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, Takeharu Nagai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish
Article number5996
JournalNature Communications
Volume14
Issue number1
DOIs
StatePublished - 2023/12

ASJC Scopus subject areas

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy

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