This dataset include individual locations of the Independent Living Section of the Department of Rehabilitation (DOR). Independent Living Centers (ILC) and Traumatic Brain Injury (TBI) locations are dedicated to the ideal that communities become fully accessible and integrated so that all persons with disabilities can live, work, shop, and play where they choose, without barriers. This data is public information and also shared on the DOR website.

Reproducible Brain Charts (RBC) is an open data resource that harmonizes several of the largest studies of brain development in youth.

The Cuban Human Brain Mapping Project (CHBMP) repository is an open multimodal neuroimaging and cognitive dataset from 282 healthy participants (31.9 ± 9.3 years, age range 18–68 years). This dataset was acquired from 2004 to 2008 as a subset of a larger stratified random sample of 2,019 participants from La Lisa municipality in La Habana, Cuba. The exclusion included presence of disease or brain dysfunctions. The information made available for all participants comprises: high-density (64-120 channels) resting state electroencephalograms (EEG), magnetic resonance images (MRI), psychological tests (MMSE, Wechsler Adult Intelligence Scale -WAIS III, computerized reaction time tests using a go no-go paradigm), as well as general information (age, gender, education, ethnicity, handedness and weight). The EEG data contains recordings with at least 30 minutes duration including the following conditions: eyes closed, eyes open, hyperventilation and subsequent recovery. The MRI consisted in anatomical T1 as well as diffusion weighted (DWI) images acquired on a 1.5 Tesla system. The data is available for registered users on the LORIS database which is part of the MNI neuroinformatics ecosystem.

Tetralogy of Fallot Neurodevelopment Research: An Open-Source Dataset

1. Introduction

This dataset presents MRI and neurodevelopmental data from 31 pediatric participants (18 males; age range: 4–33 months) who underwent surgical intervention for Tetralogy of Fallot (TOF) at the Children's Hospital of Nanjing Medical University between 2022 and 2023. The study aims to explore the relationship between neurodevelopmental disorders (NDDs) and perioperative risk factors in congenital heart disease (CHD) patients.

2. Dataset Overview

This open-source dataset provides neuroimaging, clinical, and behavioral data for investigating neurodevelopmental outcomes in TOF patients. The dataset is structured as follows: - Demographic and Clinical Data - Age at admission, gender, BMI, family income, parental education - Surgery-related metrics: age at surgery, cardiopulmonary bypass (CPB) time, aortic cross-clamp time, mechanical ventilation duration, ICU stay, etc. - Hemodynamic and respiratory parameters: tidal volume, oxygen saturation, heart rate, blood pressure - Neurodevelopmental Assessment Data - Wechsler Intelligence Scale (WPPSI-IV) scores for cognitive evaluation - GESELL developmental scale assessment for younger participants (<2.5 years) - Perioperative monitoring of cerebral oxygenation, EEG, and pulse oximetry - Follow-up assessments at 1, 3, 6 months, 1 year, and 2 years post-discharge - Neuroimaging Data - Structural MRI (T1, T2, FLAIR, DWI, DTI): Total brain volume, white/gray matter, cortical thickness, hippocampal volume - Functional MRI (if applicable) - Diffusion Tensor Imaging (DTI) metrics: Fractional anisotropy (FA), Mean Diffusivity (MD), Radial Diffusivity (RD), Axial Diffusivity (AD)

3. Data Acquisition

Neuroimaging data were collected using Siemens Avento 1.5T and Philips Ingenia 3.0T MR scanners. For children under 3 years old, sedation with 5% chloral hydrate (1ml/kg) was used before scanning. - MRI Sequence Parameters: - T1-weighted (3D MPRAGE/TFE): High-resolution anatomical imaging - T2-weighted (FSE): Structural integrity assessment - FLAIR (Fluid Attenuated Inversion Recovery): Detection of periventricular abnormalities - Diffusion-Weighted Imaging (DWI): Water molecule diffusion properties - Diffusion Tensor Imaging (DTI): White matter microstructural analysis - Post-processing Methods: - Structural MRI: Cortical segmentation, volumetric analysis - DTI Processing: Head motion correction, fiber tracking, FA/MD calculations - DWI Analysis: ADC mapping and feature extraction using Python-based MRIcron processing

4. Data Access

This dataset is open-source and can be accessed through our research portal: 🔗 GitHub Repository To apply for dataset access, please complete the online form with the following details: - Full Name - Email Address - Affiliation (Institution/University/Hospital) - Research Purpose - Contact Information - Data access approval is subject to manual review to ensure ethical compliance.

5. Ethical Approval

This research has been approved by the Ethics Committee of Nanjing Medical University's Affiliated Children's Hospital. All data collection procedures were conducted in accordance with institutional guidelines. Informed consent was obtained from all participants' guardians before data collection.

6. Limitations & Future Directions

• Single-Center Data: Currently, the dataset is limited to a single hospital, potentially limiting generalizability.
• Loss to Follow-Up: Some participants may not complete long-term neurodevelopmental assessments.
• Multi-Factorial Influences: Genetic, environmental, and socio-economic factors may affect neurodevelopment beyond the scope of this study.
• Future Expansion: We aim to extend this dataset to a multi-center collaboration and integrate AI-driven predictive analytics. ## 7. Citation If you use this dataset in your research, please cite our work: [Author names], "Tetralogy of Fallot Neurodevelopment Research: An Open-Source Dataset," Children's Hospital of Nanjing Medical University, 2025.

For any inquiries, please contact: xuyang3141@gmail.com

Fragility Multi-Center Retrospective Study

iEEG and EEG data from 5 centers is organized in our study with a total of 100 subjects. We publish 4 centers' dataset here due to data sharing issues.

Acquisitions include ECoG and SEEG. Each run specifies a different snapshot of EEG data from that specific subject's session. For seizure sessions, this means that each run is a EEG snapshot around a different seizure event.

For additional clinical metadata about each subject, refer to the clinical Excel table in the publication.

Data Availability

NIH, JHH, UMMC, and UMF agreed to share. Cleveland Clinic did not, so requires an additional DUA.

All data, except for Cleveland Clinic was approved by their centers to be de-identified and shared. All data in this dataset have no PHI, or other identifiers associated with patient. In order to access Cleveland Clinic data, please forward all requests to Amber Sours, SOURSA@ccf.org:

Amber Sours, MPH Research Supervisor | Epilepsy Center Cleveland Clinic | 9500 Euclid Ave. S3-399 | Cleveland, OH 44195 (216) 444-8638

You will need to sign a data use agreement (DUA).

Sourcedata

For each subject, there was a raw EDF file, which was converted into the BrainVision format with mne_bids. Each subject with SEEG implantation, also has an Excel table, called electrode_layout.xlsx, which outlines where the clinicians marked each electrode anatomically. Note that there is no rigorous atlas applied, so the main points of interest are: WM, GM, VENTRICLE, CSF, and OUT, which represent white-matter, gray-matter, ventricle, cerebrospinal fluid and outside the brain. WM, Ventricle, CSF and OUT were removed channels from further analysis. These were labeled in the corresponding BIDS channels.tsv sidecar file as status=bad. The dataset uploaded to openneuro.org does not contain the sourcedata since there was an extra anonymization step that occurred when fully converting to BIDS.

Derivatives

Derivatives include: * fragility analysis * frequency analysis * graph metrics analysis * figures

These can be computed by following the following paper: Neural Fragility as an EEG Marker for the Seizure Onset Zone

Events and Descriptions

Within each EDF file, there contain event markers that are annotated by clinicians, which may inform you of specific clinical events that are occuring in time, or of when they saw seizures onset and offset (clinical and electrographic).

During a seizure event, specifically event markers may follow this time course:

* eeg onset, or clinical onset - the onset of a seizure that is either marked electrographically, or by clinical behavior. Note that the clinical onset may not always be present, since some seizures manifest without clinical behavioral changes.
* Marker/Mark On - these are usually annotations within some cases, where a health practitioner injects a chemical marker for use in ICTAL SPECT imaging after a seizure occurs. This is commonly done to see which portions of the brain are active metabolically.
* Marker/Mark Off - This is when the ICTAL SPECT stops imaging.
* eeg offset, or clinical offset - this is the offset of the seizure, as determined either electrographically, or by clinical symptoms.

Other events included may be beneficial for you to understand the time-course of each seizure. Note that ICTAL SPECT occurs in all Cleveland Clinic data. Note that seizure markers are not consistent in their description naming, so one might encode some specific regular-expression rules to consistently capture seizure onset/offset markers across all dataset. In the case of UMMC data, all onset and offset markers were provided by the clinicians on an Excel sheet instead of via the EDF file. So we went in and added the annotations manually to each EDF file.

Seizure Electrographic and Clinical Onset Annotations

For various datasets, there are seizures present within the dataset. Generally there is only one seizure per EDF file. When seizures are present, they are marked electrographically (and clinically if present) via standard approaches in the epilepsy clinical workflow.

Clinical onset are just manifestation of the seizures with clinical syndromes. Sometimes the maker may not be present.

Seizure Onset Zone Annotations

What is actually important in the evaluation of datasets is the clinical annotations of their localization hypotheses of the seizure onset zone.

These generally include:

* early onset: the earliest onset electrodes participating in the seizure that clinicians saw
* early/late spread (optional): the electrodes that showed epileptic spread activity after seizure onset. Not all seizures has spread contacts annotated.

Surgical Zone (Resection or Ablation) Annotations

For patients with the post-surgical MRI available, then the segmentation process outlined above tells us which electrodes were within the surgical removed brain region.

Otherwise, clinicians give us their best estimate, of which electrodes were resected/ablated based on their surgical notes.

For surgical patients whose postoperative medical records did not explicitly indicate specific resected or ablated contacts, manual visual inspection was performed to determine the approximate contacts that were located in later resected/ablated tissue. Postoperative T1 MRI scans were compared against post-SEEG implantation CT scans or CURRY coregistrations of preoperative MRI/post SEEG CT scans. Contacts of interest in and around the area of the reported resection were selected individually and the corresponding slice was navigated to on the CT scan or CURRY coregistration. After identifying landmarks of that slice (e.g. skull shape, skull features, shape of prominent brain structures like the ventricles, central sulcus, superior temporal gyrus, etc.), the location of a given contact in relation to these landmarks, and the location of the slice along the axial plane, the corresponding slice in the postoperative MRI scan was navigated to. The resected tissue within the slice was then visually inspected and compared against the distinct landmarks identified in the CT scans, if brain tissue was not present in the corresponding location of the contact, then the contact was marked as resected/ablated. This process was repeated for each contact of interest.

References

Adam Li, Chester Huynh, Zachary Fitzgerald, Iahn Cajigas, Damian Brusko, Jonathan Jagid, Angel Claudio, Andres Kanner, Jennifer Hopp, Stephanie Chen, Jennifer Haagensen, Emily Johnson, William Anderson, Nathan Crone, Sara Inati, Kareem Zaghloul, Juan Bulacio, Jorge Gonzalez-Martinez, Sridevi V. Sarma. Neural Fragility as an EEG Marker of the Seizure Onset Zone. bioRxiv 862797; doi: https://doi.org/10.1101/862797

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

Holdgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D'Ambrosio, S., David, O., … Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7

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

README

Details related to access to the data

• [x] Data user agreement

Dataset publicly available under the Creative Commons CC0 license after a grace period of 36 months.

• [x] Contact person

Edmund Wascher, IfADo, wascher@ifado.de, ORCID: 0000-0003-3616-9767 Daniel Schneider, IfADo, schneiderd@ifado.de, ORCID: 0000-0002-2867-2613 Patrick D. Gajewski, IfADo, gajewski@ifado.de, ORCID: 0000-0001-8240-1702 Stephan Getzmann, IfADo, getzmann@ifado.de, ORCID: 0000-0002-6382-0183

• [x] Practical information to access the data

The data are provided at OpenNeuro (dataset accession number: xxx, version xxx, DOI: xxx https://xxx) in BIDS format.

Overview

• [x] Project name

Resting-state EEG activity before and after cognitive activity at baseline and a 5-years follow-up.

• [x] Year(s) that the project ran

2016-2024

• [x] Brief overview of the tasks in the experiment

Resting-state EEG (rs-EEG) is a non-invasive measure of the spontaneous electrical activity of the brain, measured while remaining still and relaxed, and without performing any assigned cognitive tasks. Changes in rs-EEG are associated with numerous psychiatric disorders, but also with normal aging and with factors such as fatigue and motivation. Analyses of longitudinal rs-EEG measurements in healthy subjects over the entire adult lifespan could help to better understand the underlying brain processes, their development across the lifespan, and differences in brain activity between healthy and clinically relevant groups. The data set is part of the Dortmund Vital Study (ClinicalTrials.gov Identifier: NCT05155397), a prospective study on the determinants of healthy cognitive aging. The experiments comprised the recording of resting-state EEG data before and after a 2-hour block of cognitive experimental tasks. There are baseline measurements and about 5-years follow-up measurements of a subsample of healthy adult participants (for more information, Gajewski et al., 2022, doi: 10.2196/32352).

• [x] Description of the contents of the dataset

The dataset consists of 64-channels resting-state EEG recordings of 608 participants aged between 20 and 70 years, measured for three minutes with eyes open and eyes closed before and after a 2-hour block of demanding cognitive experimental tasks. Additional follow-up measurements are available from 208 subjects who also took part in the baseline measurement. The baseline measurements took place between 2016 and 2023, the follow-up measurements at intervals of around 5 years, starting 2021. The years of the baseline and follow-up measurements are specified in the sub-xxx_sessions.tsv file for each subject. The procedure for this (ongoing) follow-up measurement is exactly the same as for the first measurement.

• [x] Independent variables

Information on Age, Sex, Handeness of the participants are provided.

• [x] Dependent variables

Spontaneous EEG Activity is measured.

• [ ] Control variables

n/a

• [x] Quality assessment of the data

The data was checked for completeness and includes the non-preprocessed raw EEG.

An estimate of the reliability of the rs-EEG data was provided by a study, in which the intra-class correlation (ICC) in absolute EEG alpha power (8-13 Hz) of all four recordings at the first measurement (session 1) was examined on selected frontal and parietal electrodes in a subgroup of 370 participants (Metzen et al., 2022, doi: 10.1007/s00429-021-02399-1). The ICC ranged between 0.92 and 0.94 in the eyes-closed condition and between 0.87 and 0.90 in the eyes-open condition, indicating good to excellent ratings of alpha power reliability. A recent analysis of the reliability of EEG microstate indicated good to excellent short-term retest-reliability of microstate durations, occurrences, and coverages in a subgroup of 583 participants, as well as good overall short, intermediate, and long-term re-test reliability of these microstate characteristics across session 1 and session 2, covering a period of more than half a year (Kleinert et al., 2024, doi: 10.1007/s10548-023-00982-9).

Methods

Subjects

The subject pool consists of participants in the Dortmund Vital Study and includes people of working age between 20 and 70 years. 61.8% of the subjects of the baseline measurement are female, 93.1% are right-handed. The participants reported to be healthy and free of medication that might affect their attention during the experimental sessions. In general, the study population can be considered as representative in terms of age distribution, genetics, cognitive performance parameters, and occupation, whereas there were differences in gender distribution and educational qualifications compared to the general population in Germany (for details, see Gajewski et al., 2022, doi: 10.2196/32352).

• [x] Information about the recruitment procedure

The participants were recruited from local companies, and public institutions, and through advertisements in newspapers and public media.

• [ ] Subject inclusion criteria (if relevant)
• [x] Subject exclusion criteria (if relevant)

Exclusion criteria were history of severe diseases, namely neurological diseases (such as dementia, Parkinson disease, or stroke); cardiovascular, oncological, and eye diseases; psychiatric and affective disorders; head injuries, head surgery, and head implants; use of psychotropic drugs and neuroleptics; limited physical fitness and mobility.

Apparatus

The measurements took place in a quiet laboratory room while the subject was sitting. The resting-state EEG was recorded using a 64-channel elastic cap (actiCap system, Brain Products GmbH; Munich, Germany) arranged based on the 10-20 system with FCz electrode as on-line reference, and a BrainVision Brainamp DC amplifier and BrainVision Recorder software (BrainProducts GmbH). The EEG signal was recorded with 1000-Hz sampling rate and filtered online by a 250-Hz low-pass filter. Impedances were kept below 10 kOhm.

Initial setup

After arriving at institute there was an introductory meeting to clarify the procedure and open questions, to explain the aim of the study, and to explain open questions regarding informed consent forms and anonymization of the data. In the next step the EEG cap was mounted, and the tasks explained.

Task organization

• [x] Was task order counter-balanced?

The resting-state EEG was always recorded first with the eyes closed and then with the eyes open.

• [x] What other activities were interspersed between tasks?

The resting-state EEG with eyes closed and eyes open was measured before and after a 2-hour block of cognitive tasks.

• [x] In what order were the tasks and other activities performed?

The cognitive block comprised five tasks on visual attention, vigilance, stimulus-response conflict processing, updating and statistical learning, and speech-in-noise perception and auditory selective attention. The tasks were carried out one after the other with short breaks (for details, see Gajewski et al., 2022, doi: 10.2196/32352).

Task details

Recordings consists of resting-state EEG epochs measured for three minutes with eyes open and eyes closed before and after a 2-hour block of cognitive experimental tasks. During the resting-state EEG measurement, the subjects should sit quietly and relaxed.

Additional data acquired

n/a

Experimental location

The measurements took place at the Leibniz Research Centre for Working Environment and Human Factors at Dortmund University (IfADo) in Dortmund, Germany.

Missing data

• Information on handeness is missing for two subjects.

Notes

n/a

The Voxelwise Encoding Model framework (VEM) is a powerful approach for functional brain mapping. In the VEM framework, features are extracted from the stimulus (or task) and used in an encoding model to predict brain activity. If the encoding model is able to predict brain activity in some part of the brain, then one may conclude that some information represented in the features is also encoded in the brain. In VEM, a separate encoding model is fitted on each spatial sample (i.e., each voxel). VEM has many benefits compared to other methods for analyzing and modeling neuroimaging data. Most importantly, VEM can use large numbers of features simultaneously, which enables the analysis of complex naturalistic stimuli and tasks. Therefore, VEM can produce high-dimensional functional maps that reflect the selectivity of each voxel to large numbers of features. Moreover, because model performance is estimated on a separate test dataset not used during fitting, VEM minimizes overfitting and inflated Type I error confounds that plague other approaches, and the results of VEM generalize to new subjects and new stimuli. Despite these benefits, VEM is still not widely used in neuroimaging, partly because no tutorials on this method are available currently. To demystify the VEM framework and ease its dissemination, this paper presents a series of hands-on tutorials accessible to novice practitioners. The VEM tutorials are based on free open-source tools and public datasets, and reproduce the analysis presented in previously published work.

Raw data for the #EEGManyLabs replication of Experiment 2 of Eimer's seminal N2pc study (1996).

This is a registration of the systematic review methodology after the completion of data collection. This review synthesized the existing literature on MRI harmonization using machine learning techniques and algorithms. The goal was to provide insights into the effectiveness and limitations of current harmonization methods and to identify areas for further research and development. The summary of the methodologies and the reason of conducting the resignation are attached below.

An EEG study examining pre-stimulus preparatory event-related potentials in a task-switching design. Participants are cued to switch between an episodic source memory task and a non-episodic task. ERPs are separated according to task, trial sequence and accuracy.

Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing

Latest version of files: repository_v2.0.zip, Behavior Data_v2.0.xlsx and MRI IDs Testing&Replication Cohort.xlsx (please ignore repository.zip)

Open data repository Knab et al. Prediction of stroke outcome in mice based on non-invasvive MRI and behavioral testing

Open code and documentation of prediction models available via https://github.com/major-s/mouse-mcao-outcome-predictor

Content:

README.txt

This information

dat

Contains MRI data in NIFTI format and secondary data from atlas registration. For documentation of atlas registration files see https://pubmed.ncbi.nlm.nih.gov/28829217/Files used for the manuscript:t2.nii: t2 weighted image acquired 24 h post strokemasklesion.nii: manually delineated lesionx_masklesion.nii: lesion in atlas spaceix_ANO.nii: Allen brain atlas in native space (i.e. matching t2.nii)Lesion volume was calculated by volume of voxels unequal 0 in x_masklesion.niiOverlap of regions defined by ix_ANO.nii with masklesion.nii were used for calculating percent damage in each atlas region

prediction_models

Contains separated training and test data as xlsx and csv files with lesion volumes in cubic mm of the Allen brain atlas space, percent damage per atlas region and behavioral data. The training data was used as input for training prediction models in MATLAB, the results were created using the test data.The files have following sturcture:Column 1: animal IDColumns 2-537: MRI regions (column title corresponds to the region number as used in the Allen common coordinate framework)Column 538: lesion volumeColumn 539: initial performance (subacute deficit) = mean performance/deficit on days 2-6Column 540: mean performance/deficit on days 2-6 = initial performance (subacute deficit) - this column equals column 539 but has different header which was used to train the residual from initial deficitColumn 541: residual performance/deficitColumn 542: test or training groupConsecutive rows contain data for each animal specified by the animal id

The repository also contains all trained models, prediction results for the test data and tables with resulting median absolute error (MedAE) and 5th, 25th, 75th and 95 absolute error quantiles for each model.The model files end with '_models.mat' and contain 50 independently trained models each. Each model version is specified by number 1-50.The result files end with '_test_results.mat' or '_test_results.xlsx', files with MedAE and quantiles end with '_test_errors.xlsx' or '_test_errors.csv. The common part of filenames specifies the used paradigmFolder 'subacute deficit prediction' contains: - initial_performance_from_lesion_volume: prediction of subacute deficit using lesion volume - initial_performance_from_segmented_mri: prediction of subacute deficit using segmented mriFolder 'long-term outcome prediction' contains: - lesion_volume: prediction of residual deficit using lesion volume - segmented_mri: prediction of residual deficit using segmented_mri - initial_performance: prediction of residual deficit using subacute deficitFolder 'mri_inc_oob_imp' contains models trained using increasing number of mri segments sorted according to the out-of-bag importance. The number of used segments is given in the file name. The models, results and errors are separated in subfolders.

Files with equal file name and different extension always contain the same data

templatesAllen atlas, template, brain mask, hemisphere masks, tissue probability masks in NIFTI format including annotations of region IDs and parameter.m file for use in MATLAB toolbox ANTx2

methodology behind the paper "tau accumulations in the brains of woodpeckers"