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.
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.
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.
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
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 .<br /> ```<br /> <br /> ## Example usage<br /> <br /> One can import the entire package with `import ibc_public` or use specific parts of the package:<br /> <br /> ```python<br /> from ibc_public import utils_data<br /> utils_data.make_surf_db(derivatives="/path/to/ibc/derivatives", mesh="fsaverage5")<br /> ```<br /> <br /> ## Details<br /> <br /> These script make it possible to preprocess the data<br /> * run topup distortion correction<br /> * run motion correction<br /> * run coregistration of the fMRI scans to the individual T1 image<br /> * run spatial normalization of the data<br /> * run a general linear model to obtain brain activity maps for the main contrasts of the experiment.<br /> <br /> ## Core scripts<br /> <br /> The core scripts are in the `scripts` folder<br /> <br /> - `pipeline.py` lunches the full analysis on fMRI data (pre-processing + GLM)<br /> - `glm_only.py` launches GLM analyses on the data<br /> - `surface_based_analyses` launches surface extraction and registration with Freesurfer; it also projects fMRI data to the surface<br /> - `surface_glm_analysis.py` runs glm analyses on the surface<br /> - `dmri_preprocessing` (WIP) is for diffusion daat. It relies on dipy.<br /> - `anatomical mapping` (WIP) yields T1w, T2w and MWF surrogates from anatomical acquisitions.<br /> - `script_retino.py` yields some post-processing for retinotopic acquisitions (derivation of retinotopic representations from fMRI maps)<br /> <br /> ## Dependencies<br /> <br /> Dependencies are :<br /> * FSL (topup)<br /> * SPM12 for preprocessing<br /> * Freesurfer for surface-based analysis<br /> * Nipype to call SPM12 functions<br /> * Pypreprocess to generate preprocessing reports<br /> * Nilearn for various functions<br /> * Nistats to run general Linear models.<br /> <br /> The scripts have been used with the following versions of software and environment:<br /> <br /> * Python 3.5<br /> * Ubuntu 16.04<br /> * Nipype v0.14.0<br /> * Pypreprocess v0.0.1.dev<br /> * FSL v5.0.9<br /> * SPM12 rev 7219<br /> * Nilearn v0.4.0<br /> * Nistats v0.0.1.a<br /> <br /> ## Future work<br /> <br /> - More high-level analyses scripts<br /> - Scripts for additional datasets not yet available<br /> - scripts for surface-based analysis<br /> <br /> ## Contributions<br /> <br /> Please feel free to report any issue and propose improvements on Github.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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).
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)
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.
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