Differential Diagnosis Of Parkinsonian Disorders Research Articles

Radiomics-Guided Deep Learning Networks Classify Differential Diagnosis of Parkinsonism.

The differential diagnosis between atypical Parkinsonian syndromes may be challenging and critical. We aimed to proposed a radiomics-guided deep learning (DL) model to discover interpretable DL features and further verify the proposed model through the differential diagnosis of Parkinsonian syndromes. We recruited 1495 subjects for 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scanning, including 220 healthy controls and 1275 patients diagnosed with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA), or progressive supranuclear palsy (PSP). Baseline radiomics and two DL models were developed and tested for the Parkinsonian diagnosis. The DL latent features were extracted from the last layer and subsequently guided by radiomics. The radiomics-guided DL model outperformed the baseline radiomics approach, suggesting the effectiveness of the DL approach. DenseNet showed the best diagnosis ability (sensitivity: 95.7%, 90.1%, and 91.2% for IPD, MSA, and PSP, respectively) using retained DL features in the test dataset. The retained DL latent features were significantly associated with radiomics features and could be interpreted through biological explanations of handcrafted radiomics features. The radiomics-guided DL model offers interpretable high-level abstract information for differential diagnosis of Parkinsonian disorders and holds considerable promise for personalized disease monitoring.

Parkinson's disease and Parkinsonism syndromes: Evaluating iron deposition in the putamen using magnetic susceptibility MRI techniques - A systematic review and literature analysis

Magnetic resonance imaging (MRI) techniques, such as quantitative susceptibility mapping (QSM) and susceptibility-weighted imaging (SWI), can detect iron deposition in the brain. Iron accumulation in the putamen (PUT) can contribute to the pathogenesis of Parkinson's disease (PD) and atypical Parkinsonian disorders. This systematic review aimed to synthesize evidence on iron deposition in the PUT assessed by MRI susceptibility techniques in PD and Parkinsonism syndromes. The PubMed and Scopus databases were searched for relevant studies. Thirty-four studies from January 2007 to October 2023 that used QSM, SWI, or other MRI susceptibility methods to measure putaminal iron in PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and healthy controls (HCs) were included. Most studies have found increased putaminal iron levels in PD patients versus HCs based on higher quantitative susceptibility. Putaminal iron accumulation correlates with worse motor scores and cognitive decline in patients with PD. Evidence regarding differences in susceptibility between PD and atypical Parkinsonism is emerging, with several studies showing greater putaminal iron deposition in PSP and MSA than in PD patients. Alterations in putaminal iron levels help to distinguish these disorders from PD. Increased putaminal iron levels appear to be associated with increased disease severity and progression. Thus, magnetic susceptibility MRI techniques can detect abnormal iron accumulation in the PUT of patients with Parkinsonism. Moreover, quantifying putaminal susceptibility may serve as an MRI biomarker to monitor motor and cognitive changes in PD and aid in the differential diagnosis of Parkinsonian disorders.

The utility of neuroimaging in the differential diagnosis of parkinsonian disorders

The utility of neuroimaging in the differential diagnosis of parkinsonian disorders

Differentiating progressive supranuclear palsy from other movement disorders using transcranial sonography: a systematic review and meta-analysis.

Progressive supranuclear palsy (PSP) is an atypical parkinsonism that presents with different phenotypes. There are still no validated diagnostic biomarkers for early diagnosis of PSP. Transcranial sonography (TCS) is a promising tool in the differential diagnosis of parkinsonian disorders; however, there are no systematic investigations about the application of TCS in PSP patients. Therefore, we performed a systematic review and meta-analysis to discuss the role of TCS in diagnosing PSP by systematically searching PubMed, Cochrane Library, Chinese National Knowledge Infrastructure and Wan Fang databases. Of 66 obtained records, 16 articles, including 366 patients with PSP, were included. Our results showed the estimated random-effects pooled prevalence of substantia nigra hyperechogenicity in patients with PSP was 22% (95% CI 12-32%), lenticular nucleus hyperechogenicity was 70% (95% CI 52-82%), and enlarged third ventricle was 71% (95% CI 55-85%). Additionally, a normal echogenicity substantia nigra in TCS showed 70% sensitivity (95% CI 56-81%) and 86% specificity (95% CI 75-86%) to differentiate PSP from Parkinson's disease. In conclusion, TCS is an important supplementary biomarker for diagnosing PSP. At the same time, the diagnostic value of TCS in discriminating PSP from other atypical parkinsonism and between different PSP phenotypes needs further exploration.

Electrochemical Aptasensors for Parkinson's Disease Biomarkers Detection.

Biomarkers are characteristic molecules that can serve as indicators of biological process status or condition; here, they are being studied with special relevance to Parkinson's Disease (PD). This disease is a chronic neurodegenerative disorder very difficult to study given the site of pathology and due to a clinical phenotype that fluctuates over time. Currently, there is no definitive diagnostic test for Parkinson's Disease; thus, clinicians hope that the detection of crucial biomarkers will help in the symptomatic and presymptomatic diagnostics and provide surrogate endpoints to demonstrate the clinical efficacy of new treatments. Electrochemical aptasensors are excellent analytical tools that are used in the detection of PD biomarkers, as they are portable, easy to use, and perform real-time analysis. In this review, we discuss the most important clinical biomarkers for PD, highlighting their physiological role and function in the disease. Herein, we review, for the first time, innovative aptasensors for the detection of current potential PD biomarkers based on electrochemical techniques and discuss future alternatives, including ideal analytical platforms for point-of-care diagnostics. These new tools will be critical not only in the discovery of sensitive, specific, and reliable biomarkers of preclinical PD, but also in the development of tests that can assist in the early detection and differential diagnosis of parkinsonian disorders and in monitoring disease progression. Various methods for fixing aptamers onto the sensor surfaces, enabling quantitative and specific PD biomarker detection present in synthetic and clinical samples, will also be discussed.

Assessment of Bioenergetic Deficits in Patients With Parkinson Disease and Progressive Supranuclear Palsy Using 31P-MRSI.

Bioenergetic disturbance, mainly caused by mitochondrial dysfunction, is an established pathophysiologic phenomenon in neurodegenerative movement disorders. The in vivo assessment of brain energy metabolism by 31phosphorus magnetic resonance spectroscopy imaging could provide pathophysiologic insights and serve in the differential diagnosis of parkinsonian disorders. In this study, we investigated such aspects of the underlying pathophysiology in patients with idiopathic Parkinson disease (PwPD) and progressive supranuclear palsy (PwPSP). In total, 30 PwPD, 16 PwPSP, and 25 healthy control subjects (HCs) underwent a clinical examination, structural magnetic resonance imaging, and 31phosphorus magnetic resonance spectroscopy imaging of the forebrain and basal ganglia in a cross-sectional study. High-energy phosphate metabolites were remarkably decreased in PwPD, particularly in the basal ganglia (-42% compared with HCs and -43% compared with PwPSP, p < 0.0001). This result was not confounded by morphometric brain differences. By contrast, PwPSP had normal levels of high-energy energy metabolites. Thus, the combination of morphometric and metabolic neuroimaging was able to discriminate PwPD from PwPSP with an accuracy of up to 0.93 [95%-CI: 0.91-0.94]. Our study shows that mitochondrial dysfunction and bioenergetic depletion contribute to idiopathic Parkinson disease pathophysiology but not to progressive supranuclear palsy. Combined morphometric and metabolic imaging could serve as an accompanying diagnostic biomarker in the neuroimaging-guided differential diagnosis of these parkinsonian disorders. This study provides Class III evidence that 31phosphorus magnetic resonance spectroscopy imaging combined with morphometric MRI can differentiate PwPD from PwPSP.

Imaging Dopaminergic Neurotransmission in Neurodegenerative Disorders.

Imaging of dopaminergic transmission in neurodegenerative disorders such as Parkinson disease (PD) or dementia with Lewy bodies plays a major role in clinical practice and in clinical research. We here review the role of imaging of the nigrostriatal pathway, as well as of striatal receptors and dopamine release, in common neurodegenerative disorders in clinical practice and research. Imaging of the nigrostriatal pathway has a high diagnostic accuracy to detect nigrostriatal degeneration in disorders characterized by nigrostriatal degeneration, such as PD and dementia with Lewy bodies, and disorders of more clinical importance, namely in patients with clinically uncertain parkinsonism. Imaging of striatal dopamine D2/3 receptors is not recommended for the differential diagnosis of parkinsonian disorders in clinical practice anymore. Regarding research, recently the European Medicines Agency has qualified dopamine transporter imaging as an enrichment biomarker for clinical trials in early PD, which underlines the high diagnostic accuracy of this imaging tool and will be implemented in future trials. Also, imaging of the presynaptic dopaminergic system plays a major role in, for example, examining the extent of nigrostriatal degeneration in preclinical and premotor phases of neurodegenerative disorders and to examine subtypes of PD. Also, imaging of postsynaptic dopamine D2/3 receptors plays a role in studying, for example, the neuronal substrate of impulse control disorders in PD, as well as in measuring endogenous dopamine release to examine, for example, motor complications in the treatment of PD. Finally, novel MRI sequences as neuromelanin-sensitive MRI are promising new tools to study nigrostriatal degeneration invivo.

Dopaminergic Correlates of Regional Cerebral Blood Flow in Parkinsonian Disorders.

Cerebral blood flow (CBF) and dopamine transporter (DAT) images are clinically used for the differential diagnosis of parkinsonian disorders. This study aimed to examine the correlation of CBF with striatal DAT in patients with Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) and evaluate the diagnostic power of DAT-correlated CBF in PD through machine learning with each imaging modality alone or in combination. Fifty-eight patients with PD and 71 with APS (24 with multiple system atrophy, 21 with progressive supranuclear palsy, and 26 with corticobasal syndrome) underwent 123 I-IMP and 123 I-FP-CIT single-photon emission computed tomography. Multiple regression analyses for CBF and striatal DAT binding were conducted on each group. PD probability was predicted by machine learning and receiver operating characteristic curves. The PD group showed more affected striatal DAT binding positively correlated with the ipsilateral prefrontal perfusion and negatively with the bilateral cerebellar perfusion. In corticobasal syndrome, striatal DAT binding positively correlated with the ipsilateral prefrontal perfusion and negatively with the contralateral precentral perfusion. In Richardson's syndrome, striatal DAT binding positively correlated with perfusion in the ipsilateral precentral cortex and basal ganglia. Machine learning showed that the combination of CBF and DAT was better for delineating PD from APS (area under the curve [AUC]=0.87) than either CBF (0.67) or DAT (0.50) alone. In PD and four-repeat tauopathy, prefrontal perfusion was related to ipsilateral nigrostriatal dopaminergic function. This dual-tracer frontostriatal relationship may be effectively used as a diagnostic tool for delineating PD from APS. © 2022 International Parkinson and Movement Disorder Society.

Computer-Aided Classification Framework of Parkinsonian Disorders Using 11C-CFT PET Imaging.

PurposeTo investigate the usefulness of a novel computer-aided classification framework for the differential diagnosis of parkinsonian disorders (PDs) based on 11C-methyl-N-2β-carbomethoxy-3β-(4-fluorophenyl)-tropanel (11C-CFT) positron emission tomography (PET) imaging.MethodsPatients with different forms of PDs—including Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP)—underwent dopamine transporter (DAT) imaging with 11C-CFT PET. A novel multistep computer-aided classification framework—consisting of magnetic resonance imaging (MRI)-assisted PET segmentation, feature extraction and prediction, and automatic subject classification—was developed. A random forest method was used to assess the diagnostic relevance of different regions to the classification process. Finally, the performance of the computer-aided classification system was tested using various training strategies involving patients with early and advanced disease stages.ResultsAccuracy values for identifying PD, MSA, and PSP were 85.0, 82.2, and 89.7%, respectively—with an overall accuracy of 80.4%. The caudate and putamen provided the highest diagnostic relevance to the proposed classification framework, whereas the contribution of midbrain was negligible. With the exception of sensitivity for diagnosing PSP, the strategy comprising both early and advanced disease stages performed better in terms of sensitivity, specificity, positive predictive value, and negative predictive value within each PDs subtype.ConclusionsThe proposed computer-aided classification framework based on 11C-CFT PET imaging holds promise for improving the differential diagnosis of PDs.

Comparison of perfusion 18F-FP-CIT PET and 99mTc-ECD SPECT in parkinsonian disorders.

Early and accurate identification of various conditions that can cause parkinsonian symptoms is important for determining treatment policies. Currently dopamine transporter (DAT) imaging using FP-CIT, glucose metabolism imaging using fluorodeoxyglucose, cerebral blood flow image using ethyl cysteinate dimer (ECD), and others are used for differentiation. However, the use of multiple modalities is inconvenient and costly. In the present retrospective study, we evaluated the correlation between regional brain uptake ratios (URs) in perfusion FP-CIT PET and ECD SPECT images.Twenty patients with Parkinson's symptoms underwent perfusion DAT positron emission tomography (18F-FP-CIT PET/CT) and cerebral blood flow tomography (99mTc-ECD SPECT) within a 2-week period. Perfusion 18F-FP-CIT PET/CT and 99mTc-ECD SPECT URs of 19 brain regions (bilateral frontal, temporal, parietal and occipital lobes, bilateral caudate nucleus, bilateral putamen, bilateral insula, bilateral cingulate gyrus, bilateral thalamus, and brainstem) were directly compared and correlations were analyzed.Average 18F-FP-CIT PET/CT regional perfusion URs were higher than 99mTc-ECD SPECT URs. Uptake ratios were well correlated in all 19 regions (except right putamen), and especially in dopamine poor regions (cerebral cortex). In left putamen, URs were significantly correlated, but the correlation coefficient was lower than those of other regions.A single tracer dual phase N-3-fluoropropyl-2-beta-carboxymethoxy-3-beta-(4-iodophenyl) nortropane test seems to be helpful for differential diagnosis of parkinsonian disorders. Large-scale, longitudinal studies on complementary diseases with parkinsonian patterns are required to investigate differences in correlations between perfusion 18F-FP-CIT PET/CT and 99mTc-ECD SPECT over time.

Diagnostic accuracy of dual-phase 18F-FP-CIT PET imaging for detection and differential diagnosis of Parkinsonism

Delayed phase 18F-FP-CIT PET (dCIT) can assess the striatal dopamine transporter binding to detect degenerative parkinsonism (DP). Early phase 18F-FP-CIT (eCIT) can assess the regional brain activity for differential diagnosis among parkinsonism similar with 18F-FDG PET. We evaluated the diagnostic performance of dual phase 18F-FP-CIT PET (dual CIT) and 18F-FDG PET compared with clinical diagnosis in 141 subjects [36 with idiopathic Parkinson’s disease (IPD), 77 with multiple system atrophy (MSA), 18 with progressive supranuclear palsy (PSP), and 10 with non-DP)]. Visual assessment of eCIT, dCIT, dual CIT, 18F-FDG and 18F-FDG PET with dCIT was in agreement with the clinical diagnosis in 61.7%, 69.5%, 95.7%, 81.6%, and 97.2% of cases, respectively. ECIT showed about 90% concordance with non-DP and MSA, and 8.3% and 27.8% with IPD and PSP, respectively. DCIT showed ≥ 88% concordance with non-DP, IPD, and PSP, and 49.4% concordance with MSA. Dual CIT showed ≥ 90% concordance in all groups. 18F-FDG PET showed ≥ 90% concordance with non-DP, MSA, and PSP, but only 33.3% concordance with IPD. The combination of 18F-FDG and dCIT yielded ≥ 90% concordance in all groups. Dual CIT may represent a powerful alternative to the combination of 18F-FDG PET and dCIT for differential diagnosis of parkinsonian disorders.

Dopamine transporter SPECT imaging in Parkinson’s disease and parkinsonian disorders

The dopamine transporter (DAT) imaging provides an objective tool for the assessment of dopaminergic function of presynaptic terminals which is valuable for the differential diagnosis of parkinsonian disorders related to a striatal dopaminergic deficiency from movement disorders not related a striatal dopaminergic deficiency. DAT imaging with single-photon emission computed tomography (SPECT) can be used to confirm or exclude a diagnosis of dopamine deficient parkinsonism in cases where the diagnosis is unclear. It can also detect the dopaminergic dysfunction in presymptomatic subjects at risk for Parkinson’s disease (PD) since the reduced radiotracer binding to DATs in striatum is already present in the prodromal stage of PD. This review covers the rationale of using DAT SPECT imaging in the diagnosis of PD and other parkinsonian disorders, specifically focusing on the practical aspects of imaging and routine clinical indications.

Contribution of the Mesencephalon Indices to Differential Diagnosis of Parkinsonian Disorders.

This study aimed to assess the sensitivity and specificity of brain stem morphologic changes to differentiate the progressive supranuclear palsy (PSP) from Parkinson disease (PD) and multiple system atrophy (MSA), by single and combined magnetic resonance imaging (MRI) measurements. Peduncle angle (PA), pons area (P), mesencephalon area (M), middle cerebellar peduncles (MCPs), and superior cerebellar peduncles (SCPs) were measured in 14 PSP, 43 PD, 8 MSA patients, and 45 age-matched control participants on T1-weighted MRI. Neurologists clinically diagnosed all patients. Additionally, P/M ratio, MCPs/SCPs ratio, the previously defined Magnetic Resonance Parkinsonism Index, MRPI: (P/M) · (MCP/SCP), and also the Akdeniz Index (AKI) that we termed were calculated, AKI: (P/M) · (PA/180). Two blinded radiologists evaluated all MR images and inter-/intraobserver variations were measured. Both M and SCPs were significantly lower and P/M, MCPs/SCPs, and PA were significantly higher in PSP patients than the other groups (P < .001). This significance was related to patients with PSP and PD. But all single measurements showed some overlapping values. Therefore, previously defined MRPI was calculated and shown to distinguish patients (negative predictive values: 92%, sensitivity: 78%, specificity: 82%). In this study, interobserver correlation (0.68) was found low for MRPI. Therefore, we identified a more practical index: the Akdeniz Index, which has same diagnostic power with MRPI and higher interobserver correlation (0.91). The Akdeniz Index identified in our study is a practical index with high diagnostic power and can reinforce radiological distinguishing of PSP and PD, which are clinically difficult to distinguish.

Towards accurate and unbiased imaging-based differentiation of Parkinson’s disease, progressive supranuclear palsy and corticobasal syndrome

The early and accurate differential diagnosis of parkinsonian disorders is still a significant challenge for clinicians. In recent years, a number of studies have used magnetic resonance imaging data combined with machine learning and statistical classifiers to successfully differentiate between different forms of Parkinsonism. However, several questions and methodological issues remain, to minimize bias and artefact-driven classification. In this study, we compared different approaches for feature selection, as well as different magnetic resonance imaging modalities, with well-matched patient groups and tightly controlling for data quality issues related to patient motion. Our sample was drawn from a cohort of 69 healthy controls, and patients with idiopathic Parkinson’s disease (n = 35), progressive supranuclear palsy Richardson’s syndrome (n = 52) and corticobasal syndrome (n = 36). Participants underwent standardized T1-weighted and diffusion-weighted magnetic resonance imaging. Strict data quality control and group matching reduced the control and patient numbers to 43, 32, 33 and 26, respectively. We compared two different methods for feature selection and dimensionality reduction: whole-brain principal components analysis, and an anatomical region-of-interest based approach. In both cases, support vector machines were used to construct a statistical model for pairwise classification of healthy controls and patients. The accuracy of each model was estimated using a leave-two-out cross-validation approach, as well as an independent validation using a different set of subjects. Our cross-validation results suggest that using principal components analysis for feature extraction provides higher classification accuracies when compared to a region-of-interest based approach. However, the differences between the two feature extraction methods were significantly reduced when an independent sample was used for validation, suggesting that the principal components analysis approach may be more vulnerable to overfitting with cross-validation. Both T1-weighted and diffusion magnetic resonance imaging data could be used to successfully differentiate between subject groups, with neither modality outperforming the other across all pairwise comparisons in the cross-validation analysis. However, features obtained from diffusion magnetic resonance imaging data resulted in significantly higher classification accuracies when an independent validation cohort was used. Overall, our results support the use of statistical classification approaches for differential diagnosis of parkinsonian disorders. However, classification accuracy can be affected by group size, age, sex and movement artefacts. With appropriate controls and out-of-sample cross validation, diagnostic biomarker evaluation including magnetic resonance imaging based classifiers may be an important adjunct to clinical evaluation.

Metabolic pattern analysis of 18F-FDG PET as a marker for Parkinson's disease: a systematic review and meta-analysis.

A large number of articles have assessed the diagnostic accuracy of the metabolic pattern analysis of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) in Parkinson's disease (PD); however, different studies involved small samples with various controls and methods, leading to discrepant conclusions. This study aims to consolidate the available observational studies and provide a comprehensive evaluation of the clinical utility of 18F-FDG PET for PD. The methods included a systematic literature search and a hierarchical summary receiver operating characteristic approach. Sensitivity analyses according to different pattern analysis methods (statistical parametric mapping versus scaled subprofile modeling/principal component analysis) and control population [healthy controls (HCs) versus atypical parkinsonian disorder (APD) patients] were performed to verify the consistency of the main results. Additional analyses for multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) were conducted. Fifteen studies comprising 1446 subjects (660 PD patients, 499 APD patients, and 287 HCs) were included. The overall diagnostic accuracy of 18F-FDG in differentiating PD from APDs and HCs was quite high, with a pooled sensitivity of 0.88 [95% confidence interval (95% CI), 0.85-0.91] and a pooled specificity of 0.92 (95% CI, 0.89-0.94), with sensitivity analyses indicating statistically consistent results. Additional analyses showed an overall sensitivity and specificity of 0.87 (95% CI, 0.76-0.94) and 0.93 (95% CI, 0.89-0.96) for MSA and 0.91 (95% CI, 0.78-0.95) and 0.96 (95% CI, 0.92-0.98) for PSP. Our study suggests that the metabolic pattern analysis of 18F-FDG PET has high diagnostic accuracy in the differential diagnosis of parkinsonian disorders.

Cerebrospinal fluid NFL in the differential diagnosis of parkinsonian disorders: A meta-analysis

Neurofilament light chain (NFL) in cerebrospinal fluid (CSF) is a promising biomarker candidate which may discriminate atypical parkinsonian disorders (APD), mainly including multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD), from Parkinson’s disease (PD). We aim to evaluate the diagnostic accuracy of CSF NFL level as a differentiating biomarker between APD and PD. Databases of PubMed, OVID and Web of Science were searched for studies (published until May 31, 2017) that reported on CSF NFL as a diagnostic biomarker between APD and PD. Eight studies were pooled in this meta-analysis, including 341 PD and 396 APD patients and 388 healthy controls. The pooled sensitivity was 82% (95% CI, 68%–91%) and specificity was 85% (95% CI, 79%–89%) in differentiating APD from PD. The pooled positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR) were 5.4 (95% CI, 3.6%–8.1%), 0.21 (95% CI, 0.11%–0.40%), and 25 (95% CI, 9%–69%) respectively; and the area under the curve (AUC) was 0.89 (95% CI, 0.86%-0.91%). Subgroup analysis revealed sensitivity and specificity were significantly influenced by study design. The APD subtypes, disease duration and severity were the main heterogeneity sources in specificity. The results of Deeks’ test revealed a low risk of publication bias. The CSF NFL level may be used as a biomarker in discriminating APD from PD with high diagnostic accuracy at an early stage of disease. Large and longitudinal studies are still needed on individuals who are suspected to have APD.

Biomarkers for cognitive impairment in Lewy body disorders: Status and relevance for clinical trials.

Biomarkers have the potential to improve diagnosis and prognosis, and guide clinical treatment decisions. In research, biomarkers can be used for patient selection and as outcome measures in clinical trials. A range of biochemical and imaging biomarkers are relevant to patients with Lewy body disorders, including PD, PD dementia, and dementia with Lewy bodies. Dopaminergic imaging is used for differential diagnosis of parkinsonian disorders versus tremor disorders without dopamingeric deficits, and also to differentiate dementia with Lewy bodies from Alzheimer's disease. Markers of underlying Alzheimer's disease pathology have been applied to PD patients experiencing cognitive decline to determine the extent of mixed pathology in these cases. Assessment of alpha-synuclein species in spinal fluid is possible, and more specific assays attempt to identify alpha-synuclein aggregates or phosphorylated alpha-synuclein. While alpha-synuclein markers are intended to measure the pathology most central to PD dementia and dementia with Lewy bodies, convincing evidence of robust reliability and validity from multiple laboratories is lacking. Similarly, alpha-synuclein imaging by PET or single-photon emission computed tomography, while an important research goal, is not yet available. In addition to their uses in the clinic, biomarkers have natural uses in therapeutic trials that target cognitive and neuropsychiatric features of Lewy body disorders. The biomarkers most likely to be incorporated into trials are dopaminergic and amyloid imaging for the purpose of accurate patient selection, and possibly to demonstrate the utility of antiamyloid treatments in Lewy body disorders patients with mixed pathology. © 2018 International Parkinson and Movement Disorder Society.

The utility of FDG-PET in the differential diagnosis of Parkinsonism

Introduction: Differential diagnosis of parkinsonian disorders can be difficult on clinical grounds, especially in the early stage. Recent advancements in 18-F-fluorodeoxyglucose positron emission tomography (FDG-PET) imaging reveals different patterns of regional glucose metabolism in idiopathic Parkinson’s disease (IPD) and atypical parkinsonian syndromes, such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), which may help differentiating between these conditions.Purpose: To assess the utility of FDG-PET imaging in differential diagnosis of Parkinsonism in clinical practice.Methods: FDG-PET was performed in 72 patients with parkinsonism (age 34–80 years) referred to our center by movement disorder specialists. FDG-PET diagnosis was obtained by visual assessment of individual scans combined with voxel-based statistical parametric mapping analysis. FDG-PET diagnosis assigned at the time of imaging was compared with the final clinical diagnosis made by the movement disorder specialists after ≥2 years follow-up.Results: FDG-PET findings were consistent with IPD in 27, MSA in 18, PSP in 19 and CBS in 2 patients. The final clinical diagnosis was IPD in 29, MSA in 20, PSP in 21 and CBS in 2 patients. Concordance between the FDG-PET and clinical diagnoses was 92% in the overall sample (IPD 93%, MSA 90%, PSP 91% and CBS 100%). The diagnostic accuracy of FDG-PET was 93% for IPD and MSA and 97% for PSP.Conclusion: FDG-PET may help differentiate between IPD, MSA, PSP and CBS among patients presenting with parkinsonian symptoms, which is important for patient counselling and making early decisions about treatment.

Increased Rab35 expression is a potential biomarker and implicated in the pathogenesis of Parkinson's disease.

Parkinson's disease (PD) is the second common neurodegenerative disease. Identification of biomarkers for early diagnosis and prediction of disease progression is important. The present comparative proteomic study of serum samples using two-dimensional fluorescence differential gel electrophoresis followed by ELISA confirmation demonstrated that protein expression of Rab35 was increased in PD patients compared with matched control subjects and other parkinsonian disorders, progressive supranuclear palsy (PSP) and multiple system atrophy (MSA). The serum level of Rab35 was significantly correlated with the age at onset of PD. The median age of onset in patients with higher Rab35 serum level was 5 years younger than those with lower Rab35 serum level. There was a positive correlation between the Rab35 level and disease duration of PD. Moreover, the protein expression of Rab35 was increased in the substantia nigra but not in the striatum of mouse models of PD, including MPTP-treated mice, rotenone-treated mice, (R1441C) LRRK2 or (G2019S) LRRK2 transgenic mice. Furthermore, overexpression of Rab35 increased the aggregation and secretion of mutant A53T α-synuclein in dopaminergic SH-SY5Y cells. Co-expression of Rab35 with wild-type or A53T α-synuclein in SH-SY5Y cells deteriorated cell death. Our results suggest that Rab35 is potentially useful in the differential diagnosis of parkinsonian disorders and is implicated in the pathogenesis of PD.

Free-water imaging in Parkinson's disease and atypical parkinsonism.

Conventional single tensor diffusion analysis models have provided mixed findings in the substantia nigra of Parkinson's disease, but recent work using a bi-tensor analysis model has shown more promising results. Using a bi-tensor model, free-water values were found to be increased in the posterior substantia nigra of Parkinson's disease compared with controls at a single site and in a multi-site cohort. Further, free-water increased longitudinally over 1 year in the posterior substantia nigra of Parkinson's disease. Here, we test the hypothesis that other parkinsonian disorders such as multiple system atrophy and progressive supranuclear palsy have elevated free-water in the substantia nigra. Equally important, however, is whether the bi-tensor diffusion model is able to detect alterations in other brain regions beyond the substantia nigra in Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy and to accurately distinguish between these diseases. Free-water and free-water-corrected fractional anisotropy maps were compared across 72 individuals in the basal ganglia, midbrain, thalamus, dentate nucleus, cerebellar peduncles, cerebellar vermis and lobules V and VI, and corpus callosum. Compared with controls, free-water was increased in the anterior and posterior substantia nigra of Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy. Despite no other changes in Parkinson's disease, we observed elevated free-water in all regions except the dentate nucleus, subthalamic nucleus, and corpus callosum of multiple system atrophy, and in all regions examined for progressive supranuclear palsy. Compared with controls, free-water-corrected fractional anisotropy values were increased for multiple system atrophy in the putamen and caudate, and increased for progressive supranuclear palsy in the putamen, caudate, thalamus, and vermis, and decreased in the superior cerebellar peduncle and corpus callosum. For all disease group comparisons, the support vector machine 10-fold cross-validation area under the curve was between 0.93-1.00 and there was high sensitivity and specificity. The regions and diffusion measures selected by the model varied across comparisons and are consistent with pathological studies. In conclusion, the current study used a novel bi-tensor diffusion analysis model to indicate that all forms of parkinsonism had elevated free-water in the substantia nigra. Beyond the substantia nigra, both multiple system atrophy and progressive supranuclear palsy, but not Parkinson's disease, showed a broad network of elevated free-water and altered free-water corrected fractional anisotropy that included the basal ganglia, thalamus, and cerebellum. These findings may be helpful in the differential diagnosis of parkinsonian disorders, and thereby facilitate the development and assessment of targeted therapies.