Bayesian group activation analysis for functional neuroimaging
IEEE 15th Signal Processing and Communications Applications Conference, Eskişehir, Türkiye, 11 - 13 Haziran 2007, ss.873-874, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası:
- Doi Numarası: 10.1109/siu.2007.4298693
- Basıldığı Şehir: Eskişehir
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.873-874
- Acıbadem Mehmet Ali Aydınlar Üniversitesi Adresli: Hayır
Özet
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.