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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


A MATLAB® toolbox that given a three-dimensional spine reconstruction computes a set of characteristic morphological measures that unequivocally determine the spine shape.

Modelling and simulation


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

Android app for multimodal data acquisition from wearables

An app for acquiring and storing data from multiple sensors. Currently, can be used with the following devices: Empatica E4 Tablet/Smartphone built-in sensors MetaMotion R In order to improve reliability, a bipartite structure has been implemented. In particular, the Main Activity acts as an interface between the user and the main service that constitutes the principal actor. The latter performs scans, handles the user's requests to connect remote devices, all the unexpected disconnection that may happen and receives the data from the wireless sensors.



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


Arbor is a high-performance library for computational neuroscience simulations with multi-compartment, morphologically-detailed cells, from single cell models to very large networks. Arbor is written from the ground up with many-cpu and gpu architectures in mind, to help neuroscientists effectively use contemporary and future HPC systems to meet their simulation needs. Arbor supports NVIDIA and AMD GPUs as well as explicit vectorization on CPUs from Intel (AVX, AVX2 and AVX512) and ARM (Neon and SVE). When coupled with low memory overheads, this makes Arbor an order of magnitude faster than the most widely-used comparable simulation software. Arbor is open source and openly developed, and we use development practices such as unit testing, continuous integration, and validation.

Modelling and simulationCellular level simulation


A SciUnit library for data-driven testing of basal ganglia models. Employed for testing via the HBP Validation Framework. This test shall take as input a BluePyOpt optimized output file, containing a hall_of_fame.json file specifying a collection of parameter sets. The validation test would then evaluate the model for all (or specified) parameter sets against various eFEL features.

Validation and inference

Bids Manager & Pipeline

Manually driven processes for data storing can lead to human errors, which cannot be tolerated in the context of a clinical data sets. The Bids manager offers a secure system to import and structure patient’s clinical data sets in Brain Imaging Data Structure (BIDS). BIDS is an initiative aiming at establishing a common standard to describe data and its organization on disk for both neuroimaging and electrophysiological data. Bids Manager is a software for clinicians and researchers with a user-friendly interface.

Share dataData

BioExcel Building Blocks

BioExcel Building Blocks Workflows is a collection of biomolecular workflows to explore the flexibility and dynamics of macromolecules, including signal transduction proteins or molecules related to the Central Nervous System. Molecular dynamics setup for protein and protein-ligand complexes are examples of workflows available as Jupyter Notebooks. The workflows are built using the BioBB software library, developed in the framework of the BioExcel Centre of Excellence. BioBBis a collection of Python wrappers on top of popular biomolecular simulation tools, offering a layer of interoperability between the wrapped tools, which make them compatible and prepared to be directly interconnected to build complex biomolecular workflows.

Modelling and simulationMolecular and subcellular simulation


BioExcel-CV19 is a platform designed to provide web-access to atomistic-molecular dynamics trajectories for macromolecules involved in the COVID-19 disease. The BioExcel-CV19 web server interface presents simulated trajectories, with a set of quality control analyses, system information and interactive and graphical information on key structural and flexibility features. All the analyses integrated in the web portal are completely interactive. Whenever possible, a direct link from the analysis to the 3D representation is offered, using the NGL viewer tool. All data produced is available to download from an associated programmatic access API.

Modelling and simulationMolecular and subcellular simulation


BluePyEfe aims at easing the process of reading experimental recordings and extracting batches of electrical features from these recordings. To do so, it combines trace reading functions and features extraction functions from the eFel library. BluePyEfe outputs protocols and features files in the format used by BluePyOpt for neuron electrical model building.

Modelling and simulation


When building a network simulation, biophysically detailed electrical models (e-models) need to be tested for every morphology that is possibly used in the circuit. E-models can e.g. be obtained using BluePyOpt by data-driven model parameter optimization. Developing e-models can take a lot of time and computing resources. Therefore, these models are not reoptimized for every morphology in the network. Instead we want to test if an existing e-model matches that particular morphology `well enough’. This process is called Cell Model Management (MM). It takes as input a morphology release, a circuit recipe and a set of e-models with some extra information. Next, it finds all possible (morphology, e-model)-combinations (me-combos) based on e-type, m-type, and layer as described by the circuit recipe, and calculates the scores for every combination. Finally, it writes out the resulting accepted me-combos to a database, and produces a report with information on the number of matches – BluePyMM. This software is also part of the Human Brain Project Brain Simulation Platform.

Modelling and simulation


The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible framework for data-driven model parameter optimisation that wraps and standardises several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures.

Modelling and simulation


Simulate or emulate spiking neural networks on BrainScaleS. Models and simulation experiments can be described in a Python script using the PyNN API and submitted either through the EBRAINS Collaboratory website or via our web API (python client available). Results can be viewed via browser and downloaded as data files for analysis, making use e.g. of the data analysis capabilities EBRAINS offers.

Neuromorphic computingModelling and simulation

Brain Simulation Platform Service Account

The Brain Simulation Platform Service Account allows developers to submit jobs, on behalf of the HBP Collaboratory users, on the remote HPC systems made available in the HBP framework. The service leverages the UNICORE APIs and uses the token generated for individual users (upon logging in on the HBP Collaboratory) for tracking general information on job submission and status.

Modelling and simulation

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