NCS-FR: Protecting the Aging Brain: Self-Organizing Networks and Multi-Scale Dynamics under Energy Constraints

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The brain's ability to use energy has been strongly implicated in age-based cognitive impairment, which will dramatically affect a disproportionally aging demographic. Globally, the number of adults aged 65 or older is estimated to more than double by 2030, with dementia rates exponentially increasing from 1-2% of the population for those age 65, to 58% for those age 94. This project probes the hypothesis that age-based cognitive impairment reflects insulin resistance (Type 2 diabetes) within the brain, limiting neurons' access to blood sugar, and tests whether one can reverse aging effects through the use of an alternative brain fuel: ketones. This project addresses individuality and variation, leveraging the interdisciplinary team's expertise in neuroscience, statistical physics, and machine learning, to tackle one of neurobiology's most fundamental unanswered questions: what are the "rules" by which the brain self-organizes in response to resource constraints? Unifying the project across scales and disciplines is a computational model designed to predict single-subject network trajectories in response to tightening and releasing of energy constraints, a first step towards understanding individuality and variation in brain aging. The project team have previously shown that aging is associated with destabilization of brain networks, an effect that the team's preliminary results suggest can be modulated by switching neuro-metabolism from glucose to ketones. Others have shown that age-based cognitive deterioration accelerates with insulin resistance. Thus, the team hypothesizes that network destabilization may result from reorganization as the brain attempts to optimize networks to conserve energy in response to neuron insulin-resistance. Using insulin resistance to tighten energy constraints and ketones to release them, the team plans to use animal (DREADD/patch-clamp/calcium imaging) and human (31P/1H-MRS, 7T fMRI) data to characterize changes in excitatory/inhibitory neuron firing dynamics and their implications for connectivity. Techniques adapted from "optimization under constraint" problems in statistical physics (e.g., Maximum Caliber) will then be applied to these data to identify cellular automaton-like "rules" that neurons might follow in guiding emergent self-organization. In so doing, the project considers optimization based upon biological principles as well as developing generative techniques for identifying constraints unbiased by the a priori hypotheses. Using an iterative approach, in which each individual subject's network trajectory provides 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. These would provide first steps towards personalized neurology, by being able to simulate - for a single individual - the potential consequences of different initial conditions and/or clinical interventions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.