Automated structural imaging analysis detects premanifest Huntington's disease neurodegeneration within 1 year
Identifieur interne : 000094 ( Istex/Corpus ); précédent : 000093; suivant : 000095Automated structural imaging analysis detects premanifest Huntington's disease neurodegeneration within 1 year
Auteurs : D. S. Adnan Majid ; Diederick Stoffers ; Sarah Sheldon ; Samar Hamza ; Wesley K. Thompson ; Jody Goldstein ; Jody Corey-Bloom ; Adam R. AronSource :
- Movement Disorders [ 0885-3185 ] ; 2011-07.
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- KwdEn :
Abstract
Intense efforts are underway to evaluate neuroimaging measures as biomarkers for neurodegeneration in premanifest Huntington's disease (preHD). We used a completely automated longitudinal analysis method to compare structural scans in preHD individuals and controls. Using a 1‐year longitudinal design, we analyzed T1‐weighted structural scans in 35 preHD individuals and 22 age‐matched controls. We used the SIENA (Structural Image Evaluation, using Normalization, of Atrophy) software tool to yield overall percentage brain volume change (PBVC) and voxel‐level changes in atrophy. We calculated sample sizes for a hypothetical disease‐modifying (neuroprotection) study. We found significantly greater yearly atrophy in preHD individuals versus controls (mean PBVC controls, −0.149%; preHD, −0.388%; P = .031, Cohen's d = .617). For a preHD subgroup closest to disease onset, yearly atrophy was more than 3 times that of controls (mean PBVC close‐to‐onset preHD, −0.510%; P = .019, Cohen's d = .920). This atrophy was evident at the voxel level in periventricular regions, consistent with well‐established preHD basal ganglia atrophy. We estimated that a neuroprotection study using SIENA would only need 74 close‐to‐onset individuals in each arm (treatment vs placebo) to detect a 50% slowing in yearly atrophy with 80% power. Automated whole‐brain analysis of structural MRI can reliably detect preHD disease progression in 1 year. These results were attained with a readily available imaging analysis tool, SIENA, which is observer independent, automated, and robust with respect to image quality, slice thickness, and different pulse sequences. This MRI biomarker approach could be used to evaluate neuroprotection in preHD. © 2011 Movement Disorder Society
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DOI: 10.1002/mds.23656
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<front><div type="abstract" xml:lang="en">Intense efforts are underway to evaluate neuroimaging measures as biomarkers for neurodegeneration in premanifest Huntington's disease (preHD). We used a completely automated longitudinal analysis method to compare structural scans in preHD individuals and controls. Using a 1‐year longitudinal design, we analyzed T1‐weighted structural scans in 35 preHD individuals and 22 age‐matched controls. We used the SIENA (Structural Image Evaluation, using Normalization, of Atrophy) software tool to yield overall percentage brain volume change (PBVC) and voxel‐level changes in atrophy. We calculated sample sizes for a hypothetical disease‐modifying (neuroprotection) study. We found significantly greater yearly atrophy in preHD individuals versus controls (mean PBVC controls, −0.149%; preHD, −0.388%; P = .031, Cohen's d = .617). For a preHD subgroup closest to disease onset, yearly atrophy was more than 3 times that of controls (mean PBVC close‐to‐onset preHD, −0.510%; P = .019, Cohen's d = .920). This atrophy was evident at the voxel level in periventricular regions, consistent with well‐established preHD basal ganglia atrophy. We estimated that a neuroprotection study using SIENA would only need 74 close‐to‐onset individuals in each arm (treatment vs placebo) to detect a 50% slowing in yearly atrophy with 80% power. Automated whole‐brain analysis of structural MRI can reliably detect preHD disease progression in 1 year. These results were attained with a readily available imaging analysis tool, SIENA, which is observer independent, automated, and robust with respect to image quality, slice thickness, and different pulse sequences. This MRI biomarker approach could be used to evaluate neuroprotection in preHD. © 2011 Movement Disorder Society</div>
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<notesStmt><note type="content">*Relevant conflicts of interest/financial disclosures: This study was supported by the CHDI Foundation. Jody Corey‐Bloom has received research support from the Huntington's Study Group, and Adam R. Aron receives research support from CHDI. Full financial disclosures and author roles may be found in the online version of this article.</note>
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<copyright ownership="thirdParty">Copyright © 2011 Movement Disorder Society</copyright>
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<correspondenceTo>Department of Psychology, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093‐0109, USA</correspondenceTo>
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<titleGroup><title type="main" xml:lang="en">Automated structural imaging analysis detects premanifest Huntington's disease neurodegeneration within 1 year<link href="#fn5"></link>
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<title type="short" xml:lang="en">Longitudinal Biomarker in Premanifest HD</title>
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<creators><creator xml:id="au1" creatorRole="author" affiliationRef="#af1 #af2"><personName><givenNames>D.S. Adnan</givenNames>
<familyName>Majid</familyName>
<degrees>BS</degrees>
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<creator xml:id="au2" creatorRole="author" affiliationRef="#af3"><personName><givenNames>Diederick</givenNames>
<familyName>Stoffers</familyName>
<degrees>PhD</degrees>
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<creator xml:id="au3" creatorRole="author" affiliationRef="#af4"><personName><givenNames>Sarah</givenNames>
<familyName>Sheldon</familyName>
<degrees>BS</degrees>
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<creator xml:id="au4" creatorRole="author" affiliationRef="#af4"><personName><givenNames>Samar</givenNames>
<familyName>Hamza</familyName>
<degrees>BS</degrees>
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<creator xml:id="au5" creatorRole="author" affiliationRef="#af5"><personName><givenNames>Wesley K.</givenNames>
<familyName>Thompson</familyName>
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<creator xml:id="au6" creatorRole="author" affiliationRef="#af4"><personName><givenNames>Jody</givenNames>
<familyName>Goldstein</familyName>
<degrees>BS</degrees>
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<creator xml:id="au7" creatorRole="author" affiliationRef="#af4"><personName><givenNames>Jody</givenNames>
<familyName>Corey‐Bloom</familyName>
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<creator xml:id="au8" creatorRole="author" affiliationRef="#af1 #af2" corresponding="yes"><personName><givenNames>Adam R.</givenNames>
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<affiliationGroup><affiliation xml:id="af1" countryCode="US" type="organization"><unparsedAffiliation>Department of Psychology, University of California, San Diego (UCSD), San Diego, California, USA</unparsedAffiliation>
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<affiliation xml:id="af4" countryCode="US" type="organization"><unparsedAffiliation>Department of Neurosciences, University of California, San Diego (UCSD), San Diego, California, USA</unparsedAffiliation>
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<affiliation xml:id="af5" countryCode="US" type="organization"><unparsedAffiliation>Department of Psychiatry, University of California, San Diego (UCSD), San Diego, California, USA</unparsedAffiliation>
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<keywordGroup xml:lang="en" type="author"><keyword xml:id="kwd1">MRI biomarker</keyword>
<keyword xml:id="kwd2">premanifest HD</keyword>
<keyword xml:id="kwd3">longitudinal atrophy</keyword>
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<abstractGroup><abstract type="main" xml:lang="en"><title type="main">Abstract</title>
<p>Intense efforts are underway to evaluate neuroimaging measures as biomarkers for neurodegeneration in premanifest Huntington's disease (preHD). We used a completely automated longitudinal analysis method to compare structural scans in preHD individuals and controls. Using a 1‐year longitudinal design, we analyzed T<sub>1</sub>
‐weighted structural scans in 35 preHD individuals and 22 age‐matched controls. We used the SIENA (Structural Image Evaluation, using Normalization, of Atrophy) software tool to yield overall percentage brain volume change (PBVC) and voxel‐level changes in atrophy. We calculated sample sizes for a hypothetical disease‐modifying (neuroprotection) study. We found significantly greater yearly atrophy in preHD individuals versus controls (mean PBVC controls, −0.149%; preHD, −0.388%; <i>P</i>
= .031, Cohen's d = .617). For a preHD subgroup closest to disease onset, yearly atrophy was more than 3 times that of controls (mean PBVC close‐to‐onset preHD, −0.510%; <i>P</i>
= .019, Cohen's d = .920). This atrophy was evident at the voxel level in periventricular regions, consistent with well‐established preHD basal ganglia atrophy. We estimated that a neuroprotection study using SIENA would only need 74 close‐to‐onset individuals in each arm (treatment vs placebo) to detect a 50% slowing in yearly atrophy with 80% power. Automated whole‐brain analysis of structural MRI can reliably detect preHD disease progression in 1 year. These results were attained with a readily available imaging analysis tool, SIENA, which is observer independent, automated, and robust with respect to image quality, slice thickness, and different pulse sequences. This MRI biomarker approach could be used to evaluate neuroprotection in preHD. © 2011 Movement Disorder Society</p>
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<noteGroup><note xml:id="fn5"><p><b>Relevant conflicts of interest/financial disclosures:</b>
This study was supported by the CHDI Foundation. Jody Corey‐Bloom has received research support from the Huntington's Study Group, and Adam R. Aron receives research support from CHDI. Full financial disclosures and author roles may be found in the online version of this article.</p>
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<titleInfo type="abbreviated" lang="en"><title>Longitudinal Biomarker in Premanifest HD</title>
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<titleInfo type="alternative" contentType="CDATA" lang="en"><title>Automated structural imaging analysis detects premanifest Huntington's disease neurodegeneration within 1 year</title>
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<abstract lang="en">Intense efforts are underway to evaluate neuroimaging measures as biomarkers for neurodegeneration in premanifest Huntington's disease (preHD). We used a completely automated longitudinal analysis method to compare structural scans in preHD individuals and controls. Using a 1‐year longitudinal design, we analyzed T1‐weighted structural scans in 35 preHD individuals and 22 age‐matched controls. We used the SIENA (Structural Image Evaluation, using Normalization, of Atrophy) software tool to yield overall percentage brain volume change (PBVC) and voxel‐level changes in atrophy. We calculated sample sizes for a hypothetical disease‐modifying (neuroprotection) study. We found significantly greater yearly atrophy in preHD individuals versus controls (mean PBVC controls, −0.149%; preHD, −0.388%; P = .031, Cohen's d = .617). For a preHD subgroup closest to disease onset, yearly atrophy was more than 3 times that of controls (mean PBVC close‐to‐onset preHD, −0.510%; P = .019, Cohen's d = .920). This atrophy was evident at the voxel level in periventricular regions, consistent with well‐established preHD basal ganglia atrophy. We estimated that a neuroprotection study using SIENA would only need 74 close‐to‐onset individuals in each arm (treatment vs placebo) to detect a 50% slowing in yearly atrophy with 80% power. Automated whole‐brain analysis of structural MRI can reliably detect preHD disease progression in 1 year. These results were attained with a readily available imaging analysis tool, SIENA, which is observer independent, automated, and robust with respect to image quality, slice thickness, and different pulse sequences. This MRI biomarker approach could be used to evaluate neuroprotection in preHD. © 2011 Movement Disorder Society</abstract>
<note type="content">*Relevant conflicts of interest/financial disclosures: This study was supported by the CHDI Foundation. Jody Corey‐Bloom has received research support from the Huntington's Study Group, and Adam R. Aron receives research support from CHDI. Full financial disclosures and author roles may be found in the online version of this article.</note>
<subject lang="en"><genre>Keywords</genre>
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<identifier type="ISSN">0885-3185</identifier>
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