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Chougar L, Faucher A, Faouzi J, Lejeune FX, Gama Lobo G, Jovanovic C, Cormier F, Dupont G, Vidailhet M, Corvol JC, Colliot O, Lehéricy S, Grabli D, Degos B. Contribution of MRI for the Early Diagnosis of Parkinsonism in Patients with Diagnostic Uncertainty. Mov Disord 2024; 39:825-835. [PMID: 38486423 DOI: 10.1002/mds.29760] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/16/2024] [Accepted: 02/16/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND International clinical criteria are the reference for the diagnosis of degenerative parkinsonism in clinical research, but they may lack sensitivity and specificity in the early stages. OBJECTIVES To determine whether magnetic resonance imaging (MRI) analysis, through visual reading or machine-learning approaches, improves diagnostic accuracy compared with clinical diagnosis at an early stage in patients referred for suspected degenerative parkinsonism. MATERIALS Patients with initial diagnostic uncertainty between Parkinson's disease (PD), progressive supranuclear palsy (PSP), and multisystem atrophy (MSA), with brain MRI performed at the initial visit (V1) and available 2-year follow-up (V2), were included. We evaluated the accuracy of the diagnosis established based on: (1) the international clinical diagnostic criteria for PD, PSP, and MSA at V1 ("Clin1"); (2) MRI visual reading blinded to the clinical diagnosis ("MRI"); (3) both MRI visual reading and clinical criteria at V1 ("MRI and Clin1"), and (4) a machine-learning algorithm ("Algorithm"). The gold standard diagnosis was established by expert consensus after a 2-year follow-up. RESULTS We recruited 113 patients (53 with PD, 31 with PSP, and 29 with MSA). Considering the whole population, compared with clinical criteria at the initial visit ("Clin1": balanced accuracy, 66.2%), MRI visual reading showed a diagnostic gain of 14.3% ("MRI": 80.5%; P = 0.01), increasing to 19.2% when combined with the clinical diagnosis at the initial visit ("MRI and Clin1": 85.4%; P < 0.0001). The algorithm achieved a diagnostic gain of 9.9% ("Algorithm": 76.1%; P = 0.08). CONCLUSION Our study shows the use of MRI analysis, whether by visual reading or machine-learning methods, for early differentiation of parkinsonism. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Lydia Chougar
- Department of Neuroradiology, Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Paris, France
- ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France
- ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France
- Department of Neuroradiology, Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Paris, France
| | - Alice Faucher
- Assistance Publique Hôpitaux de Paris, Service de Neurologie, Hôpital Avicenne, Hôpitaux Universitaires de Paris Seine-Saint-Denis, Sorbonne Paris Nord, NS-PARK/FCRIN Network, Bobigny, France
| | - Johann Faouzi
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
- CREST, ENSAI, Campus de Ker-Lann, Bruz, France
| | - François-Xavier Lejeune
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, Paris, France
- ICM, Data Analysis Core (DAC), Paris, France
| | - Gonçalo Gama Lobo
- Neuroradiology Department, Centro Hospitalar Universitário de Lisboa Central, Lisboa, Portugal
| | - Carna Jovanovic
- Neurology Clinic, University Clinical Center of Serbia, Belgrade, Serbia
| | - Florence Cormier
- Département de Neurologie, Hôpital Pitié-Salpêtrière, Assistance Publique Hôpitaux de Paris, Clinique des Mouvements Anormaux, Clinical Investigation Center for Neurosciences, Paris, France
| | - Gwendoline Dupont
- Université de Bourgogne, Dijon, France
- Département de Neurologie, Centre Hospitalier Universitaire François Mitterrand, Dijon, France
| | - Marie Vidailhet
- ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, Paris, France
| | - Jean-Christophe Corvol
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, Paris, France
- Département de Neurologie, Hôpital Pitié-Salpêtrière, Assistance Publique Hôpitaux de Paris, Clinique des Mouvements Anormaux, Clinical Investigation Center for Neurosciences, Paris, France
| | - Olivier Colliot
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stéphane Lehéricy
- ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France
- ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France
- Department of Neuroradiology, Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Paris, France
| | - David Grabli
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, Paris, France
- Département de Neurologie, Hôpital Pitié-Salpêtrière, Assistance Publique Hôpitaux de Paris, Clinique des Mouvements Anormaux, Clinical Investigation Center for Neurosciences, Paris, France
| | - Bertrand Degos
- Assistance Publique Hôpitaux de Paris, Service de Neurologie, Hôpital Avicenne, Hôpitaux Universitaires de Paris Seine-Saint-Denis, Sorbonne Paris Nord, NS-PARK/FCRIN Network, Bobigny, France
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, Université PSL, Paris, France
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Lee S, Kovacs GG. The Irony of Iron: The Element with Diverse Influence on Neurodegenerative Diseases. Int J Mol Sci 2024; 25:4269. [PMID: 38673855 PMCID: PMC11049980 DOI: 10.3390/ijms25084269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/07/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Iron accumulation in the brain is a common feature of many neurodegenerative diseases. Its involvement spans across the main proteinopathies involving tau, amyloid-beta, alpha-synuclein, and TDP-43. Accumulating evidence supports the contribution of iron in disease pathologies, but the delineation of its pathogenic role is yet challenged by the complex involvement of iron in multiple neurotoxicity mechanisms and evidence supporting a reciprocal influence between accumulation of iron and protein pathology. Here, we review the major proteinopathy-specific observations supporting four distinct hypotheses: (1) iron deposition is a consequence of protein pathology; (2) iron promotes protein pathology; (3) iron protects from or hinders protein pathology; and (4) deposition of iron and protein pathology contribute parallelly to pathogenesis. Iron is an essential element for physiological brain function, requiring a fine balance of its levels. Understanding of disease-related iron accumulation at a more intricate and systemic level is critical for advancements in iron chelation therapies.
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Affiliation(s)
- Seojin Lee
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON M5T 0S8, Canada;
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Gabor G. Kovacs
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON M5T 0S8, Canada;
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Edmond J. Safra Program in Parkinson’s Disease, Rossy Program for PSP Research and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, ON M5T 2S8, Canada
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Bendetowicz D, Fabbri M, Sirna F, Fernagut PO, Foubert-Samier A, Saulnier T, Le Traon AP, Proust-Lima C, Rascol O, Meissner WG. Recent Advances in Clinical Trials in Multiple System Atrophy. Curr Neurol Neurosci Rep 2024; 24:95-112. [PMID: 38416311 DOI: 10.1007/s11910-024-01335-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/13/2024] [Indexed: 02/29/2024]
Abstract
PURPOSE OF REVIEW This review summarizes previous and ongoing neuroprotection trials in multiple system atrophy (MSA), a rare and fatal neurodegenerative disease characterized by parkinsonism, cerebellar, and autonomic dysfunction. It also describes the preclinical therapeutic pipeline and provides some considerations relevant to successfully conducting clinical trials in MSA, i.e., diagnosis, endpoints, and trial design. RECENT FINDINGS Over 30 compounds have been tested in clinical trials in MSA. While this illustrates a strong treatment pipeline, only two have reached their primary endpoint. Ongoing clinical trials primarily focus on targeting α-synuclein, the neuropathological hallmark of MSA being α-synuclein-bearing glial cytoplasmic inclusions. The mostly negative trial outcomes highlight the importance of better understanding underlying disease mechanisms and improving preclinical models. Together with efforts to refine clinical measurement tools, innovative statistical methods, and developments in biomarker research, this will enhance the design of future neuroprotection trials in MSA and the likelihood of positive outcomes.
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Affiliation(s)
- David Bendetowicz
- Univ. Bordeaux, CNRS, IMN, UMR5293, Bordeaux, France.
- CHU Bordeaux, Service de Neurologie des Maladies Neurodégénératives, IMNc, CRMR AMS, NS-Park/FCRIN Network, Bordeaux, France.
| | - Margherita Fabbri
- MSA French Reference Center, Univ. Hospital Toulouse, Toulouse, France
- Univ. Toulouse, CIC-1436, Departments of Clinical Pharmacology and Neurosciences, NeuroToul COEN Center, NS-Park/FCRIN Network, Toulouse University Hospital, Inserm, U1048/1214, Toulouse, France
| | - Federico Sirna
- Univ. Bordeaux, INSERM, BPH, U1219, IPSED, Bordeaux, France
| | - Pierre-Olivier Fernagut
- Université de Poitiers, Laboratoire de Neurosciences Expérimentales et Cliniques, INSERM UMR-S 1084, Poitiers, France
| | - Alexandra Foubert-Samier
- Univ. Bordeaux, CNRS, IMN, UMR5293, Bordeaux, France
- CHU Bordeaux, Service de Neurologie des Maladies Neurodégénératives, IMNc, CRMR AMS, NS-Park/FCRIN Network, Bordeaux, France
- Univ. Bordeaux, INSERM, BPH, U1219, IPSED, Bordeaux, France
| | | | - Anne Pavy Le Traon
- MSA French Reference Center, Univ. Hospital Toulouse, Toulouse, France
- Univ. Toulouse, CIC-1436, Departments of Clinical Pharmacology and Neurosciences, NeuroToul COEN Center, NS-Park/FCRIN Network, Toulouse University Hospital, Inserm, U1048/1214, Toulouse, France
| | | | - Olivier Rascol
- MSA French Reference Center, Univ. Hospital Toulouse, Toulouse, France
- Univ. Toulouse, CIC-1436, Departments of Clinical Pharmacology and Neurosciences, NeuroToul COEN Center, NS-Park/FCRIN Network, Toulouse University Hospital, Inserm, U1048/1214, Toulouse, France
| | - Wassilios G Meissner
- Univ. Bordeaux, CNRS, IMN, UMR5293, Bordeaux, France
- CHU Bordeaux, Service de Neurologie des Maladies Neurodégénératives, IMNc, CRMR AMS, NS-Park/FCRIN Network, Bordeaux, France
- Department of Medicine, University of Otago, Christchurch, and New Zealand Brain Research Institute, Christchurch, New Zealand
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Kawabata K, Krismer F, Heim B, Hussl A, Mueller C, Scherfler C, Gizewski ER, Seppi K, Poewe W. A Blinded Evaluation of Brain Morphometry for Differential Diagnosis of Atypical Parkinsonism. Mov Disord Clin Pract 2024; 11:381-390. [PMID: 38314609 PMCID: PMC10982602 DOI: 10.1002/mdc3.13987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 01/14/2024] [Indexed: 02/06/2024] Open
Abstract
BACKGROUND Advanced imaging techniques have been studied for differential diagnosis between PD, MSA, and PSP. OBJECTIVES This study aims to validate the utility of individual voxel-based morphometry techniques for atypical parkinsonism in a blinded fashion. METHODS Forty-eight healthy controls (HC) T1-WI were used to develop a referential dataset and fit a general linear model after segmentation into gray matter (GM) and white matter (WM) compartments. Segmented GM and WM with PD (n = 96), MSA (n = 18), and PSP (n = 20) were transformed into z-scores using the statistics of referential HC and individual voxel-based z-score maps were generated. An imaging diagnosis was assigned by two independent raters (trained and untrained) blinded to clinical information and final diagnosis. Furthermore, we developed an observer-independent index for ROI-based automated differentiation. RESULTS The diagnostic performance using voxel-based z-score maps by rater 1 and rater 2 for MSA yielded sensitivities: 0.89, 0.94 (95% CI: 0.74-1.00, 0.84-1.00), specificities: 0.94, 0.80 (0.90-0.98, 0.73-0.87); for PSP, sensitivities: 0.85, 0.90 (0.69-1.00, 0.77-1.00), specificities: 0.98, 0.94 (0.96-1.00, 0.90-0.98). Interrater agreement was good for MSA (Cohen's kappa: 0.61), and excellent for PSP (0.84). Receiver operating characteristic analysis using the ROI-based new index showed an area under the curve (AUC): 0.89 (0.77-1.00) for MSA, and 0.99 (0.98-1.00) for PSP. CONCLUSIONS These evaluations provide support for the utility of this imaging technique in the differential diagnosis of atypical parkinsonism demonstrating a remarkably high differentiation accuracy for PSP, suggesting potential use in clinical settings in the future.
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Affiliation(s)
- Kazuya Kawabata
- Department of NeurologyMedical University InnsbruckInnsbruckAustria
- Department of NeurologyNagoya University Graduate School of MedicineNagoyaJapan
| | - Florian Krismer
- Department of NeurologyMedical University InnsbruckInnsbruckAustria
- Neuroimaging Research Core FacilityMedical University InnsbruckInnsbruckAustria
| | - Beatrice Heim
- Department of NeurologyMedical University InnsbruckInnsbruckAustria
- Neuroimaging Research Core FacilityMedical University InnsbruckInnsbruckAustria
| | - Anna Hussl
- Department of NeurologyMedical University InnsbruckInnsbruckAustria
| | | | - Christoph Scherfler
- Department of NeurologyMedical University InnsbruckInnsbruckAustria
- Neuroimaging Research Core FacilityMedical University InnsbruckInnsbruckAustria
| | - Elke R. Gizewski
- Neuroimaging Research Core FacilityMedical University InnsbruckInnsbruckAustria
- Department of NeuroradiologyMedical University InnsbruckInnsbruckAustria
| | - Klaus Seppi
- Department of NeurologyMedical University InnsbruckInnsbruckAustria
- Neuroimaging Research Core FacilityMedical University InnsbruckInnsbruckAustria
| | - Werner Poewe
- Department of NeurologyMedical University InnsbruckInnsbruckAustria
- Neuroimaging Research Core FacilityMedical University InnsbruckInnsbruckAustria
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Li X, Chen C, Pan T, Zhou X, Sun X, Zhang Z, Wu D, Chen X. Trends and hotspots in non-motor symptoms of Parkinson's disease: a 10-year bibliometric analysis. Front Aging Neurosci 2024; 16:1335550. [PMID: 38298610 PMCID: PMC10827952 DOI: 10.3389/fnagi.2024.1335550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/05/2024] [Indexed: 02/02/2024] Open
Abstract
Non-motor symptoms are prevalent among individuals with Parkinson's disease (PD) and seriously affect patient quality of life, even more so than motor symptoms. In the past decade, an increasing number of studies have investigated non-motor symptoms in PD. The present study aimed to comprehensively analyze the global literature, trends, and hotspots of research investigating non-motor symptoms in PD through bibliometric methods. Studies addressing non-motor symptoms in the Web of Science Core Collection (WoSCC), published between January 2013 and December 2022, were retrieved. Bibliometric methods, including the R package "Bibliometrix," VOS viewer, and CiteSpace software, were used to investigate and visualize parameters, including yearly publications, country/region, institution, and authors, to collate and quantify information. Analysis of keywords and co-cited references explored trends and hotspots. There was a significant increase in the number of publications addressing the non-motor symptoms of PD, with a total of 3,521 articles retrieved. The United States was ranked first in terms of publications (n = 763) and citations (n = 11,269), maintaining its leadership position among all countries. King's College London (United Kingdom) was the most active institution among all publications (n = 133) and K Ray Chaudhuri was the author with the most publications (n = 131). Parkinsonism & Related Disorders published the most articles, while Movement Disorders was the most cited journal. Reference explosions have shown that early diagnosis, biomarkers, novel magnetic resonance imaging techniques, and deep brain stimulation have become research "hotspots" in recent years. Keyword clustering revealed that alpha-synuclein is the largest cluster for PD. The keyword heatmap revealed that non-motor symptoms appeared most frequently (n = 1,104), followed by quality of life (n = 502), dementia (n = 403), and depression (n = 397). Results of the present study provide an objective, comprehensive, and systematic analysis of these publications, and identifies trends and "hot" developments in this field of research. This work will inform investigators worldwide to help them conduct further research and develop new therapies.
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Affiliation(s)
- Xuefeng Li
- Changchun University of Chinese Medicine, Changchun, China
| | - Chunhai Chen
- The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Ting Pan
- The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Xue Zhou
- Changchun University of Chinese Medicine, Changchun, China
| | - Xiaozhou Sun
- Center of Children's Clinic, The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Ziyang Zhang
- Changchun University of Chinese Medicine, Changchun, China
| | - Dalong Wu
- The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Xinhua Chen
- The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China
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Krismer F, Péran P, Beliveau V, Seppi K, Arribarat G, Pavy-Le Traon A, Meissner WG, Foubert-Samier A, Fabbri M, Schocke MM, Gordon MF, Wenning GK, Poewe W, Rascol O, Scherfler C. Progressive Brain Atrophy in Multiple System Atrophy: A Longitudinal, Multicenter, Magnetic Resonance Imaging Study. Mov Disord 2024; 39:119-129. [PMID: 37933745 DOI: 10.1002/mds.29633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 08/27/2023] [Accepted: 09/28/2023] [Indexed: 11/08/2023] Open
Abstract
OBJECTIVE To determine the rates of brain atrophy progression in vivo in patients with multiple system atrophy (MSA). BACKGROUND Surrogate biomarkers of disease progression are a major unmet need in MSA. Small-scale longitudinal studies in patients with MSA using magnetic resonance imaging (MRI) to assess progression of brain atrophy have produced inconsistent results. In recent years, novel MRI post-processing methods have been developed enabling reliable quantification of brain atrophy in an automated fashion. METHODS Serial 3D-T1-weighted MRI assessments (baseline and after 1 year of follow-up) of 43 patients with MSA were analyzed and compared to a cohort of early-stage Parkinson's disease (PD) patients and healthy controls (HC). FreeSurfer's longitudinal analysis stream was used to determine the brain atrophy rates in an observer-independent fashion. RESULTS Mean ages at baseline were 64.4 ± 8.3, 60.0 ± 7.5, and 59.8 ± 9.2 years in MSA, PD patients and HC, respectively. A mean disease duration at baseline of 4.1 ± 2.5 years in MSA patients and 2.3 ± 1.4 years in PD patients was observed. Brain regions chiefly affected by MSA pathology showed progressive atrophy with annual rates of atrophy for the cerebellar cortex, cerebellar white matter, pons, and putamen of -4.24 ± 6.8%, -8.22 ± 8.8%, -4.67 ± 4.9%, and - 4.25 ± 4.9%, respectively. Similar to HC, atrophy rates in PD patients were minimal with values of -0.41% ± 1.8%, -1.47% ± 4.1%, -0.04% ± 1.8%, and -1.54% ± 2.2% for cerebellar cortex, cerebellar white matter, pons, and putamen, respectively. CONCLUSIONS Patients with MSA show significant brain volume loss over 12 months, and cerebellar, pontine, and putaminal volumes were the most sensitive to change in mid-stage disease. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Florian Krismer
- Department of Neurology, Medical University Innsbruck, Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University Innsbruck, Innsbruck, Austria
| | - Patrice Péran
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France
| | - Vincent Beliveau
- Department of Neurology, Medical University Innsbruck, Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University Innsbruck, Innsbruck, Austria
| | - Klaus Seppi
- Department of Neurology, Medical University Innsbruck, Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University Innsbruck, Innsbruck, Austria
| | - Germain Arribarat
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France
| | - Anne Pavy-Le Traon
- French Reference Center for MSA, Neurology Department, University Hospital of Toulouse and INSERM-Institute of Cardiovascular and Metabolic Diseases (I2MC) UMR1297, Toulouse, France
| | - Wassilios G Meissner
- CHU Bordeaux, Service de Neurologie des Maladies Neurodégénératives, IMNc, CRMR AMS, Bordeaux, France
- University of Bordeaux, CNRS, IMN, UMR 5293, Bordeaux, France
- Department of Medicine, University of Otago, Christchurch, and New Zealand Brain Research Institute, Christchurch, New Zealand
| | - Alexandra Foubert-Samier
- CHU Bordeaux, Service de Neurologie des Maladies Neurodégénératives, IMNc, CRMR AMS, Bordeaux, France
- University of Bordeaux, CNRS, IMN, UMR 5293, Bordeaux, France
- INSERM, UMR1219, Bordeaux Population Health Research Center, University of Bordeaux, ISPED, Bordeaux, France
| | - Margherita Fabbri
- French Reference Center for MSA, Clinical Investigation Center CIC1436, Departments of Clinical Pharmacology and Neurosciences, NS-Park/FCRIN Network and NeuroToul Center of Excellence for Neurodegeneration, INSERM, University Hospital of Toulouse and University of Toulouse, Toulouse, France
| | - Michael M Schocke
- Neuroimaging Research Core Facility, Medical University Innsbruck, Innsbruck, Austria
| | | | - Gregor K Wenning
- Department of Neurology, Medical University Innsbruck, Innsbruck, Austria
| | - Werner Poewe
- Department of Neurology, Medical University Innsbruck, Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University Innsbruck, Innsbruck, Austria
| | - Olivier Rascol
- French Reference Center for MSA, Clinical Investigation Center CIC1436, Departments of Clinical Pharmacology and Neurosciences, NS-Park/FCRIN Network and NeuroToul Center of Excellence for Neurodegeneration, INSERM, University Hospital of Toulouse and University of Toulouse, Toulouse, France
| | - Christoph Scherfler
- Department of Neurology, Medical University Innsbruck, Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University Innsbruck, Innsbruck, Austria
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Bu S, Pang H, Li X, Zhao M, Wang J, Liu Y, Yu H. Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson's disease and multiple system atrophy. BMC Med Imaging 2023; 23:204. [PMID: 38066432 PMCID: PMC10709839 DOI: 10.1186/s12880-023-01169-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
OBJECTIVES This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson's disease (PD) and multiple system atrophy (MSA). METHODS A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process. RESULTS The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively. CONCLUSIONS Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences.
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Affiliation(s)
- Shuting Bu
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Huize Pang
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Xiaolu Li
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Mengwan Zhao
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Juzhou Wang
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Yu Liu
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Hongmei Yu
- Department of Neurology, the First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, Liaoning, 110001, PR China.
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Wang Z, Mo J, Zhang J, Feng T, Zhang K. Surface-Based Neuroimaging Pattern of Multiple System Atrophy. Acad Radiol 2023; 30:2999-3009. [PMID: 37495425 DOI: 10.1016/j.acra.2023.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 07/28/2023]
Abstract
RATIONALE AND OBJECTIVES Overlapping parkinsonism, cerebellar ataxia, and pyramidal signs render challenges in the clinical diagnosis of multiple system atrophy (MSA). The neuroimaging pattern is valuable to understand its pathophysiology and improve its diagnostic effect. MATERIALS AND METHODS We retrospectively obtained magnetic resonance imaging and susceptibility-weighted imaging in patients with MSA (including parkinsonian type [MSA-P] and cerebellar type [MSA-C]), Parkinson's disease, and normal controls. We quantified neuroimaging features to identify the optimal threshold for diagnosis. Furthermore, we explore neuroimaging patterns of MSA by mapping the subcortical morphological alterations and constructing a diagnostic model. RESULTS Compared to controls, normalized putaminal volume significantly decreased in patients with MSA-P (P < .001) and normalized pontine volume significantly decreased in patients with MSA-C (P < .001). The Youden index of the threshold-based clinical prediction model was 0.871-0.928 in patients with MSA. The neuroimaging pattern in patients with MSA was jointly located in the lateral putamen, and the neuroimaging pattern prediction model achieved a classification accuracy of 83.9%-100%. CONCLUSION The quantitative neuroimaging features and surface-based morphologic anomalies represent the markers of MSA and open new avenues for personalized clinical diagnosis.
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Affiliation(s)
- Zhan Wang
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (Z.W., T.F.); China National Clinical Research Center for Neurological Disease, NCRC-ND, Beijing, China (Z.W., T.F.)
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Beijing Key Laboratory of Neurostimulation, Beijing, China (J.M., J.Z., K.Z.)
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Beijing Key Laboratory of Neurostimulation, Beijing, China (J.M., J.Z., K.Z.)
| | - Tao Feng
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (Z.W., T.F.); China National Clinical Research Center for Neurological Disease, NCRC-ND, Beijing, China (Z.W., T.F.)
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Beijing Key Laboratory of Neurostimulation, Beijing, China (J.M., J.Z., K.Z.).
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9
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Hussain SS, Degang X, Shah PM, Islam SU, Alam M, Khan IA, Awwad FA, Ismail EAA. Classification of Parkinson's Disease in Patch-Based MRI of Substantia Nigra. Diagnostics (Basel) 2023; 13:2827. [PMID: 37685365 PMCID: PMC10486663 DOI: 10.3390/diagnostics13172827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 08/25/2023] [Accepted: 08/26/2023] [Indexed: 09/10/2023] Open
Abstract
Parkinson's disease (PD) is a chronic and progressive neurological disease that mostly shakes and compromises the motor system of the human brain. Patients with PD can face resting tremors, loss of balance, bradykinesia, and rigidity problems. Complex patterns of PD, i.e., with relevance to other neurological diseases and minor changes in brain structure, make the diagnosis of this disease a challenge and cause inaccuracy of about 25% in the diagnostics. The research community utilizes different machine learning techniques for diagnosis using handcrafted features. This paper proposes a computer-aided diagnostic system using a convolutional neural network (CNN) to diagnose PD. CNN is one of the most suitable models to extract and learn the essential features of a problem. The dataset is obtained from Parkinson's Progression Markers Initiative (PPMI), which provides different datasets (benchmarks), such as T2-weighted MRI for PD and other healthy controls (HC). The mid slices are collected from each MRI. Further, these slices are registered for alignment. Since the PD can be found in substantia nigra (i.e., the midbrain), the midbrain region of the registered T2-weighted MRI slice is selected using the freehand region of interest technique with a 33 × 33 sized window. Several experiments have been carried out to ensure the validity of the CNN. The standard measures, such as accuracy, sensitivity, specificity, and area under the curve, are used to evaluate the proposed system. The evaluation results show that CNN provides better accuracy than machine learning techniques, such as naive Bayes, decision tree, support vector machine, and artificial neural network.
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Affiliation(s)
| | - Xu Degang
- School of Automation, Central South University, Changsha 410010, China;
| | - Pir Masoom Shah
- Department of Computer Science, Bacha Khan University Charsadda, Charsadda 24540, Pakistan; (P.M.S.); (I.A.K.)
- School of Computer Science and Engineering, Central South University, Changsha 410010, China;
| | - Saif Ul Islam
- Department of Computer Science, Institute of Space Technology, Islamabad 44000, Pakistan;
| | - Mahmood Alam
- School of Computer Science and Engineering, Central South University, Changsha 410010, China;
| | - Izaz Ahmad Khan
- Department of Computer Science, Bacha Khan University Charsadda, Charsadda 24540, Pakistan; (P.M.S.); (I.A.K.)
| | - Fuad A. Awwad
- Department of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh 11587, Saudi Arabia; (F.A.A.); (E.A.A.I.)
| | - Emad A. A. Ismail
- Department of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh 11587, Saudi Arabia; (F.A.A.); (E.A.A.I.)
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10
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Wan L, Zhu S, Chen Z, Qiu R, Tang B, Jiang H. Multidimensional biomarkers for multiple system atrophy: an update and future directions. Transl Neurodegener 2023; 12:38. [PMID: 37501056 PMCID: PMC10375766 DOI: 10.1186/s40035-023-00370-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 07/11/2023] [Indexed: 07/29/2023] Open
Abstract
Multiple system atrophy (MSA) is a fatal progressive neurodegenerative disease. Biomarkers are urgently required for MSA to improve the diagnostic and prognostic accuracy in clinic and facilitate the development and monitoring of disease-modifying therapies. In recent years, significant research efforts have been made in exploring multidimensional biomarkers for MSA. However, currently few biomarkers are available in clinic. In this review, we systematically summarize the latest advances in multidimensional biomarkers for MSA, including biomarkers in fluids, tissues and gut microbiota as well as imaging biomarkers. Future directions for exploration of novel biomarkers and promotion of implementation in clinic are also discussed.
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Affiliation(s)
- Linlin Wan
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National International Collaborative Research Center for Medical Metabolomics, Central South University, Changsha, 410008, China
| | - Sudan Zhu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Zhao Chen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, 410008, China
| | - Rong Qiu
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Beisha Tang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, 410008, China
| | - Hong Jiang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Department of Neurology, The Third Xiangya Hospital, Central South University, Changsha, 410013, China.
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, 410008, China.
- National International Collaborative Research Center for Medical Metabolomics, Central South University, Changsha, 410008, China.
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11
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Belić M, Radivojević Z, Bobić V, Kostić V, Đurić-Jovičić M. Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson’s disease and atypical parkinsonisms. Heliyon 2023; 9:e14824. [PMID: 37077676 PMCID: PMC10107087 DOI: 10.1016/j.heliyon.2023.e14824] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 03/06/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
Background Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence rises with age, yet clinical diagnosis is still a challenging task due to similar manifestations of other neurodegenerative movement disorders. In untreated patients or those with unclear responses to medication, correct percentages of early diagnoses go as low as 26%. Technology has been used in various forms to facilitate discerning between persons with PD and healthy individuals, but much less work has been dedicated to separating PD and atypical parkinsonisms. Methods A wearable system was developed based on inertial sensors that capture the movements of fingers during repetitive finger tapping. A k-nearest-neighbor classifier was used on features extracted from gyroscope recordings for quick aid in differential diagnostics, discerning patients with PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and healthy controls (HC). Results The overall classification accuracy achieved was 85.18% in the multiclass setup. MSA and HC groups were the easiest to discern (100%), while PSP was the most elusive diagnosis, as some patients were incorrectly assigned to MSA and HC groups. Conclusions The system shows potential for use as a tool for quick diagnostic aid, and in the era of big data, offers a means of standardization of data collection that could allow scientists to aggregate multi-center data for further research.
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12
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Chougar L, Lejeune FX, Faouzi J, Morino B, Faucher A, Hoyek N, Grabli D, Cormier F, Vidailhet M, Corvol JC, Colliot O, Degos B, Lehéricy S. Comparison of mean diffusivity, R2* relaxation rate and morphometric biomarkers for the clinical differentiation of parkinsonism. Parkinsonism Relat Disord 2023; 108:105287. [PMID: 36706616 DOI: 10.1016/j.parkreldis.2023.105287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 12/15/2022] [Accepted: 01/14/2023] [Indexed: 01/19/2023]
Abstract
INTRODUCTION Quantitative biomarkers for clinical differentiation of parkinsonian syndromes are still lacking. Our aim was to evaluate the value of combining clinically feasible manual measurements of R2* relaxation rates and mean diffusivity (MD) in subcortical regions and brainstem morphometric measurements to improve the discrimination of parkinsonian syndromes. METHODS Twenty-two healthy controls (HC), 25 patients with Parkinson's disease (PD), 19 with progressive supranuclear palsy (PSP) and 27 with multiple system atrophy (MSA, 21 with the parkinsonian variant -MSAp, 6 with the cerebellar variant -MSAc) were recruited. R2*, MD measurements and morphometric biomarkers including the midbrain to pons area ratio and the Magnetic Resonance Parkinsonism Index (MRPI) were compared between groups and their diagnostic performances were assessed. RESULTS Morphometric biomarkers discriminated better patients with PSP (ratio: AUC 0.89, MRPI: AUC 0.89) and MSAc (ratio: AUC 0.82, MRPI: AUC 0.75) from other groups. R2* and MD measurements in the posterior putamen performed better in separating patients with MSAp from PD (R2*: AUC 0.89; MD: AUC 0.89). For the three-class classification "MSA vs PD vs PSP", the combination of MD and R2* measurements in the posterior putamen with morphometric biomarkers (AUC: 0.841) outperformed each marker separately. At the individual-level, there were seven discordances between imaging-based prediction and clinical diagnosis involving MSA. Using the new Movement Disorder Society criteria for the diagnosis of MSA, three of these seven patients were clinically reclassified as predicted by quantitative imaging. CONCLUSION Combining R2* and MD measurements in the posterior putamen with morphometric biomarkers improves the discrimination of parkinsonism.
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Affiliation(s)
- Lydia Chougar
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Department of Neuroradiology, F-75013, Paris, France; ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France; ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Department of Neuroradiology, F-75013, Paris, France.
| | - François-Xavier Lejeune
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, F-75013, Paris, France; ICM, Data and Analysis Core, Paris, France
| | - Johann Faouzi
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, F-75013, Paris, France; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France
| | - Benjamin Morino
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Department of Neuroradiology, F-75013, Paris, France
| | - Alice Faucher
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, Université PSL, Paris, France; Service de Neurologie, Hôpital Avicenne, Hôpitaux Universitaires de Paris Seine-Saint-Denis, APHP, Bobigny, France
| | - Nadine Hoyek
- Department of Radiology, Hotel Dieu de France University Hospital, Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - David Grabli
- Clinique des mouvements anormaux, Département de Neurologie, Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Paris, France; ICM, Centre d'Investigation Clinique Neurosciences, Paris, France
| | - Florence Cormier
- Clinique des mouvements anormaux, Département de Neurologie, Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Paris, France; ICM, Centre d'Investigation Clinique Neurosciences, Paris, France
| | - Marie Vidailhet
- ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, F-75013, Paris, France; Clinique des mouvements anormaux, Département de Neurologie, Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Paris, France; ICM, Centre d'Investigation Clinique Neurosciences, Paris, France
| | - Jean-Christophe Corvol
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, F-75013, Paris, France; Clinique des mouvements anormaux, Département de Neurologie, Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Paris, France; ICM, Centre d'Investigation Clinique Neurosciences, Paris, France
| | - Olivier Colliot
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, F-75013, Paris, France; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France
| | - Bertrand Degos
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, Université PSL, Paris, France; Service de Neurologie, Hôpital Avicenne, Hôpitaux Universitaires de Paris Seine-Saint-Denis, APHP, Bobigny, France
| | - Stéphane Lehéricy
- ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France; ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Department of Neuroradiology, F-75013, Paris, France
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13
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Shusharina N, Yukhnenko D, Botman S, Sapunov V, Savinov V, Kamyshov G, Sayapin D, Voznyuk I. Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression. Diagnostics (Basel) 2023; 13:diagnostics13030573. [PMID: 36766678 PMCID: PMC9914271 DOI: 10.3390/diagnostics13030573] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/27/2023] [Accepted: 01/29/2023] [Indexed: 02/09/2023] Open
Abstract
This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented.
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Affiliation(s)
- Natalia Shusharina
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
- Correspondence:
| | - Denis Yukhnenko
- Department of Social Security and Humanitarian Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia
| | - Stepan Botman
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Viktor Sapunov
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Vladimir Savinov
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Gleb Kamyshov
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Dmitry Sayapin
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Igor Voznyuk
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
- Department of Neurology, Pavlov First Saint Petersburg State Medical University, 197022 Saint Petersburg, Russia
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14
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Doan TT, Pham TD, Nguyen DD, Ngo DHA, Le TB, Nguyen TT. Multiple system atrophy-cerebellar: A case report and literature review. Radiol Case Rep 2023; 18:1121-1126. [PMID: 36660581 PMCID: PMC9842541 DOI: 10.1016/j.radcr.2022.12.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 12/21/2022] [Indexed: 01/09/2023] Open
Abstract
We reported a case of a 48-year-old female patient admitted to the hospital due to balance disorder which progressed rapidly within 1 week. Cerebral magnetic resonance imaging showed significant atrophy and hyperintensities at the middle cerebellar peduncles and the "hot cross bun" sign of the pons. The final diagnosis was probable multiple system atrophy, cerebellar subtype. The clinical and imaging findings will be discussed as well as a brief literature review.
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Affiliation(s)
- Thi Thuong Doan
- Department of Radiology, University of Medicine and Pharmacy, Hue University, 06 Ngo Quyen str., 530000 Hue city, Vietnam
| | - Thuy Dung Pham
- Department of Radiology, University of Medicine and Pharmacy, Hue University, 06 Ngo Quyen str., 530000 Hue city, Vietnam
| | - Duy Duan Nguyen
- Department of Internal Medicine, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
| | - Dac Hong An Ngo
- Department of Radiology, University of Medicine and Pharmacy, Hue University, 06 Ngo Quyen str., 530000 Hue city, Vietnam,Corresponding author.
| | - Trong Binh Le
- Department of Radiology, University of Medicine and Pharmacy, Hue University, 06 Ngo Quyen str., 530000 Hue city, Vietnam
| | - Thanh Thao Nguyen
- Department of Radiology, University of Medicine and Pharmacy, Hue University, 06 Ngo Quyen str., 530000 Hue city, Vietnam
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15
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Tinaz S. Magnetic resonance imaging modalities aid in the differential diagnosis of atypical parkinsonian syndromes. Front Neurol 2023; 14:1082060. [PMID: 36816565 PMCID: PMC9932598 DOI: 10.3389/fneur.2023.1082060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
Accurate and timely diagnosis of atypical parkinsonian syndromes (APS) remains a challenge. Especially early in the disease course, the clinical manifestations of the APS overlap with each other and with those of idiopathic Parkinson's disease (PD). Recent advances in magnetic resonance imaging (MRI) technology have introduced promising imaging modalities to aid in the diagnosis of APS. Some of these MRI modalities are also included in the updated diagnostic criteria of APS. Importantly, MRI is safe for repeated use and more affordable and accessible compared to nuclear imaging. These advantages make MRI tools more appealing for diagnostic purposes. As the MRI field continues to advance, the diagnostic use of these techniques in APS, alone or in combination, are expected to become commonplace in clinical practice.
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Affiliation(s)
- Sule Tinaz
- Division of Movement Disorders, Department of Neurology, Yale School of Medicine, New Haven, CT, United States
- Department of Neurology, Clinical Neurosciences Imaging Center, Yale School of Medicine, New Haven, CT, United States
- *Correspondence: Sule Tinaz ✉
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16
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Speech acoustic indices for differential diagnosis between Parkinson's disease, multiple system atrophy and progressive supranuclear palsy. NPJ Parkinsons Dis 2022; 8:142. [PMID: 36302780 PMCID: PMC9613976 DOI: 10.1038/s41531-022-00389-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 09/01/2022] [Indexed: 11/05/2022] Open
Abstract
While speech disorder represents an early and prominent clinical feature of atypical parkinsonian syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), little is known about the sensitivity of speech assessment as a potential diagnostic tool. Speech samples were acquired from 215 subjects, including 25 MSA, 20 PSP, 20 Parkinson's disease participants, and 150 healthy controls. The accurate differential diagnosis of dysarthria subtypes was based on the quantitative acoustic analysis of 26 speech dimensions related to phonation, articulation, prosody, and timing. A semi-supervised weighting-based approach was then applied to find the best feature combinations for separation between PSP and MSA. Dysarthria was perceptible in all PSP and MSA patients and consisted of a combination of hypokinetic, spastic, and ataxic components. Speech features related to respiratory dysfunction, imprecise consonants, monopitch, slow speaking rate, and subharmonics contributed to worse performance in PSP than MSA, whereas phonatory instability, timing abnormalities, and articulatory decay were more distinctive for MSA compared to PSP. The combination of distinct speech patterns via objective acoustic evaluation was able to discriminate between PSP and MSA with very high accuracy of up to 89% as well as between PSP/MSA and PD with up to 87%. Dysarthria severity in MSA/PSP was related to overall disease severity. Speech disorders reflect the differing underlying pathophysiology of tauopathy in PSP and α-synucleinopathy in MSA. Vocal assessment may provide a low-cost alternative screening method to existing subjective clinical assessment and imaging diagnostic approaches.
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17
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Pasquini J, Firbank MJ, Ceravolo R, Silani V, Pavese N. Diffusion Magnetic Resonance Imaging Microstructural Abnormalities in Multiple System Atrophy: A Comprehensive Review. Mov Disord 2022; 37:1963-1984. [PMID: 36036378 DOI: 10.1002/mds.29195] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 07/22/2022] [Accepted: 08/01/2022] [Indexed: 01/07/2023] Open
Abstract
Multiple system atrophy (MSA) is a neurodegenerative disease characterized by autonomic failure, ataxia, and/or parkinsonism. Its prominent pathological alterations can be investigated using diffusion magnetic resonance imaging (dMRI), a technique that exploits the characteristics of water random motion inside brain tissue. The aim of this report was to review currently available literature on the application of dMRI in MSA and to describe microstructural abnormalities, diagnostic applications, and pathophysiological correlates. Sixty-four published studies involving microstructural investigation using dMRI in MSA were included. Widespread microstructural abnormalities of white matter were described, especially in the middle cerebellar peduncle, corticospinal tract, and hemispheric fibers. Gray matter degeneration was identified as well, with diffuse involvement of subcortical structures, especially in the putamina. Diagnostic applications of dMRI were mostly explored for the differential diagnosis between MSA parkinsonism and Parkinson's disease. Recently, machine learning algorithms for image processing and disease classification have demonstrated high diagnostic accuracy, showing potential for translation into clinical practice. To a lesser extent, clinical correlates of microstructural abnormalities have also been investigated, and abnormalities related to motor, ocular, and cognitive impairments were described. dMRI in MSA has contributed to in vivo identification of known pathological abnormalities. Translation into clinical practice of the latest advancements for the differential diagnosis between MSA and other forms of parkinsonism seems feasible. Current limitations involve the possibility of correctly diagnosing MSA in the very early stages, when the clinical diagnosis is most uncertain. Furthermore, pathophysiological correlates of microstructural abnormalities remain understudied. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Jacopo Pasquini
- Clinical Ageing Research Unit, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Michael J Firbank
- Positron Emission Tomography Centre, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Roberto Ceravolo
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.,Neurodegenerative Diseases Center, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Vincenzo Silani
- Department of Neurology and Laboratory of Neuroscience, Istituto Auxologico Italiano IRCCS, Milan, Italy.,Department of Pathophysiology and Transplantation, Dino Ferrari Center, Università degli Studi di Milano, Milan, Italy
| | - Nicola Pavese
- Clinical Ageing Research Unit, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark
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18
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Pang H, Yu Z, Yu H, Chang M, Cao J, Li Y, Guo M, Liu Y, Cao K, Fan G. Multimodal striatal neuromarkers in distinguishing parkinsonian variant of multiple system atrophy from idiopathic Parkinson's disease. CNS Neurosci Ther 2022; 28:2172-2182. [PMID: 36047435 PMCID: PMC9627351 DOI: 10.1111/cns.13959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/10/2022] [Accepted: 08/11/2022] [Indexed: 02/06/2023] Open
Abstract
AIMS To develop an automatic method of classification for parkinsonian variant of multiple system atrophy (MSA-P) and Idiopathic Parkinson's disease (IPD) in early to moderately advanced stages based on multimodal striatal alterations and identify the striatal neuromarkers for distinction. METHODS 77 IPD and 75 MSA-P patients underwent 3.0 T multimodal MRI comprising susceptibility-weighted imaging, resting-state functional magnetic resonance imaging, T1-weighted imaging, and diffusion tensor imaging. Iron-radiomic features, volumes, functional and diffusion scalars of bilateral 10 striatal subregions were calculated and provided to the support vector machine for classification RESULTS: A combination of iron-radiomic features, function, diffusion, and volumetric measures optimally distinguished IPD and MSA-P in the testing dataset (accuracy 0.911 and area under the receiver operating characteristic curves [AUC] 0.927). The diagnostic performance further improved when incorporating clinical variables into the multimodal model (accuracy 0.934 and AUC 0.953). The most crucial factor for classification was the functional activity of the left dorsolateral putamen. CONCLUSION The machine learning algorithm applied to multimodal striatal dysfunction depicted dorsal striatum and supervening prefrontal lobe and cerebellar dysfunction through the frontostriatal and cerebello-striatal connections and facilitated accurate classification between IPD and MSA-P. The dorsolateral putamen was the most valuable neuromarker for the classification.
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Affiliation(s)
- Huize Pang
- Department of RadiologyThe first Affiliated Hospital of China Medical UniversityShenyangChina
| | - Ziyang Yu
- School of MedicineXiamen UniversityXiamenChina
| | - Hongmei Yu
- Department of NeurologyThe first Affiliated Hospital of China Medical UniversityShenyangChina
| | - Miao Chang
- Department of RadiologyThe first Affiliated Hospital of China Medical UniversityShenyangChina
| | - Jibin Cao
- Department of RadiologyThe first Affiliated Hospital of China Medical UniversityShenyangChina
| | - Yingmei Li
- Department of RadiologyThe first Affiliated Hospital of China Medical UniversityShenyangChina
| | - Miaoran Guo
- Department of RadiologyThe first Affiliated Hospital of China Medical UniversityShenyangChina
| | - Yu Liu
- Department of RadiologyThe first Affiliated Hospital of China Medical UniversityShenyangChina
| | - Kaiqiang Cao
- Department of RadiologyThe first Affiliated Hospital of China Medical UniversityShenyangChina
| | - Guoguang Fan
- Department of RadiologyThe first Affiliated Hospital of China Medical UniversityShenyangChina
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19
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Abstract
Multiple system atrophy (MSA) is a rare neurodegenerative disease that is characterized by neuronal loss and gliosis in multiple areas of the central nervous system including striatonigral, olivopontocerebellar and central autonomic structures. Oligodendroglial cytoplasmic inclusions containing misfolded and aggregated α-synuclein are the histopathological hallmark of MSA. A firm clinical diagnosis requires the presence of autonomic dysfunction in combination with parkinsonism that responds poorly to levodopa and/or cerebellar ataxia. Clinical diagnostic accuracy is suboptimal in early disease because of phenotypic overlaps with Parkinson disease or other types of degenerative parkinsonism as well as with other cerebellar disorders. The symptomatic management of MSA requires a complex multimodal approach to compensate for autonomic failure, alleviate parkinsonism and cerebellar ataxia and associated disabilities. None of the available treatments significantly slows the aggressive course of MSA. Despite several failed trials in the past, a robust pipeline of putative disease-modifying agents, along with progress towards early diagnosis and the development of sensitive diagnostic and progression biomarkers for MSA, offer new hope for patients.
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20
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Bagchi AD. Multiple System Atrophy. J Nurse Pract 2022. [DOI: 10.1016/j.nurpra.2022.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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21
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Advanced brain aging in multiple system atrophy compared to Parkinson's disease. Neuroimage Clin 2022; 34:102997. [PMID: 35397330 PMCID: PMC8987993 DOI: 10.1016/j.nicl.2022.102997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 03/07/2022] [Accepted: 03/28/2022] [Indexed: 11/24/2022]
Abstract
MSA, but not PD, exhibits advanced brain aging in gray matter and white matter. Brain age of gray matter is correlated with that of white matter in PD. Brain age measures can partly reveal associations with symptom severity. Brain features underlying brain age difference between MSA and PD are identified.
Multiple system atrophy (MSA) and Parkinson’s disease (PD) belong to alpha-synucleinopathy, but they have very different clinical courses and prognoses. An imaging biomarker that can differentiate between the two diseases early in the disease course is desirable for appropriate treatment. Neuroimaging-based brain age paradigm provides an individualized marker to differentiate aberrant brain aging patterns in neurodegenerative diseases. In this study, patients with MSA (N = 23), PD (N = 33), and healthy controls (N = 34; HC) were recruited. A deep learning approach was used to estimate brain-predicted age difference (PAD) of gray matter (GM) and white matter (WM) based on image features extracted from T1-weighted and diffusion-weighted magnetic resonance images, respectively. Spatial normative models of image features were utilized to quantify neuroanatomical impairments in patients, which were then used to estimate the contributions of image features to brain age measures. For PAD of GM (GM-PAD), patients with MSA had significantly older brain age (9.33 years) than those with PD (0.75 years; P = 0.002) and HC (-1.47 years; P < 0.001), and no significant difference was found between PD and HC (P = 1.000). For PAD of WM (WM-PAD), it was significantly greater in MSA (9.27 years) than that in PD (1.90 years; P = 0.037) and HC (-0.74 years; P < 0.001); there was no significant difference between PD and HC (P = 0.087). The most salient image features that contributed to PAD in MSA and PD were different. For GM, they were the orbitofrontal regions and the cuneus in MSA and PD, respectively, and for WM, they were the central corpus callosum and the uncinate fasciculus in MSA and PD, respectively. Our results demonstrated that MSA revealed significantly greater PAD than PD, which might be related to markedly different neuroanatomical contributions to brain aging. The image features with distinct contributions to brain aging might be of value in the differential diagnosis of MSA and PD.
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22
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Wenning GK, Stankovic I, Vignatelli L, Fanciulli A, Calandra-Buonaura G, Seppi K, Palma JA, Meissner WG, Krismer F, Berg D, Cortelli P, Freeman R, Halliday G, Höglinger G, Lang A, Ling H, Litvan I, Low P, Miki Y, Panicker J, Pellecchia MT, Quinn N, Sakakibara R, Stamelou M, Tolosa E, Tsuji S, Warner T, Poewe W, Kaufmann H. The Movement Disorder Society Criteria for the Diagnosis of Multiple System Atrophy. Mov Disord 2022; 37:1131-1148. [PMID: 35445419 PMCID: PMC9321158 DOI: 10.1002/mds.29005] [Citation(s) in RCA: 204] [Impact Index Per Article: 102.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 01/25/2022] [Accepted: 02/28/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The second consensus criteria for the diagnosis of multiple system atrophy (MSA) are widely recognized as the reference standard for clinical research, but lack sensitivity to diagnose the disease at early stages. OBJECTIVE To develop novel Movement Disorder Society (MDS) criteria for MSA diagnosis using an evidence-based and consensus-based methodology. METHODS We identified shortcomings of the second consensus criteria for MSA diagnosis and conducted a systematic literature review to answer predefined questions on clinical presentation and diagnostic tools relevant for MSA diagnosis. The criteria were developed and later optimized using two Delphi rounds within the MSA Criteria Revision Task Force, a survey for MDS membership, and a virtual Consensus Conference. RESULTS The criteria for neuropathologically established MSA remain unchanged. For a clinical MSA diagnosis a new category of clinically established MSA is introduced, aiming for maximum specificity with acceptable sensitivity. A category of clinically probable MSA is defined to enhance sensitivity while maintaining specificity. A research category of possible prodromal MSA is designed to capture patients in the earliest stages when symptoms and signs are present, but do not meet the threshold for clinically established or clinically probable MSA. Brain magnetic resonance imaging markers suggestive of MSA are required for the diagnosis of clinically established MSA. The number of research biomarkers that support all clinical diagnostic categories will likely grow. CONCLUSIONS This set of MDS MSA diagnostic criteria aims at improving the diagnostic accuracy, particularly in early disease stages. It requires validation in a prospective clinical and a clinicopathological study. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Gregor K Wenning
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Iva Stankovic
- Neurology Clinic, University Clinical Center of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Luca Vignatelli
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | | | - Giovanna Calandra-Buonaura
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Klaus Seppi
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Jose-Alberto Palma
- Department of Neurology, Dysautonomia Center, Langone Medical Center, New York University School of Medicine, New York, New York, USA
| | - Wassilios G Meissner
- French Reference Center for MSA, Department of Neurology for Neurodegenerative Diseases, University Hospital Bordeaux, 33076 Bordeaux and Institute of Neurodegenerative Diseases, University Bordeaux, CNRS, Bordeaux, France.,Department of Medicine, University of Otago, Christchurch, and New Zealand Brain Research Institute, Christchurch, New Zealand
| | - Florian Krismer
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Daniela Berg
- Department of Neurodegeneration and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,Department of Neurology, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Pietro Cortelli
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Roy Freeman
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Glenda Halliday
- Brain and Mind Centre, Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, New South Wales, Australia
| | - Günter Höglinger
- Department of Neurology, Hanover Medical School, Hanover, Germany.,German Center for Neurodegenerative Diseases, Munich, Germany
| | - Anthony Lang
- Edmond J. Safra Program in Parkinson's Disease, University Health Network and the Division of Neurology, University of Toronto, Toronto, Canada
| | - Helen Ling
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, United Kingdom.,Reta Lila Weston Institute of Neurological Studies, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Irene Litvan
- Department of Neurosciences, Parkinson and Other Movement Disorders Center, University of California, San Diego, California, USA
| | - Phillip Low
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Yasuo Miki
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, United Kingdom.,Department of Neuropathology, Institute of Brain Science, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Jalesh Panicker
- UCL Queen Square Institute of Neurology, London, United Kingdom.,Department of Uro-Neurology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
| | - Maria Teresa Pellecchia
- Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", Neuroscience Section, University of Salerno, Salerno, Italy
| | - Niall Quinn
- UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Ryuji Sakakibara
- Neurology, Internal Medicine, Sakura Medical Center, Toho University, Sakura, Japan
| | - Maria Stamelou
- Parkinson's Disease and Movement Disorders Department, HYGEIA Hospital, and Aiginiteion Hospital, University of Athens, Athens, Greece.,Philipps University Marburg, Germany and European University of Cyprus, Nicosia, Cyprus
| | - Eduardo Tolosa
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED) Hospital Clínic, IDIBAPS, Universitat de Barcelona, Catalonia, Spain.,Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Catalonia, Spain
| | - Shoji Tsuji
- Department of Molecular Neurology, The University of Tokyo, Graduate School of Medicine, Tokyo, Japan.,International University of Health and Welfare, Chiba, Japan
| | - Tom Warner
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Werner Poewe
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Horacio Kaufmann
- Department of Neurology, Dysautonomia Center, Langone Medical Center, New York University School of Medicine, New York, New York, USA
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23
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Jucaite A, Cselényi Z, Kreisl WC, Rabiner EA, Varrone A, Carson RE, Rinne JO, Savage A, Schou M, Johnström P, Svenningsson P, Rascol O, Meissner WG, Barone P, Seppi K, Kaufmann H, Wenning GK, Poewe W, Farde L. Glia Imaging Differentiates Multiple System Atrophy from Parkinson's Disease: A Positron Emission Tomography Study with [ 11 C]PBR28 and Machine Learning Analysis. Mov Disord 2021; 37:119-129. [PMID: 34609758 DOI: 10.1002/mds.28814] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 09/06/2021] [Accepted: 09/10/2021] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND The clinical diagnosis of multiple system atrophy (MSA) is challenged by overlapping features with Parkinson's disease (PD) and late-onset ataxias. Additional biomarkers are needed to confirm MSA and to advance the understanding of pathophysiology. Positron emission tomography (PET) imaging of the translocator protein (TSPO), expressed by glia cells, has shown elevations in MSA. OBJECTIVE In this multicenter PET study, we assess the performance of TSPO imaging as a diagnostic marker for MSA. METHODS We analyzed [11 C]PBR28 binding to TSPO using imaging data of 66 patients with MSA and 24 patients with PD. Group comparisons were based on regional analysis of parametric images. The diagnostic readout included visual reading of PET images against clinical diagnosis and machine learning analyses. Sensitivity, specificity, and receiver operating curves were used to discriminate MSA from PD and cerebellar from parkinsonian variant MSA. RESULTS We observed a conspicuous pattern of elevated regional [11 C]PBR28 binding to TSPO in MSA as compared with PD, with "hotspots" in the lentiform nucleus and cerebellar white matter. Visual reading discriminated MSA from PD with 100% specificity and 83% sensitivity. The machine learning approach improved sensitivity to 96%. We identified MSA subtype-specific TSPO binding patterns. CONCLUSIONS We found a pattern of significantly increased regional glial TSPO binding in patients with MSA. Intriguingly, our data are in line with severe neuroinflammation in MSA. Glia imaging may have potential to support clinical MSA diagnosis and patient stratification in clinical trials on novel drug therapies for an α-synucleinopathy that remains strikingly incurable. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Aurelija Jucaite
- PET Science Centre, Personalized Medicine and Biosamples, R&D, AstraZeneca, Stockholm, Sweden.,Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - Zsolt Cselényi
- PET Science Centre, Personalized Medicine and Biosamples, R&D, AstraZeneca, Stockholm, Sweden.,Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - William C Kreisl
- Taub Institute, Department of Neurology, Columbia University Irving Medical Centre, New York, New York, USA
| | - Eugenii A Rabiner
- Invicro, London, UK.,Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Andrea Varrone
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | | | - Juha O Rinne
- Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland
| | | | - Magnus Schou
- PET Science Centre, Personalized Medicine and Biosamples, R&D, AstraZeneca, Stockholm, Sweden.,Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - Peter Johnström
- PET Science Centre, Personalized Medicine and Biosamples, R&D, AstraZeneca, Stockholm, Sweden.,Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - Per Svenningsson
- Section of Neurology, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Olivier Rascol
- French MSA Reference Centre, Clinical Investigation Centre CIC1436, Department of Neurosciences and Clinical Pharmacology, NeuroToul COEN Centre, UMR 1 214-ToNIC and University Hospital of Toulouse, INSERM and University of Toulouse 3, Toulouse, France
| | - Wassilios G Meissner
- CRMR AMS, Service de Neurologie-Maladies Neurodégénératives, CHU Bordeaux, Bordeaux, France.,University Bordeaux, CNRS, IMN, UMR 5293, Bordeaux, France.,Department of Medicine, University of Otago, Christchurch, New Zealand Brain Research Institute, Christchurch, New Zealand
| | - Paolo Barone
- Neurodegenerative Disease Centre, University of Salerno, Salerno, Italy
| | - Klaus Seppi
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Horacio Kaufmann
- Department of Medicine, NYU Grossman School of Medicine, New York, New York, USA
| | - Gregor K Wenning
- Division of Clinical Neurobiology, Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Werner Poewe
- Division of Clinical Neurobiology, Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Lars Farde
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
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24
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Dan X, Hu Y, Sun J, Gao L, Zhou Y, Ma J, Doyon J, Wu T, Chan P. Altered Cerebellar Resting-State Functional Connectivity in Early-Stage Parkinson's Disease Patients With Cognitive Impairment. Front Neurol 2021; 12:678013. [PMID: 34512503 PMCID: PMC8425347 DOI: 10.3389/fneur.2021.678013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 07/30/2021] [Indexed: 01/01/2023] Open
Abstract
Background: Cognitive impairment is one of the most prominent non-motor symptoms in Parkinson's disease (PD), due in part to known cerebellar dysfunctions. Furthermore, previous studies have reported altered cerebellar functional connectivity (FC) in PD patients. Yet whether these changes are also due to the cognitive deficits in PD remain unclear. Methods: A total of 122 non-dementia participants, including 64 patients with early PD and 58 age- and gender-matched elderly controls were stratified into four groups based on their cognitive status (normal cognition vs. cognitive impairment). Cerebellar volumetry and FC were investigated by analyzing, respectively, structural and resting-state functional MRI data among groups using quality control and quantitative measures. Correlation analysis between MRI metrics and clinical features (motor and cognitive scores) were performed. Results: Compared to healthy control subjects with no cognitive deficits, altered cerebellar FC were observed in early PD participants with both motor and cognitive deficits, but not in PD patients with normal cognition, nor elderly subjects showing signs of a cognitive impairment. Moreover, connectivity between the "motor" cerebellum and SMA was positively correlated with motor scores, while intracerebellar connectivity was positively correlated with cognitive scores in PD patients with cognitive impairment. No cerebellar volumetric difference was observed between groups. Conclusions: These findings show that altered cerebellar FC during resting state in early PD patients may be driven not solely by the motor deficits, but by cognitive deficits as well, hence highlighting the interplay between motor and cognitive functioning, and possibly reflecting compensatory mechanisms, in the early PD.
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Affiliation(s)
- Xiaojuan Dan
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Key Laboratory on Neurodegenerative Disorders of Ministry of Education, Key Laboratory on Parkinson's Disease of Beijing, Beijing, China
| | - Yang Hu
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junyan Sun
- Department of Neurobiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Linlin Gao
- Department of Neurobiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yongtao Zhou
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Jinghong Ma
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Julien Doyon
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Tao Wu
- Department of Neurobiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Piu Chan
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Key Laboratory on Neurodegenerative Disorders of Ministry of Education, Key Laboratory on Parkinson's Disease of Beijing, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Beijing Institute for Brain Disorders Parkinson's Disease Center, Capital Medical University, Beijing, China
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25
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Marsili L, Giannini G, Cortelli P, Colosimo C. Early recognition and diagnosis of multiple system atrophy: best practice and emerging concepts. Expert Rev Neurother 2021; 21:993-1004. [PMID: 34253122 DOI: 10.1080/14737175.2021.1953984] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Introduction: Multiple system atrophy (MSA) is a progressive degenerative disorder of the central and autonomic nervous systems characterized by parkinsonism, cerebellar ataxia, dysautonomia, and pyramidal signs. The confirmatory diagnosis is pathological, but clinical-diagnostic criteria have been developed to help clinicians. To date, the early diagnosis of MSA is challenging due to the lack of reliable diagnostic biomarkers.Areas covered: The authors reappraised the main clinical, neurophysiological, imaging, genetic, and laboratory evidence to help in the early diagnosis of MSA in the clinical and in the research settings. They also addressed the practical clinical issues in the differential diagnosis between MSA and other parkinsonian and cerebellar syndromes. Finally, the authors summarized the unmet needs in the early diagnosis of MSA and proposed the next steps for future research efforts in this field.Expert opinion: In the last decade, many advances have been achieved to help the correct MSA diagnosis since early stages. In the next future, the early diagnosis and correct classification of MSA, together with a better knowledge of the causative mechanisms of the disease, will hopefully allow the identification of suitable candidates to enroll in clinical trials and select the most appropriate disease-modifying strategies to slow down disease progression.
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Affiliation(s)
- Luca Marsili
- Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, OH, USA
| | - Giulia Giannini
- IRCCS Istituto delle Scienze Neurologiche di Bologna, UOC Clinica Neurologica NeuroMet, Ospedale Bellaria, Bologna, Italy.,Dipartimento di Scienze Biomediche e Neuromotorie, Università Bologna, Bologna, Italy
| | - Pietro Cortelli
- IRCCS Istituto delle Scienze Neurologiche di Bologna, UOC Clinica Neurologica NeuroMet, Ospedale Bellaria, Bologna, Italy.,Dipartimento di Scienze Biomediche e Neuromotorie, Università Bologna, Bologna, Italy
| | - Carlo Colosimo
- Department of Neurology, Santa Maria University Hospital, Terni, Italy
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26
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Feraco P, Gagliardo C, La Tona G, Bruno E, D’angelo C, Marrale M, Del Poggio A, Malaguti MC, Geraci L, Baschi R, Petralia B, Midiri M, Monastero R. Imaging of Substantia Nigra in Parkinson's Disease: A Narrative Review. Brain Sci 2021; 11:brainsci11060769. [PMID: 34207681 PMCID: PMC8230134 DOI: 10.3390/brainsci11060769] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 06/02/2021] [Accepted: 06/05/2021] [Indexed: 12/15/2022] Open
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder, characterized by motor and non-motor symptoms due to the degeneration of the pars compacta of the substantia nigra (SNc) with dopaminergic denervation of the striatum. Although the diagnosis of PD is principally based on a clinical assessment, great efforts have been expended over the past two decades to evaluate reliable biomarkers for PD. Among these biomarkers, magnetic resonance imaging (MRI)-based biomarkers may play a key role. Conventional MRI sequences are considered by many in the field to have low sensitivity, while advanced pulse sequences and ultra-high-field MRI techniques have brought many advantages, particularly regarding the study of brainstem and subcortical structures. Nowadays, nigrosome imaging, neuromelanine-sensitive sequences, iron-sensitive sequences, and advanced diffusion weighted imaging techniques afford new insights to the non-invasive study of the SNc. The use of these imaging methods, alone or in combination, may also help to discriminate PD patients from control patients, in addition to discriminating atypical parkinsonian syndromes (PS). A total of 92 articles were identified from an extensive review of the literature on PubMed in order to ascertain the-state-of-the-art of MRI techniques, as applied to the study of SNc in PD patients, as well as their potential future applications as imaging biomarkers of disease. Whilst none of these MRI-imaging biomarkers could be successfully validated for routine clinical practice, in achieving high levels of accuracy and reproducibility in the diagnosis of PD, a multimodal MRI-PD protocol may assist neuroradiologists and clinicians in the early and differential diagnosis of a wide spectrum of neurodegenerative disorders.
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Affiliation(s)
- Paola Feraco
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Via S. Giacomo 14, 40138 Bologna, Italy;
- Neuroradiology Unit, S. Chiara Hospital, 38122 Trento, Italy;
| | - Cesare Gagliardo
- Section of Radiological Sciences, Department of Biomedicine, Neurosciences & Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (G.L.T.); (E.B.); (C.D.); (M.M.)
- Correspondence:
| | - Giuseppe La Tona
- Section of Radiological Sciences, Department of Biomedicine, Neurosciences & Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (G.L.T.); (E.B.); (C.D.); (M.M.)
| | - Eleonora Bruno
- Section of Radiological Sciences, Department of Biomedicine, Neurosciences & Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (G.L.T.); (E.B.); (C.D.); (M.M.)
| | - Costanza D’angelo
- Section of Radiological Sciences, Department of Biomedicine, Neurosciences & Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (G.L.T.); (E.B.); (C.D.); (M.M.)
| | - Maurizio Marrale
- Department of Physics and Chemistry, University of Palermo, 90128 Palermo, Italy;
| | - Anna Del Poggio
- Department of Neuroradiology and CERMAC, San Raffaele Scientific Institute, San Raffaele Vita-Salute University, 20132 Milan, Italy;
| | | | - Laura Geraci
- Diagnostic and Interventional Neuroradiology Unit, A.R.N.A.S. Civico-Di Cristina-Benfratelli, 90127 Palermo, Italy;
| | - Roberta Baschi
- Section of Neurology, Department of Biomedicine, Neurosciences & Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (R.B.); (R.M.)
| | | | - Massimo Midiri
- Section of Radiological Sciences, Department of Biomedicine, Neurosciences & Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (G.L.T.); (E.B.); (C.D.); (M.M.)
| | - Roberto Monastero
- Section of Neurology, Department of Biomedicine, Neurosciences & Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (R.B.); (R.M.)
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Update on neuroimaging for categorization of Parkinson's disease and atypical parkinsonism. Curr Opin Neurol 2021; 34:514-524. [PMID: 34010220 DOI: 10.1097/wco.0000000000000957] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
PURPOSE OF REVIEW Differential diagnosis of Parkinsonism may be difficult. The objective of this review is to present the work of the last three years in the field of imaging for diagnostic categorization of parkinsonian syndromes focusing on progressive supranuclear palsy (PSP) and multiple system atrophy (MSA). RECENT FINDINGS Two main complementary approaches are being pursued. The first seeks to develop and validate manual qualitative or semi-quantitative imaging markers that can be easily used in clinical practice. The second is based on quantitative measurements of magnetic resonance imaging abnormalities integrated in a multimodal approach and in automatic categorization machine learning tools. SUMMARY These two complementary approaches obtained high diagnostic around 90% and above in the classical Richardson form of PSP and probable MSA. Future work will determine if these techniques can improve diagnosis in other PSP variants and early forms of the diseases when all clinical criteria are not fully met.
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Shi D, Zhang H, Wang S, Wang G, Ren K. Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson's Disease: A Histogram Analysis. Front Aging Neurosci 2021; 13:624731. [PMID: 34045953 PMCID: PMC8144304 DOI: 10.3389/fnagi.2021.624731] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 03/22/2021] [Indexed: 01/08/2023] Open
Abstract
This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson's disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.
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Affiliation(s)
| | | | | | | | - Ke Ren
- Department of Radiology, Xiang’an Hospital of Xia Men University, Xiamen, China
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Tolosa E, Garrido A, Scholz SW, Poewe W. Challenges in the diagnosis of Parkinson's disease. Lancet Neurol 2021; 20:385-397. [PMID: 33894193 PMCID: PMC8185633 DOI: 10.1016/s1474-4422(21)00030-2] [Citation(s) in RCA: 391] [Impact Index Per Article: 130.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 01/08/2021] [Accepted: 01/14/2021] [Indexed: 12/17/2022]
Abstract
Parkinson's disease is the second most common neurodegenerative disease and its prevalence has been projected to double over the next 30 years. An accurate diagnosis of Parkinson's disease remains challenging and the characterisation of the earliest stages of the disease is ongoing. Recent developments over the past 5 years include the validation of clinical diagnostic criteria, the introduction and testing of research criteria for prodromal Parkinson's disease, and the identification of genetic subtypes and a growing number of genetic variants associated with risk of Parkinson's disease. Substantial progress has been made in the development of diagnostic biomarkers, and genetic and imaging tests are already part of routine protocols in clinical practice, while novel tissue and fluid markers are under investigation. Parkinson's disease is evolving from a clinical to a biomarker-supported diagnostic entity, for which earlier identification is possible, different subtypes with diverse prognosis are recognised, and novel disease-modifying treatments are in development.
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Affiliation(s)
- Eduardo Tolosa
- Parkinson’s disease and Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic, IDIBAPS, Universitat de Barcelona, Barcelona, Spain
| | - Alicia Garrido
- Parkinson’s disease and Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic, IDIBAPS, Universitat de Barcelona, Barcelona, Spain
| | - Sonja W. Scholz
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Werner Poewe
- Department of Neurology, Medical University Innsbruck, Innsbruck, Austria
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30
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Hu X, Sun X, Hu F, Liu F, Ruan W, Wu T, An R, Lan X. Multivariate radiomics models based on 18F-FDG hybrid PET/MRI for distinguishing between Parkinson's disease and multiple system atrophy. Eur J Nucl Med Mol Imaging 2021; 48:3469-3481. [PMID: 33829415 DOI: 10.1007/s00259-021-05325-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 03/20/2021] [Indexed: 01/08/2023]
Abstract
PURPOSE To construct multivariate radiomics models using hybrid 18F-FDG PET/MRI for distinguishing between Parkinson's disease (PD) and multiple system atrophy (MSA). METHODS Ninety patients (60 with PD and 30 with MSA) were randomized to training and test sets in a 7:3 ratio. All patients underwent 18F-fluorodeoxyglucose (18F-FDG) PET/MRI to simultaneously obtain metabolic images (18F-FDG), structural MRI images (T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) and T2-weighted fluid-attenuated inversion recovery (T2/FLAIR)) and functional MRI images (susceptibility-weighted imaging (SWI) and apparent diffusion coefficient). Using PET and five MRI sequences, we extracted 1172 radiomics features from the putamina and caudate nuclei. The radiomics signatures were constructed with the least absolute shrinkage and selection operator algorithm in the training set, with progressive optimization through single-sequence and double-sequence radiomics models. Multivariable logistic regression analysis was used to develop a clinical-radiomics model, combining the optimal multi-sequence radiomics signature with clinical characteristics and SUV values. The diagnostic performance of the models was assessed by receiver operating characteristic and decision curve analysis (DCA). RESULTS The radiomics signatures showed favourable diagnostic efficacy. The optimal model comprised structural (T1WI), functional (SWI) and metabolic (18F-FDG) sequences (RadscoreFDG_T1WI_SWI) with the area under curves (AUCs) of the training and test sets of 0.971 and 0.957, respectively. The integrated model, incorporating RadscoreFDG_T1WI_SWI, three clinical symptoms (disease duration, dysarthria and autonomic failure) and SUVmax, demonstrated satisfactory calibration and discrimination in the training and test sets (0.993 and 0.994, respectively). DCA indicated the highest clinical benefit of the clinical-radiomics integrated model. CONCLUSIONS The radiomics signature with metabolic, structural and functional information provided by hybrid 18F-FDG PET/MRI may achieve promising diagnostic efficacy for distinguishing between PD and MSA. The clinical-radiomics integrated model performed best.
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Affiliation(s)
- Xuehan Hu
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Ave, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Xun Sun
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Ave, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Fan Hu
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Ave, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Fang Liu
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Ave, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Weiwei Ruan
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Ave, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Tingfan Wu
- GE Healthcare, Pudong New Town, No.1, Huatuo Road, Shanghai, 200000, China
| | - Rui An
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Ave, Wuhan, 430022, China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
| | - Xiaoli Lan
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Ave, Wuhan, 430022, China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
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Li Y, Sethi SK, Zhang C, Miao Y, Yerramsetty KK, Palutla VK, Gharabaghi S, Wang C, He N, Cheng J, Yan F, Haacke EM. Iron Content in Deep Gray Matter as a Function of Age Using Quantitative Susceptibility Mapping: A Multicenter Study. Front Neurosci 2021; 14:607705. [PMID: 33488350 PMCID: PMC7815653 DOI: 10.3389/fnins.2020.607705] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 12/07/2020] [Indexed: 12/21/2022] Open
Abstract
PURPOSE To evaluate the effect of resolution on iron content using quantitative susceptibility mapping (QSM); to verify the consistency of QSM across field strengths and manufacturers in evaluating the iron content of deep gray matter (DGM) of the human brain using subjects from multiple sites; and to establish a susceptibility baseline as a function of age for each DGM structure using both a global and regional iron analysis. METHODS Data from 623 healthy adults, ranging from 20 to 90 years old, were collected across 3 sites using gradient echo imaging on one 1.5 Tesla and two 3.0 Tesla MR scanners. Eight subcortical gray matter nuclei were semi-automatically segmented using a full-width half maximum threshold-based analysis of the QSM data. Mean susceptibility, volume and total iron content with age correlations were evaluated for each measured structure for both the whole-region and RII (high iron content regions) analysis. For the purpose of studying the effect of resolution on QSM, a digitized model of the brain was applied. RESULTS The mean susceptibilities of the caudate nucleus (CN), globus pallidus (GP) and putamen (PUT) were not significantly affected by changing the slice thickness from 0.5 to 3 mm. But for small structures, the susceptibility was reduced by 10% for 2 mm thick slices. For global analysis, the mean susceptibility correlated positively with age for the CN, PUT, red nucleus (RN), substantia nigra (SN), and dentate nucleus (DN). There was a negative correlation with age in the thalamus (THA). The volumes of most nuclei were negatively correlated with age. Apart from the GP, THA, and pulvinar thalamus (PT), all the other structures showed an increasing total iron content despite the reductions in volume with age. For the RII regional high iron content analysis, mean susceptibility in most of the structures was moderately to strongly correlated with age. Similar to the global analysis, apart from the GP, THA, and PT, all structures showed an increasing total iron content. CONCLUSION A reasonable estimate for age-related iron behavior can be obtained from a large cross site, cross manufacturer set of data when high enough resolutions are used. These estimates can be used for correcting for age related iron changes when studying diseases like Parkinson's disease, Alzheimer's disease, and other iron related neurodegenerative diseases.
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Affiliation(s)
- Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sean K. Sethi
- Department of Radiology, Wayne State University, Detroit, MI, United States
- MR Innovations, Inc., Bingham Farms, MI, United States
- SpinTech, Inc., Bingham Farms, MI, United States
| | - Chunyan Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanwei Miao
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | | | | | | | - Chengyan Wang
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ewart Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Radiology, Wayne State University, Detroit, MI, United States
- MR Innovations, Inc., Bingham Farms, MI, United States
- SpinTech, Inc., Bingham Farms, MI, United States
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32
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De Barros A, Arribarat G, Lotterie JA, Dominguez G, Chaynes P, Péran P. Iron distribution in the lentiform nucleus: A post-mortem MRI and histology study. Brain Struct Funct 2021; 226:351-364. [PMID: 33389044 DOI: 10.1007/s00429-020-02175-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 11/09/2020] [Indexed: 01/19/2023]
Abstract
Iron plays an important role in many neurobiological processes, especially in the basal ganglia, the brain structures with the highest concentration. Composed of the pallidum and putamen, the lentiform nucleus plays a key role in the basal ganglia circuitry. With MRI advances, iron-based sequences such as R2* and quantitative susceptibility mapping (QSM) are now available for detecting and quantifying iron in different brain structures. Since their validation using classic iron detection techniques (histology or physical techniques), these sequences have attracted growing clinical attention, especially in the field of extrapyramidal syndromes that particularly affect the basal nuclei. Accurate mapping of iron in these nuclei and their connections is needed to gain a better understanding of this specific anatomy, before considering its involvement in the physiopathological processes. We performed R2* and QSM along with Perls histology, to gain new insights into the distribution of iron in the lentiform nucleus and its surrounding structures, based on four specimens obtained from voluntary donors. We found that iron is preferentially distributed in the anterior part of the globus pallidus externus and the posterior part of the putamen. The lateral wall of the putamen is iron-poor, compared with the lateral medullary lamina and intraputaminal fibers. The relevance of perivascular iron concentration, along with pallido- and putaminofugal iron-rich fibers, is discussed.
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Affiliation(s)
- Amaury De Barros
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier-INSERM, CHU Purpan, Pavillon Baudot, Place du Dr Baylac, 31024, Toulouse, Cedex 3, France. .,Department of Anatomy, Toulouse Faculty of Medicine, Toulouse federal University, Toulouse, France. .,Neuroscience (Neurosurgery) Center, Toulouse University Hospital, Toulouse, France.
| | - Germain Arribarat
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier-INSERM, CHU Purpan, Pavillon Baudot, Place du Dr Baylac, 31024, Toulouse, Cedex 3, France
| | - Jean Albert Lotterie
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier-INSERM, CHU Purpan, Pavillon Baudot, Place du Dr Baylac, 31024, Toulouse, Cedex 3, France.,Neuroscience (Neurosurgery) Center, Toulouse University Hospital, Toulouse, France
| | - Gaelle Dominguez
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier-INSERM, CHU Purpan, Pavillon Baudot, Place du Dr Baylac, 31024, Toulouse, Cedex 3, France.,Neuropathology Unit, University Pathology Laboratory, Toulouse University Hospital-University of Toulouse III-Paul Sabatier, Toulouse, France
| | - Patrick Chaynes
- Department of Anatomy, Toulouse Faculty of Medicine, Toulouse federal University, Toulouse, France.,Neuroscience (Neurosurgery) Center, Toulouse University Hospital, Toulouse, France
| | - Patrice Péran
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier-INSERM, CHU Purpan, Pavillon Baudot, Place du Dr Baylac, 31024, Toulouse, Cedex 3, France
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33
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Pang H, Yu Z, Li R, Yang H, Fan G. MRI-Based Radiomics of Basal Nuclei in Differentiating Idiopathic Parkinson's Disease From Parkinsonian Variants of Multiple System Atrophy: A Susceptibility-Weighted Imaging Study. Front Aging Neurosci 2020; 12:587250. [PMID: 33281598 PMCID: PMC7689200 DOI: 10.3389/fnagi.2020.587250] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Accepted: 10/08/2020] [Indexed: 12/11/2022] Open
Abstract
Objectives To investigate the value of MRI-based radiomic model based on the radiomic features of different basal nuclei in differentiating idiopathic Parkinson's disease (IPD) from Parkinsonian variants of multiple system atrophy (MSA-P). Methods Radiomics was applied to the 3T susceptibility- weighted imaging (SWI) from 102 MSA-P patients and 83 IPD patients (allocated to a training and a testing cohort, 7:3 ratio). The substantia nigra (SN), caudate nucleus (CN), putamen (PUT), globus pallidus (GP), red nucleus (RN), and subthalamic nucleus (STN) were manually segmented, and 396 features were extracted. After feature selection, support vector machine (SVM) was generated, and its predictive performance was calculated in both the training and testing cohorts using the area under receiver operating characteristic curve (AUC). Results Seven radiomic features were selected from the PUT, by which the SVM classifier achieved the best diagnostic performance with an AUC of 0.867 in the training cohort and an AUC of 0.862 in the testing cohort. Furthermore, the combined model, which incorporating part III of the Parkinson's Disease Rating Scale (UPDRSIII) scores into radiomic features of the PUT, further improved the diagnostic performance. However, radiomic features extracted from RN, SN, GP, CN, and STN had moderate to poor diagnostic performance, with AUC values that ranged from 0.610 to 0.788 in the training cohort and 0.583 to 0.766 in the testing cohort. Conclusion Radiomic features derived from the PUT had optimal value in differentiating IPD from MSA-P. A combined radiomic model, which contained radiomic features of the PUT and UPDRSIII scores, further improved performance and may represent a promising tool for distinguishing between IPD and MSA-P.
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Affiliation(s)
- Huize Pang
- Department of Radiology, The first affiliated hospital of China Medical University, China Medical University, Shenyang, China
| | - Ziyang Yu
- School of Medicine, Xiamen University, Xiamen, China
| | - Renyuan Li
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China.,The Affiliated Sir Run Run Shaw hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Huaguang Yang
- Department of Radiology, The first affiliated hospital of China Medical University, China Medical University, Shenyang, China
| | - Guoguang Fan
- Department of Radiology, The first affiliated hospital of China Medical University, China Medical University, Shenyang, China
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Chougar L, Faouzi J, Pyatigorskaya N, Yahia‐Cherif L, Gaurav R, Biondetti E, Villotte M, Valabrègue R, Corvol J, Brice A, Mariani L, Cormier F, Vidailhet M, Dupont G, Piot I, Grabli D, Payan C, Colliot O, Degos B, Lehéricy S. Automated Categorization of Parkinsonian Syndromes Using
Magnetic Resonance Imaging
in a Clinical Setting. Mov Disord 2020; 36:460-470. [DOI: 10.1002/mds.28348] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 09/15/2020] [Indexed: 02/06/2023] Open
Affiliation(s)
- Lydia Chougar
- Paris Brain Institute–ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UMR S 1127, CNRS UMR 7225 Paris France
- ICM, “Movement Investigations and Therapeutics” Team (MOV'IT) Paris France
- ICM, Centre de NeuroImagerie de Recherche–CENIR Paris France
- Department of Neuroradiology Pitié‐Salpêtrière University Hospital, APHP Paris France
| | - Johann Faouzi
- Paris Brain Institute–ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UMR S 1127, CNRS UMR 7225 Paris France
- INRIA, Aramis Team Paris France
| | - Nadya Pyatigorskaya
- Paris Brain Institute–ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UMR S 1127, CNRS UMR 7225 Paris France
- ICM, “Movement Investigations and Therapeutics” Team (MOV'IT) Paris France
- ICM, Centre de NeuroImagerie de Recherche–CENIR Paris France
- Department of Neuroradiology Pitié‐Salpêtrière University Hospital, APHP Paris France
| | - Lydia Yahia‐Cherif
- Paris Brain Institute–ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UMR S 1127, CNRS UMR 7225 Paris France
- ICM, Centre de NeuroImagerie de Recherche–CENIR Paris France
| | - Rahul Gaurav
- Paris Brain Institute–ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UMR S 1127, CNRS UMR 7225 Paris France
- ICM, “Movement Investigations and Therapeutics” Team (MOV'IT) Paris France
- ICM, Centre de NeuroImagerie de Recherche–CENIR Paris France
| | - Emma Biondetti
- Paris Brain Institute–ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UMR S 1127, CNRS UMR 7225 Paris France
- ICM, “Movement Investigations and Therapeutics” Team (MOV'IT) Paris France
- ICM, Centre de NeuroImagerie de Recherche–CENIR Paris France
| | - Marie Villotte
- Faculté de Médecine Université Denis Diderot Paris France
| | - Romain Valabrègue
- Paris Brain Institute–ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UMR S 1127, CNRS UMR 7225 Paris France
- ICM, Centre de NeuroImagerie de Recherche–CENIR Paris France
| | - Jean‐Christophe Corvol
- Paris Brain Institute–ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UMR S 1127, CNRS UMR 7225 Paris France
- ICM, Centre d'Investigation Clinique Neurosciences Paris France
| | - Alexis Brice
- Paris Brain Institute–ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UMR S 1127, CNRS UMR 7225 Paris France
- ICM, Team Neurogénétique Fondamentale et Translationnelle Paris France
| | - Louise‐Laure Mariani
- Paris Brain Institute–ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UMR S 1127, CNRS UMR 7225 Paris France
- ICM, Centre d'Investigation Clinique Neurosciences Paris France
- Clinique des Mouvements Anormaux, Département des Maladies du Système Nerveux, Hôpital Pitié‐Salpêtrière, APHP Paris France
| | - Florence Cormier
- Clinique des Mouvements Anormaux, Département des Maladies du Système Nerveux, Hôpital Pitié‐Salpêtrière, APHP Paris France
| | - Marie Vidailhet
- Paris Brain Institute–ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UMR S 1127, CNRS UMR 7225 Paris France
- ICM, “Movement Investigations and Therapeutics” Team (MOV'IT) Paris France
- Clinique des Mouvements Anormaux, Département des Maladies du Système Nerveux, Hôpital Pitié‐Salpêtrière, APHP Paris France
| | - Gwendoline Dupont
- Université de Bourgogne Dijon France
- Centre Hospitalier Universitaire François Mitterrand, Département de Neurologie Dijon France
| | - Ines Piot
- Department of Neuroradiology Pitié‐Salpêtrière University Hospital, APHP Paris France
| | - David Grabli
- Clinique des Mouvements Anormaux, Département des Maladies du Système Nerveux, Hôpital Pitié‐Salpêtrière, APHP Paris France
| | - Christine Payan
- BESPIM, Hôpital Universitaire de Nîmes Nîmes France
- Service de Pharmacologie Clinique, Hôpital Pitié‐Salpêtrière, APHP Paris France
| | - Olivier Colliot
- Paris Brain Institute–ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UMR S 1127, CNRS UMR 7225 Paris France
- INRIA, Aramis Team Paris France
| | - Bertrand Degos
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology Collège de France, CNRS UMR7241/INSERM U1050, MemoLife Labex Paris France
- Department of Neurology, Avicenne University Hospital Sorbonne Paris Nord University Bobigny France
| | - Stéphane Lehéricy
- Paris Brain Institute–ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UMR S 1127, CNRS UMR 7225 Paris France
- ICM, “Movement Investigations and Therapeutics” Team (MOV'IT) Paris France
- ICM, Centre de NeuroImagerie de Recherche–CENIR Paris France
- Department of Neuroradiology Pitié‐Salpêtrière University Hospital, APHP Paris France
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35
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Song P, Li S, Wang S, Wei H, Lin H, Wang Y. Repetitive transcranial magnetic stimulation of the cerebellum improves ataxia and cerebello-fronto plasticity in multiple system atrophy: a randomized, double-blind, sham-controlled and TMS-EEG study. Aging (Albany NY) 2020; 12:20611-20622. [PMID: 33085647 PMCID: PMC7655163 DOI: 10.18632/aging.103946] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 08/04/2020] [Indexed: 11/25/2022]
Abstract
Cerebellar ataxia is the predominant motor feature of multiple system atrophy cerebellar subtype (MSA-C). Although repetitive transcranial magnetic stimulation (TMS) of the cerebellum is growingly applied in MSA, the mechanism is unknown. We examined dynamic connectivity changes of 20 patients with MSA and 25 healthy controls using TMS combined with electroencephalography. Observations that significantly decreased dynamic cerebello-frontal connectivity in patients have inspired attempts to modulate cerebellar connectivity in order to benefit MSA. We further explore the therapeutic potential of a 10-day treatment of cerebellar intermittent theta burst stimulation (iTBS) in MSA by a randomized, double-blind, sham-controlled trial. The functional reorganization of cerebellar networks was investigated after the end of treatment in active and sham groups. The severity of the symptoms was evaluated using the Scale for Assessment and Rating of Ataxia scores. Patients treated with active stimulation showed an improvement of cerebello-frontal connectivity and balance functions, as revealed by a significant decrease in the ataxia scores (P < 0.01). Importantly, the neural activity of frontal connectivity from 80 to 100 ms after a single TMS was significantly related to the severity of the disease. Our study provides new proof that cerebellar iTBS improves motor imbalance in MSA by acting on cerebello-cortical plasticity.
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Affiliation(s)
- Penghui Song
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China.,Central Laboratory, Xuanwu Hospital, Capital Medical University, Beijing 100053, China.,Beijing Geriatric Medical Research Center, Beijing 100053, China
| | - Siran Li
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Suobin Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Hua Wei
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Hua Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Yuping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China.,Beijing Key Laboratory of Neuromodulation, Beijing 100053, China
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36
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Saeed U, Lang AE, Masellis M. Neuroimaging Advances in Parkinson's Disease and Atypical Parkinsonian Syndromes. Front Neurol 2020; 11:572976. [PMID: 33178113 PMCID: PMC7593544 DOI: 10.3389/fneur.2020.572976] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 09/02/2020] [Indexed: 12/11/2022] Open
Abstract
Parkinson's disease (PD) and atypical Parkinsonian syndromes are progressive heterogeneous neurodegenerative diseases that share clinical characteristic of parkinsonism as a common feature, but are considered distinct clinicopathological disorders. Based on the predominant protein aggregates observed within the brain, these disorders are categorized as, (1) α-synucleinopathies, which include PD and other Lewy body spectrum disorders as well as multiple system atrophy, and (2) tauopathies, which comprise progressive supranuclear palsy and corticobasal degeneration. Although, great strides have been made in neurodegenerative disease research since the first medical description of PD in 1817 by James Parkinson, these disorders remain a major diagnostic and treatment challenge. A valid diagnosis at early disease stages is of paramount importance, as it can help accommodate differential prognostic and disease management approaches, enable the elucidation of reliable clinicopathological relationships ideally at prodromal stages, as well as facilitate the evaluation of novel therapeutics in clinical trials. However, the pursuit for early diagnosis in PD and atypical Parkinsonian syndromes is hindered by substantial clinical and pathological heterogeneity, which can influence disease presentation and progression. Therefore, reliable neuroimaging biomarkers are required in order to enhance diagnostic certainty and ensure more informed diagnostic decisions. In this article, an updated presentation of well-established and emerging neuroimaging biomarkers are reviewed from the following modalities: (1) structural magnetic resonance imaging (MRI), (2) diffusion-weighted and diffusion tensor MRI, (3) resting-state and task-based functional MRI, (4) proton magnetic resonance spectroscopy, (5) transcranial B-mode sonography for measuring substantia nigra and lentiform nucleus echogenicity, (6) single photon emission computed tomography for assessing the dopaminergic system and cerebral perfusion, and (7) positron emission tomography for quantifying nigrostriatal functions, glucose metabolism, amyloid, tau and α-synuclein molecular imaging, as well as neuroinflammation. Multiple biomarkers obtained from different neuroimaging modalities can provide distinct yet corroborative information on the underlying neurodegenerative processes. This integrative "multimodal approach" may prove superior to single modality-based methods. Indeed, owing to the international, multi-centered, collaborative research initiatives as well as refinements in neuroimaging technology that are currently underway, the upcoming decades will mark a pivotal and exciting era of further advancements in this field of neuroscience.
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Affiliation(s)
- Usman Saeed
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Anthony E Lang
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada.,Edmond J Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Mario Masellis
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada.,L.C. Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Center, Toronto, ON, Canada.,Cognitive and Movement Disorders Clinic, Sunnybrook Health Sciences Center, Toronto, ON, Canada
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37
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Zhang Y, Burock MA. Diffusion Tensor Imaging in Parkinson's Disease and Parkinsonian Syndrome: A Systematic Review. Front Neurol 2020; 11:531993. [PMID: 33101169 PMCID: PMC7546271 DOI: 10.3389/fneur.2020.531993] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 08/18/2020] [Indexed: 12/21/2022] Open
Abstract
Diffusion tensor imaging (DTI) allows measuring fractional anisotropy and similar microstructural indices of the brain white matter. Lower than normal fractional anisotropy as well as higher than normal diffusivity is associated with loss of microstructural integrity and neurodegeneration. Previous DTI studies in Parkinson's disease (PD) have demonstrated abnormal fractional anisotropy in multiple white matter regions, particularly in the dopaminergic nuclei and dopaminergic pathways. However, DTI is not considered a diagnostic marker for the earliest Parkinson's disease since anisotropic alterations present a temporally divergent pattern during the earliest Parkinson's course. This article reviews a majority of clinically employed DTI studies in PD, and it aims to prove the utilities of DTI as a marker of diagnosing PD, correlating clinical symptomatology, tracking disease progression, and treatment effects. To address the challenge of DTI being a diagnostic marker for early PD, this article also provides a comparison of the results from a longitudinal, early stage, multicenter clinical cohort of Parkinson's research with previous publications. This review provides evidences of DTI as a promising marker for monitoring PD progression and classifying atypical PD types, and it also interprets the possible pathophysiologic processes under the complex pattern of fractional anisotropic changes in the first few years of PD. Recent technical advantages, limitations, and further research strategies of clinical DTI in PD are additionally discussed.
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Affiliation(s)
- Yu Zhang
- Department of Psychiatry, War Related Illness and Injury Study Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, United States
| | - Marc A Burock
- Department of Psychiatry, Mainline Health, Bryn Mawr Hospital, Bryn Mawr, PA, United States
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38
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Krismer F, Beliveau V, Seppi K, Mueller C, Goebel G, Gizewski ER, Wenning GK, Poewe W, Scherfler C. Automated Analysis of Diffusion-Weighted Magnetic Resonance Imaging for the Differential Diagnosis of Multiple System Atrophy from Parkinson's Disease. Mov Disord 2020; 36:241-245. [PMID: 32935402 PMCID: PMC7891649 DOI: 10.1002/mds.28281] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 08/14/2020] [Accepted: 08/17/2020] [Indexed: 12/14/2022] Open
Abstract
Background Manual region‐of‐interest analysis of putaminal and middle cerebellar peduncle diffusivity distinguishes patients with multiple system atrophy (MSA) and Parkinson's disease (PD) with high diagnostic accuracy. However, a recent meta‐analysis found substantial between‐study heterogeneity of diagnostic accuracy due to the lack of harmonized imaging protocols and standardized analyses pipelines. Objective Evaluation of diagnostic accuracy of observer‐independent analysis of microstructural integrity as measured by diffusion‐tensor imaging in patients with MSA and PD. Methods A total of 29 patients with MSA and 19 patients with PD (matched for age, gender, and disease duration) with 3 years of follow‐up were investigated with diffusion‐tensor imaging and T1‐weighted magnetic resonance imaging. Automated localization of relevant brain regions was obtained, and mean diffusivity and fractional anisotropy values were averaged within the regions of interest. The classification was performed using a C5.0 hierachical decision tree algorithm. Results Mean diffusivity of the middle cerebellar peduncle and cerebellar gray and white matter compartment as well as the putamen were significantly increased in patients with MSA and showed superior effect sizes compared to the volumetric analysis of these regions. A classifier model identified mean diffusivity of the middle cerebellar peduncle and putamen as the most predictive parameters. Cross‐validation of the classification model yields a Cohen's κ and overall diagnostic accuracy of 0.823 and 0.914, respectively. Conclusion Analysis of microstructural integrity within the middle cerebellar peduncle and putamen yielded a superior effect size compared to the volumetric measures, resulting in excellent diagnostic accuracy to discriminate patients with MSA from PD in the early to moderate disease stages. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society
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Affiliation(s)
- Florian Krismer
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria.,Neuroimaging Research Core Facility, Medical University of Innsbruck, Innsbruck, Austria
| | - Vincent Beliveau
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria.,Neuroimaging Research Core Facility, Medical University of Innsbruck, Innsbruck, Austria
| | - Klaus Seppi
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria.,Neuroimaging Research Core Facility, Medical University of Innsbruck, Innsbruck, Austria
| | - Christoph Mueller
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria.,Neuroimaging Research Core Facility, Medical University of Innsbruck, Innsbruck, Austria
| | - Georg Goebel
- Department of Medical Statistics, Informatics and Health Economics, Medical University of Innsbruck, Innsbruck, Austria
| | - Elke R Gizewski
- Neuroimaging Research Core Facility, Medical University of Innsbruck, Innsbruck, Austria.,Department of Neuroradiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Gregor K Wenning
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Werner Poewe
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria.,Neuroimaging Research Core Facility, Medical University of Innsbruck, Innsbruck, Austria
| | - Christoph Scherfler
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria.,Neuroimaging Research Core Facility, Medical University of Innsbruck, Innsbruck, Austria
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Pellecchia MT, Stankovic I, Fanciulli A, Krismer F, Meissner WG, Palma JA, Panicker JN, Seppi K, Wenning GK. Can Autonomic Testing and Imaging Contribute to the Early Diagnosis of Multiple System Atrophy? A Systematic Review and Recommendations by the Movement Disorder Society Multiple System Atrophy Study Group. Mov Disord Clin Pract 2020; 7:750-762. [PMID: 33043073 DOI: 10.1002/mdc3.13052] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 05/08/2020] [Accepted: 05/23/2020] [Indexed: 01/01/2023] Open
Abstract
Background In the current consensus diagnostic criteria, the diagnosis of probable multiple system atrophy (MSA) is based solely on clinical findings, whereas neuroimaging findings are listed as aid for the diagnosis of possible MSA. There are overlapping phenotypes between MSA-parkinsonian type and Parkinson's disease, progressive supranuclear palsy, and dementia with Lewy bodies, and between MSA-cerebellar type and sporadic adult-onset ataxia resulting in a significant diagnostic delay and misdiagnosis of MSA during life. Objectives In light of an ongoing effort to revise the current consensus criteria for MSA, the Movement Disorders Society Multiple System Atrophy Study Group performed a systematic review of original articles published before August 2019. Methods We included articles that studied at least 10 patients with MSA as well as participants with another disorder or control group for comparison purposes. MSA was defined by neuropathological confirmation, or as clinically probable, or clinically probable plus possible according to consensus diagnostic criteria. Results We discuss the pitfalls and benefits of each diagnostic test and provide specific recommendations on how to evaluate patients in whom MSA is suspected. Conclusions This systematic review of relevant studies indicates that imaging and autonomic function tests significantly contribute to increasing the accuracy of a diagnosis of MSA.
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Affiliation(s)
- Maria Teresa Pellecchia
- Center for Neurodegenerative Diseases, Department of Medicine, Neuroscience Section, University of Salerno Fisciano Italy
| | - Iva Stankovic
- Neurology Clinic, Clinical Center of Serbia School of Medicine, University of Belgrade Belgrade Serbia
| | | | - Florian Krismer
- Department of Neurology Innsbruck Medical University Innsbruck Austria
| | - Wassilios G Meissner
- French Reference Center for MSA, Department of Neurology University Hospital Bordeaux, Bordeaux and Institute of Neurodegenerative Disorders, University Bordeaux, Centre National de la Recherche Scientifique Unite Mixte de Recherche Bordeaux Bordeaux France
| | - Jose-Alberto Palma
- Dysautonomia Center, Langone Medical Center New York University School of Medicine New York New York USA
| | - Jalesh N Panicker
- Institute of Neurology, University College London London United Kingdom.,Department of Uro-Neurology The National Hospital for Neurology and Neurosurgery London United Kingdom
| | - Klaus Seppi
- Department of Neurology Innsbruck Medical University Innsbruck Austria
| | - Gregor K Wenning
- Department of Neurology Innsbruck Medical University Innsbruck Austria
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Arribarat G, De Barros A, Péran P. Modern Brainstem MRI Techniques for the Diagnosis of Parkinson's Disease and Parkinsonisms. Front Neurol 2020; 11:791. [PMID: 32849237 PMCID: PMC7417676 DOI: 10.3389/fneur.2020.00791] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 06/25/2020] [Indexed: 01/22/2023] Open
Abstract
The brainstem is the earliest vulnerable structure in many neurodegenerative diseases like in Multiple System Atrophy (MSA) or Parkinson's disease (PD). Up-to-now, MRI studies have mainly focused on whole-brain data acquisition. Due to its spatial localization, size, and tissue characteristics, brainstem poses particular challenges for MRI. We provide a brief overview on recent advances in brainstem-related MRI markers in Parkinson's disease and Parkinsonism's. Several MRI techniques investigating brainstem, mainly the midbrain, showed to be able to discriminate PD patients from controls or to discriminate PD patients from atypical parkinsonism patients: iron-sensitive MRI, nigrosome imaging, neuromelanin-sensitive MRI, diffusion tensor imaging and advanced diffusion imaging. A standardized multimodal brainstem-dedicated MRI approach at high resolution able to quantify microstructural modification in brainstem nuclei would be a promising tool to detect early changes in parkinsonian syndromes.
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Affiliation(s)
- Germain Arribarat
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France.,Centre de Recherche Cerveau et Cognition (CNRS, Cerco, UMR5549), UPS, Toulouse, France
| | - Amaury De Barros
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France.,Department of Anatomy, Toulouse Faculty of Medicine, Toulouse, France
| | - Patrice Péran
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France
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Chougar L, Pyatigorskaya N, Degos B, Grabli D, Lehéricy S. The Role of Magnetic Resonance Imaging for the Diagnosis of Atypical Parkinsonism. Front Neurol 2020; 11:665. [PMID: 32765399 PMCID: PMC7380089 DOI: 10.3389/fneur.2020.00665] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Accepted: 06/03/2020] [Indexed: 12/14/2022] Open
Abstract
The diagnosis of Parkinson's disease and atypical Parkinsonism remains clinically difficult, especially at the early stage of the disease, since there is a significant overlap of symptoms. Multimodal MRI has significantly improved diagnostic accuracy and understanding of the pathophysiology of Parkinsonian disorders. Structural and quantitative MRI sequences provide biomarkers sensitive to different tissue properties that detect abnormalities specific to each disease and contribute to the diagnosis. Machine learning techniques using these MRI biomarkers can effectively differentiate atypical Parkinsonian syndromes. Such approaches could be implemented in a clinical environment and improve the management of Parkinsonian patients. This review presents different structural and quantitative MRI techniques, their contribution to the differential diagnosis of atypical Parkinsonian disorders and their interest for individual-level diagnosis.
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Affiliation(s)
- Lydia Chougar
- Institut du Cerveau et de la Moelle épinière-ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UPMC Univ Paris 06, UMRS 1127, CNRS UMR 7225, Paris, France.,ICM, "Movement Investigations and Therapeutics" Team (MOV'IT), Paris, France.,ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France.,Service de Neuroradiologie, Hôpital Pitié-Salpêtrière, APHP, Paris, France
| | - Nadya Pyatigorskaya
- Institut du Cerveau et de la Moelle épinière-ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UPMC Univ Paris 06, UMRS 1127, CNRS UMR 7225, Paris, France.,ICM, "Movement Investigations and Therapeutics" Team (MOV'IT), Paris, France.,ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France.,Service de Neuroradiologie, Hôpital Pitié-Salpêtrière, APHP, Paris, France
| | - Bertrand Degos
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, MemoLife Labex, Paris, France.,Department of Neurology, Avicenne University Hospital, Sorbonne Paris Nord University, Bobigny, France
| | - David Grabli
- Département des Maladies du Système Nerveux, Hôpital Pitié-Salpêtrière, APHP, Paris, France
| | - Stéphane Lehéricy
- Institut du Cerveau et de la Moelle épinière-ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UPMC Univ Paris 06, UMRS 1127, CNRS UMR 7225, Paris, France.,ICM, "Movement Investigations and Therapeutics" Team (MOV'IT), Paris, France.,ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France.,Service de Neuroradiologie, Hôpital Pitié-Salpêtrière, APHP, Paris, France
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42
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Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges. Curr Opin Neurol 2020; 33:439-450. [DOI: 10.1097/wco.0000000000000838] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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43
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Altered white matter microarchitecture in Parkinson's disease: a voxel-based meta-analysis of diffusion tensor imaging studies. Front Med 2020; 15:125-138. [PMID: 32458190 DOI: 10.1007/s11684-019-0725-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Accepted: 10/12/2019] [Indexed: 02/05/2023]
Abstract
This study aimed to define the most consistent white matter microarchitecture pattern in Parkinson's disease (PD) reflected by fractional anisotropy (FA), addressing clinical profiles and methodology-related heterogeneity. Web-based publication databases were searched to conduct a meta-analysis of whole-brain diffusion tensor imaging studies comparing PD with healthy controls (HC) using the anisotropic effect size-signed differential mapping. A total of 808 patients with PD and 760 HC coming from 27 databases were finally included. Subgroup analyses were conducted considering heterogeneity with respect to medication status, disease stage, analysis methods, and the number of diffusion directions in acquisition. Compared with HC, patients with PD had decreased FA in the left middle cerebellar peduncle, corpus callosum (CC), left inferior fronto-occipital fasciculus, and right inferior longitudinal fasciculus. Most of the main results remained unchanged in subgroup meta-analyses of medicated patients, early stage patients, voxel-based analysis, and acquisition with 30 diffusion directions. The subgroup meta-analysis of medication-free patients showed FA decrease in the right olfactory cortex. The cerebellum and CC, associated with typical motor impairment, showed the most consistent FA decreases in PD. Medication status, analysis approaches, and the number of diffusion directions have an important impact on the findings, needing careful evaluation in future meta-analyses.
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44
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Arribarat G, Péran P. Quantitative MRI markers in Parkinson's disease and parkinsonian syndromes. Curr Opin Neurol 2020; 33:222-229. [DOI: 10.1097/wco.0000000000000796] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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45
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Diagnosing multiple system atrophy at the prodromal stage. Clin Auton Res 2020; 30:197-205. [PMID: 32232688 DOI: 10.1007/s10286-020-00682-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 03/16/2020] [Indexed: 02/07/2023]
Abstract
Identifying individuals at the earliest disease stage becomes crucial as we aim to develop disease-modifying treatments for neurodegenerative disorders. Prodromal diagnostic criteria were recently developed for Parkinson's disease (PD) and are forthcoming for dementia with Lewy bodies (DLB). The latest 2008 version of diagnostic criteria for multiple system atrophy (MSA) have improved diagnostic accuracy in early disease stages compared to previous criteria, but we do not yet have formal criteria for prodromal MSA. Building on similar approaches as for PD and DLB, we can identify features on history-taking, clinical examination, and ancillary clinical testing that can predict the likelihood of an individual developing MSA, while also distinguishing it from PD and DLB. The main clinical hallmarks of MSA are REM sleep behavior disorder (RBD) and autonomic dysfunction (particularly orthostatic hypotension and urogenital symptoms), and may be the primary means by which patients with potential prodromal MSA are identified. Preserved olfaction, absence of significant cognitive deficits, urinary retention, and respiratory symptoms such as stridor and respiratory insufficiency can be clinical features that help distinguish MSA from PD and DLB. Finally, ancillary test results including neuroimaging as well as serological and cerebrospinal fluid (CSF) biomarkers may lend further weight to quantifying the likelihood of phenoconversion into MSA. For prodromal criteria, the primary challenges are MSA's lower prevalence, shorter lead time to diagnosis, and strong overlap with other synucleinopathies. Future prodromal criteria may need to first embed the diagnosis into a general umbrella of prodromal alpha-synucleinopathies, followed by identification of features that suggest prodromal MSA as the specific cause.
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46
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Li SJ, Ren YD, Li J, Cao B, Ma C, Qin SS, Li XR. The role of iron in Parkinson's disease monkeys assessed by susceptibility weighted imaging and inductively coupled plasma mass spectrometry. Life Sci 2019; 240:117091. [PMID: 31760102 DOI: 10.1016/j.lfs.2019.117091] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 11/15/2019] [Accepted: 11/18/2019] [Indexed: 01/18/2023]
Abstract
Mounting evidences indicated that elevated iron levels in the substantia nigra (SN) have been concerned as the underlying mechanisms of neurodegenerative diseases, including Parkinson's disease (PD). The present study used the 1-Methyl-4-phenyl-1, 2, 3, 6 -tetrahydropyridine (MPTP)-treated cynomolgus monkeys for PD to evaluate the usability of SWI for assessing iron deposition in the cerebral nuclei of PD. The results showed that susceptibility-weighted imaging (SWI) phase values of the ipsilateral (MPTP-lesion side) SN of MPTP-treated monkeys were lower than those in the contralateral SN of MPTP-treated monkeys and the same side of Control monkeys, suggesting that iron deposition were elevated in the affected side SN of MPTP-treated monkeys. Whereas MPTP has not effects on the SWI phase values in other detected brain regions of monkeys, including red nucleus (RN), putamen (PUT) and caudate nucleus (CA). Furthermore, ICP-MS results showed that MPTP increased the iron levels in MPTP injection side, but no in the ipsilateral striatum. Additionally, MPTP treatment did not affect the calcium and manganese levels in the detected brain regions of monkeys. However, Pearson correlation analysis results indicated that there were not relationship between SWI phase values in MPTP-lesion side of SN with the behavioral score, tyrosine hydroxylase (TH)-positive cells number and iron levels in the MPTP-lesion side of midbrain. Taken together, the results confirm the involvement of SN iron accumulations in the MPTP-treated monkey models for PD, and indirectly verify the usability of SWI for the measurement of iron deposition in the cerebral nuclei of PD.
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Affiliation(s)
- Shao-Jun Li
- Department of Toxicology, School of Public Health, Guangxi Medical University, No. 22, Shuangyong Road, Nanning 530021, Guangxi Province, China
| | - Yan-De Ren
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266003, Shandong Province, China.
| | - Jin Li
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6, Shuangyong Road, Nanning 530021, Guangxi Province, China
| | - Bin Cao
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266003, Shandong Province, China
| | - Chi Ma
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266003, Shandong Province, China
| | - Shan-Shan Qin
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266003, Shandong Province, China
| | - Xiang-Rong Li
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6, Shuangyong Road, Nanning 530021, Guangxi Province, China.
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Fanciulli A, Stankovic I, Krismer F, Seppi K, Levin J, Wenning GK. Multiple system atrophy. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2019; 149:137-192. [PMID: 31779811 DOI: 10.1016/bs.irn.2019.10.004] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Multiple system atrophy (MSA) is a sporadic, adult-onset, relentlessly progressive neurodegenerative disorder, clinically characterized by various combinations of autonomic failure, parkinsonism and ataxia. The neuropathological hallmark of MSA are glial cytoplasmic inclusions consisting of misfolded α-synuclein. Selective atrophy and neuronal loss in striatonigral and olivopontocerebellar systems underlie the division into two main motor phenotypes of MSA-parkinsonian type and MSA-cerebellar type. Isolated autonomic failure and REM sleep behavior disorder are common premotor features of MSA. Beyond the core clinical symptoms, MSA manifests with a number of non-motor and motor features. Red flags highly specific for MSA may provide clues for a correct diagnosis, but in general the diagnostic accuracy of the second consensus criteria is suboptimal, particularly in early disease stages. In this chapter, the authors discuss the historical milestones, etiopathogenesis, neuropathological findings, clinical features, red flags, differential diagnosis, diagnostic criteria, imaging and other biomarkers, current treatment, unmet needs and future treatments for MSA.
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Affiliation(s)
| | - Iva Stankovic
- Neurology Clinic, Clinical Center of Serbia, School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Florian Krismer
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Klaus Seppi
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-Universität München, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V., Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Gregor K Wenning
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
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Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography. Sci Rep 2019; 9:16488. [PMID: 31712681 PMCID: PMC6848175 DOI: 10.1038/s41598-019-52829-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 10/02/2019] [Indexed: 02/06/2023] Open
Abstract
Recent studies combining diffusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson’s disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be used to classify MSA and PD patients at the single-patient level. The classification performance of subcortical FA and MD was also evaluated to compare the discriminant ability between diffusion tensor-derived metrics and NOS. Using diffusion-weighted images acquired in a 3 T MRI scanner and probabilistic tractography, we reconstructed the white matter tracts between 18 subcortical structures from a sample of 54 healthy controls, 31 MSA patients and 65 PD patients. NOS between subcortical structures were compared between groups and entered as features into a machine learning algorithm. Reduced NOS in MSA compared with controls and PD were found in connections between the putamen, pallidum, ventral diencephalon, thalamus, and cerebellum, in both right and left hemispheres. The classification procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classified. NOS features outperformed the discrimination performance obtained with FA and MD. Our findings suggest that structural connectivity derived from tractography has the potential to correctly distinguish between MSA and PD patients. Furthermore, NOS measures obtained from tractography might be more useful than diffusion tensor-derived metrics for the detection of MSA.
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Meissner WG, Fernagut PO, Dehay B, Péran P, Traon APL, Foubert-Samier A, Lopez Cuina M, Bezard E, Tison F, Rascol O. Multiple System Atrophy: Recent Developments and Future Perspectives. Mov Disord 2019; 34:1629-1642. [PMID: 31692132 DOI: 10.1002/mds.27894] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 09/03/2019] [Accepted: 09/15/2019] [Indexed: 02/06/2023] Open
Abstract
Multiple system atrophy (MSA) is a rare and fatal neurodegenerative disorder characterized by a variable combination of parkinsonism, cerebellar impairment, and autonomic dysfunction. The pathologic hallmark is the accumulation of aggregated α-synuclein in oligodendrocytes, forming glial cytoplasmic inclusions, which qualifies MSA as a synucleinopathy together with Parkinson's disease and dementia with Lewy bodies. The underlying pathogenesis is still not well understood. Some symptomatic treatments are available, whereas neuroprotection remains an urgent unmet treatment need. In this review, we critically appraise significant developments of the past decade with emphasis on pathogenesis, diagnosis, prognosis, and treatment development. We further discuss unsolved questions and highlight some perspectives. © 2019 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Wassilios G Meissner
- CRMR Atrophie Multisystématisée, CHU Bordeaux, Service de Neurologie, Bordeaux, France.,Institut des Maladies Neurodégénératives, Univ. de Bordeaux, Bordeaux, France.,CNRS, Institut des Maladies Neurodégénératives, Bordeaux, France.,Dept. of Medicine, University of Otago, Christchurch, New Zealand Brain Research Institute, Christchurch, New Zealand
| | - Pierre-Olivier Fernagut
- Institut des Maladies Neurodégénératives, Univ. de Bordeaux, Bordeaux, France.,CNRS, Institut des Maladies Neurodégénératives, Bordeaux, France.,Laboratoire de Neurosciences Expérimentales et Cliniques, Université de Poitiers, Poitiers, France.,INSERM, Laboratoire de Neurosciences Expérimentales et Cliniques, Poitiers, France
| | - Benjamin Dehay
- Institut des Maladies Neurodégénératives, Univ. de Bordeaux, Bordeaux, France.,CNRS, Institut des Maladies Neurodégénératives, Bordeaux, France
| | - Patrice Péran
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Toulouse, France
| | - Anne Pavy-Le Traon
- Services de Neurologie, CRMR Atrophie Multisystématisée, Toulouse, Institut des Maladies Métaboliques et Cardiovasculaires, Toulouse, France
| | - Alexandra Foubert-Samier
- CRMR Atrophie Multisystématisée, CHU Bordeaux, Service de Neurologie, Bordeaux, France.,Institut des Maladies Neurodégénératives, Univ. de Bordeaux, Bordeaux, France.,Inserm, Bordeaux Population Health Research Center, Bordeaux University, Bordeaux, France
| | - Miguel Lopez Cuina
- Institut des Maladies Neurodégénératives, Univ. de Bordeaux, Bordeaux, France.,CNRS, Institut des Maladies Neurodégénératives, Bordeaux, France
| | - Erwan Bezard
- Institut des Maladies Neurodégénératives, Univ. de Bordeaux, Bordeaux, France.,CNRS, Institut des Maladies Neurodégénératives, Bordeaux, France
| | - François Tison
- CRMR Atrophie Multisystématisée, CHU Bordeaux, Service de Neurologie, Bordeaux, France.,Institut des Maladies Neurodégénératives, Univ. de Bordeaux, Bordeaux, France.,CNRS, Institut des Maladies Neurodégénératives, Bordeaux, France
| | - Olivier Rascol
- Services de Neurologie et de Pharmacologie Clinique, Centre de Reference AMS, Centre d'Investigation Clinique, Réseau NS-Park/FCRIN et Centre of Excellence for Neurodegenerative Disorders (COEN) de Toulouse, CHU de Toulouse, Toulouse 3 University, Toulouse, France
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Seki M, Seppi K, Mueller C, Potrusil T, Goebel G, Reiter E, Nocker M, Kremser C, Wildauer M, Schocke M, Gizewski ER, Wenning GK, Poewe W, Scherfler C. Diagnostic Potential of Multimodal MRI Markers in Atypical Parkinsonian Disorders. JOURNAL OF PARKINSONS DISEASE 2019; 9:681-691. [DOI: 10.3233/jpd-181568] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Morinobu Seki
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University of Innsbruck, Innsbruck, Austria
| | - Klaus Seppi
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University of Innsbruck, Innsbruck, Austria
| | - Christoph Mueller
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Thomas Potrusil
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University of Innsbruck, Innsbruck, Austria
| | - Georg Goebel
- Department of Medical Statistics, Informatics and Health Economics, Medical University of Innsbruck, Innsbruck, Austria
| | - Eva Reiter
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Michael Nocker
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Christian Kremser
- Neuroimaging Research Core Facility, Medical University of Innsbruck, Innsbruck, Austria
- Department of Neuroradiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Matthias Wildauer
- Neuroimaging Research Core Facility, Medical University of Innsbruck, Innsbruck, Austria
- Department of Neuroradiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Michael Schocke
- Department of Neuroradiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Elke R. Gizewski
- Neuroimaging Research Core Facility, Medical University of Innsbruck, Innsbruck, Austria
- Department of Neuroradiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Gregor K. Wenning
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Werner Poewe
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Christoph Scherfler
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
- Neuroimaging Research Core Facility, Medical University of Innsbruck, Innsbruck, Austria
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