The EBRAINS Model Catalog contains information about models developed and/or used within the EBRAINS research infrastructure. It allows you to find information about models and results obtained using those models, showcasing how those models have been validated against experimental findings.
The Electrophys Feature Extraction Library (eFEL) allows neuroscientists to automatically extract features from time series data recorded from neurons (both in vitro and in silico). Examples are the action potential width and amplitude in voltage traces recorded during whole-cell patch clamp experiments. The user of the library provides a set of traces and selects the features to be calculated. The library will then extract the requested features and return the values to the user. The core of the library is written in C++, and a Python wrapper is included. At the moment we provide a way to automatically compile and install the library as a Python module.
This test shall take as input a BluePyOpt optimized output file. The validation test would then evaluate the model for all parameter sets against various eFEL features. It should be noted that the reference data used is that located within the model, so this test can be considered as a quantification of the goodness of fitting the model. The results are registered on the HBP Validation Framework app.
The Python library Electrophysiology Analysis Toolkit (Elephant) provides tools for analysing neuronal activity data, such as spike trains, local field potentials and intracellular data. In addition to providing a platform for sharing analysis codes from different laboratories, Elephant provides a consistent and homogeneous framework for data analysis built on a modular foundation. The underlying data model is the Neo library. This framework easily captures a wide range of neuronal data types and methods, including dozens of file formats and network simulation tools. A common data description, as the Neo library provides, is essential for developing interoperable analysis workflows.
This software provides an efficient implementation for embedded devices of a QRS complex detection method that works on non-uniformly sampled ECG signals.
ExploreASL is a pipeline and toolbox for image processing and statistics of arterial spin labeling perfusion MR images. It is designed as a multi-OS, open source, collaborative framework that facilitates cross-pollination between image processing method developers and clinical investigators. The software provides a complete head-to-tail approach that runs fully automatically, encompassing all necessary tasks from data import and structural segmentation, registration and normalization, up to CBF quantification. In addition, the software package includes and quality control (QC) procedures and region-of-interest (ROI) as well as voxel-wise analysis on the extracted data. To-date, ExploreASL has been used for processing ~10000 ASL datasets from all major MRI vendors and ASL sequences, and a variety of patient populations, representing ~30 studies. The ultimate goal of ExploreASL is to combine data from multiple studies to identify disease related perfusion patterns that may prove crucial in using ASL as a diagnostic tool and enhance our understanding of the interplay of perfusion and structural changes in neurodegenerative pathophysiology. Additionally, this (semi-)automatic pipeline allows us to minimize manual intervention, which increases the reproducibility of studies.
Extrae is a dynamic instrumentation package to trace programs compiled and run with the shared memory model (like OpenMP and pthreads), the message passing (MPI) programming model or both programming models (different MPI processes using OpenMP or pthreads within each MPI process). Instrumentation of CUDA, CUPTI, OpenCL, Python multiprocessing, Java threads and relevant system, dynamic memory and I/O calls is also supported. Extrae generates trace files that can be visualized with Paraver. Extrae is currently available on different architectures and operating systems, including: GNU/Linux (x86, x86_64, ARM, POWER), IBM AIX, SGI Altix, Open Solaris, FreeBSD, Android, Fujitsu FX10/100, Cray XT, IBM Blue Gene, Intel Xeon Phi, GPUs and FPGAs. The combined use of Extrae and Paraver offers an enormous analysis potential, both qualitative and quantitative. With these tools the actual performance bottlenecks of parallel applications can be identified. The microscopic view of the program behavior that the tools provide is very useful to optimize the parallel program performance.
Extra-P is an automatic performance-modeling tool that supports the user in the identification of scalability bugs. A scalability bug is a part of the program whose scaling behavior is unintentionally poor, that is, much worse than expected. Extra-P uses measurements of various performance metrics at different processor configurations as input to represent the performance of code regions (including their calling context) as a function of the number of processes. All it takes to search for scalability issues even in full-blown codes is to run a manageable number of small-scale performance experiments, launch Extra-P, and compare the asymptotic or extrapolated performance of the worst instances to the expectations. Besides the number of processes, it is also possible to consider other parameters such as the input problem size. Extra-P generates not only a list of potential scalability bugs but also human-readable models for all performance metrics available such as floating-point operations or bytes sent by MPI calls that can be further analyzed and compared to identify the root causes of scalability issues.
The fiber architecture constructor (FAConstructor) allows a simple and effective creation of fiber models based on mathematical functions or the manual input of data points. Models are visualized during creation and can be interacted with by translating them in the 3-dimensional space.
Rationale The approach assumes that segmented (into GM, WM and background) images have been aligned, so does not require the additional complexity of a convolutional approach. The use of segmented images is to make the approach less dependent on the particular image contrasts so it generalises better to a wider variety of brain scans. The approach assumes that there are only a relatively small number of labelled images, but many images that are unlabelled. It therefore uses a semi-supervised learning approach, with an underlying Bayesian generative model that has relatively few weights to learn. Model The approach is patch based. For each patch, a set of basis functions model both the (categorical) image to label, and the corresponding (categorical) label map. A common set of latent variables control the two sets of basis functions, and the results are passed through a softmax so that the model encodes the means of a multinouli distribution (Böhning, 1992; Khan et al, 2010). Continuity over patches is achieved by modelling the probability of the latent variables within each patch conditional on the values of the latent variables in the six adjacent patches, which is a type of conditional random field (Zhang et al, 2015; Brudfors et al, 2019). This model (with Wishart priors) gives the prior mean and covariance of a Gaussian prior over the latent variables of each patch. Patches are updated using an iterative red-black checkerboard scheme. Labelling After training, labelling a new image is relatively fast because optimising the latent variables can be formulated within a scheme similar to a recurrent Res-Net (He et al, 2016)."
fairgraph is a Python library for working with metadata in the HBP/EBRAINS Knowledge Graph, with a particular focus on data reuse, although it is also useful in metadata registration/curation. The basic idea of the library is to represent metadata nodes from the Knowledge Graph as Python objects. Communication with the Knowledge Graph service is through a client object, for which an access token associated with an EBRAINS account is needed.
The Fiber Architecture Simulation Toolbox for PLI (fastpli) is a toolbox for polarized light imaging (PLI) with three main purposes: Sandbox - designing of nerve fiber models: The first module allows the user to create different types of nerve fiber bundles and additionally fill them with individual nerve fibers. * Details * Tutorial Solver - generating collision free models: The second module takes as input a configuration of nerve fibers and checks them for spatial collisions. Since nerve fibers cannot overlap in reality, one must ensure that the models follow the same rules. The solver module implements a simple algorithm that checks for collisions and, if it finds any, pushes the colliding segments of the fibers slightly apart. This is repeated until all collisions are solved. * Details * Tutorial Simulation - simulation of 3D-Polarized Light Imaging: The simulation module enables the simulation of 3D Polarized Light Imaging (3D-PLI). This is a microscopic technique that allows the polarization change of light moving through a brain section to be measured. Due to the birefringence property of the myelin surrounding the nerve fibers, the polarization state changes. This change enables the calculation of the 3d orientation of the nerve fibers in the brain slice. * Details * Tutorial
A collection of repositories for applications of fast spike-based sampling: compared to conventional neural networks, physical model devices offer a fast, efficient, and inherently parallel substrate capable of related forms of Markov chain Monte Carlo sampling. This software suite enables the use of a neuromorphic chip to replicate the properties of quantum systems through spike-based sampling.
The Feature Extraction Graphical User Interface (GUI) is a web application that allows users to extract an ensemble of electrophysiological properties from voltage traces recorded upon electrical stimulation of neuronal cells. The main outcome of the application is the generation of two files - features.json and protocol.json - that can be used for later model parameter optimizations.
MATLAB implementation of Feature-specific Information Transfer (FIT) and conditional FIT (cFIT) measures. FIT quantifies the amount of information transmitted from a sender brain region X to a receiver brain region Y about a specific external feature S. This approach goes beyond classic methodologies to study causal communication, such as Transfer Entropy (TE), which quantify the overall activity propagated from the sender to the receiver region. By isolating the feature-specific information flowing from one region to another, FIT sheds light on the actual content of the communication. For the details on FIT mathematical derivation and test on simulated and rael data please read: M. Celotto et al. An information-theoretic quantification of the content of communication between brain regions. biorXiv, 2023.
This library is meant to be a light-weight Python library that handles functional alignment tasks. It is compatible with and inspired from Nilearn. Alternative implementations of these ideas can be found in the pymvpa or brainiak packages.
Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. fMRIPrep is an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for task-based and resting fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. fMRIPrep robustly produces high-quality results on diverse fMRI data. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results. The workflow is based on Nipype and encompases a large set of tools from well-known neuroimaging packages, including [FSL](<https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/), ANTs, FreeSurfer, AFNI, and Nilearn. This pipeline was designed to provide the best software implementation for each state of preprocessing, and will be updated as newer and better neuroimaging software becomes available. fMRIPrep performs basic preprocessing steps (coregistration, normalization, unwarping, noise component extraction, segmentation, skullstripping etc.) providing outputs that can be easily submitted to a variety of group level analyses, including task-based or resting-state fMRI, graph theory measures, surface or volume-based statistics, etc. fMRIPrep allows you to easily do the following: Take fMRI data from unprocessed (only reconstructed) to ready for analysis. Implement tools from different software packages. Achieve optimal data processing quality by using the best tools available. Generate preprocessing-assessment reports, with which the user can easily identify problems. Receive verbose output concerning the stage of preprocessing for each subject, including meaningful errors. Automate and parallelize processing steps, which provides a significant speed-up from typical linear, manual processing.
Frites allows the characterisation of task-related cognitive brain networks. Neural correlates of cognitive functions can be extracted both at the single brain area (or channel) and network level. The toolbox includes time-resolved directed (e.g., Granger causality) and undirected (e.g., Mutual Information) Functional Connectivity metrics. In addition, it includes cluster-based and permutation-based statistical methods for single-subject and group-level inference.
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