A new neuroinformatics approach to personalized medicine in neurology: The Virtual Brain

Abstract

Purpose of review An exciting advance in the field of neuroimaging is the acquisition and processing of very large data sets (so called ‘big data’), permitting large-scale inferences that foster a greater understanding of brain function in health and disease. Yet what we are clearly lacking are quantitative integrative tools to translate this understanding to the individual level to lay the basis for personalized medicine. Recent findings Here we address this challenge through a review on how the relatively new field of neuroinformatics modeling has the capacity to track brain network function at different levels of inquiry, from microscopic to macroscopic and from the localized to the distributed. In this context, we introduce a new and unique multiscale approach, The Virtual Brain (TVB), that effectively models individualized brain activity, linking large-scale (macroscopic) brain dynamics with biophysical parameters at the microscopic level. We also show how TVB modeling provides unique biological interpretable data in epilepsy and stroke. Summary These results establish the basis for a deliberate integration of computational biology and neuroscience into clinical approaches for elucidating cellular mechanisms of disease. In the future, this can provide the means to create a collection of disease-specific models that can be applied on the individual level to personalize therapeutic interventions. Video abstract http://links.lww.com/CONR/A41

Fifty shades of The Virtual Brain: Converging optimal working points yield biologically plausible electrophysiological and imaging features

“…In the current study, we conducted a large parameter space exploration with TVB, simulating BOLD fMRI and electrophysiological neural signals for 50 individual human brains. TVB has previously been used to study how neural networks behave in healthy and diseased states (8,10,13,18,21,26). Here, we assert that goodness-of-fit of simulated to empirical data converge at a particular parameter set across multiple tenets of the EEG and fMRI signal, and that there are reliable inter-individual differences in biophysical parameters that lead to optimal functioning.…”

“…In a second set of simulations, we explored a narrower range of global coupling and reproduced alpha frequency (8)(9)(10)(11)(12)(13)(14)(15) Hz) oscillations in the neural signal, a characteristic feature of human empirical EEG (37,38), which has previously been modelled as a function of global coupling in MEG (30). Here, it was shown that transmission delays and global cortical coupling affect the mean power in an MEG model, and the best model fit was found in the alpha band power.…”

“…Optimal model fit (simulated FC to empirical FC) is highly dependent on the global coupling and conduction speed values. (8,9,13,18,(20)(21)(22)(23). See equation 1 in Methods for a mathematical description of these parameters.…”

Differentiation of Alzheimer’s disease based on local and global parameters in personalized Virtual Brain models

“…Previous work has shown that biophysical parameters derived from TVB modeling can describe healthy neural dynamics (Jirsa et al, 2010;Kunze et al, 2016;Roy et al, 2014), as well as disease. As a proof of concept, it has been shown that these biophysical parameters correlate with motor recovery from stroke (Adhikari et al, 2015;Falcon et al, 2016a;Falcon et al, 2016b;Falcon et al, 2015), and the generation of epileptic seizures , along with seizure progression (Jirsa et al, 2017). The biophysical parameters derived from TVB modeling have been shown to correlate with a variety of clinical phenotypes, and as such offer a potential for translation to clinical applications.…”

Data augmentation based on dynamical systems for the classification of brain states

“…However, this cannot be taken for granted in surrogates obtained from models fitted to individual 310 patients, where higher inter-subject variability may arise from abnormalities in brain structure and function. Since these limitations could be informative of such abnormalities, low dimensional whole-brain models should be further ex-plored in the context of reproducing single subject FC from the individual SC of the patients [24].…”