Gene expression as a critical indicator of hippocampal function

神经科学面临的最大挑战之一是理解认知的细胞基础，这至关重要地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)在整个大脑中提供较低的时空分辨率；而微电极记录提供精确的分辨率，但采样能力有限。直到最近，一直缺少的是一种用细胞分辨率来揭示多个行为时期的功能活动的方法--这个问题最近通过可视化即刻早期基因(IEGs)得到了解决。当神经元参与高频活动时，它们转录的IEG在细胞核内立即可见。随后(~30min)，信使核糖核酸被转移到细胞质。因为信使核糖核酸可以在两个细胞隔间中分解，所以有可能在同一动物的两个行为时期识别活跃的神经元。因此，与功能磁共振成像或正电子发射计算机断层扫描一样，IEGS提供了同一受试者的重复活动测量，但具有细胞分辨率，以描绘参与特定行为的神经网络。在空间探索中，海马体细胞在不同的物理位置激发，共同创建一张特定于环境的“地图”，以建立要记住的事件的背景。这些细胞还可以可靠地转录IEG，从而可以估计位置图的大小/一致性，可与直接神经元记录相媲美。然而，与直接记录不同，IEG表达允许进行无限样本，准确地了解有多少细胞参与到一个环境中，以及活动群体的大小/一致性如何随环境变化。因此，当前的授权利用了使用IEG表达式的独特优势来(1)创建在(1)行为和(2)随后的与记忆相关的处理期间活动细胞的数量如何根据环境的物理特征(例如，环境的大小/复杂性)以及(3)这些变化如何与学习相关的变化的估计。这些研究提供了关于最可靠地影响海马体活动的信息的关键数据，并提供了对定义行为背景的计算的性质的洞察。

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.