Cellular Mediators of Dentate Pattern Separation in Epilepsy

颞叶癫痫(TLE)占所有癫痫病例的60%，并累及海马体，导致 记忆和认知缺陷。在海马体内是齿状回(DG)，这是一种选择性过滤器，它 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(INS)的协调激活，在TLE中，INS是 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)的主要投射 神经元，火的不精确导致模式分离失败。DG中两个重要的IN子类型是 小白蛋白(PV)INS，它通过提供可靠的周身抑制来调节GC的激发，从而影响产量 信号，以及生长抑素(SOM)INS，它通过突触到远端来调制传入信号 树枝状结构。然而，它们对模式分离计算的单独贡献还有待确定。 最近，半月颗粒细胞(Sgcs)，一种由其广泛的树突识别的兴奋性神经元，已经被 假设是为了帮助维持对未被解雇的GC的压制。SGCs和GC在分子上的不同 并且连接配置文件目前是未知的。有趣的是，SGC已被证明是主要的来源 对PV-INS的周周兴奋，潜在地加强了对局部GC的反馈抑制。然而，如何 SGC影响网络活动，它们对TLE模式分离的贡献尚不清楚。我假设 与GCS相比，SGC将显示出更少的固有模式分离，并且SGC驱动的PV-IN活动更多 有力地支持模式分离，而不是通过SOM-INS反馈树枝状抑制。此外，试验性的 TLE会破坏SGC对PV/SOM-IN介导的抑制的精确度，导致模式分离缺陷。 这项提议将研究SGCs的独特连接体，并使用体外时间模式分离 范型以及电子DG网络模型，以阐明PV-INS和SOM-INS对 正常和癫痫环路中SGCs和GCs的模式分离。一起，识别本地电路 齿状回模式分离的潜在机制及其在癫痫发生过程中如何受损将为 管理癫痫患者记忆相关并发症的新策略的方法。

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.