Dopaminergic correlates of metabolic network activity in Parkinson's disease

Spontaneous blink-related beta power increase and theta phase reset in subthalamic nucleus of Parkinson patients during walking

OBJECTIVE

Both blinking and walking are altered in Parkinson's disease and both motor outputs have been shown to be linked in healthy subjects. Additionally, studies suggest an involvement of basal ganglia activity and striatal dopamine in blink generation. We investigated the role of the basal ganglia circuitry on spontaneous blinking and if this role is dependent on movement state and striatal dopamine.

METHODS

We analysed subthalamic nucleus (STN) activity in seven chronically implanted patients for deep brain stimulation (DBS) with respect to blinks and movement state (resting state and unperturbed walking). Neurophysiological recordings were combined with individual molecular brain imaging assessing the dopamine reuptake transporter (DAT) density for the left and right striatum separately.

RESULTS

We found a significantly higher blink rate during walking compared to resting. The blink rate during walking positively correlated with the DAT density of the left caudate nucleus. During walking only, spontaneous blinking was followed by an increase in the right STN beta power and a bilateral subthalamic phase reset in the low frequencies. The right STN blink-related beta power modulation correlated negatively with the DAT density of the contralateral putamen. The left STN blink-related beta power correlated with the DAT density of the putamen in the less dopamine-depleted hemisphere. Both correlations were specific to the walking condition and to beta power following a blink.

CONCLUSION

Our findings show that spontaneous blinking is related to striatal dopamine and has a frequency specific deployment in the STN. This correlation depends on the current movement state such as walking.

SIGNIFICANCE

This work indicates that subcortical activity following a motor event as well as the relationship between dopamine and motor events can be dependent on the motor state. Accordingly, disease related changes in brain activity should be assessed during natural movement.

Patterns of regional cerebral hypoperfusion in early Parkinson's disease: Clinical implications

INTRODUCTION

This study aimed to investigate whether regional cerebral perfusion patterns on early-phase 18F-FP-CIT PET scans, which is typically coupled to cerebral metabolism, predict the long-term prognosis of Parkinson's disease (PD).

METHODS

We enrolled 397 drug-naïve patients with early-stage PD who underwent dual-phase 18F-FP-CIT PET scans. After quantifying the early-phase 18F-FP-CIT PET images, cluster analysis was performed to delineate the PD subtypes according to the patterns of regional cerebral perfusion. We compared the risk of developing levodopa-induced dyskinesia (LID), wearing-off, freezing of gait (FOG), and dementia between the PD subtypes.

RESULTS

Cluster analysis classified patients into three subtypes: cluster 1 (relatively preserved cortical uptake; n = 175), cluster 2 (decreased uptake in the frontal, parietal, and temporal regions; n = 151), and cluster 3 (decreased uptake in more extensive regions, additionally involving the lateral occipital regions; n = 71). Cluster 1 was characterized by a younger age-of-onset, less severe motor deficits, less severely decreased 18F-FP-CIT binding in the caudate, and better cognitive performance. Cluster 3 was characterized by an older age-of-onset, more severe motor deficits, and poorer cognitive performance. Cluster 2 was intermediate between clusters 1 and 3. Cox regression analyses demonstrated that clusters 2 and 3 had a higher risk for dementia conversion than cluster 1, whereas the risk for developing LID, wearing-off, and FOG did not differ among the clusters.

CONCLUSION

The patterns of regional cerebral perfusion can provide information on long-term prognosis with regards to cognitive, but not motor aspects of patients with early-stage PD.

Role of dopamine neuron activity in Parkinson's disease pathophysiology

Neural activity is finely tuned to produce normal behaviors, and disruptions in activity likely occur early in the course of many neurodegenerative diseases. However, how neural activity is altered, and how these changes influence neurodegeneration is poorly understood. Here, we focus on evidence that the activity of dopamine neurons is altered in Parkinson's disease (PD), either as a compensatory response to degeneration or as a result of circuit dynamics or pathologic proteins, based on available human data and studies in animal models of PD. We then discuss how this abnormal activity may augment other neurotoxic phenomena in PD, including mitochondrial deficits, protein aggregation and spread, dopamine toxicity, and excitotoxicity. A more complete picture of how activity is altered and the resulting effects on dopaminergic neuron health and function may inform future therapeutic interventions to target and protect dopamine neurons from degeneration.

99mTc-HMPAO SPECT Perfusion Signatures Associated With Clinical Progression in Patients With Isolated REM Sleep Behavior Disorder

BACKGROUND AND OBJECTIVES

Idiopathic/isolated REM sleep behavior disorder (iRBD) is associated with dementia with Lewy bodies and Parkinson disease. Despite evidence of abnormal cerebral perfusion in iRBD, there is currently no pattern that can predict whether an individual will develop dementia with Lewy bodies or Parkinson disease. The objective was to identify a perfusion signature associated with conversion to dementia with Lewy bodies in iRBD.

METHODS

Patients with iRBD underwent video-polysomnography, neurologic and neuropsychological assessments, and baseline 99mTc-HMPAO SPECT to assess relative cerebral blood flow. Partial least squares correlation was used to identify latent variables that maximized covariance between 27 clinical features and relative gray matter perfusion. Patient-specific scores on the latent variables were used to test the association with conversion to dementia with Lewy bodies compared with that with Parkinson disease. The signature's expression was also assessed in 24 patients with iRBD who underwent a second perfusion scan, 22 healthy controls, and 19 individuals with Parkinson disease.

RESULTS

Of the 137 participants, 93 underwent SPECT processing, namely 52 patients with iRBD (67.9 years, 73% men), 19 patients with Parkinson disease (67.3 years, 37% men), and 22 controls (67.0 years, 73% men). Of the 47 patients with iRBD followed up longitudinally (4.5 years), 12 (26%) developed a manifest synucleinopathy (4 dementia with Lewy bodies and 8 Parkinson disease). Analysis revealed 2 latent variables between relative blood flow and clinical features: the first was associated with a broad set of features that included motor, cognitive, and perceptual variables, age, and sex; the second was mostly associated with cognitive features and RBD duration. When brought back into the patient's space, the expression of the first variable was associated with conversion to a manifest synucleinopathy, whereas the second was associated with conversion to dementia with Lewy bodies. The expression of the patterns changed over time and was associated with worse motor features.

DISCUSSION

This study identified a brain perfusion signature associated with cognitive impairment in iRBD and transition to dementia with Lewy bodies. This signature, which can be derived from individual scans, has the potential to be developed into a biomarker that predicts dementia with Lewy bodies in at-risk individuals.

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.

Parkinson's disease progression: Increasing expression of an invariant common core subnetwork

Notable success has been achieved in the study of neurodegenerative conditions using reduction techniques such as principal component analysis (PCA) and sparse inverse covariance estimation (SICE) in positron emission tomography (PET) data despite their widely differing approach. In a recent study of SICE applied to metabolic scans from Parkinson's disease (PD) patients, we showed that by using PCA to prespecify disease-related partition layers, we were able to optimize maps of functional metabolic connectivity within the relevant networks. Here, we show the potential of SICE, enhanced by disease-specific subnetwork partitions, to identify key regional hubs and their connections, and track their associations in PD patients with increasing disease duration. This approach enabled the identification of a core zone that included elements of the striatum, pons, cerebellar vermis, and parietal cortex and provided a deeper understanding of progressive changes in their connectivity. This subnetwork constituted a robust invariant disease feature that was unrelated to phenotype. Mean expression levels for this subnetwork increased steadily in a group of 70 PD patients spanning a range of symptom durations between 1 and 21 years. The findings were confirmed in a validation sample of 69 patients with up to 32 years of symptoms. The common core elements represent possible targets for disease modification, while their connections to external regions may be better suited for symptomatic treatment.

The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification

18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely employed to reveal metabolic abnormalities linked to Parkinson's disease (PD) at a systemic level. However, the individual metabolic connectome details with PD based on 18F-FDG PET remain largely unknown. To alleviate this issue, we derived a novel brain network estimation method for individual metabolic connectome, that is, Jensen-Shannon Divergence Similarity Estimation (JSSE). Further, intergroup difference between the individual's metabolic brain network and its global/local graph metrics was analyzed to investigate the metabolic connectome's alterations. To further improve the PD diagnosis performance, multiple kernel support vector machine (MKSVM) is conducted for identifying PD from normal control (NC), which combines both topological metrics and connection. Resultantly, PD individuals showed higher nodal topological properties (including assortativity, modularity score, and characteristic path length) than NC individuals, whereas global efficiency and synchronization were lower. Moreover, 45 most significant connections were affected. Further, consensus connections in occipital, parietal, and frontal regions were decrease in PD while increase in subcortical, temporal, and prefrontal regions. The abnormal metabolic network measurements depicted an ideal classification in identifying PD of NC with an accuracy up to 91.84%. The JSSE method identified the individual-level metabolic connectome of 18F-FDG PET, providing more dimensional and systematic mechanism insights for PD.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Microglia and Astrocytes Dysfunction and Key Neuroinflammation-Based Biomarkers in Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disease, with symptoms such as tremor, bradykinesia with rigidity, and depression appearing in the late stage of life. The key hallmark of PD is the loss or death of dopaminergic neurons in the region substantia nigra pars compacta. Neuroinflammation plays a key role in the etiology of PD, and the contribution of immunity-related events spurred the researchers to identify anti-inflammatory agents for the treatment of PD. Neuroinflammation-based biomarkers have been identified for diagnosing PD, and many cellular and animal models have been used to explain the underlying mechanism; however, the specific cause of neuroinflammation remains uncertain, and more research is underway. So far, microglia and astrocyte dysregulation has been reported in PD. Patients with PD develop neural toxicity, inflammation, and inclusion bodies due to activated microglia and a-synuclein-induced astrocyte conversion into A1 astrocytes. Major phenotypes of PD appear in the late stage of life, so there is a need to identify key early-stage biomarkers for proper management and diagnosis. Studies are under way to identify key neuroinflammation-based biomarkers for early detection of PD. This review uses a constructive analysis approach by studying and analyzing different research studies focused on the role of neuroinflammation in PD. The review summarizes microglia, astrocyte dysfunction, neuroinflammation, and key biomarkers in PD. An approach that incorporates multiple biomarkers could provide more reliable diagnosis of PD.

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.

Seed-Based Connectivity Prediction of Initial Outcome of Subthalamic Nuclei Deep Brain Stimulation

Subthalamic nuclei deep brain stimulation (STN-DBS) is a well-established treatment for Parkinson's disease (PD). Some studies have confirmed the long-term efficacy is associated with brain connectivity; however, whether the initial outcome is associated with brain connectivity and efficacy of prediction based on these factors has not been well investigated. In the present study, a total of 98 patients were divided into a training set (n = 78) and a test set (n = 20). The stimulation and medication responses were calculated based on the motor performance. The functional and structural connectomes were established based on a public database and used to measure the association between stimulation response and brain connectivity. The prediction of initial outcome was achieved via a machine learning algorithm-support vector machine based on the model established with the training set. It was found that the initial outcome of STN-DBS was associated with functional/structural connectivities between the volume of tissue activated and multiple brain regions, including the supplementary motor area, precentral and frontal areas, cingulum, temporal cortex, and striatum. These factors could be used to predict the initial outcome, with an r value of 0.4978 (P = 0.0255). Our study demonstrates a correlation between a specific connectivity pattern and initial outcome of STN-DBS, which could be used to predict the initial outcome of DBS.

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.

The Parkinson's Disease Progression Neuroimaging Initiative

The Parkinson's Disease Progressive Neuroimaging Initiative (PDPNI) is a longitudinal observational clinical study. In PDPNI, the clinical and imaging data of patients diagnosed with Parkinsonian syndromes and Idiopathic rapid eye movement sleep behavior disorder (RBD) were longitudinally followed every two years, aiming to identify progression biomarkers of Parkinsonian syndromes through functional imaging modalities including FDG-PET, DAT-PET imaging, ASL MRI, and fMRI, as well as the treatment conditions, clinical symptoms, and clinical assessment results of patients. From February 2012 to March 2019, 224 subjects (including 48 healthy subjects and 176 patients with confirmed PDS) have been enrolled in PDPNI. The detailed clinical information and clinical assessment scores of all subjects were collected by neurologists from Huashan Hospital, Fudan University. All subjects enrolled in PDPNI were scanned with 18F-FDG PET, 11C-CFT PET, and MRI scan sequence. All data were collected in strict accordance with standardized data collection protocols.

Circuit imaging biomarkers in preclinical and prodromal Parkinson's disease

Parkinson's disease (PD) commences several years before the onset of motor features. Pathophysiological understanding of the pre-clinical or early prodromal stages of PD are essential for the development of new therapeutic strategies. Two categories of patients are ideal to study the early disease stages. Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents a well-known prodromal stage of PD in which pathology is presumed to have reached the lower brainstem. The majority of patients with iRBD will develop manifest PD within years to decades. Another category encompasses non-manifest mutation carriers, i.e. subjects without symptoms, but with a known mutation or genetic variant which gives an increased risk of developing PD. The speed of progression from preclinical or prodromal to full clinical stages varies among patients and cannot be reliably predicted on the individual level. Clinical trials will require inclusion of patients with a predictable conversion within a limited time window. Biomarkers are necessary that can confirm pre-motor PD status and can provide information regarding lead time and speed of progression. Neuroimaging changes occur early in the disease process and may provide such a biomarker. Studies have focused on radiotracer imaging of the dopaminergic nigrostriatal system, which can be assessed with dopamine transporter (DAT) single photon emission computed tomography (SPECT). Loss of DAT binding represents an effect of irreversible structural damage to the nigrostriatal system. This marker can be used to monitor disease progression and identify individuals at specific risk for phenoconversion. However, it is known that changes in neuronal activity precede structural changes. Functional neuro-imaging techniques, such as ¹⁸F-2-fluoro-2-deoxy-D-glucose Positron Emission Tomography (¹⁸F-FDG PET) and functional magnetic resonance imaging (fMRI), can be used to model the effects of disease on brain networks when combined with advanced analytical methods. Because these changes occur early in the disease process, functional imaging studies are of particular interest in prodromal PD diagnosis. In addition, fMRI and ¹⁸F-FDG PET may be able to predict a specific future phenotype in prodromal cohorts, which is not possible with DAT SPECT. The goal of the current review is to discuss the network-level brain changes in pre-motor PD.

Neuropsychological spectrum in early PD: Insights from controlled and automatic behavioural regulation

Initial changes in Parkinson's disease (PD) are marked by loss of automatic movements and decline of some cognitive functions. Yet, the exact profile and extent of cognitive impairments in early stages of PD as well as their mechanisms related to automatic motor dysfunction remain unclear. Our objective was to examine the neuropsychological changes in early PD and their association to automatic and controlled modes of behavioural control. Significant relationships between early PD and cognitive dysfunction in set-shifting, abstraction ability/concept formation, processing speed, visuospatial/constructional abilities and verbal-visual memory was found. We also noted that tests with a strong effortful and controlled component were similarly affected as automatic tests by early PD, particularly those testing verbal memory, processing speed and visuospatial/constructional functions. Our findings indicate that initial stages of PD sets constraints over most of the cognitive domains normally assessed and are not easily explained in terms of either automatic or controlled mechanisms, as both appear similarly altered in early PD.

A subcortical network for implicit visuo-spatial attention: Implications for Parkinson's Disease

Recent studies in humans and animal models suggest a primary role of the basal ganglia in the extraction of stimulus-value regularities, then exploited to orient attentional shift and build up sensorimotor memories. The tail of the caudate and the posterior putamen both receive early visual input from the superficial layers of the superior colliculus, thus forming a closed-loop. We portend that the functional value of this circuit is to manage the selection of visual stimuli in a rapid and automatic way, once sensory-motor associations are formed and stored in the posterior striatum. In Parkinson's Disease, the nigrostriatal dopamine depletion starts and tends to be more pronounced in the posterior putamen. Thus, at least some aspect of the visuospatial attention deficits observed since the early stages of the disease could be the behavioral consequences of a cognitive system that has lost the ability to translate high-level processing in stable sensorimotor memories.

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.

In vivo MRI Structural and PET Metabolic Connectivity Study of Dopamine Pathways in Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is characterized by an involvement of brain dopamine (DA) circuitry, the presence of which has been associated with emergence of both neuropsychiatric symptoms and cognitive deficits.

OBJECTIVE

In order to investigate whether and how the DA pathways are involved in the pathophysiology of AD, we assessed by in vivo neuroimaging the structural and metabolic alterations of subcortical and cortical DA pathways and targets.

METHODS

We included 54 healthy control participants, 53 amyloid-positive subjects with mild cognitive impairment due to AD (MCI-AD), and 60 amyloid-positive patients with probable dementia due to AD (ADD), all with structural 3T MRI and 18F-FDG-PET scans. We assessed MRI-based gray matter reductions in the MCI-AD and ADD groups within an anatomical a priori-defined Nigrostriatal and Mesocorticolimbic DA pathways, followed by 18F-FDG-PET metabolic connectivity analyses to evaluate network-level metabolic connectivity changes.

RESULTS

We found significant tissue loss in the Mesocorticolimbic over the Nigrostriatal pathway. Atrophy was evident in the ventral striatum, orbitofrontal cortex, and medial temporal lobe structures, and already plateaued in the MCI-AD stage. Degree of atrophy in Mesocorticolimbic regions positively correlated with the severity of depression, anxiety, and apathy in MCI-AD and ADD subgroups. Additionally, we observed significant alterations of metabolic connectivity between the ventral striatum and fronto-cingulate regions in ADD, but not in MCI-AD. There were no metabolic connectivity changes within the Nigrostriatal pathway.

CONCLUSION

Our cross-sectional data support a clinically-meaningful, yet stage-dependent, involvement of the Mesocorticolimbic system in AD. Longitudinal and clinical correlation studies are needed to further establish the relevance of DA system involvement in AD.

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.

Clinical implications for dopaminergic and functional neuroimage research in cognitive symptoms of Parkinson's disease

Evidence from dopaminergic image and cerebral blood flow/metabolism images have shed light on symptomatology of cognitive aspects in brain physiology of healthy human as well as patients with Parkinson's disease. Cognitive impairment in Parkinson's disease is characterized by executive, visuospatial, attentional disturbances. Dopaminergic system includes triadic parallel pathways. The mesostriatal pathway consist of posterolateral putamen and motor areas, the mesocortical pathway of dorsal caudate nucleus and dorsolateral prefrontal cortex, and the mesolimbic pathway of ventral striatum, anterior cingulate cortex. The mesocortical pathway is responsible for the executive function which may change by administration of dopaminergic medication. The mesolimbic pathway is associated with motivation and reward prediction which may result in depression or apathy when dopamine level was suboptimal, impulse control disorder and punding when dopamine was over the optimal level. Abnormal brain metabolism/perfusion related to cognitive impairment in Parkinson's disease are relatively reduced activity located in frontal and parietal association areas and relatively increased activity in the cerebellum. In the anterior brain, the mesocortical pathway, is responsible for verbal memory and executive function, which originates with caudate dopaminergic system and account for mild cognitive impairment of Parkinson's disease. The posterior brain system which includes the parietal, temporal, and occipital cortices, is responsible for the memory and visuospatial function, and related to cholinergic dysfunction and possibly glucocerebrosidase gene variants, relating to dementia in Parkinson's disease. The role of cerebellum in Parkinson's disease remains unclear but emerging evidence suggests that it may relate to the sequencing detection and affective symptoms. The dual syndrome hypothesis is helpful for understanding the mechanism of cognitive impairment in Parkinson's disease and optimal symptom management.

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.

Modulating brain networks associated with cognitive deficits in Parkinson's disease

Parkinson's disease (PD) is a relatively well characterised neurological disorder that primarily affects motor and cognitive functions. This paper reviews on how transcranial direct current stimulation (tDCS) can be used to modulate brain networks associated with cognitive deficits in PD. We first provide an overview of brain network abnormalities in PD, by introducing the brain network modulation approaches such as pharmacological interventions and brain stimulation techniques. We then present the potential underlying mechanisms of tDCS technique, and specifically highlight how tDCS can be applied to modulate brain network abnormality associated with cognitive dysfunction among PD patients. More importantly, we address the limitations of existing studies and suggest possible future directions, with the aim of helping researchers to further develop the use of tDCS technique in clinical settings.

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.

Spectral guided sparse inverse covariance estimation of metabolic networks in Parkinson's disease

In neurodegenerative disorders, a clearer understanding of the underlying aberrant networks facilitates the search for effective therapeutic targets and potential cures. [¹⁸F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging data of brain metabolism reflects the distribution of glucose consumption known to be directly related to neural activity. In FDG PET resting-state metabolic data, characteristic disease-related patterns have been identified in group analysis of various neurodegenerative conditions using principal component analysis of multivariate spatial covariance. Notably, among several parkinsonian syndromes, the identified Parkinson’s disease-related pattern (PDRP) has been repeatedly validated as an imaging biomarker of PD in independent groups worldwide. Although the primary nodal associations of this network are known, its connectivity is not fully understood. Here, we describe a novel approach to elucidate functional principal component (PC) network connections by performing graph theoretical sparse network derivation directly within the disease relevant PC partition layer of the whole brain data rather than by searching for associations retrospectively in whole brain sparse representations. Using sparse inverse covariance estimation of each overlapping PC partition layer separately, a single coherent network is detected for each layer in contrast to more spatially modular segmentation in whole brain data analysis. Using this approach, the major nodal hubs of the PD disease network are identified and their characteristic functional pathways are clearly distinguished within the basal ganglia, midbrain and parietal areas. Network associations are further clarified using Laplacian spectral analysis of the adjacency matrices. In addition, the innate discriminative capacity of the eigenvector centrality of the graph derived networks in differentiating PD versus healthy external data provides evidence of their validity.

Hemispheric Network Expression in Parkinson's Disease: Relationship to Dopaminergic Asymmetries

BACKGROUND

Parkinson's disease (PD) is characterized by brain metabolic networks, specifically associated with motor and cognitive manifestations. Few studies have investigated network changes in cerebral hemispheres ipsilateral and contralateral to the clinically more affected body side.

OBJECTIVE

We examined hemispheric network abnormalities and their relationship to striatal dopaminergic deficits in PD patients at different stages.

METHODS

45 PD patients underwent dual-tracer positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) and 18F-fluorodopa (FDOPA) in a high-resolution PET scanner. In all patients, we computed expression levels for the PD-related motor/cognition metabolic patterns (PDRP/PDCP) as well as putamen/caudate FDOPA uptake values in both hemispheres. Resulting hemispheric measures in the PD group were compared with corresponding healthy control values and assessed across disease stages.

RESULTS

Hemispheric PDRP and PDCP expression was significantly elevated contralateral and ipsilateral to the more affected body side in patients with unilateral symptoms (H&Y 1: p < 0.01) and in patients with bilateral limb involvement (H&Y 2-3: p < 0.001; H&Y 4: p < 0.003). Elevations in pattern expression were symmetrical at all disease stages. By contrast, FDOPA uptake in the caudate and putamen was reduced bilaterally (p < 0.002), with lower values on both sides at more advanced disease stages. Hemispheric uptake was asymmetrical in both striatal regions, with lower contralateral values at all disease stages. The magnitude of hemispheric uptake asymmetry was smaller with more advanced disease, reflecting greater change ipsilaterally.

CONCLUSION

Symmetrical network expression in PD represents bilateral functional effects unrelated to nigrostriatal dopaminergic asymmetries.

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.

Nigrostriatal Degeneration in the Cognitive Part of the Striatum in Parkinson Disease Is Associated With Frontomedial Hypometabolism

PURPOSE

The present study investigated possible associations between cortical dysfunction/degeneration as measured by F-FDG PET and nigrostriatal degeneration according to the specific I-FP-CIT binding ratio (SBR) in striatal subregions defined by striato-cortical anatomical connectivity in Parkinson disease (PD) patients.

MATERIALS AND METHODS

The study included 41 patients (61.4 ± 12.8 years) with PD-typical reduction of striatal FP-CIT SBR and no sign of atypical parkinsonian syndrome on FDG PET. FP-CIT SBR was determined separately in the cognitive (composite of executive and limbic) and sensorimotor part of the striatum according to the Oxford-GSK-Imanova Striatal Connectivity Atlas. Scaled FDG uptake was tested voxelwise for correlation with FP-CIT SBR (familywise error corrected P < 0.05).

RESULTS

A large cluster (17.6 mL) of significant correlation of scaled FDG uptake with FP-CIT SBR in the cognitive part of the striatum, corrected for SBR in the sensorimotor part, was detected in the bilateral medial frontal cortex and the anterior cingulate cortex (partial correlation coefficient R = 0.767); small clusters were detected in ipsilateral caudate and ipsilateral thalamus. There was a small contralateral occipital cluster (3.0 mL) of significant correlation between FDG uptake and sensorimotor SBR corrected for cognitive SBR (R = 0.709).

CONCLUSIONS

The correlation between nigrostriatal degeneration in the cognitive striatum and reduced cerebral glucose metabolism in the medial parts of the frontal cortex including the anterior cingulate suggests that nigrostriatal degeneration is specifically involved in the pathogenesis of cognitive deficits associated with medial frontal dysfunction such as impaired inhibitory control.

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

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

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.

Correlations of Neuropsychological and Metabolic Brain Changes in Parkinson's Disease and Other α-Synucleinopathies

Cognitive impairment is a common feature in Parkinson's disease (PD) and other α-synucleinopathies as 80% of PD patients develop dementia within 20 years. Early cognitive changes in PD patients present as a dysexecutive syndrome, broadly characterized as a disruption of the fronto-striatal dopamine network. Cognitive deficits in other domains (recognition memory, attention processes and visuospatial abilities) become apparent with the progression of PD and development of dementia. In dementia with Lewy bodies (DLB) the cognitive impairment develops early or even precedes parkinsonism and it is more pronounced in visuospatial skills and memory. Cognitive impairment in the rarer α-synucleinopathies (multiple system atrophy and pure autonomic failure) is less well studied. Metabolic brain imaging with positron emission tomography and [¹⁸F]-fluorodeoxyglucose (FDG-PET) is a well-established diagnostic method in neurodegenerative diseases, including dementias. Changes in glucose metabolism precede those seen on structural magnetic resonance imaging (MRI). Reduction in glucose metabolism and atrophy have been suggested to represent consecutive changes of neurodegeneration and are linked to specific cognitive disorders (e.g., dysexecutive syndrome, memory impairment, visuospatial deficits etc.). Advances in the statistical analysis of FDG-PET images enabling a network analysis broadened our understanding of neurodegenerative brain processes. A specific cognitive pattern related to PD was identified by applying voxel-based network modeling approach. The magnitude of this pattern correlated significantly with patients' cognitive skills. Specific metabolic brain changes were observed also in patients with DLB as well as in a prodromal phase of α-synucleinopathy: REM sleep behavior disorder. Metabolic brain imaging with FDG-PET is a reliable biomarker of neurodegenerative brain diseases throughout their course, precisely reflecting their topographic distribution, stage and functional impact.

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.

Distinguishing between dementia with Lewy bodies and Alzheimer's disease using metabolic patterns

Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) are the 2 most common causes of dementia. We compared the regional metabolism on ¹⁸F-fluorodeoxyglucose positron emission tomography (PET) among 21 control subjects and cognitively impaired patients due to DLB (N = 63) and AD (N = 38). All participants underwent ¹⁸F-Florbetaben (FBB) PET, and all DLB patients had abnormality on dopamine transporter PET. Both the FBB-positive DLB (N = 38) and FBB-negative DLB (N = 25) groups had increased metabolism in the bilateral central cerebellum, posterior putamen, and somatomotor cortices compared with the control and AD groups. Compared with the control group, the DLB and AD groups commonly exhibited hypometabolism in the bilateral lateral temporal, temporo-parietal junction, posterior cingulate, and precuneus cortices. Both DLB groups had additional hypometabolism in the bilateral thalami and dorsolateral prefrontal cortices, whereas the AD group did in the bilateral entorhinal cortices and hippocampi. Our results suggest that hypermetabolism in the somatomotor cortex, posterior putamen, or central cerebellum could be a useful imaging biomarker for detecting DLB patients, while entorhinal/hippocampal hypometabolism could be a specific biomarker for AD.

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.

Brain Molecular Connectivity in Neurodegenerative Diseases: Recent Advances and New Perspectives Using Positron Emission Tomography

Positron emission tomography (PET) represents a unique molecular tool to get in vivo access to a wide spectrum of biological and neuropathological processes, of crucial relevance for neurodegenerative conditions. Although most PET findings are based on massive univariate approaches, in the last decade the increasing interest in multivariate methods has paved the way to the assessment of unexplored cerebral features, spanning from resting state brain networks to whole-brain connectome properties. Currently, the combination of molecular neuroimaging techniques with multivariate connectivity methods represents one of the most powerful, yet still emerging, approach to achieve novel insights into the pathophysiology of neurodegenerative diseases. In this review, we will summarize the available evidence in the field of PET molecular connectivity, with the aim to provide an overview of how these studies may increase the understanding of the pathogenesis of neurodegenerative diseases, over and above "traditional" structural/functional connectivity studies. Considering the available evidence, a major focus will be represented by molecular connectivity studies using [18F]FDG-PET, today applied in the major neuropathological spectra, from amyloidopathies and tauopathies to synucleinopathies and beyond. Pioneering studies using PET tracers targeting brain neuropathology and neurotransmission systems for connectivity studies will be discussed, their strengths and limitations highlighted with reference to both applied methodology and results interpretation. The most common methods for molecular connectivity assessment will be reviewed, with particular emphasis on the available strategies to investigate molecular connectivity at the single-subject level, of potential relevance for not only research but also diagnostic purposes. Finally, we will highlight possible future perspectives in the field, with reference in particular to newly available PET tracers, which will expand the application of molecular connectivity to new, exciting, unforeseen possibilities.

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

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

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.

FDG-PET hypometabolism is more sensitive than MRI atrophy in Parkinson's disease: A whole-brain multimodal imaging meta-analysis

Recently, revised diagnostic criteria for Parkinson's disease (PD) were introduced (Postuma et al., 2015). Yet, except for well-established dopaminergic imaging, validated imaging biomarkers for PD are still missing, though they could improve diagnostic accuracy. We conducted systematic meta-analyses to identify PD-specific markers in whole-brain structural magnetic resonance imaging (MRI), [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET) and diffusion tensor imaging (DTI) studies. Overall, 74 studies were identified including 2323 patients and 1767 healthy controls. Studies were first grouped according to imaging modalities (MRI 50; PET 14; DTI 10) and then into subcohorts based on clinical phenotypes. To ensure reliable results, we combined established meta-analytical algorithms - anatomical likelihood estimation and seed-based D mapping - and cross-validated them in a conjunction analysis. Glucose hypometabolism was found using FDG-PET extensively in bilateral inferior parietal cortex and left caudate nucleus with both meta-analytic methods. This hypometabolism pattern was confirmed in subcohort analyses and related to cognitive deficits (inferior parietal cortex) and motor symptoms (caudate nucleus). Structural MRI showed only small focal gray matter atrophy in the middle occipital gyrus that was not confirmed in subcohort analyses. DTI revealed fractional anisotropy reductions in the cingulate bundle near the orbital and anterior cingulate gyri in PD. Our results suggest that FDG-PET reliably identifies consistent functional brain abnormalities in PD, whereas structural MRI and DTI show only focal alterations and rather inconsistent results. In conclusion, FDG-PET hypometabolism outperforms structural MRI in PD, although both imaging methods do not offer disease-specific imaging biomarkers for PD.

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.

Advances in PET Methodology

Recent advances in disease understanding, instrumentation technology, and computationally demanding image analysis approaches are opening new frontiers in the investigation of movement disorders and brain disease in general. A key aspect is the recognition of the need to determine molecular correlates to early functional and metabolic connectivity alterations, which are increasingly recognized as useful signatures of specific clinical disease phenotypes. Such multi-modal approaches are highly likely to provide new information on pathogenic mechanisms and to help the identification of novel therapeutic targets. This chapter describes recent methodological developments in PET starting with a very brief overview of radiotracers relevant to movement disorders while emphasizing the development of instrumentation, algorithms and imaging analysis methods relevant to multi-modal investigation of movement disorders.