Tutorial on how to perform multi-scale simulation of Alzheimer's disease on The Virtual Brain Simulation Platform

Related publication

Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease, published in frontiers in Computational Neuroscience, August 2019, by Leon Stefanovski, Paul Triebkorn, Andreas Spiegler, Margarita-Arimatea Diaz-Cortes, Ana Solodkin, Viktor Jirsa, Anthony Randal McIntosh and Petra Ritter

doi: 10.3389/fncom.2019.00054

Abstract

Introduction: While the prevalence of neurodegenerative diseases associated with dementia such as Alzheimer's disease (AD) increases, our knowledge on the underlying mechanisms, outcome predictors, or therapeutic targets is limited. In this work, we demonstrate how computational multi-scale brain modeling links phenomena of different scales and therefore identifies potential disease mechanisms leading the way to improved diagnostics and treatment.

Methods: The Virtual Brain (TVB; thevirtualbrain.org) neuroinformatics platform allows standardized large-scale structural connectivity-based simulations of whole brain dynamics. We provide proof of concept for a novel approach that quantitatively links the effects of altered molecular pathways onto neuronal population dynamics. As a novelty, we connect chemical compounds measured with positron emission tomography (PET) with neural function in TVB addressing the phenomenon of hyperexcitability in AD related to the protein amyloid beta (Abeta). We construct personalized virtual brains based on an averaged healthy connectome and individual PET derived distributions of Abeta in patients with mild cognitive impairment (MCI, N = 8) and Alzheimer's Disease (AD, N = 10) and in age-matched healthy controls (HC, N = 15) using data from ADNI-3 data base (http://adni.loni.usc.edu). In the personalized virtual brains, individual Abeta burden modulates regional Excitation-Inhibition balance, leading to local hyperexcitation with high Abeta loads. We analyze simulated regional neural activity and electroencephalograms (EEG).

Results: Known empirical alterations of EEG in patients with AD compared to HCs were reproduced by simulations. The virtual AD group showed slower frequencies in simulated local field potentials and EEG compared to MCI and HC groups. The heterogeneity of the Abeta load is crucial for the virtual EEG slowing which is absent for control models with homogeneous Abeta distributions. Slowing phenomena primarily affect the network hubs, independent of the spatial distribution of Abeta. Modeling the N-methyl-D-aspartate (NMDA) receptor antagonism of memantine in local population models, reveals potential functional reversibility of the observed large-scale alterations (reflected by EEG slowing) in virtual AD brains.

Discussion: We demonstrate how TVB enables the simulation of systems effects caused by pathogenetic molecular candidate mechanisms in human virtual brains.

More resources for this lesson

• GitHub repository with code
• Jupyter notebook with scripting tutorial

Tutorial on how to simulate brain tumor brains with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls.

Related publication

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain, published in eNeuro, May 2018, by Hannelore Aerts, Michael Schirner, Ben Jeurissen, Dirk Van Roost, Eric Achten, Petra Ritter and Daniele Marinazzo

doi: 10.1523/ENEURO.0083-18.2018

Abstract

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed.

However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention.

Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics.

As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong–Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed.

Results showed

• (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters;
• (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain;
• and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

More resources for this lesson

• Publication website with more data, explanations and examples
• GitHub repository with code
• Jupyter notebook with scripting tutorial

This tutorial on simulating The Virutal Mouse Brain by Patrik Bey is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc... TVB is a full brain simulation platform.

Topics covered in this lesson by Patrik Bey

• Step-by-step guide to simulating The Virtual Mouse Brain

This tutorlal on modeling a virtual macaque brain is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.

Topics covered in this lesson by Julie Courtiol

• Step-by-step guide to modeling a virtual macaque brain using The Virtual Brain

This lecture on generating 3D brain model outside The Virtual Brain is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc... TVB is a full brain simulation platform.

Topics covered in this lesson by Michael Schirner

• 3D visualization with Python and mayavi (code)
• 3D visualizatiom with mrview (video)
• 3D visualization with MATLAB and Surfice (video and code)

This presentation is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging. brain simulation. personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.

Topics covered in this lesson by Dionysios Perdikis

• Introduction to TVB-multi-scale extension
• Introduction to TVB-multi-scale/TVB_nest interface
• Proof of concept in the HBP Collaboratory
• Implementations in Jupyter notebooks

This lecture on multi-scale entropy is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.

Topics covered in this lesson by Jil Meier

• Introduction to entropy
• Introduction to multi-scale entropy (MSE)
• Evidence from clinical studies
• Local vs global entropy
• Calculating MSE using TVB