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

Web game that provides an innovative infrastructure for labeling to enable an alternative to expert raters for medical image labeling through statistical analysis of the collaborative efforts of many, minimally-trained raters. Statistical atlases of regional brain anatomy have proven to be extremely useful in characterizing the relationship between the structure and function of the human nervous system. Typically, an expert human rater manually examines each slice of a three-dimensional volume. This approach can be exceptionally time and resource intensive, so cost severely limits the clinical studies where subject-specific labeling is feasible. Methods for improved efficiency and reliability of manual labeling would be of immense benefit for clinical investigation into morphological correlates of brain function.

Proper citation: Web Game for Collaborative Labeling (RRID:SCR_006685) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/polgui/

An interface between MATLAB and the Polhemus Fastrak digitizer used to digitize fiducial locations and scalp EEG electrode locations. There are 5 versions all of which work under MATLAB R14 (on both linux and windows platforms), # polgui_ver1_r14 : works with 1 receiver (stylus pen) # polgui_ver2_r14 : works with 2 receivers (including the pen) # polgui_ver3_r14 : works with 3 receivers(including the pen) # polgui_ver4_r14 : works with 4 receivers (including the pen) # polgui_ver5_r14 : Generic version which works with 1/2/3/4 receivers (WARNING: Ver 5 might be buggy; not fully tested) Requirements: MATLAB R14 (Linux/Windows)

Proper citation: POLGUI - Matlab Polhemus Interface (RRID:SCR_006752) Copy   

•   Source: SciCrunch Registry

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

User software contributions for XNAT - The Extensible Neuroimaging Archive Toolkit, http://www.xnat.org

Proper citation: XNAT Extras (RRID:SCR_004759) Copy   

•   Source: SciCrunch Registry

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

Software to: view dicom files and assemble them into 3D volumes. View and convert between Analyze, Nifti, and Interfile. Classify and organize dicoms and 3D volumes using metadata. Search and report on a collection of scans.

Proper citation: DIAMOND (RRID:SCR_009457) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/cbs-tools/

A fully automated processing pipeline for cortical analysis of structural MR images at a resolution of up to 400������m, including skull stripping, whole brain segmentation, cortical extraction, surface inflation and mapping, as well as dedicated tools for profile estimation across the cortical thickness. The tools are released as a set of plug-ins for the MIPAV software package and the JIST pipeline environment. They are therefore cross-platform and compatible with a wide variety of file formats.

Proper citation: CBS High-Res Brain Processing Tools (RRID:SCR_009452) Copy   

•   Source: SciCrunch Registry

http://www.pstnet.com/hardware.cfm?ID=91

Instrument that accurately gathers participant responses and verifies signals. The Celeritas Series response units are assembled using high-impact, chemical resistant, medical grade plastic. The response units include a tactile indicator to ensure correct finger placement during experiments and comfortably attach to the participant?s wrists. The units communicate button presses through fiber optic cabling which connects to a Fiber Optic Interface Console located in the control room through an available wave guide. The interface console provides real-time feedback of participant responses via LED indicators and includes a set of switches which can be used to make responses for the participant as needed.

Proper citation: Fiber Optic Button Response System (RRID:SCR_009577) Copy   

•   Source: SciCrunch Registry

http://www.nordicneurolab.com

From state of the art post-processing and visualization software for BOLD, Diffusion / DTI, and Perfusion / DCE imaging to fMRI hardware for audio and visual stimulation, eye tracking, and patient response collection, they provide products and solutions that define the field of functional MR imaging. They are dedicated to bringing the most advanced neuro-imaging tools to market while making functional MRI programs easy to implement. Through collaboration with research and clinical teams from both academic and medical centers, MR system manufacturers, and third party vendors they develop and manufacture hardware and software solutions that meet the needs of very experienced centers while developing training programs to make fMRI easy to adopt for more novice users. Their products are used around the world by researchers and clinicians alike.

Proper citation: NordicNeuroLab (RRID:SCR_009632) Copy   

•   Source: SciCrunch Registry

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

A reliable patient response system designed specifically for use in an fMRI. Lumina was developed to satisfy the requirements of both the clinical and research fields.

Proper citation: Lumina LP- 400 Response System (RRID:SCR_009596) Copy   

•   Source: SciCrunch Registry

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

This program contains Python modules for modeling and reconstruction of diffusion weighted MRI data. It is a subset of the code internally used in the CVGMI lab at the University of Florida. Three different reconstruction methods are currently included in this program, namely, Mixture of Wisharts (MOW), Diffusion Orientation Transform (DOT) and Q-ball Imaging (QBI). This program is mainly developed and maintained by Bing Jian, as part of his Ph.D. research, supervised by Prof. Baba Vemuri. Please note that the source code of this program is hosted at Google Code, see the Source Code link on the left.

Proper citation: Multi-fiber Reconstruction from DW-MRI (RRID:SCR_009509) Copy   

•   Source: SciCrunch Registry

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

This repository stores and provides opportunities for collaboration through Matlab code, libraries, and configuration information for projects in early stage development. The MASI research laboratory concentrates on analyzing large-scale cross-sectional and longitudinal neuroimaging data. Specifically, they are interested in population characterization with magnetic resonance imaging (MRI), multi-parametric studies (DTI, sMRI, qMRI), and shape modeling.

Proper citation: MASIMatlab (RRID:SCR_009506) Copy   

•   Source: SciCrunch Registry

http://www.smivision.com/en/gaze-and-eye-tracking-systems/products/iview-x-mri-meg.html

A non-invasive, long-range eye tracking system for use in the fMRI environment. Some features of the system include: * Elaborate faraday shielding and fiber optics to avoid noise in high-field magnets. * Includes stimulus presentation software ?Experiment Center? and is compatible with 3rd party products such as ?Presentation? by NeuroBS. * Utilizes mirror box customized for large field of view. * Includes powerful analysis software ?BeGaze2? for graphical and statistical analysis of eye movements. * Includes fixation, saccade and blink detection, and area-of-interest based statistics * Real-time data available via digital or analog output

Proper citation: iView X MRI-LR - Eye Tracking for fMRI (RRID:SCR_009627) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/masi-fusion/

Tool that provides a unified framework for testing and applying statistical and voting label fusion techniques. The project will include implementations of several different voting techniques including majority vote, weighted voting, and regionally weighted voting. Additionally, multiple statistical fusion methods will be included, notably, STAPLE, Spatial STAPLE, STAPLER and COLLATE. In addition to the fusion algorithms, code for running specialized simulations and various tools and utilities to test the efficacy of the algorithms will be provided.

Proper citation: MASI Label Fusion (RRID:SCR_009505) Copy   

•   Source: SciCrunch Registry

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

Automatically creates a mask based on the "foreground" of an anatomical scan volume.

Proper citation: BRAINSROIAuto (RRID:SCR_009501) Copy   

•   Source: SciCrunch Registry

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

This project is used for students enrolled in courses using the JIST framework. Content in this CVS is freely available, but it is not intended for any specific purpose.

Proper citation: JHU Proj. in Applied Medical Imaging (RRID:SCR_009499) Copy   

•   Source: SciCrunch Registry

http://www.vpixx.com/products/visual-stimulators/datapixx.html

Supplies a complete multi-function data and video processing USB peripheral for vision research. In addition to a dual-display video processor, the DATAPixx includes an array of peripherals which often need to be synchronized to video during an experiment, including a stereo audio stimulator, a button box port for precise reaction-time measurement, triggers for electrophysiology equipment, and even a complete analog I/O subsystem. Because we implemented the video controller and peripheral control on the same circuit board, you can now successfully synchronize all of your subject I/O to video refresh with microsecond precision.

Proper citation: DATAPixx (RRID:SCR_009648) Copy   

•   Source: SciCrunch Registry

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

To enable widespread application of the Biological parametric mapping (BPM) approach, they introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model. Biological parametric mapping (BPM) has extended the widely popular statistical parametric approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects.

Proper citation: Robust Biological Parametric Mapping (RRID:SCR_009642) Copy   

•   Source: SciCrunch Registry

http://www.connectomics.org/cfflib/

A container format for multi-modal neuroimaging data. It comprises connectome objects of type: CMetadata, CNetwork, CVolume, CSurface, CTrack, CScript, CData, CTimeseries, CImagestack. The Python library cfflib provides read/write functionality.

Proper citation: Connectome File Format (RRID:SCR_009551) Copy   

•   Source: SciCrunch Registry

http://www.cise.ufl.edu/~tichen/cdfHC.zip

A Matlab demo for group wise point set registration using a novel CDF-based Havrda-Charvat Divergence, which is based on the paper: Ting Chen, Baba C. Vemuri, Anand Rangarajan and Stephan J. Eisenschenk, Group-wise Point-set registration using a novel CDF-based Havrda-Charvat Divergence. In IJCV : International Journal of Computer Vision, 86(1):111-124, January, 2010.

Proper citation: CDF-HC PointSetReg (RRID:SCR_009544) Copy   

•   Source: SciCrunch Registry

http://cocomac.g-node.org/main/index.php?

Online access (html or xml) to structural connectivity ("wiring") data on the Macaque brain. The database has become by far the largest of its kind, with data extracted from more than four hundred published tracing studies. The main database, contains data from tracing studies on anatomical connectivity in the macaque cerebral cortex. Also available are a variety of tools including a graphical simulation workbench, map displays and the CoCoMac-Paxinos-3D viewer. Submissions are welcome. To overcome the problem of divergent brain maps ORT (Objective Relational Transformation) was developed, an algorithmic method to convert data in a coordinate- independent way based on logical relations between areas in different brain maps. CoCoMac data is used to analyze the organization of the cerebral cortex, and to establish its structure- function relationships. This includes multi-variate statistics and computer simulation of models that take into account the real anatomy of the primate cerebral cortex. This site * Provides full, scriptable open access to the data in CoCoMac (you must adhere to the citation policy) * Powers the graphical interface to CoCoMac provided by the Scalable Brain Atlas * Sports an extensive search/browse wizard, which automatically constructs complex search queries and lets you further explore the database from the results page. * Allows you to get your hands dirty, by using the custom SQL query service. * Displays connectivity data in tabular form, through the axonal projections service. CoCoMac 2 was initiated at the Donders Institute for Brain, Cognition and Behaviour, and is currently supported by the German neuroinformatics node and the Computational and Systems Neuroscience group at the Juelich research institute.

Proper citation: CoCoMac (RRID:SCR_007277) Copy   

•   Source: SciCrunch Registry

http://www.oasis-brains.org/

Project aimed at making neuroimaging data sets of brain freely available to scientific community. By compiling and freely distributing neuroimaging data sets, future discoveries in basic and clinical neuroscience are facilitated.

Proper citation: Open Access Series of Imaging Studies (RRID:SCR_007385) Copy   

•   Source: SciCrunch Registry