Investigating AD and non-AD [¹⁸F]flortaucipir distribution patterns in a memory clinic cohort

Area of Science:

• Neuroimaging
• Neurology
• Biomarkers

Background:

• Alzheimer's disease (AD) diagnosis relies on identifying amyloid (A) and tau (T) pathology using PET scans.
• Distinguishing AD from non-AD neurodegenerative conditions with distinct tauopathies is crucial.
• [18F]flortaucipir tau PET shows different topographic patterns in various neurodegenerative disorders.

Purpose of the Study:

• To determine if [18F]flortaucipir distribution patterns, analyzed by SSM/PCA, can differentiate amyloid-positive (A+) from amyloid-negative (A-) individuals.
• To explore the potential of [18F]flortaucipir PET to replace amyloid PET in AD diagnosis.

Main Methods:

• 81 subjects were analyzed: 40 with AD (A+T+), 13 with suspected non-Alzheimer's pathology (A-T+), and 28 controls (A-T-).
• Scaled subprofile modelling using principal component analysis (SSM/PCA) identified amyloid-positive (A+DP) and amyloid-negative (A-DP) patterns in [18F]flortaucipir PET.
• Machine learning models (SVM, Random Forest, MLP) predicted amyloid status using SSM/PCA similarity scores; Spearman correlations assessed biomarker associations.

Main Results:

• Two distinct disease patterns (DPs) were identified, correlating with known disease characteristics and plasma biomarkers.
• The A+DP accurately predicted AD pathology, with machine learning models correctly classifying at least 36 out of 40 AD cases.
• The A-DP showed limited ability to identify non-AD conditions, with a higher misclassification rate (best performance: 7/13).

Conclusions:

• [18F]flortaucipir PET, analyzed with SSM/PCA, provides information on both amyloid and tau status, suggesting its utility as a primary diagnostic tool for suspected AD.
• For non-AD conditions, amyloid PET scans remain recommended.
• SSM/PCA enhances the diagnostic value of tau PET imaging in memory clinics.