Brain activity detection in single- and multi-subject PET data by Bayesian analysis

Abstract

Positron emission tomography (PET) is a functional neuroimaging method that maps brain activity non-invasively. Statistical methods play an essential part in understanding and analysing functional PET data. Several Bayesian approaches have been proposed for neuroimaging techniques that arrange information on the brain structure or activity function. In this paper, Bayesian analysis was used to detect functional brain activity in single- and multiple-subject PET data. Free PET dataset analyses for a single subject and five multiple subjects were conducted. A total of 72 functional PET images were processed, 12 for each one of the five multiple subjects and for the single subject. Several ways to design multiple-subject PET data were introduced in this work. Bayesian analysis was performed on the five multiple-subject and the single-subject PET data. A comparison was presented to determine which statistical matrix design is applicable for brain detection activity in PET data. The results of the design matrix and brain activity detection were presented for each selected design matrix. The Bayesian estimation of each case of the PET dataset for all the subjects was plotted. The brain activity was plotted as voxels on a transparent brain image in three orthogonal planes. The voxels were visualised using the maximum intensity projection method. Results showed that brain activity could not be detected easily in single-subject PET data. Finding the activity in multiple subjects depended on the design matrix used for PET data analysis.