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, [6] propose a method for inferring effective connectivity networks from multi-electrode array data. In this paper, a novel statistical method called spectral mirror estimation [2] is applied to a 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. A classical change point algorithm is then applied to this representation, which successfully detects a neuroscientifically significant change point coinciding with the time inhibitory neurons start appearing and the percentage of astrocytes increases dramatically [9]. 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，[6]提出了一种从多电极阵列数据推断有效连接网络的方法。本文采用一种名为谱镜估计[2]的新颖统计方法，应用于推断出的类器官有效连接网络的时间序列。该方法生成网络时间序列动态的一维iso-mirror表示，随后对该表示应用经典变化点算法，成功检测到一个具有神经科学意义的变化点，该变化点恰好与抑制性神经元开始出现及星形胶质细胞比例急剧增加的时间点相吻合[9]。这一发现证明了将iso-mirror动态结构发现方法应用于推断的类器官网络有效连接时间序列的潜在用途。