Eigenvector Centrality Dynamics From Resting-State fMRI: Gender and Age Differences in Healthy Subjects
With the increasing use of functional brain network properties as markers of brain disorders, efficient visualization and evaluation methods have become essential. Eigenvector centrality mapping (ECM) of functional MRI (fMRI) data enables the representation of per-node graph theoretical measures as...
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| Vydáno v: | Frontiers in neuroscience Ročník 13; s. 648 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Switzerland
Frontiers Research Foundation
27.06.2019
Frontiers Media S.A |
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| ISSN: | 1662-453X, 1662-4548, 1662-453X |
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| Abstract | With the increasing use of functional brain network properties as markers of brain disorders, efficient visualization and evaluation methods have become essential. Eigenvector centrality mapping (ECM) of functional MRI (fMRI) data enables the representation of per-node graph theoretical measures as brain maps. This paper studies the use of centrality dynamics for measuring group differences in imaging studies. Imaging data were used from a publicly available imaging study, which included resting fMRI data. After warping the images to a standard space and masking cortical regions, ECM were computed in a sliding window. The dual regression method was used to identify dynamic centrality differences inside well-known resting-state networks between gender and age groups. Gender-related differences were found in the medial and lateral visual, motor, default mode, and executive control RSN, where male subjects had more consistent centrality variations within the network. Age-related differences between the youngest and oldest subjects, based on a median split, were found in the medial visual, executive control and left frontoparietal networks, where younger subjects had more consistent centrality variations within the network. Our findings show that centrality dynamics can be used to identify between-group functional brain network centrality differences, and that age and gender distributions studies need to be taken into account in functional imaging studies. |
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| AbstractList | With the increasing use of functional brain network properties as markers of brain disorders, efficient visualization and evaluation methods have become essential. Eigenvector centrality mapping (ECM) of functional MRI (fMRI) data enables the representation of per-node graph theoretical measures as brain maps. This paper studies the use of centrality dynamics for measuring group differences in imaging studies. Imaging data were used from a publicly available imaging study, which included resting fMRI data. After warping the images to a standard space and masking cortical regions, ECM were computed in a sliding window. The dual regression method was used to identify dynamic centrality differences inside well-known resting-state networks between gender and age groups. Gender-related differences were found in the medial and lateral visual, motor, default mode, and executive control RSN, where male subjects had more consistent centrality variations within the network. Age-related differences between the youngest and oldest subjects, based on a median split, were found in the medial visual, executive control and left frontoparietal networks, where younger subjects had more consistent centrality variations within the network. Our findings show that centrality dynamics can be used to identify between-group functional brain network centrality differences, and that age and gender distributions studies need to be taken into account in functional imaging studies. With the increasing use of functional brain network properties as markers of brain disorders, efficient visualisation and evaluation methods have become essential. Eigenvector centrality mapping (ECM) of functional MRI (fMRI) data enables the representation of per-node graph theoretical measures as brain maps. This paper studies the use of centrality dynamics for measuring group differences in imaging studies. Imaging data were used from a publicly available imaging study, which included resting fMRI data. After warping the images to a standard space and masking cortical regions, ECM were computed in a sliding window. The dual regression method was used to identify dynamic centrality differences inside well-known resting state networks between gender and age groups. Gender-related differences were found in the medial and lateral visual, motor, default mode, and executive control RSN, where male subjects had more consistent centrality variations within the network. Age-related differences between the youngest and oldest subjects, based on a median split, were found in the medial visual, executive control and left frontoparietal networks, where younger subjects had more consistent centrality variations within the network. Our findings show that centrality dynamics can be used to identify between-group functional brain network centrality differences, and that age and gender distributions studies need to be taken into account in functional imaging studies. With the increasing use of functional brain network properties as markers of brain disorders, efficient visualization and evaluation methods have become essential. Eigenvector centrality mapping (ECM) of functional MRI (fMRI) data enables the representation of per-node graph theoretical measures as brain maps. This paper studies the use of centrality dynamics for measuring group differences in imaging studies. Imaging data were used from a publicly available imaging study, which included resting fMRI data. After warping the images to a standard space and masking cortical regions, ECM were computed in a sliding window. The dual regression method was used to identify dynamic centrality differences inside well-known resting-state networks between gender and age groups. Gender-related differences were found in the medial and lateral visual, motor, default mode, and executive control RSN, where male subjects had more consistent centrality variations within the network. Age-related differences between the youngest and oldest subjects, based on a median split, were found in the medial visual, executive control and left frontoparietal networks, where younger subjects had more consistent centrality variations within the network. Our findings show that centrality dynamics can be used to identify between-group functional brain network centrality differences, and that age and gender distributions studies need to be taken into account in functional imaging studies.With the increasing use of functional brain network properties as markers of brain disorders, efficient visualization and evaluation methods have become essential. Eigenvector centrality mapping (ECM) of functional MRI (fMRI) data enables the representation of per-node graph theoretical measures as brain maps. This paper studies the use of centrality dynamics for measuring group differences in imaging studies. Imaging data were used from a publicly available imaging study, which included resting fMRI data. After warping the images to a standard space and masking cortical regions, ECM were computed in a sliding window. The dual regression method was used to identify dynamic centrality differences inside well-known resting-state networks between gender and age groups. Gender-related differences were found in the medial and lateral visual, motor, default mode, and executive control RSN, where male subjects had more consistent centrality variations within the network. Age-related differences between the youngest and oldest subjects, based on a median split, were found in the medial visual, executive control and left frontoparietal networks, where younger subjects had more consistent centrality variations within the network. Our findings show that centrality dynamics can be used to identify between-group functional brain network centrality differences, and that age and gender distributions studies need to be taken into account in functional imaging studies. |
| Author | Wink, Alle Meije |
| AuthorAffiliation | Radiology and Nuclear Medicine, Amsterdam University Medical Center , Amsterdam , Netherlands |
| AuthorAffiliation_xml | – name: Radiology and Nuclear Medicine, Amsterdam University Medical Center , Amsterdam , Netherlands |
| Author_xml | – sequence: 1 givenname: Alle Meije surname: Wink fullname: Wink, Alle Meije |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31316335$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3233_JAD_230048 crossref_primary_10_1007_s10578_022_01432_6 crossref_primary_10_1016_j_neuroimage_2022_119125 crossref_primary_10_1186_s10194_021_01341_4 crossref_primary_10_1371_journal_pone_0326449 crossref_primary_10_1002_brb3_2334 crossref_primary_10_1016_j_physrep_2019_12_004 crossref_primary_10_3390_cancers15020556 crossref_primary_10_1038_s41380_021_01421_6 crossref_primary_10_1016_j_bpsc_2024_07_021 crossref_primary_10_1212_WNL_0000000000012341 crossref_primary_10_3389_fnins_2023_1206604 crossref_primary_10_1007_s00429_021_02435_0 |
| Cites_doi | 10.1016/S1053-8119(09)71511-3 10.1002/hbm.1058 10.1016/j.neuroimage.2014.03.042 10.1038/nn.4497 10.1089/brain.2011.0036 10.1371/journal.pone.0013701 10.1073/pnas.0911855107 10.1103/PhysRevE.79.061922 10.3389/fphys.2012.00015 10.3389/fpsyg.2015.00663 10.1016/j.neuroimage.2008.03.061 10.1016/j.neuroimage.2015.03.047 10.1142/S0129065717500137 10.1371/journal.pone.0010232 10.1523/JNEUROSCI.3874-05.2006 10.1177/1073858410386492 10.1371/journal.pone.0184661 10.1016/j.neuroimage.2015.11.055 10.1073/pnas.0905267106 10.3389/fnins.2016.00381 10.3389/fnagi.2014.00256 10.1073/pnas.0803652105 10.1016/j.neuroimage.2005.02.018 10.1002/hbm.22335 10.3389/fnagi.2013.00073 10.1073/pnas.0601417103 10.1016/j.neurobiolaging.2011.07.003 10.1016/j.neuroimage.2011.10.018 10.1371/journal.pone.0124577 10.1089/brain.2012.0087 10.1038/s41598-017-12993-12991 10.3389/fnins.2017.00115 10.1016/j.neuroscience.2016.09.034 10.1016/j.neuron.2013.07.035 10.1016/j.neuroimage.2016.12.061 10.1093/cercor/bhy109 10.1177/1352458513516892 10.1002/brb3.1080 10.3389/fnins.2016.00515 10.1002/hbm.23617 10.1002/hbm.21514 10.3389/fnhum.2015.00478 10.1212/WNL.0000000000003689 10.1371/journal.pcbi.1006196 10.1093/cercor/bhu012 10.1016/j.neuropsychologia.2012.05.025 10.1016/j.neuroimage.2013.08.048 |
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| Copyright | 2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2019 Wink. 2019 Wink |
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| Keywords | graph theory – graph algorithms imaging studies age-related trees functional MRI (fMRI) methods gender-related |
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