bsp-usecase-wizard

Web application that displays scientific use cases and creates the environment that allows end users to run them flawlessly.

Different categories of use cases like morphology analysis, trace analysis, simulation and different scales such as brain area, single cell, etc. are shown to be run in Ebrains infrastructure. Some of the use cases are web apps, others jupyter notebooks.

This is part of Interactive Workflows for Cellular Level Modeling in Ebrains

Other software

All software

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

3DSpineMFE

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

3DSpineS

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

Other software

3D Slicer

3DSpineMFE

3DSpineS

Make the most out of EBRAINS