Covariance statistics and network analysis of brain PET imaging studies

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 ([ 18 F]FDG, [ 18 F]FDOPA and [ 11 C]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 [ 18 F]FDG brain PET covariance, including the definition of an entropy metric, proved to be particularly relevant for Alzheimer’s disease, showing an association with the progression of the pathology. This study shows that covariance statistics can be applied to PET neuroimaging data to investigate the topological characteristics of the tracer kinetics and its related targets, although sensitivity to experimental variables, group inhomogeneities and image resolution need to be considered when the method is applied to cross-sectional studies.

Metabolic and haemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study

“…Consequently, static FDG-PET images cannot be used to estimate metabolic connectivity within single subjects, greatly limiting their application as a disease biomarker (cf. Yakushev et al, 2017;Veronese et al, 2019).…”

From metabolic connectivity to molecular connectivity: application to dopaminergic pathways

“…These findings highlight the importance of the choice of the correct target to assess molecular connectivity. To date, only three studies have examined dopaminergic connectivity with the PET modality [13,44,45]. The first study targeted postsynaptic dopamine D2 receptors in control subjects but did not find significant correlations between the striatum and extrastriatal areas using IRCA with different ROIs which did not include the midbrain [44].…”

“…The authors found a decrease in connectivity within the nigrostriatal pathway in patients with early Parkinson's disease [45]. The third study using 18 F-FDOPA PET in 52 healthy subjects aimed to assess the feasibility of graph theory analysis [13].…”

“…Compared to functional magnetic resonance imaging (fMRI), metabolic connectivity provides better signal-to-noise ratios, variance concentration and out-of-sample replication. This original approach could be extended to other targets by disclosing associations based on molecular information from single photon emission computed tomography (SPECT) and PET imaging techniques [9][10][11][12][13].…”

Development and Porting Of Mathematical Model to Design a PixTra Algorithm for Detection of Brain Tumor

“…Existing research have got investigated the evolution and management of co-operation in the prisoner"s problem and also various other types [14]. Spatial games are actually especially important to the research of malignancy development as cell duplication and useful resource rivals is localized at the time of cancerous growth [15,16]. For proposed research we formulated the classification as well as, investigation of statistics with game theory pixel rendering and set theory.…”