How do parvalbumin interneuron-generated gamma oscillations organize prefrontal networks to promote behavioral adaptation?

点击翻译按钮获取中文摘要

PROJECT SUMMARY Rhythmic fluctuations of electrical activity in the brain are frequently observed during cognitive tasks. In many cases these oscillations are synchronized across brain regions. Synchronization in the gamma-frequency (~30- 100 Hz) range has been hypothesized to promote communication between brain regions, thereby facilitating cognitive functions. Conversely, deficits in gamma synchrony have been hypothesized to contribute to cognitive deficits at the heart of schizophrenia, Alzheimer’s disease, and related disorders. However, whether gamma synchrony actually contributes to brain function remains highly controversial. The specific circuit-level mechanisms through which gamma synchrony acts are also unclear. This proposal will take advantage of two recent developments in our laboratory. First, we have developed a new method for analyzing signals from genetically encoded voltage indicators in order to quantify changes in gamma synchrony within freely behaving mice. Second, using this method and optogenetics, we have found that interhemispheric gamma synchrony between parvalbumin (PV) interneurons in the prefrontal cortex plays a key role when mice learn new cue- reward associations. We hypothesize that: 1) gamma-frequency activity in PV interneurons entrains activity in prefrontal neurons which project to specific targets; 2) the activity of these projection neurons encodes key information related to learning; 3) thus, gamma-frequency synchronization allows prefrontal output to converge constructively in specific downstream targets, facilitating the transmission of critical task-relevant information across an extended prefrontal network that mediates learning. This proposal will test these hypotheses by studying whether gamma synchrony is transmitted from prefrontal PV interneurons to various classes of prefrontal projection neurons which encode task-relevant information and/or to downstream regions. We will then construct a computational model to test which hypothesized functions of gamma synchrony are consistent with our experimental observations. This will reveal circuit-level mechanisms whereby gamma synchrony is transmitted across neural networks in ways that can facilitate inter-regional communication and learning.