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Huang J, Liang Y, Wang J, Shan Y, Zhao C, Li Q, Dong H, Lu J. Quantitative synthetic MRI for evaluation of hippocampus in patients with multiple sclerosis. Brain Res 2024:149298. [PMID: 39490955 DOI: 10.1016/j.brainres.2024.149298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 09/18/2024] [Accepted: 10/23/2024] [Indexed: 11/05/2024]
Abstract
OBJECTIVE To identify early changes in hippocampal quantitative parameters in multiple sclerosis (MS) patients using synthetic MRI, and to correlate these changes with clinical variables. METHODS 45 MS patients and 26 healthy controls (HCs) underwent synthetic MRI and 3D-T1 MRI. The hippocampus volumes were assessed by using voxel-based morphometry. Synthetic MRI parameters (T1, T2, and proton density (PD)) from hippocampus and its subfield were measured and compared, and their associations with the Expanded Disability Status Scale (EDSS), Symbol Digit Modalities Test (SDMT) scores were further investigated. RESULTS There was no significant difference in hippocampal volume between MS patients and HCs. Compared with HCs, the T1, T2 and PD values of hippocampus and its subfield increased in MS patients. T2 values showed positive correlation with EDSS and negative correlation with SDMT. CONCLUSIONS Synthetic MRI can detect subtle quantitative changes of the hippocampus in MS patients with normal hippocampal volume. Specifically, Synthetic MRI parameters may apply as potentially effective imaging biomarker for hippocampus evaluation.
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Affiliation(s)
- Jing Huang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Yan Liang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jiyuan Wang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Yi Shan
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Cheng Zhao
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Qiongge Li
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Huiqing Dong
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China.
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Young G, Nguyen VS, Howlett-Prieto Q, Abuaf AF, Carroll TJ, Kawaji K, Javed A. T1 mapping from routine 3D T1-weighted inversion recovery sequences in clinical practice: comparison against reference inversion recovery fast field echo T1 scans and feasibility in multiple sclerosis. Neuroradiology 2024; 66:1709-1719. [PMID: 38880824 DOI: 10.1007/s00234-024-03400-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 06/06/2024] [Indexed: 06/18/2024]
Abstract
BACKGROUND AND PURPOSE Quantitative T1 mapping can be an essential tool for assessing tissue injury in multiple sclerosis (MS). We introduce T1-REQUIRE, a method that converts a single high-resolution anatomical 3D T1-weighted Turbo Field Echo (3DT1TFE) scan into a parametric T1 map that could be used for quantitative assessment of tissue damage. We present the accuracy and feasibility of this method in MS. METHODS 14 subjects with relapsing-remitting MS and 10 healthy subjects were examined. T1 maps were generated from 3DT1TFE images using T1-REQUIRE, which estimates T1 values using MR signal equations and internal tissue reference T1 values. Estimated T1 of lesions, white, and gray matter regions were compared with reference Inversion-Recovery Fast Field Echo T1 values and analyzed via correlation and Bland-Altman (BA) statistics. RESULTS 159 T1-weighted (T1W) hypointense MS lesions and 288 gray matter regions were examined. T1 values for MS lesions showed a Pearson's correlation of r = 0.81 (p < 0.000), R2 = 0.65, and Bias = 4.18%. BA statistics showed a mean difference of -53.95 ms and limits of agreement (LOA) of -344.20 and 236.30 ms. Non-lesional normal-appearing white matter had a correlation coefficient of r = 0.82 (p < 0.000), R2 = 0.67, Bias = 8.78%, mean difference of 73.87 ms, and LOA of -55.67 and 203.41 ms. CONCLUSIONS We demonstrate the feasibility of retroactively derived high-resolution T1 maps from routinely acquired anatomical images, which could be used to quantify tissue pathology in MS. The results of this study will set the stage for testing this method in larger clinical studies for examining MS disease activity and progression.
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Affiliation(s)
- Griffin Young
- Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Vivian S Nguyen
- Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Quentin Howlett-Prieto
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, USA
| | | | - Timothy J Carroll
- Department of Radiology, The University of Chicago, Chicago, IL, USA
| | - Keigo Kawaji
- Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Adil Javed
- Department of Neurology, The University of Chicago, Chicago, IL, 5841 South Maryland Avenue, MC2030, 60637, USA.
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3
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Dean DC, Tisdall MD, Wisnowski JL, Feczko E, Gagoski B, Alexander AL, Edden RAE, Gao W, Hendrickson TJ, Howell BR, Huang H, Humphreys KL, Riggins T, Sylvester CM, Weldon KB, Yacoub E, Ahtam B, Beck N, Banerjee S, Boroday S, Caprihan A, Caron B, Carpenter S, Chang Y, Chung AW, Cieslak M, Clarke WT, Dale A, Das S, Davies-Jenkins CW, Dufford AJ, Evans AC, Fesselier L, Ganji SK, Gilbert G, Graham AM, Gudmundson AT, Macgregor-Hannah M, Harms MP, Hilbert T, Hui SCN, Irfanoglu MO, Kecskemeti S, Kober T, Kuperman JM, Lamichhane B, Landman BA, Lecour-Bourcher X, Lee EG, Li X, MacIntyre L, Madjar C, Manhard MK, Mayer AR, Mehta K, Moore LA, Murali-Manohar S, Navarro C, Nebel MB, Newman SD, Newton AT, Noeske R, Norton ES, Oeltzschner G, Ongaro-Carcy R, Ou X, Ouyang M, Parrish TB, Pekar JJ, Pengo T, Pierpaoli C, Poldrack RA, Rajagopalan V, Rettmann DW, Rioux P, Rosenberg JT, Salo T, Satterthwaite TD, Scott LS, Shin E, Simegn G, Simmons WK, Song Y, Tikalsky BJ, Tkach J, van Zijl PCM, Vannest J, Versluis M, Zhao Y, Zöllner HJ, Fair DA, Smyser CD, Elison JT. Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol. Dev Cogn Neurosci 2024; 70:101452. [PMID: 39341120 PMCID: PMC11466640 DOI: 10.1016/j.dcn.2024.101452] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 08/29/2024] [Accepted: 09/13/2024] [Indexed: 09/30/2024] Open
Abstract
The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the study's core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain.
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Affiliation(s)
- Douglas C Dean
- Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, USA; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, Madison, WI, USA.
| | - M Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jessica L Wisnowski
- Department of Pediatrics, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA, USA; Department of Radiology, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, MA, USA; Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Andrew L Alexander
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Wei Gao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Timothy J Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA; Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN, USA
| | - Brittany R Howell
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA; Department of Human Development and Family Science, Virginia Tech, Blacksburg, VA, USA
| | - Hao Huang
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kathryn L Humphreys
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, USA
| | - Tracy Riggins
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA; Taylor Family Institute for Innovative Psychiatric Research, Washington University in St. Louis, St. Louis, MO, USA
| | - Kimberly B Weldon
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Banu Ahtam
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Natacha Beck
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | | | - Sergiy Boroday
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | | | - Bryan Caron
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Samuel Carpenter
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | | | - Ai Wern Chung
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - William T Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Anders Dale
- Department of Radiology, University of California San Diego, La Jolla, CA, USA; Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Samir Das
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Christopher W Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Alexander J Dufford
- Department of Psychiatry and Center for Mental Health Innovation, Oregon Health & Science University, Portland, OR, USA
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Laetitia Fesselier
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Sandeep K Ganji
- MR Clinical Science, Philips Healthcare, Best, the Netherlands
| | - Guillaume Gilbert
- MR Clinical Science, Philips Healthcare, Mississauga, Ontario, Canada
| | - Alice M Graham
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Aaron T Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Maren Macgregor-Hannah
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Michael P Harms
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland,; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland,; LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Steve C N Hui
- Developing Brain Institute, Children's National Hospital, Washington, DC, USA; Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA; Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - M Okan Irfanoglu
- Quantitative Medical Imaging Laboratory, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
| | | | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland,; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland,; LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Joshua M Kuperman
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Bidhan Lamichhane
- Center for Health Sciences, Oklahoma State University, Tulsa, OK, USA
| | - Bennett A Landman
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Xavier Lecour-Bourcher
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Erik G Lee
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA; Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN, USA
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Leigh MacIntyre
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; Lasso Informatics, Canada
| | - Cecile Madjar
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Mary Kate Manhard
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | | | - Kahini Mehta
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Saipavitra Murali-Manohar
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Cristian Navarro
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Sharlene D Newman
- Alabama Life Research Institute, University of Alabama, Tuscaloosa, AL, USA; Department of Psychology, University of Alabama, Tuscaloosa, AL, USA
| | - Allen T Newton
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Monroe Carell Jr. Children's Hospital at Vandebrilt, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Elizabeth S Norton
- Department of Communication Sciences and Disorders, School of Communication, Northwestern University, Evanston, IL, USA; Department of Medical Social Sciences, Feinberg School of Medicine, Chicago, IL, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Regis Ongaro-Carcy
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Xiawei Ou
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA; Arkansas Children's Research Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Minhui Ouyang
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Todd B Parrish
- Department of Radiology, Feinberg School of Medicine, Chicago, IL, USA; Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | - James J Pekar
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Thomas Pengo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Carlo Pierpaoli
- Quantitative Medical Imaging Laboratory, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
| | | | - Vidya Rajagopalan
- Department of Pediatrics, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA, USA; Department of Radiology, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | | | - Pierre Rioux
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Jens T Rosenberg
- Advanced Magnetic Resonance Imaging and Spectroscopy Facility, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | - Taylor Salo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lisa S Scott
- Department of Psychology, University of Florida, Gainesville, FL, USA
| | - Eunkyung Shin
- Department of Psychology, Pennsylvania State University, University Park, PA, USA
| | - Gizeaddis Simegn
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - W Kyle Simmons
- Department of Pharmacology and Physiology, Oklahoma State University Center for Health Sciences, Tulsa, OK, USA; OSU Biomedical Imaging Center, Oklahoma State University Center for Health Sciences, Tulsa, OK, USA
| | - Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Barry J Tikalsky
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Jean Tkach
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Peter C M van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jennifer Vannest
- Department of Communication Sciences and Disorders, University of Cincinnati, Cincinnati, OH, USA; Communication Sciences Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | - Yansong Zhao
- MR Clinical Science, Philips Healthcare, Cleveland, OH, USA
| | - Helge J Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Damien A Fair
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Institute of Child Development, University of Minnesota, Minneapolis, MN, USA.
| | - Christopher D Smyser
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA; Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
| | - Jed T Elison
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Institute of Child Development, University of Minnesota, Minneapolis, MN, USA.
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4
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Harper JG, York EN, Meijboom R, Kampaite A, Thrippleton MJ, Kearns PKA, Valdés Hernández MDC, Chandran S, Waldman AD. Quantitative T 1 brain mapping in early relapsing-remitting multiple sclerosis: longitudinal changes, lesion heterogeneity and disability. Eur Radiol 2024; 34:3826-3839. [PMID: 37943312 PMCID: PMC11166797 DOI: 10.1007/s00330-023-10351-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/20/2023] [Accepted: 08/29/2023] [Indexed: 11/10/2023]
Abstract
OBJECTIVES To quantify brain microstructural changes in recently diagnosed relapsing-remitting multiple sclerosis (RRMS) using longitudinal T1 measures, and determine their associations with clinical disability. METHODS Seventy-nine people with recently diagnosed (< 6 months) RRMS were recruited from a single-centre cohort sub-study, and underwent baseline and 1-year brain MRI, including variable flip angle T1 mapping. Median T1 was measured in white matter lesions (WML), normal-appearing white matter (NAWM), cortical/deep grey matter (GM), thalami, basal ganglia and medial temporal regions. Prolonged T1 (≥ 2.00 s) and supramedian T1 (relative to cohort WML values) WML voxel counts were also measured. Longitudinal change was assessed with paired t-tests and compared with Bland-Altman limits of agreement from healthy control test-retest data. Regression analyses determined relationships with Expanded Disability Status Scale (EDSS) score and dichotomised EDSS outcomes (worsening or stable/improving). RESULTS Sixty-two people with RRMS (mean age 37.2 ± 10.9 [standard deviation], 48 female) and 11 healthy controls (age 44 ± 11, 7 female) contributed data. Prolonged and supramedian T1 WML components increased longitudinally (176 and 463 voxels, respectively; p < .001), and were associated with EDSS score at baseline (p < .05) and follow-up (supramedian: p < .01; prolonged: p < .05). No cohort-wide median T1 changes were found; however, increasing T1 in WML, NAWM, cortical/deep GM, basal ganglia and thalami was positively associated with EDSS worsening (p < .05). CONCLUSION T1 is sensitive to brain microstructure changes in early RRMS. Prolonged WML T1 components and subtle changes in NAWM and GM structures are associated with disability. CLINICAL RELEVANCE STATEMENT MRI T1 brain mapping quantifies disability-associated white matter lesion heterogeneity and subtle microstructural damage in normal-appearing brain parenchyma in recently diagnosed RRMS, and shows promise for early objective disease characterisation and stratification. KEY POINTS • Quantitative T1 mapping detects brain microstructural damage and lesion heterogeneity in recently diagnosed relapsing-remitting multiple sclerosis. • T1 increases in lesions and normal-appearing parenchyma, indicating microstructural damage, are associated with worsening disability. • Brain T1 measures are objective markers of disability-relevant pathology in early multiple sclerosis.
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Affiliation(s)
- James G Harper
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK
| | - Elizabeth N York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK.
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK.
- Anne Rowling Regenerative Neurology Clinic, Edinburgh, UK.
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Patrick K A Kearns
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK
- Anne Rowling Regenerative Neurology Clinic, Edinburgh, UK
| | - Maria Del C Valdés Hernández
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK
- Anne Rowling Regenerative Neurology Clinic, Edinburgh, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh BioQuarter: Chancellors Building, Edinburgh, EH16 4SB, UK.
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK.
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5
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Santini T, Chen C, Zhu W, Liou JJ, Walker E, Venkatesh S, Farhat N, Sajewski A, Alkhateeb S, Saranathan M, Xia Z, Ibrahim TS. Hippocampal subfields and thalamic nuclei associations with clinical outcomes in multiple sclerosis: An ultrahigh field MRI study. Mult Scler Relat Disord 2024; 86:105520. [PMID: 38582026 PMCID: PMC11081814 DOI: 10.1016/j.msard.2024.105520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/14/2024] [Accepted: 02/25/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Previous studies have shown that thalamic and hippocampal neurodegeneration is associated with clinical decline in Multiple Sclerosis (MS). However, contributions of the specific thalamic nuclei and hippocampal subfields require further examination. OBJECTIVE Using 7 Tesla (7T) magnetic resonance imaging (MRI), we investigated the cross-sectional associations between functionally grouped thalamic nuclei and hippocampal subfields volumes and T1 relaxation times (T1-RT) and subsequent clinical outcomes in MS. METHODS High-resolution T1-weighted and T2-weighted images were acquired at 7T (n=31), preprocessed, and segmented using the Thalamus Optimized Multi Atlas Segmentation (THOMAS, for thalamic nuclei) and the Automatic Segmentation of Hippocampal Subfields (ASHS, for hippocampal subfields) packages. We calculated Pearson correlations between hippocampal subfields and thalamic nuclei volumes and T1-RT and subsequent multi-modal rater-determined and patient-reported clinical outcomes (∼2.5 years after imaging acquisition), correcting for confounders and multiple tests. RESULTS Smaller volume bilaterally in the anterior thalamus region correlated with worse performance in gait function, as measured by the Patient Determined Disease Steps (PDDS). Additionally, larger volume in most functional groups of thalamic nuclei correlated with better visual information processing and cognitive function, as measured by the Symbol Digit Modalities Test (SDMT). In bilateral medial and left posterior thalamic regions, there was an inverse association between volumes and T1-RT, potentially indicating higher tissue degeneration in these regions. We also observed marginal associations between the right hippocampal subfields (both volumes and T1-RT) and subsequent clinical outcomes, though they did not survive correction for multiple testing. CONCLUSION Ultrahigh field MRI identified markers of structural damage in the thalamic nuclei associated with subsequently worse clinical outcomes in individuals with MS. Longitudinal studies will enable better understanding of the role of microstructural integrity in these brain regions in influencing MS outcomes.
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Affiliation(s)
- Tales Santini
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Chenyi Chen
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Wen Zhu
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jr-Jiun Liou
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Elizabeth Walker
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Shruthi Venkatesh
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Nadim Farhat
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Andrea Sajewski
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Salem Alkhateeb
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States
| | | | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States.
| | - Tamer S Ibrahim
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States; Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States.
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6
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Fujita S, Gagoski B, Hwang KP, Hagiwara A, Warntjes M, Fukunaga I, Uchida W, Saito Y, Sekine T, Tachibana R, Muroi T, Akatsu T, Kasahara A, Sato R, Ueyama T, Andica C, Kamagata K, Amemiya S, Takao H, Hoshino Y, Tomizawa Y, Yokoyama K, Bilgic B, Hattori N, Abe O, Aoki S. Cross-vendor multiparametric mapping of the human brain using 3D-QALAS: A multicenter and multivendor study. Magn Reson Med 2024; 91:1863-1875. [PMID: 38192263 DOI: 10.1002/mrm.29939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 11/06/2023] [Accepted: 11/06/2023] [Indexed: 01/10/2024]
Abstract
PURPOSE To evaluate a vendor-agnostic multiparametric mapping scheme based on 3D quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) for whole-brain T1, T2, and proton density (PD) mapping. METHODS This prospective, multi-institutional study was conducted between September 2021 and February 2022 using five different 3T systems from four prominent MRI vendors. The accuracy of this technique was evaluated using a standardized MRI system phantom. Intra-scanner repeatability and inter-vendor reproducibility of T1, T2, and PD values were evaluated in 10 healthy volunteers (6 men; mean age ± SD, 28.0 ± 5.6 y) who underwent scan-rescan sessions on each scanner (total scans = 100). To evaluate the feasibility of 3D-QALAS, nine patients with multiple sclerosis (nine women; mean age ± SD, 48.2 ± 11.5 y) underwent imaging examination on two 3T MRI systems from different manufacturers. RESULTS Quantitative maps obtained with 3D-QALAS showed high linearity (R2 = 0.998 and 0.998 for T1 and T2, respectively) with respect to reference measurements. The mean intra-scanner coefficients of variation for each scanner and structure ranged from 0.4% to 2.6%. The mean structure-wise test-retest repeatabilities were 1.6%, 1.1%, and 0.7% for T1, T2, and PD, respectively. Overall, high inter-vendor reproducibility was observed for all parameter maps and all structure measurements, including white matter lesions in patients with multiple sclerosis. CONCLUSION The vendor-agnostic multiparametric mapping technique 3D-QALAS provided reproducible measurements of T1, T2, and PD for human tissues within a typical physiological range using 3T scanners from four different MRI manufacturers.
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Affiliation(s)
- Shohei Fujita
- Department of Radiology, Juntendo University, Tokyo, Japan
- Department of Radiology, The University of Tokyo, Tokyo, Japan
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Marcel Warntjes
- SyntheticMR, Linköping, Sweden
- Center for Medical Imaging Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Issei Fukunaga
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Yuya Saito
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Towa Sekine
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Rina Tachibana
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Tomoya Muroi
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Toshiya Akatsu
- Department of Radiology, Juntendo University, Tokyo, Japan
| | | | - Ryo Sato
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Ueyama
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Christina Andica
- Department of Radiology, Juntendo University, Tokyo, Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Shiori Amemiya
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Hidemasa Takao
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | | | - Yuji Tomizawa
- Department of Neurology, Juntendo University, Tokyo, Japan
| | | | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard/MIT Health Sciences and Technology, Cambridge, Massachusetts, USA
| | | | - Osamu Abe
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University, Tokyo, Japan
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7
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Cho J, Gagoski B, Kim TH, Wang F, Manhard MK, Dean D, Kecskemeti S, Caprihan A, Lo WC, Splitthoff DN, Liu W, Polak D, Cauley S, Setsompop K, Grant PE, Bilgic B. Time-efficient, high-resolution 3T whole-brain relaxometry using 3D-QALAS with wave-CAIPI readouts. Magn Reson Med 2024; 91:630-639. [PMID: 37705496 DOI: 10.1002/mrm.29865] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 07/16/2023] [Accepted: 08/25/2023] [Indexed: 09/15/2023]
Abstract
PURPOSE Volumetric, high-resolution, quantitative mapping of brain-tissue relaxation properties is hindered by long acquisition times and SNR challenges. This study combines time-efficient wave-controlled aliasing in parallel imaging (wave-CAIPI) readouts with the 3D quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS), enabling full-brain quantitative T1 , T2 , and proton density (PD) maps at 1.15-mm3 isotropic voxels in 3 min. METHODS Wave-CAIPI readouts were embedded in the standard 3D-QALAS encoding scheme, enabling full-brain quantitative parameter maps (T1 , T2 , and PD) at acceleration factors of R = 3 × 2 with minimum SNR loss due to g-factor penalties. The quantitative parameter maps were estimated using a dictionary-based mapping algorithm incorporating inversion efficiency and B1 -field inhomogeneity effects. The parameter maps using the accelerated protocol were quantitatively compared with those obtained from the conventional 3D-QALAS sequence using GRAPPA acceleration of R = 2 in the ISMRM/NIST phantom, and in 10 healthy volunteers. RESULTS When tested in both the ISMRM/NIST phantom and 10 healthy volunteers, the quantitative maps using the accelerated protocol showed excellent agreement against those obtained from conventional 3D-QALAS at RGRAPPA = 2. CONCLUSION Three-dimensional QALAS enhanced with wave-CAIPI readouts enables time-efficient, full-brain quantitative T1 , T2 , and PD mapping at 1.15 mm3 in 3 min at R = 3 × 2 acceleration. The quantitative maps obtained from the accelerated wave-CAIPI 3D-QALAS protocol showed very similar values to those from the standard 3D-QALAS (R = 2) protocol, alluding to the robustness and reliability of the proposed method.
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Affiliation(s)
- Jaejin Cho
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Tae Hyung Kim
- Department of Computer Engineering, Hongik University, Seoul, South Korea
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Mary Kate Manhard
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Douglas Dean
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Steven Kecskemeti
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | | | - Wei-Ching Lo
- Siemens Medical Solutions USA, Inc., Charlestown, Massachusetts, USA
| | | | - Wei Liu
- Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Stephen Cauley
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Kawin Setsompop
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Patricia Ellen Grant
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard/MIT Health Sciences and Technology, Cambridge, Massachusetts, USA
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8
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Ontaneda D, Gulani V, Deshmane A, Shah A, Guruprakash DK, Jiang Y, Ma D, Fisher E, Rudick RA, Raza P, Kilbane M, Cohen JA, Sakaie K, Lowe MJ, Griswold MA, Nakamura K. Magnetic resonance fingerprinting in multiple sclerosis. Mult Scler Relat Disord 2023; 79:105024. [PMID: 37783196 DOI: 10.1016/j.msard.2023.105024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 08/15/2023] [Accepted: 09/23/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND In this cross sectional study, we used MRF to investigate tissue properties of normal-appearing white matter, gray matter, and lesions in relapsing remitting MS (n = 21), secondary progressive MS (n = 16) and healthy controls (n = 9). A FISP-based MRF sequence was used for acquisition, imaging time 5 min 15 s. MRF T1 and T2 relaxation times were measured from lesional tissue, normal-appearing frontal white matter, corpus callous, thalamus, and caudate. Differences between healthy controls and MS were examined using ANCOVA adjusted for age and sex. Spearman rank correlations were assessed between T1 and T2 relaxation times and clinical measures. OBJECTIVES To examine brain T1 and T2 values using magnetic resonance fingerprinting (MRF) in healthy controls and MS. METHODS The subjects included 21 relapsing-remitting (RR) MS, 16 secondary progressive (SP) MS, and 9 age- and sex-matched HC without manifest neurological disease participating in a longitudinal MRI study. A 3T/ FISP-based MRF sequence was acquired. Regions of interest were drawn for lesions and normal appearing white matter. ANCOVA adjusted for age and sex were used to compare the groups with significance set at 0.05. RESULTS A step-wise increase in T1 and T2 relaxation times was found between healthy controls, relapsing remitting MS, and secondary progressive MS. Significant differences were found in T1 and T2 between MS and healthy controls in the frontal normal-appearing white matter, corpus callosum, and thalamus (p < 0.04 for all). Significant differences in T1 and T2 between RR and SPMS were found in the frontal normal-appearing white matter and T2 lesions (p < 0.02 for all). T1 relaxation from the frontal normal-appearing white matter correlated with the Expanded Disability Status Scale [ρ = 0.62, p < 0.001], timed 25 foot walk (ρ = 0.45, p = 0.01), 9 hole peg test (ρ = 0.62, p < 0.001), and paced auditory serial addition test (ρ = -0.4, p = 0.01). CONCLUSION These results suggest that MRF may be a clinically feasible quantitative approach for characterizing tissue damage in MS.
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Affiliation(s)
- Daniel Ontaneda
- Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, United States.
| | - Vikas Gulani
- Department of Radiology, University of Michigan, Michigan, United States
| | - Anagha Deshmane
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Amisha Shah
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, United States
| | - Deepti K Guruprakash
- Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
| | - Yun Jiang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, United States; Department of Radiology, University of Michigan, Ann Arbor, United States
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, United States
| | - Elizabeth Fisher
- Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
| | - Richard A Rudick
- Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, United States
| | - Praneeta Raza
- Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, United States
| | - Meghan Kilbane
- Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, United States
| | - Jeffrey A Cohen
- Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, United States
| | - Ken Sakaie
- Imaging Institute, Cleveland Clinic, Cleveland, United States
| | - Mark J Lowe
- Imaging Institute, Cleveland Clinic, Cleveland, United States
| | - Mark A Griswold
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, United States
| | - Kunio Nakamura
- Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
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9
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O'Grady KP, Satish S, Owen QR, Box BA, Bagnato F, Combes AJE, Cook SR, Westervelt HJ, Feiler HR, Lawless RD, Sarma A, Malone SD, Ndolo JM, Yoon K, Dortch RD, Rogers BP, Smith SA. Relaxation-Compensated Chemical Exchange Saturation Transfer MRI in the Brain at 7T: Application in Relapsing-Remitting Multiple Sclerosis. Front Neurol 2022; 13:764690. [PMID: 35299614 PMCID: PMC8923037 DOI: 10.3389/fneur.2022.764690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 02/01/2022] [Indexed: 11/16/2022] Open
Abstract
Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) can probe tissue biochemistry in vivo with high resolution and sensitivity without requiring exogenous contrast agents. Applying CEST MRI at ultrahigh field provides advantages of increasing spectral resolution and improving sensitivity to metabolites with faster proton exchange rates such as glutamate, a critical neurotransmitter in the brain. Prior magnetic resonance spectroscopy and CEST MRI studies have revealed altered regulation of glutamate in patients with multiple sclerosis (MS). While CEST imaging facilitates new strategies for investigating the pathology underlying this complex and heterogeneous neurological disease, CEST signals are contaminated or diluted by concurrent effects (e.g., semi-solid magnetization transfer (MT) and direct water saturation) and are scaled by the T1 relaxation time of the free water pool which may also be altered in the context of disease. In this study of 20 relapsing-remitting MS patients and age- and sex-matched healthy volunteers, glutamate-weighted CEST data were acquired at 7.0 T. A Lorentzian fitting procedure was used to remove the asymmetric MT contribution from CEST z-spectra, and the apparent exchange-dependent relaxation (AREX) correction was applied using an R1 map derived from an inversion recovery sequence to further isolate glutamate-weighted CEST signals from concurrent effects. Associations between AREX and cognitive function were examined using the Minimal Assessment of Cognitive Function in MS battery. After isolating CEST effects from MT, direct water saturation, and T1 effects, glutamate-weighted AREX contrast remained higher in gray matter than in white matter, though the difference between these tissues decreased. Glutamate-weighted AREX in normal-appearing gray and white matter in MS patients did not differ from healthy gray and white matter but was significantly elevated in white matter lesions. AREX in some cortical regions and in white matter lesions correlated with disability and measures of cognitive function in MS patients. However, further studies with larger sample sizes are needed to confirm these relationships due to potential confounding effects. The application of MT and AREX corrections in this study demonstrates the importance of isolating CEST signals for more specific characterization of the contribution of metabolic changes to tissue pathology and symptoms in MS.
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Affiliation(s)
- Kristin P. O'Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Sanjana Satish
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Quinn R. Owen
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Bailey A. Box
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Francesca Bagnato
- Neuroimaging Unit, Division of Neuroimmunology, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Neurology, Nashville VA Medical Center, TN Valley Healthcare System, Nashville, TN, United States
| | - Anna J. E. Combes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Sarah R. Cook
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Holly James Westervelt
- Division of Behavioral and Cognitive Neurology, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Haley R. Feiler
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Richard D. Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Asha Sarma
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Shekinah D. Malone
- School of Medicine, Meharry Medical College, Nashville, TN, United States
| | - Josephine M. Ndolo
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Keejin Yoon
- Neuroimaging Unit, Division of Neuroimmunology, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Richard D. Dortch
- Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, AZ, United States
| | - Baxter P. Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
- Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Seth A. Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
- Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
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10
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Ricigliano VAG, Tonietto M, Hamzaoui M, Poirion É, Lazzarotto A, Bottlaender M, Gervais P, Maillart E, Stankoff B, Bodini B. Spontaneous remyelination in lesions protects the integrity of surrounding tissues over time in multiple sclerosis. Eur J Neurol 2022; 29:1719-1729. [DOI: 10.1111/ene.15285] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 02/06/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Vito A. G. Ricigliano
- Sorbonne Université Paris Brain Institute ICM CNRS Inserm Paris France
- Neurology Department St Antoine Hospital APHP Paris France
| | - Matteo Tonietto
- Sorbonne Université Paris Brain Institute ICM CNRS Inserm Paris France
- Université Paris‐Saclay CEA CNRS Inserm, BioMaps Service Hospitalier Frédéric Joliot Orsay France
| | - Mariem Hamzaoui
- Sorbonne Université Paris Brain Institute ICM CNRS Inserm Paris France
| | - Émilie Poirion
- Sorbonne Université Paris Brain Institute ICM CNRS Inserm Paris France
- Service dImagerie Médicale Hôpital Fondation Adolphe de Rothschild Paris France
| | - Andrea Lazzarotto
- Sorbonne Université Paris Brain Institute ICM CNRS Inserm Paris France
- Neurology Department St Antoine Hospital APHP Paris France
| | - Michel Bottlaender
- Université Paris‐Saclay CEA CNRS Inserm, BioMaps Service Hospitalier Frédéric Joliot Orsay France
| | - Philippe Gervais
- Université Paris‐Saclay CEA CNRS Inserm, BioMaps Service Hospitalier Frédéric Joliot Orsay France
| | | | - Bruno Stankoff
- Sorbonne Université Paris Brain Institute ICM CNRS Inserm Paris France
- Neurology Department St Antoine Hospital APHP Paris France
| | - Benedetta Bodini
- Sorbonne Université Paris Brain Institute ICM CNRS Inserm Paris France
- Neurology Department St Antoine Hospital APHP Paris France
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11
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Thaler C, Hartramph I, Stellmann JP, Heesen C, Bester M, Fiehler J, Gellißen S. T1 Relaxation Times in the Cortex and Thalamus Are Associated With Working Memory and Information Processing Speed in Patients With Multiple Sclerosis. Front Neurol 2021; 12:789812. [PMID: 34925222 PMCID: PMC8678069 DOI: 10.3389/fneur.2021.789812] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 11/03/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Cortical and thalamic pathologies have been associated with cognitive impairment in patients with multiple sclerosis (MS). Objective: We aimed to quantify cortical and thalamic damage in patients with MS using a high-resolution T1 mapping technique and to evaluate the association of these changes with clinical and cognitive impairment. Methods: The study group consisted of 49 patients with mainly relapsing-remitting MS and 17 age-matched healthy controls who received 3T MRIs including a T1 mapping sequence (MP2RAGE). Mean T1 relaxation times (T1-RT) in the cortex and thalami were compared between patients with MS and healthy controls. Additionally, correlation analysis was performed to assess the relationship between MRI parameters and clinical and cognitive disability. Results: Patients with MS had significantly decreased normalized brain, gray matter, and white matter volumes, as well as increased T1-RT in the normal-appearing white matter, compared to healthy controls (p < 0.001). Partial correlation analysis with age, sex, and disease duration as covariates revealed correlations for T1-RT in the cortex (r = -0.33, p < 0.05), and thalami (right thalamus: r = -0.37, left thalamus: r = -0.50, both p < 0.05) with working memory and information processing speed, as measured by the Symbol-Digit Modalities Test. Conclusion: T1-RT in the cortex and thalamus correlate with information processing speed in patients with MS.
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Affiliation(s)
- Christian Thaler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Isabelle Hartramph
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan-Patrick Stellmann
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Institute for Neuroimmunology and Multiple Sclerosis, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,APHM La Timone, CEMEREM and Department of Neuroradiology, Marseille, France.,Aix-Marseille University, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Christoph Heesen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Institute for Neuroimmunology and Multiple Sclerosis, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Maxim Bester
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Susanne Gellißen
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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12
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Rakić M, Vercruyssen S, Van Eyndhoven S, de la Rosa E, Jain S, Van Huffel S, Maes F, Smeets D, Sima DM. icobrain ms 5.1: Combining unsupervised and supervised approaches for improving the detection of multiple sclerosis lesions. Neuroimage Clin 2021; 31:102707. [PMID: 34111718 PMCID: PMC8193144 DOI: 10.1016/j.nicl.2021.102707] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 01/03/2023]
Abstract
Multiple sclerosis (MS) is a chronic autoimmune, inflammatory neurological disease of the central nervous system. Its diagnosis nowadays commonly includes performing an MRI scan, as it is the most sensitive imaging test for MS. MS plaques are commonly identified from fluid-attenuated inversion recovery (FLAIR) images as hyperintense regions that are highly varying in terms of their shapes, sizes and locations, and are routinely classified in accordance to the McDonald criteria. Recent years have seen an increase in works that aimed at development of various semi-automatic and automatic methods for detection, segmentation and classification of MS plaques. In this paper, we present an automatic combined method, based on two pipelines: a traditional unsupervised machine learning technique and a deep-learning attention-gate 3D U-net network. The deep-learning network is specifically trained to address the weaker points of the traditional approach, namely difficulties in segmenting infratentorial and juxtacortical plaques in real-world clinical MRIs. It was trained and validated on a multi-center multi-scanner dataset that contains 159 cases, each with T1 weighted (T1w) and FLAIR images, as well as manual delineations of the MS plaques, segmented and validated by a panel of raters. The detection rate was quantified using lesion-wise Dice score. A simple label fusion is implemented to combine the output segmentations of the two pipelines. This combined method improves the detection of infratentorial and juxtacortical lesions by 14% and 31% respectively, in comparison to the unsupervised machine learning pipeline that was used as a performance assessment baseline.
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Affiliation(s)
- Mladen Rakić
- icometrix, Leuven, Belgium; KU Leuven, Department of Electrical Engineering (ESAT), Processing Speech and Images (PSI) and Medical Imaging Research Center, 3001 Leuven, Belgium.
| | | | | | - Ezequiel de la Rosa
- icometrix, Leuven, Belgium; Technical University of Munich, Department of Computer Science, Munich, Germany
| | | | - Sabine Van Huffel
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, 3001 Leuven, Belgium
| | - Frederik Maes
- KU Leuven, Department of Electrical Engineering (ESAT), Processing Speech and Images (PSI) and Medical Imaging Research Center, 3001 Leuven, Belgium
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13
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Seiler A, Nöth U, Hok P, Reiländer A, Maiworm M, Baudrexel S, Meuth S, Rosenow F, Steinmetz H, Wagner M, Hattingen E, Deichmann R, Gracien RM. Multiparametric Quantitative MRI in Neurological Diseases. Front Neurol 2021; 12:640239. [PMID: 33763021 PMCID: PMC7982527 DOI: 10.3389/fneur.2021.640239] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 02/12/2021] [Indexed: 11/27/2022] Open
Abstract
Magnetic resonance imaging (MRI) is the gold standard imaging technique for diagnosis and monitoring of many neurological diseases. However, the application of conventional MRI in clinical routine is mainly limited to the visual detection of macroscopic tissue pathology since mixed tissue contrasts depending on hardware and protocol parameters hamper its application for the assessment of subtle or diffuse impairment of the structural tissue integrity. Multiparametric quantitative (q)MRI determines tissue parameters quantitatively, enabling the detection of microstructural processes related to tissue remodeling in aging and neurological diseases. In contrast to measuring tissue atrophy via structural imaging, multiparametric qMRI allows for investigating biologically distinct microstructural processes, which precede changes of the tissue volume. This facilitates a more comprehensive characterization of tissue alterations by revealing early impairment of the microstructural integrity and specific disease-related patterns. So far, qMRI techniques have been employed in a wide range of neurological diseases, including in particular conditions with inflammatory, cerebrovascular and neurodegenerative pathology. Numerous studies suggest that qMRI might add valuable information, including the detection of microstructural tissue damage in areas appearing normal on conventional MRI and unveiling the microstructural correlates of clinical manifestations. This review will give an overview of current qMRI techniques, the most relevant tissue parameters and potential applications in neurological diseases, such as early (differential) diagnosis, monitoring of disease progression, and evaluating effects of therapeutic interventions.
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Affiliation(s)
- Alexander Seiler
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany
| | - Ulrike Nöth
- Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - Pavel Hok
- Department of Neurology, Palacký University Olomouc and University Hospital Olomouc, Olomouc, Czechia
| | - Annemarie Reiländer
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany
| | - Michelle Maiworm
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - Simon Baudrexel
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany
| | - Sven Meuth
- Department of Neurology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Rosenow
- Department of Neurology, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany.,Epilepsy Center Frankfurt Rhine-Main, Center of Neurology and Neurosurgery, University Hospital, Frankfurt, Germany
| | - Helmuth Steinmetz
- Department of Neurology, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - Marlies Wagner
- Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - Elke Hattingen
- Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany.,Department of Neuroradiology, Goethe University, Frankfurt, Germany
| | - Ralf Deichmann
- Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - René-Maxime Gracien
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
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14
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Cafiero R, Brauer J, Anwander A, Friederici AD. The Concurrence of Cortical Surface Area Expansion and White Matter Myelination in Human Brain Development. Cereb Cortex 2020; 29:827-837. [PMID: 30462166 PMCID: PMC6319170 DOI: 10.1093/cercor/bhy277] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 10/11/2018] [Indexed: 02/03/2023] Open
Abstract
The human brain undergoes dramatic structural changes during childhood that co-occur with behavioral development. These age-related changes are documented for the brain’s gray matter and white matter. However, their interrelation is largely unknown. In this study, we investigated age-related effects in cortical thickness (CT) and in cortical surface area (SA) as parts of the gray matter volume as well as age effects in T1 relaxation times in the white matter. Data from N = 170 children between the ages of 3 and 7 years contributed to the sample. We found a high spatial overlap of age-related correlations between SA and T1 relaxation times of the corresponding white matter connections, but no such relation between SA and CT. These results indicate that during childhood the developmental expansion of the cortical surface goes hand-in-hand with age-related increase of white matter fiber connections terminating in the cortical surface.
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Affiliation(s)
- Riccardo Cafiero
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jens Brauer
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Alfred Anwander
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Angela D Friederici
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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15
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Kirov II, Tal A. Potential clinical impact of multiparametric quantitative MR spectroscopy in neurological disorders: A review and analysis. Magn Reson Med 2020; 83:22-44. [PMID: 31393032 PMCID: PMC6814297 DOI: 10.1002/mrm.27912] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 06/06/2019] [Accepted: 06/29/2019] [Indexed: 12/12/2022]
Abstract
PURPOSE Unlike conventional MR spectroscopy (MRS), which only measures metabolite concentrations, multiparametric MRS also quantifies their longitudinal (T1 ) and transverse (T2 ) relaxation times, as well as the radiofrequency transmitter inhomogeneity (B1+ ). To test whether knowledge of these additional parameters can improve the clinical utility of brain MRS, we compare the conventional and multiparametric approaches in terms of expected classification accuracy in differentiating controls from patients with neurological disorders. THEORY AND METHODS A literature review was conducted to compile metabolic concentrations and relaxation times in a wide range of neuropathologies and regions of interest. Simulations were performed to construct receiver operating characteristic curves and compute the associated areas (area under the curve) to examine the sensitivity and specificity of MRS for detecting each pathology in each region. Classification accuracy was assessed using metabolite concentrations corrected using population-averages for T1 , T2 , and B1+ (conventional MRS); using metabolite concentrations corrected using per-subject values (multiparametric MRS); and using an optimal linear multiparametric estimator comprised of the metabolites' concentrations and relaxation constants (multiparametric MRS). Additional simulations were conducted to find the minimal intra-subject precision needed for each parameter. RESULTS Compared with conventional MRS, multiparametric approaches yielded area under the curve improvements for almost all neuropathologies and regions of interest. The median area under the curve increased by 0.14 over the entire dataset, and by 0.24 over the 10 instances with the largest individual increases. CONCLUSIONS Multiparametric MRS can substantially improve the clinical utility of MRS in diagnosing and assessing brain pathology, motivating the design and use of novel multiparametric sequences.
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Affiliation(s)
- Ivan I. Kirov
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, Department of Radiology, 660 1 Avenue, New York, NY 10016, United States of America
| | - Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, 234 Herzel St., Rehovot 7610001, Israel
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16
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Abdelhafeez MA, Zamzam DA, Foad MM, Swelam MS, Abdelnasser A, Aref HA, Ibrahim YA, Khater NH, Darwish EA, Zakaria MF. Magnetic resonance imaging markers of disability in Egyptian multiple sclerosis patients. Mult Scler Relat Disord 2019; 36:101417. [DOI: 10.1016/j.msard.2019.101417] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 09/24/2019] [Accepted: 09/25/2019] [Indexed: 11/25/2022]
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17
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Rahmanzadeh R, Brück W, Minagar A, Sahraian MA. Multiple sclerosis pathogenesis: missing pieces of an old puzzle. Rev Neurosci 2019; 30:67-83. [PMID: 29883325 DOI: 10.1515/revneuro-2018-0002] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Accepted: 03/30/2018] [Indexed: 11/15/2022]
Abstract
Traditionally, multiple sclerosis (MS) was considered to be a CD4 T cell-mediated CNS autoimmunity, compatible with experimental autoimmune encephalitis model, which can be characterized by focal lesions in the white matter. However, studies of recent decades revealed several missing pieces of MS puzzle and showed that MS pathogenesis is more complex than the traditional view and may include the following: a primary degenerative process (e.g. oligodendroglial pathology), generalized abnormality of normal-appearing brain tissue, pronounced gray matter pathology, involvement of innate immunity, and CD8 T cells and B cells. Here, we review these findings and discuss their implications in MS pathogenesis.
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Affiliation(s)
- Reza Rahmanzadeh
- MS Research Center, Neuroscience Institute, Tehran University of Medical Science, Department of Neurology, Sina Hospital, 1136746911 Tehran, Iran
| | - Wolfgang Brück
- Institute of Neuropathology, University Medical Center, D-37075 Göttingen, Germany
| | - Alireza Minagar
- Department of Neurology, LSU Health Sciences Center, Shreveport, LA 71130, USA
| | - Mohammad Ali Sahraian
- MS Research Center, Neuroscience Institute, Tehran University of Medical Science, Department of Neurology, Sina Hospital, 1136746911 Tehran, Iran.,Iranian Center for Neurological Research, Neuroscience Institute, Tehran University of Medical Science, 1136746890 Tehran, Iran
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18
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Koskimäki F, Bernard J, Yong J, Arndt N, Carroll T, Lee SK, Reder AT, Javed A. Gray matter atrophy in multiple sclerosis despite clinical and lesion stability during natalizumab treatment. PLoS One 2018; 13:e0209326. [PMID: 30576361 PMCID: PMC6303064 DOI: 10.1371/journal.pone.0209326] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 12/04/2018] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Brain volume loss is an important surrogate marker for assessing disability in MS; however, contribution of gray and white matter to the whole brain volume loss needs further examination in the context of specific MS treatment. OBJECTIVES To examine whole and segmented gray, white, thalamic, and corpus callosum volume loss in stable patients receiving natalizumab for 2-5 years. METHODS This was a retrospective study of 20 patients undergoing treatment with natalizumab for 24-68 months. Whole brain volume loss was determined with SIENA. Gray and white matter segmentation was done using FAST. Thalamic and corpus callosum volumes were determined using Freesurfer. T1 relaxation values of chronic hypointense lesions (black holes) were determined using a quantitative, in-house developed method to assess lesion evolution. RESULTS Over a mean of 36.6 months, median percent brain volume change (PBVC) was -2.0% (IQR 0.99-2.99). There was decline in gray (p = 0.001) but not white matter (p = 0.6), and thalamic (p = 0.01) but not corpus callosum volume (p = 0.09). Gray matter loss correlated with PBVC (Spearman's r = 0.64, p = 0.003) but not white matter (Spearman's r = 0.42, p = 0.07). Age significantly influenced whole brain volume loss (p = 0.010, multivariate regression), but disease duration and baseline T2 lesion volume did not. There was no change in T1 relaxation values of lesions or T2 lesion volume over time. All patients remained clinically stable. CONCLUSIONS These results demonstrate that brain volume loss in MS is primarily driven by gray matter changes and may be independent of clinically effective treatment.
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Affiliation(s)
- Fredrika Koskimäki
- Division of Clinical Neurosciences, Turku University Hospital and University of Turku, Turku, Finland
| | - Jacqueline Bernard
- Department of Neurology, Oregon Health Science University, Portland, Oregon, United States of America
| | - Jeong Yong
- Northwestern University, Biomedical Engineering, Chicago, Illinois, United States of America
| | - Nancy Arndt
- Department of Neurology, The University of Chicago, Chicago, Illinois, United States of America
| | - Timothy Carroll
- Department of Radiology, The University of Chicago, Chicago, Illinois, United States of America
| | - Seon-Kyu Lee
- Department of Radiology, The University of Chicago, Chicago, Illinois, United States of America
| | - Anthony T. Reder
- Department of Neurology, The University of Chicago, Chicago, Illinois, United States of America
| | - Adil Javed
- Department of Neurology, The University of Chicago, Chicago, Illinois, United States of America
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19
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Bonnier G, Fischi-Gomez E, Roche A, Hilbert T, Kober T, Krueger G, Granziera C. Personalized pathology maps to quantify diffuse and focal brain damage. NEUROIMAGE-CLINICAL 2018; 21:101607. [PMID: 30502080 PMCID: PMC6413479 DOI: 10.1016/j.nicl.2018.11.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 10/02/2018] [Accepted: 11/18/2018] [Indexed: 01/04/2023]
Abstract
Background and objectives Quantitative MRI (qMRI) permits the quantification of brain changes compatible with inflammation, degeneration and repair in multiple sclerosis (MS) patients. In this study, we propose a new method to provide personalized maps of tissue alterations and longitudinal brain changes based on different qMRI metrics, which provide complementary information about brain pathology. Methods We performed baseline and two-years follow-up on (i) 13 relapsing-remitting MS patients and (ii) four healthy controls. A group consisting of up to 65 healthy controls was used to compute the reference distribution of qMRI metrics in healthy tissue. All subjects underwent 3T MRI examinations including T1, T2, T2* relaxation and Magnetization Transfer Ratio (MTR) imaging. We used a recent partial volume estimation algorithm to estimate the concentration of different brain tissue types on T1 maps; then, we computed a deviation map (z-score map) for each contrast at both time-points. Finally, we subtracted those deviation maps only for voxels showing a significant difference with healthy tissue in one of the time points, to obtain a difference map for each subject. Results and conclusion Control subjects did not show any significant z-score deviations or longitudinal z-score changes. On the other hand, MS patients showed brain regions with cross-sectional and longitudinal concomitant increase in T1, T2, T2* z-scores and decrease of MTR z-scores, suggesting brain tissue degeneration/loss. In the lesion periphery, we observed areas with cross-sectional and longitudinal decreased T1/T2 and slight decrease in T2* most likely related to iron accumulation. Moreover, we measured longitudinal decrease in T1, T2 - and to a lesser extent in T2* - as well as a concomitant increase in MTR, suggesting remyelination/repair. In summary, we have developed a method that provides whole-brain personalized maps of cross-sectional and longitudinal changes in MS patients, which are computed in patient space. These maps may open new perspectives to complement and support radiological evaluation of brain damage for a given patient.
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Affiliation(s)
- G Bonnier
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - E Fischi-Gomez
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - A Roche
- Advanced Clinical Imaging Technology (HC CEMEA SUI DI PI), Siemens Healthcare AG, Switzerland; Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - T Hilbert
- Advanced Clinical Imaging Technology (HC CEMEA SUI DI PI), Siemens Healthcare AG, Switzerland; Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - T Kober
- Advanced Clinical Imaging Technology (HC CEMEA SUI DI PI), Siemens Healthcare AG, Switzerland; Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - G Krueger
- Siemens Healthcare AG (HC CEMEA DI), Zürich, Switzerland
| | - C Granziera
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
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20
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Reeth EV, Ratiney H, Tse Ve Koon K, Tesch M, Grenier D, Beuf O, Glaser SJ, Sugny D. A simplified framework to optimize MRI contrast preparation. Magn Reson Med 2018; 81:424-438. [PMID: 30265759 DOI: 10.1002/mrm.27417] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 05/28/2018] [Accepted: 06/01/2018] [Indexed: 11/08/2022]
Abstract
PURPOSE This article proposes a rigorous optimal control framework for the design of preparation schemes that optimize MRI contrast based on relaxation time differences. METHODS Compared to previous optimal contrast preparation schemes, a drastic reduction of the optimization parameter number is performed. The preparation scheme is defined as a combination of several block pulses whose flip angles, phase terms and inter-pulse delays are optimized to control the magnetization evolution. RESULTS The proposed approach reduces the computation time of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>B</mml:mi> <mml:mn>0</mml:mn></mml:msub> </mml:math> -robust preparation schemes to around a minute (whereas several hours were required with previous schemes), with negligible performance loss. The chosen parameterization allows to formulate the total preparation duration as a constraint, which improves the overall compromise between contrast performance and preparation time. Simulation, in vitro and in vivo results validate this improvement, illustrate the straightforward applicability of the proposed approach, and point out its flexibility in terms of achievable contrasts. Major improvement is especially achieved for short-T2 enhancement, as shown by the acquisition of a non-trivial contrast on a rat brain, where a short-T2 white matter structure (corpus callosum) is enhanced compared to surrounding gray matter tissues (hippocampus and neocortex). CONCLUSIONS This approach proposes key advances for the design of optimal contrast preparation sequences, that emphasize their ability to generate non-standard contrasts, their potential benefit in a clinical context, and their straightforward applicability on any MR system.
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Affiliation(s)
- Eric Van Reeth
- CNRS, Inserm, CREATIS UMR 5220, U1206, Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, Lyon, France
| | - Hélène Ratiney
- CNRS, Inserm, CREATIS UMR 5220, U1206, Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, Lyon, France
| | - Kevin Tse Ve Koon
- CNRS, Inserm, CREATIS UMR 5220, U1206, Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, Lyon, France
| | - Michael Tesch
- Department of Chemistry, Technical University of Munich, Munich, Germany
| | - Denis Grenier
- CNRS, Inserm, CREATIS UMR 5220, U1206, Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, Lyon, France
| | - Olivier Beuf
- CNRS, Inserm, CREATIS UMR 5220, U1206, Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, Lyon, France
| | - Steffen J Glaser
- Department of Chemistry, Technical University of Munich, Munich, Germany
| | - Dominique Sugny
- ICB, CNRS UMR5209, Université de Bourgogne, France.,Institute for Advanced Study, Technical University of Munich, Garching, Germany
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21
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Vavasour IM, Meyers SM, Mädler B, Harris T, Fu E, Li DK, Traboulsee A, MacKay AL, Laule C. Multicenter Measurements of T1
Relaxation and Diffusion Tensor Imaging: Intra and Intersite Reproducibility. J Neuroimaging 2018; 29:42-51. [DOI: 10.1111/jon.12559] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 08/20/2018] [Accepted: 08/26/2018] [Indexed: 12/25/2022] Open
Affiliation(s)
- Irene M. Vavasour
- Department of Radiology; University of British Columbia, UBC MRI Research Centre; Vancouver British Columbia Canada
| | - Sandra M. Meyers
- Department of Physics and Astronomy; University of British Columbia; Vancouver British Columbia Canada
| | | | - Trudy Harris
- Department of Radiology; University of British Columbia, UBC MRI Research Centre; Vancouver British Columbia Canada
| | - Eric Fu
- Department of Statistics; University of British Columbia; Vancouver British Columbia Canada
| | - David K.B. Li
- Department of Radiology; University of British Columbia, UBC MRI Research Centre; Vancouver British Columbia Canada
- Department of Medicine; University of British Columbia; Vancouver British Columbia Canada
| | - Anthony Traboulsee
- Department of Medicine; University of British Columbia; Vancouver British Columbia Canada
| | - Alex L. MacKay
- Department of Radiology; University of British Columbia, UBC MRI Research Centre; Vancouver British Columbia Canada
- Department of Physics and Astronomy; University of British Columbia; Vancouver British Columbia Canada
| | - Cornelia Laule
- Department of Radiology; University of British Columbia, UBC MRI Research Centre; Vancouver British Columbia Canada
- Department of Physics and Astronomy; University of British Columbia; Vancouver British Columbia Canada
- Department of Pathology and Laboratory Medicine; University of British Columbia; Vancouver British Columbia Canada
- International Collaboration on Repair Discoveries (ICORD); University of British Columbia; Vancouver British Columbia Canada
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22
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Sinnecker T, Granziera C, Wuerfel J, Schlaeger R. Future Brain and Spinal Cord Volumetric Imaging in the Clinic for Monitoring Treatment Response in MS. Curr Treat Options Neurol 2018; 20:17. [PMID: 29679165 DOI: 10.1007/s11940-018-0504-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
PURPOSE OF REVIEW Volumetric analysis of brain imaging has emerged as a standard approach used in clinical research, e.g., in the field of multiple sclerosis (MS), but its application in individual disease course monitoring is still hampered by biological and technical limitations. This review summarizes novel developments in volumetric imaging on the road towards clinical application to eventually monitor treatment response in patients with MS. RECENT FINDINGS In addition to the assessment of whole-brain volume changes, recent work was focused on the volumetry of specific compartments and substructures of the central nervous system (CNS) in MS. This included volumetric imaging of the deep brain structures and of the spinal cord white and gray matter. Volume changes of the latter indeed independently correlate with clinical outcome measures especially in progressive MS. Ultrahigh field MRI and quantitative MRI added to this trend by providing a better visualization of small compartments on highly resolving MR images as well as microstructural information. New developments in volumetric imaging have the potential to improve sensitivity as well as specificity in detecting and hence monitoring disease-related CNS volume changes in MS.
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Affiliation(s)
- Tim Sinnecker
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Medical Image Analysis Center Basel AG, Basel, Switzerland
- NeuroCure Clinical Research Center, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Cristina Granziera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Wuerfel
- Medical Image Analysis Center Basel AG, Basel, Switzerland
- NeuroCure Clinical Research Center, Charité Universitätsmedizin Berlin, Berlin, Germany
- Berlin Ultrahigh Field Facility, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Regina Schlaeger
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
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23
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24
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Influence of Free Radicals on the Intrinsic MRI Relaxation Properties. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 977:73-79. [DOI: 10.1007/978-3-319-55231-6_11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
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25
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Davies GR, Hadjiprocopis A, Altmann DR, Chard DT, Griffin CM, Rashid W, Parker GJ, Tofts PS, Kapoor R, Thompson AJ, Miller DH. Normal-appearing grey and white matter T1 abnormality in early relapsing–remitting multiple sclerosis: a longitudinal study. Mult Scler 2017; 13:169-77. [PMID: 17439881 DOI: 10.1177/1352458506070726] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Objective To investigate the presence and evolution of T1 relaxation time abnormalities in normal-appearing white matter (NAWM) and grey matter (GM), early in the course of relapsing–remitting multiple sclerosis (MS). Methods Twenty-three patients with early relapsing–remitting MS and 14 healthy controls were imaged six monthly for up to three years. Mean follow-up was 26 months for MS patients and 24 months for controls. Dual-echo fast-spin echo and gradient-echo proton-density and T1-weighted data sets (permitting the calculation of a T1 map) were acquired in all subjects. GM and NAWM T1 histograms were produced and a hierarchical regression model was used to investigate changes in T1 over time. Results At baseline, significant patient-control differences were seen, both in NAWM (P = 0.001) and in GM (P = 0.01). At follow-up, there was no evidence for a serial change in either mean T1 or peak-location for either NAWM or GM. There was weak evidence for a decline in patient NAWM peak-height and also evidence for a decline in control GM peak-height. Conclusion There are significant and persistent abnormalities of NAWM and GM T1 in early relapsing-remitting MS. Further studies should address whether such T1 measures have a role in prognosis or therapeutic monitoring. Multiple Sclerosis 2007; 13:169–177. http://msj.sagepub.com
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Affiliation(s)
- G R Davies
- NMR Research Unit, Institute of Neurology, University College London, Queen Square, London, UK
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Gracien RM, Jurcoane A, Wagner M, Reitz SC, Mayer C, Volz S, Hof SM, Fleischer V, Droby A, Steinmetz H, Zipp F, Hattingen E, Deichmann R, Klein JC. The Relationship between Gray Matter Quantitative MRI and Disability in Secondary Progressive Multiple Sclerosis. PLoS One 2016; 11:e0161036. [PMID: 27513853 PMCID: PMC4981438 DOI: 10.1371/journal.pone.0161036] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Accepted: 07/28/2016] [Indexed: 11/18/2022] Open
Abstract
PURPOSE In secondary progressive Multiple Sclerosis (SPMS), global neurodegeneration as a driver of disability gains importance in comparison to focal inflammatory processes. However, clinical MRI does not visualize changes of tissue composition outside MS lesions. This quantitative MRI (qMRI) study investigated cortical and deep gray matter (GM) proton density (PD) values and T1 relaxation times to explore their potential to assess neuronal damage and its relationship to clinical disability in SPMS. MATERIALS AND METHODS 11 SPMS patients underwent quantitative T1 and PD mapping. Parameter values across the cerebral cortex and deep GM structures were compared with 11 healthy controls, and correlation with disability was investigated for regions exhibiting significant group differences. RESULTS PD was increased in the whole GM, cerebral cortex, thalamus, putamen and pallidum. PD correlated with disability in the whole GM, cerebral cortex, putamen and pallidum. T1 relaxation time was prolonged and correlated with disability in the whole GM and cerebral cortex. CONCLUSION Our study suggests that the qMRI parameters GM PD (which likely indicates replacement of neural tissue with water) and cortical T1 (which reflects cortical damage including and beyond increased water content) are promising qMRI candidates for the assessment of disease status, and are related to disability in SPMS.
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Affiliation(s)
- René-Maxime Gracien
- Department of Neurology, Goethe University, Frankfurt/Main, Germany
- Brain Imaging Center, Goethe University, Frankfurt/Main, Germany
- * E-mail:
| | - Alina Jurcoane
- Department of Neuroradiology, Goethe University, Frankfurt/Main, Germany
- Brain Imaging Center, Goethe University, Frankfurt/Main, Germany
| | - Marlies Wagner
- Department of Neuroradiology, Goethe University, Frankfurt/Main, Germany
- Brain Imaging Center, Goethe University, Frankfurt/Main, Germany
| | - Sarah C. Reitz
- Department of Neurology, Goethe University, Frankfurt/Main, Germany
- Brain Imaging Center, Goethe University, Frankfurt/Main, Germany
| | - Christoph Mayer
- Department of Neurology, Goethe University, Frankfurt/Main, Germany
| | - Steffen Volz
- Brain Imaging Center, Goethe University, Frankfurt/Main, Germany
| | - Stephanie-Michelle Hof
- Department of Neurology, Goethe University, Frankfurt/Main, Germany
- Brain Imaging Center, Goethe University, Frankfurt/Main, Germany
| | - Vinzenz Fleischer
- Department of Neurology, Johannes Gutenberg University, Mainz, Germany
- Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), Johannes Gutenberg-University, Mainz, Germany
| | - Amgad Droby
- Department of Neurology, Johannes Gutenberg University, Mainz, Germany
- Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), Johannes Gutenberg-University, Mainz, Germany
| | | | - Frauke Zipp
- Department of Neurology, Johannes Gutenberg University, Mainz, Germany
- Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), Johannes Gutenberg-University, Mainz, Germany
| | - Elke Hattingen
- Department of Neuroradiology, Goethe University, Frankfurt/Main, Germany
- Brain Imaging Center, Goethe University, Frankfurt/Main, Germany
| | - Ralf Deichmann
- Brain Imaging Center, Goethe University, Frankfurt/Main, Germany
| | - Johannes C. Klein
- Department of Neurology, Goethe University, Frankfurt/Main, Germany
- Brain Imaging Center, Goethe University, Frankfurt/Main, Germany
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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Steenwijk MD, Vrenken H, Jonkman LE, Daams M, Geurts JJG, Barkhof F, Pouwels PJW. High-resolution T1-relaxation time mapping displays subtle, clinically relevant, gray matter damage in long-standing multiple sclerosis. Mult Scler 2016; 22:1279-88. [DOI: 10.1177/1352458515615953] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2015] [Accepted: 10/14/2015] [Indexed: 01/02/2023]
Abstract
Background: Gray matter (GM) pathology has high clinical relevance in multiple sclerosis (MS), but conventional magnetic resonance imaging (MRI) is insufficiently sensitive to visualize the rather subtle damage. Objective: To investigate whether high spatial resolution T1-relaxation time (T1-RT) measurements can detect changes in the normal-appearing GM of patients with long-standing MS and whether these changes are associated with physical and cognitive impairment. Methods: High spatial resolution (1.05 × 1.05 × 1.2 mm3) T1-RT measurements were performed at 3 T in 156 long-standing MS patients and 54 healthy controls. T1-RT histogram parameters in several regions were analyzed to investigate group differences. Stepwise linear regression analyses were used to assess the relation of T1-RT with physical and cognitive impairment. Results: In both thalamus and cortex, T1-RT histogram skewness was higher in patients than controls. In the cortex, this was driven by the frontal and temporal lobes. No differences were found in other GM histogram parameters. Cortical skewness, thalamus volume, and average white matter (WM) lesion T1-RT emerged as the strongest predictors for cognitive performance (adjusted R2 = 0.39). Conclusion: Subtle GM damage was present in the cortex and thalamus of MS patients, as indicated by increased T1-RT skewness. Increased cortical skewness emerged as an independent predictor of cognitive dysfunction.
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Affiliation(s)
- Martijn D Steenwijk
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands/Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands/Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Laura E Jonkman
- Department of Anatomy and Neurosciences, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Marita Daams
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands/Department of Anatomy and Neurosciences, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Jeroen JG Geurts
- Department of Anatomy and Neurosciences, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Petra JW Pouwels
- Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
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Castriota-Scanderbeg A, Fasano F, Filippi M, Caltagirone C. T1 relaxation maps allow differentiation between pathologic tissue subsets in relapsing-remitting and secondary progressive multiple sclerosis. Mult Scler 2016; 10:556-61. [PMID: 15471373 DOI: 10.1191/1352458504ms1073oa] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
In an attempt to clarify whether T1 relaxation time mapping may assist in characterizing the pathological brain tissue substrate of multiple sclerosis (MS), we compared the T1 relaxation times of lesions, areas of normal-appearing white matter (NAWM) located proximal to lesions, and areas of NAWM located distant from lesions in 12 patients with the relapsing-remitting and 12 with the secondary progressive (SP) subtype of disease. Nine healthy volunteers served as controls. Calculated mean T1 values were averaged across all patients within each clinical group, and comparisons were made by means of the Mann-Whitney U-test. Significant differences were found between all investigated brain regions within each clinical subgroup. Significant differences were also detected for each investigated brain region among clinical subgroups. While T1 values of NAWM were significantly higher in patients with SP disease than in normal white matter (NWM) of controls, no differences were detected when corresponding brain areas of patients with RR MS were compared with NWM of controls. T1 maps identify areas of the brain that are damaged to a different extent in patients with MS, and may be of help in monitoring disease progression.
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Gracien RM, Jurcoane A, Wagner M, Reitz SC, Mayer C, Volz S, Hof SM, Fleischer V, Droby A, Steinmetz H, Groppa S, Hattingen E, Deichmann R, Klein JC. Multimodal quantitative MRI assessment of cortical damage in relapsing-remitting multiple sclerosis. J Magn Reson Imaging 2016; 44:1600-1607. [DOI: 10.1002/jmri.25297] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 04/19/2016] [Indexed: 11/05/2022] Open
Affiliation(s)
- René-Maxime Gracien
- Department of Neurology; Goethe University; Frankfurt/Main Germany
- Brain Imaging Center; Goethe University; Frankfurt/Main Germany
| | - Alina Jurcoane
- Department of Neuroradiology; Goethe University; Frankfurt/Main Germany
- Brain Imaging Center; Goethe University; Frankfurt/Main Germany
| | - Marlies Wagner
- Department of Neuroradiology; Goethe University; Frankfurt/Main Germany
- Brain Imaging Center; Goethe University; Frankfurt/Main Germany
| | - Sarah C. Reitz
- Department of Neurology; Goethe University; Frankfurt/Main Germany
- Brain Imaging Center; Goethe University; Frankfurt/Main Germany
| | - Christoph Mayer
- Department of Neurology; Goethe University; Frankfurt/Main Germany
| | - Steffen Volz
- Brain Imaging Center; Goethe University; Frankfurt/Main Germany
| | - Stephanie-Michelle Hof
- Department of Neurology; Goethe University; Frankfurt/Main Germany
- Brain Imaging Center; Goethe University; Frankfurt/Main Germany
| | - Vinzenz Fleischer
- Department of Neurology; Johannes Gutenberg University; Mainz Germany
- Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN); Johannes Gutenberg-University; Mainz Germany
| | - Amgad Droby
- Department of Neurology; Johannes Gutenberg University; Mainz Germany
- Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN); Johannes Gutenberg-University; Mainz Germany
| | | | - Sergiu Groppa
- Department of Neurology; Johannes Gutenberg University; Mainz Germany
- Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN); Johannes Gutenberg-University; Mainz Germany
| | - Elke Hattingen
- Department of Neuroradiology; Goethe University; Frankfurt/Main Germany
- Brain Imaging Center; Goethe University; Frankfurt/Main Germany
| | - Ralf Deichmann
- Brain Imaging Center; Goethe University; Frankfurt/Main Germany
| | - Johannes C. Klein
- Department of Neurology; Goethe University; Frankfurt/Main Germany
- Brain Imaging Center; Goethe University; Frankfurt/Main Germany
- Nuffield Department of Clinical Neurosciences; University of Oxford; UK
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Multi-parametric quantitative MRI of normal appearing white matter in multiple sclerosis, and the effect of disease activity on T2. Brain Imaging Behav 2016; 11:744-753. [DOI: 10.1007/s11682-016-9550-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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31
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Mejia AF, Sweeney EM, Dewey B, Nair G, Sati P, Shea C, Reich DS, Shinohara RT. Statistical estimation of T1 relaxation times using conventional magnetic resonance imaging. Neuroimage 2015; 133:176-188. [PMID: 26732403 DOI: 10.1016/j.neuroimage.2015.12.037] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 12/18/2015] [Accepted: 12/22/2015] [Indexed: 12/11/2022] Open
Abstract
Quantitative T1 maps estimate T1 relaxation times and can be used to assess diffuse tissue abnormalities within normal-appearing tissue. T1 maps are popular for studying the progression and treatment of multiple sclerosis (MS). However, their inclusion in standard imaging protocols remains limited due to the additional scanning time and expert calibration required and susceptibility to bias and noise. Here, we propose a new method of estimating T1 maps using four conventional MR images, which are intensity-normalized using cerebellar gray matter as a reference tissue and related to T1 using a smooth regression model. Using cross-validation, we generate statistical T1 maps for 61 subjects with MS. The statistical maps are less noisy than the acquired maps and show similar reproducibility. Tests of group differences in normal-appearing white matter across MS subtypes give similar results using both methods.
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Affiliation(s)
- Amanda F Mejia
- Department of Biostatistics, The Johns Hopkins University, Baltimore, MD 21205, USA
| | - Elizabeth M Sweeney
- Department of Biostatistics, The Johns Hopkins University, Baltimore, MD 21205, USA; Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Blake Dewey
- Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Govind Nair
- Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Pascal Sati
- Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Colin Shea
- Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Daniel S Reich
- Department of Biostatistics, The Johns Hopkins University, Baltimore, MD 21205, USA; Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Russell T Shinohara
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Chalah MA, Riachi N, Ahdab R, Créange A, Lefaucheur JP, Ayache SS. Fatigue in Multiple Sclerosis: Neural Correlates and the Role of Non-Invasive Brain Stimulation. Front Cell Neurosci 2015; 9:460. [PMID: 26648845 PMCID: PMC4663273 DOI: 10.3389/fncel.2015.00460] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2015] [Accepted: 11/11/2015] [Indexed: 12/21/2022] Open
Abstract
Multiple sclerosis (MS) is a chronic progressive inflammatory disease of the central nervous system (CNS) and the major cause of non-traumatic disability in young adults. Fatigue is a frequent symptom reported by the majority of MS patients during their disease course and drastically affects their quality of life. Despite its significant prevalence and impact, the underlying pathophysiological mechanisms are not well elucidated. MS fatigue is still considered the result of multifactorial and complex constellations, and is commonly classified into “primary” fatigue related to the pathological changes of the disease itself, and “secondary” fatigue attributed to mimicking symptoms, comorbid sleep and mood disorders, and medications side effects. Radiological, physiological, and endocrine data have raised hypotheses regarding the origin of this symptom, some of which have succeeded in identifying an association between MS fatigue and structural or functional abnormalities within various brain networks. Hence, the aim of this work is to reappraise the neural correlates of MS fatigue and to discuss the rationale for the emergent use of noninvasive brain stimulation (NIBS) techniques as potential treatments. This will include a presentation of the various NIBS modalities and a suggestion of their potential mechanisms of action in this context. Specific issues related to the value of transcranial direct current stimulation (tDCS) will be addressed.
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Affiliation(s)
- Moussa A Chalah
- EA 4391, Excitabilité Nerveuse et Thérapeutique, Université Paris-Est-Créteil Créteil, France ; Service de Physiologie - Explorations Fonctionnelles, Hôpital Henri Mondor, Assistance Publique - Hôpitaux de Paris Créteil, France
| | - Naji Riachi
- Neurology Division, University Medical Center Rizk Hospital Beirut, Lebanon
| | - Rechdi Ahdab
- EA 4391, Excitabilité Nerveuse et Thérapeutique, Université Paris-Est-Créteil Créteil, France ; Neurology Division, University Medical Center Rizk Hospital Beirut, Lebanon
| | - Alain Créange
- EA 4391, Excitabilité Nerveuse et Thérapeutique, Université Paris-Est-Créteil Créteil, France ; Service de Neurologie, Hôpital Henri Mondor, Assistance Publique - Hôpitaux de Paris Créteil, France
| | - Jean-Pascal Lefaucheur
- EA 4391, Excitabilité Nerveuse et Thérapeutique, Université Paris-Est-Créteil Créteil, France ; Service de Physiologie - Explorations Fonctionnelles, Hôpital Henri Mondor, Assistance Publique - Hôpitaux de Paris Créteil, France
| | - Samar S Ayache
- EA 4391, Excitabilité Nerveuse et Thérapeutique, Université Paris-Est-Créteil Créteil, France ; Service de Physiologie - Explorations Fonctionnelles, Hôpital Henri Mondor, Assistance Publique - Hôpitaux de Paris Créteil, France
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Al-Radaideh A, Mougin OE, Lim SY, Chou IJ, Constantinescu CS, Gowland P. Histogram analysis of quantitative T1 and MT maps from ultrahigh field MRI in clinically isolated syndrome and relapsing-remitting multiple sclerosis. NMR IN BIOMEDICINE 2015; 28:1374-1382. [PMID: 26346925 DOI: 10.1002/nbm.3385] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2015] [Revised: 07/19/2015] [Accepted: 07/27/2015] [Indexed: 06/05/2023]
Abstract
This study used quantitative MRI to study normal appearing white matter (NAWM) in patients with clinically isolated syndromes suggestive of multiple sclerosis and relapsing-remitting multiple sclerosis (RRMS). This was done at ultrahigh field (7 T) for greater spatial resolution and sensitivity. 17 CIS patients, 11 RRMS patients, and 20 age-matched healthy controls were recruited. They were scanned using a 3D inversion recovery turbo field echo sequence to measure the longitudinal relaxation time (T1). A 3D magnetization transfer prepared turbo field echo (MT-TFE) sequence was also acquired, first without a presaturation pulse and then with the MT presaturation pulse applied at -1.05 kHz and +1.05 kHz off resonance from water to produce two magnetization transfer ratio maps (MTR(-) and MTR(+)). Histogram analysis was performed on the signal from the voxels in the NAWM mask. The upper quartile cut-off of the T1 histogram was significantly higher in RRMS patients than in controls (p < 0.05), but there was no difference in CIS. In contrast, MTR was significantly different between CIS or RRMS patients and controls (p < 0.05) for most histogram measures considered. The difference between MTR(+) and MTR(-) signals showed that NOE contributions dominated the changes found. There was a weak negative correlation (r = -0.46, p < 0.05) between the mode of T1 distributions and healthy controls' age; this was not significant for MTR(+) (r = -0.34, p > 0.05) or MTR(-) (r = 0.13, p > 0.05). There was no significant correlation between the median of T1, MTR(-), or MTR(+) and the age of healthy controls. Furthermore, no significant correlation was observed between EDSS or disease duration and T1, MTR(-), or MTR(+) for either CIS or RRMS patients. In conclusion, MTR was found to be more sensitive to early changes in MS disease than T1.
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Affiliation(s)
- Ali Al-Radaideh
- Medical Imaging, The Hashemite University, Zarqa, Jordan
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
| | - Olivier E Mougin
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
| | - Su-Yin Lim
- Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - I-Jun Chou
- Clinical Neuroscience, University of Nottingham, Nottingham, UK
- Paediatric Neurology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | | | - Penny Gowland
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
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Hattingen E, Jurcoane A, Nelles M, Müller A, Nöth U, Mädler B, Mürtz P, Deichmann R, Schild HH. Quantitative MR Imaging of Brain Tissue and Brain Pathologies. Clin Neuroradiol 2015. [PMID: 26223371 DOI: 10.1007/s00062-015-0433-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Measurement of basic quantitative magnetic resonance (MR) parameters (e.g., relaxation times T1, T2*, T2 or respective rates R (1/T)) corrected for radiofrequency (RF) coil bias yields different conventional and new tissue contrasts as well as volumes for tissue segmentation. This approach also provides quantitative measures of microstructural and functional tissue changes. We herein demonstrate some prospects of quantitative MR imaging in neurological diagnostics and science.
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Affiliation(s)
- E Hattingen
- Neuroradiologie, Radiologische Klinik des Universitätsklinikums Bonn, Sigmund Freud Strasse 25, 53127, Bonn, Germany.
| | - A Jurcoane
- Neuroradiologie, Radiologische Klinik des Universitätsklinikums Bonn, Sigmund Freud Strasse 25, 53127, Bonn, Germany
| | - M Nelles
- Neuroradiologie, Radiologische Klinik des Universitätsklinikums Bonn, Sigmund Freud Strasse 25, 53127, Bonn, Germany
| | - A Müller
- Neuroradiologie, Radiologische Klinik des Universitätsklinikums Bonn, Sigmund Freud Strasse 25, 53127, Bonn, Germany
| | - U Nöth
- Brain Imaging Center, Universitätsklinikum Frankfurt, Frankfurt/Main, Germany
| | - B Mädler
- Philips Medical Systems, Philips GmbH, Hamburg, Germany
| | - P Mürtz
- Neuroradiologie, Radiologische Klinik des Universitätsklinikums Bonn, Sigmund Freud Strasse 25, 53127, Bonn, Germany
| | - R Deichmann
- Brain Imaging Center, Universitätsklinikum Frankfurt, Frankfurt/Main, Germany
| | - H H Schild
- Radiologische Klinik des Universitätsklinikums Bonn, Sigmund Freud Strasse 25, 53127, Bonn, Germany
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Krauss W, Gunnarsson M, Andersson T, Thunberg P. Accuracy and reproducibility of a quantitative magnetic resonance imaging method for concurrent measurements of tissue relaxation times and proton density. Magn Reson Imaging 2015; 33:584-91. [PMID: 25708264 DOI: 10.1016/j.mri.2015.02.013] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Revised: 01/29/2015] [Accepted: 02/16/2015] [Indexed: 11/16/2022]
Affiliation(s)
- Wolfgang Krauss
- Department of Radiology, Faculty of Medicine and Health, Örebro University, Sweden.
| | - Martin Gunnarsson
- Department of Neurology and Neurophysiology, Faculty of Medicine and Health, Örebro University, Sweden; Faculty of Medicine and Health, Örebro University, Sweden
| | | | - Per Thunberg
- Faculty of Medicine and Health, Örebro University, Sweden; Department of Medical Physics, Faculty of Medicine and Health, Örebro University, Sweden
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Pulli B, Bure L, Wojtkiewicz GR, Iwamoto Y, Ali M, Li D, Schob S, Hsieh KLC, Jacobs AH, Chen JW. Multiple sclerosis: myeloperoxidase immunoradiology improves detection of acute and chronic disease in experimental model. Radiology 2014; 275:480-9. [PMID: 25494298 DOI: 10.1148/radiol.14141495] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
PURPOSE To test if MPO-Gd, a gadolinium-based magnetic resonance (MR) imaging probe that is sensitive and specific for the proinflammatory and oxidative enzyme myeloperoxidase (MPO), which is secreted by certain inflammatory cells, is more sensitive than diethylenetriaminepentaacetic acid (DTPA)-Gd in revealing early subclinical and chronic disease activity in the brain in experimental autoimmune encephalomyelitis (EAE), a mouse model of multiple sclerosis. MATERIALS AND METHODS The protocol for animal experiments was approved by the institutional animal care committee. A total of 61 female SJL mice were induced with EAE. Mice underwent MPO-Gd- or DTPA-Gd-enhanced MR imaging on days 6, 8, and 10 after induction, before clinical disease develops, and during chronic disease at remission and the first relapse. Brains were harvested at these time points for flow cytometric evaluation of immune cell subtypes and immunohistochemistry. Statistical analysis was performed, and P < .05 was considered to indicate a significant difference. RESULTS MPO-Gd helps detect earlier (5.2 vs 2.3 days before symptom onset, P = .004) and more (3.1 vs 0.3, P = .008) subclinical inflammatory lesions compared with DTPA-Gd, including in cases in which there was no evidence of overt blood-brain barrier (BBB) breakdown detected with DTPA-Gd enhancement. The number of MPO-Gd-enhancing lesions correlated with early infiltration of MPO-secreting monocytes and neutrophils into the brain (r = 0.91). MPO-Gd also helped detect more lesions during subclinical disease at remission (5.5 vs 1.3, P = .006) and at the first relapse (9.0 vs 2.7, P = .03) than DTPA-Gd, which also correlated well with the presence and accumulation of MPO-secreting inflammatory cells in the brain (r = 0.93). CONCLUSION MPO-Gd specifically reveals lesions with inflammatory monocytes and neutrophils, which actively secrete MPO. These results demonstrate the feasibility of detection of subclinical inflammatory disease activity in vivo, which is different from overt BBB breakdown.
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Affiliation(s)
- Benjamin Pulli
- From the Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, 185 Cambridge St, Boston, MA 02114 (B.P., L.B., G.R.W., Y.I., M.A., D.L., S.S., K.L.C.H., J.W.C.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (B.P., J.W.C.); and European Institute for Molecular Imaging, University of Münster, Münster, Germany (A.H.J.)
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Simioni S, Amarù F, Bonnier G, Kober T, Rotzinger D, Du Pasquier R, Schluep M, Meuli R, Sbarbati A, Thiran JP, Krueger G, Granziera C. MP2RAGE provides new clinically-compatible correlates of mild cognitive deficits in relapsing-remitting multiple sclerosis. J Neurol 2014; 261:1606-13. [PMID: 24912471 DOI: 10.1007/s00415-014-7398-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2014] [Revised: 05/23/2014] [Accepted: 06/03/2014] [Indexed: 12/01/2022]
Abstract
Despite that cognitive impairment is a known early feature present in multiple sclerosis (MS) patients, the biological substrate of cognitive deficits in MS remains elusive. In this study, we assessed whether T1 relaxometry, as obtained in clinically acceptable scan times by the recent Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) sequence, may help identifying the structural correlate of cognitive deficits in relapsing-remitting MS patients (RRMS). Twenty-nine healthy controls (HC) and forty-nine RRMS patients underwent high-resolution 3T magnetic resonance imaging to obtain optimal cortical lesion (CL) and white matter lesion (WML) count/volume and T1 relaxation times. T1 z scores were then obtained between T1 relaxation times in lesion and the corresponding HC tissue. Patient cognitive performance was tested using the Brief Repeatable Battery of Neuro-psychological Tests. Multivariate analysis was applied to assess the contribution of MRI variables (T1 z scores, lesion count/volume) to cognition in patients and Bonferroni correction was applied for multiple comparison. T1 z scores were higher in WML (p < 0.001) and CL-I (p < 0.01) than in the corresponding normal-appearing tissue in patients, indicating relative microstructural loss. (1) T1 z scores in CL-I (p = 0.01) and the number of CL-II (p = 0.04) were predictors of long-term memory; (2) T1 z scores in CL-I (β = 0.3; p = 0.03) were independent determinants of long-term memory storage, and (3) lesion volume did not significantly influenced cognitive performances in patients. Our study supports evidence that T1 relaxometry from MP2RAGE provides information about microstructural properties in CL and WML and improves correlation with cognition in RRMS patients, compared to conventional measures of disease burden.
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Affiliation(s)
- Samanta Simioni
- Division of Neurology, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne, Lausanne, Switzerland
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Dieringer MA, Deimling M, Santoro D, Wuerfel J, Madai VI, Sobesky J, von Knobelsdorff-Brenkenhoff F, Schulz-Menger J, Niendorf T. Rapid parametric mapping of the longitudinal relaxation time T1 using two-dimensional variable flip angle magnetic resonance imaging at 1.5 Tesla, 3 Tesla, and 7 Tesla. PLoS One 2014; 9:e91318. [PMID: 24621588 PMCID: PMC3951399 DOI: 10.1371/journal.pone.0091318] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2013] [Accepted: 02/08/2014] [Indexed: 11/18/2022] Open
Abstract
INTRODUCTION Visual but subjective reading of longitudinal relaxation time (T1) weighted magnetic resonance images is commonly used for the detection of brain pathologies. For this non-quantitative measure, diagnostic quality depends on hardware configuration, imaging parameters, radio frequency transmission field (B1+) uniformity, as well as observer experience. Parametric quantification of the tissue T1 relaxation parameter offsets the propensity for these effects, but is typically time consuming. For this reason, this study examines the feasibility of rapid 2D T1 quantification using a variable flip angles (VFA) approach at magnetic field strengths of 1.5 Tesla, 3 Tesla, and 7 Tesla. These efforts include validation in phantom experiments and application for brain T1 mapping. METHODS T1 quantification included simulations of the Bloch equations to correct for slice profile imperfections, and a correction for B1+. Fast gradient echo acquisitions were conducted using three adjusted flip angles for the proposed T1 quantification approach that was benchmarked against slice profile uncorrected 2D VFA and an inversion-recovery spin-echo based reference method. Brain T1 mapping was performed in six healthy subjects, one multiple sclerosis patient, and one stroke patient. RESULTS Phantom experiments showed a mean T1 estimation error of (-63±1.5)% for slice profile uncorrected 2D VFA and (0.2±1.4)% for the proposed approach compared to the reference method. Scan time for single slice T1 mapping including B1+ mapping could be reduced to 5 seconds using an in-plane resolution of (2×2) mm2, which equals a scan time reduction of more than 99% compared to the reference method. CONCLUSION Our results demonstrate that rapid 2D T1 quantification using a variable flip angle approach is feasible at 1.5T/3T/7T. It represents a valuable alternative for rapid T1 mapping due to the gain in speed versus conventional approaches. This progress may serve to enhance the capabilities of parametric MR based lesion detection and brain tissue characterization.
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Affiliation(s)
- Matthias A. Dieringer
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrueck Center for Molecular Medicine, Berlin, Germany
- Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center, a joint cooperation between the Charité Medical Faculty and the Max-Delbrueck Center for Molecular Medicine, HELIOS Clinics Berlin Buch, Department of Cardiology and Nephrology, Berlin, Germany
- * E-mail:
| | - Michael Deimling
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrueck Center for Molecular Medicine, Berlin, Germany
- Siemens Healthcare, Erlangen, Germany
| | - Davide Santoro
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Jens Wuerfel
- Institute of Neuroradiology, University Medicine Göttingen, Göttingen, Germany
- NeuroCure Clinical Research Center, Charité University Medicine Berlin, Berlin, Germany
| | - Vince I. Madai
- Clinic for Neurology & Center for Stroke Research Berlin, Charité Medical Faculty Berlin, Berlin, Germany
| | - Jan Sobesky
- Clinic for Neurology & Center for Stroke Research Berlin, Charité Medical Faculty Berlin, Berlin, Germany
| | - Florian von Knobelsdorff-Brenkenhoff
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrueck Center for Molecular Medicine, Berlin, Germany
- Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center, a joint cooperation between the Charité Medical Faculty and the Max-Delbrueck Center for Molecular Medicine, HELIOS Clinics Berlin Buch, Department of Cardiology and Nephrology, Berlin, Germany
| | - Jeanette Schulz-Menger
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrueck Center for Molecular Medicine, Berlin, Germany
- Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center, a joint cooperation between the Charité Medical Faculty and the Max-Delbrueck Center for Molecular Medicine, HELIOS Clinics Berlin Buch, Department of Cardiology and Nephrology, Berlin, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrueck Center for Molecular Medicine, Berlin, Germany
- Experimental and Clinical Research Center, a joint cooperation between the Charité Medical Faculty and the Max-Delbrueck Center for Molecular Medicine, Berlin, Germany
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Nayak NB, Salah R, Huang JC, Hathout GM. A comparison of sagittal short T1 inversion recovery and T2-weighted FSE sequences for detection of multiple sclerosis spinal cord lesions. Acta Neurol Scand 2014; 129:198-203. [PMID: 23980614 DOI: 10.1111/ane.12168] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2013] [Indexed: 11/27/2022]
Abstract
PURPOSE Multiple sclerosis (MS) is the most common disabling CNS disease of young adults. MRI is routinely used for the detection of MS plaques in the brain and spinal cord. A significant portion of patients with MS demonstrates spinal cord lesions at the time of initial workup, and these lesions are an important part of the McDonald criteria for diagnosis. However, whereas brain imaging sequences are now fairly standardized, there continues to be debate about the optimal sequences for imaging the spinal cord. The short T1 inversion recovery (STIR) sequence has been shown in the current literature to improve lesion detection with its additive T1/T2 weighting, but current spinal cord imaging protocols from the Consortium on MS Center Consensus Guidelines do not include the STIR sequence. We demonstrate that not only do STIR sequences improve lesion detection when compared directly with conventional T2-weighted sequences, but that they also significantly improve lesion conspicuity, facilitating earlier positive diagnosis and management. MATERIALS AND METHODS Dedicated MR spinal cord imaging of twenty-nine sequential patients with clinically confirmed multiple sclerosis was retrospectively analyzed by two independent neuroradiologists in a novel study design. Sagittal T2-weighted and STIR sequence images from the same study for each patient were examined for MS plaques using a double-blinded review of individual images 'separated in time and space', such that STIR and T2 image pairs were never analyzed simultaneously. Number of lesions and lesion conspicuity for each lesion, using a subjective scale (1-5), were tallied for each sequence. Averages for each observer were compared using a paired t-test analysis for statistical significance, and assessment of inter-rater agreement was assessed using Cohen's kappa index. RESULTS Significantly, more MS lesions were detected on STIR than on T2-weighted sequences for both observers (P = 0.001 and P = 0.005). In seven patients, the conventional T2 sequence detected no lesions at all, whereas STIR sequences showed significant cord involvement. Lesion conspicuity was also significantly better on STIR for both observers (P < 0.0005). This improved conspicuity leads to more uniform lesion detection. On the conventional T2-weighted sequence, there was a statistically significant difference in the number of lesions detected between the two observers (P = 0.003), but there was no statistically significant difference on STIR (P = 0.43). The kappa index showed greater interobserver agreement in both lesion count and lesion conspicuity on the STIR sequence as compared with T2. CONCLUSIONS Short T1 inversion recovery sequence imaging not only significantly improves detection of MS lesions within the spinal cord, but also provides better contrast and conspicuity of visible lesions, creating a more confident diagnostic measure of MS extent and progression. Short T1 inversion recovery sequences of the spinal cord should be routinely obtained during initial and routine follow-up of MS.
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Affiliation(s)
- N. B. Nayak
- Department of Radiology; University of California; Los Angeles CA USA
| | - R. Salah
- David Geffen School of Medicine at University of California; Los Angeles CA USA
| | - J. C. Huang
- Department of Radiology; West Los Angeles Veterans Administration Medical Center; Los Angeles CA USA
- Department of Radiology; Olive View-UCLA Medical Center; Sylmar CA USA
| | - G. M. Hathout
- Department of Radiology; University of California; Los Angeles CA USA
- Department of Radiology; West Los Angeles Veterans Administration Medical Center; Los Angeles CA USA
- Department of Radiology; Olive View-UCLA Medical Center; Sylmar CA USA
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Cook SD, Dhib-Jalbut S, Dowling P, Durelli L, Ford C, Giovannoni G, Halper J, Harris C, Herbert J, Li D, Lincoln JA, Lisak R, Lublin FD, Lucchinetti CF, Moore W, Naismith RT, Oehninger C, Simon J, Sormani MP. Use of Magnetic Resonance Imaging as Well as Clinical Disease Activity in the Clinical Classification of Multiple Sclerosis and Assessment of Its Course: A Report from an International CMSC Consensus Conference, March 5-7, 2010. Int J MS Care 2014; 14:105-14. [PMID: 24453741 DOI: 10.7224/1537-2073-14.3.105] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
It has recently been suggested that the Lublin-Reingold clinical classification of multiple sclerosis (MS) be modified to include the use of magnetic resonance imaging (MRI). An international consensus conference sponsored by the Consortium of Multiple Sclerosis Centers (CMSC) was held from March 5 to 7, 2010, to review the available evidence on the need for such modification of the Lublin-Reingold criteria and whether the addition of MRI or other biomarkers might lead to a better understanding of MS pathophysiology and disease course over time. The conference participants concluded that evidence of new MRI gadolinium-enhancing (Gd+) T1-weighted lesions and unequivocally new or enlarging T2-weighted lesions (subclinical activity, subclinical relapses) should be added to the clinical classification of MS in distinguishing relapsing inflammatory from progressive forms of the disease. The consensus was that these changes to the classification system would provide more rigorous definitions and categorization of MS course, leading to better insights as to the evolution and treatment of MS.
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Affiliation(s)
- Stuart D Cook
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Suhayl Dhib-Jalbut
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Peter Dowling
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Luca Durelli
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Corey Ford
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Gavin Giovannoni
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - June Halper
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Colleen Harris
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Joseph Herbert
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - David Li
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - John A Lincoln
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Robert Lisak
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Fred D Lublin
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Claudia F Lucchinetti
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Wayne Moore
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Robert T Naismith
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Carlos Oehninger
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Jack Simon
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
| | - Maria Pia Sormani
- University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (SDC); Department of Neurology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, NJ (SDJ); VA Medical Center-East Orange, East Orange, NJ, USA (PD); Department of Clinical and Biological Sciences, San Luigi Gonzaga Medical School, University of Torino, Orbassano, Italy (LD); Multiple Sclerosis Clinic, University of New Mexico Health Sciences Center, Albuquerque, NM, USA (CF); Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, Whitechapel, London, UK (GG); Consortium of Multiple Sclerosis Centers, Hackensack, NJ, USA (J Halper); Multiple Sclerosis Clinic, Foothills Medical Centre, Calgary, Alberta, Canada (CH); MS Comprehensive Care Center, NYU Langone Medical Center, New York, NY, USA (J Herbert); MS Clinic, University of British Columbia Hospital, Vancouver, British Columbia, Canada (DL); MS Research Group, University of Texas Health, Houston, TX, USA (JAL); Comprehensive Clinical and Research MS Center, Wayne State University, Detroit, MI, USA (RL); Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, NY, USA (FDL); Department of Neurology, Mayo Clinic, Rochester, MN, USA (CFL); Vancouver General Hospital, Vancouver, British Columbia, Canada (WM); Department of Neurology, Washington University, St. Louis, MO, USA (RTN); LACTRIMS and Institute of Neurology, Montevideo, Uruguay (CO); VA Medical Center, Portland, OR, USA (JS); and Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy (MPS)
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Bagnato F, Ohayon JM, Ehrmantraut M, Chiu AW, Riva M, Ikonomidou VN. Clinical and imaging metrics for monitoring disease progression in patients with multiple sclerosis. Expert Rev Neurother 2014; 6:599-612. [PMID: 16623658 DOI: 10.1586/14737175.6.4.599] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Multiple sclerosis (MS) is an autoimmune disease of the CNS leading to clinical disability in 250,000-350,000 young adults in the USA and Europe. The disease affects both white matter (WM) and gray matter (GM) tissues of the brain and spinal cord. While WM disease is easily quantified using currently available magnetic resonance imaging (MRI) techniques, identification and quantification of GM disease present a daily challenge. Nonconventional brain and spinal cord MRI techniques, including magnetization transfer, MRI spectroscopy and diffusion tensor imaging, have improved our understanding of MS pathology in the deep GM. The sensitivity of high-resolution MRI obtained at a high magnetic field will improve the detection of spinal cord and brain cortical GM disease. The appropriate use of the above-mentioned techniques has the potential to more accurately explain the level of disability in MS patients.
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Affiliation(s)
- Francesca Bagnato
- Neuroimmunology Branch, NIND-NIH, 10 Center Drive, Building 10, Room 5B16, Bethesda, MD 20892-1400, USA.
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Filippi M, Charil A, Rovaris M, Absinta M, Rocca MA. Insights from magnetic resonance imaging. HANDBOOK OF CLINICAL NEUROLOGY 2014; 122:115-149. [PMID: 24507516 DOI: 10.1016/b978-0-444-52001-2.00006-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Recent years have witnessed impressive advancements in the use of magnetic resonance imaging (MRI) for the assessment of patients with multiple sclerosis (MS). Complementary to the clinical evaluation, conventional MRI (cMRI) provides crucial pieces of information for the diagnosis of MS, the understanding of its natural history, and monitoring the efficacy of experimental treatments. Measures derived from cMRI present clear advantages over the clinical assessment, including their more objective nature and an increased sensitivity to MS-related changes. However, the correlation between these measures and the clinical manifestations of the disease remains weak, and this can be explained, at least partially, by the limited ability of cMRI to characterize and quantify the heterogeneous features of MS pathology. Quantitative MR-based techniques have the potential to overcome the limitations of cMRI. Magnetization transfer MRI, diffusion-weighted and diffusion tensor MRI with fiber tractography, proton magnetic resonance spectroscopy, T1 and T2 relaxation time measurement, and functional MRI are contributing to elucidate the mechanisms that underlie injury, repair, and functional adaptation in patients with MS. All conventional and nonconventional MR techniques will benefit from the use of high-field MR systems (3.0T or more).
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Affiliation(s)
- Massimo Filippi
- Neuroimaging Research Unit, Department of Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.
| | - Arnaud Charil
- Neuroimaging Research Unit, Department of Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Marco Rovaris
- Neuroimaging Research Unit, Department of Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Martina Absinta
- Neuroimaging Research Unit, Department of Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Maria Assunta Rocca
- Neuroimaging Research Unit, Department of Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
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Verma RK, Slotboom J, Heldner MR, Kellner-Weldon F, Kottke R, Ozdoba C, Weisstanner C, Kamm CP, Wiest R. Characterization of microcirculation in multiple sclerosis lesions by dynamic texture parameter analysis (DTPA). PLoS One 2013; 8:e67610. [PMID: 23874432 PMCID: PMC3713008 DOI: 10.1371/journal.pone.0067610] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2012] [Accepted: 05/25/2013] [Indexed: 01/22/2023] Open
Abstract
Objective Texture analysis is an alternative method to quantitatively assess MR-images. In this study, we introduce dynamic texture parameter analysis (DTPA), a novel technique to investigate the temporal evolution of texture parameters using dynamic susceptibility contrast enhanced (DSCE) imaging. Here, we aim to introduce the method and its application on enhancing lesions (EL), non-enhancing lesions (NEL) and normal appearing white matter (NAWM) in multiple sclerosis (MS). Methods We investigated 18 patients with MS and clinical isolated syndrome (CIS), according to the 2010 McDonald's criteria using DSCE imaging at different field strengths (1.5 and 3 Tesla). Tissues of interest (TOIs) were defined within 27 EL, 29 NEL and 37 NAWM areas after normalization and eight histogram-based texture parameter maps (TPMs) were computed. TPMs quantify the heterogeneity of the TOI. For every TOI, the average, variance, skewness, kurtosis and variance-of-the-variance statistical parameters were calculated. These TOI parameters were further analyzed using one-way ANOVA followed by multiple Wilcoxon sum rank testing corrected for multiple comparisons. Results Tissue- and time-dependent differences were observed in the dynamics of computed texture parameters. Sixteen parameters discriminated between EL, NEL and NAWM (pAVG = 0.0005). Significant differences in the DTPA texture maps were found during inflow (52 parameters), outflow (40 parameters) and reperfusion (62 parameters). The strongest discriminators among the TPMs were observed in the variance-related parameters, while skewness and kurtosis TPMs were in general less sensitive to detect differences between the tissues. Conclusion DTPA of DSCE image time series revealed characteristic time responses for ELs, NELs and NAWM. This may be further used for a refined quantitative grading of MS lesions during their evolution from acute to chronic state. DTPA discriminates lesions beyond features of enhancement or T2-hypersignal, on a numeric scale allowing for a more subtle grading of MS-lesions.
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Affiliation(s)
- Rajeev Kumar Verma
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Berne, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Berne, Bern, Switzerland
- * E-mail:
| | - Mirjam Rahel Heldner
- Department of Neurology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Frauke Kellner-Weldon
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Berne, Bern, Switzerland
| | - Raimund Kottke
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Berne, Bern, Switzerland
| | - Christoph Ozdoba
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Berne, Bern, Switzerland
| | - Christian Weisstanner
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Berne, Bern, Switzerland
| | - Christian Philipp Kamm
- Department of Neurology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Berne, Bern, Switzerland
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Jurcoane A, Wagner M, Schmidt C, Mayer C, Gracien RM, Hirschmann M, Deichmann R, Volz S, Ziemann U, Hattingen E. Within-lesion differences in quantitative MRI parameters predict contrast enhancement in multiple sclerosis. J Magn Reson Imaging 2013; 38:1454-61. [PMID: 23554005 DOI: 10.1002/jmri.24107] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2012] [Accepted: 02/11/2013] [Indexed: 01/17/2023] Open
Abstract
PURPOSE To investigate the relationship between quantitative magnetic resonance imaging (qMRI) and contrast enhancement in multiple sclerosis (MS) lesions. We compared maps of T1 relaxation time, proton density (PD), and magnetization transfer ratio (MTR) between lesions with and without contrast enhancement as quantified by the amount of T1 shortening postcontrast agent (CA). MATERIALS AND METHODS In 17 patients with relapsing-remitting MS (RRMS), 15 with progressive MS (PMS), and 17 healthy controls, T1, PD, and MTR were measured at 3T and T1-mapping was repeated after CA administration. Manually drawn MS-lesions (3D-FLAIR) were labeled as enhancing if post-CA T1-shortening exceeded mean T1-shortening in normal-appearing white matter (NAWM) by at least 2 standard deviations. Precontrast T1, PD, and MTR were compared in enhancing lesions, nonenhancing lesions, NAWM, and gray matter. RESULTS Precontrast T1, PD, and MTR differed significantly between enhancing and nonenhancing lesions in RRMS and PMS patients (all P < 0.01). In PMS patients, PD of NAWM, enhancing, and nonenhancing lesions and MTR and T1 of gray matter differed significantly from RRMS and controls. Only MTR of gray matter differed between RRMS and controls. CONCLUSION Contrast enhancement in MS quantified by relative T1 shortening may be predicted by precontrast abnormalities of T1, PD, and MTR and likely represents blood-brain barrier damage.
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Affiliation(s)
- Alina Jurcoane
- Institute of Neuroradiology, Goethe University, Frankfurt am Main, Germany
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Steen C, D’haeseleer M, Hoogduin JM, Fierens Y, Cambron M, Mostert JP, Heersema DJ, Koch MW, De Keyser J. Cerebral white matter blood flow and energy metabolism in multiple sclerosis. Mult Scler 2013; 19:1282-9. [DOI: 10.1177/1352458513477228] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Cerebral blood flow (CBF) is reduced in normal-appearing white matter (NAWM) of subjects with multiple sclerosis (MS), but the underlying mechanism is unknown. Objective: The objective of this article is to assess the relationship between reduced NAWM CBF and both axonal mitochondrial metabolism and astrocytic phosphocreatine (PCr) metabolism. Methods: Ten healthy controls and 25 MS subjects were studied with 3 Tesla magnetic resonance imaging. CBF was measured using pseudo-continuous arterial spin labeling. N-acetylaspartate/creatine (NAA/Cr) ratios (axonal mitochondrial metabolism) were obtained using 1H-MR spectroscopy and PCr/β-ATP ratios using 31P-MR spectroscopy. In centrum semiovale NAWM, we assessed correlations between CBF and both NAA/Cr and PCr/β-ATP ratios. Results: Subjects with MS had a widespread reduction in CBF of NAWM (centrum semiovale, periventricular, frontal and occipital), and gray matter (frontoparietal cortex and thalamus). Compared to controls, NAA/Cr in NAWM of the centrum semiovale of MS subjects was decreased, whereas PCr/β-ATP was increased. We found no correlations between CBF and PCr/β-ATP. CBF and NAA/Cr correlated in controls ( p = 0.02), but not in MS subjects ( p = 0.68). Conclusions: Our results suggest that in MS patients there is no relationship between reduced CBF in NAWM and impaired axonal mitochondrial metabolism or astrocytic PCr metabolism.
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Affiliation(s)
- Christel Steen
- Department of Neurology, Universitair Medisch Centrum Groningen, The Netherlands
| | - Miguel D’haeseleer
- Department of Neurology, Universitair Ziekenhuis Brussel, Center for Neurosciences, Vrije Universiteit Brussel (VUB), Belgium
| | - Johannes M Hoogduin
- Department of Neurology and Neurosurgery, Universitair Medisch Centrum Utrecht, The Netherlands
| | - Yves Fierens
- Department of Medical Physics, Department of Radiology, Universitair Ziekenhuis Brussel, Belgium
| | - Melissa Cambron
- Department of Neurology, Universitair Ziekenhuis Brussel, Center for Neurosciences, Vrije Universiteit Brussel (VUB), Belgium
| | - Jop P Mostert
- Department of Neurology, Rijnstate Hospital, The Netherlands
| | - Dorothea J Heersema
- Department of Neurology, Universitair Medisch Centrum Groningen, The Netherlands
| | - Marcus W Koch
- Department of Clinical Neurosciences, Division of Neurology, University of Calgary, Canada
| | - Jacques De Keyser
- Department of Neurology, Universitair Medisch Centrum Groningen, The Netherlands
- Department of Neurology, Universitair Ziekenhuis Brussel, Center for Neurosciences, Vrije Universiteit Brussel (VUB), Belgium
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Lund H, Krakauer M, Skimminge A, Sellebjerg F, Garde E, Siebner HR, Paulson OB, Hesse D, Hanson LG. Blood-brain barrier permeability of normal appearing white matter in relapsing-remitting multiple sclerosis. PLoS One 2013; 8:e56375. [PMID: 23441184 PMCID: PMC3575471 DOI: 10.1371/journal.pone.0056375] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2012] [Accepted: 01/08/2013] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Multiple sclerosis (MS) affects the integrity of the blood-brain barrier (BBB). Contrast-enhanced T1 weighted magnetic resonance imaging (MRI) is widely used to characterize location and extent of BBB disruptions in focal MS lesions. We employed quantitative T1 measurements before and after the intravenous injection of a paramagnetic contrast agent to assess BBB permeability in the normal appearing white matter (NAWM) in patients with relapsing-remitting MS (RR-MS). METHODOLOGY/PRINCIPAL FINDINGS Fifty-nine patients (38 females) with RR-MS undergoing immunomodulatory treatment and nine healthy controls (4 females) underwent quantitative T1 measurements at 3 tesla before and after injection of a paramagnetic contrast agent (0.2 mmol/kg Gd-DTPA). Mean T1 values were calculated for NAWM in patients and total cerebral white matter in healthy subjects for the T1 measurements before and after injection of Gd-DTPA. The pre-injection baseline T1 of NAWM (945±55 [SD] ms) was prolonged in RR-MS relative to healthy controls (903±23 ms, p = 0.028). Gd-DTPA injection shortened T1 to a similar extent in both groups. Mean T1 of NAWM was 866±47 ms in the NAWM of RR-MS patients and 824±13 ms in the white matter of healthy controls. The regional variability of T1 values expressed as the coefficient of variation (CV) was comparable between the two groups at baseline, but not after injection of the contrast agent. After intravenous Gd-DTPA injection, T1 values in NAWM were more variable in RR-MS patients (CV = 0.198±0.046) compared to cerebral white matter of healthy controls (CV = 0.166±0.018, p = 0.046). CONCLUSIONS/SIGNIFICANCE We found no evidence of a global BBB disruption within the NAWM of RR-MS patients undergoing immunomodulatory treatment. However, the increased variation of T1 values in NAWM after intravenous Gd-DTPA injection points to an increased regional inhomogeneity of BBB function in NAWM in relapsing-remitting MS.
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Affiliation(s)
- Henrik Lund
- Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital, Hvidovre, Denmark.
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García-Lorenzo D, Francis S, Narayanan S, Arnold DL, Collins DL. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med Image Anal 2013; 17:1-18. [DOI: 10.1016/j.media.2012.09.004] [Citation(s) in RCA: 153] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2011] [Revised: 09/06/2012] [Accepted: 09/17/2012] [Indexed: 01/21/2023]
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Weise G, Stoll G. Magnetic resonance imaging of blood brain/nerve barrier dysfunction and leukocyte infiltration: closely related or discordant? Front Neurol 2012; 3:178. [PMID: 23267343 PMCID: PMC3527731 DOI: 10.3389/fneur.2012.00178] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2012] [Accepted: 12/03/2012] [Indexed: 11/13/2022] Open
Abstract
Unlike other organs the nervous system is secluded from the rest of the organism by the blood brain barrier (BBB) or blood nerve barrier (BNB) preventing passive influx of fluids from the circulation. Similarly, leukocyte entry to the nervous system is tightly controlled. Breakdown of these barriers and cellular inflammation are hallmarks of inflammatory as well as ischemic neurological diseases and thus represent potential therapeutic targets. The spatiotemporal relationship between BBB/BNB disruption and leukocyte infiltration has been a matter of debate. We here review contrast-enhanced magnetic resonance imaging (MRI) as a non-invasive tool to depict barrier dysfunction and its relation to macrophage infiltration in the central and peripheral nervous system under pathological conditions. Novel experimental contrast agents like Gadofluorine M (Gf) allow more sensitive assessment of BBB dysfunction than conventional Gadolinium (Gd)-DTPA enhanced MRI. In addition, Gf facilitates visualization of functional and transient alterations of the BBB remote from lesions. Cellular contrast agents such as superparamagnetic iron oxide particles (SPIO) and perfluorocarbons enable assessment of leukocyte (mainly macrophage) infiltration by MR technology. Combined use of these MR contrast agents disclosed that leukocytes can enter the nervous system independent from a disturbance of the BBB, and vice versa, a dysfunctional BBB/BNB by itself is not sufficient to attract inflammatory cells from the circulation. We will illustrate these basic imaging findings in animal models of multiple sclerosis, cerebral ischemia, and traumatic nerve injury and review corresponding findings in patients.
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Affiliation(s)
- Gesa Weise
- Department of Neurology, University of Wuerzburg Wuerzburg, Germany ; Fraunhofer Institute for Cell Therapy and Immunology Leipzig, Germany ; Translational Center for Regenerative Medicine Leipzig, Germany
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The age-related deficit in LTP is associated with changes in perfusion and blood-brain barrier permeability. Neurobiol Aging 2012; 33:1005.e23-35. [DOI: 10.1016/j.neurobiolaging.2011.09.035] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2011] [Revised: 09/22/2011] [Accepted: 09/30/2011] [Indexed: 12/11/2022]
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