Alzheimer’s disease (AD) is the most common cause of dementia and represents a growing public health challenge worldwide. Despite extensive research, early diagnosis remains difficult because brain changes begin many years before symptoms appear. This PhD project investigates how cerebral glucose metabolism, blood flow, and amyloid plaque accumulation differ between healthy aging and pathological aging in individuals with Mild Cognitive Impairment (MCI) and Alzheimer’s disease.
Using advanced multimodal PET/MR imaging, the study will examine structural, metabolic, and functional changes in the brain. Imaging biomarkers will be combined with blood-based biomarkers to improve understanding of early disease mechanisms. Artificial intelligence (AI), specifically convolutional neural networks (CNN), will be applied to identify subtle patterns in complex imaging data that may not be detectable using conventional methods.
The project builds on previous research investigating healthy aging and extends the analysis to patients with cognitive impairment. By comparing healthy and pathological trajectories, the study aims to identify early indicators of disease progression such as blood biomarkers that may support earlier diagnosis and improved treatment strategies. Early detection could enable timely intervention, potentially slowing disease progression and improving quality of life for patients and their families.
Overall, the project integrates neuroimaging, blood biomarker analysis, and AI-driven data analysis to advance knowledge of AD mechanisms and contribute to the development of precision medicine approaches in neurodegenerative disorders.