Abnormal metabolic networks in atypical parkinsonism

Impulse Control Disorders in Parkinson's Disease and Atypical Parkinsonian Syndromes-Is There a Difference?

BACKGROUND AND OBJECTIVES

Impulse control disorders (ICDs) are characterized by potentially harmful actions resulting from disturbances in the self-control of emotions and behavior. ICDs include disorders such as gambling, hypersexuality, binge eating, and compulsive buying. ICDs are known non-motor symptoms in Parkinson's disease (PD) and are associated primarily with the use of dopaminergic treatment (DRT) and especially dopamine agonists (DA). However, in atypical parkinsonism (APS), such as progressive supranuclear palsy (PSP) or multiple system atrophy (MSA), there are only single case reports of ICDs without attempts to determine the risk factors for their occurrence. Moreover, numerous reports in the literature indicate increased impulsivity in PSP. Our study aimed to determine the frequency of individual ICDs in APS compared to PD and identify potential factors for developing ICDs in APS.

MATERIALS AND METHODS

Our prospective study included 185 patients with PD and 35 with APS (27 patients with PSP and 9 with MSA) hospitalized between 2020 and 2023 at the Neurological Department of University Central Hospital in Katowice. Each patient was examined using the Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease (QUIP) to assess ICDs. Additionally, other scales were used to assess the advancement of the disease, the severity of depression, and cognitive impairment. Information on age, gender, age of onset, disease duration, and treatment used were collected from medical records and patient interviews.

RESULTS

ICDs were detected in 23.39% of patients with PD (including binge eating in 11.54%, compulsive buying in 10.44%, hypersexuality in 8.79%, and pathological gambling in 4.40%), in one patient with MSA (hypersexuality and pathological gambling), and in 18.52% of patients with PSP (binge eating in 3.70%, compulsive buying in 7.41%, and hypersexuality in 11.11%). We found no differences in the frequency of ICDs between individual diseases (p = 0.4696). We confirmed that the use of higher doses of DA and L-dopa in patients with PD, as well as a longer disease duration and the presence of motor complications, were associated with a higher incidence of ICDs. However, we did not find any treatment effect on the incidence of ICDs in APS.

CONCLUSIONS

ICDs are common and occur with a similar frequency in PD and APS. Well-described risk factors for ICDs in PD, such as the use of DRT or longer disease duration, are not fully reflected in the risk factors for ICDs in APS. This applies especially to PSP, which, unlike PD and MSA, is a tauopathy in which, in addition to the use of DRT, other mechanisms related to the disease, such as disorders in neuronal loops and neurotransmitter deficits, may influence the development of ICDs. Further prospective multicenter studies recruiting larger groups of patients are needed to fully determine the risk factors and mechanisms of ICD development in APS.

New Perspectives in Radiological and Radiopharmaceutical Hybrid Imaging in Progressive Supranuclear Palsy: A Systematic Review

Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized by four-repeat tau deposition in various cell types and anatomical regions, and can manifest as several clinical phenotypes, including the most common phenotype, Richardson's syndrome. The limited availability of biomarkers for PSP relates to the overlap of clinical features with other neurodegenerative disorders, but identification of a growing number of biomarkers from imaging is underway. One way to increase the reliability of imaging biomarkers is to combine different modalities for multimodal imaging. This review aimed to provide an overview of the current state of PSP hybrid imaging by combinations of positron emission tomography (PET) and magnetic resonance imaging (MRI). Specifically, combined PET and MRI studies in PSP highlight the potential of [18F]AV-1451 to detect tau, but also the challenge in differentiating PSP from other neurodegenerative diseases. Studies over the last years showed a reduced synaptic density in [11C]UCB-J PET, linked [11C]PK11195 and [18F]AV-1451 markers to disease progression, and suggested the potential role of [18F]RO948 PET for identifying tau pathology in subcortical regions. The integration of quantitative global and regional gray matter analysis by MRI may further guide the assessment of reduced cortical thickness or volume alterations, and diffusion MRI could provide insight into microstructural changes and structural connectivity in PSP. Challenges in radiopharmaceutical biomarkers and hybrid imaging require further research targeting markers for comprehensive PSP diagnosis.

Brain MRI in Progressive Supranuclear Palsy with Richardson's Syndrome and Variant Phenotypes

BACKGROUND

Brain magnetic resonance imaging (MRI) is used to support the diagnosis of progressive supranuclear palsy (PSP). However, the value of visual descriptive, manual planimetric, automatic volumetric MRI markers and fully automatic categorization is unclear, particularly regarding PSP predominance types other than Richardson's syndrome (RS).

OBJECTIVES

To compare different visual reading strategies and automatic classification of T1-weighted MRI for detection of PSP in a typical clinical cohort including PSP-RS and (non-RS) variant PSP (vPSP) patients.

METHODS

Forty-one patients (21 RS, 20 vPSP) and 46 healthy controls were included. Three readers using three strategies performed MRI analysis: exclusively visual reading using descriptive signs (hummingbird, morning-glory, Mickey-Mouse), visual reading supported by manual planimetry measures, and visual reading supported by automatic volumetry. Fully automatic classification was performed using a pre-trained support vector machine (SVM) on the results of atlas-based volumetry.

RESULTS

All tested methods achieved higher specificity than sensitivity. Limited sensitivity was driven to large extent by false negative vPSP cases. Support by automatic volumetry resulted in the highest accuracy (75.1% ± 3.5%) among the visual strategies, but performed not better than the midbrain area (75.9%), the best single planimetric measure. Automatic classification by SVM clearly outperformed all other methods (accuracy, 87.4%), representing the only method to provide clinically useful sensitivity also in vPSP (70.0%).

CONCLUSIONS

Fully automatic classification of volumetric MRI measures using machine learning methods outperforms visual MRI analysis without and with planimetry or volumetry support, particularly regarding diagnosis of vPSP, suggesting the use in settings with a broad phenotypic PSP spectrum. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease

Network analysis of functional brain scans acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET, to map cerebral glucose metabolism), or resting-state functional magnetic resonance imaging (rs-fMRI, to map blood oxygen level-dependent brain activity) has increasingly been used to identify and validate reproducible circuit abnormalities associated with neurodegenerative disorders such as Parkinson's disease (PD). In addition to serving as imaging markers of the underlying disease process, these networks can be used singly or in combination as an adjunct to clinical diagnosis and as a screening tool for therapeutics trials. Disease networks can also be used to measure rates of progression in natural history studies and to assess treatment responses in individual subjects. Recent imaging studies in PD subjects scanned before and after treatment have revealed therapeutic effects beyond the modulation of established disease networks. Rather, other mechanisms of action may be at play, such as the induction of novel functional brain networks directly by treatment. To date, specific treatment-induced networks have been described in association with novel interventions for PD such as subthalamic adeno-associated virus glutamic acid decarboxylase (AAV2-GAD) gene therapy, as well as sham surgery or oral placebo under blinded conditions. Indeed, changes in the expression of these networks with treatment have been found to correlate consistently with clinical outcome. In aggregate, these attributes suggest a role for functional brain networks as biomarkers in future clinical trials.

Automatic covariance pattern analysis outperforms visual reading of 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) in variant progressive supranuclear palsy

BACKGROUND

To date, studies on positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) in progressive supranuclear palsy (PSP) usually included PSP cohorts overrepresenting patients with Richardson's syndrome (PSP-RS).

OBJECTIVES

To evaluate FDG-PET in a patient sample representing the broad phenotypic PSP spectrum typically encountered in routine clinical practice.

METHODS

This retrospective, multicenter study included 41 PSP patients, 21 (51%) with RS and 20 (49%) with non-RS variants of PSP (vPSP), and 46 age-matched healthy controls. Two state-of-the art methods for the interpretation of FDG-PET were compared: visual analysis supported by voxel-based statistical testing (five readers) and automatic covariance pattern analysis using a predefined PSP-related pattern.

RESULTS

Sensitivity and specificity of the majority visual read for the detection of PSP in the whole cohort were 74% and 72%, respectively. The percentage of false-negative cases was 10% in the PSP-RS subsample and 43% in the vPSP subsample. Automatic covariance pattern analysis provided sensitivity and specificity of 93% and 83% in the whole cohort. The percentage of false-negative cases was 0% in the PSP-RS subsample and 15% in the vPSP subsample.

CONCLUSIONS

Visual interpretation of FDG-PET supported by voxel-based testing provides good accuracy for the detection of PSP-RS, but only fair sensitivity for vPSP. Automatic covariance pattern analysis outperforms visual interpretation in the detection of PSP-RS, provides clinically useful sensitivity for vPSP, and reduces the rate of false-positive findings. Thus, pattern expression analysis is clinically useful to complement visual reading and voxel-based testing of FDG-PET in suspected PSP. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Comparison of univariate and multivariate analyses for brain [18F]FDG PET data in α-synucleinopathies

BACKGROUND

Brain imaging with [18F]FDG-PET can support the diagnostic work-up of patients with α-synucleinopathies. Validated data analysis approaches are necessary to evaluate disease-specific brain metabolism patterns in neurodegenerative disorders. This study compared the univariate Statistical Parametric Mapping (SPM) single-subject procedure and the multivariate Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) in a cohort of patients with α-synucleinopathies.

METHODS

We included [18F]FDG-PET scans of 122 subjects within the α-synucleinopathy spectrum: Parkinson's Disease (PD) normal cognition on long-term follow-up (PD - low risk to dementia (LDR); n = 28), PD who developed dementia on clinical follow-up (PD - high risk of dementia (HDR); n = 16), Dementia with Lewy Bodies (DLB; n = 67), and Multiple System Atrophy (MSA; n = 11). We also included [18F]FDG-PET scans of isolated REM sleep behaviour disorder (iRBD; n = 51) subjects with a high risk of developing a manifest α-synucleinopathy. Each [18F]FDG-PET scan was compared with 112 healthy controls using SPM procedures. In the SSM/PCA approach, we computed the individual scores of previously identified patterns for PD, DLB, and MSA: PD-related patterns (PDRP), DLBRP, and MSARP. We used ROC curves to compare the diagnostic performances of SPM t-maps (visual rating) and SSM/PCA individual pattern scores in identifying each clinical condition across the spectrum. Specifically, we used the clinical diagnoses ("gold standard") as our reference in ROC curves to evaluate the accuracy of the two methods. Experts in movement disorders and dementia made all the diagnoses according to the current clinical criteria of each disease (PD, DLB and MSA).

RESULTS

The visual rating of SPM t-maps showed higher performance (AUC: 0.995, specificity: 0.989, sensitivity 1.000) than PDRP z-scores (AUC: 0.818, specificity: 0.734, sensitivity 1.000) in differentiating PD-LDR from other α-synucleinopathies (PD-HDR, DLB and MSA). This result was mainly driven by the ability of SPM t-maps to reveal the limited or absent brain hypometabolism characteristics of PD-LDR. Both SPM t-maps visual rating and SSM/PCA z-scores showed high performance in identifying DLB (DLBRP = AUC: 0.909, specificity: 0.873, sensitivity 0.866; SPM t-maps = AUC: 0.892, specificity: 0.872, sensitivity 0.910) and MSA (MSARP: AUC: 0.921, specificity: 0.811, sensitivity 1.000; SPM t-maps: AUC: 1.000, specificity: 1.000, sensitivity 1.000) from other α-synucleinopathies. PD-HDR and DLB were comparable for the brain hypo and hypermetabolism patterns, thus not allowing differentiation by SPM t-maps or SSM/PCA. Of note, we found a gradual increase of PDRP and DLBRP expression in the continuum from iRBD to PD-HDR and DLB, where the DLB patients had the highest scores. SSM/PCA could differentiate iRBD from DLB, reflecting specifically the differences in disease staging and severity (AUC: 0.938, specificity: 0.821, sensitivity 0.941).

CONCLUSIONS

SPM-single subject maps and SSM/PCA are both valid methods in supporting diagnosis within the α-synucleinopathy spectrum, with different strengths and pitfalls. The former reveals dysfunctional brain topographies at the individual level with high accuracy for all the specific subtype patterns, and particularly also the normal maps; the latter provides a reliable quantification, independent from the rater experience, particularly in tracking the disease severity and staging. Thus, our findings suggest that differences in data analysis approaches exist and should be considered in clinical settings. However, combining both methods might offer the best diagnostic performance.

Principal component analysis of synaptic density measured with [11C]UCB-J PET in early Alzheimer's disease

BACKGROUND

Synaptic loss is considered an early pathological event and major structural correlate of cognitive impairment in Alzheimer's disease (AD). We used principal component analysis (PCA) to identify regional patterns of covariance in synaptic density using [11C]UCB-J PET and assessed the association between principal components (PC) subject scores with cognitive performance.

METHODS

[11C]UCB-J binding was measured in 45 amyloid + participants with AD and 19 amyloid- cognitively normal participants aged 55-85. A validated neuropsychological battery assessed performance across five cognitive domains. PCA was applied to the pooled sample using distribution volume ratios (DVR) standardized (z-scored) by region from 42 bilateral regions of interest (ROI).

RESULTS

Parallel analysis determined three significant PCs explaining 70.2% of the total variance. PC1 was characterized by positive loadings with similar contributions across the majority of ROIs. PC2 was characterized by positive and negative loadings with strongest contributions from subcortical and parietooccipital cortical regions, respectively, while PC3 was characterized by positive and negative loadings with strongest contributions from rostral and caudal cortical regions, respectively. Within the AD group, PC1 subject scores were positively correlated with performance across all cognitive domains (Pearson r = 0.24-0.40, P = 0.06-0.006), PC2 subject scores were inversely correlated with age (Pearson r = -0.45, P = 0.002) and PC3 subject scores were significantly correlated with CDR-sb (Pearson r = 0.46, P = 0.04). No significant correlations were observed between cognitive performance and PC subject scores in CN participants.

CONCLUSIONS

This data-driven approach defined specific spatial patterns of synaptic density correlated with unique participant characteristics within the AD group. Our findings reinforce synaptic density as a robust biomarker of disease presence and severity in the early stages of AD.

Structural and Molecular Imaging for Clinically Uncertain Parkinsonism

Neuroimaging is an important adjunct to the clinical assessment of Parkinson disease (PD). Parkinsonism can be challenging to differentiate, especially in early disease stages, when it mimics other movement disorders or when there is a poor response to dopaminergic therapies. There is also a discrepancy between the phenotypic presentation of degenerative parkinsonism and the pathological outcome. The emergence of more sophisticated and accessible neuroimaging can identify molecular mechanisms of PD, the variation between clinical phenotypes, and the compensatory mechanisms that occur with disease progression. Ultra-high-field imaging techniques have improved spatial resolution and contrast that can detect microstructural changes, disruptions in neural pathways, and metabolic and blood flow alterations. We highlight the imaging modalities that can be accessed in clinical practice and recommend an approach to the diagnosis of clinically uncertain parkinsonism.

The challenging quest of neuroimaging: From clinical to molecular-based subtyping of Parkinson disease and atypical parkinsonisms

The current framework of Parkinson disease (PD) focuses on phenotypic classification despite its considerable heterogeneity. We argue that this method of classification has restricted therapeutic advances and therefore limited our ability to develop disease-modifying interventions in PD. Advances in neuroimaging have identified several molecular mechanisms relevant to PD, variation within and between clinical phenotypes, and potential compensatory mechanisms with disease progression. Magnetic resonance imaging (MRI) techniques can detect microstructural changes, disruptions in neural pathways, and metabolic and blood flow alterations. Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging have informed the neurotransmitter, metabolic, and inflammatory dysfunctions that could potentially distinguish disease phenotypes and predict response to therapy and clinical outcomes. However, rapid advancements in imaging techniques make it challenging to assess the significance of newer studies in the context of new theoretical frameworks. As such, there needs to not only be a standardization of practice criteria in molecular imaging but also a rethinking of target approaches. In order to harness precision medicine, a coordinated shift is needed toward divergent rather than convergent diagnostic approaches that account for interindividual differences rather than similarities within an affected population, and focus on predictive patterns rather than already lost neural activity.

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.

Additive value of [18F]PI-2620 perfusion imaging in progressive supranuclear palsy and corticobasal syndrome

PURPOSE

Early after [18F]PI-2620 PET tracer administration, perfusion imaging has potential for regional assessment of neuronal injury in neurodegenerative diseases. This is while standard late-phase [18F]PI-2620 tau-PET is able to discriminate the 4-repeat tauopathies progressive supranuclear palsy and corticobasal syndrome (4RTs) from disease controls and healthy controls. Here, we investigated whether early-phase [18F]PI-2620 PET has an additive value for biomarker based evaluation of 4RTs.

METHODS

Seventy-eight patients with 4RTs (71 ± 7 years, 39 female), 79 patients with other neurodegenerative diseases (67 ± 12 years, 35 female) and twelve age-matched controls (69 ± 8 years, 8 female) underwent dynamic (0-60 min) [18F]PI-2620 PET imaging. Regional perfusion (0.5-2.5 min p.i.) and tau load (20-40 min p.i.) were measured in 246 predefined brain regions [standardized-uptake-value ratios (SUVr), cerebellar reference]. Regional SUVr were compared between 4RTs and controls by an ANOVA including false-discovery-rate (FDR, p < 0.01) correction. Hypoperfusion in resulting 4RT target regions was evaluated at the patient level in all patients (mean value - 2SD threshold). Additionally, perfusion and tau pattern expression levels were explored regarding their potential discriminatory value of 4RTs against other neurodegenerative disorders, including validation in an independent external dataset (n = 37), and correlated with clinical severity in 4RTs (PSP rating scale, MoCA, activities of daily living).

RESULTS

Patients with 4RTs had significant hypoperfusion in 21/246 brain regions, most dominant in thalamus, caudate nucleus, and anterior cingulate cortex, fitting to the topology of the 4RT disease spectrum. However, single region hypoperfusion was not specific regarding the discrimination of patients with 4RTs against patients with other neurodegenerative diseases. In contrast, perfusion pattern expression showed promise for discrimination of patients with 4RTs from other neurodegenerative diseases (AUC: 0.850). Discrimination by the combined perfusion-tau pattern expression (AUC: 0.903) exceeded that of the sole tau pattern expression (AUC: 0.864) and the discriminatory power of the combined perfusion-tau pattern expression was replicated in the external dataset (AUC: 0.917). Perfusion but not tau pattern expression was associated with PSP rating scale (R = 0.402; p = 0.0012) and activities of daily living (R =  - 0.431; p = 0.0005).

CONCLUSION

[18F]PI-2620 perfusion imaging mirrors known topology of regional hypoperfusion in 4RTs. Single region hypoperfusion is not specific for 4RTs, but perfusion pattern expression may provide an additive value for the discrimination of 4RTs from other neurodegenerative diseases and correlates closer with clinical severity than tau pattern expression.

Differences in Striatal Metabolism in [18F]FDG PET in Parkinson's Disease and Atypical Parkinsonism

Neurodegenerative parkinsonisms affect mainly cognitive and motor functions and are syndromes of overlapping symptoms and clinical manifestations such as tremor, rigidness, and bradykinesia. These include idiopathic Parkinson's disease (PD) and the atypical parkinsonisms, namely progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), multiple system atrophy (MSA) and dementia with Lewy body (DLB). Differences in the striatal metabolism among these syndromes are evaluated using [¹⁸F]FDG PET, caused by alterations to the dopaminergic activity and neuronal loss. A study cohort of three patients with PD, 29 with atypical parkinsonism (10 PSP, 6 CBD, 2 MSA, 7 DLB, and 4 non-classifiable), and a control group of 25 patients with normal striatal metabolism is available. Standardized uptake value ratios (SUVR) are extracted from the striatum, and the caudate and the putamen separately. SUVRs are compared among the study groups. In addition, hemispherical and caudate-putamen differences are evaluated in atypical parkinsonisms. Striatal hypermetabolism is detected in patients with PD, while atypical parkinsonisms show hypometabolism, compared to the control group. Hemispherical differences are observed in CBD, MSA and DLB, with the latter also showing statistically significant caudate-putamen asymmetry (p = 0.018). These results indicate disease-specific metabolic uptake patterns in the striatum that can support the differential diagnosis.

Stereotyped Relationship Between Motor and Cognitive Metabolic Networks in Parkinson's Disease

BACKGROUND

Idiopathic Parkinson's disease (iPD) is associated with two distinct brain networks, PD-related pattern (PDRP) and PD-related cognitive pattern (PDCP), which correlate respectively with motor and cognitive symptoms. The relationship between the two networks in individual patients is unclear.

OBJECTIVE

To determine whether a consistent relationship exists between these networks, we measured the difference between PDRP and PDCP expression, termed delta, on an individual basis in independent populations of patients with iPD (n = 356), patients with idiopathic REM sleep behavioral disorder (iRBD) (n = 21), patients with genotypic PD (gPD) carrying GBA1 variants (n = 12) or the LRRK2-G2019S mutation (n = 14), patients with atypical parkinsonian syndromes (n = 238), and healthy control subjects (n = 95) from the United States, Slovenia, India, and South Korea.

METHODS

We used [18 F]-fluorodeoxyglucose positron emission tomography and resting-state fMRI to quantify delta and to compare the measure across samples; changes in delta over time were likewise assessed in longitudinal patient samples. Lastly, we evaluated delta in prodromal individuals with iRBD and subjects with gPD.

RESULTS

Delta was abnormally elevated in each of the four iPD samples (P < 0.05), as well as in the at-risk iRBD group (P < 0.05), with increasing values over time (P < 0.001). PDRP predominance was also present in gPD, with higher values in patients with GBA1 variants compared with the less aggressive LRRK2-G2019S mutation (P = 0.005). This trend was not observed in patients with atypical parkinsonian syndromes, who were accurately discriminated from iPD based on PDRP expression and delta (area under the curve = 0.85; P < 0.0001).

CONCLUSIONS

PDRP predominance, quantified by delta, assays the spread of dysfunction from motor to cognitive networks in patients with PD. Delta may therefore aid in differential diagnosis and in tracking disease progression in individual patients. © 2022 International Parkinson and Movement Disorder Society.

Nigral glucose metabolism as a diagnostic marker of neurodegenerative parkinsonian syndromes

Parkinson’s disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP) are characterized by nigrostriatal degeneration. We used [18F]FDG PET to assess glucose metabolism of the substantia nigra (SN) in patients with these diseases and evaluated its ability to discriminate neurodegenerative parkinsonian syndromes (NP) from controls. We retrospectively evaluated [18F]FDG PET scans of 171 patients with NP (n = 115 PD, n = 35 MSA, n = 21 PSP) and 48 controls (13 healthy controls [HC] and 35 control patients). Mean normalized bilateral [18F]FDG uptake in the SN was calculated and compared between groups with covariance and receiver operating characteristic (ROC) analyses (selection of the optimal cut-off required a minimum specificity of 90% to meet the clinical need of a confirmatory test). PD patients were additionally stratified by the expression of the well-established PD-related metabolic pattern (PDRP; elevated expression defined as 2 standard deviations above the mean value of HC). [18F]FDG uptake was significantly lower in NP (Cohen’s d = 1.09, p < 0.001) and its subgroups (PD, d = 1.10, p < 0.001; MSA, d = 0.97, p < 0.001; PSP, d = 1.79, p < 0.001) than in controls. ROC analysis for discriminating NP vs. controls revealed an area under the curve of 0.81 and a sensitivity and specificity of 56 and 92%. Moreover, nigral metabolism was below the cut-off in 60% of PD patients without elevated PDRP expression. Glucose metabolism of the SN can distinguish patients with NP from controls with good diagnostic accuracy and can be used as a marker of nigral degeneration. Its evaluation is particularly valuable in PD patients without elevated PDRP expression and may thus help to narrow the diagnostic gap of [18F]FDG PET in neurodegenerative parkinsonism (i.e., identification of patients with PD without cortical involvement).

A bibliometric analysis of neuroimaging biomarkers in Parkinson disease based on Web of Science

BACKGROUND

This study aimed to analyze and summarize the research hotspots and trends in neuroimaging biomarkers (NMBM) in Parkinson disease (PD) based on the Web of Science core collection database and provide new references for future studies.

METHODS

Literature regarding NMBM in PD from 1998 to 2022 was analyzed using the Web of Science core collection database. We utilized CiteSpace software (6.1R2) for bibliometric analyses of countries/institutions/authors, keywords, keyword bursts, references, and their clusters.

RESULTS

A total of 339 studies were identified with a continually increasing annual trend. The most productive country and collaboration was the United States. The top research hotspot is PD cognitive disorder. NMBM and artificial intelligence medical imaging have been applied in the clinical diagnosis, differential diagnosis, treatment, and prognosis of PD. The trends in this field include research on T1 weighted structure magnetic resonance imaging in accordance with voxel-based morphometry, PD cognitive disorder, and neuroimaging features of Lewy body dementia and Alzheimer disease.

CONCLUSION

The development of NMBM in PD will be effectively promoted by drawing on international research hotspots and cutting-edge technologies, emphasizing international collaboration and institutional cooperation at the national level, and strengthening interdisciplinary research.

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.

The Frontal and Cerebellar Metabolism Related to Cognitive Dysfunction in Multiple System Atrophy

Background

Cognitive dysfunctions have been reported in multiple system atrophy (MSA). However the underlying mechanisms remain to be elucidated. This study aimed to explore the possible cerebral metabolism associated with domain-specific cognitive performances in MSA.

Methods

A total of 84 patients were diagnosed as probable or possible MSA, comprised of 27 patients as MSA with predominant parkinsonism (MSA-P) and 57 patients as MSA with predominant cerebellar ataxia (MSA-C). The comprehensive neuropsychological tests and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) imaging were performed. Z-score was calculated to non-dimensionalize and unify indicators of different tests in the domains of executive function, attention, language, memory, and visuospatial function. Correlations between specific Z-score and cerebral ¹⁸F-FDG uptake were analyzed using statistical parametric mapping. The cognition-related metabolic differences between patients with MSA-P and MSA-C were analyzed using the post-hoc test.

Results

Z-scores of the domains including attention, executive function, and language correlated positively with the metabolism in the superior/inferior frontal gyrus and cerebellum, but negatively with that in the insula and fusiform gyrus (p < 0.001). No significant differences in neuropsychological performances and frontal metabolism were found between patients with MSA-P and MSA-C. Only lower metabolism in the cerebellum was observed in MSA-C.

Conclusion

Metabolic changes in the frontal lobe and cerebellum may participate in the cognitive impairments of patients with MSA. Nevertheless, cognitive and corresponding metabolic differences between the two subtypes of MSA still need more exploration.

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.

Pragmatic Approach on Neuroimaging Techniques for the Differential Diagnosis of Parkinsonisms

Background

Rapid advances in neuroimaging technologies in the exploration of the living human brain also apply to movement disorders. However, the accurate diagnosis of Parkinson's disease (PD) and atypical parkinsonian disorders (APDs) still remains a challenge in daily practice.

Methods

We review the literature and our own experience as the Movement Disorder Society-Neuroimaging Study Group in Movement Disorders with the aim of providing a practical approach to the use of imaging technologies in the clinical setting.

Results

The enormous amount of articles published so far and our increasing recognition of imaging technologies contrast with a lack of imaging protocols and updated algorithms for differential diagnosis. The distinctive pathological involvement in different brain structures and the correlation with imaging findings obtained with magnetic resonance, positron emission tomography, or single-photon emission computed tomography illustrate what qualitative and quantitative measures may be useful in the clinical setting.

Conclusion

We delineate a pragmatic approach to discuss imaging technologies, updated imaging algorithms, and their implications for differential diagnoses in PD and APDs.

EANM procedure guidelines for brain PET imaging using [18F]FDG, version 3

The present procedural guidelines summarize the current views of the EANM Neuro-Imaging Committee (NIC). The purpose of these guidelines is to assist nuclear medicine practitioners in making recommendations, performing, interpreting, and reporting results of [¹⁸F]FDG-PET imaging of the brain. The aim is to help achieve a high-quality standard of [¹⁸F]FDG brain imaging and to further increase the diagnostic impact of this technique in neurological, neurosurgical, and psychiatric practice. The present document replaces a former version of the guidelines that have been published in 2009. These new guidelines include an update in the light of advances in PET technology such as the introduction of digital PET and hybrid PET/MR systems, advances in individual PET semiquantitative analysis, and current broadening clinical indications (e.g., for encephalitis and brain lymphoma). Further insight has also become available about hyperglycemia effects in patients who undergo brain [¹⁸F]FDG-PET. Accordingly, the patient preparation procedure has been updated. Finally, most typical brain patterns of metabolic changes are summarized for neurodegenerative diseases. The present guidelines are specifically intended to present information related to the European practice. The information provided should be taken in the context of local conditions and regulations.

Evolving concepts in progressive supranuclear palsy and other 4-repeat tauopathies

Tauopathies are classified according to whether tau deposits predominantly contain tau isoforms with three or four repeats of the microtubule-binding domain. Those in which four-repeat (4R) tau predominates are known as 4R-tauopathies, and include progressive supranuclear palsy, corticobasal degeneration, argyrophilic grain disease, globular glial tauopathies and conditions associated with specific MAPT mutations. In these diseases, 4R-tau deposits are found in various cell types and anatomical regions of the brain and the conditions share pathological, pathophysiological and clinical characteristics. Despite being considered 'prototype' tauopathies and, therefore, ideal for studying neuroprotective agents, 4R-tauopathies are still severe and untreatable diseases for which no validated biomarkers exist. However, advances in research have addressed the issues of phenotypic overlap, early clinical diagnosis, pathophysiology and identification of biomarkers, setting a road map towards development of treatments. New clinical criteria have been developed and large cohorts with early disease are being followed up in prospective studies. New clinical trial readouts are emerging and biomarker research is focused on molecular pathways that have been identified. Lessons learned from failed trials of neuroprotective drugs are being used to design new trials. In this Review, we present an overview of the latest research in 4R-tauopathies, with a focus on progressive supranuclear palsy, and discuss how current evidence dictates ongoing and future research goals.

The role of neuroimaging in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Two hallmarks of PD are the accumulation of alpha-synuclein and the loss of dopaminergic neurons in the brain. There is no cure for PD, and all existing treatments focus on alleviating the symptoms. PD diagnosis is also based on the symptoms, such as abnormalities of movement, mood, and cognition observed in the patients. Molecular imaging methods such as magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and positron emission tomography (PET) can detect objective alterations in the neurochemical machinery of the brain and help diagnose and study neurodegenerative diseases. This review addresses the application of functional MRI, PET, and SPECT in PD patients. We provide an overview of the imaging targets, discuss the rationale behind target selection, the agents (tracers) with which the imaging can be performed, and the main findings regarding each target's state in PD. Molecular imaging has proven itself effective in supporting clinical diagnosis of PD and has helped reveal that PD is a heterogeneous disorder, which has important implications for the development of future therapies. However, the application of molecular imaging for early diagnosis of PD or for differentiation between PD and atypical parkinsonisms has remained challenging. The final section of the review is dedicated to new imaging targets with which one can detect the PD-related pathological changes upstream from dopaminergic degeneration. The foremost of those targets is alpha-synuclein. We discuss the progress of tracer development achieved so far and challenges on the path toward alpha-synuclein imaging in humans.

Early recognition and diagnosis of multiple system atrophy: best practice and emerging concepts

Introduction: Multiple system atrophy (MSA) is a progressive degenerative disorder of the central and autonomic nervous systems characterized by parkinsonism, cerebellar ataxia, dysautonomia, and pyramidal signs. The confirmatory diagnosis is pathological, but clinical-diagnostic criteria have been developed to help clinicians. To date, the early diagnosis of MSA is challenging due to the lack of reliable diagnostic biomarkers.Areas covered: The authors reappraised the main clinical, neurophysiological, imaging, genetic, and laboratory evidence to help in the early diagnosis of MSA in the clinical and in the research settings. They also addressed the practical clinical issues in the differential diagnosis between MSA and other parkinsonian and cerebellar syndromes. Finally, the authors summarized the unmet needs in the early diagnosis of MSA and proposed the next steps for future research efforts in this field.Expert opinion: In the last decade, many advances have been achieved to help the correct MSA diagnosis since early stages. In the next future, the early diagnosis and correct classification of MSA, together with a better knowledge of the causative mechanisms of the disease, will hopefully allow the identification of suitable candidates to enroll in clinical trials and select the most appropriate disease-modifying strategies to slow down disease progression.

Implication of metabolic and dopamine transporter PET in dementia with Lewy bodies

To evaluate the implication of ¹⁸F-fluorodeoxyglucose (FDG)- and dopamine transporter (DAT)-positron emission tomography (PET) in the diagnosis and clinical symptoms of dementia with Lewy bodies (DLB), 55 DLB patients and 49 controls underwent neuropsychological evaluation and FDG-, DAT-, and ¹⁸F-Florbetaben (FBB) PET. DAT- and FDG-uptake and FDG/DAT ratio were measured in the anterior and posterior striatum. The first principal component (PC1) of FDG subject residual profiles was identified for each subject. Receiver operating characteristic curve analyses for the diagnosis of DLB were performed using FDG- and DAT-PET biomarkers as predictors, and general linear models for motor severity and cognitive scores were performed adding FBB standardized uptake value ratio as a predictor. Increased metabolism in the bilateral putamen, vermis, and somato-motor cortices, which characterized PC1, was observed in the DLB group, compared to the control group. A combination of posterior putamen FDG/DAT ratio and PC1 showed the highest diagnostic accuracy (91.8% sensitivity and 96.4% specificity), which was significantly greater than that obtained by DAT uptake alone. Striatal DAT uptake and PC1 independently contributed to motor severity and language, memory, frontal/executive, and general cognitive dysfunction in DLB patients, while only PC1 contributed to attention and visuospatial dysfunction.

Abnormal structural connectivity in progressive supranuclear palsy-Richardson syndrome

OBJECTIVES

Progressive supranuclear palsy-Richardson syndrome (PSP-RS) is characterized by symmetrical parkinsonism with postural instability and frontal dysfunction. This study aims to use the whole brain structural connectome (SC) to gain insights into the underlying disconnectivity which may be implicated in the clinical features of PSP-RS.

METHODS

Sixteen patients of PSP-RS and 12 healthy controls were recruited. Disease severity was quantified using PSP rating scale (PSPRS), and mini-mental scale was applied to evaluate cognition. Thirty-two direction diffusion MRIs were acquired and used to compute the structural connectome of the whole brain using deterministic fiber tracking. Group analyses were performed at the edge-wise, nodal, and global levels. Age and gender were used as nuisance covariates for all the subsequent analyses, and FDR correction was applied.

RESULTS

Network-based statistics revealed a 34-edge network with significantly abnormal edge-wise connectivity in the patient group. Of these, 25 edges were cortical connections, of which 68% were frontal connections. Abnormal deep gray matter connections were predominantly comprised of connections between structures of the basal ganglia. The characteristic path length of the SC was lower in PSP-RS, and nodal analysis revealed abnormal degree, strength, local efficiency, betweenness centrality, and participation coefficient in several nodes.

CONCLUSIONS

Significant alterations in the structural connectivity of the whole brain connectome were observed in PSP-RS. The higher degree of abnormality observed in nodes belonging to the frontal lobe and basal ganglia substantiates the predominant frontal dysfunction and parkinsonism observed in PSP-RS. The findings of this study support the concept that PSP-RS may be a network-based disorder.

Brain Molecular Connectivity in Neurodegenerative Conditions

Positron emission tomography (PET) allows for the in vivo assessment of early brain functional and molecular changes in neurodegenerative conditions, representing a unique tool in the diagnostic workup. The increased use of multivariate PET imaging analysis approaches has provided the chance to investigate regional molecular processes and long-distance brain circuit functional interactions in the last decade. PET metabolic and neurotransmission connectome can reveal brain region interactions. This review is an overview of concepts and methods for PET molecular and metabolic covariance assessment with evidence in neurodegenerative conditions, including Alzheimer’s disease and Lewy bodies disease spectrum. We highlight the effects of environmental and biological factors on brain network organization. All of the above might contribute to innovative diagnostic tools and potential disease-modifying interventions.

Neuroimaging Advances in Parkinson's Disease and Atypical Parkinsonian Syndromes

Parkinson's disease (PD) and atypical Parkinsonian syndromes are progressive heterogeneous neurodegenerative diseases that share clinical characteristic of parkinsonism as a common feature, but are considered distinct clinicopathological disorders. Based on the predominant protein aggregates observed within the brain, these disorders are categorized as, (1) α-synucleinopathies, which include PD and other Lewy body spectrum disorders as well as multiple system atrophy, and (2) tauopathies, which comprise progressive supranuclear palsy and corticobasal degeneration. Although, great strides have been made in neurodegenerative disease research since the first medical description of PD in 1817 by James Parkinson, these disorders remain a major diagnostic and treatment challenge. A valid diagnosis at early disease stages is of paramount importance, as it can help accommodate differential prognostic and disease management approaches, enable the elucidation of reliable clinicopathological relationships ideally at prodromal stages, as well as facilitate the evaluation of novel therapeutics in clinical trials. However, the pursuit for early diagnosis in PD and atypical Parkinsonian syndromes is hindered by substantial clinical and pathological heterogeneity, which can influence disease presentation and progression. Therefore, reliable neuroimaging biomarkers are required in order to enhance diagnostic certainty and ensure more informed diagnostic decisions. In this article, an updated presentation of well-established and emerging neuroimaging biomarkers are reviewed from the following modalities: (1) structural magnetic resonance imaging (MRI), (2) diffusion-weighted and diffusion tensor MRI, (3) resting-state and task-based functional MRI, (4) proton magnetic resonance spectroscopy, (5) transcranial B-mode sonography for measuring substantia nigra and lentiform nucleus echogenicity, (6) single photon emission computed tomography for assessing the dopaminergic system and cerebral perfusion, and (7) positron emission tomography for quantifying nigrostriatal functions, glucose metabolism, amyloid, tau and α-synuclein molecular imaging, as well as neuroinflammation. Multiple biomarkers obtained from different neuroimaging modalities can provide distinct yet corroborative information on the underlying neurodegenerative processes. This integrative "multimodal approach" may prove superior to single modality-based methods. Indeed, owing to the international, multi-centered, collaborative research initiatives as well as refinements in neuroimaging technology that are currently underway, the upcoming decades will mark a pivotal and exciting era of further advancements in this field of neuroscience.

Dopamine transporter imaging in neurodegenerative movement disorders: PET vs. SPECT

Purpose

The dopamine transporter (DAT) serves as biomarker for parkinsonian syndromes. DAT can be measured in vivo with single-photon emission computed tomography (SPECT) and positron emission tomography (PET). DAT-SPECT is the current clinical molecular imaging standard. However, PET has advantages over SPECT measurements, and PET radioligands with the necessary properties for clinical applications are on the rise. Therefore, it is time to review the role of DAT imaging with SPECT compared to PET.

Methods

PubMed and Web of Science were searched for relevant literature of the previous 10 years. Four topics for comparison were used: diagnostic accuracy, quantitative accuracy, logistics, and flexibility.

Results

There are a few studies directly comparing DAT-PET and DAT-SPECT. PET and SPECT both perform well in discriminating neurodegenerative from non-neurodegenerative parkinsonism. Clinical DAT-PET imaging seems feasible only recently, thanks to simplified DAT assessments and better availability of PET radioligands and systems. The higher resolution of PET makes more comprehensive assessments of disease progression in the basal ganglia possible. Additionally, it has the possibility of multimodal target assessment.

Conclusion

DAT-SPECT is established for differentiating degenerative from non-degenerative parkinsonism. For further differentiation within neurodegenerative Parkinsonian syndromes, DAT-PET has essential benefits. Nowadays, because of wider availability of PET systems and radioligand production centers, and the possibility to use simplified quantification methods, DAT-PET imaging is feasible for clinical use. Therefore, DAT-PET needs to be considered for a more active role in the clinic to take a step forward to a more comprehensive understanding and assessment of Parkinson’s disease.

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

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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.

Multicenter Validation of Metabolic Abnormalities Related to PSP According to the MDS-PSP Criteria

It remains unclear whether the supportive imaging features described in the diagnostic criteria for progressive supranuclear palsy (PSP) are suitable for the full clinical spectrum. The aim of the current study was to define and cross-validate the pattern of glucose metabolism in the brain associated with a diagnosis of different PSP variants. A retrospective multicenter cohort study performed on 73 PSP patients who were referred for a fluorodeoxyglucose positron emission tomography PET scan: PSP-Richardson's syndrome, n = 47; PSP-parkinsonian variant, n = 18; and progressive gait freezing, n = 8. In addition, we included 55 healthy controls and 58 Parkinson's disease (PD) patients. Scans were normalized by global mean activity. We analyzed the regional differences in metabolism between the groups. Moreover, we applied a multivariate analysis to obtain a PSP-related pattern that was cross-validated in independent populations at the individual level. Group analysis showed relative hypometabolism in the midbrain, basal ganglia, thalamus, and frontoinsular cortices and hypermetabolism in the cerebellum and sensorimotor cortices in PSP patients compared with healthy controls and PD patients, the latter with more severe involvement in the basal ganglia and occipital cortices. The PSP-related pattern obtained confirmed the regions described above. At the individual level, the PSP-related pattern showed optimal diagnostic accuracy to distinguish between PSP and healthy controls (sensitivity, 80.4%; specificity, 96.9%) and between PSP and PD (sensitivity, 80.4%; specificity, 90.7%). Moreover, PSP-Richardson's syndrome and PSP-parkinsonian variant patients showed significantly more PSP-related pattern expression than PD patients and healthy controls. The glucose metabolism assessed by fluorodeoxyglucose PET is a useful and reproducible supportive diagnostic tool for PSP-Richardson's syndrome and PSP-parkinsonian variant. © 2020 International Parkinson and Movement Disorder Society.

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.

Brain Glucose Metabolism Heterogeneity in Idiopathic REM Sleep Behavior Disorder and in Parkinson's Disease

BACKGROUND/OBJECTIVE

Idiopathic REM sleep behavior disorder (iRBD) often precedes Parkinson's disease (PD) and other alpha-synucleinopathies. The aim of the study is to investigate brain glucose metabolism of patients with RBD and PD by means of a multidimensional scaling approach, using18F-FDG-PET as a biomarker of synaptic function.

METHODS

Thirty-six iRBD patients (64.1±6.5 y, 32 M), 72 PD patients, and 79 controls (65.6±9.4 y, 53 M) underwent brain 18F-FDG-PET. PD patients were divided according to the absence (PD, 32 subjects; 68.4±8.5 y, 15 M) or presence (PDRBD, 40 subjects; 71.8±6.6 y, 29 M) of RBD. 18F-FDG-PET scans were used to independently discriminate subjects belonging to four categories: controls (RBD no, PD no), iRBD (RBD yes, PD no), PD (RBD no, PD yes) and PDRBD (RBD yes, PD yes).

RESULTS

The discriminant analysis was moderately accurate in identifying the correct category. This is because the model mostly confounds iRBD and PD, thus the intermediate classes. Indeed, iRBD, PD and PDRBD were progressively located at increasing distance from controls and are ordered along a single dimension (principal coordinate analysis) indicating the presence of a single flux of variation encompassing both RBD and PD conditions.

CONCLUSION

Data-driven approach to brain 18F-FDG-PET showed only moderate discrimination between iRBD and PD patients, highlighting brain glucose metabolism heterogeneity among such patients. iRBD should be considered as a marker of an ongoing condition that may be picked-up in different stages across patients and thus express different brain imaging features and likely different clinical trajectories.

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.

Diagnosing multiple system atrophy at the prodromal stage

Identifying individuals at the earliest disease stage becomes crucial as we aim to develop disease-modifying treatments for neurodegenerative disorders. Prodromal diagnostic criteria were recently developed for Parkinson's disease (PD) and are forthcoming for dementia with Lewy bodies (DLB). The latest 2008 version of diagnostic criteria for multiple system atrophy (MSA) have improved diagnostic accuracy in early disease stages compared to previous criteria, but we do not yet have formal criteria for prodromal MSA. Building on similar approaches as for PD and DLB, we can identify features on history-taking, clinical examination, and ancillary clinical testing that can predict the likelihood of an individual developing MSA, while also distinguishing it from PD and DLB. The main clinical hallmarks of MSA are REM sleep behavior disorder (RBD) and autonomic dysfunction (particularly orthostatic hypotension and urogenital symptoms), and may be the primary means by which patients with potential prodromal MSA are identified. Preserved olfaction, absence of significant cognitive deficits, urinary retention, and respiratory symptoms such as stridor and respiratory insufficiency can be clinical features that help distinguish MSA from PD and DLB. Finally, ancillary test results including neuroimaging as well as serological and cerebrospinal fluid (CSF) biomarkers may lend further weight to quantifying the likelihood of phenoconversion into MSA. For prodromal criteria, the primary challenges are MSA's lower prevalence, shorter lead time to diagnosis, and strong overlap with other synucleinopathies. Future prodromal criteria may need to first embed the diagnosis into a general umbrella of prodromal alpha-synucleinopathies, followed by identification of features that suggest prodromal MSA as the specific cause.

Differences in cerebellar perfusion between Parkinson's disease and multiple system atrophy

INTRODUCTION

Objective biomarkers are required for differential diagnosis of Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP).

OBJECTIVE

We aimed to determine if cerebellar blood flow, measured using N-isopropyl-[¹²³I] p-iodoamphetamine single photon emission computed tomography (¹²³I -IMP-SPECT), was useful for differentiating between PD, MSA and PSP.

METHODS

Twenty-four patients with PD, seventeen patients with MSA with predominant parkinsonian features (MSA-P), sixteenth patients with MSA with predominant cerebellar ataxia (MSA-C) and eight patients with PSP were enrolled. Twenty-seven normal controls' data were used for the calculation of z score. All patients underwent ¹²³I -IMP-SPECT, and data were analyzed using a three-dimensional-stereotactic surface projection program.

RESULTS

Cerebellar perfusion in MSA-P (MSA-P vs PD, P = .002; MSA-P vs PSP, P < .001) and MSA-C (MSA-C vs PD, P < .001; MSA-C vs PSP, P < .001) were significantly decreased compared with PD or PSP. There was no significant difference in perfusion between PD and PSP groups (P = .061). The area under the receiver operating characteristic curve for cerebellar perfusion between MSA-P and PD was 0.858.

CONCLUSION

Our findings revealed that cerebellar perfusion by ¹²³I-IMP-SPECT was useful for differentiating between PD and MSA-P.

Multiple System Atrophy: Recent Developments and Future Perspectives

Multiple system atrophy (MSA) is a rare and fatal neurodegenerative disorder characterized by a variable combination of parkinsonism, cerebellar impairment, and autonomic dysfunction. The pathologic hallmark is the accumulation of aggregated α-synuclein in oligodendrocytes, forming glial cytoplasmic inclusions, which qualifies MSA as a synucleinopathy together with Parkinson's disease and dementia with Lewy bodies. The underlying pathogenesis is still not well understood. Some symptomatic treatments are available, whereas neuroprotection remains an urgent unmet treatment need. In this review, we critically appraise significant developments of the past decade with emphasis on pathogenesis, diagnosis, prognosis, and treatment development. We further discuss unsolved questions and highlight some perspectives. © 2019 International Parkinson and Movement Disorder Society.

Recent Advancement and Clinical Implications of 18FDG-PET in Parkinson's Disease, Atypical Parkinsonisms, and Other Movement Disorders

PURPOSE OF REVIEW

The molecular imaging field has been very instrumental in identifying the multiple network interactions that compose the human brain. The cerebral glucose metabolism is associated with neural function. 18F-fluoro-deoxyglucose-PET (FDG-PET) studies reflect brain metabolism in a pattern-specific manner. This article reviews FDG-PET studies in Parkinson's disease (PD), atypical parkinsonism (AP), Huntington's disease (HD), and dystonia.

RECENT FINDINGS

The metabolic pattern of PD, disease progression, non-motor symptoms such as fatigue, depression, apathy, impulse control disorders, and cognitive impairment, and the risk of progression to dementia have been identified with FDG-PET studies. In prodromal PD, the REM sleep behavior disorder-related covariance pattern has been described. In AP, FDG-PET studies have demonstrated to be superior to D2/D3 SPECT in differentiating PD from AP. The metabolic patterns of HD and dystonia have also been described. FDG-PET studies are an excellent tool to identify patterns of brain metabolism.

Factors affecting the harmonization of disease-related metabolic brain pattern expression quantification in [18F]FDG-PET (PETMETPAT)

Introduction

The implementation of spatial-covariance [¹⁸F]fluorodeoxyglucose positron emission tomography–based disease-related metabolic brain patterns as biomarkers has been hampered by intercenter imaging differences. Within the scope of the JPND-PETMETPAT working group, we illustrate the impact of these differences on Parkinson's disease–related pattern (PDRP) expression scores.

Methods

Five healthy controls, 5 patients with idiopathic rapid eye movement sleep behavior disorder, and 5 patients with Parkinson's disease were scanned on one positron emission tomography/computed tomography system with multiple image reconstructions. In addition, one Hoffman 3D Brain Phantom was scanned on several positron emission tomography/computed tomography systems using various reconstructions. Effects of image contrast on PDRP scores were also examined.

Results

Human and phantom raw PDRP scores were systematically influenced by scanner and reconstruction effects. PDRP scores correlated inversely to image contrast. A Gaussian spatial filter reduced contrast while decreasing intercenter score differences.

Discussion

Image contrast should be considered in harmonization efforts. A Gaussian filter may reduce noise and intercenter effects without sacrificing sensitivity. Phantom measurements will be important for correcting PDRP score offsets.

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.

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.

Principal Components Analysis of Brain Metabolism Predicts Development of Alzheimer Dementia

The value of ¹⁸F-FDG PET for predicting conversion from mild cognitive impairment (MCI) to Alzheimer dementia (AD) is currently under debate. We used a principal components analysis (PCA) to identify a metabolic AD conversion-related pattern (ADCRP) and investigated the prognostic value of the resulting pattern expression score (PES). Methods: ¹⁸F-FDG PET scans of 544 MCI patients were obtained from the Alzheimer Disease Neuroimaging Initiative database and analyzed. We implemented voxel-based PCA and standard Statistical Parametric Mapping analysis (as a reference) to disclose cerebral metabolic patterns associated with conversion from MCI to AD. By Cox proportional hazards regression, we examined the prognostic value of candidate predictors. Also, we constructed prognostic models with clinical, imaging, and clinical and imaging variables in combination. Results: PCA revealed an ADCRP that involved regions with relative decreases in metabolism (temporoparietal, frontal, posterior cingulate, and precuneus cortices) and relative increases in metabolism (sensorimotor and occipital cortices, cerebellum, and left putamen). Among the predictor variables age, sex, Functional Activities Questionnaire, Mini-Mental State Examination, apolipoprotein E, PES, and normalized ¹⁸F-FDG uptake (regions with significant hypo- and hypermetabolism in patients with conversion vs. those without conversion), PES was the best independent predictor of conversion (hazard ratio, 1.77, per z score increase; 95% CI, 1.24-2.52; P < 0.001). Moreover, adding PES to the model including the clinical variables significantly increased its prognostic value. Conclusion: The ADCRP expression score was a valid predictor of conversion. A combination of clinical variables and PES yielded a higher accuracy than each single tool in predicting conversion from MCI to AD, underlining the incremental utility of ¹⁸F-FDG PET.

PET Imaging in Movement Disorders

Positron emission tomography (PET) has revealed key insights into the pathophysiology of movement disorders. This paper will focus on how PET investigations of pathophysiology are particularly relevant to Parkinson disease, a neurodegenerative condition usually starting later in life marked by a varying combination of motor and nonmotor deficits. Various molecular imaging modalities help to determine what changes in brain herald the onset of pathology; can these changes be used to identify presymptomatic individuals who may be appropriate for to-be-developed treatments that may forestall onset of symptoms or slow disease progression; can PET act as a biomarker of disease progression; can molecular imaging help enrich homogenous cohorts for clinical studies; and what other pathophysiologic mechanisms relate to nonmotor manifestations. PET methods include measurements of regional cerebral glucose metabolism and blood flow, selected receptors, specific neurotransmitter systems, postsynaptic signal transducers, and abnormal protein deposition. We will review each of these methodologies and how they are relevant to important clinical issues pertaining to Parkinson disease.

European Association of Nuclear Medicine and European Academy of Neurology recommendations for the use of brain 18 F-fluorodeoxyglucose positron emission tomography in neurodegenerative cognitive impairment and dementia: Delphi consensus

BACKGROUND AND PURPOSE

Recommendations for using fluorodeoxyglucose positron emission tomography (FDG-PET) to support the diagnosis of dementing neurodegenerative disorders are sparse and poorly structured.

METHODS

Twenty-one questions on diagnostic issues and on semi-automated analysis to assist visual reading were defined. Literature was reviewed to assess study design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiver operating characteristic curve, and positive/negative likelihood ratio of FDG-PET in detecting the target conditions. Using the Delphi method, an expert panel voted for/against the use of FDG-PET based on published evidence and expert opinion.

RESULTS

Of the 1435 papers, 58 papers provided proper quantitative assessment of test performance. The panel agreed on recommending FDG-PET for 14 questions: diagnosing mild cognitive impairment due to Alzheimer's disease (AD), frontotemporal lobar degeneration (FTLD) or dementia with Lewy bodies (DLB); diagnosing atypical AD and pseudo-dementia; differentiating between AD and DLB, FTLD or vascular dementia, between DLB and FTLD, and between Parkinson's disease and progressive supranuclear palsy; suggesting underlying pathophysiology in corticobasal degeneration and progressive primary aphasia, and cortical dysfunction in Parkinson's disease; using semi-automated assessment to assist visual reading. Panellists did not support FDG-PET use for pre-clinical stages of neurodegenerative disorders, for amyotrophic lateral sclerosis and Huntington disease diagnoses, and for amyotrophic lateral sclerosis or Huntington-disease-related cognitive decline.

CONCLUSIONS

Despite limited formal evidence, panellists deemed FDG-PET useful in the early and differential diagnosis of the main neurodegenerative disorders, and semi-automated assessment helpful to assist visual reading. These decisions are proposed as interim recommendations.

The effects of image reconstruction algorithms on topographic characteristics, diagnostic performance and clinical correlation of metabolic brain networks in Parkinson's disease

PURPOSE

The purpose of this study was to evaluate the effects of different image reconstruction algorithms on topographic characteristics and diagnostic performance of the Parkinson's disease related pattern (PDRP).

METHODS

FDG-PET brain scans of 20 Parkinson's disease (PD) patients and 20 normal controls (NC) were reconstructed with six different algorithms in order to derive six versions of PDRP. Additional scans of 20 PD, 25 atypical parkinsonism (AP) patients and 20 NC subjects were used for validation. PDRP versions were compared by assessing differences in topographies, individual subject scores and correlations with patient's clinical ratings. Discrimination of PD from NC and AP subjects was evaluated across cohorts.

RESULTS

The region weights of the six PDRPs highly correlated (R ≥ 0.991; p < 0.0001). All PDRPs' expressions were significantly elevated in PD relative to NC and AP subjects (p < 0.0001) and correlated with clinical ratings (R ≥ 0.47; p < 0.05). Subject scores of the six PDRPs highly correlated within each of individual healthy and parkinsonian groups (R ≥ 0.972, p < 0.0001) and were consistent across the algorithms when using the same reconstruction methods in PDRP derivation and validation. However, when derivation and validation reconstruction algorithms differed, subject scores were notably lower compared to the reference PDRP, in all subject groups.

CONCLUSION

PDRP proves to be highly reproducible across FDG-PET image reconstruction algorithms in topography, ability to differentiate PD from NC and AP subjects and clinical correlation. When calculating PDRP scores in scans that have different reconstruction algorithms and imaging systems from those used for PDRP derivation, a calibration with NC subjects is advisable.

Clinical Routine FDG-PET Imaging of Suspected Progressive Supranuclear Palsy and Corticobasal Degeneration: A Gatekeeper for Subsequent Tau-PET Imaging?

Background: F-18-fluordeoxyglucose positron emission tomography (FDG-PET) is widely used for discriminative diagnosis of tau-positive atypical parkinsonian syndromes (T+APS). This approach now stands to be augmented with more specific tau tracers. Therefore, we retrospectively analyzed a large clinical routine dataset of FDG-PET images for evaluation of the strengths and limitations of stand-alone FDG-PET.

Methods: A total of 117 patients (age 68.4 ± 11.1 y) underwent an FDG-PET exam. Patients were followed clinically for a minimum of one year and their final clinical diagnosis was recorded. FDG-PET was rated visually (positive/negative) and categorized as high, moderate or low likelihood of T+APS and other neurodegenerative disorders. We then calculated positive and negative predictive values (PPV/NPV) of FDG-PET readings for the different subgroups relative to their final clinical diagnosis.

Results: Suspected diagnoses were confirmed by clinical follow-up (≥1 y) for 62 out of 117 (53%) patients. PPV was excellent when FDG-PET indicated a high likelihood of T+APS in combination with low to moderate likelihood of another neurodegenerative disorder. PPV was distinctly lower when FDG-PET indicated only a moderate likelihood of T+APS or when there was deemed equal likelihood of other neurodegenerative disorder. NPV of FDG-PET with a low likelihood for T+APS was high.

Conclusions: FDG-PET has high value in clinical routine evaluation of suspected T+APS, gaining satisfactory differential diagnosis in two thirds of the patients. One third of patients would potentially profit from further evaluation by more specific radioligands, with FDG-PET serving gatekeeper function for the more expensive methods.

Clinical utility of FDG PET in Parkinson's disease and atypical parkinsonism associated with dementia

Purpose

There are no comprehensive guidelines for the use of FDG PET in the following three clinical scenarios: (1) diagnostic work-up of patients with idiopathic Parkinson’s disease (PD) at risk of future cognitive decline, (2) discriminating idiopathic PD from progressive supranuclear palsy, and (3) identifying the underlying neuropathology in corticobasal syndrome.

Methods

We therefore performed three literature searches and evaluated the selected studies for quality of design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were the sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiving operating characteristic curve, and positive/negative likelihood ratio of FDG PET in detecting the target condition. Using the Delphi method, a panel of seven experts voted for or against the use of FDG PET based on published evidence and expert opinion.

Results

Of 91 studies selected from the three literature searches, only four included an adequate quantitative assessment of the performance of FDG PET. The majority of studies lacked robust methodology due to lack of critical outcomes, inadequate gold standard and no head-to-head comparison with an appropriate reference standard. The panel recommended the use of FDG PET for all three clinical scenarios based on nonquantitative evidence of clinical utility.

Conclusion

Despite widespread use of FDG PET in clinical practice and extensive research, there is still very limited good quality evidence for the use of FDG PET. However, in the opinion of the majority of the panellists, FDG PET is a clinically useful imaging biomarker for idiopathic PD and atypical parkinsonism associated with dementia.

Reproducible network and regional topographies of abnormal glucose metabolism associated with progressive supranuclear palsy: Multivariate and univariate analyses in American and Chinese patient cohorts

Progressive supranuclear palsy (PSP) is a rare movement disorder and often difficult to distinguish clinically from Parkinson's disease (PD) and multiple system atrophy (MSA) in early phases. In this study, we report reproducible disease-related topographies of brain network and regional glucose metabolism associated with PSP in clinically-confirmed independent cohorts of PSP, MSA, and PD patients and healthy controls in the USA and China. Using ¹⁸ F-FDG PET images from PSP and healthy subjects, we applied spatial covariance analysis with bootstrapping to identify a PSP-related pattern (PSPRP) and estimate its reliability, and evaluated the ability of network scores for differential diagnosis. We also detected regional metabolic differences using statistical parametric mapping analysis. We produced a highly reliable PSPRP characterized by relative metabolic decreases in the middle prefrontal cortex/cingulate, ventrolateral prefrontal cortex, striatum, thalamus and midbrain, covarying with relative metabolic increases in the hippocampus, insula and parieto-temporal regions. PSPRP network scores correlated positively with PSP duration and accurately discriminated between healthy, PSP, MSA and PD groups in two separate cohorts of parkinsonian patients at both early and advanced stages. Moreover, PSP patients shared many overlapping areas with abnormal metabolism in the same cortical and subcortical regions as in the PSPRP. With rigorous cross-validation, this study demonstrated highly comparable and reproducible PSP-related metabolic topographies at network and regional levels across different patient populations and PET scanners. Metabolic brain network activity may serve as a reliable and objective marker of PSP, although cross-validation applying recent diagnostic criteria and classification is warranted.