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http://www.nitrc.org/projects/jist/
A native Java-based imaging processing environment similar to the ITK/VTK paradigm. Initially developed as an extension to MIPAV (CIT, NIH, Bethesda, MD), the JIST processing infrastructure provides automated GUI generation for application plug-ins, graphical layout tools, and command line interfaces. This repository maintains the current multi-institutional JIST development tree and is recommended for public use and extension. JIST was originally developed at IACL and MedIC (Johns Hopkins University) and is now also supported by MASI (Vanderbilt University).
Proper citation: JIST: Java Image Science Toolkit (RRID:SCR_008887) Copy
http://www.nitrc.org/projects/dti_rat_atlas/
3D DTI anatomical rat brain atlases have been created by the UNC- Chapel Hill Department of Psychiatry and the CAMID research collaboration. There are three age groups, postnatal day 5, postnatal day 14, and postnatal day 72. The subjects were Sprague-Dawley rats that were controls in a study on cocaine abuse and development. The P5 and P14 templates were made from scans of twenty rats each (ten female, ten male); the P72, from six females. The individual cases have been resampled to isotropic resolution, manually skull-stripped, and deformably registered via an unbiased atlas building method to create a template for each age group. Each template was then manually segmented using itk-SNAP software. Each atlas is made up of 3 files, a template image, a segmentation, and a label file.
Proper citation: 3D DTI Atlas of the Rat Brain In Postnatal Day 5 14 and Adulthood (RRID:SCR_009437) Copy
http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases
Probabilistic atlases covering 48 cortical and 21 subcortical structural areas, derived from structural data and segmentations kindly provided by the Harvard Center for Morphometric Analysis. T1-weighted images of 21 healthy male and 16 healthy female subjects (ages 18-50) were individually segmented by the CMA using semi-automated tools developed in-house. The T1-weighted images were affine-registered to MNI152 space using FLIRT (FSL), and the transforms then applied to the individual labels. Finally, these were combined across subjects to form population probability maps for each label. Segmentations used to create these atlases were provided by: David Kennedy and Christian Haselgrove, Centre for Morphometric Analysis, Harvard; Bruce Fischl, the Martinos Center for Biomedical Imaging, MGH; Janis Breeze and Jean Frazier from the Child and Adolescent Neuropsychiatric Research Program, Cambridge Health Alliance; Larry Seidman and Jill Goldstein from the Department of Psychiatry of Harvard Medical School.
Proper citation: Harvard - Oxford Cortical Structural Atlas (RRID:SCR_001476) Copy
Project focused on advancing knowledge of prognosis, trial design and treatment in Traumatic Brain Injury. IMPACT has developed and validated prognostic models for classification and characterization of TBI series, and participated in development of standardization of data collection in TBI studies.
Proper citation: IMPACT: International Mission for Prognosis and Analysis of Clinical Trials in TBI (RRID:SCR_000539) Copy
http://www.nitrc.org/projects/mni2orfromxyz/
Input either normalized MNI coordinates from a 3D image, or input real world XYZ matrix coordinates, and this code will convert coordinates of one type to the other.
Proper citation: Convert MNI coordinates to or from XYZ (RRID:SCR_000406) Copy
http://www.nitrc.org/projects/frats/
Software for the analysis of multiple diffusion properties along fiber bundle as functions in an infinite dimensional space and their association with a set of covariates of interest, such as age, diagnostic status and gender, in real applications. The resulting analysis pipeline can be used for understanding normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles.
Proper citation: Functional Regression Analysis of DTI Tract Statistics (RRID:SCR_002293) Copy
Open source Java based image processing software program designed for scientific multidimensional images. ImageJ has been transformed to ImageJ2 application to improve data engine to be sufficient to analyze modern datasets.
Proper citation: ImageJ (RRID:SCR_003070) Copy
https://www.rarediseasesnetwork.org/cms/create/researchers/biorepository
Biorepository of samples collected from patients with ALS, ALS-frontotemporal dementia (ALS-FTD), primary lateral sclerosis (PLS), progressive muscular atrophy (PMA), hereditary spastic paraplegia (HSP) and multisystem proteinopathy (MSP). Used by Consortium members and the scientific community to advance therapeutic development through study of the relationship between clinical phenotype and underlying genotype, and also through the discovery and development of biomarkers.
Proper citation: CReATE (RRID:SCR_016436) Copy
Software tool as robust preprocessing pipeline for functional MRI.Used for preprocessing of diverse fMRI data.
Proper citation: fMRIPrep (RRID:SCR_016216) Copy
Project to create network based understanding of biology by cataloging changes in gene expression and other cellular processes when cells are exposed to genetic and environmental stressors. Program to develop therapies that might restore pathways and networks to their normal states. Has LINCS Data Coordination and Integration Center and six Data and Signature Generation Centers: Drug Toxicity Signature Generation Center, HMS LINCS Center, LINCS Center for Transcriptomics, LINCS Proteomic Characterization Center for Signaling and Epigenetics, MEP LINCS Center, and NeuroLINCS Center.
Proper citation: LINCS Project (RRID:SCR_016486) Copy
https://bwhbioinfo.shinyapps.io/powerEQTL/
Software R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis.
Proper citation: powereQTL (RRID:SCR_021653) Copy
https://github.com/denisecailab/minian
Software miniscope analysis pipeline that requires low memory and computational demand so it can be run without specialized hardware. Offers interactive visualization that allows users to see how parameters in each step of pipeline affect output.
Proper citation: Minian (RRID:SCR_022601) Copy
https://github.com/Cai-Lab-at-University-of-Michigan/nTracer
Software tool as plug-in for ImageJ software. Used for tracing microscopic images.
Proper citation: nTracer (RRID:SCR_023032) Copy
https://github.com/mne-tools/mne-bids/
Software Python package to link Brain Imaging Data Structure and MNE-Python software for analyzing neurophysiology data with goal to make analyses faster to code, more robust to errors, and easily shareable with colleagues. Provides programmable interface for BIDS datasets in electrophysiology with MNE-Python. Used for organizing electrophysiological data into BIDS format and facilitating their analysis.
Proper citation: MNE-BIDS (RRID:SCR_018766) Copy
A web-based, light-weight 3D volume viewer that serves large volumes (typically the whole brain) of high-resolution mouse brain images (~1.5 TB per brain, ~1 um resolution) from the Knife-Edge Scanning Microscope (KESM), invented by Bruce H. McCormick. Currently, KESMBA serves the following data sets: * Mouse: Whole-brain-scale Golgi (acquired 2008 spring): neuronal morphology: Choe et al. (2009) * Mouse: Whole-brain India Ink (acquired 2008 spring): vascular network: Choe et al. (2009); Mayerich et al. (2011); * Mouse: Whole-brain Golgi (acquired 2011 summer): neuronal morphology: Choe et al. (2011); Chung et al. (2011); * Mouse: Whole-brain Nissl (acquired 2009-2010 winter): somata (Choe et al. 2010) (Coming soon) They will ship you the full data set on a hard drive if you provide them with the hard drive and shipping cost.
Proper citation: KESM brain atlas (RRID:SCR_001559) Copy
THIS RESOURCE IS NO LONGER IN SERVICE, documented on December 02, 2011. Notice: This domain name expired on 10/29/11 and is pending renewal or deletion PD-DOC is a portal and a database resource, hosting a database and linking to other databases and data sets of clinical and translational data. PD-DOC functions to organize and facilitate clinical and translational research in Parkinson's disease. The PD-DOC Database contains standardized data collected by user institutions on large numbers of patients with Parkinsons disease and other parkinsonian disorders. In some cases, data is obtained at a single point in time, while in others data is collected repeatedly over time. The PD-DOC Database is composed of the Core Data Set (CDS) which consists of those variables required to be gathered for each subject whose data is entered into the PD-DOC database. In 2005, working groups of Udall Center and invited experts deliberated to establish the components of each CDS section (e.g. General Clinical, Cognitive/Behavioral, Postmortem Brain Neuropathological Findings). The PD-DOC CDS was established and designed to optimize data analyses and data mining for large numbers of subjects participating in a variety of research studies. In most cases corresponding DNA samples are available form the NINDS Human Genetic Repository (at Coriell). Much of the website is publicly available for viewing. To request access to sections of the website dealing with downloading or requesting data, requesting a consultation, or submitting data or other information you will need to register. Before registering, you should read the PD-DOC Policies. Note that PD-DOC data can be used for research purposes only. Once your registration is successfully completed you will be automatically logged into the website.
Proper citation: PD-DOC (RRID:SCR_001596) Copy
Project to define a roadmap for diffusion MR imaging of traumatic brain imaging and design an infrastructure to implement the recommendations and tested to ensure feasibility, disseminate results, and facilitate deployment and adoption. The research roadmap and infrastructure development will concentrate on three areas: 1) standardization of diffusion imaging methodology, 2) trial design and patient selection for acute or chronic therapy, and 3) development of multi-center collaborations and repositories for evaluating whether advanced diffusion imaging does improve decision making and TBI patients' outcomes. # DTI MRI reproducability: One of the major areas of investigation in this project is to study the reproducibility of data acquisition and image analysis algorithms. Understanding reproducibility defines a base level of deviation from which scans can be analyzed with statistical significance. As part of this work they are also developing site qualification criteria with the intention of setting limits on the MR system minimal performance for acceptable use in TBI evaluation. # Infrastructure for image storage, analysis and visualization: There is a continuing need to refine and extend software methods for diffusion MRI data analysis and visualization. Not only to translate tools into clinical practice, but also to encourage continuation of the innovation and development of new tools and techniques. To deliver upon these goals they are designing and implementing a storage and computational infrastructure to provide access to shared datasets and intuitive interfaces for analysis and visualization through a variety of tools. A strong emphasis has been placed on providing secure data sharing and the ability to add community defined common data elements. The infrastructure is built upon a Software-as-a-Service model, in which tools are hosted and managed remotely allowing users access through well-defined interfaces. The final service will also facilitate composition or orchestration of workflows composed of different analysis and processing tasks (for example using LONI or XNAT pipelines) with the ultimate goal of providing automated no-click evaluations of diffusion MRI data. # Tool development: The final aspect of this project aims to facilitate and encourage tool development and contribution. By providing access to open datasets, they will create a platform on which tool developers can compare and improve and their tools. When tools are sufficiently mature they can be exposed in the infrastructure mentioned above and used by researchers and other developers.
Proper citation: Diffusion MRI of Traumatic Brain Injury (RRID:SCR_001637) Copy
http://neuromorpho.org/index.jsp
Centrally curated inventory of digitally reconstructed neurons associated with peer-reviewed publications that contains some of the most complete axonal arborizations digitally available in the community. Each neuron is represented by a unique identifier, general information (metadata), the original and standardized ASCII files of the digital morphological reconstruction, and a set of morphometric features. It contains contributions from over 100 laboratories worldwide and is continuously updated as new morphological reconstructions are collected, published, and shared. Users may browse by species, brain region, cell type or lab name. Users can also download morphological reconstructions for research and analysis. Deposition and distribution of reconstruction files ultimately prevents data loss. Centralized curation and annotation aims at minimizing the effort required by data owners while ensuring a unified format. It also provides a one-stop entry point for all available reconstructions, thus maximizing data visibility and impact.
Proper citation: NeuroMorpho.Org (RRID:SCR_002145) Copy
National resource for investigators utilizing human post-mortem brain tissue and related biospecimens for their research to understand conditions of the nervous system. Federated network of brain and tissue repositories in the United States that collects, evaluates, stores, and makes available to researchers, brain and other tissues in a way that is consistent with the highest ethical and research standards. The NeuroBioBank ensures protection of the privacy and wishes of donors. Provides information to the public about the need for tissue donation and how to register as a donor.
Proper citation: NIH NeuroBioBank (RRID:SCR_003131) Copy
The U.S. National Institutes of Health Final NIH Statement on Sharing Research Data (NIH-OD-03-032) is now in effect. It specifies that all high-direct-cost NIH grant applications include plans for sharing of research data. To support and encourage collegial, enabling, and rewarding data sharing for neuroscience and beyond, the Laboratory of Neuroinformatics at Weill Medical College of Cornell University has established this site. A source of, and portal to, tools and proposals supporting the informed exchange of neuroscience data.
Proper citation: Datasharing.net (RRID:SCR_003312) Copy
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