Date of Award

Spring 5-8-2024

Author's School

McKelvey School of Engineering

Author's Department

Electrical & Systems Engineering

Degree Name

Master of Science (MS)

Degree Type



With the escalating prevalence of dementia, particularly Alzheimer's Disease (AD), the need for early and precise diagnostic techniques is rising. This study delves into the comparative efficacy of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) and T1-weighted Magnetic Resonance Imaging (MRI) in diagnosing AD, where the integration of multimodal models is becoming a trend. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we employed linear Support Vector Machines (SVM) to assess the diagnostic potential of these modalities, both individually and in combination, within the AD continuum. Our analysis, under the A/T/N framework's 'N' category, reveals that FDG-PET consistently outperforms T1w-MRI across various stages of cognitive impairment. Contrary to expectations and previous studies that suggested enhanced diagnostic accuracy through the fusion of neuroimaging modalities—including CSF markers—our findings do not demonstrate a significant improvement in diagnostic performance from combining FDG-PET and MRI data. This outcome aligns with Narazani et al. (2022), challenging the prevailing assumption about the added value of multimodal data fusion in AD diagnosis. Through the interpretation of activation maps, our study further elucidates the distinct yet complementary roles of FDG-PET and MRI in highlighting the pathological underpinnings of AD, contributing to a nuanced understanding of neuroimaging biomarkers in clinical settings. Our research underscores the critical need for refined strategies in neuroimaging data integration, advocating for a more discerning application of single and multimodal approaches in the early detection of AD.


English (en)


Aristeidis Sotiras, Institute for Informatics

Committee Members

Abhinav Kumar Jha, James Feher