Dynamic Causal Modeling applied to fMRI data shows high reliability.

Dynamic Causal Modeling applied to fMRI data shows high reliability.
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DOI:
10.1016/j.neuroimage.2009.07.015
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发表时间:
2010-01-01
期刊:
影响因子:
5.7
通讯作者:
Davidson, Richard J.
Davidson, Richard J.
中科院分区:
医学1区
文献类型:
--
作者:
Schuyler, Brianna;Ollinger, John M.;Oakes, Terrence R.;Johnstone, Tom;Davidson, Richard J.

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灵敏度、特异性和可重复性对于解释功能性磁共振成像(fMRI)实验的神经科学结果至关重要。在这里,我们检查了在同一扫描会话中进行的扫描中使用动态因果模型(DCM)估计的百分比信号变化(PSC)和参数的扫描重新扫描可靠性,间隔不到五分钟。我们发现公平的PSC的可靠性良好的区域参与的任务,公平的优秀的可靠性与DCM。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。
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 five minutes 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.
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