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<meta name="DC.Title" content="Alterations of Directional Connectivity among Resting-State Networks in Alzheimer Disease" />
<meta name="DC.Identifier" content="10.3174/ajnr.A3197" />
<meta name="DC.Date" content="2013-02-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: AD has been documented as a kind of disconnection syndrome by functional neuroimaging studies. The primary focus of this study was to examine, with the use of resting-state fMRI, whether AD would impact connectivity among RSNs.

MATERIALS AND METHODS: Fourteen patients with AD and 16 NC were recruited and scanned by using resting-state fMRI. Group independent-component analysis and the BN learning approach were used, respectively, to separate the RSNs and construct the network-to-network connectivity patterns for each group. The convergence index for the special network DMN was measured.

RESULTS: Three of the 4 connections were significantly lower in AD compared with NC. Although numerically the AD group had more connections, none was statistically different from that in the NC group except for 1 increased connection from the DMN to the DAN. The convergence index for the DMN node was lower in AD than in NC.

CONCLUSIONS: Connections among cognitive networks in AD were more vulnerable to impairment than sensory networks. The DMN decreased its integration function for other RSNs but may also play a role in compensating for the disrupted connections in AD.

AD
:   Alzheimer disease
AN
:   auditory network
BN
:   Bayesian network
DAN
:   dorsal attention network
DMN
:   default-mode network
LVN
:   lateral visual network
MVN
:   medial visual network
NC
:   healthy controls
RSN
:   resting-state network
SMN
:   sensory-motor network
SRN
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VAN
:   ventral attention network" />
<meta name="DC.Contributor" content="R. Li" />
<meta name="DC.Contributor" content="X. Wu" />
<meta name="DC.Contributor" content="K. Chen" />
<meta name="DC.Contributor" content="A.S. Fleisher" />
<meta name="DC.Contributor" content="E.M. Reiman" />
<meta name="DC.Contributor" content="L. Yao" />
<meta name="article:published_time" content="2013-02-01" />
<meta name="article:section" content="Functional" />
<meta name="citation_title" content="Alterations of Directional Connectivity among Resting-State Networks in Alzheimer Disease" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;AD has been documented as a kind of disconnection syndrome by functional neuroimaging studies. The primary focus of this study was to examine, with the use of resting-state fMRI, whether AD would impact connectivity among RSNs.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Fourteen patients with AD and 16 NC were recruited and scanned by using resting-state fMRI. Group independent-component analysis and the BN learning approach were used, respectively, to separate the RSNs and construct the network-to-network connectivity patterns for each group. The convergence index for the special network DMN was measured.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Three of the 4 connections were significantly lower in AD compared with NC. Although numerically the AD group had more connections, none was statistically different from that in the NC group except for 1 increased connection from the DMN to the DAN. The convergence index for the DMN node was lower in AD than in NC.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Connections among cognitive networks in AD were more vulnerable to impairment than sensory networks. The DMN decreased its integration function for other RSNs but may also play a role in compensating for the disrupted connections in AD.&lt;/p&gt;" />
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<meta name="citation_author" content="R. Li" />
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<meta name="twitter:title" content="Alterations of Directional Connectivity among Resting-State Networks in Alzheimer Disease" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: AD has been documented as a kind of disconnection syndrome by functional neuroimaging studies. The primary focus of this study was to examine, with the use of resting-state fMRI, whether AD would impact connectivity among RSNs.

MATERIALS AND METHODS: Fourteen patients with AD and 16 NC were recruited and scanned by using resting-state fMRI. Group independent-component analysis and the BN learning approach were used, respectively, to separate the RSNs and construct the network-to-network connectivity patterns for each group. The convergence index for the special network DMN was measured.

RESULTS: Three of the 4 connections were significantly lower in AD compared with NC. Although numerically the AD group had more connections, none was statistically different from that in the NC group except for 1 increased connection from the DMN to the DAN. The convergence index for the DMN node was lower in AD than in NC.

CONCLUSIONS: Connections among cognitive networks in AD were more vulnerable to impairment than sensory networks. The DMN decreased its integration function for other RSNs but may also play a role in compensating for the disrupted connections in AD.

AD
:   Alzheimer disease
AN
:   auditory network
BN
:   Bayesian network
DAN
:   dorsal attention network
DMN
:   default-mode network
LVN
:   lateral visual network
MVN
:   medial visual network
NC
:   healthy controls
RSN
:   resting-state network
SMN
:   sensory-motor network
SRN
:   self-referential network
VAN
:   ventral attention network" />
<meta name="og-title" property="og:title" content="Alterations of Directional Connectivity among Resting-State Networks in Alzheimer Disease" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/34/2/340" />
<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: AD has been documented as a kind of disconnection syndrome by functional neuroimaging studies. The primary focus of this study was to examine, with the use of resting-state fMRI, whether AD would impact connectivity among RSNs.

MATERIALS AND METHODS: Fourteen patients with AD and 16 NC were recruited and scanned by using resting-state fMRI. Group independent-component analysis and the BN learning approach were used, respectively, to separate the RSNs and construct the network-to-network connectivity patterns for each group. The convergence index for the special network DMN was measured.

RESULTS: Three of the 4 connections were significantly lower in AD compared with NC. Although numerically the AD group had more connections, none was statistically different from that in the NC group except for 1 increased connection from the DMN to the DAN. The convergence index for the DMN node was lower in AD than in NC.

CONCLUSIONS: Connections among cognitive networks in AD were more vulnerable to impairment than sensory networks. The DMN decreased its integration function for other RSNs but may also play a role in compensating for the disrupted connections in AD.

AD
:   Alzheimer disease
AN
:   auditory network
BN
:   Bayesian network
DAN
:   dorsal attention network
DMN
:   default-mode network
LVN
:   lateral visual network
MVN
:   medial visual network
NC
:   healthy controls
RSN
:   resting-state network
SMN
:   sensory-motor network
SRN
:   self-referential network
VAN
:   ventral attention network" />
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      <h1 class="highwire-cite-title" id="page-title">Alterations of Directional Connectivity among Resting-State Networks in Alzheimer Disease</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">R. Li</span>, <span class="highwire-citation-author" data-delta="1">X. Wu</span>, <span class="highwire-citation-author" data-delta="2">K. Chen</span>, <span class="highwire-citation-author" data-delta="3">A.S. Fleisher</span>, <span class="highwire-citation-author" data-delta="4">E.M. Reiman</span> and <span class="highwire-citation-author" data-delta="5">L. Yao</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">February 2013,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">34 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(2) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">340-345; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A3197 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-12254--61905474635" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">R. Li </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Center on Aging Psychology (R.L.), Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 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=R.%2BLi%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=Li%20R&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%3AR.%2BLi%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-1"><div class="author-tooltip-name">X. Wu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>State Key Laboratory of Cognitive Neuroscience and Learning (X.W., L.Y.)</div><div class='author-affiliation'><span class='nlm-sup'>c</span>School of Information Science and Technology (X.W., L.Y.), Beijing Normal University, Beijing, 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=X.%2BWu%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=Wu%20X&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%3AX.%2BWu%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-2"><div class="author-tooltip-name">K. Chen </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Banner Alzheimer's Institute and Banner Good Samaritan PET Center (K.C., A.S.F., E.M.R.), Phoenix, Arizona</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=K.%2BChen%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=Chen%20K&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%3AK.%2BChen%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-3"><div class="author-tooltip-name">A.S. Fleisher </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Banner Alzheimer's Institute and Banner Good Samaritan PET Center (K.C., A.S.F., E.M.R.), Phoenix, Arizona</div><div class='author-affiliation'><span class='nlm-sup'>e</span>Department of Neuroscience (A.S.F.), University of California, San Diego, San Diego, California.</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=A.S.%2BFleisher%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=Fleisher%20A&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%3AA.S.%2BFleisher%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">E.M. Reiman </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Banner Alzheimer's Institute and Banner Good Samaritan PET Center (K.C., A.S.F., E.M.R.), Phoenix, Arizona</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=E.M.%2BReiman%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=Reiman%20E&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%3AE.M.%2BReiman%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-5"><div class="author-tooltip-name">L. Yao </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>State Key Laboratory of Cognitive Neuroscience and Learning (X.W., L.Y.)</div><div class='author-affiliation'><span class='nlm-sup'>c</span>School of Information Science and Technology (X.W., L.Y.), Beijing Normal University, Beijing, 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=L.%2BYao%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=Yao%20L&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%3AL.%2BYao%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div></div></div>  </div>

  
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<p id="p-2"><strong>BACKGROUND AND PURPOSE:</strong> AD has been documented as a kind of disconnection syndrome by functional neuroimaging studies. The primary focus of this study was to examine, with the use of resting-state fMRI, whether AD would impact connectivity among RSNs.</p>
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<p id="p-3"><strong>MATERIALS AND METHODS:</strong> Fourteen patients with AD and 16 NC were recruited and scanned by using resting-state fMRI. Group independent-component analysis and the BN learning approach were used, respectively, to separate the RSNs and construct the network-to-network connectivity patterns for each group. The convergence index for the special network DMN was measured.</p>
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<p id="p-4"><strong>RESULTS:</strong> Three of the 4 connections were significantly lower in AD compared with NC. Although numerically the AD group had more connections, none was statistically different from that in the NC group except for 1 increased connection from the DMN to the DAN. The convergence index for the DMN node was lower in AD than in NC.</p>
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<p id="p-5"><strong>CONCLUSIONS:</strong> Connections among cognitive networks in AD were more vulnerable to impairment than sensory networks. The DMN decreased its integration function for other RSNs but may also play a role in compensating for the disrupted connections in AD.</p>
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<h2>ABBREVIATIONS:</h2>
<dl id="def-list-1"><dt id="def-item-1">AD<span class="def-item-dt-sep"></span></dt><dd>Alzheimer disease<span class="def-item-dd-sep"></span></dd><dt id="def-item-2">AN<span class="def-item-dt-sep"></span></dt><dd>auditory network<span class="def-item-dd-sep"></span></dd><dt id="def-item-3">BN<span class="def-item-dt-sep"></span></dt><dd>Bayesian network<span class="def-item-dd-sep"></span></dd><dt id="def-item-4">DAN<span class="def-item-dt-sep"></span></dt><dd>dorsal attention network<span class="def-item-dd-sep"></span></dd><dt id="def-item-5">DMN<span class="def-item-dt-sep"></span></dt><dd>default-mode network<span class="def-item-dd-sep"></span></dd><dt id="def-item-6">LVN<span class="def-item-dt-sep"></span></dt><dd>lateral visual network<span class="def-item-dd-sep"></span></dd><dt id="def-item-7">MVN<span class="def-item-dt-sep"></span></dt><dd>medial visual network<span class="def-item-dd-sep"></span></dd><dt id="def-item-8">NC<span class="def-item-dt-sep"></span></dt><dd>healthy controls<span class="def-item-dd-sep"></span></dd><dt id="def-item-9">RSN<span class="def-item-dt-sep"></span></dt><dd>resting-state network<span class="def-item-dd-sep"></span></dd><dt id="def-item-10">SMN<span class="def-item-dt-sep"></span></dt><dd>sensory-motor network<span class="def-item-dd-sep"></span></dd><dt id="def-item-11">SRN<span class="def-item-dt-sep"></span></dt><dd>self-referential network<span class="def-item-dd-sep"></span></dd><dt id="def-item-12">VAN<span class="def-item-dt-sep"></span></dt><dd>ventral attention network</dd></dl>
</div><ul class="copyright-statement"><li class="fn" id="copyright-statement-1">© 2013 by American Journal of Neuroradiology</li></ul><div class="license" id="license-1"><p id="p-1">Indicates open access to non-subscribers at <a href="http://www.ajnr.org">www.ajnr.org</a></p></div><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div><a href="http://ajnr-stage2.stage.highwire.org/content/34/2/340.full" class="hw-link hw-link-article-full-text" data-icon-position="" data-hide-link-title="0">View Full Text</a></div>  </div>

  
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