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Spatial Bayesian Modelling for Neuroimaging

  • Johnson, T. D., Liu, Z., Bartsch, A. J., & Nichols, T. E. (2012). A Bayesian non-parametric Potts model with application to pre-surgical FMRI data. Statistical methods in medical research. doi:10.1177/0962280212448970
  • Kang, J., Johnson, T. D., Nichols, T. E., & Wager, T. D. (2011). Meta Analysis of Functional Neuroimaging Data via Bayesian Spatial Point Processes. Journal of the American Statistical Association, 106(493), 124–134. doi:10.1198/jasa.2011.ap09735
  • Xu, L., Johnson, T. D., Nichols, T. E., & Nee, D. E. (2009). Modeling Inter-Subject Variability in fMRI Activation Location: A Bayesian Hierarchical Spatial Model. 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


Contact Info

Room D0.03
Deptment of Statistics
University of Warwick
United Kingdom

Tel: +44(0)24 761 51086
Email: t.e.nichols 'at'
Blog: NISOx blog

[Book Cover]

Handbook of fMRI Data Analysis by Russ Poldrack, Thomas Nichols and Jeanette Mumford