通过iso-mirror发现类器官网络时间序列中的变点与分段线性结构
Discovering a change point and piecewise linear structure in a time series of organoid networks via the iso-mirror
AI总结：
本文提出将谱镜像估计与变点检测相结合，应用于类器官网络有效连接时间序列，成功识别出与抑制性神经元出现及星形胶质细胞增加相关的变点，验证了该方法在动态结构发现中的潜力。
摘要：
近年来，基于细胞的体外神经元网络（即类器官）的开发取得了最新进展。为了更好地理解这些类器官的网络结构，已提出一种超选择性算法，用于从多电极阵列数据中推断有效连接网络。本文中，我们将一种名为谱镜像估计的新颖统计方法应用于推断出的有效连接类器官网络的时间序列。该方法为网络时间序列的动力学产生了一维iso-mirror表示，该表示展现出分段线性结构。随后，将经典变点算法应用于该表示，成功检测到一个变点，该变点与神经科学上重要的时间点（抑制性神经元开始出现且星形胶质细胞比例急剧增加）相吻合。这一发现证明了将iso-mirror动态结构发现方法应用于推断出的类器官网络有效连接时间序列的潜在效用。
英文摘要：
Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids, a super-selective algorithm has been proposed for inferring the effective connectivity networks from multi-electrode array data. In this paper, we apply a novel statistical method called spectral mirror estimation to the time series of inferred effective connectivity organoid networks. This method produces a one-dimensional iso-mirror representation of the dynamics of the time series of the networks which exhibits a piecewise linear structure. A classical change point algorithm is then applied to this representation, which successfully detects a change point coinciding with the neuroscientifically significant time inhibitory neurons start appearing and the percentage of astrocytes increases dramatically. This finding demonstrates the potential utility of applying the iso-mirror dynamic structure discovery method to inferred effective connectivity time series of organoid networks.
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