title: Nonlinear estimation of BOLD signals with the aid of cerebral blood volume imaging.
abstract: BACKGROUND
  The hemodynamic balloon model describes the change in coupling from underlying neural activity to observed blood oxygen level dependent (BOLD) response. It plays an increasing important role in brain research using magnetic resonance imaging (MRI) techniques. However, changes in the BOLD signal are sensitive to the resting blood volume fraction (i.e., [Formula: see text]) associated with the regional vasculature. In previous studies the value was arbitrarily set to a physiologically plausible value to circumvent the ill-posedness of the inverse problem. These approaches fail to explore actual [Formula: see text] value and could yield inaccurate model estimation.
  
  
  METHODS
  The present study represents the first empiric attempt to derive the actual [Formula: see text] from data obtained using cerebral blood volume imaging, with the aim of augmenting the existing estimation schemes. Bimanual finger tapping experiments were performed to determine how [Formula: see text] influences the model estimation of BOLD signals within a single-region and multiple-regions (i.e., dynamic causal modeling). In order to show the significance of applying the true [Formula: see text], we have presented the different results obtained when using the real [Formula: see text] and assumed [Formula: see text] in terms of single-region model estimation and dynamic causal modeling.
  
  
  RESULTS
  The results show that [Formula: see text] significantly influences the estimation results within a single-region and multiple-regions. Using the actual [Formula: see text] might yield more realistic and physiologically meaningful model estimation results.
  
  
  CONCLUSIONS
  Incorporating regional venous information in the analysis of the hemodynamic model can provide more reliable and accurate parameter estimations and model predictions, and improve the inference about brain connectivity based on fMRI data.
authors.affiliation: College of Optical and Electronic Technology, China Jiliang University, Xueyuan Street 258, Hangzhou, 310018, China. zyshally@cjlu.edu.cn.
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: College of Optical and Electronic Technology, China Jiliang University, Xueyuan Street 258, Hangzhou, 310018, China. zuliwang@163.com.
authors.forename: Zuli
authors.initials: Z
authors.lastname: Wang
authors.affiliation: College of Optical Science and Engineering, Zhejiang University, Zheda Road 38, Hangzhou, 310027, China. zhongzhou.cai@sap.com.
authors.forename: Zhongzhou
authors.initials: Z
authors.lastname: Cai
authors.affiliation: Center for Optics and Optoelectronics Research, College of Science, Zhejiang University of Technology, Liuhe Road 288, Hangzhou, 310023, China. qlin@zjut.edu.cn.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Lin
authors.affiliation: Center for Optics and Optoelectronics Research, College of Science, Zhejiang University of Technology, Liuhe Road 288, Hangzhou, 310023, China. zhenghui@zjut.edu.cn.
authors.forename: Zhenghui
authors.initials: Z
authors.lastname: Hu
mesh_terms: D001810:Blood Volume; D001921:Brain / Q000098:blood supply* / Q000502:physiology; D006439:Hemodynamics; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008954:Models, Biological; D010100:Oxygen / Q000097:blood*
chemical_list: D010100:Oxygen