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Variability of regional glucose metabolism and the topology of functional networks in the human brain.

Authors :
Palombit A
Silvestri E
Volpi T
Aiello M
Cecchin D
Bertoldo A
Corbetta M
Source :
NeuroImage [Neuroimage] 2022 Aug 15; Vol. 257, pp. 119280. Date of Electronic Publication: 2022 May 04.
Publication Year :
2022

Abstract

The brain consumes the most energy per relative mass amongst the organs in the human body. Theoretical and empirical studies have shown that behavioral processes are relatively inexpensive metabolically, and that most energy goes to maintaining the status quo, i.e., the balance of cell membranes' resting potentials and subthreshold spontaneous activity. Spontaneous activity fluctuates across brain regions in a correlated fashion that defines multi-scale hierarchical networks called resting-state networks (RSNs). Different regions of the brain display different metabolic consumption, but the relationship between regional brain metabolism and RSNs is still under investigation. Here, we examine the variability of glucose metabolism across brain regions, measured with the relative standard uptake value (SUVR) using <superscript>18</superscript> F-FDG PET, and the topology of RSNs, measured through graph analysis applied to fMRI resting-state functional connectivity (FC). We found a moderate linear relationship between the strength (STR) of pairwise regional FC and metabolism. Moreover, the linear correlation between SUVR and STR grew stronger as we considered more connected regions (hubs). Regions connecting different RSNs, or connector hubs, showed higher SUVR than regions connecting nodes within the same RSN, or provincial hubs. Our results show that functional connections as probed by fMRI are related to glucose metabolism, especially in a system of provincial and connector hubs.<br />Competing Interests: Declaration of Competing Interest None.<br /> (Copyright © 2022. Published by Elsevier Inc.)

Details

Language :
English
ISSN :
1095-9572
Volume :
257
Database :
MEDLINE
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
35525522
Full Text :
https://doi.org/10.1016/j.neuroimage.2022.119280