Cognition-Weighted Multimodal MRI and FDG-PET Features for Classification of Mild Cognitive Impairment, Alzheimer’s Disease, and Frontotemporal Dementia

Background: Differentiating between mild cognitive impairment (MCI), Alzheimer’s disease (AD), and frontotemporal dementia (FTD) subtypes remains a clinical challenge due to overlapping cognitive symptoms, structural atrophy, and metabolic patterns, especially in the early stages. Multimodal classification approaches integrating neuroimaging and cognitive scores may offer early, accurate characterization and, subsequently, improved diagnostic precision. Methods: In this study, we included 100 participants (50 AD, 30 FTD [14 behavioral variant FTD (bvFTD) and 16 primary progressive aphasia (PPA)], and 20 MCI) who underwent simultaneous structural MRI and FDG-PET imaging. Cortical thickness (CTH) from anatomical MRI and standardized uptake values from FDG-PET were extracted using FreeSurfer and PETSurfer pipelines, respectively. CTH and FDG-PET features were combined into a single vector through a logistic weighting function derived from ACE-III scores, capturing the progressive nature of cognitive decline in dementia. A Naive Bayes classifier was then trained to differentiate diagnostic groups based on the merged features. Results: The model achieved classification accuracies of 83% for MCI vs. dementia (AD + FTD), 85% for MCI vs. FTD, 87% for MCI vs. PPA, 71% for MCI vs. bvFTD, 64% for MCI vs. AD, and 69% for AD vs. FTD. The overall classification accuracy was 68%, with the highest discriminative performance observed in separating MCI from FTD subtypes. Conclusions: This study presents a novel, cognition-weighted multimodal approach combining structural and metabolic imaging to enhance the classification of neurodegenerative syndromes. Findings from this study underscore the potential of integrating ACE-III scores with neuroimaging biomarkers to accurately characterize and differentiate early-stage MCI, AD, and FTD variants.