A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment

Abstract

Objective. Glucose-based positron emission tomography (PET) imaging has been widely used to predict the progression of mild cognitive impairment (MCI) into Alzheimer’s disease (AD) clinically. However, existing discriminant methods are unsubtle to reveal pathophysiological changes. Therefore, we present a novel metabolic connectome-based predictive modeling to predict progression from MCI to AD accurately. Methods. In this study, we acquired fluorodeoxyglucose PET images and clinical assessments from 420 MCI patients with 36 months follow-up. Individual metabolic network based on connectome analysis was constructed, and the metabolic connectivity in this network was extracted as predictive features. Three different classification strategies were implemented to interrogate the predictive performance. To verify the effectivity of selected features, specific brain regions associated with MCI conversion were identified based on these features and compared with prior knowledge. Results. As a result, 4005 connectome features were obtained, and 153 in which were selected as efficient features. Our proposed feature extraction method had achieved 85.2% accuracy for MCI conversion prediction (sensitivity: 88.1%; specificity: 81.2%; and AUC: 0.933). The discriminative brain regions associated with MCI conversion were mainly located in the precentral gyrus, precuneus, lingual, and inferior frontal gyrus. Conclusion. Overall, the results suggest that our proposed individual metabolic connectome method has great potential to predict whether MCI patients will progress to AD. The metabolic connectome may help to identify brain metabolic dysfunction and build a clinically applicable biomarker to predict the MCI progression.

The Effect of Aging on Brain Glucose Metabolic Connectivity Revealed by [18F]FDG PET-MR and Individual Brain Networks

“…In terms of methodology, we applied a novel KLSE approach to define metabolic connectivity at a subject level. This approach has already been validated and successfully implemented to predict the progression from MCI to AD using [ 18 F]FDG PET ( Wang et al, 2020a , b ). To determine an individual metabolic network, the KLSE approach relies on a predefined atlas for brain parcellation and defining the nodes of each individual network.…”

Neuroimaging statistical approaches for determining neural correlates of Alzheimer's disease via positron emission tomography imaging

“…Applied to all region‐pairs, this technique generates a connectivity matrix representative of one particular scan. Wang, Yan, et al (2020) used KLSE to characterize the connectivity patterns between stable and progressive MCI patients using the AAL parcellation. Comparing the patterns of stable and progressive MCI patients they were able to identify a difference pattern that implicated regions associated with conversion to AD.…”

Health care intelligent system: A neural network based method for early diagnosis of Alzheimer's disease using MRI images

“…Research studies show that 15% to 20% of MCI patients progress to AD, but it takes years. If the MCI stage is examined in‐depth, it can decrease AD's more high‐risk population (Wang et al, 2020). Early diagnosis of AD produced promising outcomes.…”