Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
Hum Brain Mapp. 2024 Jun 1;45(8):e26751. doi: 10.1002/hbm.26751.
Effective connectivity (EC) refers to directional or causal influences between interacting neuronal populations or brain regions and can be estimated from functional magnetic resonance imaging (fMRI) data via dynamic causal modeling (DCM). In contrast to functional connectivity, the impact of data processing varieties on DCM estimates of task-evoked EC has hardly ever been addressed. We therefore investigated how task-evoked EC is affected by choices made for data processing. In particular, we considered the impact of global signal regression (GSR), block/event-related design of the general linear model (GLM) used for the first-level task-evoked fMRI analysis, type of activation contrast, and significance thresholding approach. Using DCM, we estimated individual and group-averaged task-evoked EC within a brain network related to spatial conflict processing for all the parameters considered and compared the differences in task-evoked EC between any two data processing conditions via between-group parametric empirical Bayes (PEB) analysis and Bayesian data comparison (BDC). We observed strongly varying patterns of the group-averaged EC depending on the data processing choices. In particular, task-evoked EC and parameter certainty were strongly impacted by GLM design and type of activation contrast as revealed by PEB and BDC, respectively, whereas they were little affected by GSR and the type of significance thresholding. The event-related GLM design appears to be more sensitive to task-evoked modulations of EC, but provides model parameters with lower certainty than the block-based design, while the latter is more sensitive to the type of activation contrast than is the event-related design. Our results demonstrate that applying different reasonable data processing choices can substantially alter task-evoked EC as estimated by DCM. Such choices should be made with care and, whenever possible, varied across parallel analyses to evaluate their impact and identify potential convergence for robust outcomes.
有效连通性 (EC) 是指相互作用的神经元群体或脑区之间的方向或因果影响，可以通过动态因果建模 (DCM) 从功能磁共振成像 (fMRI) 数据中估计。与功能连接不同，数据处理种类对任务诱发 EC 的 DCM 估计的影响几乎没有被涉及。因此，我们研究了任务诱发 EC 如何受到数据处理选择的影响。特别是，我们考虑了全局信号回归 (GSR)、用于一级任务诱发 fMRI 分析的广义线性模型 (GLM) 的块/事件相关设计、激活对比类型和显著阈值方法的影响。使用 DCM，我们在与空间冲突处理相关的大脑网络中估计了个体和组平均的任务诱发 EC，考虑了所有参数，并通过组间参数经验贝叶斯 (PEB) 分析和贝叶斯数据分析比较 (BDC) 比较了任何两种数据处理条件之间任务诱发 EC 的差异。我们观察到，组平均 EC 的模式因数据处理选择而有很大的不同。特别是，任务诱发 EC 和参数确定性受到 GLM 设计和激活对比类型的强烈影响，如 PEB 和 BDC 分别揭示的那样，而它们受 GSR 和显著阈值类型的影响很小。事件相关 GLM 设计似乎对 EC 的任务诱发调制更敏感，但与基于块的设计相比，提供的模型参数确定性较低，而后者对激活对比类型的敏感性比事件相关设计高。我们的结果表明，应用不同合理的数据处理选择可以极大地改变 DCM 估计的任务诱发 EC。这些选择应该谨慎做出，并且尽可能在平行分析中变化，以评估它们的影响并确定稳健结果的潜在收敛性。
