Predicting MCI progression with FDG-PET and cognitive scores: a longitudinal study

Area of Science:

• Neuroimaging
• Biostatistics
• Gerontology

Background:

• Mild cognitive impairment (MCI) represents a transitional stage between normal aging and dementia.
• Understanding MCI progression is crucial for developing preventative strategies against Alzheimer's disease (AD).
• 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) is a valuable tool for assessing cerebral glucose metabolism.

Purpose of the Study:

• To develop and evaluate a classification framework for predicting MCI progression.
• To utilize both baseline and longitudinal FDG-PET scans alongside cognitive scores for MCI prediction.
• To differentiate between progressive MCI (pMCI) and stable MCI (sMCI) patients.

Main Methods:

• PET image normalization and registration to the Brainnetome Atlas (BNA).
• Extraction of static (metabolic intensity) and dynamic (intensity variation over time) features from FDG-PET scans.
• Inclusion of Mini-Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale-Cognitive (ADAS-cog) scores as cognitive features.
• Feature selection using F-score and classification using Support Vector Machine (SVM) with an RBF kernel.

Main Results:

• Dynamic features achieved higher classification accuracy (88.61%) compared to static features (78.48%).
• The combination of cognitive and dynamic features improved classification specificity to 95.65% and Area Under the Curve (AUC) to 0.9308.
• Dynamic features demonstrated superior performance in longitudinal MCI prediction.

Conclusions:

• Dynamic FDG-PET features are more informative for longitudinal MCI prediction.
• Integrating dynamic features with cognitive scores enhances classification performance, particularly in specificity and AUC.
• These findings offer potential for predicting disease trajectories and clinical changes in individuals with MCI.