A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis

@article{Mejia2020ABG,
  title={A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis},
  author={Amanda F. Mejia and Yu Ryan Yue and David Bolin and Finn Lindgren and Martin A. Lindquist},
  journal={Journal of the American Statistical Association},
  year={2020},
  volume={115},
  pages={501 - 520}
}
Abstract Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering better whole-brain visualization, dimension reduction, removal of extraneous tissue types, and improved alignment of cortical areas across subjects, it is also more compatible with common assumptions of Bayesian spatial models. However, as no spatial Bayesian model has been proposed for cs-fMRI data, most analyses continue to… 
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