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Basic Science and Pathogenesis.

Alexei Taylor1, Alexandra Sahl1, Hansoo Chang1

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This study introduces a new framework to define Alzheimer's disease (AD) subtypes using postmortem data to guide clustering. This approach improves subtype prediction accuracy compared to traditional methods.

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Area of Science:

  • Neuroscience
  • Biomedical Imaging
  • Computational Biology

Background:

  • Alzheimer's disease (AD) cognitive decline correlates with tau neuropathology, which spreads through the brain in predictable patterns.
  • Atypical tau patterns and challenges in defining AD subtypes hinder individual-level comparison and understanding of distinct pathological features.
  • Existing methods for identifying AD subtypes using in-vivo imaging data have limitations in definition and comparison.

Purpose of the Study:

  • To propose a novel framework for interpreting AD subtype replicability by integrating hypothesis-driven postmortem findings with data-driven clustering.
  • To address challenges in defining and comparing AD subtypes, particularly concerning the spatiotemporal relationship between tau pathology and gray matter atrophy.
  • To develop a method that aids in the consistent and stable assignment of individuals to AD subtypes.

Main Methods:

  • Utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
  • Computed AD subtype likelihood for each participant based on hypothesis-driven protocols.
  • Employed semi-supervised clustering, incorporating subtype likelihood to cluster ADNI data, and compared stability with unsupervised methods using nested cross-validation.
  • Evaluated clustering performance using cross-sectional tau features (n=222) and atrophy-related features (n=527).

Main Results:

  • Semi-supervised clustering demonstrated consistent subtype prediction on a held-out test set (Adjusted Rand Index: 0.57 for tau features, 0.12 for atrophy features).
  • Unsupervised clustering methods (hierarchical, k-means) yielded chance-level results, highlighting the limitations of purely data-driven approaches.
  • The proposed framework showed improved performance in predicting AD subtypes based on tau and atrophy features.

Conclusions:

  • Relying solely on unsupervised clustering for AD subtype interpretation carries significant risks.
  • A framework combining hypothesis-driven definitions with data-driven clustering is recommended for defining, training, and evaluating AD subtypes.
  • This approach enhances the reliability and interpretability of AD subtype identification, grounded in established neuropathological findings.