The identification of the organization principles on the basis of the brain connectivity can be performed in terms of structural (i.e., morphological), functional (i.e., statistical), or effective (i.e., causal) connectivity. If structural connectivity is based on the detection of the morphological (synaptically mediated) links among neurons, functional and effective relationships derive from the recording of the patterns of electrophysiological activity (e.g., spikes, local field potentials). Correlation or information theory-based algorithms are typical routes pursued to find statistical dependencies and to build a functional connectivity matrix. As long as the matrix collects the possible associations among the network nodes, each interaction between the neuron i and j is different from zero, even though there was no morphological, statistical or causal connection between them. Hence, it becomes essential to find and identify only the significant functional connections that are predictive of the structural ones. For this reason, a robust, fast, and automatized procedure should be implemented to discard the "noisy" connections. In this work, we present a Double Threshold (DDT) algorithm based on the definition of two statistical thresholds. The main goal is not to lose weak but significant links, whose arbitrary exclusion could generate functional networks with a too small number of connections and altered topological properties. The algorithm allows overcoming the limits of the simplest threshold-based methods in terms of precision and guaranteeing excellent computational performances compared to shuffling-based approaches. The presented DDT algorithm was compared with other methods proposed in the literature by using a benchmarking procedure based on synthetic data coming from the simulations of large-scale neuronal networks with different structural topologies.

中文翻译：

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阈值功能连接矩阵以恢复大规模神经元网络的拓扑特性。

可以在结构（即形态）、功能（即统计）或有效（即因果）连接方面进行基于大脑连通性的组织原则的识别。如果结构连通性是基于对神经元之间形态（突触介导）联系的检测，则功能和有效关系源自电生理活动模式（例如，尖峰、局部场电位）的记录。基于相关性或信息论的算法是寻找统计依赖关系和构建功能连接矩阵的典型途径。只要矩阵收集了网络节点之间可能的关联，神经元 i 和 j 之间的每个交互都不为零，即使没有形态，它们之间的统计或因果关系。因此，找到和识别可预测结构连接的重要功能连接变得至关重要。出于这个原因，应该实施一个健壮、快速和自动化的程序来丢弃“嘈杂”的连接。在这项工作中，我们提出了一种基于两个统计阈值定义的双阈值 (DDT) 算法。主要目标不是丢失薄弱但重要的链接，其任意排除可能会生成连接数量过少和拓扑属性改变的功能网络。与基于混洗的方法相比，该算法可以克服最简单的基于阈值的方法在精度方面的限制，并保证出色的计算性能。