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<meta name="DC.Title" content="Combining MR Imaging, Positron-Emission Tomography, and CSF Biomarkers in the Diagnosis and Prognosis of Alzheimer Disease" />
<meta name="DC.Identifier" content="10.3174/ajnr.A1809" />
<meta name="DC.Date" content="2010-02-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
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Indicates open access to non-subscribers at www.ajnr.org" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Different biomarkers for AD may potentially be complementary in diagnosis and prognosis of AD. Our aim was to combine MR imaging, FDG-PET, and CSF biomarkers in the diagnostic classification and 2-year prognosis of MCI and AD, by examining the following: 1) which measures are most sensitive to diagnostic status, 2) to what extent the methods provide unique information in diagnostic classification, and 3) which measures are most predictive of clinical decline.

MATERIALS AND METHODS: ADNI baseline MR imaging, FDG-PET, and CSF data from 42 controls, 73 patients with MCI, and 38 patients with AD; and 2-year clinical follow-up data for 36 controls, 51 patients with MCI, and 25 patients with AD were analyzed. The hippocampus and entorhinal, parahippocampal, retrosplenial, precuneus, inferior parietal, supramarginal, middle temporal, lateral, and medial orbitofrontal cortices were used as regions of interest. CSF variables included Aβ42, t-tau, p-tau, and ratios of t-tau/Aβ42 and p-tau/Aβ42. Regression analyses were performed to determine the sensitivity of measures to diagnostic status as well as 2-year change in CDR-SB, MMSE, and delayed logical memory in MCI.

RESULTS: Hippocampal volume, retrosplenial thickness, and t-tau/Aβ42 uniquely predicted diagnostic group. Change in CDR-SB was best predicted by retrosplenial thickness; MMSE, by retrosplenial metabolism and thickness; and delayed logical memory, by hippocampal volume.

CONCLUSIONS: All biomarkers were sensitive to the diagnostic group. Combining MR imaging morphometry and CSF biomarkers improved diagnostic classification (controls versus AD). MR imaging morphometry and PET were largely overlapping in value for discrimination. Baseline MR imaging and PET measures were more predictive of clinical change in MCI than were CSF measures.

Aβ42
:   β amyloid 1–42
AD
:   Alzheimer disease
ADNI
:   Alzheimer's Disease Neuroimaging Initiative
AUC
:   area under the curve
B
:   B coefficient for each predictor in the regression equation
CDR-SB
:   Clinical Dementia Rating sum of boxes
Corr. Class.
:   correlation classification
CSHC
:   Center for the Study of Human Cognition
FDA
:   US Food and Drug Administration
18F-FDG
:   [18F] fluorodeoxyglucose
FDG-PET
:   fluorodeoxyglucose–positron-emission tomography
inf.
:   inferior
lat.
:   lateral
LM-del
:   delayed Logical Memory from the Wechsler Memory Scale Logical Memory II 
M
:   mean
MCI
:   mild cognitive impairment
med. orb. front.
:   medial orbital frontal
mid
:   middle
MMSE
:   Mini-Mental State Examination
MRI
:   MR imaging
NIH
:   National Institutes of Health
NC
:   healthy control
orb. front.
:   orbital frontal
p-tau
:   phosphorylated tau protein 181
parahippoc.
:   parahippocampus
PET
:   positron-emission tomography
t-tau
:   tau protein
ROC
:   receiver operating characteristics" />
<meta name="DC.Contributor" content="K.B. Walhovd" />
<meta name="DC.Contributor" content="A.M. Fjell" />
<meta name="DC.Contributor" content="J. Brewer" />
<meta name="DC.Contributor" content="L.K. McEvoy" />
<meta name="DC.Contributor" content="C. Fennema-Notestine" />
<meta name="DC.Contributor" content="D.J. Hagler" />
<meta name="DC.Contributor" content="R.G. Jennings" />
<meta name="DC.Contributor" content="D. Karow" />
<meta name="DC.Contributor" content="A.M. Dale" />
<meta name="DC.Contributor" content="the Alzheimer's Disease Neuroimaging Initiative" />
<meta name="article:published_time" content="2010-02-01" />
<meta name="article:section" content="Brain" />
<meta name="citation_title" content="Combining MR Imaging, Positron-Emission Tomography, and CSF Biomarkers in the Diagnosis and Prognosis of Alzheimer Disease" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Different biomarkers for AD may potentially be complementary in diagnosis and prognosis of AD. Our aim was to combine MR imaging, FDG-PET, and CSF biomarkers in the diagnostic classification and 2-year prognosis of MCI and AD, by examining the following: 1) which measures are most sensitive to diagnostic status, 2) to what extent the methods provide unique information in diagnostic classification, and 3) which measures are most predictive of clinical decline.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;ADNI baseline MR imaging, FDG-PET, and CSF data from 42 controls, 73 patients with MCI, and 38 patients with AD; and 2-year clinical follow-up data for 36 controls, 51 patients with MCI, and 25 patients with AD were analyzed. The hippocampus and entorhinal, parahippocampal, retrosplenial, precuneus, inferior parietal, supramarginal, middle temporal, lateral, and medial orbitofrontal cortices were used as regions of interest. CSF variables included Aβ42, t-tau, p-tau, and ratios of t-tau/Aβ42 and p-tau/Aβ42. Regression analyses were performed to determine the sensitivity of measures to diagnostic status as well as 2-year change in CDR-SB, MMSE, and delayed logical memory in MCI.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Hippocampal volume, retrosplenial thickness, and t-tau/Aβ42 uniquely predicted diagnostic group. Change in CDR-SB was best predicted by retrosplenial thickness; MMSE, by retrosplenial metabolism and thickness; and delayed logical memory, by hippocampal volume.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;All biomarkers were sensitive to the diagnostic group. Combining MR imaging morphometry and CSF biomarkers improved diagnostic classification (controls versus AD). MR imaging morphometry and PET were largely overlapping in value for discrimination. Baseline MR imaging and PET measures were more predictive of clinical change in MCI than were CSF measures.&lt;/p&gt;" />
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<meta name="twitter:title" content="Combining MR Imaging, Positron-Emission Tomography, and CSF Biomarkers in the Diagnosis and Prognosis of Alzheimer Disease" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:image" content="http://ajnr-stage2.stage.highwire.org/sites/default/files/highwire/ajnr/31/2.cover.gif" />
<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Different biomarkers for AD may potentially be complementary in diagnosis and prognosis of AD. Our aim was to combine MR imaging, FDG-PET, and CSF biomarkers in the diagnostic classification and 2-year prognosis of MCI and AD, by examining the following: 1) which measures are most sensitive to diagnostic status, 2) to what extent the methods provide unique information in diagnostic classification, and 3) which measures are most predictive of clinical decline.

MATERIALS AND METHODS: ADNI baseline MR imaging, FDG-PET, and CSF data from 42 controls, 73 patients with MCI, and 38 patients with AD; and 2-year clinical follow-up data for 36 controls, 51 patients with MCI, and 25 patients with AD were analyzed. The hippocampus and entorhinal, parahippocampal, retrosplenial, precuneus, inferior parietal, supramarginal, middle temporal, lateral, and medial orbitofrontal cortices were used as regions of interest. CSF variables included Aβ42, t-tau, p-tau, and ratios of t-tau/Aβ42 and p-tau/Aβ42. Regression analyses were performed to determine the sensitivity of measures to diagnostic status as well as 2-year change in CDR-SB, MMSE, and delayed logical memory in MCI.

RESULTS: Hippocampal volume, retrosplenial thickness, and t-tau/Aβ42 uniquely predicted diagnostic group. Change in CDR-SB was best predicted by retrosplenial thickness; MMSE, by retrosplenial metabolism and thickness; and delayed logical memory, by hippocampal volume.

CONCLUSIONS: All biomarkers were sensitive to the diagnostic group. Combining MR imaging morphometry and CSF biomarkers improved diagnostic classification (controls versus AD). MR imaging morphometry and PET were largely overlapping in value for discrimination. Baseline MR imaging and PET measures were more predictive of clinical change in MCI than were CSF measures.

Aβ42
:   β amyloid 1–42
AD
:   Alzheimer disease
ADNI
:   Alzheimer's Disease Neuroimaging Initiative
AUC
:   area under the curve
B
:   B coefficient for each predictor in the regression equation
CDR-SB
:   Clinical Dementia Rating sum of boxes
Corr. Class.
:   correlation classification
CSHC
:   Center for the Study of Human Cognition
FDA
:   US Food and Drug Administration
18F-FDG
:   [18F] fluorodeoxyglucose
FDG-PET
:   fluorodeoxyglucose–positron-emission tomography
inf.
:   inferior
lat.
:   lateral
LM-del
:   delayed Logical Memory from the Wechsler Memory Scale Logical Memory II 
M
:   mean
MCI
:   mild cognitive impairment
med. orb. front.
:   medial orbital frontal
mid
:   middle
MMSE
:   Mini-Mental State Examination
MRI
:   MR imaging
NIH
:   National Institutes of Health
NC
:   healthy control
orb. front.
:   orbital frontal
p-tau
:   phosphorylated tau protein 181
parahippoc.
:   parahippocampus
PET
:   positron-emission tomography
t-tau
:   tau protein
ROC
:   receiver operating characteristics" />
<meta name="og-title" property="og:title" content="Combining MR Imaging, Positron-Emission Tomography, and CSF Biomarkers in the Diagnosis and Prognosis of Alzheimer Disease" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/31/2/347" />
<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: Different biomarkers for AD may potentially be complementary in diagnosis and prognosis of AD. Our aim was to combine MR imaging, FDG-PET, and CSF biomarkers in the diagnostic classification and 2-year prognosis of MCI and AD, by examining the following: 1) which measures are most sensitive to diagnostic status, 2) to what extent the methods provide unique information in diagnostic classification, and 3) which measures are most predictive of clinical decline.

MATERIALS AND METHODS: ADNI baseline MR imaging, FDG-PET, and CSF data from 42 controls, 73 patients with MCI, and 38 patients with AD; and 2-year clinical follow-up data for 36 controls, 51 patients with MCI, and 25 patients with AD were analyzed. The hippocampus and entorhinal, parahippocampal, retrosplenial, precuneus, inferior parietal, supramarginal, middle temporal, lateral, and medial orbitofrontal cortices were used as regions of interest. CSF variables included Aβ42, t-tau, p-tau, and ratios of t-tau/Aβ42 and p-tau/Aβ42. Regression analyses were performed to determine the sensitivity of measures to diagnostic status as well as 2-year change in CDR-SB, MMSE, and delayed logical memory in MCI.

RESULTS: Hippocampal volume, retrosplenial thickness, and t-tau/Aβ42 uniquely predicted diagnostic group. Change in CDR-SB was best predicted by retrosplenial thickness; MMSE, by retrosplenial metabolism and thickness; and delayed logical memory, by hippocampal volume.

CONCLUSIONS: All biomarkers were sensitive to the diagnostic group. Combining MR imaging morphometry and CSF biomarkers improved diagnostic classification (controls versus AD). MR imaging morphometry and PET were largely overlapping in value for discrimination. Baseline MR imaging and PET measures were more predictive of clinical change in MCI than were CSF measures.

Aβ42
:   β amyloid 1–42
AD
:   Alzheimer disease
ADNI
:   Alzheimer's Disease Neuroimaging Initiative
AUC
:   area under the curve
B
:   B coefficient for each predictor in the regression equation
CDR-SB
:   Clinical Dementia Rating sum of boxes
Corr. Class.
:   correlation classification
CSHC
:   Center for the Study of Human Cognition
FDA
:   US Food and Drug Administration
18F-FDG
:   [18F] fluorodeoxyglucose
FDG-PET
:   fluorodeoxyglucose–positron-emission tomography
inf.
:   inferior
lat.
:   lateral
LM-del
:   delayed Logical Memory from the Wechsler Memory Scale Logical Memory II 
M
:   mean
MCI
:   mild cognitive impairment
med. orb. front.
:   medial orbital frontal
mid
:   middle
MMSE
:   Mini-Mental State Examination
MRI
:   MR imaging
NIH
:   National Institutes of Health
NC
:   healthy control
orb. front.
:   orbital frontal
p-tau
:   phosphorylated tau protein 181
parahippoc.
:   parahippocampus
PET
:   positron-emission tomography
t-tau
:   tau protein
ROC
:   receiver operating characteristics" />
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      <h1 class="highwire-cite-title" id="page-title">Combining MR Imaging, Positron-Emission Tomography, and CSF Biomarkers in the Diagnosis and Prognosis of Alzheimer Disease</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">K.B. Walhovd</span>, <span class="highwire-citation-author" data-delta="1">A.M. Fjell</span>, <span class="highwire-citation-author" data-delta="2">J. Brewer</span>, <span class="highwire-citation-author" data-delta="3">L.K. McEvoy</span>, <span class="highwire-citation-author" data-delta="4">C. Fennema-Notestine</span>, <span class="highwire-citation-author" data-delta="5">D.J. Hagler</span>, <span class="highwire-citation-author" data-delta="6">R.G. Jennings</span>, <span class="highwire-citation-author" data-delta="7">D. Karow</span>, <span class="highwire-citation-author" data-delta="8">A.M. Dale</span> and <span class="highwire-citation-author" data-delta="9"><span class="nlm-collab" data-xlink-type="simple" data-hwp-id="collab-1">the Alzheimer's Disease Neuroimaging Initiative</span></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 2010,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">31 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(2) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">347-354; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A1809 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-9747--61866549493" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">K.B. Walhovd </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.B.%2BWalhovd%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=Walhovd%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.B.%2BWalhovd%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">A.M. 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