Tutorial on how to perform multi-scale simulation of Alzheimer's disease on The Virtual Brain Simulation Platform. Authors: L. Stefanovski, P. Triebkorn, M.A. Diaz-Cortes, A. Solodkin, V. Jirsa, A.R. McIntosh, P. Ritter
A brief overview of the Python programming language, with an emphasis on tools relevant to data scientists. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
The practical usage of The Virtual brain in its graphical user interface and via python scripts is introduced. In the graphical user interface, you are guided through its data repository, simulator, phase plane exploration tool, connectivity editor, stimulus generator and the provided analyses. The implemented iPython notebooks of TVB are presented, and since they are public, can be used for further exploration of The Virtual brain.
Get to know the TVB graphical user interface and start your first simulation. The hands-on focuses on a brief introduction to the GUI of TVB. You will visualize a structural connectome and use it for simulation. The local neural mass model will be explored through the phase plane viewer and a parameter space exploration will be performed to observe different dynamics of the large-scale brain model.
Simulate your own stimulation with the TVB graphical user interface. This hands-on shows you how to configure a stimulus for a specific brain region and apply it to the simulation. Afterwards the results are visualized with the TVB 3D viewer.
Manipulate the default connectome provided with TVB to see how structural lesions effect brain dynamics. In this hands-on session you will insert lesions into the connectome within the TVB graphical user interface. Afterwards the modified connectome will be used for simulations and the resulting activity will be analysed using functional connectivity.
Learn how to simulate strokes with the simulation platform, The Virtual Brain. We will go through two papers: Functional Mechanisms of Recovery after Stroke: Modeling with The Virtual Brain and The Virtual Brain: Modeling Biological Correlates of Recovery After Chronic Stroke, and apply the same processes with our own structural connectivity data set in The Virtual Brain.
Learn how to simulate seizure events and epilepsy in The Virtual Brain. We will look at the paper: On the Nature of Seizure Dynamics which describes a new local model called the Epileptor, and apply this same model in The Virtual Brain. This is part 1 of 2 in a series explaining how to use the Epileptor. In this part, we focus on setting up the parameters.
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.
This lecture presents the Graphical (GUI) and Command Line (CLI) User Interface of TVB. Alongside with the speakers, explore and interact with all means necessary to generate, manipulate and visualize connectivity and network dynamics. Speakers: Paula Popa & Mihai Andrei
Tutorial on how to use the image processing pipeline with the HBP collab. Authors: M. Schirner, P. Triebkorn, P. Ritter
Audio slides presentation to accompany the paper titled: An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data. Authors: M. Schirner, S. Rothmeier, V. Jirsa, A.R. McIntosh, P. Ritter.
Supplement video for the publication: Inferring multi-scale neural mechanisms with brain network modelling. Authors: M. Schirner, A.R. McIntosh, V. Jirsa, G. Deco, P. Ritter
Research Resource Identifiers (RRIDs) are ID numbers assigned to help researchers cite key resources (antibodies, model organisms and software projects) in the biomedical literature to improve transparency of research methods.
Lecture on functional brain parcellations and a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation which were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
This lecture is part of the Neuromatch Academy (NMA), a massive, interactive online summer school held in 2020 that provided participants with experiences spanning from hands-on modeling experience to meta-science interpretation skills across just about everything that could reasonably be included in the label "computational neuroscience".
This lecture on model types introduces the advantages of modeling, provide examples of different model types, and explain what modeling is all about. This lecture contains links to 3 tutorials, lecture/tutorial slides, suggested reading list, and 3 recorded question and answer sessions.