Advances in imaging in Parkinson's disease

Clinical biomarkers for Lewy body diseases

Synucleinopathies are a group of neurodegenerative disorders characterized by pathologic aggregates of neural and glial α-synuclein (α-syn) in the form of Lewy bodies (LBs), Lewy neurites, and cytoplasmic inclusions in both neurons and glia. Two major classes of synucleinopathies are LB disease and multiple system atrophy. LB diseases include Parkinson's disease (PD), PD with dementia, and dementia with LBs. All are increasing in prevalence. Effective diagnostics, disease-modifying therapies, and therapeutic monitoring are urgently needed. Diagnostics capable of differentiating LB diseases are based on signs and symptoms which might overlap. To date, no specific diagnostic test exists despite disease-specific pathologies. Diagnostics are aided by brain imaging and cerebrospinal fluid evaluations, but more accessible biomarkers remain in need. Mechanisms of α-syn evolution to pathologic oligomers and insoluble fibrils can provide one of a spectrum of biomarkers to link complex neural pathways to effective therapies. With these in mind, we review promising biomarkers linked to effective disease-modifying interventions.

An interpretable multiparametric radiomics model of basal ganglia to predict dementia conversion in Parkinson's disease

Cognitive impairment in Parkinson's disease (PD) severely affects patients' prognosis, and early detection of patients at high risk of dementia conversion is important for establishing treatment strategies. We aimed to investigate whether multiparametric MRI radiomics from basal ganglia can improve the prediction of dementia development in PD when integrated with clinical profiles. In this retrospective study, 262 patients with newly diagnosed PD (June 2008-July 2017, follow-up >5 years) were included. MRI radiomic features (n = 1284) were extracted from bilateral caudate and putamen. Two models were developed to predict dementia development: (1) a clinical model-age, disease duration, and cognitive composite scores, and (2) a combined clinical and radiomics model. The area under the receiver operating characteristic curve (AUC) were calculated for each model. The models' interpretabilities were studied. Among total 262 PD patients (mean age, 68 years ± 8 [standard deviation]; 134 men), 51 (30.4%), and 24 (25.5%) patients developed dementia within 5 years of PD diagnosis in the training (n = 168) and test sets (n = 94), respectively. The combined model achieved superior predictive performance compared to the clinical model in training (AUCs 0.928 vs. 0.894, P = 0.284) and test set (AUCs 0.889 vs. 0.722, P = 0.016). The cognitive composite scores of the frontal/executive function domain contributed most to predicting dementia. Radiomics derived from the caudate were also highly associated with cognitive decline. Multiparametric MRI radiomics may have an incremental prognostic value when integrated with clinical profiles to predict future cognitive decline in PD.

Differentiating Parkinson’s Disease from Essential Tremor Using Transcranial Sonography: A Systematic Review and Meta-Analysis

Background:

Essential tremor (ET) and the tremor of Parkinson’s disease (PD) are the most common tremors encountered in clinical practice. Especially in early disease stages, discrimination between the tremors of ET and PD can be challenging.

Objective:

The aim of this study was to evaluate the diagnostic accuracy of transcranial sonography (TCS) of the substantia nigra echogenicity for differential diagnosis of PD versus ET.

Methods:

A systematic PubMed search identified 512 studies. Sensitivity and specificity of substantia nigra hyperechogenicity was estimated. Data synthesis was carried applying a random effects bivariate binomial model. To assess study quality and risk of bias, the QUADAS-2 tool was used.

Results:

Eighteen studies were suitable for analysis including 1,264 PD and 824 ET patients. The meta analysis showed a pooled sensitivity and specificity for TCS in the differential diagnosis of PD versus ET of 84.6% (95% CI, 79.4–88.6%) and 83.9% (95% CI, 78.4–88.2%), respectively. Furthermore, we found nearly similar results in sensitivity and specificity comparing TCS and DaTSCAN in a subgroup-analysis of three studies using both diagnostic tools including 107 patients with PD and 62 patients with ET. The QUADAS-2 toolbox revealed a high risk of bias regarding the methodological quality of patient selection.

Conclusion:

Substantia nigra hyperechogenicity yield high diagnostic accuracy for the discrimination of PD from ET. TCS is a low cost, widely available, non-invasive marker without radiation Therefore, a diagnostic algorithm based on presence or absence of substantia nigra hyperechogenicity is highly warranted.

Quercetin attenuates neurotoxicity induced by iron oxide nanoparticles

Iron oxide nanoparticles (IONPs) have been proposed as targeted carriers to deliver therapeutic molecules in the central nervous system (CNS). However, IONPs may damage neural tissue via free iron accumulation, protein aggregation, and oxidative stress. Neuroprotective effects of quercetin (QC) have been proven due to its antioxidant and anti-inflammatory properties. However, poor solubility and low bioavailability of QC have also led researchers to make various QC-involved nanoparticles to overcome these limitations. We wondered how high doses or prolonged treatment with quercetin conjugated superparamagnetic iron oxide nanoparticles (QCSPIONs) could improve cognitive dysfunction and promote neurogenesis without any toxicity. It can be explained that the QC inhibits protein aggregation and acts against iron overload via iron-chelating activity, iron homeostasis genes regulation, radical scavenging, and attenuation of Fenton/Haber-Weiss reaction. In this review, first, we present brain iron homeostasis, molecular mechanisms of iron overload that induced neurotoxicity, and the role of iron in dementia-associated diseases. Then by providing evidence of IONPs neurotoxicity, we discuss how QC neutralizes IONPs neurotoxicity, and finally, we make a brief comparison between QC and conventional iron chelators. In this review, we highlight that QC as supplementation and especially in conjugated form reduces iron oxide nanoparticles neurotoxicity in clinical application.

Multivariate radiomics models based on 18F-FDG hybrid PET/MRI for distinguishing between Parkinson's disease and multiple system atrophy

PURPOSE

To construct multivariate radiomics models using hybrid ¹⁸F-FDG PET/MRI for distinguishing between Parkinson's disease (PD) and multiple system atrophy (MSA).

METHODS

Ninety patients (60 with PD and 30 with MSA) were randomized to training and test sets in a 7:3 ratio. All patients underwent ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/MRI to simultaneously obtain metabolic images (¹⁸F-FDG), structural MRI images (T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) and T2-weighted fluid-attenuated inversion recovery (T2/FLAIR)) and functional MRI images (susceptibility-weighted imaging (SWI) and apparent diffusion coefficient). Using PET and five MRI sequences, we extracted 1172 radiomics features from the putamina and caudate nuclei. The radiomics signatures were constructed with the least absolute shrinkage and selection operator algorithm in the training set, with progressive optimization through single-sequence and double-sequence radiomics models. Multivariable logistic regression analysis was used to develop a clinical-radiomics model, combining the optimal multi-sequence radiomics signature with clinical characteristics and SUV values. The diagnostic performance of the models was assessed by receiver operating characteristic and decision curve analysis (DCA).

RESULTS

The radiomics signatures showed favourable diagnostic efficacy. The optimal model comprised structural (T1WI), functional (SWI) and metabolic (¹⁸F-FDG) sequences (Radscore_{FDG_T1WI_SWI}) with the area under curves (AUCs) of the training and test sets of 0.971 and 0.957, respectively. The integrated model, incorporating Radscore_{FDG_T1WI_SWI}, three clinical symptoms (disease duration, dysarthria and autonomic failure) and SUV_max, demonstrated satisfactory calibration and discrimination in the training and test sets (0.993 and 0.994, respectively). DCA indicated the highest clinical benefit of the clinical-radiomics integrated model.

CONCLUSIONS

The radiomics signature with metabolic, structural and functional information provided by hybrid ¹⁸F-FDG PET/MRI may achieve promising diagnostic efficacy for distinguishing between PD and MSA. The clinical-radiomics integrated model performed best.

Networks Are Associated With Depression in Patients With Parkinson's Disease: A Resting-State Imaging Study

Background

Disturbance of networks was recently proposed to be associated with the occurrence of depression in Parkinson's disease (PD). However, the neurobiological mechanism of depression underlying PD remains unclear.

Objective

This study was conducted to investigate whether intra-network and inter-network brain connectivity is differently changed in PD patients with and without depression (PDD and PDND patients, respectively).

Methods

Forty-one PDD patients, 64 PDND patients, and 55 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (fMRI). The default mode network (DMN), executive control network (ECN), salience network (SN), precuneus network (PCUN), and sensorimotor network (SMN) were extracted using independent component analysis (ICA), and then the functional connectivity (FC) values within and between these networks were measured.

Results

PDD patients exhibited abnormal FC values within the DMN, ECN, SN, PCUN, and SMN. In addition, PDD patients demonstrated decreased connectivity between anterior SN (aSN) and bilateral ECN, between posterior SN (pSN) and dorsal DMN (dDMN), and between PCUN and dDMN/SMN/bilateral ECN. Connectivity within the left hippocampus of dDMN and the right medial superior frontal gyrus of aSN was a significant predictor of depression level in PD patients.

Conclusions

Aberrant intra- and inter-network FC is involved in several important hubs in the large-scale networks, which can be a biomarker for distinguishing PDD from PDND.

Clinical Characteristics and Electrophysiological Biomarkers of Parkinson's Disease Developed From Essential Tremor

Background and Objective: Parkinson's disease developed from essential tremor (ET-PD) is a distinct clinical syndrome that is different from essential tremor (ET) and Parkinson's disease (PD). There is currently a lack of research on ET-PD. Tremor characteristics (amplitude and frequency) are primary quantitative indexes for diagnosing and monitoring of tremors. In this study, we aimed to explore specific clinical and electrophysiological biomarkers for the identification of ET-PD. Methods: The study included patients with ET-PD (n = 22), ET (n = 42), and tremor-dominant PD (t-PD, n = 47). We collected demographic data, clinical characteristics (including motor and non-motor symptoms), and tremor analysis. The frequency, amplitude, contracting patterns of resting tremor and postural tremor were collected. The analysis of ET-PD and ET/t-PD was compared. The receiver operating characteristic (ROC) curve was used to analyze the electrophysiological features in distinguishing ET-PD from ET or t-PD. Results: Compared with ET, hyposmia, bradykinesia, rigidity, postural abnormality, and resting tremor were more common in the ET-PD group (P = 0.01, 0.003, 0.001, 0.001, 0.019, respectively). The postural tremor frequencies of the head, upper limbs, and lower limbs were significantly lower in the ET-PD than in the ET (P = 0.007, 0.003, 0.035, respectively), which were the most appropriate variables for distinguishing ET-PD from ET (AUC: 0.775, 0.727, and 0.701, respectively). Compared with t-PD, bradykinesia, rigidity, postural abnormality (both P < 0.001), and resting tremor (P = 0.024) were less common in the ET-PD. The postural tremor amplitudes of the head and upper limbs were significantly higher in the ET-PD than in the t-PD (P = 0.022, 0.001, respectively), which were the most appropriate variables for distinguishing ET-PD from t-PD (AUC: 0.793 and 0.716). Conclusions: Hyposmia and electrophysiological biomarkers (postural tremor frequencies and amplitudes) help early recognition of ET-PD.

Automated Classification of Postural Control for Individuals With Parkinson's Disease Using a Machine Learning Approach: A Preliminary Study

The purposes of the study were (1) to compare postural sway between participants with Parkinson's disease (PD) and healthy controls and (2) to develop and validate an automated classification of PD postural control patterns using a machine learning approach. A total of 9 participants in the early stage of PD and 12 healthy controls were recruited. Participants were instructed to stand on a force plate and maintain stillness for 2 minutes with eyes open and eyes closed. The center of pressure data were collected at 50 Hz. Linear displacements, standard deviations, total distances, sway areas, and multiscale entropy of center of pressure were calculated and compared using mixed-model analysis of variance. Five supervised machine learning algorithms (ie, logistic regression, K-nearest neighbors, Naïve Bayes, decision trees, and random forest) were used to classify PD postural control patterns. Participants with PD exhibited greater center of pressure sway and variability compared with controls. The K-nearest neighbor method exhibited the best prediction performance with an accuracy rate of up to 0.86. In conclusion, participants with PD exhibited impaired postural stability and their postural sway features could be identified by machine learning algorithms.

Evaluation of Wearable Sensor Devices in Parkinson's Disease: A Review of Current Status and Future Prospects

Parkinson's disease (PD) decreases the quality of life of the affected individuals. The incidence of PD is expected to increase given the growing aging population. Motor symptoms associated with PD render the patients unable to self-care and function properly. Given that several drugs have been developed to control motor symptoms, highly sensitive scales for clinical evaluation of drug efficacy are needed. Among such scales, the objective and continuous evaluation of wearable devices is increasingly utilized by clinicians and patients. Several electronic technologies have revolutionized the clinical monitoring of PD development, especially its motor symptoms. Here, we review and discuss the recent advances in the development of wearable devices for bradykinesia, tremor, gait, and myotonia. Our aim is to capture the experiences of patients and clinicians, as well as expand our understanding on the application of wearable technology. In so-doing, we lay the foundation for further research into the use of wearable technology in the management of PD.

Infuse-as-you-go convective delivery to enhance coverage of elongated brain targets: technical note

OBJECTIVE

To develop and assess a convective delivery technique that enhances the effectiveness of drug delivery to nonspherical brain nuclei, the authors developed an occipital "infuse-as-you-go" approach to the putamen and compared it to the currently used transfrontal approach.

METHODS

Eleven nonhuman primates received a bilateral putamen injection of adeno-associated virus with 2 mM gadolinium-DTPA by real-time MR-guided convective perfusion via either a transfrontal (n = 5) or occipital infuse-as-you-go (n = 6) approach.

RESULTS

MRI provided contemporaneous assessment and monitoring of putaminal infusions for transfrontal (2 to 3 infusion deposits) and occipital infuse-as-you-go (stepwise infusions) putaminal approaches. The infuse-as-you-go technique was more efficient than the transfrontal approach (mean 35 ± 1.1 vs 88 ± 8.3 minutes [SEM; p < 0.001]). More effective perfusion of the postcommissural and total putamen was achieved with the infuse-as-you-go versus transfronatal approaches (100-µl infusion volumes; mean posterior commissural coverage 76.2% ± 5.0% vs 32.8% ± 2.9% [p < 0.001]; and mean total coverage 53.5% ± 3.0% vs 38.9% ± 2.3% [p < 0.01]).

CONCLUSIONS

The infuse-as-you-go approach, paralleling the longitudinal axis of the target structure, provides a more effective and efficient method for convective infusate coverage of elongated, irregularly shaped subcortical brain nuclei.

MRI biomarkers of motor and non-motor symptoms in Parkinson's disease

Parkinson's disease is a heterogeneous disorder with both motor and non-motor symptoms that contribute to functional impairment. To develop effective, disease modifying treatments for these symptoms, biomarkers are necessary to detect neuropathological changes early in the disease course and monitor changes over time. Advances in MRI scan sequences and analytical techniques present numerous promising metrics to detect changes within the nigrostriatal system, implicated in the cardinal motor symptoms of the disease, and detect broader dysfunction involved in the non-motor symptoms, such as cognitive impairment. There is emerging evidence that iron sensitive, neuromelanin sensitive, diffusion sensitive, and resting state functional magnetic imaging measures can capture changes within the nigrostriatal system. Iron, neuromelanin, and diffusion sensitive measures demonstrate high specificity and sensitivity in distinguishing Parkinson's disease relative to controls, with inconsistent results differentiating Parkinson's disease relative to atypical parkinsonian disorders. They may also serve as useful monitoring biomarkers, with each possibly detecting different aspects of the disease course (early nigrosome changes versus broader substantia nigra changes). Investigations of non-motor symptoms, such as cognitive impairment, require careful consideration of the nature of cognitive deficits to characterize regional and network specific impairment. While the early, executive dysfunction observed is consistent with nigrostriatal degeneration, the memory and visuospatial impairments, the harbingers of a dementia process reflect dopaminergic independent dysfunction involving broader regions of the brain.

Monitoring Neuroinflammation with an HOCl-Activatable and Blood-Brain Barrier Permeable Upconversion Nanoprobe

A reliable tool for real-time tracking the neuroinflammatory progress is highly desired for interpretation and treatment of neurological disorders. Herein, a blood-brain barrier (BBB) permeable and HOCl-activatable upconversion (UC) nanoprobe with NIR emission was designed for visual study on neuroinflammation (NI) in vivo. This UC probe consists of three parts: upconversion nanoparticles (UCNPs) as signal reporter, the Cy-HOCl dye acting as energy acceptor of UCNPs as well as the recognition unit of HOCl, and amphiphilic polymers endowing the probe with biocompatibility and BBB permeability. Upon intravenous injection into mice, the probe crossed the BBB via low-density lipoprotein receptor related protein (LRP) mediated transcytosis and was then lightened up by overproduced HOCl in an NI process. This probe was able to differentiate inflammation and the normal state of the brain in LPS-induced NI and monitor the progress of NI occurring in mice with cerebral stroke, providing a practical tool for noninvasive and visual assessment of NI.

Longitudinal Positron Emission Tomography of Dopamine Synthesis in Subjects with GBA1 Mutations

Mutations in GBA1, the gene mutated in Gaucher disease, are a common genetic risk factor for Parkinson disease, although the penetrance is low. We performed [¹⁸ F]-fluorodopa positron emission tomography studies of 57 homozygous and heterozygous GBA1 mutation carriers (15 with parkinsonism) and 98 controls looking for early indications of dopamine loss using voxelwise analyses to identify group differences in striatal [¹⁸ F]-fluorodopa uptake (K_i ). Forty-eight subjects were followed longitudinally. Cross-sectional and longitudinal comparisons of K_i and K_i change found significant effects of Parkinson disease. However, at baseline and over time, striatal [¹⁸ F]-fluorodopa uptake in mutation carriers without parkinsonism did not significantly differ from controls. ANN NEUROL 2020;87:652-657.

VMAT2 imaging agent, D6-[18F]FP-(+)-DTBZ: Improved radiosynthesis, purification by solid-phase extraction and characterization

OBJECTIVES

Recently, a deuterated tracer, D6-[¹⁸F]FP-(+)-DTBZ, 9-O-hexadeutero-3-[¹⁸F]fluoropropoxyl-(+)-dihydrotetrabenazine ([¹⁸F]9), targeting vesicular monoamine transporter 2 (VMAT2) in the central nervous system, was reported as a useful imaging agent for the diagnosis of Parkinson's disease (PD). The production of [¹⁸F]9 was optimized and simplified by using solid-phase extraction (SPE) purification.

METHODS

Three major nonradioactive impurities were synthesized and characterized. The preparation of [¹⁸F]9 was optimized by using different labeling conditions, and an SPE purification method was evaluated. The influence of chemical impurities in the final dose of [¹⁸F]9 was assessed by an in vitro binding assay, an assay of the in vivo biodistribution in mice, and ex vivo and in vitro autoradiography of brain sections.

RESULTS

Optimized fluorination conditions for [¹⁸F]9 were found - heating at 130 °C for 10 min in DMSO, and a high radiochemical yield and three major chemical impurities were observed. An SPE method involving a Sep-Pak® tC18 Plus Light cartridge with a two-step elution process was successfully implemented. This process gave a good radiochemical yield (38.7 ± 10.5%, decay corrected; radiochemical purity >99%) and low chemical impurities. An in vivo biodistribution study and autoradiography of brain sections showed that there was no significant difference between HPLC-purified and SPE-purified [¹⁸F]9.

CONCLUSION

A VMAT2 targeting imaging agent, D6-[¹⁸F]FP-(+)-DTBZ, [¹⁸F]9, was prepared by optimized labeling conditions and an easy SPE purification. This method offers a short preparation time and operational simplicity. In conjunction with PET imaging, this new VMAT2 agent might be a useful clinical tool for diagnosing PD.

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.

Neuroimaging and clinical features in adults with a 22q11.2 deletion at risk of Parkinson's disease

The recurrent 22q11.2 deletion is a genetic risk factor for early-onset Parkinson's disease. Adults with the associated 22q11.2 deletion syndrome (22q11.2DS) may exhibit phenotypes that could help identify those at highest risk and reveal disease trajectories. We investigated clinical and neuroimaging features relevant to Parkinson's disease in 26 adults: 13 with 22q11.2DS at genetic risk of Parkinson's disease (mean age = 41.5 years, standard deviation = 9.7), 12 healthy age and sex-matched controls, and a 22q11.2DS patient with l-DOPA-responsive early-onset Parkinson's disease. Neuroimaging included transcranial sonography and positron emission tomography using 11C-dihydrotetrabenazine (11C-DTBZ), a radioligand that binds to the presynaptic vesicular monoamine transporter. The 22q11.2DS group without Parkinson's disease demonstrated significant motor and olfactory deficits relative to controls. Eight (61.5%) were clinically classified with parkinsonism. Transcranial sonography showed a significantly larger mean area of substantia nigra echogenicity in the 22q11.2DS risk group compared with controls (P = 0.03). The 22q11.2DS patient with Parkinson's disease showed the expected pattern of severely reduced striatal 11C-DTBZ binding. The 22q11.2DS group without Parkinson's disease however showed significantly elevated striatal 11C-DTBZ binding relative to controls (∼33%; P < 0.01). Results were similar within the 22q11.2DS group for those with (n = 7) and without (n = 6) psychotic illness. These findings suggest that manifestations of parkinsonism and/or evolution to Parkinson's disease in this genetic at-risk population may include a hyperdopaminergic mechanism. Adequately powered longitudinal studies and animal models are needed to evaluate the relevance of the observed clinical and imaging phenotypes to Parkinson's disease and other disorders that are more prevalent in 22q11.2DS, such as schizophrenia.

Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images

No disease modifying therapies for Parkinson's disease (PD) have been found effective to date. To properly power clinical trials for discovery of such therapies, the ability to predict outcome in PD is critical, and there is a significant need for discovery of prognostic biomarkers of PD. Dopamine transporter (DAT) SPECT imaging is widely used for diagnostic purposes in PD. In the present work, we aimed to evaluate whether longitudinal DAT SPECT imaging can significantly improve prediction of outcome in PD patients. In particular, we investigated whether radiomics analysis of DAT SPECT images, in addition to use of conventional non-imaging and imaging measures, could be used to predict motor severity at year 4 in PD subjects. We selected 64 PD subjects (38 male, 26 female; age at baseline (year 0): 61.9 ± 7.3, range [46,78]) from the Parkinson's Progressive Marker Initiative (PPMI) database. Inclusion criteria included (i) having had at least 2 SPECT scans at years 0 and 1 acquired on a similar scanner, (ii) having undergone a high-resolution 3 T MRI scan, and (iii) having motor assessment (MDS-UPDRS-III) available in year 4 used as outcome measure. Image analysis included automatic region-of-interest (ROI) extraction on MRI images, registration of SPECT images onto the corresponding MRI images, and extraction of radiomic features. Non-imaging predictors included demographics, disease duration as well as motor and non-motor clinical measures in years 0 and 1. The image predictors included 92 radiomic features extracted from the caudate, putamen, and ventral striatum of DAT SPECT images at years 0 and 1 to quantify heterogeneity and texture in uptake. Random forest (RF) analysis with 5000 trees was used to combine both non-imaging and imaging variables to predict motor outcome (UPDRS-III: 27.3 ± 14.7, range [3,77]). The RF prediction was evaluated using leave-one-out cross-validation. Our results demonstrated that addition of radiomic features to conventional measures significantly improved (p < 0.001) prediction of outcome, reducing the absolute error of predicting MDS-UPDRS-III from 9.00 ± 0.88 to 4.12 ± 0.43. This shows that radiomics analysis of DAT SPECT images has a significant potential towards development of effective prognostic biomarkers in PD.

Connectivity Changes in Parkinson's Disease

Parkinson's disease (PD) is a chronic and progressive movement disorder of the central nervous system characterized by widespread alterations in several non-motor aspects such as mood, sleep, olfactory, and cognition in addition to motor dysfunctions. Advanced neuroimaging using functional connectivity reconstruction of the human brain has provided a vast knowledge on the pathophysiological mechanisms underlying this disorder, but this, however, does not cover the overall inter-/intra-individual variability of PD phenotypes. The present review is aimed at discussing to what extent the evidence provided by group-based neuroimaging analysis in this field of study (using seed-based, network-based, or graph theory approaches) may be generalized. In particular, we summarized the literature on the application of resting-state functional connectivity studies to explore different neural correlates of motor and non-motor symptoms of PD and the neural mechanisms involved in treatment effects: effects of levodopa or deep brain stimulation. The lesson learnt from one decade of studies provides consistent evidence on the role of the altered communication between the striato-frontal pathways as a marker of PD-related motor degeneration, whereas in the non-motor domain, several missing pieces of a complex puzzle are provided. However, the main target is to present a new era of intelligent neuroimaging applications, where automated multivariate analysis of functional connectivity data may be used for moving from group-level statistical results to personalized predictions in a clinical setting. Although in its relative infancy, the evidence gathered so far suggests a new era of clinical neuroimaging is starting.

Resting-state functional reorganization in Parkinson's disease: An activation likelihood estimation meta-analysis

Parkinson's disease (PD) is a common progressive neurodegenerative disorder. Studies using resting-state functional magnetic resonance imaging (fMRI) to investigate underlying pathophysiology of motor and non-motor symptoms in PD yielded largely inconsistent results. This quantitative neuroimaging meta-analysis aims to identify consistent abnormal intrinsic functional patterns in PD across studies. We used PubMed to retrieve suitable resting-state studies and stereotactic data were extracted from 28 individual between-group comparisons. Convergence across their findings was tested using the activation likelihood estimation (ALE) approach. We found convergent evidence for intrinsic functional disturbances in bilateral inferior parietal lobule (IPL) and the supramarginal gyrus in PD patients compared to healthy subjects. In follow-up task-based and task-independent functional connectivity (FC) analyses using two independent healthy subject data sets, we found that the regions showing convergent aberrations in PD formed an interconnected network mainly with the default mode network (DMN). Behavioral characterization of these regions using the BrainMap database suggested associated dysfunction of perception and executive processes. Taken together, our findings highlight the role of parietal cortex in the pathophysiology of PD.

Depletion of AADC activity in caudate nucleus and putamen of Parkinson's disease patients; implications for ongoing AAV2-AADC gene therapy trial

In Parkinson’s disease (PD), aromatic L-amino acid decarboxylase (AADC) is the rate-limiting enzyme in the conversion of L-DOPA (Sinemet) to dopamine (DA). Previous studies in PD animal models demonstrated that lesion of dopaminergic neurons is associated with profound loss of AADC activity in the striatum, blocking efficient conversion of L-DOPA to DA. Relatively few studies have directly analyzed AADC in PD brains. Thus, the aim of this study was to gain a better understanding of regional changes in AADC activity, DA, serotonin and their monoamine metabolites in the striatum of PD patients and experimentally lesioned animals (rat and MPTP-treated nonhuman primate, NHP). Striatal AADC activity was determined post mortem in neuropathologically confirmed PD subjects, animal models and controls. A regional analysis was performed for striatal AADC activity and monoamine levels in NHP tissue. Interestingly, analysis of putaminal AADC activity revealed that control human striatum contained much less AADC activity than rat and NHP striata. Moreover, a dramatic loss of AADC activity in PD striatum compared to controls was detected. In MPTP-treated NHP, caudate nucleus was almost as greatly affected as putamen, although mean DA turnover was higher in caudate nucleus. Similarly, DA and DA metabolites were dramatically reduced in different regions of PD brains, including caudate nucleus, whereas serotonin was relatively spared. After L-DOPA administration in MPTP-treated NHP, very poor conversion to DA was detected, suggesting that AADC in NHP nigrostriatal fibers is mainly responsible for L-DOPA to DA conversion. These data support further the rationale behind viral gene therapy with AAV2-hAADC to restore AADC levels in putamen and suggest further the advisability of expanding vector delivery to include coverage of anterior putamen and the caudate nucleus.

MicroRNAs in Parkinson's disease and emerging therapeutic targets

Parkinson's disease (PD) is the second most common age-related neurodegenerative disorder, with the clinical main symptoms caused by a loss of dopaminergic neurons in the substantia nigra, corpus striatum and brain cortex. Over 90% of patients with PD have sporadic PD and occur in people with no known family history of the disorder. Currently there is no cure for PD. Treatment with medications to increase dopamine relieves the symptoms but does not slow down or reverse the damage to neurons in the brain. Increasing evidence points to inflammation as a chief mediator of PD with inflammatory response mechanisms, involving microglia and leukocytes, activated following loss of dopaminergic neurons. Oxidative stress is also recognized as one of the main causes of PD, and excessive reactive oxygen species (ROS) and reactive nitrogen species can lead to dopaminergic neuron vulnerability and eventual death. MicroRNAs control a range of physiological and pathological functions, and may serve as potential targets for intervention against PD to mitigate damage to the brain. Several studies have demonstrated that microRNAs can regulate oxidative stress and prevent ROS-mediated damage to dopaminergic neurons, suggesting that specific microRNAs may be putative targets for novel therapeutic strategies in PD. Recent human and animal studies have identified a large number of dysregulated microRNAs in PD brain tissue samples, many of which were downregulated. The dysregulated microRNAs affect downstream targets such as SNCA, PARK2, LRRK2, TNFSF13B, LTA, SLC5A3, PSMB2, GSR, GBA, LAMP-2A, HSC. Apart from one study, none of the studies reviewed had used agomirs or antagomirs to reverse the levels of downregulated or upregulated microRNAs, respectively, in mouse models of PD or with isolated human or mouse dopaminergic cells. Further large-scale studies of brain tissue samples collected with short postmortem interval from human PD patients are warranted to provide more information on the microRNA profiles in different brain regions and to test for gender differences.

Does Side of Onset Influence the Pattern of Cerebral Atrophy in Parkinson's Disease?

Background

Imaging studies have revealed widespread neurodegeneration in Parkinson’s disease (PD), but only a few considered the issue of asymmetrical clinical presentations.

Objective

To investigate if the side of onset influences the pattern of gray matter (GM) atrophy in PD.

Methods

Sixty patients (57.87 ± 10.27 years) diagnosed with idiopathic PD according to the U.K. Brain Bank criteria, 26 with right-sided disease onset (RDO) and 34 with left-sided disease onset (LDO), were compared to 80 healthy controls (HC) (57.1 ± 9.47 years). We acquired T1-weighted images on a 3 T scanner. Images were processed and analyzed with VBM8 (SPM8/Dartel) on Matlab R2012b platform. Statistic assessments included a two-sample test (family-wise error p < 0.05) with extent threshold of 20 voxels.

Results

Compared to HC, LDO patients had GM atrophy in the insula, putamen, anterior cingulate, frontotemporal cortex, and right caudate, while the RDO group showed atrophy at the anterior cingulate, insula, frontotemporal, and occipital cortex.

Conclusion

This study revealed widespread GM atrophy in PD, predominantly in the left hemisphere, regardless of the side of onset. Future investigations should also consider handedness and side of onset to better characterize cerebral involvement and its progression in PD.

Neuromelanin Imaging and Dopaminergic Loss in Parkinson's Disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder in which the major pathologic substrate is a loss of dopaminergic neurons from the substantia nigra. Our main objective was to determine the correspondence between changes in the substantia nigra, evident in neuromelanin and iron sensitive magnetic resonance imaging (MRI), and dopaminergic striatal innervation loss in patients with PD. Eighteen patients and 18 healthy control subjects were included in the study. Using neuromelanin-MRI, we measured the volume of the substantia nigra and the contrast-to-noise-ratio between substantia nigra and a background region. The apparent transverse relaxation rate and magnetic susceptibility of the substantia nigra were calculated from dual-echo MRI. Striatal dopaminergic innervation was measured as density of dopamine transporter (DAT) by means of single-photon emission computed tomography and [¹²³I] N-ω-fluoropropyl-2b-carbomethoxy-3b-(4-iodophenyl) tropane. Patients showed a reduced volume of the substantia nigra and contrast-to-noise-ratio and both positively correlated with the corresponding striatal DAT density. The apparent transverse relaxation rate and magnetic susceptibility values of the substantia nigra did not differ between patients and healthy controls. The best predictor of DAT reduction was the volume of the substantia nigra. Clinical and imaging correlations were also investigated for the locus coeruleus. Our results suggest that neuromelanin-MRI can be used for quantifying substantia nigra pathology in PD where it closely correlates with dopaminergic striatal innervation loss. Longitudinal studies should further explore the role of Neuromelanin-MRI as an imaging biomarker of PD, especially for subjects at risk of developing the disease.

Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments

Dopamine transporter (DAT) SPECT imaging is increasingly utilized for diagnostic purposes in suspected Parkinsonian syndromes. We performed a cross-sectional study to investigate whether assessment of texture in DAT SPECT radiotracer uptake enables enhanced correlations with severity of motor and cognitive symptoms in Parkinson's disease (PD), with the long-term goal of enabling clinical utility of DAT SPECT imaging, beyond standard diagnostic tasks, to tracking of progression in PD. Quantitative analysis in routine DAT SPECT imaging, if performed at all, has been restricted to assessment of mean regional uptake. We applied a framework wherein textural features were extracted from the images. Notably, the framework did not require registration to a common template, and worked in the subject-native space. Image analysis included registration of SPECT images onto corresponding MRI images, automatic region-of-interest (ROI) extraction on the MRI images, followed by computation of Haralick texture features. We analyzed 141 subjects from the Parkinson's Progressive Marker Initiative (PPMI) database, including 85 PD and 56 healthy controls (HC) (baseline scans with accompanying 3 T MRI images). We performed univariate and multivariate regression analyses between the quantitative metrics and different clinical measures, namely (i) the UPDRS (part III - motor) score, disease duration as measured from (ii) time of diagnosis (DD-diag.) and (iii) time of appearance of symptoms (DD-sympt.), as well as (iv) the Montreal Cognitive Assessment (MoCA) score. For conventional mean uptake analysis in the putamen, we showed significant correlations with clinical measures only when both HC and PD were included (Pearson correlation r = − 0.74, p-value < 0.001). However, this was not significant when applied to PD subjects only (r = − 0.19, p-value = 0.084), and no such correlations were observed in the caudate. By contrast, for the PD subjects, significant correlations were observed in the caudate when including texture metrics, with (i) UPDRS (p-values < 0.01), (ii) DD-diag. (p-values < 0.001), (iii) DD-sympt (p-values < 0.05), and (iv) MoCA (p-values < 0.01), while no correlations were observed for conventional analysis (p-values = 0.94, 0.34, 0.88 and 0.96, respectively). Our results demonstrated the ability to capture valuable information using advanced texture metrics from striatal DAT SPECT, enabling significant correlations of striatal DAT binding with clinical, motor and cognitive outcomes, and suggesting that textural features hold potential as biomarkers of PD severity and progression.

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Aim to enable image-based tracking of progression in Parkinson's disease

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Texture analysis of clinical dopamine transporter (DAT) SPECT images (DaTscans)

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Significant correlations with clinical, motor and cognitive outcomes

Toward precision medicine in Parkinson's disease

Precision medicine refers to an innovative approach selected for disease prevention and health promotion according to the individual characteristics of each patient. The goal of precision medicine is to formulate prevention and treatment strategies based on each individual with novel physiological and pathological insights into a certain disease. A multidimensional data-driven approach is about to upgrade "precision medicine" to a higher level of greater individualization in healthcare, a shift towards the treatment of individual patients rather than treating a certain disease including Parkinson's disease (PD). As one of the most common neurodegenerative diseases, PD is a lifelong chronic disease with clinical and pathophysiologic complexity, currently it is treatable but neither preventable nor curable. At its advanced stage, PD is associated with devastating chronic complications including both motor dysfunction and non-motor symptoms which impose an immense burden on the life quality of patients. Advances in computational approaches provide opportunity to establish the patient's personalized disease data at the multidimensional levels, which finally meeting the need for the current concept of precision medicine via achieving the minimal side effects and maximal benefits individually. Hence, in this review, we focus on highlighting the perspectives of precision medicine in PD based on multi-dimensional information about OMICS, molecular imaging, deep brain stimulation (DBS) and wearable sensors. Precision medicine in PD is expected to integrate the best evidence-based knowledge to individualize optimal management in future health care for those with PD.

Association of specific frequency bands of functional MRI signal oscillations with motor symptoms and depression in Parkinson's disease

A novel empirical mode decomposition method was adopted to investigate the dissociative or interactive neural impact of depression and motor impairments in Parkinson’s disease (PD). Resting-state fMRI data of 59 PD subjects were first decomposed into characteristic frequency bands, and the main effects of motor severity and depression and their interaction on the energy of blood-oxygen-level-dependent signal oscillation in specific frequency bands were then evaluated. The results show that the severity of motor symptoms is negatively correlated with the energy in the frequency band of 0.10–0.25 Hz in the bilateral thalamus, but positively correlated with 0.01–0.027 Hz band energy in the bilateral postcentral gyrus. The severity of depression, on the other hand, is positively correlated with the energy of 0.10–0.25 Hz but negatively with 0.01–0.027 Hz in the bilateral subgenual gyrus. Notably, the interaction between motor and depressive symptoms is negatively correlated with the energy of 0.10–0.25 Hz in the substantia nigra, hippocampus, inferior orbitofrontal cortex, and temporoparietal junction, but positively correlated with 0.02–0.05 Hz in the same regions. These findings indicate unique associations of fMRI band signals with motor and depressive symptoms in PD in specific brain regions, which may underscore the neural impact of the comorbidity and the differentiation between the two PD-related disorders.

Parkinson's Disease in Saudi Patients: A Genetic Study

Parkinson’s disease (PD) is one of the major causes of parkinsonism syndrome. Its characteristic motor symptoms are attributable to dopaminergic neurons loss in the midbrain. Genetic advances have highlighted underlying molecular mechanisms and provided clues to potential therapies. However, most of the studies focusing on the genetic component of PD have been performed on American, European and Asian populations, whereas Arab populations (excluding North African Arabs), particularly Saudis remain to be explored. Here we investigated the genetic causes of PD in Saudis by recruiting 98 PD-cases (sporadic and familial) and screening them for potential pathogenic mutations in PD-established genes; SNCA, PARKIN, PINK1, PARK7/DJ1, LRRK2 and other PD-associated genes using direct sequencing. To our surprise, the screening revealed only three pathogenic point mutations; two in PINK1 and one in PARKIN. In addition to mutational analysis, CNV and cDNA analysis was performed on a subset of patients. Exon/intron dosage alterations in PARKIN were detected and confirmed in 2 cases. Our study suggests that mutations in the ORF of the screened genes are not a common cause of PD in Saudi population; however, these findings by no means exclude the possibility that other genetic events such as gene expression/dosage alteration may be more common nor does it eliminate the possibility of the involvement of novel genes.

Multipinhole collimator with 20 apertures for a brain SPECT application

PURPOSE

Several new technologies for single photon emission computed tomography (SPECT) instrumentation with parallel-hole collimation have been proposed to improve detector sensitivity and signal collection efficiency. Benefits from improved signal efficiency include shorter acquisition times and lower dose requirements. In this paper, the authors show a possibility of over an order of magnitude enhancement in photon detection efficiency (from 7.6 × 10(-5) to 1.6 × 10(-3)) for dopamine transporter (DaT) imaging of the striatum over the conventional SPECT parallel-hole collimators by use of custom-designed 20 multipinhole (20-MPH) collimators with apertures of 0.75 cm diameter.

METHODS

Quantifying specific binding ratio (SBR) of (123)I-ioflupane or (123)I-iometopane's signal at the striatal region is a common brain imaging method to confirm the diagnosis of the Parkinson's disease. The authors performed imaging of a striatal phantom filled with aqueous solution of I-123 and compared camera recovery ratios of SBR acquired between low-energy high-resolution (LEHR) parallel-hole collimators and 20-MPH collimators.

RESULTS

With only two-thirds of total acquisition time (20 min against 30 min), a comparable camera recovery ratio of SBR was achieved using 20-MPH collimators in comparison to that from the LEHR collimator study.

CONCLUSIONS

Their systematic analyses showed that the 20-MPH collimator could be a promising alternative for the DaT SPECT imaging for brain over the traditional LEHR collimator, which could give both shorter scan time and improved diagnostic accuracy.

Emerging therapies for gait disability and balance impairment: promises and pitfalls

Therapeutic management of gait and balance impairment during aging and neurodegeneration has long been a neglected topic. This has changed considerably during recent years, for several reasons: (1) an increasing recognition that gait and balance deficits are among the most relevant determinants of an impaired quality of life and increased mortality for affected individuals; (2) the arrival of new technology, which has allowed for new insights into the anatomy and functional (dis)integrity of gait and balance circuits; and (3) based in part on these improved insights, the development of new, more specific treatment strategies in the field of pharmacotherapy, deep brain surgery, and physiotherapy. The initial experience with these emerging treatments is encouraging, although much work remains to be done. The objective of this narrative review is to discuss several promising developments in the field of gait and balance treatment. We also address several pitfalls that can potentially hinder a fast and efficient continuation of this vital progress. Important issues that should be considered in future research include a clear differentiation between gait and balance as two distinctive targets for treatment and recognition of compensatory mechanisms as a separate target for therapeutic intervention.

Magnetic resonance support vector machine discriminates essential tremor with rest tremor from tremor-dominant Parkinson disease

BACKGROUND

The aim of the current study was to distinguish patients who had tremor-dominant Parkinson's disease (tPD) from those who had essential tremor with rest tremor (rET).

METHODS

We combined voxel-based morphometry-derived gray matter and white matter volumes and diffusion tensor imaging-derived mean diffusivity and fractional anisotropy in a support vector machine (SVM) to evaluate 15 patients with rET and 15 patients with tPD. Dopamine transporter single-photon emission computed tomography imaging was used as ground truth.

RESULTS

SVM classification of individual patients showed that no single predictor was able to fully discriminate patients with tPD from those with rET. By contrast, when all predictors were combined in a multi-modal algorithm, SVM distinguished patients with rET from those with tPD with an accuracy of 100%.

CONCLUSIONS

SVM is an operator-independent and automatic technique that may help distinguish patients with tPD from those with rET at the individual level.

Affective disorders in Parkinson's disease

PURPOSE OF REVIEW

This review explores recent literature pertaining to affective disorders associated with Parkinson's disease.

RECENT FINDINGS

Nonmotor symptoms including affective disorders are becoming more widely recognized as complications of Parkinson's disease. As awareness of these symptoms increases, and new neuroimaging tools are developed and become more accessible, more studies are being conducted pertaining to behavioral complications in Parkinson's disease. The functional connectivity of the basal ganglia can predispose people with Parkinson's to develop affective disorders. Furthermore, dopaminergic treatments may exacerbate or trigger behavioral symptoms. It is now understood that changes associated with Parkinson's disease are widespread, affecting striatal and extrastriatal regions and resulting in alterations in gray matter, white matter, blood flow, metabolism, and dopaminergic and serotonergic function.

SUMMARY

Neuroimaging is advancing our knowledge of the mechanisms involved in Parkinson's disease, and their role in the development of behavioral disorders. An increased understanding of these disorders may lead to the discovery of new therapeutic targets, or the identification of risk factors for the development of these disorders. If preventive therapies become available, identification of risk factors will be important for the identification and treatment of susceptible individuals.

A voxel-based morphometry and diffusion tensor imaging analysis of asymptomatic Parkinson's disease-related G2019S LRRK2 mutation carriers

BACKGROUND

Patients with Parkinson's disease have reduced gray matter volume and fractional anisotropy in both cortical and sub-cortical structures, yet changes in the pre-motor phase of the disease are unknown.

METHODS

A comprehensive imaging study using voxel-based morphometry and diffusion tensor imaging tract-based spatial statistics analysis was performed on 64 Ashkenazi Jewish asymptomatic first degree relatives of patients with Parkinson's disease (30 mutation carriers), who carry the G2019S mutation in the leucine-rich repeat kinase 2 (LRRK2) gene.

RESULTS

No between-group differences in gray matter volume could be noted in either whole-brain or volume-of-interest analysis. Diffusion tensor imaging analysis did not identify group differences in white matter areas, and volume-of-interest analysis identified no differences in diffusivity parameters in Parkinson's disease-related structures.

CONCLUSIONS

G2019S carriers do not manifest changes in gray matter volume or diffusivity parameters in Parkinson's disease-related structures prior to the appearance of motor symptoms.

Imaging brain iron and diffusion patterns: a follow-up study of Parkinson's disease in the initial stages

RATIONALE AND OBJECTIVES

The aim of this study was to examine changes of brain iron content and diffusion patterns longitudinally in early-stage Parkinson's disease (PD) patients using T2- and T2*-based magnetic resonance imaging (MRI) over 2-year follow-up.

MATERIALS AND METHODS

We imaged 32 PD patients with tremor and 19 healthy controls. A follow-up study (median 25 months, range 22-31 months) was accomplished for 25 patients (men:women = 11:14; age range 44-87 years, median 73 years). All patients and healthy volunteers underwent clinical, neuropsychological, and MRI examinations on the same day. Three different MRI sequences were used and their results were compared: T2-weighted imaging, susceptibility-weighted imaging, and T2* mapping. Additionally, we evaluated diffusion tensor data between groups using tract-based spatial statistics.

RESULTS

Over the 2-year follow-up, the iron-related relaxation increased in the globus pallidus anterior and the caudate nucleus and slightly in the substantia nigra pars compacta (SNc). In the globus pallidus anterior and medial SNc, the change was associated with mild cognitive impairment. In the caudate nucleus, the increase was pronounced in patients with disease onset at 67 years or older. In the SNc, medial transverse relaxation was increased, and in the thalamus, it was decreased, in patients with PD compared with healthy volunteers at 2-year follow-up. Tract-based spatial statistical data did not differ between groups based on gender or Unified Parkinson's Disease Rating Scale, but a slight tendency to decreasing fractional anisotropy (P < .10) in the genu of corpus callosum and bilaterally in corona radiata was seen over 2 years.

CONCLUSIONS

PD-related changes were found in putative iron content over 2 years. Although mild in the initial stages, these changes were consistent over MRI sequences. Rather than correlating with disease duration, the rate of changes was associated with individual characters, such as cognitive decline and age.

In vivo detection of monoaminergic degeneration in early Parkinson disease by (18)F-9-fluoropropyl-(+)-dihydrotetrabenzazine PET

PET with (18)F-9-fluoropropyl-(+)-dihydrotetrabenzazine ((18)F-DTBZ), a novel radiotracer targeting vesicular monoamine transporter type 2 (VMAT2), has been proven as a useful imaging marker to measure dopaminergic integrity.

METHODS

The aim of this study was to evaluate the capability of (18)F-DTBZ PET in detecting the monoaminergic degeneration in early Parkinson disease (PD) in vivo. Seventeen age-matched healthy subjects and 30 PD patients at early stage of disease (duration of disease ≤ 5 y) with mild and unilateral motor symptoms underwent (18)F-DTBZ PET scans. The severity of disease, including Unified Parkinson Disease Rating Scale and modified Hoehn and Yahr Stage (mHY), were recorded at off-medication states. The standardized volumes of interest were applied to the spatial normalized image for quantification analysis. The specific uptake ratios (SURs) were calculated according to the formula (specific volumes-of-interest counts/occipital cortex counts) - 1. SUR measurements were summarized for each brain region.

RESULTS

The mean duration of disease in the PD group was 3.2 ± 2.1 y (range, 0.5-5 y). The mean mHY was 1.0 ± 0.1 (range, 1-1.5). The SURs of bilateral caudate, anterior putamen, posterior putamen, substantia nigra, and nucleus accumbens were significantly lower in PD patients than those of healthy subjects. The reduction of SURs was most severe in the contralateral (the brain regions that are located opposite to the symptomatic side) posterior putamen (-81%), followed by the ipsilateral posterior putamen (-67%). Receiver-operating-characteristic curve analysis showed that the SURs of the bilateral posterior putamen and contralateral anterior putamen had a sensitivity of 100% and specificity of 100% in differentiating PD patients from healthy subjects.

CONCLUSION

(18)F-DTBZ PET was as an excellent tool for the early diagnosis of PD. The obvious decline of (18)F-DTBZ uptake in the ipsilateral (asymptomatic) striatum suggested that (18)F-DTBZ PET might serve as an in vivo biomarker to detect the monoaminergic degeneration in the premotor phase of PD.