Cell-type-specific contributions to theta-gamma coupled rhythms in the hippocampus

Distinct inhibitory cell types participate in cognitively relevant nested brain rhythms, and particular changes in such rhythms are known to occur in disease states. Specifically, the coexpression of theta and gamma rhythms in the hippocampus is believed to represent a general coding scheme, but cellular-based generation mechanisms for these coupled rhythms are currently unclear. We develop a population rate model of the CA1 hippocampus that encompasses circuits of three inhibitory cell types (bistratified cells and parvalbumin [PV]-expressing and cholecystokinin [CCK]-expressing basket cells) and pyramidal cells to examine this. We constrain parameters and perform numerical and theoretical analyses. The theory, in combination with the numerical explorations, predicts circuit motifs and specific cell-type mechanisms that are essential for the coexistence of theta and gamma oscillations. We find that CCK-expressing basket cells initiate the coupled rhythms and regularize theta, and PV-expressing basket cells enhance both theta and gamma rhythms. Pyramidal and bistratified cells govern the generation of theta rhythms, and PV-expressing basket and pyramidal cells play dominant roles in controlling theta frequencies. Our circuit motifs for the theta-gamma coupled rhythm generation could be applicable to other brain regions.

There are many different types of inhibitory cells in our brains that are differentially affected in disease. Concomitantly, coupled rhythms change in particular ways with disease. To help understand cell-type-specific changes in coupled rhythms, we develop a mathematical network model that is both respective of the cell type and also amenable to analyses. We focus on theta-gamma coupled rhythms in the hippocampus and include three different inhibitory cell types in our model circuits. By combining a theoretical analysis and numerical explorations, we find distinct contributions of these inhibitory cell types to coupled rhythms and predict motifs that are essential for the expression of theta-gamma coupled rhythms. Moving forward, we can leverage our model insights to help unravel cell-type contributions in disease states.