Metabolic network expression in parkinsonism: Clinical and dopaminergic correlations

The value of PET/CT in the diagnosis and differential diagnosis of Parkinson's disease: a dual-tracer study

Positron emission tomography/computed tomography (PET/CT) is a molecular imaging method commonly used to diagnose and differentiate Parkinson's disease (PD). This study aimed to evaluate the performance of PET/CT with 11C-2β-Carbomethoxy-3β-(4-fluorophenyl) tropane (11C-CFT) and 18F-fluorodeoxyglucose (18F-FDG) tracers in the differential diagnosis between PD, multiple system atrophy parkinsonian type (MSA-P), progressive supranuclear palsy (PSP) and vascular parkinsonism (VP) using the data of 220 patients with clinical PD-like symptoms. Of the 220 enrolled patients, 166 (PD, n = 80; MSA-P, n = 54; PSP, n = 15; VP, n = 17) completed the motor, cognitive and PET/CT assessment and were included in this study. 11C-CFT and 18F-FDG PET/CT images were analyzed using the SNBPI toolbox and CortexID Suite software. The uptake values of 11C-CFT and 18F-FDG PET/CT were compared among the groups after controlling for covariates using generalized linear models. Receiver operating characteristic (ROC) curves were generated to estimate the diagnostic values. Patients with PSP showed the most significant reduction on 11C-CFT PET/CT, while patients with PD and MSA-P showed similar reductions, and patients with VP did not show any significant reduction in 11C-CFT uptake. The areas under the curve (AUCs) for 11C-CFT PET/CT for distinguishing PD from VP, PSP, and MSA-P were 0.902, 0.830, and 0.580, respectively, and 0.728 for distinguishing advanced-stage PD from PSP. On 18F-FDG PET/CT, the AUCs for distinguishing PD from PSP and MSA-P were 0.968 and 0.963, respectively. These results suggest that 11C-CFT and 18F-FDG PET/CT complement each other in improving the accuracy in differential diagnosis of PD.

Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission

PURPOSE

Metabolic network analysis of FDG-PET utilizes an index of inter-regional correlation of resting state glucose metabolism and has been proven to provide complementary information regarding the disease process in parkinsonian syndromes. The goals of this study were (i) to evaluate pattern similarities of glucose metabolism and network connectivity in dementia with Lewy bodies (DLB) subjects with subthreshold dopaminergic loss compared to advanced disease stages and to (ii) investigate metabolic network alterations of FDG-PET for discrimination of patients with early DLB from other neurodegenerative disorders (Alzheimer's disease, Parkinson's disease, multiple system atrophy) at individual patient level via principal component analysis (PCA).

METHODS

FDG-PETs of subjects with probable or possible DLB (n = 22) without significant dopamine deficiency (z-score < 2 in putamen binding loss on DaT-SPECT compared to healthy controls (HC)) were scaled by global-mean, prior to volume-of-interest-based analyses of relative glucose metabolism. Single region metabolic changes and network connectivity changes were compared against HC (n = 23) and against DLB subjects with significant dopamine deficiency (n = 86). PCA was applied to test discrimination of patients with DLB from disease controls (n = 101) at individual patient level.

RESULTS

Similar patterns of hypo- (parietal- and occipital cortex) and hypermetabolism (basal ganglia, limbic system, motor cortices) were observed in DLB patients with and without significant dopamine deficiency when compared to HC. Metabolic connectivity alterations correlated between DLB patients with and without significant dopamine deficiency (R2 = 0.597, p < 0.01). A PCA trained by DLB patients with dopamine deficiency and HC discriminated DLB patients without significant dopaminergic loss from other neurodegenerative parkinsonian disorders at individual patient level (area-under-the-curve (AUC): 0.912).

CONCLUSION

Disease-specific patterns of altered glucose metabolism and altered metabolic networks are present in DLB subjects without significant dopaminergic loss. Metabolic network alterations in FDG-PET can act as a supporting biomarker in the subgroup of DLB patients without significant dopaminergic loss at symptoms onset.

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.

Neuroimaging in Dementia

OBJECTIVE

Neurodegenerative diseases are significant health concerns with regard to morbidity and social and economic hardship around the world. This review describes the state of the field of neuroimaging measures as biomarkers for detection and diagnosis of both slowly progressing and rapidly progressing neurodegenerative diseases, specifically Alzheimer disease, vascular cognitive impairment, dementia with Lewy bodies or Parkinson disease dementia, frontotemporal lobar degeneration spectrum disorders, and prion-related diseases. It briefly discusses findings in these diseases in studies using MRI and metabolic and molecular-based imaging (eg, positron emission tomography [PET] and single-photon emission computerized tomography [SPECT]).

LATEST DEVELOPMENTS

Neuroimaging studies with MRI and PET have demonstrated differential patterns of brain atrophy and hypometabolism in different neurodegenerative disorders, which can be useful in differential diagnoses. Advanced MRI sequences, such as diffusion-based imaging, and functional MRI (fMRI) provide important information about underlying biological changes in dementia and new directions for development of novel measures for future clinical use. Finally, advancements in molecular imaging allow clinicians and researchers to visualize dementia-related proteinopathies and neurotransmitter levels.

ESSENTIAL POINTS

Diagnosis of neurodegenerative diseases is primarily based on symptomatology, although the development of in vivo neuroimaging and fluid biomarkers is changing the scope of clinical diagnosis, as well as the research into these devastating diseases. This article will help inform the reader about the current state of neuroimaging in neurodegenerative diseases, as well as how these tools might be used for differential diagnoses.

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.

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.

Cognition-Related Functional Topographies in Parkinson's Disease: Localized Loss of the Ventral Default Mode Network

Cognitive dysfunction in Parkinson's disease (PD) is associated with increased expression of the PD cognition-related pattern (PDCP), which overlaps with the normal default mode network (DMN). Here, we sought to determine the degree to which the former network represents loss of the latter as a manifestation of the disease process. To address this, we first analyzed metabolic images (fluorodeoxyglucose positron emission tomography [PET]) from a large PD sample with varying cognitive performance. Cognitive impairment in these patients correlated with increased PDCP expression as well as DMN loss. We next determined the spatial relationship of the 2 topographies at the subnetwork level. To this end, we analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from an independent population. This approach uncovered a significant PD cognition-related network that resembled previously identified PET- and rs-fMRI-based PDCP topographies. Further analysis revealed selective loss of the ventral DMN subnetwork (precuneus and posterior cingulate cortex) in PD, whereas the anterior and posterior components were not affected by the disease. Importantly, the PDCP also included a number of non-DMN regions such as the dorsolateral prefrontal and medial temporal cortex. The findings show that the PDCP is a reproducible cognition-related network that is topographically distinct from the normal DMN.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

CLASSIFICATION OF EVIDENCE

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

Molecular Imaging of Extrapyramidal Movement Disorders With Dementia: The 4R Tauopathies

Two pathologically distinct neurodegenerative conditions, progressive supranuclear palsy and corticobasal degeneration, share in common deposits of tau proteins that differ both molecularly and ultrastructurally from the common tau deposits diagnostic of Alzheimer disease. The proteinopathy in these disorders is characterized by fibrillary aggregates of 4R tau proteins. The clinical presentations of progressive supranuclear palsy and of corticobasal degeneration are often confused with more common disorders such as Parkinson disease or subtypes of frontotemporal lobar degeneration. Neither of these 4R tau disorders has effective therapy, and while there are emerging molecular imaging approaches to identify patients earlier in the course of disease, there are as yet no reliably sensitive and specific approaches to diagnoses in life. In this review, aspects of the clinical syndromes, neuropathology, and molecular biomarker imaging studies applicable to progressive supranuclear palsy and to corticobasal degeneration will be presented. Future development of more accurate molecular imaging approaches is proposed.

Co-registration Analysis of Fluorodopa and Fluorodeoxyglucose Positron Emission Tomography for Differentiating Multiple System Atrophy Parkinsonism Type From Parkinson's Disease

It is difficult to differentiate between Parkinson's disease and multiple system atrophy parkinsonian subtype (MSA-P) because of the overlap of their signs and symptoms. Enormous efforts have been made to develop positron emission tomography (PET) imaging to differentiate these diseases. This study aimed to investigate the co-registration analysis of ¹⁸F-fluorodopa and ¹⁸F-flurodeoxyglucose PET images to visualize the difference between Parkinson's disease and MSA-P. We enrolled 29 Parkinson's disease patients, 28 MSA-P patients, and 10 healthy controls, who underwent both ¹⁸F-fluorodopa and ¹⁸F-flurodeoxyglucose PET scans. Patients with Parkinson's disease and MSA-P exhibited reduced bilateral striatal ¹⁸F-fluorodopa uptake (p < 0.05, vs. healthy controls). Both regional specific uptake ratio analysis and statistical parametric mapping analysis of ¹⁸F-flurodeoxyglucose PET revealed hypometabolism in the bilateral putamen of MSA-P patients and hypermetabolism in the bilateral putamen of Parkinson's disease patients. There was a significant positive correlation between ¹⁸F-flurodeoxyglucose uptake and ¹⁸F-fluorodopa uptake in the contralateral posterior putamen of MSA-P patients (rs = 0.558, p = 0.002). Both ¹⁸F-flurodeoxyglucose and ¹⁸F-fluorodopa PET images showed that the striatum was rabbit-shaped in the healthy control group segmentation analysis. A defective rabbit-shaped striatum was observed in the ¹⁸F-fluorodopa PET image of patients with Parkinson's disease and MSA-P. In the segmentation analysis of ¹⁸F-flurodeoxyglucose PET image, an intact rabbit-shaped striatum was observed in Parkinson's disease patients, whereas a defective rabbit-shaped striatum was observed in MSA-P patients. These findings suggest that there were significant differences in the co-registration analysis of ¹⁸F-flurodeoxyglucose and ¹⁸F-fluorodopa PET images, which could be used in the individual analysis to differentiate Parkinson's disease from MSA-P.

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

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

Gender-Related Vulnerability of Dopaminergic Neural Networks in Parkinson's Disease

Background: In Parkinson's disease (PD), neurodegeneration of dopaminergic systems leads to motor and non-motor abnormalities. Sex might influence the clinical PD phenotypes and progression. Previous molecular imaging data focused only on the nigro-striato-cortical dopamine system that appeared more preserved in women. There is still a lack of evidence on gender/sex differences in the mesolimbic dopaminergic system. We aimed at assessing PD gender differences in both the dopaminergic pathways, by using a brain metabolic connectivity approach. This is based on the evidence of a significant coupling between the neurotransmission and metabolic impairments. Methods: We included 34 idiopathic PD patients (Female/Male: 16/18) and 34 healthy controls for comparison. The molecular architecture of both the dopaminergic networks was estimated throughout partial correlation analyses using brain metabolism data obtained by fluorine-18-fluorodeoxyglucose positron emission tomography (threshold set at p < 0.01, corrected for Bonferroni multiple comparisons). Results: Male patients were characterized by a widespread altered connectivity in the nigro-striato-cortical network and a sparing of the mesolimbic pathway. On the contrary, PD females showed a severe altered connectivity in the mesolimbic network and only a partial reconfiguration of the nigro-striato-cortical network. Discussion: Our findings add remarkable knowledge on the neurobiology of gender differences in PD, with the identification of specific neural vulnerabilities. The gender differences here revealed might be due to the combination of both biological and sociodemographic life factors. Gender differences in PD should be considered also for treatments and the targeting of modifiable risk factors.

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.

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.

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

Rationale

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

Methods

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

Results

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

Conclusion

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

Electronic supplementary material

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

Network Structure and Function in Parkinson's Disease

Little is known of the structural and functional properties of abnormal brain networks associated with neurological disorders. We used a social network approach to characterize the properties of the Parkinson's disease (PD) metabolic topography in 4 independent patient samples and in an experimental non-human primate model. The PD network exhibited distinct features. Dense, mutually facilitating functional connections linked the putamen, globus pallidus, and thalamus to form a metabolically active core. The periphery was formed by weaker connections linking less active cortical regions. Notably, the network contained a separate module defined by interconnected, metabolically active nodes in the cerebellum, pons, frontal cortex, and limbic regions. Exaggeration of the small-world property was a consistent feature of disease networks in parkinsonian humans and in the non-human primate model; this abnormality was only partly corrected by dopaminergic treatment. The findings point to disease-related alterations in network structure and function as the basis for faulty information processing in this disorder.

Brain metabolic connectome classify mild cognitive impairment into Alzheimer's dementia. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019;2019:32-35. [PMID: 31945838 DOI: 10.1109/embc.2019.8857104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Identifying whether patients with mild cognitive impairment (MCI) are converting to Alzheimer's disease (AD) is clinically important, but there are still controversies and doubts. We aimed to develop a novel connectome approach which could accurately and precisely predict whether MCI patients are converted to AD using ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET). In our study, FDG-PET images were acquired from 84 patients with MCI who converted to AD within 48 months and 109 patients with MCI without conversion within 48 months from the Alzheimer's Disease Neuroimaging Initiative database. The experimental results showed that the classification performance about whether an MCI patient would convert to AD were 92.1%, 87.1%, 94.4% and 0.95 (Accuracy, Sensitivity, Specificity and AUC). The abnormality of functional connection was located at Middle frontal gyrus, Posterior cingulate gyrus, Precentral gyrus, Precuneus and Temporal lobe. These finding showed the brain connectome as a practical approach for developing predictive neuroimaging biomarker.

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

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

Cis-4-[18F]fluoro-D-proline detects neurodegeneration in patients with akinetic-rigid parkinsonism

OBJECTIVES

This study aimed to investigate whether the amino acid PET tracer cis-4-[F]fluoro-D-proline [D-cis-[F]FPro] shows increased uptake in the basal ganglia of patients with neurodegenerative akinetic-rigid parkinsonism. D-Cis-[F]FPro is a sensitive PET tracer for inflammation-associated neurodegeneration in animal models. We hypothesized that D-cis-[F]FPro might also be a sensitive marker of alterations of the basal ganglia in parkinsonian syndromes.

PARTICIPANTS AND METHODS

Ten patients with neurodegenerative akinetic-rigid parkinsonism (five with idiopathic Parkinson's disease and five with atypical parkinsonian syndromes) were imaged with D-cis-[F]FPro and compared with 13 patients with brain tumors who had no basal ganglia involvement. PET images 20-50 min after injection were evaluated and tracer uptake in the basal ganglia was quantified using volume-of-interest analysis with basal ganglia to background ratios. The severity of disease was assessed with unified Parkinson's disease rating scale III and correlated with D-cis-[F]FPro uptake.

RESULTS

In patients with parkinsonism, volume-of-interest analysis showed mild, but significantly increased D-cis-[F]FPro uptake in the basal ganglia, pronounced in the lenticular nucleus. Disease severity correlated with D-cis-[F]FPro uptake in the right pallidum (r=-0.687, P=0.041).

CONCLUSION

Data suggest that D-cis-[F]FPro is a sensitive marker of inflammation-associated degenerative processes in parkinsonian syndromes.

Neuroimaging in aging and neurologic diseases

Neuroimaging biomarkers for neurologic diseases are important tools, both for understanding pathology associated with cognitive and clinical symptoms and for differential diagnosis. This chapter explores neuroimaging measures, including structural and functional measures from magnetic resonance imaging (MRI) and molecular measures primarily from positron emission tomography (PET), in healthy aging adults and in a number of neurologic diseases. The spectrum covers neuroimaging measures from normal aging to a variety of dementias: late-onset Alzheimer's disease [AD; including mild cognitive impairment (MCI)], familial and nonfamilial early-onset AD, atypical AD syndromes, posterior cortical atrophy (PCA), logopenic aphasia (lvPPA), cerebral amyloid angiopathy (CAA), vascular dementia (VaD), sporadic and familial behavioral-variant frontotemporal dementia (bvFTD), semantic dementia (SD), progressive nonfluent aphasia (PNFA), frontotemporal dementia with motor neuron disease (FTD-MND), frontotemporal dementia with amyotrophic lateral sclerosis (FTD-ALS), corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), dementia with Lewy bodies (DLB), Parkinson's disease (PD) with and without dementia, and multiple systems atrophy (MSA). We also include a discussion of the appropriate use criteria (AUC) for amyloid imaging and conclude with a discussion of differential diagnosis of neurologic dementia disorders in the context of neuroimaging.

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.

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.

Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia

Utilizing the publicly available neuroimaging database enabled by Alzheimer's disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/ ), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). General linear model, scaled subprofile modeling and support vector machines were examined. Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation. We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S. Korea) and Asan Medical Centre (Seoul, S. Korea). Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson's Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET.

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.

Validation of abnormal glucose metabolism associated with Parkinson's disease in Chinese participants based on 18F-fluorodeoxyglucose positron emission tomography imaging

PURPOSE

We previously identified disease-related cerebral metabolic characteristics associated with Parkinson's disease (PD) in the Chinese population using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) imaging. The present study aims to assess data reproducibility and robustness of the metabolic activity characteristics across independent cohorts.

PATIENTS AND METHODS

Forty-eight patients with PD and 48 healthy controls from Chongqing district, in addition to 33 patients with PD and 33 healthy controls from Shanghai district were recruited. Each subject underwent brain 18F-FDG PET/CT imaging in a resting state. Based on the brain images, differences between the groups and PD-related cerebral metabolic activities were graphically and quantitatively evaluated.

RESULTS

Both PD patient cohorts exhibited analogous cerebral patterns characterized by metabolic increase in the putamen, globus pallidus, thalamus, pons, sensorimotor cortex and cerebellum, along with metabolic decrease in parieto-occipital areas. Additionally, the metabolic pattern was highly indicative of the disease, with a significant elevation in PD patients compared with healthy controls (p<0.001) in both the derivation (Shanghai) and validation (Chongqing) cohorts.

CONCLUSION

This dual-center study demonstrated the high comparability and reproducibility of PD-related cerebral metabolic activity patterns across independent Chinese cohorts and may serve as an objective diagnostic marker for the disease.

Update on Molecular Imaging in Parkinson's Disease

Advances in radionuclide tracers have allowed for more accurate imaging that reflects the actions of numerous neurotransmitters, energy metabolism utilization, inflammation, and pathological protein accumulation. All of these achievements in molecular brain imaging have broadened our understanding of brain function in Parkinson's disease (PD). The implementation of molecular imaging has supported more accurate PD diagnosis as well as assessment of therapeutic outcome and disease progression. Moreover, molecular imaging is well suited for the detection of preclinical or prodromal PD cases. Despite these advances, future frontiers of research in this area will focus on using multi-modalities combining positron emission tomography and magnetic resonance imaging along with causal modeling with complex algorithms.