The Allen Mouse Brain Atlas is a genome-wide, high-resolution atlas of gene expression throughout the adult mouse brain. This tutorial describes the basic search and navigation features of the Allen Mouse Brain Atlas.
The Allen Developing Mouse Brain Atlas is a detailed atlas of gene expression across mouse brain development. This tutorial describes the basic search and navigation features of the Allen Developing Mouse Brain Atlas.
This tutorial demonstrates how to use the differential search feature of the Allen Mouse Brain Atlas to find gene markers for different regions of the brain, as well as to visualize this gene expression in three-dimensional space. Differential search is also available for the Allen Developing Mouse Brain Atlas and the Allen Human Brain Atlas.
The Mouse Phenome Database (MPD) provides access to primary experimental trait data, genotypic variation, protocols and analysis tools for mouse genetic studies. Data are contributed by investigators worldwide and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD ensures rigorous curation of phenotype data and supporting documentation using relevant ontologies and controlled vocabularies. As a repository of curated and integrated data, MPD provides a means to access/re-use baseline data, as well as allows users to identify sensitized backgrounds for making new mouse models with genome editing technologies, analyze trait co-inheritance, benchmark assays in their own laboratories, and many other research applications. MPD’s primary source of funding is NIDA. For this reason, a majority of MPD data is neuro- and behavior-related.
This lesson provides a demonstration of GeneWeaver, a system for the integration and analysis of heterogeneous functional genomics data.
In this tutorial on simulating whole-brain activity using Python, participants can follow along using corresponding code and repositories, learning the basics of neural oscillatory dynamics, evoked responses and EEG signals, ultimately leading to the design of a network model of whole-brain anatomical connectivity.
This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks.
This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.
Neuronify is an educational tool meant to create intuition for how neurons and neural networks behave. You can use it to combine neurons with different connections, just like the ones we have in our brain, and explore how changes on single cells lead to behavioral changes in important networks. Neuronify is based on an integrate-and-fire model of neurons. This is one of the simplest models of neurons that exist. It focuses on the spike timing of a neuron and ignores the details of the action potential dynamics. These neurons are modeled as simple RC circuits. When the membrane potential is above a certain threshold, a spike is generated and the voltage is reset to its resting potential. This spike then signals other neurons through its synapses.
Neuronify aims to provide a low entry point to simulation-based neuroscience.
This short video walks you through the steps of publishing a dataset on brainlife, an open-source, free and secure reproducible neuroscience analysis platform.
This video shows how to use the brainlife.io interface to edit the participants' info file. This file is the ParticipantInfo.json file of the Brain Imaging Data Structure (BIDS).
This video will document the process of running an app on brainlife, from data staging to archiving of the final data outputs.
This video will document the process of visualizing the provenance of each step performed to generate a data object on brainlife.
This video will document the process of downloading and running the "reproduce.sh" script, which will automatically run all of the steps to generate a data object locally on a user's machine.
This video will document the process of creating a pipeline rule for batch processing on brainlife.
This video will document the process of launching a Jupyter Notebook for group-level analyses directly from brainlife.
This brief video walks you through the steps necessary when creating a project on brainlife.io.
This brief video rus through how to make an accout on brainlife.io.
This video will document how to run a correlation analysis between the gray matter volume of two different structures using the output from brainlife app-freesurfer-stats.
This lecture introduces you to the basics of the Amazon Web Services public cloud. It covers the fundamentals of cloud computing and goes through both the motivations and processes involved in moving your research computing to the cloud.