A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment
Joint Authors
Jiang, Jiehui
Zuo, Chuantao
Yan, Zhuangzhi
Xiao, Shu-yun
Wang, Min
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-18
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Min& Yan, Zhuangzhi& Xiao, Shu-yun& Zuo, Chuantao& Jiang, Jiehui. 2020. A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment. Behavioural Neurology،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1138374
Modern Language Association (MLA)
Wang, Min…[et al.]. A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment. Behavioural Neurology No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1138374
American Medical Association (AMA)
Wang, Min& Yan, Zhuangzhi& Xiao, Shu-yun& Zuo, Chuantao& Jiang, Jiehui. A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment. Behavioural Neurology. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1138374
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138374