Indirect relation based individual metabolic network for identification of mild cognitive impairment

Background: Optimized abnormalities of individual brain network may allow earlier detection of mild cognitive impairment (MCI) and accurate prediction of its conversion to Alzheimer's disease (AD). Currently, most studies constructed individual networks based on region-to-region correlation without employing multi-region information. In order to develop the potential discriminative power of network and provide supportive evidence for feasibility of individual metabolic network study, we propose a new approach to extract features from network with indirect relation based on ¹⁸F-FDG PET data. New Method: Direct relation based individual network is first constructed using Gaussian kernel function. After that, the lattice-close-degree in fuzzy mathematics is applied to reflect region-to-region indirect relation using the direct relations of regions and their common neighbors. The proposed approach has been evaluated on 199 MCI subjects and 166 normal controls (NC) using SVM classifier. Results: The indirect relation based network features significantly promote classification performance in separating MCI from normal controls (NC) as well as MCI converters from non-converters. Specially, further improvements can be obtained by combining indirect relation features with ADAS-cog scores. Moreover, the discriminative regions we found are consistent with previous studies, indicating the efficacy of our constructed network in identifying correct biomarkers for diagnosing MCI and predicting its conversion. Comparison with Existing Method(s): More accurate MCI identification of PET data can be achieved by features of network with indirect relation. Conclusions: This work provides a new way to investigate brain network from metabolic perspective for accurate identification of MCI.