DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 최정균 | - |
dc.contributor.author | Oh, Jae Ho | - |
dc.contributor.author | 오재호 | - |
dc.date.accessioned | 2024-08-08T19:31:07Z | - |
dc.date.available | 2024-08-08T19:31:07Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1099233&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/322021 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 바이오및뇌공학과, 2024.2,[iii, 79 p. :] | - |
dc.description.abstract | Cognitive functions that are based on numerous molecular processes including changes in the expression of genes are among the crucial processes in the brain. Prefrontal cortex (PFC) has been well-established as a crucial hub in the neural circuitry underlying cognitive function processes. For decades, Immediate Early Genes (IEGs) have served as indirect markers for measuring neuronal activity. Several studies have revealed the involvement of N-methyl-D-aspartate receptors (NMDARs) expression in cognitive function within neurons of PFC. Recently, numerous studies have been conducted in specific disease models to elucidate the mechanisms of cognitive function changes, encompassing working memory, decision-making, and learning and memory. However, despite the considerable progress in research related to cognitive function, there remains a deficiency in investigations exploring changes in cognitive function over learning time at the single-cell resolution. In this study, we focused on comprehending global changes among cell types by utilizing single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data in the PFC of the DNMP T-maze task model. Also, to investigate the mechanisms in EN during the learning period, we conducted network and subtype analysis using scRNA and scATAC data. As a result, our study not only developed a new learning model based on the DNMP T-maze task with respect to learning time but also investigated the mechanisms in Excitatory Neurons (EN) in the PFC. Particularly, we elucidated a mechanism where the early activation of Immediate Early Genes (IEGs) such as Fos, Jun, Egr1, and Arc in the early stages of learning enhances learning efficiency through interactions with key target genes like Ahi1, Basp1, and Nrgn. Additionally, we found that this mechanism operates specifically in IT L2/3 Ptgs2+ and PT L5b subtypes of EN. Therefore, these discovered mechanisms and target genes are expected to be utilized as potential therapeutic targets for diseases related to cognitive function. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 인지기능▼a단일세포 전사체▼a전전두엽▼a네트워크 분석 | - |
dc.subject | Cognitive function▼aSingle-cell RNA▼aPFC▼aNetwork analysis | - |
dc.title | Understanding of learning mechanisms at single-cell resolution | - |
dc.title.alternative | 단일세포 분석을 통한 학습 메커니즘의 이해 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :바이오및뇌공학과, | - |
dc.contributor.alternativeauthor | Choi, Jung Kyoon | - |
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