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Bilgic B, Costagli M, Chan KS, Duyn J, Langkammer C, Lee J, Li X, Liu C, Marques JP, Milovic C, Robinson SD, Schweser F, Shmueli K, Spincemaille P, Straub S, van Zijl P, Wang Y. Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group. Magn Reson Med 2024; 91:1834-1862. [PMID: 38247051 PMCID: PMC10950544 DOI: 10.1002/mrm.30006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/31/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi-echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting.
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Affiliation(s)
- Berkin Bilgic
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy
| | - Kwok-Shing Chan
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Jeff Duyn
- Advanced MRI Section, NINDS, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Carlos Milovic
- School of Electrical Engineering (EIE), Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Simon Daniel Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Centre of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, New York, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, New York, USA
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Pascal Spincemaille
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA
| | - Peter van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yi Wang
- MRI Research Institute, Departments of Radiology and Biomedical Engineering, Cornell University, New York, New York, USA
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Reeves JA, Mohebbi M, Wicks T, Salman F, Bartnik A, Jakimovski D, Bergsland N, Schweser F, Weinstock-Guttman B, Dwyer MG, Zivadinov R. Paramagnetic rim lesions predict greater long-term relapse rates and clinical progression over 10 years. Mult Scler 2024; 30:535-545. [PMID: 38366920 PMCID: PMC11009059 DOI: 10.1177/13524585241229956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
BACKGROUND Paramagnetic rim lesions (PRLs) have been linked to higher clinical disease severity and relapse frequency. However, it remains unclear whether PRLs predict future, long-term disease progression. OBJECTIVES The study aimed to assess whether baseline PRLs were associated with subsequent long-term (10 years) Expanded Disability Status Scale (EDSS) increase and relapse frequency and, if so, whether PRL-associated EDSS increase was mediated by relapse. METHODS This retrospective analysis included 172 people with multiple sclerosis (pwMS) with 1868 yearly clinical visits over a mean follow-up time of 10.2 years. 3T magnetic resonance imaging (MRI) was acquired at baseline and PRLs were assessed on quantitative susceptibility mapping (QSM) images. The associations between PRLs, relapse, and rate of EDSS change were assessed using linear models. RESULTS PRL+ pwMS had greater overall annual relapse rate (β = 0.068; p = 0.010), three times greater overall odds of relapse (exp(β) = 3.472; p = 0.009), and greater rate of yearly EDSS change (β = 0.045; p = 0.010) than PRL- pwMS. Greater PRL number was associated with greater odds of at least one progression independent of relapse activity (PIRA) episode over follow-up (exp(β) = 1.171, p = 0.009). Mediation analysis showed that the association between PRL presence (yes/no) and EDSS increase was 96.7% independent of relapse number. CONCLUSION PRLs are a marker of aggressive ongoing disease inflammatory activity, including more frequent future clinical relapses and greater long-term, relapse-independent disability progression.
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Affiliation(s)
- Jack A Reeves
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Maryam Mohebbi
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Taylor Wicks
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Fahad Salman
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Alexander Bartnik
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
- Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | | | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
- Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
- Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
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3
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Reeves JA, Mohebbi M, Zivadinov R, Bergsland N, Dwyer MG, Salman F, Schweser F, Jakimovski D. Reliability of paramagnetic rim lesion classification on quantitative susceptibility mapping (QSM) in people with multiple sclerosis: Single-site experience and systematic review. Mult Scler Relat Disord 2023; 79:104968. [PMID: 37716210 DOI: 10.1016/j.msard.2023.104968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 07/15/2023] [Accepted: 08/28/2023] [Indexed: 09/18/2023]
Abstract
BACKGROUND Recent developments in iron-sensitive MRI techniques have enabled visualization of chronic active lesions as paramagnetic rim lesions (PRLs) in vivo. Although PRLs have potential as a diagnostic and prognostic tool for multiple sclerosis (MS), limited studies have reported the reliability of PRL assessment. Further evaluation of PRL reliability, through original investigations and review of PRL literature, are warranted. METHODS A single-center cohort study was conducted to evaluate the inter-rater reliability of PRL identification on quantitative susceptibiltiy mapping (QSM) in 10 people with MS, 5 people with clinically isolated syndrome, and 5 healthy controls. An additional systematic literature search was then conducted of published PRL reliability data, and these results were synthesized. RESULTS In the single-center study, both inter-rater and intra-rater reliability of per-subject PRL number were at an "Excellent" (intraclass correlation coefficient (ICC) of 0.901 for both) level with only 2-years lesion classification experience. Across the reported literature values, reliability of per-lesion rim presence was on average "Near perfect" (for intra-rater; Cohen's κ = 0.833) and "Substantial" (for inter-rater; Cohens κ = 0.687), whereas inter-rater reliability of per-subject PRL number was "Good" (ICC = 0.874). Only 4/22 studies reported complete information on rater experience, rater level of training, detailed PRL classification criteria, and reliability cohort size and disease subtypes. CONCLUSION PRLs can be reliably detected both at per-lesion and per-subject level. We recommend that future PRL studies report detailed reliability results, including rater experience level, and use a standardized set of reliability metrics (Cohen's κ or ICC) for improved comparability between studies.
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Affiliation(s)
- Jack A Reeves
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA
| | - Maryam Mohebbi
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA; Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA
| | - Fahad Salman
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA; Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY 14203, USA.
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Pol S, Dhanraj R, Taher A, Crever M, Charbonneau T, Schweser F, Dwyer M, Zivadinov R. Effect of Siponimod on Brain and Spinal Cord Imaging Markers of Neurodegeneration in the Theiler's Murine Encephalomyelitis Virus Model of Demyelination. Int J Mol Sci 2023; 24:12990. [PMID: 37629171 PMCID: PMC10455446 DOI: 10.3390/ijms241612990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/05/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023] Open
Abstract
Siponimod (Sp) is a Sphingosine 1-phosphate (S1P) receptor modulator, and it suppresses S1P- mediated autoimmune lymphocyte transport and inflammation. Theiler's murine encephalomyelitis virus (TMEV) infection mouse model of multiple sclerosis (MS) exhibits inflammation-driven acute and chronic phases, spinal cord lesions, brain and spinal cord atrophy, and white matter injury. The objective of the study was to investigate whether Sp treatment could attenuate inflammation-induced pathology in the TMEV model by inhibiting microglial activation and preventing the atrophy of central nervous tissue associated with neurodegeneration. Clinical disability score (CDS), body weight (BW), and rotarod retention time measures were used to assess Sp's impact on neurodegeneration and disease progression in 4 study groups of 102 animals, including 44 Sp-treated (SpT), 44 vehicle-treated, 6 saline-injected, and 8 age-matched healthy controls (HC). Next, 58 (22 SpT, 22 vehicle, 6 saline injected, and 8 HC) out of the 102 animals were further evaluated to assess the effect of Sp on brain region-specific and spinal cord volume changes, as well as microglial activation. Sp increased CDS and decreased BW and rotarod retention time in TMEV mice, but did not significantly affect most brain region volumes, except for lateral ventricle volume. Sp suppressed ventricular enlargement, suggesting reduced TMEV-induced inflammation in LV. No significant differences in spine volume changes were observed between Sp- and vehicle-treated animals, but there were differences between HC and TMEV groups, indicating TMEV-induced inflammation contributed to increased spine volume. Spine histology revealed no significant microglial density differences between groups in gray matter, but HC animals had higher type 1 morphology and lower type 2 morphology percentages in gray and white matter regions. This suggests that Sp did not significantly affect microglial density but may have modulated neuroinflammation in the spinal cord. Sp may have some effects on neuroinflammation and ventricular enlargement. However, it did not demonstrate a significant impact on neurodegeneration, spinal volume, or lesion volume in the TMEV mouse model. Further investigation is required to fully understand Sp's effect on microglial activation and its relevance to the pathophysiology of MS. The differences between the current study and previous research using other MS models, such as EAE, highlight the differences in pathological processes in these two disease models.
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Affiliation(s)
- Suyog Pol
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA; (S.P.); (R.D.); (A.T.); (M.C.); (T.C.); (F.S.); (M.D.)
- Center for Biomedical Imaging, Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY 14203, USA
| | - Ravendra Dhanraj
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA; (S.P.); (R.D.); (A.T.); (M.C.); (T.C.); (F.S.); (M.D.)
| | - Anissa Taher
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA; (S.P.); (R.D.); (A.T.); (M.C.); (T.C.); (F.S.); (M.D.)
| | - Mateo Crever
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA; (S.P.); (R.D.); (A.T.); (M.C.); (T.C.); (F.S.); (M.D.)
| | - Taylor Charbonneau
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA; (S.P.); (R.D.); (A.T.); (M.C.); (T.C.); (F.S.); (M.D.)
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA; (S.P.); (R.D.); (A.T.); (M.C.); (T.C.); (F.S.); (M.D.)
- Center for Biomedical Imaging, Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY 14203, USA
| | - Michael Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA; (S.P.); (R.D.); (A.T.); (M.C.); (T.C.); (F.S.); (M.D.)
- Center for Biomedical Imaging, Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY 14203, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA; (S.P.); (R.D.); (A.T.); (M.C.); (T.C.); (F.S.); (M.D.)
- Center for Biomedical Imaging, Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY 14203, USA
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Bilgic B, Costagli M, Chan KS, Duyn J, Langkammer C, Lee J, Li X, Liu C, Marques JP, Milovic C, Robinson S, Schweser F, Shmueli K, Spincemaille P, Straub S, van Zijl P, Wang Y. Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. ArXiv 2023:arXiv:2307.02306v1. [PMID: 37461418 PMCID: PMC10350101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with - as a minimum - the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting.
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Affiliation(s)
- Berkin Bilgic
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy
| | - Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jeff Duyn
- Advanced MRI Section, NINDS, National Institutes of Health, Bethesda, MD, United States
| | | | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Carlos Milovic
- School of Electrical Engineering (EIE), Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Simon Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, NY, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, NY, United States
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Pascal Spincemaille
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, FL, United States
| | - Peter van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Yi Wang
- MRI Research Institute, Departments of Radiology and Biomedical Engineering, Cornell University, New York, NY, United States
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Reeves JA, Weinstock Z, Zivadinov R, Dwyer MG, Bergsland N, Salman F, Schweser F, Weinstock-Guttman B, Benedict RH, Jakimovski D. Paramagnetic rim lesions are associated with greater incidence of relapse and worse cognitive recovery following relapse. Mult Scler 2023; 29:1033-1038. [PMID: 37161349 PMCID: PMC10523891 DOI: 10.1177/13524585231169466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
BACKGROUND Paramagnetic rim lesions (PRL) may be linked to relapse risk of people with relapsing-remitting multiple sclerosis (pwRRMS). OBJECTIVE To determine the relationship between presence of PRL lesions and cognitive recovery after relapse. METHODS PRL load was compared between acutely relapsing pwRRMS and matched stable pwRRMS controls (each group n = 21). In addition, cognitive recovery was compared between acutely relapsing pwRRMS with at least one PRL (PRL+) and those without any PRL (PRL-). RESULTS Acutely relapsing pwRRMS had significantly greater prevalence and number of PRL (p = 0.004 and p = 0.003) compared with stable controls. These findings remained significant after adjusting for global neuroinflammatory burden (enhancing and non-enhancing lesions). In addition, acutely relapsing PRL + pwRRMS (n = 10) had worse recovery of verbal memory following relapse compared with acutely relapsing PRL - pwRRMS (n = 7; p = 0.027). CONCLUSION These findings may partially explain previously suggested associations between presence of PRL with more severe disease course.
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Affiliation(s)
- Jack A Reeves
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Zachary Weinstock
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA/Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA/Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA/IRCCS, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Fahad Salman
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA/Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Bianca Weinstock-Guttman
- Department of Neurology, Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ralph Hb Benedict
- Department of Neurology, Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
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Reeves JA, Bergsland N, Dwyer MG, Wilding GE, Jakimovski D, Salman F, Sule B, Meineke N, Weinstock-Guttman B, Zivadinov R, Schweser F. Susceptibility networks reveal independent patterns of brain iron abnormalities in multiple sclerosis. Neuroimage 2022; 261:119503. [PMID: 35878723 PMCID: PMC10097440 DOI: 10.1016/j.neuroimage.2022.119503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 07/06/2022] [Accepted: 07/21/2022] [Indexed: 11/24/2022] Open
Abstract
Brain iron homeostasis is necessary for healthy brain function. MRI and histological studies have shown altered brain iron levels in the brains of patients with multiple sclerosis (MS), particularly in the deep gray matter (DGM). Previous studies were able to only partially separate iron-modifying effects because of incomplete knowledge of iron-modifying processes and influencing factors. It is therefore unclear to what extent and at which stages of the disease different processes contribute to brain iron changes. We postulate that spatially covarying magnetic susceptibility networks determined with Independent Component Analysis (ICA) reflect, and allow for the study of, independent processes regulating iron levels. We applied ICA to quantitative susceptibility maps for 170 individuals aged 9-81 years without neurological disease ("Healthy Aging" (HA) cohort), and for a cohort of 120 patients with MS and 120 age- and sex-matched healthy controls (HC; together the "MS/HC" cohort). Two DGM-associated "susceptibility networks" identified in the HA cohort (the Dorsal Striatum and Globus Pallidus Interna Networks) were highly internally reproducible (i.e. "robust") across multiple ICA repetitions on cohort subsets. DGM areas overlapping both robust networks had higher susceptibility levels than DGM areas overlapping only a single robust network, suggesting that these networks were caused by independent processes of increasing iron concentration. Because MS is thought to accelerate brain aging, we hypothesized that associations between age and the two robust DGM-associated networks would be enhanced in patients with MS. However, only one of these networks was altered in patients with MS, and it had a null age association in patients with MS rather than a stronger association. Further analysis of the MS/HC cohort revealed three additional disease-related networks (the Pulvinar, Mesencephalon, and Caudate Networks) that were differentially altered between patients with MS and HCs and between MS subtypes. Exploratory regression analyses of the disease-related networks revealed differential associations with disease duration and T2 lesion volume. Finally, analysis of ROI-based disease effects in the MS/HC cohort revealed an effect of disease status only in the putamen ROI and exploratory regression analysis did not show associations between the caudate and pulvinar ROIs and disease duration or T2 lesion volume, showing the ICA-based approach was more sensitive to disease effects. These results suggest that the ICA network framework increases sensitivity for studying patterns of brain iron change, opening a new avenue for understanding brain iron physiology under normal and disease conditions.
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Affiliation(s)
- Jack A Reeves
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA; MR Research Laboratory, IRCCS, Don Gnocchi Foundation ONLUS, Milan, Italy
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, Clinical and Translational Research Center, State University of New York at Buffalo, 6045C, 875 Ellicott Street, Buffalo, NY 14203, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Gregory E Wilding
- Department of Biostatistics, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, NY, USA
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Fahad Salman
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Balint Sule
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Nicklas Meineke
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Bianca Weinstock-Guttman
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA; Jacobs Neurological Institute, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, Clinical and Translational Research Center, State University of New York at Buffalo, 6045C, 875 Ellicott Street, Buffalo, NY 14203, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, Clinical and Translational Research Center, State University of New York at Buffalo, 6045C, 875 Ellicott Street, Buffalo, NY 14203, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA.
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Nakuci J, McGuire M, Schweser F, Poulsen D, Muldoon SF. Differential Patterns of Change in Brain Connectivity Resulting from Severe Traumatic Brain Injury. Brain Connect 2022; 12:799-811. [PMID: 35302399 PMCID: PMC9805864 DOI: 10.1089/brain.2021.0168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Background: Traumatic brain injury (TBI) damages white matter tracts, disrupting brain network structure and communication. There exists a wide heterogeneity in the pattern of structural damage associated with injury, as well as a large heterogeneity in behavioral outcomes. However, little is known about the relationship between changes in network connectivity and clinical outcomes. Materials and Methods: We utilize the rat lateral fluid-percussion injury model of severe TBI to study differences in brain connectivity in 8 animals that received the insult and 11 animals that received only a craniectomy. Diffusion tensor imaging is performed 5 weeks after the injury and network theory is used to investigate changes in white matter connectivity. Results: We find that (1) global network measures are not able to distinguish between healthy and injured animals; (2) injury induced alterations predominantly exist in a subset of connections (subnetworks) distributed throughout the brain; and (3) injured animals can be divided into subgroups based on changes in network motifs-measures of local structural connectivity. In addition, alterations in predicted functional connectivity indicate that the subgroups have different propensities to synchronize brain activity, which could relate to the heterogeneity of clinical outcomes. Discussion: These results suggest that network measures can be used to quantify progressive changes in brain connectivity due to injury and differentiate among subpopulations with similar injuries, but different pathological trajectories.
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Affiliation(s)
- Johan Nakuci
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, New York, USA
| | - Matthew McGuire
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, New York, USA
- Department of Neurosurgery, University at Buffalo, SUNY, Buffalo, New York, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, SUNY, Buffalo, New York, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, SUNY, Buffalo, New York, USA
| | - David Poulsen
- Department of Neurosurgery, University at Buffalo, SUNY, Buffalo, New York, USA
| | - Sarah F. Muldoon
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, New York, USA
- Department of Mathematics and CDSE Program, University at Buffalo, SUNY, Buffalo, New York, USA
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Bordeau BM, Polli JR, Schweser F, Grimm HP, Richter WF, Balthasar JP. Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Monoclonal Antibody Tumor Disposition. Int J Mol Sci 2022; 23:679. [PMID: 35054865 PMCID: PMC8775965 DOI: 10.3390/ijms23020679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/30/2021] [Accepted: 01/05/2022] [Indexed: 11/16/2022] Open
Abstract
The prediction of monoclonal antibody (mAb) disposition within solid tumors for individual patients is difficult due to inter-patient variability in tumor physiology. Improved a priori prediction of mAb pharmacokinetics in tumors may facilitate the development of patient-specific dosing protocols and facilitate improved selection of patients for treatment with anti-cancer mAb. Here, we report the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), with tumor penetration of the contrast agent gadobutrol used as a surrogate, to improve physiologically based pharmacokinetic model (PBPK) predictions of cetuximab pharmacokinetics in epidermal growth factor receptor (EGFR) positive xenografts. In the initial investigations, mice bearing Panc-1, NCI-N87, and LS174T xenografts underwent DCE-MRI imaging with the contrast agent gadobutrol, followed by intravenous dosing of an 125Iodine-labeled, non-binding mAb (8C2). Tumor concentrations of 8C2 were determined following the euthanasia of mice (3 h-6 days after 8C2 dosing). Potential predictor relationships between DCE-MRI kinetic parameters and 8C2 PBPK parameters were evaluated through covariate modeling. The addition of the DCE-MRI parameter Ktrans alone or Ktrans in combination with the DCE-MRI parameter Vp on the PBPK parameters for tumor blood flow (QTU) and tumor vasculature permeability (σTUV) led to the most significant improvement in the characterization of 8C2 pharmacokinetics in individual tumors. To test the utility of the DCE-MRI covariates on a priori prediction of the disposition of mAb with high-affinity tumor binding, a second group of tumor-bearing mice underwent DCE-MRI imaging with gadobutrol, followed by the administration of 125Iodine-labeled cetuximab (a high-affinity anti-EGFR mAb). The MRI-PBPK covariate relationships, which were established with the untargeted antibody 8C2, were implemented into the PBPK model with considerations for EGFR expression and cetuximab-EGFR interaction to predict the disposition of cetuximab in individual tumors (a priori). The incorporation of the Ktrans MRI parameter as a covariate on the PBPK parameters QTU and σTUV decreased the PBPK model prediction error for cetuximab tumor pharmacokinetics from 223.71 to 65.02%. DCE-MRI may be a useful clinical tool in improving the prediction of antibody pharmacokinetics in solid tumors. Further studies are warranted to evaluate the utility of the DCE-MRI approach to additional mAbs and additional drug modalities.
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Affiliation(s)
- Brandon M. Bordeau
- Department of Pharmaceutical Sciences, University at Buffalo, 450 Pharmacy Building, Buffalo, NY 14214, USA; (B.M.B.); (J.R.P.)
| | - Joseph Ryan Polli
- Department of Pharmaceutical Sciences, University at Buffalo, 450 Pharmacy Building, Buffalo, NY 14214, USA; (B.M.B.); (J.R.P.)
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA;
- Clinical and Translational Science Institute, Center for Biomedical Imaging, University at Buffalo, Buffalo, NY 14203, USA
| | - Hans Peter Grimm
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland; (H.P.G.); (W.F.R.)
| | - Wolfgang F. Richter
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland; (H.P.G.); (W.F.R.)
| | - Joseph P. Balthasar
- Department of Pharmaceutical Sciences, University at Buffalo, 450 Pharmacy Building, Buffalo, NY 14214, USA; (B.M.B.); (J.R.P.)
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Kaliyappan K, Nakuci J, Preda M, Schweser F, Muldoon S, Krishnan Muthaiah VP. Correlation of Histomorphometric Changes with Diffusion Tensor Imaging for Evaluation of Blast-Induced Auditory Neurodegeneration in Chinchilla. J Neurotrauma 2021; 38:3248-3259. [PMID: 34605670 DOI: 10.1089/neu.2020.7556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
In the present study, we have evaluated the blast-induced auditory neurodegeneration in chinchilla by correlating the histomorphometric changes with diffusion tensor imaging. The chinchillas were exposed to single unilateral blast-overpressure (BOP) at ∼172dB peak sound pressure level (SPL) and the pathological changes were compared at 1 week and 1 month after BOP. The functional integrity of the auditory system was assessed by auditory brainstem response (ABR) and distortion product otoacoustic emissions (DPOAE). The axonal integrity was assessed using diffusion tensor imaging at regions of interests (ROIs) of the central auditory neuraxis (CAN) including the cochlear nucleus (CN), inferior colliculus (IC), and auditory cortex (AC). Post-BOP, cyto-architecture metrics such as viable cells, degenerating neurons, and apoptotic cells were quantified at the CAN ROIs using light microscopic studies using cresyl fast violet, hematoxylin and eosin, and modified Crossmon's trichrome stains. We observed mean ABR threshold shifts of 30- and 10-dB SPL at 1 week and 1 month after BOP, respectively. A similar pattern was observed in DPAOE amplitudes shift. In the CAN ROIs, diffusion tensor imaging studies showed a decreased axial diffusivity in CN 1 month after BOP and a decreased mean diffusivity and radial diffusivity at 1 week after BOP. However, morphometric measures such as decreased viable cells and increased degenerating neurons and apoptotic cells were observed at CN, IC, and AC. Specifically, increased degenerating neurons and reduced viable cells were high on the ipsilateral side when compared with the contralateral side. These results indicate that a single blast significantly damages structural and functional integrity at all levels of CAN ROIs.
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Affiliation(s)
- Kathiravan Kaliyappan
- Department of Rehabilitation Sciences, School of Public Health and Health Professions, College of Arts and Sciences, University at Buffalo, Buffalo, New York, USA
| | - Johan Nakuci
- Neuroscience Program, College of Arts and Sciences, University at Buffalo, Buffalo, New York, USA
| | - Marilena Preda
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, College of Arts and Sciences, University at Buffalo, Buffalo, New York, USA.,Center for Biomedical Imaging, Clinical and Translational Science Institute, College of Arts and Sciences, University at Buffalo, Buffalo, New York, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, College of Arts and Sciences, University at Buffalo, Buffalo, New York, USA.,Center for Biomedical Imaging, Clinical and Translational Science Institute, College of Arts and Sciences, University at Buffalo, Buffalo, New York, USA
| | - Sarah Muldoon
- Department of Mathematics, College of Arts and Sciences, University at Buffalo, Buffalo, New York, USA
| | - Vijaya Prakash Krishnan Muthaiah
- Department of Rehabilitation Sciences, School of Public Health and Health Professions, College of Arts and Sciences, University at Buffalo, Buffalo, New York, USA
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11
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Bilgic B, Langkammer C, Marques JP, Meineke J, Milovic C, Schweser F. QSM reconstruction challenge 2.0: Design and report of results. Magn Reson Med 2021; 86:1241-1255. [PMID: 33783037 DOI: 10.1002/mrm.28754] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 01/25/2021] [Accepted: 02/08/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE The aim of the second quantitative susceptibility mapping (QSM) reconstruction challenge (Oct 2019, Seoul, Korea) was to test the accuracy of QSM dipole inversion algorithms in simulated brain data. METHODS A two-stage design was chosen for this challenge. The participants were provided with datasets of multi-echo gradient echo images synthesized from two realistic in silico head phantoms using an MR simulator. At the first stage, participants optimized QSM reconstructions without ground truth data available to mimic the clinical setting. At the second stage, ground truth data were provided for parameter optimization. Submissions were evaluated using eight numerical metrics and visual ratings. RESULTS A total of 98 reconstructions were submitted for stage 1 and 47 submissions for stage 2. Iterative methods had the best quantitative metric scores, followed by deep learning and direct inversion methods. Priors derived from magnitude data improved the metric scores. Algorithms based on iterative approaches and total variation (and its derivatives) produced the best overall results. The reported results and analysis pipelines have been made public to allow researchers to compare new methods to the current state of the art. CONCLUSION The synthetic data provide a consistent framework to test the accuracy and robustness of QSM algorithms in the presence of noise, calcifications and minor voxel dephasing effects. Total Variation-based algorithms produced the best results among all metrics. Future QSM challenges should assess whether this good performance with synthetic datasets translates to more realistic scenarios, where background fields and dipole-incompatible phase contributions are included.
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Affiliation(s)
- Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | | | - José P Marques
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands
| | | | - Carlos Milovic
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, New York, USA
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12
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Marques JP, Meineke J, Milovic C, Bilgic B, Chan K, Hedouin R, van der Zwaag W, Langkammer C, Schweser F. QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures. Magn Reson Med 2021; 86:526-542. [PMID: 33638241 PMCID: PMC8048665 DOI: 10.1002/mrm.28716] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 01/12/2021] [Accepted: 01/15/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE To create a realistic in silico head phantom for the second QSM reconstruction challenge and for future evaluations of processing algorithms for QSM. METHODS We created a digital whole-head tissue property phantom by segmenting and postprocessing high-resolution (0.64 mm isotropic), multiparametric MRI data acquired at 7 T from a healthy volunteer. We simulated the steady-state magnetization at 7 T using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based processing. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the phantom in the future, such as the inclusion of pathologies as well as the simulation of a wide range of acquisition protocols. Specifically, the following parameters and effects were implemented: TR and TE, voxel size, background fields, and RF phase biases. Diffusion-weighted imaging phantom data are provided, allowing future investigations of tissue-microstructure effects in phase and QSM algorithms. RESULTS The brain part of the phantom featured realistic morphology with spatial variations in relaxation and susceptibility values similar to the in vivo setting. We demonstrated some of the phantom's properties, including the possibility of generating phase data with nonlinear evolution over TE due to partial-volume effects or complex distributions of frequency shifts within the voxel. CONCLUSION The presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative susceptibility reconstruction algorithms.
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Affiliation(s)
- José P. Marques
- Donders Institute for Brain, Cognition and BehaviorRadboud UniversityNijmegenthe Netherlands
| | | | - Carlos Milovic
- Department of Electrical EngineeringPontificia Universidad Catolica de ChileSantiagoChile
- Biomedical Imaging CenterPontificia Universidad Catolica de ChileSantiagoChile
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUnited Kingdom
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical ImagingCharlestownMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
- Harvard‐MIT Health Sciences and TechnologyMITCambridgeMassachusettsUSA
| | - Kwok‐Shing Chan
- Donders Institute for Brain, Cognition and BehaviorRadboud UniversityNijmegenthe Netherlands
| | - Renaud Hedouin
- Donders Institute for Brain, Cognition and BehaviorRadboud UniversityNijmegenthe Netherlands
- Centre Inria Rennes ‐ Bretagne AtlantiqueRennesFrance
| | | | | | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis CenterDepartment of NeurologyJacobs School of Medicine and Biomedical SciencesUniversity at BuffaloThe State University of New YorkBuffaloNew YorkUSA
- Center for Biomedical Imaging, Clinical and Translational Science InstituteUniversity at BuffaloThe State University of New YorkBuffaloNew YorkUSA
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Dwyer M, Lyman C, Ferrari H, Bergsland N, Fuchs TA, Jakimovski D, Schweser F, Weinstock-Guttmann B, Benedict RHB, Riolo J, Silva D, Zivadinov R. DeepGRAI (Deep Gray Rating via Artificial Intelligence): Fast, feasible, and clinically relevant thalamic atrophy measurement on clinical quality T2-FLAIR MRI in multiple sclerosis. Neuroimage Clin 2021; 30:102652. [PMID: 33872992 PMCID: PMC8080069 DOI: 10.1016/j.nicl.2021.102652] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 03/15/2021] [Accepted: 03/26/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Thalamic volume loss is a key marker of neurodegeneration in multiple sclerosis (MS). T2-FLAIR MRI is a common denominator in clinical routine MS imaging, but current methods for thalamic volumetry are not applicable to it. OBJECTIVE To develop and validate a robust algorithm to measure thalamic volume using clinical routine T2-FLAIR MRI. METHODS A dual-stage deep learning approach based on 3D U-net (DeepGRAI - Deep Gray Rating via Artificial Intelligence) was created and trained/validated/tested on 4,590 MRI exams (4288 2D-FLAIR, 302 3D-FLAIR) from 59 centers (80/10/10 train/validation/test split). As training/test targets, FIRST was used to generate thalamic masks from 3D T1 images. Masks were reviewed, corrected, and aligned into T2-FLAIR space. Additional validation was performed to assess inter-scanner reliability (177 subjects at 1.5 T and 3 T within one week) and scan-rescan-reliability (5 subjects scanned, repositioned, and then re-scanned). A longitudinal dataset including assessment of disability and cognition was used to evaluate the predictive value of the approach. RESULTS DeepGRAI automatically quantified thalamic volume in approximately 7 s per case, and has been made publicly available. Accuracy on T2-FLAIR relative to 3D T1 FIRST was 99.4% (r = 0.94, p < 0.001,TPR = 93.0%, FPR = 0.3%). Inter-scanner error was 3.21%. Scan-rescan error with repositioning was 0.43%. DeepGRAI-derived thalamic volume was associated with disability (r = -0.427,p < 0.001) and cognition (r = -0.537,p < 0.001), and was a significant predictor of longitudinal cognitive decline (R2 = 0.081, p = 0.024; comparatively, FIRST-derived volume was R2 = 0.080, p = 0.025). CONCLUSIONS DeepGRAI provides fast, reliable, and clinically relevant thalamic volume measurement on multicenter clinical-quality T2-FLAIR images. This indicates potential for real-world thalamic volumetry, as well as quantification on legacy datasets without 3D T1 imaging.
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Affiliation(s)
- Michael Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; Jacobs MS Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.
| | - Cassondra Lyman
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Hannah Ferrari
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; IRCCS, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Tom A Fuchs
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; Jacobs MS Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Bianca Weinstock-Guttmann
- Jacobs MS Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ralph H B Benedict
- Jacobs MS Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Jon Riolo
- Bristol Myers Squibb, Summit, NJ, USA
| | | | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; Jacobs MS Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
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14
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Bergsland N, Benedict RHB, Dwyer MG, Fuchs TA, Jakimovski D, Schweser F, Tavazzi E, Weinstock-Guttman B, Zivadinov R. Thalamic Nuclei Volumes and Their Relationships to Neuroperformance in Multiple Sclerosis: A Cross-Sectional Structural MRI Study. J Magn Reson Imaging 2021; 53:731-739. [PMID: 33044013 DOI: 10.1002/jmri.27389] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 09/25/2020] [Accepted: 09/25/2020] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Although reduced thalamic volume is associated with multiple sclerosis (MS)-related clinical impairment, the role of individual thalamic nuclei remains poorly understood. PURPOSE/HYPOTHESIS To test whether individual thalamic nuclei volumes are more strongly associated with clinical disability than the whole thalamic volume. STUDY TYPE Retrospective analysis of a prospective dataset. SUBJECTS A total of 108 MS patients and 48 age- and sex-matched healthy controls (HCs) FIELD STRENGTH: 3T. SEQUENCES 3D T1 -weighted inversion recovery spoiled gradient echo; 2D T2 -weighted fluid-attenuated inversion recovery spin echo; 2D dual-echo proton density-weighted/T2 -weighted spin echo. ASSESSMENTS Clinical assessments included the Expanded Disability Status Scale (EDSS), Nine-Hole Peg Test (9HPT), Timed 25-Foot Walk (T25FW), Symbol Digit Modalities Test (SDMT), Brief Visuospatial Memory Test-Revised (BVMTR), and the California Verbal Learning Test (CVLT2). FreeSurfer provided anterior, intralaminar, lateral, medial, ventral, posterior, and total volumes. STATISTICAL TESTS False discovery rate-corrected partial correlations (controlling for age, sex, and education) to assess the relationships between volumes and neuroperformance. RESULTS Compared to HCs, MS patients presented with lower thalamic nuclei volumes (P < 0.05) except for the intralaminar nucleus (P = 0.279) and scored worse on all neuroperformance scales (P ≤ 0.05) except for CVLT2 (P = 0.151). All nuclei except intralaminar were associated with EDSS (correlation coefficient range: -0.233 to -0.395), SDMT (range: 0.247-0.423), and 9HPT (range: -0.232 to -0.303) (all P < 0.05). BVMTR was associated with anterior (r = 0.319), lateral (r = 0.31), and medial (r = 0.304) volumes (all P < 0.05). T25FW correlated with ventral (r = -0.392) and total (r = -0.309) volumes (both P < 0.05), with the latter being significantly greater (P < 0.05). DATA CONCLUSION Assessing individual nuclei volume can aid in unraveling the relationship between thalamic pathology and disparate aspects of MS-related disability. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Ralph H B Benedict
- Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Tom A Fuchs
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
- Center for Biomedical Imaging at Clinical Translational Science Institute, The State University of New York, Buffalo, New York, USA
| | - Eleonora Tavazzi
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Bianca Weinstock-Guttman
- Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
- Center for Biomedical Imaging at Clinical Translational Science Institute, The State University of New York, Buffalo, New York, USA
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15
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Schweser F, Hagemeier J, Dwyer MG, Bergsland N, Hametner S, Weinstock-Guttman B, Zivadinov R. Decreasing brain iron in multiple sclerosis: The difference between concentration and content in iron MRI. Hum Brain Mapp 2020; 42:1463-1474. [PMID: 33378095 PMCID: PMC7927296 DOI: 10.1002/hbm.25306] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 10/21/2020] [Accepted: 11/20/2020] [Indexed: 12/11/2022] Open
Abstract
Increased brain iron concentration is often reported concurrently with disease development in multiple sclerosis (MS) and other neurodegenerative diseases. However, it is unclear whether the higher iron concentration in patients stems from an influx of iron into the tissue or a relative reduction in tissue compartments without much iron. By taking into account structural volume, we investigated tissue iron content in the deep gray matter (DGM) over 2 years, and compared findings to previously reported changes in iron concentration. 120 MS patients and 40 age‐ and sex‐matched healthy controls were included. Clinical testing and MRI were performed both at baseline and after 2 years. Overall, iron content was calculated from structural MRI and quantitative susceptibility mapping in the thalamus, caudate, putamen, and globus pallidus. MS patients had significantly lower iron content than controls in the thalamus, with progressive MS patients demonstrating lower iron content than relapsing–remitting patients. Over 2 years, iron content decreased in the DGM of patients with MS, while it tended to increase or remain stable among controls. In the thalamus, decreasing iron content over 2 years was associated with disability progression. Our study showed that temporally increasing magnetic susceptibility in MS should not be considered as evidence for iron influx because it may be explained, at least partially, by disease‐related atrophy. Declining DGM iron content suggests that, contrary to the current understanding, iron is being removed from the DGM in patients with MS.
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Affiliation(s)
- Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA.,Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Jesper Hagemeier
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA.,Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA.,IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Simon Hametner
- Department of Neuroimmunology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Bianca Weinstock-Guttman
- Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA.,Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, New York, USA
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16
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Zivadinov R, Schweser F, Dwyer MG, Pol S. Detection of Monocyte/Macrophage and Microglia Activation in the TMEV Model of Chronic Demyelination Using USPIO-Enhanced Ultrahigh-Field Imaging. J Neuroimaging 2020; 30:769-778. [PMID: 32866329 DOI: 10.1111/jon.12768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 07/20/2020] [Accepted: 07/28/2020] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND AND PURPOSE Blood-derived monocytes/macrophages can be labeled with ultrasmall superparamagnetic iron oxides (USPIO) at periphery and subsequently migrate into areas of inflammation in the brain. We investigated temporal pattern of migration of peripheral immune cells in Theiler's murine encephalomyelitis virus (TMEV) model of chronic demyelination by USPIO-enhanced imaging. METHODS Fifteen SJL mice (Envigo, Indianapolis, IN) were injected with TMEV (n = 12) or saline (n = 3) at 7 weeks of age. Brain MRI of 9.4 T was performed at 3 months postinfection (mpi) (the peak of inflammatory phase), at 4, 5, and 7 mpi (throughout neurodegenerative phase) using T2*-weighted gradient echo MRI, and performed 24 hours after USPIO injection. Contrast enhancing lesion (CEL) number and volume were measured and development of brain atrophy was assessed across serial time points. Clinical disability scale and rotarod score assessed disease progression. RESULTS CEL was detected in a total of eight (66.7%) TMEV-infected animals and none of the Controls. The CEL was present in four (33.3%) TMEV-infected animals at 3 mpi, two (16.7%) at 4 mpi, six (54.5%) at 5 mpi, and four (44.4%) at 7 mpi, respectively. In TMEV-infected animals, the CEL number and volume increased significantly from 3 to 7 mpi (P < .01 for both). The correlation between total CEL number and volume with clinical and MRI outcomes was trending (P < .05). On histopathology analysis, CEL showed increased density of Iba1 staining for microglia activity. CONCLUSIONS Serial USPIO imaging is a promising biomarker for investigating the effect of therapeutic interventions on monocytes/macrophages and microglia activation and neurodegeneration in TMEV-infected animals.
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Affiliation(s)
- Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY
- Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, NY
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY
- Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, NY
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY
- Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, NY
| | - Suyog Pol
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY
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17
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Pol S, Liang S, Schweser F, Dhanraj R, Schubart A, Preda M, Sveinsson M, Ramasamy DP, Dwyer MG, Weckbecker G, Zivadinov R. Subcutaneous anti-CD20 antibody treatment delays gray matter atrophy in human myelin oligodendrocyte glycoprotein-induced EAE mice. Exp Neurol 2020; 335:113488. [PMID: 32991933 DOI: 10.1016/j.expneurol.2020.113488] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 09/18/2020] [Accepted: 09/25/2020] [Indexed: 02/04/2023]
Abstract
BACKGROUND The human myelin oligodendrocyte glycoprotein-induced experimental autoimmune encephalomyelitis (huMOG-EAE) model, generates B-cell driven demyelination in mice, making it a suitable multiple sclerosis model to study B cell depletion. OBJECTIVES We investigated the effect of subcutaneous anti-CD20 antibody treatment on huMOG-EAE gray matter (GM) pathology. METHODS C57Bl/6, 8-week old mice were immunized with 200 huMOG1-125 and treated with 50 μg/mouse of anti-CD20 antibody (n = 16) or isotype control (n = 16). Serial brain volumetric 9.4 T MRI scans was performed at baseline, 1 and 5 wkPI. Disease severity was measured by clinical disability score (CDS) and performance on rotarod test. RESULTS Anti-CD20 antibody significantly reduced brain volume loss compared with the isotype control across all timepoints longitudinally in the basal ganglia (p = 0.01), isocortex (p = 0.025) and thalamus (p = 0.023). The CDS was reduced significantly with anti-CD20 antibody vs. the isotype control at 3 (p = 0.003) and 4 (p = 0.03) wkPI, while a trend was observed at 5 (p = 0.057) and 6 (p = 0.086) wkPI. Performance on rotarod was also improved significantly at 3 (p = 0.007) and 5 (p = 0.01) wkPI compared with the isotype control. At cellular level, anti-CD20 therapy suppressed the percentage of proliferative nuclear antigen positive microglia in huMOG-EAE isocortex (p = 0.016). Flow cytometry confirmed that anti-CD20 antibody strongly depleted the CD19-expressing B cell fraction in peripheral blood mononuclear cells, reducing it from 39.7% measured in isotype control to 1.59% in anti-CD20 treated mice (p < 0.001). CONCLUSIONS Anti-CD20 antibody treatment delayed brain tissue neurodegeneration in GM, and showed clinical benefit on measures of disease severity in huMOG-EAE mice.
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Affiliation(s)
- Suyog Pol
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Serena Liang
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, NY, USA
| | - Ravendra Dhanraj
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Anna Schubart
- Novartis Institutes of BioMedical Research, Department of Transplantation and Immunology, Novartis, Basel, Switzerland
| | - Marilena Preda
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Michele Sveinsson
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Deepa P Ramasamy
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, NY, USA
| | - Gisbert Weckbecker
- Novartis Institutes of BioMedical Research, Department of Transplantation and Immunology, Novartis, Basel, Switzerland
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, NY, USA.
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18
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Bergsland N, Tavazzi E, Schweser F, Jakimovski D, Hagemeier J, Dwyer MG, Zivadinov R. Targeting Iron Dyshomeostasis for Treatment of Neurodegenerative Disorders. CNS Drugs 2019; 33:1073-1086. [PMID: 31556017 PMCID: PMC6854324 DOI: 10.1007/s40263-019-00668-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
While iron has an important role in the normal functioning of the brain owing to its involvement in several physiological processes, dyshomeostasis has been found in many neurodegenerative disorders, as evidenced by both histopathological and imaging studies. Although the exact causes have remained elusive, the fact that altered iron levels have been found in disparate diseases suggests that iron may contribute to their development and/or progression. As such, the processes involved in iron dyshomeostasis may represent novel therapeutic targets. There are, however, many questions about the exact interplay between neurodegeneration and altered iron homeostasis. Some insight can be gained by considering the parallels with respect to what occurs in healthy aging, which is also characterized by increased iron throughout many regions in the brain along with progressive neurodegeneration. Nevertheless, the exact mechanisms of iron-mediated damage are likely disease specific to a certain degree, given that iron plays a crucial role in many disparate biological processes, which are not always affected in the same way across different neurodegenerative disorders. Moreover, it is not even entirely clear yet whether iron actually has a causative role in all of the diseases where altered iron levels have been noted. For example, there is strong evidence of iron dyshomeostasis leading to neurodegeneration in Parkinson's disease, but there is still some question as to whether changes in iron levels are merely an epiphenomenon in multiple sclerosis. Recent advances in neuroimaging now offer the possibility to detect and monitor iron levels in vivo, which allows for an improved understanding of both the temporal and spatial dynamics of iron changes and associated neurodegeneration compared to post-mortem studies. In this regard, iron-based imaging will likely play an important role in the development of therapeutic approaches aimed at addressing altered iron dynamics in neurodegenerative diseases. Currently, the bulk of such therapies have focused on chelating excess iron. Although there is some evidence that these treatment options may yield some benefit, they are not without their own limitations. They are generally effective at reducing brain iron levels, as assessed by imaging, but clinical benefits are more modest. New drugs that specifically target iron-related pathological processes may offer the possibility to prevent, or at the least, slow down irreversible neurodegeneration, which represents an unmet therapeutic target.
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Affiliation(s)
- Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High St., Buffalo, NY, 14203, USA.
| | - Eleonora Tavazzi
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA,Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Jesper Hagemeier
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Michael G. Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA,Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA,Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, USA
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19
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Karthikeyan B, Sonkawade SD, Pokharel S, Preda M, Schweser F, Zivadinov R, Kim M, Sharma UC. Tagged cine magnetic resonance imaging to quantify regional mechanical changes after acute myocardial infarction. Magn Reson Imaging 2019; 66:208-218. [PMID: 31668928 DOI: 10.1016/j.mri.2019.09.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 08/16/2019] [Accepted: 09/15/2019] [Indexed: 12/29/2022]
Abstract
PURPOSE The conventional volumetric approaches of measuring cardiac function are load-dependent, and are not able to discriminate functional changes in the infarct, transition and remote myocardium. We examined phase-dependent regional mechanical changes in the infarct, transition and remote regions after acute myocardial infarction (MI) in a preclinical mouse model using cardiovascular magnetic resonance imaging (CMR). METHODS We induced acute MI in six mice with left anterior descending coronary artery ligation. We then examined cardiac (infarct, transition and remote-zone) morphology and function utilizing 9.4 T high field CMR before and 2 weeks after the induction of acute MI. Myocardial scar tissue was evaluated by using CMR with late gadolinium enhancement (LGE). After determining global function through volumetric analysis, regional wall motion was evaluated by measuring wall thickening and radial velocities. Strain rate imaging was performed to assess circumferential contraction and relaxation at the myocardium, endocardium, and epicardium. RESULTS There was abnormal LGE in the anterior walls after acute MI suggesting a successful MI procedure. The transition zone consisted of a mixed signal intensity, while the remote zone contained viable myocardium. As expected, the infarct zone had demonstrated severely decreased myocardial velocities and strain rates, suggesting reduced contraction and relaxation function. Compared to pre-infarct baseline, systolic and diastolic velocities (vS and vD) were significantly reduced at the transition zone (vS: -1.86 ± 0.16 cm/s vs -0.68 ± 0.13 cm/s, P < 0.001; vD: 1.86 ± 0.17 cm/s vs 0.53 ± 0.06 cm/s, P < 0.001) and remote zone (vS: -1.86 ± 0.16 cm/s vs -0.65 ± 0.12 cm/s, P < 0.001; vD: 1.86 ± 0.16 cm/s vs 0.51 ± 0.04 cm/s, P < 0.001). Myocardial peak systolic and diastolic strain rates (SRS and SRD) were significantly lower in the transition zone (SRS: -4.2 ± 0.3 s-1 vs -1.3 ± 0.2 s-1, P < 0.001; SRD: 3.9 ± 0.3 s-1 vs 1.3 ± 0.2 s-1, P < 0.001) and remote zone (SRS: -3.8 ± 0.3 s-1 vs -1.4 ± 0.3 s-1, P < 0.001; SRD: 3.5 ± 0.2 s-1 vs 1.5 ± 0.4 s-1, P = 0.006). Endocardial and epicardial SRS and SRD were similarly reduced in the transition and remote zones compared to baseline. CONCLUSIONS This study, for the first time, utilized state-of-the art high-field CMR algorithms in a preclinical mouse model for a comprehensive and controlled evaluation of the regional mechanical changes in the transition and remote zones, after acute MI. Our data demonstrate that CMR can quantitatively monitor dynamic post-MI remodeling in the transition and remote zones, thereby serving as a gold standard tool for therapeutic surveillance.
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Affiliation(s)
- Badri Karthikeyan
- Department of Medicine, Division of Cardiology, Jacob's School of Medicine and Biomedical Sciences, Buffalo, NY, United States of America
| | - Swati D Sonkawade
- Department of Medicine, Division of Cardiology, Jacob's School of Medicine and Biomedical Sciences, Buffalo, NY, United States of America
| | - Saraswati Pokharel
- Department of Pathology and Laboratory Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States of America
| | - Marilena Preda
- Center for Biomedical Imaging at the Clinical and Translational Science Institute, University at Buffalo, Buffalo, NY, United States of America
| | - Ferdinand Schweser
- Center for Biomedical Imaging at the Clinical and Translational Science Institute, University at Buffalo, Buffalo, NY, United States of America
| | - Robert Zivadinov
- Center for Biomedical Imaging at the Clinical and Translational Science Institute, University at Buffalo, Buffalo, NY, United States of America
| | - Minhyung Kim
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States of America
| | - Umesh C Sharma
- Department of Medicine, Division of Cardiology, Jacob's School of Medicine and Biomedical Sciences, Buffalo, NY, United States of America.
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20
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Zivadinov R, Polak P, Schweser F, Bergsland N, Hagemeier J, Dwyer MG, Ramasamy DP, Baker JG, Leddy JJ, Willer BS. Multimodal Imaging of Retired Professional Contact Sport Athletes Does Not Provide Evidence of Structural and Functional Brain Damage. J Head Trauma Rehabil 2019; 33:E24-E32. [PMID: 30080799 DOI: 10.1097/htr.0000000000000422] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Long-term consequences of playing professional football and hockey on brain function and structural neuronal integrity are unknown. OBJECTIVES To investigate multimodal metabolic and structural brain magnetic resonance imaging (MRI) differences in retired professional contact sport athletes compared with noncontact sport athletes. METHODS Twenty-one male contact sport athletes and 21 age-matched noncontact sport athletes were scanned on a 3 tesla (3T) MRI using a multimodal imaging approach. The MRI outcomes included presence, number, and volume of focal white matter signal abnormalities, volumes of global and regional tissue-specific brain structures, diffusion-tensor imaging tract-based spatial statistics measures of mean diffusivity and fractional anisotropy, quantitative susceptibility mapping of deep gray matter, presence, number, and volume of cerebral microbleeds, MR spectroscopy N-acetyl-aspartate, glutamate, and glutamine concentrations relative to creatine and phosphor creatine of the corpus callosum, and perfusion-weighted imaging mean transit time, cerebral blood flow, and cerebral blood volume outcomes. Subjects were also classified as having mild cognitive impairment. RESULTS No significant differences were found for structural or functional MRI measures between contact sport athletes and noncontact sport athletes. CONCLUSIONS This multimodal imaging study did not show any microstructural, metabolic brain tissue injury differences in retired contact versus non-contact sport athletes.
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Affiliation(s)
- Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology (Drs Zivadinov, Polak, Schweser, Bergsland, Hagemeier, Dwyer, and Ramasamy), MR Imaging Clinical and Translational Research Center (Drs Zivadinov and Schweser), Department of Orthopaedics (Drs Baker and Leddy), Department of Nuclear Medicine (Dr Baker), and Department of Psychiatry (Dr Willer), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo
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21
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Zivadinov R, Bergsland N, Hagemeier J, Ramasamy DP, Dwyer MG, Schweser F, Kolb C, Weinstock-Guttman B, Hojnacki D. Cumulative gadodiamide administration leads to brain gadolinium deposition in early MS. Neurology 2019; 93:e611-e623. [PMID: 31285398 PMCID: PMC6709999 DOI: 10.1212/wnl.0000000000007892] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Accepted: 04/02/2019] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVE Frequent administration of gadolinium-based contrast agents in multiple sclerosis (MS) may increase signal intensity (SI) unenhanced T1-weighted imaging MRI throughout the brain. We evaluated the association between lifetime cumulative doses of gadodiamide administration and increased SI within the dentate nucleus (DN), globus pallidus (GP), and thalamus in patients with early MS. METHODS A total of 203 patients with MS (107 with baseline and follow-up MRI assessments) and 262 age- and sex-matched controls were included in this retrospective, longitudinal, 3T MRI-reader-blinded study. Patients with MS had disease duration <2 years at baseline and received exclusively gadodiamide at all MRI time points. SI ratio (SIR) to pons and CSF of lateral ventricle volume (CSF-LVV) were assessed. Analysis of covariance and correlation analyses, adjusted for age, sex, and region of interest volume, were used. RESULTS The mean follow-up time was 55.4 months, and the mean number of gadolinium-based contrast agents administrations was 9.2. At follow-up, 49.3% of patients with MS and no controls showed DN T1 hyperintensity (p < 0.001). The mean SIR of DN (p < 0.001) and of GP (p = 0.005) to pons and the mean SIR of DN, GP, and thalamus to CSF-LVV were higher in patients with MS compared to controls (p < 0.001). SIR of DN to pons was associated with number of gadodiamide doses (p < 0.001). No associations between SIR of DN, GP, and thalamus and clinical and MRI outcomes of disease severity were detected over the follow-up. CONCLUSIONS DN, GP, and thalamus gadolinium deposition in early MS is associated with lifetime cumulative gadodiamide administration without clinical or radiologic correlates of more aggressive disease.
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Affiliation(s)
- Robert Zivadinov
- From the Buffalo Neuroimaging Analysis Center (R.Z., N.B., J.H., D.P.R.) and Jacobs Comprehensive MS Treatment and Research Center (C.K., B.W.-G., D.H.), Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, and Center for Biomedical Imaging at Clinical Translational Science Institute (R.Z., M.G.D., F.S.), University at Buffalo, State University of New York.
| | - Niels Bergsland
- From the Buffalo Neuroimaging Analysis Center (R.Z., N.B., J.H., D.P.R.) and Jacobs Comprehensive MS Treatment and Research Center (C.K., B.W.-G., D.H.), Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, and Center for Biomedical Imaging at Clinical Translational Science Institute (R.Z., M.G.D., F.S.), University at Buffalo, State University of New York
| | - Jesper Hagemeier
- From the Buffalo Neuroimaging Analysis Center (R.Z., N.B., J.H., D.P.R.) and Jacobs Comprehensive MS Treatment and Research Center (C.K., B.W.-G., D.H.), Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, and Center for Biomedical Imaging at Clinical Translational Science Institute (R.Z., M.G.D., F.S.), University at Buffalo, State University of New York
| | - Deepa P Ramasamy
- From the Buffalo Neuroimaging Analysis Center (R.Z., N.B., J.H., D.P.R.) and Jacobs Comprehensive MS Treatment and Research Center (C.K., B.W.-G., D.H.), Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, and Center for Biomedical Imaging at Clinical Translational Science Institute (R.Z., M.G.D., F.S.), University at Buffalo, State University of New York
| | - Michael G Dwyer
- From the Buffalo Neuroimaging Analysis Center (R.Z., N.B., J.H., D.P.R.) and Jacobs Comprehensive MS Treatment and Research Center (C.K., B.W.-G., D.H.), Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, and Center for Biomedical Imaging at Clinical Translational Science Institute (R.Z., M.G.D., F.S.), University at Buffalo, State University of New York
| | - Ferdinand Schweser
- From the Buffalo Neuroimaging Analysis Center (R.Z., N.B., J.H., D.P.R.) and Jacobs Comprehensive MS Treatment and Research Center (C.K., B.W.-G., D.H.), Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, and Center for Biomedical Imaging at Clinical Translational Science Institute (R.Z., M.G.D., F.S.), University at Buffalo, State University of New York
| | - Channa Kolb
- From the Buffalo Neuroimaging Analysis Center (R.Z., N.B., J.H., D.P.R.) and Jacobs Comprehensive MS Treatment and Research Center (C.K., B.W.-G., D.H.), Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, and Center for Biomedical Imaging at Clinical Translational Science Institute (R.Z., M.G.D., F.S.), University at Buffalo, State University of New York
| | - Bianca Weinstock-Guttman
- From the Buffalo Neuroimaging Analysis Center (R.Z., N.B., J.H., D.P.R.) and Jacobs Comprehensive MS Treatment and Research Center (C.K., B.W.-G., D.H.), Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, and Center for Biomedical Imaging at Clinical Translational Science Institute (R.Z., M.G.D., F.S.), University at Buffalo, State University of New York
| | - David Hojnacki
- From the Buffalo Neuroimaging Analysis Center (R.Z., N.B., J.H., D.P.R.) and Jacobs Comprehensive MS Treatment and Research Center (C.K., B.W.-G., D.H.), Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, and Center for Biomedical Imaging at Clinical Translational Science Institute (R.Z., M.G.D., F.S.), University at Buffalo, State University of New York
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22
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Schweser F, Kyyriäinen J, Preda M, Pitkänen A, Toffolo K, Poulsen A, Donahue K, Levy B, Poulsen D. Visualization of thalamic calcium influx with quantitative susceptibility mapping as a potential imaging biomarker for repeated mild traumatic brain injury. Neuroimage 2019; 200:250-258. [PMID: 31201986 DOI: 10.1016/j.neuroimage.2019.06.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 06/03/2019] [Accepted: 06/11/2019] [Indexed: 11/17/2022] Open
Abstract
A key event in the pathophysiology of traumatic brain injury (TBI) is the influx of substantial amounts of Ca2+ into neurons, particularly in the thalamus. Detection of this calcium influx in vivo would provide a window into the biochemical mechanisms of TBI with potentially significant clinical implications. In the present work, our central hypothesis was that the Ca2+ influx could be imaged in vivo with the relatively recent MRI technique of quantitative susceptibility mapping (QSM). Wistar rats were divided into five groups: naive controls, sham-operated experimental controls, single mild TBI, repeated mild TBI, and single severe TBI. We employed the lateral fluid percussion injury (FPI) model, which replicates clinical TBI without skull fracture, performed 9.4 Tesla MRI with a 3D multi-echo gradient-echo sequence at weeks 1 and 4 post-injury, computed susceptibility maps using V-SHARP and the QUASAR-HEIDI technique, and performed histology. Sham, experimental controls animals, and injured animals did not demonstrate calcifications at 1 week after the injury. At week 4, calcifications were found in the ipsilateral thalamus of 25-50% of animals after a single TBI and 83% of animals after repeated mild TBI. The location and appearance of calcifications on stained sections was consistent with the appearance on the in vivo susceptibility maps (correlation of volumes: r = 0.7). Our findings suggest that persistent calcium deposits represent a primary pathology of repeated injury and that FPI-QSM has the potential to become a sensitive tool for studying pathophysiology related to mild TBI in vivo.
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Affiliation(s)
- Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, USA.
| | - Jenni Kyyriäinen
- Epilepsy Research Laboratory, A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, FI, 70211, Kuopio, Finland
| | - Marilena Preda
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Asla Pitkänen
- Epilepsy Research Laboratory, A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, FI, 70211, Kuopio, Finland
| | - Kathryn Toffolo
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Austin Poulsen
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Kaitlynn Donahue
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Benett Levy
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - David Poulsen
- Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, Buffalo, NY, USA
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23
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Bergsland N, Zivadinov R, Schweser F, Hagemeier J, Lichter D, Guttuso T. Ventral posterior substantia nigra iron increases over 3 years in Parkinson's disease. Mov Disord 2019; 34:1006-1013. [PMID: 31180615 DOI: 10.1002/mds.27730] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 05/08/2019] [Accepted: 05/10/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Parkinson's disease (PD) is characterized in part by the progressive accumulation of iron within the substantia nigra (SN); however, its spatial and temporal dynamics remain relatively poorly understood. OBJECTIVES The objective of this study was to investigate spatial patterns and temporal evolution of SN iron accumulation in PD. METHODS A total of 18 PD patients (mean disease duration = 6.2 years) receiving dopaminergic therapy and 16 healthy controls were scanned with 3T MRI at baseline and 3 years later using quantitative susceptibility mapping, an indirect marker of iron content. Iron was assessed separately in the posterior SN and anterior SN at the ventral and dorsal levels of the SN. The results were corrected for the false discovery rate. RESULTS A significant group effect was found for the ventral posterior SN (P < .001) and anterior SN (P = .042) quantitative susceptibility mapping as well as significant group x time interaction effects (P = .02 and P = .043, respectively). In addition, a significant intragroup change during 3 years of follow-up was found only in the ventral posterior SN of PD (P = .012), but not healthy controls. No significant effects were detected for any dorsal SN measures. No associations were identified with clinical measures. CONCLUSIONS We found both cross-sectional and longitudinal SN iron changes to be confined to its more ventral location in PD. Because pathology studies also show the ventral SN to degenerate early and to the greatest extent in PD, the assessment of iron levels by quantitative susceptibility mapping in this area may potentially represent a disease progression biomarker in PD. © 2019 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA.,Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA.,Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Jesper Hagemeier
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA
| | - David Lichter
- Movement Disorder Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA
| | - Thomas Guttuso
- Movement Disorder Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA
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24
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Deshpande HU, Mellis AM, Lisinski JM, Stein JS, Koffarnus MN, Paluch R, Schweser F, Zivadinov R, LaConte SM, Epstein LH, Bickel WK. Reinforcer pathology: Common neural substrates for delay discounting and snack purchasing in prediabetics. Brain Cogn 2019; 132:80-88. [PMID: 30933707 PMCID: PMC6626634 DOI: 10.1016/j.bandc.2019.03.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 03/14/2019] [Accepted: 03/17/2019] [Indexed: 01/04/2023]
Abstract
Reinforcer pathology theory stipulates that individuals with both (a) high preference for smaller, immediate over larger, delayed rewards; and (b) high demand for unhealthy commodities are uniquely susceptible to poor health outcomes. Specifically, two behavioral economic tasks (delay discounting, assessing preference for smaller, immediate or larger, delayed rewards; and purchasing, assessing purchases of commodities over changes in price) have been independently associated with conditions such as overweight/obesity and problem substance use. In the present study, we examined possible shared neural regions involved in the processes of delay discounting and demand for snack foods in a prediabetic sample. Fifty-four participants completed both of these tasks. Conjunction between delay discounting and purchasing task results indicates substantial common neural substrates recruited during these two tasks, consistent with interpretations of executive control, interoception, and attention, in the prefrontal cortex, insula, and frontoparietal cortex (superior/middle frontal cortex and superior/inferior parietal lobules), respectively. Collectively, these results suggest possible neural substrates in which the two behavioral risk factors of reinforcer pathology may interact during real-world decision-making in prediabetes.
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Affiliation(s)
| | - Alexandra M Mellis
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
| | - Jonathan M Lisinski
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
| | - Jeffrey S Stein
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
| | - Mikhail N Koffarnus
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
| | - Rocco Paluch
- University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Stephen M LaConte
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA
| | - Leonard H Epstein
- University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Warren K Bickel
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA.
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25
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Soheili-Nezhad S, Sedghi A, Schweser F, Eslami Shahr Babaki A, Jahanshad N, Thompson PM, Beckmann CF, Sprooten E, Toghae M. Structural and Functional Reorganization of the Brain in Migraine Without Aura. Front Neurol 2019; 10:442. [PMID: 31133962 PMCID: PMC6515892 DOI: 10.3389/fneur.2019.00442] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 04/10/2019] [Indexed: 01/27/2023] Open
Abstract
It remains unknown whether migraine headache has a progressive component in its pathophysiology. Quantitative MRI may provide valuable insight into abnormal changes in the migraine interictum and assist in identifying disrupted brain networks. We carried out a data-driven study of structural integrity and functional connectivity of the resting brain in migraine without aura. MRI scanning was performed in 36 patients suffering from episodic migraine without aura and 33 age-matched healthy subjects. Voxel-wise analysis of regional brain volume was performed by registration of the T1-weighted MRI scans into a common study brain template using the tensor-based morphometry (TBM) method. Changes in functional synchronicity of the brain networks were assessed using probabilistic independent component analysis (ICA). TBM revealed that migraine is associated with reduced volume of the medial prefrontal cortex (mPFC). Among 375 functional brain networks, resting-state connectivity was decreased between two components spanning the visual cortex, posterior insula, and parietal somatosensory cortex. Our study reveals structural and functional alterations of the brain in the migraine interictum that may stem from underlying disease risk factors and the "silent" aura phenomenon. Longitudinal studies will be needed to investigate whether interictal brain changes are progressive and associated with clinical disease trajectories.
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Affiliation(s)
- Sourena Soheili-Nezhad
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands.,Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands
| | - Alireza Sedghi
- Medical Informatics Laboratory, Queen's University, Kingston, ON, Canada
| | - Ferdinand Schweser
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo Neuroimaging Analysis Center, University at Buffalo, Buffalo, NY, United States.,Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, Buffalo, NY, United States
| | | | - Neda Jahanshad
- Keck School of Medicine of USC, Imaging Genetics Center, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, United States
| | - Paul M Thompson
- Keck School of Medicine of USC, Imaging Genetics Center, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, United States
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands.,Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands.,John Radcliffe Hospital, Oxford Centre for Functional MRI of the Brain, Oxford, United Kingdom
| | - Emma Sprooten
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands.,Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands
| | - Mansoureh Toghae
- Headache Department, Iranian Center of Neurological Research, Neuroscience Research Institute, Tehran University of Medical Sciences, Tehran, Iran
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26
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Pol S, Schweser F, Bertolino N, Preda M, Sveinsson M, Sudyn M, Babek N, Zivadinov R. Characterization of leptomeningeal inflammation in rodent experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis. Exp Neurol 2019; 314:82-90. [PMID: 30684521 DOI: 10.1016/j.expneurol.2019.01.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 01/14/2019] [Accepted: 01/21/2019] [Indexed: 01/15/2023]
Abstract
BACKGROUND Leptomeningeal inflammation, as evidenced by leptomeningeal contrast enhancement (LMCE), is associated to cortical pathology in multiple sclerosis. The temporal pattern of LMCE in experimental autoimmune encephalomyelitis (EAE) myelin oligodendrocyte glycoprotein (MOG) is unknown. OBJECTIVE To investigate LMCE using serial MRI in the EAE model of MS, and its association with clinical disease progression. To characterize the relationship between LMCE and underlying histological correlates. DESIGN Thirteen C57BL/6J mice, MOG-immunized (35-55 amino acid) and 8 saline injected animals were assessed at pre-induction and at 3, 6, 10, 20, 27, 32, 45 and 63 days post induction (dPI). LMCE scan was obtained using FLAIR-RARE sequence after post-contrast gadolinium administration on 9.4 T scanner. Brain cryo-sections were assessed for measuring cellular density of Iba1 positive macrophage/microglia at 10 dPI and 32 dPI, and for the presence of T, B and macrophage cells in the meningeal layer at 10 dPI and 63 dPI. RESULTS All EAE-MOG animals showed presence of LMCE and none of the control mice. The peak signal intensity of LMCE was evidenced at 10dPI in the meninges and decreased through 10-63 dPI. The peak of LMCE was associated with a weight loss starting at 1 week PI and with clinical symptoms starting at 2 weeks PI. Histological analysis of the brain tissue showed a higher density of Iba1 positive microglial cells in the EAE-MOG animals, corresponding to the areas of LMCE. Meninges of EAE mice showed higher density of Iba1 stained macrophage cells relative to saline animals. EAE animals also showed the presence of T and B cells in the meninges which were absent in the saline animals. CONCLUSIONS LMCE peak intensity in the meninges corresponds to the acute inflammatory phase of EAE-MOG disease progression, and is associated with clinical symptoms and higher inflammatory cell density.
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Affiliation(s)
- Suyog Pol
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Nicola Bertolino
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Marilena Preda
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Michele Sveinsson
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Michelle Sudyn
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Natan Babek
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA.
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27
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Taege Y, Hagemeier J, Bergsland N, Dwyer MG, Weinstock-Guttman B, Zivadinov R, Schweser F. Assessment of mesoscopic properties of deep gray matter iron through a model-based simultaneous analysis of magnetic susceptibility and R 2* - A pilot study in patients with multiple sclerosis and normal controls. Neuroimage 2019; 186:308-320. [PMID: 30445148 PMCID: PMC6481304 DOI: 10.1016/j.neuroimage.2018.11.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 10/16/2018] [Accepted: 11/08/2018] [Indexed: 11/17/2022] Open
Abstract
Most studies of brain iron relied on the effect of the iron on magnetic resonance (MR) relaxation properties, such as R2∗, and bulk tissue magnetic susceptibility, as measured by quantitative susceptibility mapping (QSM). The present study exploited the dependence of R2∗ and magnetic susceptibility on physical interactions at different length-scales to retrieve information about the tissue microenvironment, rather than the iron concentration. We introduce a method for the simultaneous analysis of brain tissue magnetic susceptibility and R2∗ that aims to isolate those biophysical mechanisms of R2∗ -contrast that are associated with the micro- and mesoscopic distribution of iron, referred to as the Iron Microstructure Coefficient (IMC). The present study hypothesized that changes in the deep gray matter (DGM) magnetic microenvironment associated with aging and pathological mechanisms of multiple sclerosis (MS), such as changes of the distribution and chemical form of the iron, manifest in quantifiable contributions to the IMC. To validate this hypothesis, we analyzed the voxel-based association between R2∗ and magnetic susceptibility in different DGM regions of 26 patients with multiple sclerosis and 33 age- and sex-matched normal controls. Values of the IMC varied significantly between anatomical regions, were reduced in the dentate and increased in the caudate of patients compared to controls, and decreased with normal aging, most strongly in caudate, globus pallidus and putamen.
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Affiliation(s)
- Yanis Taege
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, 100 High Street, Buffalo, NY, 14202, USA
| | - Jesper Hagemeier
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, 100 High Street, Buffalo, NY, 14202, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, 100 High Street, Buffalo, NY, 14202, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, 100 High Street, Buffalo, NY, 14202, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, The State University of New York, 875 Ellicott Street, Buffalo, NY, 14202, USA
| | - Bianca Weinstock-Guttman
- Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, 1010 Main St 2nd Flr, Buffalo, NY, 14202, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, 100 High Street, Buffalo, NY, 14202, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, The State University of New York, 875 Ellicott Street, Buffalo, NY, 14202, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, 100 High Street, Buffalo, NY, 14202, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, The State University of New York, 875 Ellicott Street, Buffalo, NY, 14202, USA.
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28
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Shi R, Jung JH, Schweser F. Two-dimensional local Fourier image reconstruction via domain decomposition Fourier continuation method. PLoS One 2019; 14:e0197963. [PMID: 30625147 PMCID: PMC6326477 DOI: 10.1371/journal.pone.0197963] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 12/19/2018] [Indexed: 12/03/2022] Open
Abstract
The MRI image is obtained in the spatial domain from the given Fourier coefficients in the frequency domain. It is costly to obtain the high resolution image because it requires higher frequency Fourier data while the lower frequency Fourier data is less costly and effective if the image is smooth. However, the Gibbs ringing, if existent, prevails with the lower frequency Fourier data. We propose an efficient and accurate local reconstruction method with the lower frequency Fourier data that yields sharp image profile near the local edge. The proposed method utilizes only the small number of image data in the local area. Thus the method is efficient. Furthermore the method is accurate because it minimizes the global effects on the reconstruction near the weak edges shown in many other global methods for which all the image data is used for the reconstruction. To utilize the Fourier method locally based on the local non-periodic data, the proposed method is based on the Fourier continuation method. This work is an extension of our previous 1D Fourier domain decomposition method to 2D Fourier data. The proposed method first divides the MRI image in the spatial domain into many subdomains and applies the Fourier continuation method for the smooth periodic extension of the subdomain of interest. Then the proposed method reconstructs the local image based on L2 minimization regularized by the L1 norm of edge sparsity to sharpen the image near edges. Our numerical results suggest that the proposed method should be utilized in dimension-by-dimension manner instead of in a global manner for both the quality of the reconstruction and computational efficiency. The numerical results show that the proposed method is effective when the local reconstruction is sought and that the solution is free of Gibbs oscillations.
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Affiliation(s)
- Ruonan Shi
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY, United States of America
| | - Jae-Hun Jung
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY, United States of America
- Department of Data Science, Ajou University, Yeongtung-gu, Suwon, Korea
- * E-mail:
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, NY, United States of America
- Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, Buffalo, NY, United States of America
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29
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Schweser F, Zivadinov R. Quantitative susceptibility mapping (QSM) with an extended physical model for MRI frequency contrast in the brain: a proof-of-concept of quantitative susceptibility and residual (QUASAR) mapping. NMR Biomed 2018; 31:e3999. [PMID: 30246892 PMCID: PMC6296773 DOI: 10.1002/nbm.3999] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 05/21/2018] [Accepted: 06/28/2018] [Indexed: 05/12/2023]
Abstract
Quantitative susceptibility mapping (QSM) aims to calculate the tissue's magnetic susceptibility distribution from its perturbing effect on the MRI static main magnetic field. The method is increasingly being applied to study iron and myelin in clinical and preclinical settings. However, recent experimental and theoretical findings have challenged the fundamental theoretical assumptions that form the basis of current numerical implementations of QSM algorithms. The present work introduces a new class of susceptibility mapping algorithms, termed quantitative susceptibility and residual mapping (QUASAR), which takes into account frequency contributions not related to the spatial variation of bulk magnetic susceptibility in the Lorentz sphere model. We present a simple proof-of-concept QUASAR algorithm that, unlike most of the QSM algorithms currently used widely, results in an improved anatomical accuracy of the susceptibility distribution without any a priori assumptions about the susceptibility distribution during the field-to-source inversion. The algorithm was evaluated both in silico and in vivo in the preclinical setting. Our preliminary application of QUASAR in rodents provides the first in vivo evidence that the susceptibility-field model traditionally used in the QSM field cannot fully explain the frequency contrast in brain tissues. Only when an additional local frequency contribution is added to the physical model can the frequency contrast in the brain be related properly to the underlying anatomy.
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Affiliation(s)
- Ferdinand Schweser
- University at Buffalo, The State University of New York, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, New York, United States
| | - Robert Zivadinov
- University at Buffalo, The State University of New York, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, New York, United States
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30
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Zivadinov R, Tavazzi E, Bergsland N, Hagemeier J, Lin F, Dwyer MG, Carl E, Kolb C, Hojnacki D, Ramasamy D, Durfee J, Weinstock-Guttman B, Schweser F. Brain Iron at Quantitative MRI Is Associated with Disability in Multiple Sclerosis. Radiology 2018; 289:487-496. [PMID: 30015589 PMCID: PMC6219694 DOI: 10.1148/radiol.2018180136] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 04/30/2018] [Accepted: 05/04/2018] [Indexed: 11/11/2022]
Abstract
Purpose To study deep gray matter susceptibility in multiple sclerosis (MS) by using quantitative susceptibility mapping (QSM) and to assess the relationship between susceptibility and clinical disability. Materials and Methods For this prospective study between March 2009 and November 2013, 600 participants with MS (452 with relapsing-remitting MS and 148 with secondary progressive MS) and 250 age- and sex-matched healthy control participants were imaged with 3.0-T MRI to measure magnetic susceptibility. Deep gray matter susceptibility (in parts per billion) was analyzed by using region of interest and voxelwise methods. QSM and MRI volumetric differences between study groups and associations with clinical outcomes were assessed. Analysis of covariance, multivariable linear regression, and voxelwise analyses, controlling for age and sex, were used to compare study groups and to explore associations between MRI and clinical outcomes. Results Compared with control participants, participants with MS presented with lower thalamic susceptibility (-7.5 ppb vs -1.1 ppb; P < .001) and higher susceptibility of basal ganglia (62 ppb vs 54.8 ppb; P < .001). Lower thalamic susceptibility was associated with longer disease duration (β = -0.42; P = .002), higher degree of disability (β = -0.64; P = .03), and secondary-progressive course (β = -4.3; P = .009). Higher susceptibility of the globus pallidus was associated with higher disability (β = 2; P = .03). After correcting for each individual structural volume in voxelwise analysis, lower thalamic susceptibility and higher susceptibility of the globus pallidus remained associated with clinical disability (P < .05). Conclusion Quantitative susceptibility mapping (QSM) suggests that altered deep gray matter iron is associated with the evolution of multiple sclerosis (MS) and on disability accrual, independent of tissue atrophy. © RSNA, 2018 Online supplemental material is available for this article.
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Affiliation(s)
- Robert Zivadinov
- From the Buffalo Neuroimaging Analysis Center, Department of
Neurology, Jacobs School of Medicine and Biomedical Sciences (R.Z., E.T., N.B.,
J.H., F.L., M.G.D., E.C., D.R., J.D., F.S.), Center for Biomedical Imaging,
Clinical Translational Science Institute (R.Z.), and Jacobs Multiple Sclerosis
Center, Department of Neurology, School of Medicine and Biomedical Sciences
(C.K., D.H., B.W.G.), University at Buffalo, State University of New York, 100
High St, Buffalo, NY 14203
| | - Eleonora Tavazzi
- From the Buffalo Neuroimaging Analysis Center, Department of
Neurology, Jacobs School of Medicine and Biomedical Sciences (R.Z., E.T., N.B.,
J.H., F.L., M.G.D., E.C., D.R., J.D., F.S.), Center for Biomedical Imaging,
Clinical Translational Science Institute (R.Z.), and Jacobs Multiple Sclerosis
Center, Department of Neurology, School of Medicine and Biomedical Sciences
(C.K., D.H., B.W.G.), University at Buffalo, State University of New York, 100
High St, Buffalo, NY 14203
| | - Niels Bergsland
- From the Buffalo Neuroimaging Analysis Center, Department of
Neurology, Jacobs School of Medicine and Biomedical Sciences (R.Z., E.T., N.B.,
J.H., F.L., M.G.D., E.C., D.R., J.D., F.S.), Center for Biomedical Imaging,
Clinical Translational Science Institute (R.Z.), and Jacobs Multiple Sclerosis
Center, Department of Neurology, School of Medicine and Biomedical Sciences
(C.K., D.H., B.W.G.), University at Buffalo, State University of New York, 100
High St, Buffalo, NY 14203
| | - Jesper Hagemeier
- From the Buffalo Neuroimaging Analysis Center, Department of
Neurology, Jacobs School of Medicine and Biomedical Sciences (R.Z., E.T., N.B.,
J.H., F.L., M.G.D., E.C., D.R., J.D., F.S.), Center for Biomedical Imaging,
Clinical Translational Science Institute (R.Z.), and Jacobs Multiple Sclerosis
Center, Department of Neurology, School of Medicine and Biomedical Sciences
(C.K., D.H., B.W.G.), University at Buffalo, State University of New York, 100
High St, Buffalo, NY 14203
| | - Fuchun Lin
- From the Buffalo Neuroimaging Analysis Center, Department of
Neurology, Jacobs School of Medicine and Biomedical Sciences (R.Z., E.T., N.B.,
J.H., F.L., M.G.D., E.C., D.R., J.D., F.S.), Center for Biomedical Imaging,
Clinical Translational Science Institute (R.Z.), and Jacobs Multiple Sclerosis
Center, Department of Neurology, School of Medicine and Biomedical Sciences
(C.K., D.H., B.W.G.), University at Buffalo, State University of New York, 100
High St, Buffalo, NY 14203
| | - Michael G. Dwyer
- From the Buffalo Neuroimaging Analysis Center, Department of
Neurology, Jacobs School of Medicine and Biomedical Sciences (R.Z., E.T., N.B.,
J.H., F.L., M.G.D., E.C., D.R., J.D., F.S.), Center for Biomedical Imaging,
Clinical Translational Science Institute (R.Z.), and Jacobs Multiple Sclerosis
Center, Department of Neurology, School of Medicine and Biomedical Sciences
(C.K., D.H., B.W.G.), University at Buffalo, State University of New York, 100
High St, Buffalo, NY 14203
| | - Ellen Carl
- From the Buffalo Neuroimaging Analysis Center, Department of
Neurology, Jacobs School of Medicine and Biomedical Sciences (R.Z., E.T., N.B.,
J.H., F.L., M.G.D., E.C., D.R., J.D., F.S.), Center for Biomedical Imaging,
Clinical Translational Science Institute (R.Z.), and Jacobs Multiple Sclerosis
Center, Department of Neurology, School of Medicine and Biomedical Sciences
(C.K., D.H., B.W.G.), University at Buffalo, State University of New York, 100
High St, Buffalo, NY 14203
| | - Channa Kolb
- From the Buffalo Neuroimaging Analysis Center, Department of
Neurology, Jacobs School of Medicine and Biomedical Sciences (R.Z., E.T., N.B.,
J.H., F.L., M.G.D., E.C., D.R., J.D., F.S.), Center for Biomedical Imaging,
Clinical Translational Science Institute (R.Z.), and Jacobs Multiple Sclerosis
Center, Department of Neurology, School of Medicine and Biomedical Sciences
(C.K., D.H., B.W.G.), University at Buffalo, State University of New York, 100
High St, Buffalo, NY 14203
| | - David Hojnacki
- From the Buffalo Neuroimaging Analysis Center, Department of
Neurology, Jacobs School of Medicine and Biomedical Sciences (R.Z., E.T., N.B.,
J.H., F.L., M.G.D., E.C., D.R., J.D., F.S.), Center for Biomedical Imaging,
Clinical Translational Science Institute (R.Z.), and Jacobs Multiple Sclerosis
Center, Department of Neurology, School of Medicine and Biomedical Sciences
(C.K., D.H., B.W.G.), University at Buffalo, State University of New York, 100
High St, Buffalo, NY 14203
| | - Deepa Ramasamy
- From the Buffalo Neuroimaging Analysis Center, Department of
Neurology, Jacobs School of Medicine and Biomedical Sciences (R.Z., E.T., N.B.,
J.H., F.L., M.G.D., E.C., D.R., J.D., F.S.), Center for Biomedical Imaging,
Clinical Translational Science Institute (R.Z.), and Jacobs Multiple Sclerosis
Center, Department of Neurology, School of Medicine and Biomedical Sciences
(C.K., D.H., B.W.G.), University at Buffalo, State University of New York, 100
High St, Buffalo, NY 14203
| | - Jacqueline Durfee
- From the Buffalo Neuroimaging Analysis Center, Department of
Neurology, Jacobs School of Medicine and Biomedical Sciences (R.Z., E.T., N.B.,
J.H., F.L., M.G.D., E.C., D.R., J.D., F.S.), Center for Biomedical Imaging,
Clinical Translational Science Institute (R.Z.), and Jacobs Multiple Sclerosis
Center, Department of Neurology, School of Medicine and Biomedical Sciences
(C.K., D.H., B.W.G.), University at Buffalo, State University of New York, 100
High St, Buffalo, NY 14203
| | - Bianca Weinstock-Guttman
- From the Buffalo Neuroimaging Analysis Center, Department of
Neurology, Jacobs School of Medicine and Biomedical Sciences (R.Z., E.T., N.B.,
J.H., F.L., M.G.D., E.C., D.R., J.D., F.S.), Center for Biomedical Imaging,
Clinical Translational Science Institute (R.Z.), and Jacobs Multiple Sclerosis
Center, Department of Neurology, School of Medicine and Biomedical Sciences
(C.K., D.H., B.W.G.), University at Buffalo, State University of New York, 100
High St, Buffalo, NY 14203
| | - Ferdinand Schweser
- From the Buffalo Neuroimaging Analysis Center, Department of
Neurology, Jacobs School of Medicine and Biomedical Sciences (R.Z., E.T., N.B.,
J.H., F.L., M.G.D., E.C., D.R., J.D., F.S.), Center for Biomedical Imaging,
Clinical Translational Science Institute (R.Z.), and Jacobs Multiple Sclerosis
Center, Department of Neurology, School of Medicine and Biomedical Sciences
(C.K., D.H., B.W.G.), University at Buffalo, State University of New York, 100
High St, Buffalo, NY 14203
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Pol S, Sveinsson M, Sudyn M, Babek N, Siebert D, Bertolino N, Modica CM, Preda M, Schweser F, Zivadinov R. Teriflunomide's Effect on Glia in Experimental Demyelinating Disease: A Neuroimaging and Histologic Study. J Neuroimaging 2018; 29:52-61. [PMID: 30232810 DOI: 10.1111/jon.12561] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 08/31/2018] [Accepted: 09/04/2018] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND AND PURPOSE Teriflunomide reduces disability progression and brain atrophy in multiple sclerosis patients. The exact mechanism of action by which teriflunomide exerts these effects is currently unknown. We assessed the effect of teriflunomide on brain glial cells in the Theiler's murine encephalomyelitis virus (TMEV) by using a histological approach in combination with neuroimaging. METHODS Forty-eight SJL female mice received an intracerebral injection of TMEV at 6-8 weeks of age and were then treated with teriflunomide (n = 24) or placebo (n = 24) for 9 months. They were examined with MRI and behavioral testing at 2, 6, and 9 months postinduction (mPI). Of those, 18 teriflunomide-treated and 17 controls mice were analyzed histologically at 9 mPI to sample from different brain regions for myelination status, microglial density, and oligodendroglial lineage. The histological and MRI outcomes were correlated. RESULTS Corpus callosum microglial density was numerically lower in the teriflunomide-treated mice compared to the control group (141.1 ± 21.7 SEM vs. 214.74 ± 34.79 SEM, Iba1+ cells/mm2 , P = .087). Basal ganglia (BG) microglial density in the teriflunomide group exhibited a negative correlation with fractional anisotropy (P = .021) and a positive correlation with mean diffusivity (P = .034), indicating less inflammation and axonal damage. Oligodendroglial lineage cell and myelin density were not significantly different between treatment groups. However, a significant positive correlation between BG oligodendrocytes and BG volume (P = .027), and with N-acetyl aspartate concentration (P = .008), was found in the teriflunomide group, indicating less axonal loss. CONCLUSION Teriflunomide altered microglia density and oligodendrocytes differentiation, which was associated with less evident microstructural damage on MRI.
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Affiliation(s)
- Suyog Pol
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY
| | - Michele Sveinsson
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY
| | - Michelle Sudyn
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY
| | - Natan Babek
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY
| | - Danielle Siebert
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY
| | - Nicola Bertolino
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY
| | - Claire M Modica
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY
| | - Marilena Preda
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY.,Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY.,Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY.,Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY
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32
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Ziliotto N, Bernardi F, Jakimovski D, Baroni M, Marchetti G, Bergsland N, Ramasamy DP, Weinstock-Guttman B, Schweser F, Zamboni P, Ramanathan M, Zivadinov R. Hemostasis biomarkers in multiple sclerosis. Eur J Neurol 2018; 25:1169-1176. [PMID: 29758118 DOI: 10.1111/ene.13681] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 05/03/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND PURPOSE The aim was to investigate the plasma levels of hemostasis components in multiple sclerosis (MS) and their association with clinical and magnetic resonance imaging (MRI) outcomes. METHODS In all, 138 MS patients [85 with relapsing-remitting MS (RR-MS) and 53 with progressive MS (P-MS) with a mean age of 54 years; 72.5% female; median Expanded Disability Status Scale 3.5; mean disease duration 21 years] and 42 age- and sex-matched healthy individuals (HI) were studied. All subjects were examined with 3 T MRI and clinical examinations. Plasma levels of hemostasis factors [procoagulant, factor XII (FXII)] and inhibitors [tissue factor pathway inhibitor (TFPI), thrombomodulin, heparin cofactor II, a disintegrin-like and metalloprotease with thrombospondin type 1 motif 13 (ADAMTS13) and plasminogen activator inhibitor 1 (PAI-1)] were evaluated by magnetic Luminex assays and enzyme-linked immunosorbent assay. Associations between hemostasis plasma levels and clinical and MRI outcomes were assessed. RESULTS Lower ADAMTS13 levels were found in MS patients compared to HI (P = 0.008) and in MS patients presenting with cerebral microbleeds compared to those without (P = 0.034). Higher PAI-1 levels were found in MS patients compared to HI (P = 0.02). TFPI levels were higher in the P-MS subgroup compared to RR-MS patients (P = 0.011) and compared to HI (P = 0.002). No significant associations between hemostasis plasma levels and clinical or MRI outcomes were found. CONCLUSIONS Decreased ADAMTS13, particularly in MS patients with cerebral microbleeds, which deserves further investigation, and increased PAI-1 and TFPI levels were observed in MS patients, which deserves further investigation. No relationship between hemostasis plasma levels and measures of disease severity was detected.
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Affiliation(s)
- N Ziliotto
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy.,Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - F Bernardi
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | - D Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - M Baroni
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | - G Marchetti
- Department of Biomedical and Specialty Surgical Sciences, University of Ferrara, Ferrara, Italy
| | - N Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - D P Ramasamy
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - B Weinstock-Guttman
- Jacobs Comprehensive MS Treatment and Research Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - F Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.,Center for Biomedical Imaging, Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - P Zamboni
- Department of Morphology, Surgery and Experimental Medicine, University of Ferrara, Ferrara, Italy
| | - M Ramanathan
- Department of Pharmaceutical Sciences, State University of New York, Buffalo, NY, USA
| | - R Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.,Center for Biomedical Imaging, Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
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33
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Bergsland N, Schweser F, Dwyer MG, Weinstock-Guttman B, Benedict RHB, Zivadinov R. Thalamic white matter in multiple sclerosis: A combined diffusion-tensor imaging and quantitative susceptibility mapping study. Hum Brain Mapp 2018; 39:4007-4017. [PMID: 29923266 DOI: 10.1002/hbm.24227] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 04/15/2018] [Accepted: 05/14/2018] [Indexed: 12/25/2022] Open
Abstract
Thalamic white matter (WM) injury in multiple sclerosis (MS) remains relatively poorly understood. Combining multiple imaging modalities, sensitive to different tissue properties, may aid in further characterizing thalamic damage. Forty-five MS patients and 17 demographically-matched healthy controls (HC) were scanned with 3T MRI to obtain quantitative measures of diffusivity and magnetic susceptibility. Participants underwent cognitive evaluation with the Brief International Cognitive Assessment for Multiple Sclerosis battery. Tract-based spatial statistics identified thalamic WM. Non-parametric combination (NPC) analysis was used to perform joint inference on fractional anisotropy (FA), mean diffusivity (MD) and magnetic susceptibility measures. The association of surrounding WM lesions and thalamic WM pathology was investigated with lesion probability mapping. Compared to HCs, the greatest extent of thalamic WM damage was reflected by the combination of increased MD and decreased magnetic susceptibility (63.0% of thalamic WM, peak p = .001). Controlling for thalamic volume resulted in decreased FA and magnetic susceptibility (34.1%, peak p = .004) as showing the greatest extent. In MS patients, the most widespread association with information processing speed was found with the combination of MD and magnetic susceptibility (67.6%, peak p = .0005), although this was not evident after controlling for thalamic volume. For memory measures, MD alone yielded the most widespread associations (45.9%, peak p = .012 or 76.7%, peak p = .001), even after considering thalamic volume, albeit with smaller percentages. White matter lesions were related to decreased FA (peak p = .0063) and increased MD (peak p = .007), but not magnetic susceptibility, of thalamic WM. Our study highlights the complex nature of thalamic pathology in MS.
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Affiliation(s)
- Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York.,Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, New York
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York.,Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, New York
| | - Bianca Weinstock-Guttman
- Jacobs Comprehensive MS Treatment and Research Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York
| | - Ralph H B Benedict
- Jacobs Comprehensive MS Treatment and Research Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York.,Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, New York
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Zivadinov R, Ramasamy DP, Hagemeier J, Kolb C, Bergsland N, Schweser F, Dwyer MG, Weinstock-Guttman B, Hojnacki D. Evaluation of Leptomeningeal Contrast Enhancement Using Pre-and Postcontrast Subtraction 3D-FLAIR Imaging in Multiple Sclerosis. AJNR Am J Neuroradiol 2018; 39:642-647. [PMID: 29439125 DOI: 10.3174/ajnr.a5541] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Accepted: 11/28/2017] [Indexed: 01/13/2023]
Abstract
BACKGROUND AND PURPOSE Leptomeningeal contrast enhancement is found in patients with multiple sclerosis, though reported rates have varied. The use of 3D-fluid-attenuated inversion recovery pre- and postcontrast subtraction imaging may more accurately determine the frequency of leptomeningeal contrast enhancement. The purpose of this study was to investigate the frequency of leptomeningeal contrast enhancement using the pre- and postcontrast subtraction approach and to evaluate 3 different methods of assessing the presence of leptomeningeal contrast enhancement. MATERIALS AND METHODS We enrolled 258 consecutive patients with MS (212 with relapsing-remitting MS, 32 with secondary-progressive MS, and 14 with clinically isolated syndrome) who underwent both pre- and 10-minute postcontrast 3D-FLAIR sequences after a single dose of gadolinium injection on 3T MR imaging. The analysis included leptomeningeal contrast-enhancement evaluation on 3D-FLAIR postcontrast images in native space (method A), on pre- and postcontrast 3D-FLAIR images in native space (method B), and on pre-/postcontrast 3D-FLAIR coregistered and subtracted images (method C, used as the criterion standard). RESULTS In total, 51 (19.7%) patients with MS showed the presence of leptomeningeal contrast enhancement using method A; 39 (15.1%), using method B; and 39 (15.1%), using method C (P = .002). Compared with method C as the criterion standard, method A showed 89.8% sensitivity and 92.7% specificity, while method B showed 84.6% sensitivity and 97.3% specificity (P < .001) at the patient level. Reproducibility was the highest using method C (κ agreement, r = 088, P < .001). The mean time to analyze the 3D-FLAIR images was significantly lower with method C compared with methods A and B (P < .001). CONCLUSIONS 3D-FLAIR postcontrast imaging offers a sensitive method for detecting leptomeningeal contrast enhancement in patients with MS. However, the use of subtraction imaging helped avoid false-positive cases, decreased reading time, and increased the accuracy of leptomeningeal contrast-enhancement foci detection in a clinical routine.
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Affiliation(s)
- R Zivadinov
- From the Department of Neurology (R.Z., D.P.R., J.H., N.B., F.S., M.G.D.), Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences
- Department of Neurology (R.Z., C.K., D.H.), Jacobs Comprehensive MS Treatment and Research Center, University at Buffalo, State University of New York, Buffalo, New York
| | - D P Ramasamy
- From the Department of Neurology (R.Z., D.P.R., J.H., N.B., F.S., M.G.D.), Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences
| | - J Hagemeier
- From the Department of Neurology (R.Z., D.P.R., J.H., N.B., F.S., M.G.D.), Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences
| | - C Kolb
- Department of Neurology (R.Z., C.K., D.H.), Jacobs Comprehensive MS Treatment and Research Center, University at Buffalo, State University of New York, Buffalo, New York
| | - N Bergsland
- From the Department of Neurology (R.Z., D.P.R., J.H., N.B., F.S., M.G.D.), Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences
| | - F Schweser
- From the Department of Neurology (R.Z., D.P.R., J.H., N.B., F.S., M.G.D.), Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences
| | - M G Dwyer
- From the Department of Neurology (R.Z., D.P.R., J.H., N.B., F.S., M.G.D.), Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences
| | - B Weinstock-Guttman
- Translational Imaging Center at Clinical Translational Science Institute (B.W.-G.)
| | - D Hojnacki
- Department of Neurology (R.Z., C.K., D.H.), Jacobs Comprehensive MS Treatment and Research Center, University at Buffalo, State University of New York, Buffalo, New York
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35
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Langkammer C, Schweser F, Shmueli K, Kames C, Li X, Guo L, Milovic C, Kim J, Wei H, Bredies K, Buch S, Guo Y, Liu Z, Meineke J, Rauscher A, Marques JP, Bilgic B. Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge. Magn Reson Med 2018; 79:1661-1673. [PMID: 28762243 PMCID: PMC5777305 DOI: 10.1002/mrm.26830] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 06/03/2017] [Accepted: 06/17/2017] [Indexed: 01/10/2023]
Abstract
PURPOSE The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully. METHODS Gradient-echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution. A reference susceptibility map was provided, which was computed using the susceptibility tensor imaging algorithm on data acquired at 12 head orientations. Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map. Deviations were quantified using the following metrics: root mean squared error (RMSE), structure similarity index (SSIM), high-frequency error norm (HFEN), and the error in selected white and gray matter regions. RESULTS Twenty-seven submissions were evaluated. Most of the best scoring approaches estimated the spatial frequency content in the ill-conditioned domain of the dipole kernel using compressed sensing strategies. The top 10 maps in each category had similar error metrics but substantially different visual appearance. CONCLUSION Because QSM algorithms were optimized to minimize error metrics, the resulting susceptibility maps suffered from over-smoothing and conspicuity loss in fine features such as vessels. As such, the challenge highlighted the need for better numerical image quality criteria. Magn Reson Med 79:1661-1673, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; Clinical and Translational Science Institute, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Christian Kames
- UBC MRI Research Centre, Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Li Guo
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Carlos Milovic
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile; Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Jinsuh Kim
- Department of Radiology, University of Illinois at Chicago, IL, USA
| | - Hongjiang Wei
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Kristian Bredies
- Institute of Mathematics and Scientific Computing, University of Graz, Austria
| | - Sagar Buch
- The MRI Institute for Biomedical Research, Waterloo, Ontario, Canada
| | - Yihao Guo
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Zhe Liu
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | | | - Alexander Rauscher
- UBC MRI Research Centre, Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
| | - José P. Marques
- Donders Centre for Cognitive Neuroimaging, Radboud University, The Netherlands
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, MGH, Boston, MA, USA
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Schweser F, Raffaini Duarte Martins AL, Hagemeier J, Lin F, Hanspach J, Weinstock-Guttman B, Hametner S, Bergsland N, Dwyer MG, Zivadinov R. Mapping of thalamic magnetic susceptibility in multiple sclerosis indicates decreasing iron with disease duration: A proposed mechanistic relationship between inflammation and oligodendrocyte vitality. Neuroimage 2018; 167:438-452. [PMID: 29097315 PMCID: PMC5845810 DOI: 10.1016/j.neuroimage.2017.10.063] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 10/24/2017] [Accepted: 10/27/2017] [Indexed: 12/13/2022] Open
Abstract
Recent advances in susceptibility MRI have dramatically improved the visualization of deep gray matter brain regions and the quantification of their magnetic properties in vivo, providing a novel tool to study the poorly understood iron homeostasis in the human brain. In this study, we used an advanced combination of the recent quantitative susceptibility mapping technique with dedicated analysis methods to study intra-thalamic tissue alterations in patients with clinically isolated syndrome (CIS) and multiple sclerosis (MS). Thalamic pathology is one of the earliest hallmarks of MS and has been shown to correlate with cognitive dysfunction and fatigue, but the mechanisms underlying the thalamic pathology are poorly understood. We enrolled a total of 120 patients, 40 with CIS, 40 with Relapsing Remitting MS (RRMS), and 40 with Secondary Progressive MS (SPMS). For each of the three patient groups, we recruited 40 controls, group matched for age- and sex (120 total). We acquired quantitative susceptibility maps using a single-echo gradient echo MRI pulse sequence at 3 T. Group differences were studied by voxel-based analysis as well as with a custom thalamus atlas. We used threshold-free cluster enhancement (TFCE) and multiple regression analyses, respectively. We found significantly reduced magnetic susceptibility compared to controls in focal thalamic subregions of patients with RRMS (whole thalamus excluding the pulvinar nucleus) and SPMS (primarily pulvinar nucleus), but not in patients with CIS. Susceptibility reduction was significantly associated with disease duration in the pulvinar, the left lateral nuclear region, and the global thalamus. Susceptibility reduction indicates a decrease in tissue iron concentration suggesting an involvement of chronic microglia activation in the depletion of iron from oligodendrocytes in this central and integrative brain region. Not necessarily specific to MS, inflammation-mediated iron release may lead to a vicious circle that reduces the protection of axons and neuronal repair.
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Affiliation(s)
- Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, USA.
| | - Ana Luiza Raffaini Duarte Martins
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Jesper Hagemeier
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Fuchun Lin
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Jannis Hanspach
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - Bianca Weinstock-Guttman
- Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Simon Hametner
- Department of Neuroimmunology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, USA
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Abstract
Susceptibility Weighted Imaging (SWI) is an established part of the clinical neuroimaging toolbox and, since its inception, has also successfully been used in various preclinical studies. Exploiting the effect of variations of magnetic susceptibility between different tissues on the externally applied, static, homogeneous magnetic field, the method visualizes venous vasculature, hemorrhages and blood degradation products, calcifications, and tissue iron deposits. The chapter describes in vivo and ex vivo protocols for preclinical SWI in rodents.
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Affiliation(s)
- Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA.
- Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, USA.
| | - Marilena Preda
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, USA
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38
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Tutino VM, Rajabzadeh-Oghaz H, Chandra AR, Gutierrez LC, Schweser F, Preda M, Chien A, Vakharia K, Ionita C, Siddiqui A, Kolega J. 9.4T Magnetic Resonance Imaging of the Mouse Circle of Willis Enables Serial Characterization of Flow-Induced Vascular Remodeling by Computational Fluid Dynamics. Curr Neurovasc Res 2018; 15:312-325. [PMID: 30484404 DOI: 10.2174/1567202616666181127165943] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 09/27/2018] [Accepted: 10/10/2018] [Indexed: 11/22/2022]
Abstract
BACKGROUND The neurovasculature dynamically responds to changes in cerebral blood flow by vascular remodeling processes. Serial imaging studies in mouse models could help characterize pathologic and physiologic flow-induced remodeling of the Circle of Willis (CoW). METHOD We induced flow-driven pathologic cerebral vascular remodeling in the CoW of mice (n=3) by ligation of the left Common Carotid Artery (CCA), and the right external carotid and pterygopalatine arteries, increasing blood flow through the basilar and the right internal carotid arteries. One additional mouse was used as a wild-type control. Magnetic Resonance Imaging (MRI) at 9.4 Tesla (T) was used to serially image the mouse CoW over three months, and to obtain threedimensional images for use in Computational Fluid Dynamic (CFD) simulations. Terminal vascular corrosion casting and scanning electron microscope imaging were used to identify regions of macroscopic and microscopic arterial damage. RESULTS We demonstrated the feasibility of detecting and serially measuring pathologic cerebral vascular changes in the mouse CoW, specifically in the anterior vasculature. These changes were characterized by bulging and increased vessel tortuosity on the anterior cerebral artery and aneurysm- like remodeling at the right olfactory artery origin. The resolution of the 9.4T system further allowed us to perform CFD simulations in the anterior CoW, which showed a correlation between elevated wall shear stress and pathological vascular changes. CONCLUSION In the future, serial high-resolution MRI could be useful for characterizing the flow environments corresponding to other pathologic remodeling processes in the mouse CoW, such as aneurysm formation, subarachnoid hemorrhage, and ischemia.
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Affiliation(s)
- Vincent M Tutino
- Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14260, United States
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260, United States
| | | | - Anusha R Chandra
- Cannon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14260, United States
| | - Liza C Gutierrez
- Cannon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14260, United States
| | - Ferdinand Schweser
- Department of Neurology, Buffalo Neuroimaging Analysis Center, University at Buffalo, Buffalo, NY 14260, United States
| | - Marilena Preda
- Department of Neurology, Buffalo Neuroimaging Analysis Center, University at Buffalo, Buffalo, NY 14260, United States
| | - Aichi Chien
- Department of Radiology, University of California, Los Angeles, Los Angeles, CA 90095, United States
| | - Kunal Vakharia
- Cannon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14260, United States
| | - Ciprian Ionita
- Cannon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14260, United States
| | - Adnan Siddiqui
- Cannon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14260, United States
| | - John Kolega
- Cannon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14260, United States
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39
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Hagemeier J, Ramanathan M, Schweser F, Dwyer MG, Lin F, Bergsland N, Weinstock-Guttman B, Zivadinov R. Iron-related gene variants and brain iron in multiple sclerosis and healthy individuals. Neuroimage Clin 2017; 17:530-540. [PMID: 29201641 PMCID: PMC5699896 DOI: 10.1016/j.nicl.2017.11.003] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 11/01/2017] [Accepted: 11/03/2017] [Indexed: 12/14/2022]
Abstract
Brain iron homeostasis is known to be disturbed in multiple sclerosis (MS), yet little is known about the association of common gene variants linked to iron regulation and pathological tissue changes in the brain. In this study, we investigated the association of genetic determinants linked to iron regulation with deep gray matter (GM) magnetic susceptibility in both healthy controls (HC) and MS patients. Four hundred (400) patients with MS and 150 age- and sex-matched HCs were enrolled and obtained 3 T MRI examination. Three (3) single nucleotide polymorphisms (SNPs) associated with iron regulation were genotyped: two SNPs in the human hereditary hemochromatosis protein gene HFE: rs1800562 (C282Y mutation) and rs1799945 (H63D mutation), as well as the rs1049296 SNP in the transferrin gene (C2 mutation). The effects of disease and genetic status were studied using quantitative susceptibility mapping (QSM) voxel-based analysis (VBA) and region-of-interest (ROI) analysis of the deep GM. The general linear model framework was used to compare groups. Analyses were corrected for age and sex, and adjusted for false discovery rate. We found moderate increases in susceptibility in the right putamen of participants with the C282Y (+ 6.1 ppb) and H63D (+ 6.9 ppb) gene variants vs. non-carriers, as well as a decrease in thalamic susceptibility of progressive MS patients with the C282Y mutation (left: − 5.3 ppb, right: − 6.7 ppb, p < 0.05). Female MS patients had lower susceptibility in the caudate (− 6.0 ppb) and putamen (left: − 3.9 ppb, right: − 4.6 ppb) than men, but only when they had a wild-type allele (p < 0.05). Iron-gene linked increases in putamen susceptibility (in HC and relapsing remitting MS) and decreases in thalamus susceptibility (in progressive MS), coupled with apparent sex interactions, indicate that brain iron in healthy and disease states may be influenced by genetic factors. Magnetic susceptibility and common gene variants linked to iron were investigated. The C282Y and H63D alleles were associated with putamen and thalamus susceptibility changes. Dependent on allele status, men and women differed in deep GM susceptibility in MS.
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Key Words
- EDSS, Expanded Disability Status Scale
- FDR, false discovery rate
- FWE, family-wise error rate
- GLM, general linear model
- GM, gray matter
- GRE, gradient recalled echo
- HC, healthy control
- HFE, human hemochromatosis gene
- Iron
- Iron related genes
- MS, multiple sclerosis
- MSSS, multiple sclerosis severity scale
- Multiple sclerosis
- QSM
- QSM, quantitative susceptibility mapping
- Quantitative susceptibility mapping
- ROI, region of interest
- RRMS, relapsing-remitting multiple sclerosis
- SNP, single nucleotide polymorphism
- T1w, T1-weighted
- TF, transferrin
- TFCE, threshold-free cluster enhancement
- VBA, voxel-based analysis
- ppb, parts per billion
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Affiliation(s)
- Jesper Hagemeier
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA.
| | - Murali Ramanathan
- Department of Pharmaceutical Sciences, School of Medicine and Biomedical Sciences, State University of New York, Buffalo, NY, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Fuchun Lin
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Bianca Weinstock-Guttman
- Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
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40
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Modica CM, Schweser F, Sudyn ML, Bertolino N, Preda M, Polak P, Siebert DM, Krawiecki JC, Sveinsson M, Hagemeier J, Dwyer MG, Pol S, Zivadinov R. Effect of teriflunomide on cortex-basal ganglia-thalamus (CxBGTh) circuit glutamatergic dysregulation in the Theiler's Murine Encephalomyelitis Virus mouse model of multiple sclerosis. PLoS One 2017; 12:e0182729. [PMID: 28796815 PMCID: PMC5552032 DOI: 10.1371/journal.pone.0182729] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2017] [Accepted: 07/24/2017] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Pathology of gray matter is associated with development of physical and cognitive disability in patients with multiple sclerosis. In particular, glutamatergic dysregulation in the cortex-basal ganglia-thalamus (CxBGTh) circuit could be associated with decline in these behaviors. OBJECTIVES To investigate the effect of an immunomodulatory therapy (teriflunomide, Aubagio®) on changes of the CxBGTh loop in the Theiler's Murine Encephalomyelitis Virus, (TMEV) mouse model of MS. METHODS Forty-eight (48) mice were infected with TMEV, treated with teriflunomide (24) or control vehicle (24) and followed for 39 weeks. Mice were examined with MRS and volumetric MRI scans (0, 8, 26, and 39 weeks) in the cortex, basal ganglia and thalamus, using a 9.4T scanner, and with behavioral tests (0, 4, 8, 12, 17, 26, and 39 weeks). Within conditions, MRI measures were compared between two time points by paired samples t-test and across multiple time points by repeated measures ANOVA (rmANOVA), and between conditions by independent samples t-test and rmANOVA, respectively. Data were considered as significant at the p<0.01 level and as a trend at p<0.05 level. RESULTS In the thalamus, the teriflunomide arm exhibited trends toward decreased glutamate levels at 8 and 26 weeks compared to the control arm (p = 0.039 and p = 0.026), while the control arm exhibited a trend toward increased glutamate between 0 to 8 weeks (p = 0.045). In the basal ganglia, the teriflunomide arm exhibited a trend toward decreased glutamate earlier than the control arm, from 0 to 8 weeks (p = 0.011), resulting in decreased glutamate compared to the control arm at 8 weeks (p = 0.016). CONCLUSIONS Teriflunomide may reduce possible excitotoxicity in the thalamus and basal ganglia by lowering glutamate levels.
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Affiliation(s)
- Claire M Modica
- Neuroscience Program, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America.,Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Ferdinand Schweser
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America.,Translational Imaging Center, Clinical and Translational Science Institute, University at Buffalo, Buffalo, New York, United States of America
| | - Michelle L Sudyn
- Neuroscience Program, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America.,Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Nicola Bertolino
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Marilena Preda
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America.,Translational Imaging Center, Clinical and Translational Science Institute, University at Buffalo, Buffalo, New York, United States of America
| | - Paul Polak
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Danielle M Siebert
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America.,Exercise Science, School of Public Health and Health Professions, University at Buffalo, Buffalo, New York, United States of America
| | - Jacqueline C Krawiecki
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America.,Department of Geology, University at Buffalo, Buffalo, New York, United States of America
| | - Michele Sveinsson
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Jesper Hagemeier
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Michael G Dwyer
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Suyog Pol
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Robert Zivadinov
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America.,Translational Imaging Center, Clinical and Translational Science Institute, University at Buffalo, Buffalo, New York, United States of America
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41
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Hagemeier J, Zivadinov R, Dwyer MG, Polak P, Bergsland N, Weinstock-Guttman B, Zalis J, Deistung A, Reichenbach JR, Schweser F. Changes of deep gray matter magnetic susceptibility over 2 years in multiple sclerosis and healthy control brain. Neuroimage Clin 2017; 18:1007-1016. [PMID: 29868452 PMCID: PMC5984575 DOI: 10.1016/j.nicl.2017.04.008] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 04/07/2017] [Accepted: 04/09/2017] [Indexed: 01/21/2023]
Abstract
In multiple sclerosis, pathological changes of both tissue iron and myelin occur, yet these factors have not been characterized in a longitudinal fashion using the novel iron- and myelin-sensitive quantitative susceptibility mapping (QSM) MRI technique. We investigated disease-relevant tissue changes associated with myelin loss and iron accumulation in multiple sclerosis deep gray matter (DGM) over two years. One-hundred twenty (120) multiple sclerosis patients and 40 age- and sex-matched healthy controls were included in this prospective study. Written informed consent and local IRB approval were obtained from all participants. Clinical testing and QSM were performed both at baseline and at follow-up. Brain magnetic susceptibility was measured in major DGM structures. Temporal (baseline vs. follow-up) and cross-sectional (multiple sclerosis vs. controls) differences were studied using mixed factorial ANOVA analysis and appropriate t-tests. At either time-point, multiple sclerosis patients had significantly higher susceptibility in the caudate and globus pallidus and lower susceptibility in the thalamus. Over two years, susceptibility increased significantly in the caudate of both controls and multiple sclerosis patients. Inverse thalamic findings among MS patients suggest a multi-phase pathology explained by simultaneous myelin loss and/or iron accumulation followed by iron depletion and/or calcium deposition at later stages.
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Affiliation(s)
- Jesper Hagemeier
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA.
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; MRI Clinical and Translational Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Paul Polak
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; IRCCS Don Gnocchi Foundation, Milan, Italy
| | - Bianca Weinstock-Guttman
- Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Joshua Zalis
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Andreas Deistung
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Germany; Section of Experimental Neurology, Department of Neurology, Essen University Hospital, Essen, Germany; Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Germany; Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University Jena, Germany
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; MRI Clinical and Translational Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
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42
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Özbay PS, Deistung A, Feng X, Nanz D, Reichenbach JR, Schweser F. A comprehensive numerical analysis of background phase correction with V-SHARP. NMR Biomed 2017; 30:10.1002/nbm.3550. [PMID: 27259117 PMCID: PMC5136354 DOI: 10.1002/nbm.3550] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2015] [Revised: 03/18/2016] [Accepted: 04/11/2016] [Indexed: 05/19/2023]
Abstract
Sophisticated harmonic artifact reduction for phase data (SHARP) is a method to remove background field contributions in MRI phase images, which is an essential processing step for quantitative susceptibility mapping (QSM). To perform SHARP, a spherical kernel radius and a regularization parameter need to be defined. In this study, we carried out an extensive analysis of the effect of these two parameters on the corrected phase images and on the reconstructed susceptibility maps. As a result of the dependence of the parameters on acquisition and processing characteristics, we propose a new SHARP scheme with generalized parameters. The new SHARP scheme uses a high-pass filtering approach to define the regularization parameter. We employed the variable-kernel SHARP (V-SHARP) approach, using different maximum radii (Rm ) between 1 and 15 mm and varying regularization parameters (f) in a numerical brain model. The local root-mean-square error (RMSE) between the ground-truth, background-corrected field map and the results from SHARP decreased towards the center of the brain. RMSE of susceptibility maps calculated with a spatial domain algorithm was smallest for Rm between 6 and 10 mm and f between 0 and 0.01 mm-1 , and for maps calculated with a Fourier domain algorithm for Rm between 10 and 15 mm and f between 0 and 0.0091 mm-1 . We demonstrated and confirmed the new parameter scheme in vivo. The novel regularization scheme allows the use of the same regularization parameter irrespective of other imaging parameters, such as image resolution. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Pinar Senay Özbay
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, University of Zurich and ETH Zürich, Zurich, Switzerland
| | - Andreas Deistung
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany
| | - Xiang Feng
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Daniel Nanz
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Jürgen Rainer Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany
- Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University Jena, Jena, Germany
| | - Ferdinand Schweser
- MRI Clinical and Translational Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
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43
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Robinson SD, Bredies K, Khabipova D, Dymerska B, Marques JP, Schweser F. An illustrated comparison of processing methods for MR phase imaging and QSM: combining array coil signals and phase unwrapping. NMR Biomed 2017; 30:e3601. [PMID: 27619999 PMCID: PMC5348291 DOI: 10.1002/nbm.3601] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2015] [Revised: 06/14/2016] [Accepted: 07/18/2016] [Indexed: 05/11/2023]
Abstract
Phase imaging benefits from strong susceptibility effects at very high field and the high signal-to-noise ratio (SNR) afforded by multi-channel coils. Combining the information from coils is not trivial, however, as the phase that originates in local field effects (the source of interesting contrast) is modified by the inhomogeneous sensitivity of each coil. This has historically been addressed by referencing individual coil sensitivities to that of a volume coil, but alternative approaches are required for ultra-high field systems in which no such coil is available. An additional challenge in phase imaging is that the phase that develops up to the echo time is "wrapped" into a range of 2π radians. Phase wraps need to be removed in order to reveal the underlying phase distribution of interest. Beginning with a coil combination using a homogeneous reference volume coil - the Roemer approach - which can be applied at 3 T and lower field strengths, we review alternative methods for combining single-echo and multi-echo phase images where no such reference coil is available. These are applied to high-resolution data acquired at 7 T and their effectiveness assessed via an index of agreement between phase values over channels and the contrast-to-noise ratio in combined images. The virtual receiver coil and COMPOSER approaches were both found to be computationally efficient and effective. The main features of spatial and temporal phase unwrapping methods are reviewed, placing particular emphasis on recent developments in temporal phase unwrapping and Laplacian approaches. The features and performance of these are illustrated in application to simulated and high-resolution in vivo data. Temporal unwrapping was the fastest of the methods tested and the Laplacian the most robust in images with low SNR. © 2016 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.
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Affiliation(s)
- Simon Daniel Robinson
- High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Kristian Bredies
- Institute of Mathematics and Scientific Computing, University of Graz, Austria
| | - Diana Khabipova
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Switzerland
- Donders Institute for Brain, Cognition and Behaviou, Radboud University Nijmegen, The Netherlands
| | - Barbara Dymerska
- High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - José P Marques
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Switzerland
- Donders Institute for Brain, Cognition and Behaviou, Radboud University Nijmegen, The Netherlands
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, New York, USA
- MRI Clinical and Translational Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, New York, USA
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Deistung A, Schweser F, Reichenbach JR. Overview of quantitative susceptibility mapping. NMR Biomed 2017; 30:e3569. [PMID: 27434134 DOI: 10.1002/nbm.3569] [Citation(s) in RCA: 167] [Impact Index Per Article: 23.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 05/03/2016] [Accepted: 05/09/2016] [Indexed: 06/06/2023]
Abstract
Magnetic susceptibility describes the magnetizability of a material to an applied magnetic field and represents an important parameter in the field of MRI. With the recently introduced method of quantitative susceptibility mapping (QSM) and its conceptual extension to susceptibility tensor imaging (STI), the non-invasive assessment of this important physical quantity has become possible with MRI. Both methods solve the ill-posed inverse problem to determine the magnetic susceptibility from local magnetic fields. Whilst QSM allows the extraction of the spatial distribution of the bulk magnetic susceptibility from a single measurement, STI enables the quantification of magnetic susceptibility anisotropy, but requires multiple measurements with different orientations of the object relative to the main static magnetic field. In this review, we briefly recapitulate the fundamental theoretical foundation of QSM and STI, as well as computational strategies for the characterization of magnetic susceptibility with MRI phase data. In the second part, we provide an overview of current methodological and clinical applications of QSM with a focus on brain imaging. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Andreas Deistung
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, The State University of New York at Buffalo, NY, USA
- MRI Clinical and Translational Research Center, Jacobs School of Medicine and Biomedical Sciences, The State University of New York at Buffalo, NY, USA
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany
- Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University Jena, Jena, Germany
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Schweser F, Robinson S, de Rochefort L, Li W, Bredies K. An illustrated comparison of processing methods for phase MRI and QSM: removal of background field contributions from sources outside the region of interest. NMR Biomed 2017; 30:10.1002/nbm.3604. [PMID: 27717080 PMCID: PMC5587182 DOI: 10.1002/nbm.3604] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Revised: 06/10/2016] [Accepted: 07/18/2016] [Indexed: 05/08/2023]
Abstract
The elimination of so-called background fields is an essential step in phase MRI and quantitative susceptibility mapping (QSM). Background fields, which are caused by sources outside the region of interest (ROI), are often one to two orders of magnitude stronger than tissue-related field variations from within the ROI, hampering quantitative interpretation of field maps. This paper reviews the current literature on background elimination algorithms for QSM and provides insights into similarities and differences between the many algorithms proposed. We discuss the basic theoretical foundations and derive fundamental limitations of background field elimination. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Ferdinand Schweser
- Department of Neurology, Buffalo Neuroimaging Analysis Center, University at Buffalo, The State University of New York – Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA. University at Buffalo, The State University of New York – MRI Molecular and Translational Imaging Center, Buffalo, NY, USA
| | - Simon Robinson
- Medical University of Vienna – Department of Biomedical Imaging and Image-Guided Therapy, Vienna, Austria
| | - Ludovic de Rochefort
- Centre de Résonance Magnétique Biologique et Médicale (CRMBM), UMR 7339, CNRS, Aix-Marseille Université, France
| | - Wei Li
- University Texas Health Science Center at San Antonio Research Imaging Institute, San Antonio, TX, USA
| | - Kristian Bredies
- University of Graz – Institute for Mathematics and Scientific Computing, Graz, Austria
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Hanspach J, Dwyer MG, Bergsland NP, Feng X, Hagemeier J, Bertolino N, Polak P, Reichenbach JR, Zivadinov R, Schweser F. Methods for the computation of templates from quantitative magnetic susceptibility maps (QSM): Toward improved atlas- and voxel-based analyses (VBA). J Magn Reson Imaging 2017; 46:1474-1484. [PMID: 28263417 DOI: 10.1002/jmri.25671] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 01/30/2017] [Indexed: 12/31/2022] Open
Abstract
PURPOSE To develop and assess a method for the creation of templates for voxel-based analysis (VBA) and atlas-based approaches using quantitative magnetic susceptibility mapping (QSM). MATERIALS AND METHODS We studied four strategies for the creation of magnetic susceptibility brain templates, derived as successive extensions of the conventional template generation (CONV) based on only T1 -weighted (T1 w) images. One method that used only T1 w images involved a minor improvement of CONV (U-CONV). One method used only magnetic susceptibility maps as input for template generation (DIRECT), and the other two used a linear combination of susceptibility and T1 w images (HYBRID) and an algorithm that directly used both image modalities (MULTI), respectively. The strategies were evaluated in a group of N = 10 healthy human subjects and semiquantitatively assessed by three experienced raters. Template quality was compared statistically via worth estimates (WEs) obtained with a log-linear Bradley-Terry model. RESULTS The overall quality of the templates was better for strategies including both susceptibility and T1 w contrast (MULTI: WE = 0.62; HYBRID: WE = 0.21), but the best method depended on the anatomical region of interest. While methods using only one modality resulted in lower WEs, lowest overall WEs were obtained when only T1 w images were used (DIRECT: WE = 0.12; U-CONV: WE = 0.05). CONCLUSION Template generation strategies that employ only magnetic susceptibility contrast or both magnetic susceptibility and T1 w contrast produce templates with the highest quality. The optimal approach depends on the anatomical structures of interest. The established approach of using only T1 w images (CONV) results in reduced image quality compared to all other approaches studied. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1474-1484.
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Affiliation(s)
- Jannis Hanspach
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA
| | - Niels P Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA.,Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy
| | - Xiang Feng
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich Schiller University Jena, Jena, TH, Germany
| | - Jesper Hagemeier
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA
| | - Nicola Bertolino
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA
| | - Paul Polak
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich Schiller University Jena, Jena, TH, Germany.,Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University Jena, Jena, TH, Germany
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA.,MRI Clinical and Translational Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA.,MRI Clinical and Translational Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA
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47
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Feng X, Deistung A, Dwyer MG, Hagemeier J, Polak P, Lebenberg J, Frouin F, Zivadinov R, Reichenbach JR, Schweser F. An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM). Magn Reson Imaging 2017; 39:110-122. [PMID: 28188873 DOI: 10.1016/j.mri.2017.02.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 02/05/2017] [Accepted: 02/05/2017] [Indexed: 12/13/2022]
Abstract
Accurate and robust segmentation of subcortical gray matter (SGM) nuclei is required in many neuroimaging applications. FMRIB's Integrated Registration and Segmentation Tool (FIRST) is one of the most popular software tools for automated subcortical segmentation based on T1-weighted (T1w) images. In this work, we demonstrate that FIRST tends to produce inaccurate SGM segmentation results in the case of abnormal brain anatomy, such as present in atrophied brains, due to a poor spatial match of the subcortical structures with the training data in the MNI space as well as due to insufficient contrast of SGM structures on T1w images. Consequently, such deviations from the average brain anatomy may introduce analysis bias in clinical studies, which may not always be obvious and potentially remain unidentified. To improve the segmentation of subcortical nuclei, we propose to use FIRST in combination with a special Hybrid image Contrast (HC) and Non-Linear (nl) registration module (HC-nlFIRST), where the hybrid image contrast is derived from T1w images and magnetic susceptibility maps to create subcortical contrast that is similar to that in the Montreal Neurological Institute (MNI) template. In our approach, a nonlinear registration replaces FIRST's default linear registration, yielding a more accurate alignment of the input data to the MNI template. We evaluated our method on 82 subjects with particularly abnormal brain anatomy, selected from a database of >2000 clinical cases. Qualitative and quantitative analyses revealed that HC-nlFIRST provides improved segmentation compared to the default FIRST method.
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Affiliation(s)
- Xiang Feng
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany.
| | - Andreas Deistung
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany; Section of Experimental Neurology, Department of Neurology, Essen University Hospital, Essen, Germany; Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Dept. of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, United States
| | - Jesper Hagemeier
- Buffalo Neuroimaging Analysis Center, Dept. of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, United States
| | - Paul Polak
- Buffalo Neuroimaging Analysis Center, Dept. of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, United States
| | - Jessica Lebenberg
- UNATI, CEA DRF/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin Center, Gif/Yvette, France
| | - Frédérique Frouin
- Inserm/CEA/Université Paris Sud/CNRS, CEA/I2BM/SHFJ, Laboratoire IMIV, Orsay, France
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Dept. of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, United States; MRI Molecular and Translational Imaging Center, Buffalo CTRC, State University of New York at Buffalo, Buffalo, NY, United States
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany; Center of Medical Optics and Photonics, Friedrich Schiller University Jena, Germany; Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University Jena, Germany
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Dept. of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, United States; MRI Molecular and Translational Imaging Center, Buffalo CTRC, State University of New York at Buffalo, Buffalo, NY, United States
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Langkammer C, Pirpamer L, Seiler S, Deistung A, Schweser F, Franthal S, Homayoon N, Katschnig-Winter P, Koegl-Wallner M, Pendl T, Stoegerer EM, Wenzel K, Fazekas F, Ropele S, Reichenbach JR, Schmidt R, Schwingenschuh P. Quantitative Susceptibility Mapping in Parkinson's Disease. PLoS One 2016; 11:e0162460. [PMID: 27598250 PMCID: PMC5012676 DOI: 10.1371/journal.pone.0162460] [Citation(s) in RCA: 151] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Accepted: 08/23/2016] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Quantitative susceptibility mapping (QSM) and R2* relaxation rate mapping have demonstrated increased iron deposition in the substantia nigra of patients with idiopathic Parkinson's disease (PD). However, the findings in other subcortical deep gray matter nuclei are converse and the sensitivity of QSM and R2* for morphological changes and their relation to clinical measures of disease severity has so far been investigated only sparsely. METHODS The local ethics committee approved this study and all subjects gave written informed consent. 66 patients with idiopathic Parkinson's disease and 58 control subjects underwent quantitative MRI at 3T. Susceptibility and R2* maps were reconstructed from a spoiled multi-echo 3D gradient echo sequence. Mean susceptibilities and R2* rates were measured in subcortical deep gray matter nuclei and compared between patients with PD and controls as well as related to clinical variables. RESULTS Compared to control subjects, patients with PD had increased R2* values in the substantia nigra. QSM also showed higher susceptibilities in patients with PD in substantia nigra, in the nucleus ruber, thalamus, and globus pallidus. Magnetic susceptibility of several of these structures was correlated with the levodopa-equivalent daily dose (LEDD) and clinical markers of motor and non-motor disease severity (total MDS-UPDRS, MDS-UPDRS-I and II). Disease severity as assessed by the Hoehn & Yahr scale was correlated with magnetic susceptibility in the substantia nigra. CONCLUSION The established finding of higher R2* rates in the substantia nigra was extended by QSM showing superior sensitivity for PD-related tissue changes in nigrostriatal dopaminergic pathways. QSM additionally reflected the levodopa-dosage and disease severity. These results suggest a more widespread pathologic involvement and QSM as a novel means for its investigation, more sensitive than current MRI techniques.
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Affiliation(s)
| | - Lukas Pirpamer
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Stephan Seiler
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Andreas Deistung
- Medical Physics Group, University Hospital-Friedrich Schiller University Jena, Jena, Germany
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, University at Buffalo, The State University of New York, Buffalo, NY, United States of America
- MRI Molecular and Translational Imaging Center, Clinical and Translational Research Center, University at Buffalo, The State University of New York, Buffalo, NY, United States of America
| | | | - Nina Homayoon
- Department of Neurology, Medical University of Graz, Graz, Austria
| | | | | | - Tamara Pendl
- Department of Neurology, Medical University of Graz, Graz, Austria
| | | | - Karoline Wenzel
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Franz Fazekas
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Jürgen Rainer Reichenbach
- Medical Physics Group, University Hospital-Friedrich Schiller University Jena, Jena, Germany
- Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University Jena, Jena, Germany
| | - Reinhold Schmidt
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Petra Schwingenschuh
- Department of Neurology, Medical University of Graz, Graz, Austria
- Division of Neuroradiology, Department of Radiology, Medical University of Graz, Graz, Austria
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Klohs J, Deistung A, Ielacqua GD, Seuwen A, Kindler D, Schweser F, Vaas M, Kipar A, Reichenbach JR, Rudin M. Quantitative assessment of microvasculopathy in arcAβ mice with USPIO-enhanced gradient echo MRI. J Cereb Blood Flow Metab 2016; 36:1614-24. [PMID: 26661253 PMCID: PMC5010097 DOI: 10.1177/0271678x15621500] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Accepted: 07/06/2015] [Indexed: 01/04/2023]
Abstract
Magnetic resonance imaging employing administration of iron oxide-based contrast agents is widely used to visualize cellular and molecular processes in vivo. In this study, we investigated the ability of [Formula: see text] and quantitative susceptibility mapping to quantitatively assess the accumulation of ultrasmall superparamagnetic iron oxide (USPIO) particles in the arcAβ mouse model of cerebral amyloidosis. Gradient-echo data of mouse brains were acquired at 9.4 T after injection of USPIO. Focal areas with increased magnetic susceptibility and [Formula: see text] values were discernible across several brain regions in 12-month-old arcAβ compared to 6-month-old arcAβ mice and to non-transgenic littermates, indicating accumulation of particles after USPIO injection. This was concomitant with higher [Formula: see text] and increased magnetic susceptibility differences relative to cerebrospinal fluid measured in USPIO-injected compared to non-USPIO-injected 12-month-old arcAβ mice. No differences in [Formula: see text] and magnetic susceptibility were detected in USPIO-injected compared to non-injected 12-month-old non-transgenic littermates. Histological analysis confirmed focal uptake of USPIO particles in perivascular macrophages adjacent to small caliber cerebral vessels with radii of 2-8 µm that showed no cerebral amyloid angiopathy. USPIO-enhanced [Formula: see text] and quantitative susceptibility mapping constitute quantitative tools to monitor such functional microvasculopathies.
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Affiliation(s)
- Jan Klohs
- Institute for Biomedical Engineering, ETH & University of Zurich, Zurich, Switzerland Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Andreas Deistung
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital-Friedrich Schiller University Jena, Jena, Germany
| | - Giovanna D Ielacqua
- Institute for Biomedical Engineering, ETH & University of Zurich, Zurich, Switzerland
| | - Aline Seuwen
- Institute for Biomedical Engineering, ETH & University of Zurich, Zurich, Switzerland
| | - Diana Kindler
- Institute for Biomedical Engineering, ETH & University of Zurich, Zurich, Switzerland
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA MRI Clinical and Translational Research Center, School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Markus Vaas
- Institute for Biomedical Engineering, ETH & University of Zurich, Zurich, Switzerland Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Anja Kipar
- Laboratory for Animal Model Pathology, Institute of Veterinary Pathology, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital-Friedrich Schiller University Jena, Jena, Germany Abbe School of Photonics, Friedrich Schiller University Jena, Jena, Germany Center of Medical Optics and Photonics, Friedrich Schiller University Jena, Jena, Germany Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University Jena, Jena, Germany
| | - Markus Rudin
- Institute for Biomedical Engineering, ETH & University of Zurich, Zurich, Switzerland Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
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Zivadinov R, Ramasamy DP, Benedict RRH, Polak P, Hagemeier J, Magnano C, Dwyer MG, Bergsland N, Bertolino N, Weinstock-Guttman B, Kolb C, Hojnacki D, Utriainen D, Haacke EM, Schweser F. Cerebral Microbleeds in Multiple Sclerosis Evaluated on Susceptibility-weighted Images and Quantitative Susceptibility Maps: A Case-Control Study. Radiology 2016; 281:884-895. [PMID: 27308776 DOI: 10.1148/radiol.2016160060] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Purpose To assess cerebral microbleed (CMB) prevalence in patients with multiple sclerosis (MS) and clinically isolated syndrome (CIS) and associations with clinical outcomes. Materials and Methods CMBs are associated with aging and neurodegenerative disorders. The prevalence of CMBs has not previously been well established. In this study, 445 patients with MS (266 with relapsing-remitting MS, 138 with secondary progressive MS, and 41 with primary progressive MS), 45 patients with CIS, 51 patients with other neurological diseases, and 177 healthy control subjects (HCs) underwent 3-T magnetic resonance (MR) imaging and clinical examinations. A subset of 168 patients with MS and 50 HCs underwent neuropsychological testing. Number of CMBs was assessed on susceptibility-weighted minimum intensity projections by using the Microbleed Anatomic Rating Scale; volume was calculated by using quantitative susceptibility maps. Differences between groups were analyzed with the χ2 test, Fisher exact test, Student t test, and analysis of variance; associations of CMBs with clinical and other MR imaging outcomes were explored with correlation and regression analyses. Because CMB frequency increases with age, prevalence was investigated in participants at least 50 years of age and younger than 50 years. Results Significantly more patients with MS than HCs had CMBs (19.8% vs 7.4%, respectively; P = .01) in the group at least 50 years old. A trend toward greater presence of CMBs was found in patients with MS (P = .016) and patients with CIS who were younger than 50 years (P = .039) compared with HCs. In regression analysis adjusted for age, hypertension, and normalized brain volume, increased number of CMBs was significantly associated with increased physical disability in the MS population (R2 = 0.23, P < .0001). In correlation analysis, increased number of CMBs was significantly associated with deteriorated auditory and verbal learning and memory (P = .006) and visual information processing speed trends (P = .049) in patients with MS. Conclusion Monitoring CMBs may be relevant in patients with MS and CIS at higher risk for developing cognitive and physical disability. © RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Robert Zivadinov
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
| | - Deepa P Ramasamy
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
| | - Ralph R H Benedict
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
| | - Paul Polak
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
| | - Jesper Hagemeier
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
| | - Christopher Magnano
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
| | - Michael G Dwyer
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
| | - Niels Bergsland
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
| | - Nicola Bertolino
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
| | - Bianca Weinstock-Guttman
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
| | - Channa Kolb
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
| | - David Hojnacki
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
| | - David Utriainen
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
| | - E Mark Haacke
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
| | - Ferdinand Schweser
- From the Buffalo Neuroimaging Analysis Ctr, Dept of Neurology (R.Z., D.P.R., P.P., J.H., C.M., M.G.D., N. Bergsland, N. Bertolino, F.S.), MR Imaging Clinical and Translational Research Ctr (R.Z., C.M., F.S.), and Jacobs Multiple Sclerosis Ctr, Dept of Neurology (R.R.H.B., B.W.G., C.K., D.H.), Jacobs School of Medicine and Biomedical Sciences, Univ at Buffalo, The State Univ of New York, 100 High St, Buffalo, NY 14203; GE Healthcare, Waukesha, Wis (C.M.); Magnetic Resonance Laboratory, IRCCS Don Gnocchi Foundation, Milan, Italy (N. Bergsland); Magnetic Resonance Innovations, Detroit, Mich (D.U.); Dept of Radiology, Wayne State Univ, Detroit, Mich (E.M.H.); School of Biomedical Engineering, McMaster Univ, Hamilton, Ontario, Canada (E.M.H.); and Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univ, Shanghai, China (E.M.H.)
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