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.

翻译：近年来，基于细胞的体外神经元网络（即类脑器官）的研发取得了重要进展。为深入理解这些类脑器官的网络结构，研究人员提出了一种超选择性算法，用于从多电极阵列数据中推断有效连接网络。本文针对推断出的类脑器官有效连接网络时间序列，应用了一种称为谱镜像估计的统计新方法。该方法能够生成网络时间序列动态的一维 iso-mirror 表示，该表示呈现出分段线性结构。进而对这一表示应用经典变点检测算法，成功检测到一个变点，该变点恰好与神经科学中具有重要意义的时刻——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类脑器官推断有效连接时间序列的潜在应用价值。