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

Brain activity detection in single- and multi-subject PET data by Bayesian analysis PurposePositron 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.MethodsFree 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.ResultsThe 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.ConclusionResults 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

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

Aljobouri, Hadeel K.
Research on Biomedical Engineering , Volume 36 (3) – Sep 26, 2020
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Publisher
Springer Journals
Copyright
Copyright © Sociedade Brasileira de Engenharia Biomedica 2020
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-020-00071-x
Publisher site
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Abstract

PurposePositron 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.MethodsFree 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.ResultsThe 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.ConclusionResults 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.

Journal

Research on Biomedical Engineering – Springer Journals

Published: Sep 26, 2020

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