Allows users to work about magnetic resonance imaging (MRI) brain imaging data. FSL can realize functional magnetic resonance imaging (FMRI) analysis (brain extraction, smoothing, statistics, registration). This tool can analyze a wide range of MR modalities (task FMRI, resting FMRI, ASL, diffusion, structure), and can be easily scripted and run over computing clusters.
Simulates the dynamics of large-scale brain networks with biologically realistic connectivity. The Virtual Brain uses tractographic data (DTI/DSI) to generate connectivity matrices and build cortical and subcortical brain networks. The connectivity matrix defines the connection strengths and time delays via signal transmission between all network nodes. Various neural mass models are available in the repertoire of The Virtual Brain and define the dynamics of a network node. Together, the neural mass models at the network nodes and the connectivity matrix define the Virtual Brain. The Virtual Brain simulates and generates the time courses of various forms of neural activity including Local Field Potentials (LFP) and firing rate, as well as brain imaging data such as EEG, MEG and BOLD activations as observed in fMRI.
Gathers several in vivo dynamic contrast-enhanced imaging subtools for analyzing various functional changes associated with disease initiation, progression and responses to therapy. CAM-Java incorporates three Convex Analysis of Mixtures (CAM) based algorithms: (1) compartment modeling (CAM-CM), (2) non-negative independent component analysis (CAM nICA) and (3) non-negative well-grounded component analysis (CAM-nWCA). These subtools address real-world blind source separation (BSS) problems.
Allows diffusion of imaging research in a clinical oncology setting and provides tools for end-to-end diffusion image analysis as well as interoperation with clinical imaging systems. SlicerDMRI is a suite of open-source software tools for diffusion magnetic resonance imaging (dMRI) research. The software is built upon and integrated with 3D Slicer, an NIH-supported open-source platform for medical image computing. SlicerDMRI is used for both neuroscience research and cancer imaging research.
Allows to study diffusion Magnetic Resonance Imaging (MRI) data. Dipy is a program allowing users to share their code and experiments. One of its objectives is to provide transparent implementations for all the different steps of the dMRI analysis with a uniform programming interface. It implements two interfaces for probabilistic Markov fiber tracking: (1) it allows the user to provide the distribution evaluated on a discrete set of possible tracking directions, and (2) it accommodates tracking methods where the fiber orientation distribution function (fODF) cannot be easily computed.
Allows the analysis and visualization of structural and functional neuroimaging data from cross-sectional or longitudinal studies. FreeSurfer proposes a suite of tools that provide extensive and automated analysis of key features in the human brain. This includes skull stripping, image registration, subcortical segmentation, cortical surface reconstruction, cortical segmentation, cortical thickness estimation, longitudinal processing, fMRI analysis, tractography, FreeView Visualization GUI, etc... FreeSurfer is freely available, runs on a wide variety of hardware and software platforms, and is open source.
A MATLAB toolbox for a comprehensive pipeline processing of dMRI dataset, aiming to facilitate image processing for the across-subject analysis of diffusion metrics and brain network constructions. Of note, the processing pipelines in this toolbox have been completely set up, allowing the end-users of dMRI to process the data immediately. After the user sets the input/output and processing parameters through the friendly graphical user interface (GUI), PANDA fully automates all processing steps for datasets of any number of subjects, and results in data pertaining to many diffusion metrics that are ready for statistical analysis at three levels (Voxel-level, ROI-level, and TBSS-level). Additionally, anatomical brain networks can be automatically generated using either deterministic or probabilistic tractography techniques. Particularly, PANDA can run processing jobs in parallel with multiple cores either in a single computer or within a distributed computing environment using a Sun Grid Engine (SGE) system, thus allowing for maximum usage of the available computing resources.
Facilitates analysis and comparison of structural and functional network brain networks. GAT provides a graphical user interface (GUI) that facilitates construction and analysis of brain networks, comparison of regional and global topological properties between networks, analysis of network hub and modules, and analysis of resilience of the networks to random failure and targeted attacks. Area under a curve (AUC) and functional data analyses (FDA), in conjunction with permutation testing, is employed for testing the differences in network topologies; analyses that are less sensitive to the thresholding process.
Enables the implementation of custom magnetic resonance imaging (MRI) spectrometers using commercially-available software defined radios (SDRs). gr-MRI includes a signal generation and recording blocks for GNU Radio. It can implement: system timing calibrations; center frequency and transmit power optimization; shaped radiofrequency (RF) and gradient pulses; image reconstruction; and three representative magnetic MRI sequences: gradient echo, spin echo, and inversion recovery.
Allows users to visualize and quantify analysis of Diffusion Tensor Imaging (DTI). SATURN aids researchers to find statistical differences between control and patient DTI data groups, using standard and advanced measures over regions of interest (ROIs), and over fiber bundles. Moreover, it can be used for the exploration of fiber tracts and study them in different ways.
Allows users to explore multimodality brain data to compare effect sizes and associated brain anatomical structures and genomics factors. ENIGMA-Viewer is an interactive visualization tool that provides a series of design strategies for spatial and non-spatial data integration in the context of meta-analysis of brain imaging and genetics. It also assists reporting and interpreting effect size measures.
Allows detailed investigation and evaluation of multidimensional biomedical images. AnalyzePro can serve for magnetic resonance imaging, radionuclide emission tomography, ultrasound tomography, and 3-D imaging modalities based on x-ray computed tomography. It contains features for interactive display, manipulation and measurement of multidimensional image data.
Provides a workflow to classify white matter (WM) fibers. FiberNET provides an automated method with the aim of eliminating false positive fibers from a fiber bundle. It was tested by using a public dataset from the Parkinson’s Progression Markers Initiative (PPMI). This tool can reduce the effects of false positive streamlines. It can be employed to predict or cluster single shell tractography results.
Processes multiple different analysis on magnetic resonance imaging (MRI) data such as functional MRI and diffusion MRI. VISTASOFT contains several modules that allows users to: align functional and anatomical data, display MR data on rendered 3D surface representations of the brain, handle anatomical MRI data, analyze functional MRI data or provide a list of scripts.
Hosts heterogeneous tools dedicated to neuroimaging research. BrainVISA aims to help researchers in developing new neuroimaging tools, sharing data and distributing software. It offers a way to define viewers which may use any visualization software. Thanks to its data management functions, the tool can define the data types handled by the software, associate key attributes for indexation, and filename patterns to make the link between the filesystem and the database schema.
Serves for diffusion magnetic resonance imaging (MRI) processing. Camino contains several techniques such as probabilistic tractography, deterministic, diffusion tensor fitting or mapping fractional anisotropy. It enables of processing pipelines thanks to its modular design. It implements a data processing pipeline allowing easy scripting and flexible integration with other software.
Allows users to measure brain networks from structural and functional magnetic resonance imaging (MRI) data. bNets gathers functions for pre-processing and preparing diffusion tensor imaging (DTI) data in order to proceed to connectome studies. It is able to isolate individual network contribution from group level connectivity matrices and to compute random attack analysis on edges.
Allows image processing, analysis and visualization. MRtrix3 can determine fibre orientation distributions using constrained spherical deconvolution. It serves for fixel-based analysis of apparent fibre density and fibre cross-section, quantitative structural connectivity study and non-linear spatial registration of fibre orientation distribution images using a probabilisitic streamlines algorithm.
Allows users to visualize, manipulate, and understand data from imaging modalities such as computed tomography, microscopy or Magnetic resonance imaging (MRI). Amira 3D Software for Life Sciences provides features to import and process 2D and 3D images data, visualization techniques and tools for visual analysis. Users can also create and share presentations. The base product can be customized by adding functional extensions to fit special needs in different application areas.
Processes the diffusion of magnetic resonance imaging (MRI) data. TORTOISE contains three main modules: DIFF_PREP-software for image resampling, motion, eddy current distortion, and EPI distortion correction using a structural image as target, and for re-orientation of data to a common space; DIFF_CALC-software for tensor fitting, error analysis, directionally encoded color map visualization and ROI analysis; DR-BUDDI-software for EPI distortion correction using pairs of diffusion data sets acquired with opposite phase encoding (blip-up blip-down acquisitions).
Analyzes white matter morphometry using diffusion tensor imaging (DTI) data. DTI-TK is standalone software that permits users to register, examine and visualize DTI images. This software can build atlas, permits spatial normalization and can be employed as a construction toolkit. It performs a state-of-the-art registration algorithm with the aim of ameliorating the power of statistical inference in clinicals settings.
A “profilometry” framework for the multimetric analysis of white matter tracts. Mrpipe is a statistical framework that combine a multivariate analysis of covariance (MANCOVA) and a linear discriminant analysis (LDA) both accounting for age and gender, with multiple comparison corrections. This methodology can not only combine tract profile analyses in a single statistical model but can also take into account covariates such as age and gender which are of particular importance in most clinical investigations. This project is still a skeleton, the tool is not ready for use.