Tools

NeuroScheme

NeuroScheme uses schematic representations, such as icons and glyphs to encode attributes of neural structures (i.e. neurons, columns, layers, populations, etc.). This abstraction alleviates problems with displaying, navigating, and analysing, large datasets. It has been designed specifically to manage hierarchically organised neural structures; one can navigate through the levels of the hierarchy, and hone in on their desired level of details. NeuroScheme works using what we call "domains". These domains specify which entities, attributes and relationships are going to be used for a specific use case. NeuroScheme currently has two built-in domains: “cortex” and “congen”. The “cortex” domain is designed for navigating and analysing cerebral cortex structures (i.e. neurons, micro-columns, columns, layers, etc.). The “congen” domain can be used to define the properties of both cells and connections, create circuits composed of neurons, and build populations. Groups of populations can be easily moved to a higher level of abstraction (such as column or layer), allowing one to create complex networks with little effort. These circuits can be exported afterwards and used for further analysis and simulations.

Modelling and simulationCellular level simulationData analysis and visualisation

NeuroSuites-BNs

NeuroSuites is an online platform to run multiple neuroscience tools in a very easy way. To start using NeuroSuites just click on your preferred category on the tab at the top of the page. It provides you multiple tools to analyze neuroscience data. You will not need to install anything to run the tools provided, everything is online. Furthermore, when you are done, you can export your results to your own computer. There are many available tools, some are focused in analyzing neurons morphology reconstructions and the others are general purpose tools like the statistics engine, supervised classification models, Bayesian networks, etc.

Data analysis and visualisation

NeuroTessMesh

Visualise neurons and neural circuits consisting of a large number of cells with NeuroTessMesh on your desktop. It enables the visualisation of the 3D morphology of cells included in open databases, such as NeuroMorpho, and provides the tools needed to approximate missing information such as the soma’s morphology. NeuroTessMesh takes morphological tracings of cells acquired by neuroscientists as its only input. It generates 3D models that approximate the neuronal membrane. The resolution of the models can be adapted at the time of visualisation. NeuroTessMesh can assign different colours to different morphologies, in order to visually codify relevant morphological variables, or even neuronal activity.

Modelling and simulationCellular level simulationData analysis and visualisation

Nilearn

Nilearn makes it easy to use many advanced machine learning, pattern recognition and multivariate statistical techniques on neuroimaging data for applications such as MVPA (Mutli-Voxel Pattern Analysis), decoding, predictive modelling, functional connectivity, brain parcellations, connectomes. Nilearn can readily be used on task fMRI, resting-state, or VBM data. For a machine-learning expert, the value of nilearn can be seen as domain-specific feature engineering construction, that is, shaping neuroimaging data into a feature matrix well suited to statistical learning, or vice versa.

Data analysis and visualisation

Nutil

Nutil aims to simplify the pre-and-post processing of 2D brain section image data from mouse, rat and other small animal models. It can be used to preprocess images in preparation for analysis, and used as part of the QUINT workflow to perform spatial analysis of labelled features relative to a reference brain atlas. Nutil is developed as a stand-alone application with a simple user-interface, requiring little-to-no experience to execute.

Brain atlasesData analysis and visualisation

Paraver

Paraver was developed to respond to the need to have a qualitative global perception of the application behavior by visual inspection and then to be able to focus on the detailed quantitative analysis of the problems. Expressive power, flexibility and the capability of efficiently handling large traces are key features addressed in the design of Paraver. The clear and modular structure of Paraver plays a significant role towards achieving these targets. Paraver is a very flexible data browser that is part of the CEPBA-Tools toolkit. Its analysis power is based on two main pillars. First, its trace format has no semantics; extending the tool to support new performance data or new programming models requires no changes to the visualizer, just to capture such data in a Paraver trace. The second pillar is that the metrics are not hardwired on the tool but programmed. To compute them, the tool offers a large set of time functions, a filter module, and a mechanism to combine two time lines. This approach allows displaying a huge number of metrics with the available data. To capture the experts knowledge, any view or set of views can be saved as a Paraver configuration file. After that, re-computing the view with new data is as simple as loading the saved file. The tool has been demonstrated to be very useful for performance analysis studies, giving much more details about the applications behaviour than most performance tools. Some Paraver features are the support for: Detailed quantitative analysis of program performance Concurrent comparative analysis of several traces Customizable semantics of the visualized information Cooperative work, sharing views of the tracefile Building of derived metrics

Data analysis and visualisation

ParaView

ParaView is an open-source, multi-platform data analysis and visualization application. ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using ParaView’s batch processing capabilities. ParaView was developed to analyze extremely large datasets using distributed memory computing resources. It can be run on supercomputers to analyze datasets of petascale as well as on laptops for smaller data. ParaView is an application framework as well as a turn-key application. The ParaView code base is designed in such a way that all of its components can be reused to quickly develop vertical applications. This flexibility allows ParaView developers to quickly develop applications that have specific functionality for a specific problem domain. ParaView runs on distributed and shared memory parallel and single processor systems. It has been successfully deployed on Windows, Mac OS X, Linux, SGI, IBM Blue Gene, Cray and various Unix workstations, clusters and supercomputers. Under the hood, ParaView uses the Visualization Toolkit (VTK) as the data processing and rendering engine and has a user interface written using Qt® The goals of the ParaView team include the following: Develop an open-source, multi-platform visualization application. Support distributed computation models to process large data sets. Create an open, flexible, and intuitive user interface. Develop an extensible architecture based on open standards.

Data analysis and visualisation

PLIViewer

The PLIViewer is visualization software for 3D-Polarized Light Imaging (3D-PLI), to interactively explore the scalar and vector datasets; it provides additional methods to transform data, thus revealing new insights that are not available in the raw representations. The high resolution provided by 3D-PLI produces massive, terabyte-scale datasets, which makes visualization challenging. The PLIViewer tackles this problem by providing functionality to select areas of interests from the dataset, and options for downscaling. It makes it possible to interactively compute and visualize Orientation Distribution Functions (ODFs) and polar plots from the vector field, which reveal mesoscopic and macroscopic scale information from the microscopic dataset without significant loss of detail. The PLIViewer equips the neuroscientist with specialized visualization tools needed to explore 3D-PLI datasets through direct and interactive visualization of the data.

Data analysis and visualisation

PoSCE

PoSCE (Population Shrinkage Covariance Embedding) is a functional connectivity estimator from rfMRI (resting-state functional Magnetic Resonance Images) timeseries. It relies on the Riemannian geometry of covariances and integrates prior knowledge of covariance distribution over a population. This is an implementation of the work introduced in: M. Rahim, B. Thirion and G. Varoquaux. Population shrinkage of covariance (PoSCE) for better individual brain functional-connectivity estimation, in Medical Image Analysis (2019).

Data analysis and visualisation

PyJuGex

Find a set of differentially expressed genes between two user defined volumes of interest based on JuBrain maps. The tool downloads expression values of user specified sets of genes from Allen Brain API. Then, it uses zscores to find which genes are expressed differentially between the user specified regions of interests. This tool is available as a Python package.

Data analysis and visualisation

Remote Connection Manager

The Remote Connection Manager (RCM) is an application that wraps vnc client. It allows HPC-users to perform remote visualization on HPC clusters. The tool offers to: Visualize the data produced on Cineca’s HPC systems (scientific visualization); Analyse and inspect data directly on the systems; Debug and profile parallel codes running on the HPC clusters. Debugging and profiling tools have to be interfaced to the compute nodes which execute the parallel code; they can benefit from tools enabling a graphic connection to the compute nodes. Scientific visualization can exploit the hardware (GPUs, memory and CPUs) available on the server side, enabling the user to remotely access their data and display them in an efficient way on their local client. The graphical interface of RCM allows the HPC users to easily create remote displays and to manage them (connect, kill, refresh).

Data analysis and visualisation

ReMoToo

ReMoToo is a system service that is able to stream the desktop to web remote clients, making it possible to have interactive sessions over remote high performance systems or even regular systems through a standard web browser. The key aspects of ReMoToo are the high quality visualization it provides as well as its low latency. To achieve it, ReMoToo uses video compression on the server side and sends the generated video stream to the client in a transparent and easy way. On the server side, several ReMoToo instances are managed by another service called ReMoLON. This service is in charge of the initiation, control and stop of ReMoToo visualization streams. The ReMoLON system service is connected through the ReMoLON_FrontEnd, a simple web server running on the login node/s. This FrontEnd is in charge of the user authentication and configuration of the ReMoToo instances through the ReMoLON system services.

Data analysis and visualisation

rsHRF

This toolbox is aimed to retrieve the onsets of pseudo-events triggering an hemodynamic response from resting state fMRI BOLD voxel-wise signal. It is based on point process theory, and fits a model to retrieve the optimal lag between the events and the HRF onset, as well as the HRF shape, using a choice of basis functions (the canonical shape with two derivatives, (smoothed) Finite Impulse Response, mixture of gammas).

Data analysis and visualisation

RTNeuron

RTNeuron is a scalable real-time rendering tool for the visualisation of neuronal simulations based on cable models. Its main utility is twofold: the interactive visual inspection of structural and functional features of the cortical column model and the generation of high quality movies and images for presentations and publications. The package provides three main components: A high level C++ library. A Python module that wraps the C++ library and provides additional tools. The Python application script rtneuron-app.py A wide variety of scenarios is covered by rtneuron-app.py. In case the user needs a finer control of the rendering, such as in movie production or to speed up the exploration of different data sets, the Python wrapping is the way to go. The Python wrapping can be used through an IPython shell started directly from rtneuron-app.py or importing the module rtneuron into own Python programs. GUI overlays can be created for specific use cases using PyQt and QML.

Data analysis and visualisationModelling and simulation

siibra-explorer

siibra-explorer is built around an interactive 3D view of the brain displaying a unique selection of detailed templates and parcellation maps for the human, macaque, rat or mouse brain, including BigBrain as a microscopic resolution human brain model at its full resolution of 20 micrometres.

Data analysis and visualisationBrain atlases

SNT

SNT is ImageJ's framework for semi-automated tracing, visualization, quantitative analyses and modeling of neuronal morphology distributed with Fiji. SNT supports modern multi-dimensional microscopy data, features advanced visualization and quantification tools, and interacts with all major morphology databases. All aspects of the program can be controlled from a user-friendly interface or programmatically, using several of Fiji's supported scripting languages.

Data analysis and visualisationModelling and simulation

SpectralSegmentation

Spectralsegmentation is a pipeline that can be used to detect active neurons and dendrites in ca-imageing data. A series of steps are defined to achieve this. After image stabilization and transposing an image sequence to the (time x pixel) space, Cross-spectral analysis is applied to low frequency(<1Hz) decimated pixel traces. This results in images representing the cross spectral power of each pixel with it's surrounding pixels at increasing frequency components (0.017Hz steps - 0.4Hz). These images are used to define preliminary ROIs using morphological criteria. The ROIs are then constrained to contain only pixels with possitive correlations. The pipeline includes a graphical user interface to edit the automatically extracted ROIs, to add new ones or delete ROIs by further constraining their properties.

Data analysis and visualisation

SpykeViewer

It is based on the Neo library, which enables it to load a wide variety of data formats used in electrophysiology. At its core, Spyke Viewer includes functionality for navigating Neo object hierarchies and performing operations on them. A central design goal of Spyke Viewer is flexibility. For this purpose, it includes an embedded Python console for exploratory analysis, a filtering system, and a plugin system. Filters are used to semantically define data subsets of interest. Spyke Viewer comes with a variety of plugins implementing common neuroscientific plots (e.g. rasterplot, peristimulus time histogram, correlogram, and signal plot). Custom plugins for other analyses or plots can be easily created and modified using the integrated Python editor or external editors. Users can download and share additional plugins and other extensions at the Spyke Repository. Among the extensions hosted at the site are plugins for spike detection and spike sorting.

Data analysis and visualisation

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