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

ImaGIN

SPM-based Matlab toolbox for processing intracranial EEG recordings (SEEG and ECOG), including basic (data conversion, electrode positioning, montage, filtering, time-frequency decomposition) and advanced (epileptogenicity mapping, statistical analyses) for the analysis of epileptic seizures and cortico-cortical evoked potentials. All the functions can be used both from the toolbox interface and from Matlab scripts. They are written in Matlab and assume the data are saved in SPM EEG data format.

InDiProv

This server-side tool is meant to be used for the creation of provenance tracks in context of interactive analysis tools and visualization applications. It is capable of tracking multi-view and multiple applications for one user using this ensemble. It further is able to extract these tracks from the internal data base into a XML-based standard format, such as the W3C Prov-Model or the OPM format. This enables the integration to other tools used for provenance tracking and will finally end up in the UP.

Insite

Insite provides a middleware that enables users to acquire data from neural simulators via the in-transit paradigm. In-transit approaches allow users to access data from a running simulation while the simulation is still going on. In the traditional approach data from simulations is written to disk first, and can only be accessed after the simulation has finished. However, this has two main constraints: Data can only be further processed after the whole simulation has finished. Disk speed can be a bottleneck when simulating, as data has to be written out. Data must be completely stored on the machine, leading to large files. Using Insite allows users to develop data consumer, such as visualizations and analysis tools that allow early insight into the data without storing data with virtually zero dependencies. Insite* was specifically designed to be ease-to-integrate and easy-to-use to allow a wide range of users to take advantage of in-transit approaches in the context of brain simulation. Insite uses off-the-shelf dataformats and protocols to make integration as easy as possible. Data can be queried via an HTTP REST API from Insite's access node, which represents a single point of contact for the user. Insite support the following three simulators: NEST, Arbor, TVB.

Interactive Atlas Viewer

The Human Brain Project hosts a rich web-based 3D atlas viewer („NeHuBa“), that is capable of displaying very large brain volumes, including oblique slicing, a whole brain overview, surface meshes, and maps. It allows to interactively choose different template spaces and reference parcellations, find brain areas by name or visual selection, and browse additional region-specific multimodal data. The rendering of large volumetric data builds on the opensource project neuroglancer. Some important atlases and templates can be directly accessed, including the „Big Brain“ (Amunts et al., Science 2013) the JulichBrain cytoarchitectonic atlas and the Waxholm Space Atlas of the Sprague Dawley Rat Brain.

Brain atlases

Interactive Workflows for Cellular Level Modeling

Work through a number of pipelines for single cell model optimization of different brain region cells, run in silico experiments of individual neurons, small circuits and entire brain regions, perform ad hoc data analysis on electrophysiological data, synaptic events fitting, morphology analysis and visualization.

Modelling and simulationCellular level simulation

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

JuGEx

JuGEx is a tool that combines data from two repositories, the Allen Human Brain Atlas and the EBRAINS Human Brain Atlas, to provide detailed insights into how gene activities and microanatomical architectures contribute to brain function and dysfunction. The Allen Human Brain Atlas contains regional gene expression data, while the EBRAINS human brain atlas offers three-dimensional cytoarchitectonic maps reflecting interindividual variability. JuGEx integrates tissue transcriptome and probabilistic brain segregation data, allowing researchers to explore the complex relationships between genetic expression, brain structure, and function. With JuGEx, researchers can gain a deeper understanding of the intricate connections between genes and cognition, which can inform the development of new treatments for brain disorders and improve our understanding of brain function in health and disease.

Brain atlases

Jupyter Lab

An extensible environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture. Currently ready for users. JupyterLab is the next-generation user interface for Project Jupyter offering all the familiar building blocks of the classic Jupyter Notebook (notebook, terminal, text editor, file browser, rich outputs, etc.) in a flexible and powerful user interface. JupyterLab will eventually replace the classic Jupyter Notebook. JupyterLab can be extended using npm packages that use our public APIs. To find JupyterLab extensions, search for the npm keyword jupyterlab-extension or the GitHub topic jupyterlab-extension. To learn more about extensions, see the user documentation. The current JupyterLab releases are suitable for general usage, and the extension APIs will continue to evolve for JupyterLab extension developers.

Data

KG Core

The heart of the EBRAINS Knowledge Graph is called "KG Core" and provides the API you are looking for if you want to integrate or interact with the EBRAINS Knowledge Graph by scripts. It consists of a graph database and a service layer providing the necessary business logic. The KG Core is also responsible for the authorization of the users.

KG Editor

Collaboratively create and edit the metadata through the Knowledge Graph Editor which supports you by input aids and built-in ontologies. With the KG Editor, managing data "on the graph", reviewing and publishing revisions of your metadata is easy to achieve and allows to keep up with high quality standards.

KG Query Builder

You want to query metadata to use in your own application or reports? Make use of the easy-to-use KG Query Builder - a UI that allows you to define your queries and collect just the metadata you want conveniently in your web browser.

KG Search

Finding data through the EBRAINS Knowledge Graph is easy: Use EBRAINS Find data, the publicly available Search User Interface, to search, filter, explore and download neuroscientific data.

KnowledgeSpace

KnowledgeSpace (KS) is a community-based encyclopedia for neuroscience that links brain research concepts to the data, models, and literature that support them. The KS framework: combines the general descriptions of neuroscience concepts found in wikipedia with more their more detailed desrciptions from InterLex links the latest PubMed citations with the descriptions of neuroscience concepts, and provides users with access to the data and models linked to neuroscience research concepts found in some of the world’s leading neuroscience repositories. Further, it serves as a framework where large-scale neuroscience projects can expose their data to the neuroscience community-at-large. KnowledgeSpace is a joint development between the Human Brain Project (HBP), the International Neuroinformatics Coordinating Facility (INCF), and the Neuroscience Information Framework (NIF).

Data

LeAPP

The lesion aware automated processing pipeline (LeAPP, Bey et al. 2023) allows the user to automatically create TVB-ready neuroimaging data for patients with pathological artefacts, in particular ischemic stroke lesions, in a reproducible and FAIR way. Building upon the human connectome minimal processing pipeline (Glasser et al. 2013) the presented pipeline introduces established correction measures, such as cost function masking (Brett et al. 2001) and virtual brain transplant (Solodkin et al. 2010) and implemented processing steps appropriate for modalities and properties commonly found in the clinical context of acute stroke patient data acquisition. The current software is provided as containerized image for robust and reproducible dissemination. We acknowledge the important contribution of our colleagues from UKE who by providing the data set Longitudinal multimodal magnetic resonance imaging data for ischemic stroke patients and healthy controls – a BIDS data set enabled the development of the presented pipeline.*

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

LePetitPrince

Given a sequence of stimuli, fMRI data from subjects exposed to these sequence one or several models making quantitative predictions from each stimulus in the sequence, the code allows you build a flexible analysis pipeline combining functions that allow you to generate R² or r maps. Function types can be for example: Data compression methods (already coded or that you can add) Data transformation methods (standardization, convolution with a kernel,or whatever your heart desires...) Splitting strategies Encoding models Any task that you might find useful (and that you are willing to code) For example, the pipeline programmed in main.py fits stimuli-representations of several lanuage models to fMRI data obtained from participants listening to an audiobook (in 9 runs). R² are computed from a nested-cross validated Ridge-regression. The pipeline runs for tuples (1 subject, 1 model), to facilitate distributing the code on a cluster.

Data

LFPy

LFPy is a Python module for the calculation of extracellular potentials and magnetic signals from activity in multicompartment neuron and network models. It relies on the NEURON simulator (http://www.neuron.yale.edu/neuron) and uses the Python interface (http://www.frontiersin.org/neuroinformatics/10.3389/neuro.11.001.2009/abstract) it provides.

Modelling and simulation

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