Medical Research

Stony Brook University

Lilianne R. Mujica-Parodi, Ken Dill, Steven Skiena, Steven Stufflebeam, Jacob Hooker
Stony Brook, NY
June 2017

In moving towards the goal of personalized medicine, investigators at Stony Brook University in collaboration with researchers at Massachusetts General Hospital/Harvard Medical School approach brain network connectivity, assessed by functional magnetic resonance imaging (fMRI) and associated cognitive function, as a dynamic emergent phenomenon.  They plan to integrate human neuroimaging data (7-Tesla fMRI and positron emission tomography, the latter to measure nutrient consumption by brain cells) with multi-scale biomimetic modeling, to test hypotheses with respect to how energy constraints (from diet to mitochondria) affect neural efficiency with age.  The interdisciplinary team of researchers will also experimentally investigate the use of exogenous ketones, a fuel source that is alternative to glucose, as a way to ameliorate age-related effects.  Based upon single subject-specific parameters, models will predict how networks self-organize in response to changes in energy supply and demand, then will be compared against human network trajectories.  Using an iterative approach, in which human data provide feedback, informing the models, which then make predictions that are tested against the next individual’s data, models will eventually converge in predicting human network trajectories based upon individually variable parameters.  In addition to generating fundamental understanding of how nutrition of brain neurons affects cognitive capacity and aging in humans, the project could provide a critical first step towards personalized neurology.  This would be accomplished by simulating—for a single individual—the potential consequences of different dietary interventions in protecting the aging brain.

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