Section of Brainimaging, Department of Psychiatry, University of Marburg, 35039 Marburg, Germany; Department of Child and Adolescent Psychiatry, University of Marburg, 35039 Marburg, Germany.
Translational Neuromodeling Unit (TNU), Institute of Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, London, UK.
Neuroimage. 2015 Aug 15;117:56-66. doi: 10.1016/j.neuroimage.2015.05.040. Epub 2015 May 22.
Dynamic causal modeling (DCM) is a Bayesian framework for inferring effective connectivity among brain regions from neuroimaging data. While the validity of DCM has been investigated in various previous studies, the reliability of DCM parameter estimates across sessions has been examined less systematically. Here, we report results of a software comparison with regard to test-retest reliability of DCM for fMRI, using a challenging scenario where complex models with many parameters were applied to relatively few data points. Specifically, we examined the reliability of different DCM implementations (in terms of the intra-class correlation coefficient, ICC) based on fMRI data from 35 human subjects performing a simple motor task in two separate sessions, one month apart. We constructed DCMs of motor regions with fair to excellent reliability of conventional activation measures. Using classical DCM (cDCM) in SPM5, we found that the test-retest reliability of DCM results was high, both concerning the model evidence (ICC=0.94) and the model parameter estimates (median ICC=0.47). However, when using a more recent DCM version (DCM10 in SPM8), test-retest reliability was reduced notably. Analyses indicated that, in our particular case, the prior distributions played a crucial role in this change in reliability across software versions. Specifically, when using cDCM priors for model inversion in DCM10, this not only restored reliability but yielded even better results than in cDCM. Analyzing each component of the objective function in DCM, we found a selective change in the reliability of posterior mean estimates. This suggests that tighter regularization afforded by cDCM priors reduces the possibility of local extrema in the objective function. We conclude this paper with an outlook to ongoing developments for overcoming the software-dependency of reliability observed in this study, including global optimization and empirical Bayesian procedures.
动态因果建模（DCM）是一种贝叶斯框架，用于从神经影像学数据中推断脑区之间的有效连通性。虽然 DCM 的有效性已经在许多先前的研究中得到了研究，但 DCM 参数估计在不同会话之间的可靠性尚未得到系统的检查。在这里，我们报告了一项软件比较的结果，该比较涉及使用具有许多参数的复杂模型应用于相对较少的数据点的具有挑战性的场景，对 fMRI 的 DCM 的测试-重测可靠性。具体来说，我们根据 35 名人类受试者在两次单独的会话中（相隔一个月）执行简单运动任务的 fMRI 数据，检查了不同 DCM 实现（基于组内相关系数，ICC）的可靠性。我们构建了运动区域的 DCM，具有传统激活测量的良好到优秀的可靠性。在 SPM5 中使用经典 DCM（cDCM），我们发现 DCM 结果的测试-重测可靠性很高，无论是关于模型证据（ICC=0.94）还是模型参数估计（中位数 ICC=0.47）。然而，当使用更新的 DCM 版本（SPM8 中的 DCM10）时，测试-重测可靠性明显降低。分析表明，在我们的特定情况下，先验分布在软件版本之间的可靠性变化中起着至关重要的作用。具体来说，当在 DCM10 中使用 cDCM 先验进行模型反演时，这不仅恢复了可靠性，而且产生了比 cDCM 更好的结果。分析 DCM 中的目标函数的每个组成部分，我们发现后验均值估计的可靠性发生了选择性变化。这表明 cDCM 先验提供的更严格正则化减少了目标函数中局部极值的可能性。我们在本文的最后展望了克服本研究中观察到的可靠性对软件的依赖性的正在进行的发展，包括全局优化和经验贝叶斯程序。
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