We present processed multimodal empirical data from a study with The Virtual Brain (TVB) based on this data. Structural and functional 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 in a previous study (Triebkorn et al. 2024). We present this large comprehensive processed data set in an annotated and structured format following BIDS standards for derivatives of MRI and 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 for a large data set of healthy subjects with a wide age range.

Overview

This dataset contains raw and pre-processed EEG data from a mobile EEG study investigating the additive effects of task load, motor demand, and environmental complexity on attention. More details will be provided once the manuscript has passed peer-review.

All preprocessing and analysis code is deposited in the code directory. The entire MATLAB pipeline can be reproduced by executing the run_pipeline.m script. In order to run these scripts, you will need to ensure you have the required MATLAB toolboxes and R packages on your system. You will also need to adapt def_local.m to specify local paths to MATLAB and EEGLAB. Descriptive statistics and mixed-effects models can be reproduced in R by running the stat_analysis.R script.

See below for software details.

Citing this dataset

For more information, see the dataset_description.json file.

Format

Dataset is formatted according to the EEG-BIDS extension (Pernet et al., 2019) and the BIDS extension proposal for common electrophysiological derivatives (BEP021) v0.0.1, which can be found here:

https://docs.google.com/document/d/1PmcVs7vg7Th-cGC-UrX8rAhKUHIzOI-uIOh69_mvdlw/edit#heading=h.mqkmyp254xh6

Note that BEP021 is still a work in progress as of 2021-03-01.

Generally, you can find data in the .tsv files and descriptions in the accompanying .json files.

An important BIDS definition to consider is the "Inheritance Principle" (see 3.5 in the BIDS specification: http://bids.neuroimaging.io/bids_spec.pdf), which states:

Any metadata file (​.json​,​.bvec​,​.tsv​, etc.) may be defined at any directory level. The values from the top level are inherited by all lower levels unless they are overridden by a file at the lower level.

Details about the experiment

Forty-four healthy adults aged 18-40 performed an oddball task involving complex tone (piano and horn) stimuli in three settings: (1) sitting in a quiet room in the lab (LAB); (2) walking around a sports field (FIELD); (3) navigating a route through a university campus (CAMPUS).

Participants performed each environmental condition twice: once while attending to oddball stimuli (i.e. counting the number of presented deviant tones; COUNT), and once while disregarding or ignoring the tone stimuli (IGNORE).

EEG signals were recorded from 32 active electrodes using a Brain Vision LiveAmp 32 amplifier. See manuscript for further details.

MATLAB software details

MATLAB Version: 9.7.0.1319299 (R2019b) Update 5 MATLAB License Number: 678256 Operating System: Microsoft Windows 10 Enterprise Version 10.0 (Build 18363) Java Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode

• MATLAB (v9.7)
• Simulink (v10.0)
• Curve Fitting Toolbox (v3.5.10)
• DSP System Toolbox (v9.9)
• Image Processing Toolbox (v11.0)
• MATLAB Compiler (v7.1)
• MATLAB Compiler SDK (v6.7)
• Parallel Computing Toolbox (v7.1)
• Signal Processing Toolbox (v8.3)
• Statistics and Machine Learning Toolbox (v11.6)
• Symbolic Math Toolbox (v8.4)
• Wavelet Toolbox (v5.3)

The following toolboxes/helper functions were also used:

• EEGLAB (v2019.1)
• ERPLAB (v8.02)
• ICLabel (v1.3)
• clean_rawdata (v2.3)
• bids-matlab-tools (v5.2)
• dipfit (v3.4)
• firfilt (v2.4)
• export_fig (v3.12)
• ColorBrewer (v3.1.0)

R software details

R version 3.6.2 (2019-12-12)

Platform: x86_64-w64-mingw32/x64 (64-bit)

locale: _LC_COLLATE=English_Australia.1252_, _LC_CTYPE=English_Australia.1252_, _LC_MONETARY=English_Australia.1252_, _LC_NUMERIC=C_ and _LC_TIME=English_Australia.1252_

attached base packages:

• stats
• graphics
• grDevices
• utils
• datasets
• methods
• base

other attached packages:

• sjPlot(v.2.8.7)
• emmeans(v.1.5.1)
• car(v.3.0-10)
• carData(v.3.0-4)
• lme4(v.1.1-23)
• Matrix(v.1.2-18)
• data.table(v.1.13.0)
• forcats(v.0.5.0)
• stringr(v.1.4.0)
• dplyr(v.1.0.2)
• purrr(v.0.3.4)
• readr(v.1.4.0)
• tidyr(v.1.1.2)
• tibble(v.3.0.4)
• ggplot2(v.3.3.2)
• tidyverse(v.1.3.0)

loaded via a namespace (and not attached):

• nlme(v.3.1-149)
• pbkrtest(v.0.4-8.6)
• fs(v.1.5.0)
• lubridate(v.1.7.9)
• insight(v.0.12.0)
• httr(v.1.4.2)
• numDeriv(v.2016.8-1.1)
• tools(v.3.6.2)
• backports(v.1.1.10)
• utf8(v.1.1.4)
• R6(v.2.4.1)
• sjlabelled(v.1.1.7)
• DBI(v.1.1.0)
• colorspace(v.1.4-1)
• withr(v.2.3.0)
• tidyselect(v.1.1.0)
• curl(v.4.3)
• compiler(v.3.6.2)
• performance(v.0.5.0)
• cli(v.2.1.0)
• rvest(v.0.3.6)
• xml2(v.1.3.2)
• sandwich(v.3.0-0)
• labeling(v.0.3)
• bayestestR(v.0.7.2)
• scales(v.1.1.1)
• mvtnorm(v.1.1-1)
• digest(v.0.6.25)
• foreign(v.0.8-76)
• minqa(v.1.2.4)
• rio(v.0.5.16)
• pkgconfig(v.2.0.3)
• dbplyr(v.1.4.4)
• rlang(v.0.4.8)
• readxl(v.1.3.1)
• rstudioapi(v.0.11)
• farver(v.2.0.3)
• generics(v.0.0.2)
• zoo(v.1.8-8)
• jsonlite(v.1.7.1)
• zip(v.2.1.1)
• magrittr(v.1.5)
• parameters(v.0.8.6)
• Rcpp(v.1.0.5)
• munsell(v.0.5.0)
• fansi(v.0.4.1)
• abind(v.1.4-5)
• lifecycle(v.0.2.0)
• stringi(v.1.4.6)
• multcomp(v.1.4-14)
• MASS(v.7.3-53)
• plyr(v.1.8.6)
• grid(v.3.6.2)
• blob(v.1.2.1)
• parallel(v.3.6.2)
• sjmisc(v.2.8.6)
• crayon(v.1.3.4)
• lattice(v.0.20-41)
• ggeffects(v.0.16.0)
• haven(v.2.3.1)
• splines(v.3.6.2)
• pander(v.0.6.3)
• sjstats(v.0.18.1)
• hms(v.0.5.3)
• knitr(v.1.30)
• pillar(v.1.4.6)
• boot(v.1.3-25)
• estimability(v.1.3)
• effectsize(v.0.3.3)
• codetools(v.0.2-16)
• reprex(v.0.3.0)
• glue(v.1.4.2)
• modelr(v.0.1.8)
• vctrs(v.0.3.4)
• nloptr(v.1.2.2.2)
• cellranger(v.1.1.0)
• gtable(v.0.3.0)
• assertthat(v.0.2.1)
• xfun(v.0.18)
• openxlsx(v.4.2.2)
• xtable(v.1.8-4)
• broom(v.0.7.1)
• coda(v.0.19-4)
• survival(v.3.2-7)
• lmerTest(v.3.1-3)
• statmod(v.1.4.34)
• TH.data(v.1.0-10)
• ellipsis(v.0.3.1)

Data repository for Brain Imaging Data: MRI, PET, MEG, EEG, etc .. similar to OpenNeuro where each dataset is shared independently. Platform is GDPR compliant, allowing to share brain imaging data as FAIRly as possibly. The catalogue allows all metadata to be open and thus findable. As EU data have to be protected, we provide the necessary privacy protection by controlling user access.

About this dataset

This dataset was acquired using the spine-generic protocol on a 38 y.o. male healthy subject, across multiple sites and multiple MRI vendors and models. The list of sites is available in participants.tsv.

The contributors have the necessary ethics & permissions to share the data publicly. The dataset does not include any identifiable personal health information, including names, zip codes, dates of birth, facial features on structural scans.

The dataset is about 1 GB and it is structured according to the BIDS convention.

Download

We are using a tool to manage large datasets called git-annex. To download this dataset, you need to have `git` installed, and also install `git-annex` at version 8. Then run:

git clone https://github.com/spine-generic/data-multi-subject && \
cd data-multi-subject && \
git annex init && \
git annex get

You may substitute `git annex get` with more specific commands if you are only interested in certain subjects. For example:

git annex get sub-nwu01/ sub-nwu03/ sub-nwu04/ sub-oxfordFmrib04/ sub-tokyoSkyra*/

Analysis

The instructions to process this dataset are available in the spine-generic documentation.

Executive Function

To find out more about this study, you can examine the documentation and scan protocolshere.

This repository contains a set of BIDS-compatible datasets with T1s, two rest runs and corresponding functional maps for three subjects. Both .nii.gz and .json files are available for each of these, along with a dataset_description.json, as well as their fMRIPrep derivatives.

Information about Files:

Provenance:

These files were obtained from Flywheel, defaced and run through fMRIPrep.

This is a collection of Structural T1w MRI scans from the notorious IXI dataset. The data was preprocessed using Freesurfer 8. It does not include all the accessory data from the preprocessing pipeline, but it does include the structural skullstripped brain (brainmask.mgz) and the aseg mask. Additional step of preprocessing are applied, which will be briefly illustrated.

The main folder (IXI) is organised in a semi BIDS format. Each subject has a specific folder (sub-IXI[digits]), with additional subfolders:

_IXI

|_sub-IXI001 <- subject folder

| |_ses-1 <- session folder

| | |_run-1 <- run folder

| | | |_anat <- anatomical folder

| | | | |_sub-IXI001_acq-GE-1.5T_mni_registered_T1w.nii <- structural T1w scan

| | | | |_sub-IXI001_acq-GE-1.5T_segmask_mni_registered_T1w.nii <- segmentation mask

The structural T1w scan is obtained from brainmask.mgz output of FS8 via a preprocessing scripts that does the following operations - mriconvert on the brainmask - fslreorient2std - flirt operation with simple affine transformation to the MNI152 Standard Template transformation matrix is computed and applied to the segmentation mask.

Additionally, 2 csvs file are provided: - subjects.csv contains demographic data of the subjects, linking to the relative path of the subject folders - thickness.csv contains information on the derivative cortical thickness measures obtained via parcellation annotation of the DKTAtlas (aparc.DKTatlas.annot). We provide both mean_thickness_weighted and mean_thickness_simple. The first one is the average for the candidate based on the vertex area size, while the other is a simple mean. Information about the computation can be found on the related github repo (TODO).

This dataset will form the basis of a forthcoming publication regarding a model-based Bayesian analysis of streamlined quantitative BOLD data to measure brain oxygenation. In the absence of a reference to this publication the methods used are outlined here. Please reference this dataset if you use it in your work.

Cherukara MT, Stone AJ, Chappell MA, Blockley NP. Data acquired to demonstrate model-based Bayesian inference of brain oxygenation using quantitative BOLD, Oxford University Research Archive 2018. doi: 10.5287/bodleian:6R5px9K0X

*Summary*

Data for Study 1 (7 subjects) of this dataset were acquired as part of a previous study [1] and are also available at via the Oxford University Research Archive [2]. Data for Study 2 (5 subjects) were acquired to demonstrate and validate a model-based Bayesian analysis method of similar data. The aim in this part was to see whether a model-based correction for CSF signal, using an independent CSF partial volume estimate, could be used in place of a FLAIR preparation [3] in order to improve image SNR and reduce total scan time. Images were acquired the streamlined qBOLD protocol [1] both with and without FLAIR preparation, using an Asymmetric Spin Echo (ASE) pulse sequence [4] with Gradient Echo Slice Excitation Profile Imaging (GESEPI) incorporated to minimise the effect of through-slice magnetic field gradients [5].

*MRI data*

Images were acquired using a Siemens Magnetom Verio scanner at 3T. The body coil was used for transmission and the manufacturer's 32-channel head coil was used for reception. For Study 1, GESEPI ASE (GASE) data were acquired with a field of view of 240x240 mm2, a 64x64 matrix, ten 5mm slices, TR/TE=3s/74ms, and an EPI bandwidth of 2004Hz/px. ASE images are acquired with varying amount of R2′ weighting determined by the spin echo displacement time, tau. Twenty four values of tau were acquired for each GASE scan: -28, -24, -20, -16, -12, -8, -4, 0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, and 64ms. For Study 2, GASE data were acquired with a 220x220 mm2 field of view, a 96x96 matrix, eight 5mm slices, TR/TE=3s/82ms, an EPI bandwidth of 2004Hz/px, and eleven tau values: -16, -8, 0, 8, 16, 24, 32, 40, 48, 56, and 64ms. For all subjects, the GESEPI magnetic field gradient correction technique required each 5mm slice to be encoded into multiple thin partitions each 1.25mm thick. Furthermore, partitions were oversampled by 100% leading to the acquisition of 8 partitions per slice. Oversampled slices were discarded during reconstruction, resulting in 40 slices being acquired for each tau value. To regain signal to noise ratio summing the slices in blocks of four is suggested. This results in the original number of prescribed slices. A FLAIR preparation was used to null the signal from CSF, with an inversion time of 1.21s, based on literature values for T1 and T2 of CSF [3]. In Study 2, GASE data were also acquired with the same protocol but without the FLAIR preparation, as well as a single GASE volume with the same parameters, except with a TE of 250ms, and tau of 0ms, and a set of eight 2D spin echo volumes with the same dimensions as the GASE data were acquired with TE values uniformly spaced from 66 to 248 ms. These were used to generate T2 weighted estimates of CSF partial volume. High resolution T1 weighted anatomical images were acquired for registration and the generation of tissue masks and T1 weighted CSF partial volume estimates. Anatomicals were “defaced” using the shell script in the code directory [6].

*Data curation*

The structure in which this data has been placed is based on the Brain Imaging Data Structure (BIDS) format [7]. However, this format (BIDS version 1.0.0-rc2) does not support ASE data, but we have followed the guiding principles of this specification.

*References*

1. Stone AJ, Blockley NP. A streamlined acquisition for mapping baseline brain oxygenation using quantitative BOLD. NeuroImage 2017:147:79-88.

2. Stone AJ, Blockley NP. Data acquired to demonstrate a streamlined approach to mapping and quantifying brain oxygenation using quantitative BOLD. Oxford University Research Archive 2016. doi: 10.5287/Bodleian:E24JbXQwO.

3. Hajnal JV, Bryant DJ, Kasuboski L, Pattany PM, De Coene B, Lewis PD, Pennock JM, Oatridge A, Young IR, Bydder GM. Use of fluid attenuated inversion recovery (FLAIR) pulse sequences in MRI of the brain. J Comput Assist Tomogr 1992;16:841–844.

4. Wismer GL, Buxton RB, Rosen BR, Fisel CR, Oot RF, Brady TJ, Davis KR. Susceptibility induced MR line broadening: applications to brain iron mapping. J Comput Assist Tomogr 1988;12:259–265.

5. Blockley NP, Stone AJ. Improving the specificity of R2′ to the deoxyhaemoglobin content of brain tissue: Prospective correction of macroscopic magnetic field gradients. Neuroimage 2016, in press. doi: 10.1016/j.neuroimage.2016.04.013

6. https://github.com/hanke/gumpdata/blob/master/scripts/conversion/convert_dicoms_anatomy

7. http://bids.neuroimaging.io

This archive contains a raw DICOM dataset acquired (with informed consent) using the ReproIn naming convention on a Siemens Skyra 3T MRI scanner. The dataset includes a T1-weighted anatomical image, four functional runs with the “prettymouth” spoken story stimulus, and one functional run with a block design emotional faces task, as well as auxiliary scans (e.g., scout, soundcheck). The “prettymouth” story stimulus created by Yeshurun et al., 2017 and is available as part of the Narratives collection, and the emotional faces task is similar to Chai et al., 2015. These data are intended for use with the Princeton Handbook for Reproducible Neuroimaging. The handbook provides guidelines for BIDS conversion and execution of BIDS apps (e.g., fMRIPrep, MRIQC). The brain data are contributed by author S.A.N. and are authorized for non-anonymized distribution.

The dataset contains raw functional MRI data from 8 congenitally blind individual who listened to natural sounds while being scanned, as well as corresponding T1-weighted anatomical images, and events files with trial order, onsets and durations. The dataset is provided in BIDS format.

Supporting materials for the GX Dataset.

The GX Dataset is a dataset of combined tES, EEG, physiological, and behavioral signals from human subjects.

Publication

A full data descriptor is published in Nature Scientific Data. Please cite this work as:

Gebodh, N., Esmaeilpour, Z., Datta, A. et al. Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial electrical stimulation. Sci Data 8, 274 (2021). https://doi.org/10.1038/s41597-021-01046-y

Descriptions

A dataset combining high-density electroencephalography (EEG) with physiological and continuous behavioral metrics during transcranial electrical stimulation (tES; including tDCS and tACS). Data includes within subject application of nine High-Definition tES (HD-tES) types targeted three brain regions (frontal, motor, parietal) with three waveforms (DC, 5Hz, 30Hz), with more than 783 total stimulation trials over 62 sessions with EEG, physiological (ECG or EKG, EOG), and continuous behavioral vigilance/alertness metrics (CTT task).

Acknowledgments

Portions of this study were funded by X (formerly Google X), the Moonshot Factory. The funding source had no influence on study conduction or result evaluation. MB is further supported by grants from the National Institutes of Health: R01NS101362, R01NS095123, R01NS112996, R01MH111896, R01MH109289, and (to NG) NIH-G-RISE T32GM136499.

We would like to thank Yuxin Xu and Michaela Chum for all their technical assistance.

Extras

For downsampled data (1 kHz ) please see (in .mat format):

• Experiment 1 : https://doi.org/10.5281/zenodo.3840614
• Experiment 2 : https://doi.org/10.5281/zenodo.3840616

Code used to import, process, and plot this dataset can be found here:

• GitHub : https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior

Additional figures for this project have been shared on Figshare. Trial-wise figures can be found here:

• PSD: https://doi.org/10.6084/m9.figshare.14810517.v1
• Topoplots During Stimulation: https://doi.org/10.6084/m9.figshare.14810478
• Voltage timeseries, spectrogram and behavior: https://doi.org/10.6084/m9.figshare.14810442.v1

The full dataset is also provided in BIDS format here:

• Data in BIDS format: https://doi.org/10.18112/openneuro.ds003670.v1.1.0

Data License
Creative Common 4.0 with attribution (CC BY 4.0)

NOTE

Please email ngebodh01@citymail.cuny.edu with any questions.

Synopsis This is the GX dataset formatted to comply with BIDS standard format.

The tES/EEG/CTT/Vigilance experiment contains 19 unique participants (some repeated experiments). Over a 70 min period EEG/ECG/EOG were recorded concurrently with a CTT where participants maintained a ball at the center of the screen and were periodically stimulated (with low-intensity noninvasive brain stimulation) for 30 secs with combinations of 9 stimulation montages.

For the raw data please see: https://zenodo.org/record/4456079

For methodological details please see corresponding article titled: Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial Electrical Stimulation

Data Descriptor Abstract We present a dataset combining human-participant high-density electroencephalography (EEG) with physiological and continuous behavioral metrics during transcranial electrical stimulation (tES). Data include within participant application of nine High-Definition tES (HD-tES) types, targeting three cortical regions (frontal, motor, parietal) with three stimulation waveforms (DC, 5 Hz, 30 Hz); more than 783 total stimulation trials over 62 sessions with EEG, physiological (ECG, EOG), and continuous behavioral vigilance/alertness metrics. Experiment 1 and 2 consisted of participants performing a continuous vigilance/alertness task over three 70-minute and two 70.5-minute sessions, respectively. Demographic data were collected, as well as self-reported wellness questionnaires before and after each session. Participants received all 9 stimulation types in Experiment 1, with each session including three stimulation types, with 4 trials per type. Participants received 2 stimulation types in Experiment 2, with 20 trials of a given stimulation type per session. Within-participant reliability was tested by repeating select sessions. This unique dataset supports a range of hypothesis testing including interactions of tDCS/tACS location and frequency, brain-state, physiology, fatigue, and cognitive performance.

For more details please see the full data descriptor article.

Code used to import and process this dataset can be found here: GitHub : https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior

For downsampled data please see: Experiment 1 : https://doi.org/10.5281/zenodo.3840615 Experiment 2 : https://doi.org/10.5281/zenodo.3840617

• Nigel Gebodh (May 26th, 2021)

Data for Reading reshapes stimulus selectivity in the visual word form area

This contains the raw and pre-processed fMRI data and structural images (T1) used in the article, "Reading reshapes stimulus selectivity in the visual word form area. The preprint is available here, and the article will be in press at eNeuro.

Additional processed data and analysis code are available in an OSF repository.

Details about the study are included here.

Participants

We recruited 17 participants (Age range 19 to 38, 21.12 ± 4.44, 4 self-identified as male, 1 left-handed) from the Barnard College and Columbia University student body. The study was approved by the Internal Review Board at Barnard College, Columbia University. All participants provided written informed consent, acquired digitally, and were monetarily compensated for their participation. All participants had learned English before the age of five.

To ensure high data quality, we used the following criteria for excluding functional runs and participants. If the participant moved by a distance greater than 2 voxels (4 mm) within a single run, that run was excluded from analysis. Additionally, if the participant responded in less than 50% of the trials in the main experiment, that run was removed. Finally, if half or more of the runs met any of these criteria for a single participant, that participant was dropped from the dataset. Using these constraints, the analysis reported here is based on data from 16 participants. They ranged in age from 19 to 38 years (mean = 21.12 ± 4.58,). 4 participants self-identified as male, and 1 was left-handed. A total of 6 runs were removed from three of the remaining participants due to excessive head motion.

Equipment

We collected MRI data at the Zuckerman Institute, Columbia University, a 3T Siemens Prisma scanner and a 64-channel head coil. In each MR session, we acquired a T1 weighted structural scan, with voxels measuring 1 mm isometrically. We acquired functional data with a T2* echo planar imaging sequences with multiband echo sequencing (SMS3) for whole brain coverage. The TR was 1.5s, TE was 30 ms and the flip angle was 62°. The voxel size was 2 mm isotropic.

Stimuli were presented on an LCD screen that the participants viewed through a mirror with a viewing distance of 142 cm. The display had a resolution of 1920 by 1080 pixels, and a refresh rate of 60 Hz. We presented the stimuli using custom code written in MATLAB and the Psychophysics Toolbox (Brainard, 1997; Pelli, 1997). Throughout the scan, we recorded monocular gaze position using an SR Research Eyelink 1000 tracker. Participants responded with their right hand via three buttons on an MR-safe response pad.

Tasks

Main Task

Participants performed three different tasks during different runs, two of which required attending to the character strings, and one that encouraged participants to ignore them. In the lexical decision task, participants reported whether the character string on each trial was a real word or not. In the stimulus color task, participants reported whether the color of the character string was red or gray. In the fixation color task, participants reported whether or not the fixation dot turned red.

On each trial, a single character string flashed for 150 ms at one of three locations: centered at fixation, 3 dva left, or 3 dva right). The stimulus was followed by a blank with only the fixation mark present for 3850 ms, during which the participant had the opportunity to respond with a button press. After every five trials, there was a rest period (no task except to fixation on the dot). The duration of the rest period was either 4, 6 or 8 s in duration (randomly and uniformly selected).

Localizer for visual category-selective ventral temporal regions

Participants viewed sequences of images, each of which contained 3 items of one category: words, pseudowords, false fonts, faces, and limbs. Participants performed a one-back repetition detection task. On 33% of the trials, the exact same images flashed twice in a row. The participant’s task was to push a button with their right index finger whenever they detected such a repetition. Each participant performed 4 runs of the localizer task. Each run consisted of 77 four-second trials, lasting for approximately 6 minutes. Each category was presented 56 times through the course of the experiment.

Localizer for language processing regions

The stimuli on each trial were a sequence of 12 written words or pronounceable pseudowords, presented one at a time. The words were presented as meaningful sentences, while pseudowords formed “Jabberwocky” phrases that served as a control condition. Participants were instructed to read the stimuli silently to themselves, and also to push a button upon seeing the icon of a hand that appeared between trials. Participants performed three runs of the language localizer. Each run included 16 trials and lasted for 6 minutes. Each trial lasted for 6s, beginning with a blank screen for 100ms, followed by the presentation of 12 words or pseudowords for 450ms each (5400s total), followed by a response prompt for 400ms and a final blank screen for 100ms. Each run also included 5 blank trials (6 seconds each).

Data organization

This repository contains three main folders, complying with BIDS specifications. - Inputs contain BIDS compliant raw data, with the only change being defacing the anatomicals using pydeface. Data was converted to BIDS format using heudiconv.
- Outputs contain preprocessed data obtained using fMRIPrep. In addition to subject specific folders, we also provide the freesurfer reconstructions obtained using fMRIPrep, with defaced anatomicals. Subject specific ROIs are also included in the label folder for each subject in the freesurfer directory. - Derivatives contain all additional whole brain analyses performed on this dataset.

GX Dataset downsampled - Experiment 1

The GX Dataset is a dataset of combined tES, EEG, physiological, and behavioral signals from human subjects.
Here the GX Dataset for Experiment 1 is downsampled to 1 kHz and saved in .MAT format which can be used in both MATLAB and Python.

Publication

A full data descriptor is published in Nature Scientific Data. Please cite this work as:

Gebodh, N., Esmaeilpour, Z., Datta, A. et al. Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial electrical stimulation. Sci Data 8, 274 (2021). https://doi.org/10.1038/s41597-021-01046-y

Descriptions

A dataset combining high-density electroencephalography (EEG) with physiological and continuous behavioral metrics during transcranial electrical stimulation (tES). Data includes within subject application of nine High-Definition tES (HD-tES) types targeted three brain regions (frontal, motor, parietal) with three waveforms (DC, 5Hz, 30Hz), with more than 783 total stimulation trials over 62 sessions with EEG, physiological (ECG, EOG), and continuous behavioral vigilance/alertness metrics.

Acknowledgments

Portions of this study were funded by X (formerly Google X), the Moonshot Factory. The funding source had no influence on study conduction or result evaluation. MB is further supported by grants from the National Institutes of Health: R01NS101362, R01NS095123, R01NS112996, R01MH111896, R01MH109289, and (to NG) NIH-G-RISE T32GM136499.

Extras

Back to Full GX Dataset : https://doi.org/10.5281/zenodo.4456079

For downsampled data (1 kHz ) please see (in .mat format):

• Experiment 1 : https://doi.org/10.5281/zenodo.3840615
• Experiment 2 : https://doi.org/10.5281/zenodo.3840617

Code used to import, process, and plot this dataset can be found here:

• GitHub : https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior

Additional figures for this project have been shared on Figshare. Trial-wise figures can be found here:

• PSD: https://doi.org/10.6084/m9.figshare.14810517.v1
• Topoplots During Stimulation: https://doi.org/10.6084/m9.figshare.14810478
• Voltage timeseries, spectrogram and behavior: https://doi.org/10.6084/m9.figshare.14810442.v1

The full dataset is also provided in BIDS format here:

• Data in BIDS format: https://doi.org/10.18112/openneuro.ds003670.v1.1.0

Data License
Creative Common 4.0 with attribution (CC BY 4.0)

NOTE

Please email ngebodh01@citymail.cuny.edu with any questions.

Follow @NigelGebodh for latest updates.

Spinal Cord fMRI Segmentation Database (Multi-Subject)

This dataset was acquired using various EPI protocols on multiple subjects, multiple sites and multiple MRI vendors and models to develop a method to automate the time-consuming segmentation of the spinal cord for fMRI. The list of subjects is available in participants.tsv.

This dataset follows the BIDS convention. The contributors have the necessary ethics & permissions to share the data publicly.

The dataset does not include any identifiable personal health information, including names, zip codes, dates of birth, facial features.

Link to the full data: https://openneuro.org/datasets/ds004926/versions/1.3.0.

If you reference this dataset in your publications, please cite the following publication: XXX.

This dataset contains a subset of the data that was collected looking at the inhibition of young amateur soccer players. All participants were male, with an average age of 16.4. Participants performed a stop signal task. The dataset contains anatomical and functional MRI images, and information about reaction times.

Summary

Metrics from diffusion imaging are sensitive and robust markers for microstructural tissue alterations in cerebral small vessel disease (SVD). Reproducibility of diffusion metrics is essential for the use in clinical routine and multi-center trials. We therefore provide this inter-site (inter-scanner) dataset of 10 patients with the genetically-defined small vessel disease CADASIL, who have been scanned on 2 different 3 Tesla Siemens MRI scanners (Magnetom Prisma und Magnetom Skyra) within less than 24 hours. The two scanners were located at the same hospital, but in different buildings with independent infrastructure, which is why we consider this an inter-site dataset.

The dataset has already been used to determine the reproducibility of commonly assessed diffusion metrics (Konieczny & Dewenter et al., Neurology 2020, https://doi.org/10.1212/wnl.0000000000011213).

Terms of Use

The dataset is provided under CC BY-NC 4.0 license and can be freely downloaded and used for non-commercial purposes. The use of the dataset must be acknowledged through proper citation of Konieczny & Dewenter et al., Neurology 2020.

Methods

Patients (n=10)

CADASIL patients (diagosed via molecular genetic testing or skin biopsy) were recruited at the Institute for Stroke and Dementia Research (Munich, Germany).

ICV = intracranial volume, IQR = inter-quartile range, WMH = white matter hyperintensities.

MRI protocol

Patients were scanned on 2 Siemens 3T Magnetom MRI scanners (Siemens Healthineers, Erlangen, Germany) in separate buildings at the LMU University Hospital. Key acquisition parameters (b-values of shells, diffusion-encoding directions, resolution) were harmonized between the two scanners. However, the weaker gradient system of the Magnetom Skyra resulted in longer repetition and echo times.

/sub-01
  /ses-prisma
    /dwi_raw
    /dwi_preprocessed
  /ses-skyra
    /dwi_raw
    /dwi_preprocessed

Aphasia Recovery Cohort (ARC) Dataset - Mini Sample

  Dataset Description

This is the Aphasia Recovery Cohort (ARC) Dataset, formatted in BIDS (Brain Imaging Data Structure). Here a mirror of the original dataset as given on OpenNeuro is provided. The Aphasia Recovery Cohort is an open-source chronic stroke repository containing neuroimaging data from aphasia patients.

  Dataset Information

Full Dataset Name: Aphasia Recovery Cohort (ARC) Dataset BIDS Version:… See the full description on the dataset page: https://huggingface.co/datasets/TobiasPitters/ds004884.

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.

About this dataset

This dataset was acquired using the spine-generic protocol on multiple subjects, multiple sites and multiple MRI vendors and models. The list of subjects is available in participants.tsv.

The contributors have the necessary ethics & permissions to share the data publicly. The dataset does not include any identifiable personal health information, including names, zip codes, dates of birth, facial features on structural scans.

The dataset is about 10 GB and it is structured according to the BIDS convention.

Download

We are using a tool to manage large datasets called git-annex. To download this dataset, you need to have `git` installed, and also install `git-annex` at version 8. Then run:

git clone https://github.com/spine-generic/data-multi-subject && \
cd data-multi-subject && \
git annex init && \
git annex get

You may substitute `git annex get` with more specific commands if you are only interested in certain subjects. For example:

git annex get sub-nwu01/ sub-nwu03/ sub-nwu04/ sub-oxfordFmrib04/ sub-tokyoSkyra*/

Analysis

The instructions to process this dataset are available in the spine-generic documentation.

Contributing

If you wish to contribute to this dataset please see the wiki. Thank you for your contribution 🎉

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.