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<meta name="DC.Title" content="Temporoparietal MR Imaging Measures of Atrophy in Subjects with Mild Cognitive Impairment That Predict Subsequent Diagnosis of Alzheimer Disease" />
<meta name="DC.Identifier" content="10.3174/ajnr.A1397" />
<meta name="DC.Date" content="2009-03-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="Copyright © American Society of Neuroradiology." />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer disease (AD). Our goal was to determine if specific temporoparietal regions can predict the time to progress from MCI to AD.

MATERIALS AND METHODS: MR images from 129 individuals with MCI were analyzed to identify the volume of 14 neocortical and 2 non-neocortical brain regions, comprising the temporal and parietal lobes. In addition, 3 neuropsychological test scores were included to determine whether they would provide independent information. After a mean follow-up time of 5 years, 44 of these individuals had progressed to a diagnosis of AD.

RESULTS: Cox proportional hazards models demonstrated significant effects for 6 MR imaging regions with the greatest differences being the following: the entorhinal cortex (hazard ratio [HR] = 0.54, P &lt; .001), inferior parietal lobule (hazard ratio [HR] = 0.64, P &lt; .005), and middle temporal gyrus (HR = 0.64, P &lt; .004), indicating decreased risk with larger volumes. A multivariable model showed that a combination of the entorhinal cortex (HR = 0.60, P &lt; .001) and the inferior parietal lobule (HR = 0.62, P &lt; .01) was the best predictor of time to progress to AD. A multivariable model reiterated the importance of including both MR imaging and neuropsychological variables in the final model.

CONCLUSIONS: These findings reaffirm the importance of the entorhinal cortex and present evidence for the importance of the inferior parietal lobule as a predictor of time to progress from MCI to AD. The inclusion of neuropsychological performance in the final model continues to highlight the importance of using these measures in a complementary fashion." />
<meta name="DC.Contributor" content="R.S. Desikan" />
<meta name="DC.Contributor" content="H.J. Cabral" />
<meta name="DC.Contributor" content="B. Fischl" />
<meta name="DC.Contributor" content="C.R.G. Guttmann" />
<meta name="DC.Contributor" content="D. Blacker" />
<meta name="DC.Contributor" content="B.T. Hyman" />
<meta name="DC.Contributor" content="M.S. Albert" />
<meta name="DC.Contributor" content="R.J. Killiany" />
<meta name="article:published_time" content="2009-03-01" />
<meta name="article:section" content="BRAIN" />
<meta name="citation_title" content="Temporoparietal MR Imaging Measures of Atrophy in Subjects with Mild Cognitive Impairment That Predict Subsequent Diagnosis of Alzheimer Disease" />
<meta name="citation_abstract" lang="en" content="&lt;p&gt;&lt;b&gt;BACKGROUND AND PURPOSE:&lt;/b&gt; Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer disease (AD). Our goal was to determine if specific temporoparietal regions can predict the time to progress from MCI to AD.&lt;/p&gt;&lt;p&gt;&lt;b&gt;MATERIALS AND METHODS:&lt;/b&gt; MR images from 129 individuals with MCI were analyzed to identify the volume of 14 neocortical and 2 non-neocortical brain regions, comprising the temporal and parietal lobes. In addition, 3 neuropsychological test scores were included to determine whether they would provide independent information. After a mean follow-up time of 5 years, 44 of these individuals had progressed to a diagnosis of AD.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS:&lt;/b&gt; Cox proportional hazards models demonstrated significant effects for 6 MR imaging regions with the greatest differences being the following: the entorhinal cortex (hazard ratio [HR] = 0.54, &lt;i&gt;P&lt;/i&gt; &amp;lt; .001), inferior parietal lobule (hazard ratio [HR] = 0.64, &lt;i&gt;P&lt;/i&gt; &amp;lt; .005), and middle temporal gyrus (HR = 0.64, &lt;i&gt;P&lt;/i&gt; &amp;lt; .004), indicating decreased risk with larger volumes. A multivariable model showed that a combination of the entorhinal cortex (HR = 0.60, &lt;i&gt;P&lt;/i&gt; &amp;lt; .001) and the inferior parietal lobule (HR = 0.62, &lt;i&gt;P&lt;/i&gt; &amp;lt; .01) was the best predictor of time to progress to AD. A multivariable model reiterated the importance of including both MR imaging and neuropsychological variables in the final model.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS:&lt;/b&gt; These findings reaffirm the importance of the entorhinal cortex and present evidence for the importance of the inferior parietal lobule as a predictor of time to progress from MCI to AD. The inclusion of neuropsychological performance in the final model continues to highlight the importance of using these measures in a complementary fashion.&lt;/p&gt;" />
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<meta name="twitter:title" content="Temporoparietal MR Imaging Measures of Atrophy in Subjects with Mild Cognitive Impairment That Predict Subsequent Diagnosis of Alzheimer Disease" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer disease (AD). Our goal was to determine if specific temporoparietal regions can predict the time to progress from MCI to AD.

MATERIALS AND METHODS: MR images from 129 individuals with MCI were analyzed to identify the volume of 14 neocortical and 2 non-neocortical brain regions, comprising the temporal and parietal lobes. In addition, 3 neuropsychological test scores were included to determine whether they would provide independent information. After a mean follow-up time of 5 years, 44 of these individuals had progressed to a diagnosis of AD.

RESULTS: Cox proportional hazards models demonstrated significant effects for 6 MR imaging regions with the greatest differences being the following: the entorhinal cortex (hazard ratio [HR] = 0.54, P &lt; .001), inferior parietal lobule (hazard ratio [HR] = 0.64, P &lt; .005), and middle temporal gyrus (HR = 0.64, P &lt; .004), indicating decreased risk with larger volumes. A multivariable model showed that a combination of the entorhinal cortex (HR = 0.60, P &lt; .001) and the inferior parietal lobule (HR = 0.62, P &lt; .01) was the best predictor of time to progress to AD. A multivariable model reiterated the importance of including both MR imaging and neuropsychological variables in the final model.

CONCLUSIONS: These findings reaffirm the importance of the entorhinal cortex and present evidence for the importance of the inferior parietal lobule as a predictor of time to progress from MCI to AD. The inclusion of neuropsychological performance in the final model continues to highlight the importance of using these measures in a complementary fashion." />
<meta name="og-title" property="og:title" content="Temporoparietal MR Imaging Measures of Atrophy in Subjects with Mild Cognitive Impairment That Predict Subsequent Diagnosis of Alzheimer Disease" />
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<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer disease (AD). Our goal was to determine if specific temporoparietal regions can predict the time to progress from MCI to AD.

MATERIALS AND METHODS: MR images from 129 individuals with MCI were analyzed to identify the volume of 14 neocortical and 2 non-neocortical brain regions, comprising the temporal and parietal lobes. In addition, 3 neuropsychological test scores were included to determine whether they would provide independent information. After a mean follow-up time of 5 years, 44 of these individuals had progressed to a diagnosis of AD.

RESULTS: Cox proportional hazards models demonstrated significant effects for 6 MR imaging regions with the greatest differences being the following: the entorhinal cortex (hazard ratio [HR] = 0.54, P &lt; .001), inferior parietal lobule (hazard ratio [HR] = 0.64, P &lt; .005), and middle temporal gyrus (HR = 0.64, P &lt; .004), indicating decreased risk with larger volumes. A multivariable model showed that a combination of the entorhinal cortex (HR = 0.60, P &lt; .001) and the inferior parietal lobule (HR = 0.62, P &lt; .01) was the best predictor of time to progress to AD. A multivariable model reiterated the importance of including both MR imaging and neuropsychological variables in the final model.

CONCLUSIONS: These findings reaffirm the importance of the entorhinal cortex and present evidence for the importance of the inferior parietal lobule as a predictor of time to progress from MCI to AD. The inclusion of neuropsychological performance in the final model continues to highlight the importance of using these measures in a complementary fashion." />
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<div id="hw-article-author-popups-top-node-7822--8764838902" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">R.S. Desikan </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.S.%2BDesikan%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=Desikan%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.S.%2BDesikan%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">H.J. 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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div xmlns:xhtml="http://www.w3.org/1999/xhtml" class="contributors"><ol class="contributor-list" id="contrib-group-1"><li class="contributor" id="contrib-1"><span class="name">R.S. Desikan</span><a id="xref-aff-1-1" class="xref-aff" href="#aff-1">a</a>, </li><li class="contributor" id="contrib-2"><span class="name">H.J. Cabral</span><a id="xref-aff-2-1" class="xref-aff" href="#aff-2">b</a>, </li><li class="contributor" id="contrib-3"><span class="name">B. Fischl</span><a id="xref-aff-4-1" class="xref-aff" href="#aff-4">d</a><span class="xref-sep">,</span><a id="xref-aff-7-1" class="xref-aff" href="#aff-7">g</a>, </li><li class="contributor" id="contrib-4"><span class="name">C.R.G. Guttmann</span><a id="xref-aff-8-1" class="xref-aff" href="#aff-8">h</a>, </li><li class="contributor" id="contrib-5"><span class="name">D. Blacker</span><a id="xref-aff-5-1" class="xref-aff" href="#aff-5">e</a>, </li><li class="contributor" id="contrib-6"><span class="name">B.T. Hyman</span><a id="xref-aff-6-1" class="xref-aff" href="#aff-6">f</a>, </li><li class="contributor" id="contrib-7"><span class="name">M.S. Albert</span><a id="xref-aff-9-1" class="xref-aff" href="#aff-9">i</a> and </li><li class="last" id="contrib-8"><span class="name">R.J. Killiany</span><a id="xref-aff-1-2" class="xref-aff" href="#aff-1">a</a><span class="xref-sep">,</span><a id="xref-aff-3-1" class="xref-aff" href="#aff-3">c</a><span class="xref-sep">,</span><a id="xref-aff-5-2" class="xref-aff" href="#aff-5">e</a><span class="xref-sep">,</span><a id="xref-aff-8-2" class="xref-aff" href="#aff-8">h</a></li></ol><ol class="affiliation-list"><li class="aff"><a id="aff-1" name="aff-1"></a><address><sup>a</sup>Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Mass</address></li><li class="aff"><a id="aff-2" name="aff-2"></a><address><sup>b</sup>Department of Biostatistics, Boston University School of Public Health, Boston, Mass</address></li><li class="aff"><a id="aff-3" name="aff-3"></a><address><sup>c</sup>Department of Environmental Health, Boston University School of Public Health, Boston, Mass</address></li><li class="aff"><a id="aff-4" name="aff-4"></a><address><sup>d</sup>Department of Radiology, Massachusetts General Hospital, Boston, Mass</address></li><li class="aff"><a id="aff-5" name="aff-5"></a><address><sup>e</sup>Athinoula A. Martinos Center for Biomedical Imaging, Psychiatry, Massachusetts General Hospital, Boston, Mass</address></li><li class="aff"><a id="aff-6" name="aff-6"></a><address><sup>f</sup>Department of Neurology, Massachusetts General Hospital, Boston, Mass</address></li><li class="aff"><a id="aff-7" name="aff-7"></a><address><sup>g</sup>Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Mass</address></li><li class="aff"><a id="aff-8" name="aff-8"></a><address><sup>h</sup>Department of Radiology, Brigham and Women's Hospital, Boston, Mass</address></li><li class="aff"><a id="aff-9" name="aff-9"></a><address><sup>i</sup>Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Md</address></li></ol><ol class="corresp-list"><li class="corresp" id="corresp-1">Please address correspondence to Ronald J. Killiany, PhD, Department of Anatomy and Neurobiology, Center of Biomedical Imaging, Boston University School of Medicine, 700 Albany St, W701, Boston, MA 02118; e-mail: <span class="em-link"><span class="em-addr">killiany{at}bu.edu</span></span></li></ol></div></div><a href="http://ajnr-stage2.stage.highwire.org/content/30/3/532.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. 30</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 3 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">March 2009 </div></div>
      
      
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