动态因果建模应用于 fMRI 数据显示出高度的可靠性。
Dynamic Causal Modeling applied to fMRI data shows high reliability.
机构信息
Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, USA.
出版信息
Neuroimage. 2010 Jan 1;49(1):603-11. doi: 10.1016/j.neuroimage.2009.07.015. Epub 2009 Jul 18.
Abstract
Sensitivity, specificity, and reproducibility are vital to interpret neuroscientific results from functional magnetic resonance imaging (fMRI) experiments. Here we examine the scan-rescan reliability of the percent signal change (PSC) and parameters estimated using Dynamic Causal Modeling (DCM) in scans taken in the same scan session, less than 5 min apart. We find fair to good reliability of PSC in regions that are involved with the task, and fair to excellent reliability with DCM. Also, the DCM analysis uncovers group differences that were not present in the analysis of PSC, which implies that DCM may be more sensitive to the nuances of signal changes in fMRI data.
摘要
从功能磁共振成像 (fMRI) 实验中解释神经科学结果时，灵敏度、特异性和可重复性至关重要。在这里，我们研究了在不到 5 分钟的时间内采集的同一扫描会话中扫描的 PSC（信号变化百分比）和使用动态因果建模 (DCM) 估计的参数的扫描间可重复性。我们发现，在与任务相关的区域中，PSC 的可靠性为中等至良好，而 DCM 的可靠性为中等至优秀。此外，DCM 分析揭示了 PSC 分析中不存在的组间差异，这意味着 DCM 可能对 fMRI 数据中信号变化的细微差别更敏感。
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