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Alzheimer's Imaging Consortium.

Leonard Pieperhoff1, Luigi Lorenzini1, Mario Tranfa1,2

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Summary
This summary is machine-generated.

Alzheimer's disease research often assumes a single amyloid-beta accumulation path. This study identified five distinct cortical patterns of amyloid-PET signal, revealing heterogeneity in Alzheimer's disease progression.

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Area of Science:

  • Neuroimaging
  • Biomarkers
  • Alzheimer's Disease Research

Background:

  • Current Alzheimer's disease (AD) research often assumes a uniform amyloid-beta (Aβ) accumulation trajectory.
  • Amyloid positron emission tomography (PET) studies indicate the existence of multiple Aβ accumulation patterns.

Purpose of the Study:

  • To characterize AD heterogeneity by analyzing amyloid-PET data using independent spatial component analysis.
  • To identify distinct spatial patterns of Aβ deposition in the brain.

Main Methods:

  • Spatial decomposition of amyloid-PET scans from two radiotracers ([18F]Flutemetamol and [18F]Florbetaben) using group-level independent component analysis (gICA).
  • Analysis included non-demented older individuals (CDR < 1, age ≥ 50) from the pan-European AMYPAD study.
  • Investigated relationships between identified components, global Aβ burden (Centiloid), and regional gray matter (GM) volumes.

Main Results:

  • Five cortical (frontal, parietal, occipital, left temporal, right temporal) and four non-cortical components of amyloid-PET signal were identified.
  • Cortical components showed strong associations with global Aβ burden (Centiloid), while non-cortical components had minimal association.
  • Cortical component loading, particularly in temporal regions, was negatively associated with GM volume, while other components showed positive associations.

Conclusions:

  • Identified five cortical and four non-cortical independent components of amyloid-PET signal with high cross-tracer consistency.
  • These components demonstrated differential relationships with gray matter volumes, highlighting AD heterogeneity.
  • The independent component analysis method effectively quantifies features capturing diverse disease processes in Alzheimer's disease.