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http://dti-tk.sourceforge.net/pmwiki/pmwiki.php

A spatial normalization and atlas construction toolkit optimized for examining white matter morphometry using DTI data with special care taken to respect the tensorial nature of the data. It implements a state-of-the-art registration algorithm that drives the alignment of white matter (WM) tracts by matching the orientation of the underlying fiber bundle at each voxel. The algorithm has been shown to both improve WM tract alignment and to enhance the power of statistical inference in clinical settings. A 2011 study published in NeuroImage ranks DTI-TK the top-performing tool in its class. Key features include: * open standard-based file IO support: NIfTI format for scalar, vector and tensor image volumes * tool chains for manipulating tensor image volumes: resampling, smoothing, warping, registration & visualization * pipelines for WM morphometry: spatial normalization & atlas construction for population-based studies * built-in cluster-computing support: support for open source Sun Grid Engine (SGE) * Interoperability with other popular DTI tools: AFNI, Camino, FSL & DTIStudio * Interoperability with ITK-SNAP: support multi-modal visualization and segmentation

Proper citation: Diffusion Tensor Imaging ToolKit (RRID:SCR_001642) Copy   

•   Source: SciCrunch Registry

http://neuroimage.usc.edu/brainstorm/

Software as collaborative, open source application dedicated to analysis of brain recordings: MEG, EEG, fNIRS, ECoG, depth electrodes and animal invasive neurophysiology. User-Friendly Application for MEG/EEG Analysis.

Proper citation: Brainstorm (RRID:SCR_001761) Copy   

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http://www.nitrc.org/projects/itk-snap/

Open source interactive software application for three dimentional medical images, manual delineation of anatomical regions of interest, and performing automatic image segmentation. Used for delineating anatomical structures and regions in MRI, CT and other 3D biomedical imaging data.WebGL-based viewer for volumetric data. It is capable of displaying arbitrary (non axis-aligned) cross-sectional views of volumetric data, as well as 3-D meshes and line-segment based models (skeletons).

Proper citation: ITK-SNAP (RRID:SCR_002010) Copy   

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https://github.com/trendscenter/gift

Software MATLAB toolbox which implements multiple algorithms for independent component analysis and blind source separation of group (and single subject) functional magnetic resonance imaging data. GIFT works on MATLAB 6.5 and higher. Many ICA algorithms were generously contributed by Dr. Andrzej Cichocki.

Proper citation: Group ICA of fMRI Toolbox (RRID:SCR_001953) Copy   

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http://mialab.mrn.org/software/eegift/index.html

Implements multiple algorithms for independent component analysis and blind source separation of group (and single subject) EEG data. This MATLAB toolbox is compatible with MATLAB 6.5 and higher.

Proper citation: Group ICA Of EEG Toolbox (RRID:SCR_002478) Copy   

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http://neuralensemble.org/trac/OpenElectrophy

Software Python module for electrophysiology data analysis.

Proper citation: OpenElectrophy (RRID:SCR_000819) Copy   

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http://www.itk.org

Open source, cross platform library that provides developers with extensive suite of software tools for image analysis. Developed through extreme programming methodologies, ITK builds on proven, spatially oriented architecture for processing, segmentation, and registration of scientific images in two, three, or more dimensions.

Proper citation: Insight Segmentation and Registration Toolkit (RRID:SCR_001149) Copy   

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http://martinos.org/mne/

Software suite for processing magnetoencephalography and electroencephalography data. Open source Python software for exploring, visualizing, and analyzing human neurophysiological data including MEG, EEG, sEEG, ECoG . Implements all functionality of MNE Matlab tools in Python and extends capabilities of MNE Matlab tools to, e.g., frequency-domain and time-frequency analyses and non-parametric statistics.

Proper citation: MNE software (RRID:SCR_005972) Copy   

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http://users.loni.ucla.edu/~shattuck/brainsuite/

Suite of image analysis tools designed to process magnetic resonance images (MRI) of the human head. BrainSuite provides an automatic sequence to extract genus-zero cortical surface mesh models from the MRI. It also provides a set of viewing tools for exploring image and surface data. The latest release includes graphical user interface and command line versions of the tools. BrainSuite was specifically designed to guide its users through the process of cortical surface extraction. NITRC has written the software to require minimal user interaction and with the goal of completing the entire process of extracting a topologically spherical cortical surface from a raw MR volume within several minutes on a modern workstation. The individual components of BrainSuite may also be used for soft tissue, skull and scalp segmentation and for surface analysis and visualization. BrainSuite was written in Microsoft Visual C using the Microsoft Foundation Classes for its graphical user interface and the OpenGL library for rendering. BrainSuite runs under the Windows 2000 and Windows XP Professional operating systems. BrainSuite features include: * Sophisticated visualization tools, such as MRI visualization in 3 orthogonal views (either separately or in 3D view), and overlayed surface visualization of cortex, skull, and scalp * Cortical surface extraction, using a multi-stage user friendly approach. * Tools including brain surface extraction, bias field correction, voxel classification, cerebellum removal, and surface generation * Topological correction of cortical surfaces, which uses a graph-based approach to remove topological defects (handles and holes) and ensure a tessellation with spherical topology * Parameterization of generated cortical surfaces, minimizing a harmonic energy functional in the p-norm * Skull and scalp surface extraction

Proper citation: BrainSuite (RRID:SCR_006623) Copy   

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http://brainatlas.mbi.ufl.edu/Database/

Comprehensive three-dimensional digital atlas database of the C57BL/6J mouse brain based on magnetic resonance microscopy images acquired on a 17.6-T superconducting magnet. This database consists of: Individual MRI images of mouse brains; three types of atlases: individual atlases, minimum deformation atlases and probabilistic atlases; the associated quantitative structural information, such as structural volumes and surface areas. Quantitative group information, such as variations in structural volume, surface area, magnetic resonance microscopy image intensity and local geometry, have been computed and stored as an integral part of the database. The database augments ongoing efforts with other high priority strains as defined by the Mouse Phenome Database focused on providing a quantitative framework for accurate mapping of functional, genetic and protein expression patterns acquired by a myriad of technologies and imaging modalities. You must register First (Mandatory) and then you may Download Images and Data.

Proper citation: MRM NeAt (Neurological Atlas) Mouse Brain Database (RRID:SCR_007053) Copy   

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http://physionet.org/physiobank/

Archive of well-characterized digital recordings of physiologic signals and related data for use by the biomedical research community. PhysioBank currently includes databases of multi-parameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and patients with a variety of conditions with major public health implications, including sudden cardiac death, congestive heart failure, epilepsy, gait disorders, sleep apnea, and aging. The PhysioBank Archives now contain over 700 gigabytes of data that may be freely downloaded. PhysioNet is seeking contributions of data sets that can be made freely available in PhysioBank. Contributions of digitized and anonymized (deidentified) physiologic signals and time series of all types are welcome. If you have a data set that may be suitable, please review PhysioNet''s guidelines for contributors and contact them.

Proper citation: Physiobank (RRID:SCR_006949) Copy   

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http://www.loni.usc.edu/Software/BrainParser

Software that uses a novel statistical-learning technique to segment brain regions of interest (ROIs) based on a training set of data and generates 3D MRI volumes. The software comes pre-trained on a provided data set but can be retrained to work with your desired regions of interest.

Proper citation: LONI Brain Parser (RRID:SCR_009572) Copy   

•   Source: SciCrunch Registry

http://loni.usc.edu/Software/CFMBIS

A computer-aided tool for 2-D brain image segmentation using an electrostatic charged fluid model. It allows researchers to perform 2-D image segmentation in brain MR image data. Each interactive visualization element corresponding to the embedded function enables the end user to easily manipulate the image data. The visual environment of this tool provides an easy-to-use means of inspection and interpretation of image data using the LONI jViewbox library. CFMBIS uses the Java Image I/O plug-in architecture to read a wide variety of common medical image file formats.

Proper citation: Charged Fluid Model for Brain Image Segmentation (RRID:SCR_008281) Copy   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/Software/MultiPhase-SEG

A segmentation software that employs the implementation of the active contours without edges level set based segmentation model. Its features include: segmentation of three-dimensional brain volumes into two or more regions (for example, regions could be WM, GM, and CSF), visualization of surfaces representing boundaries of different brain regions, and being written in Matlab with the ability to run on any platform with Matlab installed.

Proper citation: MultiPhase-SEG (RRID:SCR_008275) Copy   

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http://loni.usc.edu/Software/SVT

Software tool for determining the statistically significant regions of activation in single or multi-subject human brain functional studies. It can be also applied to structural brain data for analyzing developmental, dementia and other changes of anatomy over time. This package was originally developed to work on Sun SPARC and SGI stations using the "C" language compiler provided by Sun/SGI as part of the standard system software.

Proper citation: Sub-Volume Thresholding Analysis (RRID:SCR_008272) Copy   

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https://crispresso.pinellolab.partners.org/submission

Software suite of tools to qualitatively and quantitatively evaluate outcomes of genome editing experiments in which target loci are subject to deep sequencing and provides integrated, user friendly interface. Used for analysis of CRISPR-Cas9 genome editing outcomes from sequencing data. CRISPResso2 provides accurate and rapid genome editing sequence analysis.Used for analysis of deep sequencing data for rapid and intuitive interpretation of genome editing experiments.

Proper citation: CRISPResso (RRID:SCR_021538) Copy   

•   Source: SciCrunch Registry

http://bmsr.usc.edu/software/lysis/

Interactive software of a set of modular programs (each performing a specific task) that provide an integrated computing environment for data analysis and system modeling. Unique capabilities of LYSIS include input-output nonlinear system modeling and the novel methodology of Principal Dynamic Modes (PDMs). LYSIS is currently available in two versions: one for LYSIS 7.1 Windows and one for LYSIS 7.2 Matlab. Early versions are also available for UNIX environments, distributed as source code that can be compiled for each UNIX implementation (e.g., Solaris, HPUX, Linux). Specific features of LYSIS that cannot be found in commercially available packages include the efficient kernel estimation using Laguerre expansions and the use of Principal Dynamic Modes (PDMs). These enable input-output modeling of dynamic nonlinear systems with relatively short data-records (even in the presence of considerable noise). System Requirements * Operating System ** Windows XP/Vista/7 ** Sun/Unix: Solaris 2.x

Proper citation: LYSIS (RRID:SCR_001385) Copy   

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https://www.loni.usc.edu/research/software?name=WAIR

A software tool for the quantitative analysis of various n-dimensional (n-D) image registration techniques. The series of 'C' subroutines which comprise the WAIR library can be easily incorporated into the user's site specific programs and adapted to their particular needs. Wavelet-space triangle analysis is applicable for studying a family of warps on single or multiple n-D data sets. For each data set the WAIR routine assigns a positive real number to every warp alignment in the family, and the best warp for the given data will be the one with the smallest value. It uses the original data prior to warping and the target of the warp in determining warp ranking in reduced wavelet space. Cluster group classification (CGC) is applicable for analyzing the overall performance of a family of warps of a group of data sets. A single number is assigned to each registration alignment, based on its group-clustering characteristics. Spread group classification (SGC) gives preference to registration techniques that spread apart baseline versus activation functional signal for group data.

Proper citation: Wavelet Analysis of Image Registration (RRID:SCR_000172) Copy   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/Software/ShapeTools

Software library that is a collection of Java classes that enable Java programmers to model, manipulate and visualize geometric shapes and associated data values. It simplifies the creation of application programs by providing a ready-made set of support routines. * File format readers that implement ShapeIO interface (modeled after Java ImageIO) are automatically used when appropriate. * Storage of additional metadata of arbitrary type (other than shape vertices and interconnections) is enabled by the use of data attributes. * Shapes may contain a set of child shapes allowing for the construction and manipulation of complex hierarchies of shapes. * The various components of a shape are specified as interfaces with specific implementations, making it easy to create specialized implementations of a shape component when different performance characteristics are required.

Proper citation: LONI ShapeTools (RRID:SCR_002697) Copy   

•   Source: SciCrunch Registry

http://mouseatlas.caltech.edu/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.. Documented on October, 01, 2019.
3D digital atlas of normal mouse development constructed from magnetic resonance image data. The download is a zipped file containing the six atlases Theiler Stages (ts) 13, 21,23, 24, 25 and 26 and MRI data for an unlabeled ts19 embryo. To view the atlases, download and install MBAT from: http://mbat.loni.ucla.edu Specimens were prepared in aqueous, isotonic solutions to avoid tissue shrinkage. Limited specimen handling minimized physical perturbation of the embryos to ensure accurate geometric representations of developing mouse anatomy. Currently, the atlas contains orthogonal sections through MRI volumes, three stages of embryos that have annotated anatomy, photographs of several stages of development, lineage trees for annotated embryos and a gallery of images and movies derived from the annotations. Anatomical annotations can be viewed by selecting a transverse section and selecting a pixel on the displayed slice.

Proper citation: 3D MRI Atlas of Mouse Development (RRID:SCR_008090) Copy   

•   Source: SciCrunch Registry