Dynamic Changes in Functional Network Connectivity Involving Amyotrophic Lateral Sclerosis and Its Correlation With Disease Severity
Background Aberrant static functional connectivity (FC) has been well demonstrated in amyotrophic lateral sclerosis (ALS); however, ALS‐related alterations in FC dynamic properties remain unclear, although dynamic FC analyses contribute to uncover mechanisms underlying neurodegenerative disorders. P...
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| Veröffentlicht in: | Journal of magnetic resonance imaging 2021-07, Vol.54 (1), p.239-248 |
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| creator | Chen, Hua‐Jun Zou, Zhang‐Yu Zhang, Xiao‐Hong Shi, Jia‐Yan Huang, Nao‐Xin Lin, Yan‐Juan |
| description | Background
Aberrant static functional connectivity (FC) has been well demonstrated in amyotrophic lateral sclerosis (ALS); however, ALS‐related alterations in FC dynamic properties remain unclear, although dynamic FC analyses contribute to uncover mechanisms underlying neurodegenerative disorders.
Purpose
To explore dynamic functional network connectivity (dFNC) in ALS and its correlation with disease severity.
Study Type
Prospective.
Subjects
Thirty‐two ALS patients and 45 healthy controls.
Field Strength/Sequence
Multiband resting‐state functional images using gradient echo echo‐planar imaging and T1‐weighted images were acquired at 3.0 T.
Assessment
Disease severity was evaluated with the revised ALS Functional Rating Scale (ALSFRS‐R) and patients were stratified according to diagnostic category. Independent component analysis was conducted to identify the components of seven intrinsic brain networks (ie, visual/sensorimotor (SMN)/auditory/cognitive‐control (CCN)/default‐mode (DMN)/subcortical/cerebellar networks). A sliding‐window correlation approach was used to compute dFNC. FNC states were determined by k‐mean clustering, and state‐specific FNC and dynamic indices (fraction time/mean dwell time/transition number) were calculated.
Statistical Tests
Two‐sample t test used for comparisons on dynamic measures and Spearman's correlation analysis.
Results
ALS patients showed increased FNC between DMN‐SMN in state 1 and between CCN‐SMN in state 4. Patients remained in state 2 (showing the weakest FNC) for a significantly longer time (mean dwell time: 49.8 ± 40.1 vs. 93.6 ± 126.3; P |
| doi_str_mv | 10.1002/jmri.27521 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2487748593</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2539815737</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3571-544b1b56066a337605985cf433fb49ef286f3ae4a92de337b92d33458366e133</originalsourceid><addsrcrecordid>eNp9kcFu1DAQhi1UREvhwgMgS71USCm2x3aSY7VtYdHSSrQSx8jJTrpeEntrJ1vlzoPjZQuHHnqa0eibT5r5CfnA2RlnTHxe98GeiVwJ_ooccSVEJlShD1LPFGS8YPkheRvjmjFWllK9IYcASpWg2RH5fTE509uGzlbG3WOk1tGr0TWD9c509BqHRx9-0Zl3DtNwa4eJzt3Wd1vr7ul5P_kh-M0qCRZmwJBWbpsOg482UuOWdD7EtBwCdmanpD_tsKIXNqKJSG9xiyEZ35HXrekivn-qx-Tu6vJu9jVb3HyZz84XWQMq55mSsua10kxrA5BrpspCNa0EaGtZYisK3YJBaUqxxATUqQJIVYDWyAGOyeleuwn-YcQ4VL2NDXadcejHWAlZ5Lks0mcSevIMXfsxpI8kSkFZcJVDnqhPe6pJB8eAbbUJtjdhqjirdtFUu2iqv9Ek-OOTcqx7XP5H_2WRAL4HHm2H0wuq6tv3H_O99A-o75nS</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2539815737</pqid></control><display><type>article</type><title>Dynamic Changes in Functional Network Connectivity Involving Amyotrophic Lateral Sclerosis and Its Correlation With Disease Severity</title><source>Wiley Online Library Journals Frontfile Complete</source><creator>Chen, Hua‐Jun ; Zou, Zhang‐Yu ; Zhang, Xiao‐Hong ; Shi, Jia‐Yan ; Huang, Nao‐Xin ; Lin, Yan‐Juan</creator><creatorcontrib>Chen, Hua‐Jun ; Zou, Zhang‐Yu ; Zhang, Xiao‐Hong ; Shi, Jia‐Yan ; Huang, Nao‐Xin ; Lin, Yan‐Juan</creatorcontrib><description>Background
Aberrant static functional connectivity (FC) has been well demonstrated in amyotrophic lateral sclerosis (ALS); however, ALS‐related alterations in FC dynamic properties remain unclear, although dynamic FC analyses contribute to uncover mechanisms underlying neurodegenerative disorders.
Purpose
To explore dynamic functional network connectivity (dFNC) in ALS and its correlation with disease severity.
Study Type
Prospective.
Subjects
Thirty‐two ALS patients and 45 healthy controls.
Field Strength/Sequence
Multiband resting‐state functional images using gradient echo echo‐planar imaging and T1‐weighted images were acquired at 3.0 T.
Assessment
Disease severity was evaluated with the revised ALS Functional Rating Scale (ALSFRS‐R) and patients were stratified according to diagnostic category. Independent component analysis was conducted to identify the components of seven intrinsic brain networks (ie, visual/sensorimotor (SMN)/auditory/cognitive‐control (CCN)/default‐mode (DMN)/subcortical/cerebellar networks). A sliding‐window correlation approach was used to compute dFNC. FNC states were determined by k‐mean clustering, and state‐specific FNC and dynamic indices (fraction time/mean dwell time/transition number) were calculated.
Statistical Tests
Two‐sample t test used for comparisons on dynamic measures and Spearman's correlation analysis.
Results
ALS patients showed increased FNC between DMN‐SMN in state 1 and between CCN‐SMN in state 4. Patients remained in state 2 (showing the weakest FNC) for a significantly longer time (mean dwell time: 49.8 ± 40.1 vs. 93.6 ± 126.3; P < 0.05) and remained in state 1 (showing a relatively strong FNC) for a shorter time (fraction time: 0.27 ± 0.25 vs. 0.13 ± 0.20; P < 0.05). ALS patients exhibited less temporal variability in their FNC (transition number: 10.2 ± 4.4 vs. 7.8 ± 3.8; P < 0.05). A significant correlation was observed between ALSFRS‐R and mean dwell time in state 2 (r = −0.414, P < 0.05) and transition number (r = 0.452, P < 0.05). No significant between‐subgroup difference in dFNC properties was found (all P > 0.05).
Data Conclusion
Our findings suggest aberrant dFNC properties in ALS, which is associated with disease severity.
Level of Evidence
2
Technical Efficacy
Stage 3</description><identifier>ISSN: 1053-1807</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.27521</identifier><identifier>PMID: 33559360</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Amyotrophic lateral sclerosis ; Cerebellum ; Clustering ; Cognitive ability ; Correlation analysis ; Dwell time ; dynamic ; Field strength ; functional network connectivity ; Image acquisition ; Independent component analysis ; Magnetic resonance imaging ; Mean ; Medical imaging ; Neural networks ; Neurodegenerative diseases ; Neuroimaging ; resting‐state functional magnetic resonance imaging ; Sensorimotor system ; Sensory integration ; Statistical analysis ; Statistical tests ; Subgroups</subject><ispartof>Journal of magnetic resonance imaging, 2021-07, Vol.54 (1), p.239-248</ispartof><rights>2021 International Society for Magnetic Resonance in Medicine</rights><rights>2021 International Society for Magnetic Resonance in Medicine.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3571-544b1b56066a337605985cf433fb49ef286f3ae4a92de337b92d33458366e133</citedby><cites>FETCH-LOGICAL-c3571-544b1b56066a337605985cf433fb49ef286f3ae4a92de337b92d33458366e133</cites><orcidid>0000-0001-7568-8709 ; 0000-0003-3077-9142</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjmri.27521$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmri.27521$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,27915,27916,45565,45566</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33559360$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, Hua‐Jun</creatorcontrib><creatorcontrib>Zou, Zhang‐Yu</creatorcontrib><creatorcontrib>Zhang, Xiao‐Hong</creatorcontrib><creatorcontrib>Shi, Jia‐Yan</creatorcontrib><creatorcontrib>Huang, Nao‐Xin</creatorcontrib><creatorcontrib>Lin, Yan‐Juan</creatorcontrib><title>Dynamic Changes in Functional Network Connectivity Involving Amyotrophic Lateral Sclerosis and Its Correlation With Disease Severity</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Background
Aberrant static functional connectivity (FC) has been well demonstrated in amyotrophic lateral sclerosis (ALS); however, ALS‐related alterations in FC dynamic properties remain unclear, although dynamic FC analyses contribute to uncover mechanisms underlying neurodegenerative disorders.
Purpose
To explore dynamic functional network connectivity (dFNC) in ALS and its correlation with disease severity.
Study Type
Prospective.
Subjects
Thirty‐two ALS patients and 45 healthy controls.
Field Strength/Sequence
Multiband resting‐state functional images using gradient echo echo‐planar imaging and T1‐weighted images were acquired at 3.0 T.
Assessment
Disease severity was evaluated with the revised ALS Functional Rating Scale (ALSFRS‐R) and patients were stratified according to diagnostic category. Independent component analysis was conducted to identify the components of seven intrinsic brain networks (ie, visual/sensorimotor (SMN)/auditory/cognitive‐control (CCN)/default‐mode (DMN)/subcortical/cerebellar networks). A sliding‐window correlation approach was used to compute dFNC. FNC states were determined by k‐mean clustering, and state‐specific FNC and dynamic indices (fraction time/mean dwell time/transition number) were calculated.
Statistical Tests
Two‐sample t test used for comparisons on dynamic measures and Spearman's correlation analysis.
Results
ALS patients showed increased FNC between DMN‐SMN in state 1 and between CCN‐SMN in state 4. Patients remained in state 2 (showing the weakest FNC) for a significantly longer time (mean dwell time: 49.8 ± 40.1 vs. 93.6 ± 126.3; P < 0.05) and remained in state 1 (showing a relatively strong FNC) for a shorter time (fraction time: 0.27 ± 0.25 vs. 0.13 ± 0.20; P < 0.05). ALS patients exhibited less temporal variability in their FNC (transition number: 10.2 ± 4.4 vs. 7.8 ± 3.8; P < 0.05). A significant correlation was observed between ALSFRS‐R and mean dwell time in state 2 (r = −0.414, P < 0.05) and transition number (r = 0.452, P < 0.05). No significant between‐subgroup difference in dFNC properties was found (all P > 0.05).
Data Conclusion
Our findings suggest aberrant dFNC properties in ALS, which is associated with disease severity.
Level of Evidence
2
Technical Efficacy
Stage 3</description><subject>Amyotrophic lateral sclerosis</subject><subject>Cerebellum</subject><subject>Clustering</subject><subject>Cognitive ability</subject><subject>Correlation analysis</subject><subject>Dwell time</subject><subject>dynamic</subject><subject>Field strength</subject><subject>functional network connectivity</subject><subject>Image acquisition</subject><subject>Independent component analysis</subject><subject>Magnetic resonance imaging</subject><subject>Mean</subject><subject>Medical imaging</subject><subject>Neural networks</subject><subject>Neurodegenerative diseases</subject><subject>Neuroimaging</subject><subject>resting‐state functional magnetic resonance imaging</subject><subject>Sensorimotor system</subject><subject>Sensory integration</subject><subject>Statistical analysis</subject><subject>Statistical tests</subject><subject>Subgroups</subject><issn>1053-1807</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kcFu1DAQhi1UREvhwgMgS71USCm2x3aSY7VtYdHSSrQSx8jJTrpeEntrJ1vlzoPjZQuHHnqa0eibT5r5CfnA2RlnTHxe98GeiVwJ_ooccSVEJlShD1LPFGS8YPkheRvjmjFWllK9IYcASpWg2RH5fTE509uGzlbG3WOk1tGr0TWD9c509BqHRx9-0Zl3DtNwa4eJzt3Wd1vr7ul5P_kh-M0qCRZmwJBWbpsOg482UuOWdD7EtBwCdmanpD_tsKIXNqKJSG9xiyEZ35HXrekivn-qx-Tu6vJu9jVb3HyZz84XWQMq55mSsua10kxrA5BrpspCNa0EaGtZYisK3YJBaUqxxATUqQJIVYDWyAGOyeleuwn-YcQ4VL2NDXadcejHWAlZ5Lks0mcSevIMXfsxpI8kSkFZcJVDnqhPe6pJB8eAbbUJtjdhqjirdtFUu2iqv9Ek-OOTcqx7XP5H_2WRAL4HHm2H0wuq6tv3H_O99A-o75nS</recordid><startdate>202107</startdate><enddate>202107</enddate><creator>Chen, Hua‐Jun</creator><creator>Zou, Zhang‐Yu</creator><creator>Zhang, Xiao‐Hong</creator><creator>Shi, Jia‐Yan</creator><creator>Huang, Nao‐Xin</creator><creator>Lin, Yan‐Juan</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-7568-8709</orcidid><orcidid>https://orcid.org/0000-0003-3077-9142</orcidid></search><sort><creationdate>202107</creationdate><title>Dynamic Changes in Functional Network Connectivity Involving Amyotrophic Lateral Sclerosis and Its Correlation With Disease Severity</title><author>Chen, Hua‐Jun ; Zou, Zhang‐Yu ; Zhang, Xiao‐Hong ; Shi, Jia‐Yan ; Huang, Nao‐Xin ; Lin, Yan‐Juan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3571-544b1b56066a337605985cf433fb49ef286f3ae4a92de337b92d33458366e133</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Amyotrophic lateral sclerosis</topic><topic>Cerebellum</topic><topic>Clustering</topic><topic>Cognitive ability</topic><topic>Correlation analysis</topic><topic>Dwell time</topic><topic>dynamic</topic><topic>Field strength</topic><topic>functional network connectivity</topic><topic>Image acquisition</topic><topic>Independent component analysis</topic><topic>Magnetic resonance imaging</topic><topic>Mean</topic><topic>Medical imaging</topic><topic>Neural networks</topic><topic>Neurodegenerative diseases</topic><topic>Neuroimaging</topic><topic>resting‐state functional magnetic resonance imaging</topic><topic>Sensorimotor system</topic><topic>Sensory integration</topic><topic>Statistical analysis</topic><topic>Statistical tests</topic><topic>Subgroups</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Hua‐Jun</creatorcontrib><creatorcontrib>Zou, Zhang‐Yu</creatorcontrib><creatorcontrib>Zhang, Xiao‐Hong</creatorcontrib><creatorcontrib>Shi, Jia‐Yan</creatorcontrib><creatorcontrib>Huang, Nao‐Xin</creatorcontrib><creatorcontrib>Lin, Yan‐Juan</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Hua‐Jun</au><au>Zou, Zhang‐Yu</au><au>Zhang, Xiao‐Hong</au><au>Shi, Jia‐Yan</au><au>Huang, Nao‐Xin</au><au>Lin, Yan‐Juan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamic Changes in Functional Network Connectivity Involving Amyotrophic Lateral Sclerosis and Its Correlation With Disease Severity</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J Magn Reson Imaging</addtitle><date>2021-07</date><risdate>2021</risdate><volume>54</volume><issue>1</issue><spage>239</spage><epage>248</epage><pages>239-248</pages><issn>1053-1807</issn><eissn>1522-2586</eissn><abstract>Background
Aberrant static functional connectivity (FC) has been well demonstrated in amyotrophic lateral sclerosis (ALS); however, ALS‐related alterations in FC dynamic properties remain unclear, although dynamic FC analyses contribute to uncover mechanisms underlying neurodegenerative disorders.
Purpose
To explore dynamic functional network connectivity (dFNC) in ALS and its correlation with disease severity.
Study Type
Prospective.
Subjects
Thirty‐two ALS patients and 45 healthy controls.
Field Strength/Sequence
Multiband resting‐state functional images using gradient echo echo‐planar imaging and T1‐weighted images were acquired at 3.0 T.
Assessment
Disease severity was evaluated with the revised ALS Functional Rating Scale (ALSFRS‐R) and patients were stratified according to diagnostic category. Independent component analysis was conducted to identify the components of seven intrinsic brain networks (ie, visual/sensorimotor (SMN)/auditory/cognitive‐control (CCN)/default‐mode (DMN)/subcortical/cerebellar networks). A sliding‐window correlation approach was used to compute dFNC. FNC states were determined by k‐mean clustering, and state‐specific FNC and dynamic indices (fraction time/mean dwell time/transition number) were calculated.
Statistical Tests
Two‐sample t test used for comparisons on dynamic measures and Spearman's correlation analysis.
Results
ALS patients showed increased FNC between DMN‐SMN in state 1 and between CCN‐SMN in state 4. Patients remained in state 2 (showing the weakest FNC) for a significantly longer time (mean dwell time: 49.8 ± 40.1 vs. 93.6 ± 126.3; P < 0.05) and remained in state 1 (showing a relatively strong FNC) for a shorter time (fraction time: 0.27 ± 0.25 vs. 0.13 ± 0.20; P < 0.05). ALS patients exhibited less temporal variability in their FNC (transition number: 10.2 ± 4.4 vs. 7.8 ± 3.8; P < 0.05). A significant correlation was observed between ALSFRS‐R and mean dwell time in state 2 (r = −0.414, P < 0.05) and transition number (r = 0.452, P < 0.05). No significant between‐subgroup difference in dFNC properties was found (all P > 0.05).
Data Conclusion
Our findings suggest aberrant dFNC properties in ALS, which is associated with disease severity.
Level of Evidence
2
Technical Efficacy
Stage 3</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>33559360</pmid><doi>10.1002/jmri.27521</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-7568-8709</orcidid><orcidid>https://orcid.org/0000-0003-3077-9142</orcidid></addata></record> |
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| ispartof | Journal of magnetic resonance imaging, 2021-07, Vol.54 (1), p.239-248 |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Amyotrophic lateral sclerosis Cerebellum Clustering Cognitive ability Correlation analysis Dwell time dynamic Field strength functional network connectivity Image acquisition Independent component analysis Magnetic resonance imaging Mean Medical imaging Neural networks Neurodegenerative diseases Neuroimaging resting‐state functional magnetic resonance imaging Sensorimotor system Sensory integration Statistical analysis Statistical tests Subgroups |
| title | Dynamic Changes in Functional Network Connectivity Involving Amyotrophic Lateral Sclerosis and Its Correlation With Disease Severity |
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