Wang Jiaping, Zhu Hongtu, Fan Jianqing, Giovanello Kelly, Lin Weili
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):269-76. doi: 10.1007/978-3-642-23629-7_33.
In an event-related functional MRI data analysis, an accurate and robust extraction of the hemodynamic response function (HRF) and its associated statistics (e.g., magnitude, width, and time to peak) is critical to infer quantitative information about the relative timing of the neuronal events in different brain regions. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) to accurately estimate HRFs pertaining to each stimulus sequence across all voxels. MASM explicitly accounts for both spatial and temporal smoothness information, while incorporating such information to adaptively estimate HRFs in the frequency domain. One simulation study and a real data set are used to demonstrate the methodology and examine its finite sample performance in HRF estimation, which confirms that MASM significantly outperforms the existing methods including the smooth finite impulse response model, the inverse logit model and the canonical HRF.
在事件相关功能磁共振成像数据分析中，准确且稳健地提取血液动力学响应函数（HRF）及其相关统计量（例如幅度、宽度和峰值时间）对于推断不同脑区神经元事件相对时间的定量信息至关重要。本文的目的是开发一种多尺度自适应平滑模型（MASM），以准确估计所有体素上与每个刺激序列相关的HRF。MASM明确考虑了空间和时间平滑信息，同时将这些信息纳入以在频域中自适应估计HRF。通过一项模拟研究和一个真实数据集来演示该方法，并检验其在HRF估计中的有限样本性能，这证实了MASM显著优于包括平滑有限脉冲响应模型、逆对数几率模型和标准HRF在内的现有方法。
