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.
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 facilitate data processing. BRANT is a MATLAB-based toolbox that integrates (i) batch processing pipelines for resting-state functional magnetic resonance imaging (rs-fMRI) data preprocessing, (ii) voxel-wise spontaneous activity analysis, (iii) functional connectivity analysis, (iv) complex network analysis, as well as (v) statistical analysis and results visualization. It was designed using dynamically generated GUIs. Users can generate their own GUIs by adding a few lines of MATLAB code.
Infers flexibly changes in a network-valued random variable with a continuous trait. BNRR is a network–response regression model, which considers the brain network as an object-type response variable having conditional expectation changing flexibly. This method can be easily adapted to incorporate directed networks via two subsets of latent coordinates—for each brain region—modeling outgoing and incoming edges, respectively.
A fully automated all-in-one connectivity toolbox that offers pre-processing, connectivity and graph theoretical analyses of multimodal image data such as diffusion-weighted imaging, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). MIBCA was developed in MATLAB environment and pipelines well-known neuroimaging software tools such as Freesurfer, SPM, FSL, and Diffusion Toolkit. It further implements routines for the construction of structural, functional and effective or combined connectivity matrices, as well as, routines for the extraction and calculation of imaging and graph-theory metrics, the latter using also functions from the Brain Connectivity Toolbox. Finally, the toolbox performs group statistical analysis and enables data visualization in the form of matrices, 3D brain graphs and connectograms.
Detects functional or structural connectivity differences in neuroimaging data that is modeled as a network. NBS employs the presence of any structure exhibited by the connections comprising the effect or contrast of interest to yield greater power than what is possible by independently correcting the p-values computed for each link using a generic procedure to control the family-wise error rate (FWE).
Generates topics that are simultaneously constrained by both anatomical and functional considerations. GC-LDA learns latent topics from the meta-analytic Neurosynth database of over 11,000 published functional magnetic resonance imaging (fMRI) studies. It allows researchers to formally specify priors on the GC-LDA topics, providing a powerful means of contextualizing interpretations and accounting for prior expectations and beliefs.
Performs electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) multimodal fusion. NIT provides several modules designed for building temporal and/or spatial information for EEG-fMRI multimodal fusion investigation. It offers different hemodynamic response function (HRF) shapes, including single gamma, standard statistical parametric mapping (SPM) and Glover HRFs. This tool aims to facilitate multimodal fusion study.
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.
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.
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.
Allows users to visualize multi-modal human brain networks. CVU generates powerful, interactive visualizations of human brain networks from common matrix and imaging file formats. It is decoupled from the process of connectivity estimation and network creation, and it visualize networks from any imaging modality. This tool allows network statistics to be easily incorporated into the visualization.
Aims to the collaborative development of comprehensive models of the brain of the fruit fly Drosophila melanogaster and their execution and testing on multiple Graphics Processing Units (GPUs). Neurokernel provides a programming model that capitalizes upon the structural organization of the fly brain into a fixed number of functional modules to distinguish between these modules’ local information processing capabilities and the connectivity patterns that link them. By defining mandatory communication interfaces that specify how data is transmitted between models of each of these modules regardless of their internal design, Neurokernel explicitly enables multiple researchers to collaboratively model the fruit fly’s entire brain by integration of their independently developed models of its constituent processing units.
Allows connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized.
A multi-user web-based collaborative management system for images and volumes which allows users to view multi-terabyte datasets, annotate images with their own annotation schema, and summarize the results. Viking has several key features. (1) It works over the internet using HTTP and supports many concurrent users limited only by hardware. (2) It supports a multi-user, collaborative annotation strategy. (3) It cleanly demarcates viewing and analysis from data collection and hosting. (4) It is capable of applying transformations in real-time. (5) It has an easily extensible user interface, allowing addition of specialized modules without rewriting the viewer.
Aims to elucidate neural circuit function from connectome data in the fruit fly brain. NeuroGFX proposes a scalable computational modeling methodology that includes i) a brain emulation engine, with an architecture that can tackle the complexity of whole brain modeling, ii) a database that supports tight integration of biological and modeling data along with support for domain specific queries and circuit transformations, and iii) a graphical interface that allows for total flexibility in configuring neural circuits and visualizing run-time results, both anchored on model abstractions closely reflecting biological structure. NeuroGFX is integrated into the architecture of the Fruit Fly Brain Observatory. The computational infrastructure in NeuroGFX is provided by Neurokernel, an open source platform for the emulation of the fruit fly brain, and NeuroArch, a database for querying and executing fruit fly brain circuits. This provides an environment where computational researchers can present configurable, executable neural circuits, and experimental scientists can easily explore circuit structure and function ultimately leading to biological validation.
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.
Allows users to query, store, and analyze CoCoMac data. CoCoTools performs, in an automated way, CoCoMac queries, local caching of data to speed up subsequent access, and parsing of CoCoMac mapping and connectivity output to build graph object. The software can be useful for cognitive neuroscientists seeking to understand the connections of a particular brain region, computational neuroscientists interested in building large-scale simulations of the macaque brain, as well as scientists that use graph theory to study the large-scale topology of the brain.
Aims to extract the magnetic susceptibility of tissue from magnetic resonance imaging (MRI) phase measurements. DeepQSM assists users for inverting the magnetic dipole kernel convolution. This tool is a program able to use real-world single-orientation phase data without the need for explicit regularization terms and manual parameter tweaking. Moreover, it can be used to determine the composition of myelin sheet of nerve fibers in the brain.
Segments a cortical surface in a few parcels that have strong similarities with brain lobes. Spanol provides a segmentation of cortical surface by using a K-means clustering of Laplace-Beltrami operator eigenfunctions. It requires few low frequency descriptors of the brain geometry to provide a given number of relevant connected regions on the cortical surface. This tool is able to find statistical associations between spatial partitions independently of the partition distance used.
Enables the conversion of high-throughput sequencing (HTS) data into neuronal connection matrix. Based on the the Barcoding of Individual Neuronal Connectivity (BOINC) protocol, the proposed computational framework builds a connectivity map at a single neuron resolution using barcoded neurons by a pseudorabies virus (PVR). This method works on co-cultured neurons and is a microscopy-free neuro-connectivity method.