Authors:	Gonzalez-Escamilla, Gabriel Miederer, Isabelle Grothe, Michel J. Schreckenberger, Mathias Muthuraman, Muthuraman Groppa, Sergiu
Title:	Metabolic and amyloid PET network reorganization in Alzheimer’s disease : differential patterns and partial volume effects
Online publication date:	10-May-2021
Year of first publication:	2021
Language:	english
Abstract:	Alzheimer’s disease (AD) is a neurodegenerative disorder, considered a disconnection syndrome with regional molecular pattern abnormalities quantifiable by the aid of PET imaging. Solutions for accurate quantification of network dysfunction are scarce. We evaluate the extent to which PET molecular markers reflect quantifiable network metrics derived through the graph theory framework and how partial volume effects (PVE)-correction (PVEc) affects these PET-derived metrics 75 AD patients and 126 cognitively normal older subjects (CN). Therefore our goal is twofold: 1) to evaluate the differential patterns of [18F]FDG- and [18F]AV45-PET data to depict AD pathology; and ii) to analyse the effects of PVEc on global uptake measures of [18F]FDG- and [18F]AV45-PET data and their derived covariance network reconstructions for differentiating between patients and normal older subjects. Network organization patterns were assessed using graph theory in terms of “degree”, “modularity”, and “efficiency”. PVEc evidenced effects on global uptake measures that are specific to either [18F]FDG- or [18F]AV45-PET, leading to increased statistical differences between the groups. PVEc was further shown to influence the topological characterization of PET-derived covariance brain networks, leading to an optimised characterization of network efficiency and modularisation. Partial-volume effects correction improves the interpretability of PET data in AD and leads to optimised characterization of network properties for organisation or disconnection.
DDC:	610 Medizin 610 Medical sciences
Institution:	Johannes Gutenberg-Universität Mainz
Department:	FB 04 Medizin
Place:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-5879
Version:	Published version
Publication type:	Zeitschriftenaufsatz
License:	CC BY
Information on rights of use:	https://creativecommons.org/licenses/by/4.0/
Journal:	Brain imaging and behavior 15
Pages or article number:	190 204
Publisher:	Springer
Publisher place:	New York, NY u.a.
Issue date:	2021
ISSN:	1931-7565
Publisher URL:	https://doi.org/10.1007/s11682-019-00247-9
Publisher DOI:	10.1007/s11682-019-00247-9
Appears in collections:	JGU-Publikationen