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结合理论、模型和实验来解释在没有振荡输入的整个海马体准备中如何产生内在的θ节律。

Combining Theory, Model, and Experiment to Explain How Intrinsic Theta Rhythms Are Generated in an Whole Hippocampus Preparation without Oscillatory Inputs.

机构信息

Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.

Department of Physiology, University of Toronto, Toronto, Ontario, Canada.

出版信息

eNeuro. 2017 Aug 7;4(4). doi: 10.1523/ENEURO.0131-17.2017. eCollection 2017 Jul-Aug.

DOI:10.1523/ENEURO.0131-17.2017
PMID:28791333
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5547196/
Abstract

Scientists have observed local field potential theta rhythms (3-12 Hz) in the hippocampus for decades, but understanding the mechanisms underlying their generation is complicated by their diversity in pharmacological and frequency profiles. In addition, interactions with other brain structures and oscillatory drives to the hippocampus during distinct brain states has made it difficult to identify hippocampus-specific properties directly involved in theta generation. To overcome this, we develop cellular-based network models using a whole hippocampus preparation that spontaneously generates theta rhythms. Building on theoretical and computational analyses, we find that spike frequency adaptation and postinhibitory rebound constitute a basis for theta generation in large, minimally connected CA1 pyramidal (PYR) cell network models with fast-firing parvalbumin-positive (PV) inhibitory cells. Sparse firing of PYR cells and large excitatory currents onto PV cells are present as in experiments. The particular theta frequency is more controlled by PYR-to-PV cell interactions rather than PV-to-PYR cell interactions. We identify two scenarios by which theta rhythms can emerge, and they can be differentiated by the ratio of excitatory to inhibitory currents to PV cells, but not to PYR cells. Only one of the scenarios is consistent with data from the whole hippocampus preparation, which leads to the prediction that the connection probability from PV to PYR cells needs to be larger than from PYR to PV cells. Our models can serve as a platform on which to build and develop an understanding of theta generation.

摘要

几十年来,科学家们一直在观察海马体中的局部场电位θ节律(3-12 Hz),但其多样性在药理学和频率特征方面使理解其产生的机制变得复杂。此外,在不同的大脑状态下与其他大脑结构的相互作用以及对海马体的振荡驱动,使得难以直接识别与θ生成直接相关的海马体特异性特性。为了克服这一困难,我们使用自发产生θ节律的整个海马体准备开发基于细胞的网络模型。基于理论和计算分析,我们发现,在具有快速放电的钙结合蛋白阳性(PV)抑制性细胞的大、最小连接 CA1 锥体(PYR)细胞网络模型中,尖峰频率适应和后抑制反弹构成了θ生成的基础。PYR 细胞的稀疏放电和大量兴奋电流传入 PV 细胞,与实验中的情况一致。特定的θ频率更多地受到 PYR 到 PV 细胞相互作用的控制,而不是 PV 到 PYR 细胞相互作用的控制。我们确定了两种可以出现θ节律的情况,它们可以通过兴奋到 PV 细胞的电流与抑制到 PV 细胞的电流的比值来区分,但不能区分 PYR 细胞。只有一种情况与整个海马体准备的数据一致,这导致了这样的预测:即从 PV 到 PYR 细胞的连接概率需要大于从 PYR 到 PV 细胞的连接概率。我们的模型可以作为一个平台,在此基础上构建和发展对θ生成的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/7a176bc2b9de/enu0041723670010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/baa2052c1c24/enu0041723670001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/9ef2fcb76091/enu0041723670002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/ab866b063e19/enu0041723670003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/ff43cf669425/enu0041723670004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/a042a3c92418/enu0041723670005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/079ed9f215da/enu0041723670006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/1f1249456fb1/enu0041723670007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/3ee8d81d9e1f/enu0041723670008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/678cb9a9d834/enu0041723670009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/7a176bc2b9de/enu0041723670010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/baa2052c1c24/enu0041723670001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/9ef2fcb76091/enu0041723670002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/ab866b063e19/enu0041723670003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/ff43cf669425/enu0041723670004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/a042a3c92418/enu0041723670005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/079ed9f215da/enu0041723670006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/1f1249456fb1/enu0041723670007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/3ee8d81d9e1f/enu0041723670008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/678cb9a9d834/enu0041723670009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/946a/5547196/7a176bc2b9de/enu0041723670010.jpg

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