JupyterHub is a simple, highly extensible, multi-user system for managing per-user Jupyter Notebook servers, designed for research groups or classes. This lecture covers deploying JupyterHub on a single server, as well as deploying with Docker using GitHub for authentication.
Tutorial describing the basic search and navigation features of the Allen Mouse Brain Atlas
Tutorial describing 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 and 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 Virtual Brain is an open-source, multi-scale, multi-modal brain simulation platform. In this lesson, you get introduced to brain simulation in general and to The Virtual brain in particular. Prof. Ritter will present the newest approaches for clinical applications of The Virtual brain - that is, for stroke, epilepsy, brain tumors and Alzheimer’s disease - and show how brain simulation can improve diagnostics, therapy and understanding of neurological disease.
The concept of neural masses, an application of mean field theory, is introduced as a possible surrogate for electrophysiological signals in brain simulation. The mathematics of neural mass models and their integration to a coupled network are explained. Bifurcation analysis is presented as an important technique in the understanding of non-linear systems and as a fundamental method in the design of brain simulations. Finally, the application of the described mathematics is demonstrated in the exploration of brain stimulation regimes.
The simulation of the virtual epileptic patient is presented as an example of advanced brain simulation as a translational approach to deliver improved results in clinics. The fundamentals of epilepsy are explained. On this basis, the concept of epilepsy simulation is developed. By using an iPython notebook, the detailed process of this approach is explained step by step. In the end, you are able to perform simple epilepsy simulations your own.
In this lecture we will focus on a paper called “The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread”. Within their work, the authors used the epileptor model to simulate a patient's individual seizure. To understand the concept we will have a closer look at the equations of the epileptor model and particular the epileptogenicity index which controls the excitability of each brain region. Subsequently, we will begin to setup the epileptogenic zone in our own brain network model with TVB.
After introducing the local epileptor model in the previous 2 videos we will now use it in a large scale brain simulation. We again focus on the paper “The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread”. Two simulations with different epileptogenicity across the network are visualized to show the difference in seizure spread across the cortex.
This lecture gives an overview on the article “Individual brain structure and modelling predict seizure propagation” where 15 subjects with epilepsy were modelled to predict individual epileptogenic zones. With the TVB GUI we will model seizure spread and the effect of lesioning the connectome. The impact of cutting edges in the network on seizure spreading will be visualized.
Along the example of a patient with bi-temporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions. The patient's brain network model is then evaluated via simulation, data fitting and mathematical analysis. This lecture demonstrates how to develop novel personalized strategies towards therapy and intervention using TVB.