Functional connectivity networks (FCNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Currently, researchers have proposed many methods for FCN construction, among which the most classic example is Pearson's correlation (PC). Despite its simplicity and popularity, PC always results in dense FCNs, and thus a thresholding strategy is usually needed in practice to sparsify the estimated FCNs prior to the network analysis, which undoubtedly causes the problem of threshold parameter selection. As an alternative to PC, sparse representation (SR) can directly generate sparse FCNs due to the l₁ regularizer in the estimation model. However, similar to the thresholding scheme used in PC, it is also challenging to determine suitable values for the regularization parameter in SR. To circumvent the difficulty of parameter selection involved in these traditional methods, we propose a hyperparameter-free method for FCN construction based on the global representation among fMRI time courses. Interestingly, the proposed method can automatically generate sparse FCNs, without any thresholding or regularization parameters. To verify the effectiveness of the proposed method, we conduct experiments to identify subjects with mild cognitive impairment (MCI) and Autism spectrum disorder (ASD) from normal controls (NCs) based on the estimated FCNs. Experimental results on two benchmark databases demonstrate that the achieved classification performance of our proposed scheme is comparable to four conventional methods.

功能连接网络 (FCN) 为理解大脑组织模式和诊断神经系统疾病提供了一种潜在的方式。目前，研究人员提出了许多构建 FCN 的方法，其中最经典的例子是 Pearson 相关性（PC）。尽管 PC 简单且流行，但 PC 总是会导致密集的 FCN，因此在实践中通常需要阈值策略来在网络分析之前稀疏估计的 FCN，这无疑会导致阈值参数选择的问题。作为 PC 的替代方案，稀疏表示 (SR) 可以直接生成稀疏 FCN，因为l ₁估计模型中的正则化器。然而，类似于 PC 中使用的阈值方案，为 SR 中的正则化参数确定合适的值也具有挑战性。为了规避这些传统方法中参数选择的困难，我们提出了一种基于 fMRI 时间过程中全局表示的 FCN 构建无超参数方法。有趣的是，所提出的方法可以自动生成稀疏 FCN，无需任何阈值或正则化参数。为了验证所提出方法的有效性，我们进行了实验，根据估计的 FCN 从正常对照 (NC) 中识别出患有轻度认知障碍 (MCI) 和自闭症谱系障碍 (ASD) 的受试者。