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http://cbfbirn.ucsd.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented August 23, 2017.

A web based central repository for individual and group analysis of Arterial Spin Labeling (ASL) data sets and ASL pulse sequences developed at CMFRI UCSD for MRI researchers. This resource currently hosts more 1300 ASL data sets from 22 projects and consists of mainly two main tools 1) The Cerebral Blood Flow Database and Analysis Pipeline (CBFDAP) is a web enabled data and workflow management system extended from the HID codebase on NITRC specialized for Arterial Spin Labeling data management and analysis (including group analysis) in a centralized manner. 2) Pulse Sequence Distribution System (PSDS) for managing dissamination of ASL pulse sequences developed at the UCSD CFMRI. This resource also includes web and video tutorials for end users.

Proper citation: CBFBIRN (RRID:SCR_009543) Copy   

•   Source: SciCrunch Registry

http://www.slicer.org/slicerWiki/index.php/Slicer3:Module:Rician_Noise_Removal

Two Slicer3 modules removing rician noise in diffusion tensor MRI

Proper citation: Slicer3 Module Rician noise filter (RRID:SCR_009614) Copy   

•   Source: SciCrunch Registry

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

Geometry format under the Neuroimaging Informatics Technology Initiative (NIfTI). Basically, it is the surface-file format complement to the NIfTI volume-file format .nii. Programs which support the Gifti format, intended to allow exchange of each others surface files, include: Freesurfer, Caret, BrainVISA, Brain Voyager, CRkit, VisTrails and AFNI.

Proper citation: GIFTI (RRID:SCR_009579) Copy   

•   Source: SciCrunch Registry

http://www.easyneuroimaging.com/

Blog presenting some scripts that can be used to facilitate and automate processing and analysis of brain data. In addition, it could be helpful explaining non clear stages and steps of brain data processing using some software such as; Freesurfer, FSL, Brainvoyager QX... At the moment, there are more than 10 applescripts in the main website http://www.easyneuroimaging.com that control different tools and commands (aparcstats2table, asegstats2table, BET, dcm2nii, FIRST, fslsplit, fslswapdim, fslview, mri_convert, Qdec, Recon-all, SIENAX, tkmedit, tksurfer)

Proper citation: Neuroimaging Made Easy Blog (RRID:SCR_009611) Copy   

•   Source: SciCrunch Registry

http://erpinfo.org/erplab

A set of open source, freely available Matlab routines for analyzing Event Related Potential (ERP) data. It is tightly integrated with the EEGLAB Toolbox. ERPLAB routines can be accessed from the Matlab command window and from Matlab scripts in addition to being accessed from the EEGLAB GUI. Consequently, ERPLAB provides the ease of learning of a GUI-based system but also provides the power and flexibility of a scripted system.The development of ERPLAB Toolbox is being coordinated by Steve Luck and Javier Lopez-Calderon at the UC-Davis Center for Mind & Brain, with financial support from NIMH.

Proper citation: ERPLAB (RRID:SCR_009574) Copy   

•   Source: SciCrunch Registry

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

Software toolbox containing many kinds of kits that you may be interested in during fMRI data analysis. This toolbox is a homebrew kits built during practical ASL(arterial spin labeling) based Cerebral Blood Flow (CBF) data analysis. Meanwhile, this toolbox is also compatible with BOLD data analysis. Everyone would find something useful for their own data analysis! This toolbox is run and tested on SPM8 with MATLAB 7.6.0(R2008a) under the Linux OS. Theoretically, most of the functions (except the menu1&2 which are specially designed for the Batch Editor of SPM8) of this toolbox should be compatible with SPM5 and should also work smoothly under the Windows OS. Feel free to give feedback to authors if you encounter any bugs or problems. Senhua Zhu Center for functional Neuroimaging, University of Pennsylvania 3 W.Gates Bldg, 3400, Philadelphia, PA (19104), United States Email: [email protected] ; [email protected] QQ group number (QQ?): 60524357 Google group: https://groups.google.com/d/forum/fmri-grocer

Proper citation: fMRI Grocer (RRID:SCR_009622) Copy   

•   Source: SciCrunch Registry

http://elastix.isi.uu.nl/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 23,2023. Software toolbox for rigid and nonrigid registration of images. elastix is open source software, based on the well-known Insight Segmentation and Registration Toolkit (ITK). The software consists of a collection of algorithms that are commonly used to solve (medical) image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. A command-line interface enables automated processing of large numbers of data sets, by means of scripting. A paper describing elastix contains more details: S. Klein, M. Staring, K. Murphy, M.A. Viergever, J.P.W. Pluim, elastix: a toolbox for intensity based medical image registration,; IEEE Transactions on Medical Imaging, vol. 29, no. 1, pp. 196 - 205, January 2010., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: elastix (RRID:SCR_009619) Copy   

•   Source: SciCrunch Registry

https://www.nitrc.org/projects/w2mhs/

An open source MATLAB toolbox designed for detecting and quantifying White Matter Hyperintensities(WMH) in Alzheimer?s and aging related neurological disorders.Our toolbox provides a self-sufficient set of tools for segmenting these WMHs reliably and further quantifying their burden for down-processing studies. WMHs arise as bright regions on T2-weighted FLAIR images. They reflect comorbid neural injury or cerebral vascular disease burden. Their precise detection is of interest in Alzheimer?s disease (AD) with regard to its prognosis.

Proper citation: Wisconsin White Matter Hyperintensities Segmentation Toolbox (RRID:SCR_009652) Copy   

•   Source: SciCrunch Registry

http://web.mit.edu/evelina9/www/funcloc.html

Spm-toolbox that performs region of interest (ROI)-level and voxel-level between-subjects analyses of functional MRI data, restricting the analyses to those areas identified using subject-specific functional localizers. Methods: The toolbox implements ROI-level and voxel-level analyses, and it implements an automatic cross-validation procedure when the localizers are not orthogonal to the effects-of-interest. ROI-level analyses allow manually defined parcels of interest, as well as automatically-defined ones (GcSS procedure, Fedorenko et al. 2010). General linear model second-level analyses are implemented, including ReML and OLS estimation of population level effects. Hypothesis testing includes standard univariate tests as well as multivariate tests for mixed within- and between-subject designs (T, F, and Wilks' lambda statistics) This toolbox requires Matlab and SPM5/SPM8.

Proper citation: SPM SS - fMRI functional localizers (RRID:SCR_009644) Copy   

•   Source: SciCrunch Registry

http://www.bic.mni.mcgill.ca/software/minc/

A medical imaging data format and an associated set of tools and libraries including a 3 level API for medical image analysis with a particular focus on the needs of research. There are also a number of tools including Registration and Non-Uniformity correction.

Proper citation: MINC (RRID:SCR_002391) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/mni2orfromxyz/

Input either normalized MNI coordinates from a 3D image, or input real world XYZ matrix coordinates, and this code will convert coordinates of one type to the other.

Proper citation: Convert MNI coordinates to or from XYZ (RRID:SCR_000406) Copy   

•   Source: SciCrunch Registry

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

This matlab script and associated files will take resultant statistical images and essentially output everything you could ever want to know. It can work off of images that were previously corrected for multiple comparisons, but it can actually do the correction itself. This is because the cluster_correct script is incorporated within. It will iterate through atlases (borrowed from other software) to tell you the location of significant results. It outputs an extremely detailed report as well as a summary table for quick investigation. In addition, it will output statistics for each surviving cluster, and the image as a whole. Feedback would be much appreciated.

Proper citation: Cluster reporter (RRID:SCR_002218) Copy   

•   Source: SciCrunch Registry

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

Software for the analysis of multiple diffusion properties along fiber bundle as functions in an infinite dimensional space and their association with a set of covariates of interest, such as age, diagnostic status and gender, in real applications. The resulting analysis pipeline can be used for understanding normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles.

Proper citation: Functional Regression Analysis of DTI Tract Statistics (RRID:SCR_002293) Copy   

•   Source: SciCrunch Registry

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

Vervet (Chlorocebus aethiops sabaeus) probabilistic atlas that defines an anatomical space (template) with associated tissue and regional prior probability maps. The atlas was produced from whole head MRI of 10 normal adult animal subjects. The package consists of two atlases. The Biased directory contains the average template and probabilistic atlases for selected tissue classes constructed by registering the training population to one subject. The Unbiased directory contains the atlas constructed using unbiased estimation. The atlas is suitable for use in any segmentation tool using a probabilistic atlas, for example those in Slicer.

Proper citation: Vervet Probabilistic Atlas (RRID:SCR_000426) Copy   

•   Source: SciCrunch Registry

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

A population-specific DTI template for young adolescent Rhesus Macaque (Macaca mulatta) monkeys using 271 high-quality scans. Using such a large number of animals in generating a template allows it to account for variability in the species. Their DTI template is based on the largest number of animals ever used in generating a computational brain template. It is anticipated that their DTI template will help facilitate voxel-based and tract specific WM analyses in non-human primate species, which in turn may increase our understanding of brain function, development, and evolution.

Proper citation: DTI-TEMPLATE-RHESUS-MACAQUES (RRID:SCR_002482) Copy   

•   Source: SciCrunch Registry

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

Slicer3 module to provide a capability for performing white matter lesion classification and summary.

Proper citation: 3DSlicerLupusLesionModule (RRID:SCR_000853) Copy   

•   Source: SciCrunch Registry

http://ccb.loni.usc.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 31, 2022. Center focused on the development of computational biological atlases of different populations, subjects, modalities, and spatio-temporal scales with 3 types of resources: (1) Stand-alone computational software tools (image and volume processing, analysis, visualization, graphical workflow environments). (2) Infrastructure Resources (Databases, computational Grid, services). (3) Web-services (web-accessible resources for processing, validation and exploration of multimodal/multichannel data including clinical data, imaging data, genetics data and phenotypic data). The CCB develops novel mathematical, computational, and engineering approaches to map biological form and function in health and disease. CCB computational tools integrate neuroimaging, genetic, clinical, and other relevant data to enable the detailed exploration of distinct spatial and temporal biological characteristics. Generalizable mathematical approaches are developed and deployed using Grid computing to create practical biological atlases that describe spatiotemporal change in biological systems. The efforts of CCB make possible discovery-oriented science and the accumulation of new biological knowledge. The Center has been divided into cores organized as follows: - Core 1 is focused on mathematical and computational research. Core 2 is involved in the development of tools to be used by Core 3. Core 3 is composed of the driving biological projects; Mapping Genomic Function, Mapping Biological Structure, and Mapping Brain Phenotype. - Cores 4 - 7 provide the infrastructure for joint structure within the Center as well as the development of new approaches and procedures to augment the research and development of Cores 1-3. These cores are: (4)Infrastructure and Resources, (5) Education and Training, (6) Dissemination, and (7) Administration and Management. The main focus of the CCB is on the brain, and specifically on neuroimaging. This area has a long tradition of sophisticated mathematical and computational techniques. Nevertheless, new developments in related areas of mathematics and computational science have emerged in recent years, some from related application areas such as Computer Graphics, Computer Vision, and Image Processing, as well as from Computational Mathematics and the Computational Sciences. We are confident that many of these ideas can be applied beneficially to neuroimaging.

Proper citation: Center for Computational Biology at UCLA (RRID:SCR_000334) Copy   

•   Source: SciCrunch Registry

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

This script will take any .img file and correct it based on a cluster extent, cluster definition and voxelwise threshold. The threshold entered will be applied to positive and negative values separately, and separate pos and neg corrected images will be output. This script requires a license for the matlab image processing toolbox.

Proper citation: Cluster Extent Correction (RRID:SCR_002226) Copy   

•   Source: SciCrunch Registry

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

A software toolbox that can automatically identify many of the artifact components that are often present in independent component analysis (ICA) of functional MRI (fMRI). The method: * Does not require temporal information about the fMRI paradigm. * Does not require the user to train the algorithm. * Requires only the EPI images (additional acquisition of anatomical images is not required). * Is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin. * Can be applied to resting-state fMRI. * Is automated, requiring minimal or no human intervention.

Proper citation: SOCK (RRID:SCR_002544) Copy   

•   Source: SciCrunch Registry

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

Slicer3 modules for quantitative diffusion analysis. Modules include tools for clustering fiber tracts, summarizing measures over tract clusters, etc.

Proper citation: Quantitative Diffusion Tools (RRID:SCR_002527) Copy   

•   Source: SciCrunch Registry