Individual‐specific metabolic network based on 18F‐FDG PET revealing multi‐level aberrant metabolisms in Parkinson's disease

• Published in: Human brain mapping Vol. 45; no. 14; pp. e70026 - n/a
• Main Authors: Lu, Weizhao, Song, Tianbin, Li, Jiping, Zhang, Yuqing, Lu, Jie
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.10.2024
• Subjects: 18F‐FDG PET; Body mass; Brain; Correlation analysis; Disease; Dopamine; Glucose; Group dynamics; individual‐specific metabolic network; Lean body mass; machine learning; Medical imaging; Metabolic networks; Metabolism; Movement disorders; Network analysis; Neurodegenerative diseases; Parkinson's disease; Patients; Positron emission; Positron emission tomography; Standard scores; Tomography
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.70026

Metabolic network analysis in Parkinson's disease (PD) based on 18F‐FDG PET has revealed PD‐related metabolic patterns. However, alterations at the systemic metabolic network level and at the connection level between different brain regions still remain unknown. This study aimed to explore metabolic network alterations at multiple network levels among PD patients using an individual‐specific metabolic network (ISMN) approach. 18F‐FDG‐PET images of patients with PD (n = 34) and healthy subjects (n = 47) were collected. Healthy subjects were further separated into reference group (n = 28) and control group (n = 19) randomly. Standardized uptake value normalized by lean body mass ratio (SULr) maps was calculated from the PET images. ISMNs were constructed based on SULr maps for PD patients and controls with reference to the reference group. Comparisons of nodal and edge features were performed between PD and control groups. Correlation analysis was conducted between multilevel network properties and clinical scales in PD group. A linear classifier was trained based on nodal or edge features to distinguish PD from controls. The distance from each patient's ISMN to the group‐level difference network showed a negative correlation with Hoehn and Yahr stage (r = −0.390, p = .023). Eight nodes from ISMN were identified which exhibited significantly increased nodal degree in PD patients compared to controls (p < .05). Eleven edges were observed which demonstrated significant distinctions in Z‐score values in comparisons to the control group (p < .05). Furthermore, the nodal and edge features showed comparable performances in PD diagnosis compared to the traditional SULr values, with area under the receiver operating characteristic curve larger than 0.91. The proposed ISMN approach revealed systemic metabolic deviations, as well as nodal and edge distinctions in PD, which might be supplementary to the existing findings on PD‐related metabolic patterns. An individual‐specific metabolic network revealed systemic metabolic network with disease severity in Parkinson's disease. In addition, nodal and edge distinctions in Parkinson's disease were also observed, which might be supplementary to the existing findings on PD‐related metabolic patterns.