| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2024-12-02 |
| タイトル |
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タイトル |
Activity-dependent organization of prefrontal hub-networks for associative learning and signal transformation |
|
言語 |
en |
| 言語 |
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|
言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
Agetsuma Masakazu
Issei Sato
Yasuhiro R Tanaka
Luis Carrillo-Reid
Atsushi Kasai
Atsushi Noritake
Yoshiyuki Arai
Miki Yoshitomo
Takashi Inagaki
Yukawa Hiroshi
Hitoshi Hashimoto
Junichi Nabekura
Takeharu Nagai
|
| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
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. |
| 書誌情報 |
Nature communications
巻 14,
p. 5996,
発行日 2023-10
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| 出版者 |
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出版者 |
Springer Nature |
| ISSN |
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収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2041-1723 |
| PubMed番号 |
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識別子タイプ |
PMID |
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関連識別子 |
37803014 |
| DOI |
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識別子タイプ |
DOI |
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関連識別子 |
10.1038/s41467-023-41547-5 |