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Multiscale adaptive smoothing models for the hemodynamic response function in fMRI

Jiaping Wang Hongtu Zhu Jianqing Fan Kelly Giovanello Weili Lin

Statistics Theory and Methods mathscidoc:1912.43386

The annals of applied statistics, 7, (2), 904, 2013.6
In the event-related functional magnetic resonance imaging (fMRI) data analysis, there is an extensive interest in accurately and robustly estimating the hemodynamic response function (HRF) and its associated statistics (eg, the magnitude and duration of the activation). Most methods to date are developed in the time domain and they have utilized almost exclusively the temporal information of fMRI data without accounting for the spatial information. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) in the frequency domain by integrating the spatial and temporal information to adaptively and accurately estimate HRFs pertaining to each stimulus sequence across all voxels in a three-dimensional (3D) volume. We use two sets of simulation studies and a real data set to examine the finite sample performance of MASM in estimating HRFs. Our real and simulated data analyses confirm
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@inproceedings{jiaping2013multiscale,
  title={Multiscale adaptive smoothing models for the hemodynamic response function in fMRI},
  author={Jiaping Wang, Hongtu Zhu, Jianqing Fan, Kelly Giovanello, and Weili Lin},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114106407923946},
  booktitle={The annals of applied statistics},
  volume={7},
  number={2},
  pages={904},
  year={2013},
}
Jiaping Wang, Hongtu Zhu, Jianqing Fan, Kelly Giovanello, and Weili Lin. Multiscale adaptive smoothing models for the hemodynamic response function in fMRI. 2013. Vol. 7. In The annals of applied statistics. pp.904. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114106407923946.
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