1
|
Zamecnik CR, Sowa GM, Abdelhak A, Dandekar R, Bair RD, Wade KJ, Bartley CM, Kizer K, Augusto DG, Tubati A, Gomez R, Fouassier C, Gerungan C, Caspar CM, Alexander J, Wapniarski AE, Loudermilk RP, Eggers EL, Zorn KC, Ananth K, Jabassini N, Mann SA, Ragan NR, Santaniello A, Henry RG, Baranzini SE, Zamvil SS, Sabatino JJ, Bove RM, Guo CY, Gelfand JM, Cuneo R, von Büdingen HC, Oksenberg JR, Cree BAC, Hollenbach JA, Green AJ, Hauser SL, Wallin MT, DeRisi JL, Wilson MR. An autoantibody signature predictive for multiple sclerosis. Nat Med 2024:10.1038/s41591-024-02938-3. [PMID: 38641750 DOI: 10.1038/s41591-024-02938-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 03/21/2024] [Indexed: 04/21/2024]
Abstract
Although B cells are implicated in multiple sclerosis (MS) pathophysiology, a predictive or diagnostic autoantibody remains elusive. In this study, the Department of Defense Serum Repository (DoDSR), a cohort of over 10 million individuals, was used to generate whole-proteome autoantibody profiles of hundreds of patients with MS (PwMS) years before and subsequently after MS onset. This analysis defines a unique cluster in approximately 10% of PwMS who share an autoantibody signature against a common motif that has similarity with many human pathogens. These patients exhibit antibody reactivity years before developing MS symptoms and have higher levels of serum neurofilament light (sNfL) compared to other PwMS. Furthermore, this profile is preserved over time, providing molecular evidence for an immunologically active preclinical period years before clinical onset. This autoantibody reactivity was validated in samples from a separate incident MS cohort in both cerebrospinal fluid and serum, where it is highly specific for patients eventually diagnosed with MS. This signature is a starting point for further immunological characterization of this MS patient subset and may be clinically useful as an antigen-specific biomarker for high-risk patients with clinically or radiologically isolated neuroinflammatory syndromes.
Collapse
Affiliation(s)
- Colin R Zamecnik
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Gavin M Sowa
- University of California, San Francisco School of Medicine, San Francisco, CA, USA
- Department of Medicine, McGaw Medical Center of Northwestern University, Chicago, IL, USA
| | - Ahmed Abdelhak
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Ravi Dandekar
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Rebecca D Bair
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Kristen J Wade
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Christopher M Bartley
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Kerry Kizer
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Danillo G Augusto
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Biological Sciences, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Asritha Tubati
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Refujia Gomez
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Camille Fouassier
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Chloe Gerungan
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Colette M Caspar
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jessica Alexander
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Anne E Wapniarski
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Rita P Loudermilk
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Erica L Eggers
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Kelsey C Zorn
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Kirtana Ananth
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Nora Jabassini
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Sabrina A Mann
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub San Francisco, San Francisco, CA, USA
| | - Nicholas R Ragan
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Adam Santaniello
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Roland G Henry
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Sergio E Baranzini
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Scott S Zamvil
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Joseph J Sabatino
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Riley M Bove
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Chu-Yueh Guo
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jeffrey M Gelfand
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Richard Cuneo
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - H-Christian von Büdingen
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jorge R Oksenberg
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Bruce A C Cree
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jill A Hollenbach
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Ari J Green
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Stephen L Hauser
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Mitchell T Wallin
- Department of Veterans Affairs, Multiple Sclerosis Center of Excellence, Washington, DC, USA
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Joseph L DeRisi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub San Francisco, San Francisco, CA, USA
| | - Michael R Wilson
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
| |
Collapse
|
2
|
Shen X, Caverzasi E, Yang Y, Liu X, Green A, Henry RG, Emir U, Larson PEZ. 3D balanced SSFP UTE MRI for multiple contrasts whole brain imaging. Magn Reson Med 2024. [PMID: 38525680 DOI: 10.1002/mrm.30093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 02/14/2024] [Accepted: 03/05/2024] [Indexed: 03/26/2024]
Abstract
PURPOSE This study aimed to develop a new high-resolution MRI sequence for the imaging of the ultra-short transverse relaxation time (uT2) components in the brain, while simultaneously providing proton density (PD) contrast for reference and quantification. THEORY The sequence combines low flip angle balanced SSFP (bSSFP) and UTE techniques, together with a 3D dual-echo rosette k-space trajectory for readout. METHODS The expected image contrast was evaluated by simulations. A study cohort of six healthy volunteers and eight multiple sclerosis (MS) patients was recruited to test the proposed sequence. Subtraction between two TEs was performed to extract uT2 signals. In addition, conventional longitudinal relaxation time (T1) weighted, T2-weighted, and PD-weighted MRI sequences were also acquired for comparison. RESULTS Typical PD-contrast was found in the second TE images, while uT2 signals were selectively captured in the first TE images. The subtraction images presented signals primarily originating from uT2 components, but only if the first TE is short enough. Lesions in the MS subjects showed hyperintense signals in the second TE images but were hypointense signals in the subtraction images. The lesions had significantly lower signal intensity in subtraction images than normal white matter (WM), which indicated a reduction of uT2 components likely associated with myelin. CONCLUSION 3D isotropic sub-millimeter (0.94 mm) spatial resolution images were acquired with the novel bSSFP UTE sequence within 3 min. It provided easy extraction of uT2 signals and PD-contrast for reference within a single acquisition.
Collapse
Affiliation(s)
- Xin Shen
- Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Eduardo Caverzasi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy
| | - Yang Yang
- Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Xiaoxi Liu
- Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Ari Green
- Neurology, University of California San Francisco, San Francisco, California, USA
| | - Roland G Henry
- Neurology, University of California San Francisco, San Francisco, California, USA
| | - Uzay Emir
- School of Health Science, Purdue University, West Lafayette, Indiana, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Peder E Z Larson
- Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| |
Collapse
|
3
|
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:awae013. [PMID: 38226694 DOI: 10.1093/brain/awae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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 (MS) and have implications for non-relapsing biological progression. In recent years, the discovery of innovative magnetic resonance imaging (MRI) and PET derived biomarkers has made it possible to detect CAL, and to some extent quantify them, in the brain of persons with MS, in vivo. Paramagnetic rim lesions on susceptibility-sensitive MRI sequences, MRI-defined slowly expanding lesions on T1-weighted (T1-w) and T2-w 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.
Collapse
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
| | | | | | - Susan A Gauthier
- Department of Neurology, Weill Cornell Medicine, NYC, 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
- Vanderbilt University Institute of Imaging Science, Department of Radiology and Radiological Sciences, 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 & 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 & 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, Institute of Experimental Neurology, Division of Neuroscience, Vita-Salute San Raffaele University and Hospital, Milan, 20132, Italy
| |
Collapse
|
4
|
Daboul L, O’Donnell CM, Amin M, Rodrigues P, Derbyshire J, Azevedo C, Bar-Or A, Caverzasi E, Calabresi PA, Cree BA, Freeman L, Henry RG, Longbrake EE, Oh J, Papinutto N, Pelletier D, Prchkovska V, Raza P, Ramos M, Samudralwar RD, Schindler MK, Sotirchos ES, Sicotte NL, Solomon AJ, Shinohara RT, Reich DS, Sati P, Ontaneda D. A multicenter pilot study evaluating simplified central vein assessment for the diagnosis of multiple sclerosis. Mult Scler 2024; 30:25-34. [PMID: 38088067 PMCID: PMC11037932 DOI: 10.1177/13524585231214360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
BACKGROUND The central vein sign (CVS) is a proposed magnetic resonance imaging (MRI) biomarker for multiple sclerosis (MS); the optimal method for abbreviated CVS scoring is not yet established. OBJECTIVE The aim of this study was to evaluate the performance of a simplified approach to CVS assessment in a multicenter study of patients being evaluated for suspected MS. METHODS Adults referred for possible MS to 10 sites were recruited. A post-Gd 3D T2*-weighted MRI sequence (FLAIR*) was obtained in each subject. Trained raters at each site identified up to six CVS-positive lesions per FLAIR* scan. Diagnostic performance of CVS was evaluated for a diagnosis of MS which had been confirmed using the 2017 McDonald criteria at thresholds including three positive lesions (Select-3*) and six positive lesions (Select-6*). Inter-rater reliability assessments were performed. RESULTS Overall, 78 participants were analyzed; 37 (47%) were diagnosed with MS, and 41 (53%) were not. The mean age of participants was 45 (range: 19-64) years, and most were female (n = 55, 71%). The area under the receiver operating characteristic curve (AUROC) for the simplified counting method was 0.83 (95% CI: 0.73-0.93). Select-3* and Select-6* had sensitivity of 81% and 65% and specificity of 68% and 98%, respectively. Inter-rater agreement was 78% for Select-3* and 83% for Select-6*. CONCLUSION A simplified method for CVS assessment in patients referred for suspected MS demonstrated good diagnostic performance and inter-rater agreement.
Collapse
Affiliation(s)
- Lynn Daboul
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
- Cleveland Clinic Lerner College of Medicine, Cleveland, OH
| | - Carly M. O’Donnell
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Moein Amin
- Neurological Institute, Cleveland Clinic, Cleveland, OH
| | | | - John Derbyshire
- Functional MRI Facility, NIMH, National Institutes of Health, Bethesda, MD
| | - Christina Azevedo
- Department of Neurology, University of Southern California, Los Angeles, CA
| | - Amit Bar-Or
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Eduardo Caverzasi
- Department of Neurology, University of California at San Francisco, San Francisco, CA
| | | | - Bruce A.C. Cree
- Department of Neurology, University of California at San Francisco, San Francisco, CA
| | - Leorah Freeman
- Department of Neurology, Dell Medical School, The University of Texas, Austin, TX
| | - Roland G. Henry
- Department of Neurology, University of California at San Francisco, San Francisco, CA
| | | | - Jiwon Oh
- Division of Neurology, St. Michael’s Hospital, University of Toronto, Toronto, ON, CANADA
| | - Nico Papinutto
- Department of Neurology, University of California at San Francisco, San Francisco, CA
| | - Daniel Pelletier
- Department of Neurology, University of Southern California, Los Angeles, CA
| | | | - Praneeta Raza
- Cleveland Clinic Lerner College of Medicine, Cleveland, OH
| | - Marc Ramos
- QMENTA Cloud Platform, QMENTA Inc., Boston, MA, USA
| | | | - Matthew K. Schindler
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | | | - Nancy L. Sicotte
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Andrew J. Solomon
- Department of Neurological Sciences, Larner College of Medicine, The University of Vermont, Burlington, VT
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Daniel S. Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, OH
| |
Collapse
|
5
|
Sacco S, Virupakshaiah A, Papinutto N, Schoeps VA, Akula A, Zhao H, Arona J, Stern WA, Chong J, Hart J, Zamvil SS, Sati P, Henry RG, Waubant E. Susceptibility-based imaging aids accurate distinction of pediatric-onset MS from myelin oligodendrocyte glycoprotein antibody-associated disease. Mult Scler 2023; 29:1736-1747. [PMID: 37897254 PMCID: PMC10687802 DOI: 10.1177/13524585231204414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/07/2023] [Accepted: 09/13/2023] [Indexed: 10/30/2023]
Abstract
BACKGROUND Myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) and pediatric-onset multiple sclerosis (POMS) share clinical and magnetic resonance imaging (MRI) features but differ in prognosis and management. Early POMS diagnosis is essential to avoid disability accumulation. Central vein sign (CVS), paramagnetic rim lesions (PRLs), and central core lesions (CCLs) are susceptibility-based imaging (SbI)-related signs understudied in pediatric populations that may help discerning POMS from MOGAD. METHODS T2-FLAIR and SbI (three-dimensional echoplanar imaging (3D-EPI)/susceptibility-weighted imaging (SWI) or similar) were acquired on 1.5T/3T scanners. Two readers assessed CVS-positive rate (%CVS+), and their average score was used to build a receiver operator curve (ROC) assessing the ability to discriminate disease type. PRLs and CCLs were identified using a consensual approach. RESULTS The %CVS+ distinguished 26 POMS cases (mean age 13.7 years, 63% females, median EDSS 1.5) from 14 MOGAD cases (10.8 years, 35% females, EDSS 1.0) with ROC = 1, p < 0.0001, (cutoff 41%). PRLs were only detectable in POMS participants (mean 2.1±2.3, range 1-10), discriminating the two conditions with a sensitivity of 69% and a specificity of 100%. CCLs were more sensitive (81%) but less specific (71.43%). CONCLUSION The %CVS+ and PRLs are highly specific markers of POMS. After proper validation on larger multicenter cohorts, consideration should be given to including such imaging markers for diagnosing POMS at disease onset.
Collapse
Affiliation(s)
- Simone Sacco
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Akash Virupakshaiah
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Nico Papinutto
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Vinicius A Schoeps
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Amit Akula
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Haojun Zhao
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Jennifer Arona
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - William A Stern
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Janet Chong
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Janace Hart
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Scott S Zamvil
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Pascal Sati
- Neuroimaging Program, Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Emmanuelle Waubant
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| |
Collapse
|
6
|
Abdelhak A, Benkert P, Schaedelin S, Boscardin WJ, Cordano C, Oechtering J, Ananth K, Granziera C, Melie-Garcia L, Montes SC, Beaudry-Richard A, Achtnichts L, Oertel FC, Lalive PH, Leppert D, Müller S, Henry RG, Pot C, Matthias A, Salmen A, Oksenberg JR, Disanto G, Zecca C, D’Souza M, Du Pasquier R, Bridel C, Gobbi C, Kappos L, Hauser SL, Cree BAC, Kuhle J, Green AJ. Neurofilament Light Chain Elevation and Disability Progression in Multiple Sclerosis. JAMA Neurol 2023; 80:1317-1325. [PMID: 37930670 PMCID: PMC10628837 DOI: 10.1001/jamaneurol.2023.3997] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 08/16/2023] [Indexed: 11/07/2023]
Abstract
Importance Mechanisms contributing to disability accumulation in multiple sclerosis (MS) are poorly understood. Blood neurofilament light chain (NfL) level, a marker of neuroaxonal injury, correlates robustly with disease activity in people with MS (MS); however, data on the association between NfL level and disability accumulation have been conflicting. Objective To determine whether and when NfL levels are elevated in the context of confirmed disability worsening (CDW). Design, Setting, and Participants This study included 2 observational cohorts: results from the Expression, Proteomics, Imaging, Clinical (EPIC) study at the University of California San Francisco (since 2004) were confirmed in the Swiss Multiple Sclerosis Cohort (SMSC), a multicenter study in 8 centers since 2012. Data were extracted from EPIC in April 2022 (sampling July 1, 2004, to December 20, 2016) and SMSC in December 2022 (sampling June 6, 2012, to September 2, 2021). The study included 2 observational cohorts in tertiary MS centers. All participants of both cohorts with available NfL results were included in the study, and no eligible participants were excluded or declined to participate. Exposure Association between NfL z scores and CDW. Main Outcome Measures CDW was defined as Expanded Disability Status Scale (EDSS) worsening that was confirmed after 6 or more months and classified into CDW associated with clinical relapses (CDW-R) or independent of clinical relapses (CDW-NR). Visits were classified in relation to the disability worsening events into CDW(-2) for 2 visits preceding event, CDW(-1) for directly preceding event, CDW(event) for first diagnosis of EDSS increase, and the confirmation visit. Mixed linear and Cox regression models were used to evaluate NfL dynamics and to assess the association of NfL with future CDW, respectively. Results A total of 3906 EPIC visits (609 participants; median [IQR] age, 42.0 [35.0-50.0] years; 424 female [69.6%]) and 8901 SMSC visits (1290 participants; median [IQR] age, 41.2 [32.5-49.9] years; 850 female [65.9%]) were included. In CDW-R (EPIC, 36 events; SMSC, 93 events), NfL z scores were 0.71 (95% CI, 0.35-1.07; P < .001) units higher at CDW-R(-1) in EPIC and 0.32 (95% CI, 0.14-0.49; P < .001) in SMSC compared with stable MS samples. NfL elevation could be detected preceding CDW-NR (EPIC, 191 events; SMSC, 342 events) at CDW-NR(-2) (EPIC: 0.23; 95% CI, 0.01-0.45; P = .04; SMSC: 0.28; 95% CI, 0.18-0.37; P < .001) and at CDW-NR(-1) (EPIC: 0.27; 95% CI, 0.11-0.44; P < .001; SMSC: 0.09; 95% CI, 0-0.18; P = .06). Those findings were replicated in the subgroup with relapsing-remitting MS. Time-to-event analysis confirmed the association between NfL levels and future CDW-R within approximately 1 year and CDW-NR (in approximately 1-2 years). Conclusions and Relevance This cohort study documents the occurrence of NfL elevation in advance of clinical worsening and may hint to a potential window of ongoing dynamic central nervous system pathology that precedes the diagnosis of CDW.
Collapse
Affiliation(s)
- Ahmed Abdelhak
- Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco
| | - Pascal Benkert
- Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), Departments of Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland
- Multiple Sclerosis Center, Department of Neurology, University Hospital and University of Basel, Basel, Switzerland
| | - Sabine Schaedelin
- Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), Departments of Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland
- Multiple Sclerosis Center, Department of Neurology, University Hospital and University of Basel, Basel, Switzerland
| | - W. John Boscardin
- Departments of Medicine and Epidemiology & Biostatistics, University of California at San Francisco, San Francisco
| | - Christian Cordano
- Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco
- Department of Neurology, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Johanna Oechtering
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), Departments of Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland
- Multiple Sclerosis Center, Department of Neurology, University Hospital and University of Basel, Basel, Switzerland
| | - Kirtana Ananth
- Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco
| | - Cristina Granziera
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), Departments of Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland
- Multiple Sclerosis Center, Department of Neurology, University Hospital and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), Departments of Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland
- Multiple Sclerosis Center, Department of Neurology, University Hospital and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Shivany Condor Montes
- Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco
| | - Alexandra Beaudry-Richard
- Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco
| | - Lutz Achtnichts
- Department of Neurology, Cantonal Hospital Aarau, Aarau, Switzerland
| | - Frederike C. Oertel
- Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco
| | - Patrice H. Lalive
- Unit of Neuroimmunology, Division of Neurology, Department of Clinical Neurosciences, University Hospital of Geneva and Faculty of Medicine, Geneva, Switzerland
| | - David Leppert
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), Departments of Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland
- Multiple Sclerosis Center, Department of Neurology, University Hospital and University of Basel, Basel, Switzerland
| | - Stefanie Müller
- Department of Neurology, Cantonal Hospital St Gallen, St Gallen, Switzerland
| | - Roland G. Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco
| | - Caroline Pot
- Department of Clinical Neurosciences, Service of Neurology, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Amandine Matthias
- Department of Clinical Neurosciences, Service of Neurology, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Anke Salmen
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Jorge R. Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco
| | - Giulio Disanto
- Multiple Sclerosis Center, Department of Neurology, Neurocenter of Southern Switzerland, ECO, Lugano, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
| | - Chiara Zecca
- Multiple Sclerosis Center, Department of Neurology, Neurocenter of Southern Switzerland, ECO, Lugano, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
| | - Marcus D’Souza
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), Departments of Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland
- Multiple Sclerosis Center, Department of Neurology, University Hospital and University of Basel, Basel, Switzerland
| | - Renaud Du Pasquier
- Department of Clinical Neurosciences, Service of Neurology, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Claire Bridel
- Unit of Neuroimmunology, Division of Neurology, Department of Clinical Neurosciences, University Hospital of Geneva and Faculty of Medicine, Geneva, Switzerland
| | - Claudio Gobbi
- Multiple Sclerosis Center, Department of Neurology, Neurocenter of Southern Switzerland, ECO, Lugano, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
| | - Ludwig Kappos
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), Departments of Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland
- Multiple Sclerosis Center, Department of Neurology, University Hospital and University of Basel, Basel, Switzerland
| | - Stephen L. Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco
| | - Bruce A. C. Cree
- Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco
| | - Jens Kuhle
- Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), Departments of Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland
- Multiple Sclerosis Center, Department of Neurology, University Hospital and University of Basel, Basel, Switzerland
| | - Ari J. Green
- Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco
- Department of Ophthalmology, University of California at San Francisco, San Francisco
| |
Collapse
|
7
|
Chitnis T, Qureshi F, Gehman VM, Becich M, Bove R, Cree BAC, Gomez R, Hauser SL, Henry RG, Katrib A, Lokhande H, Paul A, Caillier SJ, Santaniello A, Sattarnezhad N, Saxena S, Weiner H, Yano H, Baranzini SE. Inflammatory and neurodegenerative serum protein biomarkers increase sensitivity to detect disease activity in multiple sclerosis. medRxiv 2023:2023.06.28.23291157. [PMID: 37461671 PMCID: PMC10350151 DOI: 10.1101/2023.06.28.23291157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Background/Objectives Serum proteomic analysis of deeply-phenotyped samples, biological pathway modeling and network analysis were performed to elucidate the inflammatory and neurodegenerative processes of multiple sclerosis (MS) and identify sensitive biomarkers of MS disease activity (DA). Methods Over 1100 serum proteins were evaluated in >600 samples from three MS cohorts to identify biomarkers of clinical and radiographic (gadolinium-enhancing lesions) new MS DA. Protein levels were analyzed and associated with presence of gadolinium-enhancing lesions, clinical relapse status (CRS), and annualized relapse rate (ARR) to create a custom assay panel. Results Twenty proteins were associated with increased clinical and radiographic MS DA. Serum neurofilament light chain (NfL) showed the strongest univariate correlation with radiographic and clinical DA measures. Multivariate modeling significantly outperformed univariate NfL to predict gadolinium lesion activity, CRS and ARR. Discussion These findings provide insight regarding correlations between inflammatory and neurodegenerative biomarkers and clinical and radiographic MS DA. Funding Octave Bioscience, Inc (Menlo Park, CA).
Collapse
|
8
|
Caverzasi E, Papinutto N, Cordano C, Kirkish G, Gundel TJ, Zhu A, Akula AV, Boscardin WJ, Neeb H, Henry RG, Chan JR, Green AJ. MWF of the corpus callosum is a robust measure of remyelination: Results from the ReBUILD trial. Proc Natl Acad Sci U S A 2023; 120:e2217635120. [PMID: 37155847 DOI: 10.1073/pnas.2217635120] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023] Open
Abstract
Myelin repair is an unrealized therapeutic goal in the treatment of multiple sclerosis (MS). Uncertainty remains about the optimal techniques for assessing therapeutic efficacy and imaging biomarkers are required to measure and corroborate myelin restoration. We analyzed myelin water fraction imaging from ReBUILD, a double-blind, randomized placebo-controlled (delayed treatment) remyelination trial, that showed a significant reduction in VEP latency in patients with MS. We focused on brain regions rich in myelin. Fifty MS subjects in two arms underwent 3T MRI at baseline and months 3 and 5. Half of the cohort was randomly assigned to receive treatment from baseline through 3 mo, whereas the other half received treatment from 3 mo to 5 mo post-baseline. We computed myelin water fraction changes occurring in normal-appearing white matter of corpus callosum, optic radiations, and corticospinal tracts. An increase in myelin water fraction was documented in the normal-appearing white matter of the corpus callosum, in correspondence with the administration of the remyelinating treatment clemastine. This study provides direct, biologically validated imaging-based evidence of medically induced myelin repair. Moreover, our work strongly suggests that significant myelin repair occurs outside of lesions. We therefore propose myelin water fraction within the normal-appearing white matter of the corpus callosum as a biomarker for clinical trials looking at remyelination.
Collapse
Affiliation(s)
- Eduardo Caverzasi
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
| | - Nico Papinutto
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
| | - Christian Cordano
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
| | - Gina Kirkish
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
| | - Tristan J Gundel
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
| | - Alyssa Zhu
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
| | - Amit Vijay Akula
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
| | - W John Boscardin
- Department of Medicine, University of California, San Francisco, CA 94143
- Department of Epidemiology & Biostatistics, University of California, San Francisco, CA 94143
| | - Heiko Neeb
- Multimodal Imaging Physics Group, Department of Mathematics and Technology, Koblenz University of Applied Sciences, 53424 Koblenz, Germany
- Institute for Medical Engineering and Information Processing, University of Koblenz and Landau, 56070 Koblenz, Germany
| | - Roland G Henry
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
| | - Jonah R Chan
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
| | - Ari J Green
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
- Department of Ophthalmology, University of California, San Francisco, CA 94143
| |
Collapse
|
9
|
Zamecnik CR, Sowa GM, Abdelhak A, Dandekar R, Bair RD, Wade KJ, Bartley CM, Tubati A, Gomez R, Fouassier C, Gerungan C, Alexander J, Wapniarski AE, Loudermilk RP, Eggers EL, Zorn KC, Ananth K, Jabassini N, Mann SA, Ragan NR, Santaniello A, Henry RG, Baranzini SE, Zamvil SS, Bove RM, Guo CY, Gelfand JM, Cuneo R, von Büdingen HC, Oksenberg JR, Cree BAC, Hollenbach JA, Green AJ, Hauser SL, Wallin MT, DeRisi JL, Wilson MR. A Predictive Autoantibody Signature in Multiple Sclerosis. medRxiv 2023:2023.05.01.23288943. [PMID: 37205595 PMCID: PMC10187343 DOI: 10.1101/2023.05.01.23288943] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Although B cells are implicated in multiple sclerosis (MS) pathophysiology, a predictive or diagnostic autoantibody remains elusive. Here, the Department of Defense Serum Repository (DoDSR), a cohort of over 10 million individuals, was used to generate whole-proteome autoantibody profiles of hundreds of patients with MS (PwMS) years before and subsequently after MS onset. This analysis defines a unique cluster of PwMS that share an autoantibody signature against a common motif that has similarity with many human pathogens. These patients exhibit antibody reactivity years before developing MS symptoms and have higher levels of serum neurofilament light (sNfL) compared to other PwMS. Furthermore, this profile is preserved over time, providing molecular evidence for an immunologically active prodromal period years before clinical onset. This autoantibody reactivity was validated in samples from a separate incident MS cohort in both cerebrospinal fluid (CSF) and serum, where it is highly specific for patients eventually diagnosed with MS. This signature is a starting point for further immunological characterization of this MS patient subset and may be clinically useful as an antigen-specific biomarker for high-risk patients with clinically- or radiologically-isolated neuroinflammatory syndromes.
Collapse
Affiliation(s)
- Colin R. Zamecnik
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Gavin M. Sowa
- Department of Medicine, McGaw Medical Center of Northwestern University, Chicago, IL, USA
| | - Ahmed Abdelhak
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Ravi Dandekar
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Rebecca D. Bair
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Kristen J. Wade
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Christopher M. Bartley
- UCSF Weill Institute for Neurosciences, Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Asritha Tubati
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Refujia Gomez
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Camille Fouassier
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Chloe Gerungan
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jessica Alexander
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Anne E. Wapniarski
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Rita P. Loudermilk
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Erica L. Eggers
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Kelsey C. Zorn
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
| | - Kirtana Ananth
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Nora Jabassini
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Sabrina A. Mann
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
| | - Nicholas R. Ragan
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Adam Santaniello
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Roland G. Henry
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Sergio E. Baranzini
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Scott S. Zamvil
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Riley M. Bove
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Chu-Yueh Guo
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jeffrey M. Gelfand
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Richard Cuneo
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - H.-Christian von Büdingen
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jorge R. Oksenberg
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Bruce AC Cree
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jill A. Hollenbach
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA USA
| | - Ari J. Green
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Stephen L. Hauser
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Mitchell T. Wallin
- Veterans Affairs, Multiple Sclerosis Center of Excellence, Washington, DC and University of Maryland School of Medicine, Baltimore, MD, USA
| | - Joseph L. DeRisi
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Michael R. Wilson
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| |
Collapse
|
10
|
Ma Q, Shams H, Didonna A, Baranzini SE, Cree BAC, Hauser SL, Henry RG, Oksenberg JR. Integration of epigenetic and genetic profiles identifies multiple sclerosis disease-critical cell types and genes. Commun Biol 2023; 6:342. [PMID: 36997638 PMCID: PMC10063586 DOI: 10.1038/s42003-023-04713-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/14/2023] [Indexed: 04/01/2023] Open
Abstract
Genome-wide association studies (GWAS) successfully identified multiple sclerosis (MS) susceptibility variants. Despite this notable progress, understanding the biological context of these associations remains challenging, due in part to the complexity of linking GWAS results to causative genes and cell types. Here, we aimed to address this gap by integrating GWAS data with single-cell and bulk chromatin accessibility data and histone modification profiles from immune and nervous systems. MS-GWAS associations are significantly enriched in regulatory regions of microglia and peripheral immune cell subtypes, especially B cells and monocytes. Cell-specific polygenic risk scores were developed to examine the cumulative impact of the susceptibility genes on MS risk and clinical phenotypes, showing significant associations with risk and brain white matter volume. The findings reveal enrichment of GWAS signals in B cell and monocyte/microglial cell-types, consistent with the known pathology and presumed targets of effective MS therapeutics.
Collapse
Affiliation(s)
- Qin Ma
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Hengameh Shams
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Alessandro Didonna
- Department of Anatomy and Cell Biology, Brody School of Medicine, East Carolina University, Greenville, NC, 27834, USA
| | - Sergio E Baranzini
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Bruce A C Cree
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Stephen L Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA.
| |
Collapse
|
11
|
Block VJ, Cheng S, Juwono J, Cuneo R, Kirkish G, Alexander AM, Khan M, Akula A, Caverzasi E, Papinutto N, Stern WA, Pletcher MJ, Marcus GM, Olgin JE, Hauser SL, Gelfand JM, Bove R, Cree BAC, Henry RG. Association of daily physical activity with brain volumes and cervical spinal cord areas in multiple sclerosis. Mult Scler 2023; 29:363-373. [PMID: 36573559 PMCID: PMC9972237 DOI: 10.1177/13524585221143726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
BACKGROUND Remote activity monitoring has the potential to evaluate real-world, motor function, and disability at home. The relationships of daily physical activity with spinal cord white matter and gray matter (GM) areas, multiple sclerosis (MS) disability and leg function, are unknown. OBJECTIVE Evaluate the association of structural central nervous system pathology with ambulatory disability. METHODS Fifty adults with progressive or relapsing MS with motor disability who could walk >2 minutes were assessed using clinician-evaluated, patient-reported outcomes, and quantitative brain and spinal cord magnetic resonance imaging (MRI) measures. Fitbit Flex2, worn on the non-dominant wrist, remotely assessed activity over 30 days. Univariate and multivariate analyses were performed to assess correlations between physical activity and other disability metrics. RESULTS Mean age was 53.3 years and median Expanded Disability Status Scale (EDSS) was 4.0. Average daily step counts (STEPS) were highly correlated with EDSS and walking measures. Greater STEPS were significantly correlated with greater C2-C3 spinal cord GM areas (ρ = 0.39, p = 0.04), total cord area (TCA; ρ = 0.35, p = 0.04), and cortical GM volume (ρ = 0.32, p = 0.04). CONCLUSION These results provide preliminary evidence that spinal cord GM area is a neuroanatomical substrate associated with STEPS. STEPS could serve as a proxy to alert clinicians and researchers to possible changes in structural nervous system pathology.
Collapse
Affiliation(s)
- Valerie J Block
- UCSF Weill Institute for Neurosciences,
Department of Neurology, University of California San Francisco, San
Francisco, CA, USA/Department of Physical Therapy and Rehabilitation
Science, University of California San Francisco, San Francisco, CA,
USA
| | - Shuiting Cheng
- UCSF Weill Institute for Neurosciences,
Department of Neurology, University of California San Francisco, San
Francisco, CA, USA
| | - Jeremy Juwono
- UCSF Weill Institute for Neurosciences,
Department of Neurology, University of California San Francisco, San
Francisco, CA, USA
| | - Richard Cuneo
- UCSF Weill Institute for Neurosciences,
Department of Neurology, University of California San Francisco, San
Francisco, CA, USA
| | - Gina Kirkish
- UCSF Weill Institute for Neurosciences,
Department of Neurology, University of California San Francisco, San
Francisco, CA, USA
| | - Amber M Alexander
- UCSF Weill Institute for Neurosciences,
Department of Neurology, University of California San Francisco, San
Francisco, CA, USA
| | - Mahir Khan
- UCSF Weill Institute for Neurosciences,
Department of Neurology, University of California San Francisco, San
Francisco, CA, USA
| | - Amit Akula
- UCSF Weill Institute for Neurosciences,
Department of Neurology, University of California San Francisco, San
Francisco, CA, USA
| | - Eduardo Caverzasi
- UCSF Weill Institute for Neurosciences,
Department of Neurology, University of California San Francisco, San
Francisco, CA, USA/Department of Brain and Behavioral Sciences, University
of Pavia, Pavia, Italy
| | - Nico Papinutto
- UCSF Weill Institute for Neurosciences,
Department of Neurology, University of California San Francisco, San
Francisco, CA, USA
| | | | - Mark J Pletcher
- Department of Epidemiology and Biostatistics,
University of California San Francisco, San Francisco, CA, USA/Department of
Medicine, University of California San Francisco, San Francisco, CA,
USA
| | - Gregory M Marcus
- Department of Epidemiology and Biostatistics,
University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey E Olgin
- Department of Epidemiology and Biostatistics,
University of California San Francisco, San Francisco, CA, USA
| | - Stephen L Hauser
- UCSF Weill Institute for Neurosciences,
Department of Neurology, University of California San Francisco, San
Francisco, CA, USA
| | - Jeffrey M Gelfand
- UCSF Weill Institute for Neurosciences,
Department of Neurology, University of California San Francisco, San
Francisco, CA, USA
| | - Riley Bove
- UCSF Weill Institute for Neurosciences,
Department of Neurology, University of California San Francisco, San
Francisco, CA, USA
| | - Bruce AC Cree
- BAC Cree UCSF Weill Institute for
Neurosciences, Department of Neurology, University of California, 1651 4th St
Suite 252, San Francisco, San Francisco, CA 94158, USA.
| | | |
Collapse
|
12
|
Shams H, Shao X, Santaniello A, Kirkish G, Harroud A, Ma Q, Isobe N, Schaefer CA, McCauley JL, Cree BAC, Didonna A, Baranzini SE, Patsopoulos NA, Hauser SL, Barcellos LF, Henry RG, Oksenberg JR. Polygenic risk score association with multiple sclerosis susceptibility and phenotype in Europeans. Brain 2023; 146:645-656. [PMID: 35253861 PMCID: PMC10169285 DOI: 10.1093/brain/awac092] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/29/2022] [Accepted: 02/15/2022] [Indexed: 11/13/2022] Open
Abstract
Polygenic inheritance plays a pivotal role in driving multiple sclerosis susceptibility, an inflammatory demyelinating disease of the CNS. We developed polygenic risk scores (PRS) of multiple sclerosis and assessed associations with both disease status and severity in cohorts of European descent. The largest genome-wide association dataset for multiple sclerosis to date (n = 41 505) was leveraged to generate PRS scores, serving as an informative susceptibility marker, tested in two independent datasets, UK Biobank [area under the curve (AUC) = 0.73, 95% confidence interval (CI): 0.72-0.74, P = 6.41 × 10-146] and Kaiser Permanente in Northern California (KPNC, AUC = 0.8, 95% CI: 0.76-0.82, P = 1.5 × 10-53). Individuals within the top 10% of PRS were at higher than 5-fold increased risk in UK Biobank (95% CI: 4.7-6, P = 2.8 × 10-45) and 15-fold higher risk in KPNC (95% CI: 10.4-24, P = 3.7 × 10-11), relative to the median decile. The cumulative absolute risk of developing multiple sclerosis from age 20 onwards was significantly higher in genetically predisposed individuals according to PRS. Furthermore, inclusion of PRS in clinical risk models increased the risk discrimination by 13% to 26% over models based only on conventional risk factors in UK Biobank and KPNC, respectively. Stratifying disease risk by gene sets representative of curated cellular signalling cascades, nominated promising genetic candidate programmes for functional characterization. These pathways include inflammatory signalling mediation, response to viral infection, oxidative damage, RNA polymerase transcription, and epigenetic regulation of gene expression to be among significant contributors to multiple sclerosis susceptibility. This study also indicates that PRS is a useful measure for estimating susceptibility within related individuals in multicase families. We show a significant association of genetic predisposition with thalamic atrophy within 10 years of disease progression in the UCSF-EPIC cohort (P < 0.001), consistent with a partial overlap between the genetics of susceptibility and end-organ tissue injury. Mendelian randomization analysis suggested an effect of multiple sclerosis susceptibility on thalamic volume, which was further indicated to be through horizontal pleiotropy rather than a causal effect. In summary, this study indicates important, replicable associations of PRS with enhanced risk assessment and radiographic outcomes of tissue injury, potentially informing targeted screening and prevention strategies.
Collapse
Affiliation(s)
- Hengameh Shams
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA.,Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Xiaorong Shao
- Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Adam Santaniello
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Gina Kirkish
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Adil Harroud
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Qin Ma
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Noriko Isobe
- Department of Neurology, Graduate School of medical Sciences, Kyushu University, Fukuoka, 812-8582, Japan
| | | | | | - Jacob L McCauley
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA.,Dr. John T. Macdonald Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Bruce A C Cree
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Alessandro Didonna
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA.,Department of Anatomy and Cell Biology, East Carolina University, Greenville, NC 27834, USA
| | - Sergio E Baranzini
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Nikolaos A Patsopoulos
- Systems Biology and Computer Science Program, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Boston, 02115 MA, USA.,Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Harvard Medical School, Boston, MA 02115, USA.,Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stephen L Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Lisa F Barcellos
- Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| |
Collapse
|
13
|
Daboul L, O'Donnell CM, Cao Q, Amin M, Rodrigues P, Derbyshire J, Azevedo C, Bar-Or A, Caverzasi E, Calabresi P, Cree BAC, Freeman L, Henry RG, Longbrake EE, Nakamura K, Oh J, Papinutto N, Pelletier D, Samudralwar RD, Suthiphosuwan S, Schindler MK, Sotirchos ES, Sicotte NL, Solomon AJ, Shinohara RT, Reich DS, Ontaneda D, Sati P. Effect of GBCA Use on Detection and Diagnostic Performance of the Central Vein Sign: Evaluation Using a 3-T FLAIR* Sequence in Patients With Suspected Multiple Sclerosis. AJR Am J Roentgenol 2023; 220:115-125. [PMID: 35975888 PMCID: PMC10016223 DOI: 10.2214/ajr.22.27731] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND. The central vein sign (CVS) is a proposed MRI biomarker of multiple sclerosis (MS). The impact of gadolinium-based contrast agent (GBCA) administration on CVS evaluation remains poorly investigated. OBJECTIVE. The purpose of this study was to assess the effect of GBCA use on CVS detection and on the diagnostic performance of the CVS for MS using a 3-T FLAIR* sequence. METHODS. This study was a secondary analysis of data from the pilot study for the prospective multicenter Central Vein Sign: A Diagnostic Biomarker in Multiple Sclerosis (CAVS-MS), which recruited adults with suspected MS from April 2018 to February 2020. Participants underwent 3-T brain MRI including FLAIR and precontrast and post-contrast echo-planar imaging T2*-weighted acquisitions. Postprocessing was used to generate combined FLAIR and T2*-weighted images (hereafter, FLAIR*). MS diagnoses were established using the 2017 McDonald criteria. Thirty participants (23 women, seven men; mean age, 45 years) were randomly selected from the CAVS-MS pilot study cohort. White matter lesions (WMLs) were marked using FLAIR* images. A single observer, blinded to clinical data and GBCA use, reviewed marked WMLs on FLAIR* images for the presence of the CVS. RESULTS. Thirteen of 30 participants had MS. Across participants, on precontrast FLAIR* imaging, 218 CVS-positive and 517 CVS-negative WMLs were identified; on post-contrast FLAIR* imaging, 269 CVS-positive and 459 CVS-negative WMLs were identified. The fraction of WMLs that were CVS-positive on precontrast and postcontrast images was 48% and 58% in participants with MS and 7% and 10% in participants without MS, respectively. The median patient-level CVS-positivity rate on precontrast and postcontrast images was 43% and 67% for participants with MS and 4% and 8% for participants without MS, respectively. In a binomial model adjusting for MS diagnoses, GBCA use was associated with an increased likelihood of at least one CVS-positive WML (odds ratio, 1.6; p < .001). At a 40% CVS-positivity threshold, the sensitivity of the CVS for MS increased from 62% on precontrast images to 92% on postcontrast images (p = .046). Specificity was not significantly different between precontrast (88%) and postcontrast (82%) images (p = .32). CONCLUSION. GBCA use increased CVS detection on FLAIR* images, thereby increasing the sensitivity of the CVS for MS diagnoses. CLINICAL IMPACT. The postcontrast FLAIR* sequence should be considered for CVS evaluation in future investigational trials and clinical practice.
Collapse
Affiliation(s)
- Lynn Daboul
- Department of Neurology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD
- Cleveland Clinic Lerner College of Medicine, Cleveland, OH
| | - Carly M O'Donnell
- Department of Biostatistics, Epidemiology, and Informatics, Penn Statistics in Imaging and Visualization Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Quy Cao
- Department of Biostatistics, Epidemiology, and Informatics, Penn Statistics in Imaging and Visualization Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Moein Amin
- Neurological Institute, Cleveland Clinic, Cleveland, OH
| | | | | | - Christina Azevedo
- Department of Neurology, University of Southern California, Los Angeles, CA
| | - Amit Bar-Or
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Eduardo Caverzasi
- Department of Neurology, University of California at San Francisco, San Francisco, CA
| | - Peter Calabresi
- Department of Neurology, Johns Hopkins University, Baltimore, MD
| | - Bruce A C Cree
- Department of Neurology, University of California at San Francisco, San Francisco, CA
| | - Leorah Freeman
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX
| | - Roland G Henry
- Department of Neurology, University of California at San Francisco, San Francisco, CA
| | | | - Kunio Nakamura
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
| | - Jiwon Oh
- Division of Neurology, St. Michael's Hospital, University of Toronto, ON, Canada
| | - Nico Papinutto
- Department of Neurology, University of California at San Francisco, San Francisco, CA
| | - Daniel Pelletier
- Department of Neurology, University of Southern California, Los Angeles, CA
| | - Rohini D Samudralwar
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX
| | - Suradech Suthiphosuwan
- Department of Medical Imaging, St. Michael's Hospital, University of Toronto, ON, Canada
| | - Matthew K Schindler
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | | | - Nancy L Sicotte
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Andrew J Solomon
- Department of Neurological Sciences, Larner College of Medicine, The University of Vermont, Burlington, VT
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Penn Statistics in Imaging and Visualization Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, OH
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA
| |
Collapse
|
14
|
Cordano C, Nourbakhsh B, Yiu HH, Papinutto N, Caverzasi E, Abdelhak A, Oertel FC, Beaudry-Richard A, Santaniello A, Sacco S, Bennett DJ, Gomez A, Sigurdson CJ, Hauser SL, Magliozzi R, Cree BA, Henry RG, Green AJ. Differences in Age-related Retinal and Cortical Atrophy Rates in Multiple Sclerosis. Neurology 2022; 99:e1685-e1693. [PMID: 36038272 PMCID: PMC9559941 DOI: 10.1212/wnl.0000000000200977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/01/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The timing of neurodegeneration in multiple sclerosis (MS) remains unclear. It is critical to understand the dynamics of neuroaxonal loss if we hope to prevent or forestall permanent disability in MS. We therefore used a deeply phenotyped longitudinal cohort to assess and compare rates of neurodegeneration in retina and brain throughout the MS disease course. METHODS We analyzed 597 patients with MS who underwent longitudinal optical coherence tomography imaging annually for 4.5 ± 2.4 years and 432 patients who underwent longitudinal MRI scans for 10 ± 3.4 years, quantifying macular ganglion cell-inner plexiform layer (GCIPL) volume and cortical gray matter (CGM) volume. The association between the slope of decline in the anatomical structure and the age of entry in the cohort (categorized by the MRI cohort's age quartiles) was assessed by hierarchical linear models. RESULTS The rate of CGM volume loss declined with increasing age of study entry (1.3% per year atrophy for the age of entry in the cohort younger than 35 years; 1.1% for older than 35 years and younger than 41; 0.97% for older than 41 years and younger than 49; 0.9% for older than 49 years) while the rate of GCIPL thinning was highest in patients in the youngest quartile, fell by more than 50% in the following age quartile, and then stabilized (0.7% per year thinning for the age of entry in the cohort younger than 35 years; 0.29% for age older than 35 and younger than 41 years; 0.34% for older than 41 and younger than 49 years; 0.33% for age older than 49 years). DISCUSSION An age-dependent reduction in retinal and cortical volume loss rates during relapsing-remitting MS suggests deceleration in neurodegeneration in the earlier period of disease and further indicates that the period of greatest adaptive immune-mediated inflammatory activity is also the period with the greatest neuroaxonal loss.
Collapse
Affiliation(s)
- Christian Cordano
- From the Department of Neurology (C.C., N.P., E.C., A.A., F.C.O., A.B.-R., A.S., S.S., D.J.B., A.G., S.L.H., B.A.C.C., R.G.H., A.J.G.), UCSF Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (B.N.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Biology (H.H.Y.), University of Maryland, College Park; Department of Pathology (C.J.S.), University of California, San Diego, La Jolla; and Department of Neurosciences (R.M.), Biomedicine and Movement Sciences, University of Verona, Italy.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
15
|
Riva M, Wilson SM, Cai R, Castellano A, Jordan KM, Henry RG, Gorno Tempini ML, Berger MS, Chang EF. Evaluating syntactic comprehension during awake intraoperative cortical stimulation mapping. J Neurosurg 2022; 138:1403-1410. [PMID: 36208435 PMCID: PMC10159588 DOI: 10.3171/2022.8.jns221335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 08/04/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE
Electrocortical stimulation mapping (ECS) is widely used to identify essential language areas, but sentence-level processing has rarely been investigated.
METHODS
While undergoing awake surgery in the dominant left hemisphere, 6 subjects were asked to comprehend sentences varying in their demands on syntactic processing.
RESULTS
In all 6 subjects, stimulation of the inferior frontal gyrus disrupted comprehension of passive sentences, which critically depend on syntactic processing to correctly assign grammatical roles, without disrupting comprehension of simpler tasks. In 4 of the 6 subjects, these sites were localized to the pars opercularis. Sentence comprehension was also disrupted by stimulation of other perisylvian sites, but in a more variable manner.
CONCLUSIONS
These findings suggest that there may be language regions that differentially contribute to sentence processing and which therefore are best identified using sentence-level tasks. The functional consequences of resecting these sites remain to be investigated.
Collapse
Affiliation(s)
- Marco Riva
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
- Department of Neurological Surgery, University of California, San Francisco, California
| | - Stephen M. Wilson
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Ruofan Cai
- Department of Speech and Hearing Sciences, University of Washington, Seattle
- Institute for Learning & Brain Sciences, University of Washington, Seattle, Washington
| | - Antonella Castellano
- Department of Neuroradiology & CERMAC, Università Vita-Salute and Ospedale San Raffaele, Milan, Italy
| | - Kesshi M. Jordan
- Bioengineering Graduate Group, University of California, San Francisco, and University of California, Berkeley
| | - Roland G. Henry
- Department of Neurology,
- Department of Radiology and Biomedical Imaging,
| | | | - Mitchel S. Berger
- Department of Neurological Surgery, University of California, San Francisco, California
| | - Edward F. Chang
- Department of Neurological Surgery, University of California, San Francisco, California
- Center for Integrative Neuroscience, University of California, San Francisco, California
| |
Collapse
|
16
|
Paoletti M, Caverzasi E, Mandelli ML, Brown JA, Henry RG, Miller BL, Rosen HJ, DeArmond SJ, Bastianello S, Seeley WW, Geschwind MD. Default Mode Network quantitative diffusion and resting-state functional magnetic resonance imaging correlates in sporadic Creutzfeldt-Jakob disease. Hum Brain Mapp 2022; 43:4158-4173. [PMID: 35662331 PMCID: PMC9374887 DOI: 10.1002/hbm.25945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 04/14/2022] [Accepted: 05/01/2022] [Indexed: 11/25/2022] Open
Abstract
Grey matter involvement is a well-known feature in sporadic Creutzfeldt-Jakob disease (sCJD), yet precise anatomy-based quantification of reduced diffusivity is still not fully understood. Default Mode Network (DMN) areas have been recently demonstrated as selectively involved in sCJD, and functional connectivity has never been investigated in prion diseases. We analyzed the grey matter involvement using a quantitatively multi-parametric MRI approach. Specifically, grey matter mean diffusivity of 37 subjects with sCJD was compared with that of 30 age-matched healthy controls with a group-wise approach. Differences in mean diffusivity were also examined between the cortical (MM(V)1, MM(V)2C, and VV1) and subcortical (VV2 and MV2K) subgroups of sCJD for those with autopsy data available (n = 27, 73%). We also assessed resting-state functional connectivity of both ventral and dorsal components of DMN in a subset of subject with a rs-fMRI dataset available (n = 17). Decreased diffusivity was predominantly present in posterior cortical regions of the DMN, but also outside of the DMN in temporal areas and in a few limbic and frontal areas, in addition to extensive deep nuclei involvement. Both subcortical and cortical sCJD subgroups showed decreased diffusivity subcortically, whereas only the cortical type expressed significantly decreased diffusivity cortically, mainly in parietal, occipital, and medial-inferior temporal cortices bilaterally. Interestingly, we found abnormally increased connectivity in both dorsal and ventral components of the DMN in sCJD subjects compared with healthy controls. The significance and possible utility of functional imaging as a biomarker for tracking disease progression in prion disease needs to be explored further.
Collapse
Affiliation(s)
- Matteo Paoletti
- Memory and Aging Center, Department of Neurology, Weill Institute for NeuroscienceUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of NeuroradiologyIRCCS Mondino FoundationPaviaItaly
| | - Eduardo Caverzasi
- Weill Institute for Neurosciences, Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of Brain and Behavioral SciencesUniversity of PaviaPaviaItaly
| | - Maria Luisa Mandelli
- Memory and Aging Center, Department of Neurology, Weill Institute for NeuroscienceUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Jesse A. Brown
- Memory and Aging Center, Department of Neurology, Weill Institute for NeuroscienceUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Roland G. Henry
- Weill Institute for Neurosciences, Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Graduate Group in BioengineeringUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Bruce L. Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for NeuroscienceUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Howard J. Rosen
- Memory and Aging Center, Department of Neurology, Weill Institute for NeuroscienceUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | | | - Stefano Bastianello
- Department of NeuroradiologyIRCCS Mondino FoundationPaviaItaly
- Department of Brain and Behavioral SciencesUniversity of PaviaPaviaItaly
| | - William W. Seeley
- Memory and Aging Center, Department of Neurology, Weill Institute for NeuroscienceUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of PathologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Michael D. Geschwind
- Memory and Aging Center, Department of Neurology, Weill Institute for NeuroscienceUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| |
Collapse
|
17
|
Block VJ, Waliman M, Xie Z, Akula A, Bove R, Pletcher MJ, Marcus GM, Olgin JE, Cree BAC, Gelfand JM, Henry RG. Making Every Step Count: Minute-by-Minute Characterization of Step Counts Augments Remote Activity Monitoring in People With Multiple Sclerosis. Front Neurol 2022; 13:860008. [PMID: 35677343 PMCID: PMC9167929 DOI: 10.3389/fneur.2022.860008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 04/21/2022] [Indexed: 11/30/2022] Open
Abstract
Background Ambulatory disability is common in people with multiple sclerosis (MS). Remote monitoring using average daily step count (STEPS) can assess physical activity (activity) and disability in MS. STEPS correlates with conventional metrics such as the expanded disability status scale (Expanded Disability Status Scale; EDSS), Timed-25 Foot walk (T25FW) and timed up and go (TUG). However, while STEPS as a summative measure characterizes the number of steps taken over a day, it does not reflect variability and intensity of activity. Objectives Novel analytical methods were developed to describe how individuals spends time in various activity levels (e.g., continuous low versus short bouts of high) and the proportion of time spent at each activity level. Methods 94 people with MS spanning the range of ambulatory impairment (unaffected to requiring bilateral assistance) were recruited into FITriMS study and asked to wear a Fitbit continuously for 1-year. Parametric distributions were fit to minute-by-minute step data. Adjusted R2 values for regressions between distributional fit parameters and STEPS with EDSS, TUG, T25FW and the patient-reported 12-item MS Walking scale (MSWS-12) were calculated over the first 4-weeks, adjusting for sex, age and disease duration. Results Distributional fits determined that the best statistically-valid model across all subjects was a 3-compartment Gaussian Mixture Model (GMM) that characterizes the step behavior within 3 levels of activity: high, moderate and low. The correlation of GMM parameters for baseline step count measures with clinical assessments was improved when compared with STEPS (adjusted R2 values GMM vs. STEPS: TUG: 0.536 vs. 0.419, T25FW: 0.489 vs. 0.402, MSWS-12: 0.383 vs. 0.378, EDSS: 0.557 vs. 0.465). The GMM correlated more strongly (Kruskal-Wallis: p = 0.0001) than STEPS and gave further information not included in STEPS. Conclusions Individuals' step distributions follow a 3-compartment GMM that better correlates with clinic-based performance measures compared with STEPS. These data support the existence of high-moderate-low levels of activity. GMM provides an interpretable framework to better understand the association between different levels of activity and clinical metrics and allows further analysis of walking behavior that takes step distribution and proportion of time at three levels of intensity into account.
Collapse
Affiliation(s)
- Valerie J. Block
- Department of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Matthew Waliman
- Department of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Zhendong Xie
- Department of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Amit Akula
- Department of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Riley Bove
- Department of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Mark J. Pletcher
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States,Department of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Gregory M. Marcus
- Department of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Jeffrey E. Olgin
- Department of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Bruce A. C. Cree
- Department of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Jeffrey M. Gelfand
- Department of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Roland G. Henry
- Department of Neurology, University of California San Francisco (UCSF) Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States,Department of Radiology, University of California, San Francisco, San Francisco, CA, United States,*Correspondence: Roland G. Henry
| |
Collapse
|
18
|
Bove R, Anderson A, Rowles W, Rankin KA, Hills NK, Carleton M, Cooper J, Cree BA, Gelfand JM, Graves J, Henry RG, Krysko KM, Rush G, Zamvil SS, Joffe H, Chan JR, Green A. A Hormonal therapy for menopausal women with MS: A Phase Ib/IIa Randomized Controlled Trial. Mult Scler Relat Disord 2022; 61:103747. [DOI: 10.1016/j.msard.2022.103747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/07/2022] [Accepted: 03/17/2022] [Indexed: 12/27/2022]
|
19
|
Bischof A, Papinutto N, Keshavan A, Rajesh A, Kirkish G, Zhang X, Mallott JM, Asteggiano C, Sacco S, Gundel TJ, Zhao C, Stern WA, Caverzasi E, Zhou Y, Gomez R, Ragan NR, Santaniello A, Zhu AH, Juwono J, Bevan CJ, Bove RM, Crabtree E, Gelfand JM, Goodin DS, Graves JS, Green AJ, Oksenberg JR, Waubant E, Wilson MR, Zamvil SS, Cree BA, Hauser SL, Henry RG. Reply to "Spinal cord atrophy is a preclinical marker of progressive MS". Ann Neurol 2022; 91:735-736. [PMID: 35233827 PMCID: PMC9511767 DOI: 10.1002/ana.26340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 02/26/2022] [Accepted: 02/28/2022] [Indexed: 12/03/2022]
Affiliation(s)
- Antje Bischof
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA.,Department of Neurology with Institute for Translational Neurology, University Hospital Münster, Germany
| | - Nico Papinutto
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Anisha Keshavan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Anand Rajesh
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Gina Kirkish
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Xinheng Zhang
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jacob M Mallott
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Carlo Asteggiano
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Simone Sacco
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Tristan J Gundel
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Chao Zhao
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - William A Stern
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Eduardo Caverzasi
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Yifan Zhou
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Refujia Gomez
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Nicholas R Ragan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Adam Santaniello
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Alyssa H Zhu
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jeremy Juwono
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Carolyn J Bevan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Riley M Bove
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Elizabeth Crabtree
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jeffrey M Gelfand
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Douglas S Goodin
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jennifer S Graves
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Ari J Green
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Emmanuelle Waubant
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Michael R Wilson
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Scott S Zamvil
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | -
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Bruce A Cree
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Stephen L Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| |
Collapse
|
20
|
Ma Q, Caillier SJ, Muzic S, Wilson MR, Henry RG, Cree BAC, Hauser SL, Didonna A, Oksenberg JR. Specific hypomethylation programs underpin B cell activation in early multiple sclerosis. Proc Natl Acad Sci U S A 2021; 118:e2111920118. [PMID: 34911760 PMCID: PMC8713784 DOI: 10.1073/pnas.2111920118] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2021] [Indexed: 12/12/2022] Open
Abstract
Epigenetic changes have been consistently detected in different cell types in multiple sclerosis (MS). However, their contribution to MS pathogenesis remains poorly understood partly because of sample heterogeneity and limited coverage of array-based methods. To fill this gap, we conducted a comprehensive analysis of genome-wide DNA methylation patterns in four peripheral immune cell populations isolated from 29 MS patients at clinical disease onset and 24 healthy controls. We show that B cells from new-onset untreated MS cases display more significant methylation changes than other disease-implicated immune cell types, consisting of a global DNA hypomethylation signature. Importantly, 4,933 MS-associated differentially methylated regions in B cells were identified, and this epigenetic signature underlies specific genetic programs involved in B cell differentiation and activation. Integration of the methylome to changes in gene expression and susceptibility-associated regions further indicates that hypomethylated regions are significantly associated with the up-regulation of cell activation transcriptional programs. Altogether, these findings implicate aberrant B cell function in MS etiology.
Collapse
Affiliation(s)
- Qin Ma
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA 94158
| | - Stacy J Caillier
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA 94158
| | - Shaun Muzic
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA 94158
| | - Michael R Wilson
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA 94158
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA 94158
| | - Bruce A C Cree
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA 94158
| | - Stephen L Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA 94158
| | - Alessandro Didonna
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA 94158
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA 94158
| |
Collapse
|
21
|
Bischof A, Papinutto N, Keshavan A, Rajesh A, Kirkish G, Zhang X, Mallott JM, Asteggiano C, Sacco S, Gundel TJ, Zhao C, Stern WA, Caverzasi E, Zhou Y, Gomez R, Ragan NR, Santaniello A, Zhu AH, Juwono J, Bevan CJ, Bove RM, Crabtree E, Gelfand JM, Goodin DS, Graves JS, Green AJ, Oksenberg JR, Waubant E, Wilson MR, Zamvil SS, Cree BA, Hauser SL, Henry RG. Spinal cord atrophy predicts progressive disease in relapsing multiple sclerosis. Ann Neurol 2021; 91:268-281. [PMID: 34878197 PMCID: PMC8916838 DOI: 10.1002/ana.26281] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 12/04/2021] [Accepted: 12/06/2021] [Indexed: 11/06/2022]
Abstract
Objective A major challenge in multiple sclerosis (MS) research is the understanding of silent progression and Progressive MS. Using a novel method to accurately capture upper cervical cord area from legacy brain MRI scans we aimed to study the role of spinal cord and brain atrophy for silent progression and conversion to secondary progressive disease (SPMS). Methods From a single‐center observational study, all RRMS (n = 360) and SPMS (n = 47) patients and 80 matched controls were evaluated. RRMS patient subsets who converted to SPMS (n = 54) or silently progressed (n = 159), respectively, during the 12‐year observation period were compared to clinically matched RRMS patients remaining RRMS (n = 54) or stable (n = 147), respectively. From brain MRI, we assessed the value of brain and spinal cord measures to predict silent progression and SPMS conversion. Results Patients who developed SPMS showed faster cord atrophy rates (−2.19%/yr) at least 4 years before conversion compared to their RRMS matches (−0.88%/yr, p < 0.001). Spinal cord atrophy rates decelerated after conversion (−1.63%/yr, p = 0.010) towards those of SPMS patients from study entry (−1.04%). Each 1% faster spinal cord atrophy rate was associated with 69% (p < 0.0001) and 53% (p < 0.0001) shorter time to silent progression and SPMS conversion, respectively. Interpretation Silent progression and conversion to secondary progressive disease are predominantly related to cervical cord atrophy. This atrophy is often present from the earliest disease stages and predicts the speed of silent progression and conversion to Progressive MS. Diagnosis of SPMS is rather a late recognition of this neurodegenerative process than a distinct disease phase. ANN NEUROL 2022;91:268–281
Collapse
Affiliation(s)
- Antje Bischof
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Nico Papinutto
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Anisha Keshavan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Anand Rajesh
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Gina Kirkish
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Xinheng Zhang
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jacob M Mallott
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Carlo Asteggiano
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Simone Sacco
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Tristan J Gundel
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Chao Zhao
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - William A Stern
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Eduardo Caverzasi
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Yifan Zhou
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Refujia Gomez
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Nicholas R Ragan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Adam Santaniello
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Alyssa H Zhu
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jeremy Juwono
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Carolyn J Bevan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Riley M Bove
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Elizabeth Crabtree
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jeffrey M Gelfand
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Douglas S Goodin
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jennifer S Graves
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Ari J Green
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Emmanuelle Waubant
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Michael R Wilson
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Scott S Zamvil
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | -
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Bruce A Cree
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Stephen L Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| |
Collapse
|
22
|
Ontaneda D, Sati P, Raza P, Kilbane M, Gombos E, Alvarez E, Azevedo C, Calabresi P, Cohen JA, Freeman L, Henry RG, Longbrake EE, Mitra N, Illenberger N, Schindler M, Moreno-Dominguez D, Ramos M, Mowry E, Oh J, Rodrigues P, Chahin S, Kaisey M, Waubant E, Cutter G, Shinohara R, Reich DS, Solomon A, Sicotte NL. Central vein sign: A diagnostic biomarker in multiple sclerosis (CAVS-MS) study protocol for a prospective multicenter trial. Neuroimage Clin 2021; 32:102834. [PMID: 34592690 PMCID: PMC8482479 DOI: 10.1016/j.nicl.2021.102834] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 09/16/2021] [Accepted: 09/19/2021] [Indexed: 01/06/2023]
Abstract
The specificity and implementation of current MRI-based diagnostic criteria for multiple sclerosis (MS) are imperfect. Approximately 1 in 5 of individuals diagnosed with MS are eventually determined not to have the disease, with overreliance on MRI findings a major cause of MS misdiagnosis. The central vein sign (CVS), a proposed MRI biomarker for MS lesions, has been extensively studied in numerous cross sectional studies and may increase diagnostic specificity for MS. CVS has desirable analytical, measurement, and scalability properties. "Central Vein Sign: A Diagnostic Biomarker in Multiple Sclerosis (CAVS-MS)" is an NIH-supported, 2-year, prospective, international, multicenter study conducted by the North American Imaging in MS Cooperative (NAIMS) to evaluate CVS as a diagnostic biomarker for immediate translation into clinical care. Study objectives include determining the concordance of CVS and McDonald Criteria to diagnose MS, the sensitivity of CVS to detect MS in those with typical presentations, and the specificity of CVS among those with atypical presentations. The study will recruit a total of 400 participants (200 with typical and 200 with atypical presentations) across 11 sites. T2*-weighted, high-isotropic-resolution, segmented echo-planar MRI will be acquired at baseline and 24 months on 3-tesla scanners, and FLAIR* images (combination of FLAIR and T2*) will be generated for evaluating CVS. Data will be processed on a cloud-based platform that contains clinical and CVS rating modules. Imaging quality control will be conducted by automated methods and neuroradiologist review. CVS will be determined by Select6* and Select3* lesion methods following published criteria at each site and by central readers, including neurologists and neuroradiologists. Automated CVS detection and algorithms for incorporation of CVS into McDonald Criteria will be tested. Diagnosis will be adjudicated by three neurologists who served on the 2017 International Panel on the Diagnosis of MS. The CAVS-MS study aims to definitively establish CVS as a diagnostic biomarker that can be applied broadly to individuals presenting for evaluation of the diagnosis of MS.
Collapse
Affiliation(s)
- D Ontaneda
- Cleveland Clinic Foundation, Cleveland, OH, United States.
| | - P Sati
- Cedars Sinai, Los Angeles, CA, United States; NINDS, NIH, Bethesda, MD, United States
| | - P Raza
- Cleveland Clinic Foundation, Cleveland, OH, United States
| | - M Kilbane
- Cleveland Clinic Foundation, Cleveland, OH, United States
| | - E Gombos
- Cedars Sinai, Los Angeles, CA, United States
| | - E Alvarez
- Neurology, U of Colorado, Denver, CO, United States
| | | | - P Calabresi
- Neurology, Johns Hopkins, Baltimore, MD, United States
| | - J A Cohen
- Cleveland Clinic Foundation, Cleveland, OH, United States
| | - L Freeman
- Dell Medical School, The University of Texas at Austin, Austin, TX, United States
| | - R G Henry
- University of California San Francisco, San Francisco, CA, United States
| | | | - N Mitra
- University of Pennsylvania, Philadelphia, PA, United States
| | - N Illenberger
- University of Pennsylvania, Philadelphia, PA, United States
| | - M Schindler
- University of Pennsylvania, Philadelphia, PA, United States
| | | | - M Ramos
- QMENTA Inc, Boston, MA, United States
| | - E Mowry
- Neurology, Johns Hopkins, Baltimore, MD, United States
| | - J Oh
- University of Toronto, Toronto, ON, Canada
| | | | - S Chahin
- Washington University, St. Louis, MO, United States
| | - M Kaisey
- Cedars Sinai, Los Angeles, CA, United States
| | - E Waubant
- University of California San Francisco, San Francisco, CA, United States
| | - G Cutter
- UAB School of Public Health, Birmingham, AL, United States
| | - R Shinohara
- University of Pennsylvania, Philadelphia, PA, United States
| | - D S Reich
- NINDS, NIH, Bethesda, MD, United States
| | - A Solomon
- The University of Vermont, Burlington, VT, United States
| | - N L Sicotte
- Cedars Sinai, Los Angeles, CA, United States
| |
Collapse
|
23
|
Ontaneda D, Raza PC, Mahajan KR, Arnold DL, Dwyer MG, Gauthier SA, Greve DN, Harrison DM, Henry RG, Li DKB, Mainero C, Moore W, Narayanan S, Oh J, Patel R, Pelletier D, Rauscher A, Rooney WD, Sicotte NL, Tam R, Reich DS, Azevedo CJ. Deep grey matter injury in multiple sclerosis: a NAIMS consensus statement. Brain 2021; 144:1974-1984. [PMID: 33757115 PMCID: PMC8370433 DOI: 10.1093/brain/awab132] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 01/28/2021] [Accepted: 02/01/2021] [Indexed: 11/13/2022] Open
Abstract
Although multiple sclerosis has traditionally been considered a white matter disease, extensive research documents the presence and importance of grey matter injury including cortical and deep regions. The deep grey matter exhibits a broad range of pathology and is uniquely suited to study the mechanisms and clinical relevance of tissue injury in multiple sclerosis using magnetic resonance techniques. Deep grey matter injury has been associated with clinical and cognitive disability. Recently, MRI characterization of deep grey matter properties, such as thalamic volume, have been tested as potential clinical trial end points associated with neurodegenerative aspects of multiple sclerosis. Given this emerging area of interest and its potential clinical trial relevance, the North American Imaging in Multiple Sclerosis (NAIMS) Cooperative held a workshop and reached consensus on imaging topics related to deep grey matter. Herein, we review current knowledge regarding deep grey matter injury in multiple sclerosis from an imaging perspective, including insights from histopathology, image acquisition and post-processing for deep grey matter. We discuss the clinical relevance of deep grey matter injury and specific regions of interest within the deep grey matter. We highlight unanswered questions and propose future directions, with the aim of focusing research priorities towards better methods, analysis, and interpretation of results.
Collapse
Affiliation(s)
- Daniel Ontaneda
- Cleveland Clinic Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland, OH 44195, USA
| | - Praneeta C Raza
- Cleveland Clinic Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland, OH 44195, USA
| | - Kedar R Mahajan
- Cleveland Clinic Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland, OH 44195, USA
| | - Douglas L Arnold
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, The State University of New York, Buffalo, NY 14214, USA
| | - Susan A Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, NY 10021, USA
| | - Douglas N Greve
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02129, USA
| | - Daniel M Harrison
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Roland G Henry
- Department of Neurology, Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA
- The UC San Francisco and Berkeley Bioengineering Graduate Group, University of California San Francisco, San Francisco, CA 94143, USA
| | - David K B Li
- Department of Radiology and Medicine (Neurology), University of British Columbia, Vancouver, British Columbia V6T 2B5, Canada
| | - Caterina Mainero
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02129, USA
| | - Wayne Moore
- Department of Pathology and Laboratory Medicine, and International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Sridar Narayanan
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Jiwon Oh
- Division of Neurology, St. Michael’s Hospital, University of Toronto, Toronto, Ontario M5B 1W8, Canada
| | - Raihaan Patel
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec H4H 1R3, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Daniel Pelletier
- Department of Neurology, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA
| | - Alexander Rauscher
- Physics and Astronomy, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - William D Rooney
- Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR 97239, USA
| | - Nancy L Sicotte
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Roger Tam
- Department of Radiology and Medicine (Neurology), University of British Columbia, Vancouver, British Columbia V6T 2B5, Canada
- Biomedical Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, Bethesda, MD 20824, USA
| | - Christina J Azevedo
- Department of Neurology, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA
| | | |
Collapse
|
24
|
Krysko KM, Bischof A, Nourbakhsh B, Henry RG, Revirajan N, Manguinao M, Nguyen K, Akula A, Li Y, Waubant E. A pilot study of oxidative pathways in MS fatigue: randomized trial of N-acetyl cysteine. Ann Clin Transl Neurol 2021; 8:811-824. [PMID: 33675156 PMCID: PMC8045913 DOI: 10.1002/acn3.51325] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 01/17/2021] [Accepted: 02/03/2021] [Indexed: 11/29/2022] Open
Abstract
Objective To assess feasibility, tolerability, and safety of N‐acetyl cysteine (NAC) for fatigue in progressive MS. Secondary objectives evaluated changes in fatigue and oxidative pathway biomarkers on NAC versus placebo. Methods Individuals with progressive MS with Modified Fatigue Impact Scale (MFIS) > t38 were randomized 2:1 to NAC 1250mg TID or placebo for 4 weeks. The primary outcome was tolerability and safety. The secondary outcome to evaluate efficacy was MFIS change from baseline to week 4 between groups. Exploratory biomarker outcomes included change in blood GSH/GSSG ratio (reduced‐to‐oxidized glutathione (GSH)) and in vivo relative GSH using 7T MR spectroscopy (MRS) between groups. Fisher exact test was used for categorical and rank sum for continuous outcomes. Results Fifiteen were randomized (10 NAC, 5 placebo; mean age 56.1 years, 80% female, median EDSS 6.0). At least one adverse event (AE) occurred in 60% on NAC versus 80% on placebo (p = 0.75). There were two AEs attributed to NAC in one patient (abdominal pain and constipation), with 94% adherence to NAC. MFIS decreased in both groups at week 4, with the mean improvement of 11‐points on NAC versus 18‐points on placebo (p = 0.33). GSH/GSSG ratio decreased on placebo (−0.6) and NAC (−0.1) (p = 0.18). Change in GSH levels to total creatine in anterior and posterior cingulate cortex, insula, caudate, putamen, and thalamus did not differ between groups. Interpretation NAC was well‐tolerated in progressive MS, although reduction in fatigue on NAC was similar to placebo. Antioxidant blood and MRS biomarkers were not significantly altered by NAC, which could be due to dose, route of administration, time of sample collection, short half‐life, or lack of effect. Registered clinicaltrials.gov NCT02804594.
Collapse
Affiliation(s)
- Kristen M Krysko
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA.,Division of Neurology, Department of Medicine, St. Michael's Hospital, Li Ka Shing Knowledge Institute, University of Toronto, Toronto, Ontario, Canada
| | - Antje Bischof
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Bardia Nourbakhsh
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Roland G Henry
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Nisha Revirajan
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Michael Manguinao
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Khang Nguyen
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Amit Akula
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Yan Li
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Emmanuelle Waubant
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, California, USA
| |
Collapse
|
25
|
Bagnato F, Gauthier SA, Laule C, Moore GRW, Bove R, Cai Z, Cohen-Adad J, Harrison DM, Klawiter EC, Morrow SA, Öz G, Rooney WD, Smith SA, Calabresi PA, Henry RG, Oh J, Ontaneda D, Pelletier D, Reich DS, Shinohara RT, Sicotte NL. Imaging Mechanisms of Disease Progression in Multiple Sclerosis: Beyond Brain Atrophy. J Neuroimaging 2021; 30:251-266. [PMID: 32418324 DOI: 10.1111/jon.12700] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 02/04/2020] [Accepted: 02/18/2020] [Indexed: 12/11/2022] Open
Abstract
Clinicians involved with different aspects of the care of persons with multiple sclerosis (MS) and scientists with expertise on clinical and imaging techniques convened in Dallas, TX, USA on February 27, 2019 at a North American Imaging in Multiple Sclerosis Cooperative workshop meeting. The aim of the workshop was to discuss cardinal pathobiological mechanisms implicated in the progression of MS and novel imaging techniques, beyond brain atrophy, to unravel these pathologies. Indeed, although brain volume assessment demonstrates changes linked to disease progression, identifying the biological mechanisms leading up to that volume loss are key for understanding disease mechanisms. To this end, the workshop focused on the application of advanced magnetic resonance imaging (MRI) and positron emission tomography (PET) imaging techniques to assess and measure disease progression in both the brain and the spinal cord. Clinical translation of quantitative MRI was recognized as of vital importance, although the need to maintain a relatively short acquisition time mandated by most radiology departments remains the major obstacle toward this effort. Regarding PET, the panel agreed upon its utility to identify ongoing pathological processes. However, due to costs, required expertise, and the use of ionizing radiation, PET was not considered to be a viable option for ongoing care of persons with MS. Collaborative efforts fostering robust study designs and imaging technique standardization across scanners and centers are needed to unravel disease mechanisms leading to progression and discovering medications halting neurodegeneration and/or promoting repair.
Collapse
Affiliation(s)
- Francesca Bagnato
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
| | - Susan A Gauthier
- Judith Jaffe Multiple Sclerosis Center, Department of Neurology, Feil Family Brain and Mind Institute, and Department of Radiology, Weill Cornell Medicine, New York, NY
| | - Cornelia Laule
- Department of Radiology, Pathology, and Laboratory Medicine, Department of Physics and Astronomy, and International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada
| | - George R Wayne Moore
- Department of Pathology and Laboratory Medicine, and International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada
| | - Riley Bove
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA
| | - Zhengxin Cai
- Department of Radiology and Biomedical Imaging, PET Center, Yale University, New Haven, CT
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal and Functional Neuroimaging Unit, CRIUGM, University of Montreal, Montreal, Quebec, Canada
| | - Daniel M Harrison
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD
| | - Eric C Klawiter
- Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Sarah A Morrow
- Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, Canada
| | - Gülin Öz
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN
| | - William D Rooney
- Advanced Imaging Research Center, Departments of Biomedical Engineering, Neurology, and Behavioral Neuroscience, Oregon Health & Science University, Portland, OR
| | - Seth A Smith
- Radiology and Radiological Sciences and Vanderbilt University Imaging Institute, Vanderbilt University Medical Center, and Biomedical Engineering, Vanderbilt University, Nashville, TN
| | - Peter A Calabresi
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Roland G Henry
- Departments of Neurology, Radiology and Biomedical Imaging, and the UC San Francisco & Berkeley Bioengineering Graduate Group, University of California San Francisco, San Francisco, CA
| | - Jiwon Oh
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD.,Division of Neurology, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis, Neurological Institute, Cleveland Clinic, Cleveland, OH
| | - Daniel Pelletier
- Department of Neurology, University of Southern California Keck School of Medicine, Los Angeles, CA
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, Bethesda, MD
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA
| | - Nancy L Sicotte
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA
| | -
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
| |
Collapse
|
26
|
Sacco S, Paoletti M, Staffaroni AM, Kang H, Rojas J, Marx G, Goh SY, Luisa Mandelli M, Allen IE, Kramer JH, Bastianello S, Henry RG, Rosen H, Caverzasi E, Geschwind MD. Multimodal MRI staging for tracking progression and clinical-imaging correlation in sporadic Creutzfeldt-Jakob disease. Neuroimage Clin 2020; 30:102523. [PMID: 33636540 PMCID: PMC7906895 DOI: 10.1016/j.nicl.2020.102523] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 11/02/2020] [Accepted: 12/01/2020] [Indexed: 12/24/2022]
Abstract
Diffusion imaging is very useful for the diagnosis of sporadic Creutzfeldt-Jakob disease, but it has limitations in tracking disease progression as mean diffusivity changes non-linearly across the disease course. We previously showed that mean diffusivity changes across the disease course follow a quasi J-shaped curve, characterized by decreased values in earlier phases and increasing values later in the disease course. Understanding how MRI metrics change over-time, as well as their correlations with clinical deficits are crucial steps in developing radiological biomarkers for trials. Specifically, as mean diffusivity does not change linearly and atrophy mainly occurs in later stages, neither alone is likely to be a sufficient biomarker throughout the disease course. We therefore developed a model combining mean diffusivity and Volume loss (MRI Disease-Staging) to take into account mean diffusivity's non-linearity. We then assessed the associations between clinical outcomes and mean diffusivity alone, Volume alone and finally MRI Disease-Staging. In 37 sporadic Creutzfeldt-Jakob disease subjects and 30 age- and sex-matched healthy controls, high angular resolution diffusion and high-resolution T1 imaging was performed cross-sectionally to compute z-scores for mean diffusivity (MD) and Volume. Average MD and Volume were extracted from 41 GM volume of interest (VOI) per hemisphere, within the images registered to the Montreal Neurological Institute (MNI) space. Each subject's volume of interest was classified as either "involved" or "not involved" using a statistical threshold of ± 2 standard deviation (SD) for mean diffusivity changes and/or -2 SD for Volume. Volumes of interest were MRI Disease-Staged as: 0 = no abnormalities; 1 = decreased mean diffusivity only; 2 = decreased mean diffusivity and Volume; 3 = normal ("pseudo-normalized") mean diffusivity, reduced Volume; 4 = increased mean diffusivity, reduced Volume. We correlated Volume, MD and MRI Disease-Staging with several clinical outcomes (scales, score and symptoms) using 4 major regions of interest (Total, Cortical, Subcortical and Cerebellar gray matter) or smaller regions pre-specified based on known neuroanatomical correlates. Volume and MD z-scores correlated inversely with each other in all four major ROIs (cortical, subcortical, cerebellar and total) highlighting that ROIs with lower Volumes had higher MD and vice-versa. Regarding correlations with symptoms and scores, higher MD correlated with worse Mini-Mental State Examination and Barthel scores in cortical and cerebellar gray matter, but subjects with cortical sensory deficits showed lower MD in the primary sensory cortex. Volume loss correlated with lower Mini-Mental State Examination, Barthel scores and pyramidal signs. Interestingly, for both Volume and MD, changes within the cerebellar ROI showed strong correlations with both MMSE and Barthel. Supporting using a combination of MD and Volume to track sCJD progression, MRI Disease-Staging showed correlations with more clinical outcomes than Volume or MD alone, specifically with Mini-Mental State Examination, Barthel score, pyramidal signs, higher cortical sensory deficits, as well as executive and visual-spatial deficits. Additionally, when subjects in the cohort were subdivided into tertiles based on their Barthel scores and their percentile of disease duration/course ("Time-Ratio"), subjects in the lowest (most impaired) Barthel tertile showed a much greater proportion of more advanced MRI Disease-Stages than the those in the highest tertile. Similarly, subjects in the last Time-Ratio tertile (last tertile of disease) showed a much greater proportion of more advanced MRI Disease-Stages than the earliest tertile. Therefore, in later disease stages, as measured by time or Barthel, there is overall more Volume loss and increasing MD. A combined multiparametric quantitative MRI Disease-Staging is a useful tool to track sporadic Creutzfeldt-Jakob- disease progression radiologically.
Collapse
Affiliation(s)
- Simone Sacco
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco (UCSF), San Francisco, CA, USA
- Institute of Radiology, Department of Clinical Surgical Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Matteo Paoletti
- Advanced Imaging and Radiomics Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Adam M. Staffaroni
- UCSF Weill Institute for Neurosciences, Department of Neurology, Memory and Aging Center, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Huicong Kang
- UCSF Weill Institute for Neurosciences, Department of Neurology, Memory and Aging Center, University of California San Francisco (UCSF), San Francisco, CA, USA
- Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Julio Rojas
- UCSF Weill Institute for Neurosciences, Department of Neurology, Memory and Aging Center, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Gabe Marx
- UCSF Weill Institute for Neurosciences, Department of Neurology, Memory and Aging Center, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Sheng-yang Goh
- UCSF Weill Institute for Neurosciences, Department of Neurology, Memory and Aging Center, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Maria Luisa Mandelli
- UCSF Weill Institute for Neurosciences, Department of Neurology, Memory and Aging Center, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Isabel E. Allen
- Department of Epidemiology and Biostatistics, University of California San Francisco San Francisco (UCSF), San Francisco, CA, USA
| | - Joel H. Kramer
- UCSF Weill Institute for Neurosciences, Department of Neurology, Memory and Aging Center, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Stefano Bastianello
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Roland G. Henry
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Howie.J. Rosen
- UCSF Weill Institute for Neurosciences, Department of Neurology, Memory and Aging Center, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Eduardo Caverzasi
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Michael D. Geschwind
- UCSF Weill Institute for Neurosciences, Department of Neurology, Memory and Aging Center, University of California San Francisco (UCSF), San Francisco, CA, USA
| |
Collapse
|
27
|
Pröbstel AK, Zhou X, Baumann R, Wischnewski S, Kutza M, Rojas OL, Sellrie K, Bischof A, Kim K, Ramesh A, Dandekar R, Greenfield AL, Schubert RD, Bisanz JE, Vistnes S, Khaleghi K, Landefeld J, Kirkish G, Liesche-Starnecker F, Ramaglia V, Singh S, Tran EB, Barba P, Zorn K, Oechtering J, Forsberg K, Shiow LR, Henry RG, Graves J, Cree BAC, Hauser SL, Kuhle J, Gelfand JM, Andersen PM, Schlegel J, Turnbaugh PJ, Seeberger PH, Gommerman JL, Wilson MR, Schirmer L, Baranzini SE. Gut microbiota-specific IgA + B cells traffic to the CNS in active multiple sclerosis. Sci Immunol 2020; 5:5/53/eabc7191. [PMID: 33219152 DOI: 10.1126/sciimmunol.abc7191] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 10/29/2020] [Indexed: 01/04/2023]
Abstract
Changes in gut microbiota composition and a diverse role of B cells have recently been implicated in multiple sclerosis (MS), a central nervous system (CNS) autoimmune disease. Immunoglobulin A (IgA) is a key regulator at the mucosal interface. However, whether gut microbiota shape IgA responses and what role IgA+ cells have in neuroinflammation are unknown. Here, we identify IgA-bound taxa in MS and show that IgA-producing cells specific for MS-associated taxa traffic to the inflamed CNS, resulting in a strong, compartmentalized IgA enrichment in active MS and other neuroinflammatory diseases. Unlike previously characterized polyreactive anti-commensal IgA responses, CNS IgA cross-reacts with surface structures on specific bacterial strains but not with brain tissue. These findings establish gut microbiota-specific IgA+ cells as a systemic mediator in MS and suggest a critical role of mucosal B cells during active neuroinflammation with broad implications for IgA as an informative biomarker and IgA-producing cells as an immune subset to harness for therapeutic interventions.
Collapse
Affiliation(s)
- Anne-Katrin Pröbstel
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA. .,Neurologic Clinic and Policlinic and Research Center for Clinical Neuroimmunology and Neuroscience Basel, Departments of Medicine, Biomedicine, and Clinical Research, University Hospital of Basel, University of Basel, 4031 Basel, Switzerland
| | - Xiaoyuan Zhou
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ryan Baumann
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Sven Wischnewski
- Department of Neurology and Mannheim Center for Translational Neurosciences, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
| | - Michael Kutza
- Department of Neurology and Mannheim Center for Translational Neurosciences, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
| | - Olga L Rojas
- Department of Immunology, University of Toronto, Toronto, ON M5S 18A, Canada
| | - Katrin Sellrie
- Department of Biomolecular Systems, Max Planck Institute of Colloids and Interfaces, 14776 Potsdam, Germany
| | - Antje Bischof
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Kicheol Kim
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Akshaya Ramesh
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ravi Dandekar
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ariele L Greenfield
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ryan D Schubert
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jordan E Bisanz
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94143, USA.,Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Stephanie Vistnes
- Eli and Edythe Broad Center for Stem Cell Research and Regeneration Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Khashayar Khaleghi
- Department of Immunology, University of Toronto, Toronto, ON M5S 18A, Canada
| | - James Landefeld
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Gina Kirkish
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Friederike Liesche-Starnecker
- Department of Neuropathology, School of Medicine, Institute of Pathology, Technical University Munich, 81675 Munich, Germany
| | - Valeria Ramaglia
- Department of Neurology and Mannheim Center for Translational Neurosciences, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
| | - Sneha Singh
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Edwina B Tran
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Patrick Barba
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Kelsey Zorn
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Johanna Oechtering
- Neurologic Clinic and Policlinic and Research Center for Clinical Neuroimmunology and Neuroscience Basel, Departments of Medicine, Biomedicine, and Clinical Research, University Hospital of Basel, University of Basel, 4031 Basel, Switzerland
| | - Karin Forsberg
- Department of Clinical Science, Neurosciences, Umeå University, 90185 Umeå, Sweden
| | - Lawrence R Shiow
- Eli and Edythe Broad Center for Stem Cell Research and Regeneration Medicine, University of California, San Francisco, San Francisco, CA 94143, USA.,Department of Pediatrics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Roland G Henry
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jennifer Graves
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Bruce A C Cree
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Stephen L Hauser
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jens Kuhle
- Neurologic Clinic and Policlinic and Research Center for Clinical Neuroimmunology and Neuroscience Basel, Departments of Medicine, Biomedicine, and Clinical Research, University Hospital of Basel, University of Basel, 4031 Basel, Switzerland
| | - Jeffrey M Gelfand
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Peter M Andersen
- Department of Clinical Science, Neurosciences, Umeå University, 90185 Umeå, Sweden
| | - Jürgen Schlegel
- Department of Neuropathology, School of Medicine, Institute of Pathology, Technical University Munich, 81675 Munich, Germany
| | - Peter J Turnbaugh
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94143, USA.,Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Peter H Seeberger
- Department of Biomolecular Systems, Max Planck Institute of Colloids and Interfaces, 14776 Potsdam, Germany
| | | | - Michael R Wilson
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Lucas Schirmer
- Department of Neurology and Mannheim Center for Translational Neurosciences, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany.,Interdisciplinary Center for Neurosciences, University of Heidelberg, 69117 Heidelberg, Germany
| | - Sergio E Baranzini
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA. .,Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA.,Graduate Program in Bioinformatics, University of California, San Francisco, San Francisco, CA 94158, USA
| |
Collapse
|
28
|
Sacco S, Caverzasi E, Papinutto N, Cordano C, Bischof A, Gundel T, Cheng S, Asteggiano C, Kirkish G, Mallott J, Stern WA, Bastianello S, Bove RM, Gelfand JM, Goodin DS, Green AJ, Waubant E, Wilson MR, Zamvil SS, Cree BA, Hauser SL, Henry RG. Neurite Orientation Dispersion and Density Imaging for Assessing Acute Inflammation and Lesion Evolution in MS. AJNR Am J Neuroradiol 2020; 41:2219-2226. [PMID: 33154077 DOI: 10.3174/ajnr.a6862] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 07/29/2020] [Indexed: 01/22/2023]
Abstract
BACKGROUND AND PURPOSE MR imaging is essential for MS diagnosis and management, yet it has limitations in assessing axonal damage and remyelination. Gadolinium-based contrast agents add value by pinpointing acute inflammation and blood-brain barrier leakage, but with drawbacks in safety and cost. Neurite orientation dispersion and density imaging (NODDI) assesses microstructural features of neurites contributing to diffusion imaging signals. This approach may resolve the components of MS pathology, overcoming conventional MR imaging limitations. MATERIALS AND METHODS Twenty-one subjects with MS underwent serial enhanced MRIs (12.6 ± 9 months apart) including NODDI, whose key metrics are the neurite density and orientation dispersion index. Twenty-one age- and sex-matched healthy controls underwent unenhanced MR imaging with the same protocol. Fifty-eight gadolinium-enhancing and non-gadolinium-enhancing lesions were semiautomatically segmented at baseline and follow-up. Normal-appearing WM masks were generated by subtracting lesions and dirty-appearing WM from the whole WM. RESULTS The orientation dispersion index was higher in gadolinium-enhancing compared with non-gadolinium-enhancing lesions; logistic regression indicated discrimination, with an area under the curve of 0.73. At follow-up, in the 58 previously enhancing lesions, we identified 2 subgroups based on the neurite density index change across time: Type 1 lesions showed increased neurite density values, whereas type 2 lesions showed decreased values. Type 1 lesions showed greater reduction in size with time compared with type 2 lesions. CONCLUSIONS NODDI is a promising tool with the potential to detect acute MS inflammation. The observed heterogeneity among lesions may correspond to gradients in severity and clinical recovery after the acute phase.
Collapse
Affiliation(s)
- S Sacco
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California.,Institute of Radiology (S.S., C.A.), Department of Clinical Surgical Diagnostic and Pediatric Sciences
| | - E Caverzasi
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - N Papinutto
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - C Cordano
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - A Bischof
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - T Gundel
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - S Cheng
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - C Asteggiano
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California.,Institute of Radiology (S.S., C.A.), Department of Clinical Surgical Diagnostic and Pediatric Sciences
| | - G Kirkish
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - J Mallott
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - W A Stern
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - S Bastianello
- Department of Brain and Behavioral Sciences (S.B.), University of Pavia, Pavia, Italy.,Neuroradiology Department (S.B.), Istituto Di Ricovero e Cura a Carattere Scientifico Mondino Foundation, Pavia, Italy
| | - R M Bove
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - J M Gelfand
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - D S Goodin
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - A J Green
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - E Waubant
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - M R Wilson
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - S S Zamvil
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - B A Cree
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - S L Hauser
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - R G Henry
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | | |
Collapse
|
29
|
Caverzasi E, Cordano C, Zhu AH, Zhao C, Bischof A, Kirkish G, Bennett DJ, Devereux M, Baker N, Inman J, Yiu HH, Papinutto N, Gelfand JM, Cree BAC, Hauser SL, Henry RG, Green AJ. Imaging correlates of visual function in multiple sclerosis. PLoS One 2020; 15:e0235615. [PMID: 32745132 PMCID: PMC7398529 DOI: 10.1371/journal.pone.0235615] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 06/19/2020] [Indexed: 11/18/2022] Open
Abstract
No single neuroimaging technique or sequence is capable of reflecting the functional deficits manifest in MS. Given the interest in imaging biomarkers for short- to medium-term studies, we aimed to assess which imaging metrics might best represent functional impairment for monitoring in clinical trials. Given the complexity of functional impairment in MS, however, it is useful to isolate a particular functionally relevant pathway to understand the relationship between imaging and neurological function. We therefore analyzed existing data, combining multiparametric MRI and OCT to describe MS associated visual impairment. We assessed baseline data from fifty MS patients enrolled in ReBUILD, a prospective trial assessing the effect of a remyelinating drug (clemastine). Subjects underwent 3T MRI imaging, including Neurite Orientation Dispersion and Density Imaging (NODDI), myelin content quantification, and retinal imaging, using OCT. Visual function was assessed, using low-contrast letter acuity. MRI and OCT data were studied to model visual function in MS, using a partial, least-squares, regression analysis. Measures of neurodegeneration along the entire visual pathway, described most of the observed variance in visual disability, measured by low contrast letter acuity. In those patients with an identified history of ON, however, putative myelin measures also showed correlation with visual performance. In the absence of clinically identifiable inflammatory episodes, residual disability correlates with neurodegeneration, whereas after an identifiable exacerbation, putative measures of myelin content are additionally informative.
Collapse
Affiliation(s)
- Eduardo Caverzasi
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Christian Cordano
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Alyssa H Zhu
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, United States of America
| | - Chao Zhao
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Antje Bischof
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America.,Neurology and Immunology Clinic, University Hospital Basel, Switzerland
| | - Gina Kirkish
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Daniel J Bennett
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Michael Devereux
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Nicholas Baker
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Justin Inman
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Hao H Yiu
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Nico Papinutto
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Jeffrey M Gelfand
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Bruce A C Cree
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Stephen L Hauser
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Roland G Henry
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Ari J Green
- Division of Neuroimmunology and Glial Biology UCSF, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America.,Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, United States of America
| |
Collapse
|
30
|
Papinutto N, Cordano C, Asteggiano C, Caverzasi E, Mandelli ML, Lauricella M, Yabut N, Neylan M, Kirkish G, Gorno-Tempini ML, Henry RG. MRI Measurement of Upper Cervical Spinal Cord Cross-Sectional Area in Children. J Neuroimaging 2020; 30:598-602. [PMID: 32639671 DOI: 10.1111/jon.12758] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/24/2020] [Accepted: 06/26/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND AND PURPOSE Neurological and neurodegenerative diseases can affect the spinal cord (SC) of pediatric patients. Magnetic resonance imaging (MRI) allows for in vivo quantification of SC atrophy via cross-sectional area (CSA). The study of CSA values in the general population is important to disentangle disease-related changes from intersubject variability. This study aimed at providing normative values for cervical CSA in children, extending our previous work performed with adults. METHODS Seventy-eight children (age 7-17 years) were selected from a Developmental Dyslexia study. All subjects underwent a 3T brain MRI session and any incidental findings were reported on the scans. A sagittal 1 mm3 3-dimensional T1 -weighted brain acquisition extended to the upper cervical cord was used to measure CSA at C2-C3, as well as spinal canal area and skull volume (V-scale). These three metrics were linearly fitted as a function of age to extract trends and percentage annual changes. Sex differences of CSA were assessed using least squares regression analyses, adjusting for age. We tested normalization strategies proven to be effective in reducing the intersubject variability of adults' CSA. RESULTS CSA changed as a function of age at a faster rate when compared with skull volume (CSA: 1.82% increase, V-scale: .60% reduction). Sex had a statistically significant effect on CSA. Normalization methods based on canal area and skull volume reduced the CSA intersubject variability up to 16.84%. CONCLUSIONS We present CSA normative values in a large cohort of children, reporting on sources of intersubject variability and how to reduce them applying normalization methods previously developed.
Collapse
Affiliation(s)
- Nico Papinutto
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Christian Cordano
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Carlo Asteggiano
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Eduardo Caverzasi
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Maria Luisa Mandelli
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Michael Lauricella
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Nicole Yabut
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Matthew Neylan
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Gina Kirkish
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Maria Luisa Gorno-Tempini
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| | - Roland G Henry
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, CA
| |
Collapse
|
31
|
Schleimer E, Pearce J, Barnecut A, Rowles W, Lizee A, Klein A, Block VJ, Santaniello A, Renschen A, Gomez R, Keshavan A, Gelfand JM, Henry RG, Hauser SL, Bove R. A Precision Medicine Tool for Patients With Multiple Sclerosis (the Open MS BioScreen): Human-Centered Design and Development. J Med Internet Res 2020; 22:e15605. [PMID: 32628124 PMCID: PMC7381029 DOI: 10.2196/15605] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 12/16/2019] [Accepted: 02/04/2020] [Indexed: 01/11/2023] Open
Abstract
Background Patients with multiple sclerosis (MS) face several challenges in accessing clinical tools to help them monitor, understand, and make meaningful decisions about their disease course. The University of California San Francisco MS BioScreen is a web-based precision medicine tool initially designed to be clinician facing. We aimed to design a second, openly available tool, Open MS BioScreen, that would be accessible, understandable, and actionable by people with MS. Objective This study aimed to describe the human-centered design and development approach (inspiration, ideation, and implementation) for creating the Open MS BioScreen platform. Methods We planned an iterative and cyclical development process that included stakeholder engagement and iterative feedback from users. Stakeholders included patients with MS along with their caregivers and family members, MS experts, generalist clinicians, industry representatives, and advocacy experts. Users consisted of anyone who wants to track MS measurements over time and access openly available tools for people with MS. Phase I (inspiration) consisted of empathizing with users and defining the problem. We sought to understand the main challenges faced by patients and clinicians and what they would want to see in a web-based app. In phase II (ideation), our multidisciplinary team discussed approaches to capture, display, and make sense of user data. Then, we prototyped a series of mock-ups to solicit feedback from clinicians and people with MS. In phase III (implementation), we incorporated all concepts to test and iterate a minimally viable product. We then gathered feedback through an agile development process. The design and development were cyclical—many times throughout the process, we went back to the drawing board. Results This human-centered approach generated an openly available, web-based app through which patients with MS, their clinicians, and their caregivers can access the site and create an account. Users can enter information about their MS (basic level as well as more advanced concepts), visualize their data longitudinally, access a series of algorithms designed to empower them to make decisions about their treatments, and enter data from wearable devices to encourage realistic goal setting about their ambulatory activity. Agile development will allow us to continue to incorporate precision medicine tools, as these are validated in the clinical research arena. Conclusions After engaging intended users into the iterative human-centered design of the Open MS BioScreen, we will now monitor the adaptation and dissemination of the tool as we expand its functionality and reach. The insights generated from this approach can be applied to the development of a number of self-tracking, self-management, and user engagement tools for patients with chronic conditions.
Collapse
Affiliation(s)
- Erica Schleimer
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | | | - Andrew Barnecut
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - William Rowles
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Antoine Lizee
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Arno Klein
- Child Mind Institute, New York, NY, United States
| | - Valerie J Block
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Adam Santaniello
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Adam Renschen
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Refujia Gomez
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Anisha Keshavan
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Jeffrey M Gelfand
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Roland G Henry
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Stephen L Hauser
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Riley Bove
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| |
Collapse
|
32
|
Jordan KM, Keshavan A, Caverzasi E, Osorio J, Papinutto N, Amirbekian B, Berger MS, Henry RG. Longitudinal Disconnection Tractograms to Investigate the Functional Consequences of White Matter Damage: An Automated Pipeline. J Neuroimaging 2020; 30:443-457. [PMID: 32436352 DOI: 10.1111/jon.12713] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 03/27/2020] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE Neurosurgical resection is one of the few opportunities researchers have to image the human brain pre- and postfocal damage. A major challenge associated with brains undergoing surgical resection is that they often do not fit brain templates most image-processing methodologies are based on. Manual intervention is required to reconcile the pathology, requiring time investment and introducing reproducibility concerns, and extreme cases must be excluded. METHODS We propose an automatic longitudinal pipeline based on High Angular Resolution Diffusion Imaging acquisitions to facilitate a Pathway Lesion Symptom Mapping analysis relating focal white matter injury to functional deficits. This two-part approach includes (i) automatic segmentation of focal white matter injury from anisotropic power differences, and (ii) modeling disconnection using tractography on the single-subject level, which specifically identifies the disconnections associated with focal white matter damage. RESULTS The advantages of this approach stem from (1) objective and automatic lesion segmentation and tractogram generation, (2) objective and precise segmentation of affected tissue likely to be associated with damage to long-range white matter pathways (defined by anisotropic power), (3) good performance even in the cases of anatomical distortions by use of nonlinear tensor-based registration, which aligns images using an approach sensitive to white matter microstructure. CONCLUSIONS Mapping a system as variable and complex as the human brain requires sample sizes much larger than the current technology can support. This pipeline can be used to execute large-scale, sufficiently powered analyses by meeting the need for an automatic approach to objectively quantify white matter disconnection.
Collapse
Affiliation(s)
- Kesshi M Jordan
- UCSF-UC Berkeley Graduate Group in Bioengineering, San Francisco, CA.,Department of Neurology, University of California, San Francisco, CA
| | - Anisha Keshavan
- UCSF-UC Berkeley Graduate Group in Bioengineering, San Francisco, CA.,Department of Neurology, University of California, San Francisco, CA
| | - Eduardo Caverzasi
- Department of Neurology, University of California, San Francisco, CA
| | - Joseph Osorio
- Division of Neurosurgery, Department of Surgery, University of California, San Diego, CA
| | - Nico Papinutto
- Department of Neurology, University of California, San Francisco, CA
| | - Bagrat Amirbekian
- UCSF-UC Berkeley Graduate Group in Bioengineering, San Francisco, CA.,Department of Neurology, University of California, San Francisco, CA
| | - Mitchel S Berger
- Department of Neurosurgery, University of California, San Francisco, CA
| | - Roland G Henry
- UCSF-UC Berkeley Graduate Group in Bioengineering, San Francisco, CA.,Department of Neurology, University of California, San Francisco, CA.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA
| |
Collapse
|
33
|
Sanvito F, Caverzasi E, Riva M, Jordan KM, Blasi V, Scifo P, Iadanza A, Crespi SA, Cirillo S, Casarotti A, Leonetti A, Puglisi G, Grimaldi M, Bello L, Gorno-Tempini ML, Henry RG, Falini A, Castellano A. fMRI-Targeted High-Angular Resolution Diffusion MR Tractography to Identify Functional Language Tracts in Healthy Controls and Glioma Patients. Front Neurosci 2020; 14:225. [PMID: 32296301 PMCID: PMC7136614 DOI: 10.3389/fnins.2020.00225] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 03/02/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND MR Tractography enables non-invasive preoperative depiction of language subcortical tracts, which is crucial for the presurgical work-up of brain tumors; however, it cannot evaluate the exact function of the fibers. PURPOSE A systematic pipeline was developed to combine tractography reconstruction of language fiber bundles, based on anatomical landmarks (Anatomical-T), with language fMRI cortical activations. A fMRI-targeted Tractography (fMRI-T) was thus obtained, depicting the subsets of the anatomical tracts whose endpoints are located inside a fMRI activation. We hypothesized that fMRI-T could provide additional functional information regarding the subcortical structures, better reflecting the eloquent white matter structures identified intraoperatively. METHODS Both Anatomical-T and fMRI-T of language fiber tracts were performed on 16 controls and preoperatively on 16 patients with left-hemisphere brain tumors, using a q-ball residual bootstrap algorithm based on High Angular Resolution Diffusion Imaging (HARDI) datasets (b = 3000 s/mm2; 60 directions); fMRI ROIs were obtained using picture naming, verbal fluency, and auditory verb generation tasks. In healthy controls, normalized MNI atlases of fMRI-T and Anatomical-T were obtained. In patients, the surgical resection of the tumor was pursued by identifying eloquent structures with intraoperative direct electrical stimulation mapping and extending surgery to the functional boundaries. Post-surgical MRI allowed to identify Anatomical-T and fMRI-T non-eloquent portions removed during the procedure. RESULTS MNI Atlases showed that fMRI-T is a subset of Anatomical-T, and that different task-specific fMRI-T involve both shared subsets and task-specific subsets - e.g., verbal fluency fMRI-T strongly involves dorsal frontal tracts, consistently with the phonogical-articulatory features of this task. A quantitative analysis in patients revealed that Anatomical-T removed portions of AF-SLF and IFOF were significantly greater than verbal fluency fMRI-T ones, suggesting that fMRI-T is a more specific approach. In addition, qualitative analyses showed that fMRI-T AF-SLF and IFOF predict the exact functional limits of resection with increased specificity when compared to Anatomical-T counterparts, especially the superior frontal portion of IFOF, in a subcohort of patients. CONCLUSION These results suggest that performing fMRI-T in addition to the 'classic' Anatomical-T may be useful in a preoperative setting to identify the 'high-risk subsets' that should be spared during the surgical procedure.
Collapse
Affiliation(s)
- Francesco Sanvito
- Neuroradiology Unit and CERMAC, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Eduardo Caverzasi
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Marco Riva
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy
- Neurosurgical Oncology Unit, Humanitas Clinical and Research Center – IRCCS, Rozzano, Italy
| | - Kesshi M. Jordan
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | | | - Paola Scifo
- Nuclear Medicine Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Antonella Iadanza
- Neuroradiology Unit and CERMAC, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Sofia Allegra Crespi
- Neuroradiology Unit and CERMAC, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy
| | - Sara Cirillo
- Neuroradiology Unit and CERMAC, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Alessandra Casarotti
- Neurosurgical Oncology Unit, Humanitas Clinical and Research Center – IRCCS, Rozzano, Italy
| | - Antonella Leonetti
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy
| | - Guglielmo Puglisi
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy
| | - Marco Grimaldi
- Neuroradiology Unit, Humanitas Clinical and Research Center – IRCCS, Rozzano, Italy
| | - Lorenzo Bello
- Neurosurgical Oncology Unit, Humanitas Clinical and Research Center – IRCCS, Rozzano, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Maria Luisa Gorno-Tempini
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Roland G. Henry
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| |
Collapse
|
34
|
Bakshi R, Healy BC, Dupuy SL, Kirkish G, Khalid F, Gundel T, Asteggiano C, Yousuf F, Alexander A, Hauser SL, Weiner HL, Henry RG. Brain MRI Predicts Worsening Multiple Sclerosis Disability over 5 Years in the SUMMIT Study. J Neuroimaging 2020; 30:212-218. [PMID: 31994814 DOI: 10.1111/jon.12688] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 01/16/2020] [Accepted: 01/16/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND AND PURPOSE Brain MRI-derived lesions and atrophy are related to multiple sclerosis (MS) disability. In the Serially Unified Multicenter MS Investigation (SUMMIT), from Brigham and Women's Hospital (BWH) and University of California, San Francisco (UCSF), we assessed whether MRI methodologic heterogeneity may limit the ability to pool multisite data sets to assess 5-year clinical-MRI associations. METHODS Patients with relapsing-remitting (RR) MS (n = 100 from each site) underwent baseline brain MRI and baseline and 5-year clinical evaluations. Patients were matched on sex (74 women each), age, disease duration, and Expanded Disability Status Scale (EDSS) score. MRI was performed with differences between sites in both acquisition (field strength, voxel size, pulse sequences), and postprocessing pipeline to assess brain parenchymal fraction (BPF) and T2 lesion volume (T2LV). RESULTS The UCSF cohort showed higher correlation than the BWH cohort between T2LV and disease duration. UCSF showed a higher inverse correlation between BPF and age than BWH. UCSF showed a higher inverse correlation than BWH between BPF and 5-year EDSS score. Both cohorts showed inverse correlations between BPF and T2LV, with no between-site difference. The pooled but not individual cohort data showed a link between a lower baseline BPF and the subsequent 5-year worsening in disability in addition to other stronger relationships in the data. CONCLUSIONS MRI acquisition and processing differences may result in some degree of heterogeneity in assessing brain lesion and atrophy measures in patients with MS. Pooling of data across sites is beneficial to correct for potential biases in individual data sets.
Collapse
Affiliation(s)
- Rohit Bakshi
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA.,Department of Radiology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA
| | - Brian C Healy
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA
| | - Sheena L Dupuy
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA
| | - Gina Kirkish
- Department of Neurology, University of California, San Francisco, CA
| | - Fariha Khalid
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA
| | - Tristan Gundel
- Department of Neurology, University of California, San Francisco, CA
| | - Carlo Asteggiano
- Department of Neurology, University of California, San Francisco, CA
| | - Fawad Yousuf
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA
| | - Amber Alexander
- Department of Neurology, University of California, San Francisco, CA
| | - Stephen L Hauser
- Department of Neurology, University of California, San Francisco, CA
| | - Howard L Weiner
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA
| | - Roland G Henry
- Department of Neurology, University of California, San Francisco, CA
| | -
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA
| |
Collapse
|
35
|
Cantó E, Barro C, Zhao C, Caillier SJ, Michalak Z, Bove R, Tomic D, Santaniello A, Häring DA, Hollenbach J, Henry RG, Cree BAC, Kappos L, Leppert D, Hauser SL, Benkert P, Oksenberg JR, Kuhle J. Association Between Serum Neurofilament Light Chain Levels and Long-term Disease Course Among Patients With Multiple Sclerosis Followed up for 12 Years. JAMA Neurol 2019; 76:1359-1366. [PMID: 31403661 DOI: 10.1001/jamaneurol.2019.2137] [Citation(s) in RCA: 115] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Importance Blood sample-based biomarkers that are associated with clinically meaningful outcomes for patients with multiple sclerosis (MS) have not been developed. Objective To evaluate the potential of serum neurofilament light chain (sNFL) measurements as a biomarker of disease activity and progression in a longitudinal MS data set. Design, Setting, and Participants Single-center, ongoing, prospective observational cohort study of 607 patients with MS from the longitudinal EPIC (Expression, Proteomics, Imaging, Clinical) study at the University of California, San Francisco from July 1, 2004, through August 31, 2017. Clinical evaluations and sample collection were performed annually for 5 years, then at different time points for up to 12 years, with a median follow-up duration of 10 (interquartile range, 7-11) years. Serum NFL levels were measured using a sensitive single molecule array platform and compared with clinical and magnetic resonance imaging variables with the use of univariable and multivariable analyses. Main Outcomes and Measures The main outcomes were disability progression defined as clinically significant worsening on the Expanded Disability Status Scale (EDSS) score and brain fraction atrophy. Results Mean (SD) age of the 607 study participants at study entry was 42.5 (9.8) years; 423 (69.7%) were women; and all participants were of non-Hispanic European descent. Of 3911 samples sequentially collected, 3904 passed quality control for quantification of sNFL. Baseline sNFL levels showed significant associations with EDSS score (β, 1.080; 95% CI, 1.047-1.114; P < .001), MS subtype (β, 1.478; 95% CI, 1.279-1.707; P < .001), and treatment status (β, 1.120; 95% CI, 1.007-1.245; P = .04). A significant interaction between EDSS worsening and change in levels of sNFL over time was found (β, 1.015; 95% CI, 1.007-1.023; P < .001). Baseline sNFL levels alone were associated with approximately 11.6% of the variance in brain fraction atrophy at year 10. In a multivariable analysis that considered sex, age, and disease duration, baseline sNFL levels were associated with 18.0% of the variance in brain fraction atrophy at year 10. After 5 years' follow-up, active treatment was associated with lower levels of sNFL, with high-potency treatments associated with the greater decreases in sNFL levels compared with platform therapies (high-potency vs untreated: β, 0.946; 95% CI, 0.915-0.976; P < .001; high-potency vs platform: β, 0.972; 95% CI, 0.948-0.998; P = .04). Conclusions and Relevance This study found that statistically significant associations of sNFL with relevant clinical and neuroimaging outcomes in MS were confirmed and extended, supporting the potential of sNFL as an objective surrogate of ongoing MS disease activity. In this data set of patients with MS who received early treatment, the prognostic power of sNFL for relapse activity and long-term disability progression was limited. Further prospective studies are necessary to assess the assay's utility for decision-making in individual patients.
Collapse
Affiliation(s)
- Ester Cantó
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Christian Barro
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine, and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Chao Zhao
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Stacy J Caillier
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Zuzanna Michalak
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine, and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Riley Bove
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | | | - Adam Santaniello
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | | | - Jill Hollenbach
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Roland G Henry
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Bruce A C Cree
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine, and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.,Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - David Leppert
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine, and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Stephen L Hauser
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Pascal Benkert
- Clinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Jorge R Oksenberg
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine, and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| |
Collapse
|
36
|
Krysko KM, Henry RG, Cree BAC, Lin J, Caillier S, Santaniello A, Zhao C, Gomez R, Bevan C, Smith DL, Stern W, Kirkish G, Hauser SL, Oksenberg JR, Graves JS. Telomere Length Is Associated with Disability Progression in Multiple Sclerosis. Ann Neurol 2019; 86:671-682. [PMID: 31486104 DOI: 10.1002/ana.25592] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 08/23/2019] [Accepted: 09/01/2019] [Indexed: 12/13/2022]
Abstract
OBJECTIVE To assess whether biological aging as measured by leukocyte telomere length (LTL) is associated with clinical disability and brain volume loss in multiple sclerosis (MS). METHODS Adults with MS/clinically isolated syndrome in the University of California, San Francisco EPIC cohort study were included. LTL was measured on DNA samples by quantitative polymerase chain reaction and expressed as telomere to somatic DNA (T/S) ratio. Expanded Disability Status Scale (EDSS) and 3-dimensional T1-weighted brain magnetic resonance imaging were performed at baseline and follow-up. Associations of baseline LTL with cross-sectional and longitudinal outcomes were assessed using simple and mixed effects linear regression models. A subset (n = 46) had LTL measured over time, and we assessed the association of LTL change with EDSS change with mixed effects models. RESULTS Included were 356 women and 160 men (mean age = 43 years, median disease duration = 6 years, median EDSS = 1.5 [range = 0-7], mean T/S ratio = 0.97 [standard deviation = 0.18]). In baseline analyses adjusted for age, disease duration, and sex, for every 0.2 lower LTL, EDSS was 0.27 higher (95% confidence interval [CI] = 0.13-0.42, p < 0.001) and brain volume was 7.4mm3 lower (95% CI = 0.10-14.7, p = 0.047). In longitudinal adjusted analyses, those with lower baseline LTL had higher EDSS and lower brain volumes over time. In adjusted analysis of the subset, LTL change was associated with EDSS change over 10 years; for every 0.2 LTL decrease, EDSS was 0.34 higher (95% CI = 0.08-0.61, p = 0.012). INTERPRETATION Shorter telomere length was associated with disability independent of chronological age, suggesting that biological aging may contribute to neurological injury in MS. Targeting aging-related mechanisms is a potential therapeutic strategy against MS progression. ANN NEUROL 2019;86:671-682.
Collapse
Affiliation(s)
- Kristen M Krysko
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Roland G Henry
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Bruce A C Cree
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jue Lin
- Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA
| | -
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Stacy Caillier
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Adam Santaniello
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Chao Zhao
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Refujia Gomez
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Carolyn Bevan
- Department of Neurology, Northwestern University, Evanston, IL
| | - Dana L Smith
- Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA
| | - William Stern
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Gina Kirkish
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Stephen L Hauser
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jorge R Oksenberg
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jennifer S Graves
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA.,Department of Neurosciences, University of California, San Diego, San Diego, CA
| |
Collapse
|
37
|
Papinutto N, Asteggiano C, Bischof A, Gundel TJ, Caverzasi E, Stern WA, Bastianello S, Hauser SL, Henry RG. Intersubject Variability and Normalization Strategies for Spinal Cord Total Cross-Sectional and Gray Matter Areas. J Neuroimaging 2019; 30:110-118. [PMID: 31571307 DOI: 10.1111/jon.12666] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 09/02/2019] [Accepted: 09/16/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND AND PURPOSE The quantification of spinal cord (SC) atrophy by MRI has assumed an important role in assessment of neuroinflammatory/neurodegenerative diseases and traumatic SC injury. Recent technical advances make possible the quantification of gray matter (GM) and white matter tissues in clinical settings. However, the goal of a reliable diagnostic, prognostic or predictive marker is still elusive, in part due to large intersubject variability of SC areas. Here, we investigated the sources of this variability and explored effective strategies to reduce it. METHODS One hundred twenty-nine healthy subjects (mean age: 41.0 ± 15.9) underwent MRI on a Siemens 3T Skyra scanner. Two-dimensional PSIR at the C2-C3 vertebral level and a sagittal 1 mm3 3D T1-weighted brain acquisition extended to the upper cervical cord were acquired. Total cross-sectional area and GM area were measured at C2-C3, as well as measures of the vertebra, spinal canal and the skull. Correlations between the different metrics were explored using Pearson product-moment coefficients. The most promising metrics were used to normalize cord areas using multiple regression analyses. RESULTS The most effective normalization metrics were the V-scale (from SienaX) and the product of the C2-C3 spinal canal diameters. Normalization methods based on these metrics reduced the intersubject variability of cord areas of up to 17.74%. The measured cord areas had a statistically significant sex difference, while the effect of age was moderate. CONCLUSIONS The present work explored in a large cohort of healthy subjects the source of intersubject variability of SC areas and proposes effective normalization methods for its reduction.
Collapse
Affiliation(s)
- Nico Papinutto
- Department of Neurology, University of California, San Francisco, CA
| | - Carlo Asteggiano
- Department of Neurology, University of California, San Francisco, CA.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Antje Bischof
- Department of Neurology, University of California, San Francisco, CA
| | - Tristan J Gundel
- Department of Neurology, University of California, San Francisco, CA
| | - Eduardo Caverzasi
- Department of Neurology, University of California, San Francisco, CA.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - William A Stern
- Department of Neurology, University of California, San Francisco, CA
| | - Stefano Bastianello
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Stephen L Hauser
- Department of Neurology, University of California, San Francisco, CA
| | - Roland G Henry
- Department of Neurology, University of California, San Francisco, CA
| |
Collapse
|
38
|
Gupta N, Henry RG, Kang SM, Strober J, Lim DA, Ryan T, Perry R, Farrell J, Ulman M, Rajalingam R, Gage A, Huhn SL, Barkovich AJ, Rowitch DH. Long-Term Safety, Immunologic Response, and Imaging Outcomes following Neural Stem Cell Transplantation for Pelizaeus-Merzbacher Disease. Stem Cell Reports 2019; 13:254-261. [PMID: 31378671 PMCID: PMC6700500 DOI: 10.1016/j.stemcr.2019.07.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 07/04/2019] [Accepted: 07/05/2019] [Indexed: 01/23/2023] Open
Abstract
Four boys with Pelizaeus-Merzbacher disease, an X-linked leukodystrophy, underwent transplantation with human allogeneic central nervous system stem cells (HuCNS-SC). Subsequently, all subjects were followed for an additional 4 years in this separate follow-up study to evaluate safety, neurologic function, magnetic resonance imaging (MRI) data, and immunologic response. The neurosurgical procedure, immunosuppression, and HuCNS-SC transplantation were well tolerated and all four subjects were alive at the conclusion of the study period. At year 2, all subjects exhibited diffusion MRI changes at the implantation sites as well as in more distant brain regions. There were persistent, increased signal changes in the three patients who were studied up to year 5. Two of four subjects developed donor-specific HLA alloantibodies, demonstrating that neural stem cells can elicit an immune response when injected into the CNS, and suggesting the importance of monitoring immunologic parameters and identifying markers of engraftment in future studies. Neural stem cell transplantation has a safe long-term safety profile Donor-specific alloantibodies develop after stem cell transplantation into the CNS Diffusion MR imaging is a potential surrogate for CNS myelination
Collapse
Affiliation(s)
- Nalin Gupta
- Department of Neurological Surgery, University of California San Francisco, 550 16(th) Street, 4(th) Floor, San Francisco, CA 94143-0137, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA 94143, USA.
| | - Roland G Henry
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA; Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; Bioengineering Graduate Group, University of California San Francisco & Berkeley, San Francisco, CA 94143, USA
| | - Sang-Mo Kang
- Division of Transplantation, Department of Surgery, University of California San Francisco, San Francisco, CA 94143, USA
| | - Jonathan Strober
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA 94143, USA
| | - Daniel A Lim
- Department of Neurological Surgery, University of California San Francisco, 550 16(th) Street, 4(th) Floor, San Francisco, CA 94143-0137, USA
| | - Tamara Ryan
- Fetal Treatment Center, University of California San Francisco, San Francisco, CA 94143, USA
| | - Rachel Perry
- Fetal Treatment Center, University of California San Francisco, San Francisco, CA 94143, USA
| | - Jody Farrell
- Fetal Treatment Center, University of California San Francisco, San Francisco, CA 94143, USA
| | - Mary Ulman
- Department of Pediatrics, University of California San Francisco, San Francisco, CA 94143, USA
| | - Raja Rajalingam
- Immunogenetics and Transplantation Laboratory, Department of Surgery, University of California San Francisco, San Francisco, CA 94143, USA
| | | | | | - A James Barkovich
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA; Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA 94143, USA
| | - David H Rowitch
- Department of Neurological Surgery, University of California San Francisco, 550 16(th) Street, 4(th) Floor, San Francisco, CA 94143-0137, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA 94143, USA; Department of Paediatrics, University of Cambridge, Cambridge CB2 1TN, UK
| |
Collapse
|
39
|
Han SJ, Morshed RA, Troncon I, Jordan KM, Henry RG, Hervey-Jumper SL, Berger MS. Subcortical stimulation mapping of descending motor pathways for perirolandic gliomas: assessment of morbidity and functional outcome in 702 cases. J Neurosurg 2019; 131:201-208. [PMID: 30117770 DOI: 10.3171/2018.3.jns172494] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Accepted: 03/19/2018] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Herein, the authors report their experience with intraoperative stimulation mapping to locate the descending subcortical motor pathways in patients undergoing surgery for hemispheric gliomas within or adjacent to the rolandic cortex, with particular description of the morbidity and functional outcomes associated with this technique. METHODS This is a retrospective analysis of patients who, in the period between 1997 and 2016, had undergone resection of hemispheric perirolandic gliomas within or adjacent to descending motor pathways. Data regarding intraoperative stimulation mapping and patient postoperative neurological status were collected. RESULTS Of 702 patients, stimulation mapping identified the descending motor pathways in 300 cases (43%). A new or worsened motor deficit was seen postoperatively in 210 cases (30%). Among these 210 cases, there was improvement in motor function to baseline levels by 3 months postoperatively in 161 cases (77%), whereas the deficit remained in 49 cases (23%). The majority (65%) of long-term deficits (persisting beyond 3 months) were mild or moderate (antigravity strength or better). On multivariate analysis, patients in whom the subcortical motor pathways had been identified with stimulation mapping during surgery were more likely to develop an additional and/or worsened motor deficit postoperatively than were those in whom the subcortical pathways had not been found (45% vs 19%, respectively, p < 0.001). This difference remained when considering the likelihood of a long-term deficit (i.e., persisting > 3 months; 12% vs 3.2%, p < 0.001). A higher tumor grade and the presence of a preoperative motor deficit were also associated with higher rates of motor deficits persisting long-term. A region of restricted diffusion adjacent to the resection cavity was seen in 20 patients with long-term deficits (41%) and was more common in cases in which the motor pathways were not identified (69%). Long-term deficits that occur in settings in which the subcortical motor pathways are not identified seem in large part due to ischemic injury to descending tracts. CONCLUSIONS Stimulation mapping allows surgeons to identify the descending motor pathways during resection of tumors in perirolandic regions and to attain an acceptable rate of morbidity in these high-risk cases.
Collapse
Affiliation(s)
- Seunggu J Han
- 1Department of Neurological Surgery, University of California, San Francisco, California.,2Department of Neurological Surgery, Oregon Health and Science University, Portland, Oregon
| | - Ramin A Morshed
- 1Department of Neurological Surgery, University of California, San Francisco, California
| | - Irene Troncon
- 3Department of Neurological Surgery, Padua University Hospital, Padua, Italy; and
| | - Kesshi M Jordan
- 4Department of Neurology, University of California, San Francisco, California
| | - Roland G Henry
- 4Department of Neurology, University of California, San Francisco, California
| | - Shawn L Hervey-Jumper
- 1Department of Neurological Surgery, University of California, San Francisco, California
| | - Mitchel S Berger
- 1Department of Neurological Surgery, University of California, San Francisco, California
| |
Collapse
|
40
|
Papinutto N, Henry RG. Evaluation of Intra- and Interscanner Reliability of MRI Protocols for Spinal Cord Gray Matter and Total Cross-Sectional Area Measurements. J Magn Reson Imaging 2019; 49:1078-1090. [PMID: 30198209 PMCID: PMC6620602 DOI: 10.1002/jmri.26269] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 07/04/2018] [Accepted: 07/05/2018] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND In vivo quantification of spinal cord atrophy in neurological diseases using MRI has attracted increasing attention. PURPOSE To compare across different platforms the most promising imaging techniques to assess human spinal cord atrophy. STUDY TYPE Test/retest multiscanner study. SUBJECTS Twelve healthy volunteers. FIELD STRENGTH/SEQUENCE Three different 3T scanner platforms (Siemens, Philips, and GE) / optimized phase sensitive inversion recovery (PSIR), T1 -weighted (T1 -w), and T2 *-weighted (T2 *-w) protocols. ASSESSMENT On all images acquired, two operators assessed contrast-to-noise ratio (CNR) between gray matter (GM) and white matter (WM), and between WM and cerebrospinal fluid (CSF); one experienced operator measured total cross-sectional area (TCA) and GM area using JIM and the Spinal Cord Toolbox (SCT). STATISTICAL TESTS Coefficient of variation (COV); intraclass correlation coefficient (ICC); mixed effect models; analysis of variance (t-tests). RESULTS For all the scanners, GM/WM CNR was higher for PSIR than T2 *-w (P < 0.0001) and WM/CSF CNR for T1 -w was the highest (P < 0.0001). For TCA, using JIM, median COVs were smaller than 1.5% and ICC >0.95, while using SCT, median COVs were in the range 2.2-2.75% and ICC 0.79-0.95. For GM, despite some failures of the automatic segmentation, median COVs using SCT on T2 *-w were smaller than using JIM manual PSIR segmentations. In the mixed effect models, the subject was always the main contributor to the variance of area measurements and scanner often contributed to TCA variance (P < 0.05). Using JIM, TCA measurements on T2 *-w were different than on PSIR (P = 0.0021) and T1 -w (P = 0.0018), while using SCT, no notable differences were found between T1 -w and T2 *-w (P = 0.18). JIM and SCT-derived TCA were not different on T1 -w (P = 0.66), while they were different for T2 *-w (P < 0.0001). GM area derived using SCT/T2 *-w versus JIM/PSIR were different (P < 0.0001). DATA CONCLUSION The present work sets reference values for the magnitude of the contribution of different effects to cord area measurement intra- and interscanner variability. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;49:1078-1090.
Collapse
Affiliation(s)
- Nico Papinutto
- Department of NeurologyUniversity of California San Francisco94158San FranciscoCAUSA
| | - Roland G. Henry
- Department of NeurologyUniversity of California San Francisco94158San FranciscoCAUSA
| |
Collapse
|
41
|
Cree BAC, Hollenbach JA, Bove R, Kirkish G, Sacco S, Caverzasi E, Bischof A, Gundel T, Zhu AH, Papinutto N, Stern WA, Bevan C, Romeo A, Goodin DS, Gelfand JM, Graves J, Green AJ, Wilson MR, Zamvil SS, Zhao C, Gomez R, Ragan NR, Rush GQ, Barba P, Santaniello A, Baranzini SE, Oksenberg JR, Henry RG, Hauser SL. Silent progression in disease activity-free relapsing multiple sclerosis. Ann Neurol 2019; 85:653-666. [PMID: 30851128 PMCID: PMC6518998 DOI: 10.1002/ana.25463] [Citation(s) in RCA: 241] [Impact Index Per Article: 48.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 03/05/2019] [Accepted: 03/06/2019] [Indexed: 12/17/2022]
Abstract
Objective Rates of worsening and evolution to secondary progressive multiple sclerosis (MS) may be substantially lower in actively treated patients compared to natural history studies from the pretreatment era. Nonetheless, in our recently reported prospective cohort, more than half of patients with relapsing MS accumulated significant new disability by the 10th year of follow‐up. Notably, “no evidence of disease activity” at 2 years did not predict long‐term stability. Here, we determined to what extent clinical relapses and radiographic evidence of disease activity contribute to long‐term disability accumulation. Methods Disability progression was defined as an increase in Expanded Disability Status Scale (EDSS) of 1.5, 1.0, or 0.5 (or greater) from baseline EDSS = 0, 1.0–5.0, and 5.5 or higher, respectively, assessed from baseline to year 5 (±1 year) and sustained to year 10 (±1 year). Longitudinal analysis of relative brain volume loss used a linear mixed model with sex, age, disease duration, and HLA‐DRB1*15:01 as covariates. Results Relapses were associated with a transient increase in disability over 1‐year intervals (p = 0.012) but not with confirmed disability progression (p = 0.551). Relative brain volume declined at a greater rate among individuals with disability progression compared to those who remained stable (p < 0.05). Interpretation Long‐term worsening is common in relapsing MS patients, is largely independent of relapse activity, and is associated with accelerated brain atrophy. We propose the term silent progression to describe the insidious disability that accrues in many patients who satisfy traditional criteria for relapsing–remitting MS. Ann Neurol 2019;85:653–666
Collapse
Affiliation(s)
| | - Bruce A C Cree
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jill A Hollenbach
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Riley Bove
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Gina Kirkish
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Simone Sacco
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Eduardo Caverzasi
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Antje Bischof
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Tristan Gundel
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Alyssa H Zhu
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Nico Papinutto
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - William A Stern
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Carolyn Bevan
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Andrew Romeo
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Douglas S Goodin
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jeffrey M Gelfand
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jennifer Graves
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Ari J Green
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Michael R Wilson
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Scott S Zamvil
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Chao Zhao
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Refujia Gomez
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Nicholas R Ragan
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Gillian Q Rush
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Patrick Barba
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Adam Santaniello
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Sergio E Baranzini
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jorge R Oksenberg
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Roland G Henry
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Stephen L Hauser
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| |
Collapse
|
42
|
Cree BAC, Niu J, Hoi KK, Zhao C, Caganap SD, Henry RG, Dao DQ, Zollinger DR, Mei F, Shen YAA, Franklin RJM, Ullian EM, Xiao L, Chan JR, Fancy SPJ. Clemastine rescues myelination defects and promotes functional recovery in hypoxic brain injury. Brain 2019; 141:85-98. [PMID: 29244098 DOI: 10.1093/brain/awx312] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 10/14/2017] [Indexed: 11/14/2022] Open
Abstract
Hypoxia can injure brain white matter tracts, comprised of axons and myelinating oligodendrocytes, leading to cerebral palsy in neonates and delayed post-hypoxic leukoencephalopathy (DPHL) in adults. In these conditions, white matter injury can be followed by myelin regeneration, but myelination often fails and is a significant contributor to fixed demyelinated lesions, with ensuing permanent neurological injury. Non-myelinating oligodendrocyte precursor cells are often found in lesions in plentiful numbers, but fail to mature, suggesting oligodendrocyte precursor cell differentiation arrest as a critical contributor to failed myelination in hypoxia. We report a case of an adult patient who developed the rare condition DPHL and made a nearly complete recovery in the setting of treatment with clemastine, a widely available antihistamine that in preclinical models promotes oligodendrocyte precursor cell differentiation. This suggested possible therapeutic benefit in the more clinically prevalent hypoxic injury of newborns, and we demonstrate in murine neonatal hypoxic injury that clemastine dramatically promotes oligodendrocyte precursor cell differentiation, myelination, and improves functional recovery. We show that its effect in hypoxia is oligodendroglial specific via an effect on the M1 muscarinic receptor on oligodendrocyte precursor cells. We propose clemastine as a potential therapy for hypoxic brain injuries associated with white matter injury and oligodendrocyte precursor cell maturation arrest.
Collapse
Affiliation(s)
- Bruce A C Cree
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Jianqin Niu
- Department of Pediatrics, University of California at San Francisco, San Francisco, CA 94158, USA.,Department of Histology and Embryology, Collaborative Innovation Center for Brain Research, Third Military Medical University, Chongqing 400038, China
| | - Kimberly K Hoi
- Department of Pediatrics, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Chao Zhao
- Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AH, UK
| | - Scott D Caganap
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Roland G Henry
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Dang Q Dao
- Department of Ophthalmology, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Daniel R Zollinger
- Department of Pediatrics, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Feng Mei
- Department of Histology and Embryology, Collaborative Innovation Center for Brain Research, Third Military Medical University, Chongqing 400038, China
| | - Yun-An A Shen
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Robin J M Franklin
- Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AH, UK
| | - Erik M Ullian
- Department of Ophthalmology, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Lan Xiao
- Department of Histology and Embryology, Collaborative Innovation Center for Brain Research, Third Military Medical University, Chongqing 400038, China
| | - Jonah R Chan
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Stephen P J Fancy
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California at San Francisco, San Francisco, CA 94158, USA.,Department of Pediatrics, University of California at San Francisco, San Francisco, CA 94158, USA.,Division of Neonatology, University of California at San Francisco, San Francisco, CA 94158, USA.,Newborn Brain Research Institute, University of California at San Francisco, San Francisco, CA 94158, USA
| |
Collapse
|
43
|
Greenfield AL, Dandekar R, Ramesh A, Eggers EL, Wu H, Laurent S, Harkin W, Pierson NS, Weber MS, Henry RG, Bischof A, Cree BA, Hauser SL, Wilson MR, von Büdingen HC. Longitudinally persistent cerebrospinal fluid B cells can resist treatment in multiple sclerosis. JCI Insight 2019; 4:126599. [PMID: 30747723 DOI: 10.1172/jci.insight.126599] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Accepted: 02/05/2019] [Indexed: 12/27/2022] Open
Abstract
B cells are key contributors to chronic autoimmune pathology in multiple sclerosis (MS). Clonally related B cells exist in the cerebrospinal fluid (CSF), meninges, and CNS parenchyma of MS patients. We sought to investigate the presence of clonally related B cells over time by performing Ig heavy chain variable region repertoire sequencing on B cells from longitudinally collected blood and CSF samples of MS patients (n = 10). All patients were untreated at the time of the initial sampling; the majority (n = 7) were treated with immune-modulating therapies 1.2 (±0.3 SD) years later during the second sampling. We found clonal persistence of B cells in the CSF of 5 patients; these B cells were frequently Ig class-switched and CD27+. Specific blood B cell subsets appear to provide input into CNS repertoires over time. We demonstrate complex patterns of clonal B cell persistence in CSF and blood, even in patients on immune-modulating therapy. Our findings support the concept that peripheral B cell activation and CNS-compartmentalized immune mechanisms can in part be therapy resistant.
Collapse
Affiliation(s)
- Ariele L Greenfield
- UCSF Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, California, USA
| | - Ravi Dandekar
- UCSF Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, California, USA
| | - Akshaya Ramesh
- UCSF Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, California, USA
| | - Erica L Eggers
- UCSF Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, California, USA
| | - Hao Wu
- UCSF Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, California, USA
| | - Sarah Laurent
- UCSF Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, California, USA
| | - William Harkin
- UCSF Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, California, USA
| | - Natalie S Pierson
- UCSF Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, California, USA
| | - Martin S Weber
- Institute of Neuropathology, Department of Neurology, University Medical Center Göttingen, Germany
| | - Roland G Henry
- UCSF Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, California, USA
| | - Antje Bischof
- UCSF Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, California, USA
| | - Bruce Ac Cree
- UCSF Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, California, USA
| | - Stephen L Hauser
- UCSF Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, California, USA
| | - Michael R Wilson
- UCSF Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, California, USA
| | - H-Christian von Büdingen
- UCSF Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, California, USA
| |
Collapse
|
44
|
Schwartz DL, Tagge I, Powers K, Ahn S, Bakshi R, Calabresi PA, Todd Constable R, Grinstead J, Henry RG, Nair G, Papinutto N, Pelletier D, Shinohara R, Oh J, Reich DS, Sicotte NL, Rooney WD. Multisite reliability and repeatability of an advanced brain MRI protocol. J Magn Reson Imaging 2019; 50:878-888. [PMID: 30652391 DOI: 10.1002/jmri.26652] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 12/28/2018] [Accepted: 12/31/2018] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND MRI is the imaging modality of choice for diagnosis and intervention assessment in neurological disease. Its full potential has not been realized due in part to challenges in harmonizing advanced techniques across multiple sites. PURPOSE To develop a method for the assessment of reliability and repeatability of advanced multisite-multisession neuroimaging studies and specifically to assess the reliability of an advanced MRI protocol, including multiband fMRI and diffusion tensor MRI, in a multisite setting. STUDY TYPE Prospective. POPULATION Twice repeated measurement of a single subject with stable relapsing-remitting multiple sclerosis (MS) at seven institutions. FIELD STRENGTH/SEQUENCE A 3 T MRI protocol included higher spatial resolution anatomical scans, a variable flip-angle longitudinal relaxation rate constant (R1 ≡ 1/T1 ) measurement, quantitative magnetization transfer imaging, diffusion tensor imaging, and a resting-state fMRI (rsFMRI) series. ASSESSMENT Multiple methods of assessing intrasite repeatability and intersite reliability were evaluated for imaging metrics derived from each sequence. STATISTICAL TESTS Student's t-test, Pearson's r, and intraclass correlation coefficient (ICC) (2,1) were employed to assess repeatability and reliability. Two new statistical metrics are introduced that frame reliability and repeatability in the respective units of the measurements themselves. RESULTS Intrasite repeatability was excellent for quantitative R1 , magnetization transfer ratio (MTR), and diffusion-weighted imaging (DWI) based metrics (r > 0.95). rsFMRI metrics were less repeatable (r = 0.8). Intersite reliability was excellent for R1 , MTR, and DWI (ICC >0.9), and moderate for rsFMRI metrics (ICC∼0.4). DATA CONCLUSION From most reliable to least, using a new reliability metric introduced here, MTR > R1 > DWI > rsFMRI; for repeatability, MTR > DWI > R1 > rsFMRI. A graphical method for at-a-glance assessment of reliability and repeatability, effect sizes, and outlier identification in multisite-multisession neuroimaging studies is introduced. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:878-888.
Collapse
Affiliation(s)
- Daniel L Schwartz
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Ian Tagge
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Katherine Powers
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Sinyeob Ahn
- Siemens Healthineers, Malvern, Pennsylvania, USA
| | - Rohit Bakshi
- Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | | | - John Grinstead
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Siemens Healthineers, Malvern, Pennsylvania, USA
| | - Roland G Henry
- University of California San Francisco, San Francisco, California, USA
| | - Govind Nair
- National Institute of Neurological Disease and Stroke, National Institute of Health, Bethesda, Maryland, USA
| | - Nico Papinutto
- University of California San Francisco, San Francisco, California, USA
| | - Daniel Pelletier
- University of Southern California, Keck School of Medicine, Los Angeles, California, USA
| | | | - Jiwon Oh
- Johns Hopkins University, Baltimore, Maryland, USA.,University of Toronto, Ontario, Canada
| | - Daniel S Reich
- National Institute of Neurological Disease and Stroke, National Institute of Health, Bethesda, Maryland, USA
| | | | - William D Rooney
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | | |
Collapse
|
45
|
Olney NT, Bischof A, Rosen H, Caverzasi E, Stern WA, Lomen-Hoerth C, Miller BL, Henry RG, Papinutto N. Measurement of spinal cord atrophy using phase sensitive inversion recovery (PSIR) imaging in motor neuron disease. PLoS One 2018; 13:e0208255. [PMID: 30496320 PMCID: PMC6264489 DOI: 10.1371/journal.pone.0208255] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 11/14/2018] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The spectrum of motor neuron disease (MND) includes numerous phenotypes with various life expectancies. The degree of upper and lower motor neuron involvement can impact prognosis. Phase sensitive inversion recovery (PSIR) imaging has been shown to detect in vivo gray matter (GM) and white matter (WM) atrophy in the spinal cord of other patient populations but has not been explored in MND. METHODS In this study, total cord, WM and GM areas of ten patients with a diagnosis within the MND spectrum were compared to those of ten healthy controls (HC). Patients' diagnosis included amyotrophic lateral sclerosis (ALS), primary lateral sclerosis, primary muscular atrophy, facial onset sensory and motor neuronopathy and ALS-Frontotemporal dementia. Axial 2D PSIR images were acquired at four cervical disc levels (C2-C3, C3-C4, C5-C6 and C7-T1) with a short acquisition time (2 minutes) protocol. Total cross-sectional areas (TCA), GM and WM areas were measured using a combination of highly reliable manual and semi-automated methods. Cord areas in MND patients were compared with HC using linear regression analyses adjusted for age and sex. Correlation of WM and GM areas in MND patients was explored to gain insights into underlying atrophy patterns. RESULTS MND patients as a group had significantly smaller cervical cord GM area compared to HC at all four levels (C2-C3: p = .009; C3-C4: p = .001; C5-C6: p = .006; C7-T1: p = .002). WM area at C5-C6 level was significantly smaller (p = .001). TCA was significantly smaller at C3-C4 (p = .018) and C5-C6 (p = .002). No significant GM and WM atrophy was detected in the two patients with predominantly bulbar phenotype. Concomitant GM and WM atrophy was detected in solely upper or lower motor neuron level phenotypes. There was a significant correlation between GM and WM areas at all four levels in this diverse population of MND. CONCLUSION Spinal cord GM and WM atrophy can be detected in vivo in patients within the MND spectrum using a short acquisition time 2D PSIR imaging protocol. PSIR imaging shows promise as a method for quantifying spinal cord involvement and thus may be useful for diagnosis, prognosis and for monitoring disease progression.
Collapse
Affiliation(s)
- Nicholas T. Olney
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, California, United States of America
- Department of Neurology, University of California San Francisco Amyotrophic Lateral Sclerosis Center, University of California San Francisco, San Francisco, California, United States of America
- * E-mail:
| | - Antje Bischof
- Department of Neurology, University of California San Francisco, San Francisco, California, United States of America
- Department of Neurology and Immunology Clinic, Departments of Medicine, Biomedicine and Clinical Research, University Hospital Basel, Basel, Switzerland
| | - Howard Rosen
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, California, United States of America
| | - Eduardo Caverzasi
- Department of Neurology, University of California San Francisco, San Francisco, California, United States of America
| | - William A. Stern
- Department of Neurology, University of California San Francisco, San Francisco, California, United States of America
| | - Catherine Lomen-Hoerth
- Department of Neurology, University of California San Francisco Amyotrophic Lateral Sclerosis Center, University of California San Francisco, San Francisco, California, United States of America
| | - Bruce L. Miller
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, California, United States of America
| | - Roland G. Henry
- Department of Neurology, University of California San Francisco, San Francisco, California, United States of America
| | - Nico Papinutto
- Department of Neurology, University of California San Francisco, San Francisco, California, United States of America
| |
Collapse
|
46
|
Lee H, Nam Y, Lee HJ, Hsu JJ, Henry RG, Kim DH. Improved three-dimensional multi-echo gradient echo based myelin water fraction mapping with phase related artifact correction. Neuroimage 2018; 169:1-10. [DOI: 10.1016/j.neuroimage.2017.11.058] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 11/21/2017] [Accepted: 11/25/2017] [Indexed: 12/17/2022] Open
|
47
|
Dworkin JD, Linn KA, Oguz I, Fleishman GM, Bakshi R, Nair G, Calabresi PA, Henry RG, Oh J, Papinutto N, Pelletier D, Rooney W, Stern W, Sicotte NL, Reich DS, Shinohara RT. An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions. AJNR Am J Neuroradiol 2018; 39:626-633. [PMID: 29472300 PMCID: PMC5895493 DOI: 10.3174/ajnr.a5556] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 12/10/2017] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Lesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest association with clinical outcome. Lesion count, which encapsulates the natural history of lesion formation and is thought to provide complementary information, is difficult to assess in patients with confluent (ie, spatially overlapping) lesions. We introduce a statistical technique for cross-sectionally counting pathologically distinct lesions. MATERIALS AND METHODS MR imaging was used to assess the probability of a lesion at each location. The texture of this map was quantified using a novel technique, and clusters resembling the center of a lesion were counted. Validity compared with a criterion standard count was demonstrated in 60 subjects observed longitudinally, and reliability was determined using 14 scans of a clinically stable subject acquired at 7 sites. RESULTS The proposed count and the criterion standard count were highly correlated (r = 0.97, P < .001) and not significantly different (t59 = -.83, P = .41), and the variability of the proposed count across repeat scans was equivalent to that of lesion load. After accounting for lesion load and age, lesion count was negatively associated (t58 = -2.73, P < .01) with the Expanded Disability Status Scale. Average lesion size had a higher association with the Expanded Disability Status Scale (r = 0.35, P < .01) than lesion load (r = 0.10, P = .44) or lesion count (r = -.12, P = .36) alone. CONCLUSIONS This study introduces a novel technique for counting pathologically distinct lesions using cross-sectional data and demonstrates its ability to recover obscured longitudinal information. The proposed count allows more accurate estimation of lesion size, which correlated more closely with disability scores than either lesion load or lesion count alone.
Collapse
Affiliation(s)
- J D Dworkin
- From the Departments of Biostatistics, Epidemiology, and Informatics (J.D.D., K.A.L., R.T.S.)
| | - K A Linn
- From the Departments of Biostatistics, Epidemiology, and Informatics (J.D.D., K.A.L., R.T.S.)
| | - I Oguz
- Radiology (I.O., G.M.F.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - G M Fleishman
- Radiology (I.O., G.M.F.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - R Bakshi
- Laboratory for Neuroimaging Research (R.B.), Partners Multiple Sclerosis Center, Ann Romney Center for Neurologic Diseases
- Departments of Neurology (R.B.)
- Radiology (R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - G Nair
- Translational Neuroradiology Section (G.N., D.S.R.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland
| | - P A Calabresi
- Department of Neurology (P.A.C., J.O., D.S.R.), the Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - R G Henry
- Department of Neurology (R.G.H., N.P., W.S.), University of California, San Francisco, San Francisco, California
| | - J Oh
- Department of Neurology (P.A.C., J.O., D.S.R.), the Johns Hopkins University School of Medicine, Baltimore, Maryland
- Keenan Research Centre for Biomedical Science (J.O.), St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
| | - N Papinutto
- Department of Neurology (R.G.H., N.P., W.S.), University of California, San Francisco, San Francisco, California
| | - D Pelletier
- Department of Neurology (D.P.), Keck School of Medicine, University of Southern California, Los Angeles, California
| | - W Rooney
- Advanced Imaging Research Center (W.R.), Oregon Health & Science University, Portland, Oregon
| | - W Stern
- Department of Neurology (R.G.H., N.P., W.S.), University of California, San Francisco, San Francisco, California
| | - N L Sicotte
- Department of Neurology (N.L.S.), Cedars-Sinai Medical Center, Los Angeles, California. A complete list of the NAIMS participants is provided in the acknowledgment section
| | - D S Reich
- Translational Neuroradiology Section (G.N., D.S.R.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland
- Department of Neurology (P.A.C., J.O., D.S.R.), the Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - R T Shinohara
- From the Departments of Biostatistics, Epidemiology, and Informatics (J.D.D., K.A.L., R.T.S.)
| | | |
Collapse
|
48
|
Damotte V, Lizée A, Tremblay M, Agrawal A, Khankhanian P, Santaniello A, Gomez R, Lincoln R, Tang W, Chen T, Lee N, Villoslada P, Hollenbach JA, Bevan CD, Graves J, Bove R, Goodin DS, Green AJ, Baranzini SE, Cree BAC, Henry RG, Hauser SL, Gelfand JM, Gourraud PA. Harnessing electronic medical records to advance research on multiple sclerosis. Mult Scler 2018; 25:408-418. [DOI: 10.1177/1352458517747407] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Background: Electronic medical records (EMR) data are increasingly used in research, but no studies have yet evaluated similarity between EMR and research-quality data and between characteristics of an EMR multiple sclerosis (MS) population and known natural MS history. Objectives: To (1) identify MS patients in an EMR system and extract clinical data, (2) compare EMR-extracted data with gold-standard research data, and (3) compare EMR MS population characteristics to expected MS natural history. Methods: Algorithms were implemented to identify MS patients from the University of California San Francisco EMR, de-identify the data and extract clinical variables. EMR-extracted data were compared to research cohort data in a subset of patients. Results: We identified 4142 MS patients via search of the EMR and extracted their clinical data with good accuracy. EMR and research values showed good concordance for Expanded Disability Status Scale (EDSS), timed-25-foot walk, and subtype. We replicated several expected MS epidemiological features from MS natural history including higher EDSS for progressive versus relapsing–remitting patients and for male versus female patients and increased EDSS with age at examination and disease duration. Conclusion: Large real-world cohorts algorithmically extracted from the EMR can expand opportunities for MS clinical research.
Collapse
Affiliation(s)
- Vincent Damotte
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Antoine Lizée
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Université de Nantes, INSERM, UMR 1064, ATIP-Avenir, Equipe 5 Centre de Recherche en Transplantation et Immunologie, Nantes, France
| | - Matthew Tremblay
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Department of Neurology, John Dempsey Hospital, University of Connecticut Health Center, Farmington, CT, USA
| | - Alisha Agrawal
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Pouya Khankhanian
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam Santaniello
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Refujia Gomez
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Robin Lincoln
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Wendy Tang
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Tiffany Chen
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Nelson Lee
- Information Technology, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Pablo Villoslada
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/IDIBAPS—Hospital Clinic of Barcelona, Barcelona, Spain
| | - Jill A Hollenbach
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Carolyn D Bevan
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Jennifer Graves
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Riley Bove
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Douglas S Goodin
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Ari J Green
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Sergio E Baranzini
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Bruce AC Cree
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Roland G Henry
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Stephen L Hauser
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Jeffrey M Gelfand
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Pierre-Antoine Gourraud
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Université de Nantes, INSERM, UMR 1064, ATIP-Avenir, Equipe 5 Centre de Recherche en Transplantation et Immunologie, Nantes, France
| |
Collapse
|
49
|
Graves JS, Henry RG, Cree BAC, Lambert-Messerlian G, Greenblatt RM, Waubant E, Cedars MI, Zhu A, Bacchetti P, Hauser SL, Oksenberg JR. Ovarian aging is associated with gray matter volume and disability in women with MS. Neurology 2017; 90:e254-e260. [PMID: 29273686 DOI: 10.1212/wnl.0000000000004843] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 10/02/2017] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVE To determine if ovarian aging as measured by levels of anti-Müllerian hormone (AMH) is associated with pattern of multiple sclerosis (MS) progression in women. METHODS Women with MS and healthy controls were included from a longitudinal research cohort with up to 10 years follow-up. Plasma AMH levels were measured by ELISA for baseline and years 3, 5, and 8-10. Mixed effects logistic and linear regression models were employed, with adjustments for age, disease duration, and other covariables as appropriate. RESULTS AMH levels were similar (0.98-fold difference, 95% confidence interval [CI] 0.69-1.37, p = 0.87) in women with MS (n = 412, mean age 42.6 years) and healthy controls (n = 180, mean age 44 years). In a multivariable model of women with MS, including adjustments for age, body mass index, and disease duration, 10-fold lower AMH level was associated with 0.43-higher Expanded Disability Status Scale (EDSS) score (95% CI 0.15-0.70, p = 0.003), 0.25-unit worse MS Functional Composite z score (95% CI -0.40 to -0.10, p = 0.0015), and 7.44 mm3 lower cortical gray matter volume (95% CI -14.6 to -0.30; p = 0.041) at baseline. In a multivariable random-intercept-random-slope model using all observations over time, 10-fold decrease in AMH was associated with a 0.27 increase in EDSS (95% CI 0.11-0.43, p = 0.006) and 5.48 mm3 (95% CI 11.3-0.33, p = 0.065) and 4.55 mm3 (95% CI 9.33-0.23, p = 0.062) decreases in total gray and cortical gray matter, respectively. CONCLUSION As a marker of ovarian aging, lower AMH levels were associated with greater disability and gray matter loss in women with MS independent of chronological age and disease duration.
Collapse
Affiliation(s)
- Jennifer S Graves
- From the Departments of Neurology (J.S.G., R.G.H., B.A.C.C., E.W., A.Z., S.L.H., J.R.O.), Pharmacology (R.M.G.), Obstetrics, Gynecology and Reproductive Sciences (M.I.C.), and Epidemiology and Biostatistics (P.B.), University of California, San Francisco; and Women and Infants Hospital and the Alpert Medical School at Brown University (G.L.-M.), Providence, RI.
| | - Roland G Henry
- From the Departments of Neurology (J.S.G., R.G.H., B.A.C.C., E.W., A.Z., S.L.H., J.R.O.), Pharmacology (R.M.G.), Obstetrics, Gynecology and Reproductive Sciences (M.I.C.), and Epidemiology and Biostatistics (P.B.), University of California, San Francisco; and Women and Infants Hospital and the Alpert Medical School at Brown University (G.L.-M.), Providence, RI
| | - Bruce A C Cree
- From the Departments of Neurology (J.S.G., R.G.H., B.A.C.C., E.W., A.Z., S.L.H., J.R.O.), Pharmacology (R.M.G.), Obstetrics, Gynecology and Reproductive Sciences (M.I.C.), and Epidemiology and Biostatistics (P.B.), University of California, San Francisco; and Women and Infants Hospital and the Alpert Medical School at Brown University (G.L.-M.), Providence, RI
| | - Geralyn Lambert-Messerlian
- From the Departments of Neurology (J.S.G., R.G.H., B.A.C.C., E.W., A.Z., S.L.H., J.R.O.), Pharmacology (R.M.G.), Obstetrics, Gynecology and Reproductive Sciences (M.I.C.), and Epidemiology and Biostatistics (P.B.), University of California, San Francisco; and Women and Infants Hospital and the Alpert Medical School at Brown University (G.L.-M.), Providence, RI
| | - Ruth M Greenblatt
- From the Departments of Neurology (J.S.G., R.G.H., B.A.C.C., E.W., A.Z., S.L.H., J.R.O.), Pharmacology (R.M.G.), Obstetrics, Gynecology and Reproductive Sciences (M.I.C.), and Epidemiology and Biostatistics (P.B.), University of California, San Francisco; and Women and Infants Hospital and the Alpert Medical School at Brown University (G.L.-M.), Providence, RI
| | - Emmanuelle Waubant
- From the Departments of Neurology (J.S.G., R.G.H., B.A.C.C., E.W., A.Z., S.L.H., J.R.O.), Pharmacology (R.M.G.), Obstetrics, Gynecology and Reproductive Sciences (M.I.C.), and Epidemiology and Biostatistics (P.B.), University of California, San Francisco; and Women and Infants Hospital and the Alpert Medical School at Brown University (G.L.-M.), Providence, RI
| | - Marcelle I Cedars
- From the Departments of Neurology (J.S.G., R.G.H., B.A.C.C., E.W., A.Z., S.L.H., J.R.O.), Pharmacology (R.M.G.), Obstetrics, Gynecology and Reproductive Sciences (M.I.C.), and Epidemiology and Biostatistics (P.B.), University of California, San Francisco; and Women and Infants Hospital and the Alpert Medical School at Brown University (G.L.-M.), Providence, RI
| | - Alyssa Zhu
- From the Departments of Neurology (J.S.G., R.G.H., B.A.C.C., E.W., A.Z., S.L.H., J.R.O.), Pharmacology (R.M.G.), Obstetrics, Gynecology and Reproductive Sciences (M.I.C.), and Epidemiology and Biostatistics (P.B.), University of California, San Francisco; and Women and Infants Hospital and the Alpert Medical School at Brown University (G.L.-M.), Providence, RI
| | | | - Peter Bacchetti
- From the Departments of Neurology (J.S.G., R.G.H., B.A.C.C., E.W., A.Z., S.L.H., J.R.O.), Pharmacology (R.M.G.), Obstetrics, Gynecology and Reproductive Sciences (M.I.C.), and Epidemiology and Biostatistics (P.B.), University of California, San Francisco; and Women and Infants Hospital and the Alpert Medical School at Brown University (G.L.-M.), Providence, RI
| | - Stephen L Hauser
- From the Departments of Neurology (J.S.G., R.G.H., B.A.C.C., E.W., A.Z., S.L.H., J.R.O.), Pharmacology (R.M.G.), Obstetrics, Gynecology and Reproductive Sciences (M.I.C.), and Epidemiology and Biostatistics (P.B.), University of California, San Francisco; and Women and Infants Hospital and the Alpert Medical School at Brown University (G.L.-M.), Providence, RI
| | - Jorge R Oksenberg
- From the Departments of Neurology (J.S.G., R.G.H., B.A.C.C., E.W., A.Z., S.L.H., J.R.O.), Pharmacology (R.M.G.), Obstetrics, Gynecology and Reproductive Sciences (M.I.C.), and Epidemiology and Biostatistics (P.B.), University of California, San Francisco; and Women and Infants Hospital and the Alpert Medical School at Brown University (G.L.-M.), Providence, RI
| |
Collapse
|
50
|
Abstract
Neuroimaging has emerged as a powerful technology that has enabled visualization of the impact of multiple sclerosis (MS) on the central nervous system in vivo with unprecedented precision. It has played a crucial role in disentangling the chronology of inflammation and neurodegeneration, developing and understanding mechanisms of novel therapeutics, and diagnosing and monitoring the disease in the clinical setting. However, challenges pertaining to the limited resolution, lack of specificity, inherent technological biases, and processing of increasingly big datasets have hindered comprehensive insights into the pathology underlying disability.Here, we review the advances in neuroimaging for MS that have moved the field forward in recent years by addressing the above-mentioned issues, thereby enhancing our knowledge of this yet enigmatic disease. We discuss complementary imaging technologies, including magnetic resonance imaging, positron emission tomography, and optical coherence tomography, the most recent tool in the MS imaging armamentarium that holds promise to act as a surrogate of pathological changes in the central nervous system in a more easily accessible way.
Collapse
Affiliation(s)
- Antje Bischof
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California.,Department of Neurology and Immunology Clinic, University Hospital Basel, Basel, Switzerland
| | - Eduardo Caverzasi
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California
| | - Christian Cordano
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California
| | - Stephen L Hauser
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California
| | - Roland G Henry
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California
| |
Collapse
|