Tools

Gabaclassifier

Classifies the given interneuron morphology into one of the 7 possible classes. The model has been trained with layer L2/3 to layer L6 interneurons and thus only interneurons from those layers are allowed as input.

Ginkgo/GlobalTractography

Among fiber tracking methods, spin glass tractography approaches propose an efficient framework to perform a global optimization of the inference of the structural brain connectivity from diffusion MRI HARDI or HYDI dataset. In addition, spin-glass based global tractography allows to add further regularization potentials to better constrain the energy landscape using anatomical or microstructural priors and thus help discard false positives. The proposed global tractography tools allows to compute from any diffusion MRI dataset a dense tractogram of virtual white matter fibers, under the constraint of a bending energy ensuring low curvature of fibres and robust inference of fibers in regions depicting several fiber populations (kissings, crossings, splittings), of anatomical prior (pial surface to drive the ending of fibers), and of microstructural priors (like the intraxonal volume fraction or the orientation dispersion of fibers, to allow sharp turns of fibres when connecting to the cortical ribbon).

DataValidation and inference

Ginkgo/MEDUSA

Ginkgo/MEDUSA (Microstructure Environment Designer Using Sphere Atoms) is a HPC compatible simulation tool that allows an all-in one simulation of brain tissue microstructure and their diffusion MRI signal relying on 3 simulation features: Simulation of realistic geometries representing cell membranes populating brain gray and white matters to create virtual tissues using a generative approach called MEDUSA, Simulation of the diffusion process of water molecules present within tissues using a Monte-Carlo approach,Simulation of the attenuation of the diffusion MRI signal for any tuning of a diffusion-weighted MRI pulse sequence (Pulsed Gradient Spin Echo, Oscillating Gradient Spin Echo, Abritrary Gradient Spin Echo, ….). The Ginkgo/MEDUSA tool is dedicated to the development of computational models of brain tissue microstructure in order to go beyond existing analytical models known to be limited to accurately represent the complexity of brain cellular environments

Modelling and simulation

GraLL

The Glycine Receptor Allosteric Ligand Library (GRALL) is the first database of allosteric modulators of a synaptic receptor with structural annotation.

gridspeccer

Plotting tool to make plotting with many subfigures easier, especially for publications. After installation gridspeccer can be used from the command line to create plots.

hal-cgp

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

hbp-spatial-backend

An HTTP backend for transforming coordinates and data between the core template spaces of the HBP. The cross-template transformations are diffeomorphisms, which are computed based on the alignment of the folding pattern across the different brains (DISCO method) and maximization of the grey–white matter segmentation overlap (DARTEL).

HBP Validation Framework - Python Client

A Python package for working with the Human Brain Project Model Validation Framework.

Modelling and simulationValidation and inference

HealthDataCloud

The foundation for the EBRAINS HealthDataCloud is an existing GDPR compliant and EBRAINS interoperableVirtual Research Environment (VRE)– located at the Charité - that provides a secure and scalable data platform enabling multi-institutional research teams to store, share and analyze complex multi-modal health datasets.

HDC

HiBoP

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

HippoUnit

This package contains validation tests for models of hippocampus, based on the SciUnit framework and the NeuronUnit package. As in SciUnit, in HippoUnit tests four main classes are implemented: the Test class, the Model class, the Capabilities class and the Score class. The tests of HippoUnit automatically run simulations on single-cell models that mimic the electrophysiological protocol from which the target experimental data were derived. Then the behavior of the model is evaluated and quantitatively compared to the experimental data using various feature-based error functions. Current tests cover somatic behavior and signal propagation and integration in apical dendrites of hippocampal CA1 pyramidal cell models.

Validation and inference

Hodgkin Huxley Neuron Builder

The Hodgkin-Huxley Neuron Builder implements a Use Case of the Brain Simulation Platform. It allows the user to interactively go through the entire cell model building pipeline. The workflow consists of three steps: 1) electrophysiological feature extraction from voltage traces; 2) model parameter optimization; 3) in silico experiments using the optimized model cell. The user is provided with a friendly interface enabling to interact with both the HBP Collaboratory storage and the High Performance Computing (HPC) resources. The application has been built in a flexible way to allow the user to enter the workflow at any desired step, by either interacting with HBP resources or uploading his own files.

Modelling and simulation

Human Brain Atlas

The EBRAINS multilevel human brain atlas provides detailed information on anatomy, connectivity, and function. It links macroanatomical concepts and their intersubject variability with measurements of the microstructural composition and intrinsic variance of brain regions.

Brain atlases

Human Brain Project HPC Status Monitor

The HPC Status Monitor allows to check the status of the HPC systems available for job submission from the HBP Collaboratory and the remaining quotas reserved to the user on each of them. In order to run a job in the HPC systems, the HBP user needs to be mapped on and to be part of (at least) a project on those systems. If the user does not have any access and allocation to any systems, she/he can still submit jobs (with limited quotas) through the available service accounts.

Modelling and simulation

Human Intracerebral EEG Platform

The Human Intracerebral EEG Platform (HIP) is an open-source platform designed for collecting, managing, analyzing, and sharing iEEG data at an international level. Its primary mission is to promote the development of large-scale iEEG research projects by facilitating international collaborations in the field.

HIP

Hybrid MM/CG Webserver

The Hybrid MM/CG Webserver automatizes and speeds up the hybrid Molecular-Mechanics/CoarseGrained (MM/CG) simulations set-up of G-Protein coupled receptors/ligand complexes. The server allows for the equilibration of the systems, either fully automatically or interactively. It allows the visualization of results online (using both interactive 3D visualizations and analysis plots), helping the user to identify possible issues and modify the set-up parameters accordingly

ibc-public

This Python package gives the pipeline used to process the MRI data obtained in the Individual Brain Charting Project. More info on the data can be found at IBC public protocols and IBC webpage. Latest collection of raw data is available on OpenNeuro, data accession no.002685. Latest collection of unthresholded statistical maps can be found on NeuroVault, id collection=6618. Install Under the main working directory of this repository in your computer, run the following command in a command prompt: pip install -e . ``` ## Example usage One can import the entire package with `import ibc_public` or use specific parts of the package: ```python from ibc_public import utils_data utils_data.make_surf_db(derivatives="/path/to/ibc/derivatives", mesh="fsaverage5") ``` ## Details These script make it possible to preprocess the data * run topup distortion correction * run motion correction * run coregistration of the fMRI scans to the individual T1 image * run spatial normalization of the data * run a general linear model to obtain brain activity maps for the main contrasts of the experiment. ## Core scripts The core scripts are in the `scripts` folder - `pipeline.py` lunches the full analysis on fMRI data (pre-processing + GLM) - `glm_only.py` launches GLM analyses on the data - `surface_based_analyses` launches surface extraction and registration with Freesurfer; it also projects fMRI data to the surface - `surface_glm_analysis.py` runs glm analyses on the surface - `dmri_preprocessing` (WIP) is for diffusion daat. It relies on dipy. - `anatomical mapping` (WIP) yields T1w, T2w and MWF surrogates from anatomical acquisitions. - `script_retino.py` yields some post-processing for retinotopic acquisitions (derivation of retinotopic representations from fMRI maps) ## Dependencies Dependencies are : * FSL (topup) * SPM12 for preprocessing * Freesurfer for surface-based analysis * Nipype to call SPM12 functions * Pypreprocess to generate preprocessing reports * Nilearn for various functions * Nistats to run general Linear models. The scripts have been used with the following versions of software and environment: * Python 3.5 * Ubuntu 16.04 * Nipype v0.14.0 * Pypreprocess v0.0.1.dev * FSL v5.0.9 * SPM12 rev 7219 * Nilearn v0.4.0 * Nistats v0.0.1.a ## Future work - More high-level analyses scripts - Scripts for additional datasets not yet available - scripts for surface-based analysis ## Contributions Please feel free to report any issue and propose improvements on Github.

Data

ilastik

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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