Cognitive Neuroscience Embedded in Large-Scale Models of Systems Dynamics

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Identifying structure-function relationships in task-related brain networks. a) We identify brain regions that belong to task-positive and task-negative networks, and label all remaining regions as “other” regions. We examine three types of couplings, highlighted here in the axial view of the representative brain network: those between two task-negative regions (NN), between two task-positive regions (PP), and between a task-positive and a task-negative region (PN). (b) We compute measures of structural (SC) and functional (FC) connectivity between each pair of regions by measuring the number of white matter streamlines linking two regions (SC) and the strength of functional correlation between BOLD time series measured within regions (FC). The pie chart shows the decomposition of all structural connections into those that link two task-positive (nPP), two task-negative (nNN), a task-positive and a task-negative (nPN), and all other regions (n*O). (c) We assess variations Δn in these number densities for each of these types of structural connections, showing the change in composition of the pie chart in (b) while incrementally biasing toward region pairs with strong functional correlations above a threshold value τR. The inset shows the complementary cumulative distribution, which gives the probability of finding FC > FCR for every value of FCR. (d) The changes in connection density are represented by comparing the degree of within-network coupling (ΔnPP-ΔnNN) with the degree of between-network coupling (ΔnPN). This reveals that strong resting-state function is supported by strong local coupling within the task-negative network, and weak coupling between task-positive and task-negative networks.

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We develop theoretical systems-level models and data-driven analysis of anatomical architecture and cognitive function in the human brain, thereby describing human motor control, dynamic evolution of network organization, anatomical connectivity patterns, and responses to risk and pressure across a range of cognitive conditions. Through the application of tools from control theory and dynamical systems engineering, we integrate anatomical architecture, cognitive function, and behavioral performance into a theoretical framework that suggests approaches for efficient training, optimal decision-making, and resource allocation under relevant Army scenarios. These advances may further enable the identification of biomarkers for cognitive flexibility, fatigue, and performance under pressure, and may help capture important aspects of human cognition for the development of adaptable machine systems.

University: 

UCSB

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