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Unsupervised [

Giulia Quattrini1,2, Clarissa Ferrari3,4, Michela Pievani1

  • 1Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125, Brescia, Italy.

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|June 5, 2023
PubMed
Summary
This summary is machine-generated.

This study developed data-driven cutoffs for tau PET positivity in Alzheimer's disease using Gaussian mixture models. These new thresholds improve accuracy in detecting tau pathology compared to existing methods.

Keywords:
Alzheimer’s diseaseCutoffGaussian mixture modelTau PETTau positivity[18F]Flortaucipir

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Area of Science:

  • Neurology
  • Radiology
  • Biostatistics

Background:

  • Establishing accurate tau positron emission tomography (PET) cutoffs is crucial for Alzheimer's disease (AD) diagnosis.
  • Existing [18F]Flortaucipir cutoffs lack data-driven validation, potentially impacting diagnostic reliability.

Purpose of the Study:

  • To establish and validate unsupervised, data-driven cutoffs for tau PET positivity (T+) in Alzheimer's disease.
  • To apply Gaussian mixture models (GMM) for determining reliable [18F]Flortaucipir thresholds.

Main Methods:

  • Utilized Gaussian mixture models (GMM) to derive unsupervised T+ cutoffs from the temporal meta-ROI.
  • Included amyloid-negative cognitively normal (CN) and amyloid-positive AD-related dementia (ADRD) subjects from the ADNI cohort.
  • Validated GMM-based cutoffs using independent ADNI and Geneva Memory Clinic (GMC) cohorts, comparing against published cutoffs and visual ratings.

Main Results:

  • GMM-based cutoffs classified fewer subjects as T+ compared to existing methods, particularly in amyloid-negative CN and amyloid-positive CN groups.
  • Demonstrated higher agreement with visual rating (ICC=0.91) compared to previously published cutoffs (ICC<0.62).

Conclusions:

  • Provided reliable, data-driven [18F]Flortaucipir cutoffs for in vivo tau PET positivity detection in Alzheimer's disease.
  • These validated cutoffs can aid in selecting appropriate participants for clinical trials and research studies.