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Yang Y, Li X, Lu J, Ge J, Chen M, Yao R, Tian M, Wang J, Liu F, Zuo C. Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in parkinsonism. Neural Regen Res 2025; 20:93-106. [PMID: 38767479 PMCID: PMC11246150 DOI: 10.4103/1673-5374.391180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 11/18/2023] [Indexed: 05/22/2024] Open
Abstract
Nowadays, presynaptic dopaminergic positron emission tomography, which assesses deficiencies in dopamine synthesis, storage, and transport, is widely utilized for early diagnosis and differential diagnosis of parkinsonism. This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism. We conducted a thorough literature search using reputable databases such as PubMed and Web of Science. Selection criteria involved identifying peer-reviewed articles published within the last 5 years, with emphasis on their relevance to clinical applications. The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis. Moreover, when employed in conjunction with other imaging modalities and advanced analytical methods, presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker. This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion. In summary, the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials, ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.
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Affiliation(s)
- Yujie Yang
- Key Laboratory of Arrhythmias, Ministry of Education, Department of Medical Genetics, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xinyi Li
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiaying Lu
- Department of Nuclear Medicine & PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jingjie Ge
- Department of Nuclear Medicine & PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Mingjia Chen
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Ruixin Yao
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Mei Tian
- Department of Nuclear Medicine & PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- International Human Phenome Institutes (Shanghai), Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Jian Wang
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Fengtao Liu
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Chuantao Zuo
- Department of Nuclear Medicine & PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
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Eidelberg D, Tang C, Nakano Y, Vo A, Nguyen N, Schindlbeck K, Poston K, Gagnon JF, Postuma R, Niethammer M, Ma Y, Peng S, Dhawan V. Longitudinal Network Changes and Phenoconversion Risk in Isolated REM Sleep Behavior Disorder. RESEARCH SQUARE 2024:rs.3.rs-4427198. [PMID: 38853923 PMCID: PMC11160876 DOI: 10.21203/rs.3.rs-4427198/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal syndrome for Parkinson's disease (PD) and related α-synucleinopathies. We conducted a longitudinal imaging study of network changes in iRBD and their relationship to phenoconversion. Expression levels for the PD-related motor and cognitive networks (PDRP and PDCP) were measured at baseline, 2 and 4 years, along with dopamine transporter (DAT) binding. PDRP and PDCP expression increased over time, with higher values in the former network. While abnormal functional connections were identified initially within the PDRP, others bridging the two networks appeared later. A model based on the rates of PDRP progression and putamen dopamine loss predicted phenoconversion within 1.2 years in individuals with iRBD. In aggregate, the data suggest that maladaptive reorganization of brain networks takes place in iRBD years before phenoconversion. Network expression and DAT binding measures can be used together to assess phenoconversion risk in these individuals.
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Affiliation(s)
| | - Chris Tang
- The Feinstein Institutes for Medical Research
| | | | - An Vo
- The Feinstein Institutes for Medical Research
| | | | | | | | | | | | | | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
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Lövdal SS, Carli G, Orso B, Biehl M, Arnaldi D, Mattioli P, Janzen A, Sittig E, Morbelli S, Booij J, Oertel WH, Leenders KL, Meles SK. Investigating the aspect of asymmetry in brain-first versus body-first Parkinson's disease. NPJ Parkinsons Dis 2024; 10:74. [PMID: 38555343 PMCID: PMC10981719 DOI: 10.1038/s41531-024-00685-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 03/13/2024] [Indexed: 04/02/2024] Open
Abstract
Parkinson's disease (PD) is characterized by a progressive loss of dopaminergic neurons in the substantia nigra. Recent literature has proposed two subgroups of PD. The "body-first subtype" is associated with a prodrome of isolated REM-sleep Behavior Disorder (iRBD) and a relatively symmetric brain degeneration. The "brain-first subtype" is suggested to have a more asymmetric degeneration and a prodromal stage without RBD. This study aims to investigate the proposed difference in symmetry of the degeneration pattern in the presumed body and brain-first PD subtypes. We analyzed 123I-FP-CIT (DAT SPECT) and 18F-FDG PET brain imaging in three groups of patients (iRBD, n = 20, de novo PD with prodromal RBD, n = 22, and de novo PD without RBD, n = 16) and evaluated dopaminergic and glucose metabolic symmetry. The RBD status of all patients was confirmed with video-polysomnography. The PD groups did not differ from each other with regard to the relative or absolute asymmetry of DAT uptake in the putamen (p = 1.0 and p = 0.4, respectively). The patient groups also did not differ from each other with regard to the symmetry of expression of the PD-related metabolic pattern (PDRP) in each hemisphere. The PD groups had no difference in symmetry considering mean FDG uptake in left and right regions of interest and generally had the same degree of symmetry as controls, while the iRBD patients had nine regions with abnormal left-right differences (p < 0.001). Our findings do not support the asymmetry aspect of the "body-first" versus "brain-first" hypothesis.
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Affiliation(s)
- S S Lövdal
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, Netherlands.
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands.
| | - G Carli
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, Netherlands
| | - B Orso
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - M Biehl
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands
- SMQB, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - D Arnaldi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Neurophysiopathology Unit, IRCCS Ospedale Policlinico S. Martino, Genoa, Italy
| | - P Mattioli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Neurophysiopathology Unit, IRCCS Ospedale Policlinico S. Martino, Genoa, Italy
| | - A Janzen
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | - E Sittig
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | - S Morbelli
- Department of Health Sciences, University of Genoa, Genoa, Italy
- Nuclear Medicine Unit, IRCCS Ospedale Policlinico S. Martino, Genoa, Italy
| | - J Booij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - W H Oertel
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | - K L Leenders
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, Netherlands
| | - S K Meles
- Department of Neurology, University Medical Center Groningen, Groningen, Netherlands
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Stockbauer A, Beyer L, Huber M, Kreuzer A, Palleis C, Katzdobler S, Rauchmann BS, Morbelli S, Chincarini A, Bruffaerts R, Vandenberghe R, Kramberger MG, Trost M, Garibotto V, Nicastro N, Lathuilière A, Lemstra AW, van Berckel BNM, Pilotto A, Padovani A, Ochoa-Figueroa MA, Davidsson A, Camacho V, Peira E, Bauckneht M, Pardini M, Sambuceti G, Aarsland D, Nobili F, Gross M, Vöglein J, Perneczky R, Pogarell O, Buerger K, Franzmeier N, Danek A, Levin J, Höglinger GU, Bartenstein P, Cumming P, Rominger A, Brendel M. Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission. Eur J Nucl Med Mol Imaging 2024; 51:1023-1034. [PMID: 37971501 PMCID: PMC10881642 DOI: 10.1007/s00259-023-06493-w] [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: 03/09/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023]
Abstract
PURPOSE Metabolic network analysis of FDG-PET utilizes an index of inter-regional correlation of resting state glucose metabolism and has been proven to provide complementary information regarding the disease process in parkinsonian syndromes. The goals of this study were (i) to evaluate pattern similarities of glucose metabolism and network connectivity in dementia with Lewy bodies (DLB) subjects with subthreshold dopaminergic loss compared to advanced disease stages and to (ii) investigate metabolic network alterations of FDG-PET for discrimination of patients with early DLB from other neurodegenerative disorders (Alzheimer's disease, Parkinson's disease, multiple system atrophy) at individual patient level via principal component analysis (PCA). METHODS FDG-PETs of subjects with probable or possible DLB (n = 22) without significant dopamine deficiency (z-score < 2 in putamen binding loss on DaT-SPECT compared to healthy controls (HC)) were scaled by global-mean, prior to volume-of-interest-based analyses of relative glucose metabolism. Single region metabolic changes and network connectivity changes were compared against HC (n = 23) and against DLB subjects with significant dopamine deficiency (n = 86). PCA was applied to test discrimination of patients with DLB from disease controls (n = 101) at individual patient level. RESULTS Similar patterns of hypo- (parietal- and occipital cortex) and hypermetabolism (basal ganglia, limbic system, motor cortices) were observed in DLB patients with and without significant dopamine deficiency when compared to HC. Metabolic connectivity alterations correlated between DLB patients with and without significant dopamine deficiency (R2 = 0.597, p < 0.01). A PCA trained by DLB patients with dopamine deficiency and HC discriminated DLB patients without significant dopaminergic loss from other neurodegenerative parkinsonian disorders at individual patient level (area-under-the-curve (AUC): 0.912). CONCLUSION Disease-specific patterns of altered glucose metabolism and altered metabolic networks are present in DLB subjects without significant dopaminergic loss. Metabolic network alterations in FDG-PET can act as a supporting biomarker in the subgroup of DLB patients without significant dopaminergic loss at symptoms onset.
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Affiliation(s)
- Anna Stockbauer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Leonie Beyer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Maria Huber
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Annika Kreuzer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Carla Palleis
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Sabrina Katzdobler
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Department of Neuroradiology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Silvia Morbelli
- Nuclear Medicine Uni, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Health Sciences, University of Genoa, Genoa, Italy
| | - Andrea Chincarini
- National Institute of Nuclear Physics (INFN), Genoa Section, Genoa, Italy
| | - Rose Bruffaerts
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Louvain, Belgium
- Neurology Department, University Hospitals Leuven, Louvain, Belgium
- Biomedical Research Institute, Hasselt University, Hasselt, Belgium
- Experimental Neurobiology Unit, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Louvain, Belgium
- Neurology Department, University Hospitals Leuven, Louvain, Belgium
| | - Milica G Kramberger
- Department of Neurology and Department for Nuclear Medicine, University Medical Centre, Ljubljana, Slovenia
| | - Maja Trost
- Department of Neurology and Department for Nuclear Medicine, University Medical Centre, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and NIMTLab, Geneva University, Geneva, Switzerland
| | - Nicolas Nicastro
- Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland
| | - Aurélien Lathuilière
- LANVIE (Laboratoire de Neuroimagerie du Vieillissement), Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Afina W Lemstra
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Andrea Pilotto
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- Parkinson's Disease Rehabilitation Centre, FERB ONLUS - S. Isidoro Hospital, Trescore Balneario, BG, Italy
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Miguel A Ochoa-Figueroa
- Department of Clinical Physiology in Linköping, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Department of Diagnostic Radiology, Linköping University Hospital, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Anette Davidsson
- Department of Clinical Physiology in Linköping, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Valle Camacho
- Servicio de Medicina Nuclear, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Enrico Peira
- National Institute of Nuclear Physics (INFN), Genoa Section, Genoa, Italy
- Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy
| | - Matteo Bauckneht
- Nuclear Medicine Uni, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Matteo Pardini
- Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy
- Clinical Neurology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Gianmario Sambuceti
- Nuclear Medicine Uni, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Health Sciences, University of Genoa, Genoa, Italy
| | - Dag Aarsland
- Centre of Age-Related Medicine (SESAM), Stavanger University Hospital, Stavanger, Norway
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College, London, UK
| | - Flavio Nobili
- Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy
- Clinical Neurology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Mattes Gross
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Jonathan Vöglein
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, S10 2HQ, UK
- Ageing Epidemiology (AGE) Research Unit, School of Public Health, Imperial College, London, UK
| | - Oliver Pogarell
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institut for Stroke and Dementia Research, University of Munich, Munich, Germany
| | | | - Adrian Danek
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Johannes Levin
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Günter U Höglinger
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Paul Cumming
- Department of Nuclear Medicine, University of Bern, Inselspital Bern, Bern, Switzerland
- School of Psychology and Counselling and IHBI, Queensland University of Technology, Brisbane, Australia
| | - Axel Rominger
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
- Department of Nuclear Medicine, University of Bern, Inselspital Bern, Bern, Switzerland
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
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Barbero JA, Unadkat P, Choi YY, Eidelberg D. Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease. Neurotherapeutics 2023; 20:1653-1668. [PMID: 37684533 PMCID: PMC10684458 DOI: 10.1007/s13311-023-01433-w] [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] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
Network analysis of functional brain scans acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET, to map cerebral glucose metabolism), or resting-state functional magnetic resonance imaging (rs-fMRI, to map blood oxygen level-dependent brain activity) has increasingly been used to identify and validate reproducible circuit abnormalities associated with neurodegenerative disorders such as Parkinson's disease (PD). In addition to serving as imaging markers of the underlying disease process, these networks can be used singly or in combination as an adjunct to clinical diagnosis and as a screening tool for therapeutics trials. Disease networks can also be used to measure rates of progression in natural history studies and to assess treatment responses in individual subjects. Recent imaging studies in PD subjects scanned before and after treatment have revealed therapeutic effects beyond the modulation of established disease networks. Rather, other mechanisms of action may be at play, such as the induction of novel functional brain networks directly by treatment. To date, specific treatment-induced networks have been described in association with novel interventions for PD such as subthalamic adeno-associated virus glutamic acid decarboxylase (AAV2-GAD) gene therapy, as well as sham surgery or oral placebo under blinded conditions. Indeed, changes in the expression of these networks with treatment have been found to correlate consistently with clinical outcome. In aggregate, these attributes suggest a role for functional brain networks as biomarkers in future clinical trials.
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Affiliation(s)
- János A Barbero
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
| | - Prashin Unadkat
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY, 11030, USA
| | - Yoon Young Choi
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA.
- Molecular Medicine and Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA.
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Spetsieris PG, Eidelberg D. Parkinson's disease progression: Increasing expression of an invariant common core subnetwork. Neuroimage Clin 2023; 39:103488. [PMID: 37660556 PMCID: PMC10491857 DOI: 10.1016/j.nicl.2023.103488] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/31/2023] [Accepted: 08/02/2023] [Indexed: 09/05/2023]
Abstract
Notable success has been achieved in the study of neurodegenerative conditions using reduction techniques such as principal component analysis (PCA) and sparse inverse covariance estimation (SICE) in positron emission tomography (PET) data despite their widely differing approach. In a recent study of SICE applied to metabolic scans from Parkinson's disease (PD) patients, we showed that by using PCA to prespecify disease-related partition layers, we were able to optimize maps of functional metabolic connectivity within the relevant networks. Here, we show the potential of SICE, enhanced by disease-specific subnetwork partitions, to identify key regional hubs and their connections, and track their associations in PD patients with increasing disease duration. This approach enabled the identification of a core zone that included elements of the striatum, pons, cerebellar vermis, and parietal cortex and provided a deeper understanding of progressive changes in their connectivity. This subnetwork constituted a robust invariant disease feature that was unrelated to phenotype. Mean expression levels for this subnetwork increased steadily in a group of 70 PD patients spanning a range of symptom durations between 1 and 21 years. The findings were confirmed in a validation sample of 69 patients with up to 32 years of symptoms. The common core elements represent possible targets for disease modification, while their connections to external regions may be better suited for symptomatic treatment.
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Affiliation(s)
- Phoebe G Spetsieris
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, United States
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, United States; Molecular Medicine and Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, United States.
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7
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Perovnik M, Rus T, Schindlbeck KA, Eidelberg D. Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat Rev Neurol 2023; 19:73-90. [PMID: 36539533 DOI: 10.1038/s41582-022-00753-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2022] [Indexed: 12/24/2022]
Abstract
Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Tomaž Rus
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | | | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA.
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Prange S, Theis H, Banwinkler M, van Eimeren T. Molecular Imaging in Parkinsonian Disorders—What’s New and Hot? Brain Sci 2022; 12:brainsci12091146. [PMID: 36138882 PMCID: PMC9496752 DOI: 10.3390/brainsci12091146] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 12/02/2022] Open
Abstract
Highlights Abstract Neurodegenerative parkinsonian disorders are characterized by a great diversity of clinical symptoms and underlying neuropathology, yet differential diagnosis during lifetime remains probabilistic. Molecular imaging is a powerful method to detect pathological changes in vivo on a cellular and molecular level with high specificity. Thereby, molecular imaging enables to investigate functional changes and pathological hallmarks in neurodegenerative disorders, thus allowing to better differentiate between different forms of degenerative parkinsonism, improve the accuracy of the clinical diagnosis and disentangle the pathophysiology of disease-related symptoms. The past decade led to significant progress in the field of molecular imaging, including the development of multiple new and promising radioactive tracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) as well as novel analytical methods. Here, we review the most recent advances in molecular imaging for the diagnosis, prognosis, and mechanistic understanding of parkinsonian disorders. First, advances in imaging of neurotransmission abnormalities, metabolism, synaptic density, inflammation, and pathological protein aggregation are reviewed, highlighting our renewed understanding regarding the multiplicity of neurodegenerative processes involved in parkinsonian disorders. Consequently, we review the role of molecular imaging in the context of disease-modifying interventions to follow neurodegeneration, ensure stratification, and target engagement in clinical trials.
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Affiliation(s)
- Stéphane Prange
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Faculty of Medicine, University Hospital of Cologne, University of Cologne, 50937 Cologne, Germany
- Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR 5229, Université de Lyon, 69675 Bron, France
- Correspondence: (S.P.); (T.v.E.); Tel.: +49-221-47882843 (T.v.E.)
| | - Hendrik Theis
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Faculty of Medicine, University Hospital of Cologne, University of Cologne, 50937 Cologne, Germany
- Department of Neurology, Faculty of Medicine, University Hospital of Cologne, University of Cologne, 50937 Cologne, Germany
| | - Magdalena Banwinkler
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Faculty of Medicine, University Hospital of Cologne, University of Cologne, 50937 Cologne, Germany
| | - Thilo van Eimeren
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Faculty of Medicine, University Hospital of Cologne, University of Cologne, 50937 Cologne, Germany
- Department of Neurology, Faculty of Medicine, University Hospital of Cologne, University of Cologne, 50937 Cologne, Germany
- Correspondence: (S.P.); (T.v.E.); Tel.: +49-221-47882843 (T.v.E.)
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Meles SK, Oertel WH, Leenders KL. Circuit imaging biomarkers in preclinical and prodromal Parkinson's disease. Mol Med 2021; 27:111. [PMID: 34530732 PMCID: PMC8447708 DOI: 10.1186/s10020-021-00327-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 06/02/2021] [Indexed: 11/10/2022] Open
Abstract
Parkinson's disease (PD) commences several years before the onset of motor features. Pathophysiological understanding of the pre-clinical or early prodromal stages of PD are essential for the development of new therapeutic strategies. Two categories of patients are ideal to study the early disease stages. Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents a well-known prodromal stage of PD in which pathology is presumed to have reached the lower brainstem. The majority of patients with iRBD will develop manifest PD within years to decades. Another category encompasses non-manifest mutation carriers, i.e. subjects without symptoms, but with a known mutation or genetic variant which gives an increased risk of developing PD. The speed of progression from preclinical or prodromal to full clinical stages varies among patients and cannot be reliably predicted on the individual level. Clinical trials will require inclusion of patients with a predictable conversion within a limited time window. Biomarkers are necessary that can confirm pre-motor PD status and can provide information regarding lead time and speed of progression. Neuroimaging changes occur early in the disease process and may provide such a biomarker. Studies have focused on radiotracer imaging of the dopaminergic nigrostriatal system, which can be assessed with dopamine transporter (DAT) single photon emission computed tomography (SPECT). Loss of DAT binding represents an effect of irreversible structural damage to the nigrostriatal system. This marker can be used to monitor disease progression and identify individuals at specific risk for phenoconversion. However, it is known that changes in neuronal activity precede structural changes. Functional neuro-imaging techniques, such as 18F-2-fluoro-2-deoxy-D-glucose Positron Emission Tomography (18F-FDG PET) and functional magnetic resonance imaging (fMRI), can be used to model the effects of disease on brain networks when combined with advanced analytical methods. Because these changes occur early in the disease process, functional imaging studies are of particular interest in prodromal PD diagnosis. In addition, fMRI and 18F-FDG PET may be able to predict a specific future phenotype in prodromal cohorts, which is not possible with DAT SPECT. The goal of the current review is to discuss the network-level brain changes in pre-motor PD.
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Affiliation(s)
- Sanne K Meles
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, PO Box 30.001, 9700 RB, Groningen, The Netherlands.
| | - Wolfgang H Oertel
- Department of Neurology, Philipps-Universität Marburg, Marburg, Germany.,Institute for Neurogenomics, Helmholtz Center for Health and Environment, Munich, Germany
| | - Klaus L Leenders
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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Rommal A, Vo A, Schindlbeck KA, Greuel A, Ruppert MC, Eggers C, Eidelberg D. Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study. NEUROIMAGE: REPORTS 2021. [DOI: 10.1016/j.ynirp.2021.100026] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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11
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Peng S, Tang C, Schindlbeck K, Rydzinski Y, Dhawan V, Spetsieris PG, Ma Y, Eidelberg D. Dynamic 18F-FPCIT PET: Quantification of Parkinson's disease metabolic networks and nigrostriatal dopaminergic dysfunction in a single imaging session. J Nucl Med 2021; 62:jnumed.120.257345. [PMID: 33741649 PMCID: PMC8612203 DOI: 10.2967/jnumed.120.257345] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 03/09/2021] [Accepted: 03/09/2021] [Indexed: 11/16/2022] Open
Abstract
Previous multi-center imaging studies with 18F-FDG PET have established the presence of Parkinson's disease motor- and cognition-related metabolic patterns termed PDRP and PDCP in patients with this disorder. Given that in PD cerebral perfusion and glucose metabolism are typically coupled in the absence of medication, we determined whether subject expression of these disease networks can be quantified in early-phase images from dynamic 18F-FPCIT PET scans acquired to assess striatal dopamine transporter (DAT) binding. Methods: We studied a cohort of early-stage PD patients and age-matched healthy control subjects who underwent 18F-FPCIT at baseline; scans were repeated 4 years later in a smaller subset of patients. The early 18F-FPCIT frames, which reflect cerebral perfusion, were used to compute PDRP and PDCP expression (subject scores) in each subject, and compared to analogous measures computed based on 18F-FDG PET scan when additionally available. The late 18F-FPCIT frames were used to measure caudate and putamen DAT binding in the same individuals. Results: PDRP subject scores from early-phase 18F-FPCIT and 18F-FDG scans were elevated and striatal DAT binding reduced in PD versus healthy subjects. The PDRP scores from 18F-FPCIT correlated with clinical motor ratings, disease duration, and with corresponding measures from 18F-FDG PET. In addition to correlating with disease duration and analogous 18F-FDG PET values, PDCP scores correlated with DAT binding in the caudate/anterior putamen. PDRP and PDCP subject scores using either method rose over 4 years whereas striatal DAT binding declined over the same time period. Conclusion: Early-phase images obtained with 18F-FPCIT PET can provide an alternative to 18F-FDG PET for PD network quantification. This technique therefore allows PDRP/PDCP expression and caudate/putamen DAT binding to be evaluated with a single tracer in one scanning session.
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Affiliation(s)
- Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York; and
| | - Chris Tang
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York; and
| | - Katharina Schindlbeck
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York; and
| | - Yaacov Rydzinski
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Vijay Dhawan
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York; and
| | - Phoebe G. Spetsieris
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York; and
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York; and
| | - David Eidelberg
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York; and
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