Covariance statistics and network analysis of brain PET imaging studies

Scientific Reports
Mattia VeroneseFederico E Turkheimer

Abstract

The analysis of structural and functional neuroimaging data using graph theory has increasingly become a popular approach for visualising and understanding anatomical and functional relationships between different cerebral areas. In this work we applied a network-based approach for brain PET studies using population-based covariance matrices, with the aim to explore topological tracer kinetic differences in cross-sectional investigations. Simulations, test-retest studies and applications to cross-sectional datasets from three different tracers ([18F]FDG, [18F]FDOPA and [11C]SB217045) and more than 400 PET scans were investigated to assess the applicability of the methodology in healthy controls and patients. A validation of statistics, including the assessment of false positive differences in parametric versus permutation testing, was also performed. Results showed good reproducibility and general applicability of the method within the range of experimental settings typical of PET neuroimaging studies, with permutation being the method of choice for the statistical analysis. The use of graph theory for the quantification of [18F]FDG brain PET covariance, including the definition of an entropy metric, proved to be particularly r...Continue Reading

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Citations

Jun 4, 2019·European Journal of Nuclear Medicine and Molecular Imaging·Paolo Zanotti-FregonaraJoseph C Masdeu
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Aug 20, 2021·Cerebral Cortex·Arianna SalaIgor Yakushev

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Software Mentioned

Brain Connectivity Toolbox ( BCT )
PVElab

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