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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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Updated: Jan 8, 2026

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
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Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451

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Alzheimer's Imaging Consortium.

Sarah F Ackley1, Jason R Gantenberg1, Margo B Heston2

  • 1Brown University, Providence, RI, USA.

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

Amyloid positron emission tomography (PET) imaging in Alzheimer's disease (AD) shows no evidence of a plateau in amyloid accumulation, challenging existing temporal models. Further research with advanced quantitative tools will refine our understanding of AD biomarker evolution.

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Area of Science:

  • Neuroimaging and Biomarker Research
  • Alzheimer's Disease Pathogenesis

Background:

  • Longitudinal amyloid positron emission tomography (PET) data and advanced analysis methods are enabling empirical determination of amyloid trajectories in Alzheimer's disease (AD).
  • Previous models suggesting a physiologic ceiling for amyloid accumulation are not consistently supported by newer analyses, particularly those using nonparametric algorithms like sampled iterative local approximation (SILA).

Purpose of the Study:

  • To empirically evaluate the presence or absence of a physiologic ceiling for amyloid accumulation using longitudinal neuroimaging data.
  • To compare observed amyloid trajectories with those predicted by established models of AD pathogenesis.

Main Methods:

  • Simulated amyloid trajectories using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Jack model.
  • Incorporated empirically informed stochastic parameters including age at first PET scan, number of scans, and inter-scan intervals.
  • Estimated age of amyloid positivity onset based on published literature, assuming interindividual variability in accumulation rates and ceiling effects.

Main Results:

  • Reimplementation of SILA on ADNI data indicated an apparent lack of a physiologic ceiling for amyloid accumulation.
  • Simulations incorporating a physiologic ceiling produced trajectories qualitatively different from those observed in ADNI data.
  • Observed ADNI data lacked the characteristic density increase at high Centiloids indicative of approaching a ceiling.

Conclusions:

  • Longitudinal amyloid PET data, analyzed with SILA, suggest no physiologic ceiling for amyloid accumulation in Alzheimer's disease.
  • Findings contrast with prior studies that inferred a ceiling based on clinical stage and baseline amyloid levels.
  • Current influential models of AD biomarker evolution do not generate trajectories consistent with empirical ADNI data, highlighting the need for further model refinement.