Adaptively and Spatially Estimating the Hemodynamic Response Functions in fMRI
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.
KeywordsStatistical Parametric Mapping Stimulus Sequence Hemodynamic Response Function Linear Time Invariant fMRI Time Series
- 1.Bai, P., Truong, Y., Huang, X.: Nonparametric estimation of hemodynamic response function: a frequency domain approach. In: Optimality: The Third Erich L. Lehmann Symposium. IMS Lecture Notes-Monograph Series, vol. 57, pp. 190–215 (2009)Google Scholar
- 4.Friston, K.J.: Statistical Parametric Mapping: the Analysis of Functional Brain Images. Academic Press, London (2007)Google Scholar