Learning from experience is a complex and dynamic process that is central to adaptive behavior, requiring an individual to combine information across sensory, motor, and affective domains in the service of future decisions. As such, it relies on the flexible reconfiguration of large-scale brain circuitry. Given that both learning deficits and brain network abnormalities have been observed across a number of psychiatric disorders, a mechanistic understanding of this process represents an important goal for cognitive neuroscience; however, the lack of tools to assess the dynamics of brain networks has served as a serious impediment to reaching this goal. Recently, advances in dynamic network neuroscience have begun to allow for time-resolved descriptions of large-scale network coordination during motor learning. In this proposal, we seek to utilize human neuroimaging along with novel methods from this emerging field in order to characterize the role of dynamic network coordination in more cognitive forms of learning, of the sort implicated in mental disorders.
Aim 1 will demonstrate the role of dynamic connectivity in frontal-striatal circuits during reinforcement learning. These results will describe the dynamic region-network interactions taking place during learning from feedback, using a measure known as flexibility, which quantifies the extent to which brain regions participate in multiple networks over time.
Aim 2 will characterize the distinct contributions of this network flexibility to multiple learning systems in the brain. In doing so, this aim will provide a direct test of our proposed hypothesis that dynamic coordination between learning systems and large-scale networks represents a process of information integration essential for successful learning. In addition, collaboration with researchers studying learning abnormalities in patients with anorexia will demonstrate the clinical relevance of our approach. Together, these results will provide a novel framework for understanding the complex interplay between regional and network brain processes during feedback learning, and will have significant implications for understanding psychiatric illnesses with a learning component.
Learning deficits and large-scale brain network abnormalities have both been demonstrated in a number of mental disorders, but a mechanistic link between learning and connectivity has proved elusive due to a lack of methods for assessing the dynamics of large-scale networks. The aim of this research is to characterize the role of dynamic network coordination in multiple forms of learning from feedback, utilizing recent theoretical advances in quantifying the evolution of networks over time. This proposal provides a novel framework for interpreting the relationship between learning and dynamic connectivity, advancing our understanding of this relationship in both health and disease.
| Gerraty, Raphael T; Davidow, Juliet Y; Foerde, Karin et al. (2018) Dynamic flexibility in striatal-cortical circuits supports reinforcement learning. J Neurosci : |
