Bayesian spatiotemporal model for detecting of active areas in brain for analyzing of fMRI data

References

• 1.
  Poldrack RA, Mumford JA, Nichols TE. Handbook of functional MRI data analysis: Cambridge University Press; 2011.
• 2.
  Handwerker DA, Ollinger JM, D'Esposito M. Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. Neuroimage 2004; 21: 1639-1651.
• 3.
  Lu Y, Grova C, Kobayashi E, Dubeau F, Gotman J. Using voxel-specific hemodynamic response function in EEG-fMRI data analysis: An estimation and detection model. Neuroimage 2007; 34: 195-203.
• 4.
  Marrelec G, Benali H, Ciuciu P, PlgriniIssac M, Poline JB. Robust Bayesian estimation of the hemodynamic response function in eventrelated BOLD fMRI using basic physiological information. Human Brain Mapping 2003; 19: 1-17.
• 5.
  Lee KJ, Jones GL, Caffo BS, Bassett SS. Spatial Bayesian variable selection models on functional magnetic resonance imaging time-series data. Bayesian Anal 2014; 9: 699-732.
• 6.
  Quirs A, Diez RM, Gamerman D. Bayesian spatiotemporal model of fMRI data. NeuroImage 2010; 49: 442-456.
• 7.
  Woolrich MW, Jenkinson M, Brady JM, Smith SM. Fully Bayesian spatio-temporal modeling of fMRI data. IEEE Trans Med Imaging 2004; 23: 213-231.
• 8.
  Zhang L, Guindani M, Versace F, Vannucci M. A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses. NeuroImage 2014; 95: 162-175.
• 9.
  Zhang L, Guindani M, Vannucci M. Bayesian models for functional magnetic resonance imaging data analysis. Wiley Interdiscip Rev Comput Stat 2015; 7: 21-41.
• 10.
  Zellner A. On assessing prior distributions and Bayesian regression analysis with g-prior distributions. Bayesian inference and decision techniques: Essays in Honor of Bruno De Finetti 1986; 6: 233-243.
• 11.
  Harbison JI, Atkins SM, Dougherty MR, editors. N-back training task performance: Analysis and model. Proceedings of the Cognitive Science Society; 2011.
• 12.
  Poldrack RA, Laumann TO, Koyejo O, Gregory B, Hover A, Chen MY, et al. Long-term neural and physiological phenotyping of a single human. Nat Commun 2015; 6: 8885.
• 13.
  Penny W, Kiebel S, Friston K. Variational Bayesian inference for fMRI time series. NeuroImage 2003; 19: 727-741.
• 14.
  Flandin G, Penny WD. Bayesian fMRI data analysis with sparse spatial basis function priors. NeuroImage 2007; 34: 1108-1125.
• 15.
  Gssl C, Auer DP, Fahrmeir L. Bayesian spatiotemporal inference in functional magnetic resonance imaging. Biometrics 2001; 57: 554-562.
• 16.
  Harrison LM, Green GG. A Bayesian spatiotemporal model for very large data sets. NeuroImage 2010; 50: 1126-1141.
• 17.
  Smith M, Fahrmeir L. Spatial Bayesian variable selection with application to functional magnetic resonance imaging. J Am Stat Assoc 2007; 102: 417-431.
• 18.
  Owen AM, McMillan KM, Laird AR, Bullmore E. Nback working memory paradigm: A metaanalysis of normative functional neuroimaging studies. Human Brain Mapping 2005; 25: 46-59.
• 19.
  Ragland JD, Turetsky BI, Gur RC, Gunning-Dixon F, Turner T, Schroeder L, Chan R, Gur RE. Working memory for complex figures: an fMRI comparison of letter and fractal n-back tasks. Neuropsychology 2002; 16: 370.