Cellular Mediators of Dentate Pattern Separation in Epilepsy

颞叶癫痫（TLE）占所有癫痫病例的60%，并涉及海马体，导致 记忆和认知缺陷。海马内是齿状回（DG），其是选择性过滤器， 生成上下文相似输入的唯一表示，这一过程称为模式分离。图案 分离依赖于多种类型的中间神经元（IN）的协调激活，在TLE中， 易受细胞死亡和重组影响。如果没有适当的抑制，颗粒细胞（GC），主要的投影 神经元不精确地放电，导致模式分离失败。DG中两种重要的IN亚型是 小清蛋白（PV）INs，通过提供可靠的体周抑制来调节GC放电，从而影响输出 信号，以及生长抑素（SOM）INs，它通过突触连接到远端来调节传入信号 树突然而，它们各自对模式分离计算的贡献还有待确定。 半月颗粒细胞（semilunar granule cells，SGCs）是一种具有广泛树突的兴奋性神经元，近年来， 假设有助于维持对非击发GC的抑制。SGCs和GC在分子水平上的差异 并且连接简档当前是未知的。有趣的是，SGCs已被证明是主要来源 可能增强对局部GC的反馈抑制。但如何 SGCs影响网络活动，它们对TLE模式分离的贡献是未知的。我假设 与GC相比，SGC将显示出降低的内在模式分离，并且SGC驱动的PV-IN活性更高。 强有力地支持模式分离比反馈树突抑制SOM-INs。此外，实验， TLE将破坏SGC对PV/SOM-IN介导的抑制的精确性，导致模式分离缺陷。 该建议将研究SGCs独特的连接体，并使用离体时间模式分离 范例以及计算机DG网络模型，以阐明PV-IN和SOM-IN对 健康和癫痫回路中SGCs和GCs的模式分离。同时，识别本地电路 齿状模式分离的潜在机制及其在癫痫发生过程中的受损方式将为研究 新的策略来管理癫痫记忆相关的合并症的方式。

Temporal lobe epilepsy (TLE) represents 60% of all epilepsy cases and involves the hippocampus resulting in memory and cognitive deficits. Within the hippocampus is the dentate gyrus (DG), a selectivity filter which generates unique representations of contextually similar inputs, a process known as pattern separation. Pattern separation relies on the coordinated activation of multiple types of interneurons (INs) which, in TLE, are susceptible to cell death and reorganize. Without proper inhibition, granule cells (GCs), the main projection neuron, fire imprecisely leading to failure in pattern separation. Two important IN subtypes in the DG are the parvalbumin (PV) INs, which modulates GC firing by delivering reliable perisomatic inhibition thus affecting output signals, and the somatostatin (SOM) INs, which modulates incoming signals by synapsing onto the distal dendrites. However, their individual contributions to pattern separation computation have yet to be determined. Recently, the semilunar granule cells (SGCs), an excitatory neuron identified by their wide dendrites, has been hypothesized to aid in maintaining suppression of the non-firing GCs. How SGCs and GCs differ in molecular and connectivity profiles is currently unknown. Interestingly, SGCs have been shown to be the primary source of perisomatic excitation onto PV-INs, potentially enhancing feedback inhibition onto local GCs. However, how SGCs affect network activity and their contribution to pattern separation in TLE is unknown. I hypothesize that SGCs will show reduced intrinsic pattern separation compared to GCs and that SGC driven PV-IN activity more robustly supports pattern separation than feedback dendritic inhibition by SOM-INs. Furthermore, experimental TLE will disrupt the precision of SGC to PV/SOM-IN mediated inhibition resulting in pattern separation deficits. This proposal will investigate the unique connectome of SGCs and, using an ex vivo temporal pattern separation paradigm as well as a in silico DG network model, to elucidate the contributions of PV-INs and SOM-INs to pattern separation in SGCs and GCs in healthy and epileptic circuits. Together, identification of the local circuit mechanisms underlying dentate pattern separation and how it is impaired during epileptogenesis will pave the way for novel strategies to manage memory related co-morbidities in epilepsy.