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Basic Science and Pathogenesis.

Kazi Noshin1, Bojian Hou2, Mary Regina Bolan3

  • 1University of Virginia, Charlottesville, VA, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 25, 2025
PubMed
Summary
This summary is machine-generated.

We developed an interpretable deep learning model, NADCSM, for Alzheimer's Disease (AD) research. It identifies key brain regions influencing AD progression, enhancing biomarker discovery and precision medicine for neurodegenerative diseases.

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Medical Imaging

Background:

  • Identifying brain regions critical to Alzheimer's Disease (AD) progression is vital for understanding pathogenesis and developing targeted therapies.
  • Deep learning models offer advanced survival analysis for AD but often lack clinical interpretability due to their
  • Our study introduces the Neural Additive Deep Clustering Survival Machines (NADCSM) framework, designed to provide interpretable insights into brain region contributions to AD progression while maintaining high predictive accuracy.

Purpose of the Study:

  • To develop an interpretable deep learning model for Alzheimer's Disease (AD) research.
  • To identify specific brain regions that significantly influence the progression of AD.
  • To bridge the gap between predictive performance and clinical utility in AD survival analysis.

Main Methods:

  • Utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including AV45 Florbetapir PET imaging.
  • Employed the NADCSM framework, which models survival times using Weibull distributions and incorporates Neural Additive Models (NAMs) for interpretability.
  • Evaluated model performance using Concordance Index (C Index) for prediction and LogRank statistic for survival curve separation and clustering.

Main Results:

  • NADCSM demonstrated competitive predictive accuracy, with a C Index of 0.7772 ± 0.0236, closely following DCSM (0.7789 ± 0.0193).
  • The LogRank statistic for NADCSM (317.84 ± 31.89) indicated strong performance in survival curve separation and clustering, comparable to DCSM (317.84 ± 31.89).
  • NADCSM identified key brain regions, such as Fusiform (Left) and Cerebelum_Crus (Right), revealing interpretable relationships between amyloid burden and AD progression.

Conclusions:

  • The NADCSM framework offers an interpretable approach to risk prediction in Alzheimer's Disease and Related Dementias (ADRD).
  • It successfully uncovers significant features and explains their impact on ADRD progression, enhancing transparency in predictive models.
  • This interpretability can accelerate precision medicine efforts and deepen the understanding of ADRD pathogenesis.