Automated structural imaging analysis detects premanifest Huntington's disease neurodegeneration within 1 year
Identifieur interne : 001850 ( Main/Merge ); précédent : 001849; suivant : 001851Automated structural imaging analysis detects premanifest Huntington's disease neurodegeneration within 1 year
Auteurs : D. S. Adnan Majid [États-Unis] ; Diederick Stoffers [Pays-Bas] ; Sarah Sheldon [États-Unis] ; Samar Hamza [États-Unis] ; Wesley K. Thompson [États-Unis] ; Jody Goldstein [États-Unis] ; Jody Corey-Bloom [États-Unis] ; Adam R. Aron [États-Unis]Source :
- Movement Disorders [ 0885-3185 ] ; 2011-07.
English descriptors
- KwdEn :
- Adult, Brain (pathology), Case-Control Studies, Cognition Disorders (diagnosis), Cognition Disorders (etiology), Disease Progression, Female, Humans, Huntington Disease (complications), Huntington Disease (diagnosis), Image Processing, Computer-Assisted, Longitudinal Studies, MRI biomarker, Magnetic Resonance Imaging (methods), Male, Middle Aged, Nerve Degeneration (diagnosis), Nerve Degeneration (etiology), longitudinal atrophy, premanifest HD.
- MESH :
- complications : Huntington Disease.
- diagnosis : Cognition Disorders, Huntington Disease, Nerve Degeneration.
- etiology : Cognition Disorders, Nerve Degeneration.
- methods : Magnetic Resonance Imaging.
- pathology : Brain.
- Adult, Case-Control Studies, Disease Progression, Female, Humans, Image Processing, Computer-Assisted, Longitudinal Studies, Male, Middle Aged.
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
Url:
- https://api.istex.fr/document/513EB143EAF43F3EE6C748913627587EA86F73BD/fulltext/pdf
- http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3136652
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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<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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<PMC><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Automated structural imaging analysis detects premanifest Huntington’s disease neurodegeneration within one year</title>
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<author><name sortKey="Goldstein, Jody" sort="Goldstein, Jody" uniqKey="Goldstein J" first="Jody" last="Goldstein">Jody Goldstein</name>
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<author><name sortKey="Thompson, Wesley K" sort="Thompson, Wesley K" uniqKey="Thompson W" first="Wesley K." last="Thompson">Wesley K. Thompson</name>
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<author><name sortKey="Aron, Adam R" sort="Aron, Adam R" uniqKey="Aron A" first="Adam R." last="Aron">Adam R. Aron</name>
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<series><title level="j">Movement disorders : official journal of the Movement Disorder Society</title>
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<term>Brain (pathology)</term>
<term>Case-Control Studies</term>
<term>Cognition Disorders (diagnosis)</term>
<term>Cognition Disorders (etiology)</term>
<term>Disease Progression</term>
<term>Female</term>
<term>Humans</term>
<term>Huntington Disease (complications)</term>
<term>Huntington Disease (diagnosis)</term>
<term>Image Processing, Computer-Assisted</term>
<term>Longitudinal Studies</term>
<term>Magnetic Resonance Imaging (methods)</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Nerve Degeneration (diagnosis)</term>
<term>Nerve Degeneration (etiology)</term>
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<term>Huntington Disease</term>
<term>Nerve Degeneration</term>
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<keywords scheme="MESH" qualifier="etiology" xml:lang="en"><term>Cognition Disorders</term>
<term>Nerve Degeneration</term>
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<term>Image Processing, Computer-Assisted</term>
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<front><div type="abstract" xml:lang="en"><sec id="S1"><title>Background</title>
<p id="P1">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 and controls.</p>
</sec>
<sec sec-type="methods" id="S2"><title>Methods</title>
<p id="P2">Using a one-year longitudinal design, we analyzedT<sub>1</sub>
-weighted structural scans in 35 preHD individuals and 22 age-matched controls. We used the SIENA software tool (Structural Image Evaluation, using Normalization, of Atrophy) 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.</p>
</sec>
<sec id="S3"><title>Results</title>
<p id="P3">We found significantly greater yearly atrophy in preHD vs. 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 over three 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 74close-to-onset individuals in each arm (treatment vs. placebo) to detect a 50% slowing in yearly atrophy with80% power.</p>
</sec>
<sec id="S4"><title>Conclusions</title>
<p id="P4">Automated whole-brain analysis of structural MRI can reliably detect preHD disease progression over one 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.</p>
</sec>
</div>
</front>
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