
==== Front
Hum Brain Mapp
Hum Brain Mapp
10.1002/(ISSN)1097-0193
HBM
Human Brain Mapping
1065-9471
1097-0193
John Wiley & Sons, Inc. Hoboken, USA

10.1002/hbm.70026
HBM70026
Research Article
Research Article
Individual‐specific metabolic network based on 18F‐FDG PET revealing multi‐level aberrant metabolisms in Parkinson's disease
Lu et al.
Lu Weizhao https://orcid.org/0000-0002-2941-9791
1 2 3
Song Tianbin 1 2 3
Li Jiping 4
Zhang Yuqing 4
Lu Jie https://orcid.org/0000-0003-0425-3921
1 2 3 imaginglu@hotmail.com

1 Department of Radiology and Nuclear Medicine, Xuanwu Hospital Capital Medical University Beijing China
2 Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics Xuanwu Hospital Beijing China
3 Key Laboratory of Neurodegenerative Diseases Ministry of Education Beijing China
4 Beijing Institute of Functional Neurosurgery, Xuanwu Hospital Capital Medical University Beijing China
* Correspondence
Jie Lu, Department of Radiology and Nuclear Medicine, Xuanwu Hospital, No.45 Changchun Road, Beijing 100053, China.
Email: imaginglu@hotmail.com

20 9 2024
10 2024
45 14 10.1002/hbm.v45.14 e7002627 8 2024
21 6 2024
02 9 2024
© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Abstract

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.

18F‐FDG PET
individual‐specific metabolic network
machine learning
Parkinson's disease
National Key Research and Development Program of China 10.13039/501100012166 2022YFC2406900 2022YFC2406904 source-schema-version-number2.0
cover-dateOctober 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:20.09.2024
Lu, W. , Song, T. , Li, J. , Zhang, Y. , & Lu, J. (2024). Individual‐specific metabolic network based on 18F‐FDG PET revealing multi‐level aberrant metabolisms in Parkinson's disease. Human Brain Mapping, 45 (14 ), e70026. 10.1002/hbm.70026
==== Body
pmc1 INTRODUCTION

Parkinson's disease (PD) is the most common movement disorder and the second‐most common neurodegenerative disorder following Alzheimer's disease (Kalia & Lang, 2015). At present, the diagnosis of PD mainly depends on clinical symptoms, however, molecular imaging modalities including single‐photon emission computed tomography (SPECT) and positron emission tomography (PET) have played critical roles in the diagnosis and study of PD (Bidesi et al., 2021). Since the primary pathological feature of PD is the degeneration and loss of dopaminergic neurons in the substantia nigra pars compacta, and the formation of Lewy bodies in dopaminergic neurons with α‐synuclein as the main proteinaceous component (Kalia & Lang, 2015; Wang et al., 2024), dopamine transporter SPECT and PET (Darcourt et al., 2014; Scherfler et al., 2007), aromatic amino acid decarboxylase and vesicular monoamine transporter 2 PET (Tian et al., 2024), which are able to detect presynaptic dopamine neuronal dysfunction, have been studied as diagnostic tools for PD.

In addition to dopamine‐specific radiotracers, imaging of brain glucose metabolism with 2‐deoxy‐2‐[fluorine‐18]fluoro‐d‐glucose (18F‐FDG) PET is also an important contributor to the study of PD (Meles et al., 2017). A disease‐specific pattern for PD, which is known as PD‐related pattern (PDRP), has been identified, with increased glucose uptake in the pallidum, putamen, thalamus, cerebellum and sensorimotor cortex, and decreased glucose uptake in the lateral frontal and parietooccipital regions (Eidelberg et al., 1994). In addition, previous studies have explored differential diagnosis of PD with atypical parkinsonian syndromes using 18F‐FDG PET (Meyer et al., 2017; Zhao et al., 2020).

Nevertheless, it is increasingly recognized that PD is characterized by stereotyped connectivity changes. Therefore, the exploration of brain metabolic networks, rather than separate regions, provides more insight in pathophysiologic mechanisms underlying PD (Meles et al., 2017). Several studies have used scaled subprofile model and principal component analysis for metabolic network analysis, and identified PD‐related metabolic patterns (Ma et al., 2007; Schindlbeck et al., 2020; Sigurdsson et al., 2022; Teune et al., 2013; Teune et al., 2014). However, alterations at the systemic level and at the connection level between different brain regions still remain unknown. Individual‐specific network is a recently proposed approach for the analysis of molecular expression of a single sample (Liu et al., 2016). Combined with whole‐body PET images, this approach has captured the metabolic dysfunction of lung cancer patients compared to healthy controls at both systemic and organ level (Sun, Wang, Wu, et al., 2022). In this study, it was hypothesized that PD patients might exhibit global changes across the brain metabolic network and covariant changes between different brain regions. Therefore, the individual‐specific network approach based on 18F‐FDG PET images was applied to reveal multilevel metabolic dysfunctions for PD patients.

2 MATERIALS AND METHODS

2.1 Subjects

This cross‐sectional study was approved by the Institutional Review Board of Xuanwu Hospital Capital Medical University. Informed consent was obtained from all participants. Participants were recruited at the Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University. Inclusion criteria were as follows: (1) age between 40 and 75 years old, (2) right‐handedness, (3) self‐reported absence of psychiatry disorders, head trauma, and other conditions that may affect the brain, (4) no solid lesions such as tumors detected in the brain via medical imaging examinations, (5) self‐reported absence of cerebral vascular diseases, (6) no current alcohol or drug abuse, (7) no PET/magnetic resonance imaging (PET/MRI) scan contradictions. PD was diagnosed according to the International Movement Disorder Society PD criteria by experienced neurologists (Li et al., 2017; Postuma et al., 2015). Specifically, the diagnosis of PD requires the presence of the following symptoms: resting tremor, muscle rigidity, and bradykinesia and simultaneously considering the three categories of diagnostic features (absolute exclusion criteria, red flags, and supportive criteria) provided by the International Movement Disorder Society (Li et al., 2017; Postuma et al., 2015). A patient was diagnosed with PD if he/she had the core motor feature, at least two supportive criteria, with no absolute exclusion criteria and red flags (Li et al., 2017; Postuma et al., 2015).

At last, 81 participants including 34 PD patients and 47 age‐ and sex‐matched healthy subjects were enrolled. The Hoehn and Yahr stage (HY stage), Unified Parkinson's Disease Rating Scale‐part III (UPDRS‐III) during off‐state, mini‐mental status exam (MMSE), and disease duration were recorded for PD patients.

2.2 PET/MR acquisition

All participants underwent PET/MR scan using a hybrid PET/MR scanner (uPMR790, United Imaging, China) with a 24‐channel head/neck coil. PD patients were instructed to not take dopaminergic medication for at least 12 h prior to the scan. After fasting for 6 h, participants were injected with 18F‐FDG based on their body weight (0.1 mCi/kg). Fifty minutes after injection, PET/MR scan was performed for 10 minutes. PET images were reconstructed using time‐of‐flight and point spread functions with a matrix size of 256 × 256, slice thickness of 2.0000 mm, voxel size of 1.5625 × 1.5625 × 2.0000 mm3, with 4 iterations and 20 subsets, Gaussian filter (full width at half maximum = 3 mm), attenuation and scatter correction were applied. In addition, high‐resolution 3D T1‐weighted images were acquired with the following parameters: repetition time = 7.9 ms, echo time = 3.8 ms, 176 sagittal slices, field of view = 256 × 256 mm2, and spatial resolution of 1 mm3.

2.3 Imaging processing

Voxel‐wise standardized uptake value (SUV) maps were calculated from the raw PET images, and were then normalized by lean body mass to obtain SUV normalized by lean body mass (SUL) maps according to the equations proposed by Hume (Methods S1 in Supporting Information) (Hume, 1966). T1‐weighted images were co‐registered to the corresponding SUL maps using SPM 12 software. PETPVE12 toolbox was used for the segmentation of the co‐registered T1‐weighted images into gray matter, white matter, and cerebrospinal fluid (Gonzalez‐Escamilla et al., 2017). Then partial volume effect (PVE) correction was applied to the SUL maps using the Müller‐Gärtner method from PETPVE12 (Gonzalez‐Escamilla et al., 2017). After PVE correction, SUL maps were normalized to the Montreal Neurological Institute (MNI) template. Specifically, the co‐registered T1‐weighted images were nonlinearly registered to the MNI template, and the deformation field was applied to the corresponding SUL map. The SUL maps were then resampled into a voxel size of 2 × 2 × 2 mm3, were standardized by the global mean value to obtain SUL ratio (SULr) maps. SULr maps were then smoothed using a Gaussian kernel with 6‐mm full width at half maximum.

2.4 Network construction

2.4.1 Separation of reference group and control group

For healthy subjects, a group‐level metabolic network was constructed based on the covariance approach. Specifically, a metabolic network was constructed using all 47 healthy subjects. Nodes in the network were defined as brain regions in the automated anatomical labeling atlas 3 (AAL3) (Rolls et al., 2020). The edge connecting each pair of nodes was the partial correlation coefficient of SULr between each pair of brain region, with age and sex as covariates. Then we evaluated the stability of the group‐level metabolic network for the healthy subjects by a resampling procedure. 8, 12, 16, 20, 24, 28, 32, 36, 40, and 44 subjects were selected from the 47 available healthy subjects as the new group. And a new group‐level metabolic network was constructed for the new group. Stability was evaluated by the correlation analysis between the new constructed network and the original group‐level network. And the resampling procedure was repeated 20 times. The results are shown in Figure S1 and Result S1 in Supporting Information, and we observed that when there were greater than or equal to 28 healthy subjects, the stability evaluated by the correlation coefficient was above 0.9 with little variance. Therefore, we randomly selected 28 healthy subjects into the reference group to build the reference network (REF), leaving 19 subjects into the control group.

2.4.2 Construction of individual‐specific metabolic network

Then, we sequentially added each PD patient to the reference group and a new metabolic network was constructed, known as the perturbed network for PD (PNP) (Figure 1). Each PNP was constructed using data from 29 subjects, including 28 healthy subjects and 1 PD patient. Next, the difference between PNP and the reference network was calculated as ΔPNP = PNP‐REF. As ΔPNP was proved to follow a symmetric distribution known as the “volcanic distribution,” which was similar to the normal distribution (Liu et al., 2016), Z‐score transform was performed to the ΔPNP as follows: (1) Z=ΔPNPn1−PNPn2n−1

FIGURE 1 The flow diagram of constructing individual‐specific metabolic network (ISMN). Firstly, a reference structure covariance network (REF) is constructed among 28 reference subjects, with nodes defined by AAL3 atlas, and edges representing the partial correlation coefficients of SULr for each pair of brain regions. Then we add PD patients or control subjects one by one and construct a new covariance network called the perturbed network (PNC n+1 or PNP n+1), which includes 28 reference subjects and 1 PD patient/control. The ISMN of each patient or control is defined as the Z‐score of the difference between the perturbed network and the REF (see Equation 1), and then the P value of each edge in the ISMN can be further obtained, representing the degree of deviation from the reference group.

Where n = 28 is the number of subjects in the reference group. In each patient's individual‐specific metabolic network (ISMN), the weight of each edge was the Z‐score obtained from Equation (1). We further calculated the P value of each edge in the ISMN for each PD patient from the Z‐score. Finally, we identified edges in each patient's ISMN that were significantly different from the REF, with Bonferroni correction to control false positives. Similarly, the left 19 controls were also sequentially added to the reference group, and the ISMN for each of the 19 controls was obtained using the above process.

In short, each ISMN was composed of 13,695 edges (165 × 166/2 = 13,695) between 166 different brain regions in the AAL3 brain atlas, where each edge represents the degree of covariance deviation between the two nodes in PD patients or controls from the standard covariance in the reference group.

2.4.3 Comparisons with traditional group‐level metabolic network

To compare with the traditional covariance network approach, we also constructed the group‐level metabolic network for PD group similar to the process of constructing REF. We defined the group‐level difference network (diffgroup) as follows: (2) diffgroup=REF−PDgroupREF+PDgroup

diffgroup was regarded as the overall network deviation of the PD patients from the reference group. Then, to further explore the relationship of each PD individual to the overall network deviation, we compared the ISMN of each PD patients with diffgroup using the correlation distance (1—Pearson's correlation coefficient between ISMN and diffgroup).

2.4.4 Calculation of nodal and edge features from ISMN

After constructing ISMN, we sorted out statistically significant edges from the ISMN for PD patients (p < 0.05 using Bonferroni correction). And we kept the same edges in the ISMN for controls. Then we defined the degree for each node as follows (Liu et al., 2016): (3) Dm=∑i≠m∣Zmi∣

Where m presents the corresponding brain region, Z mi is the Z‐score between region m and i. We sorted out the top 5% nodes (166 × 0.05 ≈ 8) with the largest degree for PD patients. In addition, we selected edges that changed significantly in at least 20% PD patients (34 × 0.20 ≈ 7 PD patients). We also calculated nodal degrees for corresponding nodes and extracted Z‐scores from corresponding edges in the control group.

2.5 Statistical analysis

Statistical analyses were performed using SPSS (version 26.0). To assess differences in demographic and clinical data among reference, control and PD groups, we performed ANOVA on the continuous data, and chi‐square test on sex. To assess differences in nodal degrees between controls and PD patients, we conducted two‐tailed two‐sample t‐tests among the identified top eight nodes. To assess metabolic connection alterations in PD patients, we also conducted two‐tailed two‐sample t‐tests among the identified edges between controls and PD patients. In addition, to assess the association between network‐derived features and clinical scales of PD, Pearson's correlation analysis was performed. p < 0.05 was considered statistically significant for the abovementioned statistical analyses.

2.6 Classification of PD patients using network properties from ISMN

To evaluate the potential of using the obtained network features from the ISMN for PD diagnosis, we used nodal degrees and edge Z‐score values to classify PD patients (n = 34) from controls (n = 19). A support vector machine (SVM) with a linear kernel was configured using LIBSVM toolbox (Chang & Lin, n.d.). A weight of 1.8 was added to the control group, making the parameter C for the control's class to 1.8 × C to maintain a balance between the two classes. Leave‐one‐out cross‐validation strategy was used to train and test the classifier. In each cross‐validation fold, 52 subjects were used for training and the remaining one subject was selected to test the model. The iteration continued for 53 times. To compare the diagnosis performance of the network‐derived features to traditional SULr values, we also extracted SULr values from PD patients and controls according to the AAL3 atlas. To keep the same number of features, we selected the top n (n equals to the number of nodes or the number of edges) brain regions with the largest differences in SULr values between PD and reference group using independent t‐test, and also trained SVM classifiers with leave‐one‐out cross‐validation to classify PD patients with controls.

To evaluate the model performance, we calculated classification accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC‐ROC).

3 RESULTS

3.1 Demographic and clinical data for the enrolled subjects

This study included 34 patients with PD and 47 age‐ and sex‐matched healthy subjects (as shown in Table S1). Furthermore, healthy subjects were randomly separated into reference (n = 28) and control (n = 19) groups. The demographic data of the three groups are shown in Table 1. There were no statistical differences among the three groups in terms of age, sex, height, and body weight.

TABLE 1 Demographic and clinical data for the three groups.

	Reference group (n = 28)	Control group (n = 19)	PD (n = 34)	p	
Age (years)	60.29 ± 4.62	59.84 ± 7.86	62.32 ± 6.40	.286	
Sex (F/M)	18/10	8/11	21/13	.269	
Height (m)	1.65 ± 0.07	1.67 ± 0.08	1.64 ± 0.07	.245	
Weight (kg)	62.89 ± 9.11	71.32 ± 13.28	64.71 ± 14.30	.071	
HY stage	—	—	3.00 ± 0.83	—	
UPDRS‐III	—	—	59.71 ± 15.00	—	
Disease duration (years)	—	—	9.58 ± 4.04	—	
MMSE	—	—	27.41 ± 2.19 a	—	
a Due to limited conditions, only 27 of the 34 PD patients had MMSE scores.

3.2 Systemic metabolic network deviations for PD patients

We first calculated the group‐level difference (diffgroup) between the reference and PD groups according to Equation (2). We then calculated the correlation distance between diffgroup and each ISMN. The mean Pearson's correlation coefficient r between diffgroup and ISMN was 0.6298 ± 0.011, indicating that the systemic deviations of each patient's ISMN contributed to the overall group difference. In addition, the distance from ISMN to the diffgroup showed a negative correlation with HY stage (r = −0.390, p = .023), as shown in Figure 2, indicating that the systemic deviations of ISMN was related to the overall disease severity.

FIGURE 2 Correlation between distance from diffgroup to ISMN and HY stage. HY stage, Hoehn and Yahr stage; ISMN, individual‐specific metabolic network.

3.3 Nodal degree comparisons between control and PD groups

The top 8 nodes with the highest degree in PD groups compared with the reference group were the left superior occipital gyrus (SOG), right angular gyrus, left postcentral gyrus, left mediodorsal medial thalamus, left reuniens nucleus of thalamus, left paracentral lobule, right middle occipital gyrus (MOG), and left precentral gyrus (Figure 3a). In addition, the nodes identified by the proposed network were similar to PD‐related metabolic patterns (Figure 3b), proving the feasibility of the proposed individual‐specific network approach. Comparisons of nodal degree between the PD and HC groups indicated that there were significant differences across all nodes between the two groups (p < .05) (Figure 3c).

FIGURE 3 The top eight nodes in PD group with largest nodal degree. (a) The location of the top eight nodes across the brain. (b) T‐map of the comparison in SULr maps between control group and PD patients (p < .05 corrected by false discovery rate method). The detailed results are given in Table S2. Brain regions in red represent decreased glucose metabolism in PD patients compared to controls, and regions in blue indicate increased glucose metabolism in PD patients compared to controls. (c) Comparisons of nodal degree between control and PD groups. Con, control; L, left; MOG, middle occipital gyrus; R, right; SOG, superior occipital gyrus; Tha.MDm, mediodorsal medial thalamus; Tha.Re, reuniens nucleus of thalamus.

3.4 Edge differences between control and PD groups

We sorted all 13,695 edges in all ISMNs based on the number of PD patients with significant changes in the corresponding edges, and identified the edges that changed in at least 20% of patients (the top 11 edges). The top 11 edges can be divided into two categories, with opposite directions of changes in PD patients in comparison to control groups (p < .05) (see Figure 4). The first category had a total of five edges, including the left postcentral gyrus with right inferior parietal gyrus, bilateral caudate nucleus with right putamen, left SOG with left pregenual anterior cingulate cortex, left inferior occipital gyrus (IOG) with right locus coeruleus (Figure 4a,b). The second category consisted a total of 6 edges, including the triangular part of the left inferior frontal gyrus with left SOG, left SOG with left IOG, left SOG with right fusiform gyrus, left calcarine with right angular gyrus, left paracentral lobule with left cerebellum region 3, and left superior temporal gyrus with left thalamic anterior pulvinar (Figure 4c,d).

FIGURE 4 The top 11 edges identified from the ISMNs of PD group. (a) Five edges with decreased metabolic connections in PD patients compared to the control group. (b) The Z‐score of the five edges between control and PD groups. (c) Six edges with significant increased metabolic connections in PD patients compared to the control group. (d) The Z‐score of the six edges between control and PD groups. The dark lines represent the mean Z‐scores in each group, and the shallow filled areas indicate standard deviation. C3, cerebellum region 3; IOG, inferior occipital gyrus; IPG, inferior parietal gyrus; L, left; LC, locus coeruleus; MOG, middle occipital gyrus; pACC, pregenual anterior cingulate cortex; R, right; SOG, superior occipital gyrus; Con, control; STG, superior temporal gyrus; Tha.PuA, thalamic anterior pulvinar; tIFG, triangular part of inferior frontal gyrus.

3.5 Association between ISMN‐derived features and clinical scales

Then we assessed the associations of nodal and edge features with clinical scales. We found a significantly positive correlation between nodal degree of the left paracentral lobule and HY stage (Figure 5a). In terms of the edges, we observed that Z‐score of the edge between the left IOG and right locus coeruleus significantly correlated with UPDRSIII (Figure 5b), Z‐score of the edge between triangular part of the left inferior frontal gyrus and left SOG was positively correlated with HY stage and UPDRSIII (Figure 5c,d), Z‐score of the edge between the paracentral lobule and left cerebellum region 3 was also positively correlated with HY stage and UPDRSIII (Figure 5e,f), Z‐score of the edge between the left superior temporal gyrus and left thalamic anterior pulvinar was negatively with MMSE score (Figure 5g).

FIGURE 5 Associations between nodal and edge features and clinical scales in PD group. Scatter plot (a) between nodal degree of the left paracentral lobule and HY stage, (b) between Z‐score of left IOG‐right locus coeruleus connection and UPDRSIII, (c) between Z‐score of left tIFG‐left SOG connection and HY stage, (d) between Z‐score of tIFG‐left SOG connection and UPDRSIII, (e) between Z‐score of left paracentral lobule‐left cerebellum region 3 connection and HY stage, (f) between Z‐score of left paracentral lobule‐left cerebellum region 3 connection and UPDRSIII, (g) between Z‐score of left superior temporal gyrus‐left thalamic anterior pulvinar and MMSE score. C3, cerebellum region 3; HY stage, Hoehn and Yahr stage; IOG, inferior occipital gyrus; L, left; LC, locus coeruleus; MMSE, mini‐mental status exam; R, right; SOG, superior occipital gyrus; STG, superior temporal gyrus; Tha_PuA, thalamic anterior pulvinar; tIFG, triangular part of inferior frontal gyrus; UPDRSIII, Unified Parkinson's Disease Rating Scale‐part III.

3.6 Diagnosis of PD using SVM classifiers

Finally, we assessed whether the 8 nodal features and 11 edge features have the potential for PD diagnosis. We selected the SULr values from the eight brain regions with the largest differences in SULr values between PD and control groups for the comparison with the eight nodal features, which were the bilateral calcarine sulcus, bilateral paracentral lobule, bilateral MOG, left IOG, and left SOG. We also used SULr values from the 11 brain regions with the largest differences in SULr values between PD and control groups for the comparison with the 11 edge features, which were the above 8 brain regions plus the left superior parietal gyrus, right IOG and right cerebellum region 9. Table 2 and Figure 6 show classification results based on nodal and edge features, as well as traditional SULr values.

TABLE 2 Evaluation metrics of the linear SVM classifier with different features.

	Accuracy (%)	Sensitivity	Specificity	AUC	
Node	86.79	0.9474	0.8529	0.9164	
SULr_8 a	79.25	0.7368	0.9412	0.9272	
Edge	84.81	1	0.8529	0.9659	
SULr_11 a	83.02	0.8947	0.8529	0.9272	
Abbreviations: SULr_8, SULr values from eight brain regions; SULr_11, SULr values from 11 brain regions; AUC, area under the curve.

a The eight regions include bilateral calcarine sulcus, bilateral paracentral lobule, bilateral MOG, left IOG, and left SOG. The 11 regions include bilateral calcarine sulcus, bilateral paracentral lobule, bilateral MOG, left IOG, left SOG, left superior parietal gyrus, right IOG and right cerebellum region 9.

FIGURE 6 Results of PD diagnosis. (a) ROC curve of the linear SVM classifier with eight nodal degree features. (b) ROC curve of the linear SVM classifier with SULr values from eight brain regions. The eight regions include bilateral calcarine sulcus, bilateral paracentral lobule, bilateral MOG, left IOG, and left SOG. (c) ROC curve of the linear SVM classifier with 11 edge features. (d) ROC curve of the linear SVM classifier with SULr values from 11 brain regions. The 11 regions include bilateral calcarine sulcus, bilateral paracentral lobule, bilateral MOG, left IOG, left SOG, left superior parietal gyrus, right IOG, and right cerebellum region 9.

As can be seen from Figure 6, the linear SVM based on all four types of features can distinguish PD from the control group. According to Table 2, the classifier based on edge features had the highest AUC, while the classifier based on nodal features had the highest accuracy. In summary, the nodal and edge features derived from the ISMN were not weaker than the traditional SULr features in the diagnosis of PD.

4 DISCUSSION

In this study, we applied a recently proposed ISMN approach based on 18F‐FDG PET images to assess metabolic network alterations in PD at systemic, node, and edge levels. Specifically, we found systemic metabolic network deviations were associated with disease severity of PD. Then we identified 8 nodal and 11 edge features which significantly changed in PD patients compared with control groups, and some of the features correlated with clinical scales. In addition, the nodal and edge features showed promise in the diagnosis of PD.

The analysis based on 18F‐FDG PET images can provide PD‐related metabolic change patterns. Previous studies have demonstrated PD‐related metabolic networks, such as PDRP (Eidelberg et al., 1994) and PD tremor‐related pattern (Mure et al., 2011). Metabolic network studies then used scaled subprofile model and principal component analysis to extract the relevant disease‐related pattern (Ma et al., 2007; Schindlbeck et al., 2020; Sigurdsson et al., 2022; Teune et al., 2013; Teune et al., 2014). The advantage of the previous approach was that once a pattern has been identified, the degree of its expression can be quantified in any 18F‐FDG PET scan (Meles et al., 2017). However, previous studies have only focused on PD‐related metabolic patterns. Currently, the changes in the whole‐brain metabolic network related to PD, as well as the metabolic relationship between different brain regions remain unclear. Individual‐specific network, on the other hand, is a recently developed approach which generates individual network for each study sample (Liu et al., 2016). Application of individual‐specific network analysis on 18F‐FDG PET images have captures systemic metabolic network deviations in lung cancer patients and overweight individuals (Lu et al., 2024; Sun, Wang, Wu, et al., 2022). In this study, we have applied the individual‐specific network approach on the 18F‐FDG PET images of patients with PD to explore metabolic alterations in PD at systemic, nodal, and edge levels.

In this study, via ISMN, we have demonstrated patterns of metabolic network changes at systemic, nodal, and edge levels in PD, which might be supplementary to the previous studies on brain regional features (Meyer et al., 2017; Zhao et al., 2020), given that PD is increasingly recognized as a disorder affecting distributed brain networks (Meles et al., 2017). In addition, metabolic network approach applied in this study might provide insights into the underlying pathophysiological mechanisms of PD, demonstrating changes in brain metabolic connections with opposite directions, reflect the underlying neurodegeneration and compensatory mechanisms. Furthermore, we revealed that the ISMN of each PD patient was associated with the traditional group‐level difference network between PD and healthy subjects, and the distance between ISMN from the group‐level difference network was negatively correlated with HY stage. In other words, the more deviation of the ISMN from the healthy subjects, the severer the disease was. In this way, a bridge from the systemic level metabolic network of PD and the disease severity was built.

Via ISMN, 8 nodes with the largest nodal degree in PD were identified, which included the left SOG, right angular gyrus, left postcentral gyrus, left mediodorsal medial thalamus, left reuniens nucleus of thalamus, left paracentral lobule, right MOG, and left precentral gyrus, and these regions were consistent with PDRP established by previous studies (Eidelberg et al., 1994; Ma et al., 2007; Teune et al., 2013; Teune et al., 2014). Occipital hypometabolism is a risk factor for PD and is associated with a higher risk toward dementia with Lewy bodies (Carli et al., 2023). In addition, occipital glucose metabolism is implicated in motor control in PD (Lee et al., 2020), and may be originated from the degeneration of cholinergic projections to the occipital region (Klein et al., 2010). The angular gyrus is a hub within the default mode network, and is linked to sleep‐related disorders in PD (Zheng et al., 2023). The decreased metabolism in the angular gyrus is also related to PD patients with cognitive impairment and dementia, with deregulation of genomic genes encoding subunits of mitochondrial complexes (Garcia‐Esparcia et al., 2018). One of the hallmarks in PD is the change in activity of thalamic neurons within the motor circuits (Blesa et al., 2016). The cortico‐basal ganglia‐thalamic neural circuit plays critical roles in PD motor complications and disease progressions (Singh, 2018). Patients with PD experience abnormal sensorimotor integration, while the postcentral gyrus is a crucial somatosensory cortex and plays a vital role in sensorimotor integration (Sun, Wang, Ji, et al., 2022). Furthermore, the postcentral gyrus may also be implicated in levodopa‐induced dyskinesia and cognitive impairment in PD (Rucco et al., 2022; Sun, Wang, Ji, et al., 2022). The precentral gyrus is located in the primary motor cortex, and is involved in motor symptoms of PD include resting tremor, muscle rigidity, bradykinesia, and postural instability (Burciu & Vaillancourt, 2018). In line with previous studies, the identified eight nodes were associated with different aspects in the progression of PD, and their nodal features deserved further study.

On one hand, compared with the control group, PD patients demonstrated significantly decreased individual‐specific metabolic connectivities in five edges, namely the left postcentral gyrus with right inferior parietal gyrus, bilateral caudate nucleus with right putamen, left SOG with left pregenual anterior cingulate cortex, left IOG with right locus coeruleus, reflecting metabolic alterations in opposite directions between each pair of regions as compared to controls. The inferior parietal gyrus is implicated in higher motor control and other functions, and has been thought to operate as a sensorimotor interface important for guidance of movement via sensory feedback for PD patients (Tahmasian et al., 2017). The caudate nucleus and putamen are within the basal ganglia, which play important roles in PD (Singh, 2018). Dopaminergic dysfunction in the caudate nucleus is commonly seen in advanced PD patients, and plays a significant role in the pathophysiology of PD, such as cognitive impairment, depression, rapid eye movement sleep behavior disorder, and gait problems (Pasquini et al., 2019; Sacheli et al., 2019). The putamen is also affected in PD, which is originated by neuronal degeneration in the substantia nigra (Dickson, 2018), and is related to motor dysfunction and cognitive decline in PD (Kinoshita et al., 2022). Pregenual anterior cingulate cortex is involved in neuropsychiatric fluctuations in PD, including apathy and depression, and the alterations may be related to degeneration of dopaminergic mesocorticolimbic pathway or ascending nondopaminergic projections (Fleury et al., 2014; Prange et al., 2019). The noradrenergic locus coeruleus is a small nucleus that produces norepinephrine for the brain. In terms of PD, alterations of the locus coeruleus are features of the early phase of PD, even exceed the Lewy pathology in the substantia nigra (Oertel et al., 2019). In addition, the dysfunction of the locus coeruleus may contribute to several symptoms in PD, including cognitive impairment and affective symptoms (Bari et al., 2020; Oertel et al., 2019). According to previous findings, the observed decreased individual‐specific metabolic connections among these brain regions were related to the pathophysiological aspects of PD, and might reflect neurodegeneration of PD.

On the other hand, PD patients exhibited significantly increased individual‐specific metabolic connections in six edges, including the triangular part of the left inferior frontal gyrus with left SOG, left SOG with left IOG, left SOG with right fusiform gyrus, left calcarine with right angular gyrus, left paracentral lobule with left cerebellum region 3, and left superior temporal gyrus with left thalamic anterior pulvinar, which might reflect co‐activation or co‐deactivation between each pair of brain regions. While previous studies have linked the dysfunction of the triangular part of inferior frontal gyrus in PD with tremor, motor, and cognitive (Lan et al., 2023; Xu et al., 2016). In terms of the cerebellum, there is a concrete link between the cerebellum and PD (Li et al., 2023). Dopaminergic neurons are degenerated and lost in the cerebellum of PD patients. In addition, alpha‐synuclein aggregation also exists in the cerebellum of PD patients (Morris et al., 2024; Zhong et al., 2022). And evidences have shown that the disease‐related changes in the cerebellum are related to motor control in PD (Li et al., 2023). Therefore, the increased individual‐specific metabolic connections among these brain regions might be implicated in the disease‐related symptoms, and might also reflect neural compensatory mechanisms in PD (Appel‐Cresswell et al., 2010; Grandi et al., 2018).

There are several limitations that need to be addressed in this study. First, only 18F‐FDG PET images were used to construct the brain metabolic network, which only reflects information related to glucose metabolism. In the future, dopamine transporter and vesicular monoamine transporter 2 PET images will be employed to build brain networks and to explore abnormalities in dopamine transport‐related networks. Second, due to the lack of clinical scales for non‐motor functions, we did not assess the association between ISMN and non‐motor functions. Future studies will collect more related scales to reveal the relationship between changes in ISMN and non‐motor symptoms in patients with PD. Third, the pathophysiological significance of the networks used in this study remains unclear, and future mechanistic studies are needed to gain a deeper understanding of the pathophysiological significance of abnormalities in global network, nodal degree, and edges of the ISMN.

In conclusion, via a novel ISMN approach, we constructed ISMN for patients with PD. We observed that the closeness of the ISMN to the group‐level difference network was related to overall disease severity. Eight nodes with significant differences in nodal degree were identified in PD, which were consistent with PDRP. Eleven edges with significant distinctions were also observed. Furthermore, the nodal and edge features had the potential to be used for the diagnosis of PD. The current study demonstrated metabolic alterations at systemic, nodal, and edge level in PD, and the underlying pathophysiological significance of the network deserved further study.

AUTHOR CONTRIBUTIONS

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Weizhao Lu, Tianbin Song, Jiping Li, and Yuqing Zhang. The first draft of the manuscript was written by Weizhao Lu and Jie Lu commented on previous versions of the manuscript. All authors read and approved the final manuscript.

CONFLICT OF INTEREST STATEMENT

The authors declare no potential conflicts of interest.

Supporting information

Data S1. Supporting information.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.
==== Refs
REFERENCES

Appel‐Cresswell, S. , de la Fuente‐Fernandez, R. , Galley, S. , & McKeown, M. J. (2010). Imaging of compensatory mechanisms in Parkinson's disease. Current Opinion in Neurology, 23 (4 ), 407–412.20610991
Bari, B. A. , Chokshi, V. , & Schmidt, K. (2020). Locus coeruleus‐norepinephrine: Basic functions and insights into Parkinson's disease. Neural Regeneration Research, 15 (6 ), 1006–1013.31823870
Bidesi, N. S. R. , Vang Andersen, I. , Windhorst, A. D. , Shalgunov, V. , & Herth, M. M. (2021). The role of neuroimaging in Parkinson's disease. Journal of Neurochemistry, 159 (4 ), 660–689.34532856
Blesa, J. , Trigo‐Damas, I. , & Obeso, J. A. (2016). Parkinson's disease and thalamus: Facts and fancy. Lancet Neurology, 15 (7 ), e2.27302241
Burciu, R. G. , & Vaillancourt, D. E. (2018). Imaging of motor cortex physiology in Parkinson's disease. Movement Disorders, 33 (11 ), 1688–1699.30280416
Carli, G. , Meles, S. K. , Janzen, A. , Sittig, E. , Kogan, R. V. , Perani, D. , Oertel, W. H. , Leenders, K. L. , & REMPET Working Group . (2023). Occipital hypometabolism is a risk factor for conversion to Parkinson's disease in isolated REM sleep behaviour disorder. European Journal of Nuclear Medicine and Molecular Imaging, 50 (11 ), 3290–3301.37310428
Chang, C.‐C. , & Lin, C.‐J. (n.d.). LIBSVM – A library for support vector machines. https://www.csie.ntu.edu.tw/~cjlin/libsvm/
Darcourt, J. , Schiazza, A. , Sapin, N. , Dufour, M. , Ouvrier, M. J. , Benisvy, D. , Fontana, X. , & Koulibaly, P. M. (2014). 18F‐FDOPA PET for the diagnosis of parkinsonian syndromes. The Quarterly Journal of Nuclear Medicine and Molecular Imaging, 58 (4 ), 355–365.25366711
Dickson, D. W. (2018). Neuropathology of Parkinson disease. Parkinsonism & Related Disorders, 46 (Suppl 1 ), S30–S33.28780180
Eidelberg, D. , Moeller, J. R. , Dhawan, V. , Spetsieris, P. , Takikawa, S. , Ishikawa, T. , Chaly, T. , Robeson, W. , Margouleff, D. , Przedborski, S. , & Fahn, S. (1994). The metabolic topography of parkinsonism. Journal of Cerebral Blood Flow and Metabolism, 14 (5 ), 783–801.8063874
Fleury, V. , Cousin, E. , Czernecki, V. , Schmitt, E. , Lhommée, E. , Poncet, A. , Fraix, V. , Troprès, I. , Pollak, P. , Krainik, A. , & Krack, P. (2014). Dopaminergic modulation of emotional conflict in Parkinson's disease. Frontiers in Aging Neuroscience, 6 , 164.25100991
Garcia‐Esparcia, P. , Koneti, A. , Rodríguez‐Oroz, M. C. , Gago, B. , Del Rio, J. A. , & Ferrer, I. (2018). Mitochondrial activity in the frontal cortex area 8 and angular gyrus in Parkinson's disease and Parkinson's disease with dementia. Brain Pathology, 28 (1 ), 43–57.27984680
Gonzalez‐Escamilla, G. , Lange, C. , Teipel, S. , Buchert, R. , & Grothe, M. J. (2017). Alzheimer's disease neuroimaging initiative. PETPVE12: An SPM toolbox for partial volume effects correction in brain PET – Application to amyloid imaging with AV45‐PET. NeuroImage, 147 , 669–677.28039094
Grandi, L. C. , Di Giovanni, G. , & Galati, S. (2018). Animal models of early‐stage Parkinson's disease and acute dopamine deficiency to study compensatory neurodegenerative mechanisms. Journal of Neuroscience Methods, 308 , 205–218.30107207
Hume, R. (1966). Prediction of lean body mass from height and weight. Journal of Clinical Pathology, 19 (4 ), 389–391.5929341
Kalia, L. V. , & Lang, A. E. (2015). Parkinson's disease. Lancet, 386 (9996 ), 896–912.25904081
Kinoshita, K. , Kuge, T. , Hara, Y. , & Mekata, K. (2022). Putamen atrophy is a possible clinical evaluation index for Parkinson's disease using human brain magnetic resonance imaging. Journal of Imaging, 8 (11 ), 299.36354872
Klein, J. C. , Eggers, C. , Kalbe, E. , Weisenbach, S. , Hohmann, C. , Vollmar, S. , Baudrexel, S. , Diederich, N. J. , Heiss, W. D. , & Hilker, R. (2010). Neurotransmitter changes in dementia with Lewy bodies and Parkinson disease dementia in vivo. Neurology, 74 (11 ), 885–892.20181924
Lan, Y. , Liu, X. , Yin, C. , Lyu, J. , Xiaoxaio, M. , Cui, Z. , Li, X. , & Lou, X. (2023). Resting‐state functional magnetic resonance imaging study comparing tremor‐dominant and postural instability/gait difficulty subtypes of Parkinson's disease. La Radiologia Medica, 128 (9 ), 1138–1147.37474664
Lee, E. J. , Oh, J. S. , Moon, H. , Kim, M. J. , Kim, M. S. , Chung, S. J. , Kim, J. S. , & Jeon, S. R. (2020). Parkinson disease‐related pattern of glucose metabolism associated with the potential for motor improvement after deep brain stimulation. Neurosurgery, 86 (4 ), 492–499.31215629
Li, J. , Jin, M. , Wang, L. , Qin, B. , & Wang, K. (2017). MDS clinical diagnostic criteria for Parkinson's disease in China. Journal of Neurology, 264 (3 ), 476–481.28025665
Li, T. , Le, W. , & Jankovic, J. (2023). Linking the cerebellum to Parkinson disease: An update. Nature Reviews. Neurology, 19 (11 ), 645–654.37752351
Liu, X. , Wang, Y. , Ji, H. , Aihara, K. , & Chen, L. (2016). Personalized characterization of diseases using sample‐specific networks. Nucleic Acids Research, 44 (22 ), e164.27596597
Lu, W. , Duan, Y. , Li, K. , Cheng, Z. , & Qiu, J. (2024). Metabolic interactions between organs in overweight and obesity using total‐body positron emission tomography. International Journal of Obesity, 48 (1 ), 94–102.37816863
Ma, Y. , Tang, C. , Spetsieris, P. G. , Dhawan, V. , & Eidelberg, D. (2007). Abnormal metabolic network activity in Parkinson's disease: Test‐retest reproducibility. Journal of Cerebral Blood Flow and Metabolism, 27 (3 ), 597–605.16804550
Meles, S. K. , Teune, L. K. , de Jong, B. M. , Dierckx, R. A. , & Leenders, K. L. (2017). Metabolic imaging in Parkinson disease. Journal of Nuclear Medicine, 58 (1 ), 23–28.27879372
Meyer, P. T. , Frings, L. , Rücker, G. , & Hellwig, S. (2017). 18F‐FDG PET in parkinsonism: Differential diagnosis and evaluation of cognitive impairment. Journal of Nuclear Medicine, 58 (12 ), 1888–1898.28912150
Morris, H. R. , Spillantini, M. G. , Sue, C. M. , & Williams‐Gray, C. H. (2024). The pathogenesis of Parkinson's disease. Lancet, 403 (10423 ), 293–304.38245249
Mure, H. , Hirano, S. , Tang, C. C. , Isaias, I. U. , Antonini, A. , Ma, Y. , Dhawan, V. , & Eidelberg, D. (2011). Parkinson's disease tremor‐related metabolic network: Characterization, progression, and treatment effects. NeuroImage, 54 (2 ), 1244–1253.20851193
Oertel, W. H. , Henrich, M. T. , Janzen, A. , & Geibl, F. F. (2019). The locus coeruleus: Another vulnerability target in Parkinson's disease. Movement Disorders, 34 (10 ), 1423–1429.31291485
Pasquini, J. , Durcan, R. , Wiblin, L. , Gersel Stokholm, M. , Rochester, L. , Brooks, D. J. , Burn, D. , & Pavese, N. (2019). Clinical implications of early caudate dysfunction in Parkinson's disease. Journal of Neurology, Neurosurgery, and Psychiatry, 90 (10 ), 1098–1104.31079063
Postuma, R. B. , Berg, D. , Stern, M. , Poewe, W. , Olanow, C. W. , Oertel, W. , Obeso, J. , Marek, K. , Litvan, I. , Lang, A. E. , Halliday, G. , Goetz, C. G. , Gasser, T. , Dubois, B. , Chan, P. , Bloem, B. R. , Adler, C. H. , & Deuschl, G. (2015). MDS clinical diagnostic criteria for Parkinson's disease. Movement Disorders, 30 (12 ), 1591–1601.26474316
Prange, S. , Metereau, E. , Maillet, A. , Lhommée, E. , Klinger, H. , Pelissier, P. , Ibarrola, D. , Heckemann, R. A. , Castrioto, A. , Tremblay, L. , Sgambato, V. , Broussolle, E. , Krack, P. , & Thobois, S. (2019). Early limbic microstructural alterations in apathy and depression in de novo Parkinson's disease. Movement Disorders, 34 (11 ), 1644–1654.31309609
Rolls, E. T. , Huang, C. C. , Lin, C. P. , Feng, J. , & Joliot, M. (2020). Automated anatomical labelling atlas 3. NeuroImage, 206 , 116189.31521825
Rucco, R. , Lardone, A. , Liparoti, M. , Lopez, E. T. , De Micco, R. , Tessitore, A. , Granata, C. , Mandolesi, L. , Sorrentino, G. , & Sorrentino, P. (2022). Brain networks and cognitive impairment in Parkinson's disease. Brain Connectivity, 12 (5 ), 465–475.34269602
Sacheli, M. A. , Neva, J. L. , Lakhani, B. , Murray, D. K. , Vafai, N. , Shahinfard, E. , English, C. , McCormick, S. , Dinelle, K. , Neilson, N. , McKenzie, J. , Schulzer, M. , McKenzie, D. C. , Appel‐Cresswell, S. , McKeown, M. J. , Boyd, L. A. , Sossi, V. , & Stoessl, A. J. (2019). Exercise increases caudate dopamine release and ventral striatal activation in Parkinson's disease. Movement Disorders, 34 (12 ), 1891–1900.31584222
Scherfler, C. , Schwarz, J. , Antonini, A. , Grosset, D. , Valldeoriola, F. , Marek, K. , Oertel, W. , Tolosa, E. , Lees, A. J. , & Poewe, W. (2007). Role of DAT‐SPECT in the diagnostic work up of parkinsonism. Movement Disorders, 22 (9 ), 1229–1238.17486648
Schindlbeck, K. A. , Lucas‐Jiménez, O. , Tang, C. C. , Morbelli, S. , Arnaldi, D. , Pardini, M. , Pagani, M. , Ibarretxe‐Bilbao, N. , Ojeda, N. , Nobili, F. , & Eidelberg, D. (2020). Metabolic network abnormalities in drug‐Naïve Parkinson's disease. Movement Disorders, 35 (4 ), 587–594.31872507
Sigurdsson, H. P. , Yarnall, A. J. , Galna, B. , Lord, S. , Alcock, L. , Lawson, R. A. , Colloby, S. J. , Firbank, M. J. , Taylor, J. P. , Pavese, N. , Brooks, D. J. , O'Brien, J. T. , Burn, D. J. , & Rochester, L. (2022). Gait‐related metabolic covariance networks at rest in Parkinson's disease. Movement Disorders, 37 (6 ), 1222–1234.35285068
Singh, A. (2018). Oscillatory activity in the cortico‐basal ganglia‐thalamic neural circuits in Parkinson's disease. The European Journal of Neuroscience, 48 (8 ), 2869–2878.29381817
Sun, H. M. , Wang, L. N. , Ji, M. , Gan, C. T. , Yuan, Y. S. , Cao, X. Y. , Zhang, H. , & Zhang, K. Z. (2022). BDNF rs6265 single‐nucleotide polymorphism is involved in levodopa‐induced dyskinesia in Parkinson's disease via its regulation of the cortical thickness of the left postcentral gyrus. Quantitative Imaging in Medicine and Surgery, 12 (6 ), 3264–3275.35655818
Sun, T. , Wang, Z. , Wu, Y. , Gu, F. , Li, X. , Bai, Y. , Shen, C. , Hu, Z. , Liang, D. , Liu, X. , Zheng, H. , Yang, Y. , El Fakhri, G. , Zhou, Y. , & Wang, M. (2022). Identifying the individual metabolic abnormities from a systemic perspective using whole‐body PET imaging. European Journal of Nuclear Medicine and Molecular Imaging, 49 (8 ), 2994–3004.35567627
Tahmasian, M. , Eickhoff, S. B. , Giehl, K. , Schwartz, F. , Herz, D. M. , Drzezga, A. , van Eimeren, T. , Laird, A. R. , Fox, P. T. , Khazaie, H. , Zarei, M. , Eggers, C. , & Eickhoff, C. R. (2017). Resting‐state functional reorganization in Parkinson's disease: An activation likelihood estimation meta‐analysis. Cortex, 92 , 119–138.28467917
Teune, L. K. , Renken, R. J. , de Jong, B. M. , Willemsen, A. T. , van Osch, M. J. , Roerdink, J. B. , Dierckx, R. A. , & Leenders, K. L. (2014). Parkinson's disease‐related perfusion and glucose metabolic brain patterns identified with PCASL‐MRI and FDG‐PET imaging. NeuroImage: Clinical, 5 , 240–244.25068113
Teune, L. K. , Renken, R. J. , Mudali, D. , De Jong, B. M. , Dierckx, R. A. , Roerdink, J. B. , & Leenders, K. L. (2013). Validation of Parkinsonian disease‐related metabolic brain patterns. Movement Disorders, 28 (4 ), 547–551.23483593
Tian, M. , Zuo, C. , Cahid Civelek, A. , Carrio, I. , Watanabe, Y. , Kang, K. W. , Murakami, K. , Prior, J. O. , Zhong, Y. , Dou, X. , Yu, C. , Jin, C. , Zhou, R. , Liu, F. , Li, X. , Lu, J. , Zhang, H. , & Wang, J. (2024). Molecular imaging‐based precision medicine task group of A3 (China‐Japan‐Korea) foresight program. International consensus on clinical use of presynaptic dopaminergic positron emission tomography imaging in parkinsonism. European Journal of Nuclear Medicine and Molecular Imaging, 51 (2 ), 434–442.37789188
Wang, J. , Dai, L. , Chen, S. , Zhang, Z. , Fang, X. , & Zhang, Z. (2024). Protein‐protein interactions regulating α‐synuclein pathology. Trends in Neurosciences, 47 (3 ), 209–226.38355325
Xu, J. , Zhang, J. , Wang, J. , Li, G. , Hu, Q. , & Zhang, Y. (2016). Abnormal fronto‐striatal functional connectivity in Parkinson's disease. Neuroscience Letters, 613 , 66–71.26724369
Zhao, P. , Zhang, B. , Gao, S. , & Li, X. (2020). Clinical features, MRI, and 18F‐FDG‐PET in differential diagnosis of Parkinson disease from multiple system atrophy. Brain and Behavior: A Cognitive Neuroscience Perspective, 10 (11 ), e01827.
Zheng, J. H. , Ma, J. J. , Sun, W. H. , Wang, Z. D. , Chang, Q. Q. , Dong, L. R. , Shi, X. X. , & Li, M. J. (2023). Excessive daytime sleepiness in Parkinson's disease is related to functional abnormalities in the left angular gyrus. Clinical Neuroradiology, 33 (1 ), 121–127.35768695
Zhong, Y. , Liu, H. , Liu, G. , Zhao, L. , Dai, C. , Liang, Y. , du, J. , Zhou, X. , Mo, L. , Tan, C. , Tan, X. , Deng, F. , Liu, X. , & Chen, L. (2022). A review on pathology, mechanism, and therapy for cerebellum and tremor in Parkinson's disease. Nature Partner Journals Parkinson's Disease, 8 (1 ), 82.
