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Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson's disease. NPJ Parkinsons Dis 2022; 8:13. [PMID: 35064123 PMCID: PMC8783003 DOI: 10.1038/s41531-021-00266-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 12/10/2021] [Indexed: 12/14/2022] Open
Abstract
Parkinson’s disease (PD) is a common, progressive, and currently incurable neurodegenerative movement disorder. The diagnosis of PD is challenging, especially in the differential diagnosis of parkinsonism and in early PD detection. Due to the advantages of machine learning such as learning complex data patterns and making inferences for individuals, machine-learning techniques have been increasingly applied to the diagnosis of PD, and have shown some promising results. Machine-learning-based imaging applications have made it possible to help differentiate parkinsonism and detect PD at early stages automatically in a number of neuroimaging studies. Comparative studies have shown that machine-learning-based SPECT image analysis applications in PD have outperformed conventional semi-quantitative analysis in detecting PD-associated dopaminergic degeneration, performed comparably well as experts’ visual inspection, and helped improve PD diagnostic accuracy of radiologists. Using combined multi-modal (imaging and clinical) data in these applications may further enhance PD diagnosis and early detection. To integrate machine-learning-based diagnostic applications into clinical systems, further validation and optimization of these applications are needed to make them accurate and reliable. It is anticipated that machine-learning techniques will further help improve differential diagnosis of parkinsonism and early detection of PD, which may reduce the error rate of PD diagnosis and help detect PD at pre-motor stage to make it possible for early treatments (e.g., neuroprotective treatment) to slow down PD progression, prevent severe motor symptoms from emerging, and relieve patients from suffering.
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Fan Y, Han J, Zhao L, Wu C, Wu P, Huang Z, Hao X, Ji Y, Chen D, Zhu M. Experimental Models of Cognitive Impairment for Use in Parkinson's Disease Research: The Distance Between Reality and Ideal. Front Aging Neurosci 2021; 13:745438. [PMID: 34912207 PMCID: PMC8667076 DOI: 10.3389/fnagi.2021.745438] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/01/2021] [Indexed: 12/14/2022] Open
Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disease. Cognitive impairment is one of the key non-motor symptoms of PD, affecting both mortality and quality of life. However, there are few experimental studies on the pathology and treatments of PD with mild cognitive impairment (PD-MCI) and PD dementia (PDD) due to the lack of representative models. To identify new strategies for developing representative models, we systematically summarized previous studies on PD-MCI and PDD and compared differences between existing models and diseases. Our initial search identified 5432 articles, of which 738 were duplicates. A total of 227 articles met our inclusion criteria and were included in the analysis. Models fell into three categories based on model design: neurotoxin-induced, transgenic, and combined. Although the neurotoxin-induced experimental model was the most common type that was used during every time period, transgenic and combined experimental models have gained significant recent attention. Unfortunately, there remains a big gap between ideal and actual experimental models. While each model has its own disadvantages, there have been tremendous advances in the development of PD models of cognitive impairment, and almost every model can verify a hypothesis about PD-MCI or PDD. Finally, our proposed strategies for developing novel models are as follows: a set of plans that integrate symptoms, biochemistry, neuroimaging, and other objective indicators to judge and identify that the novel model plays a key role in new strategies for developing representative models; novel models should simulate different clinical features of PD-MCI or PDD; inducible α-Syn overexpression and SH-SY5Y-A53T cellular models are good candidate models of PD-MCI or PDD.
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Affiliation(s)
- Yaohua Fan
- Traditional Chinese Medicine Innovation Research Center, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Jiajun Han
- Traditional Chinese Medicine Innovation Research Center, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Lijun Zhao
- Traditional Chinese Medicine Innovation Research Center, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Chunxiao Wu
- Traditional Chinese Medicine Innovation Research Center, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China.,Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Peipei Wu
- Traditional Chinese Medicine Innovation Research Center, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Zifeng Huang
- Traditional Chinese Medicine Innovation Research Center, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Xiaoqian Hao
- Traditional Chinese Medicine Innovation Research Center, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - YiChun Ji
- Shenzhen Bao'an Traditional Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Dongfeng Chen
- Traditional Chinese Medicine Innovation Research Center, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Meiling Zhu
- Guangzhou University of Chinese Medicine, Guangzhou, China
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3
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Vidal B, Levigoureux E, Chaib S, Bouillot C, Billard T, Newman-Tancredi A, Zimmer L. Different Alterations of Agonist and Antagonist Binding to 5-HT1A Receptor in a Rat Model of Parkinson’s Disease and Levodopa-Induced Dyskinesia: A MicroPET Study. JOURNAL OF PARKINSONS DISEASE 2021; 11:1257-1269. [DOI: 10.3233/jpd-212580] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background: The gold-standard treatment for Parkinson’s disease is L-DOPA, which in the long term often leads to levodopa-induced dyskinesia. Serotonergic neurons are partially responsible for this, by converting L-DOPA into dopamine leading to its uncontrolled release as a “false neurotransmitter”. The stimulation of 5-HT1A receptors can reduce involuntary movements but this mechanism is poorly understood. Objective: This study aimed to investigate the functionality of 5-HT1A receptors using positron emission tomography in hemiparkinsonian rats with or without dyskinesia induced by 3-weeks daily treatment with L-DOPA. Imaging sessions were performed “off” L-DOPA. Methods: Each rat underwent a positron emission tomography scan with [18F]F13640, a 5-HT1AR agonist which labels receptors in a high affinity state for agonists, or with [18F]MPPF, a 5-HT1AR antagonist which labels all the receptors. Results: There were decreases of [18F]MPPF binding in hemiparkinsonian rats in cortical areas. In dyskinetic animals, changes were slighter but also found in other regions. In hemiparkinsonian rats, [18F]F13640 uptake was decreased bilaterally in the globus pallidus and thalamus. On the non-lesioned side, binding was increased in the insula, the hippocampus and the amygdala. In dyskinetic animals, [18F]F13640 binding was strongly increased in cortical and limbic areas, especially in the non-lesioned side. Conclusion: These data suggest that agonist and antagonist 5-HT1A receptor-binding sites are differently modified in Parkinson’s disease and levodopa-induced dyskinesia. In particular, these observations suggest a substantial involvement of the functional state of 5-HT1AR in levodopa-induced dyskinesia and emphasize the need to characterize this state using agonist radiotracers in physiological and pathological conditions.
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Affiliation(s)
- Benjamin Vidal
- Lyon Neuroscience Research Center, Université de Lyon, Université Claude Bernard Lyon 1, CNRS, INSERM, Lyon, France
| | - Elise Levigoureux
- Lyon Neuroscience Research Center, Université de Lyon, Université Claude Bernard Lyon 1, CNRS, INSERM, Lyon, France
- Hospices Civils de Lyon, Lyon, France
| | - Sarah Chaib
- Lyon Neuroscience Research Center, Université de Lyon, Université Claude Bernard Lyon 1, CNRS, INSERM, Lyon, France
- Hospices Civils de Lyon, Lyon, France
| | | | - Thierry Billard
- CERMEP-Imaging Platform, Bron, France
- Institute of Chemistry and Biochemistry, Université de Lyon, CNRS, Villeurbanne, France
| | | | - Luc Zimmer
- Lyon Neuroscience Research Center, Université de Lyon, Université Claude Bernard Lyon 1, CNRS, INSERM, Lyon, France
- Hospices Civils de Lyon, Lyon, France
- CERMEP-Imaging Platform, Bron, France
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4
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Dorsey ER, Omberg L, Waddell E, Adams JL, Adams R, Ali MR, Amodeo K, Arky A, Augustine EF, Dinesh K, Hoque ME, Glidden AM, Jensen-Roberts S, Kabelac Z, Katabi D, Kieburtz K, Kinel DR, Little MA, Lizarraga KJ, Myers T, Riggare S, Rosero SZ, Saria S, Schifitto G, Schneider RB, Sharma G, Shoulson I, Stevenson EA, Tarolli CG, Luo J, McDermott MP. Deep Phenotyping of Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2020; 10:855-873. [PMID: 32444562 PMCID: PMC7458535 DOI: 10.3233/jpd-202006] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/01/2020] [Indexed: 12/13/2022]
Abstract
Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.
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Affiliation(s)
- E. Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Emma Waddell
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Jamie L. Adams
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Roy Adams
- Machine Learning, AI and Healthcare Lab, Johns Hopkins University, Baltimore, MD, USA
| | | | - Katherine Amodeo
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Abigail Arky
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Erika F. Augustine
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Karthik Dinesh
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
| | | | - Alistair M. Glidden
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Stella Jensen-Roberts
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Zachary Kabelac
- Department of Computer Science and Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Dina Katabi
- Department of Computer Science and Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Karl Kieburtz
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Daniel R. Kinel
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Max A. Little
- School of Computer Science, University of Birmingham, UK
- Massachusetts Institute of Technology, MA, USA
| | - Karlo J. Lizarraga
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Taylor Myers
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Sara Riggare
- Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden
| | | | - Suchi Saria
- Machine Learning, AI and Healthcare Lab, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Statistics, and Health Policy, Johns Hopkins University, MD, USA
| | - Giovanni Schifitto
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Ruth B. Schneider
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Gaurav Sharma
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Ira Shoulson
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
- Grey Matter Technologies, Sarasota, FL, USA
| | - E. Anna Stevenson
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Christopher G. Tarolli
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Jiebo Luo
- Department of Computer Science, University of Rochester, Rochester, NY, USA
| | - Michael P. McDermott
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
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Shen T, Jiang J, Lin W, Ge J, Wu P, Zhou Y, Zuo C, Wang J, Yan Z, Shi K. Use of Overlapping Group LASSO Sparse Deep Belief Network to Discriminate Parkinson's Disease and Normal Control. Front Neurosci 2019; 13:396. [PMID: 31110472 PMCID: PMC6501727 DOI: 10.3389/fnins.2019.00396] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 04/08/2019] [Indexed: 12/31/2022] Open
Abstract
As a medical imaging technology which can show the metabolism of the brain, 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) is of great value for the diagnosis of Parkinson's Disease (PD). With the development of pattern recognition technology, analysis of brain images using deep learning are becoming more and more popular. However, existing computer-aided-diagnosis technologies often over fit and have poor generalizability. Therefore, we aimed to improve a framework based on Group Lasso Sparse Deep Belief Network (GLS-DBN) for discriminating PD and normal control (NC) subjects based on FDG-PET imaging. In this study, 225 NC and 125 PD cohorts from Huashan and Wuxi 904 hospitals were selected. They were divided into the training & validation dataset and 2 test datasets. First, in the training & validation set, subjects were randomly partitioned 80:20, with multiple training iterations for the deep learning model. Next, Locally Linear Embedding was used as a dimension reduction algorithm. Then, GLS-DBN was used for feature learning and classification. Different sparse DBN models were used to compare datasets to evaluate the effectiveness of our framework. Accuracy, sensitivity, and specificity were examined to validate the results. Output variables of the network were also correlated with longitudinal changes of rating scales about movement disorders (UPDRS, H&Y). As a result, accuracy of prediction (90% in Test 1, 86% in Test 2) for classification of PD and NC patients outperformed conventional approaches. Output scores of the network were strongly correlated with UPDRS and H&Y (R = 0.705, p < 0.001; R = 0.697, p < 0.001 in Test 1; R = 0.592, p = 0.0018, R = 0.528, p = 0.0067 in Test 2). These results show the GLS-DBN is feasible method for early diagnosis of PD.
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Affiliation(s)
- Ting Shen
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.,Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai, China
| | - Wei Lin
- Department of Neurosurgery, 904 Hospital of PLA, Anhui Medical University, Wuxi, China
| | - Jingjie Ge
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yongjin Zhou
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China
| | - Jian Wang
- Department of neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhuangzhi Yan
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Kuangyu Shi
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland.,Department of Nuclear Medicine, Technische Universitat Munchen, Munich, Germany
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Wilson H, Pagano G, Politis M. Dementia spectrum disorders: lessons learnt from decades with PET research. J Neural Transm (Vienna) 2019; 126:233-251. [PMID: 30762136 PMCID: PMC6449308 DOI: 10.1007/s00702-019-01975-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 01/21/2019] [Indexed: 02/07/2023]
Abstract
The dementia spectrum encompasses a range of disorders with complex diagnosis, pathophysiology and limited treatment options. Positron emission tomography (PET) imaging provides insights into specific neurodegenerative processes underlying dementia disorders in vivo. Here we focus on some of the most common dementias: Alzheimer's disease, Parkinsonism dementias including Parkinson's disease with dementia, dementia with Lewy bodies, progressive supranuclear palsy and corticobasal syndrome, and frontotemporal lobe degeneration. PET tracers have been developed to target specific proteinopathies (amyloid, tau and α-synuclein), glucose metabolism, cholinergic system and neuroinflammation. Studies have shown distinct imaging abnormalities can be detected early, in some cases prior to symptom onset, allowing disease progression to be monitored and providing the potential to predict symptom onset. Furthermore, advances in PET imaging have identified potential therapeutic targets and novel methods to accurately discriminate between different types of dementias in vivo. There are promising imaging markers with a clinical application on the horizon, however, further studies are required before they can be implantation into clinical practice.
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Affiliation(s)
- Heather Wilson
- Neurodegeneration Imaging Group, Maurice Wohl Clinical Neuroscience Institute, 125 Coldharbour Lane, Camberwell, London, SE5 9NU, UK
| | - Gennaro Pagano
- Neurodegeneration Imaging Group, Maurice Wohl Clinical Neuroscience Institute, 125 Coldharbour Lane, Camberwell, London, SE5 9NU, UK
| | - Marios Politis
- Neurodegeneration Imaging Group, Maurice Wohl Clinical Neuroscience Institute, 125 Coldharbour Lane, Camberwell, London, SE5 9NU, UK.
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Abstract
Even before the success of combined positron emission tomography and computed tomography (PET/CT), the neuroimaging community was conceiving the idea to integrate the positron emission tomography (PET), with very high molecular quantitative data but low spatial resolution, and magnetic resonance imaging (MRI), with high spatial resolution. Several technical limitations have delayed the use of a hybrid scanner in neuroimaging studies, including the full integration of the PET detector ring within the MRI system, the optimization of data acquisition, and the implementation of reliable methods for PET attenuation, motion correction, and joint image reconstruction. To be valid and useful in clinical and research settings, this instrument should be able to simultaneously acquire PET and MRI, and generate quantitative parametric PET images comparable to PET-CT. While post hoc co-registration of combined PET and MRI data acquired separately became the most reliable technique for the generation of "fused" PET-MRI images, only hybrid PET-MRI approach allows merging these measurements naturally and correlating them in a temporal manner. Furthermore, hybrid PET-MRI represents the most accurate tool to investigate in vivo the interplay between molecular and functional aspects of brain pathophysiology. Hybrid PET-MRI technology is still in the early stages in the movement disorders field, due to the limited availability of scanners with integrated optimized methodological models. This technology is ideally suited to investigate interactions between resting-state functional/arterial spin labeling MRI and [18F]FDG PET glucose metabolism in the evaluation of the brain "hubs" particularly vulnerable to neurodegeneration, areas with a high degree of connectivity and associated with an efficient synaptic neurotransmission. In Parkinson's disease, hybrid PET-MRI is also the ideal instrument to deeper explore the relationship between resting-state functional MRI and dopamine release at [11C]raclopride PET challenge, in the identification of early drug-naïve Parkinson's disease patients at higher risk of motor complications and in the evaluation of the efficacy of novel neuroprotective treatment able to restore at the same time the altered resting state and the release of dopamine. In this chapter, we discuss the key methodological aspects of hybrid PET-MRI; the evidence in movement disorders of the key resting-state functional and perfusion MRI; [18F]FDG PET and [11C]raclopride PET challenge studies; the potential advantages of using hybrid PET-MRI to investigate the pathophysiology of movement disorders and neurodegenerative diseases. Future directions of hybrid PET-MRI will be discussed alongside with up-to-date technological innovations on hybrid systems.
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de Natale ER, Wilson H, Pagano G, Politis M. Imaging Transplantation in Movement Disorders. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2018; 143:213-263. [PMID: 30473196 DOI: 10.1016/bs.irn.2018.10.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Cell replacement therapy with graft transplantation has been tested as a disease-modifying treatment in neurodegenerative diseases characterized by the damage of a predominant cell type, such as substantia nigra dopaminergic neurons in Parkinson's disease (PD) or striatal medium spiny projection neurons in Huntington's disease (HD). The results of these trials are mixed with success in preclinical and pilot open-label trials, which were not consistently reproduced in randomized controlled trials. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) molecular imaging and functional magnetic resonance imaging allow the graft survival, and its relationship with the host tissues to be studied in vivo. In PD, PET with [18F]DOPA showed that graft survival does not necessarily correlate with the clinical improvement and PD patients with worse outcome had lower binding in the ventral striatum and a high serotonin ([11C]DASB PET) to dopamine ([18F]DOPA PET) ratio in the grafted neurons. In HD, PET with [11C]PK11195 showed the graft survival and the clinical responses may be related to the reactive activation of the host inflammatory/immune system. Findings from these studies have been used to refine study protocols and patient selection in current clinical trials, which includes identifying suitable candidates for transplantation using imaging markers and employing multiple and/or novel PET tracers to better assess graft functions and inflammatory responses to grafts.
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Affiliation(s)
- Edoardo Rosario de Natale
- Neurodegeneration Imaging Group, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, United Kingdom
| | - Heather Wilson
- Neurodegeneration Imaging Group, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, United Kingdom
| | - Gennaro Pagano
- Neurodegeneration Imaging Group, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, United Kingdom
| | - Marios Politis
- Neurodegeneration Imaging Group, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, United Kingdom.
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Abstract
Positron emission tomography (PET) has revealed key insights into the pathophysiology of movement disorders. This paper will focus on how PET investigations of pathophysiology are particularly relevant to Parkinson disease, a neurodegenerative condition usually starting later in life marked by a varying combination of motor and nonmotor deficits. Various molecular imaging modalities help to determine what changes in brain herald the onset of pathology; can these changes be used to identify presymptomatic individuals who may be appropriate for to-be-developed treatments that may forestall onset of symptoms or slow disease progression; can PET act as a biomarker of disease progression; can molecular imaging help enrich homogenous cohorts for clinical studies; and what other pathophysiologic mechanisms relate to nonmotor manifestations. PET methods include measurements of regional cerebral glucose metabolism and blood flow, selected receptors, specific neurotransmitter systems, postsynaptic signal transducers, and abnormal protein deposition. We will review each of these methodologies and how they are relevant to important clinical issues pertaining to Parkinson disease.
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Affiliation(s)
- Baijayanta Maiti
- Department of Neurology, Washington University in St. Louis, St Louis, MO.
| | - Joel S Perlmutter
- Department of Neurology, Washington University in St. Louis, St Louis, MO; Department of Radiology, Washington University in St. Louis, St Louis, MO; Department of Neuroscience, Washington University in St. Louis, St Louis, MO; Department of Physical Therapy, Washington University in St. Louis, St Louis, MO; Department of Occupational Therapy, Washington University in St. Louis, St Louis, MO
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de Natale ER, Niccolini F, Wilson H, Politis M. Molecular Imaging of the Dopaminergic System in Idiopathic Parkinson's Disease. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2018; 141:131-172. [DOI: 10.1016/bs.irn.2018.08.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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11
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Molecular Imaging of the Serotonergic System in Parkinson's Disease. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2018; 141:173-210. [DOI: 10.1016/bs.irn.2018.08.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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12
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Changes of brain structure in Parkinson’s disease patients with mild cognitive impairment analyzed via VBM technology. Neurosci Lett 2017; 658:121-132. [DOI: 10.1016/j.neulet.2017.08.028] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 08/08/2017] [Accepted: 08/11/2017] [Indexed: 12/30/2022]
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