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Huettel, S.

Publications and source records attributed to Huettel, S..

2 recordsLinked to original sources

Developmental maturation of the precuneus as a functional core of the default-mode network

Efforts to map the functional architecture of the developing human brain have shown that connectivity between and within functional neural networks changes from childhood to adulthood. While prior work has established that the adult precuneus distinctively modifies its connectivity during task versus rest states (Utevsky, Smith, & Huettel, 2014), it remains unknown how these connectivity patterns emerge over development. Here, we use functional magnetic resonance imaging (fMRI) data collected at two longitudinal timepoints from over 250 participants between the ages of 8 and 26 engaging in two cognitive tasks and a resting-state scan. By applying independent component analysis (ICA) to both task and rest data, we identified three canonical networks of interest - the rest-based default mode network (DMN) and the task-based left and right frontoparietal networks (LFPN, RFPN) - which we explored for developmental changes using dual-regression analyses. We found systematic state-dependent functional connectivity in the precuneus, such that engaging in a task (compared to rest) resulted in greater precuneus-LFPN and precuneus-RFPN connectivity, whereas being at rest (compared to task) resulted in greater precuneus-DMN connectivity. These cross-sectional results replicated across both tasks and at both developmental timepoints. Finally, we used longitudinal mixed models to show that the degree to which precuneus distinguishes between task and rest states increases with age, due to age-related increasing segregation between precuneus and LFPN at rest. Our results highlight the distinct role of the precuneus in tracking processing state, in a manner that is both present throughout and strengthened across development.

neuroscience

Bayesian Nonparametric Models Characterize Instantaneous Strategies in a Competitive Dynamic Game

Previous approaches to investigating strategic social interaction in game theory have predominantly used games with clearly-defined turns and limited choices. However, most real-world social behaviors involve dynamic, coevolving decisions by interacting agents, which pose challenges for creating tractable models of behavior. Here, using a competitive game in which human participants control the dynamics of an on-screen avatar against either another human or a computer opponent, we show that it is possible to quantify the dynamic coupling between agents using nonparametric models. We use Gaussian Processes to model the joint distributions of players' actions and identities (human or computer) as a function of game state. Borrowing from a reinforcement learning framework, we successfully approximated both the policy and the value functions used by each human player in this competitive context. This approach offers a natural set of metrics for facilitating analysis at multiple timescales and suggests new classes of tractable paradigms for assessing human behavior.

neuroscience