Differential Diagnosis Of Parkinsonian Syndromes Research Articles

Coenzyme Q10: A Biomarker in the Differential Diagnosis of Parkinsonian Syndromes.

Multiple system atrophy (MSA) is generally a sporadic neurodegenerative disease which ranks among atypical Parkinson's syndromes. The main clinical manifestation is a combination of autonomic dysfunction and parkinsonism and/or cerebellar disability. The disease may resemble other Parkinsonian syndromes, such as Parkinson's disease (PD) or progressive supranuclear palsy (PSP), from which MSA could be hardly distinguishable during the first years of progression. Due to the lack of a reliable and easily accessible biomarker, the diagnosis is still based primarily on the clinical picture. Recently, reduced levels of coenzyme Q10 (CoQ10) were described in MSA in various tissues, including the central nervous system. The aim of our study was to verify whether the level of CoQ10 in plasma and lymphocytes could serve as an easily available diagnostic biomarker of MSA. The study reported significantly lower levels of CoQ10 in the lymphocytes of patients with MSA compared to patients with PD and controls. The reduction in CoQ10 levels in lymphocytes correlated with the increasing degree of clinical involvement of patients with MSA. CoQ10 levels in lymphocytes seem to be a potential biomarker of disease progression.

Inflammation in multiple system atrophy.

Misfolding protein aggregation inside or outside cells is the major pathological hallmark of several neurodegenerative diseases. Among proteinopathies are neurodegenerative diseases with atypical Parkinsonism and an accumulation of insoluble fibrillary alpha-synuclein (synucleinopathies) or hyperphosphorylated tau protein fragments (tauopathies). As there are no therapies available to slow or halt the progression of these disea ses, targeting the inflammatory process is a promising approach. The inflammatory biomarkers could also help in the differential diagnosis of Parkinsonian syndromes. Here, we review inflammation's role in multiple systems atrophy pathogenesis, diagnosis, and treatment.

Slowing of Frontal β Oscillations in Atypical Parkinsonism.

Diagnosis of atypical parkinsonian syndromes (APS) mostly relies on clinical presentation as well as structural and molecular brain imaging. Whether parkinsonian syndromes are distinguishable based on neuronal oscillations has not been investigated so far. The aim was to identify spectral properties specific to atypical parkinsonism. We measured resting-state magnetoencephalography in 14 patients with corticobasal syndrome (CBS), 16 patients with progressive supranuclear palsy (PSP), 33 patients with idiopathic Parkinson's disease, and 24 healthy controls. We compared spectral power as well as amplitude and frequency of power peaks between groups. Atypical parkinsonism was associated with spectral slowing, distinguishing both CBS and PSP from Parkinson's disease (PD) and age-matched healthy controls. Patients with atypical parkinsonism showed a shift in β peaks (13-30 Hz) toward lower frequencies in frontal areas bilaterally. A concomitant increase in θ/α power relative to controls was observed in both APS and PD. Spectral slowing occurs in atypical parkinsonism, affecting frontal β oscillations in particular. Spectral slowing with a different topography has previously been observed in other neurodegenerative disorders, such as Alzheimer's disease, suggesting that spectral slowing might be an electrophysiological marker of neurodegeneration. As such, it might support differential diagnosis of parkinsonian syndromes in the future. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Parkinson's disease or multiple system atrophy: potential separation by quantitative susceptibility mapping.

Due to the absence of robust biomarkers, and the low sensitivity and specificity of routine imaging techniques, the differential diagnosis between Parkinson's disease (PD) and multiple system atrophy (MSA) is challenging. High-field magnetic resonance imaging (MRI) opened up new possibilities regarding the analysis of pathological alterations associated with neurodegenerative processes. Recently, we have shown that quantitative susceptibility mapping (QSM) enables visualization and quantification of two major histopathologic hallmarks observed in MSA: reduced myelin density and iron accumulation in the basal ganglia of a transgenic murine model of MSA. It is therefore emerging as a promising imaging modality on the differential diagnosis of Parkinsonian syndromes. To assess QSM on high-field MRI for the differential diagnosis of PD and MSA. We assessed 23 patients (nine PDs and 14 MSAs) and nine controls using QSM on 3T and 7T MRI scanners at two academic centers. We observed increased susceptibility in MSA at 3T in prototypical subcortical and brainstem regions. Susceptibility measures of putamen, pallidum, and substantia nigra reached excellent diagnostic accuracy to separate both synucleinopathies. Increase toward 100% sensitivity and specificity was achieved using 7T MRI in a subset of patients. Magnetic susceptibility correlated with age in all groups, but not with disease duration in MSA. Sensitivity and specificity were particularly high for possible MSA, and reached 100% in the putamen. Putaminal susceptibility measures, in particular on ultra-high-field MRI, may distinguish MSA patients from both, PD and controls, allowing an early and sensitive diagnosis of MSA.

Platelet-to-lymphocyte ratio and neutrophil-tolymphocyte ratio may reflect differences in PD and MSA-P neuroinflammation patterns.

To assess the usefulness of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in evaluating the inflammatory process in alpha-synucleinopathies. The role of neuroinflammation in PD and MSA pathogenesis is indisputable. However, there is no method available in everyday use that would enable its evaluation. We suggest that NLR and PLR, as non-specific parameters of inflammation, due to its approachability could be helpful in the assessment of inflammatory activity in alpha-synucleinopathies in everyday clinical practice. 98 patients with a clinical diagnosis of PD, 28 with MSA-P, and 99 healthy age-matched controls, were included in the study. Blood samples were analysed in order to count neutrophil and lymphocyte rates and, subsequently, NLR and PLR. The obtained parameters were compared between the groups. Results were statistically analysed. Our results indicate that patients with PD have higher values of NLR and PLR compared to controls. For MSA-P, only NLR was significantly higher in relation to the control group. There were no statistically significant differences between patients with PD and MSA-P in relation to NLR and PLR values. There was a positive average correlation between NLR and disease duration for MSA-P patients. NLR and PLR values are significantly higher in alpha-synucleinopathies (MSA-P and PD) in relation to a control group. In PD patients, both NLR and PLR values are significantly higher in relation to a control group, whereas in patients with MSA-P, only NLR is significantly increased. The observed differences may reflect distinct neuroinflammatory patterns present in these entities. NLR and PLR are features of peripheral inflammation. Their specificity is relatively low, although increased values suggest possible inflammatory pathogenesis of clinical entities. NLR is based on the observations that in chronic and acute diseases the neutrophil rate has a tendency to rise, while the lymphocyte rate tends to decline. This aspect of inflammatory processes has been primarily evaluated in Intensive Care Units. PLR is a marker presenting changes in platelet and lymphocyte counts caused by acute inflammatory or prothrombotic states. Different values of NLR and PLR in PD and MSA-P compared to healthy controls suggest that in these two alpha-synucleinopathies, different patterns of neuroinflammation might be present. The role of inflammation in the differential diagnosis of parkinsonian syndromes remains unexplored.

Skin Conditions and Movement Disorders: Hiding in Plain Sight.

Skin manifestations are well-recognized non-motor symptoms of Parkinson's disease (PD) and other hypokinetic and hyperkinetic movement disorders. Skin conditions are usually well visible during routine clinical examination and their recognition may play a major role in diagnostic work-up. In this educational review we: (1) briefly outline skin conditions related to Parkinson's disease, including therapy-related skin complications and their management; (2) discuss the role of skin biopsies in early diagnosis of PD and differential diagnosis of parkinsonian syndromes; and focus more on areas which have not been reviewed in the literature before, including (3) skin conditions related to atypical parkinsonism, and (4) skin conditions related to hyperkinetic movement disorders. In case of rare hyperkinetic movement disorders, specific dermatological manifestations, like presence of angiokeratomas, telangiectasias, Mongolian spots, lipomas, ichthyosis, progeroid skin changes and others may point to a very specific group of disorders and help guide further investigations.

A Panel of Plasma Biomarkers for Differential Diagnosis of Parkinsonian Syndromes.

ObjectiveThe aim of our study is to explore the most reliable panel of plasma biomarkers for differential diagnosis of parkinsonian syndromes (PDSs). We selected five kinds of neurodegenerative proteins in plasma: neurofilament light chain (NfL), α-synuclein (α-syn), total tau, β-amyloid 42 (Aβ42) and β-amyloid 40 (Aβ40), and investigated the diagnostic value of these biomarkers.MethodsA total of 99 plasma samples from patients with Parkinson’s disease (PD), multiple system atrophy (MSA), progressive supranuclear palsy, and age-matched healthy controls (HCs) were enrolled in our study. Plasma NfL, α-syn, total tau, Aβ42, and Aβ40 levels were quantified by ultrasensitive single molecule array immunoassay. We used logistic regression analyses to examine diagnostic accuracy of these plasma biomarkers. Disease severity was assessed by the modified Hoehn and Yahr staging scale, Unified Parkinson’s Disease Rating Scale part III (UPDRS III), and the Mini-Mental State Examination (MMSE), and subsequently, correlation analysis was performed.ResultsA combination of α-syn, Aβ42, Aβ40, Aβ42/40, and NfL could achieve a best diagnostic value in differentiating PDSs from HC and PD from HC, with an AUC of 0.983 and 0.977, respectively. By adding NfL to measurements of α-syn or Aβ42 or Aβ40 or Aβ42/40, the best discriminating panel was formed in differentiating atypical parkinsonian disorder (APD) and HC, and the discriminatory potential could reach a sensitivity of 100% and specificity of 100% (AUC = 1.000). For further distinguishing PD from APD, we found a combination of NfL, Aβ42, and total tau was the most reliable panel with equally high diagnostic accuracy. With respect to differentiating the subtypes of APD from one another, our results revealed that measurement of NfL, total tau, Aβ42, Aβ40, and Aβ42/40 was the best discriminating panel. Correlation analysis suggests that plasma Aβ42 levels were positively correlated to UPDRS part III scores in MSA. In terms of cognitive function, there was a relationship between plasma Aβ42/40 level and MMSE scores in patients with APD.ConclusionIn our study, various combinations of plasma biomarkers have great potentialities in identifying PDSs, with important clinical utility in improving diagnostic accuracy. Plasma NfL may have added value to a blood-based biomarker panel for differentiating PDSs.

Parkinson Disease: Epidemiology, Pathology, and Clinical Diagnosis

Parkinson disease (PD) is the second most common neurodegenerative disorder after Alzheimer disease. PD predominantly affects elderly people, and most countries are now facing a marked demographic change, with a progressively larger proportion of the population reaching old age. Thus, PD is expected to cause an increasing social and economic burden on society in the near future. The causes of PD are still mostly unknown. There has been a substantial progress in our understanding of the genetic basis of PD in the past 15 years; however, these mutations explain only a small fraction of all PD cases and other non-genetic factors may play a role. This review covers the epidemiology, pathology, and clinical diagnosis of PD. Figures show age-specific incidence of PD, age-specific prevalence of PD, PD risks for exposures reported in high-quality studies, PD and atypical parkinsonism clinical features, and neuroimaging alterations in PD and atypical parkinsonism. Tables list factors associated with increased or decreased risk of PD, Braak staging scheme of α-synuclein pathology in PD, United Kingdom Parkinson’s Disease Society Brain Bank clinical diagnostic for PD, National Institute of Neurological Disorders and Stroke diagnostic criteria for PD, differential diagnosis of parkinsonian syndromes, and “atypical” atypical parkinsonism.

Parkinson Disease: Epidemiology, Pathology, and Clinical Diagnosis

Parkinson disease (PD) is the second most common neurodegenerative disorder after Alzheimer disease. PD predominantly affects elderly people, and most countries are now facing a marked demographic change, with a progressively larger proportion of the population reaching old age. Thus, PD is expected to cause an increasing social and economic burden on society in the near future. The causes of PD are still mostly unknown. There has been a substantial progress in our understanding of the genetic basis of PD in the past 15 years; however, these mutations explain only a small fraction of all PD cases and other non-genetic factors may play a role. This review covers the epidemiology, pathology, and clinical diagnosis of PD. Figures show age-specific incidence of PD, age-specific prevalence of PD, PD risks for exposures reported in high-quality studies, PD and atypical parkinsonism clinical features, and neuroimaging alterations in PD and atypical parkinsonism. Tables list factors associated with increased or decreased risk of PD, Braak staging scheme of α-synuclein pathology in PD, United Kingdom Parkinson’s Disease Society Brain Bank clinical diagnostic for PD, National Institute of Neurological Disorders and Stroke diagnostic criteria for PD, differential diagnosis of parkinsonian syndromes, and “atypical” atypical parkinsonism.

A Panel of Plasma Biomarkers for Differential Diagnosis of Parkinsonian Syndromes

A Panel of Plasma Biomarkers for Differential Diagnosis of Parkinsonian Syndromes

Диагностика болезни Паркинсона. Часть 2. Возможности структурной нейровизуализации

Objective of the Review: To describe available methods of structural neuroimaging in Parkinson’s disease (PD). Key Points. PD is the second neurodegenerative disease in terms of prevalence; it is a significant medical, social and economic issue. Early diagnosis is the most promising method for the implementation of neuroprotective strategies and preventive therapy in PD. However, differential diagnosis of parkinsonian syndromes is one of the most complicated in neurology. The article describes use of methods of structural neuroimaging in PD diagnostics, in particular of MRI images weighed by magnetic susceptibility, MRI with Т1-weighed images, transcranial sonography. It is demonstrated that these methods can be efficient in patients at an early full-fledged PD stage, where differential diagnosis has to be made or confirmed. Conclusion. Modern methods of structural neuroimaging help not only prevent the causes of secondary parkinsonism, but also confirm PD, also at an early stage of the disease. Keywords: Parkinson’s disease, neuroimaging magnetic-resonance imaging, positron emission tomography, SPECT-imaging, transcranial sonography.

Parkinson's disease or atypical parkinsonism? The importance of acoustic voice analysis in differential diagnosis of speech disorders.

IntroductionSpeech disorder is a common clinical manifestation in patients with Parkinson's disease and atypical parkinsonian syndromes and tends to occur before the onset of the axial parkinsonian symptoms. Due to parkinsonian features that overlap those of Parkinson's disease, the differentiation of voice and a speech disorder is a challenge for clinicians primarily in the early stage of the disease.MethodsSpeech samples were obtained from 116 subjects including 30 cases of Parkinson's disease, 30 cases of progressive supranuclear palsy, 30 cases of multiple system atrophy, and control group consisted of 26 subjects. Differential diagnosis of dysarthria subtypes was based on the quantitative, acoustic analysis of particular speech components. Additionally, Voice Handicap Index questionnaire was taken into account to differentiate the severity of voice impairment in the study groups.ResultsOur results showed significant differences in the distribution of acoustic parameters between Parkinson's disease and atypical parkinsonian syndromes. A mixed type of dysarthria with a combination of hypokinetic, spastic, and atactic features has been found in patients with atypical parkinsonism. In patients with the clinical diagnosis of the parkinsonian variant of multiple system atrophy, ataxic components of dysarthria were observed. Patients with PD presented pure hypokinetic dysarthria. Some parameters may be used as a marker for the diagnosis of the initial stage of PD. Voice impartment was significantly more frequent and severe in atypical parkinsonism than in Parkinson's disease.ConclusionAcoustic voice analysis is a very sensitive and noninvasive tool, provides objective information for the assessment of different speech components, has the specific potential to provide quantitative data essential for the improvement of the diagnostic process, and maybe a useful instrument in the differential diagnosis of parkinsonian syndromes.

Differential diagnosis of parkinsonian syndromes: a comparison of clinical and automated - metabolic brain patterns' based approach.

Differentiation among parkinsonian syndromes may be clinically challenging, especially at early disease stages. In this study, we used 18F-FDG-PET brain imaging combined with an automated image classification algorithm to classify parkinsonian patients as Parkinson's disease (PD) or as an atypical parkinsonian syndrome (APS) at the time when the clinical diagnosis was still uncertain. In addition to validating the algorithm, we assessed its utility in a "real-life" clinical setting. One hundred thirty-seven parkinsonian patients with uncertain clinical diagnosis underwent 18F-FDG-PET and were classified using an automated image-based algorithm. For 66 patients in cohort A, the algorithm-based diagnoses were compared with their final clinical diagnoses, which were the gold standard for cohort A and were made 2.2 ± 1.1years (mean ± SD) later by a movement disorder specialist. Seventy-one patients in cohort B were diagnosed by general neurologists, not strictly following diagnostic criteria, 2.5 ± 1.6years after imaging. The clinical diagnoses were compared with the algorithm-based ones, which were considered the gold standard for cohort B. Image-based automated classification of cohort A resulted in 86.0% sensitivity, 92.3% specificity, 97.4% positive predictive value (PPV), and 66.7% negative predictive value (NPV) for PD, and 84.6% sensitivity, 97.7% specificity, 91.7% PPV, and 95.5% NPV for APS. In cohort B, general neurologists achieved 94.7% sensitivity, 83.3% specificity, 81.8% PPV, and 95.2% NPV for PD, while 88.2%, 76.9%, 71.4%, and 90.9% for APS. The image-based algorithm had a high specificity and the predictive values in classifying patients before a final clinical diagnosis was reached by a specialist. Our data suggest that it may improve the diagnostic accuracy by 10-15% in PD and 20% in APS when a movement disorder specialist is not easily available.

PET Imaging of Astrogliosis and Tau Facilitates Diagnosis of Parkinsonian Syndromes.

Neurodegenerative parkinsonian syndromes comprise a number of disorders that are characterized by similar clinical features but are separated on the basis of different pathologies, i.e., aggregates of α-synuclein or tau protein. Due to the overlap of signs and symptoms a precise differentiation is often difficult, especially early in the disease course. Enormous efforts have been taken to develop tau-selective PET imaging agents, but strong off-target binding to monoamine oxidase B (MAO-B) has been observed across first generation ligands. Nonetheless, astrogliosis-related MAO-B elevation is a common histopathological known feature of all parkinsonian syndromes and might be itself an interesting imaging target. Therefore, this study aimed to investigate the performance of [18F]-THK5351, a combined MAO-B and tau tracer for differential diagnosis of parkinsonian syndromes. [18F]-THK5351 PET was performed in 34 patients: six with Parkinson’s disease (PD), nine with multiple system atrophy with predominant parkinsonism (MSA-P), six with MSA with predominant cerebellar ataxia (MSA-C), and 13 with progressive supranuclear palsy (PSP) Richardson’s syndrome. Volume-of-interest-based quantification of standardized-uptake-values was conducted in different parkinsonian syndrome-related target regions. PET results were subjected to multinomial logistic regression to create a prediction model discriminating among groups. Furthermore, we correlated tracer uptake with clinical findings. Elevated [18F]-THK5351 uptake in midbrain and diencephalon differentiated PSP patients from PD and MSA-C. MSA-C patients were distinguishable by high tracer uptake in the pons and cerebellar deep white matter when compared to PSP and PD patients, whereas MSA-P patients tended to show higher tracer uptake in the lentiform nucleus. A multinomial logistic regression classified 33/34 patients into the correct clinical diagnosis group. Tracer uptake in the pons, cerebellar deep white matter, and striatum was closely associated with the presence of cerebellar and parkinsonian symptoms of MSA patients. The current study demonstrates that combined MAO-B and tau binding of THK5351 facilitates differential diagnosis of parkinsonian syndromes. Furthermore, our data indicate a correlation of MSA phenotype with [18F]-THK5351 uptake in certain brain regions, illustrating their relevance for the emergence of clinical symptoms and underlining the potential of THK5351 PET as a biomarker that correlates with pathological changes as well as with disease stage.

A 3D Deep Residual Convolutional Neural Network for Differential Diagnosis of Parkinsonian Syndromes on 18F-FDG PET Images.

Idiopathic Parkinsons disease and atypical parkinsonian syndromes have similar symptoms at early disease stages, which makes the early differential diagnosis difficult. Positron emission tomography with 18F-FDG shows the ability to assess early neuronal dysfunction of neurodegenerative diseases and is well established for clinical use. In the past decades, machine learning methods have been widely used for the differential diagnosis of parkinsonism based on metabolic patterns. Unlike these conventional machine learning methods relying on hand-crafted features, the deep convolutional neural networks, which have achieved significant success in medical applications recently, have the advantage of learning salient feature representations automatically and effectively. This advantage may offer more appropriate invisible features extracted from data for the enhancement of the diagnosis accuracy. Therefore, this paper develops a 3D deep convolutional neural network on 18F-FDG PET images for the automated early diagnosis. Furthermore, we depicted in saliency maps the decision mechanism of the deep learning method to assist the physiological interpretation of deep learning performance. The proposed method was evaluated on a dataset with 920 patients. In addition to improving the accuracy in the differential diagnosis of parkinsonism compared to state-of-the-art approaches, the deep learning methods also discovered saliency features in a number of critical regions (e.g., midbrain), which are widely accepted as characteristic pathological regions for movement disorders but were ignored in the conventional analysis of FDG PET images.

"One line": A method for differential diagnosis of parkinsonian syndromes.

Neurological findings are important for the differential diagnosis of Parkinson's disease (PD), multiple system atrophy with predominant parkinsonian features (MSA-P), and progressive supranuclear palsy (PSP). There is currently no fast and reliable method to distinguish these patients. To address this, we propose a novel approach to measure midbrain and pons size using a longitudinal "one line" method from the mid-sagittal view. Structural images were acquired from 101 subjects who underwent 3.0T MRI (20 controls, 44 PD, 20 MSA, 12 PSP, and 5 corticobasal syndrome). We measured the middle cerebellar peduncle (MCP), superior cerebellar peduncle (SCP), midbrain, and pons. Brainstem size was measured by area or length of the longitudinal axis, which we named the "one line" method. We conducted intraclass correlation coefficients to assess the extent of agreement and consistency among raters, and receiver operating characteristic curves were used to determine diagnostic accuracy. Intraclass correlation coefficients (ICC) of MCP width were excellent in sagittal and axial sections while those of SCP width were moderate. There were also excellent ICCs between raters for "one line" method of the midbrain and pons, while areas showed good ICCs. "One line" method and area of the midbrain were better than SCP width for the differential diagnosis of PSP from MSA-P and PD. In contrast, there was no clearly superior measurement for differentially diagnosing MSA-P. The "one line" method was comparable with area for inter-rater agreement and diagnostic accuracy even though this was a simple and fast way.

Clinical utility of (99m Tc)-TRODAT-1 scans to differentiate Parkinsonian syndromes: case studies and commentary.

The differential diagnosis of Parkinsonian syndromes is challenging, especially in the elderly as they often have complex presentations. Nuclear imaging modalities can increase diagnostic accuracy. Single-photon emission tomography (SPECT) using the ligand (99m Tc)-TRODAT-1 is used overseas but is yet to be approved in Australia. Three pilot cases of clinically unclear Parkinsonian syndromes from the Royal North Shore Hospital Aged Care Department who underwent (99m Tc)-TRODAT-1 scans are described. From experience overseas, the (99m Tc)-TRODAT-1 scan has clinical utility in the differential diagnosis of Parkinsonian syndromes. (99m Tc)-TRODAT-1 has a lower cost and greater availability than other ligands. Our initial scan findings are largely consistent with the clinical picture, but low specific binding can affect scan interpretation. (99m Tc)-TRODAT-1 is a potential tool in the differential diagnosis of Parkinsonian syndromes. Further experience regarding its effect on patient outcomes and cost effectiveness is required before routine clinical use can be recommended.

Neuroimaging in Parkinsonian Disorders.

Neuroimaging (NI) in Parkinson's disease (PD) includes functional techniques like positron emission tomography (PET) and single photon emission computed tomography (SPECT), and morphological imaging using magnetic resonance imaging (MRI) and transcranial sonography to probe different aspects of the neurobiology of PD. Changes in neurotransmitters in various regions of the brain and their influence on brain networks is the basis for the motor symptoms of PD which are interrogated by NI. The recent Movement Disorders Society Clinical Diagnostic Criteria for PD (MDS-PD) have included the results of a few of these neuroimaging techniques to serve as single supportive criteria or absolute exclusion criteria for the diagnosis of PD. While dopaminergic imaging is useful in the early stages of disease to differentiate the neurodegenerative versus non-degenerative causes of parkinsonism like essential tremors, it has also been used for the differential diagnosis of dementia with Lewy bodies (DLB) from Alzheimer's disease (AD), for inclusion of PD patients into clinical trials and for evaluating response to cell-replacement therapies in PD. Metabolic patterns on F-18 fluorodeoxyglucose positron emission tomography have been used effectively for the classification and differential diagnosis of the parkinsonian syndromes using visual and quantitative approaches. Disease related network-patterns have been used for a completely automated approach to differential diagnosis of parkinsonian syndromes on a single case basis. Structural MRI and advanced MR techniques have been used for the classification of PD and the atypical parkinsonian syndromes. Thus, multimodal imaging in PD may aid in an early, accurate and objective diagnostic classification by highlighting the underlying neurochemical and neuroanatomical changes that underlie this spectrum of disorders. The present challenge in PD is to develop radioligands which could bind selectively to alphasynuclein in-vivo.

Differential diagnosis of parkinsonian syndromes using dopamine transporter and perfusion SPECT

ObjectiveWe aimed to assess whether a combined analysis of dopamine transporter (DAT)- and perfusion-SPECT images (or either) could: (1) distinguish atypical parkinsonian syndromes (APS) from Lewy body diseases (LBD; majority Parkinson disease [PD]), and (2) differentiate among APS subgroups (progressive supranuclear palsy [PSP], corticobasal syndrome [CBS], and multiple system atrophy [MSA]). MethodsWe recruited consecutive patients with neurodegenerative parkinsonian syndromes (LBD, n = 46; APS, n = 33). Individual [123I]FP-CIT- and [123I]iodoamphetamine-SPECT images were coregistered onto anatomical MRI segmented into brain regions. Striatal DAT activity and regional perfusion were extracted from each brain region for each patient and submitted to logistic regression analyses. Stepwise procedures were used to select predictors that should be included in the models to distinguish APS from LBD, and differentiate among the APS subgroups. Receiver-operating characteristic (ROC) analyses were performed to measure diagnostic power. Leave-one-out cross-validation (LOOCV) was performed to evaluate the diagnostic accuracy. ResultsThe model to discriminate APS from LBD showed that the area under the ROC curve (AUC) was 0.923, while the total diagnostic accuracy (TDA) was 86.1% in LOOCV. In the model to distinguish PSP, CBS, and MSA from LBD, the AUC/TDA values were 0.978/94.6%, 0.978/87.0%, and 0.880/80.3%, respectively. In the model to differentiate between CBS and MSA, MSA and PSP, and PSP and CBS, the AUC/TDA values were 0.967/91.3%, 0.920/88.0%, 0.875/77.8%, respectively. ConclusionAn image-based automated classification using striatal DAT activity and regional perfusion patterns provided a good performance in the differential diagnosis of neurodegenerative parkinsonian syndromes without clinical information.

A novel device for estimating olfactory threshold for differential diagnosis of parkinsonian syndrome: A pilot study

A novel device for estimating olfactory threshold for differential diagnosis of parkinsonian syndrome: A pilot study