OpenNeuro

Posted on August 19th, 2022

OpenNeuro is a data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. Uploaded datasets are made available with minimal restrictions to the public at large, in order to permit maximal reuse. OpenNeuro accepts datasets formatted according to the Brain Imaging Data Structure (BIDS) standard, a community-developed standard that aims to achieve broad coverage of neuroimaging data types while unifying metadata.

PET2BIDS

Posted on August 19th, 2022

This repository hosts tools to curate PET brain data using the Brain Imaging Data Structure Specification. The work to create these tools is funded by Novo Nordisk Fonden (NNF20OC0063277) and the BRAIN initiative (MH002977-01). This tool extracts and outputs some of the additional blood and radiological data along with PET neuroimaging data into the Brain Imaging Data Structure. It is one of the few tools that exists to aid in this conversion and is being used to convert and share data on OpenNeuro.org.

Brain Modeling Tools: BMTK, SONATA, VND

Posted on July 20th, 2022

The Brain Modeling Toolkit (BMTK), SONATA, and Visual Neuronal Dynamics (VND) are mutually integrated software tools that are particularly suited to support large-scale bio-realistic brain modeling, but are applicable to a variety of neuronal modeling applications. BMTK is a suite for building and simulating network models at multiple levels of resolution, from biophysically-detailed, to point-neuron, to population-statistics approaches. The modular design of BMTK allows users to easily work across different scales of resolution and different simulation engines using the same code interface. The model architecture and parameters, as well as simulation configuration, input, and output are stored together in the SONATA data format. Models and their output activity can then be visualized with the powerful rendering capabilities of VND.

DeepLabCut

Posted on July 20th, 2022

Quantifying behavior is crucial for many applications in neuroscience, genetics, and biology. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time-consuming.

DeepLabCut offers an efficient method for marker-less pose estimation based on transfer learning with deep neural networks that achieves excellent results (i.e. you can match human labeling accuracy) with minimal training data (typically 50-200 frames). Its versatility has been demonstrated by tracking various body parts in multiple species across a broad collection of behaviors. The package is open source, fast, robust, and can be used to compute 3D pose estimates. It is actively maintained.

brainlife.io

Posted on June 21st, 2022

brainlife.io is a free and secure reproducible neuroscience analysis platform. The platform support data preprocessing, visualization and analysis. Users can analyze data on brainlife.io by either uploading or importing it from public archives. Over 400+ pre-processing apps are available to build your custom workflows. Thousands of jobs can be submitted using shared clusters or on your own computer. Perform group-level statistical analysis or apply machine learning methods using Jupyter notebooks. Publish your full workflow from raw data to published figures in an integrated bundle with a single DOI.

BCI2000

Posted on November 5th, 2021

BCI2000 provides a portable open-source platform to implement the most common scenarios of adaptive neurotechnology research. BCI2000 acquires, synchronizes, and stores signals from a wide range of data acquisition systems, and translates these signals into useful outputs in real-time.

OpenMind Software Tools

Posted on November 5th, 2021

This collection of software tools is geared to support research using advanced deep-brain (DBS) stimulation technology (e.g. Medtronic’s Summit RC+S system) in clinical studies. The tools have been developed and shared by OpenMind Consortim member laboratories and include read-me, explanatory videos, and other material to enable use and adaptation by users to suit their research needs. The tools encompass code for data visualization and analysis, as well as code for device programming and control via device APIs. This set of resources helps to fill a critical gap in technological capacity needed to fully utilize advanced DBS device technology in clinical studies, and brings efficiencies in cost, labor, time and knowledge-sharing to the community of advanced DBS researchers.

DataJoint Elements

Posted on November 5th, 2021

DataJoint Elements provides an efficient approach for neuroscience labs to create and manage scientific data workflows: the complex multi-step methods for data collection, preparation, processing, analysis, and modeling that scientists must perform in the course of an experimental study. The work is derived from the developments in leading neuroscience projects and uses the open-source DataJoint framework for interfacing databases and automating computations.

Neurodata Without Borders (NWB)

Posted on November 5th, 2021

Neurodata Without Borders: Neurophysiology (NWB) is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build common analysis tools for neurophysiology data. NWB is designed to store a variety of neurophysiology data, including data from intracellular and extracellular electrophysiology experiments, data from optical physiology experiments, and tracking and stimulus data. NWB is more than just a file format; it defines an ecosystem of tools, methods, and standards for storing, sharing, and analyzing complex neurophysiology data. NWB provides software for data standardization and application programming interfaces (APIs) for reading and writing the data, and is supported by a growing ecosystem of data analysis and management tools.

MoSeq

Posted on November 1st, 2021

MoSeq (or Motion Sequencing) provides a pipeline for quantifying 3D videos or keypoints from 2D/3D videos of freely behaving mice and discovering the underlying structure of mouse behavior. MoSeq automatically locates, tracks, and quantifies the mouse in each frame of the video. Unlike typical supervised behavioral classifiers that then require human labeling, the pipeline instead trains an unsupervised machine learning model to identify repeated motifs (or syllables) of behavior. The pipeline then offers a suite of visualization tools and statistical tests for understanding the discovered behaviors and comparing them across experimental conditions. MoSeq dramatically reduces human labor in exploring mouse behavior, discovers previously unknown behaviors, and allows neuroscientists to more completely relate neural activity to free behavior.