Bayesian group activation analysis for functional neuroimaging

Ciftci K., Sankur B., Kahya Y. P. , Akin A.

IEEE 15th Signal Processing and Communications Applications Conference, Eskişehir, Turkey, 11 - 13 June 2007, pp.873-874 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2007.4298693
  • City: Eskişehir
  • Country: Turkey
  • Page Numbers: pp.873-874


The main goal of hypothesis-based functional neuroimaging is to arrive at a group decision for a set of data measured in different sessions. Hierarchical general linear model (GLM) is commonly used for this type of multilevel statistical inference problems. This study proposes a method that employs Bayesian Networks for analyzing hierarchical GLM. A major goal of the study is to put the main concepts of classical statistics, fixed-, random-, mixed-effects, into a Bayesian framework. The proposed method provides the posterior distributions for all the variables in the model. It is shown that it is possible to make generalizable inferences from a set of experimental data.