title: Influence of Resting Venous Blood Volume Fraction on Dynamic Causal Modeling and System Identifiability.
abstract: Changes in BOLD signals are sensitive to the regional blood content associated with the vasculature, which is known as V0 in 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, V0 was arbitrarily set to a physiolog-ically plausible value to overcome the ill-posedness of the inverse problem. It is interesting to investigate how the V0 value 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 assumed V0 value used in the analysis procedure. The choice of V0 value 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 making V0 a free parameter); however, the conditional dependencies induced by a more complex model may create more problems than they solve. Obtaining more realistic V0 information in DCM can improve the identifiability of the system and would provide more reliable inferences about the properties of brain connectivity.
authors.affiliation: Center for Optics and Optoelectronics Research, College of Science, Zhejiang University of Technology, Hangzhou 310023, China.
authors.forename: Zhenghui
authors.initials: Z
authors.lastname: Hu
authors.affiliation: Center for Optics and Optoelectronics Research, College of Science, Zhejiang University of Technology, Hangzhou 310023, China.|Bioinformatics research center, Department of bioinformatics and genomics, College of Computing and Informatics, University if North Carolina at Charoltte, NC 28223, USA.
authors.forename: Pengyu
authors.initials: P
authors.lastname: Ni
authors.affiliation: Customer Experience Transformation Center (CETC), Huawei Technology Corporation Limited, Hangzhou, China.
authors.forename: Qun
authors.initials: Q
authors.lastname: Wan
authors.affiliation: College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: B. Thomas Golisano College of Computing and Information Sciences, Rochester Institute of Technology Rochester, NY 14623, USA.
authors.forename: Pengcheng
authors.initials: P
authors.lastname: Shi
authors.affiliation: Center for Optics and Optoelectronics Research, College of Science, Zhejiang University of Technology, Hangzhou 310023, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Lin
mesh_terms: D001288:Attention / Q000502:physiology; D001810:Blood Volume; D001921:Brain / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D010775:Photic Stimulation; D012146:Rest / Q000502:physiology*