Gene expression as a critical indicator of hippocampal function

基因表达作为海马功能的关键指标

• 批准号: 355838-2008
• 负责人: Marrone, DianoFabio
• 金额: $ 1.82万
• 依托单位: Wilfrid Laurier University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=402724
• 关键词: Gene; expression; critical; indicator; hippocampal

One of the greatest challenges facing neuroscience is to understand the cellular basis of cognition, which critically requires identifying neural activity patterns over widespread regions of the brain. Functional imaging methods (e.g., PET, fMRI) provide low spatiotemporal resolution across the brain; while microelectrode recordings provide precise resolution, but with limited sampling capabilities. What has been missing, until recently, is a method revealing functional activity during multiple epochs of behavior with cellular resolution - a problem recently solved by visualizing immediate-early genes (IEGs). When neurons engage in high frequency activity, they transcribe IEGs which are immediately visible within the nucleus. Subsequently (~30 min), the mRNA is translocated to the cytoplasm. Because mRNA can be resolved in two cellular compartments, it is possible to identify active neurons during two behavioral epochs in the same animal. Thus, like fMRI or PET, IEGs provides repeated activity measurements in the same subject, but with the cellular resolution to delineate neural networks engaged in specific behaviors. During spatial exploration, hippocampal cells fire in distinct physical locations, collectively creating an environment-specific "map" thought to establish the context of an episode to be remembered. These cells also reliably transcribe IEGs, permitting estimates of the size/consistency of place maps comparable to direct neuronal recording. Unlike direct recordings, however, IEG expression permits a limitless sample of precisely how many cells are engaged in an environment, and how the size/consistency of active populations change with the environment. Thus, the current grant capitalizes on the unique advantages of using IEG expression to (1) create estimates of how the number of active cells during (1) behavior and (2) subsequent memory-related processing change according to the physical characteristics of the environment (e.g., size/complexity of the environment) as well as (3) how these changes relate to learning. These studies provide critical data on the information that most reliably influences hippocampal activity and provide insight into the nature of the computations that define behavioral context.

神经科学面临的最大挑战之一是理解认知的细胞基础，这迫切需要识别大脑广泛区域的神经活动模式。 功能成像方法（例如，PET、fMRI）在整个大脑中提供低时空分辨率;而微电极记录提供精确分辨率，但采样能力有限。 直到最近，还缺少一种方法来揭示多个行为时期的功能活动与细胞分辨率-最近通过可视化立即早期基因（IEG）解决了这个问题。 当神经元进行高频率活动时，它们转录在核内立即可见的IEGs。 随后（约30分钟），mRNA易位至细胞质。 由于mRNA可以在两个细胞区室中分辨，因此可以在同一动物的两个行为时期识别活跃的神经元。因此，像fMRI或PET一样，IEG提供了同一受试者的重复活动测量，但具有描绘参与特定行为的神经网络的细胞分辨率。 在空间探索过程中，海马细胞在不同的物理位置放电，共同创造了一个环境特异性的“地图”，以建立要记住的情节的背景。这些细胞也可靠地转录IEG，允许与直接神经元记录相当的位置图的大小/一致性的估计。 然而，与直接记录不同的是，IEG表达允许无限的样本精确地了解有多少细胞参与环境，以及活性群体的大小/一致性如何随环境变化。 因此，当前授权利用了使用IEG表达式的独特优势来（1）创建对在（1）行为和（2）后续存储器相关处理期间活动单元的数量如何根据环境的物理特性（例如，环境的规模/复杂性）以及（3）这些变化与学习的关系。 这些研究提供了关于最可靠地影响海马活动的信息的关键数据，并提供了对定义行为背景的计算性质的深入了解。