Movement Disorders (revue)

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Basal Ganglia Atrophy in Prodromal Huntington's Disease is Detectable Over One Year Using Automated Segmentation

Identifieur interne : 001897 ( Main/Exploration ); précédent : 001896; suivant : 001898

Basal Ganglia Atrophy in Prodromal Huntington's Disease is Detectable Over One Year Using Automated Segmentation

Auteurs : D. S. Adnan Majid [États-Unis] ; Adam R. Aron [États-Unis] ; Wesley Thompson [États-Unis] ; Sarah Sheldon [États-Unis] ; Samar Hamza [États-Unis] ; Diederick Stoffers [Pays-Bas] ; Dominic Holland [États-Unis] ; Jody Goldstein [États-Unis] ; Jody Corey-Bloom [États-Unis] ; Anders M. Dale [États-Unis]

Source :

RBID : Pascal:12-0051312

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English descriptors

Abstract

Future clinical trials of neuroprotection in prodromal Huntington's (known as preHD) will require sensitive in vivo imaging biomarkers to track disease progression over the shortest period. Since basal ganglia atrophy is the most prominent structural characteristic of Huntington's pathology, systematic assessment of longitudinal subcortical atrophy holds great potential for future biomarker development. We studied 36 preHD and 22 age-matched controls using a novel method to quantify regional change from T1-weighted structural images acquired 1 year apart. We assessed cross-sectional volume differences and longitudinal volumetric change in 7 subcortical structures- the accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. At baseline, accumbens, caudate, pallidum, and putamen volumes were reduced in preHD versus controls (all P <.01). Longitudinally, atrophy was greater in preHD than controls in the caudate, pallidum, and putamen (all P < .01). Each structure showed a large between-group effect size, especially the pallidum where Cohen's d was 1.21. Using pallidal atrophy as a biomarker, we estimate that a hypothetical 1-year neuroprotection study would require only 35 preHD per arm to detect a 50% slowing in atrophy and only 138 preHD per arm to detect a 25% slowing in atrophy. The effect sizes calculated for preHD basal ganglia atrophy over 1 year are some of the largest reported to date. Consequently, this translates to strikingly small sample size estimates that will greatly facilitate any future neuroprotection study. This underscores the utility of this automatic image segmentation and longitudinal nonlinear registration method for upcoming studies of preHD and other neurodegenerative disorders.


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Le document en format XML

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<div type="abstract" xml:lang="en">Future clinical trials of neuroprotection in prodromal Huntington's (known as preHD) will require sensitive in vivo imaging biomarkers to track disease progression over the shortest period. Since basal ganglia atrophy is the most prominent structural characteristic of Huntington's pathology, systematic assessment of longitudinal subcortical atrophy holds great potential for future biomarker development. We studied 36 preHD and 22 age-matched controls using a novel method to quantify regional change from T
<sub>1</sub>
-weighted structural images acquired 1 year apart. We assessed cross-sectional volume differences and longitudinal volumetric change in 7 subcortical structures- the accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. At baseline, accumbens, caudate, pallidum, and putamen volumes were reduced in preHD versus controls (all P <.01). Longitudinally, atrophy was greater in preHD than controls in the caudate, pallidum, and putamen (all P < .01). Each structure showed a large between-group effect size, especially the pallidum where Cohen's d was 1.21. Using pallidal atrophy as a biomarker, we estimate that a hypothetical 1-year neuroprotection study would require only 35 preHD per arm to detect a 50% slowing in atrophy and only 138 preHD per arm to detect a 25% slowing in atrophy. The effect sizes calculated for preHD basal ganglia atrophy over 1 year are some of the largest reported to date. Consequently, this translates to strikingly small sample size estimates that will greatly facilitate any future neuroprotection study. This underscores the utility of this automatic image segmentation and longitudinal nonlinear registration method for upcoming studies of preHD and other neurodegenerative disorders.</div>
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<name sortKey="Hamza, Samar" sort="Hamza, Samar" uniqKey="Hamza S" first="Samar" last="Hamza">Samar Hamza</name>
<name sortKey="Holland, Dominic" sort="Holland, Dominic" uniqKey="Holland D" first="Dominic" last="Holland">Dominic Holland</name>
<name sortKey="Sheldon, Sarah" sort="Sheldon, Sarah" uniqKey="Sheldon S" first="Sarah" last="Sheldon">Sarah Sheldon</name>
<name sortKey="Thompson, Wesley" sort="Thompson, Wesley" uniqKey="Thompson W" first="Wesley" last="Thompson">Wesley Thompson</name>
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<country name="Pays-Bas">
<region name="Hollande-Septentrionale">
<name sortKey="Stoffers, Diederick" sort="Stoffers, Diederick" uniqKey="Stoffers D" first="Diederick" last="Stoffers">Diederick Stoffers</name>
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