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Detecting Amyloid Positivity Using Morphometric Magnetic Resonance Imaging.

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This study explored using MRI scans to detect amyloid-β (Aβ) positivity for Alzheimer's disease (AD) diagnosis. Machine learning models showed potential for classifying Aβ status using brain imaging and cognitive data.

Keywords:
Alzheimer’s diseaseamyloid-βdementiadiagnostic imagingmachine learning

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Area of Science:

  • Neuroimaging
  • Alzheimer's Disease Research
  • Machine Learning in Medicine

Background:

  • Early detection of amyloid-β (Aβ) positivity is crucial for Alzheimer's disease (AD) diagnosis and treatment.
  • Current methods for Aβ detection are often costly and invasive.

Purpose of the Study:

  • To classify Aβ positivity using magnetic resonance imaging (MRI) morphometric features.
  • To evaluate classification performance in mixed (AD, MCI, CN) and cognitively impaired (AD, MCI) populations.

Main Methods:

  • Combined demographic, cognitive (MMSE), regional MRI morphometry, and graph theory (GT) features.
  • Utilized a machine learning workflow to develop Aβ+ classification models.
  • Analyzed data from 302 Aβ+ and 246 Aβ- subjects.

Main Results:

  • In an AD+MCI+CN scenario, a model with 120 features (107 GT, 12 MRI, MMSE) achieved 66.9% balanced accuracy.
  • In an AD+MCI scenario, a model with 180 MRI features achieved 70.7% balanced accuracy.
  • Both models demonstrated potential for Aβ status classification.

Conclusions:

  • Regional MRI morphometric features show potential for detecting Aβ status non-invasively.
  • Combining MRI features with cognitive data may improve classification accuracy, especially in mixed populations.
  • Further research is needed to enhance clinical applicability.