Super-Selective Reconstruction of Causal and Direct Connectivity With Application to in vitro iPSC Neuronal Networks.
Super-Selective Reconstruction of Causal and Direct Connectivity With Application to in vitro iPSC Neuronal Networks.
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DOI：
10.3389/fnins.2021.647877
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发表时间：
2021
影响因子：
4.3
通讯作者：
Silva GA
中科院分区：
文献类型：
--
作者：
Puppo F;Pré D;Bang AG;Silva GA
尽管基于细胞的体外神经网络模型的发展取得了进步，但缺乏适当的计算工具限制了他们的分析。通过细胞外脉冲记录来破译神经元之间的有效连接的方法将增加体外局部神经回路的效用，特别是对基于诱导多能干细胞（hiPSC）的人类神经发育和疾病的研究。目前的技术允许对网络中的功能耦合进行统计推断，但从根本上无法正确识别神经元之间的间接和明显连接，生成冗余的地图，对网络因果动态建模的能力有限。在本文中，我们描述了一种新颖的数学严谨，无模型的方法，从多电极阵列数据映射神经网络的有效直接和因果连接。推理算法使用统计和确定性指标的组合，首先能够识别网络中所有现有的功能链接，然后通过超选择规则重构定向和因果连接图，从而对直接、间接和明显链接进行高度准确的分类。我们的方法可以普遍应用于任何体外神经网络的功能表征。在这里，我们表明，鉴于其准确性，它可以为体外hipsc衍生的神经元培养的功能发育提供重要的见解。
Despite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective—direct and causal—connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect, and apparent links. Our method can be generally applied to the functional characterization of any in vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in vitro hiPSC-derived neuronal cultures.