Emerging subspecialties: neuroinformatics

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

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.

Sex-specific patterns and differences in dementia and Alzheimer's disease using informatics approaches

The National Institutes of Health Office of Research on Women's Health recently highlighted the critical need for explicitly addressing sex differences in biomedical research, including Alzheimer's disease and dementia. The purpose of our study was to perform a sex-stratified analysis of cognitive impairment using diverse medical, clinical, and genetic factors of unprecedented scale and scope by applying informatics approaches to three large Alzheimer's databases. Analyses suggested females were 1.5 times more likely than males to have a documented diagnosis of probable Alzheimer's disease, and several other factors fell along sex-specific lines and were possibly associated with severity of cognitive impairment.