Tools   Select Another Resource Report Type

http://www.birncommunity.org/current-users/morphometry-birn/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 4th,2023. Calibration data set of spoiled gradient-recalled echo magnetic resonance imaging data from five healthy volunteers (four males and one female) scanned twice at four sites having 1.5T systems from different vendors (Siemens, GE, Marconi Medical Systems) pooled by the Morphometry Testbed's (MBIRN). Some subjects were also scanned a single time at another site. One subject was only scanned twice at three sites (subject 73213384) and once at another site. For each subject, four Fast Low-Angle Shot (FLASH) scans with flip angles of 3, 5, 20, and 30 degrees were obtained in a single scan session, from which tissue proton density and T1 maps can be derived. These data were acquired to investigate various metrics of within-site and across-site reproducibility. The images have been defaced so that no facial features can be reconstructed from these data. The Morphometry Testbed (MBIRN) of the Biomedical Informatics Research Network (BIRN) focused on pooling and analyzing of neuroimaging data acquired at multiple sites. Specific applications include potential relationships between anatomical differences and specific memory dysfunctions, such as Alzheimer's disease. With the completion of the initial BIRN testbed phase, each of the original BIRN testbeds have now been retired in order to focus on new users in other biomedical domains.

Proper citation: Morphometry BIRN (RRID:SCR_000155) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/ibsr

Data set of manually-guided expert segmentation results along with magnetic resonance brain image data. Its purpose is to encourage the development and evaluation of segmentation methods by providing raw test and image data, human expert segmentation results, and methods for comparing segmentation results. Please see the MediaWiki for more information. This repository is meant to contain standard test image data sets which will permit a standardized mechanism for evaluation of the sensitivity of a given analysis method to signal to noise ratio, contrast to noise ratio, shape complexity, degree of partial volume effect, etc. This capability is felt to be essential to further development in the field since many published algorithms tend to only operate successfully under a narrow range of conditions which may not extend to those experienced under the typical clinical imaging setting. This repository is also meant to describe and discuss methods for the comparison of results.

Proper citation: Internet Brain Segmentation Repository (RRID:SCR_001994) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/rosetta/

Public datasets that have been transcoded into multiple formats. This library of valid file format conversions (DICOM->NIFTI, DICOM->PAR/REC, etc.) will provide a reference for tool developers seeking to support multiple sources of data.

Proper citation: Rosetta Bit (RRID:SCR_001906) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/mcic/

Expertly collected, well-curated data sets consisting of comprehensive clinical characterization and raw structural, functional and diffusion-weighted DICOM images in schizophrenia patients and gender and age-matched controls are now accessible to the scientific community through an on-line data repository (coins.mrn.org). This data repository will be useful to 1) educators in the fields of neuroimaging, medical image analysis and medical imaging informatics who need exemplar data sets for courses and workshops; 2) computer scientists and software algorithm developers for testing and validating novel registration, segmentation, and other analysis software; and 3) scientists who can study schizophrenia by further analysis of this cohort and/or by pooling with other data.

Proper citation: MCIC (RRID:SCR_002310) Copy   

•   Source: SciCrunch Registry

https://www.nitrc.org/projects/nitrc_es

An on-demand, cloud based computational virtual machine pre-installed with popular NITRC neuroimaging tools built using NeuroDebian. For a listing of current NITRC-CE packages visit: http://www.nitrc.org/ce-packages. You can also use the "public Amazon Machine Interface (AMI)" to conduct your analyses on the Amazon EC2 platform.

Proper citation: NITRC Computational Environment (RRID:SCR_002171) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/minc_ex/

A reference MINC set of files that currently includes human head images only of standard modalities. The goal is to build a well curated collection of files that demonstrate the capabilities of MINC

Proper citation: MINC Example files (RRID:SCR_000859) Copy   

•   Source: SciCrunch Registry

http://cocomac.g-node.org/main/index.php?

Online access (html or xml) to structural connectivity ("wiring") data on the Macaque brain. The database has become by far the largest of its kind, with data extracted from more than four hundred published tracing studies. The main database, contains data from tracing studies on anatomical connectivity in the macaque cerebral cortex. Also available are a variety of tools including a graphical simulation workbench, map displays and the CoCoMac-Paxinos-3D viewer. Submissions are welcome. To overcome the problem of divergent brain maps ORT (Objective Relational Transformation) was developed, an algorithmic method to convert data in a coordinate- independent way based on logical relations between areas in different brain maps. CoCoMac data is used to analyze the organization of the cerebral cortex, and to establish its structure- function relationships. This includes multi-variate statistics and computer simulation of models that take into account the real anatomy of the primate cerebral cortex. This site * Provides full, scriptable open access to the data in CoCoMac (you must adhere to the citation policy) * Powers the graphical interface to CoCoMac provided by the Scalable Brain Atlas * Sports an extensive search/browse wizard, which automatically constructs complex search queries and lets you further explore the database from the results page. * Allows you to get your hands dirty, by using the custom SQL query service. * Displays connectivity data in tabular form, through the axonal projections service. CoCoMac 2 was initiated at the Donders Institute for Brain, Cognition and Behaviour, and is currently supported by the German neuroinformatics node and the Computational and Systems Neuroscience group at the Juelich research institute.

Proper citation: CoCoMac (RRID:SCR_007277) Copy   

•   Source: SciCrunch Registry

http://www.oasis-brains.org/

Project aimed at making neuroimaging data sets of brain freely available to scientific community. By compiling and freely distributing neuroimaging data sets, future discoveries in basic and clinical neuroscience are facilitated.

Proper citation: Open Access Series of Imaging Studies (RRID:SCR_007385) Copy   

•   Source: SciCrunch Registry

http://ibvd.virtualbrain.org/

A database of brain neuroanatomic volumetric observations spanning various species, diagnoses, and structures for both individual and group results. A major thrust effort is to enable electronic access to the results that exist in the published literature. Currently, there is quite limited electronic or searchable methods for the data observations that are contained in publications. This effort will facilitate the dissemination of volumetric observations by making a more complete corpus of volumetric observations findable to the neuroscience researcher. This also enhances the ability to perform comparative and integrative studies, as well as metaanalysis. Extensions that permit pre-published, non-published and other representation are planned, again to facilitate comparative analyses. Design strategy: The principle organizing data structure is the "publication". Publications report on "groups" of subjects. These groups have "demographic" information as well as "volume" information for the group as a whole. Groups are comprised of "individuals", which also have demographic and volume information for each of the individuals. The finest-grained data structure is the "individual volume record" which contains a volume observation, the units for the observation, and a pointer to the demographic record for individual upon which the observation is derived. A collection of individual volumes can be grouped into a "group volume" observation; the group can be demographically characterized by the distribution of individual demographic observations for the members of the group.

Proper citation: Internet Brain Volume Database (RRID:SCR_002060) Copy   

•   Source: SciCrunch Registry

http://epilepsy.uni-freiburg.de/database

A comprehensive database for human surface and intracranial EEG data that is suitable for a broad range of applications e.g. of time series analyses of brain activity. Currently, the EU database contains annotated EEG datasets from more than 200 patients with epilepsy, 50 of them with intracranial recordings with up to 122 channels. Each dataset provides EEG data for a continuous recording time of at least 96 hours (4 days) at a sample rate of up to 2500 Hz. Clinical patient information and MR imaging data supplement the EEG data. The total duration of EEG recordings included execeeds 30000 hours. The database is composed of different modalities: Binary files with EEG recording / MR imaging data and Relational database for supplementary meta data.

Proper citation: EPILEPSIE database (RRID:SCR_003179) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/stark_aging/

Behavioral and imaging data from about 120 participants aged 18-89. Data were collected as part of a grant to use high-resolution imaging and advanced behavioral tasks to understand how aging affects the hippocampus and how this is related to age-related cognitive decline. The full dataset includes traditional neuropsycholgical measures, hippocampal-specific behavioral measures, whole-brain DTI, high-resolution DTI of the medial temporal lobes, and structural MRI including segmentation of grey/white/CSF, of cortical regions and of hippocampal subfields.

Proper citation: Stark Cross-Sectional Aging (RRID:SCR_014171) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/ymdti/

A dataset which contains diffusion tensor images of 93 healthy, young male subjects.

Proper citation: YMDTI: Diffusion Tensor Images of Healthy Young Males (RRID:SCR_014183) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/hfh_t1_hp_seg1/

Shared dataset which consists of skull-stripped T1 MRI images and segmented hippocampi of 163 Temporal Lobe Epilepsy (TLE) patients. The T1 and hippocampal segmentation data of TLE patients are uploaded in three separate datasets which can be accessed from the main site.

Proper citation: Epilepsy T1 and Hippocampal Segmentation Datasets (RRID:SCR_014926) Copy   

•   Source: SciCrunch Registry

http://cbrain.mcgill.ca/loris

A modular and extensible web-based data management system that integrates all aspects of a multi-center study, from heterogeneous data acquisition to storage, processing and ultimately dissemination, within a streamlined platform. Through a standard web browser, users are able to perform a wide variety of tasks, such as data entry, 3D image visualization and data querying. LORIS also stores data independently from any image processing pipeline, such that data can be processed by external image analysis software tools. LORIS provides a secure web-based and database-driven infrastructure to automate the flow of clinical data for complex multi-site neuroimaging trials and studies providing researchers with the ability to easily store, link, and access significant quantities of both scalar (clinical, psychological, genomic) and multi-dimensional (imaging) data. LORIS can collect behavioral, neurological, and imaging data, including anatomical and functional 3D/4D MRI models, atlases and maps. LORIS also functions as a project monitoring and auditing platform to oversee data acquisition across multiple study sites. Confidentiality during multi-site data sharing is provided by the Subject Profile Management System, which can perform automatic removal of confidential personal information and multiple real-time quality control checks. Additionally, web interactions with the LORIS portal take place over an encrypted channel via SSL, ensuring data security. Additional features such as Double Data Entry and Statistics and Data Query GUI are included.

Proper citation: LORIS - Longitudinal Online Research and Imaging System (RRID:SCR_000590) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/atp

Autism research program that makes available post-mortem brain tissue to qualified scientists all over the world. Working directly with tissue banks, organ procurement agencies, medical examiners and the general public, this is the largest program dedicated to increasing and enhancing the availability of post-mortem brain tissue for basic research in autism. To date, the ATP has collected and stored more than 170 brains in their repositories at Harvard (US) and Oxford (UK). These brains are processed by formalin fixation and/or snap frozen to properly provide high quality tissue of all brain regions, in support of biological research in autism. The ATP is unique in that they diligently pursue all available clinical data (pre and post mortem) on tissue donors in order to create the most biologically relevant brain repository for autism research. These data, together with tissue resources from both banks and associated repositories, are presented to all interested researchers through their extensive web-based data portal (login required). The ATP is not a brain bank, but works directly with the Harvard Brain Tissue Resource Center in Boston (HBTRC), Massachusetts to serve as its tissue repository. This program augments brain bank functions by: * Creating the most biologically relevant brain tissue repository possible * Fully covering all costs associated with brain extraction and transfer to the repositories at Harvard (US and Canada) and Oxford (UK). * Providing scientific oversight of tissue distributions * Overseeing and managing all tissue grants * Clinically phenotyping and acquiring extensive medical data on all of their donors * Providing continuing family support and communication to all of their donors * Directly supporting researchers to facilitate autism research * Maintaining a robust web based data management and secure on-line global interface system * Developing and supporting ATP established scientific initiatives * Actively providing public outreach and education The ATP is not a clinical organ procurement agency, but rather they facilitate the wishes of donors and families to donate their tissue to autism research. Through the ATP's established international infrastructure, they work with any accredited tissue bank, organ procurement agency, or medical examiner that receives a family's request to donate their loved one's tissue to the program. Once contacted, the ATP will insure that the family's request to donate their loved one's tissue is faithfully met, covering all costs to the family and partnering agency as well as ensuring the tissues' proper and rapid transport to the ATP's repository at the Harvard Brain Tissue Resource Center (HBTRC) in Boston, Massachusetts.

Proper citation: Autism Tissue Program (RRID:SCR_000651) Copy   

•   Source: SciCrunch Registry

http://fcp-indi.github.io

A configurable, open-source, Nipype-based, automated processing pipeline for resting state functional MRI (R-fMRI) data, for use by both novice and expert users. C-PAC was designed to bring the power, flexibility and elegance of the Nipype platform to users in a plug and play fashion?without requiring the ability to program. Using an easy to read, text-editable configuration file, C-PAC can rapidly orchestrate automated R-fMRI processing procedures, including: - quality assurance measurements - image preprocessing based upon user specified preferences - generation of functional connectivity maps (e.g., correlation analyses) - customizable extraction of time-series data - generation of local R-fMRI metrics (e.g., regional homogeneity, voxel-matched homotopic connectivity, fALFF/ALFF) C-PAC makes it possible to use a single configuration file to launch a factorial number of pipelines differing with respect to specific processing steps.

Proper citation: C-PAC (RRID:SCR_000862) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/cbinifti/

An I/O library for Matlab/Octave Matlab and Octave library for reading and writing Nifti-1 files. cbiNifti is intended to be a small, self-contained library that makes minimal assumptions about what Nifti files should look like and allow users easy access to the raw data. cbiNifti handles compressed file formats for reading and writing, using Unix pipes for compression and decompression. More information and code examples at: http://www.pc.rhul.ac.uk/staff/J.Larsson/software.html

Proper citation: cbiNifti: Matlab/Octave Nifti library (RRID:SCR_000860) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/cabn/

Construct and analyse brain network is a brain network visualization tool, which can help researchers to visualize construct and analyse resting state functional brain networks from different levels in a quick, easy and flexible way. Entrance parameter of construct and analyse brain network is export parameters of dparsf software.It would be greatly appreciated if you have any suggestions about the package or manual.

Proper citation: BrainNetworkConstructionAnalysisPlatform (RRID:SCR_000854) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/philips_users/

Communnity project to help support the efforts of investigators using Philips Healthcare systems. This clearingsite helps users find forums, mailinglists, etc. that support this community. If you have suggestions for inclusion, let the project admin know!

Proper citation: Philips Users Community (RRID:SCR_001438) Copy   

•   Source: SciCrunch Registry

http://incf.org/programs/atlasing/projects/waxholm-space

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 1st, 2023. Coordinate based reference space for the mapping and registration of neuroanatomical data. Users can download image volumes representing the canonical Waxholm Space (WHS) adult C57BL/6J mouse brain, which include T1-, T2*-, and T2-Weighted MR volumes (generated at the Duke Center for In-Vivo Microscopy), Nissl-stained optical histology (acquired at Drexel University), and a volume of labels. All volumes are represented at 21.5μ isotropic resolution. Datasets are provided as gzipped NIFTI files.

Proper citation: Waxholm Space (RRID:SCR_001592) Copy   

•   Source: SciCrunch Registry