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PrAGMATiC / Probabilistic And Generative Model of Areas Tiling the Cortex
Provides a detailed semantic atlas. PrAGMATiC determines where functional areas appear on the cortical sheet and determines how the cortical map is produced from an arrangement of areas. It can be modified to model functional gradients explicitly. The tool can be used for determining whether the semantic maps found here are best described as homogeneous areas or as gradients. It is based on a Voronoi diagram that must assign an area to every point on the cortex.
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.
PRoNTo / Pattern Recognition for Neuroimaging Toolbox
Provides a method for multivariate analysis based on machine learning models for neuroimaging data. PRoNTo is open-source, cross-platform, MATLAB-based and Statistical Parametric Mapping (SPM) compatible, therefore being suitable for both cognitive and clinical neuroscience research. It can also be extended via the addition of new feature selection and extraction approaches, validation procedures or classification/regression models.
GC-LDA / Generalized Correspondence Latent Dirichlet Allocation
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.
Detects neuronal assemblies into a complete data processing pipeline designed for the comprehensive analysis of fluorescence imaging data. The Toolbox-Romano-et-al is a computational package that consists of (i) modules for video pre-processing, (ii) morphological image segmentation into regions of interest (ROIs) corresponding to single neurons, (iii) extraction of fluorescence signals, (iv) analysis of ROI responses to stimulus and/or behavioral variables, (v) detection of assemblies of ROIs, (vi) exploratory analysis of network dynamics and (vii) the automatic generation of surrogate shuffled datasets to act as controls for statistical purposes.
rMSPRT / Recursive Multi-hypothesis Sequential Probability Ratio Test
Characterizes the neural mechanism that underlies decisions. rMSPRT is implemented as a probabilistic, recursive, parallel procedure. It can determine that the mean decision time on the dot motion task is a decreasing function of coherence. This tool accounts for the dependence of choice reaction times on task difficulty, trial outcome, and the number of alternatives. It is able to decide faster than monkeys in the same conditions.
Proposes a simulator-independent language for building neuronal network models. PyNN makes it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators.
PANDA / Pipeline for Analyzing braiN Diffusion imAges
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.
GRETNA / GRaph thEoreTical Network Analysis
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.
A pipeline for the reconstruction of discontinuous neuronal morphology in noisy images. SparseTracer is based on two methods. One is the region-to-region connection (RRC) method for detecting the initial part of a neurite, which can effectively gather local cues, i.e., avoid the whole image analysis, and thus boosts the efficacy of computation. The other is constrained principal curves method for completing the neurite reconstruction, which uses the past reconstruction information of a neurite for current reconstruction and thus can be suitable for tracing discontinuous neurites. SparseTracer is able to deal with the large-scale image dataset.
GAT / Graph-Analysis Toolbox
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.
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.
AICHA / Atlas of Intrinsic Connectivity of Homotopic Areas
Covers the whole cerebrum, each having homogeneity of its constituting voxels intrinsic activity, and a unique homotopic contralateral counterpart with which it has maximal intrinsic connectivity. AICHA includes 192 homotopic region pairs (122 gyral, 50 sulcal, and 20 grey nuclei). It can be used in intrinsic/effective connectivity analyses, as well as investigating brain hemispheric specialization. The tool provides a categorization at a regional level of sampling that is more precise than the anatomical atlas level; however, it is less precise than the voxel level.
Budapest Reference Connectome
Generates the common edges of the connectomes of 96 distinct cortexes, each with 1015 vertices, computed from 96 MRI data sets of the Human Connectome Project. The user may set numerous parameters for the identification and filtering of common edges, and the graphs are downloadable in both csv and GraphML formats; both formats carry the anatomical annotations of the vertices, generated by the FreeSurfer program. The resulting consensus graph is also automatically visualized in a 3D rotating brain model on the website. The consensus graphs, generated with various parameter settings, can be used as reference connectomes based on different, independent MRI images, therefore they may serve as reduced-error, low-noise, robust graph representations of the human brain.
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.
Preprocessed Connectomes Project
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.
SPANOL / SPectral ANalysis Of Lobes
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.
BRAPH / BRain Analysis using graPH theory
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.
Incorporates the effects of dendritic spikes into Hebbian learning. HagaEtAl2017 provides a framework of hippocampal memory processing based on a two-compartment neuron model. It gives a plasticity rule that combines canonical correlation analysis of correlated somatic and dendritic inputs with the conventional Hebbian plasticity rule for Principal Component Analysis (PCA) of uncorrelated inputs. It predicts that inhibitory plasticity at the dendrites of pyramidal cells plays pivotal roles for the stability and functional specialization of dendritic activity during learning.
FFBO / Fruit Fly Brain Observatory
Aims to study fruit fly brain function and investigate fruit fly brain disease models that are highly relevant to the mechanisms of human neurological and psychiatric disorders. FFBO stores and processes data related to the neural circuits of the fly brain including location, morphology, connectivity and biophysical properties of every neuron. It seamlessly integrates the structural and genetic data from multiple sources that can be queried, visualized and interpreted. Furthermore, it automatically generates models of the fly brain that can be simulated efficiently using multiple Graphics Processing Units (GPUs) to help elucidate the mechanisms of human neurological disorders and identify drug targets.
Offers a platform for numerical simulations of neural activity based on neural field models. NeuroField is an open source software that is able to simulate multiple continuum spatiotemporal models as well as systems with heterogeneous time delays between populations. The application also includes a module giving access to features for running, analyzing and visualizing models. It can be used to predict a range of brain phenomena or as a validation tool for neural field models and simulators.
Furnishes a method for tracking real-time auditory attention from non-invasive M/EEG recordings. Real-time-Tracking-of-Selective-Auditory-Attention is a software, based on Bayesian filtering, that performs in three steps: (i) estimation of dynamic models of encoding and decoding in real-time; (ii) extracting an attention-modulated feature; and (iii) determination of the given feature by using a state-space simulator and translation of the results to provide an evaluation of the attentional state.
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