Data Analysis

Many excellent brain atlases exist for different species. Some of them have an API (application programming interface) to allow users to interact with the data programmatically (e.g. the excellent Allen Mouse Brain Atlas), but many do not, and there is no consistent way to process data from multiple sources.

brainrender is a python package for the visualization of three dimensional neuro-anatomical data. It can be used to render data from publicly available data set (e.g. Allen Brain atlas) as well as user generated experimental data.

JASP is a cross-platform statistical software program with a state-of-the-art graphical user interface. The JASP interface allows you to conduct statistical analyses in seconds, and without having to learn programming or risking a programming mistake.

NeuroImaging Tools & Resources Collaboratory is an award-winning free web-based resource that offers comprehensive information on an ever expanding scope of neuroinformatics software and data. Since debuting in 2007, NITRC has helped the neuroscience community make further discoveries using software and data produced from research that used to end up lost or disregarded.

ReproNim’s goal is to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research, while making the process easier and more efficient for investigators.

Several excellent computational frameworks exist that enable high-throughput and consistent tracking of freely moving unmarked animals. SimBA introduce and distribute a plug-and play pipeline that enables users to use these pose-estimation approaches in combination with behavioral annotation for the generation of supervised machine-learning behavioral predictive classifiers.

cellfinder is software from the Margrie Lab at the Sainsbury Wellcome Centre for automated 3D cell detection and registration of whole-brain images (e.g. serial two-photon or lightsheet imaging). It’s a work in progress, but cellfinder can:

DeepLabCut™ is an efficient method for 3D markerless 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).

Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

DeepLabStream is a python based multi-purpose tool that enables the realtime tracking of animals and manipulation of experiments. Our toolbox is adapted from the previously published DeepLabCut (Mathis et al., 2018) and expands on its core capabilities.