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Bagnato F, Sati P, Hemond CC, Elliott C, Gauthier SA, Harrison DM, Mainero C, Oh J, Pitt D, Shinohara RT, Smith SA, Trapp B, Azevedo CJ, Calabresi PA, Henry RG, Laule C, Ontaneda D, Rooney WD, Sicotte NL, Reich DS, Absinta M. Imaging chronic active lesions in multiple sclerosis: a consensus statement. Brain 2024; 147:2913-2933. [PMID: 38226694 DOI: 10.1093/brain/awae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 11/21/2023] [Accepted: 12/08/2023] [Indexed: 01/17/2024] Open
Abstract
Chronic active lesions (CAL) are an important manifestation of chronic inflammation in multiple sclerosis and have implications for non-relapsing biological progression. In recent years, the discovery of innovative MRI and PET-derived biomarkers has made it possible to detect CAL, and to some extent quantify them, in the brain of persons with multiple sclerosis, in vivo. Paramagnetic rim lesions on susceptibility-sensitive MRI sequences, MRI-defined slowly expanding lesions on T1-weighted and T2-weighted scans, and 18-kDa translocator protein-positive lesions on PET are promising candidate biomarkers of CAL. While partially overlapping, these biomarkers do not have equivalent sensitivity and specificity to histopathological CAL. Standardization in the use of available imaging measures for CAL identification, quantification and monitoring is lacking. To fast-forward clinical translation of CAL, the North American Imaging in Multiple Sclerosis Cooperative developed a consensus statement, which provides guidance for the radiological definition and measurement of CAL. The proposed manuscript presents this consensus statement, summarizes the multistep process leading to it, and identifies the remaining major gaps in knowledge.
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Affiliation(s)
- Francesca Bagnato
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37212, USA
- Department of Neurology, Nashville VA Medical Center, Tennessee Valley Healthcare System, Nashville, TN 37212, USA
| | - Pascal Sati
- Neuroimaging Program, Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Christopher C Hemond
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | | | - Susan A Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, NY 10021, USA
| | - Daniel M Harrison
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
- Department of Neurology, Baltimore VA Medical Center, VA Maryland Healthcare System, Baltimore, MD 21201, USA
| | - Caterina Mainero
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jiwon Oh
- Division of Neurology, St. Michael's Hospital, University of Toronto, Toronto, ON M5S, Canada
| | - David Pitt
- Department of Neurology, Yale School of Medicine, New Haven, CT 06510, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Seth A Smith
- Department of Radiology and Radiological Sciences, Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Bruce Trapp
- Department on Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Christina J Azevedo
- Department of Neurology, Keck School of Medicine of the University of Southern California, Los Angeles, CA 90007, USA
| | - Peter A Calabresi
- Departments of Neurology and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA 94158, USA
| | - Cornelia Laule
- Department of Radiology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, OH 44195, USA
| | - William D Rooney
- Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR 97239, USA
| | - Nancy L Sicotte
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Martina Absinta
- Departments of Neurology and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Translational Neuropathology Unit, Division of Neuroscience, Institute of Experimental Neurology, Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
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Mistri D, Margoni M, Pagani E, Valsasina P, Meani A, Moiola L, Filippi M, Rocca MA. Structural and functional imaging features of cognitive phenotypes in pediatric multiple sclerosis. Ann Clin Transl Neurol 2024; 11:1840-1851. [PMID: 38804116 PMCID: PMC11251463 DOI: 10.1002/acn3.52090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/29/2024] [Accepted: 05/04/2024] [Indexed: 05/29/2024] Open
Abstract
OBJECTIVE The present study aimed to identify the clinical and MRI features of the distinct cognitive phenotypes in pediatric multiple sclerosis (pedMS). METHODS PedMS patients (n = 73) and healthy controls (n = 30) underwent clinical examination and 3.0T MRI. All patients completed neuropsychological testing, and cognitive phenotypes were identified by performing K-means clustering on cognitive scores. MRI metrics included brain T2-hyperintese lesion volume and normalized brain volumes. Within seven cognitively relevant cortical networks, structural disconnectivity (i.e., the mean percentage of streamlines connecting each pair of cortical regions passing through a lesion) and resting-state (RS) functional connectivity (FC) were estimated. RESULTS Three cognitive phenotypes emerged: Preserved cognition (PC; n = 27, 37%), mild verbal learning and memory/semantic fluency involvement (MVS; n = 28, 38%), and multidomain involvement (MI; n = 18, 25%). Age, sex, and disease duration did not differ among groups. Compared with healthy subjects, PC patients had decreased RS FC within the default mode network (p = 0.045); MVS patients exhibited lower cortical volume and reduced RS FC within the frontoparietal network (all p = 0.045); and MI patients showed decreased volumes in all brain compartments except the hippocampus, and reduced RS FC within the frontoparietal network (all p ≤ 0.045). Compared to PC, MI patients had more severe disability and higher structural disconnectivity within four cortical networks (all p ≤ 0.045). Compared to PC and MVS, MI patients had lower intelligence quotient (all p ≤ 0.005). INTERPRETATION We identified three cognitive phenotypes in pedMS that demonstrate the existence of a spectrum of impairment. Such phenotypes showed distinct clinical and MRI characteristics that contributed to explain their cognitive profiles.
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Affiliation(s)
- Damiano Mistri
- Neuroimaging Research Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Monica Margoni
- Neuroimaging Research Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
- Neurorehabilitation UnitIRCCS San Raffaele Scientific InstituteMilanItaly
- Neurology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Alessandro Meani
- Neuroimaging Research Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Lucia Moiola
- Neurology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
- Neurorehabilitation UnitIRCCS San Raffaele Scientific InstituteMilanItaly
- Neurology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
- Neurophysiology ServiceIRCCS San Raffaele Scientific InstituteMilanItaly
- Vita‐Salute San Raffaele UniversityMilanItaly
| | - Maria A. Rocca
- Neuroimaging Research Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
- Neurology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
- Vita‐Salute San Raffaele UniversityMilanItaly
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3
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Baller EB, Sweeney EM, Cieslak M, Robert-Fitzgerald T, Covitz SC, Martin ML, Schindler MK, Bar-Or A, Elahi A, Larsen BS, Manning AR, Markowitz CE, Perrone CM, Rautman V, Seitz MM, Detre JA, Fox MD, Shinohara RT, Satterthwaite TD. Mapping the Relationship of White Matter Lesions to Depression in Multiple Sclerosis. Biol Psychiatry 2024; 95:1072-1080. [PMID: 37981178 PMCID: PMC11101593 DOI: 10.1016/j.biopsych.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/27/2023] [Accepted: 11/11/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND Multiple sclerosis (MS) is an immune-mediated neurological disorder, and up to 50% of patients experience depression. We investigated how white matter network disruption is related to depression in MS. METHODS Using electronic health records, 380 participants with MS were identified. Depressed individuals (MS+Depression group; n = 232) included persons who had an ICD-10 depression diagnosis, had a prescription for antidepressant medication, or screened positive via Patient Health Questionnaire (PHQ)-2 or PHQ-9. Age- and sex-matched nondepressed individuals with MS (MS-Depression group; n = 148) included persons who had no prior depression diagnosis, had no psychiatric medication prescriptions, and were asymptomatic on PHQ-2 or PHQ-9. Research-quality 3T structural magnetic resonance imaging was obtained as part of routine care. We first evaluated whether lesions were preferentially located within the depression network compared with other brain regions. Next, we examined if MS+Depression patients had greater lesion burden and if this was driven by lesions in the depression network. Primary outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain. RESULTS MS lesions preferentially affected fascicles within versus outside the depression network (β = 0.09, 95% CI = 0.08 to 0.10, p < .001). MS+Depression patients had more lesion burden (β = 0.06, 95% CI = 0.01 to 0.10, p = .015); this was driven by lesions within the depression network (β = 0.02, 95% CI = 0.003 to 0.040, p = .020). CONCLUSIONS We demonstrated that lesion location and burden may contribute to depression comorbidity in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression patients had more disease than MS-Depression patients, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.
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Affiliation(s)
- Erica B Baller
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Elizabeth M Sweeney
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Timothy Robert-Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sydney C Covitz
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Melissa L Martin
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Matthew K Schindler
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Amit Bar-Or
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ameena Elahi
- Department of Information Services, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Bart S Larsen
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Abigail R Manning
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Clyde E Markowitz
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christopher M Perrone
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Victoria Rautman
- Department of Information Services, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Madeleine M Seitz
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania.
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Vanheule E, Cambron M, Dobai A, Casselman JW. Rim lesions in MS at 3T: clinical correlation and possible radiological alternatives for daily practice at lower field strength. J Neuroradiol 2024; 51:101165. [PMID: 37907156 DOI: 10.1016/j.neurad.2023.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/28/2023] [Indexed: 11/02/2023]
Abstract
BACKGROUND AND PURPOSE Paramagnetic rim lesions (PRLs) have been described as an imaging feature specific to multiple sclerosis (MS) using high-field strength phase-sensitive MR imaging. These lesions are suggested to reflect chronic active inflammation associated with greater disease severity and a more rapid disability progression. The aim of our study is to investigate the relationship between PRLs, clinical parameters, other radiological findings and disease progression. MATERIAL AND METHODS This cross-sectional study included MS patients treated with teriflunomide, fingolimod, natalizumab or ocrelizumab for at least 2 years. PRLs seen at 3T MRI were analysed and correlated with clinical data and radiological progression, defined as an increase of the T2/FLAIR-lesion load during therapy. In the search for alternatives for these PRLs, we defined two additional radiological markers: 'FLAIR-bullet lesions', and on post-contrast black-blood (BB) images, 'BB-bullet lesions'. RESULTS We included 84 MS patients of whom 27 (32 %) had at least 1 PRL. PRLs were associated with radiological progression under therapy (p=0.039) and higher clinical disability scores, although only significant for 9-Hole Peg Test (p=0.023). Patients with FLAIR-bullet or BB-bullet lesions at 3T MRI had a higher chance of PRL (p<0.001) with a likelihood ratio of 13.2 for FLAIR-bullets and 12.6 for BB-bullet lesions, thanks to the high negative predictive value of respectively 83 % and 90 %. CONCLUSION PRLs are associated with an increase of T2/FLAIR-lesion load under therapy and unfavourable clinical outcome. Our newly defined 'bullet lesions' are associated with PRLs and might be an interesting MRI marker for centres without access to high-field SWI images.
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Affiliation(s)
- Eva Vanheule
- Department of Radiology, AZ Sint-Jan Brugge-Oostende, Campus Brugge, Ruddershove 10, Bruges 8000, Belgium.
| | - Melissa Cambron
- Department of Neurology, AZ Sint-Jan Brugge-Oostende, Campus Brugge, Ruddershove 10, Bruges 8000, Belgium
| | - Adrienn Dobai
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Szentkirályi u. 47, Budapest, 1088, Hungary; Department of Neuroradiology, Medical Imaging Centre, Semmelweis University, Balassa street 6, Budapest, 1083, Hungary
| | - Jan W Casselman
- Department of Radiology, AZ Sint-Jan Brugge-Oostende, Campus Brugge, Ruddershove 10, Bruges 8000, Belgium; Department of Diagnostic Radiology, Dalhousie University, Halifax, Nova Scotia, Canada
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5
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Tozlu C, Olafson E, Jamison KW, Demmon E, Kaunzner U, Marcille M, Zinger N, Michaelson N, Safi N, Nguyen T, Gauthier S, Kuceyeski A. The sequence of regional structural disconnectivity due to multiple sclerosis lesions. Brain Commun 2023; 5:fcad332. [PMID: 38107503 PMCID: PMC10724045 DOI: 10.1093/braincomms/fcad332] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 09/07/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023] Open
Abstract
Prediction of disease progression is challenging in multiple sclerosis as the sequence of lesion development and retention of inflammation within a subset of chronic lesions is heterogeneous among patients. We investigated the sequence of lesion-related regional structural disconnectivity across the spectrum of disability and cognitive impairment in multiple sclerosis. In a full cohort of 482 multiple sclerosis patients (age: 41.83 ± 11.63 years, 71.57% females), the Expanded Disability Status Scale was used to classify patients into (i) no or mild (Expanded Disability Status Scale <3) versus (ii) moderate or severe disability groups (Expanded Disability Status Scale ≥3). In 363 out of 482 patients, quantitative susceptibility mapping was used to identify paramagnetic rim lesions, which are maintained by a rim of iron-laden innate immune cells. In 171 out of 482 patients, Brief International Cognitive Assessment was used to identify subjects as being cognitively preserved or impaired. Network Modification Tool was used to estimate the regional structural disconnectivity due to multiple sclerosis lesions. Discriminative event-based modelling was applied to investigate the sequence of regional structural disconnectivity due to (i) all representative T2 fluid-attenuated inversion recovery lesions, (ii) paramagnetic rim lesions versus non-paramagnetic rim lesions separately across disability groups ('no to mild disability' to 'moderate to severe disability'), (iii) all representative T2 fluid-attenuated inversion recovery lesions and (iv) paramagnetic rim lesions versus non-paramagnetic rim lesions separately across cognitive status ('cognitively preserved' to 'cognitively impaired'). In the full cohort, structural disconnection in the ventral attention and subcortical networks, particularly in the supramarginal and putamen regions, was an early biomarker of moderate or severe disability. The earliest biomarkers of disability progression were structural disconnections due to paramagnetic rim lesions in the motor-related regions. Subcortical structural disconnection, particularly in the ventral diencephalon and thalamus regions, was an early biomarker of cognitive impairment. Our data-driven model revealed that the structural disconnection in the subcortical regions, particularly in the thalamus, is an early biomarker for both disability and cognitive impairment in multiple sclerosis. Paramagnetic rim lesions-related structural disconnection in the motor cortex may identify the patients at risk for moderate or severe disability in multiple sclerosis. Such information might be used to identify people with multiple sclerosis who have an increased risk of disability progression or cognitive decline in order to provide personalized treatment plans.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
| | - Emily Olafson
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
| | - Keith W Jamison
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
| | - Emily Demmon
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Ulrike Kaunzner
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Melanie Marcille
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Nicole Zinger
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Nara Michaelson
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Neha Safi
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Thanh Nguyen
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
| | - Susan Gauthier
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
- Department of Neurology, Weill Cornell Medical College, NewYork, NY, 10065, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, NewYork, NY, 10065, USA
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6
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Lipka A, Bogner W, Dal-Bianco A, Hangel GJ, Rommer PS, Strasser B, Motyka S, Hingerl L, Berger T, Leutmezer F, Gruber S, Trattnig S, Niess E. Metabolic Insights into Iron Deposition in Relapsing-Remitting Multiple Sclerosis via 7 T Magnetic Resonance Spectroscopic Imaging. Neuroimage Clin 2023; 40:103524. [PMID: 37839194 PMCID: PMC10590870 DOI: 10.1016/j.nicl.2023.103524] [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: 06/30/2023] [Revised: 09/26/2023] [Accepted: 10/07/2023] [Indexed: 10/17/2023]
Abstract
OBJECTIVE To investigate the metabolic pattern of different types of iron accumulation in multiple sclerosis (MS) lesions, and compare metabolic alterations within and at the periphery of lesions and newly emerging lesions in vivo according to iron deposition. METHODS 7 T MR spectroscopic imaging and susceptibility-weighted imaging was performed in 31 patients with relapsing-remitting MS (16 female/15 male; mean age, 36.9 ± 10.3 years). Mean metabolic ratios of four neuro-metabolites were calculated for regions of interest (ROI) of normal appearing white matter (NAWM), "non-iron" (lesion without iron accumulation on SWI), and three distinct types of iron-laden lesions ("rim": distinct rim-shaped iron accumulation; "area": iron deposition across the entire lesions; "transition": transition between "area" and "rim" accumulation shape), and for lesion layers of "non-iron" and "rim" lesions. Furthermore, newly emerging "non-iron" and "iron" lesions were compared longitudinally, as measured before their appearance and one year later. RESULTS Thirty-nine of 75 iron-containing lesions showed no distinct paramagnetic rim. Of these, "area" lesions exhibited a 65% higher mIns/tNAA (p = 0.035) than "rim" lesions. Comparing lesion layers of both "non-iron" and "rim" lesions, a steeper metabolic gradient of mIns/tNAA ("non-iron" +15%, "rim" +40%) and tNAA/tCr ("non-iron" -15%, "rim" -35%) was found in "iron" lesions, with the lesion core showing +22% higher mIns/tNAA (p = 0.005) and -23% lower tNAA/tCr (p = 0.048) in "iron" compared to "non-iron" lesions. In newly emerging lesions, 18 of 39 showed iron accumulation, with the drop in tNAA/tCr after lesion formation remaining significantly lower compared to pre-lesional tissue over time in "iron" lesions (year 0: p = 0.013, year 1: p = 0.041) as opposed to "non-iron" lesions (year 0: p = 0.022, year 1: p = 0.231). CONCLUSION 7 T MRSI allows in vivo characterization of different iron accumulation types each presenting with a distinct metabolic profile. Furthermore, the larger extent of neuronal damage in lesions with a distinct iron rim was reconfirmed via reduced tNAA/tCr concentrations, but with metabolic differences in lesion development between (non)-iron-containing lesions. This highlights the ability of MRSI to further investigate different types of iron accumulation and suggests possible implications for disease monitoring.
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Affiliation(s)
- Alexandra Lipka
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna.
| | | | - Gilbert J Hangel
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Paulus S Rommer
- Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Bernhard Strasser
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Stanislav Motyka
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lukas Hingerl
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Thomas Berger
- Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Fritz Leutmezer
- Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Stephan Gruber
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Siegfried Trattnig
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria
| | - Eva Niess
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna
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7
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Baller EB, Sweeney EM, Cieslak MC, Robert-Fitzgerald T, Covitz SC, Martin ML, Schindler MK, Bar-Or A, Elahi A, Larsen BS, Manning AR, Markowitz CE, Perrone CM, Rautman V, Seitz MM, Detre JA, Fox MD, Shinohara RT, Satterthwaite TD. Mapping the relationship of white matter lesions to depression in multiple sclerosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.09.23291080. [PMID: 37398183 PMCID: PMC10312888 DOI: 10.1101/2023.06.09.23291080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Importance Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects nearly one million people in the United States. Up to 50% of patients with MS experience depression. Objective To investigate how white matter network disruption is related to depression in MS. Design Retrospective case-control study of participants who received research-quality 3-tesla neuroimaging as part of MS clinical care from 2010-2018. Analyses were performed from May 1 to September 30, 2022. Setting Single-center academic medical specialty MS clinic. Participants Participants with MS were identified via the electronic health record (EHR). All participants were diagnosed by an MS specialist and completed research-quality MRI at 3T. After excluding participants with poor image quality, 783 were included. Inclusion in the depression group (MS+Depression) required either: 1) ICD-10 depression diagnosis (F32-F34.*); 2) prescription of antidepressant medication; or 3) screening positive via Patient Health Questionnaire-2 (PHQ-2) or -9 (PHQ-9). Age- and sex-matched nondepressed comparators (MS-Depression) included persons with no depression diagnosis, no psychiatric medications, and were asymptomatic on PHQ-2/9. Exposure Depression diagnosis. Main Outcomes and Measures We first evaluated if lesions were preferentially located within the depression network compared to other brain regions. Next, we examined if MS+Depression patients had greater lesion burden, and if this was driven by lesions specifically in the depression network. Outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain. Secondary measures included between-diagnosis lesion burden, stratified by brain network. Linear mixed-effects models were employed. Results Three hundred-eighty participants met inclusion criteria, (232 MS+Depression: age[SD]=49[12], %females=86; 148 MS-Depression: age[SD]=47[13], %females=79). MS lesions preferentially affected fascicles within versus outside the depression network (β=0.09, 95% CI=0.08-0.10, P<0.001). MS+Depression had more white matter lesion burden (β=0.06, 95% CI=0.01-0.10, P=0.015); this was driven by lesions within the depression network (β=0.02, 95% CI 0.003-0.040, P=0.020). Conclusions and Relevance We provide new evidence supporting a relationship between white matter lesions and depression in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression had more disease than MS-Depression, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.
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Affiliation(s)
- Erica B Baller
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Elizabeth M Sweeney
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Matthew C Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Timothy Robert-Fitzgerald
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Sydney C Covitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Melissa L Martin
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Matthew K Schindler
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Amit Bar-Or
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Ameena Elahi
- Department of Information Services, University of Pennsylvania, Philadelphia, PA USA
| | - Bart S Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Abigail R Manning
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Clyde E Markowitz
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Christopher M Perrone
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Victoria Rautman
- Department of Information Services, University of Pennsylvania, Philadelphia, PA USA
| | - Madeleine M Seitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA USA
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8
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Tozlu C, Olafson E, Jamison K, Demmon E, Kaunzner U, Marcille M, Zinger N, Michaelson N, Safi N, Nguyen T, Gauthier S, Kuceyeski A. The sequence of regional structural disconnectivity due to multiple sclerosis lesions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.26.525537. [PMID: 36747675 PMCID: PMC9900990 DOI: 10.1101/2023.01.26.525537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Objective Prediction of disease progression is challenging in multiple sclerosis (MS) as the sequence of lesion development and retention of inflammation within a subset of chronic lesions is heterogeneous among patients. We investigated the sequence of lesion-related regional structural disconnectivity across the spectrum of disability and cognitive impairment in MS. Methods In a full cohort of 482 patients, the Expanded Disability Status Scale was used to classify patients into (i) no or mild vs (ii) moderate or severe disability groups. In 363 out of 482 patients, Quantitative Susceptibility Mapping was used to identify paramagnetic rim lesions (PRL), which are maintained by a rim of iron-laden innate immune cells. In 171 out of 482 patients, Brief International Cognitive Assessment was used to identify subjects with cognitive impairment. Network Modification Tool was used to estimate the regional structural disconnectivity due to MS lesions. Discriminative event-based modeling was applied to investigate the sequence of regional structural disconnectivity due to all representative lesions across the spectrum of disability and cognitive impairment. Results Structural disconnection in the ventral attention and subcortical networks was an early biomarker of moderate or severe disability. The earliest biomarkers of disability progression were structural disconnections due to PRL in the motor-related regions. Subcortical structural disconnection was an early biomarker of cognitive impairment. Interpretation MS lesion-related structural disconnections in the subcortex is an early biomarker for both disability and cognitive impairment in MS. PRL-related structural disconnection in the motor cortex may identify the patients at risk for moderate or severe disability in MS.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Emily Olafson
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Emily Demmon
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Ulrike Kaunzner
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Melanie Marcille
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Nicole Zinger
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Nara Michaelson
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Neha Safi
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Thanh Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Susan Gauthier
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Department of Neurology, Weill Cornell Medical College, New York, New York, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
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9
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Conti A, Treaba CA, Mehndiratta A, Barletta VT, Mainero C, Toschi N. An Interpretable Machine Learning Model to Predict Cortical Atrophy in Multiple Sclerosis. Brain Sci 2023; 13:brainsci13020198. [PMID: 36831740 PMCID: PMC9954500 DOI: 10.3390/brainsci13020198] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/14/2023] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
To date, the relationship between central hallmarks of multiple sclerosis (MS), such as white matter (WM)/cortical demyelinated lesions and cortical gray matter atrophy, remains unclear. We investigated the interplay between cortical atrophy and individual lesion-type patterns that have recently emerged as new radiological markers of MS disease progression. We employed a machine learning model to predict mean cortical thinning in whole-brain and single hemispheres in 150 cortical regions using demographic and lesion-related characteristics, evaluated via an ultrahigh field (7 Tesla) MRI. We found that (i) volume and rimless (i.e., without a "rim" of iron-laden immune cells) WM lesions, patient age, and volume of intracortical lesions have the most predictive power; (ii) WM lesions are more important for prediction when their load is small, while cortical lesion load becomes more important as it increases; (iii) WM lesions play a greater role in the progression of atrophy during the latest stages of the disease. Our results highlight the intricacy of MS pathology across the whole brain. In turn, this calls for multivariate statistical analyses and mechanistic modeling techniques to understand the etiopathogenesis of lesions.
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Affiliation(s)
- Allegra Conti
- Department of Biomedicine and Prevention, University of Rome ‘Tor Vergata’, Via Montpellier 1, 00133 Rome, Italy
- Correspondence: ; Tel.: +39-06-72596393
| | - Constantina Andrada Treaba
- Massachusetts General Hospital, Boston, MA 02114, USA
- A. A. Martinos Center for Biomedical Imaging, Boston, MA 02129, USA
| | - Ambica Mehndiratta
- Massachusetts General Hospital, Boston, MA 02114, USA
- A. A. Martinos Center for Biomedical Imaging, Boston, MA 02129, USA
| | - Valeria Teresa Barletta
- Massachusetts General Hospital, Boston, MA 02114, USA
- A. A. Martinos Center for Biomedical Imaging, Boston, MA 02129, USA
| | - Caterina Mainero
- Massachusetts General Hospital, Boston, MA 02114, USA
- A. A. Martinos Center for Biomedical Imaging, Boston, MA 02129, USA
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome ‘Tor Vergata’, Via Montpellier 1, 00133 Rome, Italy
- Massachusetts General Hospital, Boston, MA 02114, USA
- A. A. Martinos Center for Biomedical Imaging, Boston, MA 02129, USA
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10
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Wittayer M, Weber CE, Krämer J, Platten M, Schirmer L, Gass A, Eisele P. Exploring (peri-) lesional and structural connectivity tissue damage through T1/T2-weighted ratio in iron rim multiple sclerosis lesions. Magn Reson Imaging 2023; 95:12-18. [PMID: 36270415 DOI: 10.1016/j.mri.2022.10.009] [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: 08/05/2022] [Revised: 10/11/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE In multiple sclerosis (MS), iron rim lesions (IRLs) on magnetic resonance imaging (MRI) are associated with pronounced intralesional tissue damage. The aim of this study was to investigate (peri-)lesional and structural connectivity tissue damage in IRLs compared to non-IRLs. MATERIAL AND METHODS MRI was acquired on a 3 T system. Tissue integrity was assessed using the T1/T2-weighted (T1/T2w) ratio. Furthermore, we assessed the impact on structural network connectivity accounting for differences in lesion volumes and T1/T2w values. RESULTS Seventy-six patients (38 with at least one IRL and 38 age- and sex-matched patients without IRLs) were included. In the IRL-group, T1/T2w ratios of IRLs were significantly lower compared to non-IRLs (p < 0.05). When comparing the T1/T2w ratios in non-IRLs between the IRL-group and non-IRL group, there was no significant difference (p = 0.887). We observed a centrifugal decrease in microstructural damage from lesions to the perilesional white matter. In the IRL-group, T1/T2w ratios in the perilesional white matter 3-8 mm distant to the lesion were significantly lower in IRLs compared to non-IRLs. We found no significant differences in the amount of network disruption between both lesion types (p = 0.122). CONCLUSION T1/T2w represents an interesting candidate to capture a pronounced intra- and perilesional tissue damage of IRLs. However, our preliminary results suggest that a pronounced tissue damage might not result in a higher disruption to structural connectivity networks in IRLs.
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Affiliation(s)
- Matthias Wittayer
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center of Translational Neurosciences (MCTN), Heidelberg University, Theodor-Kutzer-Ufer 1 - 3, 68167 Mannheim, Germany
| | - Claudia E Weber
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center of Translational Neurosciences (MCTN), Heidelberg University, Theodor-Kutzer-Ufer 1 - 3, 68167 Mannheim, Germany
| | - Julia Krämer
- Department of Neurology with Institute of Translational Neurology, University Hospital Münster, Albert-Schweitzer-Campus 1; Gebäude A1, Westturm, Ebene 5, 48149 Münster, Germany
| | - Michael Platten
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center of Translational Neurosciences (MCTN), Heidelberg University, Theodor-Kutzer-Ufer 1 - 3, 68167 Mannheim, Germany; German Consortium of Translational Cancer Research (DKTK), Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lucas Schirmer
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center of Translational Neurosciences (MCTN), Heidelberg University, Theodor-Kutzer-Ufer 1 - 3, 68167 Mannheim, Germany; Institute for Innate Immunoscience, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Achim Gass
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center of Translational Neurosciences (MCTN), Heidelberg University, Theodor-Kutzer-Ufer 1 - 3, 68167 Mannheim, Germany
| | - Philipp Eisele
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center of Translational Neurosciences (MCTN), Heidelberg University, Theodor-Kutzer-Ufer 1 - 3, 68167 Mannheim, Germany.
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11
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Chien C, Seiler M, Eitel F, Schmitz-Hübsch T, Paul F, Ritter K. Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity. Mult Scler J Exp Transl Clin 2022; 8:20552173221109770. [PMID: 35815061 PMCID: PMC9260586 DOI: 10.1177/20552173221109770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/10/2022] [Indexed: 11/15/2022] Open
Abstract
Background Lack of easy-to-interpret disease activity prediction methods in early MS can lead to worse patient prognosis. Objectives Using machine learning (multiple kernel learning – MKL) models, we assessed the prognostic value of various clinical and MRI measures for disease activity. Methods Early MS patients ( n = 148) with at least two associated clinical and MRI visits were investigated. T2-weighted MRIs were cropped to contain mainly the lateral ventricles (LV). High disease activity was defined as surpassing NEDA-3 Criteria more than once per year. Clinical demographic, MRI-extracted image-derived phenotypes (IDP), and MRI data were used as inputs for separate kernels to predict future disease activity with MKL. Model performance was compared using bootstrapped effect size analysis of mean differences. Results A total of 681 visits were included, where 81 (55%) patients had high disease activity in a combined end point measure using all follow-up visits. MKL model discrimination performance was moderate (AUC ≥ 0.62); however, modelling with combined clinical and cropped LV kernels gave the highest prediction performance (AUC = 0.70). Conclusions MRIs contain valuable information on future disease activity, especially in and around the LV. MKL techniques for combining different data types can be used for the prediction of disease activity in a relatively small MS cohort.
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Affiliation(s)
- Claudia Chien
- Claudia Chien,
Charité-Universitätsmedizin Berlin, NeuroCure Clinical Research Center,
Charitéplatz 1, 10117 Berlin, Germany.
| | | | - Fabian Eitel
- Charité – Universitätsmedizin Berlin, corporate
member of Freie Universität Berlin and Humboldt-Universität zu Berlin,
Department of Psychiatry and Neurosciences, Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate
member of Freie Universität Berlin and Humboldt-Universität zu Berlin,
Bernstein Center for Computational Neuroscience, Berlin Center for Advanced
Neuroimaging, Berlin, Germany
| | - Tanja Schmitz-Hübsch
- Charité – Universitätsmedizin Berlin, corporate
member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC
Experimental and Clinical Research Center, Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate
member of Freie Universität Berlin and Humboldt-Universität zu Berlin,
NeuroCure Clinical Research Center, Berlin, Germany
| | - Friedemann Paul
- Charité – Universitätsmedizin Berlin, corporate
member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC
Experimental and Clinical Research Center, Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate
member of Freie Universität Berlin and Humboldt-Universität zu Berlin,
NeuroCure Clinical Research Center, Berlin, Germany
- Charité – Universitätsmedizin Berlin,
corporate member of Freie Universität Berlin and Humboldt-Universität zu
Berlin, Department of Neurology, Berlin, Germany
| | - Kerstin Ritter
- Charité – Universitätsmedizin Berlin,
corporate member of Freie Universität Berlin and Humboldt-Universität zu
Berlin, Department of Psychiatry and Neurosciences, Berlin, Germany
- Charité – Universitätsmedizin Berlin,
corporate member of Freie Universität Berlin and Humboldt-Universität zu
Berlin, Bernstein Center for Computational Neuroscience, Berlin Center for
Advanced Neuroimaging, Berlin, Germany
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12
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Wittayer M, Weber CE, Platten M, Schirmer L, Gass A, Eisele P. Spatial distribution of multiple sclerosis iron rim lesions and their impact on disability. Mult Scler Relat Disord 2022; 64:103967. [PMID: 35728430 DOI: 10.1016/j.msard.2022.103967] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/07/2022] [Accepted: 06/13/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND In multiple sclerosis (MS), iron rim lesions (IRLs) on magnetic resonance imaging (MRI) have been suggested as an imaging marker of disease progression. However, the exact mechanisms how they contribute to disability are yet not completely known. Strategic lesion location may be an important factor concerning the impact of focal lesions on clinical disability. Therefore, the aim of this study was to investigate the spatial distribution of IRLs compared to non-IRLs and their impact on disability. METHODS We retrospectively identified 67 patients with at least one IRL on MRI and 67 age- and sex-matched patients without IRLs. We compared the spatial distribution of lesions between both groups and between IRLs and non-IRLs in patients with IRLs. Furthermore, we assessed the relationship between lesion localisation and disability on a voxel-by-voxel basis and investigated the impact on structural network disruptions. RESULTS Patients with IRLs had higher disability scores (median Expanded Disability Status Scale score (range): 3.0 (0 - 8.5) versus 1.5 (0 - 6.5); p = 0.001; median pyramidal functional system score (range): 1.0 (0 - 5) versus 0 (0 - 4); p = 0.003), significantly lower brain volumes (mean normal-appearing grey matter volume: 749.66 ± 60.58 versus 785.83 ± 53.71 mL; mean normal-appearing white matter volume: 723.58 ± 60.13 versus 753.25 ± 69.61 mL; mean deep grey matter volume: 33.21 ± 4.19 versus 35.85 ± 4.89 mL; p < 0.05 for all comparisons) and a significantly higher total T2 lesion volume (mean: 9.96 ± 11.6 versus 4.31 ± 8.9 mL; p < 0.001). We found no neuroanatomical regions that were more often affected by IRLs. Furthermore, comparing the overall network disruption in the IRL group, IRLs caused less network disruption/mL lesion size compared to non-IRLs (1.54% / mL versus 2.0% / mL; p < 0.05). CONCLUSION IRLs are associated with higher disability scores. However, our results suggest that a higher disability is not explained by the sheer topography of IRLs or their network disruption.
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Affiliation(s)
- Matthias Wittayer
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center of Translational Neurosciences (MCTN), Heidelberg University, Theodor-Kutzer-Ufer 1 - 3, Mannheim 68167, Germany
| | - Claudia E Weber
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center of Translational Neurosciences (MCTN), Heidelberg University, Theodor-Kutzer-Ufer 1 - 3, Mannheim 68167, Germany
| | - Michael Platten
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center of Translational Neurosciences (MCTN), Heidelberg University, Theodor-Kutzer-Ufer 1 - 3, Mannheim 68167, Germany; DKTK CCU Neuroimmunology and Brain Tumor Immunology, DKFZ, Heidelberg, Germany
| | - Lucas Schirmer
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center of Translational Neurosciences (MCTN), Heidelberg University, Theodor-Kutzer-Ufer 1 - 3, Mannheim 68167, Germany; Medical Faculty Mannheim, Institute for Innate Immunoscience, Heidelberg University, Mannheim, Germany
| | - Achim Gass
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center of Translational Neurosciences (MCTN), Heidelberg University, Theodor-Kutzer-Ufer 1 - 3, Mannheim 68167, Germany
| | - Philipp Eisele
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center of Translational Neurosciences (MCTN), Heidelberg University, Theodor-Kutzer-Ufer 1 - 3, Mannheim 68167, Germany.
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13
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Zinger N, Ponath G, Sweeney E, Nguyen TD, Lo CH, Diaz I, Dimov A, Teng L, Zexter L, Comunale J, Wang Y, Pitt D, Gauthier SA. Dimethyl Fumarate Reduces Inflammation in Chronic Active Multiple Sclerosis Lesions. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2022; 9:9/2/e1138. [PMID: 35046083 PMCID: PMC8771666 DOI: 10.1212/nxi.0000000000001138] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 12/10/2021] [Indexed: 12/14/2022]
Abstract
Background and Objectives To determine the effects of dimethyl fumarate (DMF) and glatiramer acetate on iron content in chronic active lesions in patients with multiple sclerosis (MS) and in human microglia in vitro. Methods This was a retrospective observational study of 34 patients with relapsing-remitting MS and clinically isolated syndrome treated with DMF or glatiramer acetate. Patients had lesions with hyperintense rims on quantitative susceptibility mapping, were treated with DMF or glatiramer acetate (GA), and had a minimum of 2 on-treatment scans. Changes in susceptibility in rim lesions were compared among treatment groups in a linear mixed effects model. In a separate in vitro study, induced pluripotent stem cell–derived human microglia were treated with DMF or GA, and treatment-induced changes in iron content and activation state of microglia were compared. Results Rim lesions in patients treated with DMF had on average a 2.77-unit reduction in susceptibility per year over rim lesions in patients treated with GA (bootstrapped 95% CI −5.87 to −0.01), holding all other variables constant. Moreover, DMF but not GA reduced inflammatory activation and concomitantly iron content in human microglia in vitro. Discussion Together, our data indicate that DMF-induced reduction of susceptibility in MS lesions is associated with a decreased activation state in microglial cells. We have demonstrated that a specific disease modifying therapy, DMF, decreases glial activity in chronic active lesions. Susceptibility changes in rim lesions provide an in vivo biomarker for the effect of DMF on microglial activity. Classification of Evidence This study provided Class III evidence that DMF is superior to GA in the presence of iron as a marker of inflammation as measured by MRI quantitative susceptibility mapping.
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Affiliation(s)
- Nicole Zinger
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Gerald Ponath
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Elizabeth Sweeney
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Thanh D Nguyen
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Chih Hung Lo
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Ivan Diaz
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Alexey Dimov
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Leilei Teng
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Lily Zexter
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Joseph Comunale
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Yi Wang
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - David Pitt
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore
| | - Susan A Gauthier
- From the Department of Neurology (N.Z., L.Z., S.A.G.), Weill Cornell Medicine, New York; Department of Neurology (G.P., C.H.L., D.P.), Yale School of Medicine, New Haven, CT; Department of Population Health Sciences (E.S., I.D.), and Department of Radiology (T.D.N., A.D., J.C., Y.W., S.A.G.), Weil Cornell Medicine, New York; Department of Medicine (L.T.), Yale New Haven Hospital, New Haven, CT; Feil Family Brain and Mind Institute (S.A.G.), Weill Cornell Medicine, New York; and Lee Kong Chian School of Medicine (C.H.L.), Nanyang Technological University, Singapore.
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Corrigendum. Brain Behav 2022; 12:e2480. [PMID: 35072986 PMCID: PMC8785637 DOI: 10.1002/brb3.2480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Tozlu C, Jamison K, Nguyen T, Zinger N, Kaunzner U, Pandya S, Wang Y, Gauthier S, Kuceyeski A. Structural disconnectivity from paramagnetic rim lesions is related to disability in multiple sclerosis. Brain Behav 2021; 11:e2353. [PMID: 34498432 PMCID: PMC8553317 DOI: 10.1002/brb3.2353] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/28/2021] [Accepted: 08/19/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND In people with multiple sclerosis (pwMS), lesions with a hyperintense rim (rim+) on Quantitative Susceptibility Mapping (QSM) have been shown to have greater myelin damage compared to rim- lesions, but their association with disability has not yet been investigated. Furthermore, how QSM rim+ and rim- lesions differentially impact disability through their disruptions to structural connectivity has not been explored. We test the hypothesis that structural disconnectivity due to rim+ lesions is more predictive of disability compared to structural disconnectivity due to rim- lesions. METHODS Ninety-six pwMS were included in our study. Individuals with Expanded Disability Status Scale (EDSS) <2 were considered to have lower disability (n = 59). For each gray matter region, a Change in Connectivity (ChaCo) score, that is, the percent of connecting streamlines also passing through a rim- or rim+ lesion, was computed. Adaptive Boosting was used to classify the pwMS into lower versus greater disability groups based on ChaCo scores from rim+ and rim- lesions. Classification performance was assessed using the area under ROC curve (AUC). RESULTS The model based on ChaCo from rim+ lesions outperformed the model based on ChaCo from rim- lesions (AUC = 0.67 vs 0.63, p-value < .05). The left thalamus and left cerebellum were the most important regions in classifying pwMS into disability categories. CONCLUSION rim+ lesions may be more influential on disability through their disruptions to the structural connectome than rim- lesions. This study provides a deeper understanding of how rim+ lesion location/size and resulting disruption to the structural connectome can contribute to MS-related disability.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Thanh Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Nicole Zinger
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Ulrike Kaunzner
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Sneha Pandya
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Susan Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
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