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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://support.brainvoyager.com/available-tools/52-matlab-tools-bvxqtools.html

A Matlab-based toolbox for the reading, writing, and processing of BrainVoyager (QX) files in Matlab. The toolbox is freely available.

Proper citation: BVQXtools (RRID:SCR_009532) Copy   

•   Source: SciCrunch Registry

http://www.birncommunity.org/tools-catalog/b0-and-eddy-current-correction-code-for-diffusion-mri/

Software tool (excecutable and source code in C and C++) to correct distortions in diffusion MR images that are generated by main magnetic field inhomogeneities and eddy current induced fields generated from the direction-dependent diffusion encoding

Proper citation: B0 and eddy current correction for DTI (RRID:SCR_009529) Copy   

•   Source: SciCrunch Registry

http://www.unicog.org/pm/pmwiki.php/MEG/RemovingArtifactsWithADJUST

A completely automatic algorithm for artifact identification and removal in EEG data. ADJUST is based on Independent Component Analysis (ICA), a successful but unsupervised method for isolating artifacts from EEG recordings. ADJUST identifies artifacted ICA components by combining stereotyped artifact-specific spatial and temporal features. Features are optimised to capture blinks, eye movements and generic discontinuities. Once artifacted IC are identified, they can be simply removed from the data while leaving the activity due to neural sources almost unaffected.

Proper citation: ADJUST (RRID:SCR_009526) Copy   

•   Source: SciCrunch Registry

https://github.com/BRAINSia/BRAINSTools/tree/master/BRAINSDemonWarp

A command line program for image registration by using different methods including Thirion and diffeomorphic demons algorithms. The function takes in a template image and a target image along with other optional parameters and registers the template image onto the target image. The resultant deformation fields and metric values can be written to a file. The program uses the Insight Toolkit (www.ITK.org) for all the computations, and can operate on any of the image types supported by that library. This a an ITK based implementation of various forms of Thirion Demons based registration (including diffeomorphic demons registration originating from Tom Vercauteren at INRIA ).

Proper citation: BRAINSDemonWarp (RRID:SCR_009524) 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.nitrc.org/projects/fmriclassify/

They demonstrate and provide R code that can classify between groups of fMRI scans based on functional network connectivity differences, requiring only 4 lines of code to be altered. In addition, they include a detailed article explaining the methods behind and motivations of this tool. This code can also be altered to perform connectivity analysis and classification using ROI based methods by reading in distance arrays previously created. They run Independent component analysis (ICA) on fMRI data to establish functional networks, measure the functional connectivity between these networks using the temporal cross-correlations between independent component to create a distance matrix and indicating the networking. Connectivity properties are used as a feature matrix for an SVM classifier. Collectively, this project provides and explains both methods and code to perform functional network connectivity and fMRI SVM classi?cation.

Proper citation: fMRI Classification in R (RRID:SCR_009519) Copy   

•   Source: SciCrunch Registry

http://www.slicer.org/slicerWiki/index.php/Slicer3:Module:Level-Set_Segmentation_Framework-Documentation

The modules in the framework support different tasks in the segmentation realization in 3DSlicer. A module called Level-set label map evolver was developed, which takes an initial label image and a feature image as input and performs a Geodesic Active Contours evolution on the label image according to the feature image and to a different terms in the level-set equation. The evolution takes place for a customizable number of iterations. The output is a label image that can be used to produce a model. Other modules were developed to accompany the main module as can be seen in http://www.slicer.org/slicerWiki/index.php/Slicer3:Module:Level-Set_Segmentation_Framework-Documentation

Proper citation: Level-set Segmentation for Slicer3 (RRID:SCR_009558) Copy   

•   Source: SciCrunch Registry

http://www.rad.upenn.edu/sbia/software/dramms/

A software designed for deformable 2D-to-2D and 3D-to-3D image registration. Some typical applications of DRAMMS include, ** Cross-subject registration of the same organ (can be brain, breast, cardiac, etc); ** Mono- and Multi-modality registration (MRI, CT, histology); Longitudinal registration (pediatric brain growth, cancer development, etc); ** Registration under partial missing correspondences (small lesions, tumors, histological cuts). DRAMMS is implemented as a Unix command-line tool. It is fully automatic and easy to use ? users input two images, and DRAMMS will output the registered image and deformation. No need for pre-segmentation of any structures, no need for any prior knowledge, and no need for human initialization or intervention.

Proper citation: DRAMMS (RRID:SCR_009555) Copy   

•   Source: SciCrunch Registry

http://www.connectomeviewer.org/viewer/

A free, open source, cross-platform Python-based software application for visualization and analysis in connectome research. Features of the software include: Connectome File Format including metadata, networks, surfaces, volumes, track files; complex network analysis toolboxes; modular plugin architecture for extensibility; Mayavi2 for 3D Scientific Visualization and Plotting; interactive data manipulation and scripting capabilities; and Neuroimaging and Diffusion in Python libraries.

Proper citation: Connectome Viewer (RRID:SCR_009552) Copy   

•   Source: SciCrunch Registry

https://www.conn-toolbox.org

Matlab based cross platform software package for computation, display, and analysis of functional connectivity in fMRI (fcMRI). Used for resting state data (rsfMRI) as well as task related designs. Covers pipeline from raw fMRI data to hypothesis testing.

Proper citation: CONN (RRID:SCR_009550) Copy   

•   Source: SciCrunch Registry

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

https://cran.r-project.org/src/contrib/Archive/brainwaver/

Characterization of small-world networks constructed from wavelet analysis of resting fMRI. This package is currently available as an R library. Futrure development will take place within the CamBA software repository

Proper citation: Brainwaver (RRID:SCR_009540) Copy   

•   Source: SciCrunch Registry

http://www.nlm.nih.gov/

NLM collects, organizes, and makes available biomedical science information to scientists, health professionals, and the public. The Library's Web-based databases, including PubMed/Medline and MedlinePlus, are used extensively around the world. NLM conducts and supports research in biomedical communications; creates information resources for molecular biology, biotechnology, toxicology, and environmental health; and provides grant and contract support for training, medical library resources, and biomedical informatics and communications research. Celebrating its 175th anniversary in 2011, the National Library of Medicine (NLM), in Bethesda, Maryland, is a part of the National Institutes of Health, U.S. Department of Health and Human Services (HHS). Since its founding in 1836 as the library of the U.S. Army Surgeon General, NLM has played a pivotal role in translating biomedical research into practice. It is the world's largest biomedical library and the developer of electronic information services that deliver trillions of bytes of data to millions of users every day. Scientists, health professionals, and the public in the United States and around the globe search the Library's online information resources more than 1 billion times each year. The Library is open to all and has many services and resources for scientists, health professionals, historians, and the general public. NLM has over 17 million books, journals, manuscripts, audiovisuals, and other forms of medical information on its shelves, making it the largest health-science library in the world. In today's increasingly digital world, NLM carries out its mission of enabling biomedical research, supporting health care and public health, and promoting healthy behavior by: * Acquiring, organizing, and preserving the world's scholarly biomedical literature; * Providing access to biomedical and health information across the country in partnership with the 5,800-member National Network of Libraries of Medicine (NN/LM); * Serving as a leading global resource for building, curating and providing sophisticated access to molecular biology and genomic information, including those from the Human Genome Project and NIH Common Fund; * Creating high-quality information services relevant to toxicology and environmental health, health services research, and public health; * Conducting research and development on biomedical communications systems, methods, technologies, and networks and information dissemination and utilization among health professionals, patients, and the general public; * Funding advanced biomedical informatics research and serving as the primary supporter of pre- and post-doctoral research training in biomedical informatics at 18 U.S. universities.

Proper citation: National Library of Medicine (RRID:SCR_011446) Copy   

•   Source: SciCrunch Registry

https://vpixx.com/products/viewpixx/

Research-grade, CRT-replacement LCD display system for vision science and psychophysics. It combines a 22.5″ 1920×1200 industrial LCD (wide 176°/176° viewing angles) with a custom panel/video controller engineered for deterministic stimulus timing and synchronized acquisition. The display supports 12-bit intensity resolution per RGB channel via custom video modes. It uses a scanning RGB LED backlight to improve temporal precision (e.g., crisp frame transitions and reduced motion artifacts) while bypassing consumer “enhancement” processing to keep output predictable for experiments. VIEWPixx also integrates microsecond-synchronized peripherals commonly needed in timing-sensitive paradigms—24-channel TTL I/O (triggers), stereo audio I/O, analog I/O, and a button-box interface—implemented on the same board as the video pipeline for tight hardware-to-video synchronization.

Proper citation: VPixx: VIEWPixx (RRID:SCR_013271) Copy   

•   Source: SciCrunch Registry

https://vpixx.com/hardware/projector/

Unique DLP LED projector which has been designed to be the most flexible display solution for vision research and neuroscience research. The PROPixx features a native resolution of 1920 x 1080, and can be driven with refresh rate up to 500Hz with deterministic timing. The PROPixx uses high brightness LEDs as a light source, giving a wide colour gamut and much longer lifetime than halogen light sources. It features high-bit depth, up to 12-bit per color for high-frequency full colour stimulation. For stereo vision applications, our high-speed ferro-electric circular polarizer can project stereoscopic stimuli with the use of passive glasses at up to 400Hz. In addition the PROPixx includes an array of peripherals which often need to be synchronized to video during an experiment, and with perfect microsecond precision.

Proper citation: VPixx: PROPixx (RRID:SCR_013299) Copy   

•   Source: SciCrunch Registry

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

The FP-CIT SPECT brain template has been created using a fully automatic procedure involving posterization of the source image to three levels: background, brain and striatum. We performed a spatial affine registration of these 40 posterized source images to a posterized reference image in the MNI space. The intensity values of the transformed images is normalized linearly, assuming that the histogram of the intensity values follows an alpha-stable distribution. Lastly, we built the [123I]FP-CIT SPECT template by the mean of the transformed and normalized images. More info: 1) Salas-Gonzalez et al. Building a FP-CIT SPECT brain template using a posterization approach. Accepted in Neuroinformatics. 2) Salas-Gonzalez et al. Linear intensity normalization of FP-CIT SPECT brain images using the alpha-stable distribution. NeuroImage, Volume 65, 2013, pp. 449-455. http://dx.doi.org/10.1016/j.neuroimage.2...

Proper citation: FP-CIT SPECT brain template in MNI space (RRID:SCR_013668) Copy   

•   Source: SciCrunch Registry

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

A toolbox that implements several EEG/MEG source localization techniques within the Maximum Entropy on the Mean (MEM) framework. These methods are particularly dedicated to estimate accurately the source of EEG/MEG generators together with their spatial extent along the cortical surface.

Proper citation: Brain Entropy in space and time (BEst) (RRID:SCR_014090) Copy   

•   Source: SciCrunch Registry

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

Brainnetome Atlas Viewer shows the anatomical connectivity-based parcellation results, including the maximum probabilistic maps, probabilistic maps and both the anatomical and functional connectivity patterns, which have been developed in Brainnetome Center, CASIA. The atlas is based on the analysis of connectional architecture with in vivo multi-modal MRI data.

Proper citation: Brainnetome Atlas Viewer (RRID:SCR_014091) Copy   

•   Source: SciCrunch Registry

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

A clinical toolbox useful for normalizing data from individuals with brain injury and/or modalities popular in the clinical environment (CT). It supports either enantiomorphic or lesion-masked normalization. It can be either scripted or used with SPM's simple graphical interface.

Proper citation: Clinical Toolbox for SPM (RRID:SCR_014096) Copy   

•   Source: SciCrunch Registry