空间约束独立分量分析中多个空间归一化管道中功能网络连通性(FNC)值的稳定性

T. DeRamus, A. Iraji, Z. Fu, Rogers F. Silva, J. Stephen, T. Wilson, Yu Ping Wang, Yuhui Du, Jingyu Liu, V. Calhoun
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引用次数: 4

摘要

使用独立成分分析(ICA)测量功能网络连通性(FNC)的可靠性在文献中经常被探索,结果显示不同程度的可靠性,并表明数据预处理过程的微小变化可以显着改变FC结果和可靠性。然而,目前文献中尚未探讨的一个重要研究途径是空间归一化技术对FNC可靠性的影响。空间约束的独立分量分析技术,如参考多目标优化(MOO-ICAR),是使用功能磁共振成像(fMRI)研究脑功能连接(FC)的众多方法之一,理论上对归一化过程中可能出现的数据变化具有鲁棒性。在这项工作中,我们在30个不同的空间归一化管道上部署了MOO-ICAR,这些管道因参与者模板、归一化模式(解剖与功能)以及一段与两段对MNI空间的扭曲而不同。大多数组分具有较高的一致性类内相关系数(ICCs),绝大多数(~80%)大于0.5。
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Stability of functional network connectivity (FNC) values across multiple spatial normalization pipelines in spatially constrained independent component analysis
The reliability of functional network connectivity (FNC) measured using independent component analysis (ICA) has frequently been explored within the literature, with results displaying varying levels of reliability and demonstrating that minor changes in data preprocessing procedures can significantly alter FC results and reliability. However, one important avenue of research that has not been explored within the current literature is the effect of spatial normalization techniques on FNC reliability. Spatially constrained independent component analysis techniques such as multi-objective optimization with reference (MOO-ICAR) is one of many methods used to study brain functional connectivity (FC) using fMRI that is theoretically robust to variations which may arise in data as a result of normalization procedures. In this work, we deploy MOO-ICAR across 30 different spatial normalization pipelines varying across participant template, normalization modality (anatomical vs functional), and one vs. two-stage warps to MNI space. Most components display relatively high consistency intraclass-correlation coefficients (ICCs), with the vast majoritv (~80%) ereater than 0.5.
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