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

• Published in: Current opinion in neurology Vol. 29; no. 4; pp. 429 - 436
• Main Authors: Falcon, Maria I, Jirsa, Viktor, Solodkin, Ana
• Format: Journal Article
• Language: English
• Published: England Lippincott, Williams & Wilkins 01-08-2016
• Subjects: Brain - anatomy & histology; Brain - physiology; Cognitive science; Computational Biology; Datasets as Topic; Epilepsy - physiopathology; Humans; Medical Informatics Computing; Models, Neurological; Nerve Net - physiology; Neural Networks (Computer); Neuroscience; Precision Medicine - methods; Stroke - physiopathology; Systems Biology
• ISSN: 1350-7540; 1473-6551
• DOI: 10.1097/WCO.0000000000000344

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.
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.
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.