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<meta name="DC.Title" content="FLAIR and Diffusion MRI Signals Are Independent Predictors of White Matter Hyperintensities" />
<meta name="DC.Identifier" content="10.3174/ajnr.A3146" />
<meta name="DC.Date" content="2013-01-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2013 by American Journal of Neuroradiology. Indicates open access to non-subscribers at www.ajnr.org" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: WMH, associated with cognitive decline and cardiovascular risk factors, may represent only the extreme end of a more widespread continuous WM injury process that progresses during aging and is poorly understood. We investigated the ability of FLAIR and DTI to characterize the longitudinal course of WMH development.

MATERIALS AND METHODS: One hundred nineteen participants (mean age, 74.5 ± 7.4), including cognitively healthy elders and subjects diagnosed with Alzheimer disease and mild cognitive impairment, received a comprehensive clinical evaluation and brain MR imaging, including FLAIR and DTI on 2 dates. The risk for each baseline normal-appearing WM voxel to convert into WMH was modeled as a function of baseline FA (model M1) and both baseline FA and standardized FLAIR (M2). Sensitivity, specificity, accuracy, and AUC for predicting conversion to WMH were compared between models.

RESULTS: Independent of clinical diagnosis, lower baseline FA ( P &lt; .001, both models) and higher baseline FLAIR intensity ( P &lt; .001, M2) were independently associated with increased risk for conversion from normal WM to WMH. M1 exhibited higher sensitivity but lower specificity, accuracy, and AUC compared with M2.

CONCLUSIONS: These findings provide further evidence that WMH result from a continuous process of WM degeneration with time. Stepwise decreases in WM integrity as measured by both DTI and FLAIR were independently associated with stepwise increases in WMH risk, emphasizing that these modalities may provide complementary information for understanding the time course of aging-associated WM degeneration.

AD
:   Alzheimer disease
AUC
:   area under the receiver operating characteristic analysis curve
CN
:   cognitively healthy
FA
:   fractional anisotropy
M1
:   first logistic regression model
M2
:   second logistic regression model
MCI
:   mild cognitive impairment
MDT
:   minimal deformation template
nFA
:   baseline normalized FA
nFL
:   baseline normalized FLAIR
OR
:   odds ratio
WMH
:   white matter hyperintensities" />
<meta name="DC.Contributor" content="P. Maillard" />
<meta name="DC.Contributor" content="O. Carmichael" />
<meta name="DC.Contributor" content="D. Harvey" />
<meta name="DC.Contributor" content="E. Fletcher" />
<meta name="DC.Contributor" content="B. Reed" />
<meta name="DC.Contributor" content="D. Mungas" />
<meta name="DC.Contributor" content="C. DeCarli" />
<meta name="article:published_time" content="2013-01-01" />
<meta name="article:section" content="Brain" />
<meta name="citation_title" content="FLAIR and Diffusion MRI Signals Are Independent Predictors of White Matter Hyperintensities" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;WMH, associated with cognitive decline and cardiovascular risk factors, may represent only the extreme end of a more widespread continuous WM injury process that progresses during aging and is poorly understood. We investigated the ability of FLAIR and DTI to characterize the longitudinal course of WMH development.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;One hundred nineteen participants (mean age, 74.5 ± 7.4), including cognitively healthy elders and subjects diagnosed with Alzheimer disease and mild cognitive impairment, received a comprehensive clinical evaluation and brain MR imaging, including FLAIR and DTI on 2 dates. The risk for each baseline normal-appearing WM voxel to convert into WMH was modeled as a function of baseline FA (model M1) and both baseline FA and standardized FLAIR (M2). Sensitivity, specificity, accuracy, and AUC for predicting conversion to WMH were compared between models.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Independent of clinical diagnosis, lower baseline FA (&lt;i&gt;P&lt;/i&gt; &amp;lt; .001, both models) and higher baseline FLAIR intensity (&lt;i&gt;P&lt;/i&gt; &amp;lt; .001, M2) were independently associated with increased risk for conversion from normal WM to WMH. M1 exhibited higher sensitivity but lower specificity, accuracy, and AUC compared with M2.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;These findings provide further evidence that WMH result from a continuous process of WM degeneration with time. Stepwise decreases in WM integrity as measured by both DTI and FLAIR were independently associated with stepwise increases in WMH risk, emphasizing that these modalities may provide complementary information for understanding the time course of aging-associated WM degeneration.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
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<meta name="citation_publication_date" content="2013/01/01" />
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<meta name="citation_issn" content="0195-6108" />
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<meta name="citation_author" content="P. Maillard" />
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<meta name="citation_author_institution" content="bDepartment of Neurology and Center for Neuroscience (P.M., O.C., E.F., B.R., D.M., C.D.), University of California, Davis, Davis, California" />
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<meta name="citation_author_institution" content="aFrom the Imaging of Dementia and Aging Laboratory (P.M., O.C., E.F., B.R., D.M., C.D.)" />
<meta name="citation_author_institution" content="bDepartment of Neurology and Center for Neuroscience (P.M., O.C., E.F., B.R., D.M., C.D.), University of California, Davis, Davis, California" />
<meta name="citation_author" content="D. Harvey" />
<meta name="citation_author_institution" content="cDivision of Biostatistics (D.H., B.R., D.M., C.D.), Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, California." />
<meta name="citation_author" content="E. Fletcher" />
<meta name="citation_author_institution" content="aFrom the Imaging of Dementia and Aging Laboratory (P.M., O.C., E.F., B.R., D.M., C.D.)" />
<meta name="citation_author_institution" content="bDepartment of Neurology and Center for Neuroscience (P.M., O.C., E.F., B.R., D.M., C.D.), University of California, Davis, Davis, California" />
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<meta name="citation_author_institution" content="aFrom the Imaging of Dementia and Aging Laboratory (P.M., O.C., E.F., B.R., D.M., C.D.)" />
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<meta name="twitter:title" content="FLAIR and Diffusion MRI Signals Are Independent Predictors of White Matter Hyperintensities" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: WMH, associated with cognitive decline and cardiovascular risk factors, may represent only the extreme end of a more widespread continuous WM injury process that progresses during aging and is poorly understood. We investigated the ability of FLAIR and DTI to characterize the longitudinal course of WMH development.

MATERIALS AND METHODS: One hundred nineteen participants (mean age, 74.5 ± 7.4), including cognitively healthy elders and subjects diagnosed with Alzheimer disease and mild cognitive impairment, received a comprehensive clinical evaluation and brain MR imaging, including FLAIR and DTI on 2 dates. The risk for each baseline normal-appearing WM voxel to convert into WMH was modeled as a function of baseline FA (model M1) and both baseline FA and standardized FLAIR (M2). Sensitivity, specificity, accuracy, and AUC for predicting conversion to WMH were compared between models.

RESULTS: Independent of clinical diagnosis, lower baseline FA ( P &lt; .001, both models) and higher baseline FLAIR intensity ( P &lt; .001, M2) were independently associated with increased risk for conversion from normal WM to WMH. M1 exhibited higher sensitivity but lower specificity, accuracy, and AUC compared with M2.

CONCLUSIONS: These findings provide further evidence that WMH result from a continuous process of WM degeneration with time. Stepwise decreases in WM integrity as measured by both DTI and FLAIR were independently associated with stepwise increases in WMH risk, emphasizing that these modalities may provide complementary information for understanding the time course of aging-associated WM degeneration.

AD
:   Alzheimer disease
AUC
:   area under the receiver operating characteristic analysis curve
CN
:   cognitively healthy
FA
:   fractional anisotropy
M1
:   first logistic regression model
M2
:   second logistic regression model
MCI
:   mild cognitive impairment
MDT
:   minimal deformation template
nFA
:   baseline normalized FA
nFL
:   baseline normalized FLAIR
OR
:   odds ratio
WMH
:   white matter hyperintensities" />
<meta name="og-title" property="og:title" content="FLAIR and Diffusion MRI Signals Are Independent Predictors of White Matter Hyperintensities" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/34/1/54" />
<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: WMH, associated with cognitive decline and cardiovascular risk factors, may represent only the extreme end of a more widespread continuous WM injury process that progresses during aging and is poorly understood. We investigated the ability of FLAIR and DTI to characterize the longitudinal course of WMH development.

MATERIALS AND METHODS: One hundred nineteen participants (mean age, 74.5 ± 7.4), including cognitively healthy elders and subjects diagnosed with Alzheimer disease and mild cognitive impairment, received a comprehensive clinical evaluation and brain MR imaging, including FLAIR and DTI on 2 dates. The risk for each baseline normal-appearing WM voxel to convert into WMH was modeled as a function of baseline FA (model M1) and both baseline FA and standardized FLAIR (M2). Sensitivity, specificity, accuracy, and AUC for predicting conversion to WMH were compared between models.

RESULTS: Independent of clinical diagnosis, lower baseline FA ( P &lt; .001, both models) and higher baseline FLAIR intensity ( P &lt; .001, M2) were independently associated with increased risk for conversion from normal WM to WMH. M1 exhibited higher sensitivity but lower specificity, accuracy, and AUC compared with M2.

CONCLUSIONS: These findings provide further evidence that WMH result from a continuous process of WM degeneration with time. Stepwise decreases in WM integrity as measured by both DTI and FLAIR were independently associated with stepwise increases in WMH risk, emphasizing that these modalities may provide complementary information for understanding the time course of aging-associated WM degeneration.

AD
:   Alzheimer disease
AUC
:   area under the receiver operating characteristic analysis curve
CN
:   cognitively healthy
FA
:   fractional anisotropy
M1
:   first logistic regression model
M2
:   second logistic regression model
MCI
:   mild cognitive impairment
MDT
:   minimal deformation template
nFA
:   baseline normalized FA
nFL
:   baseline normalized FLAIR
OR
:   odds ratio
WMH
:   white matter hyperintensities" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node12422--5" data-node-nid="12422" id="top-node-12422--6470038210" data-pisa="ajnr;34/1/54" data-pisa-master="ajnr;ajnr.A3146" data-apath="/ajnr/34/1/54.atom" data-hw-author-tooltip-instance="highwire_author_tooltip"><div class="highwire-cite highwire-cite-highwire-article highwire-citation-jcore-article-title-complete clearfix has-author-tooltip highwire-citation-highwire-article-top-a">
  
  
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      <h1 class="highwire-cite-title" id="page-title">FLAIR and Diffusion MRI Signals Are Independent Predictors of White Matter Hyperintensities</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">P. Maillard</span>, <span class="highwire-citation-author" data-delta="1">O. Carmichael</span>, <span class="highwire-citation-author" data-delta="2">D. Harvey</span>, <span class="highwire-citation-author" data-delta="3">E. Fletcher</span>, <span class="highwire-citation-author" data-delta="4">B. Reed</span>, <span class="highwire-citation-author" data-delta="5">D. Mungas</span> and <span class="highwire-citation-author" data-delta="6">C. DeCarli</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">January 2013,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">34 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(1) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">54-61; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A3146 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-12422--6470038210" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">P. Maillard </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Imaging of Dementia and Aging Laboratory (P.M., O.C., E.F., B.R., D.M., C.D.)</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Neurology and Center for Neuroscience (P.M., O.C., E.F., B.R., D.M., C.D.), University of California, Davis, Davis, 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=P.%2BMaillard%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=Maillard%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%3AP.%2BMaillard%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">O. Carmichael </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Imaging of Dementia and Aging Laboratory (P.M., O.C., E.F., B.R., D.M., C.D.)</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Neurology and Center for Neuroscience (P.M., O.C., E.F., B.R., D.M., C.D.), University of California, Davis, Davis, 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=O.%2BCarmichael%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=Carmichael%20O&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%3AO.%2BCarmichael%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">D. Harvey </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Division of Biostatistics (D.H., B.R., D.M., C.D.), Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, 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=D.%2BHarvey%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=Harvey%20D&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%3AD.%2BHarvey%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">E. Fletcher </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Imaging of Dementia and Aging Laboratory (P.M., O.C., E.F., B.R., D.M., C.D.)</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Neurology and Center for Neuroscience (P.M., O.C., E.F., B.R., D.M., C.D.), University of California, Davis, Davis, 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=E.%2BFletcher%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=Fletcher%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.%2BFletcher%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">B. Reed </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Imaging of Dementia and Aging Laboratory (P.M., O.C., E.F., B.R., D.M., C.D.)</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Neurology and Center for Neuroscience (P.M., O.C., E.F., B.R., D.M., C.D.), University of California, Davis, Davis, California</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Division of Biostatistics (D.H., B.R., D.M., C.D.), Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, 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=B.%2BReed%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=Reed%20B&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%3AB.%2BReed%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">D. Mungas </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Imaging of Dementia and Aging Laboratory (P.M., O.C., E.F., B.R., D.M., C.D.)</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Neurology and Center for Neuroscience (P.M., O.C., E.F., B.R., D.M., C.D.), University of California, Davis, Davis, California</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Division of Biostatistics (D.H., B.R., D.M., C.D.), Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, 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=D.%2BMungas%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=Mungas%20D&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%3AD.%2BMungas%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-6"><div class="author-tooltip-name">C. DeCarli </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Imaging of Dementia and Aging Laboratory (P.M., O.C., E.F., B.R., D.M., C.D.)</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Neurology and Center for Neuroscience (P.M., O.C., E.F., B.R., D.M., C.D.), University of California, Davis, Davis, California</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Division of Biostatistics (D.H., B.R., D.M., C.D.), Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, 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=C.%2BDeCarli%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=DeCarli%20C&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%3AC.%2BDeCarli%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> WMH, associated with cognitive decline and cardiovascular risk factors, may represent only the extreme end of a more widespread continuous WM injury process that progresses during aging and is poorly understood. We investigated the ability of FLAIR and DTI to characterize the longitudinal course of WMH development.</p>
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<p id="p-3"><strong>MATERIALS AND METHODS:</strong> One hundred nineteen participants (mean age, 74.5 ± 7.4), including cognitively healthy elders and subjects diagnosed with Alzheimer disease and mild cognitive impairment, received a comprehensive clinical evaluation and brain MR imaging, including FLAIR and DTI on 2 dates. The risk for each baseline normal-appearing WM voxel to convert into WMH was modeled as a function of baseline FA (model M1) and both baseline FA and standardized FLAIR (M2). Sensitivity, specificity, accuracy, and AUC for predicting conversion to WMH were compared between models.</p>
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<p id="p-4"><strong>RESULTS:</strong> Independent of clinical diagnosis, lower baseline FA (<em>P</em> &lt; .001, both models) and higher baseline FLAIR intensity (<em>P</em> &lt; .001, M2) were independently associated with increased risk for conversion from normal WM to WMH. M1 exhibited higher sensitivity but lower specificity, accuracy, and AUC compared with M2.</p>
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<p id="p-5"><strong>CONCLUSIONS:</strong> These findings provide further evidence that WMH result from a continuous process of WM degeneration with time. Stepwise decreases in WM integrity as measured by both DTI and FLAIR were independently associated with stepwise increases in WMH risk, emphasizing that these modalities may provide complementary information for understanding the time course of aging-associated WM degeneration.</p>
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</div><div class="section glossary" id="glossary-1">
<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">AUC<span class="def-item-dt-sep"></span></dt><dd>area under the receiver operating characteristic analysis curve<span class="def-item-dd-sep"></span></dd><dt id="def-item-3">CN<span class="def-item-dt-sep"></span></dt><dd>cognitively healthy<span class="def-item-dd-sep"></span></dd><dt id="def-item-4">FA<span class="def-item-dt-sep"></span></dt><dd>fractional anisotropy<span class="def-item-dd-sep"></span></dd><dt id="def-item-5">M1<span class="def-item-dt-sep"></span></dt><dd>first logistic regression model<span class="def-item-dd-sep"></span></dd><dt id="def-item-6">M2<span class="def-item-dt-sep"></span></dt><dd>second logistic regression model<span class="def-item-dd-sep"></span></dd><dt id="def-item-7">MCI<span class="def-item-dt-sep"></span></dt><dd>mild cognitive impairment<span class="def-item-dd-sep"></span></dd><dt id="def-item-8">MDT<span class="def-item-dt-sep"></span></dt><dd>minimal deformation template<span class="def-item-dd-sep"></span></dd><dt id="def-item-9">nFA<span class="def-item-dt-sep"></span></dt><dd>baseline normalized FA<span class="def-item-dd-sep"></span></dd><dt id="def-item-10">nFL<span class="def-item-dt-sep"></span></dt><dd>baseline normalized FLAIR<span class="def-item-dd-sep"></span></dd><dt id="def-item-11">OR<span class="def-item-dt-sep"></span></dt><dd>odds ratio<span class="def-item-dd-sep"></span></dd><dt id="def-item-12">WMH<span class="def-item-dt-sep"></span></dt><dd>white matter hyperintensities</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/34/1/54.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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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 34</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 1 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Jan 2013 </div></div>
      
      
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      <div class="highwire-cite-title">FLAIR and Diffusion MRI Signals Are Independent Predictors of White Matter Hyperintensities</div>  
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    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-oa-ind highwire-cite-metadata"><i class="highwire-oa-indicator"></i> </span><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Jan 2013, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">34 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(1) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">54-61; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A3146 </span></div>
  
  
  
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