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

Software framework which uses an unscented Kalman filter for performing tractography. At each point on the fiber the most consistent direction is found as a mixture of previous estimates and of the local model. It is very easy to expand the framework and to implement new fiber representations for it. Currently it is possible to tract fibers using two different 1-, 2-, or 3-tensor methods. Both methods use a mixture of Gaussian tensors. One limits the diffusion ellipsoids to a cylindrical shape (the second and third eigenvalue are assumed to be identical) and the other one uses a full tensor representation. The project is written in C++. It could be used both as a Slicer3 module and as a standalone commandline application.

Proper citation: Diffusion Tractography with Kalman Filter (RRID:SCR_002585) Copy   

•   Source: SciCrunch Registry

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

shapeAnalysisMANCOVA offers statistical shape analysis based on a parametric boundary description (SPHARM) as the point-based model computing method. The point-based models will be analyzed with the methods here proposed using multivariate analysis of covariance (MANCOVA). Here, the number of variates being tested is the dimensionality of our observations. Each point of these observations is a three dimensional displacement vector from the mean. The number of contrasts is the number of equations involved in the null-hypothesis. In order to encompass varying numbers of variates and contrasts, and to account for independent variables, a matrix computation is performed. This matrix represents the multidimensional aspects of the correlation significance and it can be transformed into a scalar measure by manipulation of its eigenvalues. Details of the methods can be found in its Insight Journal publication: http://hdl.handle.net/10380/3124

Proper citation: shapeAnalysisMANCOVA - SPHARM tools (RRID:SCR_002578) Copy   

•   Source: SciCrunch Registry

https://pydicom.github.io/

Software Python package for working with DICOM files, made for inspecting and modifying DICOM data in an easy pythonic way. The modifications can be written again to a new file. As a pure python package, it should run anywhere python runs without any other requirements.

Proper citation: pydicom (RRID:SCR_002573) Copy   

•   Source: SciCrunch Registry

http://www.egi.com/research-division-geodesic-system-components/eeg-software

A complete software package for working with electroencephalography (EEG) and event-related potential (ERP) data. You can acquire, review, analyze, and now ?see? your participant with synchronized video. Net Station also offers specialized tools and workflow options for both clinical and research applications, allows you to save different combinations of view settings (called workspaces) and helps with your reporting requirements by letting you set up and print custom cover pages. For more specialized work, Net Station also provides an optional electrical source estimation module (GeoSource) and an optional sensor location digitizer (Geodesic Photogrammetry System).

Proper citation: Net Station EEG Software (RRID:SCR_002453) Copy   

•   Source: SciCrunch Registry

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

An open source learning-based software that automatically learns how to transfer the output of a host segmentation tool closer to the user's manual segmentation using the image data and manual segmentation provided by the user. The motivation of this project is to bridge the gap between the segmentation tool developer and the tool users such that the existing segmentation tools can more effectively serve the community. More and more automatic segmentation tools are publicly available to today's researchers. However, when applied by their end-users, these segmentation tools usually can not achieve the performance that the tool developer reported. Discrepancies between the tool developer and its users in manual segmentation protocols and imaging modalities are the main reasons for such inconsistency.

Proper citation: Automatic Segmentation Tool Adapter (RRID:SCR_002481) Copy   

•   Source: SciCrunch Registry

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

Software tool to detect physiologic signals from the data itself as well as an adaptive physiologic noise removal tool (Impulse Response Function or IRF-RETROICOR) that zooms in on noise with only 6 regressors, getting all the noise that 5th order RETROICOR gets. These tools will allow you to correct your data for physiologic noise with what you currently have. These signals are equivalent to a parallel monitored pulse signal and a respiratory chest-bellows signal. Do you have 3D+time EPI data (BOLD or perfusion) but no usable physio signals for pulse and respiration? Are you concerned about the effect of physio noise on your data but don't know what to do but regress data-derived signals that mix unknown functional signal with possible physio noise signal? Are you concerned about the number of regressors you're incorporating once you add 5th order RETROICOR (20 more regressors!)? This is for you.

Proper citation: PESTICA fMRI Physio Detection/Correction (RRID:SCR_002513) Copy   

•   Source: SciCrunch Registry

http://www.lead-dbs.org/

MATLAB toolbox for deep-brain-stimulation (DBS) electrode reconstructions and visualizations based on postoperative MRI and computed tomography (CT) imaging. The toolbox also facilitates visualization of localization results in 2D/3D, analysis of DBS-electrode placement's effects on clinical results, simulation of DBS stimulations, diffusion tensor imaging (DTI) based connectivity estimates, and fiber-tracking from the VAT to other brain regions (connectomic surgery).

Proper citation: LEAD-DBS (RRID:SCR_002915) Copy   

•   Source: SciCrunch Registry

http://openmeeg.gforge.inria.fr

A C++ package for low-frequency bio-electromagnetism solving forward problems in the field of EEG and MEG with very high accuracy.

Proper citation: OpenMEEG (RRID:SCR_002510) Copy   

•   Source: SciCrunch Registry

http://www.crl.med.harvard.edu/software/STAPLE/index.php

An algorithm for the Simultaneous Truth and Performance Level Estimation, which estimates a reference standard and segmentation generator performance from a set of segmentations. It has been widely applied for the validation of image segmentation algorithms, and to compare the performance of different algorithms and experts. It has also found application in the identification of a consensus segmentation, by combination of the output of a group of segmentation algorithms, and for segmentation by registration and template fusion., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: STAPLE (RRID:SCR_002590) Copy   

•   Source: SciCrunch Registry

http://nipy.org/nipype/

A package for writing fMRI analysis pipelines and interfacing with external analysis packages (SPM, FSL, AFNI). Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. However, this has resulted in a heterogeneous collection of specialized applications without transparent interoperability or a uniform operating interface. Nipype, an open-source, community-developed initiative under the umbrella of Nipy, is a Python project that solves these issues by providing a uniform interface to existing neuroimaging software and by facilitating interaction between these packages within a single workflow. Nipype provides an environment that encourages interactive exploration of algorithms from different packages (e.g., SPM, FSL), eases the design of workflows within and between packages, and reduces the learning curve necessary to use different packages. Nipype is creating a collaborative platform for neuroimaging software development in a high-level language and addressing limitations of existing pipeline systems.

Proper citation: Nipype (RRID:SCR_002502) Copy   

•   Source: SciCrunch Registry

http://ccr.coriell.org/Sections/Collections/NINDS/?SsId=10

Open resource of biological samples (DNA, cell lines, and other biospecimens) and corresponding phenotypic data to promote neurological research. Samples from more than 34,000 unique individuals with cerebrovascular disease, dystonia, epilepsy, Huntington's Disease, motor neuron disease, Parkinsonism, and Tourette Syndrome, as well as controls (population control and unaffected relatives) have been collected. The mission of the NINDS Repository is to provide 1) genetics support for scientists investigating pathogenesis in the central and peripheral nervous systems through submissions and distribution; 2) information support for patients, families, and advocates concerned with the living-side of neurological disease and stroke.

Proper citation: NINDS Repository (RRID:SCR_004520) Copy   

•   Source: SciCrunch Registry

http://www.brain-connectivity-toolbox.net

A large selection of complex network measures in Matlab that are increasingly used to characterize structural and functional brain connectivity datasets. Several people have contributed to the toolbox, and if you wish to contribute with a new function or set of functions, please contact Olaf Sporns. All efforts have been made to avoid errors, but users are strongly urged to independently verify the accuracy and suitability of toolbox functions for the chosen application. Please report bugs or substantial improvements.

Proper citation: Brain Connectivity Toolbox (RRID:SCR_004841) Copy   

•   Source: SciCrunch Registry

http://od1n.sourceforge.net

A C++ software framework to develop, simulate and run magnetic resonance sequences on different platforms.

Proper citation: Object-Oriented Development Interface for NMR (RRID:SCR_005974) Copy   

•   Source: SciCrunch Registry

http://www.biological-networks.org/p/outliers/

Software that performs a morphology-based approach for the automatic identification of outlier neurons based on neuronal tree structures. This tool was used by Zawadzki et al. (2012), who reported on and its application to the NeuroMorpho database. For the analysis, each neuron is represented by a feature vector composed of 20 measurements, which are projected into lower dimensional space with PCA. Bivariate kernel density estimation is then used to obtain a probability distribution for cells. Cells with high probabilities are understood as archetypes, while those with the small probabilities are classified as outliers. Further details about the method and its application in other domains can be found in Costa et al. (2009) and Echtermeyer et al. (2011). This version requires Matlab (Mathworks Inc, Natick, USA) and allows the user to apply the workflow using a graphical user interface.

Proper citation: DONE: Detection of Outlier NEurons (RRID:SCR_005299) Copy   

•   Source: SciCrunch Registry

http://mcx.sf.net/mmc/

A Monte Carlo (MC) solver for photon migration in 3D turbid media. Different from existing MC software designed for layered (such as MCML) or voxel-based media (such as MMC or tMCimg), MMC can represent a complex domain using a tetrahedral mesh. This not only greatly improves the accuracy of the solutions when modeling objects with smooth/complex boundaries, but also gives an efficient way to sample the problem domain to use less memory. The current version of MMC support multi-threaded programming and can give a almost proportional speed-up when using multiple CPU cores.

Proper citation: Mesh-based Monte Carlo (MMC) (RRID:SCR_006950) Copy   

•   Source: SciCrunch Registry

http://cmic.cs.ucl.ac.uk/mig/index.php?n=Tutorial.NODDImatlab

This MATLAB toolbox implements a data fitting routine for Neurite Orientation Dispersion and Density Imaging (NODDI). NODDI is a new diffusion MRI technique for imaging brain tissue microstructure. Compared to DTI, it has the advantage of providing measures of tissue microstructure that are much more direct and hence more specific. It achieves this by adopting the model-based strategy which relates the signals from diffusion MRI to geometric models of tissue microstructure. In contrast to typical model-based techniques, NODDI is much more clinically feasible and can be acquired on standard MR scanners with an imaging time comparable to DTI.

Proper citation: NODDI Matlab Toolbox (RRID:SCR_006826) Copy   

•   Source: SciCrunch Registry

http://moose.sourceforge.net/

MOOSE is the Multiscale Object-Oriented Simulation Environment. It is the base and numerical core for large, detailed simulations including Computational Neuroscience and Systems Biology. MOOSE spans the range from single molecules to subcellular networks, from single cells to neuronal networks, and to still larger systems. it is backwards-compatible with GENESIS, and forward compatible with Python and XML-based model definition standards like SBML and MorphML. MOOSE is coordinating with the GENESIS-3 project towards the goals of developing educational resources for modeling. MOOSE is open source software, licensed under the LGPL (Lesser GNU Public License). It has absolutely no warranty. Sponsors: - National Center of Biological Sciences (NCBS) - National Institutes of Health (NIH) Collaboration - EU-India grid - Department of Atomic Energy Science Research Council (DAE/SRC) - Department of Biotechnology (DBT)

Proper citation: Multiscale Object Orientation Simulation Environment (RRID:SCR_008031) Copy   

•   Source: SciCrunch Registry

http://www.alivelearn.net/xjview8/

A viewing program for Statistical Parametric Mapping (SPM2, SPM5 and SPM8). p-value slider, displays multiple images at a time and can be used to build Region of Interest (ROI) masks. For a given region you can find the anatomical name and search the selected region in online database (wiki, Google scholar and PubMed).

Proper citation: xjView: A Viewing Program For SPM (RRID:SCR_008642) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/asl_spm8/

Quick ASL Wrapper for preprocessing arterial spin labeled (ASL) Data and computing blood flow measurements using UPenn ASL toolbox.

Proper citation: ASL spm8 (RRID:SCR_008873) Copy   

•   Source: SciCrunch Registry

https://www.humanconnectome.org/software/connectome-workbench

Software brain visualization, analysis and discovery tool for fMRI and dMRI brain imaging data, including functional and structural connectivity data generated by the Human Connectome Project. Used to map brain imaging data. Allows for visualization of outputs from HCP pipelines from single subject, or average data from group of subjects and register that data onto standard brain atlas.

Proper citation: Connectome Workbench (RRID:SCR_008750) Copy   

•   Source: SciCrunch Registry