Detecting residual brain networks in disorders of consciousness: A resting-state fNIRS study
[Display omitted] •We explored the brain network of DOC patients using rs-fNIRS for the first time.•MCS and UWS exhibited distinct patterns of topological architecture.•MCS and UWS had distinct characteristics of short- and long-distance connectivity.•BA 10 and 46 played crucial roles in the conscio...
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| Veröffentlicht in: | Brain research 2023-01, Vol.1798, p.148162-148162, Article 148162 |
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| creator | Liu, Yu Kang, Xiao-gang Chen, Bei-bei Song, Chang-geng Liu, Yan Hao, Jian-min Yuan, Fang Jiang, Wen |
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•We explored the brain network of DOC patients using rs-fNIRS for the first time.•MCS and UWS exhibited distinct patterns of topological architecture.•MCS and UWS had distinct characteristics of short- and long-distance connectivity.•BA 10 and 46 played crucial roles in the consciousness-supporting network.•Rs-fNIRS is an efficient technique to distinguish between MCS and UWS.
Functional near infrared spectroscopy (fNIRS) is an emerging non-invasive technique that allows bedside measurement of blood oxygenation level-dependent hemodynamic signals. We aimed to examine the efficacy of resting-state fNIRS in detecting the residual functional networks in patients with disorders of consciousness (DOC). We performed resting-state fNIRS in 23 DOC patients of whom 12 were in minimally conscious state (MCS) and 11 were in unresponsive wakefulness state (UWS). Ten regions of interest (ROIs) in the prefrontal cortex (PFC) were selected: both sides of Brodmann area (BA) 9, BA10, BA44, BA45, and BA46. Graph-theoretical analysis and seed-based correlation analyses were used to investigate the network topology and the strength of pairwise connections between ROIs and channels. MCS and UWS exhibited varying degrees of the loss of topological architecture, and the regional nodal properties of BA10 were significantly different between them (Nodal degree, PLeft BA10 = 0.01, PRight BA10 |
| doi_str_mv | 10.1016/j.brainres.2022.148162 |
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•We explored the brain network of DOC patients using rs-fNIRS for the first time.•MCS and UWS exhibited distinct patterns of topological architecture.•MCS and UWS had distinct characteristics of short- and long-distance connectivity.•BA 10 and 46 played crucial roles in the consciousness-supporting network.•Rs-fNIRS is an efficient technique to distinguish between MCS and UWS.
Functional near infrared spectroscopy (fNIRS) is an emerging non-invasive technique that allows bedside measurement of blood oxygenation level-dependent hemodynamic signals. We aimed to examine the efficacy of resting-state fNIRS in detecting the residual functional networks in patients with disorders of consciousness (DOC). We performed resting-state fNIRS in 23 DOC patients of whom 12 were in minimally conscious state (MCS) and 11 were in unresponsive wakefulness state (UWS). Ten regions of interest (ROIs) in the prefrontal cortex (PFC) were selected: both sides of Brodmann area (BA) 9, BA10, BA44, BA45, and BA46. Graph-theoretical analysis and seed-based correlation analyses were used to investigate the network topology and the strength of pairwise connections between ROIs and channels. MCS and UWS exhibited varying degrees of the loss of topological architecture, and the regional nodal properties of BA10 were significantly different between them (Nodal degree, PLeft BA10 = 0.01, PRight BA10 < 0.01; nodal efficiency, PLeft BA10 = 0.03, PRight BA10 < 0.01). Compared to healthy controls, UWS had impaired functions in both short- and long-distance connectivity, however, MCS had significantly impaired functions only in long-distance connectivity. The functional connectivity of right BA10 (AUC = 0.88) and the connections between left BA46 and right BA10 (AUC = 0.86) had excellent performance in differentiating MCS and UWS. MCS and UWS have different patterns of topological architecture and short- and long-distance connectivity in PFC. Intraconnections within BA10 and interhemispheric connections between BA10 and 46 are excellent resting-state fNIRS classifiers for distinguishing between MCS and UWS.</description><identifier>ISSN: 0006-8993</identifier><identifier>EISSN: 1872-6240</identifier><identifier>DOI: 10.1016/j.brainres.2022.148162</identifier><identifier>PMID: 36375509</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Brain - diagnostic imaging ; Brodmann area ; Consciousness ; Consciousness Disorders - diagnostic imaging ; Disorders of consciousness ; Functional connectivity ; Humans ; Persistent Vegetative State - diagnosis ; Prefrontal cortex ; Prefrontal Cortex - diagnostic imaging ; Resting-state fNIRS ; Wakefulness</subject><ispartof>Brain research, 2023-01, Vol.1798, p.148162-148162, Article 148162</ispartof><rights>2022 Elsevier B.V.</rights><rights>Copyright © 2022 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-8727d9c2c90ec64b456184d0eeb26d99c118f4e5925334e27061197615bdb1a83</citedby><cites>FETCH-LOGICAL-c368t-8727d9c2c90ec64b456184d0eeb26d99c118f4e5925334e27061197615bdb1a83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.brainres.2022.148162$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36375509$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Yu</creatorcontrib><creatorcontrib>Kang, Xiao-gang</creatorcontrib><creatorcontrib>Chen, Bei-bei</creatorcontrib><creatorcontrib>Song, Chang-geng</creatorcontrib><creatorcontrib>Liu, Yan</creatorcontrib><creatorcontrib>Hao, Jian-min</creatorcontrib><creatorcontrib>Yuan, Fang</creatorcontrib><creatorcontrib>Jiang, Wen</creatorcontrib><title>Detecting residual brain networks in disorders of consciousness: A resting-state fNIRS study</title><title>Brain research</title><addtitle>Brain Res</addtitle><description>[Display omitted]
•We explored the brain network of DOC patients using rs-fNIRS for the first time.•MCS and UWS exhibited distinct patterns of topological architecture.•MCS and UWS had distinct characteristics of short- and long-distance connectivity.•BA 10 and 46 played crucial roles in the consciousness-supporting network.•Rs-fNIRS is an efficient technique to distinguish between MCS and UWS.
Functional near infrared spectroscopy (fNIRS) is an emerging non-invasive technique that allows bedside measurement of blood oxygenation level-dependent hemodynamic signals. We aimed to examine the efficacy of resting-state fNIRS in detecting the residual functional networks in patients with disorders of consciousness (DOC). We performed resting-state fNIRS in 23 DOC patients of whom 12 were in minimally conscious state (MCS) and 11 were in unresponsive wakefulness state (UWS). Ten regions of interest (ROIs) in the prefrontal cortex (PFC) were selected: both sides of Brodmann area (BA) 9, BA10, BA44, BA45, and BA46. Graph-theoretical analysis and seed-based correlation analyses were used to investigate the network topology and the strength of pairwise connections between ROIs and channels. MCS and UWS exhibited varying degrees of the loss of topological architecture, and the regional nodal properties of BA10 were significantly different between them (Nodal degree, PLeft BA10 = 0.01, PRight BA10 < 0.01; nodal efficiency, PLeft BA10 = 0.03, PRight BA10 < 0.01). Compared to healthy controls, UWS had impaired functions in both short- and long-distance connectivity, however, MCS had significantly impaired functions only in long-distance connectivity. The functional connectivity of right BA10 (AUC = 0.88) and the connections between left BA46 and right BA10 (AUC = 0.86) had excellent performance in differentiating MCS and UWS. MCS and UWS have different patterns of topological architecture and short- and long-distance connectivity in PFC. Intraconnections within BA10 and interhemispheric connections between BA10 and 46 are excellent resting-state fNIRS classifiers for distinguishing between MCS and UWS.</description><subject>Brain - diagnostic imaging</subject><subject>Brodmann area</subject><subject>Consciousness</subject><subject>Consciousness Disorders - diagnostic imaging</subject><subject>Disorders of consciousness</subject><subject>Functional connectivity</subject><subject>Humans</subject><subject>Persistent Vegetative State - diagnosis</subject><subject>Prefrontal cortex</subject><subject>Prefrontal Cortex - diagnostic imaging</subject><subject>Resting-state fNIRS</subject><subject>Wakefulness</subject><issn>0006-8993</issn><issn>1872-6240</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkEtPwzAQhC0EoqXwFyofuaT4kTgxJ6ryqlSBxOOGZCX2BrmkSbETUP89Lmm5clqvNDM7_hAaUzKhhIqL5aRwua0d-AkjjE1onFHBDtCQZimLBIvJIRoSQkSUSckH6MT7ZVg5l-QYDbjgaZIQOURv19CCbm39jkOWNV1e4d9kXEP73bgPj8PbWN84A87jpsS6qb22Tedr8P4ST7fGbUDk27wFXD7Mn56xbzuzOUVHZV55ONvNEXq9vXmZ3UeLx7v5bLqINBdZG4XGqZGaaUlAi7iIE0Gz2BCAggkjpaY0K2NIJEs4j4GlRFAqU0GTwhQ0z_gInfe5a9d8dqGNWlmvoaryGkJPxVIuhEhkxoNU9FLtGu8dlGrt7Cp3G0WJ2pJVS7Unq7ZkVU82GMe7G12xAvNn26MMgqteAOGnXxacCpSg1mCsC4SVaex_N34AH4SNRg</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Liu, Yu</creator><creator>Kang, Xiao-gang</creator><creator>Chen, Bei-bei</creator><creator>Song, Chang-geng</creator><creator>Liu, Yan</creator><creator>Hao, Jian-min</creator><creator>Yuan, Fang</creator><creator>Jiang, Wen</creator><general>Elsevier B.V</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>7X8</scope></search><sort><creationdate>20230101</creationdate><title>Detecting residual brain networks in disorders of consciousness: A resting-state fNIRS study</title><author>Liu, Yu ; Kang, Xiao-gang ; Chen, Bei-bei ; Song, Chang-geng ; Liu, Yan ; Hao, Jian-min ; Yuan, Fang ; Jiang, Wen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-8727d9c2c90ec64b456184d0eeb26d99c118f4e5925334e27061197615bdb1a83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Brain - diagnostic imaging</topic><topic>Brodmann area</topic><topic>Consciousness</topic><topic>Consciousness Disorders - diagnostic imaging</topic><topic>Disorders of consciousness</topic><topic>Functional connectivity</topic><topic>Humans</topic><topic>Persistent Vegetative State - diagnosis</topic><topic>Prefrontal cortex</topic><topic>Prefrontal Cortex - diagnostic imaging</topic><topic>Resting-state fNIRS</topic><topic>Wakefulness</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Yu</creatorcontrib><creatorcontrib>Kang, Xiao-gang</creatorcontrib><creatorcontrib>Chen, Bei-bei</creatorcontrib><creatorcontrib>Song, Chang-geng</creatorcontrib><creatorcontrib>Liu, Yan</creatorcontrib><creatorcontrib>Hao, Jian-min</creatorcontrib><creatorcontrib>Yuan, Fang</creatorcontrib><creatorcontrib>Jiang, Wen</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Brain research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Yu</au><au>Kang, Xiao-gang</au><au>Chen, Bei-bei</au><au>Song, Chang-geng</au><au>Liu, Yan</au><au>Hao, Jian-min</au><au>Yuan, Fang</au><au>Jiang, Wen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detecting residual brain networks in disorders of consciousness: A resting-state fNIRS study</atitle><jtitle>Brain research</jtitle><addtitle>Brain Res</addtitle><date>2023-01-01</date><risdate>2023</risdate><volume>1798</volume><spage>148162</spage><epage>148162</epage><pages>148162-148162</pages><artnum>148162</artnum><issn>0006-8993</issn><eissn>1872-6240</eissn><abstract>[Display omitted]
•We explored the brain network of DOC patients using rs-fNIRS for the first time.•MCS and UWS exhibited distinct patterns of topological architecture.•MCS and UWS had distinct characteristics of short- and long-distance connectivity.•BA 10 and 46 played crucial roles in the consciousness-supporting network.•Rs-fNIRS is an efficient technique to distinguish between MCS and UWS.
Functional near infrared spectroscopy (fNIRS) is an emerging non-invasive technique that allows bedside measurement of blood oxygenation level-dependent hemodynamic signals. We aimed to examine the efficacy of resting-state fNIRS in detecting the residual functional networks in patients with disorders of consciousness (DOC). We performed resting-state fNIRS in 23 DOC patients of whom 12 were in minimally conscious state (MCS) and 11 were in unresponsive wakefulness state (UWS). Ten regions of interest (ROIs) in the prefrontal cortex (PFC) were selected: both sides of Brodmann area (BA) 9, BA10, BA44, BA45, and BA46. Graph-theoretical analysis and seed-based correlation analyses were used to investigate the network topology and the strength of pairwise connections between ROIs and channels. MCS and UWS exhibited varying degrees of the loss of topological architecture, and the regional nodal properties of BA10 were significantly different between them (Nodal degree, PLeft BA10 = 0.01, PRight BA10 < 0.01; nodal efficiency, PLeft BA10 = 0.03, PRight BA10 < 0.01). Compared to healthy controls, UWS had impaired functions in both short- and long-distance connectivity, however, MCS had significantly impaired functions only in long-distance connectivity. The functional connectivity of right BA10 (AUC = 0.88) and the connections between left BA46 and right BA10 (AUC = 0.86) had excellent performance in differentiating MCS and UWS. MCS and UWS have different patterns of topological architecture and short- and long-distance connectivity in PFC. Intraconnections within BA10 and interhemispheric connections between BA10 and 46 are excellent resting-state fNIRS classifiers for distinguishing between MCS and UWS.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>36375509</pmid><doi>10.1016/j.brainres.2022.148162</doi><tpages>1</tpages></addata></record> |
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| subjects | Brain - diagnostic imaging Brodmann area Consciousness Consciousness Disorders - diagnostic imaging Disorders of consciousness Functional connectivity Humans Persistent Vegetative State - diagnosis Prefrontal cortex Prefrontal Cortex - diagnostic imaging Resting-state fNIRS Wakefulness |
| title | Detecting residual brain networks in disorders of consciousness: A resting-state fNIRS study |
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