Exploring brain glucose metabolic patterns in cognitively normal adults at risk of Alzheimer’s disease: A cross-validation study with Chinese and ADNI cohorts

Tao-Ran Li; Qiu-Yue Dong; Xue-Yan Jiang; Gui-Xia Kang; Xin Li; Yun-Yan Xie; Jie-Hui Jiang; Ying Han

NeuroImage: Clinical (Jan 2022)

Exploring brain glucose metabolic patterns in cognitively normal adults at risk of Alzheimer’s disease: A cross-validation study with Chinese and ADNI cohorts

Tao-Ran Li,
Qiu-Yue Dong,
Xue-Yan Jiang,
Gui-Xia Kang,
Xin Li,
Yun-Yan Xie,
Jie-Hui Jiang,
Ying Han

Affiliations

Tao-Ran Li: Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
Qiu-Yue Dong: Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Information and Communication Engineering, Shanghai University, Shanghai 200444, China
Xue-Yan Jiang: Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China; School of Biomedical Engineering, Hainan University, Haikou 570228, China
Gui-Xia Kang: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Xin Li: School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China; Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao 066004, China
Yun-Yan Xie: Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China; Corresponding authors at: Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China (Yun-Yan Xie); School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China (Jie-Hui Jiang); Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China (Ying Han).
Jie-Hui Jiang: Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Information and Communication Engineering, Shanghai University, Shanghai 200444, China; Corresponding authors at: Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China (Yun-Yan Xie); School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China (Jie-Hui Jiang); Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China (Ying Han).
Ying Han: Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China; School of Biomedical Engineering, Hainan University, Haikou 570228, China; Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing 100053, China; National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; Corresponding authors at: Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China (Yun-Yan Xie); School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China (Jie-Hui Jiang); Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China (Ying Han).

Journal volume & issue: Vol. 33
p. 102900

Abstract

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Objective: Disease-related metabolic brain patterns have been verified for a variety of neurodegenerative diseases including Alzheimer’s disease (AD). This study aimed to explore and validate the pattern derived from cognitively normal controls (NCs) in the Alzheimer’s continuum. Methods: This study was based on two cohorts; one from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the other from the Sino Longitudinal Study on Cognitive Decline (SILCODE). Each subject underwent [18F]fluoro-2-deoxyglucose positron emission tomography (PET) and [18F]florbetapir-PET imaging. Participants were binary-grouped based on β-amyloid (Aβ) status, and the positivity was defined as Aβ+. Voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was used to generate the “at-risk AD-related metabolic pattern (ARADRP)” for NCs. The pattern expression score was obtained and compared between the groups, and receiver operating characteristic curves were drawn. Notably, we conducted cross-validation to verify the robustness and correlation analyses to explore the relationships between the score and AD-related pathological biomarkers. Results: Forty-eight Aβ+ NCs and 48 Aβ- NCs were included in the ADNI cohort, and 25 Aβ+ NCs and 30 Aβ- NCs were included in the SILCODE cohort. The ARADRPs were identified from the combined cohorts and the two separate cohorts, characterized by relatively lower regional loadings in the posterior parts of the precuneus, posterior cingulate, and regions of the temporal gyrus, as well as relatively higher values in the superior/middle frontal gyrus and other areas. Patterns identified from the two separate cohorts showed some regional differences, including the temporal gyrus, basal ganglia regions, anterior parts of the precuneus, and middle cingulate. Cross-validation suggested that the pattern expression score was significantly higher in the Aβ+ group of both cohorts (p 0.23). Conclusions: ARADRP exists for NCs, and the acquired pattern expression score shows a certain ability to discriminate Aβ+ NCs from Aβ- NCs. The SSM/PCA method is expected to be helpful in the ultra-early diagnosis of AD in clinical practice.

Published in NeuroImage: Clinical

ISSN: 2213-1582 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Medicine: Medicine (General): Computer applications to medicine. Medical informatics; Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry: Neurology. Diseases of the nervous system
Website: http://www.journals.elsevier.com/neuroimage-clinical/

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