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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

http://www.seg3d.org

A free volume processing segmenting tool that combines a flexible manual interface with powerful image processing and segmentation algorithms. Users can explore and label image volumes using slice windows and 3D volume rendering.

Proper citation: Seg3D (RRID:SCR_002552) Copy   

•   Source: SciCrunch Registry

http://www.nmr.mgh.harvard.edu/DOT/resources/tmcimg/

Software application that uses a Monte Carlo algorithm to model the transport of photons through 3D volumes with spatially varying optical properties. Both highly-scattering tissues (e.g. white matter) and weakly scattering tissues (e.g. cerebral spinal fluid) are supported. Using the anatomical information provided by MRI, X-ray CT, or ultrasound, accurate solutions to the photon migration forward problems are computed in times ranging from minutes to hours, depending on the optical properties and the computing resources available.

Proper citation: Monte Carlo Simulation Software: tMCimg (RRID:SCR_002588) Copy   

•   Source: SciCrunch Registry

http://sig.biostr.washington.edu/projects/MindSeer/index.html

A cross-platform application for 3D brain visualization for multi-modality neuroimaging data written in Java/Java3D, that runs in both standalone and client-server mode. It supports basic data management capabilities, visualization of 3D surfaces (SPM's output or OFF files), volumes (Analyze, NIFTI or Minc) and label sets. MindSeer has 2 different modes: # Client/Server is designed to allow users to visualize data that is stored centrally and enhance collaboration. # Standalone mode is available to view local data and is built for more performance than Client/Server Both modes have the same interface and support the same features. It has a modular architecture and is designed to be extensible. Requirements: # Java 5.0 or above. # Java Web Start. # Java3D (installed automatically by Web Start).

Proper citation: MindSeer (RRID:SCR_003019) Copy   

•   Source: SciCrunch Registry

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

Simulation software that generates pathological ground truth from a healthy ground truth. The software requires an input directory that describes a healthy anatomy (anatomical probabilities, mesh, diffusion tensor image, etc) and then outputs simulation images.

Proper citation: TumorSim (RRID:SCR_002604) Copy   

•   Source: SciCrunch Registry

http://wiki.na-mic.org/Wiki/index.php/2010_Winter_Project_Week_Spine_Segmentation_Module_in_Slicer3

3D Slicer module for automated segmentation of the spine. This is an implementation of a novel model-based segmentation algorithm. This work was presented at the NA-MIC Week in Salt Lake City, Jan 2010.

Proper citation: SpineSegmentation module for 3DSlicer (RRID:SCR_002593) Copy   

•   Source: SciCrunch Registry

http://nipy.org/nitime/

Software library for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code.

Proper citation: Nitime (RRID:SCR_002504) 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://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

https://neuroscienceblueprint.nih.gov/Resources-Tools/Blueprint-Resources-Tools-Library

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 22, 2023. National initiative to advance biomedical research through data sharing and online collaboration that provides data sharing infrastructure, software tools, strategies and advisory services. Groups may choose whether to share data internally or with external audiences. Hardware and data remain under control of individual user groups.

Proper citation: Biomedical Informatics Research Network (RRID:SCR_005163) Copy   

•   Source: SciCrunch Registry

Issue

https://github.com/spm

Software package for analysis of brain imaging data sequences. Sequences can be a series of images from different cohorts, or time-series from same subject. Current release is designed for analysis of fMRI, PET, SPECT, EEG and MEG.

Proper citation: SPM (RRID:SCR_007037) Copy   

•   Source: SciCrunch Registry

http://www.pstnet.com/software.cfm?ID=96

Designed for use in an MRI simulator, MoTrak software uses Ascension Technology?s Flock of Birds. The sensor attaches to the subject?s head and determines the position of the head in space relative to the transmitter. The sensor records angular rotations as well as positional displacements from an initially calibrated position. This information is displayed and logged by the program in real-time, allowing observation of head motion in an MRI simulator. In the simulator, the participant can simultaneously be habituated to the MRI environment, while being trained to remain still via feedback from the MoTrak system.

Proper citation: MoTrak Head Motion Tracking System (RRID:SCR_009607) Copy   

•   Source: SciCrunch Registry

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

Aa brain network visualization tool, which can help researchers to visualize structural and functional connectivity patterns from different levels in a quick, easy, and flexible way.

Proper citation: BrainNet Viewer (RRID:SCR_009446) Copy   

•   Source: SciCrunch Registry

http://cytoseg.googlecode.com

A tool for automatic segmentation of 3D biological datasets, with emphasis on 3D electron microscopy. It works best for 3D blob shaped objects like mitochondria, lysosomes, etc. The project is written in Python and uses the pythonxy platform (which includes scipy and ITK image processing tools).

Proper citation: Cytoseg (RRID:SCR_009553) Copy   

•   Source: SciCrunch Registry

http://www.math.mcgill.ca/keith/surfstat

A Matlab toolbox for the statistical analysis of univariate and multivariate surface data using linear mixed effects models and random field theory.

Proper citation: SurfStat (RRID:SCR_007081) Copy   

•   Source: SciCrunch Registry

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

A segmentation tool for the segmentation of a lung from CT images. The sofware can be run in two modes: fully automatic and semi-automatic with manual seeding by the user. The software also allows the user to perform basic filtering operations and manual correction to the segmentation. The VTK-based rendering implementation, along with option to view in axial, coronal, and sagittal, provides the user with better visualization of the segmented lung.

Proper citation: NIH-CIDI Lung Segmentation Tool (RRID:SCR_014150) Copy   

•   Source: SciCrunch Registry

http://cocomac.g-node.org/main/index.php?

Online access (html or xml) to structural connectivity ("wiring") data on the Macaque brain. The database has become by far the largest of its kind, with data extracted from more than four hundred published tracing studies. The main database, contains data from tracing studies on anatomical connectivity in the macaque cerebral cortex. Also available are a variety of tools including a graphical simulation workbench, map displays and the CoCoMac-Paxinos-3D viewer. Submissions are welcome. To overcome the problem of divergent brain maps ORT (Objective Relational Transformation) was developed, an algorithmic method to convert data in a coordinate- independent way based on logical relations between areas in different brain maps. CoCoMac data is used to analyze the organization of the cerebral cortex, and to establish its structure- function relationships. This includes multi-variate statistics and computer simulation of models that take into account the real anatomy of the primate cerebral cortex. This site * Provides full, scriptable open access to the data in CoCoMac (you must adhere to the citation policy) * Powers the graphical interface to CoCoMac provided by the Scalable Brain Atlas * Sports an extensive search/browse wizard, which automatically constructs complex search queries and lets you further explore the database from the results page. * Allows you to get your hands dirty, by using the custom SQL query service. * Displays connectivity data in tabular form, through the axonal projections service. CoCoMac 2 was initiated at the Donders Institute for Brain, Cognition and Behaviour, and is currently supported by the German neuroinformatics node and the Computational and Systems Neuroscience group at the Juelich research institute.

Proper citation: CoCoMac (RRID:SCR_007277) Copy   

•   Source: SciCrunch Registry

http://www.oasis-brains.org/

Project aimed at making neuroimaging data sets of brain freely available to scientific community. By compiling and freely distributing neuroimaging data sets, future discoveries in basic and clinical neuroscience are facilitated.

Proper citation: Open Access Series of Imaging Studies (RRID:SCR_007385) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/colin3t7t

High-field extension of the Colin27 single-subject atlas with additional high-resolution, quantitative, averaged scans at both 3T and 7T.

Proper citation: Colin 3T/7T High-resolution Atlas (RRID:SCR_000160) Copy   

•   Source: SciCrunch Registry

http://www.dian-info.org/default.htm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. An international research partnership of leading scientists determined to understand a rare form of Alzheimers disease that is caused by a gene mutation and to establish a research database and tissue repository to support research on Alzheimers disease by other investigators around the world. One goal of DIAN is to study possible brain changes that occur before Alzheimers disease is expressed in people who carry an Alzheimers disease mutation. Other family members without a mutation will serve as a comparison group. People in families in which a mutation has been identified will be tracked in order to detect physical or mental changes that might distinguish people who inherited the mutation from those who did not. DIAN currently involves eleven outstanding research institutions in the United States, United Kingdom, and Australia. John C. Morris, M.D., Friedman Distinguished Professor of Neurology at Washington University School of Medicine in St. Louis, is the principal investigator of the project., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: DIAN - Dominantly Inherited Alzheimer Network (RRID:SCR_000812) Copy   

•   Source: SciCrunch Registry