The Allen mouse brain atlas is a comprehensive digital resource that provides detailed information on the structure and function of the mouse brain. A wide range of structural and functional experimental data mapped to the Allen mouse brain atlas are shared via the EBRAINS research infrastructure.
MRIcroGL allows you to view 2D slices and renderings of your brain imaging data. It can display many image formats and includes a graphical interface for dcm2nii to convert DICOM images to NIfTI format. It allows you to draw regions of interest which can aid lesion mapping and fMRI analysis. It provides sophisticated volume rendering. It uses Python as a scripting language allowing repetitve tasks to be automated.
MRIcron is a cross-platform NIfTI format image viewer. It is a stand-alone program which does not require any other software that runs natively on Windows, Linux and Macintosh computers. It can load multiple layers of images, generate volume renderings and draw volumes of interest. It also provides dcm2nii for converting DICOM images to NIfTI format and NPM for statistics.
MRtrix3 provides a large suite of tools for image processing, analysis and visualisation, with a focus on the analysis of white matter using diffusion-weighted MRI. Features include the estimation of fibre orientation distributions using constrained spherical deconvolution, a probabilisitic streamlines algorithm for fibre tractography of white matter, fixel-based analysis of apparent fibre density and fibre cross-section, quantitative structural connectivity analysis, and non-linear spatial registration of fibre orientation distribution images. MRtrix3 also offers comprehensive visualisation tools in mrview.
This BIDS App enables generation and subsequent group analysis of structural connectomes generated from diffusion MRI data. The analysis pipeline relies primarily on the MRtrix3 software package, and includes a number of state-of-the-art methods for image processing, tractography reconstruction, connectome generation and inter-subject connection density normalisation.
A compact and general-purpose Python package for Multi-perturbation Shapley value Analysis.
MSPViz is a web-based visualisation tool for structural plasticity models. It uses a novel visualisation technique based on the representation of neuronal information through the use of abstract levels and a set of representations in each level. This hierarchical representation lets the user interact and change the representation, modifying the degree of detail of the information to be analysed in a simple and intuitive way, through the navigation of different views at different levels of abstraction. The designed representations in each view only contain the necessary variables to achieve the desired tasks, thus avoiding overwhelming saturation of information. The multilevel structure and the design of the representations provide organised views, which facilitate visual analysis tasks. Moreover, each view has been enhanced adding line and bar charts to analyse trends in simulation data. Filtering and sorting capabilities can be applied on each view to ease the analysis. Additionally, some other views, such as connectivity matrices and force-directed layouts, have been incorporated, enriching the already existing views and improving the analysis process. Finally, this tool has been optimised to lower render and data loading times, even from remote sources such as WebDav servers.
Multi-Brain: Unified segmentation of population neuroimaging data The Multi-Brain (MB) model has the general aim of integrating a number of disparate image analysis components within a single unified generative modelling framework (segmentation, nonlinear registration, image translation, etc.). The model is described in Brudfors et al [2020], and it builds on a number of previous works. Its objective is to achieve diffeomorphic alignment of a wide variaty of medical image modalities into a common anatomical space. This involves the ability to construct a "tissue probability template" from a population of scans through group-wise alignment [Ashburner & Friston, 2009; Blaiotta et al, 2018]. Diffeomorphic deformations are computed within a geodesic shooting framework [Ashburner & Friston, 2011], which is optimised with a Gauss-Newton strategy that uses a multi-grid approach to solve the system of linear equations [Ashburner, 2007]. Variability among image contrasts is modelled using a much more sophisticated version of the Gaussian mixture model with bias correction framework originally proposed by Ashburner & Friston [2005], and which has been extended to account for known variability of the intensity distributions of different tissues [Blaiotta et al, 2018]. This model has been shown to provide a good model of the intensity distributions of different imaging modalities [Brudfors et al, 2019].
PIPSA is a tool for the comparison of the electrostatic interaction properties of proteins. It permits the classification of proteins according to their interaction properties. The PIPSA similarity analysis procedure consists of several steps : (0) preparation step - making a directory for similarity calculations and arranging pdb files there (1) calculating protein interaction field grid (2) calculating similarity matrix from pdb files and protein interaction field grids (2a) adding additional protein(s) to an already processed set (without repeating previously done pair-wise similarity calculations) (3) phylogenic tree anaysis or other visualisation (4) correlate kinetic parameters with average interaction field differences The multipipsa python wrapper is designed to run Protein Interaction Properties comparisons on multiple sites of a protein (all CA atoms) and calculate scores according to Tong, Wade and Bruce, Proteins 2016; 84:1844-1858
This Python package offers a convenient interface to set-up co-simulation models that simulate TVB large-scale brain network models that interact with NEST spiking neuron models. NEST simulates neural activity at the microscopic spatial scale of single neurons or neuron networks. On the other hand, The Virtual brain simulates at the mesoscopic or macroscopic scales of large neural populations or brain regions. Here, both are brought together to enable neuroscientists to study how these different scales interact and how different scales inform activity "from the bottom up" and "down from the top". A generic Python interface allows users to quickly and conveniently set up a parallel simulation in TVB and in NEST and automatically handles the exchange of currents, spikes, voltages, etc. between the different scales. Although the technical aspect of this tool is realized, the scientific part is a work in progress and we are continuously enriching the coupling between scales such that biophysical plausibility is maintained. The TVB+NEST bundle software package -- available as an easy-to-use Docker image container -- combines the sophistication and flexibility of NEST's spiking neuron simulation infrastructure with TVB's whole-brain simulation, processing, analyses and visualisation capabilities.
MUSIC is an API allowing large scale neuron simulators using MPI internally to exchange data during runtime. MUSIC provides mechanisms to transfer massive amounts of event information and continuous values from one parallel application to another. Special care has been taken to ensure that existing simulators can be adapted to MUSIC. In particular, MUSIC handles data transfer between applications that use different time steps and different data allocation strategies. This is the MUSIC pilot implementation. The two most important components built from this software distribution is the music library 'libmusic.a' and the music utility 'music'. A MUSIC-aware simulator links against the C++ library and can be launched using mpirun together with the music utility as described in the README in the code repository. MUSIC can also be used from a C program using the API in music-c.h.
NEAT is a python library for the study, simulation and simplification of morphological neuron models. NEAT accepts morphologies in the de facto standard .swc format, and implements high-level tools to interact with and analyze the morphologies. NEAT also allows for the convenient definition of morphological neuron models. These models can be simulated, through an interface with the NEURON simulator, or can be analyzed with two classical methods: The separation of variables method to obtain impedance kernels as a superposition of exponentials and Koch's method to compute impedances with linearized ion channels analytically in the frequency domain. Furthermore, NEAT implements the neural evaluation tree framework and an associated C++ simulator, to analyze subunit independence. Finally, NEAT implements a new and powerful method to simplify morphological neuron models into compartmental models with few compartments. For these models, NEAT also provides a NEURON interface so that they can be simulated directly, and will soon also provide a NEST interface.
Nehuba is a core part of the SP5 atlas tool suite, and currently being extended by interactive components to cover more use cases than browsing of reference atlases. This is the viewer that will include the components for interactive analysis with ilastik and be used for overlaying volumetric data with high-resolution atlases on the web
Neo is a Python package for working with electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for writing to a subset of these formats plus non-proprietary formats including HDF5. The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to represention of data, with no functions for data analysis or visualization. Neo is used by a number of other software tools, including SpykeViewer (data analysis and visualization), Elephant (data analysis), the G-node suite (databasing), PyNN (simulations), tridesclous (spike sorting) and ephyviewer (data visualization). OpenElectrophy (data analysis and visualization) uses an older version of neo. Neo implements a hierarchical data model well adapted to intracellular and extracellular electrophysiology and EEG data with support for multi-electrodes (for example tetrodes). Neo's data objects build on the quantities package, which in turn builds on NumPy by adding support for physical dimensions. Thus Neo objects behave just like normal NumPy arrays, but with additional metadata, checks for dimensional consistency and automatic unit conversion. A project with similar aims but for neuroimaging file formats is NiBabel.
The Neo Viewer Service is a Django app that provides a REST API for reading electrophysiology data from any file format supported by Neo and exposing it in JSON format.
NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems, rather than on the exact morphology of individual neurons. It is ideal for networks of any size, including models of information processing (e.g. in the visual or auditory cortex of mammals), models of network activity dynamics (e.g. laminar cortical networks or balanced random networks) and models of learning and plasticity. NEST is openly available for download.
NEST Desktop is a web-based GUI application for NEST Simulator, an advanced simulation tool for computational neuroscience. NEST Desktop enables the rapid construction, parametrization, and instrumentation of neuronal network models. It offers interactive tools for visual network construction, running simulations in NEST and applying visualization to support the analysis of simulation results. NEST Desktop mainly consists of two views and a connection to a server-based NEST instance, which can be controlled using the web-based NEST Desktop front-end. The first view of NEST Desktop enables the user to create point neuron network models interactively. A visual modeling language is provided and a simulation script is automatically created from this visual model. The second view enables the user to analyze the returned simulation results using various visualization methods. NEST Desktop offers additional functionality, such as employing Elephant for more sophisticated statistical analyses.
The NEST Instrumentation App is a graphical interface to connect recording and stimulation devices to networks. The interface is easy to use, is versatile, and gives a good visualization of the connection between devices and neurons. After having selected the desired connections, the projections can be sent to NEST to run simulations.
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