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

A free software tool for Copy Number Variation (CNV) detection from SNP genotyping arrays. Currently it can handle signal intensity data from Illumina and Affymetrix arrays. With appropriate preparation of file format, it can also handle other types of SNP arrays and oligonucleotide arrays. PennCNV implements a hidden Markov model (HMM) that integrates multiple sources of information to infer CNV calls for individual genotyped samples. It differs form segmentation-based algorithm in that it considered SNP allelic ratio distribution as well as other factors, in addition to signal intensity alone. In addition, PennCNV can optionally utilize family information to generate family-based CNV calls by several different algorithms. Furthermore, PennCNV can generate CNV calls given a specific set of candidate CNV regions, through a validation-calling algorithm.

Proper citation: PennCNV (RRID:SCR_002518) Copy   

•   Source: SciCrunch Registry

http://www.patternlabforproteomics.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented July 5, 2018. Gene Ontology Explorer (GOEx) combines data from protein fold changes with GO over-representation statistics to help draw conclusions in proteomic experiments. It is tightly integrated within the PatternLab for Proteomics project and, thus, lies within a complete computational environment that provides parsers and pattern recognition tools designed for spectral counting. GOEx offers three independent methods to query data: an interactive directed acyclic graph, a specialist mode where key words can be searched, and an automatic search. A recent hack included in GOEx is to load the sparse matrix index file directly into GOEx, instead of going through the report generation using the AC/T-fold methods. This makes it easy for GOEx to analyze any list of proteins as long as the list follows the index file format (described in manuscript) . Please note that if using this alternative strategy, there will be no protein fold information. Platform: Windows compatible

Proper citation: GOEx - Gene Ontology Explorer (RRID:SCR_005779) Copy   

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http://www.neurogems.org/neosim/

Simulation software that includes a parallel discrete event simulation kernel for running models of spiking neurons on a cluster of workstations. Models are specified using NeuroML, and visualized using Java2D. Simulation components are distributed across a parallel machine or network and communicate using timestamped events. The successor NEOSIM2 project under the NeuroGems umbrella at Edinburgh University (http://www.neurogems.org) continues to distribute the software, http://www.neurogems.org/neosim2/ The NEOSIM project includes: * a parallel discrete event simulation kernel for running models of spiking neural networks on clusters of machines. * a modules kit for extending the behavior of neurons and connectivity patterns. * a user interface for building and running simulations. OS: Linux, MS-Windows

Proper citation: Neural Open Simulation (RRID:SCR_002916) Copy   

•   Source: SciCrunch Registry

http://krasnow1.gmu.edu/CENlab/software.html

Stochastic reaction-diffusion simulator in Java which is used for simulating neuronal signaling pathways.

Proper citation: NeuroRD (RRID:SCR_014769) Copy   

•   Source: SciCrunch Registry

http://www.cma.mgh.harvard.edu/iatr/

A centrally available listing of all image analysis tools that are available to the neuroscience community in order to facilitate the development, identification, and sharing of tools. It is hoped that this helps the tool developers to get their tools to a larger user community and to reduce redundancy (or at least utilize tool redundancy to facilitate optimal tool design) in tool development. This also helps tool users in identification of the existing tools for specific problems as they arise. The registry is designed to be self-moderated. This means that all tool entries are owned by some responsible party who enters the tool information, and keeps it up to date via the Web.

Proper citation: Internet Analysis Tools Registry (RRID:SCR_005638) Copy   

•   Source: SciCrunch Registry

http://mindboggle.info

Mindboggle (http://mindboggle.info) is open source software for analyzing the shapes of brain structures from human MRI data. The following publication in PLoS Computational Biology documents and evaluates the software: Klein A, Ghosh SS, Bao FS, Giard J, Hame Y, Stavsky E, Lee N, Rossa B, Reuter M, Neto EC, Keshavan A. (2017) Mindboggling morphometry of human brains. PLoS Computational Biology 13(3): e1005350. doi:10.1371/journal.pcbi.1005350

Proper citation: Mindboggle (RRID:SCR_002438) Copy   

•   Source: SciCrunch Registry

http://surfer.nmr.mgh.harvard.edu/fswiki/Tracula

Software tool developed for automatically reconstructing a set of major white matter pathways in the brain from diffusion weighted images using probabilistic tractography. This method utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual intervention with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. The trac-all script is used to preprocess raw diffusion data (correcting for eddy current distortion and B0 field inhomogenities), register them to common spaces, model and reconstruct major white matter pathways (included in the atlas) without any manual intervention. trac-all may be used to execute all the above steps or parts of it depending on the dataset and user''''s preference for analyzing diffusion data. Alternatively, scripts exist to execute chunks of each processing pipeline, and individual commands may be run to execute a single processing step. To explore all the options in running trac-all please refer to the trac-all wiki. In order to use this script to reconstruct tracts in Diffusion images, all the subjects in the dataset must have Freesurfer Recons.

Proper citation: TRACULA (RRID:SCR_013152) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

http://cbrain.mcgill.ca/loris

A modular and extensible web-based data management system that integrates all aspects of a multi-center study, from heterogeneous data acquisition to storage, processing and ultimately dissemination, within a streamlined platform. Through a standard web browser, users are able to perform a wide variety of tasks, such as data entry, 3D image visualization and data querying. LORIS also stores data independently from any image processing pipeline, such that data can be processed by external image analysis software tools. LORIS provides a secure web-based and database-driven infrastructure to automate the flow of clinical data for complex multi-site neuroimaging trials and studies providing researchers with the ability to easily store, link, and access significant quantities of both scalar (clinical, psychological, genomic) and multi-dimensional (imaging) data. LORIS can collect behavioral, neurological, and imaging data, including anatomical and functional 3D/4D MRI models, atlases and maps. LORIS also functions as a project monitoring and auditing platform to oversee data acquisition across multiple study sites. Confidentiality during multi-site data sharing is provided by the Subject Profile Management System, which can perform automatic removal of confidential personal information and multiple real-time quality control checks. Additionally, web interactions with the LORIS portal take place over an encrypted channel via SSL, ensuring data security. Additional features such as Double Data Entry and Statistics and Data Query GUI are included.

Proper citation: LORIS - Longitudinal Online Research and Imaging System (RRID:SCR_000590) Copy   

•   Source: SciCrunch Registry

http://becs.aalto.fi/en/research/bayes/drifter/

Model based Bayesian method for eliminating physiological noise from fMRI data. This algorithm uses image voxel analysis to isolate the cardiac and respiratory noise from the relevant data.

Proper citation: DRIFTER (RRID:SCR_014937) Copy   

•   Source: SciCrunch Registry

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

A software application developed to support computational anatomy and shape analysis. The capabilities of CAWorks include: interactive landmark placement to create segmentation (mask) of desired region of interest; specialized landmark placement plugins for subcortical structures such as hippocampus and amygdala; support for multiple Medical Imaging data formats, such as Nifti, Analyze, Freesurfer, DICOM and landmark data; Quadra Planar view visualization; and shape analysis plugin modules, such as Large Deformation Diffeomorphic Metric Mapping (LDDMM). Specific plugins are available for landmark placement of the hippocampus, amygdala and entorhinal cortex regions, as well as a browser plugin module for the Extensible Neuroimaging Archive Toolkit.

Proper citation: CAWorks (RRID:SCR_014185) Copy   

•   Source: SciCrunch Registry

http://cibsr.stanford.edu/tools/

A multiplatform, highly modular image processing and visualization application which is under development by the Center for Interdisciplinary Brain Sciences Research. The goal of this project is provide a framework application for neuroimaging which facilitates the interchange of software tools developed by researchers. BrainImageJava can: * Delineate ROIs in slices along X, Y, or Z axes, with 3D feedback in the other axes. * Create and display triangular mesh surfaces from MRI volumes. * Draw Surfaces-of-Interest (SOIs) in 3D, and edit them in a planar display. * Set Talairach grid on a volume, export an AC/PC stack, and measure the values within each grid unit. This 3D image processing and analysis program for the Apple Macintosh PowerPC is based on the public domain application, NIH Image. It includes interactive procedures for 3D MRI quantification including semi-automated procedures for removing non-brain tissues from images, fuzzy segmentation of tissue compartments, global or local parcellation (based on the Talairach atlas), region-growing, etc. The last version of the software included multiplatform capability, volume visualization and advanced image analysis tools.

Proper citation: BrainImage Software (RRID:SCR_006139) Copy   

•   Source: SciCrunch Registry

http://yogo.msu.montana.edu/

A set of software tools created to rapidly build scientific data-management applications. These applications will enhance the process of data annotation, analysis, and web publication. The system provides a set of easy-to-use software tools for data sharing by the scientific community. It enables researchers to build their own custom-designed data management systems. The problem of scientific data management rests on several challenges. These include flexible data storage, a way to share the stored data, tools to curate the data, and history of the data to show provenance. The Yogo Framework gives you the ability to build scientific data management applications that address all of these challenges. The Yogo software is being developed as part of the NeuroSys project. All tools created as part of the Yogo Data Management Framework are open source and released under an OSI approved license.

Proper citation: Yogo Data Management System (RRID:SCR_004239) Copy   

•   Source: SciCrunch Registry

http://cng.gmu.edu:8080/Lm

A freely available software tool available for the Windows and Linux platform, as well as the Online version Applet, for the analysis, comparison and search of digital reconstructions of neuronal morphologies. For the quantitative characterization of neuronal morphology, LM computes a large number of neuroanatomical parameters from 3D digital reconstruction files starting from and combining a set of core metrics. After more than six years of development and use in the neuroscience community, LM enables the execution of commonly adopted analyses as well as of more advanced functions, including: (i) extraction of basic morphological parameters, (ii) computation of frequency distributions, (iii) measurements from user-specified subregions of the neuronal arbors, (iv) statistical comparison between two groups of cells and (v) filtered selections and searches from collections of neurons based on any Boolean combination of the available morphometric measures. These functionalities are easily accessed and deployed through a user-friendly graphical interface and typically execute within few minutes on a set of 20 neurons. The tool is available for either online use on any Java-enabled browser and platform or may be downloaded for local execution under Windows and Linux.

Proper citation: L-Measure (RRID:SCR_003487) Copy   

•   Source: SciCrunch Registry

http://dally.nimh.nih.gov/matoff/matoff.html

An interactive analysis program that searches neurophysiological data and plots the results. MatOFF was developed especially for dealing with the complexities common to behavioral neurophysiological experiments. It runs under Windows 2000 or XP and relies on MATLAB version R11.1 (or above) for all operations. MatOFF searches a data file to locate and plot epochs (trials) of special interest to the investigator. Appropriate input data files have time-stamped event codes, usually including neuron action potential firing events (spikes), and digitized analog data. The user specifies a list of event code numbers that uniquely identify a sequence of events. MatOFF uses this sequence to search the raw data file, select the epochs that meet the criteria, time-shift the trials to align them on a common event, order the epochs based on user-selected criteria, and plot the results based on a collection of page formatting specifications. MatOFF will also save extracted data and some statistics to disk. Features: * Powerful, interactive searching tools for locating relevant experimental events * Compatible with Cortex data acquisition program * Compatible with Plexon data acquisition system * Flexible, publication-quality graphical display and printing * Comprehensive scripting language * Supports learning and other dynamic behavior * Integrated interface to MATLAB functions * Automatic alignment of trial data and generation of histograms * Large variety of options for selecting and ordering trial data * Descriptive and non-parametric statistics * XY analog displays * Data export with flexible format control * Up to 72 plots per page * Display templates can be saved and reloaded * Free for public or private use * Adaptable to almost any data file format

Proper citation: MatOFF (RRID:SCR_006821) Copy   

•   Source: SciCrunch Registry

http://dally.nimh.nih.gov/index.html

A program developed by the NIMH Laboratory of Neuropsychology for data acquisition and experimental control of neurophysiological experiments. The purpose of this website is to make it easier to access new versions of NIMH CORTEX and its supporting documents. Ultimately, it is also hoped that these pages will make it easier for users to report bugs, request enhancements, and obtain help. Download the latest version and unzip it into a new sub-directory. Then read the on-line documentation. For the new user, the User''s Manuals are invaluable in specifying system requirements and giving an overview of the features and necessary hardware. The Function reference goes into more detail about how to write experiments using NIMH CORTEX. The Demos reference is a good place for new and experienced users to start to get an idea of what NIMH CORTEX can do these days.

Proper citation: NIMH CORTEX (RRID:SCR_006837) Copy   

•   Source: SciCrunch Registry

http://hendrix.imm.dtu.dk/software/lyngby/

Matlab toolbox for the analysis of functional neuroimages (PET, fMRI). The toolbox contains a number of models: FIR-filter, Lange-Zeger, K-means clustering among others, visualizations and reading of neuroimaging files.

Proper citation: Lyngby (RRID:SCR_007143) Copy   

•   Source: SciCrunch Registry

http://www.bmu.psychiatry.cam.ac.uk/software/

Suite of programs developed for fMRI analysis in a Virtual Pipeline Laboratory facilitates combining program modules from different software packages into processing pipelines to create analysis solutions which are not possible with a single software package alone. Current pipelines include fMRI analysis, statistical testing based on randomization methods and fractal spectral analysis. Pipelines are continually being added. The software is mostly written in C. This fMRI analysis package supports batch processing and comprises the following general functions at the first level of individual image analysis: movement correction (interpolation and regression), time series modeling, data resampling in the wavelet domain, hypothesis testing at voxel and cluster levels. Additionally, there is code for second level analysis - group and factorial or ANOVA mapping - after co-registration of voxel statistic maps from individual images in a standard space. The main point of difference from other fMRI analysis packages is the emphasis throughout on the use of data resampling (permutation or randomization) as a basis for inference on individual, group and factorial test statistics at voxel and cluster levels of resolution.

Proper citation: Cambridge Brain Activation (RRID:SCR_007109) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

http://fmri.wfubmc.edu/software/Bpm

Software toolbox that performs SPM analysis with voxel-wise imaging covariates. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in Matlab with a user-friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely used T-field, has been implemented in the correlation analysis for more accurate results. Requirements: * SPM2 or SPM5 * MATLAB version 6.5 or higher

Proper citation: WFU Biological Parametric Mapping Toolbox (RRID:SCR_002613) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

http://brainmap.org/

A community database of published functional and structural neuroimaging experiments with both metadata descriptions of experimental design and activation locations in the form of stereotactic coordinates (x,y,z) in Talairach or MNI space. BrainMap provides not only data for meta-analyses and data mining, but also distributes software and concepts for quantitative integration of neuroimaging data. The goal of BrainMap is to develop software and tools to share neuroimaging results and enable meta-analysis of studies of human brain function and structure in healthy and diseased subjects. It is a tool to rapidly retrieve and understand studies in specific research domains, such as language, memory, attention, reasoning, emotion, and perception, and to perform meta-analyses of like studies. Brainmap contains the following software: # Sleuth: database searches and Talairach coordinate plotting (this application requires a username and password) # GingerALE: performs meta-analyses via the activation likelihood estimation (ALE) method; also converts coordinates between MNI and Talairach spaces using icbm2tal # Scribe: database entry of published functional neuroimaging papers with coordinate results

Proper citation: brainmap.org (RRID:SCR_003069) Copy   

•   Source: SciCrunch Registry

http://dx.doi.org/10.5281/zenodo.21157

A graphical source code file used for an automated motion detection and reward system for animal training (see comment for full paper title). It was designed on the LabVIEW programming system. Running the program requires the appropriate LabVIEW runtime software from National Instruments Corporation.

Proper citation: Monkey Motion (RRID:SCR_014285) Copy   

•   Source: SciCrunch Registry