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

被引:34
作者
Falcon, Maria I. [1 ]
Jirsa, Viktor [3 ,4 ]
Solodkin, Ana [1 ,2 ]
机构
[1] UC Irvine Hlth Sch Med, Dept Anat & Neurobiol, Irvine, CA USA
[2] UC Irvine Hlth Sch Med, Dept Neurol, Irvine, CA USA
[3] Aix Marseille Univ, Fac Med, Inst Neurosci Syst, Marseille, France
[4] INSERM, Marseille, France
基金
美国国家卫生研究院;
关键词
brain modeling; computational neuroscience; connectome; epilepsy; neural coupling; neuroinformatics; neurology; personalized medicine; stroke; LARGE-SCALE BRAIN; STROKE; CONNECTOMICS; DEGENERATION; RESOLUTION; NETWORKS; DYNAMICS; SIGNAL; MODEL;
D O I
10.1097/WCO.0000000000000344
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
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.
引用
收藏
页码:429 / 436
页数:8
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