A Universal Nonparametric Event Detection Framework for Neuropixels Data
A Universal Nonparametric Event Detection Framework for Neuropixels Data
复制标题
神经像素数据的通用非参数事件检测框架
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Xinyi Deng
中科院分区：
文献类型：
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作者：
Hao Chen;Shizhe Chen;Xinyi Deng
神经像素探针为神经科学提供了令人兴奋的新机遇，但如此大规模的高密度记录也给数据分析带来了前所未有的挑战。神经像素数据通常由数百或数千个长时间的连续尖峰活动组成，这些活动随着时间的推移而非平稳地发展，并且经常受到复杂的未知动态的控制。从Neuropixels记录中提取有意义的信息是一项非试验任务。在这里，我们引入了一个通用的、基于图形的统计框架，它在不强加任何参数假设的情况下，检测种群峰值活动同时发生变化的时间点，以及只发生在神经种群子集中的变化，称为“变化点”。变化点事件的序列可以解释为神经种群活动的足迹，这使我们能够将行为与同时记录的多个大脑区域的高维神经活动联系起来。我们通过分析黑暗中清醒小鼠自发行为期间的神经像素记录来证明我们方法的有效性。我们观察到，一些大脑区域的变化点动态显示出生物学上有趣的模式，暗示了功能途径，以及与行为动态的时间精确协调。我们假设自发行为背后的神经活动，尽管分布在全脑范围内，但显示出网络模块化的证据。此外，随着新的电生理记录技术继续推动数据集数量和规模的爆炸式增长，我们设想所提出的框架将成为神经科学界有用的现成分析工具。
Neuropixels probes present exciting new opportunities for neuroscience, but such large-scale high-density recordings also introduce unprecedented challenges in data analysis. Neuropixels data usually consist of hundreds or thousands of long stretches of sequential spiking activities that evolve non-stationarily over time and are often governed by complex, unknown dynamics. Extracting meaningful information from the Neuropixels recordings is a non-trial task. Here we introduce a general-purpose, graph-based statistical framework that, without imposing any parametric assumptions, detects points in time at which population spiking activity exhibits simultaneous changes as well as changes that only occur in a subset of the neural population, referred to as “change-points”. The sequence of change-point events can be interpreted as a footprint of neural population activities, which allows us to relate behavior to simultaneously recorded high-dimensional neural activities across multiple brain regions. We demonstrate the effectiveness of our method with an analysis of Neuropixels recordings during spontaneous behavior of an awake mouse in darkness. We observe that change-point dynamics in some brain regions display biologically interesting patterns that hint at functional pathways, as well as temporally-precise coordination with behavioral dynamics. We hypothesize that neural activities underlying spontaneous behavior, though distributed brainwide, show evidences for network modularity. Moreover, we envision the proposed framework to be a useful off-the-shelf analysis tool to the neuroscience community as new electrophysiological recording techniques continue to drive an explosive proliferation in the number and size of data sets.
影响因子：
2.1
作者：
Olshen, AB;Venkatraman, ES;Wigler, M
通讯作者：
Wigler, M
DOI：
10.1080/01621459.2016.1147356
发表时间：
2017-01-01
影响因子：
3.7
作者：
Chen, Hao;Friedman, Jerome H.
通讯作者：
Friedman, Jerome H.
DOI：
10.1080/01621459.2017.1307757
发表时间：
2018-01-01
影响因子：
3.7
作者：
Chen, Hao;Chen, Xu;Su, Yi
通讯作者：
Su, Yi