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

Standardizes file formats for the storage of connectivity data. These formats are developed by the Human Connectome Project and other interested parties. Use the MEDIAWIKI entry in the menu on the left for more information about the CIFTI file formats. Access the CIFTI discussion forum using the Forums entry in the menu on the left. Subscribe to the discussion forum and you will be informed about issues involving the CIFTI file formats via email.

Proper citation: CIFTI Connectivity File Format (RRID:SCR_000852) Copy   

•   Source: SciCrunch Registry

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

So far there is a lack for Random Field Theory (RFT) -based multiple comparison correction for surfaces generated in Freesurfer software package. This set of Matlab-based functions can be used for that purpose. They are based on Worsley?s SurfStat toolbox. You also need to have installed Freesurfer software package and included the Freesurfer?s matlab subdirectory in the Matlab?s search path. In addition, this tool implements the RFT-FDR hierarchical correction that can be used for optimizing the amount of smoothing in cortical thickness analyses (Neuroimage 52, 158-171).

Proper citation: RFT FDR (RRID:SCR_002533) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/nirx2nirs/

A matlab script which takes near-infrared spectroscopy data recorded by NIRx system(s) and converts it to a .nirs file format for use with the HOMER2 NIRS processing pacakge.

Proper citation: NIRx2nirs: A NIRx to .nirs data converter (RRID:SCR_002492) Copy   

•   Source: SciCrunch Registry

http://www.mbfbioscience.com/stereo-investigator

Stereo Investigator system includes microscope, computer, and Stereo Investigator software. Software works with Brightfield, Multi-Channel Fluorescence, Confocal, and Structured Illumination Microscopes. System used to provide estimates of number, length, area, and volume of cells or biological structures in tissue specimen in areas of neuroscience including neurodegenerative diseases, neuropathy, memory, and behavior, pulmonary research, spinal cord research, and toxicology.

Proper citation: Stereo Investigator (RRID:SCR_002526) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/slicer3examples/

Example Slicer3 plugins that can be built against a Slicer3 build or a Slicer3 installation. Note: these are for 3D Slicer version 3. There is now a version 4 of 3D Slicer available. Information about extensions for version 4 can be found at the following links: http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/SlicerApplication/ExtensionsManager http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Developers/Tutorials/BuildTestPackageDistributeExtensions

Proper citation: Slicer3 Example Modules (RRID:SCR_002559) Copy   

•   Source: SciCrunch Registry

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

A software toolkit that performs multiple imputation for group level, single sample t-tests. Whole brain group level statistic maps from fMRI rarely cover the entire brain as a result of missing data. Missingness between subjects in fMRI datasets can result from susceptibility artifacts, bounding box (acquisition parameters), and small differences in post-normalized morphology. The toolkit consists of several interactive command line scripts that guide the user to map the spatial distribution of missing data across contrast images, calculate spatial neighborhood averages that help impute values, perform conventional and multiple imputed t-statistics, save the results to brain maps, and create result tables. The toolkit contains an instruction manual (pdf), two Matlab scripts and one R-Statistics script, which depend on functions defined in the popular SPM toolbox and functions defined in the MICE package for (R).

Proper citation: Group Level Imputation of Statistic Maps (RRID:SCR_002397) Copy   

•   Source: SciCrunch Registry

http://imaging.indyrad.iupui.edu/projects/SPHARM/

A matlab-based 3D shape modeling and analysis toolkit, and is designed to aid statistical shape analysis for identifying morphometric changes in 3D structures of interest related to different conditions. SPHARM-MAT is implemented based on a powerful 3D Fourier surface representation method called SPHARM, which creates parametric surface models using spherical harmonics.

Proper citation: SPHARM-MAT (RRID:SCR_002545) Copy   

•   Source: SciCrunch Registry

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

This toolbox is an EEGLAB plugin for performing Measure Projection Analysis. Measure Projection Analysis (MPA) is a novel probabilistic multi-subject inference method that overcomes EEG Independent Component (IC) clustering issues by abandoning the notion of distinct IC clusters. Instead, it searches voxel by voxel for brain regions having event-related IC process dynamics that exhibit statistically significant consistency across subjects and/or sessions as quantified by the values of various EEG measures. Local-mean EEG measure values are then assigned to all such locations based on a probabilistic model of IC localization error and inter-subject anatomical and functional differences.

Proper citation: Measure Projection Toolbox (RRID:SCR_002429) Copy   

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https://sites.google.com/a/brain.org.au/ctp/

Software package with functions that will help researchers plan how many subjects per group need to be included in an MRI-based cortical thickness study to ensure a thickness difference is detected. The package requires cortical thickness mapping and co-registration to be carried out using Freesurfer. The power analyses are implemented in the R software package., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: cortex (RRID:SCR_002467) Copy   

•   Source: SciCrunch Registry

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://neuro.debian.net/pkgs/cmtk.html

A software toolkit for computational morphometry of biomedical images, CMTK comprises a set of command line tools and a back-end general-purpose library for processing and I/O. The command line tools primarily provide the following functionality: registration (affine and nonrigid; single and multi-channel; pairwise and groupwise), image correction (MR bias field estimation; interleaved image artifact correction; EPI unwarping), processing (filters; combination of segmentations via voting and STAPLE; shape-based averaging), statistics (t-tests; general linear model). CMTK is implemented in C++ with parallel processing using POSIX Threads (SMP), OpenMP (SMP), Grand Central Dispatch (SMP), and CUDA (GPU). Supported file formats include Analyze (r/w), NIFTI (r/w), Nrrd (r/w), DICOM (read), BioRad (read). Data exchange with other toolkits, such as ITK, FSL, AFNI, SPM, etc. is thus easily accomplished.

Proper citation: Computational Morphometry Toolkit (RRID:SCR_002234) 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