Tools

ImageJ

ImageJ is an open source image processing program designed for scientific multidimensional images. ImageJ is highly extensible, with thousands of plugins and scripts for performing a wide variety of tasks, and a large user community. Open source: ImageJ is a tool for the scientific community. To maintain transparency, the ImageJ application and its source code will always be freely available. Reproducible: Powerful tools such as the Script Editor and personal update sites help you develop and share reproducible analysis workflows. Interoperable: ImageJ is not an island. Use the best tool for the job, including KNIME, ITK, MATLAB, and a multitude of scripting languages.

Data analysis and visualisation

IntrAnat

A software to visualize electrodes implantation on image data and prepare database for group studies. Multimodality and electrode implantation with 3D display and easy co-registration between modalities. (MRI : T1, T2, FLAIR, fMRI, DTI; CT ; PET) Semi-automatic estimation of the volume of resection Importation of SEEG files (for now only .TRC, Micromed©) Display of cortico-cortical evoked potential mapping Automatic exportation of "dictionaries" containing the information of contact positions in the native and MNI coordinate systems, associated parcels in different atlas (MarsAtlas, Destrieux – Freesurfer, Brodmann, AAL, etc.), white/grey matter labeling, and resection labeling. Automatic exportation of dictionaries containing the total volume of the resection and percentage of MarsAtlas or Destrieux parcels which have been considered by the resection (for now only assumes no brain deformation due to the resection). Display of Epileptogenicity maps coregistered with other modalities (all statistical maps registered in the T1 pre space are loadable). groupDisplay can be used to visualize electrode contacts from many patients over images in the MNI referential and to research patients according to different keywords. IntranatElectrodes software is based on BrainVISA, Morphologist and Cortical Surface. It uses ANTs and spm12 for multimodality coregistration and spm12 for estimation of the deformation field to convert into MNI Space. It needs a Matlab license to run the normalisation and groupDisplay interface.

Data analysis and visualisation

Lempel Ziv Perturbational Complexity Index

The module allows to compute the Perturbational Complexity (LZ) of Casarotto et al. (2016). A Python Notebook gives a step-by-step explanation of the steps needed to calculate the perturbational complexity index (PCI). The same notebook illustrates how PCI changes across two different brains states, the wake and the sleep stages. It is assumed that an inverse solution has already been obtained from the TMS/EEG data. In the notebook a 3-spheres BERG method was used to obtain the cortical currents (as in the 3-sphere BERG). Inverse solutions can be obtained from TMS/EEG data by the well known MNE Python module. (Casarotto S, Comanducci A, Rosanova M, Sarasso S, Fecchio M, Napolitani M, et al. Stratification of unresponsive patients by an independently validated index of brain complexity: Complexity Index. Annals of Neurology. 2016;80: 718–729)

Data analysis and visualisation

Livre

Livre (Large-scale Interactive Volume Rendering Engine) is an out-of-core, multi-node, multi-gpu, OpenGL volume rendering engine to visualise large volumetric data sets. It provides the following major features to facilitate rendering of large volumetric data sets: Visualisation of pre-processed UVF format (source code) volume data sets. Real-time voxelisation of different data sources (surface meshes, BBP morphologies, local-field potentials, etc) through the use of plugins. Multi-node, multi-gpu rendering (Currently only sort-first rendering)

Data analysis and visualisation

LocaliZoom

LocaliZoom allows the viewing and exploring of high-resolution images with superimposed atlas overlays, and the extraction of coordinates of annotated points within those images for viewing in 3D brain atlas space. It is well suited for the extraction of a limited number of coordinates, e.g. representing an electrode track or labelling within a small region of interest.

Data analysis and visualisationBrain atlases

MD-IFP

The MD-IFP is a python workflow for the generation and analysis of protein-ligand interaction fingerprints from Molecular Dynamics trajectories. If used for the analysis of RAMD (Random Accelaration Molecular Dynamics) trajectories, it can help to investigate dissociation mechanisms by characterizing transition states as well as the determinants and hot-spots for dissociation. As such, the combined use of τRAMD and MD-IFP may assist the early stages of drug discovery campaigns for the design of new molecules or ligand optimization.

Data analysis and visualisation

MeLVin

The application of data visualization to exploratory analysis has proven its effectiveness in different scientific fields. Unfortunately, each discipline suffers from specific problems, making it difficult to apply general solutions. MeLVin is a graphical meta-framework for our MEta Language for Visualization. It was created to facilitate the design of interactive coordinated views applications for visual exploration of data using different technologies. The platform is conceived as an abstraction layer placed over existing data visualization and processing environments, enabling their integration. The data analysis workflow can be seamlessly defined using data-flow diagrams. Unlike most data-flow diagram-based data mining applications, MeLVin allows users to include visualizations as part of the analysis process and not just as a tool to display final results. Please, find additional information on our project web page: https://gmrv.es/gmrvvis/melvin/ If you are interested in the project, you can test its functionalities at: https://gmrv.es/gmrvvis/melvin/app/auth

Data analysis and visualisation

MeshView

MeshView is a web application for real-time 3D display of surface mesh data representing structural parcellations from volumetric atlases, such as the Waxholm Space Atlas of the Sprague Dawley Rat Brain.

Data analysis and visualisation

MRIcron

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.

Data analysis and visualisation

MRtrix3

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.

Data analysis and visualisation

MRtrix3_connectome

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.

Data analysis and visualisation

Multi-Brain

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].

Data analysis and visualisation

multipipsa

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

Data analysis and visualisation

Nehuba - Online visualization of large volumetric brain images

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

Data analysis and visualisation

Neo-Viewer

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.

Data analysis and visualisation

NEST

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.

Modelling and simulationNetwork level simulationData analysis and visualisation

NEST Desktop

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.

Modelling and simulationNetwork level simulationData analysis and visualisation

NeuroM

NeuroM is a Python toolkit for the analysis and processing of neuron morphologies It includes functionality to analyze (features like radial distances, volumes, neurite type counts, sholl anaylsis, etc.), visualize, and check neuron morphologies (disconnected neurites, duplicated points, zero diameters, etc). It can be used as a library, but also includes a command line interface to perform more common operations.

Data analysis and visualisation

Results: 19 - 36 of 63

Make the most out of EBRAINS