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Updated: Jan 8, 2026

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Alzheimer's Imaging Consortium.

Theyaneshwaran Jayaprakash1, Connor Lee Cornelison1, Plamena P Powla2

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Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 23, 2025
PubMed
Summary
This summary is machine-generated.

Deep learning identified three distinct Alzheimer's disease (AD) subtypes based on tau-PET imaging. These subtypes show unique tau accumulation patterns and varying cognitive and functional connectivity deficits, offering new insights into AD heterogeneity.

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Alzheimer's disease (AD) is characterized by amyloid plaques and neurofibrillary tangles.
  • AD patients display heterogeneous tau-PET accumulation patterns across the brain.

Purpose of the Study:

  • To identify distinct population subtypes within the Alzheimer's disease (AD) spectrum.
  • To utilize a novel deep learning algorithm analyzing spatial tau-PET patterns for subtype identification.

Main Methods:

  • Analyzed tau-PET data from 318 participants (ADNI Phase 3) using a self-supervised Gaussian Mixture Model framework.
  • Identified subtypes based on tau accumulation in 68 brain regions (Desikan-Killiany atlas).
  • Validated subtypes using clinical, volumetric, genetic (APOE4), and functional connectivity data.

Main Results:

  • Identified three distinct AD subtypes (S1, S2, S3) with 93% validation accuracy.
  • Subtypes exhibited unique tau accumulation patterns: S1 (minimal), S2 (medial temporal/posterior parietal), S3 (widespread cortical).
  • Significant differences in cognitive function (memory, language) and functional connectivity were observed between subtypes.

Conclusions:

  • Tau-PET imaging reveals significant spatial heterogeneity in Alzheimer's disease (AD) pathology.
  • Deep learning models effectively identify distinct AD subtypes with multimodal differences.
  • Findings highlight the potential of advanced AI in understanding AD progression and heterogeneity.