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Ruppert-Junck MC, Heinecke V, Librizzi D, Steidel K, Beckersjürgen M, Verburg FA, Schurrat T, Luster M, Müller HH, Timmermann L, Eggers C, Pedrosa D. Connectivity based on glucose dynamics reveals exaggerated sensorimotor network coupling on subject-level in Parkinson's disease. Eur J Nucl Med Mol Imaging 2024; 51:3630-3642. [PMID: 38884774 PMCID: PMC11445336 DOI: 10.1007/s00259-024-06796-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 06/07/2024] [Indexed: 06/18/2024]
Abstract
PURPOSE While fMRI provides information on the temporal changes in blood oxygenation, 2- [18F]fluoro-2-deoxy-D-glucose ([18F]FDG)-PET has traditionally offered a static snapshot of brain glucose consumption. As a result, studies investigating metabolic brain networks as potential biomarkers for neurodegeneration have primarily been conducted at the group level. However, recent pioneering studies introduced time-resolved [18F]FDG-PET with constant infusion, which enables metabolic connectivity studies at the individual level. METHODS In the current study, this technique was employed to explore Parkinson's disease (PD)-related alterations in individual metabolic connectivity, in comparison to inter-subject measures and hemodynamic connectivity. Fifteen PD patients and 14 healthy controls with comparable cognition underwent sequential resting-state dynamic PET with constant infusion and functional MRI. Intrinsic networks were identified by independent component analysis and interregional connectivity calculated for summed static PET images, PET time series and functional MRI. RESULTS Our findings revealed an intrinsic sensorimotor network in PD patients that has not been previously observed to this extent. In PD, a significantly higher number of connections in cortical motor areas was observed compared to elderly control subjects, as indicated by both static PET and functional MRI (pBonferroni-Holm = 0.027), as well as constant infusion PET and functional MRI connectomes (pBonferroni-Holm = 0.012). This intensified coupling was associated with disease severity (ρ = 0.56, p = 0.036). CONCLUSION Metabolic connectivity, as revealed by both static and dynamic PET, provides unique information on metabolic network activity. Subject-level metabolic connectivity based on constant infusion PET may serve as a potential marker for the metabolic network signature in neurodegeneration.
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Affiliation(s)
- Marina C Ruppert-Junck
- Neurology Department at Medical Faculty Marburg, Philipps-University Marburg, Marburg, Germany.
- Neurology Department, University Hospital of Marburg and Gießen, Baldingerstraße, 35043, Marburg, Germany.
- Center for Mind, Brain and Behavior - CMBB, Universities Marburg and Gießen, Marburg, Germany.
| | - Vanessa Heinecke
- Neurology Department at Medical Faculty Marburg, Philipps-University Marburg, Marburg, Germany
| | - Damiano Librizzi
- Nuclear Medicine Department, Philipps-University Marburg, Marburg, Germany
| | - Kenan Steidel
- Neurology Department at Medical Faculty Marburg, Philipps-University Marburg, Marburg, Germany
- Neurology Department, University Hospital of Marburg and Gießen, Baldingerstraße, 35043, Marburg, Germany
| | - Maya Beckersjürgen
- Neurology Department at Medical Faculty Marburg, Philipps-University Marburg, Marburg, Germany
| | - Frederik A Verburg
- Nuclear Medicine Department, Philipps-University Marburg, Marburg, Germany
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Tino Schurrat
- Nuclear Medicine Department, Philipps-University Marburg, Marburg, Germany
| | - Markus Luster
- Nuclear Medicine Department, Philipps-University Marburg, Marburg, Germany
| | - Hans-Helge Müller
- Institute for Medical Bioinformatics and Biostatistics, Philipps-University Marburg, Marburg, Germany
| | - Lars Timmermann
- Neurology Department at Medical Faculty Marburg, Philipps-University Marburg, Marburg, Germany
- Neurology Department, University Hospital of Marburg and Gießen, Baldingerstraße, 35043, Marburg, Germany
- Center for Mind, Brain and Behavior - CMBB, Universities Marburg and Gießen, Marburg, Germany
| | - Carsten Eggers
- Neurology Department at Medical Faculty Marburg, Philipps-University Marburg, Marburg, Germany
- Neurology Department, University Hospital of Marburg and Gießen, Baldingerstraße, 35043, Marburg, Germany
- Knappschaftskrankenhaus Bottrop GmbH, Bottrop, Germany
| | - David Pedrosa
- Neurology Department at Medical Faculty Marburg, Philipps-University Marburg, Marburg, Germany
- Neurology Department, University Hospital of Marburg and Gießen, Baldingerstraße, 35043, Marburg, Germany
- Center for Mind, Brain and Behavior - CMBB, Universities Marburg and Gießen, Marburg, Germany
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Zhao T, Wang B, Liang W, Cheng S, Wang B, Cui M, Shou J. Accuracy of 18F-FDG PET Imaging in Differentiating Parkinson's Disease from Atypical Parkinsonian Syndromes: A Systematic Review and Meta-Analysis. Acad Radiol 2024:S1076-6332(24)00579-8. [PMID: 39183130 DOI: 10.1016/j.acra.2024.08.016] [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: 07/03/2024] [Revised: 07/26/2024] [Accepted: 08/09/2024] [Indexed: 08/27/2024]
Abstract
RATIONALE AND OBJECTIVE To quantitatively assess the accuracy of 18F-FDG PET in differentiating Parkinson's Disease (PD) from Atypical Parkinsonian Syndromes (APSs). METHODS PubMed, Embase, and Web of Science databases were searched to identify studies published from the inception of the databases up to June 2024 that used 18F-FDG PET imaging for the differential diagnosis of PD and APSs. The risk of bias in the included studies was assessed using the QUADAS-2 or QUADAS-AI tool. Bivariate random-effects models were used to calculate the pooled sensitivity, specificity, and the area under the curves (AUC) of summary receiver operating characteristic (SROC). RESULTS 24 studies met the inclusion criteria, involving a total of 1508 PD patients and 1370 APSs patients. 12 studies relied on visual interpretation by radiologists, of which the pooled sensitivity, specificity, and SROC-AUC for direct visual interpretation in diagnosing PD were 96% (95%CI: 91%, 98%), 90% (95%CI: 83%, 95%), and 0.98 (95%CI: 0.96, 0.99), respectively; the pooled sensitivity, specificity, and SROC-AUC for visual interpretation supported by univariate algorithms in diagnosing PD were 93% (95%CI: 90%, 95%), 90% (95%CI: 85%, 94%), and 0.96 (95%CI: 0.94, 0.97), respectively. 12 studies relied on artificial intelligence (AI) to analyze 18F-FDG PET imaging data. The pooled sensitivity, specificity, and SROC-AUC of machine learning (ML) for diagnosing PD were 87% (95%CI: 82%, 91%), 91% (95%CI: 86%, 94%), and 0.95 (95%CI: 0.93, 0.96), respectively. The pooled sensitivity, specificity, and SROC-AUC of deep learning (DL) for diagnosing PD were 97% (95%CI: 95%, 98%), 95% (95%CI: 89%, 98%), and 0.98 (95%CI: 0.96, 0.99), respectively. CONCLUSION 18F-FDG PET has a high accuracy in differentiating PD from APS, among which AI-assisted automatic classification performs well, with a diagnostic accuracy comparable to that of radiologists, and is expected to become an important auxiliary means of clinical diagnosis in the future.
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Affiliation(s)
- Tailiang Zhao
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Bingbing Wang
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Wei Liang
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Sen Cheng
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Bin Wang
- Department of Cardiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 100000, China
| | - Ming Cui
- Department of Neurology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Jixin Shou
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, 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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Unadkat P, Vo A, Ma Y, Peng S, Nguyen N, Niethammer M, Tang CC, Dhawan V, Ramdhani R, Fenoy A, Caminiti SP, Perani D, Eidelberg D. Deep brain stimulation of the subthalamic nucleus for Parkinson's disease: A network imaging marker of the treatment response. RESEARCH SQUARE 2024:rs.3.rs-4178280. [PMID: 38766007 PMCID: PMC11100869 DOI: 10.21203/rs.3.rs-4178280/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Subthalamic nucleus deep brain stimulation (STN-DBS) alleviates motor symptoms of Parkinson's disease (PD), thereby improving quality of life. However, quantitative brain markers to evaluate DBS responses and select suitable patients for surgery are lacking. Here, we used metabolic brain imaging to identify a reproducible STN-DBS network for which individual expression levels increased with stimulation in proportion to motor benefit. Of note, measurements of network expression from metabolic and BOLD imaging obtained preoperatively predicted motor outcomes determined after DBS surgery. Based on these findings, we computed network expression in 175 PD patients, with time from diagnosis ranging from 0 to 21 years, and used the resulting data to predict the outcome of a potential STN-DBS procedure. While minimal benefit was predicted for patients with early disease, the proportion of potential responders increased after 4 years. Clinically meaningful improvement with stimulation was predicted in 18.9 - 27.3% of patients depending on disease duration.
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Affiliation(s)
| | - 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
| | | | | | | | | | - Ritesh Ramdhani
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell
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Valderhaug VD, Ramstad OH, van de Wijdeven R, Heiney K, Nichele S, Sandvig A, Sandvig I. Micro-and mesoscale aspects of neurodegeneration in engineered human neural networks carrying the LRRK2 G2019S mutation. Front Cell Neurosci 2024; 18:1366098. [PMID: 38644975 PMCID: PMC11026646 DOI: 10.3389/fncel.2024.1366098] [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: 01/05/2024] [Accepted: 03/11/2024] [Indexed: 04/23/2024] Open
Abstract
Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been widely linked to Parkinson's disease, where the G2019S variant has been shown to contribute uniquely to both familial and sporadic forms of the disease. LRRK2-related mutations have been extensively studied, yet the wide variety of cellular and network events related to these mutations remain poorly understood. The advancement and availability of tools for neural engineering now enable modeling of selected pathological aspects of neurodegenerative disease in human neural networks in vitro. Our study revealed distinct pathology associated dynamics in engineered human cortical neural networks carrying the LRRK2 G2019S mutation compared to healthy isogenic control neural networks. The neurons carrying the LRRK2 G2019S mutation self-organized into networks with aberrant morphology and mitochondrial dynamics, affecting emerging structure-function relationships both at the micro-and mesoscale. Taken together, the findings of our study points toward an overall heightened metabolic demand in networks carrying the LRRK2 G2019S mutation, as well as a resilience to change in response to perturbation, compared to healthy isogenic controls.
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Affiliation(s)
- Vibeke Devold Valderhaug
- Department of Research and Innovation, Møre and Romsdal Hospital Trust, Ålesund, Norway
- Department of Neuromedicine and Movement Science, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Ola Huse Ramstad
- Department of Neuromedicine and Movement Science, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Rosanne van de Wijdeven
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, NTNU, Trondheim, Norway
| | - Kristine Heiney
- Department of Computer Science, Faculty of Technology, Art and Design, Oslo Metropolitan University (OsloMet), Oslo, Norway
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, NTNU, Trondheim, Norway
| | - Stefano Nichele
- Department of Computer Science, Faculty of Technology, Art and Design, Oslo Metropolitan University (OsloMet), Oslo, Norway
- Department of Computer Science and Communication, Østfold University College, Halden, Norway
| | - Axel Sandvig
- Department of Neuromedicine and Movement Science, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Clinical Neuroscience, Division of Neuro, Head and Neck, Umeå University Hospital, Umeå, Sweden
- Department of Community Medicine and Rehabilitation, Umeå University, Umeå, Sweden
- Department of Neurology and Clinical Neurophysiology, St Olav’s Hospital, Trondheim, Norway
| | - Ioanna Sandvig
- Department of Neuromedicine and Movement Science, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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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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7
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Holmes SE, Honhar P, Tinaz S, Naganawa M, Hilmer AT, Gallezot JD, Dias M, Yang Y, Toyonaga T, Esterlis I, Mecca A, Van Dyck C, Henry S, Ropchan J, Nabulsi N, Louis ED, Comley R, Finnema SJ, Carson RE, Matuskey D. Synaptic loss and its association with symptom severity in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:42. [PMID: 38402233 PMCID: PMC10894197 DOI: 10.1038/s41531-024-00655-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 02/06/2024] [Indexed: 02/26/2024] Open
Abstract
Parkinson's disease (PD) is the fastest growing neurodegenerative disease, but at present there is no cure, nor any disease-modifying treatments. Synaptic biomarkers from in vivo imaging have shown promise in imaging loss of synapses in PD and other neurodegenerative disorders. Here, we provide new clinical insights from a cross-sectional, high-resolution positron emission tomography (PET) study of 30 PD individuals and 30 age- and sex-matched healthy controls (HC) with the radiotracer [11C]UCB-J, which binds to synaptic vesicle glycoprotein 2A (SV2A), and is therefore, a biomarker of synaptic density in the living brain. We also examined a measure of relative brain perfusion from the early part of the same PET scan. Our results provide evidence for synaptic density loss in the substantia nigra that had been previously reported, but also extend this to other early-Braak stage regions known to be affected in PD (brainstem, caudate, olfactory cortex). Importantly, we also found a direct association between synaptic density loss in the nigra and severity of symptoms in patients. A greater extent and wider distribution of synaptic density loss in PD patients with longer illness duration suggests that [11C]UCB-J PET can be used to measure synapse loss with disease progression. We also demonstrate lower brain perfusion in PD vs. HC groups, with a greater extent of abnormalities in those with longer duration of illness, suggesting that [11C]UCB-J PET can simultaneously provide information on changes in brain perfusion. These results implicate synaptic imaging as a useful PD biomarker for future disease-modifying interventions.
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Affiliation(s)
- Sophie E Holmes
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Praveen Honhar
- Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering and Applied Sciences, Yale University, New Haven, CT, USA
| | - Sule Tinaz
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Mika Naganawa
- Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Ansel T Hilmer
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering and Applied Sciences, Yale University, New Haven, CT, USA
| | | | - Mark Dias
- Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Yanghong Yang
- Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Takuya Toyonaga
- Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Irina Esterlis
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Adam Mecca
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Shannan Henry
- Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jim Ropchan
- Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Nabeel Nabulsi
- Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Elan D Louis
- Department of Neurology, University of Texas Southwestern Medical Center, New Haven, CT, USA
| | | | | | - Richard E Carson
- Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering and Applied Sciences, Yale University, New Haven, CT, USA
| | - David Matuskey
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
- Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
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8
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Vijiaratnam N, Foltynie T. How should we be using biomarkers in trials of disease modification in Parkinson's disease? Brain 2023; 146:4845-4869. [PMID: 37536279 PMCID: PMC10690028 DOI: 10.1093/brain/awad265] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/18/2023] [Accepted: 07/22/2023] [Indexed: 08/05/2023] Open
Abstract
The recent validation of the α-synuclein seed amplification assay as a biomarker with high sensitivity and specificity for the diagnosis of Parkinson's disease has formed the backbone for a proposed staging system for incorporation in Parkinson's disease clinical studies and trials. The routine use of this biomarker should greatly aid in the accuracy of diagnosis during recruitment of Parkinson's disease patients into trials (as distinct from patients with non-Parkinson's disease parkinsonism or non-Parkinson's disease tremors). There remain, however, further challenges in the pursuit of biomarkers for clinical trials of disease modifying agents in Parkinson's disease, namely: optimizing the distinction between different α-synucleinopathies; the selection of subgroups most likely to benefit from a candidate disease modifying agent; a sensitive means of confirming target engagement; and the early prediction of longer-term clinical benefit. For example, levels of CSF proteins such as the lysosomal enzyme β-glucocerebrosidase may assist in prognostication or allow enrichment of appropriate patients into disease modifying trials of agents with this enzyme as the target; the presence of coexisting Alzheimer's disease-like pathology (detectable through CSF levels of amyloid-β42 and tau) can predict subsequent cognitive decline; imaging techniques such as free-water or neuromelanin MRI may objectively track decline in Parkinson's disease even in its later stages. The exploitation of additional biomarkers to the α-synuclein seed amplification assay will, therefore, greatly add to our ability to plan trials and assess the disease modifying properties of interventions. The choice of which biomarker(s) to use in the context of disease modifying clinical trials will depend on the intervention, the stage (at risk, premotor, motor, complex) of the population recruited and the aims of the trial. The progress already made lends hope that panels of fluid biomarkers in tandem with structural or functional imaging may provide sensitive and objective methods of confirming that an intervention is modifying a key pathophysiological process of Parkinson's disease. However, correlation with clinical progression does not necessarily equate to causation, and the ongoing validation of quantitative biomarkers will depend on insightful clinical-genetic-pathophysiological comparisons incorporating longitudinal biomarker changes from those at genetic risk with evidence of onset of the pathophysiology and those at each stage of manifest clinical Parkinson's disease.
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Affiliation(s)
- Nirosen Vijiaratnam
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Thomas Foltynie
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
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9
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Niethammer M, Tang CC, Jamora RDG, Vo A, Nguyen N, Ma Y, Peng S, Waugh JL, Westenberger A, Eidelberg D. A Network Imaging Biomarker of X-Linked Dystonia-Parkinsonism. Ann Neurol 2023; 94:684-695. [PMID: 37376770 DOI: 10.1002/ana.26732] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/26/2023] [Accepted: 06/26/2023] [Indexed: 06/29/2023]
Abstract
OBJECTIVE The purpose of this study was to characterize a metabolic brain network associated with X-linked dystonia-parkinsonism (XDP). METHODS Thirty right-handed Filipino men with XDP (age = 44.4 ± 8.5 years) and 30 XDP-causing mutation negative healthy men from the same population (age = 37.4 ± 10.5 years) underwent [18 F]-fluorodeoxyglucose positron emission tomography. Scans were analyzed using spatial covariance mapping to identify a significant XDP-related metabolic pattern (XDPRP). Patients were rated clinically at the time of imaging according to the XDP-Movement Disorder Society of the Philippines (MDSP) scale. RESULTS We identified a significant XDPRP topography from 15 randomly selected subjects with XDP and 15 control subjects. This pattern was characterized by bilateral metabolic reductions in caudate/putamen, frontal operculum, and cingulate cortex, with relative increases in the bilateral somatosensory cortex and cerebellar vermis. Age-corrected expression of XDPRP was significantly elevated (p < 0.0001) in XDP compared to controls in the derivation set and in the remaining 15 patients (testing set). We validated the XDPRP topography by identifying a similar pattern in the original testing set (r = 0.90, p < 0.0001; voxel-wise correlation between both patterns). Significant correlations between XDPRP expression and clinical ratings for parkinsonism-but not dystonia-were observed in both XDP groups. Further network analysis revealed abnormalities of information transfer through the XDPRP space, with loss of normal connectivity and gain of abnormal functional connections linking network nodes with outside brain regions. INTERPRETATION XDP is associated with a characteristic metabolic network associated with abnormal functional connectivity among the basal ganglia, thalamus, motor regions, and cerebellum. Clinical signs may relate to faulty information transfer through the network to outside brain regions. ANN NEUROL 2023;94:684-695.
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Affiliation(s)
- Martin Niethammer
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York
| | - Roland Dominic G Jamora
- Institute for Neurosciences, St. Luke's Medical Center, Quezon City, Philippines
- Department of Neurosciences, College of Medicine and Philippine General Hospital, University of the Philippines Manila, Manila, Philippines
| | - An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York
- Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, Bronx, New York
| | - Yilong Ma
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York
- Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Shichun Peng
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York
| | - Jeff L Waugh
- Division of Pediatric Neurology, Department of Pediatrics, University of Texas Southwestern, Dallas, Texas
| | - Ana Westenberger
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
- Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
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10
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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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11
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Buchert R, Wegner F, Huppertz HJ, Berding G, Brendel M, Apostolova I, Buhmann C, Dierks A, Katzdobler S, Klietz M, Levin J, Mahmoudi N, Rinscheid A, Rogozinski S, Rumpf JJ, Schneider C, Stöcklein S, Spetsieris PG, Eidelberg D, Wattjes MP, Sabri O, Barthel H, Höglinger G. Automatic covariance pattern analysis outperforms visual reading of 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) in variant progressive supranuclear palsy. Mov Disord 2023; 38:1901-1913. [PMID: 37655363 DOI: 10.1002/mds.29581] [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: 03/24/2023] [Revised: 07/19/2023] [Accepted: 07/31/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND To date, studies on positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) in progressive supranuclear palsy (PSP) usually included PSP cohorts overrepresenting patients with Richardson's syndrome (PSP-RS). OBJECTIVES To evaluate FDG-PET in a patient sample representing the broad phenotypic PSP spectrum typically encountered in routine clinical practice. METHODS This retrospective, multicenter study included 41 PSP patients, 21 (51%) with RS and 20 (49%) with non-RS variants of PSP (vPSP), and 46 age-matched healthy controls. Two state-of-the art methods for the interpretation of FDG-PET were compared: visual analysis supported by voxel-based statistical testing (five readers) and automatic covariance pattern analysis using a predefined PSP-related pattern. RESULTS Sensitivity and specificity of the majority visual read for the detection of PSP in the whole cohort were 74% and 72%, respectively. The percentage of false-negative cases was 10% in the PSP-RS subsample and 43% in the vPSP subsample. Automatic covariance pattern analysis provided sensitivity and specificity of 93% and 83% in the whole cohort. The percentage of false-negative cases was 0% in the PSP-RS subsample and 15% in the vPSP subsample. CONCLUSIONS Visual interpretation of FDG-PET supported by voxel-based testing provides good accuracy for the detection of PSP-RS, but only fair sensitivity for vPSP. Automatic covariance pattern analysis outperforms visual interpretation in the detection of PSP-RS, provides clinically useful sensitivity for vPSP, and reduces the rate of false-positive findings. Thus, pattern expression analysis is clinically useful to complement visual reading and voxel-based testing of FDG-PET in suspected PSP. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Florian Wegner
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | | | - Georg Berding
- Department of Nuclear Medicine, Hannover Medical School, Hannover, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital of Munich, LMU, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Ivayla Apostolova
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carsten Buhmann
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexander Dierks
- Department of Nuclear Medicine, University Hospital Augsburg, Augsburg, Germany
| | - Sabrina Katzdobler
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU, Munich, Germany
| | - Martin Klietz
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU, Munich, Germany
| | - Nima Mahmoudi
- Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Andreas Rinscheid
- Medical Physics and Radiation Protection, University Hospital Augsburg, Augsburg, Germany
| | | | | | - Christine Schneider
- Department of Neurology and Clinical Neurophysiology, University Hospital Augsburg, Augsburg, Germany
| | - Sophia Stöcklein
- Department of Radiology, University Hospital of Munich, LMU, Munich, Germany
| | - Phoebe G Spetsieris
- The Feinstein Institutes for Medical Research Manhasset, Manhasset, New York, USA
| | - David Eidelberg
- The Feinstein Institutes for Medical Research Manhasset, Manhasset, New York, USA
| | - Mike P Wattjes
- Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Osama Sabri
- Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Henryk Barthel
- Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Günter Höglinger
- Department of Neurology, Hannover Medical School, Hannover, Germany
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU, Munich, Germany
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12
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陈 璋, 李 桃, 唐 向. [Application of Polysomnography in Common Neurodegenerative Diseases]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2023; 54:1058-1064. [PMID: 37866969 PMCID: PMC10579074 DOI: 10.12182/20230960304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Indexed: 10/24/2023]
Abstract
At present, the etiology and pathogenesis of most neurodegenerative diseases are still not fully understood, which poses challenges for the prevention, diagnosis, and treatment of these diseases. Sleep disorders are one of the common chief complaints of neurodegenerative diseases. When patients suffer from comorbid sleep disorder and neurodegenerative diseases, the severity of their condition increases, the quality of their life drops further, and the difficulty of treatment increases. A large number of studies have been conducted to monitor the sleep of patients with neurodegenerative diseases, and it has been found that there are significant changes in their polysomnography (PSG) results compared to those of healthy control populations. In addition, there are also significant differences between the PSG findings of patients with different neurodegenerative diseases and the differences are closely associated with the pathogenesis and development of the disease. Herein, we discussed the characteristics of the sleep structure of patients with Parkinson's disease, Alzheimer's disease, Huntington's disease, and dementia with Lewy bodies and provided a brief review of the sleep disorders and the PSG characteristics of these patients. The paper will help improve the understanding of the pathogenesis and pathological changes of neurodegenerative diseases, clarify the relationship between sleep disorders and these diseases, improve clinicians' further understanding of these diseases, and provide a basis for future research.
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Affiliation(s)
- 璋玥 陈
- 四川大学华西医院 睡眠医学中心 (成都 610041)Sleep Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 桃美 李
- 四川大学华西医院 睡眠医学中心 (成都 610041)Sleep Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 向东 唐
- 四川大学华西医院 睡眠医学中心 (成都 610041)Sleep Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, China
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13
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Wang Z, Baeken C, Wu GR. Metabolic Covariance Connectivity of Posterior Cingulate Cortex Associated with Depression Symptomatology Level in Healthy Young Adults. Metabolites 2023; 13:920. [PMID: 37623864 PMCID: PMC10456574 DOI: 10.3390/metabo13080920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 08/26/2023] Open
Abstract
Early detection in the development of a Major Depressive Disorder (MDD) could guide earlier clinical interventions. Although MDD can begin at a younger age, most people have their first episode in young adulthood. The underlying pathophysiological mechanisms relating to such an increased risk are not clear. The posterior cingulate cortex (PCC), exhibiting high levels of brain connectivity and metabolic activity, plays a pivotal role in the pathological mechanism underlying MDD. In the current study, we used the (F-18) fluorodeoxyglucose (FDG) positron emission tomography (PET) to measure metabolic covariance connectivity of the PCC and investigated its association with depression symptomatology evaluated by the Centre for Epidemiological Studies Depression Inventory-Revised (CESD-R) among 27 healthy individuals aged between 18 and 23 years. A significant negative correlation has been observed between CESD-R scale scores and the PCC metabolic connectivity with the anterior cingulate, medial prefrontal cortex, inferior and middle frontal gyrus, as well as the insula. Overall, our findings suggest that the neural correlates of depressive symptomatology in healthy young adults without a formal diagnosis involve the metabolic connectivity of the PCC. Our findings may have potential implications for early identification and intervention in people at risk of developing depression.
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Affiliation(s)
- Zhixin Wang
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing 400715, China;
| | - Chris Baeken
- Faculty of Medicine and Health Sciences, Department of Head and Skin, Ghent Experimental Psychiatry (GHEP) Lab, Ghent University, 9000 Ghent, Belgium;
| | - Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing 400715, China;
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14
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Li W, Tang Y, Peng L, Wang Z, Hu S, Gao X. The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification. MedComm (Beijing) 2023; 4:e305. [PMID: 37388240 PMCID: PMC10300308 DOI: 10.1002/mco2.305] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 05/14/2023] [Accepted: 05/22/2023] [Indexed: 07/01/2023] Open
Abstract
18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely employed to reveal metabolic abnormalities linked to Parkinson's disease (PD) at a systemic level. However, the individual metabolic connectome details with PD based on 18F-FDG PET remain largely unknown. To alleviate this issue, we derived a novel brain network estimation method for individual metabolic connectome, that is, Jensen-Shannon Divergence Similarity Estimation (JSSE). Further, intergroup difference between the individual's metabolic brain network and its global/local graph metrics was analyzed to investigate the metabolic connectome's alterations. To further improve the PD diagnosis performance, multiple kernel support vector machine (MKSVM) is conducted for identifying PD from normal control (NC), which combines both topological metrics and connection. Resultantly, PD individuals showed higher nodal topological properties (including assortativity, modularity score, and characteristic path length) than NC individuals, whereas global efficiency and synchronization were lower. Moreover, 45 most significant connections were affected. Further, consensus connections in occipital, parietal, and frontal regions were decrease in PD while increase in subcortical, temporal, and prefrontal regions. The abnormal metabolic network measurements depicted an ideal classification in identifying PD of NC with an accuracy up to 91.84%. The JSSE method identified the individual-level metabolic connectome of 18F-FDG PET, providing more dimensional and systematic mechanism insights for PD.
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Affiliation(s)
- Weikai Li
- College of Mathematics and StatisticsChongqing Jiaotong UniversityChongqingChina
- Department of Nuclear Medicine (PET Center)XiangYa HospitalChangshaHunanChina
- Department of PET/MRShanghai Universal Medical Imaging Diagnostic CenterShanghaiChina
- MIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjing University of Aeronautics and AstronauticsNanjingChina
| | - Yongxiang Tang
- Department of Nuclear Medicine (PET Center)XiangYa HospitalChangshaHunanChina
| | - Liling Peng
- Department of PET/MRShanghai Universal Medical Imaging Diagnostic CenterShanghaiChina
| | - Zhengxia Wang
- School of Computer Science and Cyberspace SecurityHainan UniversityHainanChina
| | - Shuo Hu
- Department of Nuclear Medicine (PET Center)XiangYa HospitalChangshaHunanChina
- Key Laboratory of Biological Nanotechnology of National Health CommissionXiangYa HospitalCentral South UniversityChangshaHunanChina
| | - Xin Gao
- Department of PET/MRShanghai Universal Medical Imaging Diagnostic CenterShanghaiChina
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15
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Zheng J, Chen L, Cai G, Wang Y, Huang J, Lin X, Li Y, Yu Q, Chen X, Shi Y, Ye Q. The effect of Parkin gene S/N 167 polymorphism on resting spontaneous brain functional activity in Parkinson's Disease. Parkinsonism Relat Disord 2023; 113:105484. [PMID: 37454429 DOI: 10.1016/j.parkreldis.2023.105484] [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: 02/27/2023] [Revised: 05/09/2023] [Accepted: 06/04/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Genetic susceptibility plays a significant role in Parkinson's disease (PD) development. Carriers of the Parkin S/N167 mutation may have an increased risk of PD and altered spontaneous brain activity. OBJECTIVE This study aims to investigate the potential pathogenesis of PD through a comparative analysis of the amplitude of low-frequency fluctuations (ALFF) in resting-state functional magnetic resonance imaging (rs-fMRI) of subjects with Parkin gene S/N 167 polymorphisms, and to examine the association between spontaneous brain activity and clinical scale scores of PD. METHODS A total of 69 PD patients and 84 healthy controls (HC) were included in the study. Each subject was genotyped for the Parkin gene S/N 167 polymorphism and underwent rs-fMRI scans. ALFF analysis was employed to evaluate the relationship among genotypes, interactive brain regions, and clinical symptoms in PD. RESULTS PD patients exhibited decreased ALFF values in the right anterior lobe and vermis of the cerebellum compared to HC. No significant interaction was found between the gene's main effect and the "group × genotype" effect on brain ALFF values. One-factor ANOVA revealed no significant difference in ALFF values between PD subgroups; however, the ALFF values in the right anterior lobe and vermis of the cerebellum were lower in the PD-G and PD-GA groups compared to the HC-G and HC-GA groups. Spearman correlation analysis demonstrated that ALFF values in the PD-GG and PD-GA groups were negatively associated with UPDRS-III scores in the bilateral lingual gyrus (Lingual R/L). CONCLUSION Parkin gene S/N 167 polymorphisms may influence brain functional activity in specific brain regions, and ALFF values are associated with motor symptoms in PD patients.
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Affiliation(s)
- Jingxue Zheng
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, Fujian, China; Fudan University Shanghai Cancer Center(Xiamen Branch), Xiamen, Fujian, China
| | - Lina Chen
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Guoen Cai
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Yingqing Wang
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Jieming Huang
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Xiaoling Lin
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Yueping Li
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Qianwen Yu
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Xiaochun Chen
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, Fujian, China; Institute of Neuroscience, Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, China
| | - Yanchuan Shi
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, Fujian, China; Department of Neurology, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou, Fujian, China.
| | - Qinyong Ye
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, Fujian, China; Institute of Neuroscience, Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, China.
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16
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Gnörich J, Reifschneider A, Wind K, Zatcepin A, Kunte ST, Beumers P, Bartos LM, Wiedemann T, Grosch M, Xiang X, Fard MK, Ruch F, Werner G, Koehler M, Slemann L, Hummel S, Briel N, Blume T, Shi Y, Biechele G, Beyer L, Eckenweber F, Scheifele M, Bartenstein P, Albert NL, Herms J, Tahirovic S, Haass C, Capell A, Ziegler S, Brendel M. Depletion and activation of microglia impact metabolic connectivity of the mouse brain. J Neuroinflammation 2023; 20:47. [PMID: 36829182 PMCID: PMC9951492 DOI: 10.1186/s12974-023-02735-8] [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: 07/03/2022] [Accepted: 02/13/2023] [Indexed: 02/26/2023] Open
Abstract
AIM We aimed to investigate the impact of microglial activity and microglial FDG uptake on metabolic connectivity, since microglial activation states determine FDG-PET alterations. Metabolic connectivity refers to a concept of interacting metabolic brain regions and receives growing interest in approaching complex cerebral metabolic networks in neurodegenerative diseases. However, underlying sources of metabolic connectivity remain to be elucidated. MATERIALS AND METHODS We analyzed metabolic networks measured by interregional correlation coefficients (ICCs) of FDG-PET scans in WT mice and in mice with mutations in progranulin (Grn) or triggering receptor expressed on myeloid cells 2 (Trem2) knockouts (-/-) as well as in double mutant Grn-/-/Trem2-/- mice. We selected those rodent models as they represent opposite microglial signatures with disease associated microglia in Grn-/- mice and microglia locked in a homeostatic state in Trem2-/- mice; however, both resulting in lower glucose uptake of the brain. The direct influence of microglia on metabolic networks was further determined by microglia depletion using a CSF1R inhibitor in WT mice at two different ages. Within maps of global mean scaled regional FDG uptake, 24 pre-established volumes of interest were applied and assigned to either cortical or subcortical networks. ICCs of all region pairs were calculated and z-transformed prior to group comparisons. FDG uptake of neurons, microglia, and astrocytes was determined in Grn-/- and WT mice via assessment of single cell tracer uptake (scRadiotracing). RESULTS Microglia depletion by CSF1R inhibition resulted in a strong decrease of metabolic connectivity defined by decrease of mean cortical ICCs in WT mice at both ages studied (6-7 m; p = 0.0148, 9-10 m; p = 0.0191), when compared to vehicle-treated age-matched WT mice. Grn-/-, Trem2-/- and Grn-/-/Trem2-/- mice all displayed reduced FDG-PET signals when compared to WT mice. However, when analyzing metabolic networks, a distinct increase of ICCs was observed in Grn-/- mice when compared to WT mice in cortical (p < 0.0001) and hippocampal (p < 0.0001) networks. In contrast, Trem2-/- mice did not show significant alterations in metabolic connectivity when compared to WT. Furthermore, the increased metabolic connectivity in Grn-/- mice was completely suppressed in Grn-/-/Trem2-/- mice. Grn-/- mice exhibited a severe loss of neuronal FDG uptake (- 61%, p < 0.0001) which shifted allocation of cellular brain FDG uptake to microglia (42% in Grn-/- vs. 22% in WT). CONCLUSIONS Presence, absence, and activation of microglia have a strong impact on metabolic connectivity of the mouse brain. Enhanced metabolic connectivity is associated with increased microglial FDG allocation.
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Affiliation(s)
- Johannes Gnörich
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Anika Reifschneider
- grid.5252.00000 0004 1936 973XMetabolic Biochemistry, Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-Universität München, Munich, Germany
| | - Karin Wind
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Artem Zatcepin
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Sebastian T. Kunte
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Philipp Beumers
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Laura M. Bartos
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Thomas Wiedemann
- grid.5252.00000 0004 1936 973XMetabolic Biochemistry, Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-Universität München, Munich, Germany
| | - Maximilian Grosch
- grid.5252.00000 0004 1936 973XGerman Center for Vertigo and Balance Disorders, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Xianyuan Xiang
- grid.5252.00000 0004 1936 973XMetabolic Biochemistry, Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-Universität München, Munich, Germany ,grid.9227.e0000000119573309CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055 China
| | - Maryam K. Fard
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Francois Ruch
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Georg Werner
- grid.5252.00000 0004 1936 973XMetabolic Biochemistry, Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-Universität München, Munich, Germany
| | - Mara Koehler
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Luna Slemann
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Selina Hummel
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Nils Briel
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.5252.00000 0004 1936 973XCenter for Neuropathology and Prion Research, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Tanja Blume
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.5252.00000 0004 1936 973XCenter for Neuropathology and Prion Research, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Yuan Shi
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.5252.00000 0004 1936 973XCenter for Neuropathology and Prion Research, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Gloria Biechele
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Leonie Beyer
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Florian Eckenweber
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Maximilian Scheifele
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Peter Bartenstein
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Nathalie L. Albert
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany
| | - Jochen Herms
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.5252.00000 0004 1936 973XCenter for Neuropathology and Prion Research, Ludwig-Maximilians-Universität München, Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Sabina Tahirovic
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Christian Haass
- grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ,grid.5252.00000 0004 1936 973XMetabolic Biochemistry, Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-Universität München, Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Anja Capell
- grid.5252.00000 0004 1936 973XMetabolic Biochemistry, Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-Universität München, Munich, Germany
| | - Sibylle Ziegler
- grid.5252.00000 0004 1936 973XDepartment of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377 Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistrasse 15, 81377, Munich, Germany. .,German Center for Neurodegenerative Diseases (DZNE), Munich, Germany. .,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
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17
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Vo A, Schindlbeck KA, Nguyen N, Rommal A, Spetsieris PG, Tang CC, Choi YY, Niethammer M, Dhawan V, Eidelberg D. Adaptive and pathological connectivity responses in Parkinson's disease brain networks. Cereb Cortex 2023; 33:917-932. [PMID: 35325051 PMCID: PMC9930629 DOI: 10.1093/cercor/bhac110] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 11/12/2022] Open
Abstract
Functional imaging has been used extensively to identify and validate disease-specific networks as biomarkers in neurodegenerative disorders. It is not known, however, whether the connectivity patterns in these networks differ with disease progression compared to the beneficial adaptations that may also occur over time. To distinguish the 2 responses, we focused on assortativity, the tendency for network connections to link nodes with similar properties. High assortativity is associated with unstable, inefficient flow through the network. Low assortativity, by contrast, involves more diverse connections that are also more robust and efficient. We found that in Parkinson's disease (PD), network assortativity increased over time. Assoratitivty was high in clinically aggressive genetic variants but was low for genes associated with slow progression. Dopaminergic treatment increased assortativity despite improving motor symptoms, but subthalamic gene therapy, which remodels PD networks, reduced this measure compared to sham surgery. Stereotyped changes in connectivity patterns underlie disease progression and treatment responses in PD networks.
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Affiliation(s)
| | | | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Andrea Rommal
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - Phoebe G Spetsieris
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - Yoon Young Choi
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - Martin Niethammer
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - David Eidelberg
- Corresponding author: Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA.
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18
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Diaz-Galvan P, Miyagawa T, Przybelski SA, Lesnick TG, Senjem ML, Jack CR, Forsberg LK, Min HK, St. Louis EK, Savica R, Fields JA, Benarroch EE, Lowe V, Petersen RC, Boeve BF, Kantarci K. Brain glucose metabolism and nigrostriatal degeneration in isolated rapid eye movement sleep behaviour disorder. Brain Commun 2023; 5:fcad021. [PMID: 36844148 PMCID: PMC9945851 DOI: 10.1093/braincomms/fcad021] [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: 04/04/2022] [Revised: 11/14/2022] [Accepted: 01/31/2023] [Indexed: 02/04/2023] Open
Abstract
Alterations of cerebral glucose metabolism can be detected in patients with isolated rapid eye movement sleep behaviour disorder, a prodromal feature of neurodegenerative diseases with α-synuclein pathology. However, metabolic characteristics that determine clinical progression in isolated rapid eye movement sleep behaviour disorder and their association with other biomarkers need to be elucidated. We investigated the pattern of cerebral glucose metabolism on 18F-fluorodeoxyglucose PET in patients with isolated rapid eye movement sleep behaviour disorder, differentiating between those who clinically progressed and those who remained stable over time. Second, we studied the association between 18F-fluorodeoxyglucose PET and lower dopamine transporter availability in the putamen, another hallmark of synucleinopathies. Patients with isolated rapid eye movement sleep behaviour disorder from the Mayo Clinic Alzheimer's Disease Research Center and Center for Sleep Medicine (n = 22) and age-and sex-matched clinically unimpaired controls (clinically unimpaired; n = 44) from the Mayo Clinic Study of Aging were included. All participants underwent 18F-fluorodeoxyglucose PET and dopamine transporter imaging with iodine 123-radiolabeled 2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl) nortropane on single-photon emission computerized tomography. A subset of patients with isolated rapid eye movement sleep behaviour disorder with follow-up evaluations (n = 17) was classified as isolated rapid eye movement sleep behaviour disorder progressors (n = 7) if they developed mild cognitive impairment or Parkinson's disease; or isolated rapid eye movement sleep behaviour disorder stables (n = 10) if they remained with a diagnosis of isolated rapid eye movement sleep behaviour disorder with no cognitive impairment. Glucose metabolic abnormalities in isolated rapid eye movement sleep behaviour disorder were determined by comparing atlas-based regional 18F-fluorodeoxyglucose PET uptake between isolated rapid eye movement sleep behaviour disorder and clinically unimpaired. Associations between 18F-fluorodeoxyglucose PET and dopamine transporter availability in the putamen were analyzed with Pearson's correlation within the nigrostriatal pathway structures and with voxel-based analysis in the cortex. Patients with isolated rapid eye movement sleep behaviour disorder had lower glucose metabolism in the substantia nigra, retrosplenial cortex, angular cortex, and thalamus, and higher metabolism in the amygdala and entorhinal cortex compared with clinically unimpaired. Patients with isolated rapid eye movement sleep behaviour disorder who clinically progressed over time were characterized by higher glucose metabolism in the amygdala and entorhinal cortex, and lower glucose metabolism in the cerebellum compared with clinically unimpaired. Lower dopamine transporter availability in the putamen was associated with higher glucose metabolism in the pallidum within the nigrostriatal pathway; and with higher 18F-fluorodeoxyglucose uptake in the amygdala, insula, and temporal pole on a voxel-based analysis, although these associations did not survive after correcting for multiple comparisons. Our findings suggest that cerebral glucose metabolism in isolated rapid eye movement sleep behaviour disorder is characterized by hypometabolism in regions frequently affected during the prodromal stage of synucleinopathies, potentially reflecting synaptic dysfunction. Hypermetabolism is also seen in isolated rapid eye movement sleep behaviour disorder, suggesting that synaptic metabolic disruptions may be leading to a lack of inhibition, compensatory mechanisms, or microglial activation, especially in regions associated with nigrostriatal degeneration.
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Affiliation(s)
| | - Toji Miyagawa
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Scott A Przybelski
- Department of Quantitative Health Science, Mayo Clinic, Rochester, MN 55905, USA
| | - Timothy G Lesnick
- Department of Quantitative Health Science, Mayo Clinic, Rochester, MN 55905, USA
| | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Leah K Forsberg
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hoon-Ki Min
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Rodolfo Savica
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Val Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
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19
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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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20
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Rus T, Schindlbeck KA, Tang CC, Vo A, Dhawan V, Trošt M, Eidelberg D. Stereotyped Relationship Between Motor and Cognitive Metabolic Networks in Parkinson's Disease. Mov Disord 2022; 37:2247-2256. [PMID: 36054380 PMCID: PMC9669200 DOI: 10.1002/mds.29188] [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: 04/19/2022] [Revised: 06/29/2022] [Accepted: 07/20/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Idiopathic Parkinson's disease (iPD) is associated with two distinct brain networks, PD-related pattern (PDRP) and PD-related cognitive pattern (PDCP), which correlate respectively with motor and cognitive symptoms. The relationship between the two networks in individual patients is unclear. OBJECTIVE To determine whether a consistent relationship exists between these networks, we measured the difference between PDRP and PDCP expression, termed delta, on an individual basis in independent populations of patients with iPD (n = 356), patients with idiopathic REM sleep behavioral disorder (iRBD) (n = 21), patients with genotypic PD (gPD) carrying GBA1 variants (n = 12) or the LRRK2-G2019S mutation (n = 14), patients with atypical parkinsonian syndromes (n = 238), and healthy control subjects (n = 95) from the United States, Slovenia, India, and South Korea. METHODS We used [18 F]-fluorodeoxyglucose positron emission tomography and resting-state fMRI to quantify delta and to compare the measure across samples; changes in delta over time were likewise assessed in longitudinal patient samples. Lastly, we evaluated delta in prodromal individuals with iRBD and subjects with gPD. RESULTS Delta was abnormally elevated in each of the four iPD samples (P < 0.05), as well as in the at-risk iRBD group (P < 0.05), with increasing values over time (P < 0.001). PDRP predominance was also present in gPD, with higher values in patients with GBA1 variants compared with the less aggressive LRRK2-G2019S mutation (P = 0.005). This trend was not observed in patients with atypical parkinsonian syndromes, who were accurately discriminated from iPD based on PDRP expression and delta (area under the curve = 0.85; P < 0.0001). CONCLUSIONS PDRP predominance, quantified by delta, assays the spread of dysfunction from motor to cognitive networks in patients with PD. Delta may therefore aid in differential diagnosis and in tracking disease progression in individual patients. © 2022 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Tomaž Rus
- Department of Neurology, UMC Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
- Medical Faculty, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
| | - Katharina A. Schindlbeck
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York 11030, USA
| | - Chris C. Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York 11030, USA
| | - An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York 11030, USA
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York 11030, USA
| | - Maja Trošt
- Department of Neurology, UMC Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
- Medical Faculty, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
- Department of Nuclear Medicine, UMC Ljubljana, Zaloška cesta 7, 1000 Ljubljana, Slovenia
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York 11030, USA
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21
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Boccalini C, Bortolin E, Carli G, Pilotto A, Galbiati A, Padovani A, Ferini-Strambi L, Perani D. Metabolic connectivity of resting-state networks in alpha synucleinopathies, from prodromal to dementia phase. Front Neurosci 2022; 16:930735. [PMID: 36003959 PMCID: PMC9394228 DOI: 10.3389/fnins.2022.930735] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/19/2022] [Indexed: 12/05/2022] Open
Abstract
Previous evidence suggests that the derangement of large-scale brain networks reflects structural, molecular, and functional mechanisms underlying neurodegenerative diseases. Although the alterations of multiple large-scale brain networks in Parkinson’s disease (PD) and Dementia with Lewy Bodies (DLB) are reported, a comprehensive study on connectivity reconfiguration starting from the preclinical phase is still lacking. We aimed to investigate shared and disease-specific changes in the large-scale networks across the Lewy Bodies (LB) disorders spectrum using a brain metabolic connectivity approach. We included 30 patients with isolated REM sleep behavior disorder (iRBD), 28 with stable PD, 30 with DLB, and 30 healthy controls for comparison. We applied seed-based interregional correlation analyses (IRCA) to evaluate the metabolic connectivity in the large-scale resting-state networks, as assessed by [18F]FDG-PET, in each clinical group compared to controls. We assessed metabolic connectivity changes by applying the IRCA and specific connectivity metrics, such as the weighted and unweighted Dice similarity coefficients (DC), for the topographical similarities. All the investigated large-scale brain resting-state networks showed metabolic connectivity alterations, supporting the widespread involvement of brain connectivity within the alpha-synuclein spectrum. Connectivity alterations were already evident in iRBD, severely affecting the posterior default mode, attentive and limbic networks. Strong similarities emerged in iRBD and DLB that showed comparable connectivity alterations in most large-scale networks, particularly in the posterior default mode and attentive networks. Contrarily, PD showed the main connectivity alterations limited to motor and somatosensory networks. The present findings reveal that metabolic connectivity alterations in the large-scale networks are already present in the early iRBD phase, resembling the DLB metabolic connectivity changes. This suggests and confirms iRBD as a risk condition for progression to the severe LB disease phenotype. Of note, the neurobiology of stable PD supports its more benign phenotype.
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Affiliation(s)
- Cecilia Boccalini
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy
- In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisa Bortolin
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy
| | - Giulia Carli
- In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, 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, Italy
| | - Andrea Galbiati
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy
- Department of Clinical Neuroscience, Sleep Disorders Center, San Raffaele Hospital, Milan, Italy
| | - Alessandro Padovani
- 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, Italy
| | - Luigi Ferini-Strambi
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy
- Department of Clinical Neuroscience, Sleep Disorders Center, San Raffaele Hospital, Milan, Italy
| | - Daniela Perani
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy
- In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy
- *Correspondence: Daniela Perani,
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22
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Steidel K, Ruppert MC, Greuel A, Tahmasian M, Maier F, Hammes J, van Eimeren T, Timmermann L, Tittgemeyer M, Drzezga A, Pedrosa DJ, Eggers C. Longitudinal trimodal imaging of midbrain-associated network degeneration in Parkinson's disease. NPJ Parkinsons Dis 2022; 8:79. [PMID: 35732679 PMCID: PMC9218128 DOI: 10.1038/s41531-022-00341-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 05/24/2022] [Indexed: 11/20/2022] Open
Abstract
The prevailing network perspective of Parkinson’s disease (PD) emerges not least from the ascending neuropathology traceable in histological studies. However, whether longitudinal in vivo correlates of network degeneration in PD can be observed remains unresolved. Here, we applied a trimodal imaging protocol combining 18F-fluorodeoxyglucose (FDG)- and 18F-fluoro-L-Dopa- (FDOPA)-PET with resting-state functional MRI to assess longitudinal changes in midbrain metabolism, striatal dopamine depletion and striatocortical dysconnectivity in 17 well-characterized PD patients. Whole-brain (un)paired-t-tests with focus on midbrain or striatum were performed between visits and in relation to 14 healthy controls (HC) in PET modalities. Resulting clusters of FDOPA-PET comparisons provided volumes for seed-based functional connectivity (FC) analyses between visits and in relation to HC. FDG metabolism in the left midbrain decreased compared to baseline along with caudatal FDOPA-uptake. This caudate cluster exhibited a longitudinal FC decrease to sensorimotor and frontal areas. Compared to healthy subjects, dopamine-depleted putamina indicated stronger decline in striatocortical FC at follow-up with respect to baseline. Increasing nigrostriatal deficits and striatocortical decoupling were associated with deterioration in motor scores between visits in repeated-measures correlations. In summary, our results demonstrate the feasibility of in-vivo tracking of progressive network degeneration using a multimodal imaging approach. Specifically, our data suggest advancing striatal and widespread striatocortical dysfunction via an anterior-posterior gradient originating from a hypometabolic midbrain cluster within a well-characterized and only mild to moderately affected PD cohort during a relatively short period.
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Affiliation(s)
- Kenan Steidel
- Department of Neurology, University Hospital of Marburg, Marburg, Germany.
| | - Marina C Ruppert
- Department of Neurology, University Hospital of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior-CMBB, Universities Marburg and Gießen, Marburg, Germany
| | - Andrea Greuel
- Department of Neurology, University Hospital of Marburg, Marburg, Germany
| | - Masoud Tahmasian
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Franziska Maier
- Department of Psychiatry, University Hospital Cologne, Medical Faculty, Cologne, Germany
| | - Jochen Hammes
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty University Hospital Cologne, Cologne, Germany
| | - Thilo van Eimeren
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty University Hospital Cologne, Cologne, Germany.,Department of Neurology, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Cologne, Germany.,German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
| | - Lars Timmermann
- Department of Neurology, University Hospital of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior-CMBB, Universities Marburg and Gießen, Marburg, Germany
| | - Marc Tittgemeyer
- Max Planck Institute for Metabolism Research, Cologne, Germany.,Cluster of Excellence in Cellular Stress and Aging Associated Disease (CECAD), Cologne, Germany
| | - Alexander Drzezga
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty University Hospital Cologne, Cologne, Germany.,Cluster of Excellence in Cellular Stress and Aging Associated Disease (CECAD), Cologne, Germany.,Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-2), Research Center Jülich, Jülich, Germany
| | - David J Pedrosa
- Department of Neurology, University Hospital of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior-CMBB, Universities Marburg and Gießen, Marburg, Germany
| | - Carsten Eggers
- Department of Neurology, University Hospital of Marburg, Marburg, Germany.,Department of Neurology, Knappschaftskrankenhaus Bottrop, Bottrop, Germany
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23
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Brakedal B, Dölle C, Riemer F, Ma Y, Nido GS, Skeie GO, Craven AR, Schwarzlmüller T, Brekke N, Diab J, Sverkeli L, Skjeie V, Varhaug K, Tysnes OB, Peng S, Haugarvoll K, Ziegler M, Grüner R, Eidelberg D, Tzoulis C. The NADPARK study: A randomized phase I trial of nicotinamide riboside supplementation in Parkinson's disease. Cell Metab 2022; 34:396-407.e6. [PMID: 35235774 DOI: 10.1016/j.cmet.2022.02.001] [Citation(s) in RCA: 118] [Impact Index Per Article: 59.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 11/17/2021] [Accepted: 01/31/2022] [Indexed: 02/07/2023]
Abstract
We conducted a double-blinded phase I clinical trial to establish whether nicotinamide adenine dinucleotide (NAD) replenishment therapy, via oral intake of nicotinamide riboside (NR), is safe, augments cerebral NAD levels, and impacts cerebral metabolism in Parkinson's disease (PD). Thirty newly diagnosed, treatment-naive patients received 1,000 mg NR or placebo for 30 days. NR treatment was well tolerated and led to a significant, but variable, increase in cerebral NAD levels-measured by 31phosphorous magnetic resonance spectroscopy-and related metabolites in the cerebrospinal fluid. NR recipients showing increased brain NAD levels exhibited altered cerebral metabolism, measured by 18fluoro-deoxyglucose positron emission tomography, and this was associated with mild clinical improvement. NR augmented the NAD metabolome and induced transcriptional upregulation of processes related to mitochondrial, lysosomal, and proteasomal function in blood cells and/or skeletal muscle. Furthermore, NR decreased the levels of inflammatory cytokines in serum and cerebrospinal fluid. Our findings nominate NR as a potential neuroprotective therapy for PD, warranting further investigation in larger trials.
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Affiliation(s)
- Brage Brakedal
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Christian Dölle
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Frank Riemer
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Gonzalo S Nido
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Geir Olve Skeie
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Alexander R Craven
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; Department of Clinical Engineering, Haukeland University Hospital, Bergen, Norway
| | - Thomas Schwarzlmüller
- Department of Clinical Medicine, University of Bergen, Bergen, Norway; Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Njål Brekke
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Joseph Diab
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Lars Sverkeli
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Vivian Skjeie
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Kristin Varhaug
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Ole-Bjørn Tysnes
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Kristoffer Haugarvoll
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Mathias Ziegler
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Renate Grüner
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - David Eidelberg
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Charalampos Tzoulis
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway.
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24
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Ni R, Nitsch RM. Recent Developments in Positron Emission Tomography Tracers for Proteinopathies Imaging in Dementia. Front Aging Neurosci 2022; 13:751897. [PMID: 35046791 PMCID: PMC8761855 DOI: 10.3389/fnagi.2021.751897] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
An early detection and intervention for dementia represent tremendous unmet clinical needs and priorities in society. A shared feature of neurodegenerative diseases causing dementia is the abnormal accumulation and spreading of pathological protein aggregates, which affect the selective vulnerable circuit in a disease-specific pattern. The advancement in positron emission tomography (PET) biomarkers has accelerated the understanding of the disease mechanism and development of therapeutics for Alzheimer's disease and Parkinson's disease. The clinical utility of amyloid-β PET and the clinical validity of tau PET as diagnostic biomarker for Alzheimer's disease continuum have been demonstrated. The inclusion of biomarkers in the diagnostic criteria has introduced a paradigm shift that facilitated the early and differential disease diagnosis and impacted on the clinical management. Application of disease-modifying therapy likely requires screening of patients with molecular evidence of pathological accumulation and monitoring of treatment effect assisted with biomarkers. There is currently still a gap in specific 4-repeat tau imaging probes for 4-repeat tauopathies and α-synuclein imaging probes for Parkinson's disease and dementia with Lewy body. In this review, we focused on recent development in molecular imaging biomarkers for assisting the early diagnosis of proteinopathies (i.e., amyloid-β, tau, and α-synuclein) in dementia and discussed future perspectives.
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Affiliation(s)
- Ruiqing Ni
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, ETH & University of Zurich, Zurich, Switzerland
| | - Roger M. Nitsch
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
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25
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Guedj E, Varrone A, Boellaard R, Albert NL, Barthel H, van Berckel B, Brendel M, Cecchin D, Ekmekcioglu O, Garibotto V, Lammertsma AA, Law I, Peñuelas I, Semah F, Traub-Weidinger T, van de Giessen E, Van Weehaeghe D, Morbelli S. EANM procedure guidelines for brain PET imaging using [ 18F]FDG, version 3. Eur J Nucl Med Mol Imaging 2021; 49:632-651. [PMID: 34882261 PMCID: PMC8803744 DOI: 10.1007/s00259-021-05603-w] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 10/21/2021] [Indexed: 12/13/2022]
Abstract
The present procedural guidelines summarize the current views of the EANM Neuro-Imaging Committee (NIC). The purpose of these guidelines is to assist nuclear medicine practitioners in making recommendations, performing, interpreting, and reporting results of [18F]FDG-PET imaging of the brain. The aim is to help achieve a high-quality standard of [18F]FDG brain imaging and to further increase the diagnostic impact of this technique in neurological, neurosurgical, and psychiatric practice. The present document replaces a former version of the guidelines that have been published in 2009. These new guidelines include an update in the light of advances in PET technology such as the introduction of digital PET and hybrid PET/MR systems, advances in individual PET semiquantitative analysis, and current broadening clinical indications (e.g., for encephalitis and brain lymphoma). Further insight has also become available about hyperglycemia effects in patients who undergo brain [18F]FDG-PET. Accordingly, the patient preparation procedure has been updated. Finally, most typical brain patterns of metabolic changes are summarized for neurodegenerative diseases. The present guidelines are specifically intended to present information related to the European practice. The information provided should be taken in the context of local conditions and regulations.
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Affiliation(s)
- Eric Guedj
- APHM, CNRS, Centrale Marseille, Institut Fresnel, Timone Hospital, CERIMED, Nuclear Medicine Department, Aix Marseille Univ, Marseille, France. .,Service Central de Biophysique et Médecine Nucléaire, Hôpital de la Timone, 264 rue Saint Pierre, 13005, Marseille, France.
| | - Andrea Varrone
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet and Stockholm Healthcare Services, Stockholm, Sweden
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.,Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Nathalie L Albert
- Department of Nuclear Medicine, Ludwig Maximilians-University of Munich, Munich, Germany
| | - Henryk Barthel
- Department of Nuclear Medicine, Leipzig University, Leipzig, Germany
| | - Bart van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Matthias Brendel
- Department of Nuclear Medicine, Ludwig Maximilians-University of Munich, Munich, Germany.,German Centre of Neurodegenerative Diseases (DZNE), Site Munich, Bonn, Germany
| | - Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine - DIMED, University of Padua, Padua, Italy
| | - Ozgul Ekmekcioglu
- Sisli Hamidiye Etfal Education and Research Hospital, Nuclear Medicine Dept., University of Health Sciences, Istanbul, Turkey
| | - Valentina Garibotto
- NIMTLab, Faculty of Medicine, Geneva University, Geneva, Switzerland.,Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Adriaan A Lammertsma
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.,Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Iván Peñuelas
- Department of Nuclear Medicine, Clinica Universidad de Navarra, IdiSNA, University of Navarra, Pamplona, Spain
| | - Franck Semah
- Nuclear Medicine Department, University Hospital, Lille, France
| | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Elsmarieke van de Giessen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.,Radiology and Nuclear Medicine, Amsterdam UMC, Location AMC, Meibergdreef 9, Amsterdam, The Netherlands
| | | | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Nuclear Medicine Unit, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
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26
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Wang S, Cai H, Cao Z, Li C, Wu T, Xu F, Qian Y, Chen X, Yu Y. More Than Just Static: Dynamic Functional Connectivity Changes of the Thalamic Nuclei to Cortex in Parkinson's Disease With Freezing of Gait. Front Neurol 2021; 12:735999. [PMID: 34721266 PMCID: PMC8553931 DOI: 10.3389/fneur.2021.735999] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 08/26/2021] [Indexed: 12/04/2022] Open
Abstract
Background: The thalamus is not only a key relay node of the thalamocortical circuit but also a hub in the regulation of gait. Previous studies of resting-state functional magnetic resonance imaging (fMRI) have shown static functional connectivity (FC) between the thalamus and the cortex are disrupted in Parkinson's disease (PD) patients with freezing of gait (FOG). However, temporal dynamic FC between the thalamus and the cortex has not yet been characterized in these patients. Methods: Fifty PD patients, including 25 PD patients with FOG (PD-FOG) and 25 PD patients without FOG (PD-NFOG), and 25 healthy controls (HC) underwent resting-state fMRI. Seed-voxel-wise static and dynamic FC were calculated between each thalamic nuclei and other voxels across the brain using the 14 thalamic nuclei in both hemispheres as regions of interest. Associations between altered thalamic FC based on significant inter-group differences and severity of FOG symptoms were also examined in PD-FOG. Results: Both PD-FOG and PD-NFOG showed lower static FC between the right lateral posterior thalamic nuclei and right inferior parietal lobule (IPL) compared with HC. Altered FC dynamics between the thalamic nuclei and several cortical areas were identified in PD-FOG, as shown by temporal dynamic FC analyses. Specifically, relative to PD-NFOG or HC, PD-FOG showed greater fluctuations in FC between the left intralaminar (IL) nuclei and right IPL and between the left medial geniculate and left postcentral gyrus. Furthermore, the dynamics of FC between the left pulvinar anterior nuclei and left inferior frontal gyrus were upregulated in both PD-FOG and PD-NFOG. The dynamics of FC between the right ventral lateral nuclei and left paracentral lobule were elevated in PD-NFOG but were maintained in PD-FOG and HC. The quantitative variability of FC between the left IL nuclei and right IPL was positively correlated with the clinical scales scores in PD-FOG. Conclusions: Dynamic FC between the thalamic nuclei and relevant associative cortical areas involved in sensorimotor integration or cognitive function was disrupted in PD-FOG, which was reflected by greater temporal fluctuations. Abnormal dynamic FC between the left IL nuclei of the thalamus and right IPL was related to the severity of FOG.
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Affiliation(s)
- Shangpei Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Research Center of Clinical Medical Imaging, Hefei, China.,Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Research Center of Clinical Medical Imaging, Hefei, China.,Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Zong Cao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Research Center of Clinical Medical Imaging, Hefei, China.,Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Chuan Li
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Tong Wu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Fangcheng Xu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yinfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Research Center of Clinical Medical Imaging, Hefei, China.,Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Xianwen Chen
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Research Center of Clinical Medical Imaging, Hefei, China.,Anhui Provincial Institute of Translational Medicine, Hefei, China
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27
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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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28
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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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29
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Zhang Y, Huang B, Chen Q, Wang L, Zhang L, Nie K, Huang Q, Huang R. Altered microstructural properties of superficial white matter in patients with Parkinson's disease. Brain Imaging Behav 2021; 16:476-491. [PMID: 34410610 DOI: 10.1007/s11682-021-00522-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/20/2021] [Indexed: 12/31/2022]
Abstract
Parkinson's disease (PD), a chronic neurodegenerative disease, is characterized by sensorimotor and cognitive deficits. Previous diffusion tensor imaging (DTI) studies found abnormal DTI metrics in white matter bundles, such as the corpus callosum, cingulate, and frontal-parietal bundles, in PD patients. These studies mainly focused on alterations in microstructural features of long-range bundles within the deep white matter (DWM) that connects pairs of distant cortical regions. However, less is known about the DTI metrics of the superficial white matter (SWM) that connects local cortical regions in PD patients. To determine whether the DTI metrics of the SWM were different between the PD patients and the healthy controls, we recruited DTI data from 34 PD patients and 29 gender- and age-matched healthy controls. Using a probabilistic tractographic approach, we first defined a population-based SWM mask across all the subjects. Using a tract-based spatial statistical (TBSS) analytic approach, we then identified the SWM bundles showing abnormal DTI metrics in the PD patients. We found that the PD patients showed significantly lower DTI metrics in the SWM bundles connecting the sensorimotor cortex, cingulate cortex, posterior parietal cortex (PPC), and parieto-occipital cortex than the healthy controls. We also found that the clinical measures in the PD patients was significantly negatively correlated with the fractional anisotropy in the SWM (FASWM) that connects core regions in the default mode network (DMN). The FASWM in the bundles that connected the PPC was significantly positively correlated with cognitive performance in the PD patients. Our findings suggest that SWM may serve as the brain structural basis underlying the sensorimotor deficits and cognitive degeneration in PD patients.
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Affiliation(s)
- Yichen Zhang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Biao Huang
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, 510080 , China.
| | - Qinyuan Chen
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Lijuan Wang
- Department of Neurology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, 510080, China
| | - Lu Zhang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Kun Nie
- Department of Neurology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, 510080, China
| | - Qinda Huang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Ruiwang Huang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China.
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30
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Lu J, Huang L, Lv Y, Peng S, Xu Q, Li L, Ge J, Zhang H, Guan Y, Zhao Q, Guo Q, Chen K, Wu P, Ma Y, Zuo C. A disease-specific metabolic imaging marker for diagnosis and progression evaluation of semantic variant primary progressive aphasia. Eur J Neurol 2021; 28:2927-2939. [PMID: 34110063 DOI: 10.1111/ene.14919] [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: 04/27/2021] [Accepted: 05/10/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND PURPOSE The diagnosis and monitoring of semantic variant primary progressive aphasia (sv-PPA) are clinically challenging. We aimed to establish a distinctive metabolic pattern in sv-PPA for diagnosis and severity evaluation. METHODS Fifteen sv-PPA patients and 15 controls were enrolled to identify sv-PPA-related pattern (sv-PPARP) by principal component analysis of 18 F-fluorodeoxyglucose positron emission tomography. Eighteen Alzheimer disease dementia (AD) and 14 behavioral variant frontotemporal dementia (bv-FTD) patients were enrolled to test the discriminatory power. Correspondingly, regional metabolic activities extracted from the voxelwise analysis were evaluated for the discriminatory power. RESULTS The sv-PPARP was characterized as decreased metabolic activity mainly in the bilateral temporal lobe (left predominance), middle orbitofrontal gyrus, left hippocampus/parahippocampus gyrus, fusiform gyrus, insula, inferior orbitofrontal gyrus, and striatum, with increased activity in the bilateral lingual gyrus, cuneus, calcarine gyrus, and right precentral and postcentral gyrus. The pattern expression had significant discriminatory power (area under the curve [AUC] = 0.98, sensitivity = 100%, specificity = 94.4%) in distinguishing sv-PPA from AD, and the asymmetry index offered complementary discriminatory power (AUC = 0.91, sensitivity = 86.7%, specificity = 92.9%) in distinguishing sv-PPA from bv-FTD. In sv-PPA patients, the pattern expression correlated with Boston Naming Test scores at baseline and showed significant increase in the subset of patients with follow-up. The voxelwise analysis showed similar topography, and the regional metabolic activities had equivalent or better discriminatory power and clinical correlations with Boston Naming Test scores. The ability to reflect disease progression in longitudinal follow-up seemed to be inferior to the pattern expression. CONCLUSIONS The sv-PPARP might serve as an objective biomarker for diagnosis and progression evaluation.
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Affiliation(s)
- Jiaying Lu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Lin Huang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Yingru Lv
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, USA
| | - Qian Xu
- Department of Nuclear Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Ling Li
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jingjie Ge
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Huiwei Zhang
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Qianhua Zhao
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Keliang Chen
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, USA
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medicine Imaging, Fudan University, Shanghai, China
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31
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Schindlbeck KA, Vo A, Mattis PJ, Villringer K, Marzinzik F, Fiebach JB, Eidelberg D. Cognition-Related Functional Topographies in Parkinson's Disease: Localized Loss of the Ventral Default Mode Network. Cereb Cortex 2021; 31:5139-5150. [PMID: 34148072 DOI: 10.1093/cercor/bhab148] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 05/11/2021] [Accepted: 05/12/2021] [Indexed: 02/07/2023] Open
Abstract
Cognitive dysfunction in Parkinson's disease (PD) is associated with increased expression of the PD cognition-related pattern (PDCP), which overlaps with the normal default mode network (DMN). Here, we sought to determine the degree to which the former network represents loss of the latter as a manifestation of the disease process. To address this, we first analyzed metabolic images (fluorodeoxyglucose positron emission tomography [PET]) from a large PD sample with varying cognitive performance. Cognitive impairment in these patients correlated with increased PDCP expression as well as DMN loss. We next determined the spatial relationship of the 2 topographies at the subnetwork level. To this end, we analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from an independent population. This approach uncovered a significant PD cognition-related network that resembled previously identified PET- and rs-fMRI-based PDCP topographies. Further analysis revealed selective loss of the ventral DMN subnetwork (precuneus and posterior cingulate cortex) in PD, whereas the anterior and posterior components were not affected by the disease. Importantly, the PDCP also included a number of non-DMN regions such as the dorsolateral prefrontal and medial temporal cortex. The findings show that the PDCP is a reproducible cognition-related network that is topographically distinct from the normal DMN.
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Affiliation(s)
- Katharina A Schindlbeck
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - Paul J Mattis
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA.,Department of Neurology, Northwell Health, Manhasset, NY 11030, USA
| | - Kersten Villringer
- Center for Stroke Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, Berlin 12200, Germany
| | - Frank Marzinzik
- Department of Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, Berlin 12200, Germany
| | - Jochen B Fiebach
- Center for Stroke Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, Berlin 12200, Germany
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
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32
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The Cerebellum Is a Common Key for Visuospatial Execution and Attention in Parkinson's Disease. Diagnostics (Basel) 2021; 11:diagnostics11061042. [PMID: 34204073 PMCID: PMC8229154 DOI: 10.3390/diagnostics11061042] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 04/18/2021] [Accepted: 06/02/2021] [Indexed: 11/17/2022] Open
Abstract
Cognitive decline affects the clinical course in patients with Parkinson's disease (PD) and contributes to a poor prognosis. However, little is known about the underlying network-level abnormalities associated with each cognitive domain. We aimed to identify the networks related to each cognitive domain in PD using resting-state functional magnetic resonance imaging (MRI). Forty patients with PD and 15 normal controls were enrolled. All subjects underwent MRI and the Mini-Mental State Examination. Furthermore, the cognitive function of patients with PD was assessed using the Montreal Cognitive Assessment (MoCA). We used independent component analysis of the resting-state functional MRI for functional segmentation, followed by reconstruction to identify each domain-related network, to predict scores in PD using multiple regression models. Six networks were identified, as follows: the visuospatial-executive-domain-related network (R2 = 0.54, p < 0.001), naming-domain-related network (R2 = 0.39, p < 0.001), attention-domain-related network (R2 = 0.86, p < 0.001), language-domain-related network (R2 = 0.64, p < 0.001), abstraction-related network (R2 = 0.10, p < 0.05), and orientation-domain-related network (R2 = 0.64, p < 0.001). Cerebellar lobule VII was involved in the visuospatial-executive-domain-related and attention-domain-related networks. These two domains are involved in the first three listed nonamnestic cognitive impairment in the diagnostic criteria for PD with dementia (PDD). Furthermore, Brodmann area 10 contributed most frequently to each domain-related network. Collectively, these findings suggest that cerebellar lobule VII may play a key role in cognitive impairment in nonamnestic types of PDD.
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33
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Fregni F, El-Hagrassy MM, Pacheco-Barrios K, Carvalho S, Leite J, Simis M, Brunelin J, Nakamura-Palacios EM, Marangolo P, Venkatasubramanian G, San-Juan D, Caumo W, Bikson M, Brunoni AR. Evidence-Based Guidelines and Secondary Meta-Analysis for the Use of Transcranial Direct Current Stimulation in Neurological and Psychiatric Disorders. Int J Neuropsychopharmacol 2021; 24:256-313. [PMID: 32710772 PMCID: PMC8059493 DOI: 10.1093/ijnp/pyaa051] [Citation(s) in RCA: 251] [Impact Index Per Article: 83.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 07/21/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Transcranial direct current stimulation has shown promising clinical results, leading to increased demand for an evidence-based review on its clinical effects. OBJECTIVE We convened a team of transcranial direct current stimulation experts to conduct a systematic review of clinical trials with more than 1 session of stimulation testing: pain, Parkinson's disease motor function and cognition, stroke motor function and language, epilepsy, major depressive disorder, obsessive compulsive disorder, Tourette syndrome, schizophrenia, and drug addiction. METHODS Experts were asked to conduct this systematic review according to the search methodology from PRISMA guidelines. Recommendations on efficacy were categorized into Levels A (definitely effective), B (probably effective), C (possibly effective), or no recommendation. We assessed risk of bias for all included studies to confirm whether results were driven by potentially biased studies. RESULTS Although most of the clinical trials have been designed as proof-of-concept trials, some of the indications analyzed in this review can be considered as definitely effective (Level A), such as depression, and probably effective (Level B), such as neuropathic pain, fibromyalgia, migraine, post-operative patient-controlled analgesia and pain, Parkinson's disease (motor and cognition), stroke (motor), epilepsy, schizophrenia, and alcohol addiction. Assessment of bias showed that most of the studies had low risk of biases, and sensitivity analysis for bias did not change these results. Effect sizes vary from 0.01 to 0.70 and were significant in about 8 conditions, with the largest effect size being in postoperative acute pain and smaller in stroke motor recovery (nonsignificant when combined with robotic therapy). CONCLUSION All recommendations listed here are based on current published PubMed-indexed data. Despite high levels of evidence in some conditions, it must be underscored that effect sizes and duration of effects are often limited; thus, real clinical impact needs to be further determined with different study designs.
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Affiliation(s)
- Felipe Fregni
- Neuromodulation Center and Center for Clinical Research Learning, Spaulding Rehabilitation Hospital and Massachusetts General Hospital, Boston, Massachusetts
| | - Mirret M El-Hagrassy
- Neuromodulation Center and Center for Clinical Research Learning, Spaulding Rehabilitation Hospital and Massachusetts General Hospital, Boston, Massachusetts
| | - Kevin Pacheco-Barrios
- Neuromodulation Center and Center for Clinical Research Learning, Spaulding Rehabilitation Hospital and Massachusetts General Hospital, Boston, Massachusetts
- Universidad San Ignacio de Loyola, Vicerrectorado de Investigación, Unidad de Investigación para la Generación y Síntesis de Evidencias en Salud, Lima, Peru
| | - Sandra Carvalho
- Neurotherapeutics and experimental Psychopathology Group (NEP), Psychological Neuroscience Laboratory, CIPsi, School of Psychology, University of Minho, Campus de Gualtar, Braga, Portugal
| | - Jorge Leite
- I2P-Portucalense Institute for Psychology, Universidade Portucalense, Porto, Portugal
| | - Marcel Simis
- Physical and Rehabilitation Medicine Institute of the University of Sao Paulo Medical School General Hospital, Sao Paulo, Brazil
| | - Jerome Brunelin
- CH Le Vinatier, PSYR2 team, Lyon Neuroscience Research Center, UCB Lyon 1, Bron, France
| | - Ester Miyuki Nakamura-Palacios
- Laboratory of Cognitive Sciences and Neuropsychopharmacology, Department of Physiological Sciences, Federal University of Espírito Santo, Espírito Santo, Brasil (Dr Nakamura-Palacios)
| | - Paola Marangolo
- Dipartimento di Studi Umanistici, Università Federico II, Naples, Italy
- IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Ganesan Venkatasubramanian
- Translational Psychiatry Laboratory, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Daniel San-Juan
- Neurophysiology Department, National Institute of Neurology and Neurosurgery Manuel Velasco Suárez, Mexico City, Mexico
| | - Wolnei Caumo
- Post-Graduate Program in Medical Sciences, School of Medicine, Universidade Federal do Rio Grande do Sul (UFRGS) Surgery Department, School of Medicine, UFRGS; Pain and Palliative Care Service at Hospital de Clínicas de Porto Alegre (HCPA) Laboratory of Pain and Neuromodulation at HCPA, Porto Alegre, Brazil
| | - Marom Bikson
- Department of Biomedical Engineering, The City College of New York of CUNY, New York, New York
| | - André R Brunoni
- Service of Interdisciplinary Neuromodulation, Laboratory of Neurosciences (LIM-27), Department and Institute of Psychiatry & Department of Internal Medicine, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
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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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Novel PET Biomarkers to Disentangle Molecular Pathways across Age-Related Neurodegenerative Diseases. Cells 2020; 9:cells9122581. [PMID: 33276490 PMCID: PMC7761606 DOI: 10.3390/cells9122581] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 11/25/2020] [Accepted: 11/28/2020] [Indexed: 12/11/2022] Open
Abstract
There is a need to disentangle the etiological puzzle of age-related neurodegenerative diseases, whose clinical phenotypes arise from known, and as yet unknown, pathways that can act distinctly or in concert. Enhanced sub-phenotyping and the identification of in vivo biomarker-driven signature profiles could improve the stratification of patients into clinical trials and, potentially, help to drive the treatment landscape towards the precision medicine paradigm. The rapidly growing field of neuroimaging offers valuable tools to investigate disease pathophysiology and molecular pathways in humans, with the potential to capture the whole disease course starting from preclinical stages. Positron emission tomography (PET) combines the advantages of a versatile imaging technique with the ability to quantify, to nanomolar sensitivity, molecular targets in vivo. This review will discuss current research and available imaging biomarkers evaluating dysregulation of the main molecular pathways across age-related neurodegenerative diseases. The molecular pathways focused on in this review involve mitochondrial dysfunction and energy dysregulation; neuroinflammation; protein misfolding; aggregation and the concepts of pathobiology, synaptic dysfunction, neurotransmitter dysregulation and dysfunction of the glymphatic system. The use of PET imaging to dissect these molecular pathways and the potential to aid sub-phenotyping will be discussed, with a focus on novel PET biomarkers.
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36
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Tang CC, Holtbernd F, Ma Y, Spetsieris P, Oh A, Fink GR, Timmermann L, Eggers C, Eidelberg D. Hemispheric Network Expression in Parkinson's Disease: Relationship to Dopaminergic Asymmetries. JOURNAL OF PARKINSONS DISEASE 2020; 10:1737-1749. [PMID: 32925097 DOI: 10.3233/jpd-202117] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND Parkinson's disease (PD) is characterized by brain metabolic networks, specifically associated with motor and cognitive manifestations. Few studies have investigated network changes in cerebral hemispheres ipsilateral and contralateral to the clinically more affected body side. OBJECTIVE We examined hemispheric network abnormalities and their relationship to striatal dopaminergic deficits in PD patients at different stages. METHODS 45 PD patients underwent dual-tracer positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) and 18F-fluorodopa (FDOPA) in a high-resolution PET scanner. In all patients, we computed expression levels for the PD-related motor/cognition metabolic patterns (PDRP/PDCP) as well as putamen/caudate FDOPA uptake values in both hemispheres. Resulting hemispheric measures in the PD group were compared with corresponding healthy control values and assessed across disease stages. RESULTS Hemispheric PDRP and PDCP expression was significantly elevated contralateral and ipsilateral to the more affected body side in patients with unilateral symptoms (H&Y 1: p < 0.01) and in patients with bilateral limb involvement (H&Y 2-3: p < 0.001; H&Y 4: p < 0.003). Elevations in pattern expression were symmetrical at all disease stages. By contrast, FDOPA uptake in the caudate and putamen was reduced bilaterally (p < 0.002), with lower values on both sides at more advanced disease stages. Hemispheric uptake was asymmetrical in both striatal regions, with lower contralateral values at all disease stages. The magnitude of hemispheric uptake asymmetry was smaller with more advanced disease, reflecting greater change ipsilaterally. CONCLUSION Symmetrical network expression in PD represents bilateral functional effects unrelated to nigrostriatal dopaminergic asymmetries.
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Affiliation(s)
- Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Florian Holtbernd
- RWTH Aachen University, Department of Neurology, Aachen, Germany.,JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Juelich Research Centre and RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine 4 (INM-4), Juelich Research Centre, Juelich, Germany
| | - Yilong Ma
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Phoebe Spetsieris
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Alice Oh
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Gereon R Fink
- Department of Neurology, University of Cologne, Cologne, Germany.,Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
| | - Lars Timmermann
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.,Department of Neurology, University Hospital of Giessen and Marburg, Marburg, Germany
| | - Carsten Eggers
- Department of Neurology, University Hospital of Giessen and Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, Universities Marburg and Giessen, Marburg, Germany
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
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Saeed U, Lang AE, Masellis M. Neuroimaging Advances in Parkinson's Disease and Atypical Parkinsonian Syndromes. Front Neurol 2020; 11:572976. [PMID: 33178113 PMCID: PMC7593544 DOI: 10.3389/fneur.2020.572976] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 09/02/2020] [Indexed: 12/11/2022] Open
Abstract
Parkinson's disease (PD) and atypical Parkinsonian syndromes are progressive heterogeneous neurodegenerative diseases that share clinical characteristic of parkinsonism as a common feature, but are considered distinct clinicopathological disorders. Based on the predominant protein aggregates observed within the brain, these disorders are categorized as, (1) α-synucleinopathies, which include PD and other Lewy body spectrum disorders as well as multiple system atrophy, and (2) tauopathies, which comprise progressive supranuclear palsy and corticobasal degeneration. Although, great strides have been made in neurodegenerative disease research since the first medical description of PD in 1817 by James Parkinson, these disorders remain a major diagnostic and treatment challenge. A valid diagnosis at early disease stages is of paramount importance, as it can help accommodate differential prognostic and disease management approaches, enable the elucidation of reliable clinicopathological relationships ideally at prodromal stages, as well as facilitate the evaluation of novel therapeutics in clinical trials. However, the pursuit for early diagnosis in PD and atypical Parkinsonian syndromes is hindered by substantial clinical and pathological heterogeneity, which can influence disease presentation and progression. Therefore, reliable neuroimaging biomarkers are required in order to enhance diagnostic certainty and ensure more informed diagnostic decisions. In this article, an updated presentation of well-established and emerging neuroimaging biomarkers are reviewed from the following modalities: (1) structural magnetic resonance imaging (MRI), (2) diffusion-weighted and diffusion tensor MRI, (3) resting-state and task-based functional MRI, (4) proton magnetic resonance spectroscopy, (5) transcranial B-mode sonography for measuring substantia nigra and lentiform nucleus echogenicity, (6) single photon emission computed tomography for assessing the dopaminergic system and cerebral perfusion, and (7) positron emission tomography for quantifying nigrostriatal functions, glucose metabolism, amyloid, tau and α-synuclein molecular imaging, as well as neuroinflammation. Multiple biomarkers obtained from different neuroimaging modalities can provide distinct yet corroborative information on the underlying neurodegenerative processes. This integrative "multimodal approach" may prove superior to single modality-based methods. Indeed, owing to the international, multi-centered, collaborative research initiatives as well as refinements in neuroimaging technology that are currently underway, the upcoming decades will mark a pivotal and exciting era of further advancements in this field of neuroscience.
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Affiliation(s)
- Usman Saeed
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Anthony E Lang
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada.,Edmond J Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Mario Masellis
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada.,L.C. Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Center, Toronto, ON, Canada.,Cognitive and Movement Disorders Clinic, Sunnybrook Health Sciences Center, Toronto, ON, Canada
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38
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Han X, Wu P, Alberts I, Zhou H, Yu H, Bargiotas P, Yakushev I, Wang J, Höglinger G, Förster S, Bassetti C, Oertel W, Schwaiger M, Huang SC, Cumming P, Rominger A, Jiang J, Zuo C, Shi K. Characterizing the heterogeneous metabolic progression in idiopathic REM sleep behavior disorder. NEUROIMAGE-CLINICAL 2020; 27:102294. [PMID: 32570206 PMCID: PMC7322340 DOI: 10.1016/j.nicl.2020.102294] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 05/10/2020] [Accepted: 05/11/2020] [Indexed: 11/28/2022]
Abstract
Imaging biomarkers of the metabolic trajectory from HC, iRBD and PD are identified. Frontal, limbic and occipital brain regions as imaging biomarkers in PD. Frontal, limbic and occipital brain regions as imaging biomarkers of the phenoconversion from iRBD to PD.
Objective Idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) is a prodromal stage of synucleinopathies such as Parkinson’s disease (PD). Positron emission tomography (PET) with 18F-FDG reveals metabolic perturbations, which are scored by spatial covariance analysis. However, the resultant pattern scores do not capture the spatially heterogeneous trajectories of metabolic changes between individual brain regions. Assuming metabolic progression occurs as a continuum from the healthy control (HC) condition to iRBD and then PD, we investigated spatial dynamics of progressively perturbed glucose metabolism in a cross-sectional study. Methods 19 iRBD patients, 38 PD patients and 19 HC subjects underwent 18F-FDG PET. The images were spatially normalized, scaled to the global mean uptake, and automatically parcellated. We contrasted regional metabolism by group, and allocated the inferred progression to one of several possible trajectories. We further investigated the correlations between 18F-FDG uptake and the disease duration in the iRBD and PD groups, respectively. We also explored relationships between 18F-FDG uptake and the Unified Parkinson’s Disease Rating Scale motor (UPDRS III) scores in the PD group. Results PD patients exhibited more extensive relative hyper- and hypo-metabolism than iRBD patients. We identified three dynamic metabolic trajectories, cross-sectional hypo- or hypermetabolism, cross-sectionally unchanged hypo- or hypermetabolism, cross-sectionally late hypo- or hypermetabolism, appearing only in the contrast of PD with iRBD. No correlation was found between relative 18F-FDG metabolism and disease duration in the iRBD group. Regional hyper- and hypo-metabolism in the PD patients correlated with disease duration or clinical UPDRS III scores. Conclusion Cerebral metabolism changes heterogeneously in a continuum extending from HC to iRBD and PD groups in this preliminary study. The distinctive metabolic trajectories point towards a potential neuroimaging biomarker for conversion of iRBD to frank PD, which should be amenable to advanced pattern recognition analysis in future longitudinal studies.
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Affiliation(s)
- Xianhua Han
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Ian Alberts
- Department of Nuclear Medicine, University of Bern, Switzerland
| | - Hucheng Zhou
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication ,Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Huan Yu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Panagiotis Bargiotas
- Department of Neurology, University Hospital Bern (Inselspital) and University of Bern, Bern, Switzerland; Department of Neurology, Medical School, University of Cyprus, Nicosia, Cyprus
| | - Igor Yakushev
- Department of Nuclear Medicine, Technische Universität München, Munich, Germany
| | - Jian Wang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | | | - Stefan Förster
- Department of Nuclear Medicine, Technische Universität München, Munich, Germany; Department of Nuclear Medicine, Klinikum Bayreuth, Germany
| | - Claudio Bassetti
- Department of Neurology, University Hospital Bern (Inselspital) and University of Bern, Bern, Switzerland
| | | | - Markus Schwaiger
- Klinikum r. d. Isar, Technische Universität München, Munich, Germany
| | - Sung-Cheng Huang
- Department of Molecular and Medical Pharmacology, UCLA, Los Angeles, USA
| | - Paul Cumming
- Department of Nuclear Medicine, University of Bern, Switzerland; School of Psychology and Counselling and IHBI, Queensland University of Technology, Brisbane, Australia
| | - Axel Rominger
- Department of Nuclear Medicine, University of Bern, Switzerland
| | - Jiehui Jiang
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication ,Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China; Human Phenome Institute, Fudan University, Shanghai, China; Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
| | - Kuangyu Shi
- Department of Nuclear Medicine, University of Bern, Switzerland; Dept. Informatics, Technische Universität München, Munich, Germany
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Fiorenzato E, Strafella AP, Kim J, Schifano R, Weis L, Antonini A, Biundo R. Dynamic functional connectivity changes associated with dementia in Parkinson's disease. Brain 2020; 142:2860-2872. [PMID: 31280293 DOI: 10.1093/brain/awz192] [Citation(s) in RCA: 172] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Revised: 03/29/2019] [Accepted: 04/28/2019] [Indexed: 11/14/2022] Open
Abstract
Dynamic functional connectivity captures temporal variations of functional connectivity during MRI acquisition and it may be a suitable method to detect cognitive changes in Parkinson's disease. In this study, we evaluated 118 patients with Parkinson's disease matched for age, sex and education with 35 healthy control subjects. Patients with Parkinson's disease were classified with normal cognition (n = 52), mild cognitive impairment (n = 46), and dementia (n = 20) based on an extensive neuropsychological evaluation. Resting state functional MRI and a sliding-window approach were used to study the dynamic functional connectivity. Dynamic analysis suggested two distinct connectivity 'States' across the entire group: a more frequent, segregated brain state characterized by the predominance of within-network connections, State I, and a less frequent, integrated state with strongly connected functional internetwork components, State II. In Parkinson's disease, State I occurred 13.89% more often than in healthy control subjects, paralleled by a proportional reduction of State II. Parkinson's disease subgroups analyses showed the segregated state occurred more frequently in Parkinson's disease dementia than in mild cognitive impairment and normal cognition groups. Further, patients with Parkinson's disease dementia dwelled significantly longer in the segregated State I, and showed a significant lower number of transitions to the strongly interconnected State II compared to the other subgroups. Our study indicates that dementia in Parkinson's disease is characterized by altered temporal properties in dynamic connectivity. In addition, our results show that increased dwell time in the segregated state and reduced number of transitions between states are associated with presence of dementia in Parkinson's disease. Further studies on dynamic functional connectivity changes could help to better understand the progressive dysfunction of networks between Parkinson's disease cognitive states.
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Affiliation(s)
| | - Antonio P Strafella
- Division of Brain, Imaging and Behaviour-Systems Neuroscience, Krembil Research Institute, UHN, University of Toronto, Toronto, ON, Canada.,Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.,Morton and Gloria Shulman Movement Disorder Unit and E.J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, Toronto Western Hospital, UHN, University of Toronto, Toronto, ON, Canada
| | - Jinhee Kim
- Division of Brain, Imaging and Behaviour-Systems Neuroscience, Krembil Research Institute, UHN, University of Toronto, Toronto, ON, Canada.,Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.,Morton and Gloria Shulman Movement Disorder Unit and E.J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, Toronto Western Hospital, UHN, University of Toronto, Toronto, ON, Canada
| | | | - Luca Weis
- IRCCS San Camillo Hospital, Venice, Italy
| | - Angelo Antonini
- Department of Neurosciences, University of Padua, Padua, Italy
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Iizuka T, Kameyama M. Spatial metabolic profiles to discriminate dementia with Lewy bodies from Alzheimer disease. J Neurol 2020; 267:1960-1969. [PMID: 32170446 DOI: 10.1007/s00415-020-09790-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 03/05/2020] [Accepted: 03/09/2020] [Indexed: 11/26/2022]
Abstract
BACKGROUND To differentiate dementia with Lewy bodies (DLB) from Alzheimer disease (AD) using a single imaging modality is challenging, because of their common hypometabolic findings. Scaled subprofile modeling/principal component analysis (SSM/PCA), an unsupervised artificial intelligence, has the potential to offer an alternative to image analysis. OBJECTIVE We aimed to produce spatial metabolic profiles to discriminate DLB from AD and to identify the characteristics of the profiles. METHODS Fifty individuals each with DLB, AD, and normal cognition (NL) underwent 18F-FDG-PET and MRI. The spatial metabolic profile to differentiate DLB from AD (DLB-AD discrimination profile) was determined using SSM/PCA with tenfold cross validation. For comparison, we also produced disease-related profiles that can discriminate AD and DLB from NL (AD- and DLB-related profiles, respectively). RESULTS The DLB-AD discrimination profile significantly differentiated DLB from AD with comparable accuracy to that of discriminating DLB and AD from NL. The AD- and DLB-related profiles comprised metabolic imaging features typical of each pathology. In contrast, the DLB-AD discrimination profile emphasized preservation in the posterior cingulate cortex (cingulate island sign) and medial temporal lobe, and occipital hypometabolism. Common hypometabolic findings between DLB and AD were less noticeable in the profile. The DLB-related profile significantly correlated with cognitive function and three core features of DLB, whereas the DLB-AD discrimination profile did not. CONCLUSIONS Spatial metabolic profile that could discriminate DLB from AD emphasized different imaging features and eliminated common findings between DLB and AD. Neither cognitive function nor core features were associated with the profile.
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Affiliation(s)
- Tomomichi Iizuka
- Center for Dementia, Fukujuji Hospital, Japan Anti-Tuberculosis Association, Kiyose, 204-8522, Japan.
| | - Masashi Kameyama
- Department of Diagnostic Radiology, Tokyo Metropolitan Geriatric Hospital, Tokyo, 173-0015, Japan
- Division of Nuclear Medicine, Department of Radiology, School of Medicine, Keio University, Tokyo, 160-8582, Japan
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Huang Z, Jiang C, Li L, Xu Q, Ge J, Li M, Guan Y, Wu J, Wang J, Zuo C, Yu H, Wu P. Correlations between dopaminergic dysfunction and abnormal metabolic network activity in REM sleep behavior disorder. J Cereb Blood Flow Metab 2020; 40:552-562. [PMID: 30741074 PMCID: PMC7026846 DOI: 10.1177/0271678x19828916] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 01/03/2019] [Accepted: 01/07/2019] [Indexed: 12/22/2022]
Abstract
Striatal dopamine transporter (DAT) deficiency and abnormal expression of Parkinson's disease (PD)-related pattern (PDRP) have been observed in patients with idiopathic REM sleep behavior disorder (IRBD). This study aimed to investigate the correlations between these two measures with comparison to PD using a dual tracer imaging design. Age-matched 37 IRBD patients, 86 PD patients, and 15 control subjects underwent concurrent PET scans with 11C-CFT to quantify dopaminergic dysfunction and 18F-FDG to quantify PDRP expression. IRBD patients were divided into two subgroups: those with relatively normal (IRBD-RN) or abnormal (IRBD-AB) striatal DAT binding. Significantly decreased DAT binding and increased PDRP scores were present in all patient groups, except for IRBD-RN, relative to the controls. There was a significant effect of hemisphere and hemisphere × group interaction for DAT binding but not for PDRP expression. Significant correlations were observed between DAT binding and PDRP expression in the IRBD-AB and PD groups but not in the IRBD-RN group. IRBD patients present with an intermediate state in striatal DAT distribution and PDRP activity between PD and normal controls. The modest correlations between the two measures in both IRBD and PD suggest that differences in network activity cannot be fully explained by nigrostriatal dopaminergic denervation.
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Affiliation(s)
- Zhemin Huang
- PET Center, Department of Nuclear Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chengfeng Jiang
- Department of Nuclear Medicine, Affiliated Kunshan Hospital, Jiangsu University, Kunshan, Jiangsu, China
| | - Ling Li
- PET Center, Department of Nuclear Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qian Xu
- PET Center, Department of Nuclear Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jingjie Ge
- PET Center, Department of Nuclear Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ming Li
- PET Center, Department of Nuclear Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yihui Guan
- PET Center, Department of Nuclear Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Jianjun Wu
- Department of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian Wang
- Department of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chuantao Zuo
- PET Center, Department of Nuclear Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Huan Yu
- Department of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- Sleep and Wake Disorders Center, Fudan University, Shanghai, China
| | - Ping Wu
- PET Center, Department of Nuclear Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
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Ruppert MC, Greuel A, Tahmasian M, Schwartz F, Stürmer S, Maier F, Hammes J, Tittgemeyer M, Timmermann L, van Eimeren T, Drzezga A, Eggers C. Network degeneration in Parkinson’s disease: multimodal imaging of nigro-striato-cortical dysfunction. Brain 2020; 143:944-959. [DOI: 10.1093/brain/awaa019] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 11/21/2019] [Accepted: 12/11/2019] [Indexed: 11/14/2022] Open
Abstract
Abstract
The spreading hypothesis of neurodegeneration assumes an expansion of neural pathologies along existing neural pathways. Multimodal neuroimaging studies have demonstrated distinct topographic patterns of cerebral pathologies in neurodegeneration. For Parkinson’s disease the hypothesis so far rests largely on histopathological evidence of α-synuclein spreading in a characteristic pattern and progressive nigrostriatal dopamine depletion. Functional consequences of nigrostriatal dysfunction on cortical activity remain to be elucidated. Our goal was to investigate multimodal imaging correlates of degenerative processes in Parkinson’s disease by assessing dopamine depletion and its potential effect on striatocortical connectivity networks and cortical metabolism in relation to parkinsonian symptoms. We combined 18F-DOPA-PET, 18F-fluorodeoxyglucose (FDG)-PET and resting state functional MRI to multimodally characterize network alterations in Parkinson’s disease. Forty-two patients with mild-to-moderate stage Parkinson’s disease and 14 age-matched healthy control subjects underwent a multimodal imaging protocol and comprehensive clinical examination. A voxel-wise group comparison of 18F-DOPA uptake identified the exact location and extent of putaminal dopamine depletion in patients. Resulting clusters were defined as seeds for a seed-to-voxel functional connectivity analysis. 18F-FDG metabolism was compared between groups at a whole-brain level and uptake values were extracted from regions with reduced putaminal connectivity. To unravel associations between dopaminergic activity, striatocortical connectivity, glucose metabolism and symptom severity, correlations between normalized uptake values, seed-to-cluster β-values and clinical parameters were tested while controlling for age and dopaminergic medication. Aside from cortical hypometabolism, 18F-FDG-PET data for the first time revealed a hypometabolic midbrain cluster in patients with Parkinson’s disease that comprised caudal parts of the bilateral substantia nigra pars compacta. Putaminal dopamine synthesis capacity was significantly reduced in the bilateral posterior putamen and correlated with ipsilateral nigral 18F-FDG uptake. Resting state functional MRI data indicated significantly reduced functional connectivity between the dopamine depleted putaminal seed and cortical areas primarily belonging to the sensorimotor network in patients with Parkinson’s disease. In the inferior parietal cortex, hypoconnectivity in patients was significantly correlated with lower metabolism (left P = 0.021, right P = 0.018). Of note, unilateral network alterations quantified with different modalities corresponded with contralateral motor impairments. In conclusion, our results support the hypothesis that degeneration of nigrostriatal fibres functionally impairs distinct striatocortical connections, disturbing the efficient interplay between motor processing areas and impairing motor control in patients with Parkinson’s disease. The present study is the first to reveal trimodal evidence for network-dependent degeneration in Parkinson’s disease by outlining the impact of functional nigrostriatal pathway impairment on striatocortical functional connectivity networks and cortical metabolism.
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Affiliation(s)
- Marina C Ruppert
- Department of Neurology, University Hospital of Marburg, Germany
- Center for Mind, Brain and Behavior - CMBB, Universities Marburg and Gießen, Germany
| | - Andrea Greuel
- Department of Neurology, University Hospital of Marburg, Germany
| | - Masoud Tahmasian
- Institue of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Frank Schwartz
- Department of Neurology, Hospital of the Brothers of Mercy, Trier, Germany
| | - Sophie Stürmer
- Max Planck Institute for Metabolism Research, Cologne, Germany
- Department of Neurology, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Germany
| | - Franziska Maier
- Department of Psychiatry, University Hospital Cologne, Medical Faculty, Cologne, Germany
| | - Jochen Hammes
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Germany
| | - Marc Tittgemeyer
- Max Planck Institute for Metabolism Research, Cologne, Germany
- Cluster of Excellence in Cellular Stress and Aging Associated Disease (CECAD), Cologne, Germany
| | - Lars Timmermann
- Department of Neurology, University Hospital of Marburg, Germany
- Center for Mind, Brain and Behavior - CMBB, Universities Marburg and Gießen, Germany
| | - Thilo van Eimeren
- Department of Neurology, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Germany
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Germany
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Germany
| | - Alexander Drzezga
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Germany
- German Center for Neurodegenerative Diseases (DZNE), Germany
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-2), Research Center Jülich, Germany
| | - Carsten Eggers
- Department of Neurology, University Hospital of Marburg, Germany
- Center for Mind, Brain and Behavior - CMBB, Universities Marburg and Gießen, Germany
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Zhang Y, Ren R, Sanford LD, Yang L, Zhou J, Tan L, Li T, Zhang J, Wing YK, Shi J, Lu L, Tang X. Sleep in Parkinson's disease: A systematic review and meta-analysis of polysomnographic findings. Sleep Med Rev 2020; 51:101281. [PMID: 32135452 DOI: 10.1016/j.smrv.2020.101281] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 01/21/2020] [Accepted: 01/23/2020] [Indexed: 02/08/2023]
Abstract
Polysomnographic studies have been conducted to explore sleep changes in Parkinson's disease (PD), but the relationships between sleep disturbances and PD are imperfectly understood. We conducted a systematic review of the literature exploring polysomnographic differences between PD patients and controls in EMBASE, MEDLINE, All EBM databases, CINAHL, and PsycIFNO. 67 studies were identified for systematic review, 63 of which were used for meta-analysis. Meta-analyses revealed significant reductions in total sleep time, sleep efficiency, N2 percentage, slow wave sleep, rapid eye movement sleep (REM) percentage, and increases in wake time after sleep onset, N1 percentage, REM latency, apnea hypopnea index, and periodic limb movement index in PD patients compared with controls. There were no remarkable differences in sleep continuity or sleep architecture between PD patients with and without REM sleep behavior disorder (RBD). Our study suggests that PD patients have poor sleep quality and quantity. Sex, age, disease duration, presence of RBD, medication status, cognitive impairment, and adaptation night are factors that contributed to heterogeneity between studies.
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Affiliation(s)
- Ye Zhang
- Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Mental Health Center, Translational Neuroscience Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Rong Ren
- Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Mental Health Center, Translational Neuroscience Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Larry D Sanford
- Sleep Research Laboratory, Center for Integrative Neuroscience and Inflammatory Diseases, Department of Pathology and Anatomy, Eastern Virginia Medical School, Norfolk, VA, USA.
| | - Linghui Yang
- Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Mental Health Center, Translational Neuroscience Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Junying Zhou
- Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Mental Health Center, Translational Neuroscience Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Lu Tan
- Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Mental Health Center, Translational Neuroscience Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Taomei Li
- Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Mental Health Center, Translational Neuroscience Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Jihui Zhang
- Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Yun-Kwok Wing
- Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Jie Shi
- National Institute on Drug Dependence, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Lin Lu
- National Institute on Drug Dependence, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Xiangdong Tang
- Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Mental Health Center, Translational Neuroscience Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
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Schindlbeck KA, Lucas-Jiménez O, Tang CC, Morbelli S, Arnaldi D, Pardini M, Pagani M, Ibarretxe-Bilbao N, Ojeda N, Nobili F, Eidelberg D. Metabolic Network Abnormalities in Drug-Naïve Parkinson's Disease. Mov Disord 2019; 35:587-594. [PMID: 31872507 DOI: 10.1002/mds.27960] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 11/26/2019] [Accepted: 12/02/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND An ideal imaging biomarker for a neurodegenerative disorder should be able to measure abnormalities in the earliest stages of the disease. OBJECTIVE We investigated metabolic network changes in two independent cohorts of drug-naïve Parkinson's disease (PD) patients who have not been exposed to dopaminergic medication. METHODS We scanned 85 de novo, drug-naïve PD patients and 85 age-matched healthy control subjects from Italy (n = 96) and the United States (n = 74) with [18 F]-fluorodeoxyglucose PET. All patients had clinical follow-ups to verify the diagnosis of idiopathic PD. Spatial covariance analysis was used to identify and validate de novo PD-related metabolic patterns in the Italian and U.S. cohorts. We compared the de novo PD-related metabolic patterns to the original PD-related pattern that was identified in more advanced patients who had been on chronic dopaminergic treatment. RESULTS De novo PD-related metabolic patterns were identified in each of the two independent cohorts of drug-naïve PD patients, and each differentiated PD patients from healthy control subjects. Expression values for these disease patterns were elevated in drug-naïve PD patients relative to healthy controls in the identification as well as in each of the validation subgroups. The two de novo PD-related metabolic patterns were topographically very similar to each other and to the original PD-related pattern. CONCLUSIONS Reproducible PD-related patterns are expressed in de novo, drug-naïve PD patients. In PD, disease-related metabolic patterns have stereotyped topographies that develop independently of chronic levodopa treatment. © 2019 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Katharina A Schindlbeck
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Olaia Lucas-Jiménez
- Department of Methods and Experimental Psychology, Faculty of Psychology and Education, University of Deusto, Bilbao, Spain
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Department of Health Science (DISSAL), University of Genoa, Genoa, Italy
| | - Dario Arnaldi
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother-Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Matteo Pardini
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother-Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Marco Pagani
- Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy.,Department of Nuclear Medicine, Karolinska Hospital, Stockholm, Sweden
| | - Naroa Ibarretxe-Bilbao
- Department of Methods and Experimental Psychology, Faculty of Psychology and Education, University of Deusto, Bilbao, Spain
| | - Natalia Ojeda
- Department of Methods and Experimental Psychology, Faculty of Psychology and Education, University of Deusto, Bilbao, Spain
| | - Flavio Nobili
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother-Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
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Wei H, Zhou Y, Zhao J, Zhan L. Risk Factors and Metabolism of Different Brain Regions by Positron Emission Tomography in Parkinson Disease with Disabling Dyskinesia. Curr Neurovasc Res 2019; 16:310-320. [PMID: 31622205 DOI: 10.2174/1567202616666191009102112] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 07/08/2019] [Accepted: 07/25/2019] [Indexed: 11/22/2022]
Abstract
Objective:Dyskinesia is the most common motor complication in advanced Parkinson’s Disease (PD) and has a severe impact on daily life. But the mechanism of dyskinesia is still poorly understood. This study aims to explore risk factors for disabling dyskinesia in PD and further analyze the Vesicular Monoamine Transporter 2 (VMAT2) distribution (labeled with 18F-AV133) in the corpus striatum and the 18F-fluorodeoxyglucose (18F-FDG) metabolism of different brain regions by PET-CT.Methods:This is a cross-sectional study involving 135 PD patients. They were divided into disabling dyskinesia group (DD group, N=22) and non-dyskinesia group (ND group, N=113). All the patients were agreed to undergo PET-CT scans. Clinical data were analyzed between two groups by using multivariate logistic regression analysis, and risk factors for disabling dyskinesia were then determined. The standard uptake value ratios (SUVr) of 18F-AV133 in the corpus striatum and the 18F-FDG metabolism of different brain regions were identified and calculated by the software.Results:6.3% patients have disabling dyskinesia. DD group were more likely to have longer Disease Duration, higher Hoehn-Yahr degree, more severe clinic symptoms, more frequent sleep behavior disorder, and higher levodopa dose equivalency than ND group (P < 0.05). After adjusting confounding factors by multivariate logistic regression, DD group had longer PD duration and high levodopa dose equivalency compared with ND group (P < 0.05). There is no significant difference between the VMAT2 distribution (labeled with 18F- AV133) in the putamen and caudate between two groups. And the 18F-FDG metabolic changes in cortical and subcortical regions did not show a significant difference between the two groups either (P > 0.05).Conclusion:Long PD duration and high levodopa dose equivalency were two independent risk factors for disabling dyskinesia in PD patients. Compared to non-dyskinesia PD patients, there was no significant dopamine decline of the nigrostriatal system in disabling dyskinesia PD patients. Activities of different brain regions were not different between the two groups by 18F-FDG PETCT.
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Affiliation(s)
- Huan Wei
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yongtao Zhou
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Junwu Zhao
- Department of Neurology, Weihai Municipal Hospital, Shandong, China
| | - Liping Zhan
- Department of Neurology, The Affiliated Yan'an Hospital of Kunming Medical University, Kunming, China
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Murakami N, Sako W, Haji S, Furukawa T, Otomi Y, Otsuka H, Izumi Y, Harada M, Kaji R. Differences in cerebellar perfusion between Parkinson's disease and multiple system atrophy. J Neurol Sci 2019; 409:116627. [PMID: 31865188 DOI: 10.1016/j.jns.2019.116627] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 10/25/2019] [Accepted: 12/10/2019] [Indexed: 10/25/2022]
Abstract
INTRODUCTION Objective biomarkers are required for differential diagnosis of Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). OBJECTIVE We aimed to determine if cerebellar blood flow, measured using N-isopropyl-[123I] p-iodoamphetamine single photon emission computed tomography (123I -IMP-SPECT), was useful for differentiating between PD, MSA and PSP. METHODS Twenty-four patients with PD, seventeen patients with MSA with predominant parkinsonian features (MSA-P), sixteenth patients with MSA with predominant cerebellar ataxia (MSA-C) and eight patients with PSP were enrolled. Twenty-seven normal controls' data were used for the calculation of z score. All patients underwent 123I -IMP-SPECT, and data were analyzed using a three-dimensional-stereotactic surface projection program. RESULTS Cerebellar perfusion in MSA-P (MSA-P vs PD, P = .002; MSA-P vs PSP, P < .001) and MSA-C (MSA-C vs PD, P < .001; MSA-C vs PSP, P < .001) were significantly decreased compared with PD or PSP. There was no significant difference in perfusion between PD and PSP groups (P = .061). The area under the receiver operating characteristic curve for cerebellar perfusion between MSA-P and PD was 0.858. CONCLUSION Our findings revealed that cerebellar perfusion by 123I-IMP-SPECT was useful for differentiating between PD and MSA-P.
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Affiliation(s)
- Nagahisa Murakami
- Department of Clinical Neuroscience, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan
| | - Wataru Sako
- Department of Clinical Neuroscience, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan.
| | - Shotaro Haji
- Department of Clinical Neuroscience, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan
| | - Takahiro Furukawa
- Department of Clinical Neuroscience, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan
| | - Yoichi Otomi
- Department of Radiology, Tokushima University Hospital, Tokushima, Japan
| | - Hideki Otsuka
- Department of Medical Imaging/Nuclear Medicine, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan
| | - Yuishin Izumi
- Department of Clinical Neuroscience, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan
| | - Masafumi Harada
- Department of Radiology, Tokushima University Hospital, Tokushima, Japan
| | - Ryuji Kaji
- Department of Clinical Neuroscience, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan
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Schindlbeck KA, Vo A, Nguyen N, Tang CC, Niethammer M, Dhawan V, Brandt V, Saunders-Pullman R, Bressman SB, Eidelberg D. LRRK2 and GBA Variants Exert Distinct Influences on Parkinson's Disease-Specific Metabolic Networks. Cereb Cortex 2019; 30:2867-2878. [PMID: 31813991 DOI: 10.1093/cercor/bhz280] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 08/23/2019] [Accepted: 09/17/2019] [Indexed: 12/11/2022] Open
Abstract
The natural history of idiopathic Parkinson's disease (PD) varies considerably across patients. While PD is generally sporadic, there are known genetic influences: the two most common, mutations in the LRRK2 or GBA1 gene, are associated with slower and more aggressive progression, respectively. Here, we applied graph theory to metabolic brain imaging to understand the effects of genotype on the organization of previously established PD-specific networks. We found that closely matched PD patient groups with the LRRK2-G2019S mutation (PD-LRRK2) or GBA1 variants (PD-GBA) expressed the same disease networks as sporadic disease (sPD), but PD-LRRK2 and PD-GBA patients exhibited abnormal increases in network connectivity that were not present in sPD. Using a community detection strategy, we found that the location and modular distribution of these connections differed strikingly across genotypes. In PD-LRRK2, connections were gained within the network core, with the formation of distinct functional pathways linking the cerebellum and putamen. In PD-GBA, by contrast, the majority of functional connections were formed outside the core, involving corticocortical pathways at the network periphery. Strategically localized connections within the core in PD-LRRK2 may maintain PD network activity at lower levels than in PD-GBA, resulting in a less aggressive clinical course.
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Affiliation(s)
- Katharina A Schindlbeck
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Martin Niethammer
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Vicky Brandt
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Rachel Saunders-Pullman
- Department of Neurology, Mount Sinai Beth Israel, Mount Sinai Hospital, New York, NY 10003, USA
| | - Susan B Bressman
- Department of Neurology, Mount Sinai Beth Israel, Mount Sinai Hospital, New York, NY 10003, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
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Wu Y, Jiang JH, Chen L, Lu JY, Ge JJ, Liu FT, Yu JT, Lin W, Zuo CT, Wang J. Use of radiomic features and support vector machine to distinguish Parkinson's disease cases from normal controls. ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:773. [PMID: 32042789 DOI: 10.21037/atm.2019.11.26] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background Parkinson's disease (PD) is an irreversible neurodegenerative disease. The diagnosis of PD based on neuroimaging is usually with low-level or deep learning features, which results in difficulties in achieving precision classification or interpreting the clinical significance. Herein, we aimed to extract high-order features by using radiomics approach and achieve acceptable diagnosis accuracy in PD. Methods In this retrospective multicohort study, we collected 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images and clinical scale [the Unified Parkinson's Disease Rating Scale (UPDRS) and Hoehn & Yahr scale (H&Y)] from two cohorts. One cohort from Huashan Hospital had 91 normal controls (NC) and 91 PD patients (UPDRS: 22.7±11.7, H&Y: 1.8±0.8), and the other cohort from Wuxi 904 Hospital had 26 NC and 22 PD patients (UPDRS: 20.9±11.6, H&Y: 1.7±0.9). The Huashan cohort was used as the training and test sets by 5-fold cross-validation and the Wuxi cohort was used as another separate test set. After identifying regions of interests (ROIs) based on the atlas-based method, radiomic features were extracted and selected by using autocorrelation and fisher score algorithm. A support vector machine (SVM) was trained to classify PD and NC based on selected radiomic features. In the comparative experiment, we compared our method with the traditional voxel values method. To guarantee the robustness, above processes were repeated in 500 times. Results Twenty-six brain ROIs were identified. Six thousand one hundred and ten radiomic features were extracted in total. Among them 30 features were remained after feature selection. The accuracies of the proposed method achieved 90.97%±4.66% and 88.08%±5.27% in Huashan and Wuxi test sets, respectively. Conclusions This study showed that radiomic features and SVM could be used to distinguish between PD and NC based on 18F-FDG PET images.
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Affiliation(s)
- Yue Wu
- Department of Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
| | - Jie-Hui Jiang
- Department of Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
| | - Li Chen
- Department of Medical Ultrasound, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jia-Ying Lu
- Department of PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jing-Jie Ge
- Department of PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Feng-Tao Liu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jin-Tai Yu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Wei Lin
- Department of Neurosurgery, 904 Hospital of PLA, Anhui Medical University, Wuxi 214000, China
| | - Chuan-Tao Zuo
- Department of PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jian Wang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
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Abnormal pattern of brain glucose metabolism in Parkinson's disease: replication in three European cohorts. Eur J Nucl Med Mol Imaging 2019; 47:437-450. [PMID: 31768600 PMCID: PMC6974499 DOI: 10.1007/s00259-019-04570-7] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 10/03/2019] [Indexed: 12/15/2022]
Abstract
Rationale In Parkinson’s disease (PD), spatial covariance analysis of 18F-FDG PET data has consistently revealed a characteristic PD-related brain pattern (PDRP). By quantifying PDRP expression on a scan-by-scan basis, this technique allows objective assessment of disease activity in individual subjects. We provide a further validation of the PDRP by applying spatial covariance analysis to PD cohorts from the Netherlands (NL), Italy (IT), and Spain (SP). Methods The PDRPNL was previously identified (17 controls, 19 PD) and its expression was determined in 19 healthy controls and 20 PD patients from the Netherlands. The PDRPIT was identified in 20 controls and 20 “de-novo” PD patients from an Italian cohort. A further 24 controls and 18 “de-novo” Italian patients were used for validation. The PDRPSP was identified in 19 controls and 19 PD patients from a Spanish cohort with late-stage PD. Thirty Spanish PD patients were used for validation. Patterns of the three centers were visually compared and then cross-validated. Furthermore, PDRP expression was determined in 8 patients with multiple system atrophy. Results A PDRP could be identified in each cohort. Each PDRP was characterized by relative hypermetabolism in the thalamus, putamen/pallidum, pons, cerebellum, and motor cortex. These changes co-varied with variable degrees of hypometabolism in posterior parietal, occipital, and frontal cortices. Frontal hypometabolism was less pronounced in “de-novo” PD subjects (Italian cohort). Occipital hypometabolism was more pronounced in late-stage PD subjects (Spanish cohort). PDRPIT, PDRPNL, and PDRPSP were significantly expressed in PD patients compared with controls in validation cohorts from the same center (P < 0.0001), and maintained significance on cross-validation (P < 0.005). PDRP expression was absent in MSA. Conclusion The PDRP is a reproducible disease characteristic across PD populations and scanning platforms globally. Further study is needed to identify the topography of specific PD subtypes, and to identify and correct for center-specific effects. Electronic supplementary material The online version of this article (10.1007/s00259-019-04570-7) contains supplementary material, which is available to authorized users.
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Ko JH, Spetsieris PG, Eidelberg D. Network Structure and Function in Parkinson's Disease. Cereb Cortex 2019; 28:4121-4135. [PMID: 29088324 DOI: 10.1093/cercor/bhx267] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Little is known of the structural and functional properties of abnormal brain networks associated with neurological disorders. We used a social network approach to characterize the properties of the Parkinson's disease (PD) metabolic topography in 4 independent patient samples and in an experimental non-human primate model. The PD network exhibited distinct features. Dense, mutually facilitating functional connections linked the putamen, globus pallidus, and thalamus to form a metabolically active core. The periphery was formed by weaker connections linking less active cortical regions. Notably, the network contained a separate module defined by interconnected, metabolically active nodes in the cerebellum, pons, frontal cortex, and limbic regions. Exaggeration of the small-world property was a consistent feature of disease networks in parkinsonian humans and in the non-human primate model; this abnormality was only partly corrected by dopaminergic treatment. The findings point to disease-related alterations in network structure and function as the basis for faulty information processing in this disorder.
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Affiliation(s)
- Ji Hyun Ko
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Phoebe G Spetsieris
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA.,Department of Neurology, Northwell Health, Manhasset, NY, USA
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