1
|
Li P, Zhou X, Luo N, Shen R, Zhu X, Zhong M, Huang S, He N, Lyu H, Huang Y, Yin Q, Zhou L, Lu Y, Tan Y, Liu J. Distinct patterns of electrophysiologic-neuroimaging correlations between Parkinson's disease and multiple system atrophy. Neuroimage 2024; 297:120701. [PMID: 38914210 DOI: 10.1016/j.neuroimage.2024.120701] [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: 03/14/2024] [Revised: 06/17/2024] [Accepted: 06/21/2024] [Indexed: 06/26/2024] Open
Abstract
Due to a high degree of symptom overlap in the early stages, with movement disorders predominating, Parkinson's disease (PD) and multiple system atrophy (MSA) may exhibit a similar decline in motor areas, yet they differ in their spread throughout the brain, ultimately resulting in two distinct diseases. Drawing upon neuroimaging analyses and altered motor cortex excitability, potential diffusion mechanisms were delved into, and comparisons of correlations across distinct disease groups were conducted in a bid to uncover significant pathological disparities. We recruited thirty-five PD, thirty-seven MSA, and twenty-eight matched controls to conduct clinical assessments, electromyographic recording, and magnetic resonance imaging scanning during the "on medication" state. Patients with neurodegeneration displayed a widespread decrease in electrophysiology in bilateral M1. Brain function in early PD was still in the self-compensatory phase and there was no significant change. MSA patients demonstrated an increase in intra-hemispheric function coupled with a decrease in diffusivity, indicating a reduction in the spread of neural signals. The level of resting motor threshold in healthy aged showed broad correlations with both clinical manifestations and brain circuits related to left M1, which was absent in disease states. Besides, ICF exhibited distinct correlations with functional connections between right M1 and left middle temporal gyrus in all groups. The present study identified subtle differences in the functioning of PD and MSA related to bilateral M1. By combining clinical information, cortical excitability, and neuroimaging intuitively, we attempt to bring light on the potential mechanisms that may underlie the development of neurodegenerative disease.
Collapse
Affiliation(s)
- Puyu Li
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinyi Zhou
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Ningdi Luo
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Ruinan Shen
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xue Zhu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Min Zhong
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Sijia Huang
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Naying He
- Radiology Department, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Haiying Lyu
- Radiology Department, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yufei Huang
- Radiology Department, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Qianyi Yin
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Liche Zhou
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yong Lu
- Radiology Department, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yuyan Tan
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Jun Liu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Ndayisaba A, Pitaro AT, Willett AS, Jones KA, de Gusmao CM, Olsen AL, Kim J, Rissanen E, Woods JK, Srinivasan SR, Nagy A, Nagy A, Mesidor M, Cicero S, Patel V, Oakley DH, Tuncali I, Taglieri-Noble K, Clark EC, Paulson J, Krolewski RC, Ho GP, Hung AY, Wills AM, Hayes MT, Macmore JP, Warren L, Bower PG, Langer CB, Kellerman LR, Humphreys CW, Glanz BI, Dielubanza EJ, Frosch MP, Freeman RL, Gibbons CH, Stefanova N, Chitnis T, Weiner HL, Scherzer CR, Scholz SW, Vuzman D, Cox LM, Wenning G, Schmahmann JD, Gupta AS, Novak P, Young GS, Feany MB, Singhal T, Khurana V. Clinical Trial-Ready Patient Cohorts for Multiple System Atrophy: Coupling Biospecimen and iPSC Banking to Longitudinal Deep-Phenotyping. CEREBELLUM (LONDON, ENGLAND) 2024; 23:31-51. [PMID: 36190676 PMCID: PMC9527378 DOI: 10.1007/s12311-022-01471-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/26/2022] [Indexed: 11/30/2022]
Abstract
Multiple system atrophy (MSA) is a fatal neurodegenerative disease of unknown etiology characterized by widespread aggregation of the protein alpha-synuclein in neurons and glia. Its orphan status, biological relationship to Parkinson's disease (PD), and rapid progression have sparked interest in drug development. One significant obstacle to therapeutics is disease heterogeneity. Here, we share our process of developing a clinical trial-ready cohort of MSA patients (69 patients in 2 years) within an outpatient clinical setting, and recruiting 20 of these patients into a longitudinal "n-of-few" clinical trial paradigm. First, we deeply phenotype our patients with clinical scales (UMSARS, BARS, MoCA, NMSS, and UPSIT) and tests designed to establish early differential diagnosis (including volumetric MRI, FDG-PET, MIBG scan, polysomnography, genetic testing, autonomic function tests, skin biopsy) or disease activity (PBR06-TSPO). Second, we longitudinally collect biospecimens (blood, CSF, stool) and clinical, biometric, and imaging data to generate antecedent disease-progression scores. Third, in our Mass General Brigham SCiN study (stem cells in neurodegeneration), we generate induced pluripotent stem cell (iPSC) models from our patients, matched to biospecimens, including postmortem brain. We present 38 iPSC lines derived from MSA patients and relevant disease controls (spinocerebellar ataxia and PD, including alpha-synuclein triplication cases), 22 matched to whole-genome sequenced postmortem brain. iPSC models may facilitate matching patients to appropriate therapies, particularly in heterogeneous diseases for which patient-specific biology may elude animal models. We anticipate that deeply phenotyped and genotyped patient cohorts matched to cellular models will increase the likelihood of success in clinical trials for MSA.
Collapse
Affiliation(s)
- Alain Ndayisaba
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
- Division of Clinical Neurobiology, Department of Neurology, Medical University of Innsbruck, Anichstraße 35, 6020, Innsbruck, Austria
| | - Ariana T Pitaro
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Andrew S Willett
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Kristie A Jones
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Claudio Melo de Gusmao
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Abby L Olsen
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Jisoo Kim
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Eero Rissanen
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Jared K Woods
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Sharan R Srinivasan
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
- Department of Neurology, University of Michigan, Ann Arbor, MI , 48103, USA
| | - Anna Nagy
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Amanda Nagy
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Merlyne Mesidor
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Steven Cicero
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Viharkumar Patel
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Derek H Oakley
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Idil Tuncali
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Katherine Taglieri-Noble
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Emily C Clark
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Jordan Paulson
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Richard C Krolewski
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Gary P Ho
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Albert Y Hung
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Anne-Marie Wills
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Michael T Hayes
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Jason P Macmore
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | | | - Pamela G Bower
- The Multiple System Atrophy Coalition, Inc., 7918 Jones Branch Drive, Suite 300, McLean, VA, 22102, USA
| | - Carol B Langer
- The Multiple System Atrophy Coalition, Inc., 7918 Jones Branch Drive, Suite 300, McLean, VA, 22102, USA
| | - Lawrence R Kellerman
- The Multiple System Atrophy Coalition, Inc., 7918 Jones Branch Drive, Suite 300, McLean, VA, 22102, USA
| | - Christopher W Humphreys
- Department of Pulmonary, Sleep and Critical Care Medicine, Salem Hospital, MassGeneral Brigham, Salem, MA, 01970, USA
| | - Bonnie I Glanz
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Elodi J Dielubanza
- Department of Urology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Matthew P Frosch
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Roy L Freeman
- Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02115, USA
| | - Christopher H Gibbons
- Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02115, USA
| | - Nadia Stefanova
- Division of Clinical Neurobiology, Department of Neurology, Medical University of Innsbruck, Anichstraße 35, 6020, Innsbruck, Austria
| | - Tanuja Chitnis
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Howard L Weiner
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Clemens R Scherzer
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Sonja W Scholz
- Laboratory of Neurogenetics, Disorders and Stroke, National Institute of Neurological, National Institute of Neurological Disorders and Stroke, Bethesda, MD, 20892, USA
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, 21287, USA
| | - Dana Vuzman
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Laura M Cox
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Gregor Wenning
- Division of Clinical Neurobiology, Department of Neurology, Medical University of Innsbruck, Anichstraße 35, 6020, Innsbruck, Austria
| | - Jeremy D Schmahmann
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Anoopum S Gupta
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Peter Novak
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Geoffrey S Young
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Mel B Feany
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Tarun Singhal
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Vikram Khurana
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA.
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
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: 2] [Impact Index Per Article: 2.0] [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.
Collapse
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.).
| |
Collapse
|
7
|
Kim J, Young GS, Willett AS, Pitaro AT, Crotty GF, Mesidor M, Jones KA, Bay C, Zhang M, Feany MB, Xu X, Qin L, Khurana V. Toward More Accessible Fully Automated 3D Volumetric MRI Decision Trees for the Differential Diagnosis of Multiple System Atrophy, Related Disorders, and Age-Matched Healthy Subjects. CEREBELLUM (LONDON, ENGLAND) 2023; 22:1098-1108. [PMID: 36156185 PMCID: PMC10657274 DOI: 10.1007/s12311-022-01472-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/27/2022] [Indexed: 06/16/2023]
Abstract
Differentiating multiple system atrophy (MSA) from related neurodegenerative movement disorders (NMD) is challenging. MRI is widely available and automated decision-tree analysis is simple, transparent, and resistant to overfitting. Using a retrospective cohort of heterogeneous clinical MRIs broadly sourced from a tertiary hospital system, we aimed to develop readily translatable and fully automated volumetric diagnostic decision-trees to facilitate early and accurate differential diagnosis of NMDs. 3DT1 MRI from 171 NMD patients (72 MSA, 49 PSP, 50 PD) and 171 matched healthy subjects were automatically segmented using Freesurfer6.0 with brainstem module. Decision trees employing substructure volumes and a novel volumetric pons-to-midbrain ratio (3D-PMR) were produced and tenfold cross-validation performed. The optimal tree separating NMD from healthy subjects selected cerebellar white matter, thalamus, putamen, striatum, and midbrain volumes as nodes. Its sensitivity was 84%, specificity 94%, accuracy 84%, and kappa 0.69 in cross-validation. The optimal tree restricted to NMD patients selected 3D-PMR, thalamus, superior cerebellar peduncle (SCP), midbrain, pons, and putamen as nodes. It yielded sensitivities/specificities of 94/84% for MSA, 72/96% for PSP, and 73/92% PD, with 79% accuracy and 0.62 kappa. There was correct classification of 16/17 MSA, 5/8 PSP, 6/8 PD autopsy-confirmed patients, and 6/8 MRIs that preceded motor symptom onset. Fully automated decision trees utilizing volumetric MRI data distinguished NMD patients from healthy subjects and MSA from other NMDs with promising accuracy, including autopsy-confirmed and pre-symptomatic subsets. Our open-source methodology is well-suited for widespread clinical translation. Assessment in even more heterogeneous retrospective and prospective cohorts is indicated.
Collapse
Affiliation(s)
- Jisoo Kim
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Geoffrey S Young
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Andrew S Willett
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Ariana T Pitaro
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Grace F Crotty
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Merlyne Mesidor
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Kristie A Jones
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Camden Bay
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Min Zhang
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Mel B Feany
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Xiaoyin Xu
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Lei Qin
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Department of Imaging, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
| | - Vikram Khurana
- Division of Movement Disorders, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, 02115, USA.
- Hale Building for Transformative Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA.
| |
Collapse
|
8
|
Carmona-Abellan M, Del Pino R, Murueta-Goyena A, Acera M, Tijero B, Berganzo K, Gabilondo I, Gómez-Esteban JC. Multiple system atrophy: Clinical, evolutive and histopathological characteristics of a series of cases. Neurologia 2023; 38:609-616. [PMID: 37996211 DOI: 10.1016/j.nrleng.2021.04.008] [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: 12/02/2020] [Accepted: 04/06/2021] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Multiple system atrophy is a rare and fatal neurodegenerative disorder, characterized by autonomic dysfunction in association with either parkinsonism or cerebellar signs. The pathologic hallmark is the presence of alpha-synuclein aggregates in oligodendrocytes, forming glial cytoplasmic inclusions. Clinically, it may be difficult to distinguish form other parkinsonisms or ataxias, particularly in the early stages of the disease. In this case series we aim to describe in detail the features of MSA patients. MATERIAL AND METHODS Unified MSA Rating Scale (UMSARS) score, structural and functional imaging and cardiovascular autonomic testing, are summarized since early stages of the disease. RESULTS UMSARS proved to be useful to perform a follow-up being longitudinal examination essential to stratify risk of poor outcome. Neuropathological diagnosis showed an overlap between parkinsonian and cerebellar subtypes, with some peculiarities that could help to distinguish from other subtypes. CONCLUSION A better description of MSA features with standardized test confirmed by means of neuropathological studies could help to increase sensitivity.
Collapse
Affiliation(s)
- M Carmona-Abellan
- Neurodegenerative Diseases Division, Health Research Institute Biocruces, Barakaldo, Bizkaia, Spain.
| | - R Del Pino
- Neurodegenerative Diseases Division, Health Research Institute Biocruces, Barakaldo, Bizkaia, Spain
| | - A Murueta-Goyena
- Neurodegenerative Diseases Division, Health Research Institute Biocruces, Barakaldo, Bizkaia, Spain
| | - M Acera
- Neurodegenerative Diseases Division, Health Research Institute Biocruces, Barakaldo, Bizkaia, Spain
| | - B Tijero
- Neurodegenerative Diseases Division, Health Research Institute Biocruces, Barakaldo, Bizkaia, Spain; Hospital Universitario de Cruces, Barakaldo, Bizkaia, Spain
| | - K Berganzo
- Hospital Universitario de Basurto, Bilbao, Bizkaia, Spain
| | - I Gabilondo
- Neurodegenerative Diseases Division, Health Research Institute Biocruces, Barakaldo, Bizkaia, Spain; Hospital Universitario de Cruces, Barakaldo, Bizkaia, Spain; Ikerbasque, The Basque Foundation for Science, Spain
| | - J C Gómez-Esteban
- Neurodegenerative Diseases Division, Health Research Institute Biocruces, Barakaldo, Bizkaia, Spain; Hospital Universitario de Cruces, Barakaldo, Bizkaia, Spain
| |
Collapse
|
9
|
Erlinger M, Molina-Ruiz R, Brumby A, Cordas D, Hunter M, Ferreiro Arguelles C, Yus M, Owens-Walton C, Jakabek D, Shaw M, Lopez Valdes E, Looi JCL. Striatal and thalamic automatic segmentation, morphology, and clinical correlates in Parkinsonism: Parkinson's disease, multiple system atrophy and progressive supranuclear palsy. Psychiatry Res Neuroimaging 2023; 335:111719. [PMID: 37806261 DOI: 10.1016/j.pscychresns.2023.111719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/20/2023] [Accepted: 09/23/2023] [Indexed: 10/10/2023]
Abstract
Parkinson's disease (PD), multisystem atrophy (MSA), and progressive supranuclear palsy (PSP) present similarly with bradykinesia, tremor, rigidity, and cognitive impairments. Neuroimaging studies have found differential changes in the nigrostriatal pathway in these disorders, however whether the volume and shape of specific regions within this pathway can distinguish between atypical Parkinsonian disorders remains to be determined. This paper investigates striatal and thalamic volume and morphology as distinguishing biomarkers, and their relationship to neuropsychiatric symptoms. Automatic segmentation to calculate volume and shape analysis of the caudate nucleus, putamen, and thalamus were performed in 18 PD patients, 12 MSA, 15 PSP, and 20 healthy controls, then correlated with clinical measures. PSP bilateral thalami and right putamen were significantly smaller than controls, but not MSA or PD. The left caudate and putamen significantly correlated with the Neuropsychiatric Inventory total score. Bilateral thalamus, caudate, and left putamen had significantly different morphology between groups, driven by differences between PSP and healthy controls. This study demonstrated that PSP patient striatal and thalamic volume and shape are significantly different when compared with controls. Parkinsonian disorders could not be differentiated on volumetry or morphology, however there are trends for volumetric and morphological changes associated with PD, MSA, and PSP.
Collapse
Affiliation(s)
- M Erlinger
- Research Centre for the Neurosciences of Ageing, Academic Unit of Psychiatry and Addiction Medicine, School of Clinical Medicine, Australian National University, Canberra, Australia.
| | | | - A Brumby
- Research Centre for the Neurosciences of Ageing, Academic Unit of Psychiatry and Addiction Medicine, School of Clinical Medicine, Australian National University, Canberra, Australia
| | - D Cordas
- Research Centre for the Neurosciences of Ageing, Academic Unit of Psychiatry and Addiction Medicine, School of Clinical Medicine, Australian National University, Canberra, Australia
| | - M Hunter
- Research Centre for the Neurosciences of Ageing, Academic Unit of Psychiatry and Addiction Medicine, School of Clinical Medicine, Australian National University, Canberra, Australia
| | | | - M Yus
- Hospital Clinico San Carlos, Madrid, Spain
| | - C Owens-Walton
- Research Centre for the Neurosciences of Ageing, Academic Unit of Psychiatry and Addiction Medicine, School of Clinical Medicine, Australian National University, Canberra, Australia
| | - D Jakabek
- Neuroscience Research Australia, Sydney, Australia
| | - M Shaw
- Hospital Clinico San Carlos, Madrid, Spain
| | | | - J C L Looi
- Research Centre for the Neurosciences of Ageing, Academic Unit of Psychiatry and Addiction Medicine, School of Clinical Medicine, Australian National University, Canberra, Australia
| |
Collapse
|
10
|
Rau A, Schröter N, Rijntjes M, Bamberg F, Jost WH, Zaitsev M, Weiller C, Rau S, Urbach H, Reisert M, Russe MF. Deep learning segmentation results in precise delineation of the putamen in multiple system atrophy. Eur Radiol 2023; 33:7160-7167. [PMID: 37121929 PMCID: PMC10511621 DOI: 10.1007/s00330-023-09665-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/05/2023] [Accepted: 03/10/2023] [Indexed: 05/02/2023]
Abstract
OBJECTIVES The precise segmentation of atrophic structures remains challenging in neurodegenerative diseases. We determined the performance of a Deep Neural Patchwork (DNP) in comparison to established segmentation algorithms regarding the ability to delineate the putamen in multiple system atrophy (MSA), Parkinson's disease (PD), and healthy controls. METHODS We retrospectively included patients with MSA and PD as well as healthy controls. A DNP was trained on manual segmentations of the putamen as ground truth. For this, the cohort was randomly split into a training (N = 131) and test set (N = 120). The DNP's performance was compared with putaminal segmentations as derived by Automatic Anatomic Labelling, Freesurfer and Fastsurfer. For validation, we assessed the diagnostic accuracy of the resulting segmentations in the delineation of MSA vs. PD and healthy controls. RESULTS A total of 251 subjects (61 patients with MSA, 158 patients with PD, and 32 healthy controls; mean age of 61.5 ± 8.8 years) were included. Compared to the dice-coefficient of the DNP (0.96), we noted significantly weaker performance for AAL3 (0.72; p < .001), Freesurfer (0.82; p < .001), and Fastsurfer (0.84, p < .001). This was corroborated by the superior diagnostic performance of MSA vs. PD and HC of the DNP (AUC 0.93) versus the AUC of 0.88 for AAL3 (p = 0.02), 0.86 for Freesurfer (p = 0.048), and 0.85 for Fastsurfer (p = 0.04). CONCLUSION By utilization of a DNP, accurate segmentations of the putamen can be obtained even if substantial atrophy is present. This allows for more precise extraction of imaging parameters or shape features from the putamen in relevant patient cohorts. CLINICAL RELEVANCE STATEMENT Deep learning-based segmentation of the putamen was superior to currently available algorithms and is beneficial for the diagnosis of multiple system atrophy. KEY POINTS • A Deep Neural Patchwork precisely delineates the putamen and performs equal to human labeling in multiple system atrophy, even when pronounced putaminal volume loss is present. • The Deep Neural Patchwork-based segmentation was more capable to differentiate between multiple system atrophy and Parkinson's disease than the AAL3 atlas, Freesurfer, or Fastsurfer.
Collapse
Affiliation(s)
- Alexander Rau
- Department of Neuroradiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Nils Schröter
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michel Rijntjes
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | | | - Maxim Zaitsev
- Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Cornelius Weiller
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Stephan Rau
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Horst Urbach
- Department of Neuroradiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marco Reisert
- Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, University of Freiburg, Freiburg, Germany
- Department of Stereotactic and Functional Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Maximilian F Russe
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| |
Collapse
|
11
|
Seada SA, van der Eerden AW, Boon AJW, Hernandez-Tamames JA. Quantitative MRI protocol and decision model for a 'one stop shop' early-stage Parkinsonism diagnosis: Study design. Neuroimage Clin 2023; 39:103506. [PMID: 37696098 PMCID: PMC10500558 DOI: 10.1016/j.nicl.2023.103506] [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: 03/30/2023] [Revised: 06/21/2023] [Accepted: 09/04/2023] [Indexed: 09/13/2023]
Abstract
Differentiating among early-stage parkinsonisms is a challenge in clinical practice. Quantitative MRI can aid the diagnostic process, but studies with singular MRI techniques have had limited success thus far. Our objective is to develop a multi-modal MRI method for this purpose. In this review we describe existing methods and present a dedicated quantitative MRI protocol, a decision model and a study design to validate our approach ahead of a pilot study. We present example imaging data from patients and a healthy control, which resemble related literature.
Collapse
Affiliation(s)
- Samy Abo Seada
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Anke W van der Eerden
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Agnita J W Boon
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Juan A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Imaging Physics, TU Delft, The Netherlands.
| |
Collapse
|
12
|
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.
Collapse
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.
| |
Collapse
|
13
|
Maiti B, Perlmutter JS. Imaging in Movement Disorders. Continuum (Minneap Minn) 2023; 29:194-218. [PMID: 36795878 DOI: 10.1212/con.0000000000001210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
OBJECTIVE This article reviews commonly used imaging modalities in movement disorders, particularly parkinsonism. The review includes the diagnostic utility, role in differential diagnosis, reflection of pathophysiology, and limitations of neuroimaging in the setting of movement disorders. It also introduces promising new imaging modalities and describes the current status of research. LATEST DEVELOPMENTS Iron-sensitive MRI sequences and neuromelanin-sensitive MRI can be used to directly assess the integrity of nigral dopaminergic neurons and thus may reflect disease pathology and progression throughout the full range of severity in Parkinson disease (PD). The striatal uptake of presynaptic radiotracers in their terminal axons as currently assessed using clinically approved positron emission tomography (PET) or single-photon emission computed tomography (SPECT) imaging correlates with nigral pathology and disease severity only in early PD. Cholinergic PET, using radiotracers that target the presynaptic vesicular acetylcholine transporter, constitutes a substantial advance and may provide crucial insights into the pathophysiology of clinical symptoms such as dementia, freezing, and falls. ESSENTIAL POINTS In the absence of valid, direct, objective biomarkers of intracellular misfolded α-synuclein, PD remains a clinical diagnosis. The clinical utility of PET- or SPECT-based striatal measures is currently limited given their lack of specificity and inability to reflect nigral pathology in moderate to severe PD. These scans may be more sensitive than clinical examination to detect nigrostriatal deficiency that occurs in multiple parkinsonian syndromes and may still be recommended for clinical use in the future to identify prodromal PD if and when disease-modifying treatments become available. Multimodal imaging to evaluate underlying nigral pathology and its functional consequences may hold the key to future advances.
Collapse
|
14
|
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.
Collapse
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 ✉
| |
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|
16
|
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.
Collapse
|
17
|
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]
|
18
|
Characterization and diagnostic potential of R2* in early-stage progressive supranuclear palsy variants. Parkinsonism Relat Disord 2022; 101:43-48. [DOI: 10.1016/j.parkreldis.2022.06.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 06/08/2022] [Accepted: 06/24/2022] [Indexed: 01/12/2023]
|
19
|
Lee WW, Kim HJ, Lee HJ, Kim HB, Park KS, Sohn CH, Jeon B. Semiautomated Algorithm for the Diagnosis of Multiple System Atrophy With Predominant Parkinsonism. J Mov Disord 2022; 15:232-240. [PMID: 35880384 PMCID: PMC9536910 DOI: 10.14802/jmd.21178] [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: 12/08/2021] [Accepted: 03/10/2022] [Indexed: 11/24/2022] Open
Abstract
Objective Putaminal iron deposition is an important feature that helps differentiate multiple system atrophy with predominant parkinsonism (MSA-p) from Parkinson’s disease (PD). Most previous studies used visual inspection or quantitative methods with manual manipulation to perform this differentiation. We investigated the value of a new semiautomated diagnostic algorithm using 3T-MR susceptibility-weighted imaging for MSA-p. Methods This study included 26 MSA-p, 68 PD, and 41 normal control (NC) subjects. The algorithm was developed in 2 steps: 1) determine the image containing the remarkable putaminal margin and 2) calculate the phase-shift values, which reflect the iron concentration. The next step was to identify the best differentiating conditions among several combinations. The highest phase-shift value of each subject was used to assess the most effective diagnostic set. Results The raw phase-shift values were present along the lateral margin of the putamen in each group. It demonstrates an anterior-to-posterior gradient that was identified most frequently in MSA-p. The average of anterior 5 phase shift values were used for normalization. The highest area under the receiver operating characteristic curve (0.874, 80.8% sensitivity, and 86.7% specificity) of MSA-p versus PD was obtained under the combination of 3 or 4 vertical pixels and one dominant side when the normalization methods were applied. In the subanalysis for the MSA-p patients with a longer disease duration, the performance of the algorithm improved. Conclusion This algorithm detected the putaminal lateral margin well, provided insight into the iron distribution of the putaminal rim of MSA-p, and demonstrated good performance in differentiating MSA-p from PD.
Collapse
Affiliation(s)
- Woong-Woo Lee
- Department of Neurology, Nowon Eulji Medical Center, Eulji University, Seoul, Korea.,Department of Neurology, Eulji University College of Medicine, Daejeon, Korea
| | - Han-Joon Kim
- Department of Neurology, Seoul National University Hospital, Seoul, Korea.,Department of Neurology, Seoul National University College of Medicine, Seoul, Korea
| | - Hong Ji Lee
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
| | - Han Byul Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
| | - Kwang Suk Park
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
| | - Chul-Ho Sohn
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Beomseok Jeon
- Department of Neurology, Seoul National University Hospital, Seoul, Korea.,Department of Neurology, Seoul National University College of Medicine, Seoul, Korea
| |
Collapse
|
20
|
Wenning GK, Stankovic I, Vignatelli L, Fanciulli A, Calandra‐Buonaura G, Seppi K, Palma J, 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: 267] [Impact Index Per Article: 133.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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.
Collapse
Affiliation(s)
| | - Iva Stankovic
- Neurology Clinic, University Clinical Center of Serbia, Faculty of Medicine, University of BelgradeBelgradeSerbia
| | - Luca Vignatelli
- IRCCS, Istituto delle Scienze Neurologiche di BolognaBolognaItaly
| | | | - Giovanna Calandra‐Buonaura
- IRCCS, Istituto delle Scienze Neurologiche di BolognaBolognaItaly
- Department of Biomedical and Neuromotor SciencesUniversity of BolognaBolognaItaly
| | - Klaus Seppi
- Department of NeurologyInnsbruck Medical UniversityInnsbruckAustria
| | - Jose‐Alberto Palma
- Department of Neurology, Dysautonomia Center, Langone Medical CenterNew York University School of MedicineNew YorkNew YorkUSA
| | - Wassilios G. Meissner
- French Reference Center for MSA, Department of Neurology for Neurodegenerative DiseasesUniversity Hospital Bordeaux, 33076 Bordeaux and Institute of Neurodegenerative Diseases, University Bordeaux, CNRSBordeauxFrance
- Department of MedicineUniversity of Otago, Christchurch, and New Zealand Brain Research InstituteChristchurchNew Zealand
| | - Florian Krismer
- Department of NeurologyInnsbruck Medical UniversityInnsbruckAustria
| | - Daniela Berg
- Department of Neurodegeneration and Hertie‐Institute for Clinical Brain ResearchUniversity of TübingenTübingenGermany
- Department of NeurologyChristian‐Albrechts‐University KielKielGermany
| | - Pietro Cortelli
- IRCCS, Istituto delle Scienze Neurologiche di BolognaBolognaItaly
- Department of Biomedical and Neuromotor SciencesUniversity of BolognaBolognaItaly
| | - Roy Freeman
- Department of Neurology, Beth Israel Deaconess Medical CenterHarvard Medical SchoolBostonMassachusettsUSA
| | - Glenda Halliday
- Brain and Mind Centre, Faculty of Medicine and HealthSchool of Medical Sciences, The University of SydneySydneyNew South WalesAustralia
| | - Günter Höglinger
- Department of NeurologyHanover Medical SchoolHanoverGermany
- German Center for Neurodegenerative DiseasesMunichGermany
| | - Anthony Lang
- Edmond J. Safra Program in Parkinson's DiseaseUniversity Health Network and the Division of Neurology, University of TorontoTorontoCanada
| | - Helen Ling
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
- Reta Lila Weston Institute of Neurological StudiesUCL Queen Square Institute of NeurologyLondonUnited Kingdom
| | - Irene Litvan
- Department of NeurosciencesParkinson and Other Movement Disorders Center, University of CaliforniaSan DiegoCaliforniaUSA
| | - Phillip Low
- Department of NeurologyMayo ClinicRochesterMinnesotaUSA
| | - Yasuo Miki
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
- Department of NeuropathologyInstitute of Brain Science, Hirosaki University Graduate School of MedicineHirosakiJapan
| | - Jalesh Panicker
- UCL Queen Square Institute of NeurologyLondonUnited Kingdom
- Department of Uro‐NeurologyThe National Hospital for Neurology and Neurosurgery, Queen SquareLondonUnited Kingdom
| | - Maria Teresa Pellecchia
- Department of MedicineSurgery and Dentistry “Scuola Medica Salernitana”, Neuroscience Section, University of SalernoSalernoItaly
| | - Niall Quinn
- UCL Queen Square Institute of NeurologyLondonUnited Kingdom
| | - Ryuji Sakakibara
- Neurology, Internal MedicineSakura Medical Center, Toho UniversitySakuraJapan
| | - Maria Stamelou
- Parkinson's Disease and Movement Disorders DepartmentHYGEIA Hospital, and Aiginiteion Hospital, University of AthensAthensGreece
- Philipps University Marburg, Germany and European University of CyprusNicosiaCyprus
| | - Eduardo Tolosa
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED) Hospital Clínic, IDIBAPS, Universitat de BarcelonaCataloniaSpain
- Movement Disorders Unit, Neurology ServiceHospital Clínic de BarcelonaCataloniaSpain
| | - Shoji Tsuji
- Department of Molecular NeurologyThe University of Tokyo, Graduate School of MedicineTokyoJapan
- International University of Health and WelfareChibaJapan
| | - Tom Warner
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
| | - Werner Poewe
- Department of NeurologyInnsbruck Medical UniversityInnsbruckAustria
| | - Horacio Kaufmann
- Department of Neurology, Dysautonomia Center, Langone Medical CenterNew York University School of MedicineNew YorkNew YorkUSA
| |
Collapse
|
21
|
Ruiz ST, Bakklund RV, Håberg AK, Berntsen EM. Normative Data for Brainstem Structures, the Midbrain-to-Pons Ratio, and the Magnetic Resonance Parkinsonism Index. AJNR Am J Neuroradiol 2022; 43:707-714. [PMID: 35393362 PMCID: PMC9089261 DOI: 10.3174/ajnr.a7485] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 02/11/2022] [Indexed: 12/19/2022]
Abstract
BACKGROUND AND PURPOSE Imaging biomarkers derived from different brainstem structures are suggested to differentiate among parkinsonian disorders, but clinical implementation requires normative data. The main objective was to establish high-quality, sex-specific data for relevant brainstem structures derived from MR imaging in healthy subjects from the general population in their sixth and seventh decades of life. MATERIALS AND METHODS 3D T1WI acquired on the same 1.5T scanner of 996 individuals (527 women) between 50 and 66 years of age from a prospective population study was used. The area of the midbrain and pons and the widths of the middle cerebellar peduncles and superior cerebellar peduncles were measured, from which the midbrain-to-pons ratio and Magnetic Resonance Parkinsonism Index [MRPI = (Pons Area / Midbrain Area) × (Middle Cerebellar Peduncles / Superior Cerebellar Peduncles)] were calculated. Sex differences in brainstem measures and correlations to age, height, weight, and body mass index were investigated. RESULTS Inter- and intrareliability for measuring the different brainstem structures showed good-to-excellent reliability (intraclass correlation coefficient = 0.785-0.988). There were significant sex differences for the pons area, width of the middle cerebellar peduncles and superior cerebellar peduncles, midbrain-to-pons ratio, and MRPI (all, P < .001; Cohen D = 0.44-0.98), but not for the midbrain area (P = .985). There were significant very weak-to-weak correlations between several of the brainstem measures and age, height, weight, and body mass index in both sexes. However, no systematic difference in distribution caused by these variables was found, and because age had the highest and most consistent correlations, age-/sex-specific percentiles for the brainstem measures were created. CONCLUSIONS We present high-quality, sex-specific data and age-/sex-specific percentiles for the mentioned brainstem measures. These normative data can be implemented in the neuroradiologic work-up of patients with suspected brainstem atrophy to avoid the risk of misdiagnosis.
Collapse
Affiliation(s)
- S T Ruiz
- From the Department of Circulation and Medical Imaging (S.T.R., R.V.B., E.M.B.)
| | - R V Bakklund
- From the Department of Circulation and Medical Imaging (S.T.R., R.V.B., E.M.B.)
| | - A K Håberg
- Faculty of Medicine and Health Sciences, and Neuromedicine and Movement Sciences (A.K.H.), Norwegian University of Science and Technology, Trondheim, Norway.,Department of Radiology and Nuclear Medicine (A.K.H., E.M.B.), St. Olavs University Hospital, Trondheim, Norway
| | - E M Berntsen
- From the Department of Circulation and Medical Imaging (S.T.R., R.V.B., E.M.B.) .,Department of Radiology and Nuclear Medicine (A.K.H., E.M.B.), St. Olavs University Hospital, Trondheim, Norway
| |
Collapse
|
22
|
Mangesius S, Haider L, Lenhart L, Steiger R, Prados Carrasco F, Scherfler C, Gizewski ER. Qualitative and Quantitative Comparison of Hippocampal Volumetric Software Applications: Do All Roads Lead to Rome? Biomedicines 2022; 10:biomedicines10020432. [PMID: 35203641 PMCID: PMC8962257 DOI: 10.3390/biomedicines10020432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/30/2022] [Accepted: 02/10/2022] [Indexed: 02/06/2023] Open
Abstract
Brain volumetric software is increasingly suggested for clinical routine. The present study quantifies the agreement across different software applications. Ten cases with and ten gender- and age-adjusted healthy controls without hippocampal atrophy (median age: 70; 25–75% range: 64–77 years and 74; 66–78 years) were retrospectively selected from a previously published cohort of Alzheimer’s dementia patients and normal ageing controls. Hippocampal volumes were computed based on 3 Tesla T1-MPRAGE-sequences with FreeSurfer (FS), Statistical-Parametric-Mapping (SPM; Neuromorphometrics and Hammers atlases), Geodesic-Information-Flows (GIF), Similarity-and-Truth-Estimation-for-Propagated-Segmentations (STEPS), and Quantib™. MTA (medial temporal lobe atrophy) scores were manually rated. Volumetric measures of each individual were compared against the mean of all applications with intraclass correlation coefficients (ICC) and Bland–Altman plots. Comparing against the mean of all methods, moderate to low agreement was present considering categorization of hippocampal volumes into quartiles. ICCs ranged noticeably between applications (left hippocampus (LH): from 0.42 (STEPS) to 0.88 (FS); right hippocampus (RH): from 0.36 (Quantib™) to 0.86 (FS). Mean differences between individual methods and the mean of all methods [mm3] were considerable (LH: FS −209, SPM-Neuromorphometrics −820; SPM-Hammers −1474; Quantib™ −680; GIF 891; STEPS 2218; RH: FS −232, SPM-Neuromorphometrics −745; SPM-Hammers −1547; Quantib™ −723; GIF 982; STEPS 2188). In this clinically relevant sample size with large spread in data ranging from normal aging to severe atrophy, hippocampal volumes derived by well-accepted applications were quantitatively different. Thus, interchangeable use is not recommended.
Collapse
Affiliation(s)
- Stephanie Mangesius
- Department of Neuroradiology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria; (S.M.); (L.L.); (R.S.); (E.R.G.)
- Neuroimaging Core Facility, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
| | - Lukas Haider
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, University College London Institute of Neurology, Russell Square House, Russell Square 10-12, London WC1B 5EH, UK;
- Department of Biomedical Imaging and Image Guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
- Correspondence:
| | - Lukas Lenhart
- Department of Neuroradiology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria; (S.M.); (L.L.); (R.S.); (E.R.G.)
- Neuroimaging Core Facility, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
| | - Ruth Steiger
- Department of Neuroradiology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria; (S.M.); (L.L.); (R.S.); (E.R.G.)
- Neuroimaging Core Facility, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
| | - Ferran Prados Carrasco
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, University College London Institute of Neurology, Russell Square House, Russell Square 10-12, London WC1B 5EH, UK;
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, Malet Place Engineering Building, Gower Street, London WC1E 6BT, UK
- e-Health Centre, Universitat Oberta de Catalunya, Rambla del Poblenou 156, 08018 Barcelona, Spain
| | - Christoph Scherfler
- Department of Neurology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria;
| | - Elke R. Gizewski
- Department of Neuroradiology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria; (S.M.); (L.L.); (R.S.); (E.R.G.)
- Neuroimaging Core Facility, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
| |
Collapse
|
23
|
Diagnostic Performance of the Magnetic Resonance Parkinsonism Index in Differentiating Progressive Supranuclear Palsy from Parkinson's Disease: An Updated Systematic Review and Meta-Analysis. Diagnostics (Basel) 2021; 12:diagnostics12010012. [PMID: 35054178 PMCID: PMC8774886 DOI: 10.3390/diagnostics12010012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/13/2021] [Accepted: 12/20/2021] [Indexed: 12/17/2022] Open
Abstract
Progressive supranuclear palsy (PSP) and Parkinson's disease (PD) are difficult to differentiate especially in the early stages. We aimed to investigate the diagnostic performance of the magnetic resonance parkinsonism index (MRPI) in differentiating PSP from PD. A systematic literature search of PubMed-MEDLINE and EMBASE was performed to identify original articles evaluating the diagnostic performance of the MRPI in differentiating PSP from PD published up to 20 February 2021. The pooled sensitivity, specificity, and 95% CI were calculated using the bivariate random-effects model. The area under the curve (AUC) was calculated using a hierarchical summary receiver operating characteristic (HSROC) model. Meta-regression was performed to explain the effects of heterogeneity. A total of 14 original articles involving 484 PSP patients and 1243 PD patients were included. In all studies, T1-weighted images were used to calculate the MRPI. Among the 14 studies, nine studies used 3D T1-weighted images. The pooled sensitivity and specificity for the diagnostic performance of the MRPI in differentiating PSP from PD were 96% (95% CI, 87-99%) and 98% (95% CI, 91-100%), respectively. The area under the HSROC curve was 0.99 (95% CI, 0.98-1.00). Heterogeneity was present (sensitivity: I2 = 97.29%; specificity: I2 = 98.82%). Meta-regression showed the association of the magnet field strength with heterogeneity. Studies using 3 T MRI showed significantly higher sensitivity (100%) and specificity (100%) than those of studies using 1.5 T MRI (sensitivity of 98% and specificity of 97%) (p < 0.01). Thus, the MRPI could accurately differentiate PSP from PD and support the implementation of appropriate management strategies for patients with PSP.
Collapse
|
24
|
Campabadal A, Abos A, Segura B, Monte-Rubio G, Perez-Soriano A, Giraldo DM, Muñoz E, Compta Y, Junque C, Marti MJ. Differentiation of multiple system atrophy subtypes by gray matter atrophy. J Neuroimaging 2021; 32:80-89. [PMID: 34506665 DOI: 10.1111/jon.12927] [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: 06/15/2021] [Revised: 07/30/2021] [Accepted: 08/18/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE Multiple system atrophy(MSA) is a rare adult-onset synucleinopathy that can be divided in two subtypes depending on whether the prevalence of its symptoms is more parkinsonian or cerebellar (MSA-P and MSA-C, respectively). The aim of this work is to investigate the structural MRI changes able to discriminate MSA phenotypes. METHODS The sample includes 31 MSA patients (15 MSA-C and 16 MSA-P) and 39 healthy controls. Participants underwent a comprehensive motor and neuropsychological battery. MRI data were acquired with a 3T scanner (MAGNETOM Trio, Siemens, Germany). FreeSurfer was used to obtain volumetric and cortical thickness measures. A Support Vector Machine (SVM) algorithm was used to assess the classification between patients' group using cortical and subcortical structural data. RESULTS After correction for multiple comparisons, MSA-C patients had greater atrophy than MSA-P in the left cerebellum, whereas MSA-P showed reduced volume bilaterally in the pallidum and putamen. Using deep gray matter volume ratios and mean cortical thickness as features, the SVM algorithm provided a consistent classification between MSA-C and MSA-P patients (balanced accuracy 74.2%, specificity 75.0%, and sensitivity 73.3%). The cerebellum, putamen, thalamus, ventral diencephalon, pallidum, and caudate were the most contributing features to the classification decision (z > 3.28; p < .05 [false discovery rate]). CONCLUSIONS MSA-C and MSA-P with similar disease severity and duration have a differential distribution of gray matter atrophy. Although cerebellar atrophy is a clear differentiator between groups, thalamic and basal ganglia structures are also relevant contributors to distinguishing MSA subtypes.
Collapse
Affiliation(s)
- Anna Campabadal
- Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Spain.,Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Alexandra Abos
- Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Spain
| | - Barbara Segura
- Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Spain.,Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain
| | - Gemma Monte-Rubio
- Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Spain
| | - Alexandra Perez-Soriano
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain.,Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona, Spain.,Institute of Neuroscience, University of Barcelona, Barcelona, Spain
| | - Darly Milena Giraldo
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain.,Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona, Spain.,Institute of Neuroscience, University of Barcelona, Barcelona, Spain
| | - Esteban Muñoz
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain.,Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona, Spain.,Institute of Neuroscience, University of Barcelona, Barcelona, Spain
| | - Yaroslau Compta
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain.,Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona, Spain.,Institute of Neuroscience, University of Barcelona, Barcelona, Spain
| | - Carme Junque
- Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Spain.,Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain
| | - Maria Jose Marti
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain.,Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona, Spain.,Institute of Neuroscience, University of Barcelona, Barcelona, Spain
| |
Collapse
|
25
|
Ebina J, Hara K, Watanabe H, Kawabata K, Yamashita F, Kawaguchi A, Yoshida Y, Kato T, Ogura A, Masuda M, Ohdake R, Mori D, Maesawa S, Katsuno M, Kano O, Sobue G. Individual voxel-based morphometry adjusting covariates in multiple system atrophy. Parkinsonism Relat Disord 2021; 90:114-119. [PMID: 34481140 DOI: 10.1016/j.parkreldis.2021.07.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 07/16/2021] [Accepted: 07/23/2021] [Indexed: 02/04/2023]
Abstract
INTRODUCTION This study aimed to evaluate whether novel individual voxel-based morphometry adjusting covariates (iVAC), such as age, sex, and total intracranial volume, could increase the accuracy of a diagnosis of multiple system atrophy (MSA) and enable the differentiation of MSA from Parkinson's disease (PD). METHODS We included 53 MSA patients (MSA-C: 33, MSA-P: 20), 53 PD patients, and 189 healthy controls in this study. All participants underwent high-resolution T1-weighted imaging (WI) and T2-WI with a 3.0-T MRI scanner. We evaluated the occurrence of significant atrophic findings in the pons/middle cerebellar peduncle (MCP) and putamen on iVAC and compared these findings with characteristic changes on T2-WI. RESULTS On iVAC, abnormal findings were observed in the pons/MCP of 96.2% of MSA patients and in the putamen of 80% of MSA patients; however, on T2-WI, they were both observed at a frequency of 60.4% in MSA patients. On iVAC, all but one MSA-P patient (98.1%) showed significant atrophic changes in the pons/MCP or putamen. By contrast, 69.8% of patients with MSA showed abnormal signal changes in the pons/MCP or putamen on T2-WI. iVAC yielded 95.0% sensitivity and 96.2% specificity for differentiating MSA-P from PD. CONCLUSION iVAC enabled us to recognize the morphological characteristics of MSA visually and with high accuracy compared to T2-WI, indicating that iVAC is a potential diagnostic screening tool for MSA.
Collapse
Affiliation(s)
- Junya Ebina
- Brain and Mind Research Center, Nagoya University, Aichi, Japan; Division of Neurology, Department of Internal Medicine, Toho University Graduate School of Medicine, Tokyo, Japan
| | - Kazuhiro Hara
- Department of Neurology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Hirohisa Watanabe
- Brain and Mind Research Center, Nagoya University, Aichi, Japan; Department of Neurology, Fujita Health University School of Medicine, Aichi, Japan.
| | - Kazuya Kawabata
- Brain and Mind Research Center, Nagoya University, Aichi, Japan; Department of Neurology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Fumio Yamashita
- Division of Ultrahigh-Field MRI, Institute for Biomedical Sciences, Iwate Medical University, Iwate, Japan
| | - Atsushi Kawaguchi
- Education and Research Center for Community Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Yusuke Yoshida
- Department of Neurology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Toshiyasu Kato
- Department of Neurology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Aya Ogura
- Department of Neurology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Michihito Masuda
- Department of Neurology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Reiko Ohdake
- Brain and Mind Research Center, Nagoya University, Aichi, Japan; Department of Neurology, Fujita Health University School of Medicine, Aichi, Japan
| | - Daisuke Mori
- Brain and Mind Research Center, Nagoya University, Aichi, Japan
| | - Satoshi Maesawa
- Brain and Mind Research Center, Nagoya University, Aichi, Japan; Department of Neurosurgery, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Masahisa Katsuno
- Department of Neurology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Osamu Kano
- Division of Neurology, Department of Internal Medicine, Toho University Graduate School of Medicine, Tokyo, Japan
| | - Gen Sobue
- Brain and Mind Research Center, Nagoya University, Aichi, Japan.
| |
Collapse
|
26
|
Dutta S, Hornung S, Kruayatidee A, Maina KN, Del Rosario I, Paul KC, Wong DY, Duarte Folle A, Markovic D, Palma JA, Serrano GE, Adler CH, Perlman SL, Poon WW, Kang UJ, Alcalay RN, Sklerov M, Gylys KH, Kaufmann H, Fogel BL, Bronstein JM, Ritz B, Bitan G. α-Synuclein in blood exosomes immunoprecipitated using neuronal and oligodendroglial markers distinguishes Parkinson's disease from multiple system atrophy. Acta Neuropathol 2021; 142:495-511. [PMID: 33991233 PMCID: PMC8357708 DOI: 10.1007/s00401-021-02324-0] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 05/01/2021] [Accepted: 05/03/2021] [Indexed: 12/16/2022]
Abstract
The diagnosis of Parkinson's disease (PD) and atypical parkinsonian syndromes is difficult due to the lack of reliable, easily accessible biomarkers. Multiple system atrophy (MSA) is a synucleinopathy whose symptoms often overlap with PD. Exosomes isolated from blood by immunoprecipitation using CNS markers provide a window into the brain's biochemistry and may assist in distinguishing between PD and MSA. Thus, we asked whether α-synuclein (α-syn) in such exosomes could distinguish among healthy individuals, patients with PD, and patients with MSA. We isolated exosomes from the serum or plasma of these three groups by immunoprecipitation using neuronal and oligodendroglial markers in two independent cohorts and measured α-syn in these exosomes using an electrochemiluminescence ELISA. In both cohorts, α-syn concentrations were significantly lower in the control group and significantly higher in the MSA group compared to the PD group. The ratio between α-syn concentrations in putative oligodendroglial exosomes compared to putative neuronal exosomes was a particularly sensitive biomarker for distinguishing between PD and MSA. Combining this ratio with the α-syn concentration itself and the total exosome concentration, a multinomial logistic model trained on the discovery cohort separated PD from MSA with an AUC = 0.902, corresponding to 89.8% sensitivity and 86.0% specificity when applied to the independent validation cohort. The data demonstrate that a minimally invasive blood test measuring α-syn in blood exosomes immunoprecipitated using CNS markers can distinguish between patients with PD and patients with MSA with high sensitivity and specificity. Future optimization and validation of the data by other groups would allow this strategy to become a viable diagnostic test for synucleinopathies.
Collapse
Affiliation(s)
- Suman Dutta
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Simon Hornung
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Division of Peptide Biochemistry, Technical University of Munich, 85354, Freising, Germany
| | - Adira Kruayatidee
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Katherine N Maina
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Irish Del Rosario
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA
| | - Kimberly C Paul
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA
| | - Darice Y Wong
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Aline Duarte Folle
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA
| | - Daniela Markovic
- Department of Medicine, Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Jose-Alberto Palma
- Department of Neurology, Dysautonomia Center, The Marlene and Paolo Fresco Institute for Parkinson's and Movement Disorders, New York University School of Medicine, New York, NY, 10016, USA
| | - Geidy E Serrano
- Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | - Charles H Adler
- Mayo Clinic College of Medicine, Mayo Clinic Arizona, Scottsdale, AZ, 85259, USA
| | - Susan L Perlman
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Wayne W Poon
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, 92697, USA
| | - Un Jung Kang
- Department of Neurology, Dysautonomia Center, The Marlene and Paolo Fresco Institute for Parkinson's and Movement Disorders, New York University School of Medicine, New York, NY, 10016, USA
| | - Roy N Alcalay
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, 10032, USA
| | - Miriam Sklerov
- Department of Neurology, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Karen H Gylys
- School of Nursing, University of California, Los Angeles, CA, 90095, USA
- Brain Research Institute, University of California, Los Angeles, CA, 90095, USA
| | - Horacio Kaufmann
- Department of Neurology, Dysautonomia Center, The Marlene and Paolo Fresco Institute for Parkinson's and Movement Disorders, New York University School of Medicine, New York, NY, 10016, USA
| | - Brent L Fogel
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Clinical Neurogenomics Research Center, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Brain Research Institute, University of California, Los Angeles, CA, 90095, USA
| | - Jeff M Bronstein
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Brain Research Institute, University of California, Los Angeles, CA, 90095, USA
| | - Beate Ritz
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA
- Brain Research Institute, University of California, Los Angeles, CA, 90095, USA
| | - Gal Bitan
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
- Brain Research Institute, University of California, Los Angeles, CA, 90095, USA.
- Molecular Biology Institute, University of California, Los Angeles, CA, 90095, USA.
| |
Collapse
|
27
|
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.
Collapse
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
| |
Collapse
|
28
|
Affiliation(s)
- Vikram Khurana
- From the Departments of Neurology (V.K., C.M.G.) and Pathology (J.H.), Brigham and Women's Hospital, the Department of Radiology, Massachusetts General Hospital (M.G.), and the Departments of Neurology (V.K., C.M.G.), Radiology (M.G.), and Pathology (J.H.), Harvard Medical School - all in Boston
| | - Claudio M de Gusmao
- From the Departments of Neurology (V.K., C.M.G.) and Pathology (J.H.), Brigham and Women's Hospital, the Department of Radiology, Massachusetts General Hospital (M.G.), and the Departments of Neurology (V.K., C.M.G.), Radiology (M.G.), and Pathology (J.H.), Harvard Medical School - all in Boston
| | - McKinley Glover
- From the Departments of Neurology (V.K., C.M.G.) and Pathology (J.H.), Brigham and Women's Hospital, the Department of Radiology, Massachusetts General Hospital (M.G.), and the Departments of Neurology (V.K., C.M.G.), Radiology (M.G.), and Pathology (J.H.), Harvard Medical School - all in Boston
| | - Jeffrey Helgager
- From the Departments of Neurology (V.K., C.M.G.) and Pathology (J.H.), Brigham and Women's Hospital, the Department of Radiology, Massachusetts General Hospital (M.G.), and the Departments of Neurology (V.K., C.M.G.), Radiology (M.G.), and Pathology (J.H.), Harvard Medical School - all in Boston
| |
Collapse
|
29
|
Carmona-Abellan M, Del Pino R, Murueta-Goyena A, Acera M, Tijero B, Berganzo K, Gabilondo I, Gómez-Esteban JC. Multiple system atrophy: Clinical, evolutive and histopathological characteristics of a series of cases. Neurologia 2021; 38:S0213-4853(21)00073-6. [PMID: 34052041 DOI: 10.1016/j.nrl.2021.04.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/12/2021] [Accepted: 04/06/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND AND OBJECTIVE Multiple system atrophy is a rare and fatal neurodegenerative disorder, characterized by autonomic dysfunction in association with either parkinsonism or cerebellar signs. The pathologic hallmark is the presence of alpha-synuclein aggregates in oligodendrocytes, forming glial cytoplasmic inclusions. Clinically, it may be difficult to distinguish form other parkinsonisms or ataxias, particularly in the early stages of the disease. In this case series we aim to describe in detail the features of MSA patients. MATERIAL AND METHODS Unified MSA Rating Scale (UMSARS) score, structural and functional imaging and cardiovascular autonomic testing, are summarized since early stages of the disease. RESULTS UMSARS proved to be useful to perform a follow-up being longitudinal examination essential to stratify risk of poor outcome. Neuropathological diagnosis showed an overlap between parkinsonian and cerebellar subtypes, with some peculiarities that could help to distinguish from other subtypes. CONCLUSION A better description of MSA features with standardized test confirmed by means of neuropathological studies could help to increase sensitivity.
Collapse
Affiliation(s)
- M Carmona-Abellan
- Neurodegenerative Diseases Division, Health Research Institute Biocruces, Barakaldo, Bizkaia, Spain.
| | - R Del Pino
- Neurodegenerative Diseases Division, Health Research Institute Biocruces, Barakaldo, Bizkaia, Spain
| | - A Murueta-Goyena
- Neurodegenerative Diseases Division, Health Research Institute Biocruces, Barakaldo, Bizkaia, Spain
| | - M Acera
- Neurodegenerative Diseases Division, Health Research Institute Biocruces, Barakaldo, Bizkaia, Spain
| | - B Tijero
- Neurodegenerative Diseases Division, Health Research Institute Biocruces, Barakaldo, Bizkaia, Spain; Hospital Universitario de Cruces, Barakaldo, Bizkaia, Spain
| | - K Berganzo
- Hospital Universitario de Basurto, Bilbao, Bizkaia, Spain
| | - I Gabilondo
- Neurodegenerative Diseases Division, Health Research Institute Biocruces, Barakaldo, Bizkaia, Spain; Hospital Universitario de Cruces, Barakaldo, Bizkaia, Spain; Ikerbasque, The Basque Foundation for Science, Spain
| | - J C Gómez-Esteban
- Neurodegenerative Diseases Division, Health Research Institute Biocruces, Barakaldo, Bizkaia, Spain; Hospital Universitario de Cruces, Barakaldo, Bizkaia, Spain
| |
Collapse
|
30
|
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.
Collapse
|
31
|
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: 503] [Impact Index Per Article: 167.7] [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.
Collapse
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
| |
Collapse
|
32
|
Martins R, Oliveira F, Moreira F, Moreira AP, Abrunhosa A, Januário C, Castelo-Branco M. Automatic classification of idiopathic Parkinson's disease and atypical Parkinsonian syndromes combining [ 11C]raclopride PET uptake and MRI grey matter morphometry. J Neural Eng 2021; 18. [PMID: 33848996 DOI: 10.1088/1741-2552/abf772] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 04/13/2021] [Indexed: 11/12/2022]
Abstract
Objective.To explore the viability of developing a computer-aided diagnostic system for Parkinsonian syndromes using dynamic [11C]raclopride positron emission tomography (PET) and T1-weighted magnetic resonance imaging (MRI) data.Approach.The biological heterogeneity of Parkinsonian syndromes renders their statistical classification a challenge. The unique combination of structural and molecular imaging data allowed different classifier designs to be tested. Datasets from dynamic [11C]raclopride PET and T1-weighted MRI scans were acquired from six groups of participants. There were healthy controls (CTRLn= 15), patients with Parkinson's disease (PDn= 27), multiple system atrophy (MSAn= 8), corticobasal degeneration (CBDn= 6), and dementia with Lewy bodies (DLBn= 5). MSA, CBD, and DLB patients were classified into one category designated as atypical Parkinsonism (AP). The distribution volume ratio (DVR) kinetic parameters obtained from the PET data were used to quantify the reversible tracer binding to D2/D3 receptors in the subcortical regions of interest (ROI). The grey matter (GM) volumes obtained from the MRI data were used to quantify GM atrophy across cortical, subcortical, and cerebellar ROI.Results.The classifiers CTRL vs PD and CTRL vs AP achieved the highest balanced accuracy combining DVR and GM (DVR-GM) features (96.7%, 92.1%, respectively), followed by the classifiers designed with DVR features (93.3%, 88.8%, respectively), and GM features (69.6%, 86.1%, respectively). In contrast, the classifier PD vs AP showed the highest balanced accuracy (78.9%) using DVR features only. The integration of DVR-GM (77.9%) and GM features (72.7%) produced inferior performances. The classifier CTRL vs PD vs AP showed high weighted balanced accuracy when DVR (80.5%) or DVR-GM features (79.9%) were integrated. GM features revealed poorer performance (59.5%).Significance.This work was unique in its combination of structural and molecular imaging features in binary and triple category classifications. We were able to demonstrate improved binary classification of healthy/diseased status (concerning both PD and AP) and equate performance to DVR features in multiclass classifications.
Collapse
Affiliation(s)
- Ricardo Martins
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal.,Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Francisco Oliveira
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal.,Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Fradique Moreira
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal.,Department of Neurology, Hospital and University Centre of Coimbra, Coimbra, Portugal
| | - Ana Paula Moreira
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal.,Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Antero Abrunhosa
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal.,Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Cristina Januário
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal.,Department of Neurology, Hospital and University Centre of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal.,Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| |
Collapse
|
33
|
Talai AS, Sedlacik J, Boelmans K, Forkert ND. Utility of Multi-Modal MRI for Differentiating of Parkinson's Disease and Progressive Supranuclear Palsy Using Machine Learning. Front Neurol 2021; 12:648548. [PMID: 33935946 PMCID: PMC8079721 DOI: 10.3389/fneur.2021.648548] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 03/22/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Patients with Parkinson's disease (PD) and progressive supranuclear palsy Richardson's syndrome (PSP-RS) often show overlapping clinical features, leading to misdiagnoses. The objective of this study was to investigate the feasibility and utility of using multi-modal MRI datasets for an automatic differentiation of PD patients, PSP-RS patients, and healthy control (HC) subjects. Material and Methods: T1-weighted, T2-weighted, and diffusion-tensor (DTI) MRI datasets from 45 PD patients, 20 PSP-RS patients, and 38 HC subjects were available for this study. Using an atlas-based approach, regional values of brain morphology (T1-weighted), brain iron metabolism (T2-weighted), and microstructural integrity (DTI) were measured and employed for feature selection and subsequent classification using combinations of various established machine learning methods. Results: The optimal machine learning model using regional morphology features only achieved a classification accuracy of 65% (67/103 correct classifications) differentiating PD patients, PSP-RS patients, and HC subjects. The optimal machine learning model using only quantitative T2 values performed slightly better and achieved an accuracy of 75.7% (78/103). The optimal classifier using DTI features alone performed considerably better with 95.1% accuracy (98/103). The optimal multi-modal classifier using all features also achieved an accuracy of 95.1% but required more features and achieved a slightly lower F1-score compared to the optimal model using DTI features alone. Conclusion: Machine learning models using multi-modal MRI perform significantly better than uni-modal machine learning models using morphological parameters based on T1-weighted MRI datasets alone or brain iron metabolism markers based on T2-weighted MRI datasets alone. However, machine learnig models using regional brain microstructural integrity metrics computed from DTI datasets perform similar to the optimal multi-modal machine learning model. Thus, given the results from this study cohort, it appears that morphology and brain iron metabolism markers may not provide additional value for classification compared to using DTI metrics alone.
Collapse
Affiliation(s)
- Aron S. Talai
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Jan Sedlacik
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kai Boelmans
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
- Department of Neurology, Klinikum Bremerhaven-Reinkenheide, Bremerhaven, Germany
| | - Nils D. Forkert
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| |
Collapse
|
34
|
Beliveau V, Krismer F, Skalla E, Schocke MM, Gizewski ER, Wenning GK, Poewe W, Seppi K, Scherfler C. Characterization and diagnostic potential of diffusion tractography in multiple system atrophy. Parkinsonism Relat Disord 2021; 85:30-36. [PMID: 33713904 DOI: 10.1016/j.parkreldis.2021.02.027] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/28/2021] [Accepted: 02/22/2021] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Microstructural integrity of the middle cerebellar peduncle (MCP) and the putamen captured by diffusion-tensor imaging (DTI) is differentially affected in the parkinsonian and cerebellar variants of multiple system atrophy (MSA-P, MSA-C) compared to Parkinson's disease (PD). The current study applied DTI and tractography in order to 1) characterize the distribution of DTI metrics along the tracts of the MCP and from the putamen in MSA variants, and 2) evaluate the usefulness of combining these measures for the differential diagnosis of MSA-P against PD in the clinical setting. METHODS Twenty-nine MSA patients (MSA-C, n = 10; MSA-P, n = 19), with a mean disease duration of 2.8 ± 1.7 years, 19 PD patients, and 27 healthy controls (HC) were included in the study. Automatized tractography with a masking procedure was employed to isolate the MCP tracts. DTI measures along the tracts of the MCP and within the putamen were acquired and jointly used to classify MSA vs. PD, and MSA-P vs. PD. Putamen volume was additionally tested as classification feature in post hoc analyses. RESULTS DTI measures within the MCP and putamen showed significant alterations in MSA variants compared to HC and PD. Classification accuracy for MSA vs. PD and MSA-P vs PD using diffusion measures was 91.7% and 89.5%, respectively. When replacing the putaminal DTI measure by a normalized measure of putamen volume classification accuracy improved to 95.8% and 94.7%, respectively. CONCLUSION Multimodal information from MCP tractography and putamen volume yields excellent diagnostic accuracy to discriminate between early-to-moderately advanced patients with MSA and PD.
Collapse
Affiliation(s)
- Vincent Beliveau
- Medical University of Innsbruck, Department of Neurology, Anichstrasse 35, 6020, Innsbruck, Austria; Medical University of Innsbruck, Neuroimaging Research Core Facility, Anichstrasse 35, 6020, Innsbruck, Austria
| | - Florian Krismer
- Medical University of Innsbruck, Department of Neurology, Anichstrasse 35, 6020, Innsbruck, Austria; Medical University of Innsbruck, Neuroimaging Research Core Facility, Anichstrasse 35, 6020, Innsbruck, Austria
| | - Elisabeth Skalla
- Medical University of Innsbruck, Neuroimaging Research Core Facility, Anichstrasse 35, 6020, Innsbruck, Austria; Medical University of Innsbruck, Department of Neuroradiology, Anichstrasse 35, 6020, Innsbruck, Austria
| | - Michael M Schocke
- Medical University of Innsbruck, Neuroimaging Research Core Facility, Anichstrasse 35, 6020, Innsbruck, Austria; Medical University of Innsbruck, Department of Neuroradiology, Anichstrasse 35, 6020, Innsbruck, Austria
| | - Elke R Gizewski
- Medical University of Innsbruck, Neuroimaging Research Core Facility, Anichstrasse 35, 6020, Innsbruck, Austria; Medical University of Innsbruck, Department of Neuroradiology, Anichstrasse 35, 6020, Innsbruck, Austria
| | - Gregor K Wenning
- Medical University of Innsbruck, Department of Neurology, Anichstrasse 35, 6020, Innsbruck, Austria
| | - Werner Poewe
- Medical University of Innsbruck, Department of Neurology, Anichstrasse 35, 6020, Innsbruck, Austria
| | - Klaus Seppi
- Medical University of Innsbruck, Department of Neurology, Anichstrasse 35, 6020, Innsbruck, Austria; Medical University of Innsbruck, Neuroimaging Research Core Facility, Anichstrasse 35, 6020, Innsbruck, Austria
| | - Christoph Scherfler
- Medical University of Innsbruck, Department of Neurology, Anichstrasse 35, 6020, Innsbruck, Austria; Medical University of Innsbruck, Neuroimaging Research Core Facility, Anichstrasse 35, 6020, Innsbruck, Austria.
| |
Collapse
|
35
|
Texture-based markers from structural imaging correlate with motor handicap in Parkinson's disease. Sci Rep 2021; 11:2724. [PMID: 33526820 PMCID: PMC7851138 DOI: 10.1038/s41598-021-81209-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 12/28/2020] [Indexed: 01/17/2023] Open
Abstract
There is a growing need for surrogate biomarkers for Parkinson’s disease (PD). Structural analysis using magnetic resonance imaging with T1-weighted sequences has the potential to quantify histopathological changes. Degeneration is typically measured by the volume and shape of morphological changes. However, these changes appear late in the disease, preventing their use as surrogate markers. We investigated texture changes in 108 individuals, divided into three groups, matched in terms of sex and age: (1) healthy controls (n = 32); (2) patients with early-stage PD (n = 39); and (3) patients with late-stage PD and severe L-dopa-related complications (n = 37). All patients were assessed in off-treatment conditions. Statistical analysis of first- and second-order texture features was conducted in the substantia nigra, striatum, thalamus and sub-thalamic nucleus. Regions of interest volumetry and voxel-based morphometry were performed for comparison. Significantly different texture features were observed between the three populations, with some showing a gradual linear progression between the groups. The volumetric changes in the two PD patient groups were not significantly different. Texture features were significantly associated with clinical scores for motor handicap. These results suggest that texture features, measured in the nigrostriatal pathway at PD diagnosis, may be useful in predicting clinical progression of motor handicap.
Collapse
|
36
|
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
| |
Collapse
|
37
|
Park CH, Lee PH, Lee SK, Chung SJ, Shin NY. The diagnostic potential of multimodal neuroimaging measures in Parkinson's disease and atypical parkinsonism. Brain Behav 2020; 10:e01808. [PMID: 33029883 PMCID: PMC7667347 DOI: 10.1002/brb3.1808] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 08/03/2020] [Accepted: 08/04/2020] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION For the diagnosis of Parkinson's disease (PD) and atypical parkinsonism (AP) using neuroimaging, structural measures have been largely employed since structural abnormalities are most noticeable in the diseases. Functional abnormalities have been known as well, though less clearly seen, and thus, the addition of functional measures to structural measures is expected to be more informative for the diagnosis. Here, we aimed to assess whether multimodal neuroimaging measures of structural and functional alterations could have potential for enhancing performance in diverse diagnostic classification problems. METHODS For 77 patients with PD, 86 patients with AP comprising multiple system atrophy and progressive supranuclear palsy, and 53 healthy controls (HC), structural and functional MRI data were collected. Gray matter (GM) volume was acquired as a structural measure, and GM regional homogeneity and degree centrality were acquired as functional measures. The measures were used as predictors individually or in combination in support vector machine classifiers for different problems of distinguishing between HC and each diagnostic type and between different diagnostic types. RESULTS In statistical comparisons of the measures, structural alterations were extensively seen in all diagnostic types, whereas functional alterations were limited to specific diagnostic types. The addition of functional measures to the structural measure generally yielded statistically significant improvements to classification accuracy, compared to the use of the structural measure alone. CONCLUSION We suggest the fusion of multimodal neuroimaging measures as an effective strategy that could generally cope with diverse prediction problems of clinical concerns.
Collapse
Affiliation(s)
- Chang-Hyun Park
- Department of Radiology, College of Medicine, The Catholic University of Korea, Seoul, Korea.,Center for Neuroprosthetics and Brain Mind Institute, Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology, Yonsei University College of Medicine, Seoul, Korea
| | - Seok Jong Chung
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.,Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, Korea
| | - Na-Young Shin
- Department of Radiology, College of Medicine, The Catholic University of Korea, Seoul, Korea
| |
Collapse
|
38
|
Sjöström H, Granberg T, Hashim F, Westman E, Svenningsson P. Automated brainstem volumetry can aid in the diagnostics of parkinsonian disorders. Parkinsonism Relat Disord 2020; 79:18-25. [DOI: 10.1016/j.parkreldis.2020.08.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 06/30/2020] [Accepted: 08/04/2020] [Indexed: 12/14/2022]
|
39
|
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
Collapse
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
| |
Collapse
|
40
|
Reimão S, Guerreiro C, Seppi K, Ferreira JJ, Poewe W. A Standardized MR Imaging Protocol for Parkinsonism. Mov Disord 2020; 35:1745-1750. [PMID: 32914459 DOI: 10.1002/mds.28204] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 06/08/2020] [Accepted: 06/15/2020] [Indexed: 12/11/2022] Open
Affiliation(s)
- Sofia Reimão
- Neuroimaging Department, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal.,Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal.,Laboratory of Clinical Pharmacology and Therapeutics, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Carla Guerreiro
- Neuroimaging Department, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal.,Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal.,Laboratory of Clinical Pharmacology and Therapeutics, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Klaus Seppi
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Joaquim J Ferreira
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal.,Laboratory of Clinical Pharmacology and Therapeutics, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal.,CNS - Campus Neurológico Sénior, Torres Vedras, Portugal
| | - Werner Poewe
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| |
Collapse
|
41
|
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: 30] [Impact Index Per Article: 7.5] [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.
Collapse
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
| | | |
Collapse
|
42
|
Mangesius S, Mariotto S, Ferrari S, Pereverzyev S, Lerchner H, Haider L, Gizewski ER, Wenning G, Seppi K, Reindl M, Poewe W. Novel decision algorithm to discriminate parkinsonism with combined blood and imaging biomarkers. Parkinsonism Relat Disord 2020; 77:57-63. [DOI: 10.1016/j.parkreldis.2020.05.033] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 05/20/2020] [Accepted: 05/26/2020] [Indexed: 01/23/2023]
|
43
|
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.
Collapse
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
| |
Collapse
|
44
|
Hossein‐Tehrani MR, Ghaedian T, Hooshmandi E, Kalhor L, Foroughi AA, Ostovan VR. Brain TRODAT‐SPECT Versus MRI Morphometry in Distinguishing Early Mild Parkinson's Disease from Other Extrapyramidal Syndromes. J Neuroimaging 2020; 30:683-689. [DOI: 10.1111/jon.12740] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 05/18/2020] [Accepted: 05/21/2020] [Indexed: 01/26/2023] Open
Affiliation(s)
| | - Tahereh Ghaedian
- Nuclear Medicine and Molecular Imaging Research Center, Namazi Teaching Hospital Shiraz University of Medical Sciences Shiraz Iran
| | - Etrat Hooshmandi
- Clinical Neurology Research Center Shiraz University of Medical Sciences Shiraz Iran
| | - Leila Kalhor
- Nuclear Medicine and Molecular Imaging Research Center, Namazi Teaching Hospital Shiraz University of Medical Sciences Shiraz Iran
| | - Amin Abolhasani Foroughi
- Medical Imaging Research Center Shiraz University of Medical Sciences Shiraz Iran
- Epilepsy Research Center Shiraz University of Medical Sciences Shiraz Iran
| | - Vahid Reza Ostovan
- Clinical Neurology Research Center Shiraz University of Medical Sciences Shiraz Iran
| |
Collapse
|
45
|
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]
|
46
|
Ryman SG, Poston KL. MRI biomarkers of motor and non-motor symptoms in Parkinson's disease. Parkinsonism Relat Disord 2020; 73:85-93. [PMID: 31629653 PMCID: PMC7145760 DOI: 10.1016/j.parkreldis.2019.10.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 10/03/2019] [Accepted: 10/05/2019] [Indexed: 12/19/2022]
Abstract
Parkinson's disease is a heterogeneous disorder with both motor and non-motor symptoms that contribute to functional impairment. To develop effective, disease modifying treatments for these symptoms, biomarkers are necessary to detect neuropathological changes early in the disease course and monitor changes over time. Advances in MRI scan sequences and analytical techniques present numerous promising metrics to detect changes within the nigrostriatal system, implicated in the cardinal motor symptoms of the disease, and detect broader dysfunction involved in the non-motor symptoms, such as cognitive impairment. There is emerging evidence that iron sensitive, neuromelanin sensitive, diffusion sensitive, and resting state functional magnetic imaging measures can capture changes within the nigrostriatal system. Iron, neuromelanin, and diffusion sensitive measures demonstrate high specificity and sensitivity in distinguishing Parkinson's disease relative to controls, with inconsistent results differentiating Parkinson's disease relative to atypical parkinsonian disorders. They may also serve as useful monitoring biomarkers, with each possibly detecting different aspects of the disease course (early nigrosome changes versus broader substantia nigra changes). Investigations of non-motor symptoms, such as cognitive impairment, require careful consideration of the nature of cognitive deficits to characterize regional and network specific impairment. While the early, executive dysfunction observed is consistent with nigrostriatal degeneration, the memory and visuospatial impairments, the harbingers of a dementia process reflect dopaminergic independent dysfunction involving broader regions of the brain.
Collapse
Affiliation(s)
- Sephira G Ryman
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, 300 Pasteur Dr. Room A343. MC-5235, Stanford, CA, 94305, USA.
| | - Kathleen L Poston
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, 300 Pasteur Dr. Room A343. MC-5235, Stanford, CA, 94305, USA.
| |
Collapse
|
47
|
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.
Collapse
|
48
|
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.
Collapse
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
| |
Collapse
|
49
|
Giagkou N, Höglinger GU, Stamelou M. Progressive supranuclear palsy. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2019; 149:49-86. [PMID: 31779824 DOI: 10.1016/bs.irn.2019.10.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized pathologically by 4 repeat tau deposition in various cell types and anatomical regions. Richardson's syndrome (RS) is the initially described and one of the clinical phenotypes associated with PSP pathology, characterized by vertical supranuclear gaze paly in particular downwards, postural instability with early falls and subcortical frontal dementia. PSP can manifest as several other clinical phenotypes, including PSP-parkinsonism, -pure akinesia with gait freezing, -frontotemporal dementia, - corticobasal syndrome, - speech/language impairment. RS can also have a pathologic diagnosis other than PSP, including corticobasal degeneration, FTD-TDP-43 and others. New clinical diagnostic criteria take into account this phenotypic variability in an attempt to diagnose the disease earlier, given the current lack of a validated biomarker. At present, therapeutic options for PSP are symptomatic and insufficient. Recent large neuroprotective trials have failed to provide a positive clinical outcome, however, have led to the design of better studies that are ongoing and hold promise for a neuroprotective treatment for PSP.
Collapse
Affiliation(s)
- Nikolaos Giagkou
- Parkinson's Disease and Movement Disorders Department, HYGEIA Hospital, Athens, Greece
| | - Günter U Höglinger
- Department for Neurology Hannover Medical School (MHH), Hannover, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Maria Stamelou
- Parkinson's Disease and Movement Disorders Department, HYGEIA Hospital, Athens, Greece; Aiginiteion Hospital, First Department of Neurology, University of Athens, Greece; Clinic for Neurology, Philipps University, Marburg, Germany
| |
Collapse
|
50
|
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.
Collapse
|