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.

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.

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.

This dataset uses non-augmented datasets with credits belonging to the following...

Sartaj Bhuvaji, Ankita Kadam, Prajakta Bhumkar, Sameer Dedge, and Swati Kanchan. (2020). Brain Tumor Classification (MRI) [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/1183165

git repo source = https://github.com/SartajBhuvaji/Brain-Tumor-Classification-DataSet

source credit - Kaggle dataset credits https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri

Group average map of inner (white) cortical surface area images in fsaverage 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

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 RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.. Documented October 4, 2017.A sub-project of the Cell Centered Database (http://ccdb.ucsd.edu) providing a public repository for animal imaging data sets from MRI and related techniques. The public AIDB website provides the ability for browsing, visualizing and downloading the animal subjected MRI data. The AIDB is a pilot project to serve the current need for public imaging repositories for animal imaging data. The Cell Centered Database (CCDB) is a web accessible database for high resolution 2D, 3D and 4D data from light and electron microscopy. The AIDB data model is modified from the basic model of the CCDB where microscopic images are combined to make 2D, 3D and 4D reconstructions. The CCDB has made available over 40 segmented datasets from high resolution magnetic resonance imaging of inbred mouse strains through the prototype AIDB. These data were acquired as part of the Mouse BIRN project by Drs. G. Allan Johnson and Robert Williams. More information about these data can be found in Badea et al. (2009) (Genetic dissection of the mouse CNS using magnetic resonance microscopy - Pubmed: 19542887)

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.

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

The data is collected from several websites, hospitals, and public repositories. The datasetconsists of preprocessed MRI (magnetic resonance imaging) images. All the images are resized to 128 x 128 pixels. The dataset has four classes of images. The Dataset is consists of a total of 6400 MRI images. Class - 1: Mild Demented (896 images) Class - 2: Moderate Demented (64 images) Class - 3: Non Demented (3200 images) Class - 4: Very Mild Demented (2240 images)

Motive The main motive behind sharing this dataset is to design/develop an accurate framework or architecture for the classification of Alzheimer's disease.

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

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

About this dataset

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

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

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

Download

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

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

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

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

Analysis

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

Contributing

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

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

A macaque brain MRI database characterizing the normal postnatal macaque brain development. This longitudinal primate database was acquired from a cohort of healthy macaque monkeys ranging from a few week olds up to 3-year-old adolescents. Website: https://data.kitware.com/#collection/54b582c38d777f4362aa9cb3

This work was supported by R01 MH091645 DEVELOPMENTAL BRAIN ATLAS TOOLS AND DATA APPLIED TO HUMANS AND MACAQUES http://projectreporter.nih.gov/project_info_description.cfm?aid=8454496

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.

The Brain Imaging and Neurophysiology Database (BIND) represents one of the largest multi-institutional, multimodal neuroimaging repositories, comprising 1.8 million brain scans from 38,945 subjects linked to neurophysiological recordings. This comprehensive dataset addresses critical limitations in neuroimaging research by providing unprecedented scale and diversity across pathologies and healthy controls. BIND integrates de-identified data from three major academic medical centers -- Massachusetts General Hospital, Brigham and Women's Hospital, and Stanford University Medical Center -- including 1,724,300 MRI scans (1.5T, 3T, and 7T), 54,154 CT scans, 5,720 PET scans, and 655 SPECT scans, converted to standardized NIfTI format following BIDS organization. The database spans the full age spectrum and encompasses diverse neurological conditions alongside healthy subjects. We deployed Bio-Medical Large Language Models to extract structured clinical metadata from 84,960 associated radiology reports, categorizing findings into standardized pathology classifications. All imaging data are linked to previously published EEG and polysomnography recordings from the Harvard Electroencephalography Database and Human Sleep Project, enabling unprecedented multimodal analyses. BIND is freely accessible for academic research through the Brain Data Science Platform (https://bdsp.io/). This resource facilitates large-scale neuroimaging studies, machine learning applications, and multimodal brain research to accelerate discoveries in clinical neuroscience.

Knowing the difference between left and right is generally assumed throughout the brain MRI research community. However, we note widespread occurrences of left-right orientation errors in MRI open database repositories where volumes have contained systematic left-right flips between subject EPIs and anatomicals, due to having incorrect or missing file header information. Here we present a simple method in AFNI for determining the consistency of left and right within a pair of acquired volumes for a particular subject; the presence of EPI-anatomical inconsistency, for example, is a sign that dataset header information likely requires correction. The method contains both a quantitative evaluation as well as a visualizable verification. We test the functionality using publicly available datasets. Left-right flipping is not immediately obvious in most cases, so we also present visualization methods for looking at this problem (and other potential problems), using examples from both FMRI and DTI datasets.

Overview:-

This dataset provides longitudinal Magnetic Resonance Imaging (MRI) scans of patients with Multiple Sclerosis (MS), specifically curated for the study of white matter lesion (WML) changes over time.

Each patient was scanned on two separate occasions, allowing researchers to analyze lesion progression or regression between imaging sessions. Ground truth lesion change masks, derived from expert consensus, are provided to support validation of automatic change detection algorithms.

All images have been defaced to preserve patient privacy.

Dataset Contents:-

Each patient folder includes:

• Co-registered and N4 bias-corrected MRI sequences (for both timepoints):
  • T1-weighted (T1)
  • T2-weighted (T2)
  • FLAIR
• Brain mask (patientX_brainmask.nii.gz)
• White matter lesion change mask (patientX_gt.nii.gz)
• Affine transform parameters to register images across studies and into a common space.
• RAW MRI images (in original acquisition space).

Folder Structure:- The dataset is organized per patient:

patientX/ │ ├── patientX_brainmask.nii.gz # Brain mask ├── patientX_gt.nii.gz # White matter lesion change mask ├── patientX_study1_T1_reg.nii.gz # N4-corrected & co-registered T1 (study 1) ├── patientX_study1_T2_reg.nii.gz # N4-corrected & co-registered T2 (study 1) ├── patientX_study1_FLAIR_reg.nii.gz # N4-corrected & co-registered FLAIR (study 1) ├── patientX_study2_T1_reg.nii.gz # N4-corrected & co-registered T1 (study 2) ├── patientX_study2_T2_reg.nii.gz # N4-corrected & co-registered T2 (study 2) ├── patientX_study2_FLAIR_reg.nii.gz # N4-corrected & co-registered FLAIR (study 2) ├── patientX_studyY_FLAIR_to_common_space.txt # Affine transform parameters to common space │ └── raw/ ├── patientX_study1_T1.nii.gz ├── patientX_study1_T2.nii.gz ├── patientX_study1_FLAIR.nii.gz ├── patientX_study2_T1.nii.gz ├── patientX_study2_T2.nii.gz ├── patientX_study2_FLAIR.nii.gz └── patientX_studyY_modality_intrastudy_to_FLAIR.txt

License & Reference:- License: Creative Commons Attribution (CC-BY)

Reference (please cite if used): Lesjak, Ž., Pernuš, F., Likar, B., & Špiclin, Ž. “Validation of White-Matter Lesion Change Detection Methods on a Novel Publicly Available MRI Image Database.” Neuroinformatics (2016): 1–18.

Source: Laboratory of Imaging Technologies

This is a collection of 2,888 clinical MRIs of patients admitted at a National Stroke Center, over ten years, with clinical diagnosis of acute or early subacute stroke. The collection includes diverse MRI modalities and protocols. The infarct core was manually defined in the diffusion weighted images; the images are provided in native subject space and in standard space (MNI), in Neuroimaging Informatics Technology Initiative (NIfTI) format. The data format and organization follows Brain Imaging Data Structure (BIDS) guidelines. The collection includes diverse metadata, comprised of demographic information, basic clinical profile (NIH Stroke Scale/Score (NIHSS), hospitalization duration, blood pressure at admission, BMI, and associated health conditions), and expert description of the acute lesion. This resource provides high quality, large scale, human-supervised knowledge to feed artificial intelligence models and enable further development of tools to automate several tasks that currently rely on human labor, such as lesion segmentation, labeling, calculation of disease-relevant scores, and lesion-based studies relating function to frequency lesion maps. The dataset is divided in folders with 60-70 subjects. Each folder contains the "raw data" (multimodal MRIs, in native space), "DWI-mask" (manually-defined lesion masks, brain masks, and 3D DWI, b0, and recalculated ADC), "DWI-MNI-IntensityNormalized" (DWI and lesion masks in MNI coordinates), and "phenotype" (individual ".tsv" files with metadata of each subject). The "templates" folder contains images averages and lesion frequency maps. The "documentation" contains comprehensive data documentation, the phenotypes of the whole dataset, and the data dictionary.