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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://neuro.debian.net/

Collection based on a collaborative effort of popular neuroscience research software for the Debian operating system as well as Ubuntu and other derivatives. Popular packages include AFNI, FSL, PyMVPA and many others. It contains both unofficial or prospective packages which are not (yet) available from the main Debian archive, as well as backported or simply rebuilt packages also available elsewhere. A listing of current and planned projects is available if you want to get involved. The main goal of the project is to provide a versatile and convenient environment for neuroscientific research that is based on open-source software. To this end, the project offers a package repository that complements the main Debian (and Ubuntu) archive. NeuroDebian is not yet another Linux distribution, but rather an effort inside the Debian project itself. Software packages are fully integrated into the Debian system and from there will eventually migrate into Ubuntu as well. With NeuroDebian, installing and updating neuroscience software is no different from any other part of the operating system. Maintaining a research software environment becomes as easy as installing an editor. There is also virtual machine to test NeuroDebian on Windows or Mac OS. If you want to see your software packaged for Debian, please drop them a note.

Proper citation: neurodebian (RRID:SCR_004401) Copy   

•   Source: SciCrunch Registry

http://www.picsl.upenn.edu/ANTS/

Software package designed to enable researchers with advanced tools for brain and image mapping. Many of the ANTS registration tools are diffeomorphic*, but deformation (elastic and BSpline) transformations are available. Unique components of ANTS include multivariate similarity metrics, landmark guidance, the ability to use label images to guide the mapping and both greedy and space-time optimal implementations of diffeomorphisms. The symmetric normalization (SyN) strategy is a part of the ANTS toolkit as is directly manipulated free form deformation (DMFFD). *Diffeomorphism: a differentiable map with differentiable inverse. In general, these maps are generated by integrating a time-dependent velocity field. ANTS Applications: * Gray matter morphometry based on the jacobian and/or cortical thickness. * Group and single-subject optimal templates. * Multivariate DT + T1 brain templates and group studies. * Longitudinal brain mapping -- special similarity metric options. * Neonatal and pediatric brain segmentation. * Pediatric brain mapping. * T1 brain mapping guided by tractography and connectivity. * Diffusion tensor registration based on scalar or connectivity data. * Brain mapping in the presence of lesions. * Lung and pulmonary tree registration. * User-guided hippocampus labeling, also of sub-fields. * Group studies and statistical analysis of cortical thickness, white matter volume, diffusion tensor-derived metrics such as fractional anisotropy and mean diffusion.

Proper citation: ANTS - Advanced Normalization ToolS (RRID:SCR_004757) Copy   

•   Source: SciCrunch Registry

http://brainliner.jp

Portal and tools for sharing and editing neurophysiological and behavioral data for brain-machine interface research. Users can search for existing data or login with their Google, Facebook, or Twitter account and upload new data. Their main focus is on supporting brain-machine interface research, so we encourage users to not just provide recordings of brain activity data, but also information about stimuli, etc., so that statistical relationships can be found between stimuli and/or subject behavior and brain activity. The Matlab tools are for writing, reading, and converting Neuroshare files, the common file format. A free, open source desktop tool for editing neurophysiological data for brain-machine interface research is also available: https://github.com/ATR-DNI/BrainLiner Since data formats aren''''t standardized between programs and researchers, data and analysis programs for data cannot be easily shared. Neuroshare was selected as the common file format. Neuroshare can contain several types of neurophysiological data because of its high flexibility, including analog time-series data and neuronal spike timing. Some applications have plug-ins or libraries available that can read Neuroshare format files, thus making Neuroshare somewhat readily usable. Neuroshare can contain several types of neurophysiological data, but there were no easy tools to convert data into the Neuroshare format, so they made and are providing a Neuroshare Converter Library and Simple Converter using the library. In future work they will make and provide many more useful tools for data sharing. Shared experiments include: EMG signal, Takemiya Exp, Reconstruct (Visual image reconstruction from human brain activity using a combination of multi-scale local image decoders), SPIKE data, Speech Imagery Dataset (Single-trial classification of vowel speech imagery using common spatial patterns), Functional Multineuron Calcium Imaging (fMCI), Rock-paper-scissors (The data was obtained from subject while he make finger-form of rock/paper/scissors). They also have a page at https://www.facebook.com/brainliner where you can contact us

Proper citation: BrainLiner (RRID:SCR_004951) Copy   

•   Source: SciCrunch Registry

http://openneuro.org

Open platform for analyzing and sharing neuroimaging data from human brain imaging research studies. Brain Imaging Data Structure ( BIDS) compliant database. Formerly known as OpenfMRI. Data archives to hold magnetic resonance imaging data. Platform for sharing MRI, MEG, EEG, iEEG, and ECoG data.

Proper citation: OpenNeuro (RRID:SCR_005031) Copy   

•   Source: SciCrunch Registry

http://neurolex.org/wiki/Main_Page

A freely editable semantic wiki for community-based curation of the terms used in Neuroscience. Entries are curated and eventually incorporated into the formal NIFSTD ontology. NeuroLex also includes a Resource branch for community members to freely add neuroscience relevant resources that do not become part of NIFSTD ontology but rather make up the NIF Registry. As part of the NIF, we provide a simple search interface to many different sources of neuroscience information and data. To make this search more effective, we are constructing ontologies to help organize neuroscience concepts into category hierarchies, e.g., neuron is a cell. These categories provide the means to perform more effective searches and also to organize and understand the information that is returned. But an important adjunct to this activity is to clearly define all of the terms that we use to describe our data, e.g., anatomical terms, techniques, organism names. Because wikis provide an easy interface for communities to contribute their knowledge, we started the NeuroLex.

Proper citation: NeuroLex (RRID:SCR_005402) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/

Collection of resting state fMRI (R-fMRI) datasets from sites around world. It demonstrates open sharing of R-fMRI data and aims to emphasize aggregation and sharing of well-phenotyped datasets.

Proper citation: 1000 Functional Connectomes Project (RRID:SCR_005361) Copy   

•   Source: SciCrunch Registry

http://www.biological-networks.org/pubs/suppl/sinomo/

Analysis-tool which identifies singular node motifs in a network. Network nodes can be described by node-motifs. It is an improvement to the method described in Costa et al. (2009).

Proper citation: SINOMO (RRID:SCR_005286) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/adhd200/index.html#

A grassroots initiative dedicated to accelerating the scientific community''''s understanding of the neural basis of ADHD through the implementation of open data-sharing and discovery-based science. They believe that a community-wide effort focused on advancing functional and structural imaging examinations of the developing brain will accelerate the rate at which neuroscience can inform clinical practice. The ADHD-200 Global Competition invited participants to develop diagnostic classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging (MRI) of the brain. Applying their tools, participants provided diagnostic labels for previously unlabeled datasets. The competition assessed diagnostic accuracy of each submission and invited research papers describing novel, neuroscientific ideas related to ADHD diagnosis. Twenty-one international teams, from a mix of disciplines, including statistics, mathematics, and computer science, submitted diagnostic labels, with some trying their hand at imaging analysis and psychiatric diagnosis for the first time. The data for the competition was provided by the ADHD-200 Consortium. Consortium members from institutions around the world provided de-identified, HIPAA compliant imaging datasets from almost 800 children with and without ADHD. A phenotypic file including all of the test set subjects and their diagnostic codes can be downloaded. Winner is presented. The ADHD-200 consortium included: * Brown University, Providence, RI, USA (Brown) * The Kennedy Krieger Institute, Baltimore, MD, USA (KKI) * The Donders Institute, Nijmegen, The Netherlands (NeuroImage) * New York University Medical Center, New York, NY, USA (NYU) * Oregon Health and Science University, Portland, OR, USA (OHSU) * Peking University, Beijing, P.R.China (Peking 1-3) * The University of Pittsburgh, Pittsburgh, PA, USA (Pittsburgh) * Washington University in St. Louis, St. Louis, MO, USA (WashU)

Proper citation: ADHD-200 Sample (RRID:SCR_005358) Copy   

•   Source: SciCrunch Registry

http://cbrain.mcgill.ca/

A flexible software platform for distributed processing, analysis, exchange and visualization of brain imaging data. The expected result is a middleware platform that will render the processing environment (hardware, operating systems, storage servers, etc...) transparent to a remote user. Interaction with a standard web browser allows application of complex algorithm pipelines to large datasets stored at remote locations using a mixture of network available resources such as small clusters, neuroimaging tools and databases as well as Compute Canada's High Performance Computing Centers (HPC). Though the focus of CBRAIN is providing tools for use by brain imaging researchers, the platform is generalizable to other imaging domains, such as radiology, surgical planning and heart imaging, with profound consequences for Canadian medical research. CBRAIN expanded its concept to include international partners in the US, Germany and Korea. As of December 2010, GBRAIN has made significant progress with the original three partners and has developed new partners in Singapore, China, India, and Latin America. CBRAIN is currently deployed on 6 Compute Canada HPC clusters, one German HPC cluster and 3 clusters local to McGill University Campus, totaling more than 80,000 potential CPU cores.

Proper citation: CBRAIN (RRID:SCR_005513) Copy   

•   Source: SciCrunch Registry

http://slicer.org/

A free, open source software package for visualization and image analysis including registration, segmentation, and quantification of medical image data. Slicer provides a graphical user interface to a powerful set of tools so they can be used by end-user clinicians and researchers alike. 3D Slicer is natively designed to be available on multiple platforms, including Windows, Linux and Mac Os X. Slicer is based on VTK (http://public.kitware.com/vtk) and has a modular architecture for easy addition of new functionality. It uses an XML-based file format called MRML - Medical Reality Markup Language which can be used as an interchange format among medical imaging applications. Slicer is primarily written in C++ and Tcl.

Proper citation: 3D Slicer (RRID:SCR_005619) Copy   

•   Source: SciCrunch Registry

http://www.cma.mgh.harvard.edu/iatr/

A centrally available listing of all image analysis tools that are available to the neuroscience community in order to facilitate the development, identification, and sharing of tools. It is hoped that this helps the tool developers to get their tools to a larger user community and to reduce redundancy (or at least utilize tool redundancy to facilitate optimal tool design) in tool development. This also helps tool users in identification of the existing tools for specific problems as they arise. The registry is designed to be self-moderated. This means that all tool entries are owned by some responsible party who enters the tool information, and keeps it up to date via the Web.

Proper citation: Internet Analysis Tools Registry (RRID:SCR_005638) Copy   

•   Source: SciCrunch Registry

https://sourceforge.net/projects/viste/

Open source, platform-independent application for the visualization and analysis of complex, high-dimensional imaging data such as Diffusion Tensor Imaging (DTI) and High Angular Resolution Diffusion Imaging (HARDI). It has a plugin-based architecture which allows third parties to develop new plugins to extend the tool. Overview of the many features: * vIST/e is programmed in C++. It uses the Visualization Toolkit for visualization and pipelined data processing, as well as the cross-platform toolkit Qt Framework for an easy-to-use Graphical User Interface. * vIST/e introduces a powerful new plugin system, which allows for modular development with increased extensibility and stability. * Powerful GPU-based visualization techniques allow for smooth, real-time visualization of large data sets. Using custom ray tracing algorithms created with OpenGL, vIST/e can render DTI ellipsoids and HARDI spherical harmonics glyphs up to 4th order. The high frame rates offered by modern GPU technology allows for interactive exploration of this complex data. * Diffusion Tensor Imaging data can be visualized and interactively explored in a number of ways, including multiple cross-sections, volume rendering, and tensor glyphs. Derived scalar volumes, including various different anisotropy measures, can be computed and visualized. Data from other modalities, such as structural MRI, can be shown alongside the DTI data. * Various fiber tracking methods allow for fast and accurate reconstruction of fiber pathways. Interactively defined Regions of Interest (ROIs) can be used for seeding and filtering of fibers. Fibers are visualized either as lines, optionally using a powerful, GPU-based lighting engine, or as 3D structures such as tubes. * Scalar volumes, glyphs, and fibers can be colored using a wide array of coloring option. Customizable color loop-up tables allow for highly flexible visualization of scalar data. * Visualization and processing of various different HARDI formats is supported. HARDI data is interactively visualized using highly detailed glyphs rendered on the GPU. HARDI glyphs can be visualized in combination with DTI glyphs, for a better overview of complex diffusion data. * vIST/e includes support for NVIDIA's Compute Unified Device Architecture (CUDA), which enables highly parallel, GPU-based data processing, allowing for significant speed-up of computationally expensive algorithms.

Proper citation: vIST/e (RRID:SCR_001627) Copy   

•   Source: SciCrunch Registry

http://coins.mrn.org/

A web-based neuroimaging and neuropsychology software suite that offers versatile, automatable data upload/import/entry options, rapid and secure sharing of data among PIs, querying and export all data, real-time reporting, and HIPAA and IRB compliant study-management tools suitable to large institutions as well as smaller scale neuroscience and neuropsychology researchers. COINS manages over over 400 studies, more than 265,000 clinical neuropsychological assessments, and 26,000 MRI, EEG, and MEG scan sessions collected from 18,000 participants at over ten institutions on topics related to the brain and behavior. As neuroimaging research continues to grow, dynamic neuroinformatics systems are necessary to store, retrieve, mine and share the massive amounts of data. The Collaborative Informatics and Neuroimaging Suite (COINS) has been created to facilitate communication and cultivate a data community. This tool suite offers versatile data upload/import/entry options, rapid and secure sharing of data among PIs, querying of data types and assessments, real-time reporting, and study-management tools suitable to large institutions as well as smaller scale researchers. It manages studies and their data at the Mind Research Network, the Nathan Kline Institute, University of Colorado Boulder, the Olin Neuropsychiatry Research Center (at) Hartford Hospital, and others. COINS is dynamic and evolves as the neuroimaging field grows. COINS consists of the following collaboration-centric tools: * Subject and Study Management: MICIS (Medical Imaging Computer Information System) is a centralized PostgreSQL-based web application that implements best practices for participant enrollment and management. Research site administrators can easily create and manage studies, as well as generate reports useful for reporting to funding agencies. * Scan Data Collection: An automated DICOM receiver collects, archives, and imports imaging data into the file system and COINS, requiring no user intervention. The database also offers scan annotation and behavioral data management, radiology review event reports, and scan time billing. * Assessment Data Collection: Clinical data gathered from interviews, questionnaires, and neuropsychological tests are entered into COINS through the web application called Assessment Manager (ASMT). ASMT's intuitive design allows users to start data collection with little or no training. ASMT offers several options for data collection/entry: dual data entry, for paper assessments, the Participant Portal, an online tool that allows subjects to fill out questionnaires, and Tablet entry, an offline data entry tool. * Data Sharing: De-identified neuroimaging datasets with associated clinical-data, cognitive-data, and associated meta-data are available through the COINS Data Exchange tool. The Data Exchange is an interface that allows investigators to request and share data. It also tracks data requests and keeps an inventory of data that has already been shared between users. Once requests for data have been approved, investigators can download the data directly from COINS.

Proper citation: Mind Research Network - COINS (RRID:SCR_000805) Copy   

•   Source: SciCrunch Registry

https://github.com/QMICodeBase/TORTOISEV4

An integrated and flexible software package for processing of DTI data, and in general for the correction of diffusion weighted images to be used for DTI and potentially for high angular resolution diffusion imaging (HARDI) analysis. It can be run on both Linux and Mac platforms. It is composed of two modules named DIFF PREP and DIFF CALC. * DIFF_PREP - software for image resampling, motion, eddy current distortion and susceptibility induced EPI distortion corrections, and for re-orientation of data to a common space * DIFF_CALC - software for tensor fitting, error analysis, color map visualization and ROI analysis In addition, TORTOISE contains additional Utilities, such as a tool for the analysis of multi-center phantom data.

Proper citation: TORTOISE (RRID:SCR_001645) Copy   

•   Source: SciCrunch Registry

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

VUIIS (Vanderbilt University Institute of Imaging Science) Image and Data Analysis Core's data processing tools written for MATLAB and, unless stated otherwise, capable of processing 2D/3D images (matrices). These tools are written for ease of use from within MATLAB.

Proper citation: vuTools (RRID:SCR_001704) Copy   

•   Source: SciCrunch Registry

http://www.mbfbioscience.com/neurolucida

Neurolucida is advanced scientific software for brain mapping, neuron reconstruction, anatomical mapping, and morphometry. Since its debut more than 20 years ago, Neurolucida has continued to evolve and has become the worldwide gold-standard for neuron reconstruction and 3D mapping. Neurolucida has the flexibility to handle data in many formats: using live images from digital or video cameras; stored image sets from confocal microscopes, electron microscopes, and scanning tomographic sources, or through the microscope oculars using the patented LucividTM. Neurolucida controls a motorized XYZ stage for integrated navigation through tissue sections, allowing for sophisticated analysis from many fields-of-view. Neurolucidas Serial Section Manager integrates unlimited sections into a single data file, maintaining each section in aligned 3D space for full quantitative analysis. Neurolucidas neuron tracing capabilities include 3D measurement and reconstruction of branching processes. Neurolucida also features sophisticated tools for mapping delineate and map anatomical regions for detailed morphometric analyses. Neurolucida uses advanced computer-controlled microscopy techniques to obtain accurate results and speed your work. Plug-in modules are available for confocal and MRI analysis, 3D solid modeling, and virtual slide creation. The user-friendly interface gives you rapid results, allowing you to acquire data and capture the full 3D extent of neurons and brain regions. You can reconstruct neurons or create 3D serial reconstructions directly from slides or acquired images, and Neurolucida offers full microscope control for brightfield, fluorescent, and confocal microscopes. Its added compatibility with 64-bit Microsoft Vista enables reconstructions with even larger images, image stacks, and virtual slides. Adding the Solid Modeling Module allows you to rotate and view your reconstructions in real time. Neurolucida is available in two separate versions Standard and Workstation. The Standard version enables control of microscope hardware, whereas the Workstation version is used for offline analysis away from the microscope. Neurolucida provides quantitative analysis with results presented in graphical or spreadsheet format exportable to Microsoft Excel. Overall, features include: - Tracing Neurons - Anatomical Mapping - Image Processing and Analysis Features - Editing - Morphometric Analysis - Hardware Integration - Cell Analysis - Visualization Features Sponsors: Neurolucida is supported by MBF Bioscience.

Proper citation: Neurolucida (RRID:SCR_001775) Copy   

•   Source: SciCrunch Registry

http://cobre.mrn.org/megsim/

Realistic simulated MEG datasets ranging from basic sensory to oscillatory sets that mimic functional connectivity; as well as basic visual, auditory, and somatosensory empirical sets. The simulated sets were created for the purpose of testing analysis algorithms across the different MEG systems when the truth is known. MEG baseline recordings were obtained from 5 healthy participants, using three MEG systems: VSM/CTF Omega, Elekta Neuromag Vectorview, 4-D Magnes 3600. Simulated signals were embedded within the CTF and Neuromag 306 baseline recordings (4-D to be added). Participant MRIs are available. Averaged simulation files are available as netcdf files. Neuromag 306 averaged simulations are also available in fif format. Also available: single trials of data where the simulated signal is jittered about a mean value, continuous fif files where the simulated signal is marked by a trigger, and simulations with oscillations added to mimic functional connectivity.

Proper citation: MEGSIM (RRID:SCR_002420) Copy   

•   Source: SciCrunch Registry

http://humanconnectome.org/connectome/connectomeDB.html

Data management platform that houses all data generated by the Human Connectome Project - image data, clinical evaluations, behavioral data and more. ConnectomeDB stores raw image data, as well as results of analysis and processing pipelines. Using the ConnectomeDB infrastructure, research centers will be also able to manage Connectome-like projects, including data upload and entry, quality control, processing pipelines, and data distribution. ConnectomeDB is designed to be a data-mining tool, that allows users to generate and test hypotheses based on groups of subjects. Using the ConnectomeDB interface, users can easily search, browse and filter large amounts of subject data, and download necessary files for many kinds of analysis. ConnectomeDB is designed to work seamlessly with Connectome Workbench, an interactive, multidimensional visualization platform designed specifically for handling connectivity data. De-identified data within ConnectomeDB is publicly accessible. Access to additional data may be available to qualified research investigators. ConnectomeDB is being hosted on a BlueArc storage platform housed at Washington University through the year 2020. This data platform is based on XNAT, an open-source image informatics software toolkit developed by the NRG at Washington University. ConnectomeDB itself is fully open source.

Proper citation: ConnectomeDB (RRID:SCR_004830) Copy   

•   Source: SciCrunch Registry

http://www.med.unc.edu/bric/ideagroup/free-softwares/intergroup-image-registration

Software package that provides solutions for registering two groups of images, which are the necessary steps for many brain-related applications.

Proper citation: Inter-Group Registration Toolbox (RRID:SCR_002404) Copy   

•   Source: SciCrunch Registry