Spatial Bayesian Modelling for Neuroimaging

This page lists research, authored by NISOx and NISOx collaborators, concerned with Spatial Bayesian Modelling for Neuroimaging.

Publications

• A Bayesian non-parametric Potts model with application to pre-surgical FMRI data
  Timothy D Johnson, Zhuqing Liu, Andreas J Bartsch, and Thomas E Nichols. Statistical Methods in Medical Research 22(4): 364-381. doi: 10.1177/0962280212448970. PMID: 22627277.
• Meta Analysis of Functional Neuroimaging Data via Bayesian Spatial Point Processes
  Jian Kang, Timothy D. Johnson, Thomas E. Nichols, and Tor D. Wager. Journal of the American Statistical Association 106(493): 124-134. doi: 10.1198/jasa.2011.ap09735.
• Modeling Inter-Subject Variability in fMRI Activation Location: A Bayesian Hierarchical Spatial Model
  Lei Xu, Timothy D. Johnson, Thomas E. Nichols, and Derek E. Nee. Biometrics 65(4): 1041–1051. doi: 10.1111/j.1541-0420.2008.01190.x.

Lectures on Spatial Point Processes

In September 2010 as part of a CRiSM lecture series, Dr. Tim Johnson of the University of Michigan Biostatistics gave a set of three lectures on point processes, with practical details on how to build Bayesian models and samplers for these types of data. Lecuture notes for all three lectures are here: PDF