Cargando…

Elucidating the complementarity of resting-state networks derived from dynamic [(18)F]FDG and hemodynamic fluctuations using simultaneous small-animal PET/MRI

Functional connectivity (FC) and resting-state network (RSN) analyses using functional magnetic resonance imaging (fMRI) have evolved into a growing field of research and have provided useful biomarkers for the assessment of brain function in neurological disorders. However, the underlying mechanism...

Descripción completa

Detalles Bibliográficos
Autores principales: Ionescu, Tudor M., Amend, Mario, Hafiz, Rakibul, Biswal, Bharat B., Wehrl, Hans F., Herfert, Kristina, Pichler, Bernd J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339191/
https://www.ncbi.nlm.nih.gov/pubmed/33848625
http://dx.doi.org/10.1016/j.neuroimage.2021.118045
_version_ 1783733543557398528
author Ionescu, Tudor M.
Amend, Mario
Hafiz, Rakibul
Biswal, Bharat B.
Wehrl, Hans F.
Herfert, Kristina
Pichler, Bernd J.
author_facet Ionescu, Tudor M.
Amend, Mario
Hafiz, Rakibul
Biswal, Bharat B.
Wehrl, Hans F.
Herfert, Kristina
Pichler, Bernd J.
author_sort Ionescu, Tudor M.
collection PubMed
description Functional connectivity (FC) and resting-state network (RSN) analyses using functional magnetic resonance imaging (fMRI) have evolved into a growing field of research and have provided useful biomarkers for the assessment of brain function in neurological disorders. However, the underlying mechanisms of the blood oxygen level-dependant (BOLD) signal are not fully resolved due to its inherent complexity. In contrast, [(18)F]fluorodeoxyglucose positron emission tomography ([(18)F]FDG-PET) has been shown to provide a more direct measure of local synaptic activity and may have additional value for the readout and interpretation of brain connectivity. We performed an RSN analysis from simultaneously acquired PET/fMRI data on a single-subject level to directly compare fMRI and [(18)F]FDG-PET-derived networks during the resting state. Simultaneous [(18)F]FDG-PET/fMRI scans were performed in 30 rats. Pairwise correlation analysis, as well as independent component analysis (ICA), were used to compare the readouts of both methods. We identified three RSNs with a high degree of similarity between PET and fMRI-derived readouts: the default-mode-like network (DMN), the basal ganglia network and the cerebellar-midbrain network. Overall, [(18)F]FDG connectivity indicated increased integration between different, often distant, brain areas compared to the results indicated by the more segregated fMRI-derived FC. Additionally, several networks exclusive to either modality were observed using ICA. These networks included mainly bilateral cortical networks of a limited spatial extent for fMRI and more spatially widespread networks for [(18)F]FDG-PET, often involving several subcortical areas. This is the first study using simultaneous PET/fMRI to report RSNs subject-wise from dynamic [(18)F]FDG tracer delivery and BOLD fluctuations with both independent component analysis (ICA) and pairwise correlation analysis in small animals. Our findings support previous studies, which show a close link between local synaptic glucose consumption and BOLD-fMRI-derived FC. However, several brain regions were exclusively attributed to either [(18)F]FDG or BOLD-derived networks underlining the complementarity of this hybrid imaging approach, which may contribute to the understanding of brain functional organization and could be of interest for future clinical applications.
format Online
Article
Text
id pubmed-8339191
institution National Center for Biotechnology Information
language English
publishDate 2021
record_format MEDLINE/PubMed
spelling pubmed-83391912021-08-05 Elucidating the complementarity of resting-state networks derived from dynamic [(18)F]FDG and hemodynamic fluctuations using simultaneous small-animal PET/MRI Ionescu, Tudor M. Amend, Mario Hafiz, Rakibul Biswal, Bharat B. Wehrl, Hans F. Herfert, Kristina Pichler, Bernd J. Neuroimage Article Functional connectivity (FC) and resting-state network (RSN) analyses using functional magnetic resonance imaging (fMRI) have evolved into a growing field of research and have provided useful biomarkers for the assessment of brain function in neurological disorders. However, the underlying mechanisms of the blood oxygen level-dependant (BOLD) signal are not fully resolved due to its inherent complexity. In contrast, [(18)F]fluorodeoxyglucose positron emission tomography ([(18)F]FDG-PET) has been shown to provide a more direct measure of local synaptic activity and may have additional value for the readout and interpretation of brain connectivity. We performed an RSN analysis from simultaneously acquired PET/fMRI data on a single-subject level to directly compare fMRI and [(18)F]FDG-PET-derived networks during the resting state. Simultaneous [(18)F]FDG-PET/fMRI scans were performed in 30 rats. Pairwise correlation analysis, as well as independent component analysis (ICA), were used to compare the readouts of both methods. We identified three RSNs with a high degree of similarity between PET and fMRI-derived readouts: the default-mode-like network (DMN), the basal ganglia network and the cerebellar-midbrain network. Overall, [(18)F]FDG connectivity indicated increased integration between different, often distant, brain areas compared to the results indicated by the more segregated fMRI-derived FC. Additionally, several networks exclusive to either modality were observed using ICA. These networks included mainly bilateral cortical networks of a limited spatial extent for fMRI and more spatially widespread networks for [(18)F]FDG-PET, often involving several subcortical areas. This is the first study using simultaneous PET/fMRI to report RSNs subject-wise from dynamic [(18)F]FDG tracer delivery and BOLD fluctuations with both independent component analysis (ICA) and pairwise correlation analysis in small animals. Our findings support previous studies, which show a close link between local synaptic glucose consumption and BOLD-fMRI-derived FC. However, several brain regions were exclusively attributed to either [(18)F]FDG or BOLD-derived networks underlining the complementarity of this hybrid imaging approach, which may contribute to the understanding of brain functional organization and could be of interest for future clinical applications. 2021-04-10 2021-08-01 /pmc/articles/PMC8339191/ /pubmed/33848625 http://dx.doi.org/10.1016/j.neuroimage.2021.118045 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Article
Ionescu, Tudor M.
Amend, Mario
Hafiz, Rakibul
Biswal, Bharat B.
Wehrl, Hans F.
Herfert, Kristina
Pichler, Bernd J.
Elucidating the complementarity of resting-state networks derived from dynamic [(18)F]FDG and hemodynamic fluctuations using simultaneous small-animal PET/MRI
title Elucidating the complementarity of resting-state networks derived from dynamic [(18)F]FDG and hemodynamic fluctuations using simultaneous small-animal PET/MRI
title_full Elucidating the complementarity of resting-state networks derived from dynamic [(18)F]FDG and hemodynamic fluctuations using simultaneous small-animal PET/MRI
title_fullStr Elucidating the complementarity of resting-state networks derived from dynamic [(18)F]FDG and hemodynamic fluctuations using simultaneous small-animal PET/MRI
title_full_unstemmed Elucidating the complementarity of resting-state networks derived from dynamic [(18)F]FDG and hemodynamic fluctuations using simultaneous small-animal PET/MRI
title_short Elucidating the complementarity of resting-state networks derived from dynamic [(18)F]FDG and hemodynamic fluctuations using simultaneous small-animal PET/MRI
title_sort elucidating the complementarity of resting-state networks derived from dynamic [(18)f]fdg and hemodynamic fluctuations using simultaneous small-animal pet/mri
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339191/
https://www.ncbi.nlm.nih.gov/pubmed/33848625
http://dx.doi.org/10.1016/j.neuroimage.2021.118045
work_keys_str_mv AT ionescutudorm elucidatingthecomplementarityofrestingstatenetworksderivedfromdynamic18ffdgandhemodynamicfluctuationsusingsimultaneoussmallanimalpetmri
AT amendmario elucidatingthecomplementarityofrestingstatenetworksderivedfromdynamic18ffdgandhemodynamicfluctuationsusingsimultaneoussmallanimalpetmri
AT hafizrakibul elucidatingthecomplementarityofrestingstatenetworksderivedfromdynamic18ffdgandhemodynamicfluctuationsusingsimultaneoussmallanimalpetmri
AT biswalbharatb elucidatingthecomplementarityofrestingstatenetworksderivedfromdynamic18ffdgandhemodynamicfluctuationsusingsimultaneoussmallanimalpetmri
AT wehrlhansf elucidatingthecomplementarityofrestingstatenetworksderivedfromdynamic18ffdgandhemodynamicfluctuationsusingsimultaneoussmallanimalpetmri
AT herfertkristina elucidatingthecomplementarityofrestingstatenetworksderivedfromdynamic18ffdgandhemodynamicfluctuationsusingsimultaneoussmallanimalpetmri
AT pichlerberndj elucidatingthecomplementarityofrestingstatenetworksderivedfromdynamic18ffdgandhemodynamicfluctuationsusingsimultaneoussmallanimalpetmri