Metabolic brain network in the Chinese patients with Parkinson's disease based on 18F-FDG PET imaging

PET-based brain molecular connectivity in neurodegenerative disease

PURPOSE OF REVIEW

Molecular imaging has traditionally been used and interpreted primarily in the context of localized and relatively static neurochemical processes. New understanding of brain function and development of novel molecular imaging protocols and analysis methods highlights the relevance of molecular networks that co-exist and interact with functional and structural networks. Although the concept and evidence of disease-specific metabolic brain patterns has existed for some time, only recently has such an approach been applied in the neurotransmitter domain and in the context of multitracer and multimodal studies. This review briefly summarizes initial findings and highlights emerging applications enabled by this new approach.

RECENT FINDINGS

Connectivity based approaches applied to molecular and multimodal imaging have uncovered molecular networks with neurodegeneration-related alterations to metabolism and neurotransmission that uniquely relate to clinical findings; better disease stratification paradigms; an improved understanding of the relationships between neurochemical and functional networks and their related alterations, although the directionality of these relationships are still unresolved; and a new understanding of the molecular underpinning of disease-related alteration in resting-state brain activity.

SUMMARY

Connectivity approaches are poised to greatly enhance the information that can be extracted from molecular imaging. While currently mostly contributing to enhancing understanding of brain function, they are highly likely to contribute to the identification of specific biomarkers that will improve disease management and clinical care.

Spatiotemporal patterns of brain iron-oxygen metabolism in patients with Parkinson's disease

OBJECTIVES

Iron deposition and mitochondrial dysfunction are closely associated with the genesis and progression of Parkinson's disease (PD). This study aims to extract susceptibility and oxygen extraction fraction (OEF) values of deep grey matter (DGM) to explore spatiotemporal progression patterns of brain iron-oxygen metabolism in PD.

METHODS

Ninety-five PD patients and forty healthy controls (HCs) were included. Quantitative susceptibility mapping (QSM) and OEF maps were computed from MRI multi-echo gradient echo data. Analysis of covariance (ANCOVA) was used to compare mean susceptibility and OEF values in DGM between early-stage PD (ESP), advanced-stage PD (ASP) patients and HCs. Then Granger causality analysis on the pseudo-time-series of MRI data was applied to assess the causal effect of early altered nuclei on iron content and oxygen extraction in other DGM nuclei.

RESULTS

The susceptibility values in substantia nigra (SN), red nucleus, and globus pallidus (GP) significantly increased in PD patients compared with HCs, while the iron content in GP did not elevate obviously until the late stage. The mean OEF values for the caudate nucleus, putamen, and dentate nucleus were higher in ESP patients than in ASP patients or/and HCs. We also found that iron accumulation progressively expands from the midbrain to the striatum. These alterations were correlated with clinical features and improved AUC for early PD diagnosis to 0.824.

CONCLUSIONS

Abnormal cerebral iron deposition and tissue oxygen utilization in PD measured by QSM and OEF maps could reflect pathological alterations in neurodegenerative processes and provide valuable indicators for disease identification and management.

CLINICAL RELEVANCE STATEMENT

Noninvasive assessment of cerebral iron-oxygen metabolism may serve as clinical evidence of pathological changes in PD and improve the validity of diagnosis and disease monitoring.

KEY POINTS

• Quantitative susceptibility mapping and oxygen extraction fraction maps indicated the cerebral pathology of abnormal iron accumulation and oxygen metabolism in Parkinson's disease. • Iron deposition is mainly in the midbrain, while altered oxygen metabolism is concentrated in the striatum and cerebellum. • The susceptibility and oxygen extraction fraction values in subcortical nuclei were associated with clinical severity.

Global biomarker trends in Parkinson's disease research: A bibliometric analysis

As the second most common neurodegenerative disease globally, Parkinson's disease (PD) affects millions of people worldwide. In recent years, the scientific publications related to PD biomarker research have exploded, reflecting the growing interest in unraveling the complex pathophysiology of PD. In this study, we aim to use various bibliometric tools to identify key scientific concepts, detect emerging trends, and analyze the global trends and development of PD biomarker research.The research encompasses various stages of biomarker development, including exploration, identification, and multi-modal research. MOVEMENT DISORDERS emerged as the leading journal in terms of publications and citations. Key authors such as Mollenhauer and Salem were identified, while the University of Pennsylvania and USA stood out in collaboration and research output. NEUROSCIENCES emerged as the most important research direction. Key biomarker categories include α-synuclein-related markers, neurotransmitter-related markers, inflammation and immune system-related markers, oxidative stress and mitochondrial function-related markers, and brain imaging-related markers. Furthermore, future trends in PD biomarker research focus on exosomes and plasma biomarkers, miRNA, cerebrospinal fluid biomarkers, machine learning applications, and animal models of PD. These trends contribute to early diagnosis, disease progression monitoring, and understanding the pathological mechanisms of PD.

International consensus on clinical use of presynaptic dopaminergic positron emission tomography imaging in parkinsonism

PURPOSE

Presynaptic dopaminergic positron emission tomography (PET) imaging serves as an essential tool in diagnosing and differentiating patients with suspected parkinsonism, including idiopathic Parkinson's disease (PD) and other neurodegenerative and non-neurodegenerative diseases. The PET tracers most commonly used at the present time mainly target dopamine transporters (DAT), aromatic amino acid decarboxylase (AADC), and vesicular monoamine type 2 (VMAT2). However, established standards for the imaging procedure and interpretation of presynaptic dopaminergic PET imaging are still lacking. The goal of this international consensus is to help nuclear medicine practitioners procedurally perform presynaptic dopaminergic PET imaging.

METHOD

A multidisciplinary task group formed by experts from various countries discussed and approved the consensus for presynaptic dopaminergic PET imaging in parkinsonism, focusing on standardized recommendations, procedures, interpretation, and reporting.

CONCLUSION

This international consensus and practice guideline will help to promote the standardized use of presynaptic dopaminergic PET imaging in parkinsonism. It will become an international standard for this purpose in clinical practice.

Association between gene expression and functional-metabolic architecture in Parkinson's disease

Gene expression plays a critical role in the pathogenesis of Parkinson's disease (PD). How gene expression profiles are correlated with functional-metabolic architecture remains obscure. We enrolled 34 PD patients and 25 age-and-sex-matched healthy controls for simultaneous 18 F-FDG-PET/functional MRI scanning during resting state. We investigated the functional gradients and the ratio of standard uptake value. Principal component analysis was used to further combine the functional gradients and glucose metabolism into functional-metabolic architecture. Using partial least squares (PLS) regression, we introduced the transcriptomic data from the Allen Institute of Brain Sciences to identify gene expression patterns underlying the affected functional-metabolic architecture in PD. Between-group comparisons revealed significantly higher gradient variation in the visual, somatomotor, dorsal attention, frontoparietal, default mode, and subcortical network (pFDR  < .048) in PD. Increased FDG-uptake was found in the somatomotor and ventral attention network while decreased FDG-uptake was found in the visual network (pFDR  < .008). Spatial correlation analysis showed consistently affected patterns of functional gradients and metabolism (p = 2.47 × 10-8 ). PLS analysis and gene ontological analyses further revealed that genes were mainly enriched for metabolic, catabolic, cellular response to ions, and regulation of DNA transcription and RNA biosynthesis. In conclusion, our study provided genetic pathological mechanism to explain imaging-defined brain functional-metabolic architecture of PD.

The effect of Parkin gene S/N 167 polymorphism on resting spontaneous brain functional activity in Parkinson's Disease

BACKGROUND

Genetic susceptibility plays a significant role in Parkinson's disease (PD) development. Carriers of the Parkin S/N167 mutation may have an increased risk of PD and altered spontaneous brain activity.

OBJECTIVE

This study aims to investigate the potential pathogenesis of PD through a comparative analysis of the amplitude of low-frequency fluctuations (ALFF) in resting-state functional magnetic resonance imaging (rs-fMRI) of subjects with Parkin gene S/N 167 polymorphisms, and to examine the association between spontaneous brain activity and clinical scale scores of PD.

METHODS

A total of 69 PD patients and 84 healthy controls (HC) were included in the study. Each subject was genotyped for the Parkin gene S/N 167 polymorphism and underwent rs-fMRI scans. ALFF analysis was employed to evaluate the relationship among genotypes, interactive brain regions, and clinical symptoms in PD.

RESULTS

PD patients exhibited decreased ALFF values in the right anterior lobe and vermis of the cerebellum compared to HC. No significant interaction was found between the gene's main effect and the "group × genotype" effect on brain ALFF values. One-factor ANOVA revealed no significant difference in ALFF values between PD subgroups; however, the ALFF values in the right anterior lobe and vermis of the cerebellum were lower in the PD-G and PD-GA groups compared to the HC-G and HC-GA groups. Spearman correlation analysis demonstrated that ALFF values in the PD-GG and PD-GA groups were negatively associated with UPDRS-III scores in the bilateral lingual gyrus (Lingual R/L).

CONCLUSION

Parkin gene S/N 167 polymorphisms may influence brain functional activity in specific brain regions, and ALFF values are associated with motor symptoms in PD patients.

Correlation analysis between 18F-fluorodeoxyglucose positron emission tomography and cognitive function in first diagnosed Parkinson's disease patients

Objective

Evaluation of the correlation between ¹⁸F-fluorodeoxyglucose-positron emission tomography (¹⁸F-FDG PET) and cognitive function in first-diagnosed and untreated Parkinson's disease (PD) patients.

Materials and method

This cross-sectional study included 84 first diagnosed and untreated PD patients. The individuals were diagnosed by movement disorder experts based on the 2015 MDS Parkinson's disease diagnostic criteria. The patients also underwent ¹⁸F-FDG PET scans and clinical feature assessments including the Montreal Cognitive Assessment (MoCA) scale. Glucose metabolism rates were measured in 26 brain regions using region of interest (ROI) and pixel-wise analyses with displayed Z scores. The cognitive function was assessed by professionals using the MoCA scale, which covers five cognitive domains. Spearman's linear correlation and linear regression models were used to compare the correlations between ¹⁸F-FDG metabolism in each brain region and cognitive domain, using SPSS 25.0 software.

Result

The results indicated a positive correlation between executive function and glucose metabolism in the lateral prefrontal cortex of the left hemisphere (p = 0.041). Additionally, a positive correlation between memory function and glucose metabolism in the right precuneus (p = 0.014), right lateral occipital cortex (p = 0.017), left lateral occipital cortex (p = 0.031), left primary visual cortex (p = 0.008), and right medial temporal cortex (p = 0.046). Further regression analysis showed that for every one-point decrease in the memory score, the glucose metabolism in the right precuneus would decrease by 0.3 (B = 0.30, p = 0.005), the glucose metabolism in the left primary visual cortex would decrease by 0.25 (B = 0.25, p = 0.040), the glucose metabolism in the right lateral occipital cortex would decrease by 0.38 (B = 0.38, p = 0.012), and the glucose metabolism in the left lateral occipital cortex would decrease by 0.32 (B = 0.32, p = 0.045).

Conclusion

This study indicated that cognitive impairment in PD patients mainly manifests as changes in executive function, visual-spatial function and memory functions, while glucose metabolism mainly decreases in the frontal and posterior cortex. Further analysis shows that executive function is related to glucose metabolism in the left lateral prefrontal cortex. On the other hand, memory ability involves changes in glucose metabolism in a more extensive brain region. This suggests that cognitive function assessment can indirectly reflect the level of glucose metabolism in the relevant brain regions.

Effect of the identification group size and image resolution on the diagnostic performance of metabolic Alzheimer's disease-related pattern

BACKGROUND

Alzheimer's disease-related pattern (ADRP) is a metabolic brain biomarker of Alzheimer's disease (AD). While ADRP is being introduced into research, the effect of the size of the identification cohort and the effect of the resolution of identification and validation images on ADRP's performance need to be clarified.

METHODS

240 2-[¹⁸F]fluoro-2-deoxy-D-glucose positron emission tomography images [120 AD/120 cognitive normals (CN)] were selected from the Alzheimer's disease neuroimaging initiative database. A total of 200 images (100 AD/100 CN) were used to identify different versions of ADRP using a scaled subprofile model/principal component analysis. For this purpose, five identification groups were randomly selected 25 times. The identification groups differed in the number of images (20 AD/20 CN, 30 AD/30 CN, 40 AD/40 CN, 60 AD/60 CN, and 80 AD/80 CN) and image resolutions (6, 8, 10, 12, 15 and 20 mm). A total of 750 ADRPs were identified and validated through the area under the curve (AUC) values on the remaining 20 AD/20 CN with six different image resolutions.

RESULTS

ADRP's performance for the differentiation between AD patients and CN demonstrated only a marginal average AUC increase, when the number of subjects in the identification group increases (AUC increase for about 0.03 from 20 AD/20 CN to 80 AD/80 CN). However, the average of the lowest five AUC values increased with the increasing number of participants (AUC increase for about 0.07 from 20 AD/20 CN to 30 AD/30 CN and for an additional 0.02 from 30 AD/30 CN to 40 AD/40 CN). The resolution of the identification images affects ADRP's diagnostic performance only marginally in the range from 8 to 15 mm. ADRP's performance stayed optimal even when applied to validation images of resolution differing from the identification images.

CONCLUSIONS

While small (20 AD/20 CN images) identification cohorts may be adequate in a favorable selection of cases, larger cohorts (at least 30 AD/30 CN images) shall be preferred to overcome possible/random biological differences and improve ADRP's diagnostic performance. ADRP's performance stays stable even when applied to the validation images with a resolution different than the resolution of the identification ones.

Adjustment for the Age- and Gender-Related Metabolic Changes Improves the Differential Diagnosis of Parkinsonism

Age and gender are the important factors for brain metabolic declines in both normal aging and neurodegeneration, and the confounding effects may influence early and differential diagnosis of neurodegenerative diseases based on the [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET). We aimed to explore the potential of the adjustment of age- and gender-related confounding factors on [18F]FDG PET images in differentiation of Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supra-nuclear palsy (PSP). Eight hundred and seventy-seven clinically definitely diagnosed Parkinsonian patients from a benchmark Huashan Parkinsonian PET imaging database were included. An age- and gender-adjusted Z (AGAZ) score was established based on the gender-specific longitudinal metabolic changes on healthy subjects. AGAZ scores and standardized uptake value ratio (SUVR) values were quantified at regional-level and support vector machine-based error-correcting output codes method was applied for classification. Additional references of the classifications based on metabolic pattern scores were included. The feature-based AGAZ score showed the best performance in classification (accuracy for PD, MSA, PSP: 93.1%, 96.3%, 94.8%). In both genders, the AGAZ score consistently achieved the best efficiency, and the improvements compared to the conventional SUVR value for PD, MSA, and PSP mainly laid in specificity (Male: 5.7%; Female: 11.1%), sensitivity (Male: 7.2%; Female: 7.3%), and sensitivity (Male: 7.3%; Female: 17.2%). Female patients benefited more from the adjustment on [18F]FDG PET in MSA and PSP groups (absolute net reclassification index, p < 0.001). Collectively, the adjustment of age- and gender-related confounding factors may improve the differential diagnosis of Parkinsonism. Particularly, the diagnosis of female Parkinsonian population has the best improvement from this correction.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00079-6.

Functional brain networks in the evaluation of patients with neurodegenerative disorders

Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.

Severity-dependent functional connectome and the association with glucose metabolism in the sensorimotor cortex of Parkinson's disease

Functional MRI studies have achieved promising outcomes in revealing abnormal functional connectivity in Parkinson's disease (PD). The primary sensorimotor area (PSMA) received a large amount of attention because it closely correlates with motor deficits. While functional connectivity represents signaling between PSMA and other brain regions, the metabolic mechanism behind PSMA connectivity has rarely been well established. By introducing hybrid PET/MRI scanning, the current study enrolled 33 advanced PD patients during medication-off condition and 25 age-and-sex-matched healthy controls (HCs), aiming to not only identify the abnormal functional connectome pattern of the PSMA, but also to simultaneously investigate how PSMA functional connectome correlates with glucose metabolism. We calculated degree centrality (DC) and the ratio of standard uptake value (SUVr) using resting state fMRI and ¹⁸F-FDG-PET data. A two-sample t-test revealed significantly decreased PSMA DC (P_FWE < 0.014) in PD patients. The PSMA DC also correlated negatively with H-Y stage (P = 0.031). We found a widespread reduction of H-Y stage associated (P-values < 0.041) functional connectivity between PSMA and the visual network, attention network, somatomotor network, limbic network, frontoparietal network as well as the default mode network. The PSMA DC correlated positively with FDG-uptake in the HCs (P = 0.039) but not in the PD patients (P > 0.44). In summary, we identified disease severity-dependent PSMA functional connectome which in addition uncoupled with glucose metabolism in PD patients. The current study highlighted the critical role of simultaneous PET/fMRI in revealing the functional-metabolic mechanism in the PSMA of PD patients.

Association of Structural Measurements of Brain Reserve With Motor Progression in Patients With Parkinson Disease

BACKGROUND AND OBJECTIVES

To investigate the relationship between baseline structural measurements of brain reserve and clinical progression in Parkinson disease (PD). To further provide a possible underlying mechanism for structural measurements of brain reserve in PD, we combined functional and transcriptional data and investigated their relationship with progression-associated patterns derived from structural measurements and longitudinal clinical scores.

METHODS

This longitudinal study collected data from June 2010 to March 2019 from 2 datasets. The Parkinson's Progression Markers Initiative (PPMI) included controls and patients with newly diagnosed PD from 24 participating sites worldwide. Results were confirmed using data from the Huashan dataset (Shanghai, China), which included controls and patients with PD. Clinical symptoms were assessed with Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scores and Schwab & England activities of daily living (ADL). Both datasets were followed up to 5 years. Linear mixed-effects (LME) models were performed to examine whether changes in clinical scores over time differed as a function of brain structural measurements at baseline.

RESULTS

A total of 389 patients with PD (n = 346, age 61.3 ± 10.03, 35% female, PPMI dataset; n = 43, age 59.4 ± 7.3, 38.7% female, Huashan dataset) with T1-MRI and follow-up clinical assessments were included in this study. Results of LME models revealed significant interactions between baseline structural measurements of subcortical regions and time on longitudinal deterioration of clinical scores (MDS-UPDRS Part II, absolute β > 0.27; total MDS-UPDRS scores, absolute β > 1.05; postural instability-gait difficulty (PIGD) score, absolute β > 0.03; Schwab & England ADL, absolute β > 0.59; all p < 0.05, false discovery rate corrected). The interaction of baseline structural measurements of subcortical regions and time on longitudinal deterioration of the PIGD score was replicated using data from Huashan Hospital. Furthermore, the β-coefficients of these interactions recapitulated the spatial distribution of dopaminergic, metabolic, and structural changes between patients with PD and normal controls and the spatial distribution of expression of the α-synuclein gene (SNCA).

DISCUSSION

Patients with PD with greater brain resources (that is, higher deformation-based morphometry values) had greater compensatory capacity, which was associated with slower rates of clinical progression. This knowledge could be used to stratify and monitor patients for clinical trials.

Consistent Abnormalities in Metabolic Patterns of Lewy Body Dementias

BACKGROUND

Whether dementia with Lewy bodies (DLB) and Parkinson's disease (PD) dementia (PDD) represent the same disease, distinct entities, or conditions within the same spectrum remains controversial.

OBJECTIVE

The objective of this study was to provide new insight into this debate by separately identifying disease-specific metabolic patterns and comparing them with each other and with previously established PD-related pattern (PDRP).

METHODS

Patients with DLB (n = 67), patients with PDD (n = 50), and healthy control subjects (HCs; n = 15) with brain ¹⁸ F-fluorodeoxyglucose positron emission tomography were enrolled as cohorts A and B for pattern identification and validation, respectively. Patients with PD (n = 30) were included for discrimination. Twenty-one participants had two scans. The principal component analysis was applied for pattern identification (DLB-related pattern [DLBRP], PDD-related pattern [PDDRP]). Similarities and differences among three patterns were assessed by pattern topography, pattern expression, clinical correlations cross-sectionally, and pattern expression changes longitudinally.

RESULTS

DLBRP and PDDRP shared highly similar topographies, with relative hypometabolism mainly in the middle temporal gyrus, middle occipital gyrus, lingual gyrus, precuneus, cuneus, angular gyrus, superior and inferior parietal gyrus, middle and inferior frontal gyrus, cingulate, and caudate, and relative hypermetabolism in the cerebellum, putamen, thalamus, precentral/postcentral gyrus, and paracentral lobule, which were more extensive than the PDRP. Patients with DLB and PDD could not be distinguished successfully by any pattern, but patients with PD were easily recognized, especially by DLBRP and PDDRP. The pattern expression of DLBRP and PDDRP showed similar efficacy in cross-sectional disease severity assessment and longitudinal progression monitoring.

CONCLUSIONS

The consistent abnormalities in metabolic patterns of DLB and PDD might underline the potential continuum across the clinical spectrum from PD to DLB. © 2022 International Parkinson and Movement Disorder Society.

Longitudinal trimodal imaging of midbrain-associated network degeneration in Parkinson's disease

The prevailing network perspective of Parkinson’s disease (PD) emerges not least from the ascending neuropathology traceable in histological studies. However, whether longitudinal in vivo correlates of network degeneration in PD can be observed remains unresolved. Here, we applied a trimodal imaging protocol combining 18F-fluorodeoxyglucose (FDG)- and 18F-fluoro-L-Dopa- (FDOPA)-PET with resting-state functional MRI to assess longitudinal changes in midbrain metabolism, striatal dopamine depletion and striatocortical dysconnectivity in 17 well-characterized PD patients. Whole-brain (un)paired-t-tests with focus on midbrain or striatum were performed between visits and in relation to 14 healthy controls (HC) in PET modalities. Resulting clusters of FDOPA-PET comparisons provided volumes for seed-based functional connectivity (FC) analyses between visits and in relation to HC. FDG metabolism in the left midbrain decreased compared to baseline along with caudatal FDOPA-uptake. This caudate cluster exhibited a longitudinal FC decrease to sensorimotor and frontal areas. Compared to healthy subjects, dopamine-depleted putamina indicated stronger decline in striatocortical FC at follow-up with respect to baseline. Increasing nigrostriatal deficits and striatocortical decoupling were associated with deterioration in motor scores between visits in repeated-measures correlations. In summary, our results demonstrate the feasibility of in-vivo tracking of progressive network degeneration using a multimodal imaging approach. Specifically, our data suggest advancing striatal and widespread striatocortical dysfunction via an anterior-posterior gradient originating from a hypometabolic midbrain cluster within a well-characterized and only mild to moderately affected PD cohort during a relatively short period.

Effects of Bilateral Subthalamic Nucleus Stimulation on Depressive Symptoms and Cerebral Glucose Metabolism in Parkinson's Disease: A 18F-Fluorodeoxyglucose Positron Emission Tomography/Computerized Tomography Study

Subthalamic nucleus (STN) deep brain stimulation (DBS) can improve motor symptoms in Parkinson's disease (PD), as well as potentially improving otherwise intractable comorbid depressive symptoms. To address the latter issue, we evaluated the severity of depressive symptoms along with the severity of motor symptoms in 18 PD patients (mean age, 58.4 ± 5.4 years; 9 males, 9 females; mean PD duration, 9.4 ± 4.4 years) with treatment-resistant depression (TRD) before and after approximately 1 year of STN-DBS treatment. Moreover, to gain more insight into the brain mechanism mediating the therapeutic action of STN-DBS, we utilized ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) to assess cerebral regional glucose metabolism in the patients at baseline and 1-year follow-up. Additionally, the baseline PET data from patients were compared with PET data from an age- and sex-matched control group of 16 healthy volunteers. Among them, 12 PD patients underwent post-operative follow-up PET scans. Results showed that the severity of both motor and depressive symptoms in patients with PD-TRD was reduced significantly at 1-year follow-up. Also, patients used significantly less antiparkinsonian medications and antidepressants at 1-year follow-up, as well as experiencing improved daily functioning and a better quality of life. Moreover, relative to the PET data from healthy controls, PD-TRD patients displayed widespread abnormalities in cerebral regional glucose metabolism before STN-DBS treatment, which were partially recovered at 1-year follow-up. Additionally, significant correlations were observed between the patients' improvements in depressive symptoms following STN-DBS and post-operative changes in glucose metabolism in brain regions implicated in emotion regulation. These results support the view that STN-DBS provides a promising treatment option for managing both motor and depressive symptoms in patients who suffer from PD with TRD. However, the results should be interpreted with caution due to the observational nature of the study, small sample size, and relatively short follow-up.

The NADPARK study: A randomized phase I trial of nicotinamide riboside supplementation in Parkinson's disease

We conducted a double-blinded phase I clinical trial to establish whether nicotinamide adenine dinucleotide (NAD) replenishment therapy, via oral intake of nicotinamide riboside (NR), is safe, augments cerebral NAD levels, and impacts cerebral metabolism in Parkinson's disease (PD). Thirty newly diagnosed, treatment-naive patients received 1,000 mg NR or placebo for 30 days. NR treatment was well tolerated and led to a significant, but variable, increase in cerebral NAD levels-measured by ³¹phosphorous magnetic resonance spectroscopy-and related metabolites in the cerebrospinal fluid. NR recipients showing increased brain NAD levels exhibited altered cerebral metabolism, measured by ¹⁸fluoro-deoxyglucose positron emission tomography, and this was associated with mild clinical improvement. NR augmented the NAD metabolome and induced transcriptional upregulation of processes related to mitochondrial, lysosomal, and proteasomal function in blood cells and/or skeletal muscle. Furthermore, NR decreased the levels of inflammatory cytokines in serum and cerebrospinal fluid. Our findings nominate NR as a potential neuroprotective therapy for PD, warranting further investigation in larger trials.

A replication study, systematic review and meta-analysis of automated image-based diagnosis in parkinsonism

Differential diagnosis of parkinsonism early upon symptom onset is often challenging for clinicians and stressful for patients. Several neuroimaging methods have been previously evaluated; however specific routines remain to be established. The aim of this study was to systematically assess the diagnostic accuracy of a previously developed ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET) based automated algorithm in the diagnosis of parkinsonian syndromes, including unpublished data from a prospective cohort. A series of 35 patients prospectively recruited in a movement disorder clinic in Stockholm were assessed, followed by systematic literature review and meta-analysis. In our cohort, automated image-based classification method showed excellent sensitivity and specificity for Parkinson Disease (PD) vs. atypical parkinsonian syndromes (APS), in line with the results of the meta-analysis (pooled sensitivity and specificity 0.84; 95% CI 0.79-0.88 and 0.96; 95% CI 0.91 -0.98, respectively). In conclusion, FDG-PET automated analysis has an excellent potential to distinguish between PD and APS early in the disease course and may be a valuable tool in clinical routine as well as in research applications.

Computer-Aided Classification Framework of Parkinsonian Disorders Using 11C-CFT PET Imaging

Purpose

To investigate the usefulness of a novel computer-aided classification framework for the differential diagnosis of parkinsonian disorders (PDs) based on ¹¹C-methyl-N-2β-carbomethoxy-3β-(4-fluorophenyl)-tropanel (¹¹C-CFT) positron emission tomography (PET) imaging.

Methods

Patients with different forms of PDs—including Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP)—underwent dopamine transporter (DAT) imaging with ¹¹C-CFT PET. A novel multistep computer-aided classification framework—consisting of magnetic resonance imaging (MRI)-assisted PET segmentation, feature extraction and prediction, and automatic subject classification—was developed. A random forest method was used to assess the diagnostic relevance of different regions to the classification process. Finally, the performance of the computer-aided classification system was tested using various training strategies involving patients with early and advanced disease stages.

Results

Accuracy values for identifying PD, MSA, and PSP were 85.0, 82.2, and 89.7%, respectively—with an overall accuracy of 80.4%. The caudate and putamen provided the highest diagnostic relevance to the proposed classification framework, whereas the contribution of midbrain was negligible. With the exception of sensitivity for diagnosing PSP, the strategy comprising both early and advanced disease stages performed better in terms of sensitivity, specificity, positive predictive value, and negative predictive value within each PDs subtype.

Conclusions

The proposed computer-aided classification framework based on ¹¹C-CFT PET imaging holds promise for improving the differential diagnosis of PDs.

Metabolic imaging and plasticity

Positron emission tomography greatly advanced our understanding on the underlying neural mechanisms of movement disorders. PET with flurodeoxyglucose (FDG) is especially useful as it depicts regional metabolic activity level that can predict patients' symptoms. Multivariate pattern analysis has been used to determine and quantify the co-varying brain networks associated with specific clinical traits of neurodegenerative disease. The result is a biomarker, useful for diagnosis, treatments, and follow up studies. Parkinsonian traits and parkinsonisms are associated with specific spatial pattern of metabolic abnormality useful for differential diagnosis. This approach has also been used for monitoring disease progression and novel treatment responses mostly in Parkinson's disease. In this book chapter, we, illustrate and discuss the significance of the brain networks associated with disease and their modification with neuroplastic changes.

The impact of probable rapid eye movement sleep behavior disorder on Parkinson's disease: A dual-tracer PET imaging study

OBJECTIVE

With a dual-tracer PET study design, this study aimed to identify the differences between PD patients with and without probable RBD (PD+RBD+/PD+RBD-) and the influence of timing of RBD onset [probable RBD anterior to PD onset (PD+RBDa)/probable RBD posterior to PD onset (PD+RBDp)].

METHODS

Seventy-four PD+RBD+ patients, sixty-three PD+RBD-patients and twenty healthy controls were enrolled. Clinical variates, striatal DAT tracer uptake, voxel-wise glucose metabolism and Parkinson's disease-related pattern (PDRP) expressions were compared among groups.

RESULTS

No significant difference were found on clinical characteristics between PD+RBD+ and PD+RBD-groups. Compared with PD+RBD-group, PD+RBD+ group had more severe dopaminergic dysfunction (p<0.05) except for posterior putamen in the more affected hemisphere (MAH) (p = 0.350). Meanwhile, it showed relative hypermetabolism in anterior putamen in the less affected hemisphere (LAH), bilateral anterior pallidum with wider involvement in the LAH, hippocampus and para-hippocampus in the LAH and bilateral olfactory gyrus, together with relative hypometabolism in limited bilateral posterio-parietal area (p<0.001). Significantly elevated PDRP expression was also seen in PD+RBD+ group (p < 0.01). For the timing of RBD onset, PD+RBDa patients harbored greater progression rate than PD+RBDp patients (p<0.01), greater DAT declining rates of striatal subregions and greater increasing rate of PDRP expressions than both PD+RBDp and PD+RBD-patients (p<0.05).

CONCLUSION

Our study found that PD patients with probable RBD have worse striatal dopaminergic dysfunction and higher PDRP network activity, supporting the assumption that PD with RBD may be a specific phenotype of PD. Additionally, RBD preceding PD onset may indicate a steeper disease decline.

Altered microstructural properties of superficial white matter in patients with Parkinson's disease

Parkinson's disease (PD), a chronic neurodegenerative disease, is characterized by sensorimotor and cognitive deficits. Previous diffusion tensor imaging (DTI) studies found abnormal DTI metrics in white matter bundles, such as the corpus callosum, cingulate, and frontal-parietal bundles, in PD patients. These studies mainly focused on alterations in microstructural features of long-range bundles within the deep white matter (DWM) that connects pairs of distant cortical regions. However, less is known about the DTI metrics of the superficial white matter (SWM) that connects local cortical regions in PD patients. To determine whether the DTI metrics of the SWM were different between the PD patients and the healthy controls, we recruited DTI data from 34 PD patients and 29 gender- and age-matched healthy controls. Using a probabilistic tractographic approach, we first defined a population-based SWM mask across all the subjects. Using a tract-based spatial statistical (TBSS) analytic approach, we then identified the SWM bundles showing abnormal DTI metrics in the PD patients. We found that the PD patients showed significantly lower DTI metrics in the SWM bundles connecting the sensorimotor cortex, cingulate cortex, posterior parietal cortex (PPC), and parieto-occipital cortex than the healthy controls. We also found that the clinical measures in the PD patients was significantly negatively correlated with the fractional anisotropy in the SWM (FA_SWM) that connects core regions in the default mode network (DMN). The FA_SWM in the bundles that connected the PPC was significantly positively correlated with cognitive performance in the PD patients. Our findings suggest that SWM may serve as the brain structural basis underlying the sensorimotor deficits and cognitive degeneration in PD patients.

A disease-specific metabolic imaging marker for diagnosis and progression evaluation of semantic variant primary progressive aphasia

BACKGROUND AND PURPOSE

The diagnosis and monitoring of semantic variant primary progressive aphasia (sv-PPA) are clinically challenging. We aimed to establish a distinctive metabolic pattern in sv-PPA for diagnosis and severity evaluation.

METHODS

Fifteen sv-PPA patients and 15 controls were enrolled to identify sv-PPA-related pattern (sv-PPARP) by principal component analysis of ¹⁸ F-fluorodeoxyglucose positron emission tomography. Eighteen Alzheimer disease dementia (AD) and 14 behavioral variant frontotemporal dementia (bv-FTD) patients were enrolled to test the discriminatory power. Correspondingly, regional metabolic activities extracted from the voxelwise analysis were evaluated for the discriminatory power.

RESULTS

The sv-PPARP was characterized as decreased metabolic activity mainly in the bilateral temporal lobe (left predominance), middle orbitofrontal gyrus, left hippocampus/parahippocampus gyrus, fusiform gyrus, insula, inferior orbitofrontal gyrus, and striatum, with increased activity in the bilateral lingual gyrus, cuneus, calcarine gyrus, and right precentral and postcentral gyrus. The pattern expression had significant discriminatory power (area under the curve [AUC] = 0.98, sensitivity = 100%, specificity = 94.4%) in distinguishing sv-PPA from AD, and the asymmetry index offered complementary discriminatory power (AUC = 0.91, sensitivity = 86.7%, specificity = 92.9%) in distinguishing sv-PPA from bv-FTD. In sv-PPA patients, the pattern expression correlated with Boston Naming Test scores at baseline and showed significant increase in the subset of patients with follow-up. The voxelwise analysis showed similar topography, and the regional metabolic activities had equivalent or better discriminatory power and clinical correlations with Boston Naming Test scores. The ability to reflect disease progression in longitudinal follow-up seemed to be inferior to the pattern expression.

CONCLUSIONS

The sv-PPARP might serve as an objective biomarker for diagnosis and progression evaluation.

Dopaminergic and Serotonergic Degeneration and Cortical [18 F]Fluorodeoxyglucose Positron Emission Tomography in De Novo Parkinson's Disease

BACKGROUND

Degeneration of the nigrostriatal dopaminergic (DA) and the raphe-thalamic serotonergic (SE) systems is among the earliest changes observed in Parkinson's disease (PD). The consequences of those changes on brain metabolism, especially regarding their impact on the cortex, are poorly understood.

OBJECTIVES

Using multi-tracer molecular imaging, we assessed in a cohort of drug-naive PD patients the association between cortical metabolism and DA and SE system deafferentation of either striatum or thalamus, and we explored whether this association was mediated by either striatum or thalamus metabolism.

METHODS

We recruited 96 drug-naive PD patients (aged 71.9 ± 7.5 years) who underwent [¹²³ I]ioflupane single-photon emission computed tomography ([¹²³ I]FP-CIT-SPECT) and brain [¹⁸ F]fluorodeoxyglucose positron emission tomography ([¹⁸ F]FDG-PET). We used a voxel-wise analysis of [¹⁸ F]FDG-PET images to correlate regional metabolism with striatal DA and thalamic SE innervation as assessed using [¹²³ I]FP-CIT-SPECT.

RESULTS

We found that [¹²³ I]FP-CIT specific to nondisplaceable binding ratio (SBR) and glucose metabolism positively correlated with one another in the deep gray matter (thalamus: P = 0.001, r = 0.541; caudate P = 0.001, r = 0.331; putamen P = 0.001, r = 0.423). We then observed a direct correlation between temporoparietal metabolism and caudate DA innervation, as well as a direct correlation between prefrontal metabolism and thalamus SE innervation. The effect of caudate [¹²³ I]FP-CIT SBR values on temporoparietal metabolism was mediated by caudate metabolic values (percentage mediated: 89%, P-value = 0.008), and the effect of thalamus [¹²³ I]FP-CIT SBR values on prefrontal metabolism was fully mediated by thalamus metabolic values (P < 0.001).

CONCLUSIONS

These data suggest that the impact of deep gray matter monoaminergic deafferentation on cortical function is mediated by striatal and thalamic metabolism in drug-naive PD. © 2021 International Parkinson and Movement Disorder Society.

Parkinson Disease-Related Brain Metabolic Patterns and Neurodegeneration in Isolated REM Sleep Behavior Disorder

OBJECTIVE

To elucidate the role of Parkinson disease (PD)-related brain metabolic patterns as a biomarker in isolated REM sleep behavior disorder (iRBD) for future disease conversion.

METHODS

This is a prospective cohort study consisting of 30 patients with iRBD, 25 patients with de novo PD with a premorbid history of RBD, 21 patients with longstanding PD on stable treatment, and 24 healthy controls. The iRBD group was longitudinally followed up. All participants underwent ¹⁸F-fluorodeoxyglucose (FDG) PET and were evaluated with olfaction, cognition, and the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) at baseline. From FDG-PET scans, we derived metabolic patterns from the longstanding PD group (PD-RP) and de novo PD group with RBD (dnPDRBD-RP). Subsequently, we calculated the PD-RP and dnPDRBD-RP scores in patients with iRBD. We validated the metabolic patterns in each PD group and separate iRBD cohort (n = 14).

RESULTS

The 2 patterns significantly correlated with each other and were spatially overlapping yet distinct. The MDS-UPDRS motor scores significantly correlated with PD-RP (p = 0.013) but not with dnPDRBD-RP (p = 0.076). In contrast, dnPDRBD-RP correlated with olfaction in butanol threshold test (p = 0.018) in patients with iRBD, but PD-RP did not (p = 0.21). High dnPDRBD-RP in patients with iRBD predicted future phenoconversion with all cutoff ranges from 1.5 to 3 SD of the control value, whereas predictability of PD-RP was only significant in a partial range of cutoff.

CONCLUSION

The dnPDRBD-RP is an efficient neuroimaging biomarker that reflects prodromal features of PD and predicts phenoconversion in iRBD that can be applied individually.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that a de novo PD pattern on FDG-PET predicts future conversion to neurodegenerative disease in patients with iRBD.

Dynamic 18F-FPCIT PET: Quantification of Parkinson's disease metabolic networks and nigrostriatal dopaminergic dysfunction in a single imaging session

Previous multi-center imaging studies with 18F-FDG PET have established the presence of Parkinson's disease motor- and cognition-related metabolic patterns termed PDRP and PDCP in patients with this disorder. Given that in PD cerebral perfusion and glucose metabolism are typically coupled in the absence of medication, we determined whether subject expression of these disease networks can be quantified in early-phase images from dynamic 18F-FPCIT PET scans acquired to assess striatal dopamine transporter (DAT) binding. Methods: We studied a cohort of early-stage PD patients and age-matched healthy control subjects who underwent 18F-FPCIT at baseline; scans were repeated 4 years later in a smaller subset of patients. The early 18F-FPCIT frames, which reflect cerebral perfusion, were used to compute PDRP and PDCP expression (subject scores) in each subject, and compared to analogous measures computed based on 18F-FDG PET scan when additionally available. The late 18F-FPCIT frames were used to measure caudate and putamen DAT binding in the same individuals. Results: PDRP subject scores from early-phase 18F-FPCIT and 18F-FDG scans were elevated and striatal DAT binding reduced in PD versus healthy subjects. The PDRP scores from 18F-FPCIT correlated with clinical motor ratings, disease duration, and with corresponding measures from 18F-FDG PET. In addition to correlating with disease duration and analogous 18F-FDG PET values, PDCP scores correlated with DAT binding in the caudate/anterior putamen. PDRP and PDCP subject scores using either method rose over 4 years whereas striatal DAT binding declined over the same time period. Conclusion: Early-phase images obtained with 18F-FPCIT PET can provide an alternative to 18F-FDG PET for PD network quantification. This technique therefore allows PDRP/PDCP expression and caudate/putamen DAT binding to be evaluated with a single tracer in one scanning session.

Metabolic network as an objective biomarker in monitoring deep brain stimulation for Parkinson's disease: a longitudinal study

Background

With the advance of subthalamic nucleus (STN) deep brain stimulation (DBS) in the treatment of Parkinson’s disease (PD), it is desired to identify objective criteria for the monitoring of the therapy outcome. This paper explores the feasibility of metabolic network derived from positron emission tomography (PET) with ¹⁸F-fluorodeoxyglucose in monitoring the STN DBS treatment for PD.

Methods

Age-matched 33 PD patients, 33 healthy controls (HCs), 9 PD patients with bilateral DBS surgery and 9 controls underwent ¹⁸F-FDG PET scans. The DBS patients were followed longitudinally to investigate the alternations of the PD-related metabolic covariance pattern (PDRP) expressions.

Results

The PDRP expression was abnormally elevated in PD patients compared with HCs (P < 0.001). For DBS patients, a significant decrease in the Unified Parkinson’s Disease Rating Scale (UPDRS, P = 0.001) and PDRP expression (P = 0.004) was observed 3 months after STN DBS treatment, while a rollback was observed in both UPDRS and PDRP expressions (both P < 0.01) 12 months after treatment. The changes in PDRP expression mediated by STN DBS were generally in line with UPDRS improvement. The graphical network analysis shows increased connections at 3 months and a return at 12 months confirmed by small-worldness coefficient.

Conclusions

The preliminary results demonstrate the potential of metabolic network expression as complimentary objective biomarker for the assessment and monitoring of STN DBS treatment in PD patients.

Clinical Trial Registration ChiCTR-DOC-16008645. http://www.chictr.org.cn/showproj.aspx?proj=13865.

Reproducible metabolic topographies associated with multiple system atrophy: Network and regional analyses in Chinese and American patient cohorts

•

This study produced reliable metabolic brain networks for multiple system atrophy.

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Network scores discriminated this disorder from other major forms of Parkinsonism.

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Network scores correlated with clinical stages and motor symptoms in this disorder.

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The network was highly reproducible across Chinese and American patient cohorts.

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Network scores provided a clinically useful biomarker in a multi-center setting.

Purpose

Multiple system atrophy (MSA) is an atypical parkinsonian syndrome and often difficult to discriminate clinically from progressive supranuclear palsy (PSP) and Parkinson's disease (PD) in early stages. Although a characteristic metabolic brain network has been reported for MSA, it is unknown whether this network can provide a clinically useful biomarker in different centers. This study was aimed to identify and cross-validate MSA-related brain network and assess its ability for differential diagnosis and clinical correlations in Chinese and American patient cohorts.

Methods

We included ¹⁸F-FDG PET scans retrospectively from 128 clinically diagnosed parkinsonian patients (34 MSA, 34 PSP and 60 PD) and 40 normal subjects in China and in the USA. Using PET images from 20 moderate-stage MSA patients of parkinsonian subtype and 20 normal subjects in both centers, we reproduced MSA-related pattern (MSAPRP) of spatial covariance and estimated its reliability. MSAPRP scores were evaluated in assessing differential diagnosis among moderate- and early-stage MSA, PSP or PD patients and clinical correlations with disease severity. Regional metabolic differences were detected using statistical parameter mapping analysis. MSA-related network and regional topographies of metabolic abnormality were cross-validated between the Chinese and American cohorts.

Results

We generated a highly reliable MSAPRP characterized by decreased loading in inferior frontal cortex, striatum and cerebellum, and increased loading in sensorimotor, parietal and occipital cortices. MSAPRP scores discriminated between normal, MSA, PSP and PD subjects and correlated with standardized ratings of clinical stages and motor symptoms in MSA. High similarities in MSAPRPs, network scores and corresponding maps of metabolic abnormality were observed between two different cohorts.

Conclusion

We have demonstrated reproducible metabolic topographies associated with MSA at both network and regional levels in two independent patient cohorts. Moreover, MSAPRP scores are sensitive for evaluating disease discrimination and clinical correlates. This study supports differential diagnosis of MSA regardless of different patient populations, PET scanners and imaging protocols.

Brain Metabolisms Involved in Self-Reported Quality of Mobility in Parkinson's Disease

Background

Objective motor ratings and subjective motor complaints are both widely used in Parkinson’s disease (PD). However, the objective basis to the self-perceived mobility quality is still not well elucidated.

Purposes

We aimed to figure out the relevancy between the UPDRS motor scores and PDQ39 mobility sub-scores, and further explore whether physician-assessed motor dysfunctions and patients-reported mobility deficits have some shared mechanisms.

Methods

49 patients with PD who completed the PDQ39 scale were retrospectively included. The relevancy between mobility quality and UPDRS scores was assessed, as well as the related presynaptic dopaminergic binding (¹¹C-CFT) and glucose metabolism (¹⁸F-FDG) in this dual-tracer PET imaging study.

Results

Modest correlation was found between UPDRS motor score and the PDQ39 mobility sub-score (r = 0.440, p = 0.002). No correlation was found between PDQ39 mobility SI and the dopaminergic lesions in putamen; however, the strict correlation was found with the UPDRS motor scores. In terms of global PD related pattern (PDRP) scores, the two motor scores both correlated strictly. In the further regional metabolism exploration, cerebellum correlated positively with PDQ39 mobility sub-scores, and the frontal and parietal regions mainly correlated negatively with the motor quality scores.

Conclusion

UPDRS motor scores and PDQ39 mobility scores were only modestly correlated. The mechanisms involved under mobility quality were beyond dopaminergic deficiency, including motor related cerebellum hyper-metabolism and non-motor related frontal hypo-metabolism. Conclusively, the self-reported mobility experience may have the neurophysiological basis related to both motor and non-motor manifestations in PD.

Associations of [18F]-APN-1607 Tau PET Binding in the Brain of Alzheimer's Disease Patients With Cognition and Glucose Metabolism

Molecular imaging of tauopathies is complicated by the differing specificities and off-target binding properties of available radioligands for positron emission tomography (PET). [18F]-APN-1607 ([18F]-PM-PBB3) is a newly developed PET tracer with promising properties for tau imaging. We aimed to characterize the cerebral binding of [18F]-APN-1607 in Alzheimer's disease (AD) patients compared to normal control (NC) subjects. Therefore, we obtained static late frame PET recordings with [18F]-APN-1607 and [18F]-FDG in patients with a clinical diagnosis of AD group, along with an age-matched NC group ([18F]-APN-1607 only). Using statistical parametric mapping (SPM) and volume of interest (VOI) analyses of the reference region normalized standardized uptake value ratio maps, we then tested for group differences and relationships between both PET biomarkers, as well as their associations with clinical general cognition. In the AD group, [18F]-APN-1607 binding was elevated in widespread cortical regions (P < 0.001 for VOI analysis, familywise error-corrected P < 0.01 for SPM analysis). The regional uptake in AD patients correlated negatively with Mini-Mental State Examination score (frontal lobe: R = -0.632, P = 0.004; temporal lobe: R = -0.593, P = 0.008; parietal lobe: R = -0.552, P = 0.014; insula: R = -0.650, P = 0.003; cingulum: R = -0.665, P = 0.002) except occipital lobe (R = -0.417, P = 0.076). The hypometabolism to [18F]-FDG PET in AD patients also showed negative correlations with regional [18F]-APN-1607 binding in some signature areas of AD (temporal lobe: R = -0.530, P = 0.020; parietal lobe: R = -0.637, P = 0.003; occipital lobe: R = -0.567, P = 0.011). In conclusion, our results suggested that [18F]-APN-1607 PET sensitively detected tau deposition in AD and that individual tauopathy correlated with impaired cerebral glucose metabolism and cognitive function.

Dysautonomia Is Linked to Striatal Dopamine Deficits and Regional Cerebral Perfusion in Early Parkinson Disease

PURPOSE

The aim of this study was to investigate the association between autonomic dysfunction and striatal dopamine depletion or metabolic changes in de novo Parkinson disease (PD).

METHODS

Based on the Composite Autonomic Severity Score (CASS), patients with de novo PD were classified into PD with (PD-AUT+) and without autonomic dysfunction (PD-AUT-) groups. We compared the dopamine transporter (DAT) availability in the striatum by quantitatively measuring F-FP-CIT PET between both groups. We also assessed the association between DAT availability and the CASS. In addition, we compared regional uptake in early-phase images of F-FP-CIT PET as well as cortical thickness between the PD-AUT+ and PD-AUT- groups.

RESULTS

The PD-AUT+ group had significantly lower DAT availability in all striatal subregions than did the PD-AUT- group. The total CASS was significantly correlated with DAT availability in all striatal subregions. In addition, the subscores of the adrenergic component were correlated with DAT availability in all striatal subregions. The subscores of the cardiovagal component were negatively correlated with DAT availability in the caudate and ventral striatum. In early-phase F-FP-CIT PET, the PD-AUT+ group exhibited decreased regional perfusion in the parieto-occipital areas and increased regional perfusion in the pallidothalamic, pontocerebellar, inferior frontal, and primary motor areas compared with the PD-AUT- group. There were no regions of different cortical thickness between the PD groups.

CONCLUSIONS

The present study revealed that autonomic dysfunction is closely linked to striatal dopamine depletion and prominent PD-related perfusion patterns in de novo PD, suggesting a greater pathological burden in patients with dysautonomia.

Regional SUV quantification in hybrid PET/MR, a comparison of two atlas-based automatic brain segmentation methods

BACKGROUND

Quantitative analysis of brain positron-emission tomography (PET) depends on structural segmentation, which can be time-consuming and operator-dependent when performed manually. Previous automatic segmentation usually registered subjects' images onto an atlas template (defined as RSIAT here) for group analysis, which changed the individuals' images and probably affected regional PET segmentation. In contrast, we could register atlas template to subjects' images (RATSI), which created an individual atlas template and may be more accurate for PET segmentation. We segmented two representative brain areas in twenty Parkinson disease (PD) and eight multiple system atrophy (MSA) patients performed in hybrid positron-emission tomography/magnetic resonance imaging (PET/MR). The segmentation accuracy was evaluated using the Dice coefficient (DC) and Hausdorff distance (HD), and the standardized uptake value (SUV) measurements of these two automatic segmentation methods were compared, using manual segmentation as a reference.

RESULTS

The DC of RATSI increased, and the HD decreased significantly (P < 0.05) compared with the RSIAT in PD, while the results of one-way analysis of variance (ANOVA) found no significant differences in the SUVmean and SUVmax among the two automatic and the manual segmentation methods. Further, RATSI was used to compare regional differences in cerebral metabolism pattern between PD and MSA patients. The SUVmean in the segmented cerebellar gray matter for the MSA group was significantly lower compared with the PD group (P < 0.05), which is consistent with previous reports.

CONCLUSION

The RATSI was more accurate for the caudate nucleus and putamen automatic segmentation and can be used for regional PET analysis in hybrid PET/MR.

Brain Functional and Structural Signatures in Parkinson's Disease

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.

Characterizing the heterogeneous metabolic progression in idiopathic REM sleep behavior disorder

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Imaging biomarkers of the metabolic trajectory from HC, iRBD and PD are identified.

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Frontal, limbic and occipital brain regions as imaging biomarkers in PD.

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Frontal, limbic and occipital brain regions as imaging biomarkers of the phenoconversion from iRBD to PD.

Objective

Idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) is a prodromal stage of synucleinopathies such as Parkinson’s disease (PD). Positron emission tomography (PET) with ¹⁸F-FDG reveals metabolic perturbations, which are scored by spatial covariance analysis. However, the resultant pattern scores do not capture the spatially heterogeneous trajectories of metabolic changes between individual brain regions. Assuming metabolic progression occurs as a continuum from the healthy control (HC) condition to iRBD and then PD, we investigated spatial dynamics of progressively perturbed glucose metabolism in a cross-sectional study.

Methods

19 iRBD patients, 38 PD patients and 19 HC subjects underwent ¹⁸F-FDG PET. The images were spatially normalized, scaled to the global mean uptake, and automatically parcellated. We contrasted regional metabolism by group, and allocated the inferred progression to one of several possible trajectories. We further investigated the correlations between ¹⁸F-FDG uptake and the disease duration in the iRBD and PD groups, respectively. We also explored relationships between ¹⁸F-FDG uptake and the Unified Parkinson’s Disease Rating Scale motor (UPDRS III) scores in the PD group.

Results

PD patients exhibited more extensive relative hyper- and hypo-metabolism than iRBD patients. We identified three dynamic metabolic trajectories, cross-sectional hypo- or hypermetabolism, cross-sectionally unchanged hypo- or hypermetabolism, cross-sectionally late hypo- or hypermetabolism, appearing only in the contrast of PD with iRBD. No correlation was found between relative ¹⁸F-FDG metabolism and disease duration in the iRBD group. Regional hyper- and hypo-metabolism in the PD patients correlated with disease duration or clinical UPDRS III scores.

Conclusion

Cerebral metabolism changes heterogeneously in a continuum extending from HC to iRBD and PD groups in this preliminary study. The distinctive metabolic trajectories point towards a potential neuroimaging biomarker for conversion of iRBD to frank PD, which should be amenable to advanced pattern recognition analysis in future longitudinal studies.

Differential diagnosis of parkinsonian syndromes: a comparison of clinical and automated - metabolic brain patterns' based approach

PURPOSE

Differentiation among parkinsonian syndromes may be clinically challenging, especially at early disease stages. In this study, we used ¹⁸F-FDG-PET brain imaging combined with an automated image classification algorithm to classify parkinsonian patients as Parkinson's disease (PD) or as an atypical parkinsonian syndrome (APS) at the time when the clinical diagnosis was still uncertain. In addition to validating the algorithm, we assessed its utility in a "real-life" clinical setting.

METHODS

One hundred thirty-seven parkinsonian patients with uncertain clinical diagnosis underwent ¹⁸F-FDG-PET and were classified using an automated image-based algorithm. For 66 patients in cohort A, the algorithm-based diagnoses were compared with their final clinical diagnoses, which were the gold standard for cohort A and were made 2.2 ± 1.1 years (mean ± SD) later by a movement disorder specialist. Seventy-one patients in cohort B were diagnosed by general neurologists, not strictly following diagnostic criteria, 2.5 ± 1.6 years after imaging. The clinical diagnoses were compared with the algorithm-based ones, which were considered the gold standard for cohort B.

RESULTS

Image-based automated classification of cohort A resulted in 86.0% sensitivity, 92.3% specificity, 97.4% positive predictive value (PPV), and 66.7% negative predictive value (NPV) for PD, and 84.6% sensitivity, 97.7% specificity, 91.7% PPV, and 95.5% NPV for APS. In cohort B, general neurologists achieved 94.7% sensitivity, 83.3% specificity, 81.8% PPV, and 95.2% NPV for PD, while 88.2%, 76.9%, 71.4%, and 90.9% for APS.

CONCLUSION

The image-based algorithm had a high specificity and the predictive values in classifying patients before a final clinical diagnosis was reached by a specialist. Our data suggest that it may improve the diagnostic accuracy by 10-15% in PD and 20% in APS when a movement disorder specialist is not easily available.

Correlations between dopaminergic dysfunction and abnormal metabolic network activity in REM sleep behavior disorder

Striatal dopamine transporter (DAT) deficiency and abnormal expression of Parkinson's disease (PD)-related pattern (PDRP) have been observed in patients with idiopathic REM sleep behavior disorder (IRBD). This study aimed to investigate the correlations between these two measures with comparison to PD using a dual tracer imaging design. Age-matched 37 IRBD patients, 86 PD patients, and 15 control subjects underwent concurrent PET scans with 11C-CFT to quantify dopaminergic dysfunction and 18F-FDG to quantify PDRP expression. IRBD patients were divided into two subgroups: those with relatively normal (IRBD-RN) or abnormal (IRBD-AB) striatal DAT binding. Significantly decreased DAT binding and increased PDRP scores were present in all patient groups, except for IRBD-RN, relative to the controls. There was a significant effect of hemisphere and hemisphere × group interaction for DAT binding but not for PDRP expression. Significant correlations were observed between DAT binding and PDRP expression in the IRBD-AB and PD groups but not in the IRBD-RN group. IRBD patients present with an intermediate state in striatal DAT distribution and PDRP activity between PD and normal controls. The modest correlations between the two measures in both IRBD and PD suggest that differences in network activity cannot be fully explained by nigrostriatal dopaminergic denervation.

Risk Factors and Metabolism of Different Brain Regions by Positron Emission Tomography in Parkinson Disease with Disabling Dyskinesia

Objective:

Dyskinesia is the most common motor complication in advanced Parkinson’s Disease (PD) and has a severe impact on daily life. But the mechanism of dyskinesia is still poorly understood. This study aims to explore risk factors for disabling dyskinesia in PD and further analyze the Vesicular Monoamine Transporter 2 (VMAT2) distribution (labeled with 18F-AV133) in the corpus striatum and the 18F-fluorodeoxyglucose (18F-FDG) metabolism of different brain regions by PET-CT.

Methods:

This is a cross-sectional study involving 135 PD patients. They were divided into disabling dyskinesia group (DD group, N=22) and non-dyskinesia group (ND group, N=113). All the patients were agreed to undergo PET-CT scans. Clinical data were analyzed between two groups by using multivariate logistic regression analysis, and risk factors for disabling dyskinesia were then determined. The standard uptake value ratios (SUVr) of 18F-AV133 in the corpus striatum and the 18F-FDG metabolism of different brain regions were identified and calculated by the software.

Results:

6.3% patients have disabling dyskinesia. DD group were more likely to have longer Disease Duration, higher Hoehn-Yahr degree, more severe clinic symptoms, more frequent sleep behavior disorder, and higher levodopa dose equivalency than ND group (P < 0.05). After adjusting confounding factors by multivariate logistic regression, DD group had longer PD duration and high levodopa dose equivalency compared with ND group (P < 0.05). There is no significant difference between the VMAT2 distribution (labeled with 18F- AV133) in the putamen and caudate between two groups. And the 18F-FDG metabolic changes in cortical and subcortical regions did not show a significant difference between the two groups either (P > 0.05).

Conclusion:

Long PD duration and high levodopa dose equivalency were two independent risk factors for disabling dyskinesia in PD patients. Compared to non-dyskinesia PD patients, there was no significant dopamine decline of the nigrostriatal system in disabling dyskinesia PD patients. Activities of different brain regions were not different between the two groups by 18F-FDG PETCT.

Use of radiomic features and support vector machine to distinguish Parkinson's disease cases from normal controls

Background

Parkinson's disease (PD) is an irreversible neurodegenerative disease. The diagnosis of PD based on neuroimaging is usually with low-level or deep learning features, which results in difficulties in achieving precision classification or interpreting the clinical significance. Herein, we aimed to extract high-order features by using radiomics approach and achieve acceptable diagnosis accuracy in PD.

Methods

In this retrospective multicohort study, we collected ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) images and clinical scale [the Unified Parkinson's Disease Rating Scale (UPDRS) and Hoehn & Yahr scale (H&Y)] from two cohorts. One cohort from Huashan Hospital had 91 normal controls (NC) and 91 PD patients (UPDRS: 22.7±11.7, H&Y: 1.8±0.8), and the other cohort from Wuxi 904 Hospital had 26 NC and 22 PD patients (UPDRS: 20.9±11.6, H&Y: 1.7±0.9). The Huashan cohort was used as the training and test sets by 5-fold cross-validation and the Wuxi cohort was used as another separate test set. After identifying regions of interests (ROIs) based on the atlas-based method, radiomic features were extracted and selected by using autocorrelation and fisher score algorithm. A support vector machine (SVM) was trained to classify PD and NC based on selected radiomic features. In the comparative experiment, we compared our method with the traditional voxel values method. To guarantee the robustness, above processes were repeated in 500 times.

Results

Twenty-six brain ROIs were identified. Six thousand one hundred and ten radiomic features were extracted in total. Among them 30 features were remained after feature selection. The accuracies of the proposed method achieved 90.97%±4.66% and 88.08%±5.27% in Huashan and Wuxi test sets, respectively.

Conclusions

This study showed that radiomic features and SVM could be used to distinguish between PD and NC based on 18F-FDG PET images.

Abnormal pattern of brain glucose metabolism in Parkinson's disease: replication in three European cohorts

Rationale

In Parkinson’s disease (PD), spatial covariance analysis of ¹⁸F-FDG PET data has consistently revealed a characteristic PD-related brain pattern (PDRP). By quantifying PDRP expression on a scan-by-scan basis, this technique allows objective assessment of disease activity in individual subjects. We provide a further validation of the PDRP by applying spatial covariance analysis to PD cohorts from the Netherlands (NL), Italy (IT), and Spain (SP).

Methods

The PDRP_NL was previously identified (17 controls, 19 PD) and its expression was determined in 19 healthy controls and 20 PD patients from the Netherlands. The PDRP_IT was identified in 20 controls and 20 “de-novo” PD patients from an Italian cohort. A further 24 controls and 18 “de-novo” Italian patients were used for validation. The PDRP_SP was identified in 19 controls and 19 PD patients from a Spanish cohort with late-stage PD. Thirty Spanish PD patients were used for validation. Patterns of the three centers were visually compared and then cross-validated. Furthermore, PDRP expression was determined in 8 patients with multiple system atrophy.

Results

A PDRP could be identified in each cohort. Each PDRP was characterized by relative hypermetabolism in the thalamus, putamen/pallidum, pons, cerebellum, and motor cortex. These changes co-varied with variable degrees of hypometabolism in posterior parietal, occipital, and frontal cortices. Frontal hypometabolism was less pronounced in “de-novo” PD subjects (Italian cohort). Occipital hypometabolism was more pronounced in late-stage PD subjects (Spanish cohort). PDRP_IT, PDRP_NL, and PDRP_SP were significantly expressed in PD patients compared with controls in validation cohorts from the same center (P < 0.0001), and maintained significance on cross-validation (P < 0.005). PDRP expression was absent in MSA.

Conclusion

The PDRP is a reproducible disease characteristic across PD populations and scanning platforms globally. Further study is needed to identify the topography of specific PD subtypes, and to identify and correct for center-specific effects.

Electronic supplementary material

The online version of this article (10.1007/s00259-019-04570-7) contains supplementary material, which is available to authorized users.

Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson’s Disease in 3D Nuclear Imaging Data

Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a PET technique employed to obtain a representation of brain metabolic function. In this study we employed 3D CNNs in FDG-PET brain images with the purpose of discriminating patients diagnosed with Parkinson’s disease (PD) from controls. We employed Scaled Subprofile Modeling using Principal Component Analysis as a preprocessing step to focus on specific brain regions and limit the number of voxels that are used as input for the CNNs, thereby increasing the signal-to-noise ratio in our data. We performed hyperparameter optimization on three CNN architectures to estimate the classification accuracy of the networks on new data. The best performance that we obtained was [Formula: see text] and area under the receiver operating characteristic curve [Formula: see text] on the test set. We believe that, with larger datasets, PD patients could be reliably distinguished from controls by FDG-PET scans alone and that this technique could be applied to more clinically challenging tasks, like the differential diagnosis of neurological disorders with similar symptoms, such as PD, Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA).

Frequency-Dependent Relationship Between Resting-State fMRI and Glucose Metabolism in the Elderly

Both glucose metabolism and resting-state fMRI (RS-fMRI) signal reflect hemodynamic features. The objective of this study was to investigate their relationship in the resting-state in healthy elderly participants (n = 18). For RS-fMRI signal, regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), and degree of centrality (DC) maps were generated in multiple frequency bands. Glucose uptake was acquired with ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET). Linear correlation of each pair of the FDG-PET and RS-fMRI metrics was explored both in across-voxel way and in across-subject way. We found a significant across-voxel correlation between the FDG-PET and BOLD-fMRI metrics. However, only a small portion of voxels showed significant across-subject correlation between FDG-PET and BOLD-fMRI metrics. All these results were similar across all frequency bands of RS-fMRI data. The current findings indicate that FDG-PET and RS-fMRI metrics share similar spatial pattern (significant across-voxel correlation) but have different underlying physiological importance (non-significant across-subject correlation). Specifically, FDG-PET measures the mean glucose metabolism over tens of minutes, while RS-fMRI measures the dynamic characteristics. The combination of FDG-PET and RS-fMRI provides complementary information to reveal the underlying mechanisms of the brain activity and may enable more comprehensive interpretation of clinical PET-fMRI studies. Future studies would attempt to reduce the artifacts of RS-fMRI and to analyze the dynamic feature of PET signal.

Preserved caudate function in young-onset patients with Parkinson's disease: a dual-tracer PET imaging study

Parkinson's disease (PD) is a highly heterogeneous clinical entity. Patients with young-onset PD (YOPD) show some characteristic manifestations to late-onset PD (LOPD). The current study aimed to investigate the cerebral dopaminergic and metabolic characteristics in YOPD with positron emission tomography (PET) imaging. In our study, 103 subjects (42 YOPD and 61 LOPD patients) accepted both 11C-N-2-carbomethoxy-3-(4-fluorophenyl)-tropane (11C-CFT) and 18F-fluorodeoxyglucose (18F-FDG) cerebral PET imaging. Sixty-two patients out of 103 patients in our study completed the cognition tests. In this limited subsection, YOPD patients performed better in cognitive functioning than LOPD patients of similar disease duration. In 11C-CFT imaging, dopamine transporter binding in caudate was relatively spared in YOPD compared with lesions in putamen. In 18F-FDG PET, YOPD patients showed increased metabolism in basal ganglia relative to the healthy controls. When compared with LOPD patients, YOPD patients exhibited hypermetabolism in caudate and hypometabolism in putamen. Furthermore, the regional metabolic values in caudate correlated positively and moderately with the dopaminergic binding deficiency in caudate. The findings of this imaging study might offer new perspectives in understanding the characteristic manifestations in YOPD in light of better-preserved cognition function.

Metabolic pattern analysis of 18F-FDG PET as a marker for Parkinson's disease: a systematic review and meta-analysis

A large number of articles have assessed the diagnostic accuracy of the metabolic pattern analysis of [¹⁸F]fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) in Parkinson's disease (PD); however, different studies involved small samples with various controls and methods, leading to discrepant conclusions. This study aims to consolidate the available observational studies and provide a comprehensive evaluation of the clinical utility of ¹⁸F-FDG PET for PD. The methods included a systematic literature search and a hierarchical summary receiver operating characteristic approach. Sensitivity analyses according to different pattern analysis methods (statistical parametric mapping versus scaled subprofile modeling/principal component analysis) and control population [healthy controls (HCs) versus atypical parkinsonian disorder (APD) patients] were performed to verify the consistency of the main results. Additional analyses for multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) were conducted. Fifteen studies comprising 1446 subjects (660 PD patients, 499 APD patients, and 287 HCs) were included. The overall diagnostic accuracy of ¹⁸F-FDG in differentiating PD from APDs and HCs was quite high, with a pooled sensitivity of 0.88 [95% confidence interval (95% CI), 0.85-0.91] and a pooled specificity of 0.92 (95% CI, 0.89-0.94), with sensitivity analyses indicating statistically consistent results. Additional analyses showed an overall sensitivity and specificity of 0.87 (95% CI, 0.76-0.94) and 0.93 (95% CI, 0.89-0.96) for MSA and 0.91 (95% CI, 0.78-0.95) and 0.96 (95% CI, 0.92-0.98) for PSP. Our study suggests that the metabolic pattern analysis of ¹⁸F-FDG PET has high diagnostic accuracy in the differential diagnosis of parkinsonian disorders.

Network imaging biomarkers: insights and clinical applications in Parkinson's disease

Parkinson's disease presents several practical challenges: it can be difficult to distinguish from atypical parkinsonian syndromes, clinical ratings can be insensitive as markers of disease progression, and its non-motor manifestations are not readily assessed in animal models. These challenges, along with others, are beginning to be addressed by innovative imaging methods to characterise Parkinson's disease-specific functional networks across the whole brain and measure their expression in each patient. These signatures can help improve differential diagnosis, guide selection of patients for clinical trials, and quantify treatment responses and placebo effects in individual patients. The primary Parkinson's disease-related metabolic pattern has been replicated in multiple patient populations and used as an outcome measure in clinical trials. It can also be used as a predictor of near-term phenoconversion in prodromal syndromes, such as rapid eye movement sleep behaviour disorder. Functional network imaging holds great promise for future clinical use in the management of neurodegenerative disorders.

Module differences of glucose metabolic brain network among Alzheimer's disease, Parkinson's disease dementia, Lewy body dementia and health control

Dementia has become a serious disease in elderly population. Graph theory based brain network analysis method is considered as a popular and universal technique in the field of the neurosciences. It can help neuroscientists to investigate the neuropathology of dementia, especially to understand dementia subtypes. However, brain network analysis for various dementia subtypes at the same time is still limited. The aim at this study is to investigate the similarities and differences in brain network modular parameters of three dementia subtypes: Alzheimer's disease (AD), Parkinson's disease dementia (PDD), Lewy body dementia (DLB). Health control has also been compared. All subjects of health control(HC), AD, PDD and DLB groups were from Huashan Hospital. Using topological analysis algorithms, we found that HC brain was divided into 4 modules; AD brain was divided into 4 modules; PDD brain was divided into 6 modules; and DLB brain was divided into 10 modules, which demonstrated differences in these dementia subtypes. We also found that right thalamus area was scattered in all dementia subtypes. After using the seed point correlation analysis, it can be seen that the connections between right thalamus and subcortical, frontal gyrus were enhanced and the connections with the occipital gyrus was attenuated in dementia subtypes. As a result, findings in this paper are expected to be useful for neuroscientists to further understand the pathology of AD, PDD and DLB.

Hybrid PET-MRI Applications in Movement Disorders

Even before the success of combined positron emission tomography and computed tomography (PET/CT), the neuroimaging community was conceiving the idea to integrate the positron emission tomography (PET), with very high molecular quantitative data but low spatial resolution, and magnetic resonance imaging (MRI), with high spatial resolution. Several technical limitations have delayed the use of a hybrid scanner in neuroimaging studies, including the full integration of the PET detector ring within the MRI system, the optimization of data acquisition, and the implementation of reliable methods for PET attenuation, motion correction, and joint image reconstruction. To be valid and useful in clinical and research settings, this instrument should be able to simultaneously acquire PET and MRI, and generate quantitative parametric PET images comparable to PET-CT. While post hoc co-registration of combined PET and MRI data acquired separately became the most reliable technique for the generation of "fused" PET-MRI images, only hybrid PET-MRI approach allows merging these measurements naturally and correlating them in a temporal manner. Furthermore, hybrid PET-MRI represents the most accurate tool to investigate in vivo the interplay between molecular and functional aspects of brain pathophysiology. Hybrid PET-MRI technology is still in the early stages in the movement disorders field, due to the limited availability of scanners with integrated optimized methodological models. This technology is ideally suited to investigate interactions between resting-state functional/arterial spin labeling MRI and [¹⁸F]FDG PET glucose metabolism in the evaluation of the brain "hubs" particularly vulnerable to neurodegeneration, areas with a high degree of connectivity and associated with an efficient synaptic neurotransmission. In Parkinson's disease, hybrid PET-MRI is also the ideal instrument to deeper explore the relationship between resting-state functional MRI and dopamine release at [¹¹C]raclopride PET challenge, in the identification of early drug-naïve Parkinson's disease patients at higher risk of motor complications and in the evaluation of the efficacy of novel neuroprotective treatment able to restore at the same time the altered resting state and the release of dopamine. In this chapter, we discuss the key methodological aspects of hybrid PET-MRI; the evidence in movement disorders of the key resting-state functional and perfusion MRI; [¹⁸F]FDG PET and [¹¹C]raclopride PET challenge studies; the potential advantages of using hybrid PET-MRI to investigate the pathophysiology of movement disorders and neurodegenerative diseases. Future directions of hybrid PET-MRI will be discussed alongside with up-to-date technological innovations on hybrid systems.