Cargando…
Identifying brain networks in synaptic density PET ((11)C-UCB-J) with independent component analysis
BACKGROUND: The human brain is inherently organized into distinct networks, as reported widely by resting-state functional magnetic resonance imaging (rs-fMRI), which are based on blood-oxygen-level-dependent (BOLD) signal fluctuations. (11)C-UCB-J PET maps synaptic density via synaptic vesicle prot...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452380/ https://www.ncbi.nlm.nih.gov/pubmed/34000404 http://dx.doi.org/10.1016/j.neuroimage.2021.118167 |
_version_ | 1784570052885872640 |
---|---|
author | Fang, Xiaotian T. Toyonaga, Takuya Hillmer, Ansel T. Matuskey, David Holmes, Sophie E. Radhakrishnan, Rajiv Mecca, Adam P. van Dyck, Christopher H. D’Souza, Deepak Cyril Esterlis, Irina Worhunsky, Patrick D. Carson, Richard E. |
author_facet | Fang, Xiaotian T. Toyonaga, Takuya Hillmer, Ansel T. Matuskey, David Holmes, Sophie E. Radhakrishnan, Rajiv Mecca, Adam P. van Dyck, Christopher H. D’Souza, Deepak Cyril Esterlis, Irina Worhunsky, Patrick D. Carson, Richard E. |
author_sort | Fang, Xiaotian T. |
collection | PubMed |
description | BACKGROUND: The human brain is inherently organized into distinct networks, as reported widely by resting-state functional magnetic resonance imaging (rs-fMRI), which are based on blood-oxygen-level-dependent (BOLD) signal fluctuations. (11)C-UCB-J PET maps synaptic density via synaptic vesicle protein 2A, which is a more direct structural measure underlying brain networks than BOLD rs-fMRI. METHODS: The aim of this study was to identify maximally independent brain source networks, i.e., “spatial patterns with common covariance across subjects”, in (11)C-UCB-J data using independent component analysis (ICA), a data-driven analysis method. Using a population of 80 healthy controls, we applied ICA to two 40-sample subsets and compared source network replication across samples. We examined the identified source networks at multiple model orders, as the ideal number of maximally independent components (IC) is unknown. In addition we investigated the relationship between the strength of the loading weights for each source network and age and sex. RESULTS: Thirteen source networks replicated across both samples. We determined that a model order of 18 components provided stable, replicable components, whereas estimations above 18 were not stable. Effects of sex were found in two ICs. Nine ICs showed age-related change, with 4 remaining significant after correction for multiple comparison. CONCLUSION: This study provides the first evidence that human brain synaptic density can be characterized into organized covariance patterns. Furthermore, we demonstrated that multiple synaptic density source networks are associated with age, which supports the potential utility of ICA to identify biologically relevant synaptic density source networks. |
format | Online Article Text |
id | pubmed-8452380 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-84523802021-09-20 Identifying brain networks in synaptic density PET ((11)C-UCB-J) with independent component analysis Fang, Xiaotian T. Toyonaga, Takuya Hillmer, Ansel T. Matuskey, David Holmes, Sophie E. Radhakrishnan, Rajiv Mecca, Adam P. van Dyck, Christopher H. D’Souza, Deepak Cyril Esterlis, Irina Worhunsky, Patrick D. Carson, Richard E. Neuroimage Article BACKGROUND: The human brain is inherently organized into distinct networks, as reported widely by resting-state functional magnetic resonance imaging (rs-fMRI), which are based on blood-oxygen-level-dependent (BOLD) signal fluctuations. (11)C-UCB-J PET maps synaptic density via synaptic vesicle protein 2A, which is a more direct structural measure underlying brain networks than BOLD rs-fMRI. METHODS: The aim of this study was to identify maximally independent brain source networks, i.e., “spatial patterns with common covariance across subjects”, in (11)C-UCB-J data using independent component analysis (ICA), a data-driven analysis method. Using a population of 80 healthy controls, we applied ICA to two 40-sample subsets and compared source network replication across samples. We examined the identified source networks at multiple model orders, as the ideal number of maximally independent components (IC) is unknown. In addition we investigated the relationship between the strength of the loading weights for each source network and age and sex. RESULTS: Thirteen source networks replicated across both samples. We determined that a model order of 18 components provided stable, replicable components, whereas estimations above 18 were not stable. Effects of sex were found in two ICs. Nine ICs showed age-related change, with 4 remaining significant after correction for multiple comparison. CONCLUSION: This study provides the first evidence that human brain synaptic density can be characterized into organized covariance patterns. Furthermore, we demonstrated that multiple synaptic density source networks are associated with age, which supports the potential utility of ICA to identify biologically relevant synaptic density source networks. 2021-05-15 2021-08-15 /pmc/articles/PMC8452380/ /pubmed/34000404 http://dx.doi.org/10.1016/j.neuroimage.2021.118167 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 Fang, Xiaotian T. Toyonaga, Takuya Hillmer, Ansel T. Matuskey, David Holmes, Sophie E. Radhakrishnan, Rajiv Mecca, Adam P. van Dyck, Christopher H. D’Souza, Deepak Cyril Esterlis, Irina Worhunsky, Patrick D. Carson, Richard E. Identifying brain networks in synaptic density PET ((11)C-UCB-J) with independent component analysis |
title | Identifying brain networks in synaptic density PET ((11)C-UCB-J) with independent component analysis |
title_full | Identifying brain networks in synaptic density PET ((11)C-UCB-J) with independent component analysis |
title_fullStr | Identifying brain networks in synaptic density PET ((11)C-UCB-J) with independent component analysis |
title_full_unstemmed | Identifying brain networks in synaptic density PET ((11)C-UCB-J) with independent component analysis |
title_short | Identifying brain networks in synaptic density PET ((11)C-UCB-J) with independent component analysis |
title_sort | identifying brain networks in synaptic density pet ((11)c-ucb-j) with independent component analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452380/ https://www.ncbi.nlm.nih.gov/pubmed/34000404 http://dx.doi.org/10.1016/j.neuroimage.2021.118167 |
work_keys_str_mv | AT fangxiaotiant identifyingbrainnetworksinsynapticdensitypet11cucbjwithindependentcomponentanalysis AT toyonagatakuya identifyingbrainnetworksinsynapticdensitypet11cucbjwithindependentcomponentanalysis AT hillmeranselt identifyingbrainnetworksinsynapticdensitypet11cucbjwithindependentcomponentanalysis AT matuskeydavid identifyingbrainnetworksinsynapticdensitypet11cucbjwithindependentcomponentanalysis AT holmessophiee identifyingbrainnetworksinsynapticdensitypet11cucbjwithindependentcomponentanalysis AT radhakrishnanrajiv identifyingbrainnetworksinsynapticdensitypet11cucbjwithindependentcomponentanalysis AT meccaadamp identifyingbrainnetworksinsynapticdensitypet11cucbjwithindependentcomponentanalysis AT vandyckchristopherh identifyingbrainnetworksinsynapticdensitypet11cucbjwithindependentcomponentanalysis AT dsouzadeepakcyril identifyingbrainnetworksinsynapticdensitypet11cucbjwithindependentcomponentanalysis AT esterlisirina identifyingbrainnetworksinsynapticdensitypet11cucbjwithindependentcomponentanalysis AT worhunskypatrickd identifyingbrainnetworksinsynapticdensitypet11cucbjwithindependentcomponentanalysis AT carsonricharde identifyingbrainnetworksinsynapticdensitypet11cucbjwithindependentcomponentanalysis |