Structural brain connectivity software tools data analysis
Anatomical connectivity (AC), also called structural connectivity, which forms the connectome through synaptic contacts between neighboring neurons or fiber tracks connecting neuron pools in spatially distant brain regions. The whole set of such fiber tracks in the brain is called white matter. On short time scales (sec, min), anatomical connections are quite persistent and stable, while for longer time spans substantial plasticity may be observed.
Performs comprehensive graph-based topological analyses of brain networks. GRETNA incorporates network construction, analysis and comparison modules to provide a complete and automatic pipeline for connectomics. It allows manipulation of different network analytical strategies, including structurally, functionally or randomly defined network nodes, positive or negative connectivity processing, binary or weighted network types and the choices of different thresholding procedures or ranges.
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.
Allows to create, manipulate, and study the structure, dynamics, and function of complex networks. NetworkX provides data structures for graphs or networks, with graph algorithms, generators, and drawing tools. The software provides a standard programming interface and graph implementation suitable for many applications and a rapid development environment for collaborative and multidisciplinary projects.
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.
Allows users to analyze, visualize, annotate and share whole-brain data at cellular resolution. WholeBrain supplies a method to generate brain maps containing data from neuron function, neuron identity, and connectivity. It quantifies and spatially maps multidimensional data from whole-brain experiments. It also compares results across experiments in a single standardized anatomical reference atlas.
Aims to systematically preprocess the data from the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Data-sharing Initiative (INDI) and openly share the results. The Preprocessed Connectomes Project has been initiated in 2011 with the ADHD-200 Preprocessed initiative, and has grown to include the Beijing Enhanced DTI dataset and ABIDE. To enable the comparison of different preprocessing choices and to accommodate different opinions about the best preprocessing strategies, most of the data is preprocessed using a variety of tools and parameters. Data is hosted in an Amazon Web Services Public S3 Bucket and at NITRC. A software package to run the Preprocessed Connectomes Project's protocol for assessing data quality is available for local use.
Helps researchers understand brain function at the level of the topographic maps that make up sensory and motor systems. Topographica is a software package for computational modeling of neural maps. It is intended to complement the many good low-level neuron simulators that are available, such as Genesis and Neuron. This method focuses on the large-scale structure and function that is visible only when many thousands of such neurons are connected into topographic maps containing millions of connections.