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

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.

No description was included in this Dataset collected from the OSF

Individual parcellated and structural images for ten neonates comprising the M-CRIB 2.0 atlas

Brainstorm - Auditory Dataset

License

This dataset (MEG and MRI data) was collected by the MEG Unit Lab, McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Canada. The original purpose was to serve as a tutorial data example for the Brainstorm software project (http://neuroimage.usc.edu/brainstorm). It is presently released in the Public Domain, and is not subject to copyright in any jurisdiction.

We would appreciate though that you reference this dataset in your publications: please acknowledge its authors (Elizabeth Bock, Peter Donhauser, Francois Tadel and Sylvain Baillet) and cite the Brainstorm project seminal publication (also in open access): http://www.hindawi.com/journals/cin/2011/879716/

Presentation of the experiment

Experiment

• One subject, two acquisition runs of 6 minutes each
• Subject stimulated binaurally with intra-aural earphones (air tubes+transducers)
• Each run contains:
  • 200 regular beeps (440Hz)
  • 40 easy deviant beeps (554.4Hz, 4 semitones higher)
• Random inter-stimulus interval: between 0.7s and 1.7s seconds, uniformly distributed
• The subject presses a button when detecting a deviant with the right index finger
• Auditory stimuli generated with the Matlab Psychophysics toolbox
• The specifications of this dataset were discussed initially on the FieldTrip bug tracker

MEG acquisition

• Acquisition at 2400Hz, with a CTF 275 system, subject in seating position
• Recorded at the Montreal Neurological Institute in December 2013
• Anti-aliasing low-pass filter at 600Hz, files saved with the 3rd order gradient
• Recorded channels (340):
  • 1 Stim channel indicating the presentation times of the audio stimuli: UPPT001 (#1)
  • 1 Audio signal sent to the subject: UADC001 (#316)
  • 1 Response channel recordings the finger taps in response to the deviants: UDIO001 (#2)
  • 26 MEG reference sensors (#5-#30)
  • 274 MEG axial gradiometers (#31-#304)
  • 2 EEG electrodes: Cz, Pz (#305 and #306)
  • 1 ECG bipolar (#307)
  • 2 EOG bipolar (vertical #308, horizontal #309)
  • 12 Head tracking channels: Nasion XYZ, Left XYZ, Right XYZ, Error N/L/R (#317-#328)
  • 20 Unused channels (#3, #4, #310-#315, #329-340)

• 3 datasets:

  • S01_AEF_20131218_01.ds: Run #1, 360s, 200 standard + 40 deviants

  • S01_AEF_20131218_02.ds: Run #2, 360s, 200 standard + 40 deviants

  • S01_Noise_20131218_01.ds: Empty room recordings, 30s long

  • File name: S01=Subject01, AEF=Auditory evoked field, 20131218=date(Dec 18 2013), 01=run

• Use of the .ds, not the AUX (standard at the MNI) because they are easier to manipulate in FieldTrip

Stimulation delays

• Delay #1: Production of the sound.
  Between the stim markers (channel UDIO001) and the moment where the sound card plays the sound (channel UADC001). This is mostly due to the software running on the computer (stimulation software, operating system, sound card drivers, sound card electronics). The delay can be measured from the recorded files by comparing the triggers in the two channels: Delay between 11.5ms and 12.8ms (std = 0.3ms) This delay is not constant, we will need to correct for it.
• Delay #2: Transmission of the sound.
  Between when the sound card plays the sound and when the subject receives the sound in the ears. This is the time it takes for the transducer to convert the analog audio signal into a sound, plus the time it takes to the sound to travel through the air tubes from the transducer to the subject's ears. This delay cannot be estimated from the recorded signals: before the acquisition, we placed a sound meter at the extremity of the tubes to record when the sound is delivered. Delay between 4.8ms and 5.0ms (std = 0.08ms). At a sampling rate of 2400Hz, this delay can be considered constant, we will not compensate for it.
• Delay #3: Recording of the signals.
  The CTF MEG systems have a constant delay of 4 samples between the MEG/EEG channels and the analog channels (such as the audio signal UADC001), because of an anti-aliasing filtered that is applied to the first and not the second. This translate here to a constant delay of 1.7ms.
• Delay #4: Over-compensation of delay #1.
  When correcting of delay #1, the process we use to detect the beginning of the triggers on the audio signal (UADC001) sets the trigger in the middle of the ramp between silence and the beep. We "over-compensate" the delay #1 by 1.7ms. This can be considered as constant delay of about -1.7ms.
• Uncorrected delays: We will correct for the delay #1, and keep the other delays (#2, #3 and #4). After we compensate for delay #1 our MEG signals will have a constant delay of about 4.9 + 1.7 - 1.7 = 4.9 ms. We decide not to compensate for th3se delays because they do not introduce any jitter in the responses and they are not going to change anything in the interpretation of the data.

Head shape and fiducial points

• 3D digitization using a Polhemus Fastrak device driven by Brainstorm (S01_20131218_*.pos)
• More information: Digitize EEG electrodes and head shape

• The output file is copied to each .ds folder and contains the following entries:

  • The position of the center of CTF coils
  • The position of the anatomical references we use in Brainstorm: Nasion and connections tragus/helix, as illustrated here.

• Around 150 head points distributed on the hard parts of the head (no soft tissues)

Subject anatomy

• Subject with 1.5T MRI
• Marker on the left cheek
• Processed with FreeSurfer 5.3

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

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

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

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

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

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

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

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

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

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

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

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

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

Neuronal oscillations and their synchronization between brain areas are fundamental for healthy brain function. Yet, synchronization levels exhibit large inter-individual variability that is associated with behavioral variability. We test whether individual synchronization levels are predicted by individual brain states along an extended regime of critical-like dynamics – the Griffiths phase (GP). We use computational modelling to assess how synchronization is dependent on brain criticality indexed by long-range temporal correlations (LRTCs). We analyze LRTCs and synchronization of oscillations from resting-state magnetoencephalography and stereo-electroencephalography data. Synchronization and LRTCs are both positively linearly and quadratically correlated among healthy subjects, while in epileptogenic areas they are negatively linearly correlated. These results show that variability in synchronization levels is explained by the individual position along the GP with healthy brain areas..., MEG data acquisition We recorded MEG data from 52 healthy participants (age: 31 ± 9.2, 27 male) during a 10-minute eyes-open resting-state session with a Vectorview/Triux (Elekta-Neuromag/MEGIN, Helsinki, Finland) 306-channel system (204 planar gradiometers and 102 magnetometers) at the Bio-Mag Laboratory, HUS Medical Imaging Center, Helsinki. Overall, 192 sessions of MEG data were obtained, with participants contributing on average 3.7 ± 4 sessions each. Participants were instructed to focus on a cross in the center of the screen in front of them. Bipolar horizontal and vertical electrooculography (EOG) were recorded for the detection of ocular artifacts. MEG and EOG were recorded at 1 kHz sampling rate. For each participant, T1-weighted anatomical MRI scans (MP-RAGE) at a resolution of 1 × 1 × 1 mm with a 1.5-Tesla MRI scanner (Siemens, Munich, Germany) were obtained at Helsinki University Central Hospital for head models and cortical surface reconstruction. The study protocol for MEG..., The *.mat files can be opened with Matlab or any platform that is able to import Matlab files.The *.pickle files can be opened with the pickle module in Python.

The Transdiagnostic Connectome Project

A richly phenotyped transdiagnostic dataset with behavioral and Magnetic Resonance Imaging (MRI) data from 241 individuals aged 18 to 70, comprising 148 individuals meeting diagnostic criteria for a broad range of psychiatric illnesses and a healthy comparison group of 93 individuals.

These data include high-resolution anatomical scans and 6 x resting-state, and 3 x task-based (2 x Stroop, 1 x Faces/Shapes) functional MRI runs. Participants completed over 50 psychological and cognitive questionnaires, as well as a semi-structured clinical interview.

Data was collected at the Brain Imaging Center, Yale University, New Haven, CT and McLean Hospital, Belmont, MA. This dataset will allow investigation into brain function and transdiagnostic psychopathology in a community sample. See preprint (https://www.medrxiv.org/content/10.1101/2024.06.18.24309054v1) and below for detailed information.

Inclusion Criteria

Participants in the study met the following inclusion criteria:

• Aged 18 to 64 years and spoke English
• No metal contraindications, no history of concussion or prior neurological problems, no color-blindness
• Prior history of affective or psychotic illness or no psychiatric history

Exclusion criteria

Participants meeting any of the criteria listed below were excluded from the study: * Neurological disorders * Pervasive developmental disorders (e.g., autism spectrum disorder) * Any medical condition that increases risk for MRI (e.g., pacemaker, dental braces) * MRI contraindications (e.g., claustrophobia pregnancy)

Consent

Institutional Review Board approval and consent were obtained. To characterise the sample, we collected data on race/ethnicity, income, use of psychotropic medication, and family history of medical or psychiatric conditions.

Clinical Measures

Completed by Participants:

• Health and demographics questionnaire
• Alcohol Tobacco Caffeine Use Questionnaire (ATC)
• Broad Autism Phenotype Questionnaire (BAPQ-2)
• Barratt Impulsiveness Scale (BIS)
• Behavioral Inhibition/Activation Scale (BISBAS)
• Childhood Trauma Questionnaire (CTQ)
• Domain Specific Risk Taking (DOSPERT)
• Fagerstrom Test for Nicotine Dependence (FTND)
• NEO Five Factor Inventory (NEO-FFI)
• Quick Inventory of Depressive Symptomatology (QIDS)
• Multidimensional Scale for Perceived Social Support (MSPSS)
• State-Trait Anxiety Inventory (STAI)
• Temperament Character Inventory (TCI)
• Anxiety Sensitivity Index (ASI)
• Depression Anxiety Stress Scale (DASS)
• Profile of Mood States (POMS)
• Perceived Stress Scale (PSS)
• Shipley Institute of Living Scale (Shipley)
• Temporal Experience of Pleasure Scale (TEPS)
• Cognitive Emotion Regulation Questionnaire (CERQ)
• Cognitive Failures Questionnaire (CFQ)
• Cognitive Reflections Test (CRT)
• Experiences in Close Relationships Inventory (ECR)
• Positive Urgency Measure (PUM)
• Ruminative Responses Scale (RRS)
• Retrospective Self-Report of Inhibition (RSRI)
• Snaith-Hamilton Pleasure Scale (SHAPS)
• Test My Brain (TMB)
• Stroop Task (during fMRI)
• Hammer Task (during fMRI)

Completed by Clinicians:

• Structured Clinical Interview for DSM-5 Disorder (SCID-5)
• Clinical Global Impression (CGI)
• Anxiety Symptom Chronicity (ASC)
• Columbia Suicide Severity Rating Scale (CSSR-S)
• Range of Impaired Functioning Tool (LIFE-RIFT)
• Montgomery-Asberg Depression Rating Scale (MADRS)
• Multnomah Community Ability Scale (MCAS)
• Positive and Negative Syndrome Scale (PANSS)
• Panic Disorder Severity Scale (PDSS)
• Young Mania Rating Scale (YMRS)

Clinical Measures Data

Relevant clinical measures can be found in the phenotype folder, with each measure and its items described in the relevant _definition .csv file. The 'qc' columns indicate quality control checks done on each measure (i.e., number of unanswered items by a participant.) '999' values indicate missing or skipped data.

MRI acquisition parameters

MRI data were acquired at both sites using harmonized Siemens Magnetom 3T Prisma MRI scanners and a 64-channel head coil. T1-weighted (T1-w) anatomical images were acquired using a multi-echo MPRAGE sequence following parameters: acquisition duration of 132 seconds, with a repetition time (TR) of 2.2 seconds, echo times (TE) of 1.5, 3.4, 5.2, and 7.0 milliseconds, a flip angle of 7°, an inversion time (TI) of 1.1 seconds, a sagittal orientation and anterior (A) to posterior (P) phase encoding. The slice thickness was 1.2 millimeters, and 144 slices were acquired. The image resolution was 1.2 mm3. A root mean square of the four images corresponding to each echo was computed to derive a single image. T2-weighted (T2w) anatomical images with the following parameters: TR of 2800 milliseconds, TE of 326 milliseconds, a sagittal orientation, and AP phase encoding direction. The slice thickness was 1.2 millimeters, and 144 slices were acquired. All seven functional MRI runs were acquired with the same parameters matching the HCP protocol6,9, varying only the conditions (rest/task) and separately acquired phase encoding directions (AP/PA). For the resting-state, Stroop task, and Emotional Faces task, a total of 488, 510, and 493 volumes were acquired, respectively, all using the following MRI sequence parameters: TR = 800 milliseconds, TE = 37 milliseconds, flip angle = 52°, and voxel size =2mm3. A multi-band acceleration factor of 8 was applied. An auto-align pulse sequence protocol was used to align the acquisition slices of the functional scans parallel to the anterior. To enable the correction of the distortions in the EPI images, B0-field maps were acquired in both AP and PA directions with a standard Spin Echo sequence. Detailed MRI acquisition protocols for both sites are available in Appendix B. In total, four resting-state (2 AP, 2 PA), 2 Stroop task acquisitions (1 AP [Block 1], 1 PA [Block 2]), and 1 Emotional Faces task acquisition (1 AP) acquisitions were collected. Select participants out of the total sample did not complete each functional neuroimaging run; thus the sample sizes for each run were as follows: resting-state AP run 1, n = 241; resting-state PA run 1, n = 241; resting-state AP run 2, n = 237; resting-state AP run 2, n = 235; Stroop task AP, n = 226; Stroop task PA, n = 224; and Emotional Faces task AP, n = 226.

For the Emotional Faces task, the faces are fear and anger expressing (male and female groups) from the NimStim database. The faces used in each trial are outlines in each events.tsv file.For example, FA1 = female anger stimuli set number 1, or FF1 =female fear stimuli set number 1. Unfortunately, we cannot release the actual images publicly. An important consideration here might be that this task has no neutral control nor positively valenced comparison for faces (i.e., is precisely a negatively valenced face vs non-face/shape version of the task). We will soon update the events.tsv files on OpenNeuro with more informative file names (e.g. female_fear, female_anger, male_fear, male_anger).

Detailed information and protocols regarding the dataset can be found here: https://www.medrxiv.org/content/10.1101/2024.06.18.24309054v1

This dataset includes 31 mouse models representing 23 genotypes from their ongoing study of 90+ mouse strains

This dataset presents taxonomic data, and neuropil volume measurements of sensory structures in the brains of wild caught ithomiini butterflies (Lepidoptera, Nymphalidae, Danainae), within a single community in Yasuní national park, Orellana Province, Ecuador, collected in 2011-2012. Variables measured include taxonomic detail, sex, mimicry ring, and the volumes of major sensory neuropils (the medulla, lobula plate, lobula, ventral lobula, accessory medulla, antennal lobe and anterior optic tubercule) and the central brain volume. This dataset was created to examine how sensory environments shape investment in sensory brain centers in butterflies, as part of a NERC Independent Research Fellowship NE/N014936/1.

EEG data and participant information to the study: Calce et al., Voice categorization in the 4-month-old human brain, Current Biology (2023), https://doi.org/10.1016/ j.cub.2023.11.042

No description was included in this Dataset collected from the OSF

Repository of data and code for Wellcome Open Research manuscript 15617

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.

We collected data from 167 patients with biopsy-confirmed thyroid nodules (n=192) at the Stanford University Medical Center. The dataset consists of ultrasound cine-clip images, radiologist-annotated segmentations, patient demographics, lesion size and location, TI-RADS descriptors, and histopathological diagnoses.

Center for Human Neuroscience (CHN) Retinotopic Mapping Dataset

The Center for Human Neuroscience (CHN) Retinotopic Mapping Dataset collected at the University of Washington is part of "Improving the reliability and accuracy of population receptive field measures using a 'log-bar' stimulus" by Kelly Chang, Ione Fine, and Geoffrey M. Boynton.

The full dataset is comprised of the raw, preprocessed (with fMRIPrep), and pRF estimated data from 12 participants across 2 sessions.

Dataset Organization

• dataset
  This directory contains the raw, unprocessed data for each participant.

• dataset/derivatives/fmriprep
  This directory contains the fMRIPrep processed data for each particpant.

• dataset/derivatives/freesurfer
  This directory contains the standard FreeSurfer processed data for each participant.

• dataset/derivatives/prf-estimation
  This directory contains the pRF estimation data and results for each participant.

• dataset/derivatives/prf-estimation/files
  This directory contains miscellaneous files used for pRF estimation or visualizations.

  • angle_lut.json: Custom polar angle lookup table for visualization with FreeSurfer's freeview.
  • eccen_lut.json: Custom eccentricity lookup table for visualization with FreeSurfer's freeview.
  • participants_hrf_paramters.json: Corresponding metadata for participants_hrf_paramters.tsv.
  • participants_hrf_paramters.tsv: Estimated HRF parameters used during pRF estimation by participant and hemisphere.

• dataset/derivatives/prf-estimation/stimuli
  This directory contains the stimuli used in the experiment and stimulus apertures used in pRF estimation.

  • task-(fixed|log)bar_run-(1|2|3): Name of the stimulus condition and run number.
  • *_desc-full_stim.mat: Stimulus images (uint8) at full resolution of 540 by 540 pixels and 6 Hz.
  • *_desc-down_aperture.mat: Stimulus aperature (binary) where 1s indicated stimulus and 0s indicated the background at a downsampled (down) resolution of 108 by 108 pixels and 1 Hz.

• dataset/derivatives/prf-estimation/sub-(n)/anat
  This directory contains the participant's surface (inflated and sphere) and curvature files for visualization using FreeSurfer's freeview.

• dataset/derivatives/prf-estimation/sub-(n)/func
  This directory contains the preprocessed and denoised functional data, sampled onto the participant's surface, used during pRF estimation.

• dataset/derivatives/prf-estimation/sub-(n)/prfs This directory contains the estimated pRF parameter maps separated by which data was used during estimation.

  • ses-(01|02|all): Sessions used during pRF estimation, either Session 1, Session 2, or both.
  • task-(fixedbar|logbar|all): Stimuli type used during pRF estimation, either fixed-bar, log-bar, or both.

  Within the pRF estimate directories are the estimated pRF parameter maps for: - *_angle.mgz: Polar angle maps, degrees from (-180, 180). Negative values represent the left hemifield and positive values represent the right hemifield. - *_eccen.mgz: Eccentricity maps, visual degrees. - *_sigma.mgz: pRF size maps, visual degrees. - *_vexpl.mgz: Proportion of variance explained maps. - *_x0.mgz: x-coordinate maps, visual degrees, with origin (0,0) at screen center. - *_y0.mgz: y-coordinate maps, visual degrees, with origin (0,0) at screen center.

• dataset/derivatives/prf-estimation/sub-(n)/rois
  This directory contains the roi (.label) files for each participant.

  • *_VC.label: Visual cortex (VC). A liberal ROI that covered visual cortex used for pRF estimation.
  • *_fovea.label: Foveal confluence ROI.
  • *_V1.label: V1 ROI files.
  • *_V2.label: V2 ROI files.
  • *_V3.label: V3 ROI files.
  • *_V3ab.label: V3a/b ROI files.
  • *_hV4.label: hV4 ROI files.
  • *_LOC.label: Lateral-Occipital Complex (LOC) ROI files.
  • *_TOC.label: Temporal-Occipital Complex (TOC) ROI files.

• dataset/tutorials
  This directory contains tutorial scripts in MATLAB and Python to generate log distorted images from a directory of input images.

  • create_distorted_images.[m,ipynb]: Tutorial script that generates log-distorted images when given an image input directory.
  • fixed-bar: Sample image input directory.
  • log-bar: Sample image output directory.

Epilepsy Interictal Dataset

This dataset was updated and prepared for release as part of a manuscript by Bernabei & Li et al. (in preparation). A subset of the data has been featured in [1] and [2].

Summary

This dataset comprises of de-identified subjects with interictal iEEG recordings possibly with sleep or awake state annotated. The subjects come from the following centers:

• National Institute of Health (NIH)
• Johns Hopkins Hospital (JHH)
• University of Miami Florida Jackson Memorial Hospital (UMF)

In the actual study, there is also data from Kansas University Medical Center (KUMC), University of Pittsburgh Medical Center and Cleveland Clinic, whose data is not shared due to restrictions imposed by the centers there.

Some subjects, namely with the rns prefix in their subject ID were treated with RNS rather then surgical resection/ablation.

Derivatives

The processed data corresponding to the source-sink analysis and hfo comparisons are shown in the derivatives/ folder. The HFO analysis consists of two folders, one is an RMS detector and the other is a Hilbert detector. See the paper for details.

Ties to Other Datasets

NIH pt1, pt2, pt3, JHH jh103, jh105 subjects are also datasets in https://openneuro.org/datasets/ds003029, where the ictal snapshots are stored. These correspond to the following:

• pt1: pt01
• pt2: pt2
• pt3: pt3
• jh103: jh103
• jh105: jh105

Moreover, the cclinic subjects are used in that study, but not open-access due to data sharing limitations at Cleveland Clinic. Those ictal datasets were analyzed in https://www.nature.com/articles/s41593-021-00901-w.

References

[1] Li, A., Huynh, C., Fitzgerald, Z. et al. Neural fragility as an EEG marker of the seizure onset zone. Nat Neurosci 24, 1465–1474 (2021). https://doi.org/10.1038/s41593-021-00901-w

[2] Kristin M. Gunnarsdottir, Adam Li, Rachel J. Smith, Joon-Yi Kang, Nathan E. Crone, Anna Korzeniewska, Adam Rouse, Nathaniel Cameron, Iahn Cajigas, Sara Inati, Kareem A. Zaghloul, Varina L. Boerwinkle, Sarah Wyckoff, Nirav Barot, Jorge Gonzalez-Martinez, Sridevi V. Sarma. Source-sink connectivity: a novel resting-state EEG marker of the epileptogenic zone. bioRxiv 2021.10.15.464594; doi: https://doi.org/10.1101/2021.10.15.464594

[3] 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

[4] 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