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Shape matters: Predicting Huntington's disease using progression modelling.

Mohsen Ghofrani-Jahromi1, Susmita Saha1, Adeel Razi1

  • 1Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC 3800, Australia.

Computer Methods and Programs in Biomedicine
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PubMed
Summary
This summary is machine-generated.

Subcortical brain shape analysis reveals significant associations with Huntington's Disease (HD) progression, outperforming traditional volumetric measures. This novel approach enhances prediction accuracy for clinical trials in persons with HD.

Keywords:
BiomarkersClinical TrialsDeep LearningHuntington’s DiseaseNeuroimagingSubcortical Shape

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biomedical Data Science

Background:

  • Current models for Huntington's Disease (HD) progression in clinical trials do not utilize detailed striatal morphometry (shape information).
  • This limits the precision of participant selection and treatment outcome assessment in clinical research for persons with HD (PwHD).

Purpose of the Study:

  • To investigate the utility of subcortical brain shape descriptors in modeling HD progression.
  • To assess if shape information can improve predictive models for disease biomarkers compared to volumetric data.

Main Methods:

  • Validated a deep neural network to extract shape descriptors from subcortical structures in 2,932 brain scans from 615 PwHD across three longitudinal datasets.
  • Trained a conditional generative model using shape descriptors, volumetric, genetic, and clinical data to predict disease progression biomarkers.

Main Results:

  • Anatomical shapes of key subcortical structures (putamen, lateral ventricle, pallidum, caudate, thalamus, accumbens) strongly correlated with HD progression.
  • Aggregated shape descriptors via principal component analysis showed higher correlation with disease stage (ρ = 0.72) than volumetric measurements (ρ = 0.45).
  • Incorporating subcortical shape into the generative model significantly improved predictive performance over models using only brain volumes.

Conclusions:

  • Subcortical brain shape is a significant correlate of HD progression and captures finer within-stage variability.
  • Shape-based models enhance the predictability of HD biomarkers, offering potential for more precise clinical trial participant selection.
  • This approach could lead to more objective post-intervention assessments of treatment efficacy in future HD clinical trials.