We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1,870 young healthy adults, aged 18 to 35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1,722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early aging.

Collection of neuroanatomically labeled MRI brain scans, created by neuroanatomical experts. Regions of interest include the sub-cortical structures (thalamus, caudate, putamen, hippocampus, etc), along with ventricles, brain stem, cerebellum, and gray and white matter and sub-divided cortex into parcellation units that are defined by gyral and sulcal landmarks.

The Brain/MINDS Marmoset MRI NA216 and eNA91 datasets currently constitutes the largest public marmoset brain MRI resource (483 individuals), and includes in vivo and ex vivo data for large variety of image modalities covering a wide age range of marmoset subjects.
* The in vivo part corresponds to a total of 455 individuals, ranging in age from 0.6 to 12.7 years (mean age: 3.86 ± 2.63), and standard brain data (NA216) from 216 of these individuals (mean age: 4.46 ± 2.62).
T1WI, T2WI, T1WI/T2WI, DTI metrics (FA, FAc, MD, RD, AD), DWI, rs-fMRI in awake and anesthetized states, NIfTI files (.nii.gz) of label data, individual brain and population average connectome matrix (structural and functional) csv files are included.
* The ex vivo part is ex vivo data, mainly from a subset of 91 individuals with a mean age of 5.27 ± 2.39 years.
It includes NIfTI files (.nii.gz) of standard brain, T2WI, DTI metrics (FA, FAc, MD, RD, AD), DWI, and label data, and csv files of individual brain and population average structural connectome matrices.

MRI MS DB Description: In the IMT-Segmentation folder there are 38 folders representing data for each patient 38patients). In each patient folder we have: 1) MRI TIFF Images from first and second examination (0 months, 6-12 months) 2) Lesion segmentations (*.plq files). The delineation/s can be loaded into matlab i.e load(file.plq, -.mat ); Then points can be drawn on the image. load( IM_00031_1.plq , -mat );

Database of human brain images derived from a realistic phantom and generated using a sophisticated MRI simulator. Custom simulations may be generated to match a user's selected parameters. The goal is to aid validation of computer-aided quantitative analysis of medical image data. The SBD contains a set of realistic MRI data volumes produced by an MRI simulator. These data can be used by the neuroimaging community to evaluate the performance of various image analysis methods in a setting where the truth is known. The SBD contains simulated brain MRI data based on two anatomical models: normal and multiple sclerosis (MS). For both of these, full 3-dimensional data volumes have been simulated using three sequences (T1-, T2-, and proton-density- (PD-) weighted) and a variety of slice thicknesses, noise levels, and levels of intensity non-uniformity. These data are available for viewing in three orthogonal views (transversal, sagittal, and coronal), and for downloading.

RIDER Neuro MRI contains imaging data on 19 patients with recurrent glioblastoma who underwent repeat imaging sets. These images were obtained approximately 2 days apart (with the exception of one patient, RIDER Neuro MRI-1086100996, whose images were obtained one day apart).

DCE‐MRI: All 19 patients had repeat dynamic contrast‐enhanced MRI (DCE‐MRI) datasets on the same 1.5T imaging magnet. On the basis of T2‐weighted images, technologists chose 16 image locations using 5mm thick contiguous slices for the imaging. For T1 mapping, multi‐flip 3D FLASH images were obtained using flip angles of 5, 10, 15, 20, 25 and 30 degrees, TR of 4.43 ms, TE of 2.1 ms, 2 signal averages. Dynamic images were obtained during the intravenous injection of 0.1mmol/kg of Magnevist intravenous at 3ccs/second, started 24 seconds after the scan had begun. The dynamic images were acquired using a 3D FLASH technique, using a flip angle of 25 degrees, TR of 3.8 ms, TE of 1.8 ms using a 1 x1 x 5mm voxel size. The 16 slice imaging set was obtained every 4.8 sec.

DTI: Seventeen of the 19 patients also obtained repeat diffusion tensor imaging (DTI) sets. Whole brain DTI were obtained using TR 6000ms, TE 100 ms, 90 degree flip angle, 4 signal averages, matrix 128 x 128, 1.72 x 1.72 x 5 mm voxel size, 12 tensor directions, iPAT 2, b value of 1000 sec/mm2 .

Contrast‐enhanced 3D FLASH: All 19 patients underwent whole brain 3D FLASH imaging in the sagittal plane after the administration of Magnevist. For this sequence, TR was 8.6 ms, TE 4.1 ms, 20 degree flip angle, 1 signal average, matrix 256 x 256; 1mm isotropic voxel size.

Contrast‐enhanced 3D FLAIR: All 17 patients who had repeat DTI sets also had 3D FLAIR sequences in the sagittal plane after the administration of Magnevist. For this sequence, the TR was 6000 ms, TE 353 ms, and TI 2200ms; 180 degree flip angle, 1 signal average, matrix 256 x 216; 1 mm isotropic voxel size. Note: before transmission to NCIA, all image sets with 1mm isotropic voxel size were “defaced” using MIPAV software or manually.

About the RIDER project

The Reference Image Database to Evaluate Therapy Response (RIDER) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. The National Cancer Institute (NCI) has exercised a series of contracts with specific academic sites for collection of repeat "coffee break," longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [CT] positron emission tomography [PET] CT, dynamic contrast-enhanced magnetic resonance imaging [DCE MRI], diffusion-weighted [DW] MRI) and organ sites (currently lung, breast, and neuro). The methods for data collection, analysis, and results are described in the new Combined RIDER White Paper Report (Sept 2008):

• RIDER White Paper: Combined contracts report ( Sept 2008) PDF

The long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. Thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the first RIDER white paper report (2006):

• RIDER White Paper: Executive Summary PDF
• RIDER White Paper: Editorial in Nature.com

SCORING SEGMENTATIONS

• Qualitative segmentation scores by two raters (rater1; rater2).
• Scale: excellent (4); good (3); doubtful (2) and failed (1).

DATABASES

• IXI database, Imperial College of London (https://brain-development.org/ixi-dataset/)
• Autism Brain Imaging Data Exchange (ABIDE) database (http://fcon_1000.projects.nitrc.org)
• SchizConnect database (http://schizconnect.org)

SEGMENTATION METHODS

• MR-TIM (Taberna et al., 2021), green rows
• WTS (Liu et al., 2017), red rows

TABLES

IXI_young

• 20 MRI from the IXI database, participants 20–35 years old;
• MR scanners: Philips Intera 3.0T (HH); Philips Gyroscan Intera 1.5T (G)

IXI_older

• 20 MRI from the IXI database, participants 60–75 years old;
• MR scanners: Philips Intera 3.0T (HH); Philips Gyroscan Intera 1.5T (G)

ABIDE

• 10 MRI from the ABIDE database, participants 18-25 years old;
• MR scanner: Philips Achieva 3.0T

SchizConnect

• 10 MRI from the SchizConnect database, participants 19-66 years old;
• MR scanner: Siemens Trio Tim 3.0T

REFERENCES

Liu, Q., Farahibozorg, S., Porcaro, C., Wenderoth, N., & Mantini, D. (2017). Detecting large-scale networks in the human brain using high-density electroencephalography. Hum Brain Mapp, 38(9), 4631-4643. doi:10.1002/hbm.23688

Taberna, G. A., Samogin, J., & Mantini, D. (2021). Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction. Neuroinformatics. doi:10.1007/s12021-020-09504-5

This dataset was curated in collaboration between the Computer Science and Engineering Department, University of Dhaka and the National Institute of Neuroscience, Bangladesh. It comprise 5,285 T1-weighted contrast- enhanced brain MRI images belonging to 38 categories. This work is accompanied by a paper found here http://arxiv.org/abs/2106.07333

This is the MRI scan database used in the research work of classifying Meningioma Tumor in humans by using hybrid Ensemble Deep Learning Network AlGoRes. It consist of two sets; one for training and another one for testing the Deep Learning Network AlGoRes.Training data set consist of 822 imagers with meningioma_tumor and 395 images without tumor.Testing data set consist of 115 imagers with meningioma_tumor and 104 images without tumor.

The Advanced BRain Imaging on ageing and Memory (ABRIM) MRI collection includes data of 295 participants, aged between 18-80 years old. Participants underwent a multi-modal MRI protocol and behavioural examiniation.

The present collection provides both the raw and pre-processed MRI data as well as several automated and/or manual quality control indices in Brain Imaging Data Structure (BIDS) format. In addition, the collection contains information on age, sex, height, and weight.

A complete description of the MRI data collection, pre-processing steps, and data curation is provided in our preprint: https://doi.org/10.1101/2023.11.16.567360.

Please make sure to refer to the most up-to-date publication.

Data is made for available for registered users under the data user agreement (DUA) for identifiable human data - scientific use (RU-HD-SU-1.0) via: https://doi.org/10.34973/7q0a-vj19.

For more information on the data availability and corresponding (DUA), please refer to: https://data.ru.nl/.

The ABRIM behavioural collection will be released in November 2028.

Real-time magnetic resonance imaging (RT-MRI) of human speech production is enabling significant advances in speech science, linguistics, bio-inspired speech technology development, and clinical applications. Easy access to RT-MRI is however limited, and comprehensive datasets with broad access are needed to catalyze research across numerous domains. The imaging of the rapidly moving articulators and dynamic airway shaping during speech demands high spatio-temporal resolution and robust reconstruction methods. Further, while reconstructed images have been published, to-date there is no open dataset providing raw multi-coil RT-MRI data from an optimized speech production experimental setup. Such datasets could enable new and improved methods for dynamic image reconstruction, artifact correction, feature extraction, and direct extraction of linguistically-relevant biomarkers.The present dataset offers a unique corpus of 2D sagittal-view RT-MRI videos along with synchronized audio for 75 subjects performing linguistically motivated speech tasks, alongside the corresponding first-ever public domain raw RT-MRI data. The dataset also includes 3D volumetric vocal tract MRI during sustained speech sounds and high-resolution static anatomical T2-weighted upper airway MRI for each subject.Included are the following files:dataset.zip: contains the entire dataset of 75 subjectsdataset_2drt_video_only.zip: contains only files of 2D RT-MRI videos of 75 subjectsdataset_t2w_only.zip: contains only files of T2-weighted images of 75 subjectsdataset_3d_only.zip: contains only files of 3D volumetric images of 75 subjects example_for_sub001.zip: contains all files of 1 subject (sub001)metafile_public_20210129.json: contains meta informationSubjects.xlsx: contains demographic information for each subjectStimuli.pptx: contains the experimental stimuli including scripts and pictures used for the visualization to the subjects

The Amsterdam Ultra-high field adult lifespan database (AHEAD) consists of 105 7 Tesla (T) whole-brain structural MRI scans tailored specifically to imaging of the human subcortex, including both male and female participants and covering the entire adult life span (19-80 yrs). Data was acquired at a submillimeter resolution using a single multi-echo magnetization-prepared rapid gradient echo (MP2RAGEME) sequence, resulting in complete anatomical alignment of quantitative, R1-maps, R2*-maps, T1-maps, T1-weighted images, T2*-maps, and quantitative susceptibility mapping (QSM). Probability maps were created for five individual basal ganglia structures.

Fully-Automated μMRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome - Table 1

IntroductionA multishell diffusion MRI dataset collected from three traveling subjects with identical acquisition setting in ten imaging centers. Both of the scanner type and imaging protocol for anatomical and diffusion imaging were well controlled.This dataset is expected to replenish the individual reproducible study via multicenter collaborations by providing an open resource for advanced and novel microstructure and tractography quantifications.Primary acquisition parameters• T1-weighted imagesSequence: MP2RAGEResolution = 1x1x1.2 mm3• Diffusion-weighted imagesSequence: SMS EPIResolution = 1.5x1.5x1.5 mm3b-value = 1000,2000,3000 mm2/sdirections = 30,30,30non-diffusion images = 6Data download and unarchive1. The DWI data were archived by bandizip software into multi-parts zip files as: sub-1.zip(.z01), sub-2.zip(.z01), and sub-3.zip(.z01). One pair for each subject respectively. The *.zip files are around 2.4GB each and .z01 files are 4GB each. Please use the MD5 checksum as follows to verify the integrity of your download if possible.sub-1.zip 67a5b404c2d67e74c37dbdd7f5825538sub-1.z01 727e4fbddb104138dad89ba6ea60cd0fsub-2.zip a3bd923a00a2cd35a88c56356ea3f9dfsub-2.z01 b46845a3105a0694358bd57a60e4b68bsub-3.zip 9388d9745f4dce403261e70ae5f5f9bcsub-3.z01 86aa3607462c483a85a64f1503217ace2. The pair of zip parts should share exactly the same filename by default. However, it happens the filenames may be changed during downloading for some reason. Please correct and rename the filenames properly before unarchive progress, otherwise there may occur an error.3. Instructions to extract the multi-part zip files.A) For Windows users:Consider to use applications like WinRAR, 7Zip, etc. Select one set of multi-part zip files for one subject -> Right click on files -> Select ‘Extract’ option listed in the pop-out menu.B) For Unix/Linux and Mac users:In the terminal command line, first, cd to the path where multi-part zip files are, then run the following commands to extract sub-1 for example:　cat sub-1.z > sub-1-cat.zip　unzip sub-1-cat.zipMac users may also try the free applications like The Unarchiver, Dr.Unarchiver, etc.Data structureThis dataset was orgnaized in BIDS format. However, due to the limit size of 5 GB for single file in the FigShare, the folders "sub-1", "sub-2", and "sub-3" were compressed into multi-part archives. After file extraction, you can see the whole dataset structure as follows (other folders contain similar files with sub-3/ and sub-3/ses-c10r3/):|　README|　CHANGES|　dataset_description.json|　participants.tsv|▿ code/|　　example.sh|　　Prep_diffusion.sh|　　Prep_face_removal.sh|　　Prep_gibbsring.m|▸ sub-1/|▸ sub-2/|▿ sub-3/|　　sub-3_sessions.tsv|　▸ ses-c01r1/|　▸ ses-c02r1/|　▸ ses-c03r1/|　▸ ses-c04r1/|　▸ ses-c05r1/|　▸ ses-c06r1/|　▸ ses-c07r1/|　▸ ses-c08r1/|　▸ ses-c09r1/|　▸ ses-c10r1/|　▸ ses-c10r2/|　▿ ses-c10r3/|　　▿ anat/| 　 　 　 sub-3_ses-c10r3_T1w.json| 　 　 　 sub-3_ses-c10r3_T1w.nii.gz|　　▿ dwi/| 　 　 　 sub-3_ses-c10r3_dwi.bval| 　 　 　 sub-3_ses-c10r3_dwi.bvec| 　 　 　 sub-3_ses-c10r3_dwi.json| 　 　 　 sub-3_ses-c10r3_dwi.nii.gz| 　 　 　 sub-3_ses-c10r3_dwi_mask.nii.gz|▿ derivatives/|　▿ movement/|　　▸ sub-1/|　　▸ sub-2/|　　▿ sub-3/|　　　▸ ses-c01r1/|　　　▸ ses-c02r1/|　　　▸ ses-c03r1/|　　　▸ ses-c04r1/|　　　▸ ses-c05r1/|　　　▸ ses-c06r1/|　　　▸ ses-c07r1/|　　　▸ ses-c08r1/|　　　▸ ses-c09r1/|　　　▸ ses-c10r1/|　　　▸ ses-c10r2/|　　　▿ ses-c10r3/| 　 　 　　 sub-3_ses-c10r3.eddy_parametersData use agreement To use this dataset, we would like to follow the license CC BY (https://creativecommons.org/licenses/by/4.0/).ContactFor more information on the data collection and pre-processing procedure, you can contact Qiqi Tong (tongqq@zju.edu.cn).For other information or further cooperation with us, you can contact Dr. Hongjian He (hhezju@zju.edu.cn).

Database of 141 studies which have investigated brain structure (using MRI and CT scans) in patients with bipolar disorder compared to a control group. Ninety-eight studies and 47 brain structures are included in the meta-analysis. The database and meta-analysis are contained in an Excel spreadsheet file which may be freely downloaded from this website.

Please refer to readme.txt file

Group average map of FLAIR images in standard MNI space across 1,832 MRiShare subjects.

Collection description

This collection contains group average maps presented in the associated publication "The MRi-Share database: brain imaging in a cross-sectional cohort of 1,870 university students".

Subject species

homo sapiens

Modality

Structural MRI

Analysis level

group

Cognitive paradigm (task)

None / Other

Map type

A

This dataset contains MRI k-space data of the Amsterdam Ultra-high field adult lifespan database (AHEAD). Data were scanned using the MP2RAGEME sequence for T1, T2* and Quantitative Susceptibility Mapping in one sequence at 7 Tesla. Data are motion-corrected using Fat navigators (FatNavs), and defaced in image-domain. In total 77 subjects are included, scanned with a resolution of 0.7mm isotropic. Data of the MP2RAGEME-sequence are stored according to the ISMRMRD-standard in h5-format (https://ismrmrd.github.io/). Detailed scanner parameters are included in the h5-files of all subjects. Coil sensitivity maps per subjects are included in native h5-format. Demographics of all subjects are included in a separate csv-file, being sex and age decade, covering the life span.

The Erasmus Glioma Database (EGD) contains structural magnetic resonance imaging (MRI) scans, genetic and histological features (specifying the WHO 2016 subtype), and whole tumour segmentations of patients with glioma. Pre-operative MRI data of 774 patients with glioma (281 female, 492 male, 1 unknown, age range 19–86 years) treated at the Erasmus MC between 2008 and 2018 is available. For all patients a pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, and T2-weighted FLAIR scan are available, made on a variety of scanners from four different vendors. All scans are registered to a common atlas and defaced. Genetic and histological data consists of the IDH mutation status (available for 467 patients), 1p/19q co-deletion status (available for 259 patients), and grade (available for 716 patients). The full WHO 2016 subtype is available for 415 patients. Manual segmentations are available for 374 patients and automatically generated segmentations are available for 400 patients. The dataset can be used to relate the visual appearance of the tumor on the scan with the genetic and histological features, and to develop automatic segmentation methods.

See also: https://github.com/Svdvoort/egd-downloader

A database which contains longitudinal structural MRIs, spectroscopy, DTI and correlated clinical/behavioral data from approximately 500 healthy, normally developing children, ages newborn to young adult.