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体内小脑核神经元的全细胞特性

Whole-Cell Properties of Cerebellar Nuclei Neurons In Vivo.

作者信息

Canto Cathrin B, Witter Laurens, De Zeeuw Chris I

机构信息

Netherlands Institute for Neuroscience, Royal Dutch Academy of Arts & Sciences, Amsterdam, The Netherlands.

Department of Neuroscience, Erasmus Medical Center, Rotterdam, The Netherlands.

出版信息

PLoS One. 2016 Nov 16;11(11):e0165887. doi: 10.1371/journal.pone.0165887. eCollection 2016.

DOI:10.1371/journal.pone.0165887
PMID:27851801
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5112928/
Abstract

Cerebellar nuclei neurons integrate sensorimotor information and form the final output of the cerebellum, projecting to premotor brainstem targets. This implies that, in contrast to specialized neurons and interneurons in cortical regions, neurons within the nuclei encode and integrate complex information that is most likely reflected in a large variation of intrinsic membrane properties and integrative capacities of individual neurons. Yet, whether this large variation in properties is reflected in a heterogeneous physiological cell population of cerebellar nuclei neurons with well or poorly defined cell types remains to be determined. Indeed, the cell electrophysiological properties of cerebellar nuclei neurons have been identified in vitro in young rodents, but whether these properties are similar to the in vivo adult situation has not been shown. In this comprehensive study we present and compare the in vivo properties of 144 cerebellar nuclei neurons in adult ketamine-xylazine anesthetized mice. We found regularly firing (N = 88) and spontaneously bursting (N = 56) neurons. Membrane-resistance, capacitance, spike half-width and firing frequency all widely varied as a continuum, ranging from 9.63 to 3352.1 MΩ, from 6.7 to 772.57 pF, from 0.178 to 1.98 ms, and from 0 to 176.6 Hz, respectively. At the same time, several of these parameters were correlated with each other. Capacitance decreased with membrane resistance (R2 = 0.12, P<0.001), intensity of rebound spiking increased with membrane resistance (for 100 ms duration R2 = 0.1503, P = 0.0011), membrane resistance decreased with membrane time constant (R2 = 0.045, P = 0.031) and increased with spike half-width (R2 = 0.023, P<0.001), while capacitance increased with firing frequency (R2 = 0.29, P<0.001). However, classes of neuron subtypes could not be identified using merely k-clustering of their intrinsic firing properties and/or integrative properties following activation of their Purkinje cell input. Instead, using whole-cell parameters in combination with morphological criteria revealed by intracellular labelling with Neurobiotin (N = 18) allowed for electrophysiological identification of larger (29.3-50 μm soma diameter) and smaller (< 21.2 μm) cerebellar nuclei neurons with significant differences in membrane properties. Larger cells had a lower membrane resistance and a shorter spike, with a tendency for higher capacitance. Thus, in general cerebellar nuclei neurons appear to offer a rich and wide continuum of physiological properties that stand in contrast to neurons in most cortical regions such as those of the cerebral and cerebellar cortex, in which different classes of neurons operate in a narrower territory of electrophysiological parameter space. The current dataset will help computational modelers of the cerebellar nuclei to update and improve their cerebellar motor learning and performance models by incorporating the large variation of the in vivo properties of cerebellar nuclei neurons. The cellular complexity of cerebellar nuclei neurons may endow the nuclei to perform the intricate computations required for sensorimotor coordination.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4b9/5112928/99fc05cbbd1b/pone.0165887.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4b9/5112928/ffa27f45c8c9/pone.0165887.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4b9/5112928/40fd12b0169c/pone.0165887.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4b9/5112928/99fc05cbbd1b/pone.0165887.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4b9/5112928/ffa27f45c8c9/pone.0165887.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4b9/5112928/40fd12b0169c/pone.0165887.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4b9/5112928/99fc05cbbd1b/pone.0165887.g003.jpg
摘要

小脑核神经元整合感觉运动信息,形成小脑的最终输出,并投射到运动前脑干靶点。这意味着,与皮质区域的特化神经元和中间神经元不同,核内神经元编码和整合复杂信息,这很可能反映在单个神经元内在膜特性和整合能力的巨大差异上。然而,这种特性的巨大差异是否反映在具有明确或不明确细胞类型的小脑核神经元异质生理细胞群中,仍有待确定。事实上,小脑核神经元的细胞电生理特性已在幼年啮齿动物的体外实验中得到鉴定,但这些特性是否与成年动物体内的情况相似尚未得到证实。在这项全面的研究中,我们展示并比较了成年氯胺酮-赛拉嗪麻醉小鼠中144个小脑核神经元的体内特性。我们发现了规则放电(N = 88)和自发放电(N = 56)的神经元。膜电阻、电容、动作电位半宽度和放电频率均呈连续的广泛变化,范围分别为9.63至3352.1 MΩ、6.7至772.57 pF、0.178至1.98 ms和0至176.6 Hz。同时,这些参数中的几个相互关联。电容随膜电阻降低(R2 = 0.12,P<0.001),反弹放电强度随膜电阻增加(持续100 ms时R2 = 0.1503,P = 0.0011),膜电阻随膜时间常数降低(R2 = 0.045,P = 0.031)且随动作电位半宽度增加(R2 = 0.023,P<0.001),而电容随放电频率增加(R2 = 0.29,P<0.001)。然而,仅通过对其固有放电特性和/或浦肯野细胞输入激活后的整合特性进行k聚类,无法识别神经元亚型类别。相反,结合全细胞参数和用神经生物素进行细胞内标记所揭示的形态学标准(N = 18),可以对较大(胞体直径29.3 - 五十微米)和较小(< 21.2微米)的小脑核神经元进行电生理鉴定,它们在膜特性上有显著差异。较大的细胞具有较低的膜电阻和较短的动作电位,电容有升高的趋势。因此,一般来说,小脑核神经元似乎具有丰富而广泛的生理特性连续体,这与大多数皮质区域(如大脑和小脑皮质)的神经元形成对比,在这些皮质区域中,不同类别的神经元在较窄的电生理参数空间范围内运作。当前的数据集将有助于小脑核的计算建模者通过纳入小脑核神经元体内特性的巨大差异来更新和改进他们的小脑运动学习和性能模型。小脑核神经元的细胞复杂性可能使核能够执行感觉运动协调所需的复杂计算。

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本文引用的文献

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Purkinje Cell Collaterals Enable Output Signals from the Cerebellar Cortex to Feed Back to Purkinje Cells and Interneurons.浦肯野细胞侧支使小脑皮质的输出信号能够反馈至浦肯野细胞和中间神经元。
Neuron. 2016 Jul 20;91(2):312-9. doi: 10.1016/j.neuron.2016.05.037. Epub 2016 Jun 23.
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Excitatory Cerebellar Nucleocortical Circuit Provides Internal Amplification during Associative Conditioning.兴奋性小脑核皮质回路在联合条件反射过程中提供内部放大作用。
Neuron. 2016 Feb 3;89(3):645-57. doi: 10.1016/j.neuron.2016.01.008.
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Consensus Paper: Cerebellar Development.
climbing fibers 为联想学习提供了必不可少的指导信号。
Nat Neurosci. 2024 May;27(5):940-951. doi: 10.1038/s41593-024-01594-7. Epub 2024 Apr 2.
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Synaptic mechanisms for associative learning in the cerebellar nuclei.小脑核中联想学习的突触机制。
Nat Commun. 2023 Nov 20;14(1):7459. doi: 10.1038/s41467-023-43227-w.
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Cerebellum Lecture: the Cerebellar Nuclei-Core of the Cerebellum.小脑讲座:小脑核-小脑的核心。
Cerebellum. 2024 Apr;23(2):620-677. doi: 10.1007/s12311-022-01506-0. Epub 2023 Feb 13.
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Influence of data sampling methods on the representation of neural spiking activity .数据采样方法对神经放电活动表征的影响
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