Lydon-Staley David M, Ciric Rastko, Satterthwaite Theodore D, Bassett Danielle S
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Netw Neurosci. 2019 Feb 1;3(2):427-454. doi: 10.1162/netn_a_00071. eCollection 2019.
Dynamic functional connectivity reflects the spatiotemporal organization of spontaneous brain activity in health and disease. Dynamic functional connectivity may be susceptible to artifacts induced by participant motion. This report provides a systematic evaluation of 12 commonly used participant-level confound regression strategies designed to mitigate the effects of micromovements in a sample of 393 youths (ages 8-22 years). Each strategy was evaluated according to a number of benchmarks, including (a) the residual association between participant motion and edge dispersion, (b) distance-dependent effects of motion on edge dispersion, (c) the degree to which functional subnetworks could be identified by multilayer modularity maximization, and (d) measures of module reconfiguration, including node flexibility and node promiscuity. Results indicate variability in the effectiveness of the evaluated pipelines across benchmarks. Methods that included global signal regression were the most consistently effective de-noising strategies.
动态功能连接反映了健康和疾病状态下自发脑活动的时空组织。动态功能连接可能易受参与者运动引起的伪影影响。本报告对12种常用的参与者层面混杂回归策略进行了系统评估，这些策略旨在减轻393名青少年（8至22岁）样本中微运动的影响。每种策略都根据多个基准进行评估，包括：（a）参与者运动与边缘离散度之间的残余关联；（b）运动对边缘离散度的距离依赖性影响；（c）通过多层模块化最大化可识别功能子网的程度；（d）模块重新配置的度量，包括节点灵活性和节点混杂性。结果表明，在所评估的流程中，各基准的有效性存在差异。包含全局信号回归的方法是最一致有效的去噪策略。
