Abstract
The aim of this study is to explore functional and structural properties of abnormal brain networks associated with Parkinson’s disease (PD). 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) and T1-weighted magnetic resonance imaging from 20 patients with moderate-stage PD and 20 age-matched healthy controls were acquired to identify disease-related patterns in functional and structural networks. Dual-modal images from another prospective subject of 15 PD patients were used as the validation group. Scaled Subprofile Modeling based on principal component analysis method was applied to determine disease-related patterns in both modalities, and brain connectome analysis based on graph theory was applied to verify these patterns. The results showed that the expressions of the metabolic and structural patterns in PD patients were significantly higher than healthy controls (PD1-HC, p = 0.0039, p = 0.0058; PD2-HC, p < 0.001, p = 0.044). The metabolic pattern was characterized by relative increased metabolic activity in pallidothalamic, pons, putamen, and cerebellum, associated with metabolic decreased in parietal–occipital areas. The structural pattern was characterized by relative decreased gray matter (GM) volume in pons, transverse temporal gyrus, left cuneus, right superior occipital gyrus, and right superior parietal lobule, associated with preservation in GM volume in pallidum and putamen. In addition, both patterns were verified in the connectome analysis. The findings suggest that significant overlaps between metabolic and structural patterns provide new evidence for elucidating the neuropathological mechanisms of PD.
Highlights
Parkinson’s disease (PD) is a complex, chronic, and neurodegenerative disorder, pathologically characterized predominately by a loss of substantia nigra pars compacta dopaminergic neurons, manifesting in functional and structural alterations throughout the brain (Lee and Trojanowski, 2006; Choi et al, 2013; Rocha et al, 2018)
The positron emission tomography (PET)-Parkinson’s disease-related pattern (PDRP) topography generated from a linear combination of PC1, PC3, and PC4 with expression successfully discriminated PD1 patients and healthy controls, and produced the lowest AIC value in the logistic regression model
Subject expressions for the PET-PDRP topography were significantly elevated (p = 0.0039) in PD1 compared to HC subjects (Figure 2B)
Summary
Parkinson’s disease (PD) is a complex, chronic, and neurodegenerative disorder, pathologically characterized predominately by a loss of substantia nigra pars compacta dopaminergic neurons, manifesting in functional and structural alterations throughout the brain (Lee and Trojanowski, 2006; Choi et al, 2013; Rocha et al, 2018). It is increasingly used in routine clinical practice (Teune et al, 2013). The PDRP expression values (subject scores) from various studies have shown significant positive correlations with disease progression and have decreased after effective treatments of motor symptoms (Huang et al, 2007; Peng et al, 2014b)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
