30 research outputs found
Diagnosing dementia in multiple system atrophy by applying Movement Disorder Society diagnostic criteria for Parkinson's disease dementia
No full textStudy of Multiple System Atrophy progression in the French cohort of the national reference centre : longitudinal and multidimensional statistical approach
No full textL’Atrophie Multi-Systématisée (AMS) est une maladie neurodégénérative rare et incurable, dont la progression reste à ce jour mal comprise. En raison de sa faible incidence, peu de cohortes épidémiologiques existent et peu d’études sont menées sur cette pathologie. Comme pour de nombreuses maladies neurodégénératives, les données recueillies sont complexes à analyser : elles sont souvent longitudinales, multidimensionnelles, de différentes natures, et leur processus d’observation peut être tronqué par des évènements cliniques. Cela soulève d’importants défis méthodologiques auxquels les modèles statistiques actuels ne sont pas encore complètement adaptés. Cette thèse porte sur l’étude de la progression de l’AMS par le développement de méthodes statistiques afin d’exploiter pleinement les données longitudinales de la cohorte FMSA, cohorte clinique prospective de patients diagnostiqués avec une AMS et suivis aux hôpitaux de Bordeaux ou de Toulouse. Cette cohorte est devenue l’une des plus importantes au niveau mondial avec plus de 650 patients, une durée de suivi observé jusqu’à 11 ans, et un suivi annuel complet standardisé, comprenant plusieurs mesures d’atteintes et de ressenti, et ce, jusqu’au décès des patients. Les objectifs épidémiologiques de la thèse sont de décrire l’évolution de l’AMS à travers deux perspectives : celle du patient, par l’étude de la dégradation de la qualité de vie des patients au cours de la maladie, et celle du clinicien, par l’étude de l’évolution des atteintes motrices et non-motrices induites par l’AMS, et la description de l’histoire naturelle de la maladie, afin d’établir la temporalité des différents mécanismes sous-jacents impliqués. La première partie porte sur le développement du modèle JLPM (Joint Latent Process Model), modèle visant à décrire la progression au cours du temps d’un construit latent, sous-jacent à un ou plusieurs marqueurs possiblement de différentes natures. Le modèle évalue conjointement le risque de survenue d’évènements pendant le suivi pour tenir compte de la potentielle sortie d’étude informative. La deuxième partie introduit la méthode 4S, stratégie en quatre étapes pour l’analyse longitudinale de données issues d’échelles de mesure multidimensionnelles. L’approche développée consiste à successivement structurer l’échelle en sous-dimensions (Structuring), décrire la séquence des atteintes des items au cours du temps pour chaque sous-dimension via un modèle JLPM (Sequencing), aligner l’évolution de chaque sous-dimension avec la progression de la maladie (Staging), et identifier les items les plus informatifs (Selecting). La troisième partie porte sur le développement du modèle LTSM (Latent Time Shift Model), modèle conçu pour décrire la progression d’une maladie selon son propre temps de maladie quand celui-ci n’est pas directement observé. Basé sur une recalibration individuelle du temps, le modèle permet de reconstituer le temps de maladie tout en décrivant la progression de plusieurs marqueurs, offrant ainsi une vue d’ensemble de l’évolution des atteintes. Le modèle tient également compte de la potentielle sortie d’étude informative. Cette thèse apporte une compréhension plus précise de la progression de l’AMS par de nouvelles méthodologies statistiques permettant de relever les principaux défis analytiques. Ces travaux offrent des pistes d’amélioration pour la prise en charge et le suivi individuel des patients, ainsi que pour la construction des essais cliniques en ciblant des marqueurs pertinents de la maladie pour de nouvelles approches thérapeutiques. Au-delà de l’AMS, les développements biostatistiques issus de cette thèse sont diffusés sous forme de packages R, offrant ainsi des outils accessibles pour l’étude d’autres pathologies, voire d’autres applications confrontées aux mêmes défis méthodologiques.Multiple System Atrophy (MSA) is a rare and incurable neurodegenerative disease, which progression remains poorly under-stood to date. Due to its low incidence, few epidemiological cohorts exist, and few studies are conducted on this pathology. As for many neurodegenerative diseases, the collected data are complex to analyze : they are often longitudinal, multidi-mensional, of various natures, and their observation process may be truncated by clinical events. This raises important methodological challenges that current statistical models do not yet fully tackle. This thesis focuses on the study of MSA progression through the development of statistical methods to fully exploit the lon-gitudinal data of the FMSA cohort, a prospective clinical cohort of patients diagnosed with MSA and followed in Bordeaux or Toulouse hospitals. This cohort has become one of the largest worldwide, with more than 650 patients, an observed follow-up duration up to 11 years, and a complete standardized annual follow-up including multiple measures of impairments and patient impressions, until patient’s death. The epidemiological objectives of the thesis are to describe the evolution of MSA from two perspectives : from the patient, by studying the degradation in patient quality of life over the course of the disease, and from the clinician, by studying the progression of motor and non-motor impairments induced by MSA, and describing the natural history of the disease to establish the timeline of the various underlying mechanisms involved. The first part focuses on the development of JLPM (Joint Latent Process Model), a model designed to describe the progression over time of a latent construct, underlying one or multiple markers possibly of different natures. The model jointly evaluates the risk of events occurrence during follow-up to account for potential informative dropout. The second part introduces the 4S method, a four-step strategy for the longitudinal analysis of data derived from multidi-mensional measurement scales. The developed approach consists of successively structuring the scale into subdimensions (Structuring), describing the sequence of item impairments over time for each subdimension using a JLPM model (Sequencing), aligning the evolution of each subdimension with disease progression (Staging), and identifying the most informative items (Selecting). The third part focuses on the development of LTSM (Latent Time Shift Model), a model designed to describe the progression of a disease according to itsowndisease timelinewhenit is not directly observed. Based on an individual recalibration of time, the model reconstitutes the disease timeline while describing the progression of multiple markers, offering an over-view of the evolution of impairments. The model also accounts for potential informative dropout. This thesis provides a more precise understanding of MSA progression through novel statistical methodologies that tackle the main analytical challenges. Through its findings, it highlights avenues for improving patient support and individual fol-low-up, as well as for designing clinical trials by targeting relevant disease markers for new therapeutic approaches. Beyond MSA, the biostatistical developments stemmed from this thesis are available as R packages, thereby providing accessible tools for studying other pathologies or even other applications facing similar methodological challenges
Diagnostic value of cerebrospinal fluid alpha-synuclein seed quantification in synucleinopathies
No full textSeveral studies have confirmed the α-synuclein real-time quaking-induced conversion (RT-QuIC) assay to have high sensitivity and specificity for Parkinson's disease. However, whether the assay can be used as a robust, quantitative measure to monitor disease progression, stratify different synucleinopathies and predict disease conversion in patients with idiopathic REM sleep behaviour disorder remains undetermined. The aim of this study was to assess the diagnostic value of CSF α-synuclein RT-QuIC quantitative parameters in regard to disease progression, stratification and conversion in synucleinopathies. We performed α-synuclein RT-QuIC in the CSF samples from 74 Parkinson's disease, 24 multiple system atrophy and 45 idiopathic REM sleep behaviour disorder patients alongside 55 healthy controls, analysing quantitative assay parameters in relation to clinical data. α-Synuclein RT-QuIC showed 89% sensitivity and 96% specificity for Parkinson's disease. There was no correlation between RT-QuIC quantitative parameters and Parkinson's disease clinical scores (e.g. Unified Parkinson's Disease Rating Scale motor), but RT-QuIC positivity and some quantitative parameters (e.g. Vmax) differed across the different phenotype clusters. RT-QuIC parameters also added value alongside standard clinical data in diagnosing Parkinson's disease. The sensitivity in multiple system atrophy was 75%, and CSF samples showed longer T50 and lower Vmax compared to Parkinson's disease. All RT-QuIC parameters correlated with worse clinical progression of multiple system atrophy (e.g. change in Unified Multiple System Atrophy Rating Scale). The overall sensitivity in idiopathic REM sleep behaviour disorder was 64%. In three of the four longitudinally followed idiopathic REM sleep behaviour disorder cohorts, we found around 90% sensitivity, but in one sample (DeNoPa) diagnosing idiopathic REM sleep behaviour disorder earlier from the community cases, this was much lower at 39%. During follow-up, 14 of 45 (31%) idiopathic REM sleep behaviour disorder patients converted to synucleinopathy with 9/14 (64%) of convertors showing baseline RT-QuIC positivity. In summary, our results showed that α-synuclein RT-QuIC adds value in diagnosing Parkinson's disease and may provide a way to distinguish variations within Parkinson's disease phenotype. However, the quantitative parameters did not correlate with disease severity in Parkinson's disease. The assay distinguished multiple system atrophy patients from Parkinson's disease patients and in contrast to Parkinson's disease, the quantitative parameters correlated with disease progression of multiple system atrophy. Our results also provided further evidence for α-synuclein RT-QuIC having potential as an early biomarker detecting synucleinopathy in idiopathic REM sleep behaviour disorder patients prior to conversion. Further analysis of longitudinally followed idiopathic REM sleep behaviour disorder patients is needed to better understand the relationship between α-synuclein RT-QuIC signature and the progression from prodromal to different synucleinopathies
New insights into orthostatic hypotension in multiple system atrophy: A European multicentre cohort study
No full textOrthostatic hypotension (OH) is a key feature of multiple system atrophy (MSA), a fatal progressive neurodegenerative disorder associated with autonomic failure, parkinsonism and ataxia. This study aims (1) to determine the clinical spectrum of OH in a large European cohort of patients with MSA and (2) to investigate whether a prolonged postural challenge increases the sensitivity to detect OH in MSA
New insights into orthostatic hypotension in multiple system atrophy: a European multicentre cohort study
No full textObjectives: Orthostatic hypotension (OH) is a key feature of multiple system atrophy (MSA), a fatal progressive neurodegenerative disorder associated with autonomic failure, parkinsonism and ataxia. This study aims (1) to determine the clinical spectrum of OH in a large European cohort of patients with MSA and (2) to investigate whether a prolonged postural challenge increases the sensitivity to detect OH in MSA. Methods: Assessment of OH during a 10 min orthostatic test in 349 patients with MSA from seven centres of the European MSA-Study Group (age: 63.6±8.8 years; disease duration: 4.2±2.6 years). Assessment of a possible relationship between OH and MSA subtype (P with predominant parkinsonism or C with predominant cerebellar ataxia), Unified MSA Rating Scale (UMSARS) scores and drug intake. Results: 187 patients (54%) had moderate (>20 mm Hg (systolic blood pressure (SBP)) and/or >10 mm Hg (diastolic blood pressure (DBP)) or severe OH (>30 mm Hg (SBP) and/or >15 mm Hg (DBP)) within 3 min and 250 patients (72%) within 10 min. OH magnitude was significantly associated with disease severity (UMSARS I, II and IV), orthostatic symptoms (UMSARS I) and supine hypertension. OH severity was not associated with MSA subtype. Drug intake did not differ according to OH magnitude except for antihypertensive drugs being less frequently, and antihypotensive drugs more frequently, prescribed in severe OH. Conclusions: This is the largest study of OH in patients with MSA. Our data suggest that the sensitivity to pick up OH increases substantially by a prolonged 10 min orthostatic challenge. These results will help to improve OH management and the design of future clinical trials.Fil: Pavy Le Traon, Anne. University Hospital of Toulouse; Francia. Inserm; FranciaFil: Piedvache, A.. Université Paul Sabatier; FranciaFil: Pérez Lloret, Santiago. University Hospital of Toulouse; Francia. Pontificia Universidad Católica Argentina "Santa María de los Buenos Aires". Instituto de Investigaciones Biomédicas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas; ArgentinaFil: Calandra Buonara, G.. Università di Bologna; Italia. Istituto delle Scienze Neurologiche di Bologna; ItaliaFil: Cochen De Cock, V.. University Hospital of Toulouse; Francia. University of Montpellier; FranciaFil: Colosimo, C.. Sapienza Università di Roma; ItaliaFil: Cortelli, P.. Università di Bologna; Italia. Istituto delle Scienze Neurologiche di Bologna; ItaliaFil: Debs, R.. University Hospital of Toulouse; FranciaFil: Duerr, S.. Universidad de Innsbruck; AustriaFil: Fanciulli, A.. Universidad de Innsbruck; AustriaFil: Foubert Samier, A.. Centre Hospitalier Universitaire de Bordeaux; Francia. Universite de Bordeaux; FranciaFil: Gerdelat, Angela. University Hospital of Toulouse; FranciaFil: Gurevich, T.. Tel-Aviv University; IsraelFil: Krismer, F.. Universidad de Innsbruck; AustriaFil: Poewe, W.. Universidad de Innsbruck; AustriaFil: Tison, Francois. Universite de Bordeaux; Francia. Centre Hospitalier Universitaire de Bordeaux; FranciaFil: Tranchant, C.. University Hospital Hautepierre; FranciaFil: Wenning, G.. Universidad de Innsbruck; AustriaFil: Meissner, Wassilios G.. Universite de Bordeaux; Francia. Centre Hospitalier Universitaire de Bordeaux; FranciaFil: Rascol, Olivier. University Hospital of Toulouse; Franci
Étude de la progression de l’Atrophie Multi-Systématisée dans la cohorte française du centre de référence national : approche statistique longitudinale et multidimensionnelle
No full textMultiple System Atrophy (MSA) is a rare and incurable neurodegenerative disease, which progression remains poorly under-stood to date. Due to its low incidence, few epidemiological cohorts exist, and few studies are conducted on this pathology. As for many neurodegenerative diseases, the collected data are complex to analyze : they are often longitudinal, multidi-mensional, of various natures, and their observation process may be truncated by clinical events. This raises important methodological challenges that current statistical models do not yet fully tackle. This thesis focuses on the study of MSA progression through the development of statistical methods to fully exploit the lon-gitudinal data of the FMSA cohort, a prospective clinical cohort of patients diagnosed with MSA and followed in Bordeaux or Toulouse hospitals. This cohort has become one of the largest worldwide, with more than 650 patients, an observed follow-up duration up to 11 years, and a complete standardized annual follow-up including multiple measures of impairments and patient impressions, until patient’s death. The epidemiological objectives of the thesis are to describe the evolution of MSA from two perspectives : from the patient, by studying the degradation in patient quality of life over the course of the disease, and from the clinician, by studying the progression of motor and non-motor impairments induced by MSA, and describing the natural history of the disease to establish the timeline of the various underlying mechanisms involved. The first part focuses on the development of JLPM (Joint Latent Process Model), a model designed to describe the progression over time of a latent construct, underlying one or multiple markers possibly of different natures. The model jointly evaluates the risk of events occurrence during follow-up to account for potential informative dropout. The second part introduces the 4S method, a four-step strategy for the longitudinal analysis of data derived from multidi-mensional measurement scales. The developed approach consists of successively structuring the scale into subdimensions (Structuring), describing the sequence of item impairments over time for each subdimension using a JLPM model (Sequencing), aligning the evolution of each subdimension with disease progression (Staging), and identifying the most informative items (Selecting). The third part focuses on the development of LTSM (Latent Time Shift Model), a model designed to describe the progression of a disease according to itsowndisease timelinewhenit is not directly observed. Based on an individual recalibration of time, the model reconstitutes the disease timeline while describing the progression of multiple markers, offering an over-view of the evolution of impairments. The model also accounts for potential informative dropout. This thesis provides a more precise understanding of MSA progression through novel statistical methodologies that tackle the main analytical challenges. Through its findings, it highlights avenues for improving patient support and individual fol-low-up, as well as for designing clinical trials by targeting relevant disease markers for new therapeutic approaches. Beyond MSA, the biostatistical developments stemmed from this thesis are available as R packages, thereby providing accessible tools for studying other pathologies or even other applications facing similar methodological challenges.L’Atrophie Multi-Systématisée (AMS) est une maladie neurodégénérative rare et incurable, dont la progression reste à ce jour mal comprise. En raison de sa faible incidence, peu de cohortes épidémiologiques existent et peu d’études sont menées sur cette pathologie. Comme pour de nombreuses maladies neurodégénératives, les données recueillies sont complexes à analyser : elles sont souvent longitudinales, multidimensionnelles, de différentes natures, et leur processus d’observation peut être tronqué par des évènements cliniques. Cela soulève d’importants défis méthodologiques auxquels les modèles statistiques actuels ne sont pas encore complètement adaptés. Cette thèse porte sur l’étude de la progression de l’AMS par le développement de méthodes statistiques afin d’exploiter pleinement les données longitudinales de la cohorte FMSA, cohorte clinique prospective de patients diagnostiqués avec une AMS et suivis aux hôpitaux de Bordeaux ou de Toulouse. Cette cohorte est devenue l’une des plus importantes au niveau mondial avec plus de 650 patients, une durée de suivi observé jusqu’à 11 ans, et un suivi annuel complet standardisé, comprenant plusieurs mesures d’atteintes et de ressenti, et ce, jusqu’au décès des patients. Les objectifs épidémiologiques de la thèse sont de décrire l’évolution de l’AMS à travers deux perspectives : celle du patient, par l’étude de la dégradation de la qualité de vie des patients au cours de la maladie, et celle du clinicien, par l’étude de l’évolution des atteintes motrices et non-motrices induites par l’AMS, et la description de l’histoire naturelle de la maladie, afin d’établir la temporalité des différents mécanismes sous-jacents impliqués. La première partie porte sur le développement du modèle JLPM (Joint Latent Process Model), modèle visant à décrire la progression au cours du temps d’un construit latent, sous-jacent à un ou plusieurs marqueurs possiblement de différentes natures. Le modèle évalue conjointement le risque de survenue d’évènements pendant le suivi pour tenir compte de la potentielle sortie d’étude informative. La deuxième partie introduit la méthode 4S, stratégie en quatre étapes pour l’analyse longitudinale de données issues d’échelles de mesure multidimensionnelles. L’approche développée consiste à successivement structurer l’échelle en sous-dimensions (Structuring), décrire la séquence des atteintes des items au cours du temps pour chaque sous-dimension via un modèle JLPM (Sequencing), aligner l’évolution de chaque sous-dimension avec la progression de la maladie (Staging), et identifier les items les plus informatifs (Selecting). La troisième partie porte sur le développement du modèle LTSM (Latent Time Shift Model), modèle conçu pour décrire la progression d’une maladie selon son propre temps de maladie quand celui-ci n’est pas directement observé. Basé sur une recalibration individuelle du temps, le modèle permet de reconstituer le temps de maladie tout en décrivant la progression de plusieurs marqueurs, offrant ainsi une vue d’ensemble de l’évolution des atteintes. Le modèle tient également compte de la potentielle sortie d’étude informative. Cette thèse apporte une compréhension plus précise de la progression de l’AMS par de nouvelles méthodologies statistiques permettant de relever les principaux défis analytiques. Ces travaux offrent des pistes d’amélioration pour la prise en charge et le suivi individuel des patients, ainsi que pour la construction des essais cliniques en ciblant des marqueurs pertinents de la maladie pour de nouvelles approches thérapeutiques. Au-delà de l’AMS, les développements biostatistiques issus de cette thèse sont diffusés sous forme de packages R, offrant ainsi des outils accessibles pour l’étude d’autres pathologies, voire d’autres applications confrontées aux mêmes défis méthodologiques
Describing complex disease progression using joint latent class models for multivariate longitudinal markers and clinical endpoints
No full textNeurodegenerative diseases are characterized by numerous markers of
progression and clinical endpoints. For instance, Multiple System Atrophy
(MSA), a rare neurodegenerative synucleinopathy, is characterized by various
combinations of progressive autonomic failure and motor dysfunction, and a very
poor prognosis. Describing the progression of such complex and
multi-dimensional diseases is particularly difficult. One has to simultaneously
account for the assessment of multivariate markers over time, the occurrence of
clinical endpoints, and a highly suspected heterogeneity between patients. Yet,
such description is crucial for understanding the natural history of the
disease, staging patients diagnosed with the disease, unravelling
subphenotypes, and predicting the prognosis. Through the example of MSA
progression, we show how a latent class approach modeling multiple repeated
markers and clinical endpoints can help describe complex disease progression
and identify subphenotypes for exploring new pathological hypotheses. The
proposed joint latent class model includes class-specific multivariate mixed
models to handle multivariate repeated biomarkers possibly summarized into
latent dimensions and class-and-cause-specific proportional hazard models to
handle time-to-event data. Maximum likelihood estimation procedure, validated
through simulations is available in the lcmm R package. In the French MSA
cohort comprising data of 598 patients during up to 13 years, five
subphenotypes of MSA were identified that differ by the sequence and shape of
biomarkers degradation, and the associated risk of death. In posterior
analyses, the five subphenotypes were used to explore the association between
clinical progression and external imaging and fluid biomarkers, while properly
accounting for the uncertainty in the subphenotypes membership
Structural Alterations Associated With Cardiovascular Autonomic Failure in Multiple System Atrophy
No full textInternational audienceBackground Early severe autonomic failure (AF) in multiple system atrophy (MSA) is a risk factor for poor survival. Postmortem studies suggested that AF is related to the degeneration of preganglionic autonomic neurons of the brainstem and spinal cord. Objectives Characterize cerebral alterations on brain imaging associated with cardiovascular AF. Methods Cardiovascular sympathetic failure was evaluated through orthostatic hypotension (OH) based on changes in systolic and diastolic blood pressure during tilt‐test (ΔSBP and ΔDBP). Reduced heart rate (HR) variability reflecting cardio‐vagal impairment was assessed with a composite score formed by the root‐mean square differences of successive R‐R intervals (RMSSD) and HR changes during deep breathing. Voxel‐based morphometry (SPM12), volumetry, and cortical thickness measurements (FreeSurfer 7.0) of T1‐weighted anatomical images were used to assess gray matter (GM) atrophy in sub‐tentorial structures. Multivariate analysis included age, disease severity (UMSARS), and total intracranial volume as confounding factors. Results A total of 62 MSA patients followed at the French Reference Center were retrospectively included, aged 67.3 ± 8.6 years, 69.4% MSA‐P, disease duration 4.2 ± 2.1 years. Medulla atrophy was correlated to OH ( p < 0.006). Decrease in GM volume in the left anterior cerebellum (lobule V) was correlated to ΔDBP (pFWEc = 0.017). GM loss in the left interposed nucleus was correlated to ΔSBP ( p < 0.003), whereas atrophy of the right dentate was associated with decreased HR variability ( p < 0.003). Conclusion Medulla volume was strongly correlated with OH. Cerebellar degeneration was associated with the severity of cardiovascular AF
Author Correction: Time-to-event analysis mitigates the impact of symptomatic therapy on therapeutic benefit in Parkinson’s disease trials (npj Parkinson\u27s Disease, (2025), 11, 1, (193), 10.1038/s41531-025-01041-9)
No full text\ua9 The Author(s) 2025.Correction to: npj Parkinson’s Disease (2025) 11:193; https://doi.org/10.1038/s41531-025-01041-9; published online 01 July 2025 In this article the PASADENA Investigators member Jan Kassubek was incorrectly written as R. Jan Kassubek. The original article has been corrected
Author Correction: Safety and efficacy of anti-tau monoclonal antibody gosuranemab in progressive supranuclear palsy: a phase 2, randomized, placebo-controlled trial (Nature Medicine, (2021), 27, 8, (1451-1457), 10.1038/s41591-021-01455-x)
No full textCorrection to: Nature Medicine, published online 12 August 2021. In the version of this article initially published, members of the PASSPORT Study Group were listed in Supplementary information but not included in the main article. The contributors are listed below for inclusion in authorship. PASSPORT Study Group Ikuko Aiba1, Angelo Antonini2, Diana Apetauerova3, Jean-Philippe Azulay4, Ernest Balaguer Martinez5, Jee Bang6, Paolo Barone7, Matthew Barrett8, Danny Bega9, Daniela Berg10, Koldo Berganzo Corrales11, Yvette Bordelon12, Adam L. Boxer13, Moritz Brandt14, Norbert Brueggemann15, Giovanni Castelnovo16, Roberto Ceravolo17, Rosalind Chuang18, Sun Ju Chung19, Alistair Church20, Jean-Christophe Corvol21, Paola Cudia2, Marian Dale22, Luc Defebvre23, Sophie Drapier24, Erika D Driver-Dunckley25, Georg Ebersbach26, Karla M Eggert27, Aaron Ellenbogen28, Alexandre Eusebio4, Andrew H Evans29, Natalia Fedorova30, Elizabeth Finger31, Alexandra Foubert-Samier32, Boyd Ghosh33, Lawrence Golbe34, Francisco Grandas Perez35, Murray Grossman36, Deborah Hall37, Kyoko Hamada38, Kazuko Hasegawa39, Guenter Hoeglinger40, Lawrence Honig41, David Houghton42, Xuemei Huang43, Stuart Isaacson44, SeongBeom Koh45, Jaime Kulisevsky Bojarski46, Anthony E. Lang47, Peter Nigel Leigh48, Irene Litvan49, Juan Jose Lopez Lozano50, Jose Luis Lopez-Sendon Moreno51, Albert Christian Ludolph52, Ma Rosario Luquin Piudo53, Irene Martinez Torres54, Nikolaus McFarland55, Wassilios Meissner32, Tiago Mestre56, Pablo Mir Rivera57, Eric Molho58, Britt Mollenhauer59, Huw R Morris60, Miho Murata61, Tomokazu Obi62, Fabienne Ory Magne63, Padraig O’Suilleabhain64, Rajesh Pahwa65, Alexander Pantelyat6, Nicola Pavese66, Dmitry Pokhabov67, Johannes Prudlo68, Federico Rodriguez-Porcel22, James Rowe69, Joseph Savitt70, Alfons Schnitzler71, Joerg B Schulz72, Klaus Seppi73, Binit Shah8, Holly Shill74, David Shprecher75, Maria Stamelou76, Malcolm Steiger77, Yuji Takahashi61, Hiroshi Takigawa78, Carmela Tartaglia79, Lars Toenges80, Daniel Truong81, Winona Tse82, Paul Tuite83, Dieter Volc84, Anne-Marie A Wills85, Dirk Woitalla86, Tao Xie87, Tatsuhiko Yuasa88, Sarah Elizabeth Zauber89 and Theresa Zesiewicz901Department of Neurology, National Hospital Organization Higashinagoya National Hospital, Nagoya, Japan. 2San Camillo Hospital IRCCS, Venice Lido, Italy. 3Lahey Hospital and Medical Center, Burlington, MA, USA. 4Assistance Publique Hapitaux De Marseille, Marseille, France. 5Hospital General de Catalunya, Barcelona, Spain. 6The Johns Hopkins University, Baltimore, MD, USA. 7AOU San Giovanni di Dio e Ruggi d’Aragona University di Salerno, Salerno, Italy. 8University of Virginia Health System, Charlottesville, VA, USA. 9Northwestern University, Chicago, IL, USA. 10UKSH - Campus Kiel, Kiel, Germany. 11Hospital De Cruces, Barakaldo, Spain. 12University of California, Los Angeles, CA, USA. 13Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA. 14Universitatsklinikum Carl Gustav Carus Dresden, Dresden, Germany. 15University Hospital Schleswig-Holstein, Luebeck, Germany. 16Centre Hospitalier Universitaire de Nimes - Hopital Universitaire Caremeau, Nimes, France. 17University Hospital of Pisa, Pisa, Italy. 18Swedish Health Services, Seattle, WA, USA. 19Asan Medical Center, Seoul, Republic of Korea. 20Aneurin Bevan University Health BoardClinical Research and Innovation Centre - St Woolos Hospital, Newport, UK. 21Sorbonne Université, Assistance Publique Hôpitaux de Paris, INSERM, CNRS, Institut du Cerveau – Paris Brain Institute – ICM, Hôpital Pitié-Salpêtrière, Paris, France. 22Medical University of South Carolina, Charleston, SC, USA. 23Centre Hospitalier Regional Universitaire) de Lille - Hopital Roger Salengro, Lille, France. 24CHU de Rennes - Hopital de Pontchaillou, Rennes, France. 25Mayo Clinic Arizona - Scottsdale, Scottsdale, AZ, USA. 26Movement Disorders Clinic, Beelitz-Heilstatten, Germany. 27Philipps Universitat Marburg, Marburg, Germany. 28QUEST Research Institute, Farmington Hills, MI, USA. 29The Royal Melbourne Hospital (RMH)-Flemington Neurology - North Melbourne, North Melbourne, Australia. 30Russian Medical Academy of Postgraduate Education, Moscow, Russia. 31Parkwood Institute, London, Ontario, Canada. 32CHU De Bordeaux Parkinson Expert Centre, IMNC Hopital Pellegrin, Bordeaux, France. 33University Hospital Southampton NHS Foundation Trust, Southampton UK. 34 Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA. 35Hospital General Universitario Gregorio Maranon, Madrid, Spain. 36Hospital of the University of Pennsylvania, Philadelphia, PA, USA. 37Rush University Medical Centre Chicago, IL USA. 38Shinsapporo Neurosurgical Hospital, Sapporo, Japan. 39National Hospital Organization Sagamihara National Hospital, Sagamihara, Japan. 40Department of Neurology, Technische Universität München, Munich, Germany. 41Columbia University College of Physicians and Surgeons - Gertrude H. Sergievsky Center, New York, NY, USA. 42Ochsner Medical Center, New Orleans, LA, USA. 43Penn State University-Milton S. Hershey Medical Center, Hershey, PA USA. 44Parkinson’s Disease And Movement Disorder Center Of Boca Raton, Boca Raton, FL, USA. 45Korea University Guro Hospital, Seoul, Republic of Korea. 46Hospital de la Santa Creu i Sant Pau, Barcelona, Spain. 47 Edmond J. Safra Program in Parkinson’s Disease and the Rossy PSP Centre, Toronto Western Hospital and the University of Toronto, Toronto, Ontario, Canada. 48Brighton and Sussex Medical School Trafford Centre for Biomedical Research, Brighton, UK. 49University of California, Parkinson and Other Movement Disorders Center, San Diego, CA, USA. 50Clinica Ruber Internacional, Madrid, Spain. 51Hospital Universitario Ramon y Cajal, Madrid, Spain. 52Universitats- und Rehabilitationskliniken Ulm, Ulm, Germany. 53Clinica Universidad De Navarra, Pamplona, Spain. 54Hospital la FE, Valencia, Spain. 55University of Florida Center For Movement Disorders and Neurorestoration, Gainesville, FL, USA. 56The Ottawa Hospital - Civic Campus, University of Ottawa, Ottawa, Canada. 57Hospital Universitario Virgen del Rocio, Sevilla, Spain. 58Albany Medical College, Albany, NY, USA. 59Paracelsus-Elena-Klinik Kassel, Kassel, Germany. 60National Hospital for Neurology and Neurosurgery, London, UK. 61Musashi Hospital, Kodaira-Shi, Japan. 62National Hospital Organization - Shizuoka Institute of Epilepsy and Neurological Disorders, Shizuoka, Japan. 63Hopital Purpan - Batiment Pierre Paul Riquet, Toulouse, France. 64University of Texas Southwestern Medical Center - Clinical Center For Movement Disorders, Dallas, TX, USA. 65The University of Kansas Medical Center - Parkinson’s Disease and Movement Disorder Center, Kansas City, KS, USA. 66The Newcastle upon Tyne Hospitals NHS Foundation Trust - Campus for Ageing and Vitality, Newcastle upon Tyne, UK. 67Federal State Budgetary Institution, Federal Siberian Scientific Clinical Center of Federal Medical-Biological Agency, Krasnoyarsk, Russia. 68Universitaet Rostock - Universitaetsmedizin Rostock, Rostock, Germany. 69Cambridge University, Cambridge, UK. 70University of Maryland School of Medicine, Baltimore, MD, USA. 71Center for Movement Disorders and Neuromodulation-University Hospital Dusseldorf, Dusseldorf, Germany. 72Uniklinik RWTH Aachen Medizinische Klinik III, Aachen, Germany. 73Medizinische Universitaet Innsbruck, Innsbruck, Austria. 74St. Joseph’s Hospital and Medical Center/Barrow Neurology Clinics, Phoenix, AZ, USA. 75Banner Sun Health Research Institute, Sun City, AZ, USA. 76Hygeia Hospital, Marousi, Greece. 77The Walton Center - NHS Foundation Trust, Liverpool, UK. 78Tottori University Hospital, Yonago, Japan. 79Toronto Western Hospital, University Health Network Movement Disorders Centre, Toronto, Canada. 80St. Josef - Hospital Bochum, Kardiologische Studienambulanz, Bochum, Germany. 81The Parkinson’s and Movement Disorder Institute, Fountain Valley, CA, USA. 82Mount Sinai Movement Disorders Center, New York,NY, USA. 83University of Minnesota Medical Center - Fairview - Neurology Clinic, Minneapolis, MN, USA. 84Prosenex Ambulatoriumsbetriebs GmbH – Studienzentrum, Vienna, Austria. 85Massachusetts General Hospital Cancer Center, Boston, MA, USA. 86St. JosefKrankenhaus, Essen-Kupferdreh, Essen, Germany. 87The University of Chicago Medicine - Center for Parkinson’s Disease and Movement Disorders, Chicago, IL, USA. 88Kamagaya General Hospital, Kamagaya-City, Japan. 89Indiana University Health Physicians - Neurology – Indianapolis, Indianapolis, IN, USA. 90University of South Florida - Morsani College of Medicine, Tampa, FL, USA
