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http://musc.edu/cbi/dki

A software tool for post-processing diffusional kurtosis imaging (DKI) datasets. DKE consists of a suite of command-line programs along with a graphical user interface (GUI). DKE is currently supported on 32- and 64-bit Windows platforms. Given a set of diffusion-weighted images acquired following a valid DKI protocol, DKE generates a set of kurtosis (axial, mean, radial) parametric maps. DKE also generates diffusivity (axial, mean, radial) and fractional anisotropy maps using both DKI and diffusion tensor imaging signal models. DKE features include: DICOM and NIfTI format support, interactive (GUI) as well as batch mode (command-line) processing, and rigid-body motion correction. DKE implements the methods described in the following paper: Tabesh A, Jensen JH, Ardekani BA, and Helpern JA. Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Mag Reson Med. 2011 Mar;65(3):823-36. http://www.ncbi.nlm.nih.gov/pubmed/21337412

Proper citation: Diffusional Kurtosis Estimator (RRID:SCR_009563) Copy   

•   Source: SciCrunch Registry

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

An on-line tutorial on how to use MATLAB for Diffusion-Weighted MRI processing. The following subjects are covered in this tutorial: Generation of Synthetic Diffusion-Weighted MRI datasets, Diffusion Tensor (DTI) Estimation from DW-MRI, DTI Visualization as a field of ellipsoids, Higher-order Diffusion Tensor Estimation from DW-MRI, Computing of Tensor Orientation Distribution Functions (Tensor ODF), Computing of Fiber Orientations, Higher-order Diffusion Tensor Image Visualization as fields of spherical functions, Multi-fiber reconstruction etc. The tutorial contains numerous illustrations, figures and Matlab scripts embedded in the text. The reader/user can automatically generate Matlab script for a self-designed DW-MRI experiment by selecting which steps needs to be followed. The code that corresponds to the selected steps is then appropriately merged in the Matlab Script Generator, and the user can easily copy and paste the produced code directly to the Matlab command prompt.

Proper citation: MATLAB Tutorial on Diffusion Tensor MRI (RRID:SCR_009507) Copy   

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http://neurospaces.sourceforge.net/

The GEneral NEural SImulation System (GENESIS) started as a very advanced software package in the late eighties, for biologically accurate neuronal modeling. Besides being used as a neuronal simulator, it was also applied to various domains outside computational neuroscience. The Neurospaces project is a departure from the monolithic software system design of the original GENESIS system. It is a development center for software components of computational neuroscience simulators. There are many advantages of developing independent software components: - Interfacing to an individual component is obviously more simple than interfacing to a do-all monolithic system. The compartmental solver developed for the Neurospaces project can be connected to Matlab fi. - It simplifies the individual components and encourages other developers to get involved. - It allows for separate testing of the components. More than 1000 use case tests been defined for these software components, including integration tests. - Integrating different component, gives different flavours of the same simulator, and enhances the user experienced consistency when doing multilevel simulations. - A component based software system avoids vendor-lockin. Its life-cycle is more smooth than that of a monolithic system, because software components can be upgraded one at a time. The Neurospaces project embodies many software components that all have been developed in full isolation. The core of the most important components is finished. The current development focus has shifted from component integration to the support of specific use case with an emphasis on single neuron modeling. This is a list of software components that have been developed or are under construction. Together, these tools give the core for the upcoming GENESIS 3 GUI. - GShell: a simple replacement for the Genesis 2 SLI. - Heccer: a fast compartmental solver, a backend. - Dash: a second compartmental solver faster than Heccer, for simpler models. - Neurospaces Model Container: provides a solver independent internal and external storage format for models. - Discrete event system: consists of a discrete event distributor and queuer. This is used for abstract modeling of an action potential traveling inside an axon as a ''discrete event''. - SSP: a flexible scheduler written in perl, to run simulations with the Neurospaces model container and Heccer. - The Neurospaces Studio: some tools for graphical browsing and command line usage. - The Genesis Script Language Interface: a scripting component that reads Genesis 2 scripts and feeds them to the Neurospaces model container. - The Geometry Library is a general purpose geometry library, with some essential geometrical operators, not commonly found in other geometrical libraries. - Using the Geometry Library, a Reconstruct Interface has been written. This interface supports the conversion of contours exported by the Reconstruct software to the Neurospaces declarative NDF format. - The Neurospaces project browser for browsing projects and inspecting simulation results. - The Installer package contains the Neurospaces installer and developer tools that have emerged from developing Neurospaces software components. - The Configurator package contains configuration utilities for the other tools. It is not needed for the other tools to work properly. Rather, it allows to set up model database and simulation servers in a convenient way. - There is also a Neurospaces blog and a wiki at googlecode for the Neurospaces project, with information for developers.

Proper citation: GEneral NEural SImulation System: The Neurospaces Project (RRID:SCR_008035) Copy   

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

The Longitudinal MS Lesion Imaging Archive provides Training data consisting of longitudinal images from five patients and Testing data consisting of 15 patients. Each longitudinal dataset includes T1-weighted, T2-weighted, PD-weighted, and T2-weighted FLAIR MRI with 3-5 time points acquired on a 3T MR scanner. T1-weighted images have approximately a 1mm cubic voxel resolution, while the other scans are 1mm in plane with 3mm sections. Accounting for the multiple time points, this constitutes approximately 80 individual data sets. The Training data contains manual segmentations of the MS lesions from two different raters for each of the time points provided.

Proper citation: Longitudinal MS Lesion Imaging Archive (RRID:SCR_014136) Copy   

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http://www.softpedia.com/get/Science-CAD/MeshValmet.shtml

A tool that measures surface to surface distance between two triangle meshes using user-specified uniform sampling. Thus, users can choose finer sampling level to calculate errors to gain more accuracy in theerror space, or sparser sampling to gain speed and get an approximate feeling of error distribution between boundaries. Besides its pleasant visualization using the VTK library, MeshValmet also provides useful histogram and statistical information based on the sample errors, such as mean and median distance, root mean square distance, mean square distance, mean absolute distance, Hausdorff distance, 95 percentile, 68 percentile, etc. MeshValmet is based on the work of Nicolas Aspert, etc.: MESH: Measuring Errors between Surfaces using the Hausdorff distance in the proceedings of the IEEE Int. Conf. on Multimedia and Expo 2002 (ICME), vol. I, pp. 705-708. The calculation of the Dice's Coefficient is calculated by Joshua Stough using the concept of a Riemannian sum.

Proper citation: MeshValmet: Validation Metric for Meshes (RRID:SCR_006622) Copy   

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http://www.bic.mni.mcgill.ca/ServicesAtlases/Macaque

A reference atlas of standard macaque monkey magnetic resonance images. The template brain volume that offers a common stereotaxic reference frame to localize anatomical and functional information in an organized and reliable way for comparison across individual macaque monkeys and studies. We have used MRI volumes from a group of 25 normal adult macaque monkeys (18 Macaca fascicularis, 7 Macaca mulatta) to create the individual atlas. Thus, the atlas does not rely on the anatomy of a single subject, but instead depends on nonlinear normalization of numerous macaque brains mapped to an average template image that is faithful to the location of anatomical structures. Tools for registering a native MRI to the MNI macaque atlas can be found in the Software section. Viewing the atlas and associated volumes online requires Java browser support. Additionally, you may download the atlas and associated files in your chosen format.

Proper citation: McConnell Brain Imaging Center MNI Macaque Atlas (RRID:SCR_005265) Copy   

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http://eeg.sourceforge.net/

Software toolbox to facilitate quick and easy import, visualization and measurement for Event Related Potential (ERP) data. The toolbox can open and visualise ERP averaged data (Neuroscan, ascii formats), 2D/3D electrode coordinates and 3D cerebral tissue tesselations (meshes). All the features can be explored quickly and easily using the example data provided in the toolbox. The GUI interface is simple and intuitive.

Proper citation: Bioelectromagnetism Matlab Toolbox (RRID:SCR_006090) Copy   

•   Source: SciCrunch Registry

http://neurosynth.org

Platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. It''s a website wrapped around a set of open-source Python and JavaScript packages. Neurosynth lets you run crude but useful analyses of fMRI data on a very large scale. You can: * Interactively visualize the results of over 3,000 term-based meta-analyses * Select specific locations in the human brain and view associated terms * Browse through the nearly 10,000 studies in the database Their ultimate goal is to enable dynamic real-time analysis, so that you''ll be able to select foci, tables, or entire studies for analysis and run a full-blown meta-analysis without leaving your browser. You''ll also be able to do things like upload entirely new images and obtain probabilistic estimates of the cognitive states most likely to be associated with the image.

Proper citation: NeuroSynth (RRID:SCR_006798) Copy   

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

A native Java-based imaging processing environment similar to the ITK/VTK paradigm. Initially developed as an extension to MIPAV (CIT, NIH, Bethesda, MD), the JIST processing infrastructure provides automated GUI generation for application plug-ins, graphical layout tools, and command line interfaces. This repository maintains the current multi-institutional JIST development tree and is recommended for public use and extension. JIST was originally developed at IACL and MedIC (Johns Hopkins University) and is now also supported by MASI (Vanderbilt University).

Proper citation: JIST: Java Image Science Toolkit (RRID:SCR_008887) Copy   

•   Source: SciCrunch Registry

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

Interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended). EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users'' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB ''datasets'' and/or across a collection of datasets brought together in an EEGLAB ''studyset.'' For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by publishing EEGLAB ''plug-in'' functions that appear automatically in the EEGLAB menu of users who download them. For example, novel EEGLAB plug-ins might be built and released to ''pick peaks'' in ERP or time/frequency results, or to perform specialized import/export, data visualization, or inverse source modeling of EEG, MEG, and/or ECOG data. EEGLAB Features * Graphic user interface * Multiformat data importing * High-density data scrolling * Defined EEG data structure * Open source plug-in facility * Interactive plotting functions * Semi-automated artifact removal * ICA & time/frequency transforms * Many advanced plug-in toolboxes * Event & channel location handling * Forward/inverse head/source modeling

Proper citation: EEGLAB (RRID:SCR_007292) Copy   

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http://www.nbtwiki.net/

An open source Matlab toolbox for the computation and integration of neurophysiological biomarkers. NBT offers a pipeline from data storage to statistics including artifact rejection, signal visualization, biomarker computation, and statistical testing. NBT allows for easy implementation of new biomarkers, and incorporates an online wiki that facilitates collaboration among NBT users including extensive help and tutorials. NBT is specialized in analyzing EEG data, however it allows the processing of any kind of signal. NBT can, e.g., be used to analyze ongoing oscillation between: * Eyes-closed rest of subject populations (e.g., healthy subjects and patients, males vs. females, young vs. old, etc.). * Two experimental condition (e.g., classical eyes-closed rest vs. meditation, or before vs. after consumption of a CNS-active substance (a drug, coffee, nicotine, alcohol, etc.)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Neurophysiological Biomarker Toolbox (RRID:SCR_009612) Copy   

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http://www.epilepsiae.eu/project_outputs/epilab_software

A Matlab-based software package developed for supporting researchers in performing studies on the prediction of epileptic seizures. It provides an intuitive and convenient graphical user interface. Fundamental concepts that are crucial for epileptic seizure prediction studies were implemented.This includes, for example, the development and statistical validation of prediction methodologies in long-term continuous recordings. Seizure prediction is usually based on electroencephalography (EEG) and electrocardiography (ECG) signals. EPILAB is able to process both EEG and ECG data stored in different formats. More than 35 time and frequency domain measures (features) can be extracted based on univariate and multivariate data analysis. These features can be post-processed and used for prediction purposes. The predictions may be conducted based on optimized thresholds or by applying classifications methods such as artificial neural networks, cellular neuronal networks, and support vector machines.

Proper citation: EPILAB (RRID:SCR_009573) Copy   

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http://marsbar.sourceforge.net/

A toolbox for SPM which provides routines for region of interest analysis. Features include region of interest definition, combination of regions of interest with simple algebra, extraction of data for regions with and without SPM preprocessing (scaling, filtering), and statistical analyses of ROI data using the SPM statistics machinery.

Proper citation: MarsBaR region of interest toolbox for SPM (RRID:SCR_009605) Copy   

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

An interactive 3D rendering and neuroanatomical analysis package developed for analyzing focal brain lesions and functional brain imaging data (Damasio and Frank, 1991; Frank, Damasio and Grabowski, 1997).

Proper citation: Brainvox (RRID:SCR_009448) Copy   

•   Source: SciCrunch Registry

http://www.imagevis3d.org/

A new volume rendering program developed by the NIH/NCRR Center for Integrative Biomedical Computing (CIBC). The main design goals of ImageVis3D are: simplicity, scalability, and interactivity. Simplicity is achieved with a new user interface that gives an unprecedented level of flexibility (as shown in the images). Scalability and interactivity for ImageVis3D mean that both on a notebook computer as well as on a high end graphics workstation, the user can interactively explore terabyte sized data sets. Finally, the open source nature as well as the strict component-by-component design allow developers not only to extend ImageVis3D itself but also reuse parts of it, such as the rendering core. This rendering core, for instance, is planned to replace the volume rendering subsystems in many applications at the SCI Institute and with their collaborators.

Proper citation: ImageVis3D (RRID:SCR_009566) Copy   

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http://www.nitrc.org/projects/draw3d/

Draw3D is a 3D rendering tool written entirely in VTK-TCL script. It is intended for fast command line rendering and visual inspection of datasets commonly found in medical imaging. It also allows the generation of images for reports or videos. As it is based on pure VTK, it can render whatever VTK can render, and runs wherever VTK can run. Meshinator is a simpler tool that uses VTKs isosurface functions to generate meshes from volumetric data. Please see the wiki for documentation

Proper citation: Draw3D and Meshinator (RRID:SCR_009444) Copy   

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

High quality data, open and freely available to everyone to celebrate the diversity of the vertebrate brain. Do you have data that you would like to share? Do not hesitate to contact them! The Brain Catalogue is developed by Florencia Grisanti (Taxidermy Workshop of the Natural History Museum in Paris) and Roberto Toro (Neuroscience Department of the Institut Pasteur). Many of our specimens come from the Vertebrate Brain Collection of the Jardin des Plantes, curated by Marc Herbin, and are scanned at the Institut du Cerveau et de la Moelle (ICM) by Mathieu Santin and Alexandra Petiet, from the CENIR laboratory, with financial and methodological support kindly provided by Olivier Colliot, head of the Cogimage team at the ICM.

Proper citation: Brain Catalogue (RRID:SCR_009442) Copy   

•   Source: SciCrunch Registry

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

Human DTI brain atlases have been generated at UNC-Chapel Hill for several age groups, by iterative joint deformable registration of training datasets into a single unbiased DTI average image. Atlases packages include an atlas DTI tensor image, atlas DTI property images (FA, MD, AD, RD), and single tensor tractography based fiber tracts of major tracts with related 3D planes for fiber profile information: genu, splenium, anterior and posterior limb of internal capsule, uncinate fasciculus.

Proper citation: UNC Human DTI Brain Atlas (RRID:SCR_009516) Copy   

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

A nice sign of bias field correction (nonuniformity) in medical images. This tool is fast and efficient. Technical details can be found at http://zheng.vision.googlepages.com/biasCorrection_miccai09_Zheng.pdf

Proper citation: NICE-SIGN (RRID:SCR_009629) Copy   

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http://www.gtec.at/Products/Software/g.BSanalyze-Specs-Features

An interactive environment for multimodal biosignal data processing and analysis in the fields of clinical research and life sciences. It is the most comprehensive package to analyze non-invasive and invasive brain-, heart- and muscle-functions and dysfunctions. It includes many functions such as support vector machines, event-related ECG, support for P300 and SSVEP/SSSEP BCIs, zero class detection for BCIs, compressed spectral array, minimum energy, and more! g.BSanalyze consists of a base version for data import, visualization, transformation and pre-processing and has several dedicated toolboxes. The package comes with many sample biosignal data-sets, including P300, SSVEP, motor imagery, CSP BCIs, Tilt-Table, EPs, multi-unit activity, CFM, and ERD/ERS.

Proper citation: g.BSanalyze (RRID:SCR_009625) Copy   

•   Source: SciCrunch Registry