Secondary Logo

Share this article on:

Clinical, Dopaminergic, and Metabolic Correlations in Parkinson Disease: A Dual-Tracer PET Study

Liu, Feng-Tao, MD*†; Ge, Jing-Jie, MD; Wu, Jian-Jun, MD, PhD*; Wu, Ping, MD; Ma, Yilong, PhD§; Zuo, Chuan-Tao, MD, PhD; Wang, Jian, MD, PhD*

doi: 10.1097/RLU.0000000000002148
Original Articles

Purpose Neuroimaging indicators of Parkinson disease have been developed and applied in clinical practices. Dopaminergic imaging reflects nigrostriatal dopaminergic dysfunction, and metabolic network imaging offers disease-related metabolic changes at a system level. We aimed to elucidate the association between Parkinsonian symptoms and neuroimaging, and interactions between different imaging techniques.

Methods We conducted a dual-tracer PET study for the combined assessments of dopaminergic binding (11C-CFT) and glucose metabolism (18F-FDG) in 103 participants with Parkinson disease (65 male and 38 female subjects). The detailed clinical rating scores were systematically collected in all members. The interactions among dopaminergic bindings, metabolic changes, and clinical manifestations were evaluated at voxel, regional, and network levels.

Results Striatal DAT binding correlated with akinesia-rigidity (P < 0.001) but not with tremor; the metabolic PET imaging, nonspecific to the dopaminergic dysfunction, disclosed a set of brain regions correlating with the cardinal symptoms, including tremor. In addition, the unilateral symptom correlated with the contralateral nigrostriatal dopamine loss, but with bilateral metabolic changes, suggesting their differences in the application of disease-related mechanistic studies. Further imaging-imaging correlation study revealed that dopaminergic dysfunction correlated with widely distributed metabolic changes in Parkinson disease, and the modest correlations supported the findings on the clinical-imaging correlation.

Conclusions In this dual-tracer PET study, we demonstrated the robust interactions among dopaminergic dysfunction, metabolic brain changes and clinical manifestations at voxel, regional, and network levels. Our findings might promote the understanding in the proper application of dopaminergic and metabolic PET imaging in Parkinson disease and offer more evidence in support of Parkinsonian pathophysiological mechanisms.

From the *Department of Neurology and National Clinical Research Center for Aging and Medicine, Huashan Hospital;

Department of Neurology, Huashan Hospital North;

PET Center, Huashan Hospital, Fudan University, Shanghai, People’s Republic of China; and

§Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY.

Received for publication January 18, 2018; revision accepted April 17, 2018.

Conflicts of interest and sources of funding: This work was supported by Ministry of Science and Technology of China (No. 2016YFC1306500], the National Nature Science Foundation of China (Nos. 81701250, 81771372, 81671239,81571232, 81371413, and 8136112039), Scientific Research Project from Huashan Hospital affiliated to Fudan University (No. 2016QD01), Science and Technology Commission of Shanghai Municipality (No. 15ZR1435800), the development funding for Shanghai Talents (No. 201448), and Shanghai Sailing Program (No. 18YF1403100). None declared to all authors.

F.-T.L., J.-J.G., and J.-J.W. contributed equally to this work.

Authors' contribution statement: All authors’ roles in the project and preparation of the manuscript are listed as follows: 1) Research project: conception by J.W., C.-T.Z., and Y.L.M.; organization by J.W., C.-T.Z., F.-T.L., and J.-J.G.; execution by F.-T.L., J.-J.G., J.-J.W., and P.W.; 2) Statistical analysis: design by J.W. and C.-T.Z.; execution by F.-T.L., J.-J.G., and P.W.; review and critique by Y.L.M. and C.-T.Z.; 3) Manuscript: writing of the first draft by F.-T.L., J.-J.G., and J.-J.W.; review and critique by J.W., C.-T.Z., and Y.L.M.

Correspondence to: Jian Wang, MD, PhD, Department of Neurology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200040, People’s Republic of China. E-mail: wangjian336@hotmail.com; or Chuan-Tao Zuo, MD, PhD, PET Center, Huashan Hospital, Fudan University, Shanghai 200235, People’s Republic of China. E-mail: zuoct_cn2000@126.com.

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Parkinson disease (PD) is the second most common neurodegenerative disorder, the diagnosis and disease-severity assessment of which are evaluated mainly by clinical examination and follow-up. To date, a number of neuroimaging techniques measuring fundamental features in PD have been developed and applied in clinical practices to provide more reliable and objective biomarkers.

Over the past decades, some presynaptic dopaminergic imaging approaches have focused on nigrostriatal dopaminergic dysfunction using a variety of dopaminergic radiotracers.1 According to the updated MDS clinical diagnostic criteria of PD,2 the presence of normal presynaptic dopamine transporter (DAT) neuroimaging is one of exclusions ruling out PD. However, DAT imaging is of limited value in differentiating PD from other atypical parkinsonism because of its lack of specificity.3–5 Furthermore, the dysfunction in the brain of PD goes beyond dopaminergic deficits. In such circumstances, 18F-fluorodeoxyglucose (18F-FDG) PET has been developed specifically to identify and measure metabolic abnormalities in PD at a system level.6,7 The prominent network abnormality detected in PD is characterized by increased pallidothalamic and pontine metabolic activity, associated with reductions in premotor cortex and parietal associate regions, termed as PD-related metabolic pattern (PDRP).8,9 The metabolic network is highly reproducible and specific for PD and may be helpful in distinguishing PD from atypical parkinsonism.10–13

Currently, a number of studies made the diagnosis of PD with the assistance of dopaminergic or metabolic imaging markers.14,15 Moreover, 11C-CFT and 18F-FDG PET scans were also shown to be objective tools for assessing disease severity and treatment efficacy.16 To provide credible biomarkers for that purpose, the combined application of dopaminergic and metabolic imaging was further suggested.3,17 However, the interactions among the dopaminergic and metabolic imaging measures and clinical manifestations were still to be further elucidated.

In the past decade, few studies detected the correlations among whole-brain metabolic change, striatal dopaminergic binding, and clinical ratings in PD. In a cross-sectional study of 26 de novo patients with PD, Berti et al reported that putaminal DAT values correlated with cerebral glucose metabolism in several prefrontal regions.18 In a longitudinal dual-tracer PET imaging study over 4 years,19 the association between PET imaging measures and clinical changes was explored in 15 patients with early-stage PD, showing that the metabolic changes in network activity correlated with the concurrent motor dysfunction rating and DAT binding in caudate/putamen. To further explore such correlations, a recent dual-tracer PET imaging study was performed in a cohort of 106 patients with PD.20 In that study, the authors systematically examined the relationship between presynaptic dopaminergic function and glucose metabolism at the voxel, regional, and network-wide levels. However, they did not evaluate relationships between the imaging measures and clinical manifestations. Most of these previous studies revealed modest correlations between PET imaging measures and clinical ratings17,19,20 and inspired us to examine correlations with detailed clinical ratings in a relatively large cohort with dual-tracer PET imaging to further promote the understanding on the similarities and differences between the variables in these correlations.

It is noteworthy that few studies directly studied the interactions between metabolic-dopaminergic PET imaging and clinical manifestations of PD at the voxel, regional, and network-wide levels. Herein, we conducted a dual-tracer PET study for the combined assessment of presynaptic dopaminergic binding (11C-CFT) and glucose metabolism (18F-FDG) in a Chinese cohort of 103 participants with PD to explore the interactions between the PET imaging measures and clinical ratings. We also detected the differences and associations between the 2 PET imaging indices in reflecting the cardinal parkinsonian symptoms of tremor or akinesia-rigidity (AR). Our study might offer some useful information in further understanding the interactions among metabolic and dopaminergic PET imaging markers and clinical ratings in directing their further application and offer more evidence in future studies on pathogenesis in PD.

Back to Top | Article Outline

PATIENTS AND METHODS

Patients

One hundred three patients (65 male and 38 female subjects, 54.1 ± 12.0 years), who were diagnosed as having idiopathic PD by 2 senior specialists of movement disorders according to the UK Brain Bank criteria21 and scanned with both 11C-CFT and 18F-FDG PET, were consecutively enrolled in this study between January 2010 and June 2014. All participants provided written informed consent in accordance with the Declaration of Helsinki. All aspects of the study were approved by the Studies Institutional Review Board.

Back to Top | Article Outline

Study Design

All subjects fasted overnight and withdrew antiparkinsonian medications for at least 12 hours before clinical assessment and each imaging acquisition. The clinical assessment for individual patient was evaluated using the Unified Parkinson Disease Rating Scale (UPDRS22) and Hoehn and Yahr (H&Y) scale. All patients were classified into different stages according to the H&Y scale. The UPDRS subscale ratings for tremor (Rt) and AR were then evaluated, respectively, as described previously.9 More detailed clinical information of the patients can be found in Table 1. After the clinical assessment, all subjects were scanned with 11C-CFT PET and scanned again with 18F-FDG PET at the same time on the following day.

TABLE 1

TABLE 1

Back to Top | Article Outline

PET Imaging and Imaging Processing

All subjects were scanned with a Siemens Biograph 64 HD PET/CT (Siemens, Erlangen, Germany) in 3-dimensional (3D) mode.9,23 A CT transmission scan was performed for photon attenuation correction. 11C-CFT PET imaging: a PET scan was subsequently acquired over 60 to 80 minutes after an intravenous injection of CFT (350–400 MBq) and reconstructed with the ordered subset expectation maximization method. 18F-FDG PET imaging: a PET scan was acquired over 45 to 55 minutes postinjection (150–200 MBq) and reconstructed with the ordered subset expectation maximization method. Acquisition for individual patient was performed in a resting state in a quiet and dimly lit room. 18F-FDG image was used as a relative measure of regional cerebral metabolic rate of glucose (rCMRglc).

Back to Top | Article Outline

Imaging Processing

Preprocessing of both 11C-CFT and 18F-FDG PET data was performed using SPM5 software (Statistical Parametric Mapping; Wellcome Department of Imaging Neuroscience, Institute of Neurology, London, UK) implemented in Matlab7.4.0 (MathWorks Inc., Sherborn, MA). Scans from each subject were spatially normalized into Montreal Neurological Institute (MNI) brain space with linear and nonlinear 3D transformations. The normalization of 11C-CFT images used a brain template of DAT binding in the same standard brain space. The normalized PET images were then smoothed by a Gaussian filter of 10 mm full width at half maximum) over a 3D space to increase signal-to-noise ratio for statistical analysis.

Back to Top | Article Outline

Data Analysis

Quantification of DAT Binding in the Brain

To quantify striatal 11C-CFT binding in individual patients, standardized regions of interest (ROIs) for the caudate, anterior putamen, posterior putamen, and occipital cortex were placed on the mean image summed over central slices19,23; the placement of these ROIs was adjusted manually as necessary and confirmed for the individual subject with reference to a standardized MRI brain template in SPM5. Maps of DAT binding were obtained over the whole brain by (image-occipital)/occipital counts as described previously.24 We then calculated DAT binding in the caudate, anterior putamen, and posterior putamen ROIs for each hemisphere.

Back to Top | Article Outline

Quantification of Cerebral Metabolic Network Expression (PDRP)

On the basis of PDRP derived from an independent Chinese cohort,9 we prospectively quantified network score for individual patient using a voxel-based topographic profile rating algorithm (ScAnVP software; available freely on http://www.feinsteinneuroscience.org at the Centre for Neuroscience, the Feinstein Institute for Medical Research, Manhasset, NY). The network expression in each PET scan was represented by a Z-transformed score using subject scores of the healthy controls in the Chinese derivation cohort for PDRP as described previously.9

Back to Top | Article Outline

Statistical Analysis

Global Analysis

The regional 11C-CFT binding (ROIs) in striatum and metabolic network scores (PDRP) at different clinical stages was compared with normal values by unpaired Student t tests. The group comparisons for 11C-CFT binding in striatal ROIs, metabolic network scores (PDRP), and UPDRS motor ratings among different H&Y stages were then assessed using analysis of variance (ANOVA) with Bonferroni corrections for multiple comparisons. Pairwise correlations among substriatal DAT binding, PDRP scores and corresponding clinical motor ratings in all patients were conducted using Pearson correlations analysis. All analyses were performed using SPSS 22.0 (SPSS Inc, Chicago, IL), and a 2-tailed P value of less than 0.05 was considered significant.

Back to Top | Article Outline

Voxel-Based Brain Mapping Analysis

Correlations Between Maps of DAT Binding and Different Clinical Motor Ratings

To explore the relationship between 11C-CFT uptake and clinical motor measures, we performed a multiple regression analysis using SPM5 software in all PD patients.25 To evaluate the significant correlation, we set the voxel threshold at P < 0.001 (uncorrected) over whole brain and search for clusters that survived at family-wide error–corrected P < 0.05. 11C-CFT binding values in the specific regions were then extracted post hoc and correlated with the clinical ratings of individual patients.

Back to Top | Article Outline
Correlations Between Images of Whole-Brain Metabolism and Different Clinical Motor Ratings or Striatal DAT Binding

A multiple regression analysis was also used in SPM5 to investigate the relationship between whole-brain metabolism and clinical motor measures or average DAT binding values in the striatal ROIs. Global metabolic values of individual patient were entered as covariates in the analysis of covariance model.26 To evaluate the significant correlation and confirm the previous observations,20 we set the voxel threshold at P < 0.01 (uncorrected) over whole-brain and search for clusters survived at P < 0.001. Globally normalized metabolic values in the specific regions were also extracted post hoc and correlated with the UPDRS motor scores of individual patients.

Back to Top | Article Outline
Anatomical Localization of Correlation Maps and Post Hoc Analysis of 11C-CFT and 18F-FDG Images

To further interrogate the aforementioned correlation maps, an extent threshold was empirically chosen to be more than 2 to 3 times of the expected voxels per cluster estimated in each SPM run. Significant regions were localized using Talairach Daemon software (Research Imaging Center, University of Texas Health Science Center, San Antonio, TX) after applying a MNI-to-Talairach conversion. SPM maps of t statistic in neurological convention were overlaid on a standard T1-weighted MRI brain template in stereotaxic space. To quantify DAT binding or metabolic values in specific regions, we constructed a 4-mm radius spherical volume of interest (VOI) in the image space, centered at the peak voxel of clusters that were significant in each SPM analysis. To enable post hoc correlations of imaging measures with clinical variables, we calculated the corresponding VOI values in 11C-CFT and 18F-FDG images of all patients using VOI counts/global counts with ScAnVP software described previously.

Back to Top | Article Outline

RESULTS

Changes in PET Imaging Measures and Motor Rating

In PD patients, regional 11C-CFT binding (ROIs) in striatum declined with the increase in H&Y stage (caudate: F(2,100) = 7.707, P = 0.001; anterior putamen: F(2,100) = 11.457, P < 0.001; posterior putamen: F(2,100) = 10.600, P < 0.001; ANOVA; Table 1). In PET imaging with 18F-FDG, the PDRP values increased significantly as the H&Y stages increased (F(2,100) = 17.938, P < 0.001, ANOVA; Table 1). Also, the UPDRS III motor scores increased significantly as the H&Y stages increased (F(2,100) = 78.523, P < 0.001, ANOVA; Table 1).

Back to Top | Article Outline

Correlations Between DAT Binding in the Striatal ROIs, PDRP Values, and UPDRS Scores

The average DAT binding in caudate (r = −0.352, P < 0.001), anterior putamen (r = −0.477, P < 0.001), and posterior putamen (r = −0.464, P < 0.001) correlated significantly with UPDRS motor rating scores (Fig. 1A). In correlation with the motor subscores, DAT binding in caudate and putamen correlated well with AR scores (P < 0.01) but not with tremor scores. The left AR scores negatively correlated only with the 11C-CFT uptake in the right caudate (r = −0.338, P < 0.001) and putamen (anterior putamen: r = −0.503, P < 0.001; posterior putamen: r = −0.536, P < 0.001); the right AR scores negatively correlated with the 11C-CFT uptake in the left caudate(r = −0.316 P = 0.001) and putamen (anterior putamen: r = −0.522, P < 0.001; posterior putamen: r = −0.496, P < 0.001).

FIGURE 1

FIGURE 1

The PDRP values correlated with UPDRS motor scores (r = 0.450, P < 0.001) and AR scores (Rar: r = 0.467, P < 0.001) but not with tremor scores (r = 0.127, P = 0.200). The PDRP scores also correlated with the regional DAT binding in both left striatum (caudate: r = −0.348, P < 0.001; anterior putamen: r = −0.240, P = 0.015; posterior putamen: r = −0.211, P = 0.033) and right striatum (caudate: r = −0.323, P = 0.001; anterior putamen: r = −0.205, P = 0.038; posterior putamen: r = −0.181, P = 0.068).

Back to Top | Article Outline

Voxel-Based Brain Mapping Analysis

Correlation of Motor Ratings With DAT Binding

The voxel-wise regression of UPDRS motor scores against 11C-CFT uptake suggested a significant negative correlation located primarily in the putamen extending to the caudate (Fig. 1B). These correlations could also be confirmed in the post hoc correlation analysis (left putamen: r = −0.442, P < 0.001; right putamen: r = −0.456, P < 0.001) (Fig. 1C). No correlation was found between the tremor scores and the DAT binding (Fig. 1D); however, the AR scores correlated with the DAT expression in putamen significantly (Fig. 1E, left), and the correlated regions of 11C-CFT uptake to the left or right AR scores is limited to the striatum on the opposite side (Fig. 1E, middle and right). In the post hoc analysis, the 11C-CFT uptake in the right putamen (30, 6, −4) correlated negatively with left AR scores (r = −0.524, P < 0.001), and the 11C-CFT uptake in the left putamen (−24, 6, −8) correlated negatively with right AR scores (r = −0.574, P < 0.001).

Back to Top | Article Outline

Correlation of Motor Ratings With Whole-Brain Metabolism

The voxel-wise regression of UPDRS motor scores against whole-brain 18F-FDG uptake revealed that motor ratings correlated positively with relative metabolism in the cerebellum, midbrain, internal globus pallidus (GPi), and paracentral gyrus but negatively with that in the parietal gyrus, precuneus, temporal gyrus, cuneus, and occipital gyrus, quite similar to the regions of PDRP (Fig. 2A, Table 2). These correlations were further supported by the post hoc VOI analysis (Fig. 2B).

FIGURE 2

FIGURE 2

TABLE 2

TABLE 2

AR scores correlated with relative metabolism positively in the regions including cerebellum, postcentral gyrus, caudate, and internal GPi but negatively with the regions of superior parietal lobule, precuneus, lingual gyrus, cuneus, and occipital gyrus (Fig. 2C). Tremor scores correlated positively with relative metabolism in paracentral lobule, cerebellum, internal GPi, thalamus and midbrain, and negatively with the regions of superior and inferior frontal gyrus and lingual gyrus (Fig. 2D). Furthermore, in the voxel-wise regression of the unilateral AR scores and tremor scores against the whole-brain 18F-FDG, bilateral regions were found to be correlated with the unilateral rating scores (Fig. 2E and F).

Back to Top | Article Outline

Correlation of Regional Brain Metabolism With Striatal Dopaminergic Integrity

The average 11C-CFT uptake values in the anterior putamen correlated positively with metabolic activity in the midbrain (substantia nigra), putamen, and the parietal-occipital-temporal association regions including the precuneus cortex (Fig. 3A, Table 3). Inverse correlations were evident bilaterally in the cerebellum and medial frontal gyrus (Fig. 3A, Table 3). The regions correlated with the average 11C-CFT uptake values in the caudate or posterior putamen were quite similar (data not shown here). In the post hoc VOI analysis, the values of the normalized rCMRglc in the putamen, precuneus, inferior parietal lobule, and middle occipital gyrus correlated positively, and rCMRglc in the cerebellum and medial frontal gyrus correlated negatively with the average DAT binding in the anterior putamen (Fig. 3B).

FIGURE 3

FIGURE 3

TABLE 3

TABLE 3

Back to Top | Article Outline

DISCUSSION

In this dual-tracer PET study of 103 PD patients, the pairwise analysis of correlation between imaging measurements and motor manifestations delineated the similarities and differences between 11C-CFT and 18F-FDG PET in reflecting disease severity and motor symptoms. Also, the relationship of dopaminergic dysfunction and the cerebral glucose metabolism was explored at voxel, regional, and network levels. Our work might promote the understanding of the relationship among dopaminergic PET imaging, metabolic PET imaging and clinical ratings and offer new perspective of their application in future studies of PD.

Back to Top | Article Outline

The Dual-Tracer PET Imaging in Detecting the Regions Related to AR

In our DAT imaging study, the striatal 11C-CFT binding correlated with AR symptom on both voxel and regional levels, supporting that the loss of dopaminergic innervation in striatum was a main factor in the cause of akinesia/rigidity. Similar findings could be found in studies with other dopaminergic tracers, such as 18F-DOPA27,28 and 18F-DTBZ.29 In metabolic imaging, the regions involved in the CSTC (cortex-striatum-thalamus-cortex) pathway and cerebellum were found to correlate with AR. Since its description, the classic model of motor control emphasized the role of the basal ganglia (BG) in influencing cortical function30 and elegantly posited an explanation for rigidity and bradykinesia.31 Beyond the BG model, cerebellum was also reported to be involved.32 These findings could be supported by the studies showing that the striatum and cerebellum influence some similar cortical regions, and both structures could modulate each other at the subcortical level.33 As shown in our work, the metabolism in those regions correlated with the dopaminergic dysfunction in BG on voxel, regional, and network levels, and similar dopaminergic-metabolic correlations could also be found in previous studies.18–20 Therefore, the AR-related regions validated in our study might assist in understanding the pathophysiology of certain circuits and elucidating their specific roles in causing clinical symptoms.

Back to Top | Article Outline

The Dual-Tracer PET Imaging in Detecting the Regions Related to Tremor

In our study, caudate and putamen did not correlate with tremor in dopaminergic imaging, nor in metabolic imaging, supporting that the pathological changes in striatum were not directly involved in the emergence of tremor.34 Those findings were quite different from the AR-related study, inspiring us to hypothesize that the severity of tremor might have a different mechanism from akinesia/rigidity. Our findings could be supported by previous studies, in which the tremor severity did not correlate with the rhythmic activity or lesions in striatum but with the internal GPi.35,36 In our metabolic imaging study, the cerebellum, ventralis intermedius nucleus (Vim) of the thalamus, GPi, and paracentral cortex were found to correlate with tremor, quite similar to the regions in the “cerebello-thalamo-cortical” pathways in the PD tremor-related metabolic pattern.37 Furthermore, these tremor-related regions identified in our study were identical to the regions reported in the “dimmer switch” theory, in which the striato-pallidal circuit triggers tremor episodes (light switch), whereas the cerebello-thalamo-cortical circuit produces the tremor and controls its amplitude (light dimmer).34 Conclusively, the metabolic imaging may compensate for the limitation of DAT imaging in reflecting the pathogenesis and mechanism of Parkinsonian symptoms (eg, tremor).

Back to Top | Article Outline

Unilateral or Bilateral, Different Imaging Markers May Reflect Different Facets of Parkinsonian Mechanisms

In our correlation study between clinical symptoms and dopaminergic imaging, the left or right AR scores correlated strictly with the DAT binding in the opposite striatum. Our finding supported the relation between dopaminergic dysfunction and motor symptoms as previously reported28 and revealed for the first time the strict anatomical side-to-side effects of presynaptic dopaminergic deficit on the emergence of akinesia-and-rigidity symptoms at both voxel and regional levels. These findings might further promote our understanding on dopaminergic deficits in parkinsonian pathogenesis and guide the clinical practice in the use of dopaminergic PET imaging.

Quite different from dopaminergic imaging, the unilateral symptoms might associate with bilateral regions of the brain, relatively symmetrically, as shown in the metabolic imaging in the same population. In consistent with our finding, Tang reported the metabolic activity in the hemisphere contralateral to the body side without symptom was already elevated, almost identical to the measurement in the contralateral side, and such symmetrically elevated expression lasted as disease progressed.38 In some previous fMRI studies, unilateral motor tasks were also reported to recruit bilateral motor areas.33,39 The increased recruitment of additional brain structures, even from ipsilateral hemispheres, might compensate for the functions usually carried out by the contralateral structures.40 Our correlation study supported a compensatory mechanism involved, in which symmetrical disease-related metabolic changes occurred, in concurrent with asymmetric dopaminergic loss, in the emergence of the lateralized clinical manifestations.

Back to Top | Article Outline

Modest Correlations Among Dopaminergic Deficit, Metabolic Changes, and Clinical Ratings in PD

In some previous studies, the interactions between PET imaging and clinical ratings, or the correlations between different PET imaging markers, were reported14,18,19 but only of modest degree. In the longitudinal study by Huang et al of 15 early PD patients, the changes of PDRP scores correlated with the increases of UPDRS motor scores and declines of DAT binding, with the correlation magnitude of no more than 40%.19 In the cross-sectional study by Holtbernd et al of 106 PD subjects without detailed clinical rating information, the PDRP expressions correlated with presynaptic dopaminergic deficits, with the correlation magnitude of less than 20%.20 In our multilevel correlation study of 103 PD patients, with detailed clinical information systemically collected, we reported similar modest correlation among them (R 2 = 10%–30%). In the correlation study with the clinical symptoms, we found that the brain regions involved in the parkinsonian symptoms are actually beyond the nigrostriatal dopamine system; in the correlation study between the 2 PET imaging markers, we disclosed that the metabolic brain changes in PD correlated but were not equivalent to the level of dopaminergic denervation. Therefore, there were correlations among these variables but only at modest degrees.

Back to Top | Article Outline

Study Strengths and Limitations

In this study, the relative large study population made it possible for us to detect some subtle correlations and mechanisms than the studies of smaller population size. Furthermore, the detailed clinical rating scores systematically collected in all members of this cohort make it possible for us to evaluate the motor symptom–related cerebral regions and mechanisms by analyzing the relationships between imaging measurements and different motor score subtypes.

In the voxel-based correlation analysis of whole-brain metabolism, we set the voxel threshold at P < 0.01(uncorrected), which could be accepted in hypothesis-testing analysis but relatively weak. There were some explanations for the relative weak correlations. First, the weak correlation in voxel-based analysis might be due to the modest pairwise correlation found in regional and network levels (R 2 = 10%–30%). Second, the weak correlation might be due to the complexity of the disease itself. Parkinson disease is a disease beyond dopaminergic impairments. As shown in our study, metabolic changes in PD might also reflect other underlying transmitter deficits beyond dopamine. Although we analyzed the symptoms of tremor and AR separately, it would be much better to collect various nonmotor symptoms and analyze those correlations in different subtypes of certain motor and nonmotor symptoms. Third, a longitudinal follow-up would be of necessity to verify the results reported in this cross-sectional study. Future longitudinal studies on certain populations of specific subtype of PD with nonmotor and motor information would be much more convincing.

Back to Top | Article Outline

CONCLUSIONS

In conclusion, we demonstrated the robust interactions among dopaminergic dysfunction, metabolic brain changes, and clinical manifestations at voxel, regional, and network levels in this study. Our findings in the current dual-tracer PET study might promote the understanding in proper application of dopaminergic and metabolic PET imaging in PD and offer more evidence in further pathomechanism study in PD.

Back to Top | Article Outline

ACKNOWLEDGMENTS

The authors thank Drs Ding Ding, Qian-Hua Zhao, and Xiao-Niu Liang for the suggestions in data analysis; Drs Jue Zhao, Zhen-Yang Liu, Yi-Lin Tang, Ke Yang, Kui Chen, and Lu-Lu Bu for the help in data collection.

Back to Top | Article Outline

REFERENCES

1. Nandhagopal R, Kuramoto L, Schulzer M, et al. Longitudinal progression of sporadic Parkinson's disease: a multi-tracer positron emission tomography study. Brain. 2009;132:2970–2979.
2. Postuma RB, Berg D, Stern M, et al. MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord. 2015;30:1591–1601.
3. Stoessl AJ, Martin WW, McKeown MJ, et al. Advances in imaging in Parkinson's disease. Lancet Neurol. 2011;10:987–1001.
4. Brooks DJ. Molecular imaging of dopamine transporters. Ageing Res Rev. 2016;30:114–121.
5. Stoessl AJ. Neuroimaging in Parkinson's disease: from pathology to diagnosis. Parkinsonism Relat Disord. 2012;18:S55–S59.
6. Eidelberg D, Moeller JR, Ishikawa T, et al. Early differential diagnosis of Parkinson's disease with 18F-fluorodeoxyglucose and positron emission tomography. Neurology. 1995;45:1995–2004.
7. Eidelberg D. Metabolic brain networks in neurodegenerative disorders: a functional imaging approach. Trends Neurosci. 2009;32:548–557.
8. Ma Y, Tang C, Spetsieris PG, et al. Abnormal metabolic network activity in Parkinson's disease: test-retest reproducibility. J Cereb Blood Flow Metab. 2007;27:597–605.
9. Wu P, Wang J, Peng S, et al. Metabolic brain network in the Chinese patients with Parkinson's disease based on 18F-FDG PET imaging. Parkinsonism Relat Disord. 2013;19:622–627.
10. Tang CC, Poston KL, Eckert T, et al. Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis. Lancet Neurol. 2010;9:149–158.
11. Meyer PT, Hellwig S. Update on SPECT and PET in parkinsonism—part 1: imaging for differential diagnosis. Curr Opin Neurol. 2014;27:390–397.
12. Tripathi M, Tang CC, Feigin A, et al. Automated differential diagnosis of early parkinsonism using metabolic brain networks: a validation study. J Nucl Med. 2016;57:60–66.
13. Ko JH, Lee CS, Eidelberg D. Metabolic network expression in parkinsonism: clinical and dopaminergic correlations. J Cereb Blood Flow Metab. 2017;37:683–693.
14. Niethammer M, Eidelberg D. Metabolic brain networks in translational neurology: concepts and applications. Ann Neurol. 2012;72:635–647.
15. Wang J, Hoekstra JG, Zuo C, et al. Biomarkers of Parkinson's disease: current status and future perspectives. Drug Discov Today. 2013;18:155–162.
16. Hirano S, Eckert T, Flanagan T, et al. Metabolic networks for assessment of therapy and diagnosis in Parkinson's disease. Mov Disord. 2009;24:S725–S731.
17. Eckert T, Tang C, Eidelberg D. Assessment of the progression of Parkinson's disease: a metabolic network approach. Lancet Neurol. 2007;6:926–932.
18. Berti V, Polito C, Ramat S, et al. Brain metabolic correlates of dopaminergic degeneration in de novo idiopathic Parkinson's disease. Eur J Nucl Med Mol Imaging. 2010;37:537–544.
19. Huang C, Tang C, Feigin A, et al. Changes in network activity with the progression of Parkinson's disease. Brain. 2007;130:1834–1846.
20. Holtbernd F, Ma Y, Peng S, et al. Dopaminergic correlates of metabolic network activity in Parkinson's disease. Hum Brain Mapp. 2015;36:3575–3585.
21. Gibb WR, Lees AJ. The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson's disease. J Neurol Neurosurg Psychiatry. 1988;51:745–752.
22. Fahn S, Oakes D, Shoulson I, et al. Levodopa and the progression of Parkinson's disease. N Engl J Med. 2004;351:2498–2508.
23. Liu SY, Wu JJ, Zhao J, et al. Onset-related subtypes of Parkinson's disease differ in the patterns of striatal dopaminergic dysfunction: a positron emission tomography study. Parkinsonism Relat Disord. 2015;21:1448–1453.
24. Ma Y, Dhawan V, Mentis M, et al. Parametric mapping of [18F]FPCIT binding in early stage Parkinson's disease: a PET study. Synapse. 2002;45:125–133.
25. Wang J, Zuo CT, Jiang YP, et al. 18F-FP-CIT PET imaging and SPM analysis of dopamine transporters in Parkinson's disease in various Hoehn & Yahr stages. J Neurol. 2007;254:185–190.
26. Ge J, Wu P, Peng S, et al. Assessing cerebral glucose metabolism in patients with idiopathic rapid eye movement sleep behavior disorder. J Cereb Blood Flow Metab. 2015;35:1092.
27. Eggers C, Schwartz F, Pedrosa DJ, et al. Parkinson's disease subtypes show a specific link between dopaminergic and glucose metabolism in the striatum. PLoS One. 2014;9:e96629.
28. Pikstra AR, Hoorn van der A, Leenders KL, et al. Relation of 18-F-Dopa PET with hypokinesia-rigidity, tremor and freezing in Parkinson's disease. Neuroimage Clin. 2016;11:68–72.
29. Hsiao IT, Weng YH, Hsieh CJ, et al. Correlation of Parkinson disease severity and 18F-DTBZ positron emission tomography. JAMA Neurol. 2014;71:758–766.
30. DeLong MR, Alexander GE, Georgopoulos AP, et al. Role of basal ganglia in limb movements. Hum Neurobiol. 1984;2:235–244.
31. Baradaran N, Tan SN, Liu A, et al. Parkinson's disease rigidity: relation to brain connectivity and motor performance. Front Neurol. 2013;4:67.
32. Lewis MM, Galley S, Johnson S, et al. The role of the cerebellum in the pathophysiology of Parkinson's disease. Can J Neurol Sci. 2013;40:299–306.
33. Lewis MM, Du G, Sen S, et al. Differential involvement of striato- and cerebello-thalamo-cortical pathways in tremor- and akinetic/rigid-predominant Parkinson's disease. Neuroscience. 2011;177:230–239.
34. Helmich RC, Janssen MJ, Oyen WJ, et al. Pallidal dysfunction drives a cerebellothalamic circuit into Parkinson tremor. Ann Neurol. 2011;69:269–281.
35. Otsuka M, Ichiya Y, Kuwabara Y, et al. Differences in the reduced 18F-Dopa uptakes of the caudate and the putamen in Parkinson's disease: correlations with the three main symptoms. J Neurol Sci. 1996;136:169–173.
36. Rodriguez-Oroz MC, Jahanshahi M, Krack P, et al. Initial clinical manifestations of Parkinson's disease: features and pathophysiological mechanisms. Lancet Neurol. 2009;8:1128–1139.
37. Mure H, Hirano S, Tang CC, et al. Parkinson's disease tremor-related metabolic network: characterization, progression, and treatment effects. Neuroimage. 2011;54:1244–1253.
38. Tang CC, Poston KL, Dhawan V, et al. Abnormalities in metabolic network activity precede the onset of motor symptoms in Parkinson's disease. J Neurosci. 2010;30:1049–1056.
39. Moraschi M, Giulietti G, Giove F, et al. fMRI study of motor cortex activity modulation in early Parkinson's disease. Magn Reson Imaging. 2010;28:1152–1158.
40. Appel-Cresswell S, Fuente-Fernandez de la R, Galley S, et al. Imaging of compensatory mechanisms in Parkinson's disease. Curr Opin Neurol. 2010;23:407–412.
Keywords:

clinical rating; dopamine imaging; glucose metabolism; Parkinson disease; PET

Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.