Test-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study
Recent research has demonstrated the feasibility of combining functional near-infrared spectroscopy (fNIRS) and graph theory approaches to explore the topological attributes of human brain networks. However, the test-retest (TRT) reliability of the application of graph metrics to these networks rema...
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| description | Recent research has demonstrated the feasibility of combining functional near-infrared spectroscopy (fNIRS) and graph theory approaches to explore the topological attributes of human brain networks. However, the test-retest (TRT) reliability of the application of graph metrics to these networks remains to be elucidated. Here, we used resting-state fNIRS and a graph-theoretical approach to systematically address TRT reliability as it applies to various features of human brain networks, including functional connectivity, global network metrics and regional nodal centrality metrics. Eighteen subjects participated in two resting-state fNIRS scan sessions held ∼20 min apart. Functional brain networks were constructed for each subject by computing temporal correlations on three types of hemoglobin concentration information (HbO, HbR, and HbT). This was followed by a graph-theoretical analysis, and then an intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of each network metric. We observed that a large proportion of resting-state functional connections (∼90%) exhibited good reliability (0.6< ICC |
| doi_str_mv | 10.1371/journal.pone.0072425 |
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However, the test-retest (TRT) reliability of the application of graph metrics to these networks remains to be elucidated. Here, we used resting-state fNIRS and a graph-theoretical approach to systematically address TRT reliability as it applies to various features of human brain networks, including functional connectivity, global network metrics and regional nodal centrality metrics. Eighteen subjects participated in two resting-state fNIRS scan sessions held ∼20 min apart. Functional brain networks were constructed for each subject by computing temporal correlations on three types of hemoglobin concentration information (HbO, HbR, and HbT). This was followed by a graph-theoretical analysis, and then an intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of each network metric. We observed that a large proportion of resting-state functional connections (∼90%) exhibited good reliability (0.6< ICC <0.74). For global and nodal measures, reliability was generally threshold-sensitive and varied among both network metrics and hemoglobin concentration signals. Specifically, the majority of global metrics exhibited fair to excellent reliability, with notably higher ICC values for the clustering coefficient (HbO: 0.76; HbR: 0.78; HbT: 0.53) and global efficiency (HbO: 0.76; HbR: 0.70; HbT: 0.78). Similarly, both nodal degree and efficiency measures also showed fair to excellent reliability across nodes (degree: 0.52∼0.84; efficiency: 0.50∼0.84); reliability was concordant across HbO, HbR and HbT and was significantly higher than that of nodal betweenness (0.28∼0.68). Together, our results suggest that most graph-theoretical network metrics derived from fNIRS are TRT reliable and can be used effectively for brain network research. This study also provides important guidance on the choice of network metrics of interest for future applied research in developmental and clinical neuroscience.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0072425</identifier><identifier>PMID: 24039763</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Algorithms ; Area Under Curve ; Brain ; Brain Mapping ; Brain research ; Cerebral Cortex - physiology ; Clustering ; Cognition & reasoning ; Computing time ; Correlation ; Correlation analysis ; Correlation coefficient ; Correlation coefficients ; Efficiency ; Feasibility studies ; Female ; Graph theory ; Hemoglobin ; Hemoglobins ; Humans ; I.R. radiation ; Infrared spectra ; Infrared spectroscopy ; Laboratories ; Male ; Medical imaging ; Models, Biological ; Monte Carlo simulation ; Near infrared radiation ; Near infrared spectroscopy ; Nerve Net - physiology ; Nervous system ; Network reliability ; Networks ; Neural networks ; Neurosciences ; NMR ; Nuclear magnetic resonance ; Optics ; Principal Component Analysis ; Reproducibility of Results ; Rest - physiology ; Sensors ; Spectroscopy, Near-Infrared ; Spectrum analysis ; Theoretical analysis ; Young Adult</subject><ispartof>PloS one, 2013-09, Vol.8 (9), p.e72425</ispartof><rights>COPYRIGHT 2013 Public Library of Science</rights><rights>2013 Niu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2013 Niu et al 2013 Niu et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c758t-f39fd33566435ab70cb3879ea6e51f9ca0fe18d5601eaa5ca79062f12a05b83e3</citedby><cites>FETCH-LOGICAL-c758t-f39fd33566435ab70cb3879ea6e51f9ca0fe18d5601eaa5ca79062f12a05b83e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767699/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767699/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24039763$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Sporns, Olaf</contributor><creatorcontrib>Niu, Haijing</creatorcontrib><creatorcontrib>Li, Zhen</creatorcontrib><creatorcontrib>Liao, Xuhong</creatorcontrib><creatorcontrib>Wang, Jinhui</creatorcontrib><creatorcontrib>Zhao, Tengda</creatorcontrib><creatorcontrib>Shu, Ni</creatorcontrib><creatorcontrib>Zhao, Xiaohu</creatorcontrib><creatorcontrib>He, Yong</creatorcontrib><title>Test-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Recent research has demonstrated the feasibility of combining functional near-infrared spectroscopy (fNIRS) and graph theory approaches to explore the topological attributes of human brain networks. However, the test-retest (TRT) reliability of the application of graph metrics to these networks remains to be elucidated. Here, we used resting-state fNIRS and a graph-theoretical approach to systematically address TRT reliability as it applies to various features of human brain networks, including functional connectivity, global network metrics and regional nodal centrality metrics. Eighteen subjects participated in two resting-state fNIRS scan sessions held ∼20 min apart. Functional brain networks were constructed for each subject by computing temporal correlations on three types of hemoglobin concentration information (HbO, HbR, and HbT). This was followed by a graph-theoretical analysis, and then an intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of each network metric. We observed that a large proportion of resting-state functional connections (∼90%) exhibited good reliability (0.6< ICC <0.74). For global and nodal measures, reliability was generally threshold-sensitive and varied among both network metrics and hemoglobin concentration signals. Specifically, the majority of global metrics exhibited fair to excellent reliability, with notably higher ICC values for the clustering coefficient (HbO: 0.76; HbR: 0.78; HbT: 0.53) and global efficiency (HbO: 0.76; HbR: 0.70; HbT: 0.78). Similarly, both nodal degree and efficiency measures also showed fair to excellent reliability across nodes (degree: 0.52∼0.84; efficiency: 0.50∼0.84); reliability was concordant across HbO, HbR and HbT and was significantly higher than that of nodal betweenness (0.28∼0.68). Together, our results suggest that most graph-theoretical network metrics derived from fNIRS are TRT reliable and can be used effectively for brain network research. This study also provides important guidance on the choice of network metrics of interest for future applied research in developmental and clinical neuroscience.</description><subject>Adult</subject><subject>Algorithms</subject><subject>Area Under Curve</subject><subject>Brain</subject><subject>Brain Mapping</subject><subject>Brain research</subject><subject>Cerebral Cortex - physiology</subject><subject>Clustering</subject><subject>Cognition & reasoning</subject><subject>Computing time</subject><subject>Correlation</subject><subject>Correlation analysis</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Efficiency</subject><subject>Feasibility studies</subject><subject>Female</subject><subject>Graph theory</subject><subject>Hemoglobin</subject><subject>Hemoglobins</subject><subject>Humans</subject><subject>I.R. radiation</subject><subject>Infrared spectra</subject><subject>Infrared spectroscopy</subject><subject>Laboratories</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Models, Biological</subject><subject>Monte Carlo simulation</subject><subject>Near infrared radiation</subject><subject>Near infrared spectroscopy</subject><subject>Nerve Net - physiology</subject><subject>Nervous system</subject><subject>Network reliability</subject><subject>Networks</subject><subject>Neural networks</subject><subject>Neurosciences</subject><subject>NMR</subject><subject>Nuclear magnetic resonance</subject><subject>Optics</subject><subject>Principal Component Analysis</subject><subject>Reproducibility of Results</subject><subject>Rest - physiology</subject><subject>Sensors</subject><subject>Spectroscopy, Near-Infrared</subject><subject>Spectrum analysis</subject><subject>Theoretical analysis</subject><subject>Young Adult</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNkl2L1DAYhYso7jr6D0QLguBFx6Rp0mYvhGXxY2BxYXf1NrxNk07GthmTVJ1_b8bpLlNQkEBT3jznJBxOkjzHaIlJid9u7OgG6JZbO6glQmVe5PRBcoo5yTOWI_Lw6P8keeL9BiFKKsYeJyd5gQgvGTlN6lvlQ-ZUiFvqVGegNp0Ju9TqtHWwXae9Cs5In5oh1eMgg7Hx1rR2EAeDCj-t--bPUohiH8zQZj5AUKn-vLq-SX0Ym93T5JGGzqtn075Ivnx4f3vxKbu8-ri6OL_MZEmrkGnCdUMIZawgFOoSyZpUJVfAFMWaS0Ba4aqhDGEFQCWUHLFc4xwQrSuiyCJ5efDddtaLKR4vcEEwRjnFLBKrA9FY2IitMz24nbBgxJ-Bda0AF4zslNAqbwpWKSgaWUhacwBWk7rCTHKOCUSvd9NtY92rRqohOOhmpvOTwaxFa38IUrKScR4NXk0Gzn4fY3j_ePJEtRBfZQZto5nsjZfivCgrQgmK30Wy_AsVV6N6I2NBtInzmeDNTBCZoH6FFkbvxerm-v_Zq69z9vURu1bQhbW33bhvjZ-DxQGUznrvlL5PDiOx7_ddGmLfbzH1O8peHKd-L7orNPkN5Nf2tQ</recordid><startdate>20130909</startdate><enddate>20130909</enddate><creator>Niu, Haijing</creator><creator>Li, Zhen</creator><creator>Liao, Xuhong</creator><creator>Wang, Jinhui</creator><creator>Zhao, Tengda</creator><creator>Shu, Ni</creator><creator>Zhao, Xiaohu</creator><creator>He, Yong</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20130909</creationdate><title>Test-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study</title><author>Niu, Haijing ; Li, Zhen ; Liao, Xuhong ; Wang, Jinhui ; Zhao, Tengda ; Shu, Ni ; Zhao, Xiaohu ; He, Yong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c758t-f39fd33566435ab70cb3879ea6e51f9ca0fe18d5601eaa5ca79062f12a05b83e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Adult</topic><topic>Algorithms</topic><topic>Area Under Curve</topic><topic>Brain</topic><topic>Brain Mapping</topic><topic>Brain research</topic><topic>Cerebral Cortex - physiology</topic><topic>Clustering</topic><topic>Cognition & reasoning</topic><topic>Computing time</topic><topic>Correlation</topic><topic>Correlation analysis</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Efficiency</topic><topic>Feasibility studies</topic><topic>Female</topic><topic>Graph theory</topic><topic>Hemoglobin</topic><topic>Hemoglobins</topic><topic>Humans</topic><topic>I.R. radiation</topic><topic>Infrared spectra</topic><topic>Infrared spectroscopy</topic><topic>Laboratories</topic><topic>Male</topic><topic>Medical imaging</topic><topic>Models, Biological</topic><topic>Monte Carlo simulation</topic><topic>Near infrared radiation</topic><topic>Near infrared spectroscopy</topic><topic>Nerve Net - physiology</topic><topic>Nervous system</topic><topic>Network reliability</topic><topic>Networks</topic><topic>Neural networks</topic><topic>Neurosciences</topic><topic>NMR</topic><topic>Nuclear magnetic resonance</topic><topic>Optics</topic><topic>Principal Component Analysis</topic><topic>Reproducibility of Results</topic><topic>Rest - 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However, the test-retest (TRT) reliability of the application of graph metrics to these networks remains to be elucidated. Here, we used resting-state fNIRS and a graph-theoretical approach to systematically address TRT reliability as it applies to various features of human brain networks, including functional connectivity, global network metrics and regional nodal centrality metrics. Eighteen subjects participated in two resting-state fNIRS scan sessions held ∼20 min apart. Functional brain networks were constructed for each subject by computing temporal correlations on three types of hemoglobin concentration information (HbO, HbR, and HbT). This was followed by a graph-theoretical analysis, and then an intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of each network metric. We observed that a large proportion of resting-state functional connections (∼90%) exhibited good reliability (0.6< ICC <0.74). For global and nodal measures, reliability was generally threshold-sensitive and varied among both network metrics and hemoglobin concentration signals. Specifically, the majority of global metrics exhibited fair to excellent reliability, with notably higher ICC values for the clustering coefficient (HbO: 0.76; HbR: 0.78; HbT: 0.53) and global efficiency (HbO: 0.76; HbR: 0.70; HbT: 0.78). Similarly, both nodal degree and efficiency measures also showed fair to excellent reliability across nodes (degree: 0.52∼0.84; efficiency: 0.50∼0.84); reliability was concordant across HbO, HbR and HbT and was significantly higher than that of nodal betweenness (0.28∼0.68). Together, our results suggest that most graph-theoretical network metrics derived from fNIRS are TRT reliable and can be used effectively for brain network research. This study also provides important guidance on the choice of network metrics of interest for future applied research in developmental and clinical neuroscience.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>24039763</pmid><doi>10.1371/journal.pone.0072425</doi><tpages>e72425</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Algorithms Area Under Curve Brain Brain Mapping Brain research Cerebral Cortex - physiology Clustering Cognition & reasoning Computing time Correlation Correlation analysis Correlation coefficient Correlation coefficients Efficiency Feasibility studies Female Graph theory Hemoglobin Hemoglobins Humans I.R. radiation Infrared spectra Infrared spectroscopy Laboratories Male Medical imaging Models, Biological Monte Carlo simulation Near infrared radiation Near infrared spectroscopy Nerve Net - physiology Nervous system Network reliability Networks Neural networks Neurosciences NMR Nuclear magnetic resonance Optics Principal Component Analysis Reproducibility of Results Rest - physiology Sensors Spectroscopy, Near-Infrared Spectrum analysis Theoretical analysis Young Adult |
| title | Test-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T04%3A24%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Test-retest%20reliability%20of%20graph%20metrics%20in%20functional%20brain%20networks:%20a%20resting-state%20fNIRS%20study&rft.jtitle=PloS%20one&rft.au=Niu,%20Haijing&rft.date=2013-09-09&rft.volume=8&rft.issue=9&rft.spage=e72425&rft.pages=e72425-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0072425&rft_dat=%3Cgale_plos_%3EA478353078%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1431102516&rft_id=info:pmid/24039763&rft_galeid=A478353078&rft_doaj_id=oai_doaj_org_article_fe2d468ea4dc4c5b9aa6b3b816c9913a&rfr_iscdi=true |