Abstract
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.
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Keywords
- Statistical Parametric Mapping
- Stimulus Sequence
- Hemodynamic Response Function
- Linear Time Invariant
- fMRI Time Series
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References
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)
Bowman, F.D., Patel, R., Lu, C.: Methods for detecting functional classifications in neuroimaging data. Human Brain Mapping 23, 109–119 (2004)
Breiman, L., Friedman, J.H.: Estimating optimal transformations for multiple regression and correlation. Journal of American Statistical Assocication 80, 580–598 (1985)
Friston, K.J.: Statistical Parametric Mapping: the Analysis of Functional Brain Images. Academic Press, London (2007)
Katkovnik, V., Spokoiny, V.: Spatially adaptive estimation via fitted local likelihood techniques. IEEE Transactions on Signal Processing 56, 873–886 (2008)
Lindquist, M.A., Wager, T.D.: Validity and Power in hemodynamic response modeling: A comparison study and a new approach. Human Brain Mapping 28, 764–784 (2007)
Makni, S., Idier, J., Vincent, T., Thirion, B., Dehaene-Lambertz, G., Ciuciu, P.: A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI. NeuroImage 41, 941–969 (2008)
Marchini, J.L., Ripley, B.D.: A new statistical approach to detecting significant activation in functional MRI. NeuroImage 12, 366–380 (2000)
Risser, L., Vincent, T., Ciuciu, P., Idier, J.: Extrapolation scheme for fast ISING field partition functions estimation. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 975–983. Springer, Heidelberg (2009)
Tabelow, K., Polzehl, J., Voss, H.U., Spokoiny, V.: Analyzing fMRI experiments with structural adaptive smoothing procedure. NeuroImage 33, 55–62 (2006)
Vincent, T., Risser, L., Ciuciu, P.: Spatially adaptive mixture modeling for analysis of fMRI time series. IEEE Transactions on Medical Imaging 29, 1059–1074 (2010)
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Wang, J., Zhu, H., Fan, J., Giovanello, K., Lin, W. (2011). Adaptively and Spatially Estimating the Hemodynamic Response Functions in fMRI. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6892. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23629-7_33
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DOI: https://doi.org/10.1007/978-3-642-23629-7_33
Publisher Name: Springer, Berlin, Heidelberg
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