A spike analysis method for characterizing neurons based on phase locking and scaling to the interval between two behavioral events
A spike analysis method for characterizing neurons based on phase locking and scaling to the interval between two behavioral events
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DOI：
10.1152/jn.00200.2020
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发表时间：
2020-12-01
影响因子：
2.5
通讯作者：
Isomura, Yoshikazu
中科院分区：
文献类型：
--
作者：
Kawabata, Masanori;Soma, Shogo;Isomura, Yoshikazu
神经元功能的标准分析通过将棘波序列与特定行为事件的时间(例如视觉提示)对齐来评估动物行为和神经元活动之间的时间相关性。然而，棘波活动通常涉及信息处理，依赖于两个连续事件之间的相对相位，而不是单个事件。然而，到目前为止，与两个行为事件相关的尖峰活动的这种时间特征还没有引起太多的关注。在此，我们提出“相位缩放分析”来同时评估单个神经元任务相关的峰活动中两个事件之间的相位锁定和缩放间隔。这种分析方法可以使用结合了相位锁定和区间缩放的活动变化图来区分概念性“鳞片型”神经元和“非鳞片型”神经元。通过使用不同的脉冲特性进行脉冲模拟，验证了该算法的稳健性。此外，我们应用它分析了行为大鼠初级视觉皮质(V1)、后顶叶皮质(PPC)、初级运动皮质(M1)和次级运动皮质(M2)中与任务相关的神经元的实际放电数据。用活动变异图对所有神经元进行层次聚类后，客观地将其分为四类，分别对应于非鳞片型感觉和运动神经元和鳞片型神经元，包括持续活动和斜坡活动等，V1的簇/亚簇组成不同于PPC、M1和M2。在这些区域中，V1神经元的功能活动最快。我们的方法也适用于确定时间“形式”和棘波活动变化的潜伏期。这些发现证明了它在表征神经元方面的有效性。NEW&值得注意的相位缩放分析是一种新的技术，可以无偏地表征功能神经元活动对两个行为事件的时间依赖性，并客观地确定活动变化的潜伏期和形式。这一强大的分析可以揭示几类到目前为止被忽视的潜伏功能神经元，它们可能以不同的方式参与大脑功能的中间过程。相位标度分析将对处理内部信息的神经机制产生深刻的见解。
Standard analysis of neuronal functions assesses the temporal correlation between animal behaviors and neuronal activity by aligning spike trains with the timing of a specific behavioral event, e.g., visual cue. However, spike activity is often involved in information processing dependent on a relative phase between two consecutive events rather than a single event. Nevertheless, less attention has so far been paid to such temporal features of spike activity in relation to two behavioral events. Here, we propose "PhaseScaling analysis" to simultaneously evaluate the phase locking and scaling to the interval between two events in task-related spike activity of individual neurons. This analysis method can discriminate conceptual "scaled"-type neurons from "nonscaled"-type neurons using an activity variation map that combines phase locking with scaling to the interval. Its robustness was validated by spike simulation using different spike properties. Furthermore, we applied it to analyzing actual spike data from task-related neurons in the primary visual cortex (V1), posterior parietal cortex (PPC), primary motor cortex (M1), and secondary motor cortex (M2) of behaving rats. After hierarchical clustering of all neurons using their activity variation maps, we divided them objectively into four clusters corresponding to nonscaledtype sensory and motor neurons and scaled-type neurons including sustained and ramping activities, etc. Cluster/subcluster compositions for V1 differed from those of PPC, M1, and M2. The V1 neurons showed the fastest functional activities among those areas. Our method was also applicable to determine temporal "forms" and the latency of spike activity changes. These findings demonstrate its utility for characterizing neurons.NEW & NOTEWORTHY Phase-Scaling analysis is a novel technique to unbiasedly characterize the temporal dependency of functional neuron activity on two behavioral events and objectively determine the latency and form of the activity change. This powerful analysis can uncover several classes of latently functioning neurons that have thus far been overlooked, which may participate differently in intermediate processes of a brain function. The Phase-Scaling analysis will yield profound insights into neural mechanisms for processing internal information.