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Automated structural imaging analysis detects premanifest Huntington's disease neurodegeneration within 1 year

Authors

  • D.S. Adnan Majid BS,

    1. Department of Psychology, University of California, San Diego (UCSD), San Diego, California, USA
    2. Neurosciences Graduate Program, University of California, San Diego (UCSD), San Diego, California, USA
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  • Diederick Stoffers PhD,

    1. Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
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  • Sarah Sheldon BS,

    1. Department of Neurosciences, University of California, San Diego (UCSD), San Diego, California, USA
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  • Samar Hamza BS,

    1. Department of Neurosciences, University of California, San Diego (UCSD), San Diego, California, USA
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  • Wesley K. Thompson PhD,

    1. Department of Psychiatry, University of California, San Diego (UCSD), San Diego, California, USA
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  • Jody Goldstein BS,

    1. Department of Neurosciences, University of California, San Diego (UCSD), San Diego, California, USA
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  • Jody Corey-Bloom MD, PhD,

    1. Department of Neurosciences, University of California, San Diego (UCSD), San Diego, California, USA
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  • Adam R. Aron PhD

    Corresponding author
    1. Department of Psychology, University of California, San Diego (UCSD), San Diego, California, USA
    2. Neurosciences Graduate Program, University of California, San Diego (UCSD), San Diego, California, USA
    • Department of Psychology, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0109, USA
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  • 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.

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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