Individual metabolic network for the accurate detection of Alzheimer's disease based on FDGPET imaging

The rapid development of neuroimaging technology and brain network analysis methodologies have promoted the research of Alzheimer's disease (AD). Recently, studies on brain networks reported that AD patients showed abnormal connectivity alterations and disrupted coordinated organizations compared with normal controls (NC). However, much less knowledge is about the abnormalities of metabolic network at individual level, which might be the potential marker in promoting current AD diagnosis. In the present study, we constructed the individual metabolic network based on 18F-Fluro-Deoxyglucose Positron Emission Tomography (18F-FDG-PET) data by using cubes consisted with certain numbers of voxels. Network properties, connectivity strength and metabolic cost of cubes of 111 NCs and 111 AD patients were calculated to evaluate the performance and feasibility of the proposed network via machine learning approaches. Results showed that the features we extracted were well-performed in classification, with accuracy of 95.64% and area of 0.9915 under receiver operating characteristic curve, indicating the individual metabolic network and local metabolic information are potential powerful in AD diagnosis.