Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer’s disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer’s Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer’s disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.

了解阿尔茨海默病中通过静息态磁共振成像和神经心理学/行为测量测量的大脑功能的相互关系是临床研究中神经影像分析方法进步的关键。最近在网络神经科学领域开发的边缘时间序列框架，与其他网络科学方法相结合，可以研究传统功能连接方法无法实现的大脑行为关系。来自印第安纳阿尔茨海默病研究中心样本（53 名认知正常对照、47 名主观认知下降、32 名轻度认知障碍和 20 名阿尔茨海默病参与者）的数据用于研究功能连接组件之间的关系，每个组件都源自基于时间点的子集。关于区域信号的共同波动以及特定领域神经心理功能的测量。使用组件方法可以识别传统功能连接中未发现的多种关系。这些涉及注意力、边缘系统、额顶叶和默认模式系统及其相互作用，这些系统被证明与认知、执行、语言和注意神经心理学领域相结合。此外，通过两种不同的统计策略（网络偶然相关性分析和基于网络的统计相关性）获得了重叠的结果。结果表明，基于共波动的边缘时间序列衍生的连接组件揭示了传统静态功能连接未观察到的疾病相关关系。