White Matter Microstructural Alterations and Brain Metabolism distributions in Parkinson's Disease

doi:10.21203/rs.3.rs-4938084/v1

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White Matter Microstructural Alterations and Brain Metabolism distributions in Parkinson's Disease

https://doi.org/10.21203/rs.3.rs-4938084/v1

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This study aimed to use simultaneous ¹⁸F-FDG PET/MRI based on Automated Fiber Quantification (AFQ) to determine whether there is a relationship between white matter microstructure changes and glucose metabolism distribution in PD.The study involved 38 subjects, including 23 PD patients and 15 age and sex-matched healthy controls（HC）. Primary clinical data and cognitive assessments were collected. All subjects underwent a simultaneous ¹⁸F-FDG PET/MR scan. AFQ was utilized to calculate tract-wise diffusion properties of 20 major white matter tracts. PD patients showed reduced mean Mini-Mental State Examination （MMSE） and Montreal Cognitive Assessment （MoCA）scores compared to HC subjects (P < 0.05). PD patients showed higher mean diffusivity (MD) (P = 0.047) and axial diffusivity (AD) (P = 0.02) along the right corticospinal tract (CST) compared to HC. The microstructural change of CST was mainly located in the parietal part (node 67-100). Compared to HC, PD patients had FDG hypermetabolism in the right paracentral lobule (P = 0.0204) and bilateral putamen (left: P = 0.0075; right: P = 0.0155) and hypometabolism in the right calcarine (P = 0.0489). Hypermetabolism was found in the right paracentral lobule, which connects with the cortex of the right CST, and positively correlated with MD (r = 0.612, P < 0.001) and AD (r = 0.516, P < 0.001).We observed microstructural changes and glucose metabolism distribution characteristics in PD patients. These results may provide imaging evidence for studying the pathology of PD.

Parkinson’s disease

Diffusion Tensor Imaging

[18F]-FDG PET/MRI

Multimodal Imaging

Automated Fiber Quantification

Parkinson's disease (PD) is the second most common age-related neurodegenerative disease, affecting 2–3% of the population over 65 years old(Goldman and Postuma,2014), characterized by motor symptoms including resting tremor, bradykinesia, muscle rigidity, and postural balance disorders. PD is a heterogeneous disease that, in addition to motor symptoms, is also accompanied by a variety of non-motor symptoms, such as cognitive impairment and sleep disorders, which seriously affect the quality of life of patients and bring a heavy burden to the patient's families and society. However, the underlying pathophysiological mechanism of PD is still not completely understood, which makes it challenge to conduct in-depth research on the treatment of PD. Neuroimaging significantly enhances our understanding of the pathophysiology of PD. It is well-established that the primary pathological basis of PD involves extensive degeneration and loss of dopaminergic neurons in the substantia nigra-striatum, as well as other pigmented neurons, with the formation of eosinophilic inclusions known as Lewy bodies, primarily composed of abnormally aggregated α-synuclein. In addition to the degeneration of dopaminergic neurons, the disruption of white matter pathways is also a critical factor in the pathogenesis of PD(Bohnen and Albin,2011;Tagliaferro and Burke,2016).

Diffusion Tensor Imaging (DTI) (Shih, et al.,2022) is an imaging technique that leverages the anisotropy of water molecule diffusion to probe the microstructure of tissues, which is the most common method for measuring diffusion tensors from DWI data by the single tensor model and provides key metrics to evaluate the integrity of white matter microstructure, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). Moreover, DTI enables non-invasive visualization of white matter fiber connections in vivo(Basser PJ,2000). Previous studies using DTI have shown white matter (WM) damage in PD patients, with widespread microstructural damage indicated by decreased FA and increased MD, AD, and RD in numerous fiber tracts, such as the corticospinal tract, optic radiation, corpus callosum, frontal-occipital fasciculus, nigrostriatal pathway, inferior occipitofrontal fasciculus, uncinate fasciculus and posterior thalamic radiation(Pozorski, et al.,2018;Rau, et al.,2019;Zhang, et al.,2016). However, these studies have not reached consistent conclusions and sometimes produce conflicting results(Pimer, et al.,2023;Atkinson-Clement, et al.,2017).

At present, common DTI analysis methods include the region of interest (ROI)-based analysis, voxel-based morphometry (VBM), tract-based spatial statistics (TBSS) and fiber tractography. Brain images are registered to a standard coordinate system in voxel-based analysis to compare the same voxels between subjects. However, this method may not accurately align tracts at the individual level(Wassermann D,2011) due to differences in the size and shape of fiber tracts. In contrast, TBSS, a skeleton-based method, was proposed to reduce the impact of poor local registration. Although more accurate than VBA, it still lacks the precision to characterize white matter integrity along each fiber tract(Yu, et al.,2023). Fiber tractography is widely regarded as the most accurate method for identifying white matter tracts in the living brain but has two limitations. First, it often relies on manually drawn ROIs, which is time-consuming and labor-intensive. Second, averaging along the entire length of the fiber tract may obscure important information(Yeatman, et al.,2011). Automated Fiber Quantification (AFQ) is a fully automated DTI fiber tracking technique that extracts FA, MD, AD, and RD values from 100 nodes along 20 major white matter fiber tracts in individual brains. This allows for a comprehensive analysis of the tissue characteristics along the length of the fiber tract, identifying the location of damaged segments(Yeatman, et al.,2012). This method, which considers the characteristics within regions of the tract, provides a more sensitive measurement method for neuroanatomical white matter changes in PD patients compared to whole-tract analysis methods.

Due to the lack of reliable α-synuclein tracers, functional neuroimaging cannot specifically track α-synuclein distribution in the living brain of PD patients. However, brain metabolism, primarily glucose metabolism, is closely related to neuronal and synaptic function under physiological conditions. Therefore, the distribution of glucose metabolism can reflect the relative activity of neurons. ¹⁸F-FDG PET imaging, which shows glucose uptake in the brain cortex and neural nuclei, reflects brain glucose metabolism distribution and provides a relatively accurate method for studying brain function under both physiological and pathological conditions(Meles, et al.,2017). Currently, ¹⁸F-FDG PET imaging based on the PD-related pattern (PDRP)(Tang, et al.,2010;Schindlbeck and Eidelberg,2018) is widely used in the differential diagnosis of PD and atypical parkinsonian syndromes, analysis of glucose metabolism in different clinical manifestations, and potential analysis of pathophysiological mechanisms of PD(Imarisio, et al.,2023;Kubler, et al.,2023;Kim R,2022).

Multimodal neuroimaging analysis has been used in the study of PD. However, the associations between different brain neuropathological features identified by functional imaging in PD have yet to be thoroughly examined, even poorly understood. In particular, it remains unclear whether there is an association between the integrity of white matter microstructure and glucose metabolism distribution in PD patients. Few studies currently investigate the correlation between white matter microstructure and glucose metabolism in PD patients. Therefore, this study aims to use simultaneous ¹⁸F-FDG PET/MRI based on Automated Fiber Quantification (AFQ) to focus on the relationship between white matter microstructure changes and glucose metabolism distribution in PD.

2.1 Participants

Twenty-three PD patients (aged 48–81 years; 8 women) and 15 healthy controls (aged 53–71 years; 8 women) participated in this study. Data were collected from June 2023 to April 2024 at the First Affiliated Hospital of Harbin Medical University. The clinical diagnosis of PD was made by experienced neurologists according to the MDS Clinical Diagnostic Criteria for Parkinson’s disease(Postuma, et al.,2015). Exclusion criteria included contraindications to MRI, prior head trauma, other neurological or psychiatric diseases, and current alcohol or drug abuse. All participants completed the Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), Unified Parkinson's Disease Rating Scale Part III (UPDRS-Ⅲ), Hoehn and Yahr Scale (H-Y Stage) and Non-Motor Symptom Assessment Scale for Parkinson's Disease (NMSS), where PD patients were assessed 12 hours after drug withdrawal. The detailed demographics and clinical characteristics are illustrated in Table 1. A general scheme of the current study is depicted in Fig. 1. This study was approved by the ethics committee of the First Affiliated Hospital of Harbin Medical University (Ethics number: Harbin Medical Research/Articles Ethics audits 2023241), and written informed consent was obtained for all the subjects.

Table 1. Demographics and Clinical Information

	HC	PD	P-value
Demographics
n (total n = 38)	15	23
Age, y	59.6 ± 5.1	60.9 ± 10.1	0.891
Gender (M/F)	7/8	15/8	0.319
Clinical
MMSE	29.3 ± 0.7	24.8 ± 4.1	< 0.001
MoCA	26.7 ± 0.7	19.2 ± 4.5	< 0.001
H-Y stage	0	2.1 ± 0.7	-
UPDRS-Ⅲ	0.9 ± 2.3	27 ± 11.4	< 0.001
NMSS	13.3 ± 7.3	53.3 ± 51.5	< 0.001

Abbreviations/acronyms: HC = Healthy controls; PD = Parkinson’s disease; MMSE = Mini-Mental State Examination; MoCA= Montreal Cognitive Assessment; H-Y stage = Hoehn and Yahr Scale; UPDRS-Ⅲ = the third part of MDS Unified-Parkinson Disease Rating Scale; NMSS = Non-motor Symptoms Scale

2.2 Image acquisition

All patients underwent simultaneous ¹⁸F-FDG PET/MR examinations using a uPMR790 (United Imaging). They were instructed to fast for 6 hours and stop taking anti-Parkinson's drugs for 12 hours prior to the scan. After intravenous injection of 3.7 MBq/kg ¹⁸F-FDG, the participants remained comfortable in a quiet, dimly lit room with their eyes closed. PET data were acquired in list mode over 15 minutes, 50 minutes after intravenous administration of ¹⁸F-FDG.The PET datasets were reconstructed with a 192 × 192 matrix (voxel size = 1.5625 × 1.5625 × 2 mm³) with TOF and point spread function (PSF) ordered subset expectation maximization (OSEM) algorithms with 20 subsets and 4 iterations, followed by a 3-mm Gaussian filter. MRI images were obtained simultaneously with the PET acquisition. Diffusion tensor imaging was performed using an echo-planar imaging (EPI) sequence with the following parameters: TR = 6006 ms, TE = 82 ms, matrix size = 80 × 80 × 46 voxels, voxel size = 3 × 3 × 3 mm, 64 gradient directions at b = 1,000 s/mm² and 90° flip angle. In addition, we acquired high-resolution T1-weighted 3-dimensional images with a magnetization-prepared rapid gradient echo sequence (3D-MPRAGE) and the following parameters: TR = 7.21 ms, TE = 3 ms, TI = 750 ms, matrix size = 180 × 230 × 256 voxels, 10° flip angle, and 1 mm³ spatial resolution.

2.3 Data preprocessing

The DTI data preprocessing was performed using the FMRIB Diffusion Toolbox (https://users.fmrib.ox.ac.uk), which includes corrections for geometric distortion, eddy-current, motion, skull stripping, and tensor fitting.

PET images were preprocessed in SPM12. They were spatially normalized to the standard MNI coordinates, and spatial smoothing was performed using an 8-mm FWHM Gaussian kernel. The standard uptake value (SUVr) ratio was calculated via an iterative data-driven approach that could define the unbiased reference region(Nie, et al.,2018;Zang, et al.,2022;Zang, et al.,2023). Extraction of SUVr was performed according to the Anatomical Automatic Labeling (AAL) atlas for further analysis.

2.4 Quantification of diffusion measure profiles

The preprocessed diffusion images were co-registered to their corresponding T1 images using SPM12 for each participant. Then, automated fiber quantification (AFQ, https://github.com/jyeatman/AFQ) analysis was utilized for whole-brain deterministic tractography. The complete workflow included (1) whole fiber tractography, (2) fiber tract segmentation, (3) fiber tract refinement, (4) fiber tract cleaning, (5) fiber tract clipping, (6) fiber tract quantification. A total of twenty fiber tracts were identified according to the predefined ROIs and probability maps, including thalamic radiation, corticospinal tract (CST), cingulum cingulate, cingulum hippocampus, callosum forceps major, callosum forceps minor, inferior frontoccipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF), uncinated fasciculus (UF), arcuate fasciculus (AF). For each tract, the average diffusion metrics, including FA, MD, AD, and RD values, were calculated to measure the tract-wise diffusion properties. For intra-tract analysis, each tract was resampled to 100 equally spaced nodes, and the diffusion metrics (i.e., FA, MD, AD, and RD values) were calculated as a weighted average of each fiber's measurement at each node.

2.5 Statistical analysis

The SUVr in specific brain regions and intra-tract diffusion metrics were compared using two-sample t-tests between two groups. Pearson's correlation was utilized to investigate the relationship between cerebral metabolism and white matter microstructure. FDR correction was performed to find significant results. All these analyses were finished using an in-house MATLAB script and SPSS 26.0. P < 0.05 was considered statistically significant.

3.1 Demographic and clinical information

The detailed demographic and clinical information for all the participants is listed in Table S1. PD patients showed significantly reduced MMSE (P < 0.001) and MoCA (P < 0.001) compared to the HCs. Higher UPDRS-Ⅲ (P < 0.001) and NMSS (P < 0.001) were found higher in PD patients.

3.2 White matter microstructural changes in the CST of PD patients

PD patients showed significantly higher MD (P = 0.047) and AD (P = 0.02) in right CST compared to HC, as shown in Fig. 2a. The node-wise comparisons showed more detailed white matter microstructural changes, which occurred at the terminal portions of right CST (MD: nodes 67–100; AD: nodes 67–100), left thalamic radiation (MD: nodes 1–37; AD: nodes 2–29; RD: nodes 1–34), right thalamic radiation (MD: nodes 11–47, 53–65; AD: nodes 11–32, 52–78; RD: nodes 12–45), left CST (MD: nodes 34–47), right IFOF (MD: nodes 8–32, 85–95; RD: nodes 8–32,85–95) and left UF (FA: nodes 39–65; RD: nodes 51–65). The corresponding line charts are shown in Fig. 3.

3.3 Cerebral metabolic distribution of PD patients

The ROI-wise comparison showed that PD patients had FDG hypermetabolism in the right paracentral lobule (P = 0.0204) and bilateral putamen (left: P = 0.0075; right: P = 0.0155) and hypometabolism in the right calcarine (P = 0.0489), which was shown in Fig. 2b.

3.4 Correlation between the diffusion metrics and cerebral glucose metabolism

The mean MD in the disrupted partial subsegment (nodes 67–100) of the right CST was positively correlated with FDG SUVr in the right paracentral lobule in all participants (r = 0.612, P < 0.001). We also found that the mean AD of the same subsegment was significantly correlated with FDG SUVr in the right paracentral lobule (r = 0.516, P < 0.001). The corresponding scatter plots are shown in Fig. 4a, respectively.

3.5 Correlation between the diffusion metrics and clinical scores

Among all participants, MMSE and MoCA scores showed a significantly negative correlation with mean MD (MMSE: r = -0.454, P = 0.004; MoCA: r = -0.461, P = 0.004) in the disrupted partial subsegment (nodes 67–100) of right CST. In PD patients, H-Y stage and UPDRS-Ⅲ showed significantly positive correlation with the mean MD (H-Y stage: r = 0.461, P = 0.004; UPDRS-Ⅲ: r = 0.532, P < 0.001) and AD (H-Y stage: r = 0.366, P = 0.024; UPDRS-Ⅲ: r = 0.335, P = 0.039) in the same part of right CST. The corresponding scatter plots are shown in Fig. 4b, respectively.

This study is the first to use the AFQ analysis method on simultaneous PET/MRI imaging to reveal the main white matter microstructure changes and brain glucose metabolism distribution in PD patients and their interrelationships, thereby providing a basis for PD's microstructural and functional connectivity.

The results of this study indicate that compared to HC, PD patients have significantly higher MD and AD values in the right CST. Increased MD suggests disrupted integrity of nerve fiber bundles, while increased AD reflects axonal damage. Therefore, this study reveals that the integrity of the right CST is impaired in PD patients, which is consistent with previous findings(Pimer, et al.,2023;Yang, et al.,2022). However, some studies have suggested structural changes in the CST (i.e., increased FA) in PD patients(Atkinson-Clement, et al.,2017;Chen, et al.,2018), which is inconsistent with our results. Other studies(Lu, et al.,2016) found no differences in DTI metrics of the CST between PD patients and controls. Variations in results may be due to differences in the disease severity of PD subjects included in the studies. Early-stage PD might show no or compensatory changes in the CST, whereas later stages show impaired integrity. The study of Guimarães et al.(Guimaraes, et al.,2018) supports this view, showing increased AD and RD values in severe PD compared to mild PD and HC.

However, further studies are needed to determine when CST remains unchanged, when compensatory changes occur, or whether it is potentially affected by other pathophysiological mechanisms. The discrepancies might also be due to different analysis methods. Previous studies often used ROI-based analysis, VBM, TBSS, or other methods. Our study uses the fully automated DTI fiber tracking technique AFQ(Yeatman, et al.,2012), which eliminates errors from manually drawing ROIs and avoids averaging across entire fiber bundles, which may mask critical information. In this study, using the AFQ technique for node-by-node comparisons, it was found that in PD patients, MD and AD values of the subsegments (nodes 67–100) of the CST, located near the corona radiata and cerebral peduncle, were significantly increased compared to HC. This indicates white matter microstructural changes in this region, pinpointing the precise location of fiber bundle damage. This technique has been used in studies of brain white matter microstructural changes in diseases such as Alzheimer's, epilepsy, Wilson's disease, and systemic lupus erythematosus(Chen, et al.,2020;Keller, et al.,2017;Wu, et al.,2023;Zhang, et al.,2024). However, there have been few studies on PD.

Interestingly, we found that in PD patients, the H-Y stage and UPDRS-III scores were positively correlated with the MD and AD values of the damaged subsegments of the right CST, further supporting the notion that the integrity of the CST is related to disease severity. Additionally, we found that in PD patients, MMSE and MoCA scores were negatively correlated with the MD values of the same portion of the right CST, indicating that as the integrity of the right CST deteriorates, cognitive deficits in patients become more pronounced. Our results suggest that the CST, as the primary motor pathway transmitting information from the brain to the spinal cord, primarily governing voluntary movements of skeletal muscles, is also associated with cognitive decline in PD patients when its integrity is compromised, which is consistent with the conclusions of Sang (Sang, et al.,2022).

Using AFQ technology for node-by-node comparison, we identified localized microstructural changes in fiber tracts that were not detectable over the entire tract. Compared to HC, PD patients exhibited increased MD, AD, and RD values in segments of bilateral ThR, increased MD values in segments of the right CST, increased MD and RD values in segments of the right IFOF, and decreased FA value and increased RD value in segments of the left UF. ThR connects white matter fibers that connect the frontal cortex to the thalamus and basal ganglia, and its disruption may lead to memory impairment(Niida, et al.,2018). IFOF connects the occipital and frontal lobes, including fibers linking the frontal lobe to the posterior parietal and temporal lobes(Taoka T,2006). Our study showed that the most severe IFOF changes occurred in the anterior and posterior segments, potentially explaining cognitive and motor deficits in PD. UF connects the orbitofrontal cortex to the anterior temporal lobe and plays a crucial role in memory. However, no significant correlation was found between clinical scores and these fiber tracts, likely due to our small sample size and the lack of detailed clinical assessments in specific cognitive domains (such as five cognitive areas(Han, et al.,2021): attention and working memory, working memory, executive function, language, memory, and visuospatial function), which requires further research to confirm.

The simultaneous acquisition of ¹⁸F-FDG PET images allowed us to analyze SUVr in regions of interest based on the Automated Anatomical Labeling (AAL) atlas. Results showed increased glucose metabolism in the right paramedian lobule and bilateral putamen in PD compared to HC, while the right calcarine cortex exhibited decreased metabolism. This aligns with previous findings of brain metabolic changes in PD, where relative hypermetabolism is seen in the striatum (particularly the putamen), thalamus, pons, cerebellum, and motor cortex, and hypometabolism is observed in the premotor, supplementary motor, and posterior parietal areas(Ma, et al.,2007). The PD-related pattern (PDRP), established through scaled subprofile model (SSM)/principal component analysis (PCA) and ¹⁸F-FDG PET imaging, corroborates these metabolic changes(Eidelberg,2009;Woo, et al.,2017). This metabolic pattern likely reflects dysfunction in the cortical-striatal-pallidal-thalamic-cortical (CSPTC) motor circuit(DeLong MR,2007). This was confirmed in patients with PD undergoing subthalamic nucleus deep brain stimulation (DBS)(Lin, et al.,2008), where PDRP expression values obtained by preoperative ¹⁸F-FDG PET imaging significantly correlated with spontaneous discharge frequencies in the subthalamic nucleus, suggesting PDRP is linked to neuronal activity at critical nodes in the basal ganglia output pathway. Thus, PDRP explains the widespread functional abnormalities in CSPTC and related neural pathways in PD.

However, we have found in our clinical work that not all PD patients strictly follow the described metabolic pattern, likely due to variations in disease severity and clinical subtypes. Several studies have validated our hypotheses. For instance, the Parkinson's Disease Cognitive-Related Pattern (PDCP) is characterized by decreased metabolism in the medial frontal and parietal lobes and increased cerebellum, vermis, and dentate gyrus (Ko, et al.,2017). The Parkinson's Disease Tremor-Related Pattern (PDTP) shows varying degrees of increased glucose metabolism in the cerebellum and caudate/putamen(Mure, et al.,2011;Song, et al.,2023). However, more research is needed to confirm this. This study used a univariate model and could not investigate the relationship between glucose metabolism and clinical indices. Applying covariance analysis could quantify PDRP expression and study its relationship with clinical indices or predictive capabilities(Meles, et al.,2017).

This study observed significant damage in the right CST near the cerebral cortex, along with increased glucose metabolism in the adjacent right paramedian lobule (PML). Correlation analysis revealed that the mean MD and AD values of the damaged segments of the right CST were positively correlated with the FDG SUVr of the right PML. We hypothesize that the PML increases metabolic activity to compensate for the loss of structural connectivity in the CST, indicating that increased PML metabolism may be a compensatory mechanism(Kordys, et al.,2017). Additionally, due to the impairment of the CST and other motor pathways, the brain may reconfigure other neural networks to maintain essential motor control(Chu, et al.,2023;Lizarraga, et al.,2024), which could also lead to increased metabolic activity in the PML. By simultaneous PET/MRI imaging, we linked changes in white matter microstructure with glucose metabolism distribution and their interrelationships, revealing potential pathophysiological mechanisms of PD.

4.1 Limitations

Firstly, the sample size of this study is relatively small, and it is a single-center study, which may introduce bias. Secondly, we should have performed detailed subgroup analyses of PD patients, limiting the depth of analysis regarding disease severity and different clinical subtypes. Consequently, the pathophysiological study of different clinical manifestations and disease severity must be more comprehensive. Lastly, although this study is a multimodal neuroimaging study, it did not cover more imaging methods to identify the relationships between different brain pathological features. Future research will aim to recruit more subjects to increase sample size, incorporate multicenter studies, and utilize additional neuroimaging techniques for subgroup analysis of different clinical presentations (including motor and non-motor symptoms) and severities to gain a deeper understanding of PD's pathophysiology and provide comprehensive explanations for disease symptoms and surgical treatment principles, aiding in the development of more effective treatments.

To our knowledge, this study is the first to use the AFQ analysis method to analyze simultaneous PET/MRI imaging in PD. The results indicate that patients with PD can exhibit microstructural integrity damage in the CST. AFQ point-by-point analysis detects local fiber microstructural changes not identified in the entire fiber tract, providing an effective method for further in-depth research. Concurrent ¹⁸F-FDG PET imaging can display glucose metabolism distribution in the brains of PD patients and suggest a connection with white matter fiber microstructure, deepening our understanding of PD pathophysiology.

Author contributions: Concept and design: Mengjiao Wang, Peng Fu, Changjiu Zhao. Data collection, analysis and interpretation: Mengjiao Wang, Yansong Liu, Yuyin Jiao, Yujie Hu, Yang Yang, Yangyang Wang. Drafting of the manuscript: Mengjiao Wang, Yujie Hu. Critical revision of the manuscript for important intellectual content: Wei Han, Yifeng Wang, Zhang Linhan Zhang, Changjiu Zhao.

Funding Sources: This work was supported by the Scientific Research and Innovation Fund of the First Affiliated Hospital of Harbin Medical University [grant number2024M05].

Compliance with Ethical Standards: All procedures performed were in accordance with the ethical standards of the institutional research committee and with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Informed consent and consent to participate: Written informed consent has been obtained from all participants.

Conflict of Interest statement

The authors declare no conflict of interests in the current study.

Data availability

Data will be made available on request.

Acknowledgement

This work was supported by Zhehao Lv, who provided help with language polishing.

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No competing interests reported.

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White Matter Microstructural Alterations and Brain Metabolism distributions in Parkinson's Disease

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Version 1

Abstract

Figures

1. Introduction

2 Materials and methods

2.1 Participants

2.2 Image acquisition

2.3 Data preprocessing

2.4 Quantification of diffusion measure profiles

2.5 Statistical analysis

3 Results

3.1 Demographic and clinical information

3.2 White matter microstructural changes in the CST of PD patients

3.3 Cerebral metabolic distribution of PD patients

3.4 Correlation between the diffusion metrics and cerebral glucose metabolism

3.5 Correlation between the diffusion metrics and clinical scores

4. Discussion

4.1 Limitations

5.Conclusions

Declarations

References

Additional Declarations

Status:

Version 1