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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   

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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   

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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   

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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   

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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   

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http://biosig.sourceforge.net/

Software library for processing of electroencephalogram (EEG) and other biomedical signals like electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), respiration, and so on. Biosig contains tools for quality control, artifact processing, time series analysis, feature extraction, classification and machine learning, and tools for statistical analysis. Many tools are able to handle data with missing values (statistics, time series analysis, machine learning). Another feature is that more then 40 different data formats are supported, and a number of converters for EEG,, ECG and polysomnography are provided. Biosig has been widely used for scientific research on EEG-based BraiN-Computer Interfaces (BCI), sleep research, and ECG and HRV analysis. It provides software interfaces several programming languages (C, C++, Matlab/Octave, Python), and it provides also an interactive viewing and scoring software for adding, and editing of annotations, markers and events.

Proper citation: BioSig: An Imaging Bioinformatics System for Phenotypic Analysis (RRID:SCR_008428) Copy   

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http://www.textpresso.org/

An information extracting and processing package for biological literature that can be used online or installed locally via a downloadable software package, http://www.textpresso.org/downloads.html Textpresso's two major elements are (1) access to full text, so that entire articles can be searched, and (2) introduction of categories of biological concepts and classes that relate two objects (e.g., association, regulation, etc.) or describe one (e.g., methods, etc). A search engine enables the user to search for one or a combination of these categories and/or keywords within an entire literature. The Textpresso project serves the biological and biomedical research community by providing: * Full text literature searches of model organism research and subject-specific articles at individual sites. Major elements of these search engines are (1) access to full text, so that the entire content of articles can be searched, and (2) search capabilities using categories of biological concepts and classes that relate two objects (e.g., association, regulation, etc.) or identify one (e.g., cell, gene, allele, etc). The search engines are flexible, enabling users to query the entire literature using keywords, one or more categories or a combination of keywords and categories. * Text classification and mining of biomedical literature for database curation. They help database curators to identify and extract biological entities and facts from the full text of research articles. Examples of entity identification and extraction include new allele and gene names and human disease gene orthologs; examples of fact identification and extraction include sentence retrieval for curating gene-gene regulation, Gene Ontology (GO) cellular components and GO molecular function annotations. In addition they classify papers according to curation needs. They employ a variety of methods such as hidden Markov models, support vector machines, conditional random fields and pattern matches. Our collaborators include WormBase, FlyBase, SGD, TAIR, dictyBase and the Neuroscience Information Framework. They are looking forward to collaborating with more model organism databases and projects. * Linking biological entities in PDF and online journal articles to online databases. They have established a journal article mark-up pipeline that links select content of Genetics journal articles to model organism databases such as WormBase and SGD. The entity markup pipeline links over nine classes of objects including genes, proteins, alleles, phenotypes, and anatomical terms to the appropriate page at each database. The first article published with online and PDF-embedded hyperlinks to WormBase appeared in the September 2009 issue of Genetics. As of January 2011, we have processed around 70 articles, to be continued indefinitely. Extension of this pipeline to other journals and model organism databases is planned. Textpresso is useful as a search engine for researchers as well as a curation tool. It was developed as a part of WormBase and is used extensively by C. elegans curators. Textpresso has currently been implemented for 24 different literatures, among them Neuroscience, and can readily be extended to other corpora of text.

Proper citation: Textpresso (RRID:SCR_008737) Copy   

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

A suite of tools for efficient management of neuroimaging project data. Specifically, DFBIdb was designed to allow users to quickly perform routine management tasks of sorting, archiving, exploring, exporting and organising raw data. DFBIdb was implemented as a collection of Python scripts that maintain a project-based, centralised database that is based on the XCEDE 2 data model. Project data is imported from a filesystem hierarchy of raw files, which is an often-used convention of imaging devices, using a single script that catalogues meta-data into a modified XCEDE 2 data model. During the import process data are reversibly anonymised, archived and compressed. The import script was designed to support multiple file formats and features an extensible framework that can be adapted to novel file formats. Graphical user interfaces are provided for data exploration. DFBIdb includes facilities to export, convert and organise customisable subsets of project data according to user-specified criteria.

Proper citation: DFBIdb (RRID:SCR_009456) Copy   

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http://nrg.wustl.edu/projects/fiv

A tool for visualizing functional and anatomic MRI data.

Proper citation: FIV (RRID:SCR_009575) Copy   

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

An open-source C++-based application that allows automatic as well as user-interactive segmentation of the Corpus Callosum. Via a Qt-based graphical user interface, CCSeg also performs semi-automatic segmentation.

Proper citation: CCSeg - Corpus Callosum Segmentation (RRID:SCR_009453) Copy   

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http://www.ncigt.org/pages/Research_Projects/ImagingCoreToolbox/Imaging_Toolkit

This software provides algorithms for the reconstruction of raw MR data. In particular, it supports the reconstruction of accelerated data acquisitions where k-space is subsampled and the Fourier domain encoding is complemented by temporal encoding, spatial encoding, or and/or a constrained reconstruction. This library of functions provides a number of reconstruction algorithms that accurately employ advanced MR imaging methods including: UNFOLD; parallel imaging methods such as SENSE and GRAPPA; Homodyne processing of partial-Fourier data, and gradient field inhomogeneity correction (gradwarp); EPI Nyquist Ghost correction and ramp-sampling gridding. The target audience is research groups who may be interested in exploring or employing advanced MR reconstruction techniques, but don't have the necessary expertise in-house. Inquires may be directed to: ncigt-imaging-toolkit -at- bwh.harvard.edu

Proper citation: NCIGT Fast Imaging Library (RRID:SCR_009609) Copy   

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http://www.mazesuite.com/

A complete set of tools that enables researchers to perform spatial and navigational behavior experiments within interactive, easy to create, and extendable (e.g., multiple rooms) 3D virtual environments. MazeSuite can be used to design/edit adapted 3D environments where subjects? behavioral performance can be tracked. Maze Suite consists of three main applications; an editing program to create and alter maps (MazeMaker), a visualization/rendering module (MazeWalker), and finally an analysis/mapping tool (MazeAnalyzer). Additionally, MazeSuite has the capabilities of sending signal pulses to physiological recording devices using standard computer ports. MazeSuite, with all 3 applications, is a unique and complete toolset for researchers who want to easily and rapidly deploy interactive 3D environments. Requirements Maze Suite is designed for Windows 7, Windows Vista and Windows XP. 3D rendering quality depends on available graphics card hardware; OpenGL 2.1 or above compliant is recommended. For Windows XP systems, .NET Framework Version 2.0 or above is required and can be downloaded from Microsoft's website.

Proper citation: MazeSuite (RRID:SCR_009606) Copy   

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http://ric.uthscsa.edu/mango/

A viewer for medical research images that provides analysis tools and a user interface to navigate image volumes. There are three versions of Mango, each geared for a different platform: * Mango ? Desktop ? Mac OS X, Windows, and Linux * webMango ? Browser ? Safari, Firefox, Chrome, and Internet Explorer * iMango ? Mobile ? Apple iPad Key Features: * Built-in support for DICOM, NIFTI, Analyze, and NEMA-DES formats * Customizable: Create plugins, custom filters, color tables, file formats, and atlases * ROI Editing: Threshold and component-based tools for painting and tracing ROIs * Surface Rendering: Interactive surface models supporting cut planes and overlays * Image Registration: Semi-automatic image coregistration and manual transform editing * Image Stacking: Threshold and transparency-based image overlay stacking * Analysis: Histogram, cross-section, time-series analysis, image and ROI statistics * Processing: Kernel and rank filtering, arithmetic/logic image and ROI calculators

Proper citation: Mango (RRID:SCR_009603) Copy   

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

A collection of tools for MRI T1 brain image segmentation in the Windows environment. It helps construct a complete pipeline with necessary preprocessing and postprocessing procedures besides brainparser, the core program of our fast brain segmentation. The execution of the whole pipeline can be completed in 2 hours with good segmentation results. Execution requires: FSL

Proper citation: BrainSolution (RRID:SCR_009447) Copy   

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http://visual.cs.utsa.edu/eegvis

A MATLAB toolbox for exploration of multi-channel EEG and other large array-based data sets using multi-scale drill-down techniques. The toolbox can be used directly in MATLAB at any stage in a user's processing pipeline, as a plug in for EEGLAB, or as a standalone precompiled application without MATLAB running. EEGVIS and its supporting packages are freely available under the GNU general public license. The toolbox also supplies a number of extensible base classes for users who wish to develop their own visualizations.

Proper citation: EEGVIS (RRID:SCR_009569) Copy   

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https://github.com/clementsan/DTI-Reg

An open-source C++ application that performs pair-wise DTI registration, using scalar FA map to drive the registration. Individual steps of the pair-wise registration pipeline are performed via external applications - some of them being 3D Slicer modules. Starting with two input DTI images, scalar FA maps are generated via dtiprocess. Registration is then performed between these FA maps, via BRAINSFit/BRAINSDemonWarp or ANTS -Advanced Normalization Tools-, which provide different registration schemes: rigid, affine, BSpline, diffeomorphic, logDemons. The final deformation is then applied to the source DTI image via ResampleDTI.

Proper citation: DTI-Reg (RRID:SCR_009560) Copy   

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http://code.google.com/p/psom/

A lightweight software library to manage complex multi-stage data processing. A pipeline is a collection of jobs, i.e. Matlab or Octave codes with a well identified set of options that are using files for inputs and outputs. To use PSOM, the only requirement is to generate a description of a pipeline in the form of a simple Matlab / Octave structure. PSOM then automatically offers the following services: * Run jobs in parallel using multiple CPUs or within a distributed computing environment. * Generate log files and keep track of the pipeline execution. These logs are detailed enough to fully reproduce the analysis. * Handle job failures : successful completion of jobs is checked and failed jobs can be restarted. * Handle updates of the pipeline : change options or add jobs and let PSOM figure out what to reprocess !

Proper citation: Pipeline System for Octave and Matlab (RRID:SCR_009637) Copy   

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

THIS RESOURCE IS NO LONGER IN SERVICE, documented on November 05, 2013. It has been superseeded by the CALATK, available here http://www.calatk.org c++ libraries and applications for performing fluid registration based operations on 2D and 3D images. The registration method is based on the large displacement diffeomorphic mapping (LDDM) registration method and implements discretized fluid registration. This registration method is then applied to time series analysis, cross-sectional atlas building, and longitudinal atlas building. The individual tool components are: * LDDM: Fluid registration between two images. * TimeSeries: Time series analysis of longitudinal data for a single subject. * AtlasBuilder: Cross-sectional atlas building for a population of images. * LongitudinalAtlasBuilder: Longitudinal atlas building for a population of subjects, each with a longitudinal data set. * FRATUtils: A collection of utility functions for working with volumes and time series files

Proper citation: Fluid Registration and Atlas Toolkit (RRID:SCR_009478) Copy   

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

This package contains a software implementation for joint label fusion and corrective learning, which were applied in MICCAI 2012 Grand Challenge on Multi-Atlas Labeling and finished in the first place. Joint label fusion is for combining candidate segmentations produced by registering and warping multiple atlases for a target image. Corrective learning can be applied to further reduce systematic errors produced by joint label fusion. In general, corrective learning can be applied to correct systematic errors produced by other segmentation methods as well.

Proper citation: PICSL Multi-Atlas Segmentation Tool (RRID:SCR_009633) Copy   

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http://bisp.kaist.ac.kr/NIRS-SPM

A SPM and MATLAB-based software package for statistical analysis of near-infrared spectroscopy (NIRS) signals. Based on the general linear model (GLM), and Sun's tube formula / Lipschitz-Killing curvature (LKC) based expected Euler characteristics, NIRS-SPM not only provides activation maps of oxy-, deoxy-, and total-hemoglobin, but also allows for super-resolution activation localization. Additional features, including a wavelet-minimum description length detrending algorithm and cerebral metabolic rate of oxygen (CMRO2) estimation without hypercapnia, were implemented in the NIRS-SPM software package., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: NIRS-SPM (RRID:SCR_009630) Copy   

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