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Feng H, Tu N, Wang K, Ma X, Zhang Z, Liu Z, Cheng Z, Bu L. Neuromelanin-targeted 18 F-P3BZA PET/MR imaging of the substantia nigra in rhesus macaques. EJNMMI Res 2024; 14:79. [PMID: 39225971 PMCID: PMC11372002 DOI: 10.1186/s13550-024-01136-z] [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: 01/30/2024] [Accepted: 08/02/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Neuromelanin is mostly located in dopaminergic neurons in the substantia nigra (SN) pars compacta, and can be detected by magnetic resonance imaging (MRI). It is a promising imaging-base biomarker for neurological diseases. We previously developed a melanin-specific probe N-(2-(diethylamino)-ethyl)-18F-5-fluoropicolinamide (18F-P3BZA), which was initially developed for the imaging of melanoma. 18F-P3BZA exhibited high levels of binding to the melanin in vitro and in vivo with high retention and favorable pharmacokinetics. In this study we further investigated whether 18F-P3BZA could be used to quantitatively detect neuromelanin in the SN in healthy rhesus macaques. RESULTS 18F-P3BZA exhibited desired hydrophobicity with estimated log Know 5.08 and log D7.4 1.68. 18F-P3BZA readily crossed the blood-brain barrier with brain transport coefficients (Kin) of 40 ± 8 µL g-1s-1. 18F-P3BZA accumulated specifically in neuromelanotic PC12 cells, melanin-rich melanoma cells, and melanoma xenografts. Binding of 18F-P3BZA to B16F10 cells was much higher than to SKOV3 cells at 60 min (6.17 ± 0.53%IA and 0.24 ± 0.05%IA, respectively). In the biodistribution study, 18F-P3BZA had higher accumulation in B16F10 tumors (6.31 ± 0.99%IA/g) than in SKOV3 tumors (0.25 ± 0.09%IA/g). Meanwhile, 18F-P3BZA uptake in B16F10 tumors could be blocked by excess cold 19F-P3BZA (0.81 ± 0.02%IA/g, 88% inhibition, p < 0.05). PET/MRI 18F-P3BZA provided clear visualization of neuromelanin-rich SN at 30-60 min after injection in healthy macaques. The SN to cerebella ratios were 2.7 and 2.4 times higher at 30 and 60 min after injection. In in vitro autoradiography studies 18F-P3BZA exhibited high levels of binding to the SN, and almost no binding to surrounding midbrain tissues. CONCLUSION 18F-P3BZA PET/MRI clearly images neuromelanin in the SN, and may assist in the early diagnosis of neurological diseases associated with abnormal neuromelanin expression.
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Affiliation(s)
- Hongyan Feng
- PET-CT/MRI Center, Renmin Hospital of Wuhan University, 95Zhangzhidong Road, Wuchang District, Wuhan, 430060, Hubei, China
| | - Ning Tu
- PET-CT/MRI Center, Renmin Hospital of Wuhan University, 95Zhangzhidong Road, Wuchang District, Wuhan, 430060, Hubei, China
| | - Ke Wang
- PET-CT/MRI Center, Renmin Hospital of Wuhan University, 95Zhangzhidong Road, Wuchang District, Wuhan, 430060, Hubei, China
| | - Xiaowei Ma
- Department of Nuclear Medicine, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhentao Zhang
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Zhongchun Liu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Zhen Cheng
- State Key Laboratory of Drug Research, Molecular Imaging Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
| | - Lihong Bu
- PET-CT/MRI Center, Renmin Hospital of Wuhan University, 95Zhangzhidong Road, Wuchang District, Wuhan, 430060, Hubei, China.
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Wengler K, Trujillo P, Cassidy CM, Horga G. Neuromelanin-sensitive MRI for mechanistic research and biomarker development in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01934-y. [PMID: 39160355 DOI: 10.1038/s41386-024-01934-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/21/2024] [Accepted: 07/15/2024] [Indexed: 08/21/2024]
Abstract
Neuromelanin-sensitive MRI is a burgeoning non-invasive neuroimaging method with an increasing number of applications in psychiatric research. This MRI modality is sensitive to the concentration of neuromelanin, which is synthesized from intracellular catecholamines and accumulates in catecholaminergic nuclei including the dopaminergic substantia nigra and the noradrenergic locus coeruleus. Emerging data suggest the utility of neuromelanin-sensitive MRI as a proxy measure for variability in catecholamine metabolism and function, even in the absence of catecholaminergic cell loss. Given the importance of catecholamine function to several psychiatric disorders and their treatments, neuromelanin-sensitive MRI is ideally positioned as an informative and easy-to-acquire catecholaminergic index. In this review paper, we examine basic aspects of neuromelanin and neuromelanin-sensitive MRI and focus on its psychiatric applications in the contexts of mechanistic research and biomarker development. We discuss ongoing debates and state-of-the-art research into the mechanisms of the neuromelanin-sensitive MRI contrast, standardized protocols and optimized analytic approaches, and application of cutting-edge methods such as machine learning and artificial intelligence to enhance the feasibility and predictive power of neuromelanin-sensitive-MRI-based tools. We finally lay out important future directions to allow neuromelanin-sensitive-MRI to fulfill its potential as a key component of the research, and ultimately clinical, toolbox in psychiatry.
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Affiliation(s)
- Kenneth Wengler
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Paula Trujillo
- Department of Neurology, Vanderbilt University Medical Center, Vanderbilt, TN, USA
| | - Clifford M Cassidy
- Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Guillermo Horga
- New York State Psychiatric Institute, New York, NY, USA.
- Department of Psychiatry, Columbia University, New York, NY, USA.
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Angelini L, Paparella G, Bologna M. Distinguishing essential tremor from Parkinson's disease: clinical and experimental tools. Expert Rev Neurother 2024; 24:799-814. [PMID: 39016323 DOI: 10.1080/14737175.2024.2372339] [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: 04/25/2024] [Accepted: 06/20/2024] [Indexed: 07/18/2024]
Abstract
INTRODUCTION Essential tremor (ET) and Parkinson's disease (PD) are the most common causes of tremor and the most prevalent movement disorders, with overlapping clinical features that can lead to diagnostic challenges, especially in the early stages. AREAS COVERED In the present paper, the authors review the clinical and experimental studies and emphasized the major aspects to differentiate between ET and PD, with particular attention to cardinal phenomenological features of these two conditions. Ancillary and experimental techniques, including neurophysiology, neuroimaging, fluid biomarker evaluation, and innovative methods, are also discussed for their role in differential diagnosis between ET and PD. Special attention is given to investigations and tools applicable in the early stages of the diseases, when the differential diagnosis between the two conditions is more challenging. Furthermore, the authors discuss knowledge gaps and unsolved issues in the field. EXPERT OPINION Distinguishing ET and PD is crucial for prognostic purposes and appropriate treatment. Additionally, accurate diagnosis is critical for optimizing clinical and experimental research on pathophysiology and innovative therapies. In a few years, integrated technologies could enable accurate, reliable diagnosis from early disease stages or prodromal stages in at-risk populations, but further research combining different techniques is needed.
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Affiliation(s)
| | - Giulia Paparella
- IRCCS Neuromed, Pozzilli, (IS), Italy
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Matteo Bologna
- IRCCS Neuromed, Pozzilli, (IS), Italy
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
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Yan Y, Zhang M, Ren W, Zheng X, Chang Y. Neuromelanin-sensitive magnetic resonance imaging: Possibilities and promises as an imaging biomarker for Parkinson's disease. Eur J Neurosci 2024; 59:2616-2627. [PMID: 38441250 DOI: 10.1111/ejn.16296] [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: 09/23/2023] [Revised: 02/03/2024] [Accepted: 02/07/2024] [Indexed: 05/22/2024]
Abstract
Parkinson's disease (PD) is an age-related progressive neurodegenerative disorder characterized by both motor and non-motor symptoms resulting from the death of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and noradrenergic neurons in the locus coeruleus (LC). The current diagnosis of PD primarily relies on motor symptoms, often leading to diagnoses in advanced stages, where a significant portion of SNpc dopamine neurons has already succumbed. Therefore, the identification of imaging biomarkers for early-stage PD diagnosis and disease progression monitoring is imperative. Recent studies propose that neuromelanin-sensitive magnetic resonance imaging (NM-MRI) holds promise as an imaging biomarker. In this review, we summarize the latest findings concerning NM-MRI characteristics at various stages in patients with PD and those with atypical parkinsonism. In conclusion, alterations in neuromelanin within the LC are associated with non-motor symptoms and prove to be a reliable imaging biomarker in the prodromal phase of PD. Furthermore, NM-MRI demonstrates efficacy in differentiating progressive supranuclear palsy (PSP) from PD and multiple system atrophy with predominant parkinsonism. The spatial patterns of changes in the SNpc can be indicative of PD progression and aid in distinguishing between PSP and synucleinopathies. We recommend that patients with PD and individuals at risk for PD undergo regular NM-MRI examinations. This technology holds the potential for widespread use in PD diagnosis.
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Affiliation(s)
- Yayun Yan
- Department of Neurology, China-Japan Union Hospital, Jilin University, Changchun, China
| | - Mengchao Zhang
- Department of Radiology, China-Japan Union Hospital, Jilin University, Changchun, China
| | - Wenhua Ren
- Department of Neurology, China-Japan Union Hospital, Jilin University, Changchun, China
| | - Xiaoqi Zheng
- Department of Neurology, China-Japan Union Hospital, Jilin University, Changchun, China
| | - Ying Chang
- Department of Neurology, China-Japan Union Hospital, Jilin University, Changchun, China
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Rios-Urrego CD, Rusz J, Orozco-Arroyave JR. Automatic speech-based assessment to discriminate Parkinson's disease from essential tremor with a cross-language approach. NPJ Digit Med 2024; 7:37. [PMID: 38368458 PMCID: PMC10874421 DOI: 10.1038/s41746-024-01027-6] [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: 07/07/2023] [Accepted: 02/05/2024] [Indexed: 02/19/2024] Open
Abstract
Parkinson's disease (PD) and essential tremor (ET) are prevalent movement disorders that mainly affect elderly people, presenting diagnostic challenges due to shared clinical features. While both disorders exhibit distinct speech patterns-hypokinetic dysarthria in PD and hyperkinetic dysarthria in ET-the efficacy of speech assessment for differentiation remains unexplored. Developing technology for automatic discrimination could enable early diagnosis and continuous monitoring. However, the lack of data for investigating speech behavior in these patients has inhibited the development of a framework for diagnostic support. In addition, phonetic variability across languages poses practical challenges in establishing a universal speech assessment system. Therefore, it is necessary to develop models robust to the phonetic variability present in different languages worldwide. We propose a method based on Gaussian mixture models to assess domain adaptation from models trained in German and Spanish to classify PD and ET patients in Czech. We modeled three different speech dimensions: articulation, phonation, and prosody and evaluated the models' performance in both bi-class and tri-class classification scenarios (with the addition of healthy controls). Our results show that a fusion of the three speech dimensions achieved optimal results in binary classification, with accuracies up to 81.4 and 86.2% for monologue and /pa-ta-ka/ tasks, respectively. In tri-class scenarios, incorporating healthy speech signals resulted in accuracies of 63.3 and 71.6% for monologue and /pa-ta-ka/ tasks, respectively. Our findings suggest that automated speech analysis, combined with machine learning is robust, accurate, and can be adapted to different languages to distinguish between PD and ET patients.
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Affiliation(s)
| | - Jan Rusz
- Department of Circuit Theory, Czech Technical University in Prague, Prague, Czech Republic.
| | - Juan Rafael Orozco-Arroyave
- GITA Lab, Faculty of Engineering, University of Antioquia, Medellín, Colombia.
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
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Trujillo P, Aumann MA, Claassen DO. Neuromelanin-sensitive MRI as a promising biomarker of catecholamine function. Brain 2024; 147:337-351. [PMID: 37669320 PMCID: PMC10834262 DOI: 10.1093/brain/awad300] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/17/2023] [Accepted: 08/20/2023] [Indexed: 09/07/2023] Open
Abstract
Disruptions to dopamine and noradrenergic neurotransmission are noted in several neurodegenerative and psychiatric disorders. Neuromelanin-sensitive (NM)-MRI offers a non-invasive approach to visualize and quantify the structural and functional integrity of the substantia nigra and locus coeruleus. This method may aid in the diagnosis and quantification of longitudinal changes of disease and could provide a stratification tool for predicting treatment success of pharmacological interventions targeting the dopaminergic and noradrenergic systems. Given the growing clinical interest in NM-MRI, understanding the contrast mechanisms that generate this signal is crucial for appropriate interpretation of NM-MRI outcomes and for the continued development of quantitative MRI biomarkers that assess disease severity and progression. To date, most studies associate NM-MRI measurements to the content of the neuromelanin pigment and/or density of neuromelanin-containing neurons, while recent studies suggest that the main source of the NM-MRI contrast is not the presence of neuromelanin but the high-water content in the dopaminergic and noradrenergic neurons. In this review, we consider the biological and physical basis for the NM-MRI contrast and discuss a wide range of interpretations of NM-MRI. We describe different acquisition and image processing approaches and discuss how these methods could be improved and standardized to facilitate large-scale multisite studies and translation into clinical use. We review the potential clinical applications in neurological and psychiatric disorders and the promise of NM-MRI as a biomarker of disease, and finally, we discuss the current limitations of NM-MRI that need to be addressed before this technique can be utilized as a biomarker and translated into clinical practice and offer suggestions for future research.
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Affiliation(s)
- Paula Trujillo
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Megan A Aumann
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Daniel O Claassen
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37212, USA
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Lakhani DA, Zhou X, Tao S, Patel V, Wen S, Okromelidze L, Greco E, Lin C, Westerhold EM, Straub S, Wszolek ZK, Tipton PW, Uitti RJ, Grewal SS, Middlebrooks EH. Diagnostic utility of 7T neuromelanin imaging of the substantia nigra in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:13. [PMID: 38191546 PMCID: PMC10774294 DOI: 10.1038/s41531-024-00631-3] [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: 08/30/2023] [Accepted: 01/02/2024] [Indexed: 01/10/2024] Open
Abstract
Parkinson's disease (PD) is a prevalent neurodegenerative disorder that presents a diagnostic challenge due to symptom overlap with other disorders. Neuromelanin (NM) imaging is a promising biomarker for PD, but adoption has been limited, in part due to subpar performance at standard MRI field strengths. We aimed to evaluate the diagnostic utility of ultra-high field 7T NM-sensitive imaging in the diagnosis of PD versus controls and essential tremor (ET), as well as NM differences among PD subtypes. A retrospective case-control study was conducted including PD patients, ET patients, and controls. 7T NM-sensitive 3D-GRE was acquired, and substantia nigra pars compacta (SNpc) volumes, contrast ratios, and asymmetry indices were calculated. Statistical analyses, including general linear models and ROC curves, were employed. Twenty-one PD patients, 13 ET patients, and 18 controls were assessed. PD patients exhibited significantly lower SNpc volumes compared to non-PD subjects. SNpc total volume showed 100% sensitivity and 96.8% specificity (AUC = 0.998) for differentiating PD from non-PD and 100% sensitivity and 95.2% specificity (AUC = 0.996) in differentiating PD from ET. Contrast ratio was not significantly different between PD and non-PD groups (p = 0.07). There was also significantly higher asymmetry index in SNpc volume in PD compared to non-PD cohorts (p < 0.001). NM signal loss in PD predominantly involved the inferior, posterior, and lateral aspects of SNpc. Akinetic-rigid subtype showed more significant NM signal loss compared to tremor dominant subtype (p < 0.001). 7T NM imaging demonstrates potential as a diagnostic tool for PD, including potential distinction between subtypes, allowing improved understanding of disease progression and subtype-related characteristics.
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Affiliation(s)
- Dhairya A Lakhani
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | - Xiangzhi Zhou
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | - Shengzhen Tao
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | - Vishal Patel
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | - Sijin Wen
- Department of Biostatistics, West Virginia University, Morgantown, WV, USA
| | | | - Elena Greco
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | - Chen Lin
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | | | | | - Ryan J Uitti
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Erik H Middlebrooks
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA.
- Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA.
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Shaff N, Erhardt E, Nitschke S, Julio K, Wertz C, Vakhtin A, Caprihan A, Suarez‐Cedeno G, Deligtisch A, Richardson SP, Mayer AR, Ryman SG. Comparison of automated and manual quantification methods for neuromelanin-sensitive MRI in Parkinson's disease. Hum Brain Mapp 2024; 45:e26544. [PMID: 38041476 PMCID: PMC10789205 DOI: 10.1002/hbm.26544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 11/03/2023] [Accepted: 11/09/2023] [Indexed: 12/03/2023] Open
Abstract
Neuromelanin-sensitive magnetic resonance imaging quantitative analysis methods have provided promising biomarkers that can noninvasively quantify degeneration of the substantia nigra in patients with Parkinson's disease. However, there is a need to systematically evaluate the performance of manual and automated quantification approaches. We evaluate whether spatial, signal-intensity, or subject specific abnormality measures using either atlas based or manually traced identification of the substantia nigra better differentiate patients with Parkinson's disease from healthy controls using logistic regression models and receiver operating characteristics. Inference was performed using bootstrap analyses to calculate 95% confidence interval bounds. Pairwise comparisons were performed by generating 10,000 permutations, refitting the models, and calculating a paired difference between metrics. Thirty-one patients with Parkinson's disease and 22 healthy controls were included in the analyses. Signal intensity measures significantly outperformed spatial and subject specific abnormality measures, with the top performers exhibiting excellent ability to differentiate patients with Parkinson's disease and healthy controls (balanced accuracy = 0.89; area under the curve = 0.81; sensitivity =0.86; and specificity = 0.83). Atlas identified substantia nigra metrics performed significantly better than manual tracing metrics. These results provide clear support for the use of automated signal intensity metrics and additional recommendations. Future work is necessary to evaluate whether the same metrics can best differentiate atypical parkinsonism, perform similarly in de novo and mid-stage cohorts, and serve as longitudinal monitoring biomarkers.
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Affiliation(s)
| | - Erik Erhardt
- Department of Mathematics and StatisticsUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | | | - Kayla Julio
- The Mind Research NetworkAlbuquerqueNew MexicoUSA
| | | | | | | | - Gerson Suarez‐Cedeno
- Nene and Jamie Koch Comprehensive Movement Disorder Center, Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Amanda Deligtisch
- Nene and Jamie Koch Comprehensive Movement Disorder Center, Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Sarah Pirio Richardson
- Nene and Jamie Koch Comprehensive Movement Disorder Center, Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
- New Mexico VA Health Care SystemAlbuquerqueNew MexicoUSA
| | | | - Sephira G. Ryman
- The Mind Research NetworkAlbuquerqueNew MexicoUSA
- Nene and Jamie Koch Comprehensive Movement Disorder Center, Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
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Lee DH, Heo H, Suh CH, Shim WH, Kim E, Jo S, Chung SJ, Lee CS, Kim HS, Kim SJ. Improved diagnostic performance of susceptibility-weighted imaging with compressed sensing-sensitivity encoding and neuromelanin-sensitive MRI for Parkinson's disease and atypical Parkinsonism. Clin Radiol 2024; 79:e102-e111. [PMID: 37863747 DOI: 10.1016/j.crad.2023.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 08/08/2023] [Accepted: 09/18/2023] [Indexed: 10/22/2023]
Abstract
AIM To verify the diagnostic performance of the loss of nigrosome-1 on susceptibility-weighted imaging (SWI) with compressed sensing-sensitivity encoding (CS-SENSE) and neuromelanin on neuromelanin-sensitive (NM) magnetic resonance imaging (MRI) for the diagnosis of Parkinson's disease (PD) and atypical Parkinsonism. MATERIALS AND METHODS A total of 195 patients who underwent MRI between October 2019 and February 2020, including SWI, with or without CS-SENSE, and NM-MRI, were reviewed retrospectively. Two neuroradiologists assessed the loss of nigrosome-1 on SWI and neuromelanin on the NM-MRI. The result of N-3-fluoropropyl-2-beta-carbomethoxy-3-beta-(4-iodophenyl) nortropane positron-emission tomography (PET) was set as the reference standard. RESULTS When CS-SENSE was applied for nigrosome-1 imaging on SWI, the non-diagnostic scan rate was lowered significantly from 19.3% (17/88) to 5.6% (6/107; p=0.004). Diagnosis of PD and atypical Parkinsonism based on the loss of nigrosome-1 on SWI and based on NM-MRI showed good diagnostic value (area under the curve [AUC] 0.821, 95% confidence interval [CI] = 0.755-0.875: AUC 0.832, 95% CI = 0.771-0.882, respectively) with a substantial inter-reader agreement (κ = 0.791 and 0.681, respectively). Combined SWI and neuromelanin had a similar discriminatory ability (AUC 0.830, 95% CI = 0.770-0.880). Similarly, the diagnosis of PD was excellent. CONCLUSIONS CS-SENSE may add value to the diagnostic capability of nigrosome-1 on SWI to reduce the nondiagnostic scan rates. Furthermore, loss of nigrosome-1 on SWI or volume loss of neuromelanin on NM-MRI may be helpful for diagnosing PD.
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Affiliation(s)
- D H Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea; Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - H Heo
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - C H Suh
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
| | - W H Shim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - E Kim
- Philips Healthcare Korea, Seoul, Republic of Korea
| | - S Jo
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - S J Chung
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - C S Lee
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - H S Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - S J Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
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Angelini L, Terranova R, Lazzeri G, van den Berg KRE, Dirkx MF, Paparella G. The role of laboratory investigations in the classification of tremors. Neurol Sci 2023; 44:4183-4192. [PMID: 37814130 PMCID: PMC10641063 DOI: 10.1007/s10072-023-07108-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 09/28/2023] [Indexed: 10/11/2023]
Abstract
INTRODUCTION Tremor is the most common movement disorder. Although clinical examination plays a significant role in evaluating patients with tremor, laboratory tests are useful to classify tremors according to the recent two-axis approach proposed by the International Parkinson and Movement Disorders Society. METHODS In the present review, we will discuss the usefulness and applicability of the various diagnostic methods in classifying and diagnosing tremors. We will evaluate a number of techniques, including laboratory and genetic tests, neurophysiology, and neuroimaging. The role of newly introduced innovative tremor assessment methods will also be discussed. RESULTS Neurophysiology plays a crucial role in tremor definition and classification, and it can be useful for the identification of specific tremor syndromes. Laboratory and genetic tests and neuroimaging may be of paramount importance in identifying specific etiologies. Highly promising innovative technologies are being developed for both clinical and research purposes. CONCLUSIONS Overall, laboratory investigations may support clinicians in the diagnostic process of tremor. Also, combining data from different techniques can help improve understanding of the pathophysiological bases underlying tremors and guide therapeutic management.
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Affiliation(s)
- Luca Angelini
- Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università 30, 00185, Rome, Italy.
| | - Roberta Terranova
- Department of Medical, Surgical Sciences and Advanced Technologies "GF Ingrassia," University of Catania, Catania, Italy
| | - Giulia Lazzeri
- IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurology Unit, Milan, Italy
| | - Kevin R E van den Berg
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Michiel F Dirkx
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Giulia Paparella
- Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università 30, 00185, Rome, Italy
- IRCCS Neuromed, Pozzilli (IS), Italy
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Xuan T, Fang X, Xu T, Ma T, Zhang J, Wang Z, Li H. Neuromelanin-Sensitive Magnetic Resonance Imaging as a Measure for Differential Diagnosis of Essential Tremor and Parkinson's Disease. Neurol India 2023; 71:716-724. [PMID: 37635504 DOI: 10.4103/0028-3886.383826] [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] [Indexed: 08/29/2023]
Abstract
We sought to evaluate whether the neuromelanin-sensitive magnetic resonance imaging (NM-MRI) features of the substantia nigra (SN) have utility in the differential diagnosis of Parkinson's disease (PD) and essential tremor (ET). This study enrolled 23 patients with PD, 20 patients with ET, and 18 healthy participants. All subjects underwent clinical examination, motor and cognitive assessments, and NM-MRI scans. The area and contrast-to-noise ratio (CNR) values of SN were defined according to NM-MRI images. Then, receiver operating characteristic (ROC) analysis was conducted to characterize the diagnostic power of the SN area and CNR values of SN. Compared with ET and control groups, the PD group showed a significant reduction of the area of SN (P = 0.003, PD vs. ET; P = 0.001, PD vs. control) and in the SN to midbrain area ratio in the same layer (P = 0.006, PD vs. ET; P = 0.005, PD vs. control). The SN area had a sensitivity of 65% and a specificity of 87% for distinguishing ET from PD, with an area under the curve (AUC) of 0.7630 and a Youden index of 0.5200, whereas the ratio of the SN area to midbrain area in the same layer had a sensitivity of 60% and a specificity of 87% for distinguishing ET from PD, with an AUC of 0.7478 and a Youden index of 0.4700. Compared with the ET group, the mean CNR value of the SN and the respective CNR values of the three subregions were all weakened in the PD group, and only the CNR in the middle part was significantly different from the control group (P = 0.006). The sensitivity of the CNR value of the middle part of the SN for differentiating ET from PD was 65%, the specificity was 87%, the AUC was 0.7500, and the Youden index was 0.5200.Based on our findings, we conclude that NM-MRI can improve diagnostic accuracy in PD and can be used as a specific and sensitive potential diagnostic biomarker for PD.
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Affiliation(s)
- Tingting Xuan
- Department of Neurology, Cardiovascular and Cerebrovascular Disease Hospital, General Hospital of Ningxia Medical University; School of Clinical Medicine, Ningxia Medical University, Yinchuan, China
| | - Xue Fang
- Department of Neurology, Cardiovascular and Cerebrovascular Disease Hospital, General Hospital of Ningxia Medical University; School of Clinical Medicine, Ningxia Medical University, Yinchuan, China
| | - Ting Xu
- Department of Neural Electrophysiology, Cardiovascular and Cerebrovascular Disease Hospital, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Tingting Ma
- Department of Neurology, Cardiovascular and Cerebrovascular Disease Hospital, General Hospital of Ningxia Medical University, Yinchuan; Ningxia Engineering Technology Research Center for Diagnosis and Treatment of Nervous System Diseases, Neurology Center, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China
| | - Junmei Zhang
- Department of Neurology, Cardiovascular and Cerebrovascular Disease Hospital, General Hospital of Ningxia Medical University; School of Clinical Medicine, Ningxia Medical University, Yinchuan, China
| | - Zhenhai Wang
- Ningxia Engineering Technology Research Center for Diagnosis and Treatment of Nervous System Diseases, Neurology Center; Institute of Medical Sciences, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China
| | - Haining Li
- Department of Neurology, Cardiovascular and Cerebrovascular Disease Hospital, General Hospital of Ningxia Medical University, Yinchuan; Ningxia Engineering Technology Research Center for Diagnosis and Treatment of Nervous System Diseases, Neurology Center, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China
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12
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Ahmed SY, Hassan FF. Optimizing imaging resolution in brain MRI: understanding the impact of technical factors. J Med Life 2023; 16:920-924. [PMID: 37675169 PMCID: PMC10478647 DOI: 10.25122/jml-2022-0212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 11/24/2022] [Indexed: 09/08/2023] Open
Abstract
Magnetic resonance imaging (MRI) exams are essential for diagnostic procedures, but their lengthy duration and associated costs limit their accessibility. Shorter scan times would reduce expenses and allow for more MRI exams, expanding the range of diagnostic procedures. This study investigated technical factors that could decrease scan time without compromising image quality, including field-of-view (FOV), phase field of view, phase oversampling, cross-talk, brain MRI imaging resolution, and scan time. Data were collected from September 2021 to June 2022. All patients underwent brain scans in the transverse plane following a standardized protocol using a 1.5-tesla Siemens Avanto MRI scanner. The protocol employed T2-weighted Turbo Spin Echo imaging. Twenty-four cases were included in this study. Initially, all participants underwent brain MRI scans using the original protocols with axial sections. The results indicated that altering the FOV phase and phase oversampling significantly affected the scan time, whereas other factors did not have a direct impact. The original protocol had a scan time of 3.47 minutes with a FOV of 230 mm, 90% FOV phase, and 0% phase oversampling. After implementing the modified protocol, the scan time was reduced to 2.18 minutes with a FOV of 217 mm and 93.98% phase oversampling of 13.96%. Statistical analysis confirmed the high significance of FOV phase and phase oversampling in reducing scan time. By optimizing these technical factors, MRI exams can be performed more efficiently, resulting in shorter scan times and potentially reducing costs. This would enhance patient comfort and enable a greater number of MRI exams, facilitating a more comprehensive range of diagnostic procedures.
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Affiliation(s)
- Shapol Yousif Ahmed
- Department of Basic Sciences, College of Medicine, Hawler Medical University, Erbil, Iraq
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13
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Wang X, Huang P, Haacke EM, Liu Y, Zhang Y, Jin Z, Li Y, Xu Q, Liu P, Chen S, He N, Yan F. Locus coeruleus and substantia nigra neuromelanin magnetic resonance imaging differentiates Parkinson's disease and essential tremor. Neuroimage Clin 2023; 38:103420. [PMID: 37141646 PMCID: PMC10176060 DOI: 10.1016/j.nicl.2023.103420] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 04/04/2023] [Accepted: 04/23/2023] [Indexed: 05/06/2023]
Abstract
BACKGROUND Differential diagnosis of essential tremor (ET) and Parkinson's disease (PD) can still be a challenge in clinical practice. These two tremor disorders may have different pathogenesis related to the substantia nigra (SN) and locus coeruleus (LC). Characterizing neuromelanin (NM) in these structures may help improve the differential diagnosis. METHODS Forty-three subjects with tremor-dominant PD (PDTD), 31 subjects with ET, and 30 age- and sex-matched healthy controls were included. All subjects were scanned with NM magnetic resonance imaging (NM-MRI). NM volume and contrast measures for the SN and contrast for the LC were evaluated. Logistic regression was used to calculate predicted probabilities by using the combination of SN and LC NM measures. The discriminative power of the NM measures in detecting subjects with PDTD from ET was assessed with a receiver operative characteristic curve, and the area under the curve (AUC) was calculated. RESULTS The NM contrast-to-noise ratio (CNR) of the LC, the NM volume, and CNR of the SN on the right and left sides were significantly lower in PDTD subjects than in ET subjects or healthy controls (all P < 0.05). Furthermore, when combining the best model constructed from the NM measures, the AUC reached 0.92 in differentiating PDTD from ET. CONCLUSION The NM volume and contrast measures of the SN and contrast for the LC provided a new perspective on the differential diagnosis of PDTD and ET, and the investigation of the underlying pathophysiology.
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Affiliation(s)
- Xinhui Wang
- From the Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Er Road, Shanghai 200025, China
| | - Pei Huang
- From the Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Er Road, Shanghai 200025, China
| | - Ewart Mark Haacke
- From the Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Er Road, Shanghai 200025, China; Department of Biomedical Engineering, Wayne State University, 3990 John R, Detroit, MI, USA; Department of Radiology, Wayne State University, 3990 John R, Detroit, MI, USA; Department of Neurology, Wayne State University, 3990 John R, Detroit, MI, USA
| | - Yu Liu
- From the Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Er Road, Shanghai 200025, China
| | - Youmin Zhang
- From the Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Er Road, Shanghai 200025, China
| | - Zhijia Jin
- From the Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Er Road, Shanghai 200025, China
| | - Yan Li
- From the Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Er Road, Shanghai 200025, China
| | - Qiuyun Xu
- Department of Biomedical Engineering, Wayne State University, 3990 John R, Detroit, MI, USA
| | - Peng Liu
- From the Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Er Road, Shanghai 200025, China
| | - Shengdi Chen
- From the Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Er Road, Shanghai 200025, China.
| | - Naying He
- From the Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Er Road, Shanghai 200025, China.
| | - Fuhua Yan
- From the Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Er Road, Shanghai 200025, China.
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14
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He N, Chen Y, LeWitt PA, Yan F, Haacke EM. Application of Neuromelanin MR Imaging in Parkinson Disease. J Magn Reson Imaging 2023; 57:337-352. [PMID: 36017746 PMCID: PMC10086789 DOI: 10.1002/jmri.28414] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 08/01/2022] [Accepted: 08/03/2022] [Indexed: 01/20/2023] Open
Abstract
MRI has been used to develop biomarkers for movement disorders such as Parkinson disease (PD) and other neurodegenerative disorders with parkinsonism such as progressive supranuclear palsy and multiple system atrophy. One of these imaging biomarkers is neuromelanin (NM), whose integrity can be assessed from its contrast and volume. NM is found mainly in certain brain stem structures, namely, the substantia nigra pars compacta (SNpc), the ventral tegmental area, and the locus coeruleus. Another major biomarker is brain iron, which often increases in concert with NM degeneration. These biomarkers have the potential to improve diagnostic certainty in differentiating between PD and other neurodegenerative disorders similar to PD, as well as provide a better understanding of pathophysiology. Mapping NM in vivo has clinical importance for gauging the premotor phase of PD when there is a greater than 50% loss of dopaminergic SNpc melanized neurons. As a metal ion chelator, NM can absorb iron. When NM is released from neurons, it deposits iron into the intracellular tissues of the SNpc; the result is iron that can be imaged and measured using quantitative susceptibility mapping. An increase of iron also leads to the disappearance of the nigrosome-1 sign, another neuroimage biomarker for PD. Therefore, mapping NM and iron changes in the SNpc are a practical means for improving early diagnosis of PD and in monitoring disease progression. In this review, we discuss the functions and location of NM, how NM-MRI is performed, the automatic mapping of NM and iron content, how NM-related imaging biomarkers can be used to enhance PD diagnosis and differentiate it from other neurodegenerative disorders, and potential advances in NM imaging methods. With major advances currently evolving for rapid imaging and artificial intelligence, NM-related biomarkers are likely to have increasingly important roles for enhancing diagnostic capabilities in PD. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China
| | - Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Peter A LeWitt
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA.,Department of Neurology, Henry Ford Hospital, Parkinson's Disease and Movement Disorders Program, Detroit, Michigan, USA
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China
| | - E Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China.,Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA.,Department of Radiology, Wayne State University School of Medicine, Detroit, Michigan, USA.,SpinTech, Inc, Bingham Farms, Michigan, USA
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15
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Langley J, Hwang KS, Hu XP, Huddleston DE. Nigral volumetric and microstructural measures in individuals with scans without evidence of dopaminergic deficit. Front Neurosci 2022; 16:1048945. [PMID: 36507343 PMCID: PMC9731284 DOI: 10.3389/fnins.2022.1048945] [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: 09/20/2022] [Accepted: 10/28/2022] [Indexed: 11/25/2022] Open
Abstract
Introduction Striatal dopamine transporter (DAT) imaging using 123I-ioflupane single photon positron emitted computed tomography (SPECT) (DaTScan, GE) identifies 5-20% of newly diagnosed Parkinson's disease (PD) subjects enrolling in clinical studies to have scans without evidence of dopaminergic deficit (SWEDD). These individuals meet diagnostic criteria for PD, but do not clinically progress as expected, and they are not believed to have neurodegenerative Parkinsonism. Inclusion of SWEDD participants in PD biomarker studies or therapeutic trials may therefore cause them to fail. DaTScan can identify SWEDD individuals, but it is expensive and not widely available; an alternative imaging approach is needed. Here, we evaluate the use of neuromelanin-sensitive, iron-sensitive, and diffusion contrasts in substantia nigra pars compacta (SNpc) to differentiate SWEDD from PD individuals. Methods Neuromelanin-sensitive, iron-sensitive, and diffusion imaging data for SWEDD, PD, and control subjects were downloaded from the Parkinson's progression markers initiative (PPMI) database. SNpc volume, SNpc iron (R 2), and SNpc free water (FW) were measured for each participant. Results Significantly smaller SNpc volume was seen in PD as compared to SWEDD (P < 10-3) and control (P < 10-3) subjects. SNpc FW was elevated in the PD group relative to controls (P = 0.017). No group difference was observed in SNpc R 2. Conclusion In conclusion, nigral volume and FW in the SWEDD group were similar to that of controls, while a reduction in nigral volume and increased FW were observed in the PD group relative to SWEDD and control participants. These results suggest that these MRI measures should be explored as a cost-effective alternative to DaTScan for evaluation of the nigrostriatal system.
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Affiliation(s)
- Jason Langley
- Center for Advanced Neuroimaging, University of California, Riverside, Riverside, CA, United States
| | - Kristy S. Hwang
- Department of Neurosciences, University of California, San Diego, San Diego, CA, United States
| | - Xiaoping P. Hu
- Center for Advanced Neuroimaging, University of California, Riverside, Riverside, CA, United States,Department of Bioengineering, University of California, Riverside, Riverside, CA, United States,*Correspondence: Xiaoping P. Hu,
| | - Daniel E. Huddleston
- Department of Neurology, Emory University, Atlanta, GA, United States,Daniel E. Huddleston,
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16
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Gaurav R, Valabrègue R, Yahia-Chérif L, Mangone G, Narayanan S, Arnulf I, Vidailhet M, Corvol JC, Lehéricy S. NigraNet: An automatic framework to assess nigral neuromelanin content in early Parkinson's disease using convolutional neural network. Neuroimage Clin 2022; 36:103250. [PMID: 36451356 PMCID: PMC9668659 DOI: 10.1016/j.nicl.2022.103250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 10/15/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Parkinson's disease (PD) demonstrates neurodegenerative changes in the substantia nigra pars compacta (SNc) using neuromelanin-sensitive (NM)-MRI. As SNc manual segmentation is prone to substantial inter-individual variability across raters, development of a robust automatic segmentation framework is necessary to facilitate nigral neuromelanin quantification. Artificial intelligence (AI) is gaining traction in the neuroimaging community for automated brain region segmentation tasks using MRI. OBJECTIVE Developing and validating AI-based NigraNet, a fully automatic SNc segmentation framework allowing nigral neuromelanin quantification in patients with PD using NM-MRI. METHODS We prospectively included 199 participants comprising 144 early-stage idiopathic PD patients (disease duration = 1.5 ± 1.0 years) and 55 healthy volunteers (HV) scanned using a 3 Tesla MRI including whole brain T1-weighted anatomical imaging and NM-MRI. The regions of interest (ROI) were delineated in all participants automatically using NigraNet, a modified U-net, and compared to manual segmentations performed by two experienced raters. The SNc volumes (Vol), volumes corrected by total intracranial volume (Cvol), normalized signal intensity (NSI) and contrast-to-noise ratio (CNR) were computed. One-way GLM-ANCOVA was performed while adjusting for age and sex as covariates. Diagnostic performance measurement was assessed using the receiver operating characteristic (ROC) analysis. Inter and intra-observer variability were estimated using Dice similarity coefficient (DSC). The agreements between methods were tested using intraclass correlation coefficient (ICC) based on a mean-rating, two-way, mixed-effects model estimates for absolute agreement. Cronbach's alpha and Bland-Altman plots were estimated to assess inter-method consistency. RESULTS Using both methods, Vol, Cvol, NSI and CNR measurements differed between PD and HV with an effect of sex for Cvol and CNR. ICC values between the methods demonstrated optimal agreement for Cvol and CNR (ICC > 0.9) and high reproducibility (DSC: 0.80) was also obtained. The SNc measurements also showed good to excellent consistency values (Cronbach's alpha > 0.87). Bland-Altman plots of agreement demonstrated no association of SNc ROI measurement differences between the methods and ROI average measurements while confirming that 95 % of the data points were ranging between the limits of mean difference (d ± 1.96xSD). Percentage changes between PD and HV were -27.4 % and -17.7 % for Vol, -30.0 % and -22.2 % for Cvol, -15.8 % and -14.4 % for NSI, -17.1 % and -16.0 % for CNR for automatic and manual measurements respectively. Using automatic method, in the entire dataset, we obtained the areas under the ROC curve (AUC) of 0.83 for Vol, 0.85 for Cvol, 0.79 for NSI and 0.77 for CNR whereas in the training dataset of 0.96 for Vol, 0.95 for Cvol, 0.85 for NSI and 0.85 for CNR. Disease duration correlated negatively with NSI of the patients for both the automatic and manual measurements. CONCLUSIONS We presented an AI-based NigraNet framework that utilizes a small MRI training dataset to fully automatize the SNc segmentation procedure with an increased precision and more reproducible results. Considering the consistency, accuracy and speed of our approach, this study could be a crucial step towards the implementation of a time-saving non-rater dependent fully automatic method for studying neuromelanin changes in clinical settings and large-scale neuroimaging studies.
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Affiliation(s)
- Rahul Gaurav
- Paris Brain Institute - ICM, Sorbonne University, UPMC Univ Paris 06, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France; Movement Investigations and Therapeutics Team (MOV'IT), ICM, Paris, France; Center for NeuroImaging Research - CENIR, ICM, Paris, France.
| | - Romain Valabrègue
- Paris Brain Institute - ICM, Sorbonne University, UPMC Univ Paris 06, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France; Center for NeuroImaging Research - CENIR, ICM, Paris, France
| | - Lydia Yahia-Chérif
- Paris Brain Institute - ICM, Sorbonne University, UPMC Univ Paris 06, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France; Center for NeuroImaging Research - CENIR, ICM, Paris, France
| | - Graziella Mangone
- Paris Brain Institute - ICM, Sorbonne University, UPMC Univ Paris 06, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France; INSERM, Clinical Investigation Center for Neurosciences (CIC), Pitié-Salpêtrière Hospital, Paris, France
| | - Sridar Narayanan
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec, H3A 2B4, Canada
| | - Isabelle Arnulf
- Paris Brain Institute - ICM, Sorbonne University, UPMC Univ Paris 06, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France; Movement Investigations and Therapeutics Team (MOV'IT), ICM, Paris, France; Sleep Disorders Unit, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Marie Vidailhet
- Paris Brain Institute - ICM, Sorbonne University, UPMC Univ Paris 06, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France; Movement Investigations and Therapeutics Team (MOV'IT), ICM, Paris, France; Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Jean-Christophe Corvol
- Paris Brain Institute - ICM, Sorbonne University, UPMC Univ Paris 06, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France; INSERM, Clinical Investigation Center for Neurosciences (CIC), Pitié-Salpêtrière Hospital, Paris, France; Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Stéphane Lehéricy
- Paris Brain Institute - ICM, Sorbonne University, UPMC Univ Paris 06, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France; Movement Investigations and Therapeutics Team (MOV'IT), ICM, Paris, France; Center for NeuroImaging Research - CENIR, ICM, Paris, France; Department of Neuroradiology, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
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17
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Multimodal brain and retinal imaging of dopaminergic degeneration in Parkinson disease. Nat Rev Neurol 2022; 18:203-220. [PMID: 35177849 DOI: 10.1038/s41582-022-00618-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/13/2022] [Indexed: 12/12/2022]
Abstract
Parkinson disease (PD) is a progressive disorder characterized by dopaminergic neurodegeneration in the brain. The development of parkinsonism is preceded by a long prodromal phase, and >50% of dopaminergic neurons can be lost from the substantia nigra by the time of the initial diagnosis. Therefore, validation of in vivo imaging biomarkers for early diagnosis and monitoring of disease progression is essential for future therapeutic developments. PET and single-photon emission CT targeting the presynaptic terminals of dopaminergic neurons can be used for early diagnosis by detecting axonal degeneration in the striatum. However, these techniques poorly differentiate atypical parkinsonian syndromes from PD, and their availability is limited in clinical settings. Advanced MRI in which pathological changes in the substantia nigra are visualized with diffusion, iron-sensitive susceptibility and neuromelanin-sensitive sequences potentially represents a more accessible imaging tool. Although these techniques can visualize the classic degenerative changes in PD, they might be insufficient for phenotyping or prognostication of heterogeneous aspects of PD resulting from extranigral pathologies. The retina is an emerging imaging target owing to its pathological involvement early in PD, which correlates with brain pathology. Retinal optical coherence tomography (OCT) is a non-invasive technique to visualize structural changes in the retina. Progressive parafoveal thinning and fovea avascular zone remodelling, as revealed by OCT, provide potential biomarkers for early diagnosis and prognostication in PD. As we discuss in this Review, multimodal imaging of the substantia nigra and retina is a promising tool to aid diagnosis and management of PD.
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Gaurav R, Pyatigorskaya N, Biondetti E, Valabrègue R, Yahia-Cherif L, Mangone G, Leu-Semenescu S, Corvol JC, Vidailhet M, Arnulf I, Lehéricy S. Deep Learning-Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder. Mov Disord 2022; 37:1064-1069. [PMID: 35102604 PMCID: PMC9302679 DOI: 10.1002/mds.28933] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 01/06/2022] [Accepted: 01/09/2022] [Indexed: 12/24/2022] Open
Abstract
Background Isolated REM sleep behavior disorder (iRBD) is considered a prodromal stage of parkinsonism. Neurodegenerative changes in the substantia nigra pars compacta (SNc) in parkinsonism can be detected using neuromelanin‐sensitive MRI. Objective To investigate SNc neuromelanin changes in iRBD patients using fully automatic segmentation. Methods We included 47 iRBD patients, 134 early Parkinson's disease (PD) patients and 55 healthy volunteers (HVs) scanned at 3 Tesla. SNc regions‐of‐interest were delineated automatically using convolutional neural network. SNc volumes, volumes corrected by total intracranial volume, signal‐to‐noise ratio (SNR) and contrast‐to‐noise ratio were computed. One‐way general linear models (GLM) analysis of covariance (ANCOVA) was conducted while adjusting for age and sex. Results All SNc measurements differed significantly between the three groups (except SNR in iRBD). Changes in iRBD were intermediate between those in PD and HVs. Conclusions Using fully automated SNc segmentation method and neuromelanin‐sensitive imaging, iRBD patients showed neurodegenerative changes in the SNc at a lower level than in PD patients. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society
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Affiliation(s)
- Rahul Gaurav
- Center for NeuroImaging Research (CENIR), Paris Brain Institute-ICM, Paris, France.,Sorbonne Université, Paris Brain Institute-ICM, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France.,Movement Investigations and Therapeutics - MOV'IT Team, Paris Brain Institute-ICM, Paris, France
| | - Nadya Pyatigorskaya
- Center for NeuroImaging Research (CENIR), Paris Brain Institute-ICM, Paris, France.,Sorbonne Université, Paris Brain Institute-ICM, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France.,Movement Investigations and Therapeutics - MOV'IT Team, Paris Brain Institute-ICM, Paris, France.,Department of Neuroradiology, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Emma Biondetti
- Center for NeuroImaging Research (CENIR), Paris Brain Institute-ICM, Paris, France.,Sorbonne Université, Paris Brain Institute-ICM, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France.,Movement Investigations and Therapeutics - MOV'IT Team, Paris Brain Institute-ICM, Paris, France
| | - Romain Valabrègue
- Center for NeuroImaging Research (CENIR), Paris Brain Institute-ICM, Paris, France.,Sorbonne Université, Paris Brain Institute-ICM, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France
| | - Lydia Yahia-Cherif
- Center for NeuroImaging Research (CENIR), Paris Brain Institute-ICM, Paris, France.,Sorbonne Université, Paris Brain Institute-ICM, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France
| | - Graziella Mangone
- Sorbonne Université, Paris Brain Institute-ICM, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France.,INSERM, Clinical Investigation Center for Neurosciences (CIC), Pitié-Salpêtrière Hospital, Paris, France
| | | | - Jean-Christophe Corvol
- Sorbonne Université, Paris Brain Institute-ICM, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France.,Department of Neuroradiology, Pitié-Salpêtrière Hospital, AP-HP, Paris, France.,Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Marie Vidailhet
- Sorbonne Université, Paris Brain Institute-ICM, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France.,Movement Investigations and Therapeutics - MOV'IT Team, Paris Brain Institute-ICM, Paris, France.,Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Isabelle Arnulf
- Center for NeuroImaging Research (CENIR), Paris Brain Institute-ICM, Paris, France.,Movement Investigations and Therapeutics - MOV'IT Team, Paris Brain Institute-ICM, Paris, France.,Sleep Disorders Unit, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Stéphane Lehéricy
- Center for NeuroImaging Research (CENIR), Paris Brain Institute-ICM, Paris, France.,Sorbonne Université, Paris Brain Institute-ICM, INSERM U1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France.,Movement Investigations and Therapeutics - MOV'IT Team, Paris Brain Institute-ICM, Paris, France.,INSERM, Clinical Investigation Center for Neurosciences (CIC), Pitié-Salpêtrière Hospital, Paris, France
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19
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Ueno F, Iwata Y, Nakajima S, Caravaggio F, Rubio JM, Horga G, Cassidy CM, Torres-Carmona E, de Luca V, Tsugawa S, Honda S, Moriguchi S, Noda Y, Gerretsen P, Graff-Guerrero A. Neuromelanin accumulation in patients with schizophrenia: A systematic review and meta-analysis. Neurosci Biobehav Rev 2022; 132:1205-1213. [PMID: 34718049 PMCID: PMC9059704 DOI: 10.1016/j.neubiorev.2021.10.028] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/21/2021] [Accepted: 10/24/2021] [Indexed: 11/25/2022]
Abstract
Although schizophrenia is associated with increased presynaptic dopamine function in the striatum, it remains unclear if neuromelanin levels, which are thought to serve as a biomarker for midbrain dopamine neuron function, are increased in patients with schizophrenia. We conducted a systematic review and meta-analysis of magnetic resonance imaging (MRI) and postmortem studies comparing neuromelanin (NM) levels between patients with schizophrenia and healthy controls (HCs). Standard mean differences were calculated to assess group differences in NM accumulation levels between patients with schizophrenia and HCs. This study included 7 articles in total. Five studies employed NM-sensitive MRI (NM-MRI) and two were postmortem brain studies. The patient group (n = 163) showed higher NM levels in the substantia nigra (SN) than HCs (n = 228) in both the analysis of the seven studies and the subgroup analysis of the 5 NM-MRI studies. This analysis suggest increased NM levels in the SN may be a potential biomarker for stratifying schizophrenia, warranting further research that accounts for the heterogeneity of this disorder.
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Affiliation(s)
- Fumihiko Ueno
- Brain Health Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Yusuke Iwata
- Department of Neuropsychiatry, University of Yamanashi, Faculty of Medicine, Yamanashi, Japan
| | - Shinichiro Nakajima
- Brain Health Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
| | - Fernando Caravaggio
- Brain Health Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Jose M Rubio
- Barbara and Donald Zucker School of Medicine at Hofstra University - Northwell Health, Hempstead, NY, USA; Institute of Behavioral Science, Feinstein Institutes of Medical Research, Manhasset, NY, USA; Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, New York, NY, USA; Division of Translational Imaging, New York State Psychiatric Institute, New York, NY, USA
| | - Clifford M Cassidy
- The Royal's Institute of Mental Health Research Affiliated with the University of Ottawa, Ottawa, Ontario, Canada
| | - Edgardo Torres-Carmona
- Brain Health Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Vincenzo de Luca
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, CAMH, Toronto, Ontario, Canada
| | - Sakiko Tsugawa
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Shiori Honda
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Sho Moriguchi
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Yoshihiro Noda
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Philip Gerretsen
- Brain Health Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, CAMH, Toronto, Ontario, Canada
| | - Ariel Graff-Guerrero
- Brain Health Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, CAMH, Toronto, Ontario, Canada.
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20
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Bae YJ, Kim JM, Choi BS, Song YS, Nam Y, Cho SJ, Kim JH, Kim SE. MRI Findings in Parkinson’s Disease: Radiologic Assessment of Nigrostriatal Degeneration. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2022; 83:508-526. [PMID: 36238511 PMCID: PMC9514534 DOI: 10.3348/jksr.2022.0044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/06/2022] [Accepted: 05/12/2022] [Indexed: 11/15/2022]
Abstract
파킨슨병은 중뇌 흑질에 위치한 도파민성 신경세포의 퇴행성 소실로 인해 발생하는 이상운동질환이다. 최근 다양한 자기공명영상기법의 발전으로 파킨슨병에서 일어나는 병리생태학적인 변화를 반영하는 여러 영상 소견들이 보고되었다. 여러 연구에서 이러한 영상 소견들은 파킨슨병의 진단 및 비정형 파킨슨증과의 감별 등에 유의미한 도움을 줄 수 있는 것이 밝혀졌다. 본 종설에서는, 파킨슨병에서 일어나는 흑질선조체 변성의 병태생리를 나타낼 수 있는 나이그로좀 영상 및 뉴로멜라닌 영상 등을 포함한 자기공명영상기법들과 각 영상에서 나타나는 소견에 대하여 자세히 다루었다.
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Affiliation(s)
- Yun Jung Bae
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Jong-Min Kim
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Byung Se Choi
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Yoo Sung Song
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Yoonho Nam
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea
| | - Se Jin Cho
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Jae Hyoung Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Sang Eun Kim
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
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21
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Gaurav R, Yahia‐Cherif L, Pyatigorskaya N, Mangone G, Biondetti E, Valabrègue R, Ewenczyk C, Hutchison RM, Cedarbaum JM, Corvol J, Vidailhet M, Lehéricy S. Longitudinal Changes in Neuromelanin MRI Signal in Parkinson's Disease: A Progression Marker. Mov Disord 2021; 36:1592-1602. [PMID: 33751655 PMCID: PMC8359265 DOI: 10.1002/mds.28531] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 01/07/2021] [Accepted: 01/25/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Development of reliable and accurate imaging biomarkers of dopaminergic cell neurodegeneration is necessary to facilitate therapeutic drug trials in Parkinson's disease (PD). Neuromelanin-sensitive MRI techniques have been effective in detecting neurodegeneration in the substantia nigra pars compacta (SNpc). The objective of the current study was to investigate longitudinal neuromelanin signal changes in the SNpc in PD patients. METHODS In this prospective, longitudinal, observational case-control study, we included 140 PD patients and 64 healthy volunteers divided into 2 cohorts. Cohort I included 99 early PD patients (disease duration, 1.5 ± 1.0 years) and 41 healthy volunteers analyzed at baseline (V1), where 79 PD patients and 32 healthy volunteers were rescanned after 2.0 ± 0.2 years of follow-up (V2). Cohort II included 41 progressing PD patients (disease duration, 9.3 ± 3.7 years) and 23 healthy volunteers at V1, where 30 PD patients were rescanned after 2.4 ± 0.5 years of follow-up. Subjects were scanned at 3 T MRI using 3-dimensional T1-weighted and neuromelanin-sensitive imaging. Regions of interest were delineated manually to calculate SN volumes, volumes corrected by total intracranial volume, signal-to-noise ratio, and contrast-to-noise ratio. RESULTS Results showed (1) significant reduction in volume and volume corrected by total intracranial volume between visits, greater in progressing PD than nonsignificant changes in healthy volunteers; (2) no significant effects of visit for signal intensity (signal-to-noise ratio); (3) significant interaction in volume between group and visit; (4) greater volume corrected by total intracranial volume at baseline in female patients and greater decrease in volume and increase in the contrast-to-noise ratio in progressing female PD patients compared with male patients; and (5) correlations between neuromelanin SN changes and disease severity and duration. CONCLUSIONS We observed a progressive and measurable decrease in neuromelanin-based SN signal and volume in PD, which might allow a direct noninvasive assessment of progression of SN loss and could represent a target biomarker for disease-modifying treatments. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Rahul Gaurav
- Paris Brain Institute– ICMCenter for NeuroImaging Research – CENIRParisFrance
- ICM, Sorbonne University, UPMC Univ Paris 06, Inserm U1127, CNRS UMRParisFrance
- ICM Team “Movement Investigations and Therapeutics” (MOV'IT)ParisFrance
| | - Lydia Yahia‐Cherif
- Paris Brain Institute– ICMCenter for NeuroImaging Research – CENIRParisFrance
- ICM, Sorbonne University, UPMC Univ Paris 06, Inserm U1127, CNRS UMRParisFrance
| | - Nadya Pyatigorskaya
- Paris Brain Institute– ICMCenter for NeuroImaging Research – CENIRParisFrance
- ICM, Sorbonne University, UPMC Univ Paris 06, Inserm U1127, CNRS UMRParisFrance
- ICM Team “Movement Investigations and Therapeutics” (MOV'IT)ParisFrance
- Department of NeuroradiologyPitié‐Salpêtrière Hospital, AP‐HPParisFrance
| | - Graziella Mangone
- ICM, Sorbonne University, UPMC Univ Paris 06, Inserm U1127, CNRS UMRParisFrance
- INSERM, Clinical Investigation Center for Neurosciences, Pitié‐Salpêtrière HospitalParisFrance
| | - Emma Biondetti
- Paris Brain Institute– ICMCenter for NeuroImaging Research – CENIRParisFrance
- ICM, Sorbonne University, UPMC Univ Paris 06, Inserm U1127, CNRS UMRParisFrance
- ICM Team “Movement Investigations and Therapeutics” (MOV'IT)ParisFrance
| | - Romain Valabrègue
- Paris Brain Institute– ICMCenter for NeuroImaging Research – CENIRParisFrance
- ICM, Sorbonne University, UPMC Univ Paris 06, Inserm U1127, CNRS UMRParisFrance
| | - Claire Ewenczyk
- ICM, Sorbonne University, UPMC Univ Paris 06, Inserm U1127, CNRS UMRParisFrance
- ICM Team “Movement Investigations and Therapeutics” (MOV'IT)ParisFrance
- Department of NeurologyPitié‐Salpêtrière Hospital, AP‐HPParisFrance
| | | | | | - Jean‐Christophe Corvol
- ICM, Sorbonne University, UPMC Univ Paris 06, Inserm U1127, CNRS UMRParisFrance
- INSERM, Clinical Investigation Center for Neurosciences, Pitié‐Salpêtrière HospitalParisFrance
- Department of NeurologyPitié‐Salpêtrière Hospital, AP‐HPParisFrance
| | - Marie Vidailhet
- ICM, Sorbonne University, UPMC Univ Paris 06, Inserm U1127, CNRS UMRParisFrance
- ICM Team “Movement Investigations and Therapeutics” (MOV'IT)ParisFrance
- Department of NeurologyPitié‐Salpêtrière Hospital, AP‐HPParisFrance
| | - Stéphane Lehéricy
- Paris Brain Institute– ICMCenter for NeuroImaging Research – CENIRParisFrance
- ICM, Sorbonne University, UPMC Univ Paris 06, Inserm U1127, CNRS UMRParisFrance
- ICM Team “Movement Investigations and Therapeutics” (MOV'IT)ParisFrance
- Department of NeuroradiologyPitié‐Salpêtrière Hospital, AP‐HPParisFrance
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22
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Holtbernd F, Shah NJ. Imaging the Pathophysiology of Essential Tremor-A Systematic Review. Front Neurol 2021; 12:680254. [PMID: 34220687 PMCID: PMC8244929 DOI: 10.3389/fneur.2021.680254] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 05/04/2021] [Indexed: 11/28/2022] Open
Abstract
Background: The pathophysiology underlying essential tremor (ET) still is poorly understood. Recent research suggests a pivotal role of the cerebellum in tremor genesis, and an ongoing controversy remains as to whether ET constitutes a neurodegenerative disorder. In addition, mounting evidence indicates that alterations in the gamma-aminobutyric acid neurotransmitter system are involved in ET pathophysiology. Here, we systematically review structural, functional, and metabolic neuroimaging studies and discuss current concepts of ET pathophysiology from an imaging perspective. Methods: We conducted a PubMed and Scopus search from 1966 up to December 2020, entering essential tremor in combination with any of the following search terms and their corresponding abbreviations: positron emission tomography (PET), single-photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and gamma-aminobutyric acid (GABA). Results: Altered functional connectivity in the cerebellum and cerebello-thalamico-cortical circuitry is a prevalent finding in functional imaging studies. Reports from structural imaging studies are less consistent, and there is no clear evidence for cerebellar neurodegeneration. However, diffusion tensor imaging robustly points toward microstructural cerebellar changes. Radiotracer imaging suggests that the dopaminergic axis is largely preserved in ET. Similarly, measurements of nigral iron content and neuromelanin are unremarkable in most studies; this is in contrast to Parkinson's disease (PD). PET and MRS studies provide limited evidence for cerebellar and thalamic GABAergic dysfunction. Conclusions: There is robust evidence indicating that the cerebellum plays a key role within a multiple oscillator tremor network which underlies tremor genesis. However, whether cerebellar dysfunction relies on a neurodegenerative process remains unclear. Dopaminergic and iron imaging do not suggest a substantial overlap of ET with PD pathophysiology. There is limited evidence for alterations of the GABAergic neurotransmitter system in ET. The clinical, demographical, and genetic heterogeneity of ET translates into neuroimaging and likely explains the various inconsistencies reported.
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Affiliation(s)
- Florian Holtbernd
- Institute of Neuroscience and Medicine (INM-4/INM-11), Forschungszentrum Juelich GmbH, Juelich, Germany
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Juelich GmbH, Rheinisch-Westfaelische Technische Hochschule Aachen University, Aachen, Germany
- Department of Neurology, Rheinisch-Westfaelische Technische Hochschule Aachen University, Aachen, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine (INM-4/INM-11), Forschungszentrum Juelich GmbH, Juelich, Germany
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Juelich GmbH, Rheinisch-Westfaelische Technische Hochschule Aachen University, Aachen, Germany
- Department of Neurology, Rheinisch-Westfaelische Technische Hochschule Aachen University, Aachen, Germany
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23
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Feraco P, Gagliardo C, La Tona G, Bruno E, D’angelo C, Marrale M, Del Poggio A, Malaguti MC, Geraci L, Baschi R, Petralia B, Midiri M, Monastero R. Imaging of Substantia Nigra in Parkinson's Disease: A Narrative Review. Brain Sci 2021; 11:brainsci11060769. [PMID: 34207681 PMCID: PMC8230134 DOI: 10.3390/brainsci11060769] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 06/02/2021] [Accepted: 06/05/2021] [Indexed: 12/15/2022] Open
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder, characterized by motor and non-motor symptoms due to the degeneration of the pars compacta of the substantia nigra (SNc) with dopaminergic denervation of the striatum. Although the diagnosis of PD is principally based on a clinical assessment, great efforts have been expended over the past two decades to evaluate reliable biomarkers for PD. Among these biomarkers, magnetic resonance imaging (MRI)-based biomarkers may play a key role. Conventional MRI sequences are considered by many in the field to have low sensitivity, while advanced pulse sequences and ultra-high-field MRI techniques have brought many advantages, particularly regarding the study of brainstem and subcortical structures. Nowadays, nigrosome imaging, neuromelanine-sensitive sequences, iron-sensitive sequences, and advanced diffusion weighted imaging techniques afford new insights to the non-invasive study of the SNc. The use of these imaging methods, alone or in combination, may also help to discriminate PD patients from control patients, in addition to discriminating atypical parkinsonian syndromes (PS). A total of 92 articles were identified from an extensive review of the literature on PubMed in order to ascertain the-state-of-the-art of MRI techniques, as applied to the study of SNc in PD patients, as well as their potential future applications as imaging biomarkers of disease. Whilst none of these MRI-imaging biomarkers could be successfully validated for routine clinical practice, in achieving high levels of accuracy and reproducibility in the diagnosis of PD, a multimodal MRI-PD protocol may assist neuroradiologists and clinicians in the early and differential diagnosis of a wide spectrum of neurodegenerative disorders.
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Affiliation(s)
- Paola Feraco
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Via S. Giacomo 14, 40138 Bologna, Italy;
- Neuroradiology Unit, S. Chiara Hospital, 38122 Trento, Italy;
| | - Cesare Gagliardo
- Section of Radiological Sciences, Department of Biomedicine, Neurosciences & Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (G.L.T.); (E.B.); (C.D.); (M.M.)
- Correspondence:
| | - Giuseppe La Tona
- Section of Radiological Sciences, Department of Biomedicine, Neurosciences & Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (G.L.T.); (E.B.); (C.D.); (M.M.)
| | - Eleonora Bruno
- Section of Radiological Sciences, Department of Biomedicine, Neurosciences & Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (G.L.T.); (E.B.); (C.D.); (M.M.)
| | - Costanza D’angelo
- Section of Radiological Sciences, Department of Biomedicine, Neurosciences & Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (G.L.T.); (E.B.); (C.D.); (M.M.)
| | - Maurizio Marrale
- Department of Physics and Chemistry, University of Palermo, 90128 Palermo, Italy;
| | - Anna Del Poggio
- Department of Neuroradiology and CERMAC, San Raffaele Scientific Institute, San Raffaele Vita-Salute University, 20132 Milan, Italy;
| | | | - Laura Geraci
- Diagnostic and Interventional Neuroradiology Unit, A.R.N.A.S. Civico-Di Cristina-Benfratelli, 90127 Palermo, Italy;
| | - Roberta Baschi
- Section of Neurology, Department of Biomedicine, Neurosciences & Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (R.B.); (R.M.)
| | | | - Massimo Midiri
- Section of Radiological Sciences, Department of Biomedicine, Neurosciences & Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (G.L.T.); (E.B.); (C.D.); (M.M.)
| | - Roberto Monastero
- Section of Neurology, Department of Biomedicine, Neurosciences & Advanced Diagnostics, School of Medicine, University of Palermo, 90127 Palermo, Italy; (R.B.); (R.M.)
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24
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Zhang YD, Dong Z, Wang SH, Yu X, Yao X, Zhou Q, Hu H, Li M, Jiménez-Mesa C, Ramirez J, Martinez FJ, Gorriz JM. Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2020; 64:149-187. [PMID: 32834795 PMCID: PMC7366126 DOI: 10.1016/j.inffus.2020.07.006] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/06/2020] [Accepted: 07/14/2020] [Indexed: 05/13/2023]
Abstract
Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.
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Affiliation(s)
- Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Zhengchao Dong
- Department of Psychiatry, Columbia University, USA
- New York State Psychiatric Institute, New York, NY 10032, USA
| | - Shui-Hua Wang
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- School of Architecture Building and Civil engineering, Loughborough University, Loughborough, LE11 3TU, UK
- School of Mathematics and Actuarial Science, University of Leicester, LE1 7RH, UK
| | - Xiang Yu
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Xujing Yao
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Qinghua Zhou
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Hua Hu
- Department of Psychiatry, Columbia University, USA
- Department of Neurology, The Second Affiliated Hospital of Soochow University, China
| | - Min Li
- Department of Psychiatry, Columbia University, USA
- School of Internet of Things, Hohai University, Changzhou, China
| | - Carmen Jiménez-Mesa
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Javier Ramirez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Francisco J Martinez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
- Department of Psychiatry, University of Cambridge, Cambridge CB21TN, UK
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25
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Simões RM, Castro Caldas A, Grilo J, Correia D, Guerreiro C, Pita Lobo P, Valadas A, Fabbri M, Correia Guedes L, Coelho M, Rosa MM, Ferreira JJ, Reimão S. A distinct neuromelanin magnetic resonance imaging pattern in parkinsonian multiple system atrophy. BMC Neurol 2020; 20:432. [PMID: 33243166 PMCID: PMC7694430 DOI: 10.1186/s12883-020-02007-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 11/19/2020] [Indexed: 12/21/2022] Open
Abstract
Background Parkinsonian variant of multiple system atrophy is a neurodegenerative disorder frequently misdiagnosed as Parkinson’s disease. No early imaging biomarkers currently differentiate these disorders. Methods Simple visual imaging analysis of the substantia nigra and locus coeruleus in neuromelanin-sensitive magnetic resonance imaging and nigrosome 1 in susceptibility-weighted sequences was performed in thirty patients with parkinsonian variant of multiple system atrophy fulfilling possible/probable second consensus diagnostic criteria. The neuromelanin visual pattern was compared to patients with Parkinson’s disease with the same disease duration (n = 10) and healthy controls (n = 10). Substantia nigra semi-automated neuromelanin area/signal intensity was compared to the visual data. Results Groups were similar in age, sex, disease duration, and levodopa equivalent dose. Hoehn & Yahr stage was higher in parkinsonian multiple system atrophy patients, 69% of whom had normal neuromelanin size/signal, significantly different from Parkinson’s disease patients, and similar to controls. Nigrosome 1 signal was lost in 74% of parkinsonian multiple system atrophy patients. Semi-automated neuromelanin substantia nigra signal, but not area, measurements were able to differentiate groups. Conclusions In patients with parkinsonism, simple visual magnetic resonance imaging analysis showing normal neuromelanin substantia nigra and locus coeruleus, combined with nigrosome 1 loss, allowed the distinction of the parkinsonian variant of multiple system atrophy from Parkinson’s disease and healthy controls. This easy and widely available method was superior to semi-automated measurements in identifying specific imaging changes in substantia nigra and locus coeruleus. Supplementary Information The online version contains supplementary material available at 10.1186/s12883-020-02007-5.
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Affiliation(s)
- Rita Moiron Simões
- Neurology Department, Hospital Beatriz Ângelo, Loures, Portugal.,CNS-Campus Neurológico Sénior, Torres Vedras, Portugal
| | - Ana Castro Caldas
- CNS-Campus Neurológico Sénior, Torres Vedras, Portugal.,Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal
| | - Joana Grilo
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal.,Laboratório de Farmacologia Clínica e Terapêutica, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal.,Institute for Systems and Robotics (LARSyS), Department of Bioengineering, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | - Daisy Correia
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal.,Laboratório de Farmacologia Clínica e Terapêutica, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Carla Guerreiro
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal.,Department of Neurological Imaging, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal.,Imaging University Clinic, Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal
| | - Patrícia Pita Lobo
- CNS-Campus Neurológico Sénior, Torres Vedras, Portugal.,Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal.,Department of Neurosciences and Mental Health, Serviço de Neurologia, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Anabela Valadas
- CNS-Campus Neurológico Sénior, Torres Vedras, Portugal.,Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal.,Department of Neurosciences and Mental Health, Serviço de Neurologia, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Marguerita Fabbri
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal.,Department of Neurosciences, clinical investigation center CIC 1436, Parkinson Toulouse expert center, NS-Park/FCRIN network and NeuroToul COEN center, Toulouse University Hospital, INSERM, University of Toulouse 3, Toulouse, France
| | - Leonor Correia Guedes
- CNS-Campus Neurológico Sénior, Torres Vedras, Portugal.,Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal.,Department of Neurosciences and Mental Health, Serviço de Neurologia, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Miguel Coelho
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal.,Department of Neurosciences and Mental Health, Serviço de Neurologia, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Mario Miguel Rosa
- Laboratório de Farmacologia Clínica e Terapêutica, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal.,Department of Neurosciences and Mental Health, Serviço de Neurologia, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Joaquim J Ferreira
- CNS-Campus Neurológico Sénior, Torres Vedras, Portugal. .,Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal. .,Laboratório de Farmacologia Clínica e Terapêutica, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal.
| | - Sofia Reimão
- Laboratório de Farmacologia Clínica e Terapêutica, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal.,Department of Neurological Imaging, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal.,Imaging University Clinic, Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal
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Cho SJ, Bae YJ, Kim JM, Kim D, Baik SH, Sunwoo L, Choi BS, Kim JH. Diagnostic performance of neuromelanin-sensitive magnetic resonance imaging for patients with Parkinson's disease and factor analysis for its heterogeneity: a systematic review and meta-analysis. Eur Radiol 2020; 31:1268-1280. [PMID: 32886201 DOI: 10.1007/s00330-020-07240-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 07/12/2020] [Accepted: 08/28/2020] [Indexed: 12/22/2022]
Abstract
OBJECTIVE To determine the diagnostic performance of neuromelanin-sensitive magnetic resonance imaging discriminating between patients with Parkinson's disease and normal healthy controls and to identify factors causing heterogeneity influencing the diagnostic performance. METHODS A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed for studies reporting the relevant topic before February 17, 2020. The pooled sensitivity and specificity values with their 95% confidence intervals were calculated using bivariate random-effects modeling. Subgroup and meta-regression analyses were also performed to determine factors influencing heterogeneity. RESULTS Twelve articles including 403 patients with Parkinson's disease and 298 control participants were included in this systematic review and meta-analysis. Neuromelanin-sensitive magnetic resonance imaging showed a pooled sensitivity of 89% (95% confidence interval, 86-92%) and a pooled specificity of 83% (95% confidence interval, 76-88%). In the subgroup and meta-regression analysis, a disease duration longer than 5 and 10 years, comparisons using measured volumes instead of signal intensities, a slice thickness in terms of magnetic resonance imaging parameters of more than 2 mm, and semi-/automated segmentation methods instead of manual segmentation improved the diagnostic performance. CONCLUSION Neuromelanin-sensitive magnetic resonance imaging had a favorable diagnostic performance in discriminating patients with Parkinson's disease from healthy controls. To improve diagnostic accuracy, further investigations directly comparing these heterogeneity-affecting factors and optimizing these parameters are necessary. KEY POINTS • Neuromelanin-sensitive MRI favorably discriminates patients with Parkinson's disease from healthy controls. • Disease duration, parameters used for comparison, magnetic resonance imaging slice thickness, and segmentation methods affected heterogeneity across the studies.
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Affiliation(s)
- Se Jin Cho
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam, Gyeonggi, 13620, Republic of Korea
| | - Yun Jung Bae
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam, Gyeonggi, 13620, Republic of Korea.
| | - Jong-Min Kim
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam, Gyeonggi, 13620, Republic of Korea
| | - Donghyun Kim
- Department of Radiology, Busan Paik Hospital, Inje University College of Medicine, 75, Bokji-ro, Busanjin-gu, Busan, 47392, Republic of Korea
| | - Sung Hyun Baik
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam, Gyeonggi, 13620, Republic of Korea
| | - Leonard Sunwoo
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam, Gyeonggi, 13620, Republic of Korea
| | - Byung Se Choi
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam, Gyeonggi, 13620, Republic of Korea
| | - Jae Hyoung Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam, Gyeonggi, 13620, Republic of Korea
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Biondetti E, Gaurav R, Yahia-Cherif L, Mangone G, Pyatigorskaya N, Valabrègue R, Ewenczyk C, Hutchison M, François C, Arnulf I, Corvol JC, Vidailhet M, Lehéricy S. Spatiotemporal changes in substantia nigra neuromelanin content in Parkinson’s disease. Brain 2020; 143:2757-2770. [DOI: 10.1093/brain/awaa216] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 04/11/2020] [Accepted: 05/08/2020] [Indexed: 02/03/2023] Open
Abstract
Abstract
This study aimed to investigate the spatiotemporal changes in neuromelanin-sensitive MRI signal in the substantia nigra and their relation to clinical scores of disease severity in patients with early or progressing Parkinson’s disease and patients with idiopathic rapid eye movement sleep behaviour disorder (iRBD) exempt of Parkinsonian signs compared to healthy control subjects. Longitudinal T1-weighted anatomical and neuromelanin-sensitive MRI was performed in two cohorts, including patients with iRBD, patients with early or progressing Parkinson’s disease, and control subjects. Based on the aligned substantia nigra segmentations using a study-specific brain anatomical template, parametric maps of the probability of a voxel belonging to the substantia nigra were calculated for patients with various degrees of disease severity and controls. For each voxel in the substantia nigra, probability map of controls, correlations between signal-to-noise ratios on neuromelanin-sensitive MRI in patients with iRBD and Parkinson’s disease and clinical scores of motor disability, cognition and mood/behaviour were calculated. Our results showed that in patients, compared to the healthy control subjects, the volume of the substantia nigra was progressively reduced for increasing disease severity. The neuromelanin signal changes appeared to start in the posterolateral motor areas of the substantia nigra and then progressed to more medial areas of this region. The ratio between the volume of the substantia nigra in patients with Parkinson’s disease relative to the controls was best fitted by a mono-exponential decay. Based on this model, the pre-symptomatic phase of the disease started at 5.3 years before disease diagnosis, and 23.1% of the substantia nigra volume was lost at the time of diagnosis, which was in line with previous findings using post-mortem histology of the human substantia nigra and radiotracer studies of the human striatum. Voxel-wise patterns of correlation between neuromelanin-sensitive MRI signal-to-noise ratio and motor, cognitive and mood/behavioural clinical scores were localized in distinct regions of the substantia nigra. This localization reflected the functional organization of the nigrostriatal system observed in histological and electrophysiological studies in non-human primates (motor, cognitive and mood/behavioural domains). In conclusion, neuromelanin-sensitive MRI enabled us to assess voxel-wise modifications of substantia nigra’s morphology in vivo in humans, including healthy controls, patients with iRBD and patients with Parkinson’s disease, and identify their correlation with nigral function across all motor, cognitive and behavioural domains. This insight could help assess disease progression in drug trials of disease modification.
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Affiliation(s)
- Emma Biondetti
- Institut du Cerveau – ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- ICM, Centre de NeuroImagerie de Recherche – CENIR, Paris, France
- ICM, Team “Movement Investigations and Therapeutics” (MOV’IT), Paris, France
| | - Rahul Gaurav
- Institut du Cerveau – ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- ICM, Centre de NeuroImagerie de Recherche – CENIR, Paris, France
- ICM, Team “Movement Investigations and Therapeutics” (MOV’IT), Paris, France
| | - Lydia Yahia-Cherif
- Institut du Cerveau – ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- ICM, Centre de NeuroImagerie de Recherche – CENIR, Paris, France
| | - Graziella Mangone
- Institut du Cerveau – ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- National Institute of Health and Medical Research - INSERM, Clinical Investigation Centre, Pitié-Salpêtrière Hospital, Paris, France
| | - Nadya Pyatigorskaya
- Institut du Cerveau – ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- ICM, Team “Movement Investigations and Therapeutics” (MOV’IT), Paris, France
- Department of Neuroradiology, Pitié-Salpêtrière Hospital, Public Assistance - Paris Hospitals (AP-HP), Paris, France
| | - Romain Valabrègue
- Institut du Cerveau – ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- ICM, Centre de NeuroImagerie de Recherche – CENIR, Paris, France
| | - Claire Ewenczyk
- Institut du Cerveau – ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- ICM, Team “Movement Investigations and Therapeutics” (MOV’IT), Paris, France
- Department of Neurology, Pitié-Salpêtrière Hospital, Public Assistance - Paris Hospitals (AP-HP), Paris, France
| | | | - Chantal François
- Institut du Cerveau – ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
| | - Isabelle Arnulf
- Institut du Cerveau – ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- ICM, Team “Movement Investigations and Therapeutics” (MOV’IT), Paris, France
- Sleep Disorders Unit, Pitié-Salpêtrière Hospital, Public Assistance – Paris Hospitals (AP-HP), Paris, France
| | - Jean-Christophe Corvol
- Institut du Cerveau – ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- National Institute of Health and Medical Research - INSERM, Clinical Investigation Centre, Pitié-Salpêtrière Hospital, Paris, France
- Department of Neurology, Pitié-Salpêtrière Hospital, Public Assistance - Paris Hospitals (AP-HP), Paris, France
| | - Marie Vidailhet
- Institut du Cerveau – ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- ICM, Team “Movement Investigations and Therapeutics” (MOV’IT), Paris, France
- Department of Neurology, Pitié-Salpêtrière Hospital, Public Assistance - Paris Hospitals (AP-HP), Paris, France
| | - Stéphane Lehéricy
- Institut du Cerveau – ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- ICM, Centre de NeuroImagerie de Recherche – CENIR, Paris, France
- ICM, Team “Movement Investigations and Therapeutics” (MOV’IT), Paris, France
- Department of Neuroradiology, Pitié-Salpêtrière Hospital, Public Assistance - Paris Hospitals (AP-HP), Paris, France
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29
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Merrill S, Mauler DJ, Richter KR, Raghunathan A, Leis JF, Mrugala MM. Parkinsonism as a late presentation of lymphomatosis cerebri following high-dose chemotherapy with autologous stem cell transplantation for primary central nervous system lymphoma. J Neurol 2020; 267:2239-2244. [PMID: 32296938 DOI: 10.1007/s00415-020-09819-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Revised: 03/31/2020] [Accepted: 04/02/2020] [Indexed: 12/18/2022]
Abstract
Primary central nervous system lymphoma is an aggressive form of non-Hodgkin lymphoma arising in the eyes, meninges, spinal cord, or brain. Treatment of primary CNS lymphoma with a combination of high-dose chemotherapy and autologous stem cell transplantation has been shown to have high rates of remission which is frequently sustained for multiple years. Recurrence of primary CNS lymphoma generally presents with one or multiple contrast enhancing lesions on MRI. In rare cases, lymphoma cells may proliferate diffusely within the brain parenchyma without mass formation, a pattern termed lymphomatosis cerebri. Lymphomatosis cerebri presents a significant diagnostic challenge, and has not been reported to present with parkinsonism. Here, we present a case of initially mass forming, contrast-enhancing primary CNS lymphoma which remitted following chemotherapy and autologous stem cell transplantation, and recurred 7 years post-transplant with symptoms of parkinsonism and a lack of typical lesions on imaging, with lymphomatosis cerebri confirmed at autopsy.
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Affiliation(s)
- Sarah Merrill
- Mayo Clinic Alix School of Medicine, 13400 E. Shea Blvd, Scottsdale, AZ, 85259, USA
| | - David J Mauler
- Mayo Clinic Alix School of Medicine, 13400 E. Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Kent R Richter
- Mayo Clinic Alix School of Medicine, 13400 E. Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Aditya Raghunathan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 1st St. SW, Rochester, MN, 55905, USA
| | - Jose F Leis
- Department of Medicine, Division of Hematology and Oncology, Mayo Clinic, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Maciej M Mrugala
- Department of Medicine, Division of Hematology and Oncology, Mayo Clinic, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA. .,Department of Neurology, Mayo Clinic, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA.
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30
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Wengler K, He X, Abi-Dargham A, Horga G. Reproducibility assessment of neuromelanin-sensitive magnetic resonance imaging protocols for region-of-interest and voxelwise analyses. Neuroimage 2019; 208:116457. [PMID: 31841683 PMCID: PMC7118586 DOI: 10.1016/j.neuroimage.2019.116457] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 12/05/2019] [Accepted: 12/09/2019] [Indexed: 01/02/2023] Open
Abstract
Neuromelanin-sensitive MRI (NM-MRI) provides a noninvasive measure of the content of neuromelanin (NM), a product of dopamine metabolism that accumulates with age in dopamine neurons of the substantia nigra (SN). NM-MRI has been validated as a measure of both dopamine neuron loss, with applications in neurodegenerative disease, and dopamine function, with applications in psychiatric disease. Furthermore, a voxelwise-analysis approach has been validated to resolve substructures, such as the ventral tegmental area (VTA), within midbrain dopaminergic nuclei thought to have distinct anatomical targets and functional roles. NM-MRI is thus a promising tool that could have diverse research and clinical applications to noninvasively interrogate in vivo the dopamine system in neuropsychiatric illness. Although a test-retest reliability study by Langley et al. using the standard NM-MRI protocol recently reported high reliability, a systematic and comprehensive investigation of the performance of the method for various acquisition parameters and preprocessing methods has not been conducted. In particular, most previous studies used relatively thick MRI slices (~3 mm), compared to the typical in-plane resolution (~0.5 mm) and to the height of the SN (~15 mm), to overcome technical limitations such as specific absorption rate and signal-to-noise ratio, at the cost of partial-volume effects. Here, we evaluated the effect of various acquisition and preprocessing parameters on the strength and test-retest reliability of the NM-MRI signal to determine optimized protocols for both region-of-interest (including whole SN-VTA complex and atlas-defined dopaminergic nuclei) and voxelwise measures. Namely, we determined a combination of parameters that optimizes the strength and reliability of the NM-MRI signal, including acquisition time, slice-thickness, spatial-normalization software, and degree of spatial smoothing. Using a newly developed, detailed acquisition protocol, across two scans separated by 13 days on average, we obtained intra-class correlation values indicating excellent reliability and high contrast, which could be achieved with a different set of parameters depending on the measures of interest and experimental constraints such as acquisition time. Based on this, we provide detailed guidelines covering acquisition through analysis and recommendations for performing NM-MRI experiments with high quality and reproducibility. This work provides a foundation for the optimization and standardization of NM-MRI, a promising MRI approach with growing applications throughout clinical and basic neuroscience.
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Affiliation(s)
- Kenneth Wengler
- Department of Psychiatry, Columbia University, and New York State Psychiatric Institute, New York, NY, USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.
| | - Xiang He
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Anissa Abi-Dargham
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA; Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, and New York State Psychiatric Institute, New York, NY, USA
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31
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Ferini-Strambi L, Fasiello E, Sforza M, Salsone M, Galbiati A. Neuropsychological, electrophysiological, and neuroimaging biomarkers for REM behavior disorder. Expert Rev Neurother 2019; 19:1069-1087. [PMID: 31277555 DOI: 10.1080/14737175.2019.1640603] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Introduction: Rapid eye movement (REM) sleep behavior disorder (RBD) is a REM sleep parasomnia characterized by dream enacting behaviors allowed by the loss of physiological atonia during REM sleep. This disorder is recognized as a prodromal stage of neurodegenerative disease, in particular Parkinson's Disease (PD) and Dementia with Lewy Bodies (DLB). Therefore, a timely identification of biomarkers able to predict an early conversion into neurodegeneration is of utmost importance. Areas covered: In this review, the authors provide updated evidence regarding the presence of neuropsychological, electrophysiological and neuroimaging markers in isolated RBD (iRBD) patients when the neurodegeneration is yet to come. Expert opinion: Cognitive profile of iRBD patients is characterized by the presence of impairment in visuospatial abilities and executive function that is observed in α-synucleinopathies. However, longitudinal studies showed that impaired executive functions, rather than visuospatial abilities, augmented conversion risk. Cortical slowdown during wake and REM sleep suggest the presence of an ongoing neurodegenerative process paralleled by cognitive decline. Neuroimaging findings showed that impairment nigrostriatal dopaminergic system might be a good marker to identify those patients at higher risk of short-term conversion. Although a growing body of evidence the identification of biomarkers still represents a critical issue in iRBD.
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Affiliation(s)
- Luigi Ferini-Strambi
- Department of Clinical Neurosciences, Neurology - Sleep Disorders Center, IRCCS San Raffaele Scientific Institute , Milan , Italy.,Faculty of Psychology, "Vita-Salute" San Raffaele University , Milan , Italy
| | - Elisabetta Fasiello
- Department of Clinical Neurosciences, Neurology - Sleep Disorders Center, IRCCS San Raffaele Scientific Institute , Milan , Italy.,Faculty of Psychology, "Vita-Salute" San Raffaele University , Milan , Italy
| | - Marco Sforza
- Department of Clinical Neurosciences, Neurology - Sleep Disorders Center, IRCCS San Raffaele Scientific Institute , Milan , Italy.,Faculty of Psychology, "Vita-Salute" San Raffaele University , Milan , Italy
| | - Maria Salsone
- Institute of Molecular Bioimaging and Physiology, National Research Council , Catanzaro , Italy
| | - Andrea Galbiati
- Department of Clinical Neurosciences, Neurology - Sleep Disorders Center, IRCCS San Raffaele Scientific Institute , Milan , Italy.,Faculty of Psychology, "Vita-Salute" San Raffaele University , Milan , Italy
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32
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Jin L, Wang J, Wang C, Lian D, Zhou Y, Zhang Y, Lv M, Li Y, Huang Z, Cheng X, Fei G, Liu K, Zeng M, Zhong C. Combined Visualization of Nigrosome-1 and Neuromelanin in the Substantia Nigra Using 3T MRI for the Differential Diagnosis of Essential Tremor and de novo Parkinson's Disease. Front Neurol 2019; 10:100. [PMID: 30809189 PMCID: PMC6379476 DOI: 10.3389/fneur.2019.00100] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 01/25/2019] [Indexed: 01/12/2023] Open
Abstract
Differentiating early-stage Parkinson's disease (PD) from essential tremor (ET) remains challenging. In the current study, we aimed to evaluate whether visual analyses of neuromelanin-sensitive magnetic resonance imaging (NM-MRI) combined with nigrosome-1 (N1) imaging using quantitative susceptibility mapping (QSM) in the substantia nigra (SN) are of diagnostic value in the differentiation of de novo PD from untreated ET. Sixty-eight patients with de novo PD, 25 patients with untreated ET, and 34 control participants underwent NM-MRI and QSM. NM and N1 signals in the SN on MR images were visually evaluated using a 3-point ordinal scale. Receiver operating characteristic (ROC) analyses were performed to determine the diagnostic values of the visual ratings of NM and N1. The diagnostic values of the predicted probabilities were calculated via logistic regression analysis using the combination of NM and N1 visual ratings, as well as their quadratic items. The proportions of invisible NM and invisible N1 were significantly higher in the PD group than those in the ET and control groups (p < 0.001). The sensitivity/specificity for differentiating PD from ET was 0.882/0.800 for NM and 0.794/0.920 for N1, respectively. Combining the two biomarkers, the area under the curve (AUC) of the predicted probabilities was 0.935, and the sensitivity/specificity was 0.853/0.920 when the cutoff value was set to 0.704. Our findings demonstrate that visual analyses combing NM and N1 imaging in the SN may aid in differential diagnosis of PD and ET. Furthermore, our results suggest that patients with PD exhibit larger iron deposits in the SN than those with ET.
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Affiliation(s)
- Lirong Jin
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jian Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Medical Imaging Institute, Shanghai, China
| | - Changpeng Wang
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Danlan Lian
- Department of Radiology, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, China
| | - Ying Zhou
- Department of Neurology, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, China
| | - Yong Zhang
- MR Research, GE Healthcare, Shanghai, China
| | - Minzhi Lv
- Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuanfang Li
- Department of Neurology, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, China
| | - Zhen Huang
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiaoqin Cheng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Guoqiang Fei
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Kai Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Medical Imaging Institute, Shanghai, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Medical Imaging Institute, Shanghai, China
| | - Chunjiu Zhong
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
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De Micco R, Russo A, Tessitore A. Structural MRI in Idiopathic Parkinson's Disease. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2018; 141:405-438. [PMID: 30314605 DOI: 10.1016/bs.irn.2018.08.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Among modern neuroimaging modalities, magnetic resonance imaging (MRI) is a widely available, non-invasive, and cost-effective method to detect structural and functional abnormalities related to neurodegenerative disorders. In the last decades, MRI have been widely implemented to support PD diagnosis as well as to provide further insights into motor and non-motor symptoms pathophysiology, complications and treatment-related effects. Different aspects of the brain morphology and function may be derived from a single scan, by applying different analytic approaches. Biomarkers of neurodegeneration as well as tissue microstructural changes may be extracted from structural MRI techniques. In this chapter, we analyze the role of structural imaging to differentiate PD patients from controls and to define neural substrates of motor and non-motor PD symptoms. Evidence collected in the premotor PD phase will be also critically discussed. White matter as well as gray matter integrity imaging studies has been reviewed, aiming to highlight points of strength and limits to their potential application in clinical settings.
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Affiliation(s)
- Rosa De Micco
- Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy; MRI Research Center SUN-FISM, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Antonio Russo
- Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy; MRI Research Center SUN-FISM, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Alessandro Tessitore
- Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy; MRI Research Center SUN-FISM, University of Campania "Luigi Vanvitelli", Napoli, Italy.
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