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We directly compare structural imaging markers across the largest collective imaging HD dataset to identify a set of imaging markers robust to multicenter variation and to derive upper estimates on sample sizes for clinical trials in HD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used 1 postprocessing pipeline to retrospectively analyze T1\u2010weighted magnetic resonance imaging (MRI) scans from 624 participants at 3 time points, from the PREDICT\u2010HD, TRACK\u2010HD, and IMAGE\u2010HD studies. We used mixed effects models to adjust regional brain volumes for covariates, calculate effect sizes, and simulate possible treatment effects in disease\u2010affected anatomical regions. We used our model to estimate the statistical power of possible treatment effects for anatomical regions and clinical markers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We identified a set of common anatomical regions that have similarly large standardized effect sizes (&gt;0.5) between healthy control and premanifest HD (PreHD) groups. These included subcortical, white matter, and cortical regions and nonventricular cerebrospinal fluid (CSF). We also observed a consistent spatial distribution of effect size by region across the whole brain. We found that multicenter studies were necessary to capture treatment effect variance; for a 20% treatment effect, power of &gt;80% was achieved for the caudate (n\u2009=\u2009661), pallidum (n\u2009=\u2009687), and nonventricular CSF (n\u2009=\u2009939), and, crucially, these imaging markers provided greater power than standard clinical markers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Interpretation<\/jats:title><jats:p>Our findings provide the first cross\u2010study validation of structural imaging markers in HD, supporting the use of these measurements as endpoints for both observational studies and clinical trials. <jats:bold>ANN NEUROL 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