Detection of Task-Related Activation in fMRI Data with Segmentation Information from Structural MRI

• Activity Number: 235
• Type: Contributed
• Date/Time: Tuesday, August 5, 2008 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Namhee Kim*+ and Prem K. Goel and David Q. Beversdorf
• Companies: The Ohio State University and The Ohio State University and University of Missouri-Columbia
• Keywords: Bayesian model ; combining information

Abstract

As the intensity levels in Magnetic Resonance Image (MRI) data differ by tissue type, image segmentation results have been used more for clinical purposes to investigate morphological changes in brain tissue. Segmentation procedures can produce structural MRI image partitions by relevant tissue types: grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF), and background classes. Furthermore, as task-related true activations in the brain in functional MRI (fMRI) data are expected to be mostly on grey matter tissue, use of segmentation information is expected to produce more reliable activation maps in fMRI context. Recently, the adaptation of activation maps referring to the segmentation results has been attempted. In this study, we propose a method for the detection of activation combining information from segmentation results in MRI within the Bayesian framework.