Influence of Resting Venous Blood Volume Fraction on Dynamic Causal Modeling and System Identifiability.
Influence of Resting Venous Blood Volume Fraction on Dynamic Causal Modeling and System Identifiability.
复制标题
静息静脉血容量分数对动态因果模型和系统可识别性的影响
DOI：
10.1038/srep29426
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发表时间：
2016-07-08
影响因子：
4.6
通讯作者：
Lin Q
中科院分区：
文献类型：
--
作者：
Hu Z;Ni P;Wan Q;Zhang Y;Shi P;Lin Q
BOLD信号的变化对与血管系统相关的区域血液含量敏感，在血液动力学模型中称为V0。在以往的研究中，涉及动态因果建模（DCM），体现了血流动力学模型，以反功能磁共振成像信号到神经元活动，V0被任意设置为一个生理上合理的值，以克服不适定性的反问题。研究V0值对DCM的影响是一个有趣的问题。在这项研究中，我们解决了这个问题，通过使用合成和真实的实验。结果表明，DCM分析揭示大脑因果关系的信息的能力，关键取决于在分析过程中使用的assumedV 0值。V0值的选择不仅直接影响系统连接的强度，更重要的是影响对网络结构的推断。我们的分析说明了如何参数化血流动力学过程的可能改进（即，通过使V0成为自由参数）;然而，由更复杂的模型引起的条件依赖性可能会产生比它们解决的问题更多的问题。在DCM中获得更真实的V0信息可以提高系统的可识别性，并将提供关于大脑连接特性的更可靠的推断。
Changes in BOLD signals are sensitive to the regional blood content associated with the vasculature, which is known asV0in hemodynamic models. In previous studies involving dynamic causal modeling (DCM) which embodies the hemodynamic model to invert the functional magnetic resonance imaging signals into neuronal activity,V0was arbitrarily set to a physiolog-ically plausible value to overcome the ill-posedness of the inverse problem. It is interesting to investigate how theV0value influences DCM. In this study we addressed this issue by using both synthetic and real experiments. The results show that the ability of DCM analysis to reveal information about brain causality depends critically on the assumedV0value used in the analysis procedure. The choice ofV0value not only directly affects the strength of system connections, but more importantly also affects the inferences about the network architecture. Our analyses speak to a possible refinement of how the hemody-namic process is parameterized (i.e., by makingV0a free parameter); however, the conditional dependencies induced by a more complex model may create more problems than they solve. Obtaining more realisticV0information in DCM can improve the identifiability of the system and would provide more reliable inferences about the properties of brain connectivity.