Neuroscience and Mental Health Institute, University of Alberta , Edmonton, Alberta , Canada.
Hotchkiss Brain Institute, University of Calgary , Calgary, Alberta , Canada.
J Neurophysiol. 2019 Jun 1;121(6):2001-2012. doi: 10.1152/jn.00763.2018. Epub 2019 Apr 3.
Spontaneous activity is a common feature of immature neuronal networks throughout the central nervous system and plays an important role in network development and consolidation. In postnatal rodents, spontaneous activity in the spinal cord exhibits complex, stochastic patterns that have historically proven challenging to characterize. We developed a software tool for quickly and automatically characterizing and classifying episodes of spontaneous activity generated from developing spinal networks. We recorded spontaneous activity from in vitro lumbar ventral roots of 16 neonatal [postnatal day (P)0-P3] mice. Recordings were DC coupled and detrended, and episodes were separated for analysis. Amplitude-, duration-, and frequency-related features were extracted from each episode and organized into five classes. Paired classes and features were used to train and test supervised machine learning algorithms. Multilayer perceptrons were used to classify episodes as rhythmic or multiburst. We increased network excitability with potassium chloride and tested the utility of the tool to detect changes in features and episode class. We also demonstrate usability by having a novel experimenter use the program to classify episodes collected at a later time point (P5). Supervised machine learning-based classification of episodes accounted for changes that traditional approaches cannot detect. Our tool, named SpontaneousClassification, advances the detail in which we can study not only developing spinal networks, but also spontaneous networks in other areas of the nervous system. Spontaneous activity is important for nervous system network development and consolidation. Our software uses machine learning to automatically and quickly characterize and classify episodes of spontaneous activity in the spinal cord of newborn mice. It detected changes in network activity following KCl-enhanced excitation. Using our software to classify spontaneous activity throughout development, in pathological models, or with neuromodulation, may offer insight into the development and organization of spinal circuits.
自发性活动是中枢神经系统中未成熟神经元网络的共同特征，在网络发育和巩固中发挥着重要作用。在新生啮齿动物中，脊髓中的自发性活动表现出复杂的、随机的模式，这在历史上一直难以进行特征描述。我们开发了一种软件工具，用于快速、自动地描述和分类发育中脊髓网络产生的自发性活动片段。我们从 16 只新生（出生后第 0-3 天）小鼠的体外腰腹根记录自发性活动。记录是直流耦合和去趋势的，并将片段分开进行分析。从每个片段中提取幅度、持续时间和频率相关的特征，并组织成五个类别。将配对的类别和特征用于训练和测试监督机器学习算法。多层感知器用于将片段分类为节律性或多爆发性。我们用氯化钾增加网络兴奋性，并测试该工具检测特征和片段类别的变化的实用性。我们还通过让一位新实验者使用该程序对收集到的稍后时间点（P5）的片段进行分类，展示了其可用性。基于监督机器学习的片段分类可以解释传统方法无法检测到的变化。我们的工具名为 SpontaneousClassification，可以更详细地研究不仅是发育中的脊髓网络，还有神经系统其他区域的自发性网络。自发性活动对神经系统网络的发育和巩固很重要。我们的软件使用机器学习自动且快速地描述和分类新生小鼠脊髓中的自发性活动片段。它检测到 KCl 增强兴奋后网络活动的变化。使用我们的软件对整个发育过程中的自发性活动、病理模型中的自发性活动或神经调节进行分类，可能有助于深入了解脊髓回路的发育和组织。
