Computational Models of Neurological Disorder
Virtual Brain for neurological disease modeling

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Neurological disorders are often characterized by alterations in multiple brain regions; i.e. they should be studied at the organ level. Neuroimaging techniques allow extracting information at the whole brain level, but a conceptual framework is lacking to interpret neuroimaging data. The Virtual Brain (for humans) and its extension The Virtual Mouse Brain (for rodents) can provide such a framework. These platforms enable the virtualization of individual brains based on Diffusion-weighted Tensor Imaging, thus allowing studying whole brain dynamics in silico. In addition to the analysis structure/function relationships, the models can be used to generate testable predictions, including in the clinic to improve patient’s care.

Introduction

Neurological disorders represent a large cost to society, $1.5 trillion/year, nearly 9% of the gross domestic product (World Health Organization (2006)). Most of brain disorders, such as migraine, epilepsies, Alzheimer’s disease, Parkinson’s disease, and major depression remain poorly treated. Their progression can be slowed down; but it is often just a temporary relief with a large inter-patient variability. For instance many patients cannot be treated with existing drugs, e.g. 30% for patients with epilepsy. These facts underscore the necessity to better understand patient-specific mechanisms underlying brain disorders. Historically personalized medicine uses heavily genetic information, but finds more and more response on the system level. Structural and functional neuroimaging play a key role and have contributed diagnostic tools, e.g. such as presurgical evaluation of epilepsy. One solution to this issue is to link the interpretation of neuroimaging signals to personalized computational brain models, which we discuss in the following.

Section snippets

Brain disease modeling

Two different mechanistic modeling approaches can be distinguished according to the desired effect, i.e. whether one looks for a preventive or a curative treatment. The preventive approach requires identifying predictive biomarkers and the causal factors responsible for the transformation of a “healthy” network into a pathological one. The curative approach includes repairing the system, or at least controlling the symptoms. The mechanisms that need to be targeted for preventive and curative

The Virtual Brain

The Virtual Brain (TVB) is a large-scale brain network model comprising a connectivity matrix between cortical and subcortical areas and network nodes representing brain areas. TVB connectivity for primates is typically derived from Diffusion-weighted Tensor Imaging (DTI), a fairly recent non-invasive technology allowing the reconstruction of the myelinated white matter fibers. Brain areas are modeled using neural population models of varying degrees of sophistication. The computational models

The Virtual Mouse Brain (TVMB)

There is a multiplicity of experimental models of neurological disorders. These models have been designed to explore mechanisms and therapeutic solutions. One can distinguish genetic from induced models. The most relevant genetic models are those for which a mutation found in human families is directly introduced in the rodent genome. Induced models mostly involve lesions (chemical or electrical), e.g. for stroke, epilepsy, Parkinson’s disease, and autism. Whether genetic or induced, these

Conclusion

Large-scale brain network modeling enables linking personalized brain models with patient-specific neuroimaging data [26], [27]. Despite the enormous neuroinformatics complexity of integrating brain data, high performance computing and mathematical modeling, modern computational neuroscience provides in silico platforms (TVB, TVMB) for the testing of hypotheses of brain function on the large-scale system level. Suitable paradigms allow exploring questions linked to the network and the

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