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Cagol A, Ocampo-Pineda M, Lu PJ, Weigel M, Barakovic M, Melie-Garcia L, Chen X, Lutti A, Calabrese P, Kuhle J, Kappos L, Sormani MP, Granziera C. Advanced Quantitative MRI Unveils Microstructural Thalamic Changes Reflecting Disease Progression in Multiple Sclerosis. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2024; 11:e200299. [PMID: 39270143 DOI: 10.1212/nxi.0000000000200299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
BACKGROUND AND OBJECTIVES In patients with multiple sclerosis (PwMS), thalamic atrophy occurs during the disease course. However, there is little understanding of the mechanisms leading to volume loss and of the relationship between microstructural thalamic pathology and disease progression. This cross-sectional and longitudinal study aimed to comprehensively characterize in vivo pathologic changes within thalamic microstructure in PwMS using advanced multiparametric quantitative MRI (qMRI). METHODS Thalamic microstructural integrity was evaluated using quantitative T1, magnetization transfer saturation, multishell diffusion, and quantitative susceptibility mapping (QSM) in 183 PwMS and 105 healthy controls (HCs). The same qMRI protocol was available for 127 PwMS and 73 HCs after a 2-year follow-up period. Inclusion criteria for PwMS encompassed either an active relapsing-remitting MS (RRMS) or inactive progressive MS (PMS) disease course. Thalamic alterations were compared between PwMS and HCs and among disease phenotypes. In addition, the study investigated the relationship between thalamic damage and clinical and conventional MRI measures of disease severity. RESULTS Compared with HCs, PwMS exhibited substantial thalamic alterations, indicative of microstructural and macrostructural damage, demyelination, and disruption in iron homeostasis. These alterations extended beyond focal thalamic lesions, affecting normal-appearing thalamic tissue diffusely. Over the follow-up period, PwMS displayed an accelerated decrease in myelin volume fraction [mean difference in annualized percentage change (MD-ApC) = -1.50; p = 0.041] and increase in quantitative T1 (MD-ApC = 0.92; p < 0.0001) values, indicating heightened demyelinating and neurodegenerative processes. The observed differences between PwMS and HCs were substantially driven by the subgroup with PMS, wherein thalamic degeneration was significantly accelerated, even in comparison with patients with RRMS. Thalamic qMRI alterations showed extensive correlations with conventional MRI, clinical, and cognitive disease burden measures. Disability progression over follow-up was associated with accelerated thalamic degeneration, as reflected by enhanced diffusion (β = -0.067; p = 0.039) and QSM (β = -0.077; p = 0.027) changes. Thalamic qMRI metrics emerged as significant predictors of neurologic and cognitive disability even when accounting for other established markers including white matter lesion load and brain and thalamic atrophy. DISCUSSION These findings offer deeper insights into thalamic pathology in PwMS, emphasizing the clinical relevance of thalamic damage and its link to disease progression. Advanced qMRI biomarkers show promising potential in guiding interventions aimed at mitigating thalamic neurodegenerative processes.
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Affiliation(s)
- Alessandro Cagol
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Mario Ocampo-Pineda
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Po-Jui Lu
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Matthias Weigel
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Muhamed Barakovic
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Lester Melie-Garcia
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Xinjie Chen
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Antoine Lutti
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Pasquale Calabrese
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Jens Kuhle
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Ludwig Kappos
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Maria Pia Sormani
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Cristina Granziera
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
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Samara A, Xiang B, Judge B, Ciotti JR, Yablonskiy DA, Cross AH, Brier MR. Increased periventricular thalamic damage gradient in multiple sclerosis detected by quantitative gradient echo MRI. Mult Scler Relat Disord 2024; 90:105834. [PMID: 39208571 DOI: 10.1016/j.msard.2024.105834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 08/08/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024]
Abstract
OBJECTIVE Thalamic tissue damage in multiple sclerosis (MS) follows a 'surface-in' gradient from the ventricular surface. The clinical consequences of this gradient are not completely understood. Using quantitative gradient-recalled echo (qGRE) MRI, we evaluated a periventricular thalamic gradient of tissue integrity in MS and its relationship with clinical variables. METHODS Structural and qGRE MRI scans were acquired for a cohort of MS patients and healthy controls (HC). qGRE-derived R2t* values were used as a measure of tissue integrity. Thalamic segmentations were divided into 1-mm concentric bands radiating from the ventricular surface, excluding the CSF-adjacent band. Median R2t* values within these bands were used to calculate the periventricular thalamic gradient. RESULTS We included 44 MS patients and 17 HC. R2t* increased slightly with distance from the ventricular surface in HC. MS patients had a steeper periventricular thalamic gradient compared to HC (mean slope 0.55 vs. 0.36; p < 0.001), which correlated with longer disease duration (β = 0.001 /year; p = 0.027) and higher Expanded Disability Status Scale (EDSS) score (β = 0.07 /EDSS point; p = 0.019). Left and right thalamus were symmetrically affected. CONCLUSIONS We detected an increased thalamic gradient in MS in vivo using qGRE MRI, which correlated with disease duration and greater clinical disability. These findings further support the 'surface-in' pathology hypothesis in MS and suggest a CSF-mediated process given symmetric bi-thalamic involvement.
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Affiliation(s)
- Amjad Samara
- Department of Neurology, Washington University School of Medicine, St. Louis, St. Louis, MO 63110, USA
| | - Biao Xiang
- Department of Neurology, Washington University School of Medicine, St. Louis, St. Louis, MO 63110, USA
| | - Bradley Judge
- Department of Radiology, Washington University School of Medicine, St. Louis, St. Louis, MO 63110, USA
| | - John R Ciotti
- Department of Neurology, University of South Florida, Tampa, FL, USA
| | - Dmitriy A Yablonskiy
- Department of Radiology, Washington University School of Medicine, St. Louis, St. Louis, MO 63110, USA
| | - Anne H Cross
- Department of Neurology, Washington University School of Medicine, St. Louis, St. Louis, MO 63110, USA
| | - Matthew R Brier
- Department of Neurology, Washington University School of Medicine, St. Louis, St. Louis, MO 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis, St. Louis, MO 63110, USA.
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Xu Y, Wei Y, Shi Z, Yin F, Zhu Q, Luo D, Tang Y, Wang H, Yan Z, Feng J, Li Y. Multimodal magnetic resonance longitudinal study on the deep gray matter in multiple sclerosis patients with teriflunomide. J Neuroimmunol 2024; 396:578445. [PMID: 39243674 DOI: 10.1016/j.jneuroim.2024.578445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 08/15/2024] [Accepted: 09/01/2024] [Indexed: 09/09/2024]
Abstract
Disease-modifying therapies (DMTs) are used in an increasing number of patients with multiple sclerosis (MS). However, whether DMTs have intrinsic effects on deep gray matter (DGM) microstructure and atrophy is still poorly understood. In this study, we described the quantitative susceptibility values (QSV) and diffusion kurtosis imaging (DKI) metrics of DGM in relapsing-remitting MS (RRMS) patients and their association with cognitive deficits. We recruited 62 patients with RRMS receiving DMTs and 30 patients with RRMS not receiving DMTs underwent MRI on a 3T scanner. Fractional anisotropy (FA), kurtosis fractional anisotropy (KFA), mean diffusivity (MD), mean kurtosis (MK), QSV and volumes of bilateral caudate nucleus (CAU), amygdala (AMYG), putamen (PUT), hippocampus (Hipp), globus pallidus (GP) and thalamus (THA) were measured. Correlation analysis was performed between those image indexes with longitudinal significant changes and clinical neurological scores, including Expanded Disability Status Scale (EDSS), Digit Span Testand (DST), Symbol Digit Modalities Test (SDMT), Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Significant longitudinal increases in FA, KFA and MK values were found in both groups in bilateral CAU, AMYG, PUT, Hipp, GP and THA (all p < 0.005). MD values of the right of CAU in the two groups were significant longitudinal increase (p = 0.009, p = 0.047); MD values of the right of GP (p = 0.042), the left of THA (p = 0.003), the right of THA (p = 0.001) in treated MS were significant longitudinal decrease; There were no significant longitudinal changes between treated and untreated groups in normalized deep gray matter volume. For QSV, longitudinal increase in the right of PUT (p = 0.022) in the treated MS group and in the left of Hipp (p = 0.045) in the untreated MS group. The QSV and DKI measures were highly correlated with cognitive and disability tests. The treated RRMS patients showed different longitudinal changes of MD value and QSV with untreated in several DGM regions, and these differences were correlated with cognitive and microstructural integrity.
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Affiliation(s)
- Yuhui Xu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Yiqiu Wei
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhuowei Shi
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Feiyue Yin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qiyuan Zhu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Dan Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Tang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Huajiao Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zichun Yan
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jinzhou Feng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Ludwig N, Cucinelli S, Hametner S, Muckenthaler MU, Schirmer L. Iron scavenging and myeloid cell polarization. Trends Immunol 2024; 45:625-638. [PMID: 39054114 DOI: 10.1016/j.it.2024.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 06/25/2024] [Accepted: 06/27/2024] [Indexed: 07/27/2024]
Abstract
Myeloid cells that populate all human organs and blood are a versatile class of innate immune cells. They are crucial for sensing and regulating processes as diverse as tissue homeostasis and inflammation and are frequently characterized by their roles in either regulating or promoting inflammation. Recent studies in cultured cells and mouse models highlight the role of iron in skewing the functional properties of myeloid cells in tissue damage and repair. Here, we review certain emerging concepts on how iron influences and determines myeloid cell polarization in the context of its uptake, storage, and metabolism, including in conditions such as multiple sclerosis (MS), sickle cell disease, and tumors.
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Affiliation(s)
- Natalie Ludwig
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Interdisciplinary Center for Neurosciences, Heidelberg University, Heidelberg, Germany
| | - Stefania Cucinelli
- Department of Paediatric Hematology, Oncology, and Immunology, University of Heidelberg, Heidelberg, Germany; Molecular Medicine Partnership Unit (MMPU), European Molecular Biology Laboratory and University of Heidelberg, Heidelberg, Germany
| | - Simon Hametner
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria; Medical Neuroscience Cluster, Medical University of Vienna, Vienna, Austria
| | - Martina U Muckenthaler
- Department of Paediatric Hematology, Oncology, and Immunology, University of Heidelberg, Heidelberg, Germany; Molecular Medicine Partnership Unit (MMPU), European Molecular Biology Laboratory and University of Heidelberg, Heidelberg, Germany; German Centre for Cardiovascular Research (DZHK), Partner site Heidelberg/Mannheim, Heidelberg, Germany; Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Heidelberg, Germany.
| | - Lucas Schirmer
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Interdisciplinary Center for Neurosciences, Heidelberg University, Heidelberg, Germany; Mannheim Center for Translational Neuroscience, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Mannheim Institute for Innate Immunoscience, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
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Cagol A, Tsagkas C, Granziera C. Advanced Brain Imaging in Central Nervous System Demyelinating Diseases. Neuroimaging Clin N Am 2024; 34:335-357. [PMID: 38942520 DOI: 10.1016/j.nic.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
In recent decades, advances in neuroimaging have profoundly transformed our comprehension of central nervous system demyelinating diseases. Remarkable technological progress has enabled the integration of cutting-edge acquisition and postprocessing techniques, proving instrumental in characterizing subtle focal changes, diffuse microstructural alterations, and macroscopic pathologic processes. This review delves into state-of-the-art modalities applied to multiple sclerosis, neuromyelitis optica spectrum disorders, and myelin oligodendrocyte glycoprotein antibody-associated disease. Furthermore, it explores how this dynamic landscape holds significant promise for the development of effective and personalized clinical management strategies, encompassing support for differential diagnosis, prognosis, monitoring treatment response, and patient stratification.
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Affiliation(s)
- Alessandro Cagol
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Department of Neurology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Spitalstrasse 2, 4031 Basel, Switzerland; Department of Health Sciences, University of Genova, Via A. Pastore, 1 16132 Genova, Italy. https://twitter.com/CagolAlessandr0
| | - Charidimos Tsagkas
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), 10 Center Drive, Bethesda, MD 20892, USA
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Department of Neurology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Spitalstrasse 2, 4031 Basel, Switzerland.
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Zivadinov R, Schweser F, Jakimovski D, Bergsland N, Dwyer MG. Decoding Gray Matter Involvement in Multiple Sclerosis via Imaging. Neuroimaging Clin N Am 2024; 34:453-468. [PMID: 38942527 DOI: 10.1016/j.nic.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Multiple sclerosis (MS) is increasingly understood not only as a white matter disease but also involving both the deep and cortical gray matter (GM). GM pathology in people with MS (pwMS) includes the presence of lesions, leptomeningeal inflammation, atrophy, altered iron concentration, and microstructural changes. Studies using 7T and 3T MR imaging with optimized protocols established that GM damage is a principal driver of disease progression in pwMS. Future work is needed to incorporate the assessment of these GM imaging biomarkers into the clinical workup of pwMS and the assessment of treatment efficacy.
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Affiliation(s)
- Robert Zivadinov
- Department of Neurology, Buffalo Neuroimaging Analysis Center, 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.
| | - Ferdinand Schweser
- Department of Neurology, Buffalo Neuroimaging Analysis Center, 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
| | - Dejan Jakimovski
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Niels Bergsland
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Michael G Dwyer
- Department of Neurology, Buffalo Neuroimaging Analysis Center, 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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Xie Y, Zhang Y, Wu S, Zhang S, Zhu H, Zhu W, Wang Y. Atrophy-Independent and Dependent Iron and Myelin Changes in Deep Gray Matter of Multiple Sclerosis: A Longitudinal Study Using χ-Separation Imaging. Acad Radiol 2024:S1076-6332(24)00464-1. [PMID: 39084936 DOI: 10.1016/j.acra.2024.07.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 08/02/2024]
Abstract
RATIONALE AND OBJECTIVES To investigate iron and myelin changes in deep gray matter (DGM) of relapsing-remitting multiple sclerosis (RRMS) patients and their relationship to atrophy by χ-separation imaging. MATERIALS AND METHODS 33 RRMS patients and 34 healthy controls (HC) were included in this study. The χ-separation map reconstructed from a 3D multi-echo gradient echo scan was used to measure the positive susceptibility (χpos) and negative susceptibility (χneg) of DGM. To take into account the effect of atrophy, susceptibility mass of DGM was calculated by multiplying volume by the mean bulk susceptibility. Differences in MRI metrics between baseline patients, follow-up patients, and HC were compared respectively. RESULTS Compared to HC, χpos of basal ganglia were significantly increased in follow-up patients (P < 0.05). The χpos of pallidum was significantly higher in follow-up patients than that in baseline patients (P = 0.006). The χneg of caudate, pallidum and hippocampus in baseline and follow-up patients was significantly higher than that in HC (P < 0.05). When taking into account the effect of atrophy, there was a significant decrease in χpos mass and a significant increase in χneg mass of thalamus, accumbens and amygdala in follow-up patients compared to HC (P < 0.05). The χpos mass of the thalamus was further decreased in follow-up patients compared to baseline patients (P = 0.006). CONCLUSION χ-separation imaging could generate independent information on iron and myelin changes in RRMS patients, showing atrophy-dependent iron increase in basal ganglia and atrophy-independent iron and myelin decrease in thalamus.
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Affiliation(s)
- Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shaolong Wu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongquan Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA; Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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8
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Rubin M, Pagani E, Preziosa P, Meani A, Storelli L, Margoni M, Filippi M, Rocca MA. Cerebrospinal Fluid-In Gradient of Cortical and Deep Gray Matter Damage in Multiple Sclerosis. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2024; 11:e200271. [PMID: 38896808 PMCID: PMC11197989 DOI: 10.1212/nxi.0000000000200271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 04/19/2024] [Indexed: 06/21/2024]
Abstract
BACKGROUND AND OBJECTIVES A CSF-in gradient in cortical and thalamic gray matter (GM) damage has been found in multiple sclerosis (MS). We concomitantly explored the patterns of cortical, thalamic, and caudate microstructural abnormalities at progressive distances from CSF using a multiparametric MRI approach. METHODS For this cross-sectional study, from 3T 3D T1-weighted scans, we sampled cortical layers at 25%-50%-75% depths from pial surface and thalamic and caudate bands at 2-3-4 voxels from the ventricular-GM interface. Using linear mixed models, we tested between-group comparisons of magnetization transfer ratio (MTR) and R2* layer-specific z-scores, CSF-in across-layer z-score changes, and their correlations with clinical (disease duration and disability) and structural (focal lesions, brain, and choroid plexus volume) MRI measures. RESULTS We enrolled 52 patients with MS (33 relapsing-remitting [RRMS], 19 progressive [PMS], mean age: 46.4 years, median disease duration: 15.1 years, median: EDSS 2.0) and 70 controls (mean age 41.5 ± 12.8). Compared with controls, RRMS showed lower MTR values in the outer and middle cortical layers (false-discovery rate [FDR]-p ≤ 0.025) and lower R2* values in all 3 cortical layers (FDR-p ≤ 0.016). PMS had lower MTR values in the outer and middle cortical (FDR-p ≤ 0.016) and thalamic (FDR-p ≤ 0.048) layers, and in the outer caudate layer (FDR-p = 0.024). They showed lower R2* values in the outer cortical layer (FDR-p = 0.003) and in the outer thalamic layer (FDR-p = 0.046) and higher R2* values in all 3 caudate layers (FDR-p ≤ 0.031). Both RRMS and PMS had a gradient of damage, with lower values closer to the CSF, for cortical (FDR-p ≤ 0.002) and thalamic (FDR-p ≤ 0.042) MTR. PMS showed a gradient of damage for cortical R2* (FDR-p = 0.005), thalamic R2* (FDR-p = 0.004), and caudate MTR (FDR-p ≤ 0.013). Lower MTR and R2* of outer cortical, thalamic, and caudate layers and steeper gradient of damage toward the CSF were significantly associated with older age, higher T2-hyperintense white matter lesion volume, higher thalamic lesion volume, and lower brain volume (β ≥ 0.08, all FDR-p ≤ 0.040). Lower MTR of outer caudate layer was associated with more severe disability (β = -0.26, FDR-p = 0.040). No correlations with choroid plexus volume were found. DISCUSSION CSF-in damage gradients are heterogeneous among different GM regions and through MS course, possibly reflecting different dynamics of demyelination and iron loss/accumulation.
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Affiliation(s)
- Martina Rubin
- From the Neuroimaging Research Unit (M.R., E.P., P.P., A.M., L.S., M.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (M.R., P.P., M.M., M.F., M.A.R.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.R., P.P., M.F., M.A.R.); Neurorehabilitation Unit (M.M., M.F.); and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit (M.R., E.P., P.P., A.M., L.S., M.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (M.R., P.P., M.M., M.F., M.A.R.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.R., P.P., M.F., M.A.R.); Neurorehabilitation Unit (M.M., M.F.); and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paolo Preziosa
- From the Neuroimaging Research Unit (M.R., E.P., P.P., A.M., L.S., M.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (M.R., P.P., M.M., M.F., M.A.R.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.R., P.P., M.F., M.A.R.); Neurorehabilitation Unit (M.M., M.F.); and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alessandro Meani
- From the Neuroimaging Research Unit (M.R., E.P., P.P., A.M., L.S., M.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (M.R., P.P., M.M., M.F., M.A.R.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.R., P.P., M.F., M.A.R.); Neurorehabilitation Unit (M.M., M.F.); and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Loredana Storelli
- From the Neuroimaging Research Unit (M.R., E.P., P.P., A.M., L.S., M.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (M.R., P.P., M.M., M.F., M.A.R.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.R., P.P., M.F., M.A.R.); Neurorehabilitation Unit (M.M., M.F.); and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Monica Margoni
- From the Neuroimaging Research Unit (M.R., E.P., P.P., A.M., L.S., M.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (M.R., P.P., M.M., M.F., M.A.R.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.R., P.P., M.F., M.A.R.); Neurorehabilitation Unit (M.M., M.F.); and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- From the Neuroimaging Research Unit (M.R., E.P., P.P., A.M., L.S., M.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (M.R., P.P., M.M., M.F., M.A.R.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.R., P.P., M.F., M.A.R.); Neurorehabilitation Unit (M.M., M.F.); and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria A Rocca
- From the Neuroimaging Research Unit (M.R., E.P., P.P., A.M., L.S., M.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (M.R., P.P., M.M., M.F., M.A.R.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.R., P.P., M.F., M.A.R.); Neurorehabilitation Unit (M.M., M.F.); and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
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9
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Krijnen EA, Salim Karam E, Russo AW, Lee H, Chiang FL, Schoonheim MM, Huang SY, Klawiter EC. Intrinsic and extrinsic contributors to subregional thalamic volume loss in multiple sclerosis. Ann Clin Transl Neurol 2024; 11:1405-1419. [PMID: 38725151 PMCID: PMC11187835 DOI: 10.1002/acn3.52026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 06/20/2024] Open
Abstract
OBJECTIVE To evaluate the intrinsic and extrinsic microstructural factors contributing to atrophy within individual thalamic subregions in multiple sclerosis using in vivo high-gradient diffusion MRI. METHODS In this cross-sectional study, 41 people with multiple sclerosis and 34 age and sex-matched healthy controls underwent 3T MRI with up to 300 mT/m gradients using a multi-shell diffusion protocol consisting of eight b-values and diffusion time of 19 ms. Each thalamus was parcellated into 25 subregions for volume determination and diffusion metric estimation. The soma and neurite density imaging model was applied to obtain estimates of intra-neurite, intra-soma, and extra-cellular signal fractions for each subregion and within structurally connected white matter trajectories and cortex. RESULTS Multiple sclerosis-related volume loss was more pronounced in posterior/medial subregions than anterior/ventral subregions. Intra-soma signal fraction was lower in multiple sclerosis, reflecting reduced cell body density, while the extra-cellular signal fraction was higher, reflecting greater extra-cellular space, both of which were observed more in posterior/medial subregions than anterior/ventral subregions. Lower intra-neurite signal fraction in connected normal-appearing white matter and lower intra-soma signal fraction of structurally connected cortex were associated with reduced subregional thalamic volumes. Intrinsic and extrinsic microstructural measures independently related to subregional volume with heterogeneity across atrophy-prone thalamic nuclei. Extrinsic microstructural alterations predicted left anteroventral, intrinsic microstructural alterations predicted bilateral medial pulvinar, and both intrinsic and extrinsic factors predicted lateral geniculate and medial mediodorsal volumes. INTERPRETATION Our results might be reflective of the involvement of anterograde and retrograde degeneration from white matter demyelination and cerebrospinal fluid-mediated damage in subregional thalamic volume loss.
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Affiliation(s)
- Eva A. Krijnen
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Elsa Salim Karam
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Andrew W. Russo
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Hansol Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Florence L. Chiang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Menno M. Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Location VUmcAmsterdamThe Netherlands
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Eric C. Klawiter
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
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10
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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] [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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11
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Chen L, Shin HG, van Zijl PC, Li X. Exploiting gradient-echo frequency evolution: Probing white matter microstructure and extracting bulk susceptibility-induced frequency for quantitative susceptibility mapping. Magn Reson Med 2024; 91:1676-1693. [PMID: 38102838 PMCID: PMC10880384 DOI: 10.1002/mrm.29958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 10/08/2023] [Accepted: 11/17/2023] [Indexed: 12/17/2023]
Abstract
PURPOSE This work is to investigate the microstructure-induced frequency shift in white matter (WM) with crossing fibers and to separate the microstructure-related frequency shift from the bulk susceptibility-induced frequency shift by model fitting the gradient-echo (GRE) frequency evolution for potentially more accurate quantitative susceptibility mapping (QSM). METHODS A hollow-cylinder fiber model (HCFM) with two fiber populations was developed to investigate GRE frequency evolutions in WM voxels with microstructural orientation dispersion. The simulated and experimentally measured TE-dependent local frequency shift was then fitted to a simplified frequency evolution model to obtain a microstructure-related frequency difference parameter (∆ f $$ \Delta f $$ ) and a TE-independent bulk susceptibility-induced frequency shift (C f $$ {C}_f $$ ). The obtainedC f $$ {C}_f $$ was then used for QSM reconstruction. Reconstruction performances were evaluated using a numerical head phantom and in vivo data and then compared to other multi-echo combination methods. RESULTS GRE frequency evolutions and∆ f $$ \Delta f $$ -based tissue parameters in both parallel and crossing fibers determined from our simulations were comparable to those observed in vivo. The TE-dependent frequency fitting method outperformed other multi-echo combination methods in estimatingC f $$ {C}_f $$ in simulations. The fitted∆ f $$ \Delta f $$ ,C f $$ {C}_f $$ , and QSM could be improved further by navigator-based B0 fluctuation correction. CONCLUSION A HCFM with two fiber populations can be used to characterize microstructure-induced frequency shifts in WM regions with crossing fibers. HCFM-based TE-dependent frequency fitting provides tissue contrast related to microstructure (∆ f $$ \Delta f $$ ) and in addition may help improve the quantification accuracy ofC f $$ {C}_f $$ and the corresponding QSM.
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Affiliation(s)
- Lin Chen
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Hyeong-Geol Shin
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Peter C.M. van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
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12
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Voon CC, Wiltgen T, Wiestler B, Schlaeger S, Mühlau M. Quantitative susceptibility mapping in multiple sclerosis: A systematic review and meta-analysis. Neuroimage Clin 2024; 42:103598. [PMID: 38582068 PMCID: PMC11002889 DOI: 10.1016/j.nicl.2024.103598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/07/2024] [Accepted: 03/24/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Quantitative susceptibility mapping (QSM) is a quantitative measure based on magnetic resonance imaging sensitive to iron and myelin content. This makes QSM a promising non-invasive tool for multiple sclerosis (MS) in research and clinical practice. OBJECTIVE We performed a systematic review and meta-analysis on the use of QSM in MS. METHODS Our review was prospectively registered on PROSPERO (CRD42022309563). We searched five databases for studies published between inception and 30th April 2023. We identified 83 English peer-reviewed studies that applied QSM images on MS cohorts. Fifty-five included studies had at least one of the following outcome measures: deep grey matter QSM values in MS, either compared to healthy controls (HC) (k = 13) or correlated with the score on the Expanded Disability Status Scale (EDSS) (k = 7), QSM lesion characteristics (k = 22) and their clinical correlates (k = 17), longitudinal correlates (k = 11), histological correlates (k = 7), or correlates with other imaging techniques (k = 12). Two meta-analyses on deep grey matter (DGM) susceptibility data were performed, while the remaining findings could only be analyzed descriptively. RESULTS After outlier removal, meta-analyses demonstrated a significant increase in the basal ganglia susceptibility (QSM values) in MS compared to HC, caudate (k = 9, standardized mean difference (SDM) = 0.54, 95 % CI = 0.39-0.70, I2 = 46 %), putamen (k = 9, SDM = 0.38, 95 % CI = 0.19-0.57, I2 = 59 %), and globus pallidus (k = 9, SDM = 0.48, 95 % CI = 0.28-0.67, I2 = 60 %), whereas thalamic QSM values exhibited a significant reduction (k = 12, SDM = -0.39, 95 % CI = -0.66--0.12, I2 = 84 %); these susceptibility differences in MS were independent of age. Further, putamen QSM values positively correlated with EDSS (k = 4, r = 0.36, 95 % CI = 0.16-0.53, I2 = 0 %). Regarding rim lesions, four out of seven studies, representing 73 % of all patients, reported rim lesions to be associated with more severe disability. Moreover, lesion development from initial detection to the inactive stage is paralleled by increasing, plateauing (after about two years), and gradually decreasing QSM values, respectively. Only one longitudinal study provided clinical outcome measures and found no association. Histological data suggest iron content to be the primary source of QSM values in DGM and at the edges of rim lesions; further, when also considering data from myelin water imaging, the decrease of myelin is likely to drive the increase of QSM values within WM lesions. CONCLUSIONS We could provide meta-analytic evidence for DGM susceptibility changes in MS compared to HC; basal ganglia susceptibility is increased and, in the putamen, associated with disability, while thalamic susceptibility is decreased. Beyond these findings, further investigations are necessary to establish the role of QSM in MS for research or even clinical routine.
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Affiliation(s)
- Cui Ci Voon
- Dept. of Neurology, School of Medicine and Health, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Tun Wiltgen
- Dept. of Neurology, School of Medicine and Health, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Dept. of Neuroradiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Sarah Schlaeger
- Dept. of Neuroradiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Mark Mühlau
- Dept. of Neurology, School of Medicine and Health, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine and Health, Technical University of Munich, Munich, Germany.
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Amin M, Nakamura K, Ontaneda D. Differentiating multiple sclerosis from non-specific white matter changes using a convolutional neural network image classification model. Mult Scler Relat Disord 2024; 82:105420. [PMID: 38183693 DOI: 10.1016/j.msard.2023.105420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/07/2023] [Accepted: 12/30/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND The diagnosis of multiple sclerosis (MS) relies heavily on neuroimaging with magnetic resonance imaging (MRI) and exclusion of mimics. This can be a challenging task due to radiological overlap in several disorders and may require ancillary testing or longitudinal follow up. One of the most common radiological MS mimickers is non-specific white matter disease (NSWMD). We aimed to develop and evaluate models leveraging machine learning algorithms to help distinguish MS and NSWMD. METHODS All adult patients who underwent MRI brain using a demyelinating protocol with available electronic medical records between 2015 and 2019 at Cleveland Clinic affiliated facilities were included. Diagnosis of MS and NSWMD were assessed from clinical documentation. Those with a diagnosis of MS and NSWMD were matched using total T2 lesion volume (T2LV) and used to train models with logistic regression and convolutional neural networks (CNN). Performance metrices were reported for each model. RESULTS A total of 250 NSWMD MRI scans were identified, and 250 unique MS MRI scans were matched on T2LV. Cross validated logistic regression model was able to use 20 variables (including spinal cord area, regional volumes, and fractions) to predict MS compared to NSWMD with 68.0% accuracy while the CNN model was able to classify MS compared to NSWMD in two independent validation and testing cohorts with 77% and 78% accuracy on average. CONCLUSION Automated methods can be used to differentiate MS compared to NSWMD. These methods can be used to supplement currently available diagnostic tools for patients being evaluated for MS.
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Affiliation(s)
- Moein Amin
- Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Kunio Nakamura
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio, USA
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA.
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14
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Ananthavarathan P, Sahi N, Chard DT. An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression. Expert Rev Neurother 2024; 24:201-216. [PMID: 38235594 DOI: 10.1080/14737175.2024.2304116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024]
Abstract
INTRODUCTION While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear. AREAS COVERED The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning. EXPERT OPINION Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.
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Affiliation(s)
- Piriyankan Ananthavarathan
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Nitin Sahi
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Declan T Chard
- Clinical Research Associate & Consultant Neurologist, Institute of Neurology - Queen Square Multiple Sclerosis Centre, London, UK
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15
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Li H, Jacob MA, Cai M, Duering M, Chamberland M, Norris DG, Kessels RPC, de Leeuw FE, Marques JP, Tuladhar AM. Regional cortical thinning, demyelination and iron loss in cerebral small vessel disease. Brain 2023; 146:4659-4673. [PMID: 37366338 PMCID: PMC10629800 DOI: 10.1093/brain/awad220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/07/2023] [Accepted: 06/11/2023] [Indexed: 06/28/2023] Open
Abstract
The link between white matter hyperintensities (WMH) and cortical thinning is thought to be an important pathway by which WMH contributes to cognitive deficits in cerebral small vessel disease (SVD). However, the mechanism behind this association and the underlying tissue composition abnormalities are unclear. The objective of this study is to determine the association between WMH and cortical thickness, and the in vivo tissue composition abnormalities in the WMH-connected cortical regions. In this cross-sectional study, we included 213 participants with SVD who underwent standardized protocol including multimodal neuroimaging scans and cognitive assessment (i.e. processing speed, executive function and memory). We identified the cortex connected to WMH using probabilistic tractography starting from the WMH and defined the WMH-connected regions at three connectivity levels (low, medium and high connectivity level). We calculated the cortical thickness, myelin and iron of the cortex based on T1-weighted, quantitative R1, R2* and susceptibility maps. We used diffusion-weighted imaging to estimate the mean diffusivity of the connecting white matter tracts. We found that cortical thickness, R1, R2* and susceptibility values in the WMH-connected regions were significantly lower than in the WMH-unconnected regions (all Pcorrected < 0.001). Linear regression analyses showed that higher mean diffusivity of the connecting white matter tracts were related to lower thickness (β = -0.30, Pcorrected < 0.001), lower R1 (β = -0.26, Pcorrected = 0.001), lower R2* (β = -0.32, Pcorrected < 0.001) and lower susceptibility values (β = -0.39, Pcorrected < 0.001) of WMH-connected cortical regions at high connectivity level. In addition, lower scores on processing speed were significantly related to lower cortical thickness (β = 0.20, Pcorrected = 0.030), lower R1 values (β = 0.20, Pcorrected = 0.006), lower R2* values (β = 0.29, Pcorrected = 0.006) and lower susceptibility values (β = 0.19, Pcorrected = 0.024) of the WMH-connected regions at high connectivity level, independent of WMH volumes and the cortical measures of WMH-unconnected regions. Together, our study demonstrated that the microstructural integrity of white matter tracts passing through WMH is related to the regional cortical abnormalities as measured by thickness, R1, R2* and susceptibility values in the connected cortical regions. These findings are indicative of cortical thinning, demyelination and iron loss in the cortex, which is most likely through the disruption of the connecting white matter tracts and may contribute to processing speed impairment in SVD, a key clinical feature of SVD. These findings may have implications for finding intervention targets for the treatment of cognitive impairment in SVD by preventing secondary degeneration.
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Affiliation(s)
- Hao Li
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
| | - Mina A Jacob
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
| | - Mengfei Cai
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 510080 Guangzhou, China
| | - Marco Duering
- Medical Image Analysis Center (MIAC AG) and Department of Biomedical Engineering, University of Basel, 4051 Basel, Switzerland
- LMU Munich, University Hospital, Institute for Stroke and Dementia Research (ISD), 81377 Munich, Germany
| | - Maxime Chamberland
- Donders Institute for Brain, Cognition and Behaviour, Center for Cognitive Neuroimaging, Radboud University, 6525 EN Nijmegen, The Netherlands
| | - David G Norris
- Donders Institute for Brain, Cognition and Behaviour, Center for Cognitive Neuroimaging, Radboud University, 6525 EN Nijmegen, The Netherlands
| | - Roy P C Kessels
- Department of Medical Psychology and Radboudumc Alzheimer Center, Radboud University Medical Center, 6525 GC, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 EN Nijmegen, The Netherlands
- Vincent van Gogh Institute for Psychiatry, 5803 AC Venray, The Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Center for Cognitive Neuroimaging, Radboud University, 6525 EN Nijmegen, The Netherlands
| | - Anil M Tuladhar
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
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16
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De Lury AD, Bisulca JA, Lee JS, Altaf MD, Coyle PK, Duong TQ. Magnetic resonance imaging detection of deep gray matter iron deposition in multiple sclerosis: A systematic review. J Neurol Sci 2023; 453:120816. [PMID: 37827008 DOI: 10.1016/j.jns.2023.120816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 10/14/2023]
Abstract
Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease involving immune-mediated damage. Iron deposition in deep gray matter (DGM) structures like the thalamus and basal ganglia have been suggested to play a role in MS pathogenesis. Magnetic Resonance Imaging (MRI) imaging methods like T2 and T2* imaging, susceptibility-weighted imaging, and quantitative susceptibility mapping can track iron deposition storage in the brain primarily from ferritin and hemosiderin (paramagnetic iron storage proteins) with varying levels of tissue contrast and sensitivity. In this systematic review, we evaluated the role of DGM iron deposition as detected by MRI techniques in relation to MS-related neuroinflammation and its potential as a novel therapeutic target. We searched through PubMed, Embase, and Web of Science databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, against predetermined inclusion and exclusion criteria. We included 89 articles (n = 6630 patients), and then grouped them into different categories: i) methodological techniques to measure DGM iron, ii) cross-sectional and group comparison of DGM iron content, iii) longitudinal comparisons of DGM iron, iv) associations between DGM iron and other imaging and neurobiological markers, v) associations with disability, and vi) associations with cognitive impairment. The review revealed that iron deposition in DGM is independent yet concurrent with demyelination, and that these iron deposits contribute to MS-related cognitive impairment and disability. Variability in iron distributions appears to rely on a positive feedback loop between inflammation, and release of iron by oligodendrocytes. DGM iron seems to be a promising prognostic biomarker for MS pathophysiology.
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Affiliation(s)
- Amy D De Lury
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210(th) Street, Bronx, NY, USA.
| | - Joseph A Bisulca
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210(th) Street, Bronx, NY, USA.
| | - Jimmy S Lee
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210(th) Street, Bronx, NY, USA.
| | - Muhammad D Altaf
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210(th) Street, Bronx, NY, USA.
| | - Patricia K Coyle
- Department of Neurology, Stony Brook University Medical Center, Stony Brook, NY, USA.
| | - Tim Q Duong
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210(th) Street, Bronx, NY, USA.
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17
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Yin F, Yan Z, Li Y, Ding S, Wang X, Shi Z, Feng J, Du S, Tan Z, Zeng C. Multimodal Investigation of Deep Gray Matter Nucleus in Patients with Multiple Sclerosis and Their Clinical Correlations: A Multivariate Pattern Analysis Study. J Pers Med 2023; 13:1488. [PMID: 37888099 PMCID: PMC10608176 DOI: 10.3390/jpm13101488] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 09/28/2023] [Accepted: 09/30/2023] [Indexed: 10/28/2023] Open
Abstract
Deep gray matter (DGM) nucleus are involved in patients with multiple sclerosis (MS) and are strongly associated with clinical symptoms. We used machine learning approach to further explore microstructural alterations in DGM of MS patients. One hundred and fifteen MS patients and seventy-one healthy controls (HC) underwent brain MRI. The fractional anisotropy (FA), mean diffusivity (MD), quantitative susceptibility value (QSV) and volumes of the caudate nucleus (CN), putamen (PT), globus pallidus (GP), and thalamus (TH) were measured. Multivariate pattern analysis, based on a machine-learning algorithm, was applied to investigate the most damaged regions. Partial correlation analysis was used to investigate the correlation between MRI quantitative metrics and clinical neurological scores. The area under the curve of FA-based classification model was 0.83, while they were 0.93 for MD and 0.81 for QSV. The Montreal cognitive assessment scores were correlated with the volume of the DGM and the expanded disability status scale scores were correlated with the MD of the GP and PT. The study results indicated that MS patients had involvement of DGM with the CN being the most affected. The atrophy of DGM in MS patients mainly affected cognitive function and the microstructural damage of DGM was mainly correlated with clinical disability.
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Affiliation(s)
- Feiyue Yin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (F.Y.); (Z.Y.); (Y.L.); (X.W.); (Z.S.); (S.D.); (Z.T.)
| | - Zichun Yan
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (F.Y.); (Z.Y.); (Y.L.); (X.W.); (Z.S.); (S.D.); (Z.T.)
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (F.Y.); (Z.Y.); (Y.L.); (X.W.); (Z.S.); (S.D.); (Z.T.)
| | - Shuang Ding
- Department of Radiology, The Childrens’ Hospital of Chongqing Medical University, Chongqing 400015, China;
| | - Xiaohua Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (F.Y.); (Z.Y.); (Y.L.); (X.W.); (Z.S.); (S.D.); (Z.T.)
| | - Zhuowei Shi
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (F.Y.); (Z.Y.); (Y.L.); (X.W.); (Z.S.); (S.D.); (Z.T.)
| | - Jinzhou Feng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China;
| | - Silin Du
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (F.Y.); (Z.Y.); (Y.L.); (X.W.); (Z.S.); (S.D.); (Z.T.)
| | - Zeyun Tan
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (F.Y.); (Z.Y.); (Y.L.); (X.W.); (Z.S.); (S.D.); (Z.T.)
| | - Chun Zeng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (F.Y.); (Z.Y.); (Y.L.); (X.W.); (Z.S.); (S.D.); (Z.T.)
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18
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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] [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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Li Z, Feng R, Liu Q, Feng J, Lao G, Zhang M, Li J, Zhang Y, Wei H. APART-QSM: an improved sub-voxel quantitative susceptibility mapping for susceptibility source separation using an iterative data fitting method. Neuroimage 2023; 274:120148. [PMID: 37127191 DOI: 10.1016/j.neuroimage.2023.120148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 02/06/2023] [Accepted: 04/28/2023] [Indexed: 05/03/2023] Open
Abstract
The brain tissue phase contrast in MRI sequences reflects the spatial distributions of multiple substances, such as iron, myelin, calcium, and proteins. These substances with paramagnetic and diamagnetic susceptibilities often colocalize in one voxel in brain regions. Both opposing susceptibilities play vital roles in brain development and neurodegenerative diseases. Conventional QSM methods only provide voxel-averaged susceptibility value and cannot disentangle intravoxel susceptibilities with opposite signs. Advanced susceptibility imaging methods have been recently developed to distinguish the contributions of opposing susceptibility sources for QSM. The basic concept of separating paramagnetic and diamagnetic susceptibility proportions is to include the relaxation rate R2* with R2' in QSM. The magnitude decay kernel, describing the proportionality coefficient between R2' and susceptibility, is an essential reconstruction coefficient for QSM separation methods. In this study, we proposed a more comprehensive complex signal model that describes the relationship between 3D GRE signal and the contributions of paramagnetic and diamagnetic susceptibility to the frequency shift and R2* relaxation. The algorithm is implemented as a constrained minimization problem in which the voxel-wise magnitude decay kernel and sub-voxel susceptibilities are determined alternately in each iteration until convergence. The calculated voxel-wise magnitude decay kernel could realistically model the relationship between the R2' relaxation and the volume susceptibility. Thus, the proposed method effectively prevents the errors of the magnitude decay kernel from propagating to the final susceptibility separation reconstruction. Phantom studies, ex vivo macaque brain experiments, and in vivo human brain imaging studies were conducted to evaluate the ability of the proposed method to distinguish paramagnetic and diamagnetic susceptibility sources. The results demonstrate that the proposed method provides state-of-the-art performances for quantifying brain iron and myelin compared to previous QSM separation methods. Our results show that the proposed method has the potential to simultaneously quantify whole brain iron and myelin during brain development and aging. The proposed model was also deployed with multiple-orientation complex GRE data input measurements, resulting in high-quality QSM separation maps with more faithful tissue delineation between brain structures compared to those reconstructed by single-orientation QSM separation methods.
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Affiliation(s)
- Zhenghao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ruimin Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qiangqiang Liu
- Department of Neurosurgery, Clinical Neuroscience Center Comprehensive Epilepsy Unit, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Guoyan Lao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Li
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
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20
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Margoni M, Pagani E, Preziosa P, Gueye M, Azzimonti M, Rocca MA, Filippi M. Unraveling the heterogeneous pathological substrates of relapse-onset multiple sclerosis: a multiparametric voxel-wise 3 T MRI study. J Neurol 2023:10.1007/s00415-023-11736-9. [PMID: 37093395 DOI: 10.1007/s00415-023-11736-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 04/17/2023] [Accepted: 04/17/2023] [Indexed: 04/25/2023]
Abstract
BACKGROUND In multiple sclerosis (MS), pathological processes affecting brain gray (GM) and white matter (WM) are heterogeneous. OBJECTIVE To apply a multimodal MRI approach to investigate the regional distribution of the different pathological processes occurring in the brain WM and GM of relapse-onset MS patients. METHODS Fifty-seven MS patients (forty-two relapsing remitting [RR], fifteen secondary progressive [SP]) and forty-seven age- and sex-matched healthy controls (HC) underwent a multimodal 3 T MRI acquisition. Between-group voxel-wise differences of brain WM and GM volumes, magnetization transfer ratio (MTR), T1-weighted(w)/T2w ratio, intracellular volume fraction (ICV_f), and quantitative susceptibility mapping (QSM) maps were investigated. RESULTS Compared to HC, RRMS showed significant WM, deep GM and cortical atrophy, significantly lower MTR and T1w/T2w ratio of periventricular and infratentorial WM, deep GM and several cortical areas, lower ICV_f in supratentorial and cerebellar WM and in some cortical areas, and lower QSM values in bilateral periventricular WM (p < 0.001). Compared to RRMS, SPMS patients showed significant deep GM and widespread cortical atrophy, significantly lower MTR of periventricular WM, deep GM and cerebellum, lower T1w/T2w ratio of fronto-temporal WM regions, lower ICV_f of some fronto-tempo-occipital WM and cortical areas. They also had increased QSM and T1w/T2w ratio in the pallidum, bilaterally (p < 0.001). CONCLUSION A periventricular pattern of demyelination and widespread GM and WM neuro-axonal loss are detectable in RRMS and are more severe in SPMS. Higher T1w/T2w ratio and QSM in the pallidum, possibly reflecting iron accumulation and neurodegeneration, may represent a relevant MRI marker to differentiate SPMS from RRMS.
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Affiliation(s)
- Monica Margoni
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Mor Gueye
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Matteo Azzimonti
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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21
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Clinical correlates of R1 relaxometry and magnetic susceptibility changes in multiple sclerosis: a multi-parameter quantitative MRI study of brain iron and myelin. Eur Radiol 2023; 33:2185-2194. [PMID: 36241917 PMCID: PMC9935712 DOI: 10.1007/s00330-022-09154-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 04/07/2022] [Accepted: 05/13/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES The clinical impact of brain microstructural abnormalities in multiple sclerosis (MS) remains elusive. We aimed to characterize the topography of longitudinal relaxation rate (R1) and quantitative susceptibility (χ) changes, as indices of iron and myelin, together with brain atrophy, and to clarify their contribution to cognitive and motor disability in MS. METHODS In this cross-sectional study, voxel-based morphometry, and voxel-based quantification analyses of R1 and χ maps were conducted in gray matter (GM) and white matter (WM) of 117 MS patients and 53 healthy controls. Voxel-wise between-group differences were assessed with nonparametric permutation tests, while correlations between MRI metrics and clinical variables (global disability, cognitive and motor performance) were assessed both globally and voxel-wise within clusters emerging from the between-group comparisons. RESULTS MS patients showed widespread R1 decrease associated with more limited modifications of χ, with atrophy mainly involving deep GM, posterior and infratentorial regions (p < 0.02). While R1 and χ showed a parallel reduction in several WM tracts (p < 0.001), reduced GM R1 values (p < 0.001) were associated with decreased thalamic χ (p < 0.001) and small clusters of increased χ in the caudate nucleus and prefrontal cortex (p < 0.02). In addition to the atrophy, χ values in the cingulum and corona radiata correlated with global disability and motor performance, while focal demyelination correlated with cognitive performance (p < 0.04). CONCLUSIONS We confirmed the presence of widespread R1 changes, involving both GM and WM, and atrophy in MS, with less extensive modifications of tissue χ. While atrophy and χ changes are related to global and motor disability, R1 changes are meaningful correlates of cognition. KEY POINTS • Compared to healthy controls, multiple sclerosis patients showed R1 and χ changes suggestive of iron increase within the basal ganglia and reduced iron and myelin content within (subnuclei of) the thalamus. • Thalamic volume and χ changes significantly predicted clinical disability, as well as pulvinar R1 and χ changes, independently from atrophy. • Atrophy-independent R1 and χ changes, suggestive of thalamic iron and myelin depletion, may represent a sensitive marker of subclinical inflammation.
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22
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Drvenica IT, Stančić AZ, Maslovarić IS, Trivanović DI, Ilić VL. Extracellular Hemoglobin: Modulation of Cellular Functions and Pathophysiological Effects. Biomolecules 2022; 12:1708. [PMID: 36421721 PMCID: PMC9688122 DOI: 10.3390/biom12111708] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/08/2022] [Accepted: 11/15/2022] [Indexed: 08/05/2023] Open
Abstract
Hemoglobin is essential for maintaining cellular bioenergetic homeostasis through its ability to bind and transport oxygen to the tissues. Besides its ability to transport oxygen, hemoglobin within erythrocytes plays an important role in cellular signaling and modulation of the inflammatory response either directly by binding gas molecules (NO, CO, and CO2) or indirectly by acting as their source. Once hemoglobin reaches the extracellular environment, it acquires several secondary functions affecting surrounding cells and tissues. By modulating the cell functions, this macromolecule becomes involved in the etiology and pathophysiology of various diseases. The up-to-date results disclose the impact of extracellular hemoglobin on (i) redox status, (ii) inflammatory state of cells, (iii) proliferation and chemotaxis, (iv) mitochondrial dynamic, (v) chemoresistance and (vi) differentiation. This review pays special attention to applied biomedical research and the use of non-vertebrate and vertebrate extracellular hemoglobin as a promising candidate for hemoglobin-based oxygen carriers, as well as cell culture medium additive. Although recent experimental settings have some limitations, they provide additional insight into the modulatory activity of extracellular hemoglobin in various cellular microenvironments, such as stem or tumor cells niches.
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Affiliation(s)
- Ivana T. Drvenica
- Group for Immunology, Institute for Medical Research, National Institute of Republic of Serbia, University of Belgrade, 11129 Belgrade, Serbia
| | - Ana Z. Stančić
- Group for Immunology, Institute for Medical Research, National Institute of Republic of Serbia, University of Belgrade, 11129 Belgrade, Serbia
| | - Irina S. Maslovarić
- Group for Immunology, Institute for Medical Research, National Institute of Republic of Serbia, University of Belgrade, 11129 Belgrade, Serbia
| | - Drenka I. Trivanović
- Group for Hematology and Stem Cells, Institute for Medical Research, National Institute of Republic of Serbia, University of Belgrade, 11129 Belgrade, Serbia
| | - Vesna Lj. Ilić
- Group for Immunology, Institute for Medical Research, National Institute of Republic of Serbia, University of Belgrade, 11129 Belgrade, Serbia
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23
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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] [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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24
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Hamdy E, Galeel AA, Ramadan I, Gaber D, Mustafa H, Mekky J. Iron deposition in multiple sclerosis: overall load or distribution alteration? Eur Radiol Exp 2022; 6:49. [PMID: 36074209 PMCID: PMC9458829 DOI: 10.1186/s41747-022-00279-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/14/2022] [Indexed: 11/14/2022] Open
Abstract
Background Though abnormal iron deposition has been reported in specific brain regions in multiple sclerosis (MS), no data exist about whether the overall quantity of iron in the brain is altered or not. We aimed to determine whether the noted aberrant iron deposition in MS brains was a problem of overall load or regional distribution in a cohort of MS patients. Methods An experienced neuroradiologist, a radiology software engineer, and four neurologists analysed data from quantitative susceptibility maps reconstructed from 3-T magnetic resonance brain images of 30 MS patients and 15 age- and sex-matched healthy controls. Global brain iron load was calculated, and the regional iron concentrations were assessed in 1,000 regions of interest placed in MS lesions in different locations, normal appearing white matter, thalami, and basal ganglia. Results Global brain iron load was comparable between patients and controls after adjustment for volume (p = 0.660), whereas the regional iron concentrations were significantly different in patients than in control (p ≤ 0.031). There was no significant correlation between global iron load and clinical parameters, whereas regional iron concentrations correlated with patients’ age, disease duration, and disability grade (p ≤ 0.039). Conclusions The aberrant iron deposition noted in MS seems to be a problem of regional distribution rather than an altered global brain iron load. Supplementary Information The online version contains supplementary material available at 10.1186/s41747-022-00279-9.
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Affiliation(s)
- Eman Hamdy
- Department of Neurology, Faculty of Medicine, Alexandria University, Alexandria, Egypt.
| | - Aya Abdel Galeel
- Department of Radiology, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Ismail Ramadan
- Department of Neurology, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Dina Gaber
- Department of Neurology, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | | | - Jaidaa Mekky
- Department of Neurology, Faculty of Medicine, Alexandria University, Alexandria, Egypt
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25
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He C, Guan X, Zhang W, Li J, Liu C, Wei H, Xu X, Zhang Y. Quantitative susceptibility atlas construction in Montreal Neurological Institute space: towards histological-consistent iron-rich deep brain nucleus subregion identification. Brain Struct Funct 2022:10.1007/s00429-022-02547-1. [PMID: 36038737 DOI: 10.1007/s00429-022-02547-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 07/27/2022] [Indexed: 01/25/2023]
Abstract
Iron-rich deep brain nuclei (DBN) of the human brain are involved in various motoric, emotional and cognitive brain functions. The abnormal iron alterations in the DBN are closely associated with multiple neurological and psychiatric diseases. Quantitative susceptibility mapping (QSM) provides the spatial distribution of the magnetic susceptibility of human brain tissues. Compared to traditional structural imaging, QSM provides superiority for imaging the iron-rich DBN owing to the susceptibility difference existing between brain tissues. In this study, we constructed a Montreal Neurological Institute (MNI) space unbiased QSM human brain atlas via group-wise registration from 100 healthy subjects aged 19-29 years. The atlas construction process was guided by hybrid images that were fused from multi-modal magnetic resonance images (MRI). We named it as Multi-modal-fused magnetic Susceptibility (MuSus-100) atlas. The high-quality susceptibility atlas provides extraordinary image contrast between iron-rich DBN with their surroundings. Parcellation maps of DBN and their subregions that are highly related to neurological and psychiatric pathology were then manually labeled based on the atlas set with the assistance of an image border-enhancement process. Especially, the bilateral thalamus was delineated into 64 detailed subregions referring to the Schaltenbrand-Wahren stereotactic atlas. To our best knowledge, the histological-consistent thalamic nucleus parcellation map is well defined for the first time in the MNI space. Compared with existing atlases that emphasizing DBN parcellation, the newly proposed atlas outperforms on the task of atlas-guided individual brain image DBN segmentation both in accuracy and robustness. Moreover, we applied the proposed DBN parcellation map to conduct detailed identification of the pathology-related iron content alterations in subcortical nuclei for Parkinson's Disease (PD) patients. We envision that the MuSus-100 atlas can play a crucial role in improving the accuracy of DBN segmentation for the research of neurological and psychiatric disease progress and also be helpful for target planning in deep brain stimulation surgery.
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Affiliation(s)
- Chenyu He
- School of Information Science and Technology, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China
| | - Xiaojun Guan
- Department of Radiology of The Second Affiliated Hospital, Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Weimin Zhang
- School of Information Science and Technology, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China
| | - Jun Li
- School of Information Science and Technology, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China
| | - Chunlei Liu
- Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, 94720, United States
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200030, China
| | - Xiaojun Xu
- Department of Radiology of The Second Affiliated Hospital, Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China. .,Shanghai Engineering Research Center of Intelligent Vision and Imaging, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China.
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26
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Zhao MY, Fan AP, Chen DYT, Ishii Y, Khalighi MM, Moseley M, Steinberg GK, Zaharchuk G. Using arterial spin labeling to measure cerebrovascular reactivity in Moyamoya disease: Insights from simultaneous PET/MRI. J Cereb Blood Flow Metab 2022; 42:1493-1506. [PMID: 35236136 PMCID: PMC9274857 DOI: 10.1177/0271678x221083471] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Cerebrovascular reactivity (CVR) reflects the CBF change to meet different physiological demands. The reference CVR technique is PET imaging with vasodilators but is inaccessible to most patients. DSC can measure transit time to evaluate patients suspected of stroke, but the use of gadolinium may cause side-effects. Arterial spin labeling (ASL) is a non-invasive MRI technique for CBF measurements. Here, we investigate the effectiveness of ASL with single and multiple post labeling delays (PLD) to replace PET and DSC for CVR and transit time mapping in 26 Moyamoya patients. Images were collected using simultaneous PET/MRI with acetazolamide. CVR, CBF, arterial transit time (ATT), and time-to-maximum (Tmax) were measured in different flow territories. Results showed that CVR was lower in occluded regions than normal regions (by 68 ± 12%, 52 ± 5%, and 56 ± 9%, for PET, single- and multi-PLD PCASL, respectively, all p < 0.05). Multi-PLD PCASL correlated slightly higher with PET (CCC = 0.36 and 0.32 in affected and unaffected territories respectively). Vasodilation caused ATT to reduce by 4.5 ± 3.1% (p < 0.01) in occluded regions. ATT correlated significantly with Tmax (R2 > 0.35, p < 0.01). Therefore, multi-PLD ASL is recommended for CVR studies due to its high agreement with the reference PET technique and the capability of measuring transit time.
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Affiliation(s)
- Moss Y Zhao
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Audrey P Fan
- Department of Biomedical Engineering, University of California Davis, Davis, CA, USA.,Department of Neurology, University of California Davis, Davis, CA, USA
| | - David Yen-Ting Chen
- Department of Medical Imaging, Taipei Medical University - Shuan-Ho Hospital, New Taipei City.,Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei
| | - Yosuke Ishii
- Department of Neurosurgery, Tokyo Medical and Dental University, Tokyo, Japan
| | | | - Michael Moseley
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Gary K Steinberg
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Greg Zaharchuk
- Department of Radiology, Stanford University, Stanford, CA, USA
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27
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Wang C, Martins-Bach AB, Alfaro-Almagro F, Douaud G, Klein JC, Llera A, Fiscone C, Bowtell R, Elliott LT, Smith SM, Tendler BC, Miller KL. Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging. Nat Neurosci 2022; 25:818-831. [PMID: 35606419 PMCID: PMC9174052 DOI: 10.1038/s41593-022-01074-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 04/11/2022] [Indexed: 12/17/2022]
Abstract
A key aim in epidemiological neuroscience is identification of markers to assess brain health and monitor therapeutic interventions. Quantitative susceptibility mapping (QSM) is an emerging magnetic resonance imaging technique that measures tissue magnetic susceptibility and has been shown to detect pathological changes in tissue iron, myelin and calcification. We present an open resource of QSM-based imaging measures of multiple brain structures in 35,273 individuals from the UK Biobank prospective epidemiological study. We identify statistically significant associations of 251 phenotypes with magnetic susceptibility that include body iron, disease, diet and alcohol consumption. Genome-wide associations relate magnetic susceptibility to 76 replicating clusters of genetic variants with biological functions involving iron, calcium, myelin and extracellular matrix. These patterns of associations include relationships that are unique to QSM, in particular being complementary to T2* signal decay time measures. These new imaging phenotypes are being integrated into the core UK Biobank measures provided to researchers worldwide, creating the potential to discover new, non-invasive markers of brain health.
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Affiliation(s)
- Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Aurea B Martins-Bach
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Gwenaëlle Douaud
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Johannes C Klein
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
| | - Alberto Llera
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, the Netherlands
| | - Cristiana Fiscone
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Lloyd T Elliott
- Department of Statistics and Actuarial Science, Simon Fraser University, Vancouver, British Columbia, Canada
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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28
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Cerebral Iron Deposition in Neurodegeneration. Biomolecules 2022; 12:biom12050714. [PMID: 35625641 PMCID: PMC9138489 DOI: 10.3390/biom12050714] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/12/2022] [Accepted: 05/13/2022] [Indexed: 02/04/2023] Open
Abstract
Disruption of cerebral iron regulation appears to have a role in aging and in the pathogenesis of various neurodegenerative disorders. Possible unfavorable impacts of iron accumulation include reactive oxygen species generation, induction of ferroptosis, and acceleration of inflammatory changes. Whole-brain iron-sensitive magnetic resonance imaging (MRI) techniques allow the examination of macroscopic patterns of brain iron deposits in vivo, while modern analytical methods ex vivo enable the determination of metal-specific content inside individual cell-types, sometimes also within specific cellular compartments. The present review summarizes the whole brain, cellular, and subcellular patterns of iron accumulation in neurodegenerative diseases of genetic and sporadic origin. We also provide an update on mechanisms, biomarkers, and effects of brain iron accumulation in these disorders, focusing on recent publications. In Parkinson’s disease, Friedreich’s disease, and several disorders within the neurodegeneration with brain iron accumulation group, there is a focal siderosis, typically in regions with the most pronounced neuropathological changes. The second group of disorders including multiple sclerosis, Alzheimer’s disease, and amyotrophic lateral sclerosis shows iron accumulation in the globus pallidus, caudate, and putamen, and in specific cortical regions. Yet, other disorders such as aceruloplasminemia, neuroferritinopathy, or Wilson disease manifest with diffuse iron accumulation in the deep gray matter in a pattern comparable to or even more extensive than that observed during normal aging. On the microscopic level, brain iron deposits are present mostly in dystrophic microglia variably accompanied by iron-laden macrophages and in astrocytes, implicating a role of inflammatory changes and blood–brain barrier disturbance in iron accumulation. Options and potential benefits of iron reducing strategies in neurodegeneration are discussed. Future research investigating whether genetic predispositions play a role in brain Fe accumulation is necessary. If confirmed, the prevention of further brain Fe uptake in individuals at risk may be key for preventing neurodegenerative disorders.
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29
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Nikparast F, Ganji Z, Danesh Doust M, Faraji R, Zare H. Brain pathological changes during neurodegenerative diseases and their identification methods: How does QSM perform in detecting this process? Insights Imaging 2022; 13:74. [PMID: 35416533 PMCID: PMC9008086 DOI: 10.1186/s13244-022-01207-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/13/2022] [Indexed: 12/14/2022] Open
Abstract
The presence of iron is essential for many biological processes in the body. But sometimes, for various reasons, the amount of iron deposition in different areas of the brain increases, which leads to problems related to the nervous system. Quantitative susceptibility mapping (QSM) is one of the newest magnetic resonance imaging (MRI)-based methods for assessing iron accumulation in target areas. This Narrative Review article aims to evaluate the performance of QSM compared to other methods of assessing iron deposition in the clinical field. Based on the results, we introduced related basic definitions, some neurodegenerative diseases, methods of examining iron deposition in these diseases, and their advantages and disadvantages. This article states that the QSM method can be introduced as a new, reliable, and non-invasive technique for clinical evaluations.
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Affiliation(s)
- Farzaneh Nikparast
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zohreh Ganji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammad Danesh Doust
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Reyhane Faraji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hoda Zare
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. .,Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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30
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Jung W, Bollmann S, Lee J. Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities. NMR IN BIOMEDICINE 2022; 35:e4292. [PMID: 32207195 DOI: 10.1002/nbm.4292] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 02/04/2020] [Accepted: 02/25/2020] [Indexed: 06/10/2023]
Abstract
Quantitative susceptibility mapping (QSM) has gained broad interest in the field by extracting bulk tissue magnetic susceptibility, predominantly determined by myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo. Thereby, QSM can reveal pathological changes of these key components in a variety of diseases. QSM requires multiple processing steps such as phase unwrapping, background field removal and field-to-source inversion. Current state-of-the-art techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and require a careful choice of regularization parameters. With the recent success of deep learning using convolutional neural networks for solving ill-posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM processing steps can be solved by efficient feed forward multiplications not requiring either iterative optimization or the choice of regularization parameters. Here, we review the current status of deep learning-based approaches for processing QSM, highlighting limitations and potential pitfalls, and discuss the future directions the field may take to exploit the latest advances in deep learning for QSM.
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Affiliation(s)
- Woojin Jung
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Steffen Bollmann
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
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31
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Stewart AW, Robinson SD, O'Brien K, Jin J, Widhalm G, Hangel G, Walls A, Goodwin J, Eckstein K, Tourell M, Morgan C, Narayanan A, Barth M, Bollmann S. QSMxT: Robust masking and artifact reduction for quantitative susceptibility mapping. Magn Reson Med 2021; 87:1289-1300. [PMID: 34687073 DOI: 10.1002/mrm.29048] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/30/2021] [Accepted: 09/27/2021] [Indexed: 01/15/2023]
Abstract
PURPOSE Quantitative susceptibility mapping (QSM) estimates the spatial distribution of tissue magnetic susceptibilities from the phase of a gradient-echo signal. QSM algorithms require a signal mask to delineate regions with reliable phase for subsequent susceptibility estimation. Existing masking techniques used in QSM have limitations that introduce artifacts, exclude anatomical detail, and rely on parameter tuning and anatomical priors that narrow their application. Here, a robust masking and reconstruction procedure is presented to overcome these limitations and enable automated QSM processing. Moreover, this method is integrated within an open-source software framework: QSMxT. METHODS A robust masking technique that automatically separates reliable from less reliable phase regions was developed and combined with a two-pass reconstruction procedure that operates on the separated sources before combination, extracting more information and suppressing streaking artifacts. RESULTS Compared with standard masking and reconstruction procedures, the two-pass inversion reduces streaking artifacts caused by unreliable phase and high dynamic ranges of susceptibility sources. It is also robust across a range of acquisitions at 3 T in volunteers and phantoms, at 7 T in tumor patients, and in an in silico head phantom, with significant artifact and error reductions, greater anatomical detail, and minimal parameter tuning. CONCLUSION The two-pass masking and reconstruction procedure separates reliable from less reliable phase regions, enabling a more accurate QSM reconstruction that mitigates artifacts, operates without anatomical priors, and requires minimal parameter tuning. The technique and its integration within QSMxT makes QSM processing more accessible and robust to streaking artifacts.
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Affiliation(s)
- Ashley Wilton Stewart
- ARC Training Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Queensland, Australia.,Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Simon Daniel Robinson
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.,Department of Neurology, Medical University of Graz, Graz, Austria.,Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Vienna, Austria.,Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Center, Medical University of Vienna, Vienna, Austria
| | - Kieran O'Brien
- ARC Training Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Queensland, Australia.,Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.,Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
| | - Jin Jin
- ARC Training Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Queensland, Australia.,Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.,Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
| | - Georg Widhalm
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Gilbert Hangel
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Center, Medical University of Vienna, Vienna, Austria.,Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Angela Walls
- Clinical & Research Imaging Centre, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Jonathan Goodwin
- Department of Radiation Oncology, Calvary Mater Hospital, Newcastle, New South Wales, Australia.,School of Mathematical and Physical Science, University of Newcastle, Newcastle, New South Wales, Australia
| | - Korbinian Eckstein
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Center, Medical University of Vienna, Vienna, Austria
| | - Monique Tourell
- ARC Training Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Queensland, Australia.,Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Catherine Morgan
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand.,Centre of Research Excellence, Brain Research New Zealand-Rangahau Roro Aotearoa, Auckland, New Zealand.,Centre for Advanced MRI, The University of Auckland, Auckland, New Zealand
| | - Aswin Narayanan
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Markus Barth
- ARC Training Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Queensland, Australia.,Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Steffen Bollmann
- ARC Training Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Queensland, Australia.,Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
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32
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Ontaneda D, Raza PC, Mahajan KR, Arnold DL, Dwyer MG, Gauthier SA, Greve DN, Harrison DM, Henry RG, Li DKB, Mainero C, Moore W, Narayanan S, Oh J, Patel R, Pelletier D, Rauscher A, Rooney WD, Sicotte NL, Tam R, Reich DS, Azevedo CJ. Deep grey matter injury in multiple sclerosis: a NAIMS consensus statement. Brain 2021; 144:1974-1984. [PMID: 33757115 PMCID: PMC8370433 DOI: 10.1093/brain/awab132] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 01/28/2021] [Accepted: 02/01/2021] [Indexed: 11/13/2022] Open
Abstract
Although multiple sclerosis has traditionally been considered a white matter disease, extensive research documents the presence and importance of grey matter injury including cortical and deep regions. The deep grey matter exhibits a broad range of pathology and is uniquely suited to study the mechanisms and clinical relevance of tissue injury in multiple sclerosis using magnetic resonance techniques. Deep grey matter injury has been associated with clinical and cognitive disability. Recently, MRI characterization of deep grey matter properties, such as thalamic volume, have been tested as potential clinical trial end points associated with neurodegenerative aspects of multiple sclerosis. Given this emerging area of interest and its potential clinical trial relevance, the North American Imaging in Multiple Sclerosis (NAIMS) Cooperative held a workshop and reached consensus on imaging topics related to deep grey matter. Herein, we review current knowledge regarding deep grey matter injury in multiple sclerosis from an imaging perspective, including insights from histopathology, image acquisition and post-processing for deep grey matter. We discuss the clinical relevance of deep grey matter injury and specific regions of interest within the deep grey matter. We highlight unanswered questions and propose future directions, with the aim of focusing research priorities towards better methods, analysis, and interpretation of results.
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Affiliation(s)
- Daniel Ontaneda
- Cleveland Clinic Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland, OH 44195, USA
| | - Praneeta C Raza
- Cleveland Clinic Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland, OH 44195, USA
| | - Kedar R Mahajan
- Cleveland Clinic Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland, OH 44195, USA
| | - Douglas L Arnold
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - 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, Buffalo, NY 14214, USA
| | - Susan A Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, NY 10021, USA
| | - Douglas N Greve
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02129, USA
| | - Daniel M Harrison
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Roland G Henry
- Department of Neurology, Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA
- The UC San Francisco and Berkeley Bioengineering Graduate Group, University of California San Francisco, San Francisco, CA 94143, USA
| | - David K B Li
- Department of Radiology and Medicine (Neurology), University of British Columbia, Vancouver, British Columbia V6T 2B5, Canada
| | - Caterina Mainero
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02129, USA
| | - Wayne Moore
- Department of Pathology and Laboratory Medicine, and International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Sridar Narayanan
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Jiwon Oh
- Division of Neurology, St. Michael’s Hospital, University of Toronto, Toronto, Ontario M5B 1W8, Canada
| | - Raihaan Patel
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec H4H 1R3, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Daniel Pelletier
- Department of Neurology, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA
| | - Alexander Rauscher
- Physics and Astronomy, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - William D Rooney
- Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR 97239, USA
| | - Nancy L Sicotte
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Roger Tam
- Department of Radiology and Medicine (Neurology), University of British Columbia, Vancouver, British Columbia V6T 2B5, Canada
- Biomedical Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, Bethesda, MD 20824, USA
| | - Christina J Azevedo
- Department of Neurology, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA
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Guo Z, Long L, Qiu W, Lu T, Zhang L, Shu Y, Zhang K, Fang L, Chen S. The Distributional Characteristics of Multiple Sclerosis Lesions on Quantitative Susceptibility Mapping and Their Correlation With Clinical Severity. Front Neurol 2021; 12:647519. [PMID: 34305779 PMCID: PMC8299522 DOI: 10.3389/fneur.2021.647519] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 06/08/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Multiple sclerosis (MS) patients have a wide spectrum of severity and responses to therapy; the personalization of treatment relies on sensitive and specific biomarkers. Previous studies have suggested that susceptibility contrast in demyelinated plaques is associated with iron-related pathology in multiple sclerosis which may indicate clinical severity. The aims of this study were to characterize the spatial distribution of MS lesions with different iron patterns by using quantitative susceptibility mapping and to explore neuroradiological findings that correlate with poor clinical outcome. Methods: Twenty-six patients with relapsing-remitting MS [14 men, 12 women; mean age, 29 ± 8 (standard deviation) years; age range, 21-52 years] were included in this study. Differences in lesion number, T2 volume, and susceptibility were compared among lesions subcategorized by location and by the presence or absence of a hyperintense rim on quantitative susceptibility mapping. Associations between these imaging features and clinical outcomes including Expanded Disability Status Scale scores and annual relapse rates were investigated. Results: A total of 811 unifocal MS lesions were included, and their QSM patterns were nodular hyperintensity with no rim (rim-, 540, 67%) or with a hyperintense rim on the edge (rim+, 172, 21%) and nodular isointensity (99, 12%). Rim+ lesions had significantly larger volume (115 ± 142 vs. 166 ± 185 mm3, p < 0.001) and lower susceptibility (4 ± 15 vs. 8 ± 16 ppb, p < 0.05) than rim- lesions. More rim+ lesions were found in periventricular areas [median, 45%; interquartile range (IQR), 36%], whereas a larger proportion of rim- lesions were distributed in juxtacortical (median, 32%; IQR, 21%) and deep white matter (median, 38%; IQR, 22%) areas. The annual relapse rate was positively correlated with the proportion of periventricular rim+ lesions (p < 0.001, r = 0.65) and the proportion of subtentorial rim+ lesions (p < 0.05, r = 0.40). Additionally, a significant association was found between the burden of periventricular rim+ lesions (β = 0.64, p < 0.001) and the burden of subtentorial rim- lesions (β = 0.36, p < 0.05). Conclusions: A high number or lesion burden of periventricular rim+ lesions or subtentorial lesions is associated with frequent clinical relapses.
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Affiliation(s)
- Zhuoxin Guo
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Liu Long
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wei Qiu
- Department of Neurology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Tingting Lu
- Department of Neurology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Lina Zhang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yaqing Shu
- Department of Neurology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ke Zhang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ling Fang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shaoqiong Chen
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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Chen L, Cai S, van Zijl PC, Li X. Single-step calculation of susceptibility through multiple orientation sampling. NMR IN BIOMEDICINE 2021; 34:e4517. [PMID: 33822416 PMCID: PMC8184590 DOI: 10.1002/nbm.4517] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 03/06/2021] [Accepted: 03/14/2021] [Indexed: 06/12/2023]
Abstract
Quantitative susceptibility mapping (QSM) was developed to estimate the spatial distribution of magnetic susceptibility from MR signal phase acquired using a gradient echo (GRE) sequence. The field-to-susceptibility inversion in QSM is known to be ill-posed and needs numerical stabilization through either regularization or data oversampling. The calculation of susceptibility through the multiple orientation sampling (COSMOS) method uses phase data acquired at three or more head orientations to achieve a well-conditioned field-to-susceptibility inversion and is often considered the gold standard for in vivo QSM. However, the conventional COSMOS approach, here named multistep COSMOS (MSCOSMOS), solves the dipole inversion from the local field derived from raw GRE phase through multiple steps of phase preprocessing. Error propagations between these consecutive phase processing steps can thus affect the final susceptibility quantification. On the other hand, recently proposed single-step QSM (SSQSM) methods aim to solve an integrated inversion from unprocessed or total phase to mitigate such error propagations but have been limited to single orientation QSM. This study therefore aimed to test the feasibility of using single-step COSMOS (SSCOSMOS) to jointly perform background field removal and dipole inversion with multiple orientation sampling, which could serve as a better standard for gauging SSQSM methods. We incorporated multiple spherical mean value (SMV) kernels of various radii with the dipole inversion in SSCOSMOS. QSM reconstructions with SSCOSMOS and MSCOSMOS were compared using both simulations with a numerical head phantom and in vivo human brain data. SSCOSMOS permitted integrated background removal and dipole inversion without the need to adjust any regularization parameters. In addition, with sufficiently large SMV kernels, SSCOSMOS performed consistently better than MSCOSMOS in all the tested error metrics in our simulations, giving better susceptibility quantification and smaller reconstruction error. Consistent tissue susceptibility values were obtained between SSCOSMOS and MSCOSMOS.
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Affiliation(s)
- Lin Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Shuhui Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Peter C.M. van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
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35
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RESUME N: A flexible class of multi-parameter qMRI protocols. Phys Med 2021; 88:23-36. [PMID: 34171573 DOI: 10.1016/j.ejmp.2021.04.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/16/2021] [Accepted: 04/02/2021] [Indexed: 12/30/2022] Open
Abstract
PURPOSE To introduce a class of fast 3D quantitative MRI (qMRI) schemes (RESUMEN, for N=1,…,4) that allow for a thorough characterization of microstructural properties of brain tissues. METHODS An arbitrary multi-echo GRE acquisition optimized for quantitative susceptibility mapping (QSM) is complemented with an appropriate low flip-angle GRE sequence drawn from four possible choices. The acquired signals are processed to analytically derive the longitudinal relaxation (R1) and free induction decay (R2∗) rates, as well as the proton density (PD) and QSM. A comprehensive modeling of the excitation and B1- profiles and of the RF-spoiling is included in the acquisition and processing pipeline. RESULTS The RESUMEN maps appear homogeneous throughout the field-of-view and exhibit comparable values and high SNR across the considered range of N values. CONCLUSIONS The introduced schemes represent a class of robust and flexible strategies to derive a thorough and fast qMRI study, suitable for a whole-brain acquisition with isotropic voxel resolution of 700 μm in less than 15 min.
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Pontillo G, Petracca M, Monti S, Quarantelli M, Criscuolo C, Lanzillo R, Tedeschi E, Elefante A, Brescia Morra V, Brunetti A, Cocozza S, Palma G. Unraveling Deep Gray Matter Atrophy and Iron and Myelin Changes in Multiple Sclerosis. AJNR Am J Neuroradiol 2021; 42:1223-1230. [PMID: 33888456 DOI: 10.3174/ajnr.a7093] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 01/11/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND AND PURPOSE Modifications of magnetic susceptibility have been consistently demonstrated in the subcortical gray matter of MS patients, but some uncertainties remain concerning the underlying neurobiological processes and their clinical relevance. We applied quantitative susceptibility mapping and longitudinal relaxation rate relaxometry to clarify the relative contribution of atrophy and iron and myelin changes to deep gray matter damage and disability in MS. MATERIALS AND METHODS Quantitative susceptibility mapping and longitudinal relaxation rate maps were computed for 91 patients and 55 healthy controls from MR images acquired at 3T. Applying an external model, we estimated iron and myelin concentration maps for all subjects. Subsequently, changes of deep gray matter iron and myelin concentration (atrophy-dependent) and content (atrophy-independent) were investigated globally (bulk analysis) and regionally (voxel-based and atlas-based thalamic subnuclei analyses). The clinical impact of the observed MRI modifications was evaluated via regression models. RESULTS We identified reduced thalamic (P < .001) and increased pallidal (P < .001) mean iron concentrations in patients with MS versus controls. Global myelin and iron content in the basal ganglia did not differ between the two groups, while actual iron depletion was present in the thalamus (P < .001). Regionally, patients showed increased iron concentration in the basal ganglia (P ≤ .001) and reduced iron and myelin content in thalamic posterior-medial regions (P ≤ .004), particularly in the pulvinar (P ≤ .001). Disability was predicted by thalamic volume (B = -0.341, P = .02), iron concentration (B = -0.379, P = .005) and content (B = -0.406, P = .009), as well as pulvinar iron (B = -0.415, P = .003) and myelin (B = -0.415, P = .02) content, independent of atrophy. CONCLUSIONS Quantitative MRI suggests an atrophy-related iron increase within the basal ganglia of patients with MS, along with an atrophy-independent reduction of thalamic iron and myelin correlating with disability. Absolute depletions of thalamic iron and myelin may represent sensitive markers of subcortical GM damage, which add to the clinical impact of thalamic atrophy in MS.
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Affiliation(s)
- G Pontillo
- From the Departments of Advanced Biomedical Sciences (G.P., E.T., A.E., A.B., S.C.)
| | - M Petracca
- Neurosciences and Reproductive and Odontostomatological Sciences (M.P., C.C., R.L., V.B.M.), University "Federico II," Naples, Italy
| | - S Monti
- Institute of Biostructure and Bioimaging, (S.M., M.Q., G.P.) National Research Council, Naples, Italy
| | - M Quarantelli
- Institute of Biostructure and Bioimaging, (S.M., M.Q., G.P.) National Research Council, Naples, Italy
| | - C Criscuolo
- Neurosciences and Reproductive and Odontostomatological Sciences (M.P., C.C., R.L., V.B.M.), University "Federico II," Naples, Italy
| | - R Lanzillo
- Neurosciences and Reproductive and Odontostomatological Sciences (M.P., C.C., R.L., V.B.M.), University "Federico II," Naples, Italy
| | - E Tedeschi
- From the Departments of Advanced Biomedical Sciences (G.P., E.T., A.E., A.B., S.C.)
| | - A Elefante
- From the Departments of Advanced Biomedical Sciences (G.P., E.T., A.E., A.B., S.C.)
| | - V Brescia Morra
- Neurosciences and Reproductive and Odontostomatological Sciences (M.P., C.C., R.L., V.B.M.), University "Federico II," Naples, Italy
| | - A Brunetti
- From the Departments of Advanced Biomedical Sciences (G.P., E.T., A.E., A.B., S.C.)
| | - S Cocozza
- From the Departments of Advanced Biomedical Sciences (G.P., E.T., A.E., A.B., S.C.)
| | - G Palma
- Institute of Biostructure and Bioimaging, (S.M., M.Q., G.P.) National Research Council, Naples, Italy
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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: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [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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Amin M, Ontaneda D. Thalamic Injury and Cognition in Multiple Sclerosis. Front Neurol 2021; 11:623914. [PMID: 33613423 PMCID: PMC7892763 DOI: 10.3389/fneur.2020.623914] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/30/2020] [Indexed: 11/13/2022] Open
Abstract
Multiple sclerosis (MS) produces demyelination and degeneration in both gray and white matter. Both cortical and deep gray matter injury is observed during the course of MS. Among deep gray matter structures, the thalamus has received special attention, as it undergoes volume loss in different MS subtypes and is involved in the earliest form of the disease, radiologically isolated syndrome. The thalamus plays an important role as an information relay center, and involvement of the thalamus in MS has been associated with a variety of clinical manifestations in MS, including fatigue, movement disorders, pain, and cognitive impairment (CI). Similar to thalamic volume loss, CI is seen from the earliest stages of MS and is potentially one of the most debilitating manifestations of the disease. The thalamus, particularly the dorsomedial nucleus as part of the basolateral limbic circuit and anterior thalamic nuclei through connections with the prefrontal cortex, has been shown to be involved in CI. Specifically, several cognitive performance measures such as processing speed and memory correlate with thalamic volume. Thalamic atrophy is one of the most important predictors of CI in MS, and both thalamic volume, diffusion tensor imaging measures, and functional activation correlate with the degree of CI in MS. Although the exact mechanism of thalamic atrophy is not well-understood, it is hypothesized to be secondary to degeneration following white matter injury resulting in secondary neurodegeneration and neuronal loss. The thalamus may represent an ideal biomarker for studies aiming to test neuroprotective or restorative therapies aimed at cognition.
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Affiliation(s)
- Moein Amin
- Neurological Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, OH, United States
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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] [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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Al-Radaideh A, El-Haj N, Hijjawi N. Iron deposition and atrophy in cerebral grey matter and their possible association with serum iron in relapsing-remitting multiple sclerosis. Clin Imaging 2020; 69:238-242. [PMID: 32977196 DOI: 10.1016/j.clinimag.2020.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 08/17/2020] [Accepted: 09/11/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE The present study was carried out to investigate any possible linkage between cerebral grey matter volumetric, iron changes, white matter's lesions load and serum iron levels in a group of relapsing-remitting multiple sclerosis (RRMS) patients. MATERIALS AND METHODS Sixty-five RRMS patients along with thirty-four age-matched healthy controls (HCs) were recruited. Serum samples were isolated from blood samples which were collected in vacutainer plain tubes individually from both groups. Both groups were scanned at 1.5 T magnetic resonance imaging (MRI) using the following 3D sequences; T1-weighted gradient echo (MPRAGE), T2*-weighted gradient echo and T2-weighted fluid-attenuated inversion recovery (FLAIR). RESULTS Significant differences were observed between the RRMS patients and HCs for cortical and deep grey matter (dGM) volumes where cortical and dGM volumes in RRMS patient were significantly smaller than those in HCs. While iron deposition in the cortex, putamen (PT) and globus pallidus (GP) of RRMS patients were significantly higher than those of HCs, iron levels in thalamus (TH) and serum were significantly lower in RRMS compared to those in HCs. Except for T2 lesion load, none of volumetric measures showed any association with patients' disability status. Cerebral grey matter's iron changes did not show any association with those of serum. CONCLUSION Smaller cortical and subcortical grey matter volumes in RRMS patients compared to HCs were detected. None of the volumetric measures showed any association with patients' disability status. RRMS patients showed increased iron levels in the PT, GP and cortex and decreased levels in the TH and serum.
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Affiliation(s)
- Ali Al-Radaideh
- Department of Medical Imaging, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, Jordan.
| | - Nawal El-Haj
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, Jordan
| | - Nawal Hijjawi
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, Jordan
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Al-Radaideh A, Athamneh I, Alabadi H, Hbahbih M. Deep gray matter changes in relapsing-remitting multiple sclerosis detected by multi-parametric, high-resolution magnetic resonance imaging (MRI). Eur Radiol 2020; 31:706-715. [PMID: 32851443 DOI: 10.1007/s00330-020-07199-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 07/16/2020] [Accepted: 08/14/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES To investigate a variety of magnetic resonance imaging (MRI) quantitative metrics, which reflect different aspects of microstructural damage in deep gray matter (dGM) regions and white matter T2 lesions in patients with relapsing-remitting multiple sclerosis (RRMS), and to determine the level of pathological interconnection between these two entities as well as their association with clinical disability. METHODS We recruited thirty RRMS patients along with thirty age-matched healthy controls (HCs). Both groups were scanned at 3 T MRI using 3D high-resolution T1-, T2-, and T2*-weighted, magnetization transfer (MT)-prepared gradient echo for MT ratio (MTR) mapping, and eight repeats of T1-weighted images acquired at different inversion times to create T1 maps. dGM structures were segmented from T1-weighted images using FreeSurfer, WM-T2 lesions were extracted from T2-weighted images, and iron maps were calculated from the phase part of the T2*-weighted sequence. Extracted dGM MRI indices were compared between both groups. In the RRMS group, dGM MRI indices were correlated with those of WM-T2 lesions, expanded disability status scale, and disease duration. RESULTS dGM volumetric metrics of RRMS patients were significantly (p < 0.01) smaller than those of HCs and showed a significant moderate association with lesions' load (p < 0.05) and lesions' iron concentration (p < 0.01). dGM MTRs of RRMS patients were significantly (p < 0.01) smaller than those of HCs and showed a significant (p < 0.01) moderate correlation with lesion T1 times. While T1 changes in some dGM regions of RRMS patients associated weakly with those of T2 lesions, dGM iron concentration did not show any association with any of lesions' metrics. Furthermore, lesions' MTR changes did not show any association with any dGM metrics. Most dGM metrics did not show any correlation with disease severity. Contrarily, most lesions' metrics showed weak association with disease severity. CONCLUSIONS dGM changes occur in a non-uniform pattern and, almost, do not link directly to MS disease severity. Contrarily, most WM-T2 lesions' metrics tend to correlate with MS disease severity better than those of dGM. KEY POINTS • Deep gray matter (dGM) structures are very much involved in the MS disease process and quite substantial neurodegeneration is undergone during the relapsing-remitting phase of the MS disease. • Deep gray matter (dGM) quantitative changes occur in a non-uniform and non-linked pattern and, except for CN's iron deposition, do not directly associate with the MS disease severity. • Most white matter T2 lesions' metrics tend to correlate with MS disease severity better than those of dGM structures.
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Affiliation(s)
- Ali Al-Radaideh
- Department of Medical Imaging, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, Jordan.
| | - Imad Athamneh
- Department of Radiology, King Hussein Medical Center, Jordanian Royal Medical Services, Amman, Jordan
| | - Hadeel Alabadi
- Department of Radiology, King Hussein Medical Center, Jordanian Royal Medical Services, Amman, Jordan
| | - Majed Hbahbih
- Department of Internal Medicine, Neurology, King Hussein Medical Centre, Jordanian Royal Medical Services, Amman, Jordan
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Deep Gray Matter Iron Content in Neuromyelitis Optica and Multiple Sclerosis. BIOMED RESEARCH INTERNATIONAL 2020; 2020:6492786. [PMID: 32509866 PMCID: PMC7254083 DOI: 10.1155/2020/6492786] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 04/29/2020] [Indexed: 12/14/2022]
Abstract
Background Neuromyelitis optica (NMO) and multiple sclerosis (MS) are often presenting with overlapping symptoms. The aim of this study was to determine whether and how NMO and MS differ regarding cerebral iron deposits in deep gray matter (DGM) and the correlation between iron deposition and clinical severity as well as to regional atrophy of the DGM. Methods We analyzed 20 patients with NMO, 40 patients with a relapsing-remitting (RR) form of MS, and 20 healthy controls with 1.5T MRI. Quantitative susceptibility mapping (QSM) was performed to estimate iron concentration in the DGM. Results Patients with NMO have higher magnetic susceptibility values in the substantia nigra compared to healthy controls. RRMS patients have lower magnetic susceptibility values in the thalamus compared to healthy controls and NMO patients. Atrophy of the thalamus, pulvinar, and putamen is significant both in RRMS compared to NMO patients and healthy controls. A correlation was found between the disability score (EDSS) and magnetic susceptibility in the putamen in RRMS. Conclusions This study confirms that a disturbed cerebral iron homeostasis in patients with NMO occurs in different structures than in patients with RRMS. Increased magnetic susceptibility in substantia nigra in NMO and decreased magnetic susceptibility within the thalamus in RRMS were the only significant differences in the study sample. We could confirm that iron concentration in the thalami is decreased in RRMS compared to that in the HC group. Positive association was found between putaminal iron and EDSS in RRMS.
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Tavazzi E, Zivadinov R, Dwyer MG, Jakimovski D, Singhal T, Weinstock-Guttman B, Bergsland N. MRI biomarkers of disease progression and conversion to secondary-progressive multiple sclerosis. Expert Rev Neurother 2020; 20:821-834. [PMID: 32306772 DOI: 10.1080/14737175.2020.1757435] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
INTRODUCTION Conventional imaging measures remain a key clinical tool for the diagnosis multiple sclerosis (MS) and monitoring of patients. However, most measures used in the clinic show unsatisfactory performance in predicting disease progression and conversion to secondary progressive MS. AREAS COVERED Sophisticated imaging techniques have facilitated the identification of imaging biomarkers associated with disease progression, such as global and regional brain volume measures, and with conversion to secondary progressive MS, such as leptomeningeal contrast enhancement and chronic inflammation. The relevance of emerging imaging approaches partially overcoming intrinsic limitations of traditional techniques is also discussed. EXPERT OPINION Imaging biomarkers capable of detecting tissue damage early on in the disease, with the potential to be applied in multicenter trials and at an individual level in clinical settings, are strongly needed. Several measures have been proposed, which exploit advanced imaging acquisitions and/or incorporate sophisticated post-processing, can quantify irreversible tissue damage. The progressively wider use of high-strength field MRI and the development of more advanced imaging techniques will help capture the missing pieces of the MS puzzle. The ability to more reliably identify those at risk for disability progression will allow for earlier intervention with the aim to favorably alter the disease course.
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Affiliation(s)
- 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
| | - 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.,Translational Imaging Center, Clinical and Translational Science Institute, 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, 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
| | - Tarun Singhal
- PET Imaging Program in Neurologic Diseases and Partners Multiple Sclerosis Center, Ann Romney Center for Neurologic Disease, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School , Boston, MA, USA
| | - 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, 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
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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] [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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Tokola A, Laine M, Tikkanen R, Autti T. Susceptibility-Weighted Imaging Findings in Aspartylglucosaminuria. AJNR Am J Neuroradiol 2019; 40:1850-1854. [PMID: 31649158 DOI: 10.3174/ajnr.a6288] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 09/03/2019] [Indexed: 12/19/2022]
Abstract
BACKGROUND AND PURPOSE Aspartylglucosaminuria is a rare lysosomal storage disorder that causes slowly progressive, childhood-onset intellectual disability and motor deterioration. Previous studies have shown, for example, hypointensity in the thalami in patients with aspartylglucosaminuria on T2WI, especially in the pulvinar nuclei. Susceptibility-weighted imaging is a neuroimaging technique that uses tissue magnetic susceptibility to generate contrast and is able to visualize iron and other mineral deposits in the brain. SWI findings in aspartylglucosaminuria have not been reported previously. MATERIALS AND METHODS Twenty-one patients with aspartylglucosaminuria (10 girls; 7.4-15.0 years of age) underwent 3T MR imaging. The protocol included an SWI sequence, and the images were visually evaluated. Thirteen patients (6 girls, 7.4-15.0 years of age) had good-quality SWI. Eight patients had motion artifacts and were excluded from the visual analysis. Thirteen healthy children (8 girls, 7.3-14.1 years of age) were imaged as controls. RESULTS We found a considerably uniform distribution of decreased signal intensity in SWI in the thalamic nuclei in 13 patients with aspartylglucosaminuria. The most evident hypointensity was found in the pulvinar nuclei. Patchy hypointensities were also found especially in the medial and anterior thalamic nuclei. Moreover, some hypointensity was noted in globi pallidi and substantia nigra in older patients. The filtered-phase images indicated accumulation of paramagnetic compounds in these areas. No abnormal findings were seen in the SWI of the healthy controls. CONCLUSIONS SWI indicates accumulation of paramagnetic compounds in the thalamic nuclei in patients with aspartylglucosaminuria. The finding may raise the suspicion of this rare disease in clinical practice.
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Affiliation(s)
- A Tokola
- From the HUS Medical Imaging Center, Radiology (A.T., T.A.), University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - M Laine
- Department of Child Neurology (M.L.), Helsinki University Hospital, Helsinki, Finland
| | - R Tikkanen
- Institute of Biochemistry, Medical Faculty (R.T.), University of Giessen, Giessen, Germany
| | - T Autti
- From the HUS Medical Imaging Center, Radiology (A.T., T.A.), University of Helsinki and Helsinki University Hospital, Helsinki, Finland
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Hametner S, Dal-Bianco A. 7T MRI: Measuring white matter changes in MS and iron content in brain. J Neurol Sci 2019. [DOI: 10.1016/j.jns.2019.10.099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Singhal T, O'Connor K, Dubey S, Pan H, Chu R, Hurwitz S, Cicero S, Tauhid S, Silbersweig D, Stern E, Kijewski M, DiCarli M, Weiner HL, Bakshi R. Gray matter microglial activation in relapsing vs progressive MS: A [F-18]PBR06-PET study. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2019; 6:e587. [PMID: 31355321 PMCID: PMC6624145 DOI: 10.1212/nxi.0000000000000587] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 05/15/2019] [Indexed: 11/15/2022]
Abstract
Objective To determine the value of [F-18]PBR06-PET for assessment of microglial activation in the cerebral gray matter in patients with MS. Methods Twelve patients with MS (7 relapsing-remitting and 5 secondary progressive [SP]) and 5 healthy controls (HCs) had standardized uptake value (SUV) PET maps coregistered to 3T MRI and segmented into cortical and subcortical gray matter regions. SUV ratios (SUVRs) were global brain normalized. Voxel-by-voxel analysis was performed using statistical parametric mapping (SPM). Normalized brain parenchymal volumes (BPVs) were determined from MRI using SIENAX. Results Cortical SUVRs were higher in the hippocampus, amygdala, midcingulate, posterior cingulate, and rolandic operculum and lower in the medial-superior frontal gyrus and cuneus in the MS vs HC group (all p < 0.05). Subcortical gray matter SUVR was higher in SPMS vs RRMS (+10.8%, p = 0.002) and HC (+11.3%, p = 0.055) groups. In the MS group, subcortical gray matter SUVR correlated with the Expanded Disability Status Scale (EDSS) score (r = 0.75, p = 0.005) and timed 25-foot walk (T25FW) (r = 0.70, p = 0.01). Thalamic SUVRs increased with increasing EDSS scores (r = 0.83, p = 0.0008) and T25FW (r = 0.65, p = 0.02) and with decreasing BPV (r = -0.63, p = 0.03). Putaminal SUVRs increased with increasing EDSS scores (0.71, p = 0.009) and with decreasing BPV (r = -0.67, p = 0.01). On SPM analysis, peak correlations of thalamic voxels with BPV were seen in the pulvinar and with the EDSS score and T25FW in the dorsomedial thalamic nuclei. Conclusions This study suggests that [F-18]PBR06-PET detects widespread abnormal microglial activation in the cerebral gray matter in MS. Increased translocator protein binding in subcortical gray matter regions is associated with brain atrophy and may link to progressive MS.
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Affiliation(s)
- Tarun Singhal
- Partners MS Center (T.S., K.O.C., R.C., S.C., S.T., H.L.W., R.B.), Laboratory for Neuroimaging Research, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School; Division of Nuclear Medicine and Molecular Imaging (S.D., M.K., M.D.), Department of Radiology, Brigham and Women's Hospital, Harvard Medical School; Functional Neuroimaging Laboratory (H.P., D.S., E.S.), Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School; Department of Medicine (S.H.) and Department of Radiology (E.S., R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Kelsey O'Connor
- Partners MS Center (T.S., K.O.C., R.C., S.C., S.T., H.L.W., R.B.), Laboratory for Neuroimaging Research, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School; Division of Nuclear Medicine and Molecular Imaging (S.D., M.K., M.D.), Department of Radiology, Brigham and Women's Hospital, Harvard Medical School; Functional Neuroimaging Laboratory (H.P., D.S., E.S.), Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School; Department of Medicine (S.H.) and Department of Radiology (E.S., R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Shipra Dubey
- Partners MS Center (T.S., K.O.C., R.C., S.C., S.T., H.L.W., R.B.), Laboratory for Neuroimaging Research, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School; Division of Nuclear Medicine and Molecular Imaging (S.D., M.K., M.D.), Department of Radiology, Brigham and Women's Hospital, Harvard Medical School; Functional Neuroimaging Laboratory (H.P., D.S., E.S.), Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School; Department of Medicine (S.H.) and Department of Radiology (E.S., R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Hong Pan
- Partners MS Center (T.S., K.O.C., R.C., S.C., S.T., H.L.W., R.B.), Laboratory for Neuroimaging Research, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School; Division of Nuclear Medicine and Molecular Imaging (S.D., M.K., M.D.), Department of Radiology, Brigham and Women's Hospital, Harvard Medical School; Functional Neuroimaging Laboratory (H.P., D.S., E.S.), Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School; Department of Medicine (S.H.) and Department of Radiology (E.S., R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Renxin Chu
- Partners MS Center (T.S., K.O.C., R.C., S.C., S.T., H.L.W., R.B.), Laboratory for Neuroimaging Research, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School; Division of Nuclear Medicine and Molecular Imaging (S.D., M.K., M.D.), Department of Radiology, Brigham and Women's Hospital, Harvard Medical School; Functional Neuroimaging Laboratory (H.P., D.S., E.S.), Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School; Department of Medicine (S.H.) and Department of Radiology (E.S., R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Shelley Hurwitz
- Partners MS Center (T.S., K.O.C., R.C., S.C., S.T., H.L.W., R.B.), Laboratory for Neuroimaging Research, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School; Division of Nuclear Medicine and Molecular Imaging (S.D., M.K., M.D.), Department of Radiology, Brigham and Women's Hospital, Harvard Medical School; Functional Neuroimaging Laboratory (H.P., D.S., E.S.), Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School; Department of Medicine (S.H.) and Department of Radiology (E.S., R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Steven Cicero
- Partners MS Center (T.S., K.O.C., R.C., S.C., S.T., H.L.W., R.B.), Laboratory for Neuroimaging Research, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School; Division of Nuclear Medicine and Molecular Imaging (S.D., M.K., M.D.), Department of Radiology, Brigham and Women's Hospital, Harvard Medical School; Functional Neuroimaging Laboratory (H.P., D.S., E.S.), Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School; Department of Medicine (S.H.) and Department of Radiology (E.S., R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Shahamat Tauhid
- Partners MS Center (T.S., K.O.C., R.C., S.C., S.T., H.L.W., R.B.), Laboratory for Neuroimaging Research, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School; Division of Nuclear Medicine and Molecular Imaging (S.D., M.K., M.D.), Department of Radiology, Brigham and Women's Hospital, Harvard Medical School; Functional Neuroimaging Laboratory (H.P., D.S., E.S.), Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School; Department of Medicine (S.H.) and Department of Radiology (E.S., R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - David Silbersweig
- Partners MS Center (T.S., K.O.C., R.C., S.C., S.T., H.L.W., R.B.), Laboratory for Neuroimaging Research, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School; Division of Nuclear Medicine and Molecular Imaging (S.D., M.K., M.D.), Department of Radiology, Brigham and Women's Hospital, Harvard Medical School; Functional Neuroimaging Laboratory (H.P., D.S., E.S.), Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School; Department of Medicine (S.H.) and Department of Radiology (E.S., R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Emily Stern
- Partners MS Center (T.S., K.O.C., R.C., S.C., S.T., H.L.W., R.B.), Laboratory for Neuroimaging Research, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School; Division of Nuclear Medicine and Molecular Imaging (S.D., M.K., M.D.), Department of Radiology, Brigham and Women's Hospital, Harvard Medical School; Functional Neuroimaging Laboratory (H.P., D.S., E.S.), Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School; Department of Medicine (S.H.) and Department of Radiology (E.S., R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Marie Kijewski
- Partners MS Center (T.S., K.O.C., R.C., S.C., S.T., H.L.W., R.B.), Laboratory for Neuroimaging Research, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School; Division of Nuclear Medicine and Molecular Imaging (S.D., M.K., M.D.), Department of Radiology, Brigham and Women's Hospital, Harvard Medical School; Functional Neuroimaging Laboratory (H.P., D.S., E.S.), Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School; Department of Medicine (S.H.) and Department of Radiology (E.S., R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Marcelo DiCarli
- Partners MS Center (T.S., K.O.C., R.C., S.C., S.T., H.L.W., R.B.), Laboratory for Neuroimaging Research, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School; Division of Nuclear Medicine and Molecular Imaging (S.D., M.K., M.D.), Department of Radiology, Brigham and Women's Hospital, Harvard Medical School; Functional Neuroimaging Laboratory (H.P., D.S., E.S.), Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School; Department of Medicine (S.H.) and Department of Radiology (E.S., R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Howard L Weiner
- Partners MS Center (T.S., K.O.C., R.C., S.C., S.T., H.L.W., R.B.), Laboratory for Neuroimaging Research, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School; Division of Nuclear Medicine and Molecular Imaging (S.D., M.K., M.D.), Department of Radiology, Brigham and Women's Hospital, Harvard Medical School; Functional Neuroimaging Laboratory (H.P., D.S., E.S.), Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School; Department of Medicine (S.H.) and Department of Radiology (E.S., R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Rohit Bakshi
- Partners MS Center (T.S., K.O.C., R.C., S.C., S.T., H.L.W., R.B.), Laboratory for Neuroimaging Research, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School; Division of Nuclear Medicine and Molecular Imaging (S.D., M.K., M.D.), Department of Radiology, Brigham and Women's Hospital, Harvard Medical School; Functional Neuroimaging Laboratory (H.P., D.S., E.S.), Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School; Department of Medicine (S.H.) and Department of Radiology (E.S., R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
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Jakimovski D, Kuhle J, Ramanathan M, Barro C, Tomic D, Hagemeier J, Kropshofer H, Bergsland N, Leppert D, Dwyer MG, Michalak Z, Benedict RHB, Weinstock-Guttman B, Zivadinov R. Serum neurofilament light chain levels associations with gray matter pathology: a 5-year longitudinal study. Ann Clin Transl Neurol 2019; 6:1757-1770. [PMID: 31437387 PMCID: PMC6764487 DOI: 10.1002/acn3.50872] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 07/05/2019] [Accepted: 07/29/2019] [Indexed: 01/16/2023] Open
Abstract
Background Gray matter (GM) pathology is closely associated with physical and cognitive impairment in persons with multiple sclerosis (PwMS). Similarly, serum neurofilament light chain (sNfL) levels are related to MS disease activity and progression. Objectives To assess the cross–sectional and longitudinal associations between sNfL and MRI–derived lesion and brain volume outcomes in PwMS and age–matched healthy controls (HCs). Materials and Methods Forty‐seven HCs and 120 PwMS were followed over 5 years. All subjects underwent baseline and follow–up 3T MRI and sNfL examinations. Lesion volumes (LV) and global, tissue–specific and regional brain volumes were assessed. sNfL levels were analyzed using single molecule array (Simoa) assay and quantified in pg/mL. The associations between sNfL levels and MRI outcomes were investigated using regression analyses adjusted for age, sex, baseline disease modifying treatment (DMT) use and change in DMT over the follow‐up. False discovery rate (FDR)–adjusted q‐values <0.05 were considered significant. Results In PwMS, baseline sNfL was associated with baseline T1‐, T2‐ and gadolinium‐LV (q = 0.002, q = 0.001 and q < 0.001, respectively), but not with their longitudinal changes. Higher baseline sNfL levels were associated with lower baseline deep GM (β = −0.257, q = 0.017), thalamus (β = −0.216, q = 0.0017), caudate (β = −0.263, q = 0.014) and hippocampus (β = −0.267, q = 0.015) volumes. Baseline sNfL was associated with longitudinal decline of deep GM (β = −0.386, q < 0.001), putamen (β = −0.395, q < 0.001), whole brain (β = −0.356, q = 0.002), thalamus (β = −0.272, q = 0.049), globus pallidus (β = −0.284, q = 0.017), and GM (β = −0.264, q = 0.042) volumes. No associations between sNfL and MRI–derived measures were seen in the HCs. Conclusion Higher sNfL levels were associated with baseline LVs and greater development of GM atrophy in PwMS.
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Affiliation(s)
- 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, New York
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Murali Ramanathan
- Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, New York
| | - Christian Barro
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | | | - 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
| | | | - 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
| | | | - 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 at Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, New York
| | - Zuzanna Michalak
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - 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, New York
| | - Bianca Weinstock-Guttman
- Jacobs MS 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 at Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, New York
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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: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [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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Five year iron changes in relapsing-remitting multiple sclerosis deep gray matter compared to healthy controls. Mult Scler Relat Disord 2019; 33:107-115. [DOI: 10.1016/j.msard.2019.05.028] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 05/22/2019] [Accepted: 05/29/2019] [Indexed: 12/11/2022]
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