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http://cng.gmu.edu:8080/Lm

A freely available software tool available for the Windows and Linux platform, as well as the Online version Applet, for the analysis, comparison and search of digital reconstructions of neuronal morphologies. For the quantitative characterization of neuronal morphology, LM computes a large number of neuroanatomical parameters from 3D digital reconstruction files starting from and combining a set of core metrics. After more than six years of development and use in the neuroscience community, LM enables the execution of commonly adopted analyses as well as of more advanced functions, including: (i) extraction of basic morphological parameters, (ii) computation of frequency distributions, (iii) measurements from user-specified subregions of the neuronal arbors, (iv) statistical comparison between two groups of cells and (v) filtered selections and searches from collections of neurons based on any Boolean combination of the available morphometric measures. These functionalities are easily accessed and deployed through a user-friendly graphical interface and typically execute within few minutes on a set of 20 neurons. The tool is available for either online use on any Java-enabled browser and platform or may be downloaded for local execution under Windows and Linux.

Proper citation: L-Measure (RRID:SCR_003487) Copy   

•   Source: SciCrunch Registry

http://neuroscienceblueprint.nih.gov/

Collaborative framework that includes the NIH Office of the Director and the 14 NIH Institutes and Centers that support research on the nervous system. By pooling resources and expertise, the Blueprint identifies cross-cutting areas of research, and confronts challenges too large for any single Institute or Center. The Blueprint makes collaboration a day-to-day part of how the NIH does business in neuroscience, complementing the basic missions of Blueprint partners. During each fiscal year, the partners contribute a small percentage of their funds to a common pool. Since the Blueprint's inception in 2004, this pool has comprised less than 1 percent of the total neuroscience research budget of the partners. In 2009, the Blueprint Grand Challenges were launched to catalyze research with the potential to transform our basic understanding of the brain and our approaches to treating brain disorders. * The Human Connectome Project is an effort to map the connections within the healthy brain. It is expected to help answer questions about how genes influence brain connectivity, and how this in turn relates to mood, personality and behavior. The investigators will collect brain imaging data, plus genetic and behavioral data from 1,200 adults. They are working to optimize brain imaging techniques to see the brain's wiring in unprecedented detail. * The Grand Challenge on Pain supports research to understand the changes in the nervous system that cause acute, temporary pain to become chronic. The initiative is supporting multi-investigator projects to partner researchers in the pain field with researchers in the neuroplasticity field. * The Blueprint Neurotherapeutics Network is helping small labs develop new drugs for nervous system disorders. The Network provides research funding, plus access to millions of dollars worth of services and expertise to assist in every step of the drug development process, from laboratory studies to preparation for clinical trials. Project teams across the U.S. have received funding to pursue drugs for conditions from vision loss to neurodegenerative disease to depression. Since its inception in 2004, the Blueprint has supported the development of new resources, tools and opportunities for neuroscientists. For example, the Blueprint supports several training programs to help students pursue interdisciplinary areas of neuroscience, and to bring students from underrepresented groups into the neurosciences. The Blueprint also funds efforts to develop new approaches to teaching neuroscience through K-12 instruction, museum exhibits and web-based platforms. From fiscal years 2007 to 2009, the Blueprint focused on three major themes of neuroscience - neurodegeneration, neurodevelopment, and neuroplasticity. These efforts enabled unique funding opportunities and training programs, and helped establish new resources including the Blueprint Non-Human Primate Brain Atlas.

Proper citation: NIH Blueprint for Neuroscience Research (RRID:SCR_003670) 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://openconnectomeproject.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. Connectomes repository to facilitate the analysis of connectome data by providing a unified front for connectomics research. With a focus on Electron Microscopy (EM) data and various forms of Magnetic Resonance (MR) data, the project aims to make state-of-the-art neuroscience open to anybody with computer access, regardless of knowledge, training, background, etc. Open science means open to view, play, analyze, contribute, anything. Access to high resolution neuroanatomical images that can be used to explore connectomes and programmatic access to this data for human and machine annotation are provided, with a long-term goal of reconstructing the neural circuits comprising an entire brain. This project aims to bring the most state-of-the-art scientific data in the world to the hands of anybody with internet access, so collectively, we can begin to unravel connectomes. Services: * Data Hosting - Their Bruster (brain-cluster) is large enough to store nearly any modern connectome data set. Contact them to make your data available to others for any purpose, including gaining access to state-of-the-art analysis and machine vision pipelines. * Web Viewing - Collaborative Annotation Toolkit for Massive Amounts of Image Data (CATMAID) is designed to navigate, share and collaboratively annotate massive image data sets of biological specimens. The interface is inspired by Google Maps, enhanced to allow the exploration of 3D image data. View the fork of the code or go directly to view the data. * Volume Cutout Service - RESTful API that enables you to select any arbitrary volume of the 3d database (3ddb), and receive a link to download an HDF5 file (for matlab, C, C++, or C#) or a NumPy pickle (for python). Use some other programming language? Just let them know. * Annotation Database - Spatially co-registered volumetric annotations are compactly stored for efficient queries such as: find all synapses, or which neurons synapse onto this one. Create your own annotations or browse others. *Sample Downloads - In addition to being able to select arbitrary downloads from the datasets, they have also collected a few choice volumes of interest. * Volume Viewer - A web and GPU enabled stand-alone app for viewing volumes at arbitrary cutting planes and zoom levels. The code and program can be downloaded. * Machine Vision Pipeline - They are building a machine vision pipeline that pulls volumes from the 3ddb and outputs neural circuits. - a work in progress. As soon as we have a stable version, it will be released. * Mr. Cap - The Magnetic Resonance Connectome Automated Pipeline (Mr. Cap) is built on JIST/MIPAV for high-throughput estimation of connectomes from diffusion and structural imaging data. * Graph Invariant Computation - Upload your graphs or streamlines, and download some invariants. * iPad App - WholeSlide is an iPad app that accesses utilizes our open data and API to serve images on the go.

Proper citation: Open Connectome Project (RRID:SCR_004232) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. 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., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

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

•   Source: SciCrunch Registry

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

Software to fit s-reps to segmented anatomic objects, to compute probability distributions on these s-reps, to train and to apply classifiers between two classes of anatomic objects, and to apply hypothesis testing to determine which geometric or physiological features vary significantly between two classes. Software for object segmentation from medical images may also be included. S-reps are skeletal models for anatomic objects especially suited for computing probability distributions from populations of these objects and for providing object-related coordinates for the interior of these objects. They allow classification and hypothesis testing using their geometric features and physiological features derived from medical images. They also allow the definition of shape spaces, probability-based geometric typicality functions, and appearance models used for segmentation or registration. A variety of successful applications to objects in neuroimages have already been performed.

Proper citation: S-rep Fitting Statistics and Segmentation (RRID:SCR_002540) Copy   

•   Source: SciCrunch Registry

http://scralyze.sourceforge.net

A powerful software for model-based analysis of peripheral psychophysiology (e.g. skin conductance, heart rate, pupil size etc.). General linear modelling and dynamic causal modelling of these signals provide for inference on neural states/processes. SCRalyze includes flexible data import and display, statistical inference and results display and export. Easy programming of add-ons for new data formats, signal channels, and models.

Proper citation: SCRalyze (RRID:SCR_002542) Copy   

•   Source: SciCrunch Registry

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

Project to discuss, debate, develop and deploy ontological practices for the fMRI community.

Proper citation: Resource Ontology Discussion Group (RRID:SCR_002536) Copy   

•   Source: SciCrunch Registry

http://omlc.ogi.edu/software/mc/

MCML is a Monte Carlo simulation program for Multi-layered Turbid Media with an infinitely narrow photon beam as the light source. The simulation is specified by an input text file called, for example, sample.mci, which can be modified by any simple text editor. The output is another text file called, for example, sample.mco. (The names are arbitrary.) CONV is a convolution program which uses the MCML output file to convolve for photon beams of any size in a Gaussian or flat field shape. CONV can provide a variety of output formats (reflectance, transmission, iso-fluence contours, etc.), which are compatible with standard graphics applications.

Proper citation: MCML and CONV (RRID:SCR_002419) Copy   

•   Source: SciCrunch Registry

https://github.com/mjacquem/RodentThickness

An automatic cortical thickness measurement tool for rat brains. The pipeline consists of four steps: preprocessing to create binary mask and label map, thickness measurement which produces laplacian field and thickness map in order, run particle correspondence followed by statistical analysis resulting in mean thickness color map and t-test result. By running RodentThickness, you will need to fill in informations in a Graphical User Interface, and then compute. You can also run the tool in command line without using the GUI. Using the GUI, you will be able to save or load a dataset file or a configuration file. The tool needs these other tools to work, so be sure to have these installed on your computer: * ImageMath * measureThicknessFilter * GenParaMeshCLP * ParaToSPHARMMeshCLP * ShapeWorksRun * ShapeWorksGroom * SegPostProcessCLP * BinaryToDistanceMap * MeshPointsIntensitysampling

Proper citation: Rodent Cortical Thickness Analysis (RRID:SCR_002539) Copy   

•   Source: SciCrunch Registry

http://www.kcl.ac.uk/iop/depts/neuroimaging/research/imaginganalysis/Software/rBET.aspx

A modified version of the Brain Extraction Tool (BET) that can process rodent brains.

Proper citation: Rodent Brain Extraction Tool (RRID:SCR_002538) Copy   

•   Source: SciCrunch Registry

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

Non-rigid registration / distortion correction tools for enhanced functional localization through the registration of EPI fMRI to high-resolution anatomical MRI.

Proper citation: NPTK (RRID:SCR_002496) Copy   

•   Source: SciCrunch Registry

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

Support and community integration for the enhanced NITRC services of the Image Repository (IR) and the Computational Environment (CE). The NITRC Computational Environment, an on-demand, cloud based computational virtual machine pre-installed with popular NITRC neuroimaging tools built using NeuroDebian. NITRC Image Repository is built upon XNAT and supports both NIfTI and DICOM images. The NITRC-IR offers 3,733 Subjects, and 3,743 Imaging Sessions searchable across seven projects to promote re-use and integration of valuable NIH-funded data.

Proper citation: NITRC Enhanced Services (RRID:SCR_002494) 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://pysurfer.github.com

Software Python tool for visualization and interaction with cortical surface representations of neuroimaging data from Freesurfer. It extends Mayavi powerful visualization engine with interface for working with MRI and MEG data. PySurfer offers command-line interface designed to broadly replicate Freesurfer program as well as Python library for writing scripts to explore complex datasets., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PySurfer (RRID:SCR_002524) Copy   

•   Source: SciCrunch Registry

http://mgui.wikidot.com

An open source Java-based project intended to provide a graphic user interface (GUI) for interactions between scientists (or enthusiasts) and their data. In its current (beta) form, mgui offers the following functionality: * Cross-platform functionality (with a Java Runtime installation, runs on Linux, Windows, Mac, or Solaris) * 2D rendering of data based upon Java2D, and 3D rendering based upon Java3D * The ability to organize complex datasets into intuitive mgui projects * A processing pipeline interface which allows users to process their datasets with any available Java or native software tools * An extensible I/O framework accommodating a variety of standard and non-standard file formats * Database connectivity using JDBC * Graph visualization based upon the JUNG library * An intuitive Swing-based GUI for managing, querying, and visualizing data * Various CAD-type tools for editing and creating geometry * A computational modelling framework

Proper citation: ModelGUI (RRID:SCR_002441) Copy   

•   Source: SciCrunch Registry

http://air.bmap.ucla.edu/MultiTracer2/MultiTracer.html

A Java application that allows images to be displayed in three dimensions. The tool allows anatomic structures to be traced and the tracings to be saved in a format that facilitates review and revision. It supports NIfTI-1.1 format float, double and signed and unsigned byte, short, and integer formats and provides legacy support for Analyze 7.5 8 and 16 bit images. It provides image display, editing, delineation of structure boundaries, export of traced contours and generation of masked volumes. Images are displayed in 3 orthogonal views. Time series can be displayed as averaged or contrast images and time courses can be visualized graphically. Version 2 provides enhancements to the original MultiTracer feature set.

Proper citation: MultiTracer (RRID:SCR_002445) Copy   

•   Source: SciCrunch Registry

http://slicedrop.com

A viewer for medical imaging data that supports a variety of scientific file formats out-of-the-box (see https://github.com/xtk/X/wiki/X:Fileformats for a complete list). We think that the best way to render your files is without any necessary conversions. Just drop'em on a website and they are ready to render. Just drag'n'drop some medical imaging files on this website or try one of the four examples in the right corner. Then, play with the panels on the left and click, drag and rotate the 3d content. Slice:Drop uses WebGL and HTML5 Canvas to render the data in 2D and 3D. We use our own open-source toolkit to perform the rendering, called XTK ( http://goxtk.com ).

Proper citation: Slice:Drop (RRID:SCR_002557) Copy   

•   Source: SciCrunch Registry

http://brainmap.org/sleuth/

Software application that searches the BrainMap Database for papers of interest, reads their corresponding meta-data, and plots their results as coordinates on a standard glass brain in Talairach space.

Proper citation: Sleuth (RRID:SCR_002555) Copy   

•   Source: SciCrunch Registry

http://www.med.unc.edu/bric/ideagroup/free-softwares/hammer

Software for both groupwise registration and longitudinal registration, which are the necessary steps for many brain-related applications. Specifically, groupwise registration is important for unbiased analysis of a large set of MR brain images. Therefore, in this software package, they have included two of their recently-developed groupwise registration algorithms: 1) Improved unbiased groupwise registration guided with the sharp group-mean image, and 2) Hierarchical feature-based groupwise registration with implicit template (Groupwise-HAMMER for short). On the other hand, they also included their recently-developed groupwise longitudinal registration algorithm that aligns not only the longitudinal image sequence for each subject, but also align all longitudinal image sequences of all subjects to the common space simultaneously.

Proper citation: GLIRT (RRID:SCR_002390) Copy   

•   Source: SciCrunch Registry