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Data analysis and visualisation

Data analysis provides methods and workflows to reveal hidden dynamics and extract characteristic statistical features from simulations and experiments. These toolboxes help computational neuroscientists analyse neuronal dynamics simulations and validate models against experimental evidence. Visualisation gives scientists a powerful tool for interactive data analysis, from an overview to deep insights.

3D Slicer

3D Slicer is: A software platform for the analysis (including registration and interactive segmentation) and visualization (including volume rendering) of medical images and for research in image guided therapy. A free, open source software available on multiple operating systems: Linux, MacOSX and Windows Extensible, with powerful plug-in capabilities for adding algorithms and applications. Features include: Multi organ: from head to toe. Support for multi-modality imaging including, MRI, CT, US, nuclear medicine, and microscopy. Bidirectional interface for devices. There is no restriction on use, but Slicer is not approved for clinical use and intended for research. Permissions and compliance with applicable rules are the responsibility of the user.

Data analysis and visualisation


Dendritic spines of pyramidal neurons are the targets of most excitatory synapses in the cerebral cortex and their morphology appears to be critical from the functional point of view. Thus, characterizing this morphology is necessary to link structural and functional spine data and thus interpret and make them more meaningful. We have used a large database of more than 7,000 individually 3D reconstructed dendritic spines from human cortical pyramidal neurons that is first transformed into a set of 54 quantitative features characterizing spine geometry mathematically. The resulting data set is grouped into spine clusters based on a probabilistic model with Gaussian finite mixtures. We uncover six groups of spines whose discriminative characteristics are identified with machine learning methods as a set of rules. The clustering model allows us to simulate accurate spines from human pyramidal neurons to suggest new hypotheses of the functional organization of these cells.

Data analysis and visualisationData

3D Structure Tensor Analysis

A Structure Tensor Analysis (STA) tool for the characterization of local 3D orientation in TIFF image stacks. This tool is based on the evaluation of local image intensity gradients. In addition to the local 3D orientation, it also provides a full analysis of local gradient strength, structure disarray, shape and fractional anisotropy indices.

Data analysis and visualisation


SynapsesSA is a tool designed to process and analyze patterns in the three-dimensional spatial distribution of cortical synapses. It brings a variety of both innovative and well-known techniques from the spatial statistics field and a web-based graphical interface compatible with most common browsers. Functionality: Process and visualize data from cortical synapses Model the spatial distribution of the synapses Replicate, via simulation, samples of cortical synapses Compare several indicators obtained from data of different layers

Data analysis and visualisation


AnonyMI is a tool for deidentifying MRIs while preserving the geometrical properties of the images. It uses a relatively low-resolution 3D reconstruction of the face to crop the MRI volume in order to keep the shape of the head and the face but remove identifiable information. It is implemented as a plug-in of 3D Slicer, a widely used software for 3D visualization and analysis, and includes a use-friendly interface, manual and automatic selection of the areas to mask, a batch processing mode for large datasets, fast and efficient 3D rendering of the results for quality control, and a command line interface.

Data analysis and visualisation


AnyWave is a free, multi-platform software that can be used to visualize electrophysiological data, as well as being used as a development framework in order to build custom plug-ins. AnyWave uses plug-ins to load or write files formats. A set of reader and writer plug-ins is bundled with AnyWave and brings the possibility to read several EEG or MEG manufacturers’ formats. The plug-ins are also used to add entirely new signal processing, data analysis and visualization capabilities to AnyWave. AnyWave opens and displays the contents of EEG or MEG files. Acquired signals are then displayed by AnyWave as well as markers that might be stored in the file. Markers can be read from a file, added by the user or even by a signal processing plug-in. Markers can be saved to or loaded from a specific AnyWave format.

Data analysis and visualisation


Brainstorm is a collaborative, open-source application dedicated to the analysis of brain recordings: MEG, EEG, fNIRS, ECoG, depth electrodes and animal electrophysiology. *Any time you want to use Brainstorm, be sure to pull the latest version from their GitHub Repository first.** Our objective is to share a comprehensive set of user-friendly tools with the scientific community using MEG/EEG as an experimental technique. For physicians and researchers, the main advantage of Brainstorm is its rich and intuitive graphic interface, which does not require any programming knowledge. We are also putting the emphasis on practical aspects of data analysis (e.g., with scripting for batch analysis and intuitive design of analysis pipelines) to promote reproducibility and productivity in MEG/EEG research. Finally, although Brainstorm is developed with Matlab (and Java), it does not require users to own a Matlab license: an executable, platform-independent (Windows, MacOS, Linux) version is made available in the downloadable package. To get an overview of the interface, you can watch this introduction video.

Data analysis and visualisation


Brayns is a visualizer that can interactively perform high-quality and high-fidelity rendering of neuroscience large data sets. It provides an abstraction of the underlying rendering engines, so that the best possible acceleration libraries can be used for the relevant hardware (CPU or GPU). Thanks to its client/server architecture, Brayns can be run in the cloud as well as on a supercomputer and stream the rendering to any browser, either in a web UI or a Jupyter notebook.

Data analysis and visualisation


ChemBioServer is a publicly available web application for effectively mining and filtering chemical compounds used in drug discovery. It provides researchers with the ability to (i) browse and visualize compounds along with their properties, (ii) filter chemical compounds for a variety of properties such as steric clashes and toxicity, (iii) apply perfect match substructure search, (iv) cluster compounds according to their physicochemical properties providing representative compounds for each cluster, (v) build custom compound mining pipelines and (vi) quantify through property graphs the top ranking compounds in drug discovery procedures. ChemBioServer allows for pre-processing of compounds prior to an in silico screen, as well as for post-processing of top-ranked molecules resulting from a docking exercise with the aim to increase the efficiency and the quality of compound selection that will pass to the experimental test phase.

Data analysis and visualisation

Clint Explorer

Clint Explorer is an application that uses supervised and unsupervised learning techniques to cluster neurobiological dataset. The main contributions of this software is that incorporates the expert’s know-how in the clustering process. Besides, it allows to interpret the results providing different metrics.

Data analysis and visualisation

DC Explorer

DC Explorer focuses on statistical analysis of data subsets. In this regard, it provides a treemap visualization to facilitate the subset definition. Treemapping is used to visualize the filtering operations that define each subset by grouping the data in different compartments, color coding each item and sorting them by their value. Once the subsets have been defined different statistical tests are automatically performed in order to analyze relationship between the selected subsets.

Data analysis and visualisation


The Electrophys Feature Extraction Library (eFEL) allows neuroscientists to automatically extract features from time series data recorded from neurons (both in vitro and in silico). Examples are the action potential width and amplitude in voltage traces recorded during whole-cell patch clamp experiments. The user of the library provides a set of traces and selects the features to be calculated. The library will then extract the requested features and return the values to the user. The core of the library is written in C++, and a Python wrapper is included. At the moment we provide a way to automatically compile and install the library as a Python module.

Data analysis and visualisation


The Python library Electrophysiology Analysis Toolkit (Elephant) provides tools for analysing neuronal activity data, such as spike trains, local field potentials and intracellular data. In addition to providing a platform for sharing analysis codes from different laboratories, Elephant provides a consistent and homogeneous framework for data analysis built on a modular foundation. The underlying data model is the Neo library. This framework easily captures a wide range of neuronal data types and methods, including dozens of file formats and network simulation tools. A common data description, as the Neo library provides, is essential for developing interoperable analysis workflows.

Modelling and simulationData analysis and visualisationValidation and inference


ExploreASL is a pipeline and toolbox for image processing and statistics of arterial spin labeling perfusion MR images. It is designed as a multi-OS, open source, collaborative framework that facilitates cross-pollination between image processing method developers and clinical investigators. The software provides a complete head-to-tail approach that runs fully automatically, encompassing all necessary tasks from data import and structural segmentation, registration and normalization, up to CBF quantification. In addition, the software package includes and quality control (QC) procedures and region-of-interest (ROI) as well as voxel-wise analysis on the extracted data. To-date, ExploreASL has been used for processing ~10000 ASL datasets from all major MRI vendors and ASL sequences, and a variety of patient populations, representing ~30 studies. The ultimate goal of ExploreASL is to combine data from multiple studies to identify disease related perfusion patterns that may prove crucial in using ASL as a diagnostic tool and enhance our understanding of the interplay of perfusion and structural changes in neurodegenerative pathophysiology. Additionally, this (semi-)automatic pipeline allows us to minimize manual intervention, which increases the reproducibility of studies.

Data analysis and visualisation


This library implements Cartesian genetic programming (e.g, Miller and Thomson, 2000; Miller, 2011) for symbolic regression in pure Python, targeting applications with expensive fitness evaluations. It provides Python data structures to represent and evolve two-dimensional directed graphs (genotype) that are translated into computational graphs (phenotype) implementing mathematical expressions. The computational graphs can be compiled as Python functions, SymPy expressions (Meurer et al., 2017) or PyTorch modules (Paszke et al., 2017). The library currently implements an evolutionary algorithm, specifically (mu + lambda) evolution strategies adapted from Deb et al. (2002), to evolve a population of symbolic expressions in order to optimize an objective function.

Data analysis and visualisation


Data recorded with intracerebral EEG (iEEG) electrodes in patients are notoriously hard to navigate through and synthetize, because of the high spatial variability and sparsity of the recordings. The HiBoP software is the first of its kind to allow conveniently the visualization and manipulation of multimodal data (iEEG, as well as fMRI, PET …) both at the individual level and at the group level (up to 200 and more).

Data analysis and visualisation


Ilastik is a simple, user-friendly tool for interactive image classification, segmentation and analysis. It is built as a modular software framework, which currently has workflows for automated (supervised) pixel- and object-level classification, automated and semi automated object tracking, semi-automated segmentation and object counting without detection. Most analysis operations are performed lazily, which enables targeted interactive processing of data subvolumes, followed by complete volume analysis in offline batch mode. Using it requires no experience in image processing.

Data analysis and visualisationBrain atlases

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