Automated structural imaging analysis detects premanifest Huntington’s disease neurodegeneration within one year
Identifieur interne : 003133 ( Ncbi/Curation ); précédent : 003132; suivant : 003134Automated structural imaging analysis detects premanifest Huntington’s disease neurodegeneration within one 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 : official journal of the Movement Disorder Society [ 0885-3185 ] ; 2011.
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, Magnetic Resonance Imaging (methods), Male, Middle Aged, Nerve Degeneration (diagnosis), Nerve Degeneration (etiology).
- 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 and controls.
Using a one-year longitudinal design, we analyzedT1-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.
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.
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.
Url:
DOI: 10.1002/mds.23656
PubMed: 21484871
PubMed Central: 3136652
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PMC:3136652Le document en format XML
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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>Case-Control Studies</term>
<term>Disease Progression</term>
<term>Female</term>
<term>Humans</term>
<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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