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Qiu L, Zhao Z, Bao L. SIPAS: A comprehensive susceptibility imaging process and analysis studio. Neuroimage 2024; 297:120697. [PMID: 38908725 DOI: 10.1016/j.neuroimage.2024.120697] [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: 02/07/2024] [Revised: 06/10/2024] [Accepted: 06/18/2024] [Indexed: 06/24/2024] Open
Abstract
Quantitative susceptibility mapping (QSM) is a rising MRI-based technology and quite a few QSM-related algorithms have been proposed to reconstruct maps of tissue susceptibility distribution from phase images. In this paper, we develop a comprehensive susceptibility imaging process and analysis studio (SIPAS) that can accomplish reliable QSM processing and offer a standardized evaluation system. Specifically, SIPAS integrates multiple methods for each step, enabling users to select algorithm combinations according to data conditions, and QSM maps could be evaluated by two aspects, including image quality indicators within all voxels and region-of-interest (ROI) analysis. Through a sophisticated design of user-friendly interfaces, the results of each procedure are able to be exhibited in axial, coronal, and sagittal views in real-time, meanwhile ROIs can be displayed in 3D rendering visualization. The accuracy and compatibility of SIPAS are demonstrated by experiments on multiple in vivo human brain datasets acquired from 3T, 5T, and 7T MRI scanners of different manufacturers. We also validate the QSM maps obtained by various algorithm combinations in SIPAS, among which the combination of iRSHARP and SFCR achieves the best results on its evaluation system. SIPAS is a comprehensive, sophisticated, and reliable toolkit that may prompt the QSM application in scientific research and clinical practice.
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Affiliation(s)
- Lichu Qiu
- Department of Electronic Science, Xiamen University, Xiamen 36100, China
| | - Zijun Zhao
- Department of Electronic Science, Xiamen University, Xiamen 36100, China
| | - Lijun Bao
- Department of Electronic Science, Xiamen University, Xiamen 36100, China.
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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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Xie Y, Zhang S, Wu D, Yao Y, Cho J, Lu J, Zhu H, Wang Y, Zhang Y, Zhu W. The changes of oxygen extraction fraction in different types of lesions in relapsing-remitting multiple sclerosis: A cross-sectional and longitudinal study. Neurol Sci 2024; 45:3939-3949. [PMID: 38492126 DOI: 10.1007/s10072-024-07463-2] [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: 12/17/2023] [Accepted: 03/11/2024] [Indexed: 03/18/2024]
Abstract
OBJECTIVES To explore the oxygen metabolism level of different types of lesions in relapsing-remitting multiple sclerosis (RRMS) patients by oxygen extraction fraction (OEF) both cross-sectionally and longitudinally. METHODS Forty-six RRMS patients and forty-one healthy controls (HC) went MRI examination. The quantitative susceptibility mapping (QSM) and OEF map were reconstructed from a 3D multi-echo gradient echo sequence. MS lesions in white matter were classified as contrast-enhancing lesions (CELs) on post-gadolinium T1-weighted sequence, paramagnetic rim lesions (PRLs), hyperintense lesions and non-hyperintense lesions on QSM, respectively. The susceptibility and OEF of different types of lesions were compared. The susceptibility and OEF values were measured and compared among different types of lesions. Among these RRMS patients, seventeen had follow-up MRI and 232 lesions, and baseline to follow-up longitudinal changes in susceptibility and OEF were measured. RESULTS PRLs had higher susceptibility and lower OEF than CELs, hyperintense lesions, and non-hyperintense lesions. The hyperintense lesions had higher susceptibility and lower OEF than non-hyperintense lesions. In longitudinal changes, PRLs had susceptibility increased (P < 0.001) and OEF decreased (P < 0.001). The hyperintense lesions showed significant decreases in susceptibility (P = 0.020), and non-hyperintense lesions showed significant increases in OEF during follow-up (P = 0.005). Notably, hyperintense lesions may convert to PRLs or non-hyperintense lesions as time progresses, accompanied by changes of OEF and susceptibility in the lesions. CONCLUSION This study revealed tissue damage and oxygen metabolism level in different types of MS lesions. The OEF may contribute to further understanding the evolution of MS lesions.
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Affiliation(s)
- Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Di Wu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Yihao Yao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Junghun Cho
- Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY, 14260, USA
| | - Jun Lu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Hongquan Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Yan Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China.
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China.
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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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Xi J, Huang Y, Bao L. Quantitative susceptibility mapping based basal ganglia segmentation via AGSeg: leveraging active gradient guiding mechanism in deep learning. Quant Imaging Med Surg 2024; 14:4417-4435. [PMID: 39022266 PMCID: PMC11250355 DOI: 10.21037/qims-23-1858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/14/2024] [Indexed: 07/20/2024]
Abstract
Background With better visual contrast and the ability for magnetic susceptibility quantification analysis, quantitative susceptibility mapping (QSM) has emerged as an important magnetic resonance imaging (MRI) method for basal ganglia studies. Precise segmentation of basal ganglia is a prerequisite for quantification analysis of tissue magnetic susceptibility, which is crucial for subsequent disease diagnosis and surgical planning. The conventional method of localizing and segmenting basal ganglia heavily relies on layer-by-layer manual annotation by experts, resulting in a tedious amount of workload. Although several morphology registration and deep learning based methods have been developed to automate segmentation, the voxels around the nuclei boundary remain a challenge to distinguish due to insufficient tissue contrast. This paper proposes AGSeg, an active gradient guidance-based susceptibility and magnitude information complete (MIC) network for real-time and accurate basal ganglia segmentation. Methods Various datasets, including clinical scans and data from healthy volunteers, were collected across multiple centers with different magnetic field strengths (3T/5T/7T), with a total of 210 three-dimensional (3D) susceptibility measurements. Manual segmentations following fixed rules for anatomical borders annotated by experts were used as ground truth labels. The proposed network took QSM maps and Magnitude images as two individual inputs, of which the features are selectively enhanced in the proposed magnitude information complete (MIC) module. AGSeg utilized a dual-branch architecture, with Seg-branch aiming to generate a proper segmentation map and Grad-branch to reconstruct the gradient map of regions of interest (ROIs). With the support of the newly designed active gradient module (AGM) and gradient guiding module (GGM), the Grad-branch provided attention guidance for the Seg-branch, facilitating it to focus on the boundary of target nuclei. Results Ablation studies were conducted to assess the functionality of the proposed modules. Significant performance decrement was observed after ablating relative modules. AGSeg was evaluated against several existing methods on both healthy and clinical data, achieving an average Dice similarity coefficient (DSC) =0.874 and average 95% Hausdorff distance (HD95) =2.009. Comparison experiments indicated that our model had superior performance on basal ganglia segmentation and better generalization ability over existing methods. The AGSeg outperformed all implemented comparison deep learning algorithms with average DSC enhancement ranging from 0.036 to 0.074. Conclusions The current work integrates a deep learning-based method into automated basal ganglia segmentation. The high processing speed and segmentation robustness of AGSeg contribute to the feasibility of future surgery planning and intraoperative navigation. Experiments show that leveraging active gradient guidance mechanisms and magnitude information completion can facilitate the segmentation process. Moreover, this approach also offers a portable solution for other multi-modality medical image segmentation tasks.
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Affiliation(s)
- Jiaxiu Xi
- Department of Electronic Science, Xiamen University, Xiamen, China
| | - Yuqing Huang
- Department of Electronic Science, Xiamen University, Xiamen, China
| | - Lijun Bao
- Department of Electronic Science, Xiamen University, Xiamen, China
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Zhang M, Feng R, Li Z, Feng J, Wu Q, Zhang Z, Ma C, Wu J, Yan F, Liu C, Zhang Y, Wei H. A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation. Med Image Anal 2024; 95:103173. [PMID: 38657424 DOI: 10.1016/j.media.2024.103173] [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: 10/09/2023] [Revised: 03/11/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024]
Abstract
Quantitative susceptibility mapping (QSM) is an MRI-based technique that estimates the underlying tissue magnetic susceptibility based on phase signal. Deep learning (DL)-based methods have shown promise in handling the challenging ill-posed inverse problem for QSM reconstruction. However, they require extensive paired training data that are typically unavailable and suffer from generalization problems. Recent model-incorporated DL approaches also overlook the non-local effect of the tissue phase in applying the source-to-field forward model due to patch-based training constraint, resulting in a discrepancy between the prediction and measurement and subsequently suboptimal QSM reconstruction. This study proposes an unsupervised and subject-specific DL method for QSM reconstruction based on implicit neural representation (INR), referred to as INR-QSM. INR has emerged as a powerful framework for learning a high-quality continuous representation of the signal (image) by exploiting its internal information without training labels. In INR-QSM, the desired susceptibility map is represented as a continuous function of the spatial coordinates, parameterized by a fully-connected neural network. The weights are learned by minimizing a loss function that includes a data fidelity term incorporated by the physical model and regularization terms. Additionally, a novel phase compensation strategy is proposed for the first time to account for the non-local effect of tissue phase in data consistency calculation to make the physical model more accurate. Our experiments show that INR-QSM outperforms traditional established QSM reconstruction methods and the compared unsupervised DL method both qualitatively and quantitatively, and is competitive against supervised DL methods under data perturbations.
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Affiliation(s)
- Ming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ruimin Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhenghao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qing Wu
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Zhiyong Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chengxin Ma
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jinsong Wu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, China.
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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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Fiscone C, Sighinolfi G, Manners DN, Motta L, Venturi G, Panzera I, Zaccagna F, Rundo L, Lugaresi A, Lodi R, Tonon C, Castelli M. Multiparametric MRI dataset for susceptibility-based radiomic feature extraction and analysis. Sci Data 2024; 11:575. [PMID: 38834674 DOI: 10.1038/s41597-024-03418-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 05/24/2024] [Indexed: 06/06/2024] Open
Abstract
Multiple sclerosis (MS) is a progressive demyelinating disease impacting the central nervous system. Conventional Magnetic Resonance Imaging (MRI) techniques (e.g., T2w images) help diagnose MS, although they sometimes reveal non-specific lesions. Quantitative MRI techniques are capable of quantifying imaging biomarkers in vivo, offering the potential to identify specific signs related to pre-clinical inflammation. Among those techniques, Quantitative Susceptibility Mapping (QSM) is particularly useful for studying processes that influence the magnetic properties of brain tissue, such as alterations in myelin concentration. Because of its intrinsic quantitative nature, it is particularly well-suited to be analyzed through radiomics, including techniques that extract a high number of complex and multi-dimensional features from radiological images. The dataset presented in this work provides information about normal-appearing white matter (NAWM) in a cohort of MS patients and healthy controls. It includes QSM-based radiomic features from NAWM and its tracts, and MR sequences necessary to implement the pipeline: T1w, T2w, QSM, DWI. The workflow is outlined in this article, along with an application showing feature reliability assessment.
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Affiliation(s)
- Cristiana Fiscone
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Giovanni Sighinolfi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - David Neil Manners
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.
- Department for Life Quality Sciences, University of Bologna, Bologna, Italy.
| | - Lorenzo Motta
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Greta Venturi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Ivan Panzera
- UOSI Riabilitazione Sclerosi Multipla, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Fulvio Zaccagna
- Department of Imaging, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Investigative Medicine Division, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy
| | - Alessandra Lugaresi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- UOSI Riabilitazione Sclerosi Multipla, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Mauro Castelli
- NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312, Lisbon, Portugal
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10
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Soltani S, Webb SM, Kroll T, King-Jones K. Drosophila Evi5 is a critical regulator of intracellular iron transport via transferrin and ferritin interactions. Nat Commun 2024; 15:4045. [PMID: 38744835 PMCID: PMC11094094 DOI: 10.1038/s41467-024-48165-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 04/22/2024] [Indexed: 05/16/2024] Open
Abstract
Vesicular transport is essential for delivering cargo to intracellular destinations. Evi5 is a Rab11-GTPase-activating protein involved in endosome recycling. In humans, Evi5 is a high-risk locus for multiple sclerosis, a debilitating disease that also presents with excess iron in the CNS. In insects, the prothoracic gland (PG) requires entry of extracellular iron to synthesize steroidogenic enzyme cofactors. The mechanism of peripheral iron uptake in insect cells remains controversial. We show that Evi5-depletion in the Drosophila PG affected vesicle morphology and density, blocked endosome recycling and impaired trafficking of transferrin-1, thus disrupting heme synthesis due to reduced cellular iron concentrations. We show that ferritin delivers iron to the PG as well, and interacts physically with Evi5. Further, ferritin-injection rescued developmental delays associated with Evi5-depletion. To summarize, our findings show that Evi5 is critical for intracellular iron trafficking via transferrin-1 and ferritin, and implicate altered iron homeostasis in the etiology of multiple sclerosis.
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Affiliation(s)
- Sattar Soltani
- University of Alberta, Faculty of Science, Edmonton, Alberta, T6G 2E9, Canada
| | - Samuel M Webb
- Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA, 94025, USA
| | - Thomas Kroll
- Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA, 94025, USA
| | - Kirst King-Jones
- University of Alberta, Faculty of Science, Edmonton, Alberta, T6G 2E9, Canada.
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11
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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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12
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Romanò F, Valsasina P, Pagani E, De Simone A, Parolin E, Filippi M, Rocca MA. Structural and functional correlates of disability, motor and cognitive performances in multiple sclerosis: Focus on the globus pallidus. Mult Scler Relat Disord 2024; 86:105576. [PMID: 38579567 DOI: 10.1016/j.msard.2024.105576] [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: 12/18/2023] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 04/07/2024]
Abstract
OBJECTIVES To explore structural and functional alterations of external (GPe) and internal (GPi) globus pallidus in people with multiple sclerosis (pwMS) compared to healthy controls (HC) and analyze their relationship with measures of clinical disability, motor and cognitive impairment. METHODS Sixty pwMS and 30 HC comparable for age and sex underwent 3.0T MRI, including conventional, diffusion tensor MRI and resting state (RS) functional MRI. Expanded Disability Status Scale (EDSS) scores were rated and timed 25-foot walk (T25FW) test, nine-hole peg test (9HPT), and paced auditory serial addition test (PASAT) were administered. Two operators segmented the GP into GPe and GPi. Volumes, T1/T2 ratio, diffusivity indices and seed-based RS functional connectivity (FC) of the GP and its components were assessed. RESULTS PwMS had no atrophy or altered diffusivity measures of the GP. Compared to HC, pwMS had higher T1/T2 ratio in both GP regions, which correlated with EDSS score (r = 0.26-0.39, p = 0.01-0.05). RS FC analysis highlighted component-specific functional alterations in pwMS: the GPe had decreased RS FC with fronto-parietal cortices, whereas the GPi had decreased intra-GP RS FC and increased RS FC with the thalamus. Worse EDSS, 9HPT, T25FW and PASAT scores were associated with GP RS FC modifications (r=-0.51‒0.51, p < 0.001). CONCLUSIONS Structural GP involvement in MS was homogeneous across its portions. Increased T1/T2 ratio values, possibly representing iron accumulation, were related to more severe disability. RS FC alterations of the GPe and GPi were consistent with their roles within the basal ganglia network and correlated with worse functional status, suggesting less efficient communication between structures.
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Affiliation(s)
- Francesco Romanò
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alice De Simone
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Emma Parolin
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy.
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13
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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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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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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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16
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Zierfuss B, Wang Z, Jackson AN, Moezzi D, Yong VW. Iron in multiple sclerosis - Neuropathology, immunology, and real-world considerations. Mult Scler Relat Disord 2023; 78:104934. [PMID: 37579645 DOI: 10.1016/j.msard.2023.104934] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 07/30/2023] [Accepted: 08/08/2023] [Indexed: 08/16/2023]
Abstract
Iron is an essential element involved in a multitude of bodily processes. It is tightly regulated, as elevated deposition in tissues is associated with diseases such as multiple sclerosis (MS). Iron accumulation in the central nervous system (CNS) of MS patients is linked to neurotoxicity through mechanisms including oxidative stress, glutamate excitotoxicity, misfolding of proteins, and ferroptosis. In the past decade, the combination of MRI and histopathology has enhanced our understanding of iron deposition in MS pathophysiology, including in the pro-inflammatory and neurotoxicity of iron-laden rims of chronic active lesions. In this regard, iron accumulation may not only have an impact on different CNS-resident cells but may also promote the innate and adaptive immune dysfunctions in MS. Although there are discordant results, most studies indicate lower levels of iron but higher amounts of the iron storage molecule ferritin in the circulation of people with MS. Considering the importance of iron, there is a need for evidence-guided recommendation for dietary intake in people living with MS. Potential novel therapeutic approaches include the regulation of iron levels using next generation iron chelators, as well as therapies to interfere with toxic consequences of iron overload including antioxidants in MS.
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Affiliation(s)
- Bettina Zierfuss
- The Research Center of the Centre Hospitalier de l'Université de Montréal (CRCHUM), Department of Neuroscience, Faculty of Medicine, Université de Montréal, Montréal H2X 0A9, Québec, Canada
| | - Zitong Wang
- Department of Psychiatry, College of Health Sciences, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta T6G 2B7, Canada
| | - Alexandra N Jackson
- School of Rehabilitation Therapy, Faculty of Health Sciences, Queen's University, Kingston, Ontario K7L 3N6, Canada
| | - Dorsa Moezzi
- The Hotchkiss Brain Institute and the Department of Clinical Neurosciences, University of Calgary, 3330 Hospital Dr NW, Calgary, Alberta T2N 4N1, Canada
| | - V Wee Yong
- The Hotchkiss Brain Institute and the Department of Clinical Neurosciences, University of Calgary, 3330 Hospital Dr NW, Calgary, Alberta T2N 4N1, Canada.
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17
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Fiscone C, Rundo L, Lugaresi A, Manners DN, Allinson K, Baldin E, Vornetti G, Lodi R, Tonon C, Testa C, Castelli M, Zaccagna F. Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis. Sci Rep 2023; 13:16239. [PMID: 37758804 PMCID: PMC10533494 DOI: 10.1038/s41598-023-42914-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023] Open
Abstract
Multiple Sclerosis (MS) is an autoimmune demyelinating disease characterised by changes in iron and myelin content. These biomarkers are detectable by Quantitative Susceptibility Mapping (QSM), an advanced Magnetic Resonance Imaging technique detecting magnetic properties. When analysed with radiomic techniques that exploit its intrinsic quantitative nature, QSM may furnish biomarkers to facilitate early diagnosis of MS and timely assessment of progression. In this work, we explore the robustness of QSM radiomic features by varying the number of grey levels (GLs) and echo times (TEs), in a sample of healthy controls and patients with MS. We analysed the white matter in total and within six clinically relevant tracts, including the cortico-spinal tract and the optic radiation. After optimising the number of GLs (n = 64), at least 65% of features were robust for each Volume of Interest (VOI), with no difference (p > .05) between left and right hemispheres. Different outcomes in feature robustness among the VOIs depend on their characteristics, such as volume and variance of susceptibility values. This study validated the processing pipeline for robustness analysis and established the reliability of QSM-based radiomics features against GLs and TEs. Our results provide important insights for future radiomics studies using QSM in clinical applications.
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Affiliation(s)
- Cristiana Fiscone
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy
| | - Alessandra Lugaresi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- UOSI Riabilitazione Sclerosi Multipla, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - David Neil Manners
- Department for Life Quality Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Kieren Allinson
- Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Elisa Baldin
- Epidemiology and Statistics Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Gianfranco Vornetti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Claudia Testa
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy.
| | - Mauro Castelli
- NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312, Lisbon, Portugal
| | - Fulvio Zaccagna
- Department of Imaging, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Investigative Medicine Division, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
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18
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Yu JJ, Li C, Qian ZM, Liu Y. Brain iron deposition is positively correlated with cognitive impairment in patients with chronic cerebral hypoperfusion: a MRI susceptibility mapping study. Clin Radiol 2023; 78:601-607. [PMID: 37003892 DOI: 10.1016/j.crad.2023.02.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 02/14/2023] [Accepted: 02/27/2023] [Indexed: 04/03/2023]
Abstract
AIM To investigate the relationship of brain iron deposition with cognitive impairment in patients with chronic cerebral hypoperfusion (CHP). MATERIALS AND METHODS Brain iron deposition was detected using quantitative susceptibility mapping (QSM), and cognitive function by neuropsychological tests including the Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Activities of Daily Living (ADLs), and verbal fluency tests in a total of 40 participants, 23 with CHP and 17 age- and sex-matched healthy participants without CHP (controls). RESULTS The neuropsychological tests revealed that cognitive impairment (p<0.05) and susceptibility values (p<0.05) of the bilateral hippocampus (HP) and substantia nigra (SN) in CHP patients were significantly higher than those of the controls. The susceptibility values of bilateral HP and left putamen correlated closely with the scores of neuropsychological tests in the CHP patients (p<0.05, r2>0.1). The susceptibility values in the left putamen and bilateral HP were significantly higher in CHP patients with mild cognitive impairment (MCI; n=8) than those of CHP patients without MCI (n=15; p<0.05). CONCLUSIONS The present findings indicated that brain iron deposition in specific areas may be responsible for the cognitive impairment in CHP patients, and that QSM is a useful tool to determine brain iron, predicting cognitive impairment in CHP patients.
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Affiliation(s)
- J-J Yu
- Department of Pain and Rehabilitation, The Second Affiliated (Xinqiao) Hospital, The Army (Third Military) Medical University, Chongqing 400038, China
| | - C Li
- Department of Medical Imaging, Chongqing University Central Hospital, Chongqing, China
| | - Z-M Qian
- Institute of Translational & Precision Medicine, Nantong University, 19 Qi Xiu Road, Nantong, JS 226019, China.
| | - Y Liu
- Department of Pain and Rehabilitation, The Second Affiliated (Xinqiao) Hospital, The Army (Third Military) Medical University, Chongqing 400038, China.
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19
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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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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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Wang Z, Mak HKF, Cao P. Deep learning-regularized, single-step quantitative susceptibility mapping quantification. NMR IN BIOMEDICINE 2023; 36:e4849. [PMID: 36259729 DOI: 10.1002/nbm.4849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 09/26/2022] [Accepted: 10/09/2022] [Indexed: 06/16/2023]
Abstract
The purpose of the current study was to develop deep learning-regularized, single-step quantitative susceptibility mapping (QSM) quantification, directly generating QSM from the total phase map. A deep learning-regularized, single-step QSM quantification model, named SS-POCSnet, was trained with datasets created using the QSM synthesis approach in QSM reconstruction challenge 2.0. In SS-POCSnet, a data fidelity term based on a single-step model was iteratively applied that combined the spherical mean value kernel and dipole model. Meanwhile, SS-POCSnet regularized susceptibility maps, avoiding underestimating susceptibility values. We evaluated the SS-POCSnet on 10 synthetic datasets, 24 clinical datasets with lesions of cerebral microbleed (CMB) and calcification, and 10 datasets with multiple sclerosis (MS).On synthetic datasets, SS-POCSnet showed the best performance among the methods evaluated, with a normalized root mean squared error of 37.3% ± 4.2%, susceptibility-tuned structured similarity index measure of 0.823 ± 0.02, high-frequency error norm of 37.0 ± 5.7, and peak signal-to-noise ratio of 42.8 ± 1.1. SS-POCSnet also reduced the underestimations of susceptibility values in deep brain nuclei compared with those from the other models evaluated. Furthermore, SS-POCSnet was sensitive to CMB/calcification and MS lesions, demonstrating its clinical applicability. Our method also supported variable imaging parameters, including matrix size and resolution. It was concluded that deep learning-regularized, single-step QSM quantification can mitigate underestimating susceptibility values in deep brain nuclei.
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Affiliation(s)
- Zuojun Wang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Henry Ka-Fung Mak
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Peng Cao
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
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22
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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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23
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Quantitative Susceptibility Mapping in Cognitive Decline: A Review of Technical Aspects and Applications. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10095-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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24
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Tranfa M, Pontillo G, Petracca M, Brunetti A, Tedeschi E, Palma G, Cocozza S. Quantitative MRI in Multiple Sclerosis: From Theory to Application. AJNR Am J Neuroradiol 2022; 43:1688-1695. [PMID: 35680161 DOI: 10.3174/ajnr.a7536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 02/22/2022] [Indexed: 02/01/2023]
Abstract
Quantitative MR imaging techniques allow evaluating different aspects of brain microstructure, providing meaningful information about the pathophysiology of damage in CNS disorders. In the study of patients with MS, quantitative MR imaging techniques represent an invaluable tool for studying changes in myelin and iron content occurring in the context of inflammatory and neurodegenerative processes. In the first section of this review, we summarize the physics behind quantitative MR imaging, here defined as relaxometry and quantitative susceptibility mapping, and describe the neurobiological correlates of quantitative MR imaging findings. In the second section, we focus on quantitative MR imaging application in MS, reporting the main findings in both the gray and white matter compartments, separately addressing macroscopically damaged and normal-appearing parenchyma.
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Affiliation(s)
- M Tranfa
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.)
| | - G Pontillo
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.) .,Electrical Engineering and Information Technology (G. Pontillo), University of Naples "Federico II," Naples, Italy
| | - M Petracca
- Department of Human Neurosciences (M.P.), Sapienza University of Rome, Rome, Italy
| | - A Brunetti
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.)
| | - E Tedeschi
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.)
| | - G Palma
- Institute of Nanotechnology (G. Palma), National Research Council, Lecce, Italy
| | - S Cocozza
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.)
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25
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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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26
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Zachariou V, Bauer CE, Pappas C, Gold BT. High cortical iron is associated with the disruption of white matter tracts supporting cognitive function in healthy older adults. Cereb Cortex 2022; 33:4815-4828. [PMID: 36182267 PMCID: PMC10110441 DOI: 10.1093/cercor/bhac382] [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: 07/11/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 01/25/2023] Open
Abstract
Aging is associated with brain iron accumulation, which has been linked to cognitive decline. However, how brain iron affects the structure and function of cognitive brain networks remains unclear. Here, we explored the possibility that iron load in gray matter is associated with disruption of white matter (WM) microstructure within a network supporting cognitive function, in a cohort of 95 cognitively normal older adults (age range: 60-86). Functional magnetic resonance imaging was used to localize a set of brain regions involved in working memory and diffusion tensor imaging based probabilistic tractography was used to identify a network of WM tracts connecting the functionally defined regions. Brain iron concentration within these regions was evaluated using quantitative susceptibility mapping and microstructural properties were assessed within the identified tracts using neurite orientation dispersion and density imaging. Results indicated that high brain iron concentration was associated with low neurite density (ND) within the task-relevant WM network. Further, regional associations were observed such that brain iron in cortical regions was linked with lower ND in neighboring but not distant WM tracts. Our results provide novel evidence suggesting that age-related increases in brain iron concentration are associated with the disruption of WM tracts supporting cognitive function in normal aging.
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Affiliation(s)
- Valentinos Zachariou
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States
| | - Christopher E Bauer
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States
| | - Colleen Pappas
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States
| | - Brian T Gold
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States.,Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536-0298, United States.,Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY 40536-0298, United States
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27
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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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Margoni M, Pagani E, Meani A, Storelli L, Mesaros S, Drulovic J, Barkhof F, Vrenken H, Strijbis E, Gallo A, Bisecco A, Pareto D, Sastre-Garriga J, Ciccarelli O, Yiannakas M, Palace J, Preziosa P, Rocca MA, Filippi M. Exploring in vivo multiple sclerosis brain microstructural damage through T1w/T2w ratio: a multicentre study. J Neurol Neurosurg Psychiatry 2022; 93:741-752. [PMID: 35580993 DOI: 10.1136/jnnp-2022-328908] [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] [Received: 01/20/2022] [Accepted: 03/29/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To evaluate white matter and grey matter T1-weighted (w)/T2w ratio (T1w/T2w ratio) in healthy controls and patients with multiple sclerosis, and its association with clinical disability. METHODS In this cross-sectional study, 270 healthy controls and 434 patients with multiple sclerosis were retrospectively selected from 7 European sites. T1w/T2w ratio was obtained from brain T2w and T1w scans after intensity calibration using eyes and temporal muscle. RESULTS In healthy controls, T1w/T2w ratio increased until 50-60 years both in white and grey matter. Compared with healthy controls, T1w/T2w ratio was significantly lower in white matter lesions of all multiple sclerosis phenotypes, and in normal-appearing white matter and cortex of patients with relapsing-remitting and secondary progressive multiple sclerosis (p≤0.026), but it was significantly higher in the striatum and pallidum of patients with relapsing-remitting, secondary progressive and primary progressive multiple sclerosis (p≤0.042). In relapse-onset multiple sclerosis, T1w/T2w ratio was significantly lower in white matter lesions and normal-appearing white matter already at Expanded Disability Status Scale (EDSS) <3.0 and in the cortex only for EDSS ≥3.0 (p≤0.023). Conversely, T1w/T2w ratio was significantly higher in the striatum and pallidum for EDSS ≥4.0 (p≤0.005). In primary progressive multiple sclerosis, striatum and pallidum showed significantly higher T1w/T2w ratio beyond EDSS=6.0 (p≤0.001). In multiple sclerosis, longer disease duration, higher EDSS, higher brain lesional volume and lower normalised brain volume were associated with lower lesional and cortical T1w/T2w ratio and a higher T1w/T2w ratio in the striatum and pallidum (β from -1.168 to 0.286, p≤0.040). CONCLUSIONS T1w/T2w ratio may represent a clinically relevant marker sensitive to demyelination, neurodegeneration and iron accumulation occurring at the different multiple sclerosis phases.
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Affiliation(s)
- Monica Margoni
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alessandro Meani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Loredana Storelli
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Sarlota Mesaros
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Beograd, Serbia
| | - Jelena Drulovic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Beograd, Serbia
| | - Frederik Barkhof
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,MS Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, UK
| | - Hugo Vrenken
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,MS Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Eva Strijbis
- MS Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Antonio Gallo
- Department of Advanced Medical and Surgical Sciences, and 3T MRI-Center, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Alvino Bisecco
- Department of Advanced Medical and Surgical Sciences, and 3T MRI-Center, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Jaume Sastre-Garriga
- Department of Neurology/Neuroimmunology, Multiple Sclerosis Centre of Catalonia, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Olga Ciccarelli
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, UK
| | - Marios Yiannakas
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, UK
| | - Jacqueline Palace
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy.,Vita-Salute San Raffaele University, Milano, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy .,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy.,Vita-Salute San Raffaele University, Milano, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
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29
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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: 44] [Impact Index Per Article: 22.0] [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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30
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Chen Y, Genc O, Poynton CB, Banerjee S, Hess CP, Lupo JM. Comparison of quantitative susceptibility mapping methods on evaluating radiation-induced cerebral microbleeds and basal ganglia at 3T and 7T. NMR IN BIOMEDICINE 2022; 35:e4666. [PMID: 35075701 PMCID: PMC10443943 DOI: 10.1002/nbm.4666] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 11/04/2021] [Accepted: 11/24/2021] [Indexed: 06/14/2023]
Abstract
Quantitative susceptibility mapping (QSM) has the potential for being a biomarker for various diseases because of its ability to measure tissue susceptibility related to iron deposition, myelin, and hemorrhage from the phase signal of a T2 *-weighted MRI. Despite its promise as a quantitative marker, QSM is faced with many challenges, including its dependence on preprocessing of the raw phase data, the relatively weak tissue signal, and the inherently ill posed relationship between the magnetic dipole and measured phase. The goal of this study was to evaluate the effects of background field removal and dipole inversion algorithms on noise characteristics, image uniformity, and structural contrast for cerebral microbleed (CMB) quantification at both 3T and 7T. We selected four widely used background phase removal and five dipole field inversion algorithms for QSM and applied them to volunteers and patients with CMBs, who were scanned at two different field strengths, with ground truth QSM reference calculated using multiple orientation scans. 7T MRI provided QSM images with lower noise than did 3T MRI. QSIP and VSHARP + iLSQR achieved the highest white matter homogeneity and vein contrast, with QSIP also providing the highest CMB contrast. Compared with ground truth COSMOS QSM images, overall good correlations between susceptibility values of dipole inversion algorithms and the COSMOS reference were observed in basal ganglia regions, with VSHARP + iLSQR achieving the susceptibility values most similar to COSMOS across all regions. This study can provide guidance for selecting the most appropriate QSM processing pipeline based on the application of interest and scanner field strength.
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Affiliation(s)
- Yicheng Chen
- UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley and San Francisco, CA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA
| | - Ozan Genc
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA
- Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Turkey
| | - Clare B. Poynton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA
| | | | - Christopher P. Hess
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA
- Department of Neurology, University of California, San Francisco, CA
| | - Janine M. Lupo
- UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley and San Francisco, CA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA
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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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32
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Rosenblum SL, Kosman DJ. Aberrant Cerebral Iron Trafficking Co-morbid With Chronic Inflammation: Molecular Mechanisms and Pharmacologic Intervention. Front Neurol 2022; 13:855751. [PMID: 35370907 PMCID: PMC8964494 DOI: 10.3389/fneur.2022.855751] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 02/14/2022] [Indexed: 12/24/2022] Open
Abstract
The redox properties that make iron an essential nutrient also make iron an efficient pro-oxidant. Given this nascent cytotoxicity, iron homeostasis relies on a combination of iron transporters, chaperones, and redox buffers to manage the non-physiologic aqueous chemistry of this first-row transition metal. Although a mechanistic understanding of the link between brain iron accumulation (BIA) and neurodegenerative diseases is lacking, BIA is co-morbid with the majority of cognitive and motor function disorders. The most prevalent neurodegenerative disorders, including Alzheimer's Disease (AD), Parkinson's Disease (PD), Multiple System Atrophy (MSA), and Multiple Sclerosis (MS), often present with increased deposition of iron into the brain. In addition, ataxias that are linked to mutations in mitochondrial-localized proteins (Friedreich's Ataxia, Spinocerebellar Ataxias) result in mitochondrial iron accumulation and degradation of proton-coupled ATP production leading to neuronal degeneration. A comorbidity common in the elderly is a chronic systemic inflammation mediated by primary cytokines released by macrophages, and acute phase proteins (APPs) released subsequently from the liver. Abluminal inflammation in the brain is found downstream as a result of activation of astrocytes and microglia. Reasonably, the iron that accumulates in the brain comes from the cerebral vasculature via the microvascular capillary endothelial cells whose tight junctions represent the blood-brain barrier. A premise amenable to experimental interrogation is that inflammatory stress alters both the trans- and para-cellular flux of iron at this barrier resulting in a net accumulation of abluminal iron over time. This review will summarize the evidence that lends support to this premise; indicate the mechanisms that merit delineation; and highlight possible therapeutic interventions based on this model.
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33
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Mirza AI, Zhu F, Knox N, Forbes JD, Van Domselaar G, Bernstein CN, Graham M, Marrie RA, Hart J, Yeh EA, Arnold DL, Bar-Or A, O'Mahony J, Zhao Y, Hsiao W, Banwell B, Waubant E, Tremlett H. Metagenomic Analysis of the Pediatric-Onset Multiple Sclerosis Gut Microbiome. Neurology 2022; 98:e1050-e1063. [PMID: 34937787 PMCID: PMC8967388 DOI: 10.1212/wnl.0000000000013245] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 12/13/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Little is known of the functional potential of the gut microbiome in pediatric-onset multiple sclerosis (MS). We performed metagenomic analyses using stool samples from individuals with pediatric-onset MS and unaffected controls. METHODS Persons ≤21 years old enrolled in the Canadian Pediatric Demyelinating Disease Network providing a stool sample were eligible. Twenty patients with MS (McDonald criteria) with symptom onset <18 years were matched to 20 controls by sex, age (±3 years), stool consistency, and race. Microbial taxonomy and functional potentials were estimated from stool sample-derived metagenomic reads and compared by disease status (MS vs controls) and disease-modifying drug (DMD) exposure using alpha diversity, relative abundance, and prevalence using Wilcoxon rank sum, ALDEx2, and Fisher exact tests, respectively. RESULTS Individuals with MS were aged 13.6 years (mean) at symptom onset and 8 were DMD-naive. Mean ages at stool sample were 16.1 and 15.4 years for MS and control participants, respectively; 80% were girls. Alpha diversity of enzymes and proteins did not differ by disease or DMD status (p > 0.20), but metabolic pathways, gene annotations, and microbial taxonomy did. Individuals with MS (vs controls) exhibited higher methanogenesis prevalence (odds ratio 10, p = 0.044) and Methanobrevibacter abundance (log2 fold change [LFC] 1.7, p = 0.0014), but lower homolactic fermentation abundance (LFC -0.48, p = 0.039). Differences by DMD status included lower phosphate butyryl transferase for DMD-naive vs exposed patients with MS (LFC -1.0, p = 0.033). DISCUSSION The gut microbiome's functional potential and taxonomy differed between individuals with pediatric-onset MS vs controls, including higher prevalence of a methane-producing pathway from Archaea and depletion of the lactate fermentation pathway. DMD exposure was associated with butyrate-producing enzyme enrichment. Together these findings indicate that the gut microbiome of individuals with MS may have a disturbed functional potential.
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Affiliation(s)
- Ali I Mirza
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Feng Zhu
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Natalie Knox
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Jessica D Forbes
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Gary Van Domselaar
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Charles N Bernstein
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Morag Graham
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Ruth Ann Marrie
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Janace Hart
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - E Ann Yeh
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Douglas L Arnold
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Amit Bar-Or
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Julia O'Mahony
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Yinshan Zhao
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - William Hsiao
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Brenda Banwell
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Emmanuelle Waubant
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA
| | - Helen Tremlett
- From the Department of Medicine (Neurology) (A.I.M., F.Z., Y.Z., H.T.), The University of British Columbia, Vancouver; National Microbiology Laboratory (N.K., G.V.D., M.G.), Public Health Agency of Canada; Department of Medical Microbiology and Infectious Diseases (N.K., G.V.D., M.G.), Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences (C.N.B., R.A.M.), and Inflammatory Bowel Disease Clinical and Research Centre (C.N.B.), University of Manitoba, Winnipeg; Roy Romanow Provincial Laboratory (J.D.F.), Regina; Department of Pathology and Laboratory Medicine (J.D.F.), College of Medicine, University of Saskatchewan, Saskatoon, Canada; Department of Neurology (J.H., E.W.), University of California San Francisco; Department of Pediatrics (Neurology) (E.A.Y., J.O.), The Hospital for Sick Children, Toronto; Department of Neurology and Neurosurgery (D.L.A.), Montreal Neurological Institute, McGill University, Montreal, Canada; Centre for Neuroinflammation and Experimental Therapeutics and Department of Neurology (A.B.-O.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Faculty of Health Sciences (W.H.), Simon Fraser University, Burnaby, Canada; and The Children's Hospital of Philadelphia (B.B.), PA.
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Kamma E, Lasisi W, Libner C, Ng HS, Plemel JR. Central nervous system macrophages in progressive multiple sclerosis: relationship to neurodegeneration and therapeutics. J Neuroinflammation 2022; 19:45. [PMID: 35144628 PMCID: PMC8830034 DOI: 10.1186/s12974-022-02408-y] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 01/31/2022] [Indexed: 02/08/2023] Open
Abstract
There are over 15 disease-modifying drugs that have been approved over the last 20 years for the treatment of relapsing–remitting multiple sclerosis (MS), but there are limited treatment options available for progressive MS. The development of new drugs for the treatment of progressive MS remains challenging as the pathophysiology of progressive MS is poorly understood. The progressive phase of MS is dominated by neurodegeneration and a heightened innate immune response with trapped immune cells behind a closed blood–brain barrier in the central nervous system. Here we review microglia and border-associated macrophages, which include perivascular, meningeal, and choroid plexus macrophages, during the progressive phase of MS. These cells are vital and are largely the basis to define lesion types in MS. We will review the evidence that reactive microglia and macrophages upregulate pro-inflammatory genes and downregulate homeostatic genes, that may promote neurodegeneration in progressive MS. We will also review the factors that regulate microglia and macrophage function during progressive MS, as well as potential toxic functions of these cells. Disease-modifying drugs that solely target microglia and macrophage in progressive MS are lacking. The recent treatment successes for progressive MS include include B-cell depletion therapies and sphingosine-1-phosphate receptor modulators. We will describe several therapies being evaluated as a potential treatment option for progressive MS, such as immunomodulatory therapies that can target myeloid cells or as a potential neuroprotective agent.
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Affiliation(s)
- Emily Kamma
- Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Wendy Lasisi
- Recovery and Performance Laboratory, Faculty of Medicine, Memorial University of Newfoundland, Saint John's, NL, Canada
| | - Cole Libner
- Department of Health Sciences and the Office of the Saskatchewan Multiple Sclerosis Clinical Research Chair, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Huah Shin Ng
- Division of Neurology and the Djavad Mowafaghian Centre for Brain Health, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Jason R Plemel
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, AB, Canada. .,Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada. .,Department of Medical Microbiology and Immunology, University of Alberta, Edmonton, AB, Canada. .,University of Alberta, 5-64 Heritage Medical Research Centre, Edmonton, AB, T6G2S2, Canada.
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A Distinct Hibiscus sabdariffa Extract Prevents Iron Neurotoxicity, a Driver of Multiple Sclerosis Pathology. Cells 2022; 11:cells11030440. [PMID: 35159249 PMCID: PMC8834068 DOI: 10.3390/cells11030440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/23/2022] [Accepted: 01/25/2022] [Indexed: 02/04/2023] Open
Abstract
Iron deposition in the brain begins early in multiple sclerosis (MS) and continues unabated. Ferrous iron is toxic to neurons, yet the therapies used in MS do not counter iron neurotoxicity. Extracts of Hibiscus sabdariffa (HS) are used in many cultures for medicinal purposes. We collected a distinct HS extract and found that it abolished the killing of neurons by iron in culture; medications used in MS were ineffective when similarly tested. Neuroprotection by HS was not due to iron chelation or anthocyanin content. In free radical scavenging assays, HS was equipotent to alpha lipoic acid, an anti-oxidant being tested in MS. However, alpha lipoic acid was only modestly protective against iron-mediated killing. Moreover, a subfraction of HS without radical scavenging activity negated iron toxicity, whereas a commercial hibiscus preparation with anti-oxidant activity could not. The idea that HS might have altered properties within neurons to confer neuroprotection is supported by its amelioration of toxicity caused by other toxins: beta-amyloid, rotenone and staurosporine. Finally, in a mouse model of MS, HS reduced disability scores and ameliorated the loss of axons in the spinal cord. HS holds therapeutic potential to counter iron neurotoxicity, an unmet need that drives the progression of disability in MS.
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Starčević Čizmarević N, Ćurko-Cofek B, Barac-Latas V, Peterlin B, Ristić S. Lack of association between C282Y and H63D polymorphisms in the hemochromatosis gene and risk of multiple sclerosis: A meta-analysis. Biomed Rep 2022; 16:12. [PMID: 34987796 DOI: 10.3892/br.2021.1495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/09/2021] [Indexed: 12/27/2022] Open
Abstract
Increasing evidence supports the potential role of iron metabolism in multiple sclerosis (MS). Previous studies examining the association between polymorphisms of the hemochromatosis gene (HFE) and susceptibility to MS have yielded inconsistent results. In the present study, a meta-analysis of 7 studies was performed conducted in populations of Caucasian origin using the Comprehensive Meta-analysis 3.0 software. The strength of association between the C282Y and H63D polymorphisms in HFE and MS risk was estimated by odds ratios with 95% confidence intervals. Cochran's Q statistic and I2 tests were applied to quantify heterogeneity between studies. An Egger's test was used to estimate publication bias. The C282Y and H63D polymorphisms had no significant association with increased MS risk (all P≥0.05) in the following genetic comparison models: Dominant model (YY + CY vs. CC or DD + HD vs. HH) and allele contrast (Y vs. C or D vs. H). No apparent publication bias or significant heterogeneity was found between studies. These results suggest that the HFE polymorphisms C282Y and H63D are not associated with susceptibility to MS in populations of Caucasian origin. Further studies should be performed in a larger series of MS patients to evaluate the contribution of HFE and other genetic variants associated with iron regulation in the development and progression of MS.
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Affiliation(s)
- Nada Starčević Čizmarević
- Department of Medical Biology and Genetics, Faculty of Medicine, University of Rijeka, 51000 Rijeka, Croatia
| | - Božena Ćurko-Cofek
- Department of Physiology and Immunology, Faculty of Medicine, University of Rijeka, 51000 Rijeka, Croatia
| | - Vesna Barac-Latas
- Department of Physiology and Immunology, Faculty of Medicine, University of Rijeka, 51000 Rijeka, Croatia
| | - Borut Peterlin
- Clinical Institute of Genomic Medicine, University Medical Centre Ljubljana, 1000 Ljubljana, Slovenia
| | - Smiljana Ristić
- Department of Medical Biology and Genetics, Faculty of Medicine, University of Rijeka, 51000 Rijeka, Croatia
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Zhang M, Li Z, Wang H, Chen T, Lu Y, Yan F, Zhang Y, Wei H. Simultaneous Quantitative Susceptibility Mapping of Articular Cartilage and Cortical Bone of Human Knee Joint Using Ultrashort Echo Time Sequences. Front Endocrinol (Lausanne) 2022; 13:844351. [PMID: 35273576 PMCID: PMC8901574 DOI: 10.3389/fendo.2022.844351] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 01/31/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND It is of great clinical importance to assess the microstructure of the articular cartilage and cortical bone of the human knee joint. While quantitative susceptibility mapping (QSM) is a promising tool for investigating the knee joint, however, previous QSM studies using conventional gradient recalled echo sequences or ultrashort echo time (UTE) sequences only focused on mapping the magnetic susceptibility of the articular cartilage or cortical bone, respectively. Simultaneously mapping the underlying susceptibilities of the articular cartilage and cortical bone of human in vivo has not been explored and reported. METHOD Three-dimensional multi-echo radial UTE sequences with the shortest TE of 0.07 msec and computed tomography (CT) were performed on the bilateral knee joints of five healthy volunteers for this prospective study. UTE-QSM was reconstructed from the local field map after water-fat separation and background field removal. Spearman's correlation analysis was used to explore the relationship between the magnetic susceptibility and CT values in 158 representative regions of interest of cortical bone. RESULT The susceptibility properties of the articular cartilage and cortical bone were successfully quantified by UTE-QSM. The laminar structure of articular cartilage was characterized by the difference of susceptibility value in each layer. Susceptibility was mostly diamagnetic in cortical bone. A significant negative correlation (r=-0.43, p<0.001) between the susceptibility value and CT value in cortical bone was observed. CONCLUSION UTE-QSM enables simultaneous susceptibility mapping of the articular cartilage and cortical bone of human in vivo. Good association between susceptibility and CT values in cortical bone suggests the potential of UTE-QSM for bone mapping for further clinical application.
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Affiliation(s)
- Ming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhihui Li
- Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Hanqi Wang
- Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Tongtong Chen
- Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yong Lu
- Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuyao Zhang
- School of Information and Science and Technology, ShanghaiTech University, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Hongjiang Wei,
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Eliseeva D, Zakharova M. Mechanisms of Neurodegeneration in Multiple Sclerosis. Zh Nevrol Psikhiatr Im S S Korsakova 2022; 122:5-13. [DOI: 10.17116/jnevro20221220725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Yan Z, Liu H, Chen X, Zheng Q, Zeng C, Zheng Y, Ding S, Peng Y, Li Y. Quantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum Disorder. Front Neurosci 2021; 15:765634. [PMID: 34924934 PMCID: PMC8678528 DOI: 10.3389/fnins.2021.765634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 11/01/2021] [Indexed: 11/21/2022] Open
Abstract
Objectives: To implement a machine learning model using radiomic features extracted from quantitative susceptibility mapping (QSM) in discriminating multiple sclerosis (MS) from neuromyelitis optica spectrum disorder (NMOSD). Materials and Methods: Forty-seven patients with MS (mean age = 40.00 ± 13.72 years) and 36 patients with NMOSD (mean age = 42.14 ± 12.34 years) who underwent enhanced gradient-echo T2*-weighted angiography (ESWAN) sequence in 3.0-T MRI were included between April 2017 and October 2019. QSM images were reconstructed from ESWAN, and QSM-derived radiomic features were obtained from seven regions of interest (ROIs), including bilateral putamen, globus pallidus, head of the caudate nucleus, thalamus, substantia nigra, red nucleus, and dentate nucleus. A machine learning model (logistic regression) was applied to classify MS and NMOSD, which combined radiomic signatures and demographic information to assess the classification accuracy using the area under the receiver operating characteristic (ROC) curve (AUC). Results: The radiomics-only models showed better discrimination performance in almost all deep gray matter (DGM) regions than the demographic information-only model, with the highest AUC in DN of 0.902 (95% CI: 0.840–0.955). Moreover, the hybrid model combining radiomic signatures and demographic information showed the highest discrimination performance which achieved the AUC of 0.927 (95% CI: 0.871–0.984) with fivefold cross-validation. Conclusion: The hybrid model based on QSM and powered with machine learning has the potential to discriminate MS from NMOSD.
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Affiliation(s)
- Zichun Yan
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | | | - Xiaoya Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qiao Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chun Zeng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yineng Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Shuang Ding
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuling Peng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Cortese R, Giorgio A, Severa G, De Stefano N. MRI Prognostic Factors in Multiple Sclerosis, Neuromyelitis Optica Spectrum Disorder, and Myelin Oligodendrocyte Antibody Disease. Front Neurol 2021; 12:679881. [PMID: 34867701 PMCID: PMC8636325 DOI: 10.3389/fneur.2021.679881] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 10/08/2021] [Indexed: 11/25/2022] Open
Abstract
Several MRI measures have been developed in the last couple of decades, providing a number of imaging biomarkers that can capture the complexity of the pathological processes occurring in multiple sclerosis (MS) brains. Such measures have provided more specific information on the heterogeneous pathologic substrate of MS-related tissue damage, being able to detect, and quantify the evolution of structural changes both within and outside focal lesions. In clinical practise, MRI is increasingly used in the MS field to help to assess patients during follow-up, guide treatment decisions and, importantly, predict the disease course. Moreover, the process of identifying new effective therapies for MS patients has been supported by the use of serial MRI examinations in order to sensitively detect the sub-clinical effects of disease-modifying treatments at an earlier stage than is possible using measures based on clinical disease activity. However, despite this has been largely demonstrated in the relapsing forms of MS, a poor understanding of the underlying pathologic mechanisms leading to either progression or tissue repair in MS as well as the lack of sensitive outcome measures for the progressive phases of the disease and repair therapies makes the development of effective treatments a big challenge. Finally, the role of MRI biomarkers in the monitoring of disease activity and the assessment of treatment response in other inflammatory demyelinating diseases of the central nervous system, such as neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte antibody disease (MOGAD) is still marginal, and advanced MRI studies have shown conflicting results. Against this background, this review focused on recently developed MRI measures, which were sensitive to pathological changes, and that could best contribute in the future to provide prognostic information and monitor patients with MS and other inflammatory demyelinating diseases, in particular, NMOSD and MOGAD.
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Affiliation(s)
- Rosa Cortese
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Antonio Giorgio
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Gianmarco Severa
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
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Mechanism-based criteria to improve therapeutic outcomes in progressive multiple sclerosis. Nat Rev Neurol 2021; 18:40-55. [PMID: 34732831 DOI: 10.1038/s41582-021-00581-x] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/11/2021] [Indexed: 02/07/2023]
Abstract
In contrast to the multiple disease-modifying therapies that are available for relapsing-remitting multiple sclerosis (MS), the therapeutic options for progressive MS (PMS) are limited. Recent advances in our understanding of the neuroimmunology of PMS, including the mechanisms that drive slowly expanding lesions, have fuelled optimism for improved treatment of this condition. In this Review, we highlight the commonly observed neuropathology of PMS and discuss the associated mechanisms of CNS injury. We then apply this knowledge to formulate criteria for therapeutic efficacy in PMS, beginning with the need for early treatment owing to the substantial neuropathology that is already present at the initial clinical presentation. Other requirements include: antagonism of neuroaxonal injury mediators such as pro-inflammatory microglia and lymphocytes; remediation of oxidative stress resulting from iron deposition and mitochondrial dysfunction; and promotion of neuroprotection through remyelination. We consider whether current disease-modifying therapies for relapsing-remitting MS meet the criteria for successful therapeutics in PMS and suggest that the evidence favours the early introduction of sphingosine 1-phosphate receptor modulators. Finally, we weigh up emerging medications, including repurposed generic medications and Bruton's tyrosine kinase inhibitors, against these fundamental criteria. In this new therapeutic era in PMS, success depends collectively on understanding disease mechanisms, drug characteristics (including brain penetration) and rational use.
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Kames C, Doucette J, Birkl C, Rauscher A. Recovering SWI-filtered phase data using deep learning. Magn Reson Med 2021; 87:948-959. [PMID: 34611931 DOI: 10.1002/mrm.29013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 08/27/2021] [Accepted: 08/30/2021] [Indexed: 11/07/2022]
Abstract
PURPOSE To develop a deep neural network to recover filtered phase from clinical MR phase images to enable the computation of QSMs. METHODS Eighteen deep learning networks were trained to recover combinations of 13 SWI phase-filtering pipelines. SWI-filtered data were computed offline from five multiorientation, multiecho MRI scans yielding 132 3D volumes (118/7/7 training/validation/testing). Two experiments were conducted to show the efficacy of the networks. First, using QSM processing, local fields were computed from the raw phase and subsequently filtered using the SWI-filtering pipelines. The networks were then trained to invert the filtering operation. Second, the trained networks were fine-tuned to recover unfiltered local fields from filtered local fields computed by applying QSM processing to the SWI-filtered phase. Susceptibility maps were computed from the recovered fields and compared with gold standard multiple orientation sampling reconstructions. RESULTS Susceptibility maps computed from the raw phase using standard QSM processing have a normalized root mean square error (NRMSE) of 0.732 ± 0.095. Susceptibility maps computed from the recovered phase obtained NRMSEs of 0.725 ± 0.095. The network trained using all 13 processing methods generalized well, obtaining NRMSEs of 0.725 ± 0.89 on filters it has not seen, while matching the reconstruction accuracy of networks trained to recover a single filter. CONCLUSION It is feasible to recover SWI-filtered phase using deep learning. QSM can be computed from the recovered phase from SWI acquisition with comparable accuracy to standard QSM processing.
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Affiliation(s)
- Christian Kames
- UBC MRI Research Centre, The University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Jonathan Doucette
- UBC MRI Research Centre, The University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Christoph Birkl
- UBC MRI Research Centre, The University of British Columbia, Vancouver, British Columbia, Canada.,Department of Neuroradiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Alexander Rauscher
- UBC MRI Research Centre, The University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, The University of British Columbia, Vancouver, British Columbia, Canada.,Department of Pediatrics, The University of British Columbia, Vancouver, British Columbia, Canada
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Tozlu C, Jamison K, Nguyen T, Zinger N, Kaunzner U, Pandya S, Wang Y, Gauthier S, Kuceyeski A. Structural disconnectivity from paramagnetic rim lesions is related to disability in multiple sclerosis. Brain Behav 2021; 11:e2353. [PMID: 34498432 PMCID: PMC8553317 DOI: 10.1002/brb3.2353] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/28/2021] [Accepted: 08/19/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND In people with multiple sclerosis (pwMS), lesions with a hyperintense rim (rim+) on Quantitative Susceptibility Mapping (QSM) have been shown to have greater myelin damage compared to rim- lesions, but their association with disability has not yet been investigated. Furthermore, how QSM rim+ and rim- lesions differentially impact disability through their disruptions to structural connectivity has not been explored. We test the hypothesis that structural disconnectivity due to rim+ lesions is more predictive of disability compared to structural disconnectivity due to rim- lesions. METHODS Ninety-six pwMS were included in our study. Individuals with Expanded Disability Status Scale (EDSS) <2 were considered to have lower disability (n = 59). For each gray matter region, a Change in Connectivity (ChaCo) score, that is, the percent of connecting streamlines also passing through a rim- or rim+ lesion, was computed. Adaptive Boosting was used to classify the pwMS into lower versus greater disability groups based on ChaCo scores from rim+ and rim- lesions. Classification performance was assessed using the area under ROC curve (AUC). RESULTS The model based on ChaCo from rim+ lesions outperformed the model based on ChaCo from rim- lesions (AUC = 0.67 vs 0.63, p-value < .05). The left thalamus and left cerebellum were the most important regions in classifying pwMS into disability categories. CONCLUSION rim+ lesions may be more influential on disability through their disruptions to the structural connectome than rim- lesions. This study provides a deeper understanding of how rim+ lesion location/size and resulting disruption to the structural connectome can contribute to MS-related disability.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Thanh Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Nicole Zinger
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Ulrike Kaunzner
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Sneha Pandya
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Susan Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
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44
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Pontillo G, Tommasin S, Cuocolo R, Petracca M, Petsas N, Ugga L, Carotenuto A, Pozzilli C, Iodice R, Lanzillo R, Quarantelli M, Brescia Morra V, Tedeschi E, Pantano P, Cocozza S. A Combined Radiomics and Machine Learning Approach to Overcome the Clinicoradiologic Paradox in Multiple Sclerosis. AJNR Am J Neuroradiol 2021; 42:1927-1933. [PMID: 34531195 DOI: 10.3174/ajnr.a7274] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 07/12/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Conventional MR imaging explains only a fraction of the clinical outcome variance in multiple sclerosis. We aimed to evaluate machine learning models for disability prediction on the basis of radiomic, volumetric, and connectivity features derived from routine brain MR images. MATERIALS AND METHODS In this retrospective cross-sectional study, 3T brain MR imaging studies of patients with multiple sclerosis, including 3D T1-weighted and T2-weighted FLAIR sequences, were selected from 2 institutions. T1-weighted images were processed to obtain volume, connectivity score (inferred from the T2 lesion location), and texture features for an atlas-based set of GM regions. The site 1 cohort was randomly split into training (n = 400) and test (n = 100) sets, while the site 2 cohort (n = 104) constituted the external test set. After feature selection of clinicodemographic and MR imaging-derived variables, different machine learning algorithms predicting disability as measured with the Expanded Disability Status Scale were trained and cross-validated on the training cohort and evaluated on the test sets. The effect of different algorithms on model performance was tested using the 1-way repeated-measures ANOVA. RESULTS The selection procedure identified the 9 most informative variables, including age and secondary-progressive course and a subset of radiomic features extracted from the prefrontal cortex, subcortical GM, and cerebellum. The machine learning models predicted disability with high accuracy (r approaching 0.80) and excellent intra- and intersite generalizability (r ≥ 0.73). The machine learning algorithm had no relevant effect on the performance. CONCLUSIONS The multidimensional analysis of brain MR images, including radiomic features and clinicodemographic data, is highly informative of the clinical status of patients with multiple sclerosis, representing a promising approach to bridge the gap between conventional imaging and disability.
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Affiliation(s)
- G Pontillo
- From the Departments of Advanced Biomedical Sciences (G.P., L.U., E.T., S.C.).,Electrical Engineering and Information Technology (G.P., M.Q.)
| | - S Tommasin
- Department of Human Neuroscience (S.T., C.P., P.P.), Sapienza University of Rome, Rome, Italy
| | - R Cuocolo
- Clinical Medicine and Surgery (R.C.) .,Laboratory of Augmented Reality for Health Monitoring (R.C.)
| | - M Petracca
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - N Petsas
- Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Mediterraneo (N.P., P.P.), Pozzilli, Italy
| | - L Ugga
- From the Departments of Advanced Biomedical Sciences (G.P., L.U., E.T., S.C.)
| | - A Carotenuto
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - C Pozzilli
- Department of Human Neuroscience (S.T., C.P., P.P.), Sapienza University of Rome, Rome, Italy
| | - R Iodice
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - R Lanzillo
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - M Quarantelli
- Electrical Engineering and Information Technology (G.P., M.Q.).,Institute of Biostructure and Bioimaging (M.Q.), National Research Council, Naples, Italy
| | - V Brescia Morra
- Department of Electrical Engineering and Information Technology, and Department of Neurosciences and Reproductive and Odontostomatological Sciences (M.P., A.C., R.I., R.L., V.B.M.), University of Naples "Federico II," Naples, Italy
| | - E Tedeschi
- From the Departments of Advanced Biomedical Sciences (G.P., L.U., E.T., S.C.)
| | - P Pantano
- Department of Human Neuroscience (S.T., C.P., P.P.), Sapienza University of Rome, Rome, Italy.,Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Mediterraneo (N.P., P.P.), Pozzilli, Italy
| | - S Cocozza
- From the Departments of Advanced Biomedical Sciences (G.P., L.U., E.T., S.C.)
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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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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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Schneider TM, Ma J, Wagner P, Behl N, Nagel AM, Ladd ME, Heiland S, Bendszus M, Straub S. Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain. Front Neurosci 2021; 15:661504. [PMID: 34234639 PMCID: PMC8255625 DOI: 10.3389/fnins.2021.661504] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 05/17/2021] [Indexed: 12/02/2022] Open
Abstract
Objectives To characterize subcortical nuclei by multi-parametric quantitative magnetic resonance imaging. Materials and Methods: The following quantitative multiparametric MR data of five healthy volunteers were acquired on a 7T MRI system: 3D gradient echo (GRE) data for the calculation of quantitative susceptibility maps (QSM), GRE sequences with and without off-resonant magnetic transfer pulse for magnetization transfer ratio (MTR) calculation, a magnetization−prepared 2 rapid acquisition gradient echo sequence for T1 mapping, and (after a coil change) a density-adapted 3D radial pulse sequence for 23Na imaging. First, all data were co-registered to the GRE data, volumes of interest (VOIs) for 21 subcortical structures were drawn manually for each volunteer, and a combined voxel-wise analysis of the four MR contrasts (QSM, MTR, T1, 23Na) in each structure was conducted to assess the quantitative, MR value-based differentiability of structures. Second, a machine learning algorithm based on random forests was trained to automatically classify the groups of multi-parametric voxel values from each VOI according to their association to one of the 21 subcortical structures. Results The analysis of the integrated multimodal visualization of quantitative MR values in each structure yielded a successful classification among nuclei of the ascending reticular activation system (ARAS), the limbic system and the extrapyramidal system, while classification among (epi-)thalamic nuclei was less successful. The machine learning-based approach facilitated quantitative MR value-based structure classification especially in the group of extrapyramidal nuclei and reached an overall accuracy of 85% regarding all selected nuclei. Conclusion Multimodal quantitative MR enabled excellent differentiation of a wide spectrum of subcortical nuclei with reasonable accuracy and may thus enable sensitive detection of disease and nucleus-specific MR-based contrast alterations in the future.
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Affiliation(s)
- Till M Schneider
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Jackie Ma
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - Patrick Wagner
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - Nicolas Behl
- Division of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Armin M Nagel
- Division of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany.,Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Mark E Ladd
- Division of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany.,Faculty of Physics and Astronomy and Faculty of Medicine, University of Heidelberg, Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Sina Straub
- Division of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany
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48
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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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Li Y, Sethi SK, Zhang C, Miao Y, Yerramsetty KK, Palutla VK, Gharabaghi S, Wang C, He N, Cheng J, Yan F, Haacke EM. Iron Content in Deep Gray Matter as a Function of Age Using Quantitative Susceptibility Mapping: A Multicenter Study. Front Neurosci 2021; 14:607705. [PMID: 33488350 PMCID: PMC7815653 DOI: 10.3389/fnins.2020.607705] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 12/07/2020] [Indexed: 12/21/2022] Open
Abstract
PURPOSE To evaluate the effect of resolution on iron content using quantitative susceptibility mapping (QSM); to verify the consistency of QSM across field strengths and manufacturers in evaluating the iron content of deep gray matter (DGM) of the human brain using subjects from multiple sites; and to establish a susceptibility baseline as a function of age for each DGM structure using both a global and regional iron analysis. METHODS Data from 623 healthy adults, ranging from 20 to 90 years old, were collected across 3 sites using gradient echo imaging on one 1.5 Tesla and two 3.0 Tesla MR scanners. Eight subcortical gray matter nuclei were semi-automatically segmented using a full-width half maximum threshold-based analysis of the QSM data. Mean susceptibility, volume and total iron content with age correlations were evaluated for each measured structure for both the whole-region and RII (high iron content regions) analysis. For the purpose of studying the effect of resolution on QSM, a digitized model of the brain was applied. RESULTS The mean susceptibilities of the caudate nucleus (CN), globus pallidus (GP) and putamen (PUT) were not significantly affected by changing the slice thickness from 0.5 to 3 mm. But for small structures, the susceptibility was reduced by 10% for 2 mm thick slices. For global analysis, the mean susceptibility correlated positively with age for the CN, PUT, red nucleus (RN), substantia nigra (SN), and dentate nucleus (DN). There was a negative correlation with age in the thalamus (THA). The volumes of most nuclei were negatively correlated with age. Apart from the GP, THA, and pulvinar thalamus (PT), all the other structures showed an increasing total iron content despite the reductions in volume with age. For the RII regional high iron content analysis, mean susceptibility in most of the structures was moderately to strongly correlated with age. Similar to the global analysis, apart from the GP, THA, and PT, all structures showed an increasing total iron content. CONCLUSION A reasonable estimate for age-related iron behavior can be obtained from a large cross site, cross manufacturer set of data when high enough resolutions are used. These estimates can be used for correcting for age related iron changes when studying diseases like Parkinson's disease, Alzheimer's disease, and other iron related neurodegenerative diseases.
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Affiliation(s)
- Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sean K. Sethi
- Department of Radiology, Wayne State University, Detroit, MI, United States
- MR Innovations, Inc., Bingham Farms, MI, United States
- SpinTech, Inc., Bingham Farms, MI, United States
| | - Chunyan Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanwei Miao
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | | | | | | | - Chengyan Wang
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ewart Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Radiology, Wayne State University, Detroit, MI, United States
- MR Innovations, Inc., Bingham Farms, MI, United States
- SpinTech, Inc., Bingham Farms, MI, 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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