Tools   Select Another Resource Report Type

https://neurobagel.org/

Open source software ecosystem designed to facilitate harmonization, search, and discovery of neuroimaging study cohorts across decentralized datasets. Allows researchers to find participants across different studies based on specific clinical, demographic, and imaging criteria without needing to merge all the original data into a single central database. Provides tools to connect a local neuroimaging dataset with others in decentralized framework using linked data principles.You can contribute to Neurobagel by enhancing its tools (annotation, querying), improving documentation, adding new features/models, fixing bugs, or sharing your own data/expertise to help researchers find and harmonize brain imaging cohorts through standardized metadata and knowledge graphs.

Proper citation: Neurobagel (RRID:SCR_027847) Copy   

•   Source: SciCrunch Registry
  

https://broadinstitute.github.io/warp/docs/Pipelines/snm3C/README

Software open-source, cloud-optimized computational workflow for processing single-nucleus methylome and chromatin contact (snm3C) sequencing data. The workflow is designed to demultiplex and align raw sequencing reads, call chromatin contacts, and generate summary metrics.

Proper citation: snm3C Pipeline (RRID:SCR_025041) Copy   

•   Source: SciCrunch Registry
  

http://erpinfo.org/erplab

A set of open source, freely available Matlab routines for analyzing Event Related Potential (ERP) data. It is tightly integrated with the EEGLAB Toolbox. ERPLAB routines can be accessed from the Matlab command window and from Matlab scripts in addition to being accessed from the EEGLAB GUI. Consequently, ERPLAB provides the ease of learning of a GUI-based system but also provides the power and flexibility of a scripted system.The development of ERPLAB Toolbox is being coordinated by Steve Luck and Javier Lopez-Calderon at the UC-Davis Center for Mind & Brain, with financial support from NIMH.

Proper citation: ERPLAB (RRID:SCR_009574) Copy   

•   Source: SciCrunch Registry
  

https://terra.bio

Open ecosystem of data repositories hosted by various partner organizations, who work together to ensure that their data can be federated across participating platforms. Open source platform for biomedical researchers to access data, run analysis tools, and collaborate. Terra powers important scientific projects including AnVIL, BioData Catalyst, Human Cell Atlas, BICCN, and many others. Easily access both open and access-controlled datasets hosted in cloud repositories. Explore, analyze, and visualize data using Jupyter Notebooks, RStudio, RShinyApps, and Galaxy.

Proper citation: Terra (RRID:SCR_021648) Copy   

•   Source: SciCrunch Registry
  

http://www.brainscape.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on May 23, 2013. Database for resting state functional connectivity studies. Functional connectivity has shown tremendous promise in mapping the intrinsic functional topography of the brain, evaluating neuroanatomical models, and investigating neurological and psychiatric disease. Brainscape includes a repository of public and private data and an analysis engine for exploring the correlation structure of spontaneous fluctuations in the fMRI BOLD signal. (DICOM data is the image format that can be uploaded.) With Brainscape you can upload, analyze, and share your own data. You can search for, download, and analyze studies in the repository of shared data. The analysis engine works by selecting one or more studies, typing in the coordinates of a brain region of interest, and the seed-region correlation engine computes the correlation structure across the whole brain. (T1, T2 and EPI data are the scan types Brainscape can process.) You decide who can access your data. You can keep it to yourself, share with select colleagues, or share it with everyone. The Brainscape database and analysis tools are open source and freely available.

Proper citation: Brainscape (RRID:SCR_002962) Copy   

•   Source: SciCrunch Registry
  

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

Interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended). EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users'' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB ''datasets'' and/or across a collection of datasets brought together in an EEGLAB ''studyset.'' For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by publishing EEGLAB ''plug-in'' functions that appear automatically in the EEGLAB menu of users who download them. For example, novel EEGLAB plug-ins might be built and released to ''pick peaks'' in ERP or time/frequency results, or to perform specialized import/export, data visualization, or inverse source modeling of EEG, MEG, and/or ECOG data. EEGLAB Features * Graphic user interface * Multiformat data importing * High-density data scrolling * Defined EEG data structure * Open source plug-in facility * Interactive plotting functions * Semi-automated artifact removal * ICA & time/frequency transforms * Many advanced plug-in toolboxes * Event & channel location handling * Forward/inverse head/source modeling

Proper citation: EEGLAB (RRID:SCR_007292) Copy   

•   Source: SciCrunch Registry
  

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

Open-source utilities that are C++ based command line applications that allow image analysis and processing using ITK or VTK libraries. Specifically the following utilities are contained thus far: * ImageMath - the swiss army knife image modification * ImageStat - compute stats on images * IntensityRescaler - rescale/normalize intensities using a prior brain tissue segmentation * convertITKformats - convert 3D images in all ITK formats (NRRD, NIFTI, GIPL, Meta etc) * DWI_NiftiNrrdConversion - convert DWI and DTI from/to NRRD and NIFTI, works with UNC DTI tools and FSL * CropTools - crops 3D and 4D images * PolydataMerge - Merges VTK polydata files * PolydataTransform - Transforms polydata files * TransformDeformationField - concatenates or average deformation fields (H-fields or displacement fields) * DTIAtlasBuilder - Creates a DTI average from multiple DTI images

Proper citation: NIRAL Utilities (RRID:SCR_002490) Copy   

•   Source: SciCrunch Registry
  

http://www.fz-juelich.de/ime/spm_anatomy_toolbox

A MATLAB toolbox which uses three dimensional probabilistic cytoarchitechtonic maps to correlate microscopic, anatomic and functional data of the cerebral cortex. Correlating the activation foci identified in functional imaging studies of the human brain with structural (e.g., cytoarchitectonic) information on the activated areas is a major methodological challenge for neuroscience research. We here present a new approach to make use of three-dimensional probabilistic cytoarchitectonic maps, as obtained from the analysis of human post-mortem brains, for correlating microscopical, anatomical and functional imaging data of the cerebral cortex. We introduce a new, MATLAB based toolbox for the SPM2 software package which enables the integration of probabilistic cytoarchitectonic maps and results of functional imaging studies. The toolbox includes the functionality for the construction of summary maps combining probability of several cortical areas by finding the most probable assignment of each voxel to one of these areas. Its main feature is to provide several measures defining the degree of correspondence between architectonic areas and functional foci. The software, together with the presently available probability maps, is available as open source software to the neuroimaging community. This new toolbox provides an easy-to-use tool for the integrated analysis of functional and anatomical data in a common reference space.

Proper citation: SPM Anatomy Toolbox (RRID:SCR_013273) Copy   

•   Source: SciCrunch Registry
  

https://broadinstitute.github.io/warp/docs/Pipelines/SlideTags_Pipeline/README

Software pipeline as open-source, cloud-optimized workflow for processing spatial transcriptomics data. It supports data derived from spatially barcoded sequencing technologies, including Slide-tags-based single-molecule profiling. The pipeline processes raw sequencing data into spatially resolved gene expression matrices, ensuring accurate alignment, spatial positioning, and quantification.

Proper citation: SlideTags.wdl (RRID:SCR_027567) Copy   

•   Source: SciCrunch Registry
  

http://brainml.org/goto.do?page=.home

Set of standards and practices for using XML to facilitate information exchange between user application software and neuroscience data repositories. It allows for common shared library routines to handle most of the data processing, but also supports use of structures specialized to the needs of particular neuroscience communities. This site also serves as a repository for BrainML models. (A BrainML model is an XML Schema and optional vocabulary files describing a data model for electronic representation of neuroscience data, including data types, formats, and controlled vocabulary. ) It focuses on layered definitions built over a common core in order to support community-driven extension. One such extension is provided by the new NIH-supported neuroinformatics initiative of the Society for Neuroscience, which supports the development of expert-derived terminology sets for several areas of neuroscience. Under a cooperative agreement, these term lists will be made available Open Source on this site.
The repository function of this site includes the following features:
* BrainML models are published in searchable, browsable form.
* Registered users may submit new models or new versions of existing models to accommodate data of interest. * BrainML model schema and vocabulary files are made available at fixed URLs to allow software applications to reference them.
* Users can check models and/or instance documents for correct format before submitting them using an online validation service.
To complement the BrainML modeling language, a set of protocols have been developed for BrainML document exchange between repositories and clients, for indexing of repositories, and for data query.

Proper citation: BrainML (RRID:SCR_007087) Copy   

•   Source: SciCrunch Registry
  

http://penglab.janelia.org/proj/v3d/V3D/About_V3D.html

V3D is a handy, fast, and versatile 3D/4D/5D Image Visualization & Analysis System for Bioimages & Surface Objects. It also provides many unique functions, is Open Source, supports a very simple and powerful plugin interface and thus can be extended & enhanced easily. V3D-Neuron is a powerful 3D neuron reconstruction, visualization, and editing software built on top of V3D. Both V3D and V3D-Neuron have recently been published in Nature Biotechnology (April, 2010), and Highlighted in Nature Methods (May, 2010), and Science News (April, 2010), etc. V3D is a cross-platform (Mac, Linux, and Windows) tool for visualizing large-scale (gigabytes, and 64-bit data) 3D image stacks and various surface data. It is also a container of powerful modules for 3D image analysis (cell segmentation, neuron tracing, brain registration, annotation, quantitative measurement and statistics, etc) and data management. This makes V3D suitable for various bioimage informatics applications, and a nice platform to develop new 3D image analysis algorithms for high-throughput processing. In short, V3D streamlines the workflow of visualization-assisted analysis. In the latest V3D development, it can render 5D (spatial-temporal) data directly in 3D volume-rendering mode; it supports convenient and interactive local and global 3D views at different scales. It even has a Matlab file IO toolbox. A user can now write his/her own plugins to take advantage of the V3D platform very easily.

Proper citation: V3D (RRID:SCR_008646) Copy   

•   Source: SciCrunch Registry
  

http://cvlab.epfl.ch/NeuroMorph

A toolset for the morphometric analysis and visualization of 3D models derived from electron microscopy image stacks. It is designed to import, analyze, and visualize mesh models. It has been designed specifically for the morphological analysis of 3D objects derived from serial electron microscopy images of brain tissue, although much of its functionality can be applied to any 3D mesh. These models can be generated by software that allows the images to be segmented so that 3D objects can be built. These objects can be generated by any 3D image segmentation software, such as ilastik or Fiji. The NeuroMorph toolset has been developed as a set of add-ons for Blender, a widely used free and open source 3D modeling software package.

Proper citation: NeuroMorph (RRID:SCR_002091) Copy   

•   Source: SciCrunch Registry
  

http://www.pymvpa.org

A Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run. Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. This Python-based, cross-platform, open-source software toolbox software toolbox for the application of classifier-based analysis techniques to fMRI datasets makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages.

Proper citation: PyMVPA (RRID:SCR_006099) Copy   

•   Source: SciCrunch Registry
  

http://sccn.ucsd.edu/wiki/BCILAB

Open Source MATLAB toolbox and EEGLAB plugin for the design, prototyping, testing, experimentation with, and evaluation of Brain-Computer Interfaces (BCIs), and other systems in the same computational framework. It facilitates the design and development of new methods for cognitive state estimation and their use in both offline data analysis and real-time applications. BCILAB includes an easily extensible collection of currently over 100 methods from the literature (covering signal processing, machine learning and BCI-specific methods). Aside from supporting advanced BCI research, a special aim of BCILAB is to facilitate the adoption of machine learning and advanced statistical modeling for functional neuroimaging purposes in tandem with the EEGLAB platform. The toolbox offers multiple different interfaces which link to the same backend functionality, including a GUI, scripting support (MATLAB-based), APIs for real-time processing, and a variety of extension component interfaces. MATLAB programming is not strictly necessary, as most BCILAB features can be accessed from the GUI, although it is required for batch scripting and custom extensions. The strength of MATLAB-based software lies in its resources for leading-edge scientific computing, as well as in the good support for rapid prototyping, but BCI systems developed in it can be used for real-time out-of-lab experimentation, and can in principle be deployed without the need for a MATLAB license. However, due to the complexity and overhead of the MATLAB environment, the system is best used as a research platform, and not as a product development environment -- end-user software is ideally re-implemented in a compiled language, after a suitable approach has been identified and extensively tested. The process of identifying and testing an approach involves more than just computation, but also data exploration and investigation - an area which is helped by the deep integration with the EEGLAB platform. In the future, this integration will be further strengthened, bringing rich statistical learning and signal processing into routine EEG analysis workflows. The toolbox has been developed by C. Kothe at the Swartz Center, inspired by the preceding PhyPA BCI toolbox created by C. Kothe and T. Zander at the Chair for Human-Machine Systems, Berlin Institute of Technology.

Proper citation: BCILAB (RRID:SCR_007013) Copy   

•   Source: SciCrunch Registry
  

http://www.mitre.org/news/digest/archives/2002/neuroinformatics.html

This resource''s long-term goal is to develop informatics methodologies and tools that will increase the creativity and productivity of neuroscience investigators, as they work together to use shared human brain mapping data to generate and test ideas far beyond those pursued by the data''s originators. This resource currently has four major projects supporting this goal: * Database tools: The goal of the NeuroServ project is to provide neuroscience researchers with automated information management tools that reduce the effort required to manage, analyze, query, view, and share their imaging data. It currently manages both structural magnetic resonance image (MRI) datasets and diffusion tensor image (DTI) datasets. NeuroServ is fully web-enabled: data entry, query, processing, reporting, and administrative functions are performed by qualified users through a web browser. It can be used as a local laboratory repository, to share data on the web, or to support a large distributed consortium. NeuroServ is based on an industrial-quality query middleware engine MRALD. NeuroServ includes a specialized neuroimaging schema and over 40 custom Java Server Pages supporting data entry, query, and reporting to help manage and explore stored images. NeuroServ is written in Java for platform independence; it also utilizes several open source components * Data sharing: DataQuest is a collaborative forum to facilitate the sharing of neuroimaging data within the neuroscience community. By publishing summaries of existing datasets, DataQuest enables researchers to: # Discover what data is available for collaborative research # Advertise your data to other researchers for potential collaborations # Discover which researchers may have the data you need # Discover which researchers are interested in your data. * Image quality: The approach to assessing the inherent quality of an image is to measure how distorted the image is. Using what are referred to as no-reference or blind metrics, one can measure the degree to which an image is distorted. * Content-based image retrieval: NIRV (NeuroImagery Retrieval & Visualization) is a work environment for advanced querying over imagery. NIRV will have a Java-based front-end for users to issue queries, run processing algorithms, review results, visualize imagery and assess image quality. NIRV interacts with an image repository such as NeuroServ. Users can also register images and will soon be able to filter searches based on image quality.

Proper citation: MITRE Neuroinformatics (RRID:SCR_006508) 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://www.mitk.org/DiffusionImaging

A selection of image analysis algorithms for the processing of diffusion-weighted MR images. Features & Highlights * Tensor and q-ball reconstruction * Glyph visualization * Quantification and partial volume clustering of tensor and q-ball images * Global fiber tractography, visualization, and tract post-processing * Brain network statistics and visualization (connectomics) * Interactive exploration of Tract-based spatial statistics (TBSS) results * Intra-voxel incoherent motion (IVIM) estimation * Synthetic data generation Additional system specific requirements: * Windows: If you have problems running the Windows application, please install the Microsoft Redistributable Packages for VS 2008: 32 bit or 64 bit * Linux: the Qt framework, version 4.6.2 or later Tested systems: Windows 7, Windows Vista; Ubuntu 12.04 and newer; OS X 10.6 (Snow Leopard), OS X 10.8 (Mountain Lion) The OS X 10.6 installer is compatible with OS X 10.7 (Lion) so there is no dedicated disk image build under 10.7. The MITK Diffusion application is based on the MITK research platform and the most of it is open-source. The available code is embedded into the source code of MITK as a module and can be accessed through the public git repository.

Proper citation: MITK Diffusion (RRID:SCR_006846) 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.ant-neuro.com/products/eeprobe

A complete software package for the study of event-related brain activity with high-resolution EEG/MEG. This package has been designed to suit the high standards of neuroscience research. The software has been developed originally at the Max Planck Institute for Cognitive Neuroscience in Leipzig, Germany, and is available for other institutions through ANT Neuro B.V., The Netherlands, enhanced with the EEProbe Databrowser. ERP investigations, both in psychophysiology research and clinical applications require a multitude of processing steps. Analysis of large data sets is made efficient through advanced scripting possibilities. All different aspects of data handling are efficiently available in the EEProbe Databrowser. Alternatively, external data can be imported from a multitude of formats. Processing in EEProbe makes use of open file formats (see LIBEEP) and is designed to integrate with ASA for advanced source analysis. EEProbe is available for Linux and Mac OS X.

Proper citation: EEProbe (RRID:SCR_009570) Copy   

•   Source: SciCrunch Registry
  

http://www.nitrc.org/projects/itk-snap/

Open source interactive software application for three dimentional medical images, manual delineation of anatomical regions of interest, and performing automatic image segmentation. Used for delineating anatomical structures and regions in MRI, CT and other 3D biomedical imaging data.WebGL-based viewer for volumetric data. It is capable of displaying arbitrary (non axis-aligned) cross-sectional views of volumetric data, as well as 3-D meshes and line-segment based models (skeletons).

Proper citation: ITK-SNAP (RRID:SCR_002010) Copy   

•   Source: SciCrunch Registry