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Genetic Manipulation of the Embryonic Chicken Inner Ear 鸡胚胎内耳的遗传操作
Neuromethods
Pub Date : 2022-01-01 DOI: 10.1007/978-1-0716-2022-9_4
N. Daudet, Magdalena Żak, T. Støle, Stephen Terry
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引用次数: 0
Cerebellar Modelling Using Human Induced Pluripotent Stem 利用人诱导多能干细胞建立小脑模型
Neuromethods
Pub Date : 2022-01-01 DOI: 10.1007/978-1-0716-2026-7_1
Max J. van Essen, S. Nayler, Elizabeth J. Apsley, John Jacob, Esther B. E. Becker
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引用次数: 1
Translational Neuropsychopharmacology for Major Depression: Targeting Neurotransmitter Systems 重度抑郁症的转化神经精神药理学:靶向神经递质系统
Neuromethods
Pub Date : 2022-01-01 DOI: 10.1007/978-1-0716-2083-0_15
Hyewon Kim, Yong-Ku Kim, H. Jeon
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引用次数: 0
Measurement of Human Cochlear and Auditory Nerve Potentials 人类耳蜗和听神经电位的测量
Neuromethods
Pub Date : 2022-01-01 DOI: 10.1007/978-1-0716-2022-9_14
Eric Verschooten, P. Joris
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引用次数: 1
Multi-site Extracellular Electrode Neuronal in the Rodent Cerebellar and Nuclei 啮齿类动物小脑和核中的多位点细胞外电极神经元
Neuromethods
Pub Date : 2022-01-01 DOI: 10.1007/978-1-0716-2026-7_10
T. Tarpin, Victor Llobet, G. Dugué, Z. Piwkowska, A. P. Varani, A. Khilkevich, D. DiGregorio, D. Popa, C. Léna
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引用次数: 0
Molecular Imaging of Major Depressive Disorder with Positron Emission Tomography: Translational Implications for Neurobiology and Treatment 用正电子发射断层扫描对重性抑郁症的分子成像:对神经生物学和治疗的转化意义
Neuromethods
Pub Date : 2022-01-01 DOI: 10.1007/978-1-0716-2083-0_11
M. Tiger, J. Lundberg
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引用次数: 0
Neural Circuits Underlying the Pathophysiology of Major Depression 重性抑郁症病理生理背后的神经回路
Neuromethods
Pub Date : 2022-01-01 DOI: 10.1007/978-1-0716-2083-0_3
M. Amidfar, Yong-Ku Kim
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引用次数: 0
Translational Research Approach to Neurobiology and Treatment of Major Depression: From Animal Models to Clinical Treatment 神经生物学和抑郁症治疗的转化研究方法:从动物模型到临床治疗
Neuromethods
Pub Date : 2022-01-01 DOI: 10.1007/978-1-0716-2083-0_4
M. Bourin
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引用次数: 0
The Effects of Antidepressants on Neurotransmission: Translational Insights from In Vivo Electrophysiological Studies 抗抑郁药对神经传递的影响:来自体内电生理研究的转化见解
Neuromethods
Pub Date : 2022-01-01 DOI: 10.1007/978-1-0716-2083-0_9
M. Amidfar, Yong-Ku Kim
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引用次数: 0
Measuring Mouse Somatosensory Reflexive Behaviors with High-speed Videography, Statistical Modeling, and Machine Learning. 用高速摄像、统计建模和机器学习测量小鼠体感反射行为。
Neuromethods
Pub Date : 2022-01-01 Epub Date: 2022-05-27 DOI: 10.1007/978-1-0716-2039-7_21
Ishmail Abdus-Saboor, Wenqin Luo

Objectively measuring and interpreting an animal's sensory experience remains a challenging task. This is particularly true when using preclinical rodent models to study pain mechanisms and screen for potential new pain treatment reagents. How to determine their pain states in a precise and unbiased manner is a hurdle that the field will need to overcome. Here, we describe our efforts to measure mouse somatosensory reflexive behaviors with greatly improved precision by high-speed video imaging. We describe how coupling sub-second ethograms of reflexive behaviors with a statistical reduction method and supervised machine learning can be used to create a more objective quantitative mouse "pain scale." Our goal is to provide the readers with a protocol of how to integrate some of the new tools described here with currently used mechanical somatosensory assays, while discussing the advantages and limitations of this new approach.

客观地测量和解释动物的感官体验仍然是一项具有挑战性的任务。当使用临床前啮齿动物模型来研究疼痛机制和筛选潜在的新疼痛治疗试剂时，情况尤其如此。如何以精确和公正的方式确定他们的疼痛状态是该领域需要克服的障碍。在这里，我们描述了我们通过高速视频成像测量小鼠体感反射行为的努力，该行为的精度大大提高。我们描述了如何将反射行为的亚秒行为图与统计归约方法和监督机器学习相结合，以创建更客观的定量小鼠“疼痛量表”。我们的目标是为读者提供一个协议，说明如何将本文描述的一些新工具与当前使用的机械体感测定相结合，同时讨论了这种新方法的优点和局限性。
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引用次数: 1
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