Association of PSP phenotypes with survival: A brain-bank study.

INTRODUCTION: The MDS-PSP criteria expand the phenotypic spectrum of PSP by adding to Richardson's syndrome (PSP-RS) other presentations such as PSP-parkinsonism (PSP-P), PSP-pure-gait-freezing (PSP-PGF), PSP-speech-language (PSP-SL), PSP-frontal (PSP-F), PSP-postural-instability (PSP-PI) and PSP-corticobasal-syndrome (PSP-CBS). Evidence about the prognostic differences between PSP phenotypes is scarce and focused on PSP-RS vs. non-PSP-RS. Using a brain-bank cohort we assessed PSP survival not only in PSP-RS vs. non-PSP-RS, but also in PSP-RS + cortical vs. subcortical phenotypes. Besides, we assessed sensitivity and specificity of the MDS-PSP criteria in of PSP and other degenerative parkinsonisms. METHODS: We retrospectively applied the MDS-PSP diagnostic criteria to 32 definite PSP cases and 30 cases with other degenerative parkinsonian syndromes (Parkinson's disease [PD; n = 11], multiple system atrophy [MSA; n = 11], corticobasal degeneration [CBD; n = 8]). We conducted survival statistics in neuropathologically confirmed PSP cases considering PSP-RS vs. non-PSP-RS and PSP-RS + PSP-cortical (PSP-F + PSP-SL + PSP-CBS) vs. PSP-subcortical (PSP-P + PSP-PGF) phenotypes. We also adjusted survival analyses for PSP tau scores. RESULTS: Diagnostic sensitivity was 100% and specificity ranged from 47% to 87% when excluding cases that met the "suggestive of PSP" definition early in their disease course but with other clinical features better matching with a non-PSP pathological diagnosis. Survival was significantly shorter in PSP-RS vs. non-PSP-RS cases, but it was more markedly shorter in PSP-RS + PSP-cortical vs. PSP-subcortical, independently of PSP tau scores, which were not associated with survival. CONCLUSIONS: PSP-subcortical phenotypes appear to have longer survival than PSP-RS and cortical phenotypes. This might be of prognostic relevance when informing patients upon clinical diagnosis.

Parkinsonism in children: Clinical classification and etiological spectrum.

Infantile- and childhood-onset parkinsonism is mainly due to genetic alterations and is an exceedingly rare condition, unlike Parkinson's disease (PD), which is one of the most common neurologic disorders in adulthood. The clinical characterization of parkinsonism during early stages of neuromotor development is controversial due to the lack of consensus regarding the clinical criteria of PD or parkinsonism in the immature brain. The classification here proposed is based on a review of conditions that emerge during infancy and childhood, with key symptoms evocative of adult parkinsonism. The proposed nosography is based on age at presentation, clinical features, outcome, and etiological background. It includes developmental parkinsonism, infantile degenerative parkinsonism, parkinsonism in the setting of neurodevelopmental disorders, parkinsonism in the setting of multisystem brain diseases, juvenile parkinsonism and dystonia-parkinsonism, and acquired parkinsonism. The subgroups denoting peculiar clinical presentations as a consequence of disease impact on the immature brain are developmental parkinsonism due to monoamine metabolic disorders and infantile degenerative parkinsonism caused by DAT and WASR2 defects. More tardive parkinsonisms occur in genetic conditions that cause a generalized derangement of neurodevelopmental processes, such as those due to MECP2, NR4A2, SCN1A, and RAB39B. Some conditions presenting with neurodevelopmental disorder can progress later, disclosing their neurodegenerative nature (i.e. WDR45 and KCND3). Finally, new emerging conditions with childhood-onset parkinsonism arise from the cumulative effect of multiple genetic lesions.

[Recommendation for the differentiated use of nuclear medical diagnostic for parkinsonian syndromes]. / Empfehlung zum differenzierten Einsatz nuklearmedizinischer Diagnostik bei Parkinson-Syndromen.

The present work provides an overview of the various nuclear medicine methods in the diagnosis of neurodegenerative parkinsonian syndromes and their respective evidence and is intended to enable practical decision-making aids in the application and interpretation of the methods and findings. The value of the procedures differs considerably in relation to the two relevant diagnostic questions. On the one hand, it is the question of whether there is a neurodegenerative parkinsonian syndrome at all, and on the other hand the question of which one. While the DAT-SPECT is undisputedly the method of choice for answering the first question (taking certain parameters into account), this method is not suitable for answering the second question. To categorise parkinsonian syndromes into idiopathic (i. e. Parkinson´s disease) or atypical, various procedures are used in everyday clinical practice including MIBG scintigraphy, and FDG-PET. We explain why FDG-PET currently is not only the most suitable of these methods to differentiate an idiopathic parkinsonian syndrome, from an atypical Parkinson's syndrome, but also enables sufficiently valid to distinguish the various atypical neurodegenerative Parkinson's syndromes (i. e. MSA, PSP and CBD) from each other and therefore should be reimbursed by health insurances.

Immune profiling of plasma-derived extracellular vesicles identifies Parkinson disease.

OBJECTIVE: To develop a diagnostic model based on plasma-derived extracellular vesicle (EV) subpopulations in Parkinson disease (PD) and atypical parkinsonism (AP), we applied an innovative flow cytometric multiplex bead-based platform. METHODS: Plasma-derived EVs were isolated from PD, matched healthy controls, multiple system atrophy (MSA), and AP with tauopathies (AP-Tau). The expression levels of 37 EV surface markers were measured by flow cytometry and correlated with clinical scales. A diagnostic model based on EV surface markers expression was built via supervised machine learning algorithms and validated in an external cohort. RESULTS: Distinctive pools of EV surface markers related to inflammatory and immune cells stratified patients according to the clinical diagnosis. PD and MSA displayed a greater pool of overexpressed immune markers, suggesting a different immune dysregulation in PD and MSA vs AP-Tau. The receiver operating characteristic curve analysis of a compound EV marker showed optimal diagnostic performance for PD (area under the curve [AUC] 0.908; sensitivity 96.3%, specificity 78.9%) and MSA (AUC 0.974; sensitivity 100%, specificity 94.7%) and good accuracy for AP-Tau (AUC 0.718; sensitivity 77.8%, specificity 89.5%). A diagnostic model based on EV marker expression correctly classified 88.9% of patients with reliable diagnostic performance after internal and external validations. CONCLUSIONS: Immune profiling of plasmatic EVs represents a crucial step toward the identification of biomarkers of disease for PD and AP.

Unsupervised clustering of dopamine transporter PET imaging discovers heterogeneity of parkinsonism.

Parkinsonism has heterogeneous nature, showing distinctive patterns of disease progression and prognosis. We aimed to find clusters of parkinsonism based on 18 F-fluoropropyl-carbomethoxyiodophenylnortropane (FP-CIT) PET as a data-driven approach to evaluate heterogenous dopaminergic neurodegeneration patterns. Two different cohorts of patients who received FP-CIT PET were collected. A labeled cohort (n = 94) included patients with parkinsonism who underwent a clinical follow-up of at least 3 years (mean 59.0 ± 14.6 months). An unlabeled cohort (n = 813) included all FP-CIT PET data of a single-center. All PET data were clustered by a dimension reduction method followed by hierarchical clustering. Four distinct clusters were defined according to the imaging patterns. When the diagnosis of the labeled cohort of 94 patients was compared with the corresponding cluster, parkinsonism patients were mostly included in two clusters, cluster "0" and "2." Specifically, patients with progressive supranuclear palsy were significantly more included in cluster 0. The two distinct clusters showed significantly different clinical features. Furthermore, even in PD patients, two clusters showed a trend of different clinical features. We found distinctive clusters of parkinsonism based on FP-CIT PET-derived heterogeneous neurodegeneration patterns, which were associated with different clinical features. Our results support a biological underpinning for the heterogeneity of neurodegeneration in parkinsonism.

[123I]Metaiodobenzylguanidine (MIBG) Cardiac Scintigraphy and Automated Classification Techniques in Parkinsonian Disorders.

PURPOSE: To provide reliable and reproducible heart/mediastinum (H/M) ratio cut-off values for parkinsonian disorders using two machine learning techniques, Support Vector Machines (SVM) and Random Forest (RF) classifier, applied to [123I]MIBG cardiac scintigraphy. PROCEDURES: We studied 85 subjects, 50 with idiopathic Parkinson's disease, 26 with atypical Parkinsonian syndromes (P), and 9 with essential tremor (ET). All patients underwent planar early and delayed cardiac scintigraphy after [123I]MIBG (111 MBq) intravenous injection. Images were evaluated both qualitatively and quantitatively; the latter by the early and delayed H/M ratio obtained from regions of interest (ROIt1 and ROIt2) drawn on planar images. SVM and RF classifiers were finally used to obtain the correct cut-off value. RESULTS: SVM and RF produced excellent classification performances: SVM classifier achieved perfect classification and RF also attained very good accuracy. The better cut-off for H/M value was 1.55 since it remains the same for both ROIt1 and ROIt2. This value allowed to correctly classify PD from P and ET: patients with H/M ratio less than 1.55 were classified as PD while those with values higher than 1.55 were considered as affected by parkinsonism and/or ET. No difference was found when early or late H/M ratio were considered separately thus suggesting that a single early evaluation could be sufficient to obtain the final diagnosis. CONCLUSIONS: Our results evidenced that the use of SVM and CT permitted to define the better cut-off value for H/M ratios both in early and in delayed phase thus underlining the role of [123I]MIBG cardiac scintigraphy and the effectiveness of H/M ratio in differentiating PD from other parkinsonism or ET. Moreover, early scans alone could be used for a reliable diagnosis since no difference was found between early and late. Definitely, a larger series of cases is needed to confirm this data.

Lentiform Nucleus Hyperechogenicity in Parkinsonian Syndromes: A Systematic Review and Meta-Analysis with Consideration of Molecular Pathology.

The hyperechogenicity of the substania nigra (SN) has been established as a valid finding in patients with Parkinson´s disease (PD), probably caused by an increased tissue iron concentration in the SN. The application of transcranial sonography (TCS) has been investigated for further echogenic basal ganglia alterations in patients with extrapyramidal movement disorders. Compared to PD, a hyperechogenic nucleus lentiformis (LN) has been reported to appear more frequently in atypical parkinsonian syndromes (aPS) such as the parkinsonian phenotype of multiple system atrophy (MSA-P) or the progressive supranuclear palsy (PSP). As the evidence providing study sizes are small, we conduct the first meta-analysis of the prevalence of LN hyperechogenicity in PD and aPS. We search for available studies providing prevalence of LN hyperechogenicity in patients with PD and aPS (MSA-P and PSP) detected by TCS in MEDLINE and SCOPUS databases. We calculate the prevalence rates of LN hyperechogenicity detection in patients with clinical diagnosis of PD vs. aPS under the random-effects model. We include a total of 1330 patients, 1091 PD and 239 aPS (MSA-P and PSP). We find a significantly higher prevalence of LN hyperechogenicity in aPS (76%, 95% CI: 0.62-0.88) compared to PD (16%, 95% CI: 0.10-0.23). After proving a higher prevalence of LN hyperechogenicity in aPS compared to PD, its histopathological cause needs to be investigated. Furthermore, its full diagnostic accuracy and the qualification to serve as a risk factor for MSA-P and PSP should also be questioned in future studies.

Classification of atypical parkinsonism per pathology versus phenotype.

The umbrella term "atypical parkinsonism" refers to a clinical presentation with various causes, emphasizing the clinical commonality of diseases in which atypical parkinsonism can present. This term is useful for describing the phenomenology of a movement disorder and to classify patients according to their clinical presentation. In contrast to this classification per phenotype, a classification per pathology is needed when it comes to understanding the pathogenesis and designing and delivering disease-modifying therapeutic interventions. Clinico-pathological correlation studies have revealed enormous clinical heterogeneity and vast clinical overlap in pathologically defined diseases related to atypical parkinsonism. Thus, the classification of patients with atypical parkinsonism per phenotype has limited validity for predicting the underlying pathology. This chapter will contrast the phenotype-driven classification and the pathology-driven classification of neurodegenerative diseases related to atypical parkinsonism and discuss future directions to improve pathology-specific diagnosis.

Using gait analysis' parameters to classify Parkinsonism: A data mining approach.

INTRODUCTION: Parkinson's disease (PD) is the second most common neurodegenerative disorder in the world, while Progressive Supranuclear Palsy (PSP) is an atypical Parkinsonism resembling PD, especially in early stage. Assumed that gait dysfunctions represent a major motor symptom for both pathologies, gait analysis can provide clinicians with subclinical information reflecting subtle differences between these diseases. In this scenario, data mining can be exploited in order to differentiate PD patients at different stages of the disease course and PSP using all the variables acquired through gait analysis. METHODS: A cohort of 46 subjects (divided into three groups) affected by PD patients at different stages and PSP patients was acquired through gait analysis and spatial and temporal parameters were analysed. Synthetic Minority Over-sampling Technique was used to balance our imbalanced dataset and cross-validation was applied to provide different training and testing sets. Then, Random Forests and Gradient Boosted Trees were implemented. RESULTS: Accuracy, error, precision, recall, specificity and sensitivity were computed for each group and for both algorithms, including 16 features. Random Forests obtained the highest accuracy (86.4%) but also specificity and sensitivity were particularly high, overcoming the 90% for PSP group. CONCLUSION: The novelty of the study is the use of a data mining approach on the spatial and temporal parameters of gait analysis in order to classify patients affected by typical (PD) and atypical Parkinsonism (PSP) based on gait patterns. This application would be helpful for clinicians to distinguish PSP from PD at early stage, when the differential diagnosis is particularly challenging.

Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatal 123I-FP-CIT indices.

OBJECTIVES: To provide an automated classification method for degenerative parkinsonian syndromes (PS) based on semiquantitative 123I-FP-CIT SPECT striatal indices and support-vector-machine (SVM) analysis. METHODS: 123I-FP-CIT SPECT was performed at a single-center level on 370 individuals with PS, including 280 patients with Parkinson's disease (PD), 21 with multiple system atrophy-parkinsonian type (MSA-P), 41 with progressive supranuclear palsy (PSP) and 28 with corticobasal syndrome (CBS) (mean age 70.3 years, 47% female, mean disease duration at scan 1.4 year), as well as 208 age- and gender-matched control subjects. Striatal volumes-of-interest (VOIs) uptake, VOIs asymmetry indices (AIs) and caudate/putamen (C/P) ratio were used as input for SVM individual classification using fivefold cross-validation. RESULTS: Univariate analyses showed significantly lower VOIs uptake, higher striatal AI and C/P ratio for each PS in comparison to controls (all p < 0.001). Among PS, higher degree of striatal impairment was observed in MSA-P and PSP, while CBS showed moderate uptake reduction and higher AI. Binary SVM classification showed 92.9% accuracy in distinguishing PS from controls. Classification based on each binary combination of PS ranged 62.9-83.7% accuracy with the most satisfactory results when separating CBS from the other PS. Sensitivity and specificity values were high and balanced ranging from 60 to 80% for all analyses with > 70% accuracy. Overall, striatal AI and C/P ratio on the more affected side had the highest weighting factors. CONCLUSION: Semiquantitative 123I-FP-CIT SPECT striatal evaluation combined with SVM represents a promising approach to disentangle PD from non-degenerative conditions and from atypical PS at the early stage.

Clinical approach to tremor in children.

Pediatric Movement Disorders encompass a very large and complex group of diseases, among which Tremor is one of the least studied. Evaluation of tremors in kids carries significant challenges, in particular the fact that many tremor etiologies have other associated movement disorders that make tremor identification more difficult. Also, it is sometimes difficult to differentiate tremors from other shaking disorders. Yet, the correct identification of tremor leads to appropriate treatments and sometimes practical cures. Thus, in this paper we have strived to provide a succinct, clinically useful and practical review of pediatric tremors. The most useful classification of tremors in based on their predominance during rest or activity. By far, the most common tremor in children is during action. We provide a clinical algorithm on how to assess pediatric tremors at the bedside, as well as multiple useful tables. We also review common tremor etiologies.

Parkinsonian syndromes: A review.

Since James Parkinson published his remarkable clinical observations in the "An Essay On The Shaking Palsy" in 1817, the number of diseases included in the spectrum of parkinsonian syndromes (a group of diseases that have some part of their clinical features resembling those seen in Parkinson's disease), are growing. Careful history taking, comprehensive neurological examination, and utilization of proper investigations will lead the physicians to make an accurate diagnosis of the specific disease entity present. In this recent review, we cover the issue of classification of parkinsonian syndromes, and comprehensively review the characteristic features of the commonly encountered diseases that present with this syndrome. The salient aspects of the epidemiology, key clinical features, proper investigations, and possible treatment options of these diseases have also been addressed.

An updated diagnostic approach to subtype definition of vascular parkinsonism - Recommendations from an expert working group.

This expert working group report proposes an updated approach to subtype definition of vascular parkinsonism (VaP) based on a review of the existing literature. The persistent lack of consensus on clear terminology and inconsistent conceptual definition of VaP formed the impetus for the current expert recommendation report. The updated diagnostic approach intends to provide a comprehensive tool for clinical practice. The preamble for this initiative is that VaP can be diagnosed in individual patients with possible prognostic and therapeutic consequences and therefore should be recognized as a clinical entity. The diagnosis of VaP is based on the presence of clinical parkinsonism, with variable motor and non-motor signs that are corroborated by clinical, anatomic or imaging findings of cerebrovascular disease. Three VaP subtypes are presented: (1) The acute or subacute post-stroke VaP subtype presents with acute or subacute onset of parkinsonism, which is typically asymmetric and responds to dopaminergic drugs; (2) The more frequent insidious onset VaP subtype presents with progressive parkinsonism with prominent postural instability, gait impairment, corticospinal, cerebellar, pseudobulbar, cognitive and urinary symptoms and poor responsiveness to dopaminergic drugs. A higher-level gait disorder occurs frequently as a dominant manifestation in the clinical spectrum of insidious onset VaP, and (3) With the emergence of molecular imaging biomarkers in clinical practice, our diagnostic approach also allows for the recognition of mixed or overlapping syndromes of VaP with Parkinson's disease or other neurodegenerative parkinsonisms. Directions for future research are also discussed.

Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines.

BACKGROUND AND PURPOSE: In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others. METHODS: We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy. RESULTS: When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required. CONCLUSIONS: The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis.

123I-ioflupane brain SPECT and 123I-MIBG cardiac planar scintigraphy combined use in uncertain parkinsonian disorders.

We evaluated the clinical usefulness of the combined use of I-ioflupane brain single photon emission computed tomography (SPECT) and I-metaiodobenzylguanidine (MIBG) cardiac scintigraphy in discriminating uncertain parkinsonism with vascular lesions in striatal nuclei at magnetic resonance imaging (MRI). Forty-three consecutive patients with uncertain parkinsonism and vascular lesions at MRI in striatal nuclei were retrospectively evaluated; the uncertain differential diagnosis was between Parkinson's disease and vascular parkinsonism (PD/VP) in 22 patients, between PD and other neurodegenerative parkinsonism (PD/PS) in 11 patients and between Lewy body dementia and Alzheimer disease (LBD/AD) in the remaining 10 cases. All patients underwent I-ioflupane SPECT with striatal dopaminergic activity determination as binding potentials (BP; cut-off: 3.3). I-MIBG cardiac planar scintigraphy was performed 2 weeks later, in early (15 minutes) and delayed (240 minutes) phases also calculating heart to mediastinum (H/M) ratio (cut-off: 1.56). I-Ioflupane uptake was normal in 9 patients with BP values >3.3, while it was reduced in 34/43 cases with BP values <3.3 at least in one of the striatal nuclei. I-MIBG uptake was normal in 21/43 patients (5 of whom with normal and 16 with I-ioflupane striatal defects) showing the H/M ratio >1.56 in all cases; the uptake was reduced in 22/43 cases, (4 of whom were normal and 18 were with I-ioflupane striatal defects) with the H/M ratio <1.56 in all cases. No statistical differences were found when early and delayed H/M ratios were mutually compared. Combining the 2 radioisotopic procedures, a more reliable diagnosis was achieved in 39/43 cases properly classifying 13 PD, 10 VP, 7 PS, 5 LBD, and 4 AD. However, the diagnosis remained uncertain in four patients with normal I-ioflupane and reduced I-MIBG uptake. The results of the present study confirmed that in uncertain parkinsonian syndromes associated with vascular lesions in striatal nuclei, brain I-ioflupane SPECT alone did not prove able to discriminate between the different forms of disease. Only the association with I-MIBG cardiac scintigraphy, also with the early acquisition alone, allowed the most appropriate diagnosis in 90.7% of our cases. However, patients with normal I-ioflupane and reduced I-I-MIBG uptakes need a close clinical and instrumental follow-up as sympathetic damage could precede striatal disorders in the early stage of PD and LBD.

Identification of candidate cerebrospinal fluid biomarkers in parkinsonism using quantitative proteomics.

INTRODUCTION: Neurodegenerative parkinsonian syndromes have significant clinical and pathological overlap, making early diagnosis difficult. Cerebrospinal fluid (CSF) biomarkers may aid the differentiation of these disorders, but other than α-synuclein and neurofilament light chain protein, which have limited diagnostic power, specific protein biomarkers remain elusive. OBJECTIVES: To study disease mechanisms and identify possible CSF diagnostic biomarkers through discovery proteomics, which discriminate parkinsonian syndromes from healthy controls. METHODS: CSF was collected consecutively from 134 participants; Parkinson's disease (n = 26), atypical parkinsonian syndromes (n = 78, including progressive supranuclear palsy (n = 36), multiple system atrophy (n = 28), corticobasal syndrome (n = 14)), and elderly healthy controls (n = 30). Participants were divided into a discovery and a validation set for analysis. The samples were subjected to tryptic digestion, followed by liquid chromatography-mass spectrometry analysis for identification and relative quantification by isobaric labelling. Candidate protein biomarkers were identified based on the relative abundances of the identified tryptic peptides. Their predictive performance was evaluated by analysis of the validation set. RESULTS: 79 tryptic peptides, derived from 26 proteins were found to differ significantly between atypical parkinsonism patients and controls. They included acute phase/inflammatory markers and neuronal/synaptic markers, which were respectively increased or decreased in atypical parkinsonism, while their levels in PD subjects were intermediate between controls and atypical parkinsonism. CONCLUSION: Using an unbiased proteomic approach, proteins were identified that were able to differentiate atypical parkinsonian syndrome patients from healthy controls. Our study indicates that markers that may reflect neuronal function and/or plasticity, such as the amyloid precursor protein, and inflammatory markers may hold future promise as candidate biomarkers in parkinsonism.

Differentiation of neurodegenerative parkinsonian syndromes by volumetric magnetic resonance imaging analysis and support vector machine classification.

BACKGROUND: Clinical differentiation of parkinsonian syndromes is still challenging. OBJECTIVES: A fully automated method for quantitative MRI analysis using atlas-based volumetry combined with support vector machine classification was evaluated for differentiation of parkinsonian syndromes in a multicenter study. METHODS: Atlas-based volumetry was performed on MRI data of healthy controls (n = 73) and patients with PD (204), PSP with Richardson's syndrome phenotype (106), MSA of the cerebellar type (21), and MSA of the Parkinsonian type (60), acquired on different scanners. Volumetric results were used as input for support vector machine classification of single subjects with leave-one-out cross-validation. RESULTS: The largest atrophy compared to controls was found for PSP with Richardson's syndrome phenotype patients in midbrain (-15%), midsagittal midbrain tegmentum plane (-20%), and superior cerebellar peduncles (-13%), for MSA of the cerebellar type in pons (-33%), cerebellum (-23%), and middle cerebellar peduncles (-36%), and for MSA of the parkinsonian type in the putamen (-23%). The majority of binary support vector machine classifications between the groups resulted in balanced accuracies of >80%. With MSA of the cerebellar and parkinsonian type combined in one group, support vector machine classification of PD, PSP and MSA achieved sensitivities of 79% to 87% and specificities of 87% to 96%. Extraction of weighting factors confirmed that midbrain, basal ganglia, and cerebellar peduncles had the largest relevance for classification. CONCLUSIONS: Brain volumetry combined with support vector machine classification allowed for reliable automated differentiation of parkinsonian syndromes on single-patient level even for MRI acquired on different scanners. © 2016 International Parkinson and Movement Disorder Society.

Multisystem Lewy body disease and the other parkinsonian disorders.

Here we prioritize as multisystem Lewy body disease (MLBD) those genetic forms of Parkinson's disease that point the way toward a mechanistic understanding of the majority of sporadic disease. Pathological diagnosis of genetic subtypes offers the prospect of distinguishing different mechanistic trajectories with a common mutational etiology, differing outcomes from varying allelic bases, and those disease-associated variants that can be used in gene-environment analysis. Clearly delineating parkinsonian disorders into subclasses on the basis of molecular mechanisms with well-characterized outcome expectations is the basis for refining these forms of neurodegeneration as research substrate through the use of cell models derived from affected individuals while ensuring that clinically collected data can be used for therapeutic decisions and research without increasing the noise and confusion engendered by the collection of data against a range of historically defined criteria.

Vascular Parkinsonism: deconstructing a syndrome.

Progressive ambulatory impairment and abnormal white matter (WM) signal on neuroimaging come together under the diagnostic umbrella of vascular parkinsonism (VaP). A critical appraisal of the literature, however, suggests that (1) no abnormal structural imaging pattern is specific to VaP; (2) there is poor correlation between brain MRI hyperintensities and microangiopathic brain disease and parkinsonism from available clinicopathologic data; (3) pure parkinsonism from vascular injury ("definite" vascular parkinsonism) consistently results from ischemic or hemorrhagic strokes involving the SN and/or nigrostriatal pathway, but sparing the striatum itself, the cortex, and the intervening WM; and (4) many cases reported as VaP may represent pseudovascular parkinsonism (e.g., Parkinson's disease or another neurodegenerative parkinsonism, such as PSP with nonspecific neuroimaging signal abnormalities), vascular pseudoparkinsonism (e.g., akinetic mutism resulting from bilateral mesial frontal strokes or apathetic depression from bilateral striatal lacunar strokes), or pseudovascular pseudoparkinsonism (e.g., higher-level gait disorders, including normal-pressure hydrocephalus with transependimal exudate). These syndromic designations are preferable over VaP until pathology or validated biomarkers confirm the underlying nature and relevance of the leukoaraiosis. © 2015 International Parkinson and Movement Disorder Society.