This is the coding part of the ReTune Commons initiative. The joint github organization is 1 aspect of the overarching aims of ReTune Commons in common data standards, common data infrastructure and common data exchange, in which raw and standardized data can be exchanged on joint GDPR-conform data infrastructure, in order to be analysed with containerized analytic workflows and open-source ReTune-specific softwares. The key element is the integration of the ReTune Commons into the Virtual Research Environment as the major digital research flagship of the Charité – Universitätsmedizin Berlin, Berlin Institute of Health (BIH) and Universitätsklinikum Würzburg.
The open-source coding repositories of ReTune Commons are collected here:
| GitHub name | Github Link | Short description | Publications |
|---|---|---|---|
| ANTx2 | LINK | toolbox for small animal neuroimaging atlas registration | Koch et al. (2019) DOI |
| bart | LINK | toolbox for atlas registration of rodent brain histology | de Bortoli et al. (2021) DOI |
| rodentDtiConnectomics | LINK | mrtrix scripts for reconstruction of rodent dMRI | Kuffner et al. (2022) DOI |
| separating periodic from aperiodic PSD | LINK | code based on FOOOF for separating periodic from aperiodic power spectral densities | Gerster et al. (2022) DOI |
| ROIconnect | LINK | functional connectivity analysis between regions of interests (ROIs) on source level. | Pellegrini et al. (2023) DOI |
| FCSim | LINK | Matlab code for the paper "Identifying best practices for detecting inter-regional functional connectivity from EEG" | Pellegrini et al. (2023) DOI |
| Lead-DBS | LINK | toolbox facilitating Deep Brain Stimulation electrode reconstructions and computer simulations based on postoperative MRI & CT imaging. | Horn et al. (2019) DOI |
| ReTap | LINK | open-source published finger tapping accelerometer assessment tool | Habets et al. (2023) DOI |
| Graph Diffusion Reclassification | LINK | algorithm for graph semi-supervised learning | Peach et al. (2020) DOI |
| Highly Comparative Graph Analysis | LINK | highly comparative graph analysis toolbox performs a massive feature extraction from a set of graphs, and applies supervised classification methods. | Peach et al. (2021) DOI |
| Multiscale centrality | LINK | Graph centrality is a question of scale - Multiscale centrality (MSC) is a scale dependent measure of centrality on complex networks. | Arnaudon et al. (2020) DOI |
| PyGenStability | LINK | Markov Stability: Computing the Markov Stability graph community detection algorithm in Python | Arnaudon et al. (2023) DOI |
| TVB-multiscale | LINK DOI TVB | Extension of TVB to cosimulate mean-field and spiking network models | Meier et al. (2022) DOI |
| Virtual DBS model | EBRAINS | Virtual DBS proof-of-concept model including all data | Schirner et al. (2022) DOI |
| HCP pre-processed data | LINK | Readily available pre-processed data of Human Connectome Project data | Schirner et al. (2023) DOI |
| BIDS input to TVB | LINK | Extension of TVB to input BIDS-conform data | |
| BIDS extension proposal | PR850 PR967 | BIDS extension proposal for computational modeling data | Schirner et al. (2021) DOI |
| IPID1 inferring phase isostable dynamics | LINK | implementation of the IPID-1 algorithm to infer phase/isostable response curves from time series data | Cestnik et al. (2022) DOI |
| deepflash2 | LINK | segmentation of ambiguous bioimages | Griebel et al. (2023) DOI |
| py_neuromodulation | LINK | toolbox allowing for real time capable processing of multimodal electrophysiological data | Merk et al. (2022) DOI |
| ndx_ecg | LINK | extension to convert ECG data into NWB | |
| Let_it_be_3D | LINK | pipeline for tracking mice movements from 2D in 3D | |
| Findmycells | LINK | end-to-end bioimage analysis pipeline with state-of-the-art tools for non-coding experts | |
| PyPerceive | LINK | toolbox (internal use) to import (perceive'd) Percept STN LFP data in Python | |
| PyBispectra | LINK | A Python signal analysis toolbox for computing spectral-domain interactions using bispectra. | |
| ReTune BIDS Standardization | LINK | Tools for input and conversion of human electrophysiology in BIDS | |
| Sim2BIDS | LINK | sim2bids: an app with GUI that converts computational modeling data into BIDS | |
| Dataset Filter kit | LINK | tool to filter BIDS datasets and divide it in subdataset | |
| ReTune electrophysiology workshop | LINK | MATLAB workshop for electrophysiology provided by projects B03, C01 and INF | |
| Perceive | LINK | Extract data from the Medtronic Percept bidirectional brain computer interface device for adaptive deep brain stimulation | |
| Dynamic Graph Dimensionality | LINK | Dynamic Graph Dimensionality is a methodology for computing the relative, local and global dimension of complex networks | |
| stats_n_plots | LINK | Computation and visualization of statistical analyses common in the Life sciences made easy | |
| SignDCL | LINK | An easy-to-use GUI to perform contour tracking, extract immobility and freezing episodes. | |
| neurokin | LINK | python package to support integrated analysis of neural and kinematic data | |
| BSI-Zoo | LINK | python library for M/EEG simulation and source reconstruction | |
| PAC | LINK | Matlab code for the manuscript in preparation "Distinguishing across- from within-site phase-amplitude coupling using antisymmetrized bispectra" | |
| MARBLE | LINK | MARBLE or MAnifold Representation Basis LEarning is a fully unsupervised geometric deep learning method that can intrincally represent vector fields over manifolds, perform unbiased comparisons across dynamical systems and can operate in geometry-aware or geometry-agnostic modes | |
| RVGP | LINK | Riemannian manifold vector field Gaussian Processes is a generalised Gaussian process for learning vector fields over manifolds, using the connection Laplacian operator, which introduces smoothness in the tangent bundle of manifolds. |
The open-data of ReTune Commons are collected here:
| Source | Link to the Data | Short description | Publications |
|---|---|---|---|
| Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses | Data DOI | Animal Data: Images, training datasets, codes, deep learning models and model ensembles: cFOS | Segebarth et al. (2020) DOI |
| Data from: A unified connectomic target for deep brain stimulation in obsessive-compulsive disorder | Lead-DBS | Human data: the joint DBS-based clinical improvement-predictive tract atlas of 50 patients with OCD is openly available within Lead-DBS software | Li et al. (2020) DOI |
| Data from: Personalizing Deep Brain Stimulation Using Advanced Imaging Sequences | Lead-DBS | Human data: the joint hypointensity template and the probabilistic map derived from 36 patients with Essential Tremor using FGATIR sequences is openly available within Lead-DBS software | Neudorfer et al. (2022) DOI |
| Human data: Replication data demonstrating that electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease | Data DOI | human neuroelectrophysiological data of 15 subjects with Parkinson's disease and DBS-ECOG implant | Merk et al. (2022) DOI |
| Data from: Exploring transcriptome-wide changes in the brain-localized immune cells in a mouse model of Parkinson's Disease | GEO Accession Viewer | Genetic data (animal): Bulk mRNA sequencing data from the brain-localized immune cells in a mouse PD model | Karikari et al. (2022) DOI |
| Data from: Analyzing the immune cell population changes in the brain and the gut of PD mice | GEO Accession Viewer | Genetic data (animal): Single-cell sequencing data from the CD4+, CD8+, and CD11c+ cells in the brain of a PD mouse model | McFleder et al. (2023) DOI |
| Data from: Deep learning-enabled segmentation of ambiguous bioimages with deepflash2 | Data DOI | Animal data: Dataset, trained deep learning (benchmark) models, and Python Code for deepflash2 | Griebel et al. (2023) DOI |
| Data from: Prediction of Stroke Outcome in Mice Based on Noninvasive MRI and Behavioral Testing | Data DOI | Animal data: raw MRI T2w images, lesion masks, and the registered atlases, the input for machine learning algorithms (lesion volume, segmented MRI, behavioral scores), the trained classifiers, and their output (predicted behavioral scores) | Knab et al. (2023) DOI |
