Activity-dependent organization of prefrontal hub-networks for associative learning and signal transformation
Masakazu Agetsuma, Issei Sato, Yasuhiro Tanaka, Luis Carrillo‐Reid, Atsushi Kasai, Atsushi Noritake, Yoshiyuki Arai, Miki Yoshitomo, Takashi Inagaki, Hiroshi Yukawa, Hitoshi Hashimoto, Junichi Nabekura, Takeharu Nagai
Abstract
Associative learning is crucial for adapting to environmental changes. Interactions among neuronal populations involving the dorso-medial prefrontal cortex (dmPFC) are proposed to regulate associative learning, but how these neuronal populations store and process information about the association remains unclear. Here we developed a pipeline for longitudinal two-photon imaging and computational dissection of neural population activities in male mouse dmPFC during fear-conditioning procedures, enabling us to detect learning-dependent changes in the dmPFC network topology. Using regularized regression methods and graphical modeling, we found that fear conditioning drove dmPFC reorganization to generate a neuronal ensemble encoding conditioned responses (CR) characterized by enhanced internal coactivity, functional connectivity, and association with conditioned stimuli (CS). Importantly, neurons strongly responding to unconditioned stimuli during conditioning subsequently became hubs of this novel associative network for the CS-to-CR transformation. Altogether, we demonstrate learning-dependent dynamic modulation of population coding structured on the activity-dependent formation of the hub network within the dmPFC.