Tu Danni, Wrobel Julia, Satterthwaite Theodore D, Goldsmith Jeff, Gur Ruben C, Gur Raquel E, Gertheiss Jan, Bassett Dani S, Shinohara Russell T
The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, 423 Guardian Drive, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Department of Biostatistics and Bioinformatics, 1518 Clifton Rd. NE, Emory University, Atlanta, GA, 30322, United States.
Biostatistics. 2024 Dec 31;26(1). doi: 10.1093/biostatistics/kxae026.
In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.
在大脑中，功能连接形成一个网络，其拓扑组织可以通过图论网络诊断来描述。这些包括社区结构的特征，如模块化和参与系数，已证明它们在儿童期和青少年期会发生变化。为了研究功能网络中的这种变化是否与发育过程中的认知表现变化相关，网络研究通常依赖于对预处理参数的任意选择，特别是网络边的比例阈值。由于参数的选择会影响网络诊断的值，进而影响下游结论，我们建议通过将网络诊断概念化为参数的函数来规避这种选择。与单个值不同，网络诊断曲线描述了多个尺度上的连接组拓扑结构——从最强边的最稀疏组到整个边集。为了将这些曲线与执行功能及其他协变量联系起来，我们使用函数标量回归，它比网络神经科学中以前基于功能数据的模型更灵活。然后，我们考虑网络之间的系统差异如何在诊断曲线的不对准中体现出来，并因此提出一种结合其他变量辅助信息的监督曲线对齐方法。我们的算法通过迭代、惩罚和非线性似然优化来执行功能回归和对齐。所示方法有可能提高神经科学研究的可解释性和可推广性，这些研究的目标是研究功能值和标量值测量混合中的异质性。
