Chumin Evgeny J, Cutts Sarah A, Risacher Shannon L, Apostolova Liana G, Farlow Martin R, McDonald Brenna C, Wu Yu-Chien, Betzel Richard, Saykin Andrew J, Sporns Olaf
Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States.
Indiana University Network Sciences Institute, IU, Bloomington, IN, United States.
medRxiv. 2023 Nov 18:2023.05.13.23289936. doi: 10.1101/2023.05.13.23289936.
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.
了解通过静息态磁共振成像测量的脑功能与阿尔茨海默病中神经心理学/行为测量之间的相互关系，是临床研究中神经影像分析方法进步的关键。网络神经科学领域最近开发的边缘时间序列框架，与其他网络科学方法相结合，能够研究传统功能连接方法无法实现的脑-行为关系。来自印第安纳州阿尔茨海默病研究中心样本（53名认知正常对照者、47名主观认知衰退者、32名轻度认知障碍者和20名阿尔茨海默病参与者）的数据，用于研究功能连接成分（每个成分基于区域信号的共同波动从时间点子集中导出）与特定领域神经心理学功能测量之间的关系。采用成分分析法发现了多种传统功能连接未发现的关系。这些关系涉及注意力、边缘系统、额顶叶和默认模式系统及其相互作用，显示出与认知、执行、语言和注意力神经心理学领域相关。此外，两种不同的统计策略（网络列联相关分析和基于网络的统计相关分析）得到了重叠的结果。结果表明，基于共同波动从边缘时间序列导出的连接成分揭示了传统静态功能连接未观察到的与疾病相关的关系。
