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Kudara M, Kato-Ishikura E, Ikegaya Y, Matsumoto N. Ramelteon administration enhances novel object recognition and spatial working memory in mice. J Pharmacol Sci 2023; 152:128-135. [PMID: 37169477 DOI: 10.1016/j.jphs.2023.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 03/17/2023] [Accepted: 04/10/2023] [Indexed: 05/13/2023] Open
Abstract
Ramelteon is used to ameliorate sleep disorders that negatively affect memory performance; however, it remains unknown whether ramelteon strengthens neutral memories, which do not involve reward or punishment. To address this, we monitored behavior of mice treated with vehicle/ramelteon while they performed a novel object recognition task and a spontaneous alternation task. Object memory performance in the novel object recognition task was improved only if ramelteon was injected before training, suggesting that ramelteon specifically enhances the acquisition of object recognition memory. Ramelteon also enhanced spatial working memory in the spontaneous alternation task. Altogether, acute ramelteon treatment enhances memory in quasi-natural contexts.
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Affiliation(s)
- Mikuru Kudara
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan
| | - Eriko Kato-Ishikura
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan
| | - Yuji Ikegaya
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan; Institute for AI and Beyond, The University of Tokyo, Tokyo 113-0033, Japan; Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita City, Osaka, 565-0871, Japan
| | - Nobuyoshi Matsumoto
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan; Institute for AI and Beyond, The University of Tokyo, Tokyo 113-0033, Japan.
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Kudara M, Matsumoto N, Kuga N, Yamashiro K, Yoshimoto A, Ikegaya Y, Sasaki T. An open-source application to identify the three-dimensional locations of electrodes implanted into the rat brain from computed tomography images. Neurosci Res 2023:S0168-0102(23)00069-X. [PMID: 37003370 DOI: 10.1016/j.neures.2023.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 03/15/2023] [Accepted: 03/29/2023] [Indexed: 04/03/2023]
Abstract
Electrophysiological recordings using metal electrodes implanted into the brains have been widely utilized to evaluate neuronal circuit dynamics related to behavior and external stimuli. The most common method for identifying implanted electrode tracks in the brain tissue has been histological examination following postmortem slicing and staining of the brain tissue, which consumes time and resources and occasionally fails to identify the tracks because the brain preparations have been damaged during processing. Recent studies have proposed the use of a promising alternative method, consisting of computed tomography (CT) scanning that can directly reconstruct the three-dimensional arrangements of electrodes in the brains of living animals. In this study, we developed an open-source Python-based application that estimates the location of an implanted electrode from CT image sequences in a rat. After the user manually sets reference coordinates and an area from a sequence of CT images, this application automatically overlays an estimated location of an electrode tip on a histological template image; the estimates are highly accurate, with less than 135μm of error, irrespective of the depth of the brain region. The estimation of an electrode location can be completed within a few minutes. Our simple and user-friendly application extends beyond currently available CT-based electrode localization methods and opens up the possibility of applying this technique to various electrophysiological recording paradigms.
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Affiliation(s)
- Mikuru Kudara
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan
| | - Nobuyoshi Matsumoto
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan; Institute for AI and Beyond, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Nahoko Kuga
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan; Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Tohoku University, 6-3 Aramaki-Aoba, Aoba-Ku, Sendai 980-8578, Japan
| | - Kotaro Yamashiro
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan
| | - Airi Yoshimoto
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan
| | - Yuji Ikegaya
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan; Institute for AI and Beyond, The University of Tokyo, Tokyo 113-0033, Japan; Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita City, Osaka, 565-0871, Japan.
| | - Takuya Sasaki
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan; Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Tohoku University, 6-3 Aramaki-Aoba, Aoba-Ku, Sendai 980-8578, Japan.
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