Experiment Description:

This experiment involved 12 healthy subjects with no prior experience on neurofeedback or BCI, and without any known neurological disorders. All participants are right-handed, except one ambidextrous (participant #5). All participants have provided their signed informed consent for participating in the study in accordance with the 1964 Declaration of Helsinki.

The experiment had been conducted in a laboratory environment under controlled conditions. The subjects went through three sessions lasting maximum two hours, during three consecutive days and each day at approximately at the same hour.

During each session, participants underwent three different conditions. The first condition was always the ”resting-state”: the user was asked to keep the eyes open for two minutes staring at a screen with a green cross and a red arrow pointing up, and then closed for the other two minutes. After this, two more conditions followed related to a Motor Imagery (MI) task performed in a randomized order between left|right-hand movement. The two MI conditions consisted of two phases each: a training phase and a test phase. The general experimental routine for both of them was the same: each trial lasted 6 seconds (2 seconds baseline and 4 seconds MI), forewarned by the appearance of a green cross on the screen and a concomitant beep-sound a second before the onset of the task.

Then, an arrow was appearing pointing left or right, and the subject had to imagine the movement of the corresponding arm reaching an object in front of the Baxter Robot (Rethink Robotics, Bochum, Germany). For both phases, 20 trials from left and 20 trials for right MI were generated in a randomized order, for a total of 40 trials. Finally, there was an inter-trial interval that extended randomly between 1.5 and 3.5 seconds.

Overall, this study resulted into 180 EEG datasets.

Data Description:

Data Format	General Data Format (GDF)
Sampling Rate	250 Hz
Channels	32 EEG + 3 ACC.
EEG system	LiveAmp 32 with active electrodes actiCAP (Brain Products GmbH, Gilching, Germany)

Events:

Code	Description
32775	Baseline Start
32776	Baseline Stop
768	Start of Trial, Trigger at t=0s
786	Cross on screen (BCI experiment)
33282	Beep
769	class1, Left hand - cue onset
770	class2, Right hand - cue onset
781	Feedback (continuous) - onset
800	End Of Trial
1010	End Of Session
33281	Train
32770	Experiment Stop

Directory Tree:

ROOT
| chanlocs.locs
|
|
+--- USER #
| +---SESSION #
| | +---CONDITION #
| | | \---RESTING_STATE
| | | +---1st_PERSON
| | | | TRAINING
| | | | ONLINE
| | | +---3rd_PERSON
| | | | TRAINING
| | | | ONLINE

This dataset consolidates brain tumor MRI images from multiple Kaggle data sources to create a larger, centralised dataset for research and model development purposes.

The dataset comprises of 16,269 images containing four main classes : - Glioma (3,325 Images) - Meningioma (3,266 Images) - Pituitary (2,974 Images) - Healthy (6,704 Images)

Key Notes:

Duplicate images are likely due to dataset overlaps when sourcing. We strongly recommend users perform deduplication before training.

The dataset does not apply any cleaning, resizing, or augmentation — it's intended to be raw and inclusive for flexibility.

Recommendation:

This dataset is ideal for users who want to experiment with preprocessing, augmentation, and custom cleaning pipelines on a real-world, mixed-quality dataset. Please consult medical professionals if using this data for clinical or diagnostic applications.

File Structure

The dataset is organised as follows: - Each folder represents the 4 classes - The filenames of each image contain the original dataset source (Name based on user who published the dataset to Kaggle)

Data Sources:

This dataset combines the following five Kaggle datasets:

1. Brain Tumors Dataset (Excluded their augmented images) by Seyed Mohammad Hossein Hashemi
2. PMRAM Bangladeshi Brain Cancer MRI Dataset by Orville
3. Brain Tumor MRI Images (17 Classes) by Fernando Feltrin (Only T1 glioma/meningioma/healthy images used).
4. SIAR Dataset by Masoumeh Siar (Only healthy scans used as this was a binary dataset, and did not differentiate the tumor types).
5. Brain Tumor MRI Scans by Rajarshi Mandal

These datasets were selected for their popularity, quality, and complementary class coverage. We recommend checking the original sources for more information about data collection methods and original licensing.

License

This combined dataset is released under CC BY-SA 4.0 to comply with ShareAlike requirements of source datasets:

Source Dataset	Original License
Brain Tumors Dataset	CC0
Brain Tumor MRI Scans	CC0
SIAR Dataset	Unkown. Requires citation in publications.
PMRAM Bangladeshi Brain Cancer MRI Dataset	CC BY-SA 4.0
Brain Tumor MRI Images (17 Classes)	ODbL 1.0

Dataset Description

This dataset is part of a longitudinal study investigating Parkinson's Disease (PD) and its associated cognitive impairments. Resting-state fMRI data were collected from participants, including healthy controls (HC) and Parkinson's Disease patients with normal cognition (PD-NC) or mild cognitive impairment (PD-MCI). The dataset is organized following the Brain Imaging Data Structure (BIDS) specifications.

License

This dataset is shared under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. Please acknowledge this dataset in publications by citing it appropriately.

Study Information

• Title: Resting State MRI data from healthy control (HC), Parkinson's disease with normal cognition (PD-NC), and Parkinson's disease with mild cognitive impairment (PD-MCI) cohorts
• Principal Investigator: James E. Galvin
• Ethics Approval: This study was approved by the Institutional Review Board (IRB) at New York University (NYU). All participants provided informed consent.

Data Acquisition

MRI Details

• Scanner Manufacturer: Siemens
• Model: TrioTim
• Magnetic Field Strength: 3 Tesla
• Sequence: Echo Planar Imaging (EPI)
• Repetition Time (TR): 2000 ms
• Echo Time (TE): 29 ms
• Flip Angle: 90°
• Slice Thickness: 3.5 mm
• Spacing Between Slices: 3.5 mm

Dataset Structure

The dataset contains the following main directories and files:

• /participants.json: Contains metadata describing participant demographics and group information.
• /sub-<label>/: Subdirectories for each participant containing:
  • anat/: Anatomical MRI data (T1-weighted images).
  • func/: Functional MRI data (resting-state).

Key Files

participants.tsv

A tab-separated file containing participant demographics, group, and other relevant information.

participant_id	group	age	sex
sub-MJF001	PD-MCI	68	M
sub-MJF008	HC	61	F

task-rest_bold.json

Metadata file describing the resting-state functional MRI task. Key fields include: - TaskName: "rest" - Modality: "MR" - TR: 2000 ms - EchoTime: 29 ms

Usage Notes

• Please refer to the associated publication for detailed study design and analysis pipelines.

Acknowledgments

We acknowledge the contributions of all study participants and research team members. Special thanks to the Micheal J. Fox Foundation for supporting this research.

DATASET STRUCTURE

The dataset can be downloaded from https://doi.org/10.5281/zenodo.7260705 and a detailed description is offered at "synthRAD2023_dataset_description.pdf".

The training datasets for Task1 is in Task1.zip, while for Task2 in Task2.zip. After unzipping, each Task is organized according to the following folder structure:

Task1.zip/

├── Task1

├── brain

├── 1Bxxxx

├── mr.nii.gz

├── ct.nii.gz

└── mask.nii.gz

├── ...

└── overview

├── 1_brain_train.xlsx

├── 1Bxxxx_train.png

└── ...

└── pelvis

├── 1Pxxxx

├── mr.nii.gz

├── ct.nii.gz

├── mask.nii.gz

├── ...

└── overview

├── 1_pelvis_train.xlsx

├── 1Pxxxx_train.png

└── ....

Task2.zip/

├──Task2

├── brain

├── 2Bxxxx

├── cbct.nii.gz

├── ct.nii.gz

└── mask.nii.gz

├── ...

└── overview

├── 2_brain_train.xlsx

├── 2Bxxxx_train.png

└── ...

└── pelvis

├── 2Pxxxx

├── cbct.nii.gz

├── ct.nii.gz

├── mask.nii.gz

├── ...

└── overview

├── 2_pelvis_train.xlsx

├── 2Pxxxx_train.png

└── ....

Each patient folder has a unique name that contains information about the task, anatomy, center and a patient ID. The naming follows the convention below:

[Task] [Anatomy] [Center] [PatientID]

1 B A 001

In each patient folder, three files can be found:

• ct.nii.gz: CT image

• mr.nii.gz or cbct.nii.gz (depending on the task): CBCT/MR image

• mask.nii.gz:image containing a binary mask of the dilated patient outline

For each task and anatomy, an overview folder is provided which contains the following files:

• [task]_[anatomy]_train.xlsx: This file contains information about the image acquisition protocol for each patient.

• [task][anatomy][center][PatientID]_train.png: For each patient a png showing axial, coronal and sagittal slices of CBCT/MR, CT, mask and the difference between CBCT/MR and CT is provided. These images are meant to provide a quick visual overview of the data.

DATASET DESCRIPTION

This challenge dataset contains imaging data of patients who underwent radiotherapy in the brain or pelvis region. Overall, the population is predominantly adult and no gender restrictions were considered during data collection. For Task 1, the inclusion criteria were the acquisition of a CT and MRI during treatment planning while for task 2, acquisitions of a CT and CBCT, used for patient positioning, were required. Datasets for task 1 and 2 do not necessarily contain the same patients, given the different image acquisitions for the different tasks.

Data was collected at 3 Dutch university medical centers:

• Radboud University Medical Center

• University Medical Center Utrecht

• University Medical Center Groningen

For anonymization purposes, from here on, institution names are substituted with A, B and C, without specifying which institute each letter refers to.

The following number of patients is available in the training set.

Training

	Brain	Pelvis
	Center A	Center B	Center C	Total	Center A	Center B	Center C	Total
Task 1	60	60	60	180	120	0	60	180
Task 2	60	60	60	180	60	60	60	180

Each subset generally contains equal amounts of patients from each center, except for task 1 brain, where center B had no MR scans available. To compensate for this, center A provided twice the number of patients than in other subsets.

Validation

	Brain	Pelvis
	Center A	Center B	Center C	Total	Center A	Center B	Center C	Total
Task 1	10	10	10	30	20	0	10	30
Task 2	10	10	10	30	10	10	10	30

Testing

	Brain	Pelvis
	Center A	Center B	Center C	Total	Center A	Center B	Center C	Total
Task 1	20	20	20	60	40	0	20	60
Task 2	20	20	20	60	20	20	20	60

In total, for all tasks and anatomies combined, 1080 image pairs (720 training, 120 validation, 240 testing) are available in this dataset. This repository only contains the training data.

All images were acquired with the clinically used scanners and imaging protocols of the respective centers and reflect typical images found in clinical routine. As a result, imaging protocols and scanner can vary between patients. A detailed description of the imaging protocol for each image, can be found in spreadsheets that are part of the dataset release (see dataset structure).

Data was acquired with the following scanners:

• Center A:

  • MRI: Philips Ingenia 1.5T/3.0T

  • CT: Philips Brilliance Big Bore or Siemens Biograph20 PET-CT

  • CBCT: Elekta XVI

• Center B:

  • MRI: Siemens MAGNETOM Aera 1.5T or MAGNETOM Avanto_fit 1.5T

  • CT: Siemens SOMATOM Definition AS

  • CBCT: IBA Proteus+ or Elekta XVI

• Center C:

  • MRI: Siemens Avanto fit 1.5T or Siemens MAGNETOM Vida fit 3.0T

  • CT: Philips Brilliance Big Bore

  • CBCT: Elekta XVI

For task 1, MRIs were acquired with a T1-weighted gradient echo or an inversion prepared - turbo field echo (TFE) sequence and collected along with the corresponding planning CTs for all subjects. The exact acquisition parameters vary between patients and centers. For centers B and C, selected MRIs were acquired with Gadolinium contrast, while the selected MRIs of center A were acquired without contrast.

For task 2, the CBCTs used for image-guided radiotherapy ensuring accurate patient position were selected for all subjects along with the corresponding

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

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

THE ORIGINAL DATASET ds003982 README FOLLOWS

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

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

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

Recruitment

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

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

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

Clinical Measures

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

In-person Assessments

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

Medical Evaluation

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

MRI Scan

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

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

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

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

MEG

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

Specific Measures within Dataset

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

Location	Measure	File Name
Online	Alcohol Use Disorders Identification Test (AUDIT)	audit
	Demographics	demographics
	DSM-5 Level 2 Substance Use - Adult	drug_use
	Edinburgh Handedness Inventory (EHI)	ehi
	Health History Form	health_history_questions
	Perceived Health Rating - self	health_rating
	DSM-5 Self-Rated Level 1 Cross-Cutting Symptoms Measure – Adult (modified)	mental_health_questions
	World Health Organization Disability Assessment Schedule

The dataset contains EEG and behavioral data of 47 participants who completed 9 runs (i.e. copy-spelled 9 words) in a P300 speller task, as well as a random dot motion (RDM) task and questionnaires in a single experimental session. Details of the experimental protocol can be found here:

Noble SC, Woods E, Ward T, Ringwood JV. “Adaptive P300-Based Brain-Computer Interface for Attention Training: Protocol for a Randomized Controlled Trial.” JMIR Res Protoc 2023, 12:e46135, doi: 10.2196/46135

A journal article describing the results of the study can be found here:

Noble SC, Woods E, Ward T, Ringwood JV. “Accelerating P300-Based Neurofeedback Training for Attention Enhancement Using Iterative Learning Control: A Randomised Controlled Trial.” J Neural Eng 2024, 21(2), doi: 10.1088/1741-2552/ad2c9e

Please cite the results paper when using the data.

Each participant folder contains:

• [xxx]-raw.[xxx] – unprocessed EEG signals (in mV) from 32 electrodes for all 9 P300 speller runs in Openvibe (.ov) and Matlab (.mat) file formats, see details of the runs below
• [xxx]-processed.[xxx] – contains 3 xDAWN components extracted by the xDAWN spatial filter according to the weights in “spatial-filter.cfg”
• classifier.cfg - LDA classifier weights
• spatial-filter.cfg - xDAWN spatial filter weights
• log.txt - contains the group assignment, start and end time of the experiment, and performance in the P300 speller and RDM tasks

The file “Subject Information.csv” contains the age and gender of all participants.

The file “Questionnaire scores.csv” contains the responses to the questionnaire described in the experimental protocol and the NASA Task Load Index (TLX) for all participants.

The .ov and .mat files contain data from the following runs:

Filename	Word to be copy-spelled	Number of flashes per row and column	Feedback given to participant
calibration-signal1	THE	12	no
calibration-signal2	QUICK	12	no
calibration-signals	Concatenation of calibration-signal1 and calibration-signal2
eval	DOG	12	yes
training-run-1	BEAUTIFUL	10	yes
training-run-2 to training-run-5	BEAUTIFUL	varying	yes
post-training-run	DANCE	12	yes

This research is supported by the Irish Research Council under project ID GOIPG/2020/692 and Science Foundation Ireland under grant number 12/RC/2289_P2.

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

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

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

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

THE ORIGINAL DATASET ds003982 README FOLLOWS

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

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

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

Recruitment

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

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

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

Clinical Measures

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

In-person Assessments

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

Medical Evaluation

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

MRI Scan

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

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

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

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

MEG

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

Specific Measures within Dataset

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

Location	Measure	File Name
Online	Alcohol Use Disorders Identification Test (AUDIT)	audit
	Demographics	demographics
	DSM-5 Level 2 Substance Use - Adult	drug_use
	Edinburgh Handedness Inventory (EHI)	ehi
	Health History Form	health_history_questions
	Perceived Health Rating - self	health_rating

ME/CFS vs Depression Dataset

Welcome to a synthetic dataset designed for classification tasks between Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) and Depression.
This is the first dataset of its kind created specifically to help beginners and researchers explore complex cases of differential diagnosis in mental and chronic health conditions.

🎯 Objective

Predict whether a patient has: - ME/CFS - Depression - Or both (Both)

Based on behavioral, clinical, and symptomatic features.

📋 Features

Feature Name	Description
age	Patient's age
gender	Gender (Male / Female / Other)
fatigue_severity_scale_score	Fatigue Severity Scale (FSS), 0–10
depression_phq9_score	PHQ-9 depression score, 0–27
pem_present	Whether Post-Exertional Malaise (PEM) is present (Yes/No or 1/0)
pem_duration_hours	Duration of PEM in hours
sleep_quality_index	Sleep quality (1–10 scale)
brain_fog_level	Brain fog level (1–10)
physical_pain_score	Physical pain intensity (1–10)
stress_level	Stress level (1–10)
work_status	Work status: Working / Partially working / Not working
social_activity_level	Social activity: Very low – Very high
exercise_frequency	Exercise frequency: Never – Daily
meditation_or_mindfulness	Does the patient practice mindfulness or meditation? Yes/No
hours_of_sleep_per_night	Average sleep duration per night
diagnosis	Target variable: ME/CFS, Depression, Both

⚠️ Key Characteristics

• Contains missing values (NaN) in most features (1–5%), simulating real-world data collection issues.
• All numeric features contain controlled noise to prevent perfect class separation.
• Diagnosis logic is based on clinical-like heuristics, making it suitable for training models that could support real-world decisions.

🛠 Suggested Use Cases

• Binary classification: ME/CFS vs Depression
• Multiclass classification: ME/CFS, Depression, Both
• EDA and feature engineering practice
• Missing data imputation techniques
• Medical ML modeling and interpretability

📦 Format

• CSV file
• ~1,000 rows
• UTF-8 encoding

🙌 Author

Created with ❤️ for the Kaggle community.

If you like this dataset — please upvote!
If you have any suggestions or improvements — feel free to comment.

The objective of the ACRIN 6684 multi-center clinical trial was to determine the association of baseline FMISO PET uptake (maximal tumor to blood ratio, hypoxic volume) and MRI parameters (K^trans, CBV) with overall survival, time to disease progression, and 6-month progression free survival in participants with newly diagnosed glioblastoma multiforme (GBM). The trial also collected standard brain cancer data such as Karnofsky performance status, but also pathological biomarkers that included MGMT status, HIF1-alpha, GLUT1, CAIX, CD31, and alpha-SMA expression assays.

There are two sets of volumes of interest (VOI) included with the ACRIN 6684 collection of MRI, PET and low-dose CT patient images. These include delineation of enhancing brain tumor lesions and ¹⁸F-FMISO PET hypoxia maps. More information about these masks can be found on the Detailed Description tab below. Additional information about the trial is available in the Study Protocol and Case Report Forms.

ACRIN 6684 Study Protocol

After establishing eligibility and enrollment to the study, baseline imaging of both MR and PET was performed within 2 weeks of starting therapy. FMISO, has been helpful in evaluating tumor oxygenation status, which may affect how well it responds to radiation and chemotherapy. The MRI scans were designed to measure tumor characteristics related to oxygenation status, including changes in blood flow, blood volume, and blood vessel size.

In the original protocol, following baseline imaging was an optional test-retest scan for FMISO PET only. Also included were PET and MRI scans at 3 weeks after the onset of chemo/radiation therapy, and 4 weeks following the end of standard treatment. Of the 50 patients enrolled in the study only one patient had a test-retest FMISO scan, and the requirement of scans mid and post therapy were dropped after the 4^th case. The current protocol appears in the figure on the right, and can be found online ( Protocol-ACRIN 6684 Amendment 7, 01.24.12 ). The latest protocol for ACRIN 6684 had PET and MR imaging performed only at baseline, up to 2 weeks prior to standard treatment (chemo + radiation therapy). Mid and post-therapy scans were eliminated from the protocol after Case 4, and only one patient had a retest FMISO scan. Of the 50 enrolled patients, 42 patients had evaluable imaging data for the primary aims of the study (see Gerstner et al. 2016).

Note: The MRI DWI/DTI series acquired through GE or Siemens scanners for 30 patients have been stripped of their b-values and diffusion gradient matrix DICOM header fields making them unable to be processed for ADC map production. The patients scanned with Philips MRI scanners are intact.

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Group × Time interaction on PI-related brain activation in the Difficult-to-Name (DN) condition. Highlights regions where the relationship between statistical learning performance (PI) and brain activity differs between TD and DLD groups across pre- and post-training.

Collection description

This dataset includes whole-brain statistical maps from a longitudinal fMRI study investigating visual statistical learning (SL) in children aged 6–9 years with developmental language disorder (DLD; n = 27) and typically developing (TD; n = 35) peers. Participants completed an SL task involving visual sequences under two stimulus conditions: easy-to-name(EN; animal drawings) and difficult-to-name (DN; abstract Hebrew letters). Sequences were either structured (1-back transitional probabilities) or random. Functional scans were collected at two time points: before and after one week of home-based SL training.

At the first-level analysis, individual contrast maps were computed comparing BOLD responses to structured vs. random sequences (Statistic > Random), separately for each stimulus type. At the second level, full-factorial SPM12 models (2 Group × 2 Time) were implemented separately for EN and DN conditions. Crucially, group-level models included four z-scored covariates representing individual performance index (PI) for each group and session, enabling the analysis of brain–behavior associations and their modulation by group and training. Additional analyses included F-tests on PI covariates (Group × Time interaction) and t-tests comparing group differences in PI-related activation at each time point.

All functional images were acquired on a 3T Siemens Prisma scanner. Preprocessing included motion correction, normalization to MNI space, and spatial smoothing (6 mm FWHM). Data quality control included framewise displacement scrubbing and a minimum tSNR threshold of 100.

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

group

Cognitive paradigm (task)

Other evaluation task

Map type

F

The dataset can be downloaded from https://doi.org/10.5281/zenodo.7868169 and a detailed description is offered at https://doi.org/10.5281/zenodo.7260704 in the "synthRAD2023_dataset_description.pdf".

The input of the validation datasets for Task1 is in Task1_val.zip, while for Task2 in Task2_val.zip. After unzipping, each Task is organized according to the following folder structure:

Task1_val.zip/

├── Task1

├── brain

├── 1Bxxxx

├── mr.nii.gz

└── mask.nii.gz

├── ...

└── overview

├── 1_brain_val.xlsx

├── 1Bxxxx_val.png

└── ...

└── pelvis

├── 1Pxxxx

├── mr.nii.gz

├── mask.nii.gz

├── ...

└── overview

├── 1_pelvis_val.xlsx

├── 1Pxxxx_val.png

└── ....

Task2_val.zip/

├──Task2

├── brain

├── 2Bxxxx

├── cbct.nii.gz

└── mask.nii.gz

├── ...

└── overview

├── 2_brain_val.xlsx

├── 2Bxxxx_val.png

└── ...

└── pelvis

├── 2Pxxxx

├── cbct.nii.gz

├── mask.nii.gz

├── ...

└── overview

├── 2_pelvis_val.xlsx

├── 2Pxxxx_val.png

└── ....

Each patient folder has a unique name that contains information about the task, anatomy, center and a patient ID. The naming follows the convention below:

[Task] [Anatomy] [Center] [PatientID]

1 B A 001

In each patient folder, two files can be found:

• mr.nii.gz or cbct.nii.gz (depending on the task): CBCT/MR image

• mask.nii.gz: image containing a binary mask of the dilated patient outline

For each task and anatomy, an overview folder is provided which contains the following files:

• [task]_[anatomy]_val.xlsx: This file contains information about the image acquisition protocol for each patient.

• [task][anatomy][center][PatientID]_val.png: For each patient a png showing axial, coronal and sagittal slices of CBCT/MR, CT, mask and the difference between CBCT/MR and CT is provided. These images are meant to provide a quick visual overview of the data.

DATASET DESCRIPTION

This challenge dataset contains imaging data of patients who underwent radiotherapy in the brain or pelvis region. Overall, the population is predominantly adult and no gender restrictions were considered during data collection. For Task 1, the inclusion criteria were the acquisition of a CT and MRI during treatment planning while for task 2, acquisitions of a CT and CBCT, used for patient positioning, were required. Datasets for task 1 and 2 do not necessarily contain the same patients, given the different image acquisitions for the different tasks.

Data was collected at 3 Dutch university medical centers:

• Radboud University Medical Center;

• University Medical Center Utrecht;

• University Medical Center Groningen.

For anonymization purposes, from here on, institution names are substituted with A, B and C, without specifying which institute each letter refers to.

The following number of patients is available in the validation set.

Validation

	Brain	Pelvis
	Center A	Center B	Center C	Total	Center A	Center B	Center C	Total
Task 1	10	10	10	30	20	0	10	30
Task 2	10	10	10	30	10	10	10	30

In total, for all tasks and anatomies combined, 120 image pairs are available in this dataset. This repository only contains the validation data. The training data is provided at: https://doi.org/10.5281/zenodo.7260704.

All images were acquired with the clinically used scanners and imaging protocols of the respective centers and reflect typical images found in clinical routine. As a result, imaging protocols and scanner can vary between patients. A detailed description of the imaging protocol for each image, can be found in spreadsheets that are part of the dataset release (see dataset structure).

Data was acquired with the following scanners:

• Center A:

  • MRI: Philips Ingenia 1.5T/3.0T

  • CT: Philips Brilliance Big Bore or Siemens Biograph20 PET-CT

  • CBCT: Elekta XVI

• Center B:

  • MRI: Siemens MAGNETOM Aera 1.5T or MAGNETOM Avanto_fit 1.5T

  • CT: Siemens SOMATOM Definition AS

  • CBCT: IBA Proteus+ or Elekta XVI

• Center C:

  • MRI: Siemens Avanto fit 1.5T or Siemens MAGNETOM Vida fit 3.0T

  • CT: Philips Brilliance Big Bore

  • CBCT: Elekta XVI

For task 1, MRIs were acquired with a T1-weighted gradient echo or an inversion prepared - turbo field echo (TFE) sequence and collected along with the corresponding planning CTs for all subjects. The exact acquisition parameters vary between patients and centers. For centers B and C, selected MRIs were acquired with Gadolinium contrast, while the selected MRIs of center A were acquired without contrast.

For task 2, the CBCTs used for image-guided radiotherapy ensuring accurate patient position were selected for all subjects along with the corresponding planning CT.

The following pre-processing steps were performed on the data:

• Conversion from dicom to compressed nifti (nii.gz)

• Rigid registration between CT and MR/CBCT

• Anonymization (face removal, only for brain patients)

• Patient outline segmentation (provided as a binary mask)

• Crop MR/CBCT, CT and mask to remove background and reduce file sizes

The code used to preprocess the images can be found at: https://github.com/SynthRAD2023/. Detailed information about the dataset are provided in SynthRAD2023_dataset_description.pdf published here along with the data and will also be submitted to Medical Physics.

ETHICAL APPROVAL

Each institution received ethical approval from their internal review board/Medical Ethical committee:

• UMC Utrecht approved not-WMO on 4/03/2022 with number 22/474 entitled: “Synthetizing computed tomography for radiotherapy Grand Challenge (SynthRAD)”.

• UMC Groningen approved not-WMO on 20/07/2022 with number 202200310 entitled: “Synthesizing computed tomography for radiotherapy - Grand Challenge”.

• Radboud UMC declared the study not-WMO on 17/10/2022 with number 2022-15950 entitled “Synthetizing computed tomography for radiotherapy Grand Challenge”.

CHALLENGE DESIGN

The overall challenge design can be found at https://doi.org/10.5281/zenodo.7746019.

Between-group comparison of unmodulated brain activation before training (pre-training) in the DN condition. Shows baseline neural differences between TD and DLD groups prior to SL training.

Collection description

This dataset includes whole-brain statistical maps from a longitudinal fMRI study investigating visual statistical learning (SL) in children aged 6–9 years with developmental language disorder (DLD; n = 27) and typically developing (TD; n = 35) peers. Participants completed an SL task involving visual sequences under two stimulus conditions: easy-to-name(EN; animal drawings) and difficult-to-name (DN; abstract Hebrew letters). Sequences were either structured (1-back transitional probabilities) or random. Functional scans were collected at two time points: before and after one week of home-based SL training.

At the first-level analysis, individual contrast maps were computed comparing BOLD responses to structured vs. random sequences (Statistic > Random), separately for each stimulus type. At the second level, full-factorial SPM12 models (2 Group × 2 Time) were implemented separately for EN and DN conditions. Crucially, group-level models included four z-scored covariates representing individual performance index (PI) for each group and session, enabling the analysis of brain–behavior associations and their modulation by group and training. Additional analyses included F-tests on PI covariates (Group × Time interaction) and t-tests comparing group differences in PI-related activation at each time point.

All functional images were acquired on a 3T Siemens Prisma scanner. Preprocessing included motion correction, normalization to MNI space, and spatial smoothing (6 mm FWHM). Data quality control included framewise displacement scrubbing and a minimum tSNR threshold of 100.

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

group

Cognitive paradigm (task)

Other evaluation task

Map type

T

Data

Twenty-three healthy participants underwent three counterbalanced rTMS-fMRI sessions on different days, where a prefrontal (FRO), an occipital (OCC) and a temporo-parietal control (CTR) region were identically stimulated with low-frequency (1Hz) rTMS. We measured brain activity with resting state-fMRI before (rest-pre) and immediately after stimulation (rest-post).

Analysis

The following table contains a description of the configuration's or script files needed for the data analysis:

Filename	Type	Description
pipeline_config_cpac_v0.3.9.2.yml	YAML	Configuration file pipeline for CPAC v0.3.9.2 which runs the pre-processing, FC, timeseries generation and local signal analysis
fca_file_extract.sh	Bash	Extracts and organizes the FCA's files for the statistical analysis
anova_rm_spm_batch.mat	Matlab	SPM's batch configuration file for the one-way repeated measures ANOVA second level analysis
spm_contrast_vis.py	Python	Visualizes the statistically significant contrast's images by overlaying them on a glass brain representation
cons_mod_calc.m	Matlab	Extracts the timeseries generated by CPAC and runs the consensus modularity analysis
cons_mod_stats_vis.py	Python	Visualizes and runs the statistical analysis on the consensus modularity analysis results
cv_classifier.py	Python	Extracts the features and run the cross-validated classification on them
local_signal_extraction.sh	Bash	Extracts and masks the local signal analysis files for the statistical analysis
sDCM.m	Matlab	Extracts the timeseries generated by CPAC and runs the spectral DCM analysis

DATASET DETAILS:

Participant demographics:

A total of 26 participants were included, consisting of 10 males (mean age 25.4 ± 7.4) and 16 females (mean age 23 ± 3.2). All participants were right-handed, reported normal or corrected-to-normal vision, and had no motor impairments. Three participants had previous experience with BCIs, and five participants used VR more than twice. Participants were randomly assigned to either the embodied group (N=13) or the non-embodied group (N=13), which served as a control. All participants signed an informed consent before participating in the study in accordance with the 1964 Declaration of Helsinki.

Experiment Description:

A between-subject design was used to investigate the effect of virtual embodiment priming phase on the subsequent motor-imagery training phase in VR. The experiment comprised four main blocks: (1) equipment setup and instructions (45-60 minutes), (2) resting state EEG recording (4 minutes), (3) inducing or breaking the sense of embodiment in VR (5 minutes), and (4) MI training in VR (15 minutes). The entire experiment lasted approximately 90-120 minutes. Directly after block 3, participants answered a questionnaire that measured their subjective sense of embodiment and physical presence.

Equipment:

A wireless EEG amplifier (LiveAmp; Brain Products GmbH, Gilching, Germany) was used, with 32 active EEG electrodes (+3 ACC) with a sampling rate of 500Hz. In addition, EMG, and Temperature signals (in uV) have been recorded synchronously in a bipolar montage and connected to the EEG amplifier’s AUX input through the Brain Products BIP2AUX adapter.

Visual feedback was provided through an Oculus Rift CV1 headset (Reality Labs, formerly Facebook, Inc., CA, USA).

Channel Indices:

EEG: 1-32
EMG Left (AUX1): 33
EMG Right. (AUX2): 34
Temperature (AUX3): 35
ACC: 36-38

Event codes:

Code	Description
S01	Experiment Start
S02	Baseline Start
S03	Baseline Stop
S04	Start Of Trial
S05	Cross On Screen
S07	class1, Left hand
S08	class2, Right hand
S09	Feedback Continuous
S10	End of Trial
S11	End Of Session
S12	Experiment Stop

Directory tree:

ROOT
|
+--- GROUP [Control or Embodied]
| +---USER #
| | +---TASK #
| | | +---Resting State
| | | | .eeg
| | | | .vhdr
| | | | .vmrk
| | | +---Embodiment
| | | | .eeg
| | | | .vhdr
| | | | .vmrk
| | | +---MI
| | | | .eeg
| | | | .vhdr
| | | | .vmrk

For demographics and questionnaire data, please contact the authors.

Brain body and song variables for 48 male zebra finches, their fathers and foster-fathers, including details of rearing environmentData for Environmental and genetic control of brain & song.xlsx

• The global industry was valued at US$ 30.1 Bn in 2022
• It is estimated to grow at a CAGR of 3.7% from 2023 to 2031 and reach US$ 41.6 Bn by the end of 2031

Market Introduction

Attribute	Detail
Market Drivers	Increase in Prevalence of Degenerative Neurological Disorders Advancements in Technologies

Neuroscience Market Regional Insights

Attribute	Detail
Leading Region	North America

Neuroscience Market Snapshot

Attribute	Detail
Market Size in 2022	US$ 30.1 Bn
Market Forecast (Value) in 2031	US$ 41.6 Bn
Growth Rate (CAGR)	3.7%
Forecast Period	2023-2031
Historical Data Available for	2017-2021
Quantitative Units	US$ Bn for Value
Market Analysis	It includes segment analysis as well as regional level analysis. Moreover, qualitative analysis includes drivers, restraints, opportunities, key trends, Porter’s Five Forces analysis, value chain analysis, and key trend analysis.
Competition Landscape	Market share analysis by company (2022) Company profiles section includes overview, product portfolio, sales footprint, key subsidiaries or distributors, strategy & recent developments, and key financials
Format	Electronic (PDF) + Excel
Market Segmentation	Component Instrument Software Services Technology Whole Brain Imaging Neuro-microscopy Electrophysiology Neuroproteomics Analysis Animal Behavior Analysis Neuro-functional Analysis Others End-user Hospitals Diagnostic Laboratories Academic & Research Institutions Others
Regions Covered	North America Europe Asia Pacific Latin America Middle East & Africa
Countries Covered	U.S. Canada Germany U.K. France Italy Spain China India Japan Australia & New Zealand Brazil Mexico South Africa GCC
Companies Profiled	NEURALINK Kernel BrainCo, Inc. MindMaze Paradromics NeuroPro NeuroSky EMOTIV, Inc. Cercare Medical A/S Plexon, Inc. Noldus Information Technology B.V. Femtonics Ltd. Neuralynx, Inc. Neurable, Inc. Bitbrain Technologies Halo Neuroscience NeuroNexus Technologies, Inc.
Customization Scope	Available Upon Request
Pricing	Available Upon Request

This repository provides the software and data for the paper

Causemann, M., Kuchta, M., Masri, R., & Rognes, M. E. (2025). In-silico molecular enrichment and clearance of the human intracranial space.

In particular, it contains the following data:

file	description
code.zip	The code used to run all simulations and postprocessing steps. Detailed instructions on how to install and run the software can be found in the following github repository: https://github.com/MariusCausemann/brain-PVS-SAS-transport
surfaces.zip	The surface triangulations extracted from the segmentations in .ply format, suitable for viewing in e.g. paraview or further processing.
standardmesh.zip	The mesh used to run all simulations in .xdmf format. standard.xdmf : complete mesh, including markers for parenchyma and CSF spaces. standard_outer.xdmf : only CSF spaces. *_facets.xdmf: respective boundary markers
pvsnetworks.zip	1D representation of the arterial and venous networks, as well as the 3D cylinder corresponding to the vessel diameter.
segmentation.zip	Synthseg segmentations of the T1 data and binary masks of venous and arterial networks from Hodneland et al.
modelA.zip	Simulation results (concentration values in CSF, parenchyma and PVS in 10min steps) for the baseline model in the paper.
modelA.html	Interactive visualization of the tracer spreading for the baseline model in the paper. Also available here.
modelA.mp4	Animation of the tracer spreading for the baseline model in the paper.
modelA-strongVM.zip	Simulation results (concentration values in CSF, parenchyma and PVS in 10min steps) for the high PVS flow model in the paper.
modelA-strongVM.html	Interactive visualization of the tracer spreading for the high PVS flow model in the paper. Also available here.
modelA-strongVM.mp4	Animation of the tracer spreading for the high PVS flow model in the paper.