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A computational method for computing an Alzheimer's disease progression score; experiments and validation with the
Bruno M Jedynak1, Bo Liu2, Andrew Lang3
1Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA; Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA; Laboratoire de Mathématiques Paul Painlevé, Université des Sciences et Technologies de Lille, Villeneuve d'Ascq, France.
Neurobiology of Aging
|December 3, 2014
Summary
This study validates an Alzheimer's disease (AD) progression score model using biomarkers. The model accurately tracks disease progression, aiding in therapy assessment and biomarker ordering for AD research.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Biostatistics
Background:
- Tracking Alzheimer's disease (AD) progression is crucial for assessing therapeutic efficacy.
- Biomarker changes over time are key indicators of disease advancement.
- Existing models require robust validation for clinical application.
Purpose of the Study:
- To validate a novel Alzheimer's disease (AD) progression score model.
- To quantify AD progression using multiple biomarkers and specific assumptions.
- To assess the model's performance using real-world clinical data.
Main Methods:
- Developed an AD progression score model based on unique disease progression, individual onset age/rate, and sigmoidal biomarker behavior.
- Employed an alternating least squares optimization algorithm for model parameter fitting.
- Validated the model and optimization using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort and Monte Carlo simulations.
Main Results:
- The model demonstrated tight estimation of most global parameters.
- Biomarkers were successfully ordered based on their fit to the model.
- The Rey auditory verbal learning test (30 min delay) and hippocampal volume ratio showed strong model fit.
Conclusions:
- The validated AD progression score model effectively quantifies disease advancement.
- The model provides a reliable method for ordering biomarkers by their relevance to AD progression.
- This approach aids in understanding AD and evaluating disease-modifying therapies.
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