Activity-dependent organization of prefrontal hub-networks for associative learning and signal transformation

Masakazu Agetsuma (), Issei Sato, Yasuhiro R. Tanaka, Luis Carrillo-Reid, Atsushi Kasai, Atsushi Noritake, Yoshiyuki Arai, Miki Yoshitomo, Takashi Inagaki, Hiroshi Yukawa, Hitoshi Hashimoto, Junichi Nabekura and Takeharu Nagai
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Masakazu Agetsuma: National Institute for Physiological Sciences
Issei Sato: Graduate School of Information Science and Technology, The University of Tokyo
Yasuhiro R. Tanaka: Tamagawa University
Luis Carrillo-Reid: National Autonomous University of Mexico
Atsushi Kasai: Osaka University
Atsushi Noritake: National Institute for Physiological Sciences
Yoshiyuki Arai: Osaka University
Miki Yoshitomo: National Institute for Physiological Sciences
Takashi Inagaki: National Institute for Physiological Sciences
Hiroshi Yukawa: National Institutes for Quantum Science and Technology (QST)
Hitoshi Hashimoto: Osaka University
Junichi Nabekura: National Institute for Physiological Sciences
Takeharu Nagai: Osaka University

Nature Communications, 2023, vol. 14, issue 1, 1-22

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-41547-5

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