Modeling the Relationships Between Functional Connectivity and Amyloid Deposition in Alzheimer's Disease

Glucose is the main energy source of brain. Different neural degenerative diseases such as Parkinson's disease or Alzheimer's disease have shown distinct brain glucose metabolic patterns. FDG-PET is a established non-invasive method to measures cerebral glucose metabolism and can be used to differentiate different types of neurodegenerative diseases that anatomical imaging such as CT or MRI may not be able to differentiate. Among patients whose Alzheimer's diseases have not been confirmed, the defects in brain glucose metabolism can predict future amyloid plaque deposition. On the other hand, early amyloid plaque deposition may predict the future occurrence of Alzheimer's disease as early as 15 years before the onset. This research project is focusing on the sequential change of the two biomarkers of brain glucose metabolism and amyloid plaque deposition and their correlation with clinical symptoms in patients with Alzheimer's disease. The subjects in this project will be including normal controls without cognitive impairment, patients with prodromal AD or AD. The relationship between functional connectivity of FDG-PET and amyloid deposition in different group of patients will be investigated. Further correlation with tau PET will be also discussed. In the imaging process part of this project, the standard tool, SPM (Spatial Parametric Mapping) will be applied. As machine learning/deep learning methodology is gaining popularity in medical imaging research community, collaboration with artificial intelligence core laboratory at Linkou will be pursued to investigate hidden correlation between functional connectivity, amyloid plaque, progress of clinical symptoms with time that previous statistical methods may not be able to find.

Modeling the Relationships Between Functional Connectivity and Amyloid Deposition in Alzheimer's Disease

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