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Zhang Y, Zhang C, Wang X, Liu Y, Jin Z, Haacke EM, He N, Li D, Yan F. Iron and neuromelanin imaging in basal ganglia circuitry in Parkinson's disease with freezing of gait. Magn Reson Imaging 2024; 111:229-236. [PMID: 38777243 DOI: 10.1016/j.mri.2024.05.011] [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: 05/17/2024] [Accepted: 05/18/2024] [Indexed: 05/25/2024]
Abstract
OBJECTIVE This study aimed to examine the structural alterations of the deep gray matter (DGM) in the basal ganglia circuitry of Parkinson's disease (PD) patients with freezing of gait (FOG) using quantitative susceptibility mapping (QSM) and neuromelanin-sensitive magnetic resonance imaging (NM-MRI). METHODS Twenty-five (25) PD patients with FOG (PD-FOG), 22 PD patients without FOG (PD-nFOG), and 30 age- and sex-matched healthy controls (HCs) underwent 3-dimensional multi-echo gradient recalled echo and NM-MRI scanning. The mean volume and susceptibility of the DGM on QSM data and the relative contrast (NMRC-SNpc) and volume (NMvolume-SNpc) of the substantia nigra pars compacta on NM-MRI were analyzed among groups. A multiple linear regression analysis was performed to explore the associations of FOG severity with MRI measurements and disease stage. RESULTS The PD-FOG group showed higher susceptibility in the bilateral caudal substantia nigra (SN) compared to the HC group. Both the PD-FOG and PD-nFOG groups showed lower volumes than the HC group in the bilateral caudate and putamen as determined from the QSM data. The NMvolume-SNpc on NM-MRI in the PD-FOG group was significantly lower than in the HC and PD-nFOG groups. Both the PD-FOG and PD-nFOG groups showed significantly decreased NMRC-SNpc. CONCLUSIONS The PD-FOG patients showed abnormal neostriatum atrophy, increases in iron deposition in the SN, and lower NMvolume-SNpc. The structural alterations of the DGM in the basal ganglia circuits could lead to the abnormal output of the basal ganglia circuit to trigger the FOG in PD patients.
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Affiliation(s)
- Youmin Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chencheng Zhang
- Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinhui Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhijia Jin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - E Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Radiology, Wayne State University, Detroit, MI, USA
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Dianyou Li
- Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Mohammadi S, Ghaderi S, Mohammadi H, Fatehi F. Simultaneous Increase of Mean Susceptibility and Mean Kurtosis in the Substantia Nigra as an MRI Neuroimaging Biomarker for Early-Stage Parkinson's Disease: A Systematic Review and Meta-Analysis. J Magn Reson Imaging 2024. [PMID: 39210501 DOI: 10.1002/jmri.29569] [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: 06/13/2024] [Revised: 08/01/2024] [Accepted: 08/01/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Parkinson's disease (PD) is the second most common neurodegenerative disorder. Early detection is crucial for treatment and slowing disease progression. HYPOTHESIS Simultaneous alterations in mean susceptibility (MS) from quantitative susceptibility mapping (QSM) and mean kurtosis (MK) from diffusion kurtosis imaging (DKI) can serve as reliable neuroimaging biomarkers for early-stage PD (ESPD) in the basal ganglia nuclei, including the substantia nigra (SN), putamen (PUT), globus pallidus (GP), and caudate nucleus (CN). STUDY TYPE Systematic review and meta-analysis. POPULATION One hundred eleven patients diagnosed with ESPD and 81 healthy controls (HCs) were included from four studies that utilized both QSM and DKI in both subject groups. FIELD STRENGTH/SEQUENCE Three-dimensional multi-echo gradient echo sequence for QSM and spin echo planar imaging sequence for DKI at 3 Tesla. ASSESSMENT A systematic review and meta-analysis using PRISMA guidelines searched PubMed, Web of Science, and Scopus. STATISTICAL TESTS Random-effects model, standardized mean difference (SMD) to compare MS and MK between ESPD patients and HCs, I2 statistic for heterogeneity, Newcastle-Ottawa Scale (NOS) for risk of bias, and Egger's test for publication bias. A P-value <0.05 was considered significant. RESULTS MS values were significantly higher in SN (SMD 0.72, 95% CI 0.31 to 1.12), PUT (SMD 0.68, 95% CI 0.29 to 1.07), and GP (SMD 0.53, 95% CI 0.19 to 0.87) in ESPD patients compared to HCs. CN did not show a significant difference in MS values (P = 0.15). MK values were significantly higher only in SN (SMD = 0.72, 95% CI 0.16 to 1.27). MK values were not significantly different in PUT (P = 1.00), GP (P = 0.97), and CN (P = 0.59). Studies had high quality (NOS 7-8) and no publication bias (P = 0.967). DATA CONCLUSION Simultaneous use of MS and MK may be useful as an early neuroimaging biomarker for ESPD detection and its differentiation from HCs, with significant differences observed in the SN. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Sana Mohammadi
- Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Sadegh Ghaderi
- Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Mohammadi
- Department of Bioimaging, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences (IUMS), Isfahan, Iran
| | - Farzad Fatehi
- Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
- Neurology Department, University Hospitals of Leicester NHS Trust, Leicester, UK
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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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4
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Ariz M, Martínez M, Alvarez I, Fernández-Seara MA, Castellanos G, Pastor P, Pastor MA, Ortiz de Solórzano C. Automatic Segmentation and Quantification of Nigrosome-1 Neuromelanin and Iron in MRI: A Candidate Biomarker for Parkinson's Disease. J Magn Reson Imaging 2024; 60:534-547. [PMID: 37915245 DOI: 10.1002/jmri.29073] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND There is a lack of automated tools for the segmentation and quantification of neuromelanin (NM) and iron in the nigrosome-1 (N1). Existing tools evaluate the N1 sign, i.e., the presence or absence of the "swallow-tail" in iron-sensitive MRI, or globally analyze the MRI signal in an area containing the N1, without providing a volumetric delineation. PURPOSE Present an automated method to segment the N1 and quantify differences in N1's NM and iron content between Parkinson's disease (PD) patients and healthy controls (HCs). Study whether N1 degeneration is clinically related to PD and could be used as a biomarker of the disease. STUDY TYPE Prospective. SUBJECTS Seventy-one PD (65.3 ± 10.3 years old, 34 female/37 male); 30 HC (62.7 ± 7.8 years old, 17 female/13 male). FIELD STRENGTH/SEQUENCE 3 T Anatomical T1-weighted MPRAGE, NM-MRI T1-weighted gradient with magnetization transfer, susceptibility-weighted imaging (SWI). ASSESSMENT N1 was automatically segmented in SWI images using a multi-image atlas, populated with healthy N1 structures manually annotated by a neurologist. Relative NM and iron content were quantified and their diagnostic performance assessed and compared with the substantia nigra pars compacta (SNc). The association between image parameters and clinically relevant variables was studied. STATISTICAL TESTS Nonparametric tests were used (Mann-Whitney's U, chi-square, and Friedman tests) at P = 0.05. RESULTS N1's relative NM content decreased and relative iron content increased in PD patients compared with HCs (NM-CRHC = 22.55 ± 1.49; NM-CRPD = 19.79 ± 1.92; NM-nVolHC = 2.69 × 10-5 ± 1.02 × 10-5; NM-nVolPD = 1.18 × 10-5 ± 0.96 × 10-5; Iron-CRHC = 10.51 ± 2.64; Iron-CRPD = 19.35 ± 7.88; Iron-nVolHC = 0.72 × 10-5 ± 0.81 × 10-5; Iron-nVolPD = 2.82 × 10-5 ± 2.04 × 10-5). Binary logistic regression analyses combining N1 and SNc image parameters yielded a top AUC = 0.955. Significant correlation was found between most N1 parameters and both disease duration (ρNM-CR = -0.31; ρiron-CR = 0.43; ρiron-nVol = 0.46) and the motor status (ρNM-nVol = -0.27; ρiron-CR = 0.33; ρiron-nVol = 0.28), suggesting NM reduction along with iron accumulation in N1 as the disease progresses. DATA CONCLUSION This method provides a fully automatic N1 segmentation, and the analyses performed reveal that N1 relative NM and iron quantification improves diagnostic performance and suggest a relative NM reduction along with a relative iron accumulation in N1 as the disease progresses. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Mikel Ariz
- Ciberonc and Biomedical Engineering Program, CIMA University of Navarra, Pamplona, Spain
- Department of Electrical, Electronic and Communications Engineering, Public University of Navarre, Pamplona, Spain
| | - Martín Martínez
- Neuroimaging Laboratory, University of Navarra, School of Medicine, Pamplona, Spain
| | - Ignacio Alvarez
- Movement Disorders Unit, Neurology, University Hospital Mútua de Terrassa, Terrassa, Barcelona, Spain
| | - Maria A Fernández-Seara
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
| | - Gabriel Castellanos
- Department of Physiological Sciences, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Pau Pastor
- Unit of Neurodegenerative Diseases, Department of Neurology, University Hospital Germans Trias i Pujol, and Germans Trias i Pujol Research Institute (IGTP), Badalona, Barcelona, Spain
| | - Maria A Pastor
- Neuroimaging Laboratory, University of Navarra, School of Medicine, Pamplona, Spain
- Movement Disorders Unit, Neurology, University of Navarra, Pamplona, Spain
| | - Carlos Ortiz de Solórzano
- Ciberonc and Biomedical Engineering Program, CIMA University of Navarra, Pamplona, Spain
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
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5
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Zarkali A, Thomas GEC, Zetterberg H, Weil RS. Neuroimaging and fluid biomarkers in Parkinson's disease in an era of targeted interventions. Nat Commun 2024; 15:5661. [PMID: 38969680 PMCID: PMC11226684 DOI: 10.1038/s41467-024-49949-9] [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: 07/26/2023] [Accepted: 06/19/2024] [Indexed: 07/07/2024] Open
Abstract
A major challenge in Parkinson's disease is the variability in symptoms and rates of progression, underpinned by heterogeneity of pathological processes. Biomarkers are urgently needed for accurate diagnosis, patient stratification, monitoring disease progression and precise treatment. These were previously lacking, but recently, novel imaging and fluid biomarkers have been developed. Here, we consider new imaging approaches showing sensitivity to brain tissue composition, and examine novel fluid biomarkers showing specificity for pathological processes, including seed amplification assays and extracellular vesicles. We reflect on these biomarkers in the context of new biological staging systems, and on emerging techniques currently in development.
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Affiliation(s)
- Angeliki Zarkali
- Dementia Research Centre, Institute of Neurology, UCL, London, UK.
| | | | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Rimona S Weil
- Dementia Research Centre, Institute of Neurology, UCL, London, UK
- Department of Advanced Neuroimaging, UCL, London, UK
- Movement Disorders Centre, UCL, London, UK
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6
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Stige KE, Kverneng SU, Sharma S, Skeie GO, Sheard E, Søgnen M, Geijerstam SA, Vetås T, Wahlvåg AG, Berven H, Buch S, Reese D, Babiker D, Mahdi Y, Wade T, Miranda GP, Ganguly J, Tamilselvam YK, Chai JR, Bansal S, Aur D, Soltani S, Adams S, Dölle C, Dick F, Berntsen EM, Grüner R, Brekke N, Riemer F, Goa PE, Haugarvoll K, Haacke EM, Jog M, Tzoulis C. The STRAT-PARK cohort: A personalized initiative to stratify Parkinson's disease. Prog Neurobiol 2024; 236:102603. [PMID: 38604582 DOI: 10.1016/j.pneurobio.2024.102603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/15/2024] [Accepted: 04/07/2024] [Indexed: 04/13/2024]
Abstract
The STRAT-PARK initiative aims to provide a platform for stratifying Parkinson's disease (PD) into biological subtypes, using a bottom-up, multidisciplinary biomarker-based and data-driven approach. PD is a heterogeneous entity, exhibiting high interindividual clinicopathological variability. This diversity suggests that PD may encompass multiple distinct biological entities, each driven by different molecular mechanisms. Molecular stratification and identification of disease subtypes is therefore a key priority for understanding and treating PD. STRAT-PARK is a multi-center longitudinal cohort aiming to recruit a total of 2000 individuals with PD and neurologically healthy controls from Norway and Canada, for the purpose of identifying molecular disease subtypes. Clinical assessment is performed annually, whereas biosampling, imaging, and digital and neurophysiological phenotyping occur every second year. The unique feature of STRAT-PARK is the diversity of collected biological material, including muscle biopsies and platelets, tissues particularly useful for mitochondrial biomarker research. Recruitment rate is ∼150 participants per year. By March 2023, 252 participants were included, comprising 204 cases and 48 controls. STRAT-PARK is a powerful stratification initiative anticipated to become a global research resource, contributing to personalized care in PD.
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Affiliation(s)
- Kjersti Eline Stige
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway; The Department of Neuromedicine and Movement Sciences, Norwegian University of Science and Technology, Trondheim 7491, Norway; Department of Neurology and Clinical Neurophysiology, St Olav's University Hospital, Trondheim 7006, Norway
| | - Simon Ulvenes Kverneng
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Soumya Sharma
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Geir-Olve Skeie
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Erika Sheard
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Mona Søgnen
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Solveig Af Geijerstam
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Therese Vetås
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Anne Grete Wahlvåg
- Department of Neurology and Clinical Neurophysiology, St Olav's University Hospital, Trondheim 7006, Norway
| | - Haakon Berven
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Sagar Buch
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - David Reese
- Imaging Research Laboratories, Robarts Research Institute, Ontario, London N6A 5B7, Canada
| | - Dina Babiker
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Yekta Mahdi
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Trevor Wade
- Department of Medical Biophysics, Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, Ontario, London N6A 6B7, Canada
| | - Gala Prado Miranda
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Jacky Ganguly
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Yokhesh Krishnasamy Tamilselvam
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada; Department of Electrical and Computer Engineering, Canadian Surgical Technologies and Advanced Robotics (CSTAR), University of Western Ontario (UWO), Ontario, London, Canada
| | - Jia Ren Chai
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Saurabh Bansal
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Dorian Aur
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Sima Soltani
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Scott Adams
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada; School of Communication Sciences & Disorders, Faculty of Health Sciences, Western University, Canada
| | - Christian Dölle
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Fiona Dick
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Erik Magnus Berntsen
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim 7006, Norway; Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Renate Grüner
- Department of Physics and Technology, University of Bergen, Bergen 5007, Norway; Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Post Office Box 1400, Bergen 5021, Norway
| | - Njål Brekke
- Department of Physics and Technology, University of Bergen, Bergen 5007, Norway; Radiology Department, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Frank Riemer
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Post Office Box 1400, Bergen 5021, Norway
| | - Pål Erik Goa
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim 7006, Norway; Department of Physics, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Kristoffer Haugarvoll
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - E Mark Haacke
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA; Department of Radiology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Mandar Jog
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Charalampos Tzoulis
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway.
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7
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Liu P, Wang X, Zhang Y, Huang P, Jin Z, Cheng Z, Chen Y, Xu Q, Ghassaban K, Liu Y, Chen S, He N, Yan F, Haacke EM. PENCIL imaging: A novel approach for neuromelanin sensitive MRI in Parkinson's disease. Neuroimage 2024; 291:120588. [PMID: 38537765 DOI: 10.1016/j.neuroimage.2024.120588] [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: 08/31/2023] [Revised: 03/11/2024] [Accepted: 03/25/2024] [Indexed: 04/01/2024] Open
Abstract
BACKGROUND Parkinson's disease (PD) is associated with the loss of neuromelanin (NM) and increased iron in the substantia nigra (SN). Magnetization transfer contrast (MTC) is widely used for NM visualization but has limitations in brain coverage and scan time. This study aimed to develop a new approach called Proton-density Enhanced Neuromelanin Contrast in Low flip angle gradient echo (PENCIL) imaging to visualize NM in the SN. METHODS This study included 30 PD subjects and 50 healthy controls (HCs) scanned at 3T. PENCIL and MTC images were acquired. NM volume in the SN pars compacta (SNpc), normalized image contrast (Cnorm), and contrast-to-noise ratio (CNR) were calculated. The change of NM volume in the SNpc with age was analyzed using the HC data. A group analysis compared differences between PD subjects and HCs. Receiver operating characteristic (ROC) analysis and area under the curve (AUC) calculations were used to evaluate the diagnostic performance of NM volume and CNR in the SNpc. RESULTS PENCIL provided similar visualization and structural information of NM compared to MTC. In HCs, PENCIL showed higher NM volume in the SNpc than MTC, but this difference was not observed in PD subjects. PENCIL had higher CNR, while MTC had higher Cnorm. Both methods revealed a similar pattern of NM volume in SNpc changes with age. There were no significant differences in AUCs between NM volume in SNpc measured by PENCIL and MTC. Both methods exhibited comparable diagnostic performance in this regard. CONCLUSIONS PENCIL imaging provided improved CNR compared to MTC and showed similar diagnostic performance for differentiating PD subjects from HCs. The major advantage is PENCIL has rapid whole-brain coverage and, when using STAGE imaging, offers a one-stop quantitative assessment of tissue properties.
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Affiliation(s)
- Peng Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Xinhui Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Youmin Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Pei Huang
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Zhijia Jin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, 4201St. Antoine, Detroit, MI 48201, USA
| | - Qiuyun Xu
- SpinTech MRI, Bingham Farms, MI 48025, USA
| | | | - Yu Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Shengdi Chen
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China.
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China; Faculty of Medical Imaging Technology, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| | - E Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China; Department of Neurology, Wayne State University School of Medicine, 4201St. Antoine, Detroit, MI 48201, USA; Department of Radiology, Wayne State University School of Medicine, 3990 John R Street, MRI Concourse, Detroit, MI 48201, USA.
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8
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Lee S, Kovacs GG. The Irony of Iron: The Element with Diverse Influence on Neurodegenerative Diseases. Int J Mol Sci 2024; 25:4269. [PMID: 38673855 PMCID: PMC11049980 DOI: 10.3390/ijms25084269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/07/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Iron accumulation in the brain is a common feature of many neurodegenerative diseases. Its involvement spans across the main proteinopathies involving tau, amyloid-beta, alpha-synuclein, and TDP-43. Accumulating evidence supports the contribution of iron in disease pathologies, but the delineation of its pathogenic role is yet challenged by the complex involvement of iron in multiple neurotoxicity mechanisms and evidence supporting a reciprocal influence between accumulation of iron and protein pathology. Here, we review the major proteinopathy-specific observations supporting four distinct hypotheses: (1) iron deposition is a consequence of protein pathology; (2) iron promotes protein pathology; (3) iron protects from or hinders protein pathology; and (4) deposition of iron and protein pathology contribute parallelly to pathogenesis. Iron is an essential element for physiological brain function, requiring a fine balance of its levels. Understanding of disease-related iron accumulation at a more intricate and systemic level is critical for advancements in iron chelation therapies.
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Affiliation(s)
- Seojin Lee
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON M5T 0S8, Canada;
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Gabor G. Kovacs
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON M5T 0S8, Canada;
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Edmond J. Safra Program in Parkinson’s Disease, Rossy Program for PSP Research and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, ON M5T 2S8, Canada
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9
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Duan M, Pan R, Gao Q, Wu X, Lin H, Yuan J, Zhang Y, Liu L, Tian Y, Fu T. A rapid multi-parametric quantitative MR imaging method to assess Parkinson's disease: a feasibility study. BMC Med Imaging 2024; 24:58. [PMID: 38443786 PMCID: PMC10916029 DOI: 10.1186/s12880-024-01229-0] [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: 10/19/2023] [Accepted: 02/15/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND MULTIPLEX is a single-scan three-dimensional multi-parametric MRI technique that provides 1 mm isotropic T1-, T2*-, proton density- and susceptibility-weighted images and the corresponding quantitative maps. This study aimed to investigate its feasibility of clinical application in Parkinson's disease (PD). METHODS 27 PD patients and 23 healthy control (HC) were recruited and underwent a MULTIPLEX scanning. All image reconstruction and processing were automatically performed with in-house C + + programs on the Automatic Differentiation using Expression Template platform. According to the HybraPD atlas consisting of 12 human brain subcortical nuclei, the region-of-interest (ROI) based analysis was conducted to extract quantitative parameters, then identify PD-related abnormalities from the T1, T2* and proton density maps and quantitative susceptibility mapping (QSM), by comparing patients and HCs. RESULTS The ROI-based analysis revealed significantly decreased mean T1 values in substantia nigra pars compacta and habenular nuclei, mean T2* value in subthalamic nucleus and increased mean QSM value in subthalamic nucleus in PD patients, compared to HCs (all p values < 0.05 after FDR correction). The receiver operating characteristic analysis showed all these four quantitative parameters significantly contributed to PD diagnosis (all p values < 0.01 after FDR correction). Furthermore, the two quantitative parameters in subthalamic nucleus showed hemicerebral differences in regard to the clinically dominant side among PD patients. CONCLUSIONS MULTIPLEX might be feasible for clinical application to assist in PD diagnosis and provide possible pathological information of PD patients' subcortical nucleus and dopaminergic midbrain regions.
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Affiliation(s)
- Min Duan
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Rongrong Pan
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, Jiangsu Province, China
| | - Qing Gao
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, Jiangsu Province, China
| | - Xinying Wu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, Jiangsu Province, China
| | - Hai Lin
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Yamei Zhang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, Jiangsu Province, China
| | - Lindong Liu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, Jiangsu Province, China
| | - Youyong Tian
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, Jiangsu Province, China.
| | - Tong Fu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, Jiangsu Province, China.
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10
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Alushaj E, Handfield-Jones N, Kuurstra A, Morava A, Menon RS, Owen AM, Sharma M, Khan AR, MacDonald PA. Increased iron in the substantia nigra pars compacta identifies patients with early Parkinson'sdisease: A 3T and 7T MRI study. Neuroimage Clin 2024; 41:103577. [PMID: 38377722 PMCID: PMC10944193 DOI: 10.1016/j.nicl.2024.103577] [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: 08/07/2023] [Revised: 12/19/2023] [Accepted: 02/07/2024] [Indexed: 02/22/2024]
Abstract
Degeneration in the substantia nigra (SN) pars compacta (SNc) underlies motor symptoms in Parkinson's disease (PD). Currently, there are no neuroimaging biomarkers that are sufficiently sensitive, specific, reproducible, and accessible for routine diagnosis or staging of PD. Although iron is essential for cellular processes, it also mediates neurodegeneration. MRI can localize and quantify brain iron using magnetic susceptibility, which could potentially provide biomarkers of PD. We measured iron in the SNc, SN pars reticulata (SNr), total SN, and ventral tegmental area (VTA), using quantitative susceptibility mapping (QSM) and R2* relaxometry, in PD patients and age-matched healthy controls (HCs). PD patients, diagnosed within five years of participation and HCs were scanned at 3T (22 PD and 23 HCs) and 7T (17 PD and 21 HCs) MRI. Midbrain nuclei were segmented using a probabilistic subcortical atlas. QSM and R2* values were measured in midbrain subregions. For each measure, groups were contrasted, with Age and Sex as covariates, and receiver operating characteristic (ROC) curve analyses were performed with repeated k-fold cross-validation to test the potential of our measures to classify PD patients and HCs. Statistical differences of area under the curves (AUCs) were compared using the Hanley-MacNeil method (QSM versus R2*; 3T versus 7T MRI). PD patients had higher QSM values in the SNc at both 3T (padj = 0.001) and 7T (padj = 0.01), but not in SNr, total SN, or VTA, at either field strength. No significant group differences were revealed using R2* in any midbrain region at 3T, though increased R2* values in SNc at 7T MRI were marginally significant in PDs compared to HCs (padj = 0.052). ROC curve analyses showed that SNc iron measured with QSM, distinguished early PD patients from HCs at the single-subject level with good diagnostic accuracy, using 3T (mean AUC = 0.83, 95 % CI = 0.82-0.84) and 7T (mean AUC = 0.80, 95 % CI = 0.79-0.81) MRI. Mean AUCs reported here are from averages of tests in the hold-out fold of cross-validated samples. The Hanley-MacNeil method demonstrated that QSM outperforms R2* in discriminating PD patients from HCs at 3T, but not 7T. There were no significant differences between 3T and 7T in diagnostic accuracy of QSM values in SNc. This study highlights the importance of segmenting midbrain subregions, performed here using a standardized atlas, and demonstrates high accuracy of SNc iron measured with QSM at 3T MRI in identifying early PD patients. QSM measures of SNc show potential for inclusion in neuroimaging diagnostic biomarkers of early PD. An MRI diagnostic biomarker of PD would represent a significant clinical advance.
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Affiliation(s)
- Erind Alushaj
- Department of Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 3K7, Canada; Western Institute for Neuroscience, Western University, London, Ontario N6A 3K7, Canada
| | - Nicholas Handfield-Jones
- Department of Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 3K7, Canada; Western Institute for Neuroscience, Western University, London, Ontario N6A 3K7, Canada
| | - Alan Kuurstra
- Robarts Research Institute, Western University, London, Ontario N6A 3K7, Canada; Department of Medical Biophysics, Western University, London, Ontario N6A 3K7, Canada
| | - Anisa Morava
- School of Kinesiology, Faculty of Health Sciences, Western University, London, Ontario N6A 3K7, Canada
| | - Ravi S Menon
- Robarts Research Institute, Western University, London, Ontario N6A 3K7, Canada; Department of Medical Biophysics, Western University, London, Ontario N6A 3K7, Canada
| | - Adrian M Owen
- Western Institute for Neuroscience, Western University, London, Ontario N6A 3K7, Canada; Department of Physiology and Pharmacology, Western University, London, Ontario N6A 3K7, Canada
| | - Manas Sharma
- Department of Radiology, Western University, London, Ontario N6A 3K7, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario N6A 3K7, Canada
| | - Ali R Khan
- Robarts Research Institute, Western University, London, Ontario N6A 3K7, Canada; Department of Medical Biophysics, Western University, London, Ontario N6A 3K7, Canada
| | - Penny A MacDonald
- Western Institute for Neuroscience, Western University, London, Ontario N6A 3K7, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario N6A 3K7, Canada.
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11
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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: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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12
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Lee MD. Editorial for "Enhancing Nigrosome-1 Sign Identification via Interpretable AI Using True Susceptibility Weighted Imaging". J Magn Reson Imaging 2024. [PMID: 38270224 DOI: 10.1002/jmri.29260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 12/28/2023] [Indexed: 01/26/2024] Open
Affiliation(s)
- Matthew D Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, New York, USA
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13
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Wang C, He N, Zhang Y, Li Y, Huang P, Liu Y, Jin Z, Cheng Z, Liu Y, Wang Y, Zhang C, Haacke EM, Chen S, Yan F, Yang G. Enhancing Nigrosome-1 Sign Identification via Interpretable AI using True Susceptibility Weighted Imaging. J Magn Reson Imaging 2024. [PMID: 38236577 DOI: 10.1002/jmri.29245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/05/2024] [Accepted: 01/08/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND Nigrosome 1 (N1), the largest nigrosome region in the ventrolateral area of the substantia nigra pars compacta, is identifiable by the "N1 sign" in long echo time gradient echo MRI. The N1 sign's absence is a vital Parkinson's disease (PD) diagnostic marker. However, it is challenging to visualize and assess the N1 sign in clinical practice. PURPOSE To automatically detect the presence or absence of the N1 sign from true susceptibility weighted imaging by using deep-learning method. STUDY TYPE Prospective. POPULATION/SUBJECTS 453 subjects, including 225 PD patients, 120 healthy controls (HCs), and 108 patients with other movement disorders, were prospectively recruited including 227 males and 226 females. They were divided into training, validation, and test cohorts of 289, 73, and 91 cases, respectively. FIELD STRENGTH/SEQUENCE 3D gradient echo SWI sequence at 3T; 3D multiecho strategically acquired gradient echo imaging at 3T; NM-sensitive 3D gradient echo sequence with MTC pulse at 3T. ASSESSMENT A neuroradiologist with 5 years of experience manually delineated substantia nigra regions. Two raters with 2 and 36 years of experience assessed the N1 sign on true susceptibility weighted imaging (tSWI), QSM with high-pass filter, and magnitude data combined with MTC data. We proposed NINet, a neural model, for automatic N1 sign identification in tSWI images. STATISTICAL TESTS We compared the performance of NINet to the subjective reference standard using Receiver Operating Characteristic analyses, and a decision curve analysis assessed identification accuracy. RESULTS NINet achieved an area under the curve (AUC) of 0.87 (CI: 0.76-0.89) in N1 sign identification, surpassing other models and neuroradiologists. NINet localized the putative N1 sign within tSWI images with 67.3% accuracy. DATA CONCLUSION Our proposed NINet model's capability to determine the presence or absence of the N1 sign, along with its localization, holds promise for enhancing diagnostic accuracy when evaluating PD using MR images. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Chenglong Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Youmin Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Pei Huang
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhijia Jin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yun Liu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yida Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - E Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Biomedical Engineering, Wayne State University, Detroit, Michigan, USA
| | - Shengdi Chen
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
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14
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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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15
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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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16
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Lv K, Liu Y, Chen Y, Buch S, Wang Y, Yu Z, Wang H, Zhao C, Fu D, Wang H, Wang B, Zhang S, Luo Y, Haacke EM, Shen W, Chai C, Xia S. The iron burden of cerebral microbleeds contributes to brain atrophy through the mediating effect of white matter hyperintensity. Neuroimage 2023; 281:120370. [PMID: 37716591 DOI: 10.1016/j.neuroimage.2023.120370] [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: 11/11/2022] [Revised: 05/04/2023] [Accepted: 09/08/2023] [Indexed: 09/18/2023] Open
Abstract
The goal of this work was to explore the total iron burden of cerebral microbleeds (CMBs) using a semi-automatic quantitative susceptibility mapping and to establish its effect on brain atrophy through the mediating effect of white matter hyperintensities (WMH). A total of 95 community-dwelling people were enrolled. Quantitative susceptibility mapping (QSM) combined with a dynamic programming algorithm (DPA) was used to measure the characteristics of 1309 CMBs. WMH were evaluated according to the Fazekas scale, and brain atrophy was assessed using a 2D linear measurement method. Histogram analysis was used to explore the distribution of CMBs susceptibility, volume, and total iron burden, while a correlation analysis was used to explore the relationship between volume and susceptibility. Stepwise regression analysis was used to analyze the risk factors for CMBs and their contribution to brain atrophy. Mediation analysis was used to explore the interrelationship between CMBs and brain atrophy. We found that the frequency distribution of susceptibility of the CMBs was Gaussian in nature with a mean of 201 ppb and a standard deviation of 84 ppb; however, the volume and total iron burden of CMBs were more Rician in nature. A weak but significant correlation between the susceptibility and volume of CMBs was found (r = -0.113, P < 0.001). The periventricular WMH (PVWMH) was a risk factor for the presence of CMBs (number: β = 0.251, P = 0.014; volume: β = 0.237, P = 0.042; total iron burden: β = 0.238, P = 0.020) and was a risk factor for brain atrophy (third ventricle width: β = 0.325, P = 0.001; Evans's index: β = 0.323, P = 0.001). PVWMH had a significant mediating effect on the correlation between CMBs and brain atrophy. In conclusion, QSM along with the DPA can measure the total iron burden of CMBs. PVWMH might be a risk factor for CMBs and may mediate the effect of CMBs on brain atrophy.
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Affiliation(s)
- Ke Lv
- Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Yanzhen Liu
- Department of Radiology, Tianjin Chest Hospital, Tianjin, China
| | - Yongsheng Chen
- Department of Neurology, Wayne State University, Detroit, MI, USA
| | - Sagar Buch
- Department of Neurology, Wayne State University, Detroit, MI, USA
| | - Ying Wang
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA; Department of Radiology, Wayne State University, Detroit, MI, USA
| | - Zhuo Yu
- Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Huiying Wang
- The School of Medicine, Nankai University, Tianjin, China
| | - Chenxi Zhao
- Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Dingwei Fu
- Department of Radiology, The Second Affiliated Hospital of Wannan Medical College, Wuhu, Anhui Province, China
| | - Huapeng Wang
- Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Beini Wang
- Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China
| | | | - Yu Luo
- Department of Radiology, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, China
| | - E Mark Haacke
- Department of Neurology, Wayne State University, Detroit, MI, USA; Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA; Department of Radiology, Wayne State University, Detroit, MI, USA; Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA
| | - Wen Shen
- Department of Radiology, Tianjin Institute of Imaging Medicine, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Chao Chai
- Department of Radiology, Tianjin Institute of Imaging Medicine, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Shuang Xia
- Department of Radiology, Tianjin Institute of Imaging Medicine, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
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17
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Seada SA, van der Eerden AW, Boon AJW, Hernandez-Tamames JA. Quantitative MRI protocol and decision model for a 'one stop shop' early-stage Parkinsonism diagnosis: Study design. Neuroimage Clin 2023; 39:103506. [PMID: 37696098 PMCID: PMC10500558 DOI: 10.1016/j.nicl.2023.103506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/21/2023] [Accepted: 09/04/2023] [Indexed: 09/13/2023]
Abstract
Differentiating among early-stage parkinsonisms is a challenge in clinical practice. Quantitative MRI can aid the diagnostic process, but studies with singular MRI techniques have had limited success thus far. Our objective is to develop a multi-modal MRI method for this purpose. In this review we describe existing methods and present a dedicated quantitative MRI protocol, a decision model and a study design to validate our approach ahead of a pilot study. We present example imaging data from patients and a healthy control, which resemble related literature.
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Affiliation(s)
- Samy Abo Seada
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Anke W van der Eerden
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Agnita J W Boon
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Juan A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Imaging Physics, TU Delft, The Netherlands.
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18
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Wang Y, He N, Zhang C, Zhang Y, Wang C, Huang P, Jin Z, Li Y, Cheng Z, Liu Y, Wang X, Chen C, Cheng J, Liu F, Haacke EM, Chen S, Yang G, Yan F. An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images. Hum Brain Mapp 2023. [PMID: 37335041 PMCID: PMC10365226 DOI: 10.1002/hbm.26399] [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: 09/27/2022] [Revised: 05/11/2023] [Accepted: 06/02/2023] [Indexed: 06/21/2023] Open
Abstract
Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution in deep gray matter (DGM) nuclei. We hypothesized that deep learning (DL) could be used to automatically segment all DGM nuclei and use relevant features for a better differentiation between PD and healthy controls (HC). In this study, we proposed a DL-based pipeline for automatic PD diagnosis based on QSM and T1-weighted (T1W) images. This consists of (1) a convolutional neural network model integrated with multiple attention mechanisms which simultaneously segments caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra from QSM and T1W images, and (2) an SE-ResNeXt50 model with an anatomical attention mechanism, which uses QSM data and the segmented nuclei to distinguish PD from HC. The mean dice values for segmentation of the five DGM nuclei are all >0.83 in the internal testing cohort, suggesting that the model could segment brain nuclei accurately. The proposed PD diagnosis model achieved area under the the receiver operating characteristic curve (AUCs) of 0.901 and 0.845 on independent internal and external testing cohorts, respectively. Gradient-weighted class activation mapping (Grad-CAM) heatmaps were used to identify contributing nuclei for PD diagnosis on patient level. In conclusion, the proposed approach can potentially be used as an automatic, explainable pipeline for PD diagnosis in a clinical setting.
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Affiliation(s)
- Yida Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chunyan Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Youmin Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chenglong Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Pei Huang
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhijia Jin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinhui Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chen Chen
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Fangtao Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ewart Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Biomedical Engineering, Wayne State University, Detroit, Michigan, USA
| | - Shengdi Chen
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
- Institute of Brain and Education Innovation, East China Normal University, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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19
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Si W, Guo Y, Zhang Q, Zhang J, Wang Y, Feng Y. Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs. Front Neurosci 2023; 17:1165446. [PMID: 37383103 PMCID: PMC10293650 DOI: 10.3389/fnins.2023.1165446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 05/17/2023] [Indexed: 06/30/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) quantifies the distribution of magnetic susceptibility and shows great potential in assessing tissue contents such as iron, myelin, and calcium in numerous brain diseases. The accuracy of QSM reconstruction was challenged by an ill-posed field-to-susceptibility inversion problem, which is related to the impaired information near the zero-frequency response of the dipole kernel. Recently, deep learning methods demonstrated great capability in improving the accuracy and efficiency of QSM reconstruction. However, the construction of neural networks in most deep learning-based QSM methods did not take the intrinsic nature of the dipole kernel into account. In this study, we propose a dipole kernel-adaptive multi-channel convolutional neural network (DIAM-CNN) method for the dipole inversion problem in QSM. DIAM-CNN first divided the original tissue field into high-fidelity and low-fidelity components by thresholding the dipole kernel in the frequency domain, and it then inputs the two components as additional channels into a multichannel 3D Unet. QSM maps from the calculation of susceptibility through multiple orientation sampling (COSMOS) were used as training labels and evaluation reference. DIAM-CNN was compared with two conventional model-based methods [morphology enabled dipole inversion (MEDI) and improved sparse linear equation and least squares (iLSQR) and one deep learning method (QSMnet)]. High-frequency error norm (HFEN), peak signal-to-noise-ratio (PSNR), normalized root mean squared error (NRMSE), and the structural similarity index (SSIM) were reported for quantitative comparisons. Experiments on healthy volunteers demonstrated that the DIAM-CNN results had superior image quality to those of the MEDI, iLSQR, or QSMnet results. Experiments on data with simulated hemorrhagic lesions demonstrated that DIAM-CNN produced fewer shadow artifacts around the bleeding lesion than the compared methods. This study demonstrates that the incorporation of dipole-related knowledge into the network construction has a potential to improve deep learning-based QSM reconstruction.
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Affiliation(s)
- Wenbin Si
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Yihao Guo
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Qianqian Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Jinwei Zhang
- Department of Biomedical Engineering, College of Engineering, Cornell University, Ithaca, NY, United States
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY, United States
| | - Yi Wang
- Department of Biomedical Engineering, College of Engineering, Cornell University, Ithaca, NY, United States
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY, United States
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence and Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
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20
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Martínez M, Ariz M, Alvarez I, Castellanos G, Aguilar M, Hernández-Vara J, Caballol N, Garrido A, Bayés À, Vilas D, Marti MJ, Pastor P, de Solórzano CO, Pastor MA. Brainstem neuromelanin and iron MRI reveals a precise signature for idiopathic and LRRK2 Parkinson's disease. NPJ Parkinsons Dis 2023; 9:62. [PMID: 37061532 PMCID: PMC10105708 DOI: 10.1038/s41531-023-00503-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/24/2023] [Indexed: 04/17/2023] Open
Abstract
Neuromelanin (NM) loss in substantia nigra pars compacta (SNc) and locus coeruleus (LC) reflects neuronal death in Parkinson's disease (PD). Since genetically-determined PD shows varied clinical expressivity, we wanted to accurately quantify and locate brainstem NM and iron, to discover whether specific MRI patterns are linked to Leucine-rich repeat kinase 2 G2019S PD (LRRK2-PD) or idiopathic Parkinson's disease (iPD). A 3D automated MRI atlas-based segmentation pipeline (3D-ABSP) for NM/iron-sensitive MRI images topographically characterized the SNc, LC, and red nucleus (RN) neuronal loss and calculated NM/iron contrast ratio (CR) and normalized volume (nVol). Left-side NM nVol was larger in all groups. PD had lower NM CR and nVol in ventral-caudal SNc, whereas iron increased in lateral, medial-rostral, and caudal SNc. The SNc NM CR reduction was associated with psychiatric symptoms. LC CR and nVol discriminated better among subgroups: LRRK2-PD had similar LC NM CR and nVol as that of controls, and larger LC NM nVol and RN iron CR than iPD. PD showed higher iron SNc nVol than controls, especially among LRRK2-PD. ROC analyses showed an AUC > 0.92 for most pairwise subgroup comparisons, with SNc NM being the best discriminator between HC and PD. NM measures maintained their discriminator power considering the subgroup of PD patients with less than 5 years of disease duration. The SNc iron CR and nVol increase was associated with longer disease duration in PD patients. The 3D-ABSP sensitively identified NM and iron MRI patterns strongly correlated with phenotypic PD features.
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Affiliation(s)
- Martín Martínez
- Neuroimaging Laboratory, University of Navarra, School of Medicine, Pamplona, Spain
- School of Education and Psychology, University of Navarra, Pamplona, Spain
| | - Mikel Ariz
- Ciberonc and Solid Tumours and Biomarkers Program, CIMA University of Navarra, Pamplona, Spain
| | - Ignacio Alvarez
- Movement Disorders Unit, Neurology, University Hospital Mútua de Terrassa, Terrassa, Barcelona, Spain
| | - Gabriel Castellanos
- Department of Physiological Sciences, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Miquel Aguilar
- Movement Disorders Unit, Neurology, University Hospital Mútua de Terrassa, Terrassa, Barcelona, Spain
| | - Jorge Hernández-Vara
- Neurology Department, Hospital Universitari Vall D´Hebron, Neurodegenerative Diseases Research Group, Vall D'Hebron Research Institute (VHIR), Barcelona, Spain
| | - Núria Caballol
- Department of Neurology, Complex Hospitalari Moisès Broggi, Sant Joan Despí, Barcelona, Spain
- Parkinson and Movement disorders Unit, Hospital Quirón-Teknon, Barcelona, Spain
| | - Alicia Garrido
- Parkinson's Disease and Movement Disorders Unit, Neurology Service, IDIBAPS, CIBERNED, Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas: CB06/05/0018-ISCIII), ERN-RND Hospital Clínic i Provincial de Barcelona, Barcelona, Catalonia, Spain
- Department of Medicine & Institut de Neurociències of the University of Barcelona, Barcelona, Catalonia, Spain
| | - Àngels Bayés
- Parkinson and Movement disorders Unit, Hospital Quirón-Teknon, Barcelona, Spain
| | - Dolores Vilas
- Unit of Neurodegenerative diseases, Department of Neurology, University Hospital Germans Trias i Pujol, Badalona, Spain
- Neurosciences, The Germans Trias i Pujol Research Institute (IGTP) Badalona, Badalona, Catalonia, Spain
| | - Maria Jose Marti
- Parkinson's Disease and Movement Disorders Unit, Neurology Service, IDIBAPS, CIBERNED, Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas: CB06/05/0018-ISCIII), ERN-RND Hospital Clínic i Provincial de Barcelona, Barcelona, Catalonia, Spain
- Department of Medicine & Institut de Neurociències of the University of Barcelona, Barcelona, Catalonia, Spain
| | - Pau Pastor
- Unit of Neurodegenerative diseases, Department of Neurology, University Hospital Germans Trias i Pujol, Badalona, Spain.
- Neurosciences, The Germans Trias i Pujol Research Institute (IGTP) Badalona, Badalona, Catalonia, Spain.
| | | | - Maria A Pastor
- Neuroimaging Laboratory, University of Navarra, School of Medicine, Pamplona, Spain.
- Neurosciences, School of Medicine, University of Navarra, Pamplona, Spain.
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21
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Jokar M, Jin Z, Huang P, Wang Y, Zhang Y, Li Y, Cheng Z, Liu Y, Tang R, Shi X, Min J, Liu F, Chen S, He N, Haacke EM, Yan F. Diagnosing Parkinson's disease by combining neuromelanin and iron imaging features using an automated midbrain template approach. Neuroimage 2023; 266:119814. [PMID: 36528314 DOI: 10.1016/j.neuroimage.2022.119814] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 12/01/2022] [Accepted: 12/13/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND AND PURPOSE Early diagnosis of Parkinson's disease (PD) is still a clinical challenge. Most previous studies using manual or semi-automated methods for segmenting the substantia nigra (SN) are time-consuming and, despite raters being well-trained, individual variation can be significant. In this study, we used a template-based, automatic, SN subregion segmentation pipeline to detect the neuromelanin (NM) and iron features in the SN and SN pars compacta (SNpc) derived from a single 3D magnetization transfer contrast (MTC) gradient echo (GRE) sequence in an attempt to develop a comprehensive imaging biomarker that could be used to diagnose PD. MATERIALS AND METHODS A total of 100 PD patients and 100 age- and sex-matched healthy controls (HCs) were imaged on a 3T scanner. NM-based SN (SNNM) boundaries and iron-based SN (SNQSM) boundaries and their overlap region (representing the SNpc) were delineated automatically using a template-based SN subregion segmentation approach based on quantitative susceptibility mapping (QSM) and NM images derived from the same MTC-GRE sequence. All PD and HC subjects were evaluated for the nigrosome-1 (N1) sign by two raters independently. Receiver Operating Characteristic (ROC) analyses were performed to evaluate the utility of SNNM volume, SNQSM volume, SNpc volume and iron content with a variety of thresholds as well as the N1 sign in diagnosing PD. Correlation analyses were performed to study the relationship between these imaging measures and the clinical scales in PD. RESULTS In this study, we verified the value of the fully automatic template based midbrain deep gray matter mapping approach in differentiating PD patients from HCs. The automatic segmentation of the SN in PD patients led to satisfactory DICE similarity coefficients and volume ratio (VR) values of 0.81 and 1.17 for the SNNM, and 0.87 and 1.05 for the SNQSM, respectively. For the HC group, the average DICE similarity coefficients and VR values were 0.85 and 0.94 for the SNNM, and 0.87 and 0.96 for the SNQSM, respectively. The SNQSM volume tended to decrease with age for both the PD and HC groups but was more severe for the PD group. For diagnosing PD, the N1 sign performed reasonably well by itself (Area Under the Curve (AUC) = 0.783). However, combining the N1 sign with the other quantitative measures (SNNM volume, SNQSM volume, SNpc volume and iron content) resulted in an improved diagnosis of PD with an AUC as high as 0.947 (using an SN threshold of 50ppb and an NM threshold of 0.15). Finally, the SNQSM volume showed a negative correlation with the MDS-UPDRS III (R2 = 0.1, p = 0.036) and the Hoehn and Yahr scale (R2 = 0.04, p = 0.013) in PD patients. CONCLUSION In summary, this fully automatic template based deep gray matter mapping approach performs well in the segmentation of the SN and its subregions for not only HCs but also PD patients with SN degeneration. The combination of the N1 sign with other quantitative measures (SNNM volume, SNQSM volume, SNpc volume and iron content) resulted in an AUC of 0.947 and provided a comprehensive set of imaging biomarkers that, potentially, could be used to diagnose PD clinically.
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Affiliation(s)
| | - Zhijia Jin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Pei Huang
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Ying Wang
- SpinTech MRI, Inc., Bingham Farms, MI, USA; Department of Radiology, Wayne State University, Detroit, MI, USA
| | - Youmin Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Yu Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Rongbiao Tang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Xiaofeng Shi
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Jihua Min
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Fangtao Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Shengdi Chen
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China.
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China.
| | - E Mark Haacke
- SpinTech MRI, Inc., Bingham Farms, MI, USA; Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China; Department of Radiology, Wayne State University, Detroit, MI, USA; Department of Neurology, Wayne State University, Detroit, MI, USA.
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China.
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22
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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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23
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Wen J, Guo T, Wu J, Bai X, Zhou C, Wu H, Liu X, Chen J, Cao Z, Gu L, Pu J, Zhang B, Zhang M, Guan X, Xu X. Nigral Iron Deposition Influences Disease Severity by Modulating the Effect of Parkinson's Disease on Brain Networks. JOURNAL OF PARKINSON'S DISEASE 2022; 12:2479-2492. [PMID: 36336939 PMCID: PMC9837680 DOI: 10.3233/jpd-223372] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND In Parkinson's disease (PD), excessive iron deposition in the substantia nigra may exacerbate α-synuclein aggregation, facilitating the degeneration of dopaminergic neurons and their neural projection. OBJECTIVE To investigate the interaction effect between nigral iron deposition and PD status on brain networks. METHODS Eighty-five PD patients and 140 normal controls (NC) were included. Network function and nigral iron were measured using multi-modality magnetic resonance imaging. According to the median of nigral magnetic susceptibility of NC (0.095 ppm), PD and NC were respectively divided into high and low nigral iron group. The main and interaction effects were investigated by mixed effect analysis. RESULTS The main effect of disease was observed in basal ganglia network (BGN) and visual network (VN). The interaction effect between nigral iron and PD status was observed in left inferior frontal gyrus and left insular lobe in BGN, as well as right middle occipital gyrus, right superior temporal gyrus, and bilateral cuneus in VN. Furthermore, multiple mediation analysis revealed that the functional connectivity of interaction effect clusters in BGN and medial VN partially mediated the relationship between nigral iron and Unified Parkinson's Disease Rating Scale II score. CONCLUSION Our study demonstrates an interaction of nigral iron deposition and PD status on brain networks, that is, nigral iron deposition is associated with the change of brain network configuration exclusively when in PD. We identified a potential causal mediation pathway for iron to affect disease severity that was mediated by both BGN dysfunction and VN hyperfunction in PD.
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Affiliation(s)
- Jiaqi Wen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Bai
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haoting Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingwen Chen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhengye Cao
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Pu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China,Correspondence to: Xiaojun Xu, MD, Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 310009, China. Tel.: +86 0571 87315255; Fax: +86 0571 87315255; E-mail: and Xiaojun Guan, PhD, Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Shangcheng District, Hangzhou 310009, China. Tel.: +86 0571 87315255; Fax: +86 0571 87315255;
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China,Correspondence to: Xiaojun Xu, MD, Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 310009, China. Tel.: +86 0571 87315255; Fax: +86 0571 87315255; E-mail: and Xiaojun Guan, PhD, Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Shangcheng District, Hangzhou 310009, China. Tel.: +86 0571 87315255; Fax: +86 0571 87315255;
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Zhang D, Yao J, Sun J, Tong Q, Zhu S, Wang J, Chen L, Ma J, He H, Wu T. Quantitative Susceptibility Mapping and Free Water Imaging of Substantia Nigra in Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2022; 12:2469-2478. [PMID: 36404557 DOI: 10.3233/jpd-223499] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The utility of imaging methods to detect iron content in the substantia nigra pars compacta (SNc) and free water imaging in the posterior substantia nigra (pSN) has the potential to be imaging markers for the detection of Parkinson's disease (PD). OBJECTIVE This study aimed to compare the discriminative power of above methods, and whether the combination can improve the diagnostic potential of PD. METHODS Quantitative susceptibility mapping (QSM) and diffusion-weighted data were obtained from 41 healthy controls (HC), 37 patients with idiopathic REM sleep behavior disorder (RBD), and 65 patients with PD. Mean QSM values of bilateral SNc and mean isotropic volume fraction (Viso) values of bilateral pSN (mean QSM|Viso values of bilateral SNc|pSN) were separately calculated and compared among the groups. RESULTS Mean QSM|Viso values of bilateral SNc|pSN were significantly higher for RBD and PD patients compared to HC and were significantly higher in PD patients than in RBD patients. The power of the mean QSM|Viso values of bilateral SNc|pSN and combined mean QSM and Viso values was 0.873, 0.870, and 0.961 in discriminating PD and HC, 0.779, 0.719, and 0.864 in discriminating RBD from HC, 0.634, 0.636, and 0.689 in discriminating PD and RBD patients. CONCLUSION QSM and free water imaging have similar discriminative power in the detection of prodromal and clinical PD, while combination of these two methods increases discriminative power. Our findings suggest that the combination of QSM and free water imaging has the potential to become an imaging marker for the diagnosis of PD.
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Affiliation(s)
- Dongling Zhang
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Junye Yao
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Junyan Sun
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Qiqi Tong
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Silei Zhu
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Junling Wang
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Lili Chen
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jinghong Ma
- Department of Neurobiology, Xuanwu Hospital of Capital Medical University, Beijing Institute of Geriatrics, Beijing, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,School of Physics, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tao Wu
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Parkinson's Disease Center, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
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25
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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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26
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He N, Chen Y, LeWitt PA, Yan F, Haacke EM. Response to “Neuromelanin?
MRI
of Noradrenergic and Dopaminergic Neurons”. J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 11/22/2022] Open
Affiliation(s)
- Naying He
- Department of Radiology Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai 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 Shanghai China
| | - Ewart Mark Haacke
- Department of Radiology Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai 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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27
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Pizarro-Galleguillos BM, Kunert L, Brüggemann N, Prasuhn J. Iron- and Neuromelanin-Weighted Neuroimaging to Study Mitochondrial Dysfunction in Patients with Parkinson's Disease. Int J Mol Sci 2022; 23:ijms232213678. [PMID: 36430157 PMCID: PMC9696602 DOI: 10.3390/ijms232213678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/11/2022] Open
Abstract
The underlying causes of Parkinson's disease are complex, and besides recent advances in elucidating relevant disease mechanisms, no disease-modifying treatments are currently available. One proposed pathophysiological hallmark is mitochondrial dysfunction, and a plethora of evidence points toward the interconnected nature of mitochondria in neuronal homeostasis. This also extends to iron and neuromelanin metabolism, two biochemical processes highly relevant to individual disease manifestation and progression. Modern neuroimaging methods help to gain in vivo insights into these intertwined pathways and may pave the road to individualized medicine in this debilitating disorder. In this narrative review, we will highlight the biological rationale for studying these pathways, how distinct neuroimaging methods can be applied in patients, their respective limitations, and which challenges need to be overcome for successful implementation in clinical studies.
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Affiliation(s)
- Benjamin Matis Pizarro-Galleguillos
- Facultad de Medicina, Universidad de Chile, Santiago 8380453, Chile
- Institute of Neurogenetics, University of Lübeck, 23588 Lübeck, Germany
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, 23562 Lübeck, Germany
- Center for Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
| | - Liesa Kunert
- Institute of Neurogenetics, University of Lübeck, 23588 Lübeck, Germany
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, 23562 Lübeck, Germany
- Center for Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
| | - Norbert Brüggemann
- Institute of Neurogenetics, University of Lübeck, 23588 Lübeck, Germany
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, 23562 Lübeck, Germany
- Center for Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
- Correspondence: ; Tel.: +49-451-500-43420; Fax: +49-451-500-43424
| | - Jannik Prasuhn
- Institute of Neurogenetics, University of Lübeck, 23588 Lübeck, Germany
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, 23562 Lübeck, Germany
- Center for Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
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28
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Ben Bashat D, Thaler A, Lerman Shacham H, Even-Sapir E, Hutchison M, Evans KC, Orr-Urterger A, Cedarbaum JM, Droby A, Giladi N, Mirelman A, Artzi M. Neuromelanin and T 2*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson's disease. NPJ Parkinsons Dis 2022; 8:139. [PMID: 36271084 PMCID: PMC9586960 DOI: 10.1038/s41531-022-00405-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 09/30/2022] [Indexed: 11/23/2022] Open
Abstract
MRI was suggested as a promising method for the diagnosis and assessment of Parkinson's Disease (PD). We aimed to assess the sensitivity of neuromelanin-MRI and T2* with radiomics analysis for detecting PD, identifying individuals at risk, and evaluating genotype-related differences. Patients with PD and non-manifesting (NM) participants [NM-carriers (NMC) and NM-non-carriers (NMNC)], underwent MRI and DAT-SPECT. Imaging-based metrics included 48 neuromelanin and T2* radiomics features and DAT-SPECT specific-binding-ratios (SBR), were extracted from several brain regions. Imaging values were assessed for their correlations with age, differences between groups, and correlations with the MDS-likelihood-ratio (LR) score. Several machine learning classifiers were evaluated for group classification. A total of 127 participants were included: 46 patients with PD (62.3 ± 10.0 years) [15:LRRK2-PD, 16:GBA-PD, and 15:idiopathic-PD (iPD)], 47 NMC (51.5 ± 8.3 years) [24:LRRK2-NMC and 23:GBA-NMC], and 34 NMNC (53.5 ± 10.6 years). No significant correlations were detected between imaging parameters and age. Thirteen MRI-based parameters and radiomics features demonstrated significant differences between PD and NMNC groups. Support-Vector-Machine (SVM) classifier achieved the highest performance (AUC = 0.77). Significant correlations were detected between LR scores and two radiomic features. The classifier successfully identified two out of three NMC who converted to PD. Genotype-related differences were detected based on radiomic features. SBR values showed high sensitivity in all analyses. In conclusion, neuromelanin and T2* MRI demonstrated differences between groups and can be used for the assessment of individuals at-risk in cases when DAT-SPECT can't be performed. Combining neuromelanin and T2*-MRI provides insights into the pathophysiology underlying PD, and suggests that iron accumulation precedes neuromelanin depletion during the prodromal phase.
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Affiliation(s)
- Dafna Ben Bashat
- grid.413449.f0000 0001 0518 6922Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Avner Thaler
- grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel ,grid.413449.f0000 0001 0518 6922Laboratory of Early Markers Of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Hedva Lerman Shacham
- grid.413449.f0000 0001 0518 6922Department of Nuclear Medicine, Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Einat Even-Sapir
- grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.413449.f0000 0001 0518 6922Department of Nuclear Medicine, Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel
| | | | | | - Avi Orr-Urterger
- grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel ,grid.413449.f0000 0001 0518 6922Genomic Research Laboratory for Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Jesse M. Cedarbaum
- Coeruleus Clinical Sciences LLC, Woodbridge, CT USA ,grid.47100.320000000419368710Yale University School of Medicine, New Haven, CT USA
| | - Amgad Droby
- grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel ,grid.413449.f0000 0001 0518 6922Laboratory of Early Markers Of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Nir Giladi
- grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel ,grid.413449.f0000 0001 0518 6922Laboratory of Early Markers Of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Anat Mirelman
- grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel ,grid.413449.f0000 0001 0518 6922Laboratory of Early Markers Of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Moran Artzi
- grid.413449.f0000 0001 0518 6922Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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29
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Foley PB, Hare DJ, Double KL. A brief history of brain iron accumulation in Parkinson disease and related disorders. J Neural Transm (Vienna) 2022; 129:505-520. [PMID: 35534717 PMCID: PMC9188502 DOI: 10.1007/s00702-022-02505-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 04/22/2022] [Indexed: 12/21/2022]
Abstract
Iron has a long and storied history in Parkinson disease and related disorders. This essential micronutrient is critical for normal brain function, but abnormal brain iron accumulation has been associated with extrapyramidal disease for a century. Precisely why, how, and when iron is implicated in neuronal death remains the subject of investigation. In this article, we review the history of iron in movement disorders, from the first observations in the early twentieth century to recent efforts that view extrapyramidal iron as a novel therapeutic target and diagnostic indicator.
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Affiliation(s)
| | - Dominic J. Hare
- Atomic Medicine Initiative, University of Technology, Sydney, Australia
| | - Kay L. Double
- Brain and Mind Centre and School of Medical Sciences (Neuroscience), Faculty of Medicine and Health, University of Sydney, Sydney, Australia
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30
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Foley PB, Hare DJ, Double KL. A brief history of brain iron accumulation in Parkinson disease and related disorders. J Neural Transm (Vienna) 2022; 129:505-520. [PMID: 35534717 DOI: 10.1007/s00702-022-025055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 04/22/2022] [Indexed: 05/26/2023]
Abstract
Iron has a long and storied history in Parkinson disease and related disorders. This essential micronutrient is critical for normal brain function, but abnormal brain iron accumulation has been associated with extrapyramidal disease for a century. Precisely why, how, and when iron is implicated in neuronal death remains the subject of investigation. In this article, we review the history of iron in movement disorders, from the first observations in the early twentieth century to recent efforts that view extrapyramidal iron as a novel therapeutic target and diagnostic indicator.
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Affiliation(s)
| | - Dominic J Hare
- Atomic Medicine Initiative, University of Technology, Sydney, Australia
| | - Kay L Double
- Brain and Mind Centre and School of Medical Sciences (Neuroscience), Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
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31
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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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32
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Jin Z, Wang Y, Jokar M, Li Y, Cheng Z, Liu Y, Tang R, Shi X, Zhang Y, Min J, Liu F, He N, Yan F, Haacke EM. Automatic detection of neuromelanin and iron in the midbrain nuclei using a
magnetic resonance imaging
‐based brain template. Hum Brain Mapp 2022; 43:2011-2025. [PMID: 35072301 PMCID: PMC8933249 DOI: 10.1002/hbm.25770] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/01/2021] [Accepted: 12/22/2021] [Indexed: 12/17/2022] Open
Abstract
Parkinson disease (PD) is a chronic progressive neurodegenerative disorder characterized pathologically by early loss of neuromelanin (NM) in the substantia nigra pars compacta (SNpc) and increased iron deposition in the substantia nigra (SN). Degeneration of the SN presents as a 50 to 70% loss of pigmented neurons in the ventral lateral tier of the SNpc at the onset of symptoms. Also, using magnetic resonance imaging (MRI), iron deposition and volume changes of the red nucleus (RN), and subthalamic nucleus (STN) have been reported to be associated with disease status and rate of progression. Further, the STN serves as an important target for deep brain stimulation treatment in advanced PD patients. Therefore, an accurate in‐vivo delineation of the SN, its subregions and other midbrain structures such as the RN and STN could be useful to better study iron and NM changes in PD. Our goal was to use an MRI template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC‐GRE) imaging sequence. The short echo TE = 7.5 ms data from a 3D MTC‐GRE sequence was used to find the NM‐rich region, while the second echo TE = 15 ms was used to calculate the quantitative susceptibility map for 87 healthy subjects (mean age ± SD: 63.4 ± 6.2 years old, range: 45–81 years). From these data, we created both NM and iron templates and calculated the boundaries of each midbrain nucleus in template space, mapped these boundaries back to the original space and then fine‐tuned the boundaries in the original space using a dynamic programming algorithm to match the details of each individual's NM and iron features. A dual mapping approach was used to improve the performance of the morphological mapping of the midbrain of any given individual to the template space. A threshold approach was used in the NM‐rich region and susceptibility maps to optimize the DICE similarity coefficients and the volume ratios. The results for the NM of the SN as well as the iron containing SN, STN, and RN all indicate a strong agreement with manually drawn structures. The DICE similarity coefficients and volume ratios for these structures were 0.85, 0.87, 0.75, and 0.92 and 0.93, 0.95, 0.89, 1.05, respectively, before applying any threshold on the data. Using this fully automatic template‐based deep gray matter mapping approach, it is possible to accurately measure the tissue properties such as volumes, iron content, and NM content of the midbrain nuclei.
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Affiliation(s)
- Zhijia Jin
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Ying Wang
- SpinTech MRI, Inc. Detroit Michigan USA
- Department of Radiology Wayne State University Detroit Michigan USA
| | | | - Yan Li
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Yu Liu
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Rongbiao Tang
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Xiaofeng Shi
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Youmin Zhang
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Jihua Min
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Fangtao Liu
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Naying He
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Ewart Mark Haacke
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
- SpinTech MRI, Inc. Detroit Michigan USA
- Department of Radiology Wayne State University Detroit Michigan USA
- Department of Biomedical Engineering Wayne State University Detroit Michigan USA
- Department of Neurology Wayne State University Detroit Michigan USA
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33
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Xiao B, He N, Wang Q, Shi F, Cheng Z, Haacke EM, Yan F, Shen D. Stability of AI-Enabled Diagnosis of Parkinson's Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging. Front Neurosci 2021; 15:760975. [PMID: 34887722 PMCID: PMC8650720 DOI: 10.3389/fnins.2021.760975] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 10/18/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: Parkinson's disease (PD) diagnosis algorithms based on quantitative susceptibility mapping (QSM) and image algorithms rely on substantia nigra (SN) labeling. However, the difference between SN labels from different experts (or segmentation algorithms) will have a negative impact on downstream diagnostic tasks, such as the decrease of the accuracy of the algorithm or different diagnostic results for the same sample. In this article, we quantify the accuracy of the algorithm on different label sets and then improve the convolutional neural network (CNN) model to obtain a high-precision and highly robust diagnosis algorithm. Methods: The logistic regression model and CNN model were first compared for classification between PD patients and healthy controls (HC), given different sets of SN labeling. Then, based on the CNN model with better performance, we further proposed a novel "gated pooling" operation and integrated it with deep learning to attain a joint framework for image segmentation and classification. Results: The experimental results show that, with different sets of SN labeling that mimic different experts, the CNN model can maintain a stable classification accuracy at around 86.4%, while the conventional logistic regression model yields a large fluctuation ranging from 78.9 to 67.9%. Furthermore, the "gated pooling" operation, after being integrated for joint image segmentation and classification, can improve the diagnosis accuracy to 86.9% consistently, which is statistically better than the baseline. Conclusion: The CNN model, compared with the conventional logistic regression model using radiomics features, has better stability in PD diagnosis. Furthermore, the joint end-to-end CNN model is shown to be suitable for PD diagnosis from the perspectives of accuracy, stability, and convenience in actual use.
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Affiliation(s)
- Bin Xiao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qian Wang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Feng Shi
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ewart Mark Haacke
- Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Radiology, Wayne State University, Detroit, MI, United States
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Dinggang Shen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
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34
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Langley J, Huddleston DE, Hu X. Nigral diffusivity, but not free water, correlates with iron content in Parkinson's disease. Brain Commun 2021; 3:fcab251. [PMID: 34805996 PMCID: PMC8599079 DOI: 10.1093/braincomms/fcab251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/18/2021] [Accepted: 09/20/2021] [Indexed: 11/13/2022] Open
Abstract
The loss of melanized neurons in the substantia nigra pars compacta is a primary feature in Parkinson's disease. Iron deposition occurs in conjunction with this loss. Loss of nigral neurons should remove barriers for diffusion and increase diffusivity of water molecules in regions undergoing this loss. In metrics from single-compartment diffusion tensor imaging models, these changes should manifest as increases in mean diffusivity and reductions in fractional anisotropy as well as increases in the free water compartment in metrics derived from bi-compartment models. However, studies examining nigral diffusivity changes from Parkinson's disease with single-compartment models have yielded inconclusive results and emerging evidence in control subjects indicates that iron corrupts diffusivity metrics derived from single-compartment models. We aimed to examine Parkinson's disease-related changes in nigral iron and diffusion measures from single- and bi-compartment models as well as assess the effect of iron on these diffusion measures in two separate Parkinson's cohorts. Iron-sensitive data and diffusion data were analysed in two cohorts: First, a discovery cohort consisting of 71 participants (32 control participants and 39 Parkinson's disease participants) was examined. Second, an external validation cohort, obtained from the Parkinson's Progression Marker's Initiative, consisting of 110 participants (58 control participants and 52 Parkinson's disease participants) was examined. The effect of iron on diffusion measures from single- and bi-compartment models was assessed in both cohorts. Measures sensitive to the free water compartment (discovery cohort: P = 0.006; external cohort: P = 0.01) and iron content (discovery cohort: P < 0.001; validation cohort: P = 0.02) were found to increase in substantia nigra of the Parkinson's disease group in both cohorts. However, diffusion markers derived from the single-compartment model (i.e. mean diffusivity and fractional anisotropy) were not replicated across cohorts. Correlations were seen between single-compartment diffusion measures and iron markers in the discovery cohort (iron-mean diffusivity: r = -0.400, P = 0.006) and validation cohort (iron-mean diffusivity: r = -0.387, P = 0.003) but no correlation was observed between a measure from the bi-compartment model related to the free water compartment and iron markers in either cohort. In conclusion, the variability of nigral diffusion metrics derived from the single-compartment model in Parkinson's disease may be attributed to competing influences of increased iron content, which tends to drive diffusivity down, and increases in the free water compartment, which tends to drive diffusivity up. In contrast to diffusion metrics derived from the single-compartment model, no relationship was seen between iron and the free water compartment in substantia nigra.
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Affiliation(s)
- Jason Langley
- Center for Advanced Neuroimaging, University of California Riverside, Riverside, CA 92521, USA
| | | | - Xiaoping Hu
- Center for Advanced Neuroimaging, University of California Riverside, Riverside, CA 92521, USA.,Department of Bioengineering, University of California Riverside, Riverside, CA 92521, USA
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Liu Y, Xiao B, Zhang C, Li J, Lai Y, Shi F, Shen D, Wang L, Sun B, Li Y, Jin Z, Wei H, Haacke EM, Zhou H, Wang Q, Li D, He N, Yan F. Predicting Motor Outcome of Subthalamic Nucleus Deep Brain Stimulation for Parkinson's Disease Using Quantitative Susceptibility Mapping and Radiomics: A Pilot Study. Front Neurosci 2021; 15:731109. [PMID: 34557069 PMCID: PMC8452872 DOI: 10.3389/fnins.2021.731109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 08/17/2021] [Indexed: 12/02/2022] Open
Abstract
Background Emerging evidence indicates that iron distribution is heterogeneous within the substantia nigra (SN) and it may reflect patient-specific trait of Parkinson’s Disease (PD). We assume it could account for variability in motor outcome of subthalamic nucleus deep brain stimulation (STN-DBS) in PD. Objective To investigate whether SN susceptibility features derived from radiomics with machine learning (RA-ML) can predict motor outcome of STN-DBS in PD. Methods Thirty-three PD patients underwent bilateral STN-DBS were recruited. The bilateral SN were segmented based on preoperative quantitative susceptibility mapping to extract susceptibility features using RA-ML. MDS-UPDRS III scores were recorded 1–3 days before and 6 months after STN-DBS surgery. Finally, we constructed three predictive models using logistic regression analyses: (1) the RA-ML model based on radiomics features, (2) the RA-ML+LCT (levodopa challenge test) response model which combined radiomics features with preoperative LCT response, (3) the LCT response model alone. Results For the predictive performances of global motor outcome, the RA-ML model had 82% accuracy (AUC = 0.85), while the RA-ML+LCT response model had 74% accuracy (AUC = 0.83), and the LCT response model alone had 58% accuracy (AUC = 0.55). For the predictive performance of rigidity outcome, the accuracy of the RA-ML model was 80% (AUC = 0.85), superior to those of the RA-ML+LCT response model (76% accuracy, AUC = 0.82), and the LCT response model alone (58% accuracy, AUC = 0.42). Conclusion Our findings demonstrated that SN susceptibility features from radiomics could predict global motor and rigidity outcomes of STN-DBS in PD. This RA-ML predictive model might provide a novel approach to counsel candidates for STN-DBS.
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Affiliation(s)
- Yu Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bin Xiao
- School of Biomedical Engineering, Institute for Medical Imaging Technology, Shanghai Jiao Tong University, Shanghai, China
| | - Chencheng Zhang
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junchen Li
- Department of Radiology, Changshu Hospital Affiliated to Nanjing University of Chinese Medicine, Changshu, China
| | - Yijie Lai
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Feng Shi
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Dinggang Shen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.,School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.,Department of Artificial Intelligence, Korea University, Seoul, South Korea
| | - Linbin Wang
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bomin Sun
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhijia Jin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Ewart Mark Haacke
- Department of Radiology, Wayne State University, Detroit, MI, United States
| | - Haiyan Zhou
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qian Wang
- School of Biomedical Engineering, Institute for Medical Imaging Technology, Shanghai Jiao Tong University, Shanghai, China
| | - Dianyou Li
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Wieland L, Fromm S, Hetzer S, Schlagenhauf F, Kaminski J. Neuromelanin-Sensitive Magnetic Resonance Imaging in Schizophrenia: A Meta-Analysis of Case-Control Studies. Front Psychiatry 2021; 12:770282. [PMID: 34777070 PMCID: PMC8581671 DOI: 10.3389/fpsyt.2021.770282] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 10/04/2021] [Indexed: 11/17/2022] Open
Abstract
Background: Psychiatry is in urgent need of reliable biomarkers. Novel neuromelanin-sensitive magnetic resonance imaging (NM-MRI) sequences provide a time-efficient and non-invasive way to investigate the human brain in-vivo. This gives insight into the metabolites of dopaminergic signaling and may provide further evidence for potential dopaminergic alterations in patients with schizophrenia (SCZ). The present systematic review provides a meta-analysis of case-control studies using neuromelanin-sensitive sequences in SCZ vs. healthy controls (HC). Methods: According to predefined search terms and inclusion criteria studies were extracted on PubMed. Meta-analyses with a fixed and random-effects model with inverse variance method, DerSimonian-Laird estimator for τ2, and Cohen's d were calculated. Bias was assessed using funnel plots. The primary study outcome was contrast-to-noise ratio (CNR) in the substantia nigra compared between HC and SCZ. Results: The total sample of k = 6 studies included n = 183 cases and n = 162 controls. Across all studies we found a significant elevation of CNR in the substantia nigra (d = 0.42 [0.187; 0.655], z = 3.521, p < 0.001) in cases compared to controls. We found no significant difference in the control region of locus coeruleus (d = -0.07 [-0.446; 0.302], z = -0.192, p = 0.847), with CNR for the latter only reported in k = 3 studies. Conclusion: CNR in the substantia nigra were significantly elevated in cases compared to controls. Our results support neuromelanin as a candidate biomarker for dopaminergic dysfunction in schizophrenia. Further studies need to assess this candidate marker in large, longitudinal cohorts and address potential effects of disease state, medication and correlations with symptoms.
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Affiliation(s)
- Lara Wieland
- Department of Psychiatry and Psychotherapy Charité Campus Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Department of Psychiatry and Psychotherapy Charité Campus Mitte, Charité-Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
| | - Sophie Fromm
- Department of Psychiatry and Psychotherapy Charité Campus Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Department of Psychiatry and Psychotherapy Charité Campus Mitte, Charité-Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Florian Schlagenhauf
- Department of Psychiatry and Psychotherapy Charité Campus Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jakob Kaminski
- Department of Psychiatry and Psychotherapy Charité Campus Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany
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