Inferring a binary connectivity graph from resting-state fMRI data for a single subject requires making several methodological choices and assumptions that can significantly affect the results. In this study, we investigate the robustness of existing edge detection methods when relaxing a common assumption: the sparsity of the graph. We propose a new pipeline to generate synthetic data and to benchmark the state of the art in graph inference. Simulated correlation matrices are designed to have a set of given zeros and a constraint on the signal-to-noise ratio. We compare approaches based on covariance or precision matrices, emphasizing their implications for connectivity inference. This framework allows us to assess the sensitivity of connectivity estimations and edge detection methods to different parameters.

翻译：从单个受试者的静息态fMRI数据中推断二元连接图需要做出多种方法学选择和假设，这些选择和假设会显著影响结果。在本研究中，我们探究了在放宽一个常见假设——图的稀疏性——时，现有边检测方法的鲁棒性。我们提出了一种新的流程来生成合成数据，并对图推断领域的最新技术进行基准测试。模拟的相关矩阵被设计为具有一组给定的零值，并对信噪比施加约束。我们比较了基于协方差矩阵或精度矩阵的方法，重点分析了它们对连接性推断的影响。该框架使我们能够评估连接性估计和边检测方法对不同参数的敏感性。