.04.028. OMEGA is available at: https://omega.bic.mni.mcgill.ca

Description

Experiment

• 5 subjects x 5 minute resting sessions, eyes open

MEG acquisition

• Recorded at the Montreal Neurological Institute in 2012-2016
• Acquisition with CTF 275 MEG system at 2400Hz sampling rate
• Anti-aliasing low-pass filter at 600Hz, files may be saved with or without the CTF 3rd order gradient compensation
• Recorded channels (at least 297), include:
  • 26 MEG reference sensors (#2-#27)
  • 270 MEG axial gradiometers (#28-#297)
  • 1 ECG bipolar (EEG057/#298) - Not available in the empty room recordings
  • 1 vertical EOG bipolar (EEG058/#299) - Not available in the empty room recordings
  • 1 horizontal EOG bipolar (EEG059/#300) - Not available in the empty room recordings

Head shape and fiducial points

• 3D digitization using a Polhemus Fastrak device driven by Brainstorm. The .pos files contain:
  • The center of the CTF coils
  • The anatomical references we use in Brainstorm: nasion and ears as illustrated here
  • Around 100 head points distributed on the hard parts of the head (no soft tissues).

Subject anatomy

• Structural T1 image (defaced for anonymization purposes)
• Processed with FreeSurfer 5.3
• The anatomical fiducials (NAS, LPA, RPA) have already been marked and saved in the files fiducials.m

BIDS

• The data in this dataset has been organized according to the MEG-BIDS specification (Brain Imaging Data Structure, http://bids.neuroimaging.io) (Niso et al. 2018)

• 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.M., Tadel F., Wexler J., Baillet S. (2018). MEG-BIDS: an extension to the Brain Imaging Data Structure for magnetoencephalography. Scientific Data; 5, 180110. https://doi.org/10.1038/sdata.2018.110

Release history:

• 2016-12-01: initial release
• 2018-07-18: release OpenNeuro ds000247 (00001 and 00002)

Description of the ASL sequence A sequence with pseudo-continuous labeling, background suppression and 3D RARE Stack-Of-Spirals readout with optional through-plane acceleration was implemented for this study. At the beginning of the sequence, gradients were rapidly played with alternating polarity to correct for their delay in the spiral trajectories, followed by two preparation TRs, to allow the signal to reach the steady state. A non-accelerated readout was played during the preparation TRs, in order to obtain a fully sampled k-space dataset, used for calibration of the parallel imaging reconstruction kernel, needed to reconstruct the skipped kz partitions in the accelerated images.

Description of study Perfusion data were acquired on an elderly cohort using the single-shot, accelerated sequence. For each participant, first a high-resolution anatomical T1-weighted image was acquired with a magnetization prepared rapid gradient echo (MPRAGE) sequence. Resting perfusion data were acquired with a 1-shot 1D-accelerated readout for a total scan duration of 5 min, with labeling and PLD times of 1.5 and 1.5 s. Two M0 images with long TR and no magnetization preparation were acquired per run for CBF quantification purposes.

Comments added by Openfmri Curators

===========================================

General Comments

Defacing

Pydeface was used on all anatomical images to ensure deindentification of subjects. The code can be found at https://github.com/poldracklab/pydeface

Quality Control

Mriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/latest/

Where to discuss the dataset

1) www.openfmri.org/dataset/ds000236/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000236. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email.

Known Issues

N/A

Bids-validator Output

1: This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. (code: 1 - NOT_INCLUDED) /sub-01/func/sub-01_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-01_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-02/func/sub-02_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-02_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-03/func/sub-03_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-03_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-04/func/sub-04_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-04_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-05/func/sub-05_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-05_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-06/func/sub-06_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-06_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-07/func/sub-07_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-07_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-08/func/sub-08_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-08_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-09/func/sub-09_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-09_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-10/func/sub-10_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-10_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-11/func/sub-11_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-11_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-12/func/sub-12_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-12_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-13/func/sub-13_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-13_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-14/func/sub-14_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-14_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-15/func/sub-15_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-15_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-16/func/sub-16_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-16_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-17/func/sub-17_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-17_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-18/func/sub-18_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-18_task-rest_acq-1Daccel1shot_asl.nii.gz /task-rest_asl.json This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: task-rest_asl.json

  Summary:         Available Tasks:    Available Modalities:
  61 Files, 915.87MB                T1w
  18 - Subjects
  1 - Session

This dataset is a BIDS compatible version of the Siena Scalp EEG Database. It reorganizes the file structure to comply with the BIDS specification. To this effect:

Metadata was organized according to BIDS. Data in the EEG edf files was modified to keep only the 19 channels from a 10-20 EEG system. Annotations were formatted as BIDS-score compatible tsv files.

This dataset is a BIDS-compatible version of the CHB-MIT Scalp EEG Database. It reorganizes the file structure to comply with the BIDS specification. To this effect:

The data from subject chb21 was moved to sub-01/ses-02. Metadata was organized according to BIDS. Data in the EEG edf files was modified to keep only the 18 channels from a double banana bipolar montage. Annotations were formatted as BIDS-score compatible tsv files.

Dataset description This dataset is part of a bigger dataset of intracranial EEG (iEEG) called RESPect (Registry for Epilepsy Surgery Patients), a dataset recorded at the University Medical Center of Utrecht, the Netherlands. It consists of 12 patients: six patients recorded intraoperatively using electrocorticography (acute ECoG), six patients with long-term recordings (3 patients recorded with ECoG and 3 patients recorded with stereo-encephalography SEEG). For a detailed description see (Demuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M, on behalf of the RESPect group. “A Practical Workflow for Organizing Clinical Intraoperative and Long-Term iEEG data in BIDS”.).

This data is organized according to the Brain Imaging Data Structure specification. A community- driven specification for organizing neurophysiology data along with its metadata. For more information on this data specification, see https://bids-specification.readthedocs.io/en/stable/

Each patient has their own folder (e.g., sub-RESP0280) which contains the iEEG recordings data for that patient, as well as the metadata needed to understand the raw data and event timing.

Two different implementation of the BIDS structure were done according to the different type of recordings (i.e. intraoperative or long-term) Intraoperative ECoG Surgery with intraoperative ECoG is composed of three main situations that can be logically grouped into BIDS sessions:

• Pre-resection sessions, consisting of all recordings (with different configurations of the grid and strips/depth) carried out before the surgeon has started the planned resection.

• Intermediate sessions, consisting of all subsequent recordings performed before any iterative extension of the resection area.

• Post-resection sessions, consisting of all the recordings performed after the last resection.

Each situation is labelled with an increasing number starting from 1, indicative of the period in time respective to the surgical resection and a consecutive letter (starting from A) indicative of the position of the grid and strip/depth for a given session. As an example see patient RESP0280 who had 4 sessions recorded: two pre-resection sessions, one intermediate sessions and one post-resection session. The first session is SITUATION1A consisting of the first recording, then the grid was moved to another position, resulting in SITUATION1B. After that, the surgeon resected part of the brain and then there was another recording(SITUATION2A). Finally the surgeon applied a resection for the last time and the recording after that was defined as SITUATION3A.

Long-term iEEG In long-term recordings, data that are recorded within one monitoring period are logically grouped in the same BIDS session and stored across runs indicating the day and time point of recording in the monitoring period. If extra electrodes were added/removed during this period, the session was divided into different sessions (e.g. ses-1A and ses-1b). We use the optional run key-value pair to specify the day and the start time of the recording (e.g. run-021315, day 2 after implantation, which is day 1 of the monitoring period, at 13:15). The task key-value pair in long-term iEEG recordings describes the patient’s state during the recording of this file. Different tasks have been defined, such as “rest” when a patient is awake but not doing a specific task, “sleep” when a patient is sleeping the majority of the file, or “SPESclin” when the clinical SPES protocol has been performed in this file. Other task definitions can be found in the annotation syntax (https://github.com/UMCU-EpiLAB/umcuEpi_longterm_ieeg_respect_bids/master/manuals/IFU_annotatingtrc_ECoG).

License This dataset is made available under the Public Domain Dedication and License CC v1.0, whose full text can be found at https://creativecommons.org/publicdomain/zero/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 by citing Demuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M, on behalf of the RESPect group. “A Practical Workflow for Organizing Clinical Intraoperative and Long-Term iEEG data in BIDS”. Submitted to Neuroinformatics. in any publications.

Code available at: https://github.com/UMCU-EpiLAB.

Acknowledgements We would like to thank the patients for providing their data for this dataset, the RESPect team of University Medical Center of Utrecht, for the acquisition of the dataset. Please cite Demuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M, on behalf of the RESPect group. “A Practical Workflow for Organizing Clinical Intraoperative and Long-Term iEEG data in BIDS”. Submitted to Neuroinformatics. in any publications.

Description of the ASL sequence A sequence with pseudo-continuous labeling, background suppression and 3D RARE Stack-Of-Spirals readout with optional through-plane acceleration was implemented for this study. At the beginning of the sequence, gradients were rapidly played with alternating polarity to correct for their delay in the spiral trajectories, followed by two preparation TRs, to allow the signal to reach the steady state. A non-accelerated readout was played during the preparation TRs, in order to obtain a fully sampled k-space dataset, used for calibration of the parallel imaging reconstruction kernel, needed to reconstruct the skipped kz partitions in the accelerated images.

Description of study Perfusion data were acquired on an elderly cohort using the single-shot, accelerated sequence. For each participant, first a high-resolution anatomical T1-weighted image was acquired with a magnetization prepared rapid gradient echo (MPRAGE) sequence. Resting perfusion data were acquired with a 1-shot 1D-accelerated readout for a total scan duration of 5 min, with labeling and PLD times of 1.5 and 1.5 s. Two M0 images with long TR and no magnetization preparation were acquired per run for CBF quantification purposes.

Comments added by Openfmri Curators

===========================================

General Comments

Defacing

Pydeface was used on all anatomical images to ensure deindentification of subjects. The code can be found at https://github.com/poldracklab/pydeface

Quality Control

Mriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/latest/

Where to discuss the dataset

1) www.openfmri.org/dataset/ds000236/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000236. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email.

Known Issues

N/A

Bids-validator Output

1: This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. (code: 1 - NOT_INCLUDED) /sub-01/func/sub-01_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-01_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-02/func/sub-02_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-02_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-03/func/sub-03_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-03_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-04/func/sub-04_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-04_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-05/func/sub-05_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-05_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-06/func/sub-06_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-06_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-07/func/sub-07_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-07_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-08/func/sub-08_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-08_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-09/func/sub-09_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-09_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-10/func/sub-10_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-10_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-11/func/sub-11_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-11_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-12/func/sub-12_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-12_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-13/func/sub-13_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-13_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-14/func/sub-14_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-14_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-15/func/sub-15_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-15_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-16/func/sub-16_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-16_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-17/func/sub-17_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-17_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-18/func/sub-18_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-18_task-rest_acq-1Daccel1shot_asl.nii.gz /task-rest_asl.json This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: task-rest_asl.json

  Summary:         Available Tasks:    Available Modalities:
  61 Files, 915.87MB                T1w
  18 - Subjects
  1 - Session

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.

Summary

One ultimate goal of visual neuroscience is to understand how the brain processes visual stimuli encountered in the natural environment. Achieving this goal requires records of brain responses under massive amounts of naturalistic stimuli. Although the scientific community has put in a lot of effort to collect large-scale functional magnetic resonance imaging (fMRI) data under naturalistic stimuli, more naturalistic fMRI datasets are still urgently needed. We present here the Natural Object Dataset (NOD), a large-scale fMRI dataset containing responses to 57,120 naturalistic images from 30 participants. NOD strives for a balance between sampling variation between individuals and sampling variation between stimuli. This enables NOD to be utilized not only for determining whether an observation is generalizable across many individuals, but also for testing whether a response pattern is generalized to a variety of naturalistic stimuli. We anticipate that the NOD together with existing naturalistic neuroimaging datasets will serve as a new impetus for our understanding of the visual processing of naturalistic stimuli.

Data record

The data were organized according to the Brain-Imaging-Data-Structure (BIDS) Specification version 1.7.0 and can be accessed from the OpenNeuro public repository (accession number: XXX). In short, raw data of each subject were stored in “sub-

Stimulus images The stimulus images for different fMRI experiments are deposited in separate folders: “stimuli/imagenet”, “stimuli/coco”, “stimuli/prf”, and “stimuli/floc”. Each experiment folder contains corresponding stimulus images, and the auxiliary files can be found within the “info” subfolder.

Raw MRI data Each participant folder consists of several session folders: anat, coco, imagenet, prf, floc. Each session folder in turn includes “anat”, “func”, or “fmap” folders for corresponding modality data. The scan information for each session is provided in a TSV file.

Preprocessed volume data from fMRIprep The preprocessed volume-based fMRI data are in subject's native space, saved as “sub-

Preprocessed surface-based data from ciftify The preprocessed surface-based data are in standard fsLR space, saved as “sub-

Brain activation data from surface-based GLM analyses The brain activation data are derived from GLM analyses on the standard fsLR space, saved as “sub-

The bid tabulations listed below reflect grand totals only of the valid bids received. They may not reflect any added or deducted alternatives. Evaluations will be made by the project manager or his representative for correct price extensions, meeting specifications, etc. Once those specifications are completed, the department’s governing board or commission will award the bid to the lowest, responsive, responsible bidder meeting the bid’s specifications. The bids listed may only be the top three responsive bids. Please click on the bid number below to review the results from that particular bid.

This dataset is part of a study investigating brain development in infants using a 0.35T low-field MRI system, specifically optimized for infants. The goal is to enhance our understanding of early brain development and identify potential neurological conditions that may affect long-term cognitive and behavioral outcomes.Participants were recruited from the Children’s Hospital of Zhejiang University School of Medicine. They underwent the 0.35T MRI as part of a differential diagnosis to rule out cerebral complications. Sedation was used during imaging for clinical assessment purposes. This dataset includes scans of 53 female and 47 male Chinese infants without visible abnormalities, conducted between November 9, 2022, and September 28, 2023. The subjects ranged in age from 1 to 70 days post delivery(mean age: 35.66 ± 19.80 days). The dataset includes high-resolution 2D axial T2-weighted images acquired using an optimized fast spin echo technique.Additionally, the dataset includes manual brain mask segmentations for each image, as well as whole-brain segmentations based on an atlas-based method using the MCRIB template. All data are structured according to the BIDS specification. The participants’ age and gender information is provided in the accompanying .tsv file.

BIDS Resting State fMRI Dataset

This dataset contains preprocessed resting state fMRI data collected from 2 subjects using a Siemens 3T MRI scanner. Each subject completed a single resting state scan lasting approximately 6 minutes.

Subjects

• sub-17017
• sub-19738

Scanning Parameters

• TR = 2.0 s
• TE = 30 ms
• Flip angle = 90 degrees
• 32-channel head coil
• Resolution = 3mm isotropic

Preprocessing

The fMRI data were preprocessed using FSL (version 6.0.4) and included motion correction, slice-timing correction, spatial smoothing with a 6mm FWHM Gaussian kernel, and high-pass filtering with a cutoff of 0.01 Hz.

Data Organization

The dataset is organized according to the BIDS specification (version 1.6.0), with each subject's data contained within a separate directory. The directory structure is as follows:

dataset/ ├── sub-17017/ │ ├── anat/ │ ├── func/ │ ├── fmap/ │ ├── dwi/ │ └── ... ├── sub-19738/ │ ├── anat/ │ ├── func/ │ ├── fmap/ │ ├── dwi/ │ └── ... └── README

Please see the BIDS specification for further details on the organization of the data.

Dataset description This dataset is part of a bigger dataset of intracranial EEG (iEEG) called RESPect (Registry for Epilepsy Surgery Patients), a dataset recorded at the University Medical Center of Utrecht, the Netherlands. It consists of 12 patients: six patients recorded intraoperatively using electrocorticography (acute ECoG), six patients with long-term recordings (3 patients recorded with ECoG and 3 patients recorded with stereo-encephalography SEEG). For a detailed description see (link to bids paper).

This data is organized according to the Brain Imaging Data Structure specification. A community- driven specification for organizing neurophysiology data along with its metadata. For more information on this data specification, see https://bids-specification.readthedocs.io/en/stable/

Each patient has their own folder (e.g., sub-RESP0280) which contains the iEEG recordings data for that patient, as well as the metadata needed to understand the raw data and event timing.

Two different implementation of the BIDS structure were done according to the different type of recordings (i.e. intraoperative or long-term) Intraoperative ECoG Surgery with intraoperative ECoG is composed of three main situations that can be logically grouped into BIDS sessions:

• Pre-resection sessions, consisting of all recordings (with different configurations of the grid and strips/depth) carried out before the surgeon has started the planned resection.

• Intermediate sessions, consisting of all subsequent recordings performed before any iterative extension of the resection area.

• Post-resection sessions, consisting of all the recordings performed after the last resection.

Each situation is labelled with an increasing number starting from 1, indicative of the period in time respective to the surgical resection and a consecutive letter (starting from A) indicative of the position of the grid and strip/depth for a given session. As an example see patient RESP0280 who had 4 sessions recorded: two pre-resection sessions, one intermediate sessions and one post-resection session. The first session is SITUATION1A consisting of the first recording, then the grid was moved to another position, resulting in SITUATION1B. After that, the surgeon resected part of the brain and then there was another recording(SITUATION2A). Finally the surgeon applied a resection for the last time and the recording after that was defined as SITUATION3A.

Long-term iEEG In long-term recordings, data that are recorded within one monitoring period are logically grouped in the same BIDS session and stored across runs indicating the day and time point of recording in the monitoring period. If extra electrodes were added/removed during this period, the session was divided into different sessions (e.g. ses-1A and ses-1b). We use the optional run key-value pair to specify the day and the start time of the recording (e.g. run-021315, day 2 after implantation, which is day 1 of the monitoring period, at 13:15). The task key-value pair in long-term iEEG recordings describes the patient’s state during the recording of this file. Different tasks have been defined, such as “rest” when a patient is awake but not doing a specific task, “sleep” when a patient is sleeping the majority of the file, or “SPESclin” when the clinical SPES protocol has been performed in this file. Other task definitions can be found in the annotation syntax (https://github.com/UMCU-EpiLAB/umcuEpi_longterm_ieeg_respect_bids/master/manuals/IFU_annotatingtrc_ECoG).

License This dataset is made available under the Public Domain Dedication and License CC v1.0, whose full text can be found at https://creativecommons.org/publicdomain/zero/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 by citing Demuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M, on behalf of the RESPect group. “A practical workflow for organizing clinical intraoperative and long-term iEEG data in BIDS”, submitted to NeuroInformatics in 2020, in any publications.

Code available at: https://github.com/UMCU-EpiLAB.

Acknowledgements We would like to thank the patients for providing their data for this dataset, the RESPect team of University Medical Center of Utrecht, for the acquisition of the dataset. Please cite Demuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M, on behalf of the RESPect group. “A practical workflow for organizing clinical intraoperative and long-term iEEG data in BIDS”, submitted to NeuroInformatics in 2020, in any publications.

City of Asbury Park is seeking bids for Appliances Bid Specs due 2024-11-26T06:00:00.000Z

In practice, major public procurements involve competitive bidding. A 100 score is earned where all the following conditions are met: 1) bids from competing contractors, suppliers, or vendors are invited through open advertising of the scope, specifications, and terms of the proposed contract, and 2) the criteria by which the bids are evaluated is available for scrutiny. A 50 score is earned where any of the following conditions apply: 1) bids from competing contractors, suppliers, or vendors are invited though open advertising, but the advertising doesn't leave much time for bidders to prepare their offers or it lacks basic components (scope, specifications, or terms of the proposed contract), or 2) the criteria by which the bids are evaluated is not readily available for scrutiny. A 0 score is earned where at least one of the following conditions apply: 1) bids from competing contractors, suppliers, or vendors are rarely or never invited through open advertising of the scope, specifications, and terms of the proposed contract, or 2) the criteria by which the bids are to be evaluated is rarely available for scrutiny. For variable descriptions, please refer to: https://www.africaintegrityindicators.org/data. For the methodology, please refer to: https://static1.squarespace.com/static/5e971d408be44753edfb976c/t/60a55f343d36117866628867/1621450563745/AII10+-+Methodology.docx+%281%29.pdf.

iEEG data from 1 subject (Patient_01) in paper is shown here. The data is stored using the BIDS specification.Due to restrictions on data sharing from CClinic, we were unable to release the raw iEEG data that we received from this site. Dataset from CClinic is available upon request from authors at the CClinic.

The bid tabulations listed below reflect grand totals only of the valid bids received. They may not reflect any added or deducted alternatives. Evaluations will be made by the project manager or his representative for correct price extensions, meeting specifications, etc. Once those specifications are completed, the department’s governing board or commission will award the bid to the lowest, responsive, responsible bidder meeting the bid’s specifications. The bids listed may only be the top three responsive bids. Please click on the bid number below to review the results from that particular bid.

Information

This dataset contains intracranial EEG (iEEG) recordings from 8 patients during single pulse electrical stimulation used in the publication of: Ojeda Valencia G, Gregg N, Huang H, Lundstrom B, Brinkmann B, Pal Attia T, Van Gompel J, Bernstein M, In MH, Huston J, Worrell G, Miller KJ, and Hermes D. 2023. Signatures of electrical stimulation driven network interactions in the human limbic system. Journal of Neuroscience (in press).

License

This dataset is made available under the Public Domain Dedication and License CC v1.0, whose full text can be found at https://creativecommons.org/publicdomain/zero/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 by citing the following in any publication:

Ojeda Valencia G, Gregg N, Huang H, Lundstrom B, Brinkmann B, Pal Attia T, Van Gompel J, Bernstein M, In MH, Huston J, Worrell G, Miller KJ, and Hermes D. 2023. Signatures of electrical stimulation driven network interactions in the human limbic system. Journal of Neuroscience. DOI: https://doi.org/10.1523/JNEUROSCI.2201-22.2023

Task Description

Patients were resting in the hospital bed, while single pulse stimulation was performed. The stimulation had a duration of 200 microseconds, was biphasic and had an amplitude of 6mA. For subject 7 stimulation amplitude was sometimes reduced to 4mA to minimize interictal responses.

Code

Code to analyses these data is available at: https://github.com/MultimodalNeuroimagingLab/HAPwave

Dataset

This data is organized according to the Brain Imaging Data Structure specification (BIDS version 1.12.0). A community- driven specification for organizing neurophysiology data along with its metadata. For more information on this data specification, see https://bids-specification.readthedocs.io/en/stable/ Each subject has their own folder (e.g., ‘sub-01’) containing intracranial EEG (iEEG) recordings from 8 patients during single pulse electrical stimulation, as well as the metadata needed to understand the raw data and event timing.

Acknowledgements

This project was funded by the National Institute Of Mental Health of the National Institutes of Health Brain Initiative under Award Number R01 MH122258, “CRCNS: Processing speed in the human connectome across the lifespan". The overall goal of this project is to develop a large database of single pulse stimulation data and develop tools to advance our understanding of the human connectome across the lifespan. The data was collected by Dora Hermes, Nick Gregg, Brian Lundstrom, Cindy Nelson, Gabriela Ojeda Valencia, Gregg Worrell and Kai J. Miller. The BIDS formatting was performed by Dora Hermes and Gabriela Ojeda Valencia.

Contact

Please contact Dora Hermes (hermes.dora@mayo.edu) or Gabriela Ojeda Valencia (OjedaValencia.Alma@mayo.edu) for questions.

This repository contains the EEG and behavioral data described in:

Fuglsang, S A, Märcher-Rørsted, J, Dau, T, Hjortkjær, J (2020). Effects of sensorineural hearing loss on cortical synchronization to competing speech during selective attention. Journal of Neuroscience, 40(12):2562–2572, https://doi.org/10.1523/JNEUROSCI.1936-19.2020

Please cite this paper when using the data

The data set consists of response data for 22 hearing-impaired and 22 normal-hearing participants. It includes: - EEG data: responses to two-talker and single-talker speech stimuli - Envelopes of the corresponding speech audio - EEG data: responses to 1 kHz tone beeps for ERPs - EEG data: responses to periodic tone sequences for Envelope-following responses (EFRs) - EEG resting-state data recorded with eyes-open and eyes-closed - inEar EEG data for 19 of the 44 subjects (EEG recorded inside the ear canals) - Behavioral data: speech comprehension scores, task difficulty ratings, speech-in-noise scores (SRTs), tone-in-noise scores, digit span working memory scores, SSQ questionnaire ratings - Pure-tone audiograms

For more information, see the README and 'dataset_description.json' file.

Format

The dataset is formatted according to BIDS version 1.3.0 and the BIDS standard extension for EEG (BEP006) that has been merged in the main body of the specification. For more details, see https://bids-specification.readthedocs.io/en/latest/06-extensions.html

Behavioural data are stored in the 'participants.tsv' file. Task-difficulty ratings and multiple choice questionnaire data from the selective attention experiment are stored in the events files (see 'task-selectiveattention_events.json').

Code

Code for analyzing the data is available at: https://gitlab.com/sfugl/snhl

Audio

Envelopes of the audio signals are included in the data set. For inquiries regarding the raw audio data, please send an email to jensh@drcmr.dk with the subject line "ds-eeg-snhl audio".

Acknowledgments

This work was supported by the EU H2020-ICT grant number 644732 (COCOHA: Cognitive Control of a Hearing Aid) and by the Novo Nordisk Foundation synergy grant NNF17OC0027872 (UHeal). The EarEEG were kindly provided by Eriksholm Research Centre.

This dataset contains the EEG recordings of 30 participants in a study conducted by the IT University of Copenhagen brAIn lab, designed to investigate the origins of the Uncanny Valley phenomenon. The study is a follow-up to our pilot study on the Uncanny Valley, also available on Zenodo at https://zenodo.org/records/7948158.

The dataset contains the images that have been shown to the participants, the events, and all the details about the timing and the EEG data. The structure of the dataset follows the Brain Imaging Data Structure specification.

The dataset can be analysed using the scripts available at https://github.com/itubrainlab/uncanny-valley-eeg-study-full-analysis.

The bid tabulations listed below reflect grand totals only of the valid bids received. They may not reflect any added or deducted alternatives. Evaluations will be made by the project manager or his representative for correct price extensions, meeting specifications, etc. Once those specifications are completed, the department’s governing board or commission will award the bid to the lowest, responsive, responsible bidder meeting the bid’s specifications. The bids listed may only be the top three responsive bids. Please click on the bid number below to review the results from that particular bid.