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Calabrese M, Preziosa P, Scalfari A, Colato E, Marastoni D, Absinta M, Battaglini M, De Stefano N, Di Filippo M, Hametner S, Howell OW, Inglese M, Lassmann H, Martin R, Nicholas R, Reynolds R, Rocca MA, Tamanti A, Vercellino M, Villar LM, Filippi M, Magliozzi R. Determinants and Biomarkers of Progression Independent of Relapses in Multiple Sclerosis. Ann Neurol 2024; 96:1-20. [PMID: 38568026 DOI: 10.1002/ana.26913] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/04/2024] [Accepted: 02/15/2024] [Indexed: 06/20/2024]
Abstract
Clinical, pathological, and imaging evidence in multiple sclerosis (MS) suggests that a smoldering inflammatory activity is present from the earliest stages of the disease and underlies the progression of disability, which proceeds relentlessly and independently of clinical and radiological relapses (PIRA). The complex system of pathological events driving "chronic" worsening is likely linked with the early accumulation of compartmentalized inflammation within the central nervous system as well as insufficient repair phenomena and mitochondrial failure. These mechanisms are partially lesion-independent and differ from those causing clinical relapses and the formation of new focal demyelinating lesions; they lead to neuroaxonal dysfunction and death, myelin loss, glia alterations, and finally, a neuronal network dysfunction outweighing central nervous system (CNS) compensatory mechanisms. This review aims to provide an overview of the state of the art of neuropathological, immunological, and imaging knowledge about the mechanisms underlying the smoldering disease activity, focusing on possible early biomarkers and their translation into clinical practice. ANN NEUROL 2024;96:1-20.
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Affiliation(s)
- Massimiliano Calabrese
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Paolo Preziosa
- 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
| | - Antonio Scalfari
- Centre of Neuroscience, Department of Medicine, Imperial College, London, UK
| | - Elisa Colato
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Damiano Marastoni
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Martina Absinta
- Translational Neuropathology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Battaglini
- Siena Imaging S.r.l., Siena, Italy
- 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
| | - Massimiliano Di Filippo
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Simon Hametner
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Owain W Howell
- Institute of Life Sciences, Swansea University Medical School, Swansea, UK
| | - Matilde Inglese
- Dipartimento di neuroscienze, riabilitazione, oftalmologia, genetica e scienze materno-infantili - DINOGMI, University of Genova, Genoa, Italy
| | - Hans Lassmann
- Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Roland Martin
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
- Therapeutic Design Unit, Center for Molecular Medicine, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
- Cellerys AG, Schlieren, Switzerland
| | - Richard Nicholas
- Department of Brain Sciences, Faculty of Medicine, Burlington Danes, Imperial College London, London, UK
| | - Richard Reynolds
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, London, UK
| | - 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
| | - Agnese Tamanti
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Marco Vercellino
- Multiple Sclerosis Center & Neurologia I U, Department of Neuroscience, University Hospital AOU Città della Salute e della Scienza di Torino, Turin, Italy
| | - Luisa Maria Villar
- Department of Immunology, Ramon y Cajal University Hospital. IRYCIS. REI, Madrid, Spain
| | - Massimo Filippi
- 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
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Roberta Magliozzi
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
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Huerta MM, Conway DS, Planchon SM, Thoomukuntla B, Se-Hong O, Sakaie KE, Ontaneda D, Nakamura K. Longitudinal myelin content measures of slowly expanding lesions using 7T MRI in multiple sclerosis. J Neuroimaging 2024. [PMID: 38778455 DOI: 10.1111/jon.13209] [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: 02/01/2024] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND AND PURPOSE Slowly expanding lesions (SELs) are thought to represent a subset of chronic active lesions and have been associated with clinical disability, severity, and disease progression. The purpose of this study was to characterize SELs using advanced magnetic resonance imaging (MRI) measures related to myelin and neurite density on 7 Tesla (T) MRI. METHODS The study design was retrospective, longitudinal, observational cohort with multiple sclerosis (n = 15). Magnetom 7T scanner was used to acquire magnetization-prepared 2 rapid acquisition gradient echo and advanced MRI including visualization of short transverse relaxation time component (ViSTa) for myelin, quantitative magnetization transfer (qMT) for myelin, and neurite orientation dispersion density imaging (NODDI). SELs were defined as lesions showing ≥12% of growth over 12 months on serial MRI. Comparisons of quantitative measures in SELs and non-SELs were performed at baseline and over time. Statistical analyses included two-sample t-test, analysis of variance, and mixed-effects linear model for MRI metrics between lesion types. RESULTS A total of 1075 lesions were evaluated. Two hundred twenty-four lesions (21%) were SELs, and 216 (96%) of the SELs were black holes. At baseline, compared to non-SELs, SELs showed significantly lower ViSTa (1.38 vs. 1.53, p < .001) and qMT (2.47 vs. 2.97, p < .001) but not in NODDI measures (p > .27). Longitudinally, only ViSTa showed a greater loss when comparing SEL and non-SEL (p = .03). CONCLUSIONS SELs have a lower myelin content relative to non-SELs without a difference in neurite measures. SELs showed a longitudinal decrease in apparent myelin water fraction reflecting greater tissue injury.
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Affiliation(s)
- Mina M Huerta
- School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Devon S Conway
- Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Sarah M Planchon
- Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | | | - Oh Se-Hong
- Department of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea
| | - Ken E Sakaie
- Imaging Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Kunio Nakamura
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio, USA
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Lee CH, Holloman M, Salzer JL, Zhang J. Multi-parametric MRI can detect enhanced myelination in the Gli1 -/- mouse brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.20.567957. [PMID: 38045415 PMCID: PMC10690149 DOI: 10.1101/2023.11.20.567957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
This study investigated the potential of combining multiple MR parameters to enhance the characterization of myelin in the mouse brain. We collected ex vivo multi-parametric MR data at 7 Tesla from control and Gli1 -/- mice; the latter exhibit enhanced myelination at postnatal day 10 (P10) in the corpus callosum and cortex. The MR data included relaxivity, magnetization transfer, and diffusion measurements, each targeting distinct myelin properties. This analysis was followed by and compared to myelin basic protein (MBP) staining of the same samples. Although a majority of the MR parameters included in this study showed significant differences in the corpus callosum between the control and Gli1 -/- mice, only T 2 , T 1 /T 2, and radial diffusivity (RD) demonstrated a significant correlation with MBP values. Based on data from the corpus callosum, partial least square regression suggested that combining T 2 , T 1 /T 2 , and inhomogeneous magnetization transfer ratio could explain approximately 80% of the variance in the MBP values. Myelin predictions based on these three parameters yielded stronger correlations with the MBP values in the P10 mouse brain corpus callosum than any single MR parameter. In the motor cortex, combining T 2 , T 1 /T 2, and radial kurtosis could explain over 90% of the variance in the MBP values at P10. This study demonstrates the utility of multi-parametric MRI in improving the detection of myelin changes in the mouse brain.
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Mishra S, Bapuraj J, Srinivasan A. Multiple Sclerosis Part 2: Advanced Imaging and Emerging Techniques. Magn Reson Imaging Clin N Am 2024; 32:221-231. [PMID: 38555138 DOI: 10.1016/j.mric.2024.01.002] [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: 04/02/2024]
Abstract
Multiple advanced imaging methods for multiple sclerosis (MS) have been in investigation to identify new imaging biomarkers for early disease detection, predicting disease prognosis, and clinical trial endpoints. Multiple techniques probing different aspects of tissue microstructure (ie, advanced diffusion imaging, magnetization transfer, myelin water imaging, magnetic resonance spectroscopy, glymphatic imaging, and perfusion) support the notion that MS is a global disease with microstructural changes evident in normal-appearing white and gray matter. These global changes are likely better predictors of disability compared with lesion load alone. Emerging techniques in glymphatic and molecular imaging may improve understanding of pathophysiology and emerging treatments.
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Affiliation(s)
- Shruti Mishra
- Department of Radiology, University of Michigan, 1500 East Medical Center Drive, UH B2A209, Ann Arbor, MI 48109-5030, USA.
| | - Jayapalli Bapuraj
- Department of Radiology, University of Michigan, 1500 East Medical Center Drive, UH B2A209, Ann Arbor, MI 48109-5030, USA
| | - Ashok Srinivasan
- Department of Radiology, University of Michigan, 1500 East Medical Center Drive, UH B2A209, Ann Arbor, MI 48109-5030, USA
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Bilgic B, Costagli M, Chan KS, Duyn J, Langkammer C, Lee J, Li X, Liu C, Marques JP, Milovic C, Robinson 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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Vanden Bulcke C, Stölting A, Maric D, Macq B, Absinta M, Maggi P. Comparative overview of multi-shell diffusion MRI models to characterize the microstructure of multiple sclerosis lesions and periplaques. Neuroimage Clin 2024; 42:103593. [PMID: 38520830 PMCID: PMC10978527 DOI: 10.1016/j.nicl.2024.103593] [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: 12/12/2023] [Revised: 03/01/2024] [Accepted: 03/16/2024] [Indexed: 03/25/2024]
Abstract
In multiple sclerosis (MS), accurate in vivo characterization of the heterogeneous lesional and extra-lesional tissue pathology remains challenging. Marshalling several advanced imaging techniques - quantitative relaxation time (T1) mapping, a model-free average diffusion signal approach and four multi-shell diffusion models - this study investigates the performance of multi-shell diffusion models and characterizes the microstructural damage within (i) different MS lesion types - active, chronic active, and chronic inactive - (ii) their respective periplaque white matter (WM), and (iii) the surrounding normal-appearing white matter (NAWM). In 83 MS participants (56 relapsing-remitting, 27 progressive) and 23 age and sex-matched healthy controls (HC), we analysed a total of 317 paramagnetic rim lesions (PRL+), 232 non-paramagnetic rim lesions (PRL-), 38 contrast-enhancing lesions (CEL). Consistent with previous findings and histology, our analysis revealed the ability of advanced multi-shell diffusion models to characterize the unique microstructural patterns of CEL, and to elucidate their possible evolution into a resolving (chronic inactive) vs smoldering (chronic active) inflammatory stage. In addition, we showed that the microstructural damage extends well beyond the MRI-visible lesion edge, gradually fading out while moving outward from the lesion edge into the immediate WM periplaque and the NAWM, the latter still characterized by diffuse microstructural damage in MS vs HC. This study also emphasizes the critical role of selecting appropriate diffusion models to elucidate the complex pathological architecture of MS lesions and their periplaque. More specifically, multi-compartment diffusion models based on biophysically interpretable metrics such as neurite orientation dispersion and density (NODDI; mean auc=0.8002) emerge as the preferred choice for MS applications, while simpler models based on a representation of the diffusion signal, like diffusion tensor imaging (DTI; mean auc=0.6942), consistently underperformed, also when compared to T1 mapping (mean auc=0.73375).
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Affiliation(s)
- Colin Vanden Bulcke
- Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium; ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium.
| | - Anna Stölting
- Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium
| | - Dragan Maric
- Flow and Imaging Core Facility, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Benoît Macq
- ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Martina Absinta
- Translational Neuropathology Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Pietro Maggi
- Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium; Department of Neurology, Cliniques universitaires Saint-Luc, Université catholique de Louvain, Brussels, Belgium.
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Preziosa P, Pagani E, Meani A, Storelli L, Margoni M, Yudin Y, Tedone N, Biondi D, Rubin M, Rocca MA, Filippi M. Chronic Active Lesions and Larger Choroid Plexus Explain Cognition and Fatigue in Multiple Sclerosis. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2024; 11:e200205. [PMID: 38350048 DOI: 10.1212/nxi.0000000000200205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 12/18/2023] [Indexed: 02/15/2024]
Abstract
BACKGROUND AND OBJECTIVES Chronic inflammation may contribute to cognitive dysfunction and fatigue in patients with multiple sclerosis (MS). Paramagnetic rim lesions (PRLs) and choroid plexus (CP) enlargement have been proposed as markers of chronic inflammation in MS being associated with a more severe disease course. However, their relation with cognitive impairment and fatigue has not been fully explored yet. Here, we investigated the contribution of PRL number and volume and CP enlargement to cognitive impairment and fatigue in patients with MS. METHODS Brain 3T MRI, neurologic evaluation, and neuropsychological assessment, including the Brief Repeatable Battery of Neuropsychological Tests and Modified Fatigue Impact Scale, were obtained from 129 patients with MS and 73 age-matched and sex-matched healthy controls (HC). PRLs were identified on phase images of susceptibility-weighted imaging, whereas CP volume was quantified using a fully automatic method on brain three-dimensional T1-weighted and fluid-attenuated inversion recovery MRI sequences. Predictors of cognitive impairment and fatigue were identified using random forest. RESULTS Thirty-six (27.9%) patients with MS were cognitively impaired, and 31/113 (27.4%) patients had fatigue. Fifty-nine (45.7%) patients with MS had ≥1 PRLs (median = 0, interquartile range = 0;2). Compared with HC, patients with MS showed significantly higher T2-hyperintense white matter lesion (WM) volume; lower normalized brain, thalamic, hippocampal, caudate, cortical, and WM volumes; and higher normalized CP volume (p from <0.001 to 0.040). The predictors of cognitive impairment (relative importance) (out-of-bag area under the curve [OOB-AUC] = 0.707) were normalized brain volume (100%), normalized caudate volume (89.1%), normalized CP volume (80.3%), normalized cortical volume (70.3%), number (67.3%) and volume (66.7%) of PRLs, and T2-hyperintense WM lesion volume (64.0%). Normalized CP volume was the only predictor of the presence of fatigue (OOB-AUC = 0.563). DISCUSSION Chronic inflammation, with higher number and volume of PRLs and enlarged CP, may contribute to cognitive impairment in MS in addition to gray matter atrophy. The contribution of enlarged CP in explaining fatigue supports the relevance of immune-related processes in determining this manifestation independently of disease severity. PRLs and CP enlargement may contribute to the pathophysiology of cognitive impairment and fatigue in MS, and they may represent clinically relevant therapeutic targets to limit the impact of these clinical manifestations in MS.
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Affiliation(s)
- Paolo Preziosa
- From the Neuroimaging Research Unit (P.P., E.P., A.M., L.S., M.M., Y.Y., N.T., D.B., M.R., M.A.R., M.F.), Division of Neuroscience; Neurology Unit (P.P., M.M., M.R., M.A.R., M.F.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (P.P., M.R., M.A.R., M.F.); 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 (P.P., E.P., A.M., L.S., M.M., Y.Y., N.T., D.B., M.R., M.A.R., M.F.), Division of Neuroscience; Neurology Unit (P.P., M.M., M.R., M.A.R., M.F.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (P.P., M.R., M.A.R., M.F.); 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 (P.P., E.P., A.M., L.S., M.M., Y.Y., N.T., D.B., M.R., M.A.R., M.F.), Division of Neuroscience; Neurology Unit (P.P., M.M., M.R., M.A.R., M.F.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (P.P., M.R., M.A.R., M.F.); 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 (P.P., E.P., A.M., L.S., M.M., Y.Y., N.T., D.B., M.R., M.A.R., M.F.), Division of Neuroscience; Neurology Unit (P.P., M.M., M.R., M.A.R., M.F.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (P.P., M.R., M.A.R., M.F.); 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 (P.P., E.P., A.M., L.S., M.M., Y.Y., N.T., D.B., M.R., M.A.R., M.F.), Division of Neuroscience; Neurology Unit (P.P., M.M., M.R., M.A.R., M.F.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (P.P., M.R., M.A.R., M.F.); Neurorehabilitation Unit (M.M., M.F.); and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Yury Yudin
- From the Neuroimaging Research Unit (P.P., E.P., A.M., L.S., M.M., Y.Y., N.T., D.B., M.R., M.A.R., M.F.), Division of Neuroscience; Neurology Unit (P.P., M.M., M.R., M.A.R., M.F.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (P.P., M.R., M.A.R., M.F.); Neurorehabilitation Unit (M.M., M.F.); and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Nicolò Tedone
- From the Neuroimaging Research Unit (P.P., E.P., A.M., L.S., M.M., Y.Y., N.T., D.B., M.R., M.A.R., M.F.), Division of Neuroscience; Neurology Unit (P.P., M.M., M.R., M.A.R., M.F.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (P.P., M.R., M.A.R., M.F.); Neurorehabilitation Unit (M.M., M.F.); and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Diana Biondi
- From the Neuroimaging Research Unit (P.P., E.P., A.M., L.S., M.M., Y.Y., N.T., D.B., M.R., M.A.R., M.F.), Division of Neuroscience; Neurology Unit (P.P., M.M., M.R., M.A.R., M.F.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (P.P., M.R., M.A.R., M.F.); Neurorehabilitation Unit (M.M., M.F.); and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Martina Rubin
- From the Neuroimaging Research Unit (P.P., E.P., A.M., L.S., M.M., Y.Y., N.T., D.B., M.R., M.A.R., M.F.), Division of Neuroscience; Neurology Unit (P.P., M.M., M.R., M.A.R., M.F.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (P.P., M.R., M.A.R., M.F.); 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 (P.P., E.P., A.M., L.S., M.M., Y.Y., N.T., D.B., M.R., M.A.R., M.F.), Division of Neuroscience; Neurology Unit (P.P., M.M., M.R., M.A.R., M.F.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (P.P., M.R., M.A.R., M.F.); 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 (P.P., E.P., A.M., L.S., M.M., Y.Y., N.T., D.B., M.R., M.A.R., M.F.), Division of Neuroscience; Neurology Unit (P.P., M.M., M.R., M.A.R., M.F.), IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (P.P., M.R., M.A.R., M.F.); Neurorehabilitation Unit (M.M., M.F.); and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
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8
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Correia R, Corrêa D, Doring T, Theodoro C, Correia A, Coelho V, Dib JG, Marchiori E, Alves Leon SV, Rueda Lopes FC. Severity of white matter microstructural damage in a Brazilian relapsing-remitting multiple sclerosis cohort: A possible window to optimize treatment. Neuroradiol J 2024; 37:60-67. [PMID: 37915211 PMCID: PMC10863572 DOI: 10.1177/19714009231212372] [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: 11/03/2023] Open
Abstract
BACKGROUND Multiple sclerosis (MS) is an important cause of acquired neurological disability in young adults, characterized by multicentric inflammation, demyelination, and axonal damage. OBJECTIVE The objective is to investigate white matter (WM) damage progression in a Brazilian MS patient cohort, using diffusion tensor imaging (DTI) post-processed by tract-based spatial statistics (TBSS). METHODS DTI scans were acquired from 76 MS patients and 37 sex-and-age matched controls. Patients were divided into three groups based on disease duration. DTI was performed along 30 non-collinear directions by using a 1.5T imager. For TBSS analysis, the WM skeleton was created, and a 5000 permutation-based inference with a threshold of p < .05 was used, to enable the identification of abnormalities in fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). RESULTS Decreased FA and increased RD, MD, and AD were seen in patients compared to controls and a decreased FA and increased MD and RD were seen, predominantly after the first 5 years of disease, when compared between groups. CONCLUSION Progressive WM deterioration is seen over time with a more prominent pattern after 5 years of disease onset, providing evidence that the early years might be a window to optimize treatment and prevent disability.
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Affiliation(s)
- Rafael Correia
- Department of Radiology, Federal Fluminense University (UFF), Niterói – RJ, Brazil
| | - Diogo Corrêa
- Department of Radiology, Federal Fluminense University (UFF), Niterói – RJ, Brazil
| | - Thomas Doring
- Department of Radiology, Clinicas de Diagnóstico por Imagem (CDPI), Rio de Janeiro – RJ, Brazil
| | - Carmem Theodoro
- Department of Gastroenterology, Federal Fluminense University, Niterói – RJ, Brazil
| | - Aline Correia
- Department of Internal Medicine, University of Fortaleza, Fortaleza – CE, Brazil
| | - Valeria Coelho
- Department of Neurology, Federal University of Rio de Janeiro(UFRJ), Rio de Janeiro – RJ, Brazil
| | - João Gabriel Dib
- Department of Neurology, Federal University of Rio de Janeiro(UFRJ), Rio de Janeiro – RJ, Brazil
| | - Edson Marchiori
- Department of Radiology, Federal University of Rio de Janeiro (UFRJ), Rio de janeiro – RJ, Brazil
| | - Soniza V Alves Leon
- Department of Neurology, Federal University of Rio de Janeiro(UFRJ), Rio de Janeiro – RJ, Brazil
| | - Fernanda C Rueda Lopes
- Department of Radiology, Federal Fluminense University (UFF), Niterói – RJ, Brazil
- Department of Radiology, Federal University of Rio de Janeiro (UFRJ), Rio de janeiro – RJ, Brazil
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9
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Cerdán Cerdá A, Toschi N, Treaba CA, Barletta V, Herranz E, Mehndiratta A, Gomez-Sanchez JA, Mainero C, De Santis S. A translational MRI approach to validate acute axonal damage detection as an early event in multiple sclerosis. eLife 2024; 13:e79169. [PMID: 38192199 PMCID: PMC10776086 DOI: 10.7554/elife.79169] [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: 04/01/2022] [Accepted: 12/05/2023] [Indexed: 01/10/2024] Open
Abstract
Axonal degeneration is a central pathological feature of multiple sclerosis and is closely associated with irreversible clinical disability. Current noninvasive methods to detect axonal damage in vivo are limited in their specificity and clinical applicability, and by the lack of proper validation. We aimed to validate an MRI framework based on multicompartment modeling of the diffusion signal (AxCaliber) in rats in the presence of axonal pathology, achieved through injection of a neurotoxin damaging the neuronal terminal of axons. We then applied the same MRI protocol to map axonal integrity in the brain of multiple sclerosis relapsing-remitting patients and age-matched healthy controls. AxCaliber is sensitive to acute axonal damage in rats, as demonstrated by a significant increase in the mean axonal caliber along the targeted tract, which correlated with neurofilament staining. Electron microscopy confirmed that increased mean axonal diameter is associated with acute axonal pathology. In humans with multiple sclerosis, we uncovered a diffuse increase in mean axonal caliber in most areas of the normal-appearing white matter, preferentially affecting patients with short disease duration. Our results demonstrate that MRI-based axonal diameter mapping is a sensitive and specific imaging biomarker that links noninvasive imaging contrasts with the underlying biological substrate, uncovering generalized axonal damage in multiple sclerosis as an early event.
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Affiliation(s)
| | - Nicola Toschi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
- Department of Biomedicine and Prevention, University of Rome Tor VergataRomeItaly
| | - Constantina A Treaba
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
| | - Valeria Barletta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
| | - Elena Herranz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
| | - Ambica Mehndiratta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
| | - Jose A Gomez-Sanchez
- Instituto de Neurociencias de Alicante, CSIC-UMHSan Juan de AlicanteSpain
- Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL)AlicanteSpain
- Millennium Nucleus for the Study of Pain (MiNuSPain)SantiagoChile
| | - Caterina Mainero
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
| | - Silvia De Santis
- Instituto de Neurociencias de Alicante, CSIC-UMHSan Juan de AlicanteSpain
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10
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Dong X, Zhu LW, Zhang Z, Cao R, Liu P, Shu X, Cao X, Hu Y, Bao X, Xu L, Li C, Xu Y. LLDT-8 ameliorates experimental autoimmune encephalomyelitis by mediating macrophage functions in the priming stage. Eur J Pharmacol 2024; 962:176201. [PMID: 37984728 DOI: 10.1016/j.ejphar.2023.176201] [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: 07/05/2023] [Revised: 11/09/2023] [Accepted: 11/10/2023] [Indexed: 11/22/2023]
Abstract
Multiple sclerosis (MS) is an inflammatory demyelinating disease in the central nervous system caused by T cell activation mediated by peripheral macrophages, resulting in severe neurological deficits and disability. Due to the currently limited and expensive treatments for MS, we here introduce an economic Chinese medicine extract, (5R)-5-Hydroxytriptolide (LLDT-8), which shows low toxicity and high immunosuppressive activity. We used the widely accepted mouse model of MS, experimental autoimmune encephalomyelitis (EAE), to examine the immunosuppressive effect of LLDT-8 in vivo. Through the RNA-sequence analysis of peripheral macrophages in EAE mice, we discovered that LLDT-8 alleviates the symptoms of EAE by inhibiting the proinflammatory effect of macrophages, thereby blocking the activation and proliferation of T cells. In all, we found that LLDT-8 could be a potential treatment for MS.
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Affiliation(s)
- Xiaohong Dong
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210008, China; Department of Neurology, Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, 210008, China; The Affiliated Lianyungang Hospital of Xuzhou Medical University, The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, China
| | - Li-Wen Zhu
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210008, China; Department of Neurology, Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, 210008, China; Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Zhi Zhang
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210008, China; Department of Neurology, Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, 210008, China; Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Runjing Cao
- Department of Neurology, Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, 210008, China; Center for Rehabilitation Medicine, Department of Neurology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Pinyi Liu
- Department of Neurology, Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, 210008, China; Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Xin Shu
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210008, China; Department of Neurology, Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, 210008, China; Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Xiang Cao
- Department of Neurology, Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, 210008, China; Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Yujie Hu
- Department of Neurology, Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, 210008, China; Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Xinyu Bao
- Department of Neurology, Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, 210008, China; Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Lushan Xu
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210008, China; Department of Neurology, Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, 210008, China; Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Chenggang Li
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210008, China; Department of Neurology, Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, 210008, China; Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210008, China; Department of Neurology, Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, 210008, China; Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China.
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11
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Calvi A, Mendelsohn Z, Hamed W, Chard D, Tur C, Stutters J, MacManus D, Kanber B, Wheeler-Kingshott CAMG, Barkhof F, Prados F. Treatment reduces the incidence of newly appearing multiple sclerosis lesions evolving into chronic active, slowly expanding lesions: A retrospective analysis. Eur J Neurol 2024; 31:e16092. [PMID: 37823722 DOI: 10.1111/ene.16092] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/05/2023] [Accepted: 09/21/2023] [Indexed: 10/13/2023]
Abstract
BACKGROUND AND PURPOSE Newly appearing lesions in multiple sclerosis (MS) may evolve into chronically active, slowly expanding lesions (SELs), leading to sustained disability progression. The aim of this study was to evaluate the incidence of newly appearing lesions developing into SELs, and their correlation to clinical evolution and treatment. METHODS A retrospective analysis of a fingolimod trial in primary progressive MS (PPMS; INFORMS, NCT00731692) was undertaken. Data were available from 324 patients with magnetic resonance imaging scans up to 3 years after screening. New lesions at year 1 were identified with convolutional neural networks, and SELs obtained through a deformation-based method. Clinical disability was assessed annually by Expanded Disability Status Scale (EDSS), Nine-Hole Peg Test, Timed 25-Foot Walk, and Paced Auditory Serial Addition Test. Linear, logistic, and mixed-effect models were used to assess the relationship between the Jacobian expansion in new lesions and SELs, disability scores, and treatment status. RESULTS One hundred seventy patients had ≥1 new lesions at year 1 and had a higher lesion count at screening compared to patients with no new lesions (median = 27 vs. 22, p = 0.007). Among the new lesions (median = 2 per patient), 37% evolved into definite or possible SELs. Higher SEL volume and count were associated with EDSS worsening and confirmed disability progression. Treated patients had lower volume and count of definite SELs (β = -0.04, 95% confidence interval [CI] = -0.07 to -0.01, p = 0.015; β = -0.36, 95% CI = -0.67 to -0.06, p = 0.019, respectively). CONCLUSIONS Incident chronic active lesions are common in PPMS, and fingolimod treatment can reduce their number.
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Affiliation(s)
- Alberto Calvi
- NMR Research Unit, Institute of Neurology, University College London, London, UK
- Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Fundació Clinic per a la Recerca Biomèdica, Barcelona, Spain
| | - Zoe Mendelsohn
- NMR Research Unit, Institute of Neurology, University College London, London, UK
- Department of Radiology, Charité School of Medicine and University Hospital Berlin, Berlin, Germany
| | - Weaam Hamed
- NMR Research Unit, Institute of Neurology, University College London, London, UK
- Department of Radiology, Mansoura University Hospital, Mansoura, Egypt
| | - Declan Chard
- NMR Research Unit, Institute of Neurology, University College London, London, UK
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals, London, UK
| | - Carmen Tur
- NMR Research Unit, Institute of Neurology, University College London, London, UK
- Neurology-Neuroimmunology Department, Multiple Sclerosis Centre of Catalonia, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Jon Stutters
- NMR Research Unit, Institute of Neurology, University College London, London, UK
| | - David MacManus
- NMR Research Unit, Institute of Neurology, University College London, London, UK
| | - Baris Kanber
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals, London, UK
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Institute of Neurology, University College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Frederik Barkhof
- NMR Research Unit, Institute of Neurology, University College London, London, UK
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, UK
- Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC), Vrije Universiteit, Amsterdam, the Netherlands
| | - Ferran Prados
- NMR Research Unit, Institute of Neurology, University College London, London, UK
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, UK
- e-Health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
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12
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Caranova M, Soares JF, Batista S, Castelo-Branco M, Duarte JV. A systematic review of microstructural abnormalities in multiple sclerosis detected with NODDI and DTI models of diffusion-weighted magnetic resonance imaging. Magn Reson Imaging 2023; 104:61-71. [PMID: 37775062 DOI: 10.1016/j.mri.2023.09.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/31/2023] [Accepted: 09/25/2023] [Indexed: 10/01/2023]
Abstract
Multiple sclerosis (MS), namely the phenotype of the relapsing-remitting form, is the most common white matter disease and is mostly characterized by demyelination and inflammation, which lead to neurodegeneration and cognitive decline. Its diagnosis and monitoring are performed through conventional structural MRI, in which T2-hyperintense lesions can be identified, but this technique lacks sensitivity and specificity, mainly in detecting damage to normal appearing tissues. Models of diffusion-weighted MRI such as diffusion-tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) allow to uncover microstructural abnormalities that occur in MS, mainly in normal appearing tissues such as the normal appearing white matter (NAWM), which allows to overcome limitations of conventional MRI. DTI is the standard method used for modelling this kind of data, but it has limitations, which can be tackled by using more complex diffusion models, such as NODDI, which provides additional information on morphological properties of tissues. Although there are several studies in MS using both diffusion models, there is no formal assessment that summarizes the findings of both methods in lesioned and normal appearing tissues, and whether one is more advantageous than the other. Hence, this systematic review aims to identify what microstructural abnormalities are seen in lesions and/or NAWM in relapsing-remitting MS while using two different approaches to modelling diffusion data, namely DTI and NODDI, and if one of them is more appropriate than the other or if they are complementary to each other. The search was performed using PubMed, which was last searched on November 2022, and aimed at finding studies that either utilized both DTI and NODDI in the same dataset, or only one of the methods. Eleven articles were included in this review, which included cohorts with a relatively low sample size (total number of patients = 254, total number of healthy controls = 240), and patients with a moderate disease duration, all with relapsing-remitting MS. Overall, studies found decreased fractional anisotropy (FA), neurite density index (NDI) and orientation dispersion index (ODI), and increased mean, axial and radial diffusivities (MD, AD and RD, respectively) in lesions, when compared to contralateral NAWM and healthy controls' white matter. Compared to healthy controls' white matter, NAWM showed lower FA and NDI and higher MD, AD, RD, and ODI. Results from the included articles confirm that there is active demyelination and inflammation in both lesions and NAWM, as well as loss in neurites, and that structural damage is not confined to focal lesions, which is in concordance with histological findings and results from other imaging techniques. Furthermore, NODDI is suggested to have higher sensitivity and specificity, as seen by inspecting imaging results, compared to DTI, while still being clinically feasible. The use of biomarkers derived from such advanced diffusion models in clinical practice could imply a better understanding of treatment efficacy and disease progression, without relying on the manifestation of clinical symptoms, such as relapses.
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Affiliation(s)
- Maria Caranova
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal.
| | - Júlia F Soares
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Sónia Batista
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal; Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal; Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - João Valente Duarte
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal; Faculty of Medicine, University of Coimbra, Coimbra, Portugal
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13
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Bontempi P, Rozzanigo U, Marangoni S, Fogazzi E, Ravanelli D, Cazzoletti L, Giometto B, Farace P. Non-lesional white matter in relapsing-remitting multiple sclerosis assessed by multicomponent T2 relaxation. Brain Behav 2023; 13:e3334. [PMID: 38041516 PMCID: PMC10726908 DOI: 10.1002/brb3.3334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/31/2023] [Accepted: 11/02/2023] [Indexed: 12/03/2023] Open
Abstract
INTRODUCTION The purpose of the study is to investigate, by T2 relaxation, non-lesional white matter (WM) in relapsing-remitting (RR) multiple sclerosis (MS). METHODS Twenty stable RR MS patients underwent 1.5T Magnetic Resonance Imaging (MRI) with 3D Fluid-Attenuated Inversion-Recovery (FLAIR), 3D-T1-weighted, and T2-relaxation multi-echo sequences. The Lesion Segmentation Tool processed FLAIR images to identify focal lesions (FLs), whereas T1 images were segmented to identify WM and FL sub-volumes with T1 hypo-intensity. Non-lesional WM was obtained as the segmented WM, excluding FL volumes. The multi-echo sequence allowed decomposition into myelin water, intra-extracellular water, and free water (Fw), which were evaluated on the segmented non-lesional WM. Correlation analysis was performed between the non-lesional WM relaxation parameters and Expanded Disability Status Scale (EDSS), disease duration, patient age, and T1 hypo-intense FL volumes. RESULTS The T1 hypo-intense FL volumes correlated with EDSS. On the non-lesional WM, the median Fw correlated with EDSS, disease duration, age, and T1 hypo-intense FL volumes. Bivariate EDSS correlation of FL volumes and WM T2-relaxation parameters did not improve significance. CONCLUSION T2 relaxation allowed identifying subtle WM alterations, which significantly correlated with EDSS, disease duration, and age but do not seem to be EDSS-predictors independent from FL sub-volumes in stable RR patients. Particularly, the increase in the Fw component is suggestive of an uninvestigated prodromal phenomenon in brain degeneration.
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Affiliation(s)
- Pietro Bontempi
- Department of Engineering for Innovation MedicineUniversity of VeronaVeronaItaly
| | - Umberto Rozzanigo
- Neuro‐radiology Unit, Hospital of TrentoAzienda Provinciale per i Servizi Sanitari (APSS)TrentoItaly
| | - Sabrina Marangoni
- Neurology Unit, Hospital of TrentoAzienda Provinciale per i Servizi Sanitari (APSS)TrentoItaly
| | - Elena Fogazzi
- Physics departmentUniversity of TrentoPovoTrentoItaly
| | - Daniele Ravanelli
- Medical Physics Department, Hospital of TrentoAzienda Provinciale per i Servizi Sanitari (APSS)TrentoItaly
| | - Lucia Cazzoletti
- Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public HealthUniversity of VeronaVeronaItaly
| | - Bruno Giometto
- Neurology Unit, Hospital of TrentoAzienda Provinciale per i Servizi Sanitari (APSS)TrentoItaly
| | - Paolo Farace
- Medical Physics Department, Hospital of TrentoAzienda Provinciale per i Servizi Sanitari (APSS)TrentoItaly
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14
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Cacciaguerra L, Rocca MA, Filippi M. Understanding the Pathophysiology and Magnetic Resonance Imaging of Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorders. Korean J Radiol 2023; 24:1260-1283. [PMID: 38016685 DOI: 10.3348/kjr.2023.0360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/09/2023] [Accepted: 08/21/2023] [Indexed: 11/30/2023] Open
Abstract
Magnetic resonance imaging (MRI) has been extensively applied in the study of multiple sclerosis (MS), substantially contributing to diagnosis, differential diagnosis, and disease monitoring. MRI studies have significantly contributed to the understanding of MS through the characterization of typical radiological features and their clinical or prognostic implications using conventional MRI pulse sequences and further with the application of advanced imaging techniques sensitive to microstructural damage. Interpretation of results has often been validated by MRI-pathology studies. However, the application of MRI techniques in the study of neuromyelitis optica spectrum disorders (NMOSD) remains an emerging field, and MRI studies have focused on radiological correlates of NMOSD and its pathophysiology to aid in diagnosis, improve monitoring, and identify relevant prognostic factors. In this review, we discuss the main contributions of MRI to the understanding of MS and NMOSD, focusing on the most novel discoveries to clarify differences in the pathophysiology of focal inflammation initiation and perpetuation, involvement of normal-appearing tissue, potential entry routes of pathogenic elements into the CNS, and existence of primary or secondary mechanisms of neurodegeneration.
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Affiliation(s)
- Laura Cacciaguerra
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, 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, Milano, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milano, Italy.
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15
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Zhu Q, Yan Z, Shi Z, Luo D, Ding S, Chen X, Li Y. Increased cortical lesion load contributed to pathological changes beyond focal lesion in cortical gray matter of multiple sclerosis: a diffusion kurtosis imaging analysis. Cereb Cortex 2023; 33:10867-10876. [PMID: 37718158 DOI: 10.1093/cercor/bhad332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 08/20/2023] [Accepted: 08/22/2023] [Indexed: 09/19/2023] Open
Abstract
Biomarkers specific to cortical gray matter (cGM) pathological changes of multiple sclerosis (MS) are desperately needed to better understand the disease progression. The cGM damage occurs in cortical lesion (CL) and normal-appearing cGM (NAcGM) areas. While the association between CL load and cGM damage has been reported, little is known about how different CL types, i.e. intracortical lesion (ICL) and leukocortical lesion (LCL) would be associated with cGM damage. In our study, relapsing-remitting MS patients and healthy controls were divided into 4 groups according to CL load level. NAcGM diffusion kurtosis imaging (DKI)/diffusion tensor imaging (DTI) values and cGM volume (cGMV) were used to characterize the pathological changes in cGM. Univariate general linear model was used for group comparisons and stepwise regression analysis was used to assess the effects of ICL volume and LCL volume on NAcGM damage. We found peak values in DKI/DTI values, cGMV and neuropsychological scores in high CL load group. Kurtosis fractional anisotropy (KFA) was the most sensitive in characterizing NAcGM damage, and LCL volume related more to NAcGM damage. Our findings suggested KFA could become a surrogate biomarker to cGM damage, and LCL might be the main factor in whole brain NAcGM damage.
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Affiliation(s)
- Qiyuan Zhu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Zichun Yan
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Zhuowei Shi
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Dan Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Shuang Ding
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing 400014, China
| | - Xiaoya Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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16
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Krajnc N, Schmidbauer V, Leinkauf J, Haider L, Bsteh G, Kasprian G, Leutmezer F, Kornek B, Rommer PS, Berger T, Lassmann H, Dal-Bianco A, Hametner S. Paramagnetic rim lesions lead to pronounced diffuse periplaque white matter damage in multiple sclerosis. Mult Scler 2023; 29:1406-1417. [PMID: 37712486 PMCID: PMC10580674 DOI: 10.1177/13524585231197954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 07/27/2023] [Accepted: 07/31/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Paramagnetic rim lesions (PRLs) are an imaging biomarker in multiple sclerosis (MS), associated with a more severe disease. OBJECTIVES To determine quantitative magnetic resonance imaging (MRI) metrics of PRLs, lesions with diffuse susceptibility-weighted imaging (SWI)-hypointense signal (DSHLs) and SWI-isointense lesions (SILs), their surrounding periplaque area (PPA) and the normal-appearing white matter (NAWM). METHODS In a cross-sectional study, quantitative MRI metrics were measured in people with multiple sclerosis (pwMS) using the multi-dynamic multi-echo (MDME) sequence post-processing software "SyMRI." RESULTS In 30 pwMS, 59 PRLs, 74 DSHLs, and 107 SILs were identified. Beside longer T1 relaxation times of PRLs compared to DSHLs and SILs (2030.5 (1519-2540) vs 1615.8 (1403.3-1953.5) vs 1199.5 (1089.6-1334.6), both p < 0.001), longer T1 relaxation times were observed in the PRL PPA compared to the SIL PPA and the NAWM but not the DSHL PPA. Patients with secondary progressive multiple sclerosis (SPMS) had longer T1 relaxation times in PRLs compared to patients with late relapsing multiple sclerosis (lRMS) (2394.5 (2030.5-3040) vs 1869.3 (1491.4-2451.3), p = 0.015) and also in the PRL PPA compared to patients with early relapsing multiple sclerosis (eRMS) (982 (927-1093.5) vs 904.3 (793.3-958.5), p = 0.013). CONCLUSION PRLs are more destructive than SILs, leading to diffuse periplaque white matter (WM) damage. The quantitative MRI-based evaluation of the PRL PPA could be a marker for silent progression in pwMS.
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Affiliation(s)
- Nik Krajnc
- Department of Neurology, Medical University of Vienna, Vienna, Austria/Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria/Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Victor Schmidbauer
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria/Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Joel Leinkauf
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria/Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lukas Haider
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria/Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Gabriel Bsteh
- Department of Neurology, Medical University of Vienna, Vienna, Austria/Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Gregor Kasprian
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria/Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Fritz Leutmezer
- Department of Neurology, Medical University of Vienna, Vienna, Austria/Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Barbara Kornek
- Department of Neurology, Medical University of Vienna, Vienna, Austria/Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Paulus Stefan Rommer
- Department of Neurology, Medical University of Vienna, Vienna, Austria/Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Thomas Berger
- Department of Neurology, Medical University of Vienna, Vienna, Austria/Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Hans Lassmann
- Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Assunta Dal-Bianco
- Department of Neurology, Medical University of Vienna, Vienna, Austria/Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Simon Hametner
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria/Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
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17
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Seyedmirzaei H, Nabizadeh F, Aarabi MH, Pini L. Neurite Orientation Dispersion and Density Imaging in Multiple Sclerosis: A Systematic Review. J Magn Reson Imaging 2023; 58:1011-1029. [PMID: 37042392 DOI: 10.1002/jmri.28727] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 04/13/2023] Open
Abstract
Diffusion-weighted imaging has been applied to investigate alterations in multiple sclerosis (MS). In the last years, advanced diffusion models were used to identify subtle changes and early lesions in MS. Among these models, neurite orientation dispersion and density imaging (NODDI) is an emerging approach, quantifying specific neurite morphology in both grey (GM) and white matter (WM) tissue and increasing the specificity of diffusion imaging. In this systematic review, we summarized the NODDI findings in MS. A search was conducted on PubMed, Scopus, and Embase, which yielded a total number of 24 eligible studies. Compared to healthy tissue, these studies identified consistent alterations in NODDI metrics involving WM (neurite density index), and GM lesions (neurite density index), or normal-appearing WM tissue (isotropic volume fraction and neurite density index). Despite some limitations, we pointed out the potential of NODDI in MS to unravel microstructural alterations. These results might pave the way to a deeper understanding of the pathophysiological mechanism of MS. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
| | | | | | - Lorenzo Pini
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
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18
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Schiavi S, Palombo M, Zacà D, Tazza F, Lapucci C, Castellan L, Costagli M, Inglese M. Mapping tissue microstructure across the human brain on a clinical scanner with soma and neurite density image metrics. Hum Brain Mapp 2023; 44:4792-4811. [PMID: 37461286 PMCID: PMC10400787 DOI: 10.1002/hbm.26416] [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: 11/01/2022] [Revised: 05/02/2023] [Accepted: 06/23/2023] [Indexed: 08/05/2023] Open
Abstract
Soma and neurite density image (SANDI) is an advanced diffusion magnetic resonance imaging biophysical signal model devised to probe in vivo microstructural information in the gray matter (GM). This model requires acquisitions that include b values that are at least six times higher than those used in clinical practice. Such high b values are required to disentangle the signal contribution of water diffusing in soma from that diffusing in neurites and extracellular space, while keeping the diffusion time as short as possible to minimize potential bias due to water exchange. These requirements have limited the use of SANDI only to preclinical or cutting-edge human scanners. Here, we investigate the potential impact of neglecting water exchange in the SANDI model and present a 10-min acquisition protocol that enables to characterize both GM and white matter (WM) on 3 T scanners. We implemented analytical simulations to (i) evaluate the stability of the fitting of SANDI parameters when diminishing the number of shells; (ii) estimate the bias due to potential exchange between neurites and extracellular space in such reduced acquisition scheme, comparing it with the bias due to experimental noise. Then, we demonstrated the feasibility and assessed the repeatability and reproducibility of our approach by computing microstructural metrics of SANDI with AMICO toolbox and other state-of-the-art models on five healthy subjects. Finally, we applied our protocol to five multiple sclerosis patients. Results suggest that SANDI is a practical method to characterize WM and GM tissues in vivo on performant clinical scanners.
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Affiliation(s)
- Simona Schiavi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
| | - Marco Palombo
- CUBRIC, School of PsychologyCardiff UniversityCardiffUK
- School of Computer Science and InformaticsCardiff UniversityCardiffUK
| | | | - Francesco Tazza
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
| | - Caterina Lapucci
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- HNSR, IRRCS Ospedale Policlinico San MartinoGenoaItaly
| | - Lucio Castellan
- Department of NeuroradiologyIRCCS Ospedale Policlinico San MartinoGenoaItaly
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- Laboratory of Medical Physics and Magnetic ResonanceIRCCS Stella MarisPisaItaly
| | - Matilde Inglese
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- IRCCS Ospedale Policlinico San MartinoGenoaItaly
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19
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Maggi P, Bulcke CV, Pedrini E, Bugli C, Sellimi A, Wynen M, Stölting A, Mullins WA, Kalaitzidis G, Lolli V, Perrotta G, El Sankari S, Duprez T, Li X, Calabresi PA, van Pesch V, Reich DS, Absinta M. B cell depletion therapy does not resolve chronic active multiple sclerosis lesions. EBioMedicine 2023; 94:104701. [PMID: 37437310 PMCID: PMC10436266 DOI: 10.1016/j.ebiom.2023.104701] [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: 01/23/2023] [Revised: 06/23/2023] [Accepted: 06/23/2023] [Indexed: 07/14/2023] Open
Abstract
BACKGROUND Chronic active lesions (CAL) in multiple sclerosis (MS) have been observed even in patients taking high-efficacy disease-modifying therapy, including B-cell depletion. Given that CAL are a major determinant of clinical progression, including progression independent of relapse activity (PIRA), understanding the predicted activity and real-world effects of targeting specific lymphocyte populations is critical for designing next-generation treatments to mitigate chronic inflammation in MS. METHODS We analyzed published lymphocyte single-cell transcriptomes from MS lesions and bioinformatically predicted the effects of depleting lymphocyte subpopulations (including CD20 B-cells) from CAL via gene-regulatory-network machine-learning analysis. Motivated by the results, we performed in vivo MRI assessment of PRL changes in 72 adults with MS, 46 treated with anti-CD20 antibodies and 26 untreated, over ∼2 years. FINDINGS Although only 4.3% of lymphocytes in CAL were CD20 B-cells, their depletion is predicted to affect microglial genes involved in iron/heme metabolism, hypoxia, and antigen presentation. In vivo, tracking 202 PRL (150 treated) and 175 non-PRL (124 treated), none of the treated paramagnetic rims disappeared at follow-up, nor was there a treatment effect on PRL for lesion volume, magnetic susceptibility, or T1 time. PIRA occurred in 20% of treated patients, more frequently in those with ≥4 PRL (p = 0.027). INTERPRETATION Despite predicted effects on microglia-mediated inflammatory networks in CAL and iron metabolism, anti-CD20 therapies do not fully resolve PRL after 2-year MRI follow up. Limited tissue turnover of B-cells, inefficient passage of anti-CD20 antibodies across the blood-brain-barrier, and a paucity of B-cells in CAL could explain our findings. FUNDING Intramural Research Program of NINDS, NIH; NINDS grants R01NS082347 and R01NS082347; Dr. Miriam and Sheldon G. Adelson Medical Research Foundation; Cariplo Foundation (grant #1677), FRRB Early Career Award (grant #1750327); Fund for Scientific Research (FNRS).
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Affiliation(s)
- Pietro Maggi
- Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium; Neuroinflammation Imaging Lab (NIL), Université Catholique de Louvain, Brussels, Belgium; Centre Hospitalier Universitaire Vaudois, Université de Lausanne, Lausanne, Switzerland.
| | - Colin Vanden Bulcke
- Neuroinflammation Imaging Lab (NIL), Université Catholique de Louvain, Brussels, Belgium
| | - Edoardo Pedrini
- Institute of Experimental Neurology, Division of Neuroscience, Vita-Salute San Raffaele University and IRCCS San Raffaele Hospital, Milan, Italy
| | - Céline Bugli
- Plateforme Technologique de Support en Méthodologie et Calcul Statistique, Université Catholique de Louvain, Brussels, Belgium
| | - Amina Sellimi
- Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Maxence Wynen
- Neuroinflammation Imaging Lab (NIL), Université Catholique de Louvain, Brussels, Belgium
| | - Anna Stölting
- Neuroinflammation Imaging Lab (NIL), Université Catholique de Louvain, Brussels, Belgium
| | - William A Mullins
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Grigorios Kalaitzidis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Valentina Lolli
- Hôpital Erasme, Université Libre de Bruxelles, Bruxelles, Belgium
| | - Gaetano Perrotta
- Hôpital Erasme, Université Libre de Bruxelles, Bruxelles, Belgium
| | - Souraya El Sankari
- Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Thierry Duprez
- Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Xu Li
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Peter A Calabresi
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Vincent van Pesch
- Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Martina Absinta
- Institute of Experimental Neurology, Division of Neuroscience, Vita-Salute San Raffaele University and IRCCS San Raffaele Hospital, Milan, Italy; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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20
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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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21
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Rocca MA, Margoni M, Battaglini M, Eshaghi A, Iliff J, Pagani E, Preziosa P, Storelli L, Taoka T, Valsasina P, Filippi M. Emerging Perspectives on MRI Application in Multiple Sclerosis: Moving from Pathophysiology to Clinical Practice. Radiology 2023; 307:e221512. [PMID: 37278626 PMCID: PMC10315528 DOI: 10.1148/radiol.221512] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 11/28/2022] [Accepted: 01/17/2023] [Indexed: 06/07/2023]
Abstract
MRI plays a central role in the diagnosis of multiple sclerosis (MS) and in the monitoring of disease course and treatment response. Advanced MRI techniques have shed light on MS biology and facilitated the search for neuroimaging markers that may be applicable in clinical practice. MRI has led to improvements in the accuracy of MS diagnosis and a deeper understanding of disease progression. This has also resulted in a plethora of potential MRI markers, the importance and validity of which remain to be proven. Here, five recent emerging perspectives arising from the use of MRI in MS, from pathophysiology to clinical application, will be discussed. These are the feasibility of noninvasive MRI-based approaches to measure glymphatic function and its impairment; T1-weighted to T2-weighted intensity ratio to quantify myelin content; classification of MS phenotypes based on their MRI features rather than on their clinical features; clinical relevance of gray matter atrophy versus white matter atrophy; and time-varying versus static resting-state functional connectivity in evaluating brain functional organization. These topics are critically discussed, which may guide future applications in the field.
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Affiliation(s)
- Maria Assunta Rocca
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Monica Margoni
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Marco Battaglini
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Arman Eshaghi
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Jeffrey Iliff
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Paolo Preziosa
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Loredana Storelli
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Toshiaki Taoka
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Paola Valsasina
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Massimo Filippi
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
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22
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Krijnen EA, Russo AW, Salim Karam E, Lee H, Chiang FL, Schoonheim MM, Huang SY, Klawiter EC. Detection of grey matter microstructural substrates of neurodegeneration in multiple sclerosis. Brain Commun 2023; 5:fcad153. [PMID: 37274832 PMCID: PMC10233898 DOI: 10.1093/braincomms/fcad153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/16/2023] [Accepted: 05/22/2023] [Indexed: 06/07/2023] Open
Abstract
Multiple sclerosis features complex pathological changes in grey matter that begin early and eventually lead to diffuse atrophy. Novel approaches to image grey-matter microstructural alterations in vivo are highly sought after and would enable more sensitive monitoring of disease activity and progression. This cross-sectional study aimed to assess the sensitivity of high-gradient diffusion MRI for microstructural tissue damage in cortical and deep grey matter in people with multiple sclerosis and test the hypothesis that reduced cortical cell body density is associated with cortical and deep grey-matter volume loss. Forty-one people with multiple sclerosis (age 24-72, 14 females) and 37 age- and sex-matched healthy controls were scanned on a 3 T Connectom MRI scanner equipped with 300 mT/m gradients using a multi-shell diffusion MRI protocol. The soma and neurite density imaging model was fitted to high-gradient diffusion MRI data to obtain estimates of intra-neurite, intra-cellular and extra-cellular signal fractions and apparent soma radius. Cortical and deep grey-matter microstructural imaging metrics were compared between multiple sclerosis and healthy controls and correlated with grey-matter volume, clinical disability and cognitive outcomes. People with multiple sclerosis showed significant cortical and deep grey-matter volume loss compared with healthy controls. People with multiple sclerosis showed trends towards lower cortical intra-cellular signal fraction and significantly lower intra-cellular and higher extra-cellular signal fractions in deep grey matter, especially the thalamus and caudate, compared with healthy controls. Changes were most pronounced in progressive disease and correlated with the Expanded Disability Status Scale, but not the Symbol Digit Modalities Test. In multiple sclerosis, normalized thalamic volume was associated with thalamic microstructural imaging metrics. Whereas thalamic volume loss did not correlate with cortical volume loss, cortical microstructural imaging metrics were significantly associated with thalamic volume, and not with cortical volume. Compared with the short diffusion time (Δ = 19 ms) achievable on the Connectom scanner, at the longer diffusion time of Δ = 49 ms attainable on clinical scanners, multiple sclerosis-related changes in imaging metrics were generally less apparent with lower effect sizes in cortical and deep grey matter. Soma and neurite density imaging metrics obtained from high-gradient diffusion MRI data provide detailed grey-matter characterization beyond cortical and thalamic volumes and distinguish multiple sclerosis-related microstructural pathology from healthy controls. Cortical cell body density correlates with thalamic volume, appears sensitive to the microstructural substrate of neurodegeneration and reflects disability status in people with multiple sclerosis, becoming more pronounced as disability worsens.
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Affiliation(s)
- Eva A Krijnen
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
| | - Andrew W Russo
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Elsa Salim Karam
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Hansol Lee
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Florence L Chiang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
| | - Susie Y Huang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Eric C Klawiter
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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23
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Zhang X, Liu C, Ou N, Zeng X, Zhuo Z, Duan Y, Xiong X, Yu Y, Liu Z, Liu Y, Ye C. CarveMix: A simple data augmentation method for brain lesion segmentation. Neuroimage 2023; 271:120041. [PMID: 36933626 DOI: 10.1016/j.neuroimage.2023.120041] [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: 01/20/2023] [Revised: 03/01/2023] [Accepted: 03/15/2023] [Indexed: 03/18/2023] Open
Abstract
Brain lesion segmentation provides a valuable tool for clinical diagnosis and research, and convolutional neural networks (CNNs) have achieved unprecedented success in the segmentation task. Data augmentation is a widely used strategy to improve the training of CNNs. In particular, data augmentation approaches that mix pairs of annotated training images have been developed. These methods are easy to implement and have achieved promising results in various image processing tasks. However, existing data augmentation approaches based on image mixing are not designed for brain lesions and may not perform well for brain lesion segmentation. Thus, the design of this type of simple data augmentation method for brain lesion segmentation is still an open problem. In this work, we propose a simple yet effective data augmentation approach, dubbed as CarveMix, for CNN-based brain lesion segmentation. Like other mixing-based methods, CarveMix stochastically combines two existing annotated images (annotated for brain lesions only) to obtain new labeled samples. To make our method more suitable for brain lesion segmentation, CarveMix is lesion-aware, where the image combination is performed with a focus on the lesions and preserves the lesion information. Specifically, from one annotated image we carve a region of interest (ROI) according to the lesion location and geometry with a variable ROI size. The carved ROI then replaces the corresponding voxels in a second annotated image to synthesize new labeled images for network training, and additional harmonization steps are applied for heterogeneous data where the two annotated images can originate from different sources. Besides, we further propose to model the mass effect that is unique to whole brain tumor segmentation during image mixing. To evaluate the proposed method, experiments were performed on multiple publicly available or private datasets, and the results show that our method improves the accuracy of brain lesion segmentation. The code of the proposed method is available at https://github.com/ZhangxinruBIT/CarveMix.git.
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Affiliation(s)
- Xinru Zhang
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
| | - Chenghao Liu
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
| | - Ni Ou
- School of Automation, Beijing Institute of Technology, Beijing, China
| | - Xiangzhu Zeng
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | | | | | - Zhiwen Liu
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Chuyang Ye
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
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24
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Johnson P, Vavasour IM, Stojkova BJ, Abel S, Lee LE, Laule C, Tam R, Li DKB, Ackermans N, Schabas AJ, Chan J, Cross H, Sayao AL, Devonshire V, Carruthers R, Traboulsee A, Kolind SH. Myelin heterogeneity for assessing normal appearing white matter myelin damage in multiple sclerosis. J Neuroimaging 2023; 33:227-234. [PMID: 36443960 DOI: 10.1111/jon.13069] [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: 06/16/2022] [Revised: 11/04/2022] [Accepted: 11/08/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND AND PURPOSE Conventional MRI measures of multiple sclerosis (MS) disease severity, such as lesion volume and brain atrophy, do not provide information about microstructural tissue changes, which may be driving physical and cognitive progression. Myelin damage in normal-appearing white matter (NAWM) is likely an important contributor to MS disability. Myelin water fraction (MWF) provides quantitative measurements of myelin. Mean MWF reflects average myelin content, while MWF standard deviation (SD) describes variation in myelin within regions. The myelin heterogeneity index (MHI = SD/mean MWF) is a composite metric of myelin content and myelin variability. We investigated how mean MWF, SD, and MHI compare in differentiating MS from controls and their associations with physical and cognitive disability. METHODS Myelin water imaging data were acquired from 91 MS participants and 31 healthy controls (HC). Segmented whole-brain NAWM and corpus callosum (CC) NAWM, mean MWF, SD, and MHI were compared between groups. Associations of mean MWF, SD, and MHI with Expanded Disability Status Scale and Symbol Digit Modalities Test were assessed. RESULTS NAWM and CC MHI had the highest area under the curve: .78 (95% confidence interval [CI]: .69, .86) and .84 (95% CI: .76, .91), respectively, distinguishing MS from HC. CONCLUSIONS Mean MWF, SD, and MHI provide complementary information when assessing regional and global NAWM abnormalities in MS and associations with clinical outcome measures. Examining all three metrics (mean MWF, SD, and MHI) enables a more detailed interpretation of results, depending on whether regions of interest include areas that are more heterogeneous, earlier in the demyelination process, or uniformly injured.
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Affiliation(s)
- Poljanka Johnson
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
| | - Irene M Vavasour
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada.,International Collaboration on Repair and Discoveries, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Shawna Abel
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
| | - Lisa Eunyoung Lee
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
| | - Cornelia Laule
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,International Collaboration on Repair and Discoveries, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Roger Tam
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - David K B Li
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada.,Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Nathalie Ackermans
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
| | - Alice J Schabas
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
| | - Jillian Chan
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
| | - Helen Cross
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
| | - Ana-Luiza Sayao
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
| | - Virginia Devonshire
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
| | - Robert Carruthers
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
| | - Anthony Traboulsee
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
| | - Shannon H Kolind
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada.,Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,International Collaboration on Repair and Discoveries, University of British Columbia, Vancouver, British Columbia, Canada
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25
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Kwon OW, Kim D, Koh E, Yang HJ. Korean Red Ginseng and Rb1 facilitate remyelination after cuprizone diet-induced demyelination. J Ginseng Res 2023; 47:319-328. [PMID: 36926609 PMCID: PMC10014189 DOI: 10.1016/j.jgr.2022.09.005] [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: 04/10/2022] [Revised: 08/29/2022] [Accepted: 09/27/2022] [Indexed: 03/18/2023] Open
Abstract
Background Demyelination has been observed in neurological disorders, motivating researchers to search for components for enhancing remyelination. Previously we found that Rb1, a major ginsenoside in Korean Red Ginseng (KRG), enhances myelin formation. However, it has not been studied whether Rb1 or KRG function in remyelination after demyelination in vivo. Methods Mice were fed 0.2% cuprizone-containing chow for 5 weeks and returned to normal chow with daily oral injection of vehicle, KRG, or Rb1 for 3 weeks. Brain sections were stained with luxol fast blue (LFB) staining or immunohistochemistry. Primary oligodendrocyte or astrocyte cultures were subject to normal or stress condition with KRG or Rb1 treatment to measure gene expressions of myelin, endoplasmic reticulum (ER) stress, antioxidants and leukemia inhibitory factor (LIF). Results Compared to the vehicle, KRG or Rb1 increased myelin levels at week 6.5 but not 8, when measured by the LFB+ or GST-pi+ area within the corpus callosum. The levels of oligodendrocyte precursor cells, astrocytes, and microglia were high at week 5, and reduced afterwards but not changed by KRG or Rb1. In primary oligodendrocyte cultures, KRG or Rb1 increased expression of myelin genes, ER stress markers, and antioxidants. Interestingly, under cuprizone treatment, elevated ER stress markers were counteracted by KRG or Rb1. Under rotenone treatment, reduced myelin gene expressions were recovered by Rb1. In primary astrocyte cultures, KRG or Rb1 decreased LIF expression. Conclusion KRG and Rb1 may improve myelin regeneration during the remyelination phase in vivo, potentially by directly promoting myelin gene expression.
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Affiliation(s)
- Oh Wook Kwon
- Department of Integrative Biosciences, University of Brain Education, Cheonan, Republic of Korea
| | - Dalnim Kim
- Korea Institute of Brain Science, Seoul, Republic of Korea
| | - Eugene Koh
- Temasek Life Sciences Laboratories, Singapore
| | - Hyun-Jeong Yang
- Department of Integrative Biosciences, University of Brain Education, Cheonan, Republic of Korea
- Korea Institute of Brain Science, Seoul, Republic of Korea
- Department of Integrative Healthcare, University of Brain Education, Cheonan, Republic of Korea
- Corresponding author. Department of Integrative Biosciences, University of Brain Education, 284-31, Gyochonjisan-gil, Mokcheon-eup, Dongnam-gu, Cheonan-si, Chungcheongnam-do, 31228, Republic of Korea.
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26
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Preziosa P, Pagani E, Meani A, Marchesi O, Conti L, Falini A, Rocca MA, Filippi M. NODDI, diffusion tensor microstructural abnormalities and atrophy of brain white matter and gray matter contribute to cognitive impairment in multiple sclerosis. J Neurol 2023; 270:810-823. [PMID: 36201016 DOI: 10.1007/s00415-022-11415-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/29/2022] [Accepted: 09/30/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Pathologically specific MRI measures may elucidate in-vivo the heterogeneous processes contributing to cognitive impairment in multiple sclerosis (MS). PURPOSE Using diffusion tensor and neurite orientation dispersion and density imaging (NODDI), we explored the contribution of focal lesions and normal-appearing (NA) tissue microstructural abnormalities to cognitive impairment in MS. METHODS One hundred and fifty-two MS patients underwent 3 T brain MRI and a neuropsychological evaluation. Forty-eight healthy controls (HC) were also scanned. Fractional anisotropy (FA), mean diffusivity (MD), intracellular volume fraction (ICV_f) and orientation dispersion index (ODI) were assessed in cortical and white matter (WM) lesions, thalamus, NA cortex and NAWM. Predictors of cognitive impairment were identified using random forest. RESULTS Fifty-two MS patients were cognitively impaired. Compared to cognitively preserved, impaired MS patients had higher WM lesion volume (LV), lower normalized brain volume (NBV), cortical volume (NCV), thalamic volume (NTV), and WM volume (p ≤ 0.021). They also showed lower NAWM FA, higher NAWM, NA cortex and thalamic MD, lower NAWM ICV_f, lower WM lesion ODI, and higher NAWM ODI (false discovery rate-p ≤ 0.026). Cortical lesion number and microstructural abnormalities were not significantly different. The best MRI predictors of cognitive impairment (relative importance) (out-of-bag area under the curve = 0.727) were NAWM FA (100%), NTV (96.0%), NBV (84.7%), thalamic MD (43.4%), NCV (40.6%), NA cortex MD (26.0%), WM LV (23.2%) and WM lesion ODI (17.9%). CONCLUSIONS Our multiparametric MRI study including NODDI measures suggested that neuro-axonal damage and loss of microarchitecture integrity in focal WM lesions, NAWM, and GM contribute to cognitive impairment in MS.
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Affiliation(s)
- 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
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Alessandro Meani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Olga Marchesi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Lorenzo Conti
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, Division of Neuroscience, 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, 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. .,Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy. .,Vita-Salute San Raffaele University, Milan, Italy.
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27
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Edwards EM, Stanley JA, Daugherty AM, Lynn J, Borich MR, Fritz NE. Associations between myelin water imaging and measures of fall risk and functional mobility in multiple sclerosis. J Neuroimaging 2023; 33:94-101. [PMID: 36266780 DOI: 10.1111/jon.13064] [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: 07/25/2022] [Revised: 09/26/2022] [Accepted: 10/08/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND AND PURPOSE Myelin water fraction (MWF) deficits as measured by myelin water imaging (MWI) have been related to worse motor function in persons with multiple sclerosis (PwMS). However, it is unknown if measures from MWI metrics in motor areas relate to fall risk measures in PwMS. The objective of this study was to examine the relationship between MWI measures in motor areas to performance on clinical measures of fall risk and disability in PwMS. METHODS Sixteen individuals with relapsing-remitting MS participated (1 male, 15 female; age 47.1 years [12.3]; Expanded Disability Status Scale 4.0 [range 0-6.5]) and completed measures of walking and fall risk (Timed 25 Foot Walk [T25FW] and Timed Up and Go). MWF and the geometric mean of the intra-/extracellular water T2 (geomT2IEW ) values reflecting myelin content and contribution of large-diameter axons/density, respectively, were assessed in three motor-related regions. RESULTS The geomT2IEW of the corticospinal tract (r = -.599; p = .018) and superior cerebellar peduncles (r = -.613; p = .015) demonstrated significant inverse relationships with T25FW, suggesting that decreased geomT2IEW was related to slower walking. Though not significant, MWF in the corticospinal tract and superior cerebellar peduncles also demonstrated fair relationships with the T25FW, suggesting that worse performance on the T25FW was associated with lower MWF values. CONCLUSIONS MWI of key motor regions was associated with walking performance in PwMS. Further MWI studies are needed to identify relationships between pathology and clinical function in PwMS to guide targeted rehabilitation therapies aimed at preventing falls.
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Affiliation(s)
- Erin M Edwards
- Translational Neuroscience Program, Wayne State University, Detroit, Michigan, USA.,Neuroimaging and Neurorehabilitation Laboratory, Wayne State University, Detroit, Michigan, USA
| | - Jeffrey A Stanley
- Translational Neuroscience Program, Wayne State University, Detroit, Michigan, USA.,Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, Michigan, USA
| | - Ana M Daugherty
- Department of Psychology, Wayne State University, Detroit, Michigan, USA.,Institute of Gerontology, Wayne State University, Detroit, Michigan, USA
| | - Jonathan Lynn
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, Michigan, USA
| | - Michael R Borich
- Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Nora E Fritz
- Translational Neuroscience Program, Wayne State University, Detroit, Michigan, USA.,Neuroimaging and Neurorehabilitation Laboratory, Wayne State University, Detroit, Michigan, USA.,Department of Health Care Sciences, Wayne State University, Detroit, Michigan, USA.,Department of Neurology, Wayne State University, Detroit, Michigan, USA
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Present and future of the diagnostic work-up of multiple sclerosis: the imaging perspective. J Neurol 2023; 270:1286-1299. [PMID: 36427168 PMCID: PMC9971159 DOI: 10.1007/s00415-022-11488-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 11/26/2022]
Abstract
In recent years, the use of magnetic resonance imaging (MRI) for the diagnostic work-up of multiple sclerosis (MS) has evolved considerably. The 2017 McDonald criteria show high sensitivity and accuracy in predicting a second clinical attack in patients with a typical clinically isolated syndrome and allow an earlier diagnosis of MS. They have been validated, are evidence-based, simplify the clinical use of MRI criteria and improve MS patients' management. However, to limit the risk of misdiagnosis, they should be applied by expert clinicians only after the careful exclusion of alternative diagnoses. Recently, new MRI markers have been proposed to improve diagnostic specificity for MS and reduce the risk of misdiagnosis. The central vein sign and chronic active lesions (i.e., paramagnetic rim lesions) may increase the specificity of MS diagnostic criteria, but further effort is necessary to validate and standardize their assessment before implementing them in the clinical setting. The feasibility of subpial demyelination assessment and the clinical relevance of leptomeningeal enhancement evaluation in the diagnostic work-up of MS appear more limited. Artificial intelligence tools may capture MRI attributes that are beyond the human perception, and, in the future, artificial intelligence may complement human assessment to further ameliorate the diagnostic work-up and patients' classification. However, guidelines that ensure reliability, interpretability, and validity of findings obtained from artificial intelligence approaches are still needed to implement them in the clinical scenario. This review provides a summary of the most recent updates regarding the application of MRI for the diagnosis of MS.
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Chen X, Schädelin S, Lu PJ, Ocampo-Pineda M, Weigel M, Barakovic M, Ruberte E, Cagol A, Marechal B, Kober T, Kuhle J, Kappos L, Melie-Garcia L, Granziera C. Personalized maps of T1 relaxometry abnormalities provide correlates of disability in multiple sclerosis patients. Neuroimage Clin 2023; 37:103349. [PMID: 36801600 PMCID: PMC9958406 DOI: 10.1016/j.nicl.2023.103349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 02/08/2023] [Accepted: 02/10/2023] [Indexed: 02/16/2023]
Abstract
OBJECTIVES AND AIMS Quantitative MRI (qMRI) has greatly improved the sensitivity and specificity of microstructural brain pathology in multiple sclerosis (MS) when compared to conventional MRI (cMRI). More than cMRI, qMRI also provides means to assess pathology within the normal-appearing and lesion tissue. In this work, we further developed a method providing personalized quantitative T1 (qT1) abnormality maps in individual MS patients by modeling the age dependence of qT1 alterations. In addition, we assessed the relationship between qT1 abnormality maps and patients' disability, in order to evaluate the potential value of this measurement in clinical practice. METHODS We included 119 MS patients (64 relapsing-remitting MS (RRMS), 34 secondary progressive MS (SPMS), 21 primary progressive MS (PPMS)), and 98 Healthy Controls (HC). All individuals underwent 3T MRI examinations, including Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) for qT1 maps and High-Resolution 3D Fluid Attenuated Inversion Recovery (FLAIR) imaging. To calculate personalized qT1 abnormality maps, we compared qT1 in each brain voxel in MS patients to the average qT1 obtained in the same tissue (grey/white matter) and region of interest (ROI) in healthy controls, hereby providing individual voxel-based Z-score maps. The age dependence of qT1 in HC was modeled using linear polynomial regression. We computed the average qT1 Z-scores in white matter lesions (WMLs), normal-appearing white matter (NAWM), cortical grey matter lesions (GMcLs) and normal-appearing cortical grey matter (NAcGM). Lastly, a multiple linear regression (MLR) model with the backward selection including age, sex, disease duration, phenotype, lesion number, lesion volume and average Z-score (NAWM/NAcGM/WMLs/GMcLs) was used to assess the relationship between qT1 measures and clinical disability (evaluated with EDSS). RESULTS The average qT1 Z-score was higher in WMLs than in NAWM. (WMLs: 1.366 ± 0.409, NAWM: -0.133 ± 0.288, [mean ± SD], p < 0.001). The average Z-score in NAWM in RRMS patients was significantly lower than in PPMS patients (p = 0.010). The MLR model showed a strong association between average qT1 Z-scores in white matter lesions (WMLs) and EDSS (R2 = 0.549, β = 0.178, 97.5 % CI = 0.030 to 0.326, p = 0.019). Specifically, we measured a 26.9 % increase in EDSS per unit of qT1 Z-score in WMLs in RRMS patients (R2 = 0.099, β = 0.269, 97.5 % CI = 0.078 to 0.461, p = 0.007). CONCLUSIONS We showed that personalized qT1 abnormality maps in MS patients provide measures related to clinical disability, supporting the use of those maps in clinical practice.
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Affiliation(s)
- Xinjie Chen
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Sabine Schädelin
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Mario Ocampo-Pineda
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland; Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Esther Ruberte
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Alessandro Cagol
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Benedicte Marechal
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Jens Kuhle
- Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
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Cappelle S, Pareto D, Sunaert S, Smets I, Laenen A, Dubois B, Demaerel P. T1w/FLAIR ratio standardization as a myelin marker in MS patients. Neuroimage Clin 2022; 36:103248. [PMID: 36451354 PMCID: PMC9668645 DOI: 10.1016/j.nicl.2022.103248] [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: 03/30/2022] [Revised: 10/20/2022] [Accepted: 10/23/2022] [Indexed: 11/06/2022]
Abstract
INTRODUCTION Calculation of a T1w/T2w ratio was introduced as a proxy for myelin integrity in the brain of multiple sclerosis (MS) patients. Since nowadays 3D FLAIR is commonly used for lesion detection instead of T2w images, we introduce a T1w/FLAIR ratio as an alternative for the T1w/T2w ratio. OBJECTIVES Bias and intensity variation are widely present between different scanners, between subjects and within subjects over time in T1w, T2w and FLAIR images. We present a standardized method for calculating a histogram calibrated T1w/FLAIR ratio to reduce bias and intensity variation in MR sequences from different scanners and at different time-points. MATERIAL AND METHODS 207 Relapsing Remitting MS patients were scanned on 4 different 3 T scanners with a protocol including 3D T1w, 2D T2w and 3D FLAIR images. After bias correction, T1w/FLAIR ratio maps and T1w/T2w ratio maps were calculated in 4 different ways: without calibration, with linear histogram calibration as described by Ganzetti et al. (2014), and by using 2 methods of non-linear histogram calibration. The first nonlinear calibration uses a template of extra-cerebral tissue and cerebrospinal fluid (CSF) brought from Montreal Neurological Institute (MNI) space to subject space; for the second nonlinear method we used an extra-cerebral tissue and CSF template of our own subjects. Additionally, we segmented several brain structures such as Normal Appearing White Matter (NAWM), Normal Appearing Grey Matter (NAGM), corpus callosum, thalami and MS lesions using Freesurfer and Samseg. RESULTS The coefficient of variation of T1w/FLAIR ratio in NAWM for the no calibrated, linear, and 2 nonlinear calibration methods were respectively 24, 19.1, 9.5, 13.8. The nonlinear methods of calibration showed the best results for calculating the T1w/FLAIR ratio with a smaller dispersion of the data and a smaller overlap of T1w/FLAIR ratio in the different segmented brain structures. T1w/T2w and T1w/FLAIR ratios showed a wider range of values compared to MTR values. CONCLUSIONS Calibration of T1w/T2w and T1w/FLAIR ratio maps is imperative to account for the sources of variation described above. The nonlinear calibration methods showed the best reduction of between-subject and within-subject variability. The T1w/T2w and T1w/FLAIR ratio seem to be more sensitive to smaller changes in tissue integrity than MTR. Future work is needed to determine the exact substrate of T1w/FLAIR ratio and to obtain correlations with clinical outcome.
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Affiliation(s)
- S. Cappelle
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium,Corresponding author
| | - D. Pareto
- Department of Radiology (IDI), Vall d’Hebron University Hospital, Barcelona, Spain
| | - S. Sunaert
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium,Department of Imaging & Pathology, Translational MRI, KU Leuven, Leuven, Belgium
| | - I. Smets
- Laboratory for Neuroimmunology, KU Leuven, Leuven, Belgium,Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - A. Laenen
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, KU Leuven and Hasselt University, Leuven, Belgium
| | - B. Dubois
- Laboratory for Neuroimmunology, KU Leuven, Leuven, Belgium,Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Ph. Demaerel
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium,Department of Imaging & Pathology, Translational MRI, KU Leuven, Leuven, Belgium
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31
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York EN, Meijboom R, Thrippleton MJ, Bastin ME, Kampaite A, White N, Chandran S, Waldman AD. Longitudinal microstructural MRI markers of demyelination and neurodegeneration in early relapsing-remitting multiple sclerosis: Magnetisation transfer, water diffusion and g-ratio. Neuroimage Clin 2022; 36:103228. [PMID: 36265199 PMCID: PMC9668599 DOI: 10.1016/j.nicl.2022.103228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 10/07/2022] [Accepted: 10/08/2022] [Indexed: 11/11/2022]
Abstract
INTRODUCTION Quantitative microstructural MRI, such as myelin-sensitive magnetisation transfer ratio (MTR) or saturation (MTsat), axon-sensitive water diffusion Neurite Orientation Dispersion and Density Imaging (NODDI), and the aggregate g-ratio, may provide more specific markers of white matter integrity than conventional MRI for early patient stratification in relapsing-remitting multiple sclerosis (RRMS). The aim of this study was to determine the sensitivity of such markers to longitudinal pathological change within cerebral white matter lesions (WML) and normal-appearing white matter (NAWM) in recently diagnosed RRMS. METHODS Seventy-nine people with recently diagnosed RRMS, from the FutureMS longitudinal cohort, were recruited to an extended MRI protocol at baseline and one year later. Twelve healthy volunteers received the same MRI protocol, repeated within two weeks. Ethics approval and written informed consent were obtained. 3T MRI included magnetisation transfer, and multi-shell diffusion-weighted imaging. NAWM and whole brain were segmented from 3D T1-weighted MPRAGE, and WML from T2-weighted FLAIR. MTR, MTsat, NODDI isotropic (ISOVF) and intracellular (ICVF) volume fractions, and g-ratio (calculated from MTsat and NODDI data) were measured within WML and NAWM. Brain parenchymal fraction (BPF) was also calculated. Longitudinal change in BPF and microstructural metrics was assessed with paired t-tests (α = 0.05) and linear mixed models, adjusted for confounding factors with False Discovery Rate (FDR) correction for multiple comparisons. Longitudinal changes were compared with test-retest Bland-Altman limits of agreement from healthy control white matter. The influence of longitudinal change on g-ratio was explored through post-hoc analysis in silico by computing g-ratio with realistic simulated MTsat and NODDI values. RESULTS In NAWM, g-ratio and ICVF increased, and MTsat decreased over one year (adjusted mean difference = 0.007, 0.005, and -0.057 respectively, all FDR-corrected p < 0.05). There was no significant change in MTR, ISOVF, or BPF. In WML, MTsat, NODDI ICVF and ISOVF increased over time (adjusted mean difference = 0.083, 0.024 and 0.016, respectively, all FDR-corrected p < 0.05). Group-level longitudinal changes exceeded test-retest limits of agreement for NODDI ISOVF and ICVF in WML only. In silico analysis showed g-ratio may increase due to a decrease in MTsat or ISOVF, or an increase in ICVF. DISCUSSION G-ratio and MTsat changes in NAWM over one year may indicate subtle myelin loss in early RRMS, which were not apparent with BPF or NAWM MTR. Increases in NAWM and WML NODDI ICVF were not anticipated, and raise the possibility of axonal swelling or morphological change. Increases in WML MTsat may reflect myelin repair. Changes in NODDI ISOVF are more likely to reflect alterations in water content. Competing MTsat and ICVF changes may account for the absence of g-ratio change in WML. Longitudinal changes in microstructural measures are significant at a group level, however detection in individual patients in early RRMS is limited by technique reproducibility. CONCLUSION MTsat and g-ratio are more sensitive than MTR to early pathological changes in RRMS, but complex dependence of g-ratio on NODDI parameters limit the interpretation of aggregate measures in isolation. Improvements in technique reproducibility and validation of MRI biophysical models across a range of pathological tissue states are needed.
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Affiliation(s)
- Elizabeth N York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom; Anne Rowling Regenerative Neurology Clinic, Edinburgh, United Kingdom.
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Nicole White
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Anne Rowling Regenerative Neurology Clinic, Edinburgh, United Kingdom; UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom.
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32
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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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Kolb H, Al-Louzi O, Beck ES, Sati P, Absinta M, Reich DS. From pathology to MRI and back: Clinically relevant biomarkers of multiple sclerosis lesions. Neuroimage Clin 2022; 36:103194. [PMID: 36170753 PMCID: PMC9668624 DOI: 10.1016/j.nicl.2022.103194] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/07/2022] [Accepted: 09/09/2022] [Indexed: 12/14/2022]
Abstract
Focal lesions in both white and gray matter are characteristic of multiple sclerosis (MS). Histopathological studies have helped define the main underlying pathological processes involved in lesion formation and evolution, serving as a gold standard for many years. However, histopathology suffers from an intrinsic bias resulting from over-reliance on tissue samples from late stages of the disease or atypical cases and is inadequate for routine patient assessment. Pathological-radiological correlative studies have established advanced MRI's sensitivity to several relevant MS-pathological substrates and its practicality for assessing dynamic changes and following lesions over time. This review focuses on novel imaging techniques that serve as biomarkers of critical pathological substrates of MS lesions: the central vein, chronic inflammation, remyelination and repair, and cortical lesions. For each pathological process, we address the correlative value of MRI to MS pathology, its contribution in elucidating MS pathology in vivo, and the clinical utility of the imaging biomarker.
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Affiliation(s)
- Hadar Kolb
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA,Department of Neurology, Tel Aviv Sourasky Medical Center, Tel Aviv-Yaffo, Israel,Corresponding author at: Department of Neurology, Tel Aviv Sourasky Medical Center, Tel Aviv-Yaffo, Israel.
| | - Omar Al-Louzi
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA,Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Erin S. Beck
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA,Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA,Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Martina Absinta
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA,Institute of Experimental Neurology (INSPE), IRCSS San Raffaele Hospital and Vita-Salute San Raffaele University, Milan, Italy,Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Daniel S. Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA
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Foit NA, Yung S, Lee HM, Bernasconi A, Bernasconi N, Hong SJ. A whole-brain 3D myeloarchitectonic atlas: Mapping the Vogt-Vogt legacy to the cortical surface. Neuroimage 2022; 263:119617. [PMID: 36084859 DOI: 10.1016/j.neuroimage.2022.119617] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 09/03/2022] [Accepted: 09/05/2022] [Indexed: 11/18/2022] Open
Abstract
Building precise and detailed parcellations of anatomically and functionally distinct brain areas has been a major focus in Neuroscience. Pioneer anatomists parcellated the cortical manifold based on extensive histological studies of post-mortem brain, harnessing local variations in cortical cyto- and myeloarchitecture to define areal boundaries. Compared to the cytoarchitectonic field, where multiple neuroimaging studies have recently translated this old legacy data into useful analytical resources, myeloarchitectonics, which parcellate the cortex based on the organization of myelinated fibers, has received less attention. Here, we present the neocortical surface-based myeloarchitectonic atlas based on the histology-derived maps of the Vogt-Vogt school and its 2D translation by Nieuwenhuys. In addition to a myeloarchitectonic parcellation, our package includes intracortical laminar profiles of myelin content based on Vogt-Vogt-Hopf original publications. Histology-derived myelin density mapped on our atlas demonstrated a close overlap with in vivo quantitative MRI markers for myelin and relates to cytoarchitectural features. Complementing the existing battery of approaches for digital cartography, the whole-brain myeloarchitectonic atlas offers an opportunity to validate imaging surrogate markers of myelin in both health and disease.
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Affiliation(s)
- Niels A Foit
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada; Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Seles Yung
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
| | - Hyo Min Lee
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
| | - Seok-Jun Hong
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada; Center for the Developing Brain, Child Mind Institute, NY, USA; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea.
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35
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Rahmanzadeh R, Weigel M, Lu PJ, Melie-Garcia L, Nguyen TD, Cagol A, La Rosa F, Barakovic M, Lutti A, Wang Y, Bach Cuadra M, Radue EW, Gaetano L, Kappos L, Kuhle J, Magon S, Granziera C. A comparative assessment of myelin-sensitive measures in multiple sclerosis patients and healthy subjects. Neuroimage Clin 2022; 36:103177. [PMID: 36067611 PMCID: PMC9468574 DOI: 10.1016/j.nicl.2022.103177] [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: 03/01/2022] [Revised: 08/22/2022] [Accepted: 08/27/2022] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Multiple Sclerosis (MS) is a common neurological disease primarily characterized by myelin damage in lesions and in normal - appearing white and gray matter (NAWM, NAGM). Several quantitative MRI (qMRI) methods are sensitive to myelin characteristics by measuring specific tissue biophysical properties. However, there are currently few studies assessing the relative reproducibility and sensitivity of qMRI measures to MS pathology in vivo in patients. METHODS We performed two studies. The first study assessed of the sensitivity of qMRI measures to MS pathology: in this work, we recruited 150 MS and 100 healthy subjects, who underwent brain MRI at 3 T including quantitative T1 mapping (qT1), quantitative susceptibility mapping (QSM), magnetization transfer saturation imaging (MTsat) and myelin water imaging for myelin water fraction (MWF). The sensitivity of qMRIs to MS focal pathology (MS lesions vs peri-plaque white/gray matter (PPWM/PPGM)) was studied lesion-wise; the sensitivity to diffuse normal appearing (NA) pathology was measured using voxel-wise threshold-free cluster enhancement (TFCE) in NAWM and vertex-wise inflated cortex analysis in NAGM. Furthermore, the sensitivity of qMRI to the identification of lesion tissue was investigated using a voxel-wise logistic regression analysis to distinguish MS lesion and PP voxels. The second study assessed the reproducibility of myelin-sensitive qMRI measures in a single scanner. To evaluate the intra-session and inter-session reproducibility of qMRI measures, we have investigated 10 healthy subjects, who underwent two brain 3 T MRIs within the same day (without repositioning), and one after 1-week interval. Five region of interest (ROIs) in white and deep grey matter areas were segmented, and inter- and intra- session reproducibility was studied using the intra-class correlation coefficient (ICC). Further, we also investigated the voxel-wise reproducibility of qMRI measures in NAWM and NAGM. RESULTS qT1 and QSM showed the highest sensitivity to distinguish MS focal WM and cortical pathology from peri-plaque WM (P < 0.0001), although QSM also showed the highest variance when applied to lesions. MWF and MTsat exhibited the highest sensitivity to NAWM pathology (P < 0.01). On the other hand, qT1 appeared to be the most sensitive measure to NAGM pathology (P < 0.01). All myelin-sensitive qMRI measures exhibited high inter/intra sessional ICCs in various WM and deep GM ROIs, in NAWM and in NAGM (ICC 0.82 ± 0.12). CONCLUSION This work shows that the applied qT1, MWF, MTsat and QSM are highly reproducible and exhibit differential sensitivity to focal and diffuse WM and GM pathology in MS patients.
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Affiliation(s)
- Reza Rahmanzadeh
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland,Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | - Alessandro Cagol
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland,CIBM Center for Biomedical Imaging, Lausanne, Switzerland,Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland,CIBM Center for Biomedical Imaging, Lausanne, Switzerland,Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland
| | - Ernst-Wilhelm Radue
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | | | - Ludwig Kappos
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Stefano Magon
- Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland,Corresponding author.
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Oladosu O, Liu WQ, Brown L, Pike BG, Metz LM, Zhang Y. Advanced diffusion MRI and image texture analysis detect widespread brain structural differences between relapsing-remitting and secondary progressive multiple sclerosis. Front Hum Neurosci 2022; 16:944908. [PMID: 36034111 PMCID: PMC9413838 DOI: 10.3389/fnhum.2022.944908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/18/2022] [Indexed: 11/29/2022] Open
Abstract
Introduction Disease development in multiple sclerosis (MS) causes dramatic structural changes, but the exact changing patterns are unclear. Our objective is to investigate the differences in brain structure locally and spatially between relapsing-remitting MS (RRMS) and its advanced form, secondary progressive MS (SPMS), through advanced analysis of diffusion magnetic resonance imaging (MRI) and image texture. Methods A total of 20 patients with RRMS and nine patients with SPMS from two datasets underwent 3T anatomical and diffusion tensor imaging (DTI). The DTI was harmonized, augmented, and then modeled, which generated six voxel- and sub-voxel-scale measures. Texture analysis focused on T2 and FLAIR MRI, which produced two phase-based measures, namely, phase congruency and weighted mean phase. Data analysis was 3-fold, i.e., histogram analysis of whole-brain normal appearing white matter (NAWM); region of interest (ROI) analysis of NAWM and lesions within three critical white matter tracts, namely, corpus callosum, corticospinal tract, and optic radiation; and along-tract statistics. Furthermore, by calculating the z-score of core-rim pathology within lesions based on diffusion measures, we developed a novel method to define chronic active lesions and compared them between cohorts. Results Histogram features from diffusion and all but one texture measure differentiated between RRMS and SPMS. Within-tract ROI analysis detected cohort differences in both NAWM and lesions of the corpus callosum body in three measures of neurite orientation and anisotropy. Along-tract statistics detected cohort differences from multiple measures, particularly lesion extent, which increased significantly in SPMS in posterior corpus callosum and optic radiations. The number of chronic active lesions were also significantly higher (by 5–20% over z-scores 0.5 and 1.0) in SPMS than RRMS based on diffusion anisotropy, neurite content, and diameter. Conclusion Advanced diffusion MRI and texture analysis may be promising approaches for thorough understanding of brain structural changes from RRMS to SPMS, thereby providing new insight into disease development mechanisms in MS.
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Affiliation(s)
- Olayinka Oladosu
- Department of Neuroscience, Faculty of Graduate Studies, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Wei-Qiao Liu
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Lenora Brown
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Bruce G. Pike
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Luanne M. Metz
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yunyan Zhang
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- *Correspondence: Yunyan Zhang
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Update on myelin imaging in neurological syndromes. Curr Opin Neurol 2022; 35:467-474. [PMID: 35788545 DOI: 10.1097/wco.0000000000001078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Myelin water imaging (MWI) is generally regarded as the most rigorous approach for noninvasive, in-vivo measurement of myelin content, which has been histopathologically validated. As such, it has been increasingly applied to neurological diseases with white matter involvement, especially those affecting myelin. This review provides an overview of the most recent research applying MWI in neurological syndromes. RECENT FINDINGS Myelin water imaging has been applied in neurological syndromes including multiple sclerosis, Alzheimer's disease, Huntington's disease, traumatic brain injury, Parkinson's disease, cerebral small vessel disease, leukodystrophies and HIV. These syndromes generally showed alterations observable with MWI, with decreased myelin content tending to correlate with lower cognitive scores and worse clinical presentation. MWI has also been correlated with genetic variation in the APOE and PLP1 genes, demonstrating genetic factors related to myelin health. SUMMARY MWI can detect and quantify changes not observable with conventional imaging, thereby providing insight into the pathophysiology and disease mechanisms of a diverse range of neurological syndromes.
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Rahmanzadeh R, Galbusera R, Lu PJ, Bahn E, Weigel M, Barakovic M, Franz J, Nguyen TD, Spincemaille P, Schiavi S, Daducci A, La Rosa F, Absinta M, Sati P, Cuadra MB, Radue EW, Leppert D, Kuhle J, Kappos L, Brück W, Reich DS, Stadelmann C, Wang Y, Granziera C. A new advanced MRI biomarker for remyelinated lesions in Multiple Sclerosis. Ann Neurol 2022; 92:486-502. [PMID: 35713309 PMCID: PMC9527017 DOI: 10.1002/ana.26441] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 06/12/2022] [Accepted: 06/14/2022] [Indexed: 11/28/2022]
Abstract
Objectives Neuropathological studies have shown that multiple sclerosis (MS) lesions are heterogeneous in terms of myelin/axon damage and repair as well as iron content. However, it remains a challenge to identify specific chronic lesion types, especially remyelinated lesions, in vivo in patients with MS. Methods We performed 3 studies: (1) a cross‐sectional study in a prospective cohort of 115 patients with MS and 76 healthy controls, who underwent 3 T magnetic resonance imaging (MRI) for quantitative susceptibility mapping (QSM), myelin water fraction (MWF), and neurite density index (NDI) maps. White matter (WM) lesions in QSM were classified into 5 QSM lesion types (iso‐intense, hypo‐intense, hyperintense, lesions with hypo‐intense rims, and lesions with paramagnetic rim legions [PRLs]); (2) a longitudinal study of 40 patients with MS to study the evolution of lesions over 2 years; (3) a postmortem histopathology‐QSM validation study in 3 brains of patients with MS to assess the accuracy of QSM classification to identify neuropathological lesion types in 63 WM lesions. Results At baseline, hypo‐ and isointense lesions showed higher mean MWF and NDI values compared to other QSM lesion types (p < 0.0001). Further, at 2‐year follow‐up, hypo‐/iso‐intense lesions showed an increase in MWF. Postmortem analyses revealed that QSM highly accurately identifies (1) fully remyelinated areas as hypo‐/iso‐intense (sensitivity = 88.89% and specificity = 100%), (2) chronic inactive lesions as hyperintense (sensitivity = 71.43% and specificity = 92.00%), and (3) chronic active/smoldering lesions as PRLs (sensitivity = 92.86% and specificity = 86.36%). Interpretation These results provide the first evidence that it is possible to distinguish chronic MS lesions in a clinical setting, hereby supporting with new biomarkers to develop and assess remyelinating treatments. ANN NEUROL 2022;92:486–502
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Affiliation(s)
- Reza Rahmanzadeh
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Riccardo Galbusera
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Erik Bahn
- Institute of Neuropathology, University Medical Center, Göttingen, Germany
| | - Matthias Weigel
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.,Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jonas Franz
- Institute of Neuropathology, University Medical Center, Göttingen, Germany.,Max Planck Institute for Experimental Medicine, Göttingen, Germany.,Campus Institute for Dynamics of Biological Networks, University of Göttingen, Göttingen, Germany
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | | | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Martina Absinta
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Institute of Experimental Neurology, Division of Neuroscience, Vita-Salute San Raffaele University and Hospital, Milan, Italy
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, NIH, 10 Center Drive MSC 1400, Building 10 Room 5C103, Bethesda, Maryland, USA.,Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Ernst-Wilhelm Radue
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - David Leppert
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Wolfgang Brück
- Institute of Neuropathology, University Medical Center, Göttingen, Germany
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, NIH, 10 Center Drive MSC 1400, Building 10 Room 5C103, Bethesda, Maryland, USA
| | | | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Cristina Granziera
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
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Dimov AV, Gillen KM, Nguyen TD, Kang J, Sharma R, Pitt D, Gauthier SA, Wang Y. Magnetic Susceptibility Source Separation Solely from Gradient Echo Data: Histological Validation. Tomography 2022; 8:1544-1551. [PMID: 35736875 PMCID: PMC9228115 DOI: 10.3390/tomography8030127] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/09/2022] [Accepted: 06/11/2022] [Indexed: 11/17/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) facilitates mapping of the bulk magnetic susceptibility of tissue from the phase of complex gradient echo (GRE) MRI data. QSM phase processing combined with an R2* model of magnitude of multiecho gradient echo data (R2*QSM) allows separation of dia- and para-magnetic components (e.g., myelin and iron) that contribute constructively to R2* value but destructively to the QSM value of a voxel. This R2*QSM technique is validated against quantitative histology—optical density of myelin basic protein and Perls’ iron histological stains of rim and core of 10 ex vivo multiple sclerosis lesions, as well as neighboring normal appearing white matter. We found that R2*QSM source maps are in good qualitative agreement with histology, e.g., showing increased iron concentration at the edge of the rim+ lesions and myelin loss in the lesions’ core. Furthermore, our results indicate statistically significant correlation between paramagnetic and diamagnetic tissue components estimated with R2*QSM and optical densities of Perls’ and MPB stains. These findings provide direct support for the use of R2*QSM magnetic source separation based solely on GRE complex data to characterize MS lesion composition.
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Affiliation(s)
- Alexey V. Dimov
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (A.V.D.); (K.M.G.); (T.D.N.); (J.K.); (R.S.)
| | - Kelly M. Gillen
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (A.V.D.); (K.M.G.); (T.D.N.); (J.K.); (R.S.)
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (A.V.D.); (K.M.G.); (T.D.N.); (J.K.); (R.S.)
| | - Jerry Kang
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (A.V.D.); (K.M.G.); (T.D.N.); (J.K.); (R.S.)
| | - Ria Sharma
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (A.V.D.); (K.M.G.); (T.D.N.); (J.K.); (R.S.)
| | - David Pitt
- Department of Neurology, Yale Medicine, New Haven, CT 06511, USA;
| | - Susan A. Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, NY 10022, USA;
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA; (A.V.D.); (K.M.G.); (T.D.N.); (J.K.); (R.S.)
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14850, USA
- Correspondence:
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40
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Dimov AV, Nguyen TD, Gillen KM, Marcille M, Spincemaille P, Pitt D, Gauthier SA, Wang Y. Susceptibility source separation from gradient echo data using magnitude decay modeling. J Neuroimaging 2022; 32:852-859. [PMID: 35668022 DOI: 10.1111/jon.13014] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 05/17/2022] [Accepted: 05/19/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND PURPOSE The objective is to demonstrate feasibility of separating magnetic sources in quantitative susceptibility mapping (QSM) by incorporating magnitude decay rates R 2 ∗ $R_2^{\rm{*}}$ in gradient echo (GRE) MRI. METHODS Magnetic susceptibility source separation was developed using R 2 ∗ $R_2^{\rm{*}}$ and compared with a prior method using R 2 ' = R 2 ∗ - R 2 ${R^{\prime}_2} = R_2^* - {R_2}$ that required an additional sequence to measure the transverse relaxation rate R2 . Both susceptibility separation methods were compared in multiple sclerosis (MS) patients (n = 17). Susceptibility values of negative sources estimated with R 2 ∗ $R_2^{\rm{*}}$ -based source separation in a set of enhancing MS lesions (n = 44) were correlated against longitudinal myelin water fraction (MWF) changes. RESULTS In in vivo data, linear regression of the estimated χ + ${\chi}^{+}$ and χ - ${\chi}^{-}$ susceptibility values between the R 2 ∗ $R_2^*$ - and the R 2 ' ${R^{\prime}_2}$ -based separation methods performed across 182 segmented lesions revealed correlation coefficient r = .96 and slope close .99. Correlation analysis in enhancing lesions revealed a significant positive association between the χ - ${\chi}^{-}$ increase at 1-year post-onset relative to 0 year and the MWF increase at 1 year relative to 0 year (β = -0.144, 95% confidence interval: [-0.199, -0.1], p = .0008) and good agreement between R 2 ' ${R^{\prime}_2}$ and R 2 ∗ $R_2^*$ methods (r = .79, slope = .95). CONCLUSIONS Separation of magnetic sources based solely on GRE complex data is feasible by combining magnitude decay rate modeling and phase-based QSM and χ - ${\chi}^{-}$ change may serve as a biomarker for myelin recovery or damage in acute MS lesions.
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Affiliation(s)
- Alexey V Dimov
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Kelly M Gillen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Melanie Marcille
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | | | - David Pitt
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Susan A Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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Preziosa P, Pagani E, Bonacchi R, Cacciaguerra L, Falini A, Rocca MA, Filippi M. In vivo detection of damage in multiple sclerosis cortex and cortical lesions using NODDI. J Neurol Neurosurg Psychiatry 2022; 93:628-636. [PMID: 34799405 DOI: 10.1136/jnnp-2021-327803] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/28/2021] [Indexed: 11/03/2022]
Abstract
OBJECTIVE To characterise in vivo the microstructural abnormalities of multiple sclerosis (MS) normal-appearing (NA) cortex and cortical lesions (CLs) and their relations with clinical phenotypes and disability using neurite orientation dispersion and density imaging (NODDI). METHODS One hundred and seventy-two patients with MS (101 relapsing-remitting multiple sclerosis (RRMS), 71 progressive multiple sclerosis (PMS)) and 62 healthy controls (HCs) underwent a brain 3T MRI. Brain cortex and CLs were segmented from three-dimensional T1-weighted and double inversion recovery sequences. Using NODDI on diffusion-weighted sequence, intracellular volume fraction (ICV_f) and Orientation Dispersion Index (ODI) were assessed in NA cortex and CLs with default or optimised parallel diffusivity for the cortex (D//=1.7 or 1.2 µm2/ms, respectively). RESULTS The NA cortex of patients with MS had significantly lower ICV_f versus HCs' cortex with both D// values (false discovery rate (FDR)-p <0.001). CLs showed significantly decreased ICV_f and ODI versus NA cortex of both HCs and patients with MS with both D// values (FDR-p ≤0.008). Patients with PMS versus RRMS had significantly decreased NA cortex ICV_f and ODI (FDR-p=0.050 and FDR-p=0.032) with only D//=1.7 µm2/ms. No CL microstructural differences were found between MS clinical phenotypes. MS NA cortex ICV_f and ODI were significantly correlated with disease duration, clinical disability, lesion burden and global and regional brain atrophy (r from -0.51 to 0.71, FDR-p from <0.001 to 0.045). CONCLUSIONS A significant neurite loss occurs in MS NA cortex. CLs show a further neurite density reduction and a reduced ODI suggesting a simplification of neurite complexity. NODDI is relevant to investigate in vivo the heterogeneous pathology affecting the MS cortex.
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Affiliation(s)
- Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Raffaello Bonacchi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Laura Cacciaguerra
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy.,Vita-Salute San Raffaele University, Milano, Italy
| | - Andrea Falini
- Vita-Salute San Raffaele University, Milano, Italy.,Department of Neuroradiology, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, 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, Milano, Italy .,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy.,Vita-Salute San Raffaele University, Milano, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy.,Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milano, Italy
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Abstract
PURPOSE OF REVIEW Increasingly, therapeutic strategy in multiple sclerosis (MS) is informed by imaging and laboratory biomarkers, in addition to traditional clinical factors. Here, we review aspects of monitoring the efficacy and risks of disease-modifying therapy (DMT) with both conventional and emerging MRI and laboratory measures. RECENT FINDINGS The adoption of consensus-driven, stable MRI acquisition protocols and artificial intelligence-based, quantitative image analysis is heralding an era of precision monitoring of DMT efficacy. New MRI measures of compartmentalized inflammation, neuro-degeneration and repair complement traditional metrics but require validation before use in individual patients. Laboratory markers of brain cellular injury, such as neurofilament light, are robust outcomes in DMT efficacy trials; their use in clinical practice is being refined. DMT-specific laboratory monitoring for safety is critical and may include lymphocytes, immunoglobulins, autoimmunity surveillance, John Cunningham virus serology and COVID-19 vaccination seroresponse. SUMMARY A biomarker-enhanced monitoring strategy has immediate clinical application, with growing evidence of long-term reductions in disability accrual when both clinically symptomatic and asymptomatic inflammatory activity is fully suppressed; and amelioration of the risks associated with therapy. Emerging MRI and blood-based measures will also become important tools for monitoring agents that target the innate immune system and promote neuro-repair.
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Sibgatulin R, Güllmar D, Deistung A, Enzinger C, Ropele S, Reichenbach JR. Magnetic susceptibility anisotropy in normal appearing white matter in multiple sclerosis from single-orientation acquisition. Neuroimage Clin 2022; 35:103059. [PMID: 35661471 PMCID: PMC9163587 DOI: 10.1016/j.nicl.2022.103059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 05/02/2022] [Accepted: 05/21/2022] [Indexed: 11/19/2022]
Abstract
Orientation dependence of QSM is studied in a large cohort of MS patients. Apparent magnetic susceptibility anisotropy (MSA) obtained from single-orientation QSM. Apparent MSA found decreased in optic radiation (OR) of MS patients. Apparent MSA decreases with lesion load in OR and with disease duration in splenium. Negative apparent MSA observed in SLF indicates limitations of the proposed method.
Quantitative susceptibility mapping (QSM) has been successfully applied to study changes in deep grey matter nuclei as well as in lesional tissue, but its application to white matter has been complicated by the observed orientation dependence of gradient echo signal. The anisotropic susceptibility tensor is thought to be at the origin of this orientation dependence, and magnetic susceptibility anisotropy (MSA) derived from this tensor has been proposed as a marker of the state and integrity of the myelin sheath and may therefore be of particular interest for the study of demyelinating pathologies such as multiple sclerosis (MS). Reconstruction of the susceptibility tensor, however, requires repeated measurements with multiple head orientations, rendering the approach impractical for clinical applications. In this study, we combined single-orientation QSM with fibre orientation information to assess apparent MSA in three white matter tracts, i.e., optic radiation (OR), splenium of the corpus callosum (SCC), and superior longitudinal fascicle (SLF), in two cohorts of 64 healthy controls and 89 MS patients. The apparent MSA showed a significant decrease in optic radiation in the MS cohort compared with healthy controls. It decreased in the MS cohort with increasing lesion load in OR and with disease duration in the splenium. All of this suggests demyelination in normal appearing white matter. However, the apparent MSA observed in the SLF pointed to potential systematic issues that require further exploration to realize the full potential of the presented approach. Despite the limitations of such single-orientation ROI-specific estimation, we believe that our clinically feasible approach to study degenerative changes in WM is worthy of further investigation.
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Affiliation(s)
- Renat Sibgatulin
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Philosophenweg 3, 07743 Jena, Germany
| | - Daniel Güllmar
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Philosophenweg 3, 07743 Jena, Germany
| | - Andreas Deistung
- University Clinic and Outpatient Clinic for Radiology, Department for Radiation Medicine, University Hospital Halle (Saale), Ernst-Grube-Str. 40, 06120 Halle (Saale), Germany
| | - Christian Enzinger
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036 Graz, Austria
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036 Graz, Austria
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Philosophenweg 3, 07743 Jena, Germany; Michael Stifel Center Jena for Data-Driven and Simulation Science, Friedrich-Schiller-University Jena, Jena, Germany
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44
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Advanced diffusion-weighted imaging models better characterize white matter neurodegeneration and clinical outcomes in multiple sclerosis. J Neurol 2022; 269:4729-4741. [DOI: 10.1007/s00415-022-11104-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 10/18/2022]
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Preziosa P, Pagani E, Meani A, Moiola L, Rodegher M, Filippi M, Rocca MA. Slowly Expanding Lesions Predict 9-Year Multiple Sclerosis Disease Progression. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2022; 9:9/2/e1139. [PMID: 35105685 PMCID: PMC8808355 DOI: 10.1212/nxi.0000000000001139] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 12/15/2021] [Indexed: 11/15/2022]
Abstract
Background and Objectives Chronic active lesions contribute to multiple sclerosis (MS) severity, but their association with long-term disease progression has not been evaluated yet. White matter (WM) lesions showing linear expansion over time on serial T1- and T2-weighted scans (i.e., slowly expanding lesions [SELs]) have been proposed as a marker of chronic inflammation. In this study, we assessed whether SEL burden and microstructural abnormalities were associated with Expanded Disability Status Scale (EDSS) score worsening and secondary progressive (SP) conversion at 9.1-year follow-up in patients with relapsing-remitting (RR) MS. Methods In 52 patients with RRMS, SELs were identified among WM lesions by linearly fitting the Jacobian of the nonlinear deformation field between time points obtained combining 3T brain T1- and T2-weighted scans acquired at baseline and months 6, 12, and 24. Logistic regression analysis was applied to investigate the associations of SEL number, volume, magnetization transfer ratio (MTR), and T1-weighted signal intensity with disability worsening (i.e., EDSS score increase) and SP conversion after a median follow-up of 9.1 years. Results At follow-up, 20/52 (38%) patients with MS showed EDSS score worsening; 13/52 (25%) showed SP conversion. A higher baseline EDSS score (for each point higher: OR = 3.15 [95% CI = 1.61; 8.38], p = 0.003), a higher proportion of SELs among baseline lesions (for each % increase: OR = 1.22 [1.04; 1.58], p = 0.04), and lower baseline MTR values of SELs (for each % higher: OR = 0.66 [0.41; 0.92], p = 0.033) were significant independent predictors of EDSS score worsening at follow-up (C-index = 0.892). A higher baseline EDSS score (for each point higher: OR = 6.37 [1.98; 20.53], p = 0.002) and lower baseline MTR values of SELs (for each % higher: OR = 0.48 [0.25; 0.89], p = 0.02) independently predicted SPMS conversion (C-index = 0.947). Discussion The proportion of SELs is associated with MS progression after 9 years. More severe SEL microstructural abnormalities independently predict EDSS score worsening and SPMS conversion. The quantification of SEL burden and damage using T1-, T2-weighted, and MTR sequences may identify patients with RRMS at a higher risk of long-term disability progression and SPMS conversion. Classification of Evidence This study provides Class III evidence that in patients with RRMS starting treatment with natalizumab or fingolimod, the proportion of SELs on brain MRI was associated with EDSS score worsening and SPMS conversion at 9-year follow-up.
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Affiliation(s)
- Paolo Preziosa
- From the Neuroimaging Research Unit (P.P., E.P., A.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (P.P., L.M., M.R., M.F., M.A.R.); Neurorehabilitation Unit (M.F.); Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute; and Vita-Salute San Raffaele University (M.F., M.A.R.); Milan, Italy
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit (P.P., E.P., A.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (P.P., L.M., M.R., M.F., M.A.R.); Neurorehabilitation Unit (M.F.); Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute; and Vita-Salute San Raffaele University (M.F., M.A.R.); Milan, Italy
| | - Alessandro Meani
- From the Neuroimaging Research Unit (P.P., E.P., A.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (P.P., L.M., M.R., M.F., M.A.R.); Neurorehabilitation Unit (M.F.); Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute; and Vita-Salute San Raffaele University (M.F., M.A.R.); Milan, Italy
| | - Lucia Moiola
- From the Neuroimaging Research Unit (P.P., E.P., A.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (P.P., L.M., M.R., M.F., M.A.R.); Neurorehabilitation Unit (M.F.); Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute; and Vita-Salute San Raffaele University (M.F., M.A.R.); Milan, Italy
| | - Mariaemma Rodegher
- From the Neuroimaging Research Unit (P.P., E.P., A.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (P.P., L.M., M.R., M.F., M.A.R.); Neurorehabilitation Unit (M.F.); Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute; and Vita-Salute San Raffaele University (M.F., M.A.R.); Milan, Italy
| | - Massimo Filippi
- From the Neuroimaging Research Unit (P.P., E.P., A.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (P.P., L.M., M.R., M.F., M.A.R.); Neurorehabilitation Unit (M.F.); Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute; and Vita-Salute San Raffaele University (M.F., M.A.R.); Milan, Italy
| | - Maria A Rocca
- From the Neuroimaging Research Unit (P.P., E.P., A.M., M.F., M.A.R.), Division of Neuroscience; Neurology Unit (P.P., L.M., M.R., M.F., M.A.R.); Neurorehabilitation Unit (M.F.); Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute; and Vita-Salute San Raffaele University (M.F., M.A.R.); Milan, Italy.
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Kim J, Nguyen TD, Zhang J, Gauthier SA, Marcille M, Zhang H, Cho J, Spincemaille P, Wang Y. Subsecond accurate myelin water fraction reconstruction from FAST-T 2 data with 3D UNET. Magn Reson Med 2022; 87:2979-2988. [PMID: 35092094 DOI: 10.1002/mrm.29176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 12/31/2021] [Accepted: 01/08/2022] [Indexed: 11/10/2022]
Abstract
PURPOSE To develop a 3D UNET convolutional neural network for rapid extraction of myelin water fraction (MWF) maps from six-echo fast acquisition with spiral trajectory and T2 -prep data and to evaluate its accuracy in comparison with multilayer perceptron (MLP) network. METHODS The MWF maps were extracted from 138 patients with multiple sclerosis using an iterative three-pool nonlinear least-squares algorithm (NLLS) without and with spatial regularization (srNLLS), which were used as ground-truth labels to train, validate, and test UNET and MLP networks as a means to accelerate data fitting. Network testing was performed in 63 patients with multiple sclerosis and a numerically simulated brain phantom at SNR of 200, 100 and 50. RESULTS Simulations showed that UNET reduced the MWF mean absolute error by 30.1% to 56.4% and 16.8% to 53.6% over the whole brain and by 41.2% to 54.4% and 21.4% to 49.4% over the lesions for predicting srNLLS and NLLS MWF, respectively, compared to MLP, with better performance at lower SNRs. UNET also outperformed MLP for predicting srNLLS MWF in the in vivo multiple-sclerosis brain data, reducing mean absolute error over the whole brain by 61.9% and over the lesions by 67.5%. However, MLP yielded 41.1% and 51.7% lower mean absolute error for predicting in vivo NLLS MWF over the whole brain and the lesions, respectively, compared with UNET. The whole-brain MWF processing time using a GPU was 0.64 seconds for UNET and 0.74 seconds for MLP. CONCLUSION Subsecond whole-brain MWF extraction from fast acquisition with spiral trajectory and T2 -prep data using UNET is feasible and provides better accuracy than MLP for predicting MWF output of srNLLS algorithm.
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Affiliation(s)
- Jeremy Kim
- Department of Computer Science, Stanford University, Stanford, California, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Jinwei Zhang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Susan A Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Melanie Marcille
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Hang Zhang
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Junghun Cho
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | | | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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Dal-Bianco A, Schranzer R, Grabner G, Lanzinger M, Kolbrink S, Pusswald G, Altmann P, Ponleitner M, Weber M, Kornek B, Zebenholzer K, Schmied C, Berger T, Lassmann H, Trattnig S, Hametner S, Leutmezer F, Rommer P. Iron Rims in Patients With Multiple Sclerosis as Neurodegenerative Marker? A 7-Tesla Magnetic Resonance Study. Front Neurol 2022; 12:632749. [PMID: 34992573 PMCID: PMC8724313 DOI: 10.3389/fneur.2021.632749] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 11/12/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: Multiple sclerosis (MS) is a demyelinating and neurodegenerative disease of the central nervous system, characterized by inflammatory-driven demyelination. Symptoms in MS manifest as both physical and neuropsychological deficits. With time, inflammation is accompanied by neurodegeneration, indicated by brain volume loss on an MRI. Here, we combined clinical, imaging, and serum biomarkers in patients with iron rim lesions (IRLs), which lead to severe tissue destruction and thus contribute to the accumulation of clinical disability. Objectives: Subcortical atrophy and ventricular enlargement using an automatic segmentation pipeline for 7 Tesla (T) MRI, serum neurofilament light chain (sNfL) levels, and neuropsychological performance in patients with MS with IRLs and non-IRLs were assessed. Methods: In total 29 patients with MS [15 women, 24 relapsing-remitting multiple sclerosis (RRMS), and five secondary-progressive multiple sclerosis (SPMS)] aged 38 (22–69) years with an Expanded Disability Status Score of 2 (0–8) and a disease duration of 11 (5–40) years underwent neurological and neuropsychological examinations. Volumes of lesions, subcortical structures, and lateral ventricles on 7-T MRI (SWI, FLAIR, and MP2RAGE, 3D Segmentation Software) and sNfL concentrations using the Simoa SR-X Analyzer in IRL and non-IRL patients were assessed. Results: (1) Iron rim lesions patients had a higher FLAIR lesion count (p = 0.047). Patients with higher MP2Rage lesion volume exhibited more IRLs (p <0.014) and showed poorer performance in the information processing speed tested within 1 year using the Symbol Digit Modalities Test (SDMT) (p <0.047). (2) Within 3 years, patients showed atrophy of the thalamus (p = 0.021) and putamen (p = 0.043) and enlargement of the lateral ventricles (p = 0.012). At baseline and after 3 years, thalamic volumes were lower in IRLs than in non-IRL patients (p = 0.045). (3) At baseline, IRL patients had higher sNfL concentrations (p = 0.028). Higher sNfL concentrations were associated with poorer SDMT (p = 0.004), regardless of IRL presence. (4) IRL and non-IRL patients showed no significant difference in the neuropsychological performance within 1 year. Conclusions: Compared with non-IRL patients, IRL patients had higher FLAIR lesion counts, smaller thalamic volumes, and higher sNfL concentrations. Our pilot study combines IRL and sNfL, two biomarkers considered indicative for neurodegenerative processes. Our preliminary data underscore the reported destructive nature of IRLs.
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Affiliation(s)
| | - R Schranzer
- Department of Neurology, Vienna, Austria.,Department of Medical Engineering, Carinthia University of Applied Sciences, Klagenfurt, Austria
| | - G Grabner
- Department of Neurology, Vienna, Austria.,Department of Medical Engineering, Carinthia University of Applied Sciences, Klagenfurt, Austria
| | | | - S Kolbrink
- Department of Neurology, Vienna, Austria
| | - G Pusswald
- Department of Neurology, Vienna, Austria
| | - P Altmann
- Department of Neurology, Vienna, Austria
| | | | - M Weber
- Department of Biomedical Imaging and Image-Guided Therapy, High Field Magnetic Resonance Centre, Vienna, Austria
| | - B Kornek
- Department of Neurology, Vienna, Austria
| | | | - C Schmied
- Department of Neurology, Vienna, Austria
| | - T Berger
- Department of Neurology, Vienna, Austria
| | - H Lassmann
- Department of Neuroimmunology, Center for Brain Research, Vienna, Austria
| | - S Trattnig
- Department of Biomedical Imaging and Image-Guided Therapy, High Field Magnetic Resonance Centre, Vienna, Austria
| | - S Hametner
- Department of Neurology, Vienna, Austria.,Institute of Neurology, Medical University of Vienna, Vienna, Austria
| | | | - P Rommer
- Department of Neurology, Vienna, Austria
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48
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Edwards EM, Wu W, Fritz NE. Using Myelin Water Imaging to Link Underlying Pathology to Clinical Function in Multiple Sclerosis: A Scoping Review. Mult Scler Relat Disord 2022; 59:103646. [DOI: 10.1016/j.msard.2022.103646] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 12/14/2021] [Accepted: 01/29/2022] [Indexed: 12/28/2022]
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Yik JT, Becquart P, Gill J, Petkau J, Traboulsee A, Carruthers R, Kolind SH, Devonshire V, Sayao AL, Schabas A, Tam R, Moore GRW, Li DKB, Stukas S, Wellington C, Quandt JA, Vavasour IM, Laule C. Serum neurofilament light chain correlates with myelin and axonal magnetic resonance imaging markers in multiple sclerosis. Mult Scler Relat Disord 2022; 57:103366. [PMID: 35158472 DOI: 10.1016/j.msard.2021.103366] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 10/08/2021] [Accepted: 11/01/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND Neurofilaments are cytoskeletal proteins that are detectable in the blood after neuroaxonal injury. Multiple sclerosis (MS) disease progression, greater lesion volume, and brain atrophy are associated with higher levels of serum neurofilament light chain (NfL), but few studies have examined the relationship between NfL and advanced magnetic resonance imaging (MRI) measures related to myelin and axons. We assessed the relationship between serum NfL and brain MRI measures in a diverse group of MS participants. METHODS AND MATERIALS 103 participants (20 clinically isolated syndrome, 33 relapsing-remitting, 30 secondary progressive, 20 primary progressive) underwent 3T MRI to obtain myelin water fraction (MWF), geometric mean T2 (GMT2), water content, T1; high angular resolution diffusion imaging (HARDI)-derived axial diffusivity (AD), radial diffusivity (RD), fractional anisotropy (FA); diffusion basis spectrum imaging (DBSI)-derived AD, RD, FA; restricted, hindered, water and fiber fractions; and volume measurements of normalized brain, lesion, thalamic, deep gray matter (GM), and cortical thickness. Multiple linear regressions assessed the strength of association between serum NfL (dependent variable) and each MRI measure in whole brain (WB), normal appearing white matter (NAWM) and T2 lesions (independent variables), while controlling for age, expanded disability status scale, and disease duration. RESULTS Serum NfL levels were significantly associated with metrics of axonal damage (FA: R2WB-HARDI = 0.29, R2NAWM-HARDI = 0.31, R2NAWM-DBSI = 0.30, R2Lesion-DBSI = 0.31; AD: R2WB-HARDI=0.31), myelin damage (MWF: R2WB = 0.29, R2NAWM = 0.30, RD: R2WB-HARDI = 0.32, R2NAWM-HARDI = 0.34, R2Lesion-DBSI = 0.30), edema and inflammation (T1: R2Lesion = 0.32; GMT2: R2WB = 0.31, R2Lesion = 0.31), and cellularity (restricted fraction R2WB = 0.30, R2NAWM = 0.32) across the entire MS cohort. Higher serum NfL levels were associated with significantly higher T2 lesion volume (R2 = 0.35), lower brain structure volumes (thalamus R2 = 0.31; deep GM R2 = 0.33; normalized brain R2 = 0.31), and smaller cortical thickness R2 = 0.31). CONCLUSION The association between NfL and myelin MRI markers suggest that elevated serum NfL is a useful biomarker that reflects not only acute axonal damage, but also damage to myelin and inflammation, likely due to the known synergistic myelin-axon coupling relationship.
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Affiliation(s)
- Jackie T Yik
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada; International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
| | - Pierre Becquart
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Jasmine Gill
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - John Petkau
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada
| | - Anthony Traboulsee
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Robert Carruthers
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Shannon H Kolind
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada; International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada; Department of Medicine, University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Virginia Devonshire
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Ana-Luiza Sayao
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Alice Schabas
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Roger Tam
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - G R Wayne Moore
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - David K B Li
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Sophie Stukas
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Cheryl Wellington
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Jacqueline A Quandt
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Irene M Vavasour
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Cornelia Laule
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada; International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada.
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50
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Quantification of normal-appearing white matter damage in early relapse-onset multiple sclerosis through neurite orientation dispersion and density imaging. Mult Scler Relat Disord 2021; 58:103396. [DOI: 10.1016/j.msard.2021.103396] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/02/2021] [Accepted: 11/08/2021] [Indexed: 11/23/2022]
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