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<meta name="DC.Title" content="Dynamic Changes in Long-Standing Multiple Sclerosis Revealed by Longitudinal Structural Network Analysis Using Diffusion Tensor Imaging" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8115" />
<meta name="DC.Date" content="2024-03-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: DTI can be used to derive conventional diffusion measurements, which can measure WM abnormalities in multiple sclerosis. DTI can also be used to construct structural brain networks and derive network measurements. However, few studies have compared their sensitivity in detecting brain alterations, especially in longitudinal studies. Therefore, in this study, we aimed to determine which type of measurement is more sensitive in tracking the dynamic changes over time in MS.

MATERIALS AND METHODS: Eighteen patients with MS were recruited at baseline and followed up at 6 and 12 months. All patients underwent MR imaging and clinical evaluation at 3 time points. Diffusion and network measurements were derived, and their brain changes were evaluated.

RESULTS: None of the conventional DTI measurements displayed statistically significant changes during the follow-up period; however, the nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part showed significant longitudinal changes between baseline and at 12 months, respectively.

CONCLUSIONS: The nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part may be used to monitor brain changes over time in MS.

AD
:   axial diffusivity
EDSS
:   Expanded Disability Status Scale
FA
:   fractional anisotropy
IFGoperc
:   inferior frontal gyrus, opercular part
MD
:   mean diffusivity
MFG
:   middle frontal gyrus
NAWM
:   normal-appearing WM
ORBsupmed
:   superior frontal gyrus, medial orbital part
RD
:   radial diffusivity
SPMS
:   secondary-progressive MS" />
<meta name="DC.Contributor" content="Hui-Qin Zhang" />
<meta name="DC.Contributor" content="Jacky Chi-Yan Lee" />
<meta name="DC.Contributor" content="Lu Wang" />
<meta name="DC.Contributor" content="Peng Cao" />
<meta name="DC.Contributor" content="Koon-Ho Chan" />
<meta name="DC.Contributor" content="Henry Ka-Fung Mak" />
<meta name="article:published_time" content="2024-03-01" />
<meta name="article:section" content="Ultra-High-Field MRI/Imaging of Epilepsy/Demyelinating Diseases/Inflammation/Infection" />
<meta name="citation_title" content="Dynamic Changes in Long-Standing Multiple Sclerosis Revealed by Longitudinal Structural Network Analysis Using Diffusion Tensor Imaging" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;DTI can be used to derive conventional diffusion measurements, which can measure WM abnormalities in multiple sclerosis. DTI can also be used to construct structural brain networks and derive network measurements. However, few studies have compared their sensitivity in detecting brain alterations, especially in longitudinal studies. Therefore, in this study, we aimed to determine which type of measurement is more sensitive in tracking the dynamic changes over time in MS.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Eighteen patients with MS were recruited at baseline and followed up at 6 and 12 months. All patients underwent MR imaging and clinical evaluation at 3 time points. Diffusion and network measurements were derived, and their brain changes were evaluated.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;None of the conventional DTI measurements displayed statistically significant changes during the follow-up period; however, the nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part showed significant longitudinal changes between baseline and at 12 months, respectively.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;The nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part may be used to monitor brain changes over time in MS.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
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<meta name="citation_publication_date" content="2024/03/01" />
<meta name="citation_mjid" content="ajnr;45/3/305" />
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<meta name="citation_issn" content="0195-6108" />
<meta name="citation_issn" content="1936-959X" />
<meta name="citation_doi" content="10.3174/ajnr.A8115" />
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<meta name="citation_firstpage" content="305" />
<meta name="citation_lastpage" content="311" />
<meta name="citation_author" content="Hui-Qin Zhang" />
<meta name="citation_author_institution" content="aFrom the Department of Diagnostic Radiology (H.-Q.Z.), National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China" />
<meta name="citation_author_institution" content="bDepartment of Diagnostic Radiology (H.-Q.Z., P.C., H.K.-F.M.), Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China" />
<meta name="citation_author" content="Jacky Chi-Yan Lee" />
<meta name="citation_author_institution" content="cDepartment of Medicine (J.C.-Y.L., K.-H.C.), Queen Mary Hospital, Hong Kong SAR, China" />
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<meta name="citation_author" content="Lu Wang" />
<meta name="citation_author_institution" content="dDepartment of Health Technology and Informatics (L.W.), Hong Kong Polytechnic University, Hong Kong SAR, China" />
<meta name="citation_author" content="Peng Cao" />
<meta name="citation_author_institution" content="bDepartment of Diagnostic Radiology (H.-Q.Z., P.C., H.K.-F.M.), Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China" />
<meta name="citation_author" content="Koon-Ho Chan" />
<meta name="citation_author_institution" content="cDepartment of Medicine (J.C.-Y.L., K.-H.C.), Queen Mary Hospital, Hong Kong SAR, China" />
<meta name="citation_author_institution" content="eAlzheimer’s Disease Research Network (H.K.-F.M., K.-H.C.), University of Hong Kong, Hong Kong SAR, China" />
<meta name="citation_author" content="Henry Ka-Fung Mak" />
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<meta name="twitter:title" content="Dynamic Changes in Long-Standing Multiple Sclerosis Revealed by Longitudinal Structural Network Analysis Using Diffusion Tensor Imaging" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:image" content="http://ajnr-stage2.stage.highwire.org/sites/default/files/highwire/ajnr/45/3.cover-source.jpg" />
<meta name="twitter:description" content="BACKGROUND AND PURPOSE: DTI can be used to derive conventional diffusion measurements, which can measure WM abnormalities in multiple sclerosis. DTI can also be used to construct structural brain networks and derive network measurements. However, few studies have compared their sensitivity in detecting brain alterations, especially in longitudinal studies. Therefore, in this study, we aimed to determine which type of measurement is more sensitive in tracking the dynamic changes over time in MS.

MATERIALS AND METHODS: Eighteen patients with MS were recruited at baseline and followed up at 6 and 12 months. All patients underwent MR imaging and clinical evaluation at 3 time points. Diffusion and network measurements were derived, and their brain changes were evaluated.

RESULTS: None of the conventional DTI measurements displayed statistically significant changes during the follow-up period; however, the nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part showed significant longitudinal changes between baseline and at 12 months, respectively.

CONCLUSIONS: The nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part may be used to monitor brain changes over time in MS.

AD
:   axial diffusivity
EDSS
:   Expanded Disability Status Scale
FA
:   fractional anisotropy
IFGoperc
:   inferior frontal gyrus, opercular part
MD
:   mean diffusivity
MFG
:   middle frontal gyrus
NAWM
:   normal-appearing WM
ORBsupmed
:   superior frontal gyrus, medial orbital part
RD
:   radial diffusivity
SPMS
:   secondary-progressive MS" />
<meta name="og-title" property="og:title" content="Dynamic Changes in Long-Standing Multiple Sclerosis Revealed by Longitudinal Structural Network Analysis Using Diffusion Tensor Imaging" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/45/3/305" />
<meta name="og-site-name" property="og:site_name" content="American Journal of Neuroradiology" />
<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: DTI can be used to derive conventional diffusion measurements, which can measure WM abnormalities in multiple sclerosis. DTI can also be used to construct structural brain networks and derive network measurements. However, few studies have compared their sensitivity in detecting brain alterations, especially in longitudinal studies. Therefore, in this study, we aimed to determine which type of measurement is more sensitive in tracking the dynamic changes over time in MS.

MATERIALS AND METHODS: Eighteen patients with MS were recruited at baseline and followed up at 6 and 12 months. All patients underwent MR imaging and clinical evaluation at 3 time points. Diffusion and network measurements were derived, and their brain changes were evaluated.

RESULTS: None of the conventional DTI measurements displayed statistically significant changes during the follow-up period; however, the nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part showed significant longitudinal changes between baseline and at 12 months, respectively.

CONCLUSIONS: The nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part may be used to monitor brain changes over time in MS.

AD
:   axial diffusivity
EDSS
:   Expanded Disability Status Scale
FA
:   fractional anisotropy
IFGoperc
:   inferior frontal gyrus, opercular part
MD
:   mean diffusivity
MFG
:   middle frontal gyrus
NAWM
:   normal-appearing WM
ORBsupmed
:   superior frontal gyrus, medial orbital part
RD
:   radial diffusivity
SPMS
:   secondary-progressive MS" />
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      <div class="highwire-cite-overline"><span  class="highwire-cite-metadata-article-type highwire-cite-metadata">Research Article</span><span class="separator-pipe"></span><span  class="highwire-cite-metadata-art-cat highwire-cite-metadata"><span class="wrapper">Ultra-High-Field MRI/Imaging of Epilepsy/Demyelinating Diseases/Inflammation/Infection</span></span></div>
  
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      <h1 class="highwire-cite-title" id="page-title">Dynamic Changes in Long-Standing Multiple Sclerosis Revealed by Longitudinal Structural Network Analysis Using Diffusion Tensor Imaging</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Hui-Qin Zhang</span>, <span class="highwire-citation-author" data-delta="1">Jacky Chi-Yan Lee</span>, <span class="highwire-citation-author" data-delta="2">Lu Wang</span>, <span class="highwire-citation-author" data-delta="3">Peng Cao</span>, <span class="highwire-citation-author" data-delta="4">Koon-Ho Chan</span> and <span class="highwire-citation-author" data-delta="5">Henry Ka-Fung Mak</span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">March 2024,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(3) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">305-311; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8115 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-106301--21232163660" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Hui-Qin Zhang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Diagnostic Radiology (H.-Q.Z.), National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Diagnostic Radiology (H.-Q.Z., P.C., H.K.-F.M.), Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Hui-Qin%2BZhang%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on 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class='author-affiliation'><span class='nlm-sup'>b</span>Department of Diagnostic Radiology (H.-Q.Z., P.C., H.K.-F.M.), Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Peng%2BCao%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Cao%20P&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3APeng%2BCao%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">Koon-Ho Chan </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Medicine (J.C.-Y.L., K.-H.C.), Queen Mary Hospital, Hong Kong SAR, China</div><div class='author-affiliation'><span class='nlm-sup'>e</span>Alzheimer’s Disease Research Network (H.K.-F.M., K.-H.C.), University of Hong Kong, Hong Kong SAR, China</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Koon-Ho%2BChan%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Chan%20K&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li 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