Neural Foundations of Expertise Based on Optimal Decision-making, Physical Control and Responses to Stress
Abstract
The objective of this proposal is to characterize expertise in terms of the changes within and between the neural systems that support decision-making and the closely related process of habit formation (effective action selection), the physical control of skilled action (reliable execution) and the adaptive control of stress responses (resilience). Tasking to acheive the stated objective of this proposal will include a suite of experimental tools applied to humans and non-human primate (NHP) subjects and computational approaches applied to the resulting multimodal datasets. Proposers hypothesize that integration across the decision-making, physical control of skilled action and adaptive control of stress neural systems can be observed as a function of training at the anatomic, neural, behavioral and computational levels of analysis. Proposers plan to combine commonly used neural and physiological sensing modalities (e.g., single unit and LFP electrophysiology, fMRI, heart rate, skin conductance, and pupillometry), and recently developed methods (fast scan cyclic voltammetry for dopamine neurotransmitter sensing and neurotropic viral methodologies) while subjects participate in a longitudinal approach-avoidance decision-making learning paradigm. The longitudinal behavioral study will be combined with neurochemical detection of the neurotransmitter dopamine, functional neuroimaging, electrophysiology, and cortical mapping of stress responses in decision-making neural networks in non-human primates and humans. This approach enables a systematic study of various neural contributors to the process of skill learning entailing theoretically well-grounded and hypothesis-driven experiments to assess the contribution of these various factors to learning. The proposed approach also includes a strong data analysis and modeling effort to help guide the design and interpretation of the experiments. The proposal also emphasizes the implementation of network control theory and less traditional methods from "pure" mathematics, such as algebraic topology, to identify neural networks controlling brain network dynamics and neurophysiological mechanisms responsible for skill learning.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 27, 2017
- Source ID
- W911NF1610474
Entities
People
- Scott T. Grafton
Organizations
- Army Contracting Command
- United States Army
- University of California, Santa Barbara