Memory and Aging Center, UCSF Department of Neurology, University of California San Francisco, CA, USA.
Neuroimage. 2012 Jul 16;61(4):1471-83. doi: 10.1016/j.neuroimage.2012.03.027. Epub 2012 Mar 14.
"Resting-state" or task-free fMRI can assess intrinsic connectivity network (ICN) integrity in health and disease, suggesting a potential for use of these methods as disease-monitoring biomarkers. Numerous analytical options are available, including model-driven ROI-based correlation analysis and model-free, independent component analysis (ICA). High test-retest reliability will be a necessary feature of a successful ICN biomarker, yet available reliability data remains limited. Here, we examined ICN fMRI test-retest reliability in 24 healthy older subjects scanned roughly one year apart. We focused on the salience network, a disease-relevant ICN not previously subjected to reliability analysis, as well as the default mode network. Most ICN analytical methods proved reliable (intraclass coefficients>0.4) and were further improved by wavelet analysis. Seed-based ROI correlation analysis showed high scan-wise reliability, whereas graph theoretical analysis and temporal concatenation group ICA proved most reliable at the individual unit-wise level (voxels, ROIs). Including global signal regression in ROI-based correlation analyses reduced reliability. Our study provides a direct comparison between the most commonly used ICN fMRI methods and potential guidelines for measuring intrinsic connectivity in aging control and patient populations over time.
“静息态”或无任务 fMRI 可评估健康和疾病中的固有连接网络 (ICN) 完整性，表明这些方法有可能作为疾病监测的生物标志物。有许多分析选项可用，包括基于模型的 ROI 相关分析和无模型的独立成分分析 (ICA)。高测试-重测可靠性将是成功的 ICN 生物标志物的必要特征，但可用的可靠性数据仍然有限。在这里，我们在大约一年后扫描了 24 名健康的老年受试者，检查了 ICN fMRI 的测试-重测可靠性。我们专注于突显网络，这是一种与疾病相关的 ICN，以前没有进行过可靠性分析，以及默认模式网络。大多数 ICN 分析方法被证明是可靠的（组内相关系数>0.4），并且通过小波分析进一步得到了改善。基于种子的 ROI 相关分析显示出很高的扫描间可靠性，而图论分析和时间串联组 ICA 在个体单位水平（体素、ROI）上则显示出最可靠的结果。在基于 ROI 的相关分析中包含全局信号回归会降低可靠性。我们的研究对最常用的 ICN fMRI 方法进行了直接比较，并为在老龄化控制和患者人群中随时间测量固有连接提供了潜在的指导方针。
