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

A reference for modifications, extensions, and utilities for the FMRIB Software Library (FSL).

Proper citation: FSL extensions (RRID:SCR_009472) Copy   

•   Source: SciCrunch Registry

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

A MATLAB toolbox for denoising task-based fMRI data. It derives noise regressors from voxels unrelated to the experimental paradigm and uses these regressors in a general linear model (GLM) analysis of the data. The technique only requires a design matrix indicating the experimental design and an fMRI dataset.

Proper citation: GLMdenoise: a fast, automated technique for denoising task-based fMRI data (RRID:SCR_014116) Copy   

•   Source: SciCrunch Registry

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

A software tool (with GUI) for investigating inter-regional functional connectivity in event-related fMRI data and allows the user to assess the modulation of functional connectivity by an experimental condition.

Proper citation: BetA-Series COrrelation (RRID:SCR_014086) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/gig-ica/

Software toolbox for group-information guided Independent Component Analysis (ICA). In GIG-ICA, group information captured by standard Independent Component Analysis (ICA) on the group level is used as guidance to compute individual subject specific Independent Components (ICs) using a multi-objective optimization strategy. For computing subject specific ICs, GIG-ICA is applicable to subjects that are involved or not involved in the computation of the group information. Besides the group ICs, group information captured from other imaging modalities and meta analysis could be used as the guidance in GIG-ICA too.

Proper citation: Group Information Guided ICA (RRID:SCR_009491) Copy   

•   Source: SciCrunch Registry

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

A comprehensive and open-source library of analysis tools for multi-parametric MRI of the spinal cord. The toolbox includes a template and several atlases, along with state-of-the-art methods to register any data to the template. It also includes useful scripts for data preprocessing: extraction of centerline, automatic segmentation, slice-wise motion correction, etc.

Proper citation: Spinal Cord Toolbox (RRID:SCR_014170) Copy   

•   Source: SciCrunch Registry

http://caid.cs.uga.edu/?name=software

A software toolbox to predict 358 DICCCOL landmarks (Dense Individualized and Common Connectivity-based Cortical landmarks (http://dicccol.cs.uga.edu) ) on a new brain given b0, brain surface data and DTI derived fiber data (vtk format). Each DICCCOL landmark is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. DICCCOL aims to provide large-scale cortical landmarks with finer granularity, better functional homogeneity, more accurate functional localization, and automatically-established cross-subjects correspondence.

Proper citation: DICCCOL predictor (RRID:SCR_009554) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/cudasphere/

A CUDA C based toolkit which provides a GPU based implementation of the spherical model forward solution for the 306 channel Elekta Neuromag MEG system and the EEG. The 1-Sphere forward solution for the MEG and the 4-Sphere forward solution for the EEG is implemented in CUDA C and an accelerated solution is obtained using the NVIDIA GPU when the solution is calculated for a large number of dipoles (on the order of 15000 and above) and sensor location. Speedup by a factor of 22 and 32 is obtained for the EEG and MEG solution respectively when compared to the fastest CPU implementation available in the public domain. The complete source code and pre-compiled binaries are also made available via an open source license (GPL Version 3). A CUDA enabled NVIDIA graphics card is required to use the software.

Proper citation: CUDA-SPHERE-FWD-MEEG (RRID:SCR_013225) Copy   

•   Source: SciCrunch Registry

http://www.calatk.org/

An open-source toolkit for cross-sectional and longitudinal atlas building. The CalaTK project develops innovative methods and tools for longitudinal atlases with a focus on neurodevelopment. The computational toolbox is developed with the objective to analyze the neural developmental patterns observed in human and non-human primate structural and diffusion tensor magnetic resonance (MR) images.

Proper citation: CalaTK (RRID:SCR_009547) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/maps4mipav/

The exploratory development tree of Java Image Science Toolkit (JIST), an extension to the MIPAV (Medical Image Processing, Analysis, and Visualization) plug-in framework that allows the user to design and execute pipelines, which are multi-stage processing tasks.. New features and designs are tested here before general release into the JIST project. JIST was formerly known as the MedIC Automated Pipeline Scheduler (MAPS).

Proper citation: Maps4Mipav (Exploratory JIST) (RRID:SCR_000613) Copy   

•   Source: SciCrunch Registry

http://biodev.ece.ucsb.edu/projects/bisquik/wiki

A scalable web-based system for biological image analysis, management and exploration. The Bisque system incorporates many features useful to imaging researchers from image capture to extensible image analysis and querying. At the core, bisque maintains a flexible database of images and experimental metadata. Image analyses can be incorporated into the system and deployed on clusters and desktops. Search and comparison of datasets by image data and content is supported. Novel semantic analyses are integrated into the system allowing high level semantic queries and comparison of image content. New features and testing of Bisque version: 0.5.1, among many others are: # Parallel execution of datasets # Rich interfaces for autogenerated module UI # Abstracted storage system for local, irods, etc.. They are using Mercurial for their source control system. This should be installed before proceeding. Browse source on-line, http://biodev.ece.ucsb.edu/projects/bisquik/browser Bisque Installation, http://biodev.ece.ucsb.edu/projects/bisquik/wiki/InstallationInstructions05 Bisque DOWNLOAD, http://biodev.ece.ucsb.edu/projects/bisquik/wiki/download

Proper citation: Bisque (RRID:SCR_005564) Copy   

•   Source: SciCrunch Registry

https://github.com/cni/nims

A scientific data management system specifically designed for neuroimaging data. NIMS automatically reaps data from the measurement instrument (e.g., MR scanner), sorts and organizes the data based on header information, does some basic processing on the data, and makes the data available to authorized users through a web-based interface. The data are also available from the command-line through a FUSE-based filesystem.

Proper citation: Neurobiological Image Management System (RRID:SCR_006594) Copy   

•   Source: SciCrunch Registry

http://www.birncommunity.org/collaborators/function-birn/

The FBIRN Federated Informatics Research Environment (FIRE) includes tools and methods for multi-site functional neuroimaging. This includes resources for data collection, storage, sharing and management, tracking, and analysis of large fMRI datasets. fBIRN is a national initiative to advance biomedical research through data sharing and online collaboration. BIRN provides data-sharing infrastructure, software tools, strategies and advisory services - all from a single source.

Proper citation: Function BIRN (RRID:SCR_007291) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/dkfz-diffusion/

This central project points to all open-source and open-data initiatives provided by the German Cancer Research Center in the field of diffusion MRI.

Proper citation: Diffusion MRI at DKFZ Heidelberg (RRID:SCR_009465) Copy   

•   Source: SciCrunch Registry

https://github.com/incf-nidash/XCEDE

Data management software that provides an extensive metadata hierarchy for describing and documenting research and clinical studies. The schema organizes information into five general hierarchical levels: a complete project, studies within a project, subjects involved in the studies, visits for each of the subjects, the full description of the subject's participation during each visit.

Proper citation: XCEDE Schema (RRID:SCR_002571) 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

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

http://www.theuais.org

A topical portal for the UAIS Lab of Lanzhou University which researches predicting depression and schizophrenia based on demographics and physiological information (EEG, ERPs, Genetics, MRI, fMRI, etc.). It also researches wearable bio-signal sensors and antennas, bio-signal processing, speech analysis, pervasive mental health, psycho-physiological computing, bioinformatics and multimodal data fusion and modeling.

Proper citation: Prediction and Diagnosis for Depression and Schizophrenia (RRID:SCR_014161) Copy   

•   Source: SciCrunch Registry

https://github.com/Neural-Systems-at-UIO/MeshView-for-Brain-Atlases

Web application for real time 3D display of surface mesh data representing structural parcellations and generation of user defined cut planes from volumetric atlases.

Proper citation: MeshView (RRID:SCR_017222) 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.ppmi-info.org/

An observational longitudinal clinical study partnership to identify and validate biomarkers of Parkinson disease (PD) progression and provide easy and open web-based access to the comprehensive set of correlated clinical data and biospecimens, information, and biosamples acquired from PD and age and gender matched healthy control subjects to the research community. The data and specimens have been collected in a standardized manner under strict protocols and includes clinical (demographic, motor and non-motor, cognitive and neurobehavioral), imaging (raw and processed MRI, SPECT and DAT), and blood chemistry and hematology subject assessments and biospecimen inventories (serum, plasma, whole blood, CSF, DNA, RNA and urine). All data are de-identified to protect patient privacy. PPMI will be carried out over five years at 21 clinical sites in the United States and Europe and requires the participation of 400 Parkinson's patients and 200 control participants. The PPMI database provides researchers with access to correlated clinical and imaging data, along with annotated biospecimens, all available within an open access system that encourages data sharing (http://www.ppmi-info.org/access-data-specimens/). The website hosts an Ongoing Analysis section to keep the scientific community apprised of analyses being completed, in hopes of stimulating collaborations between researchers who are using PPMI data and specimens.

Proper citation: Parkinson's Progression Markers Initiative (RRID:SCR_006431) Copy   

•   Source: SciCrunch Registry