Havlicek Martin, Roebroeck Alard, Friston Karl J, Gardumi Anna, Ivanov Dimo, Uludag Kamil
Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200MD Maastricht, The Netherlands.
Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200MD Maastricht, The Netherlands.
Neuroimage. 2017 Jul 15;155:217-233. doi: 10.1016/j.neuroimage.2017.03.017. Epub 2017 Mar 18.
Effective connectivity is commonly assessed using blood oxygenation level-dependent (BOLD) signals. In (Havlicek et al., 2015), we presented a novel, physiologically informed dynamic causal model (P-DCM) that extends current generative models. We demonstrated the improvements afforded by P-DCM in terms of the ability to model commonly observed neuronal and vascular transients in single regions. Here, we assess the ability of the novel and previous DCM variants to estimate effective connectivity among a network of five ROIs driven by a visuo-motor task. We demonstrate that connectivity estimates depend sensitively on the DCM used, due to differences in the modeling of hemodynamic response transients; such as the post-stimulus undershoot or adaptation during stimulation. In addition, using a novel DCM for arterial spin labeling (ASL) fMRI that measures BOLD and CBF signals simultaneously, we confirmed our findings (by using the BOLD data alone and in conjunction with CBF). We show that P-DCM provides better estimates of effective connectivity, regardless of whether it is applied to BOLD data alone or to ASL time-series, and that all new aspects of P-DCM (i.e. neuronal, neurovascular, hemodynamic components) constitute an improvement compared to those in the previous DCM variants. In summary, (i) accurate modeling of fMRI response transients is crucial to obtain valid effective connectivity estimates and (ii) any additional hemodynamic data, such as provided by ASL, increases the ability to disambiguate neuronal and vascular effects present in the BOLD signal.
有效连接性通常使用血氧水平依赖（BOLD）信号进行评估。在（哈夫利切克等人，2015年）的研究中，我们提出了一种新颖的、基于生理学的动态因果模型（P-DCM），该模型扩展了当前的生成模型。我们展示了P-DCM在对单个区域中常见的神经元和血管瞬变进行建模的能力方面所带来的改进。在此，我们评估了新型和先前的DCM变体估计由视觉运动任务驱动的五个感兴趣区域（ROI）网络之间有效连接性的能力。我们证明，由于血流动力学响应瞬变建模的差异，例如刺激后的负波谷或刺激期间的适应性，连接性估计对所使用的DCM敏感地依赖。此外，我们使用一种用于动脉自旋标记（ASL）功能磁共振成像的新型DCM，该成像可同时测量BOLD和脑血流量（CBF）信号，通过单独使用BOLD数据以及将其与CBF数据结合使用，证实了我们的发现。我们表明，无论P-DCM是单独应用于BOLD数据还是ASL时间序列，它都能提供更好的有效连接性估计，并且P-DCM的所有新方面（即神经元、神经血管、血流动力学成分）与先前的DCM变体相比都有改进。总之，（i）对功能磁共振成像响应瞬变进行准确建模对于获得有效的有效连接性估计至关重要，并且（ii）任何额外的血流动力学数据，如ASL提供的数据，都能增强区分BOLD信号中存在的神经元和血管效应的能力。
