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Faulkner ME, Gong Z, Guo A, Laporte JP, Bae J, Bouhrara M. Harnessing myelin water fraction as an imaging biomarker of human cerebral aging, neurodegenerative diseases, and risk factors influencing myelination: A review. J Neurochem 2024. [PMID: 38973579 DOI: 10.1111/jnc.16170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/12/2024] [Accepted: 06/19/2024] [Indexed: 07/09/2024]
Abstract
Myelin water fraction (MWF) imaging has emerged as a promising magnetic resonance imaging (MRI) biomarker for investigating brain function and composition. This comprehensive review synthesizes the current state of knowledge on MWF as a biomarker of human cerebral aging, neurodegenerative diseases, and risk factors influencing myelination. The databases used include Web of Science, Scopus, Science Direct, and PubMed. We begin with a brief discussion of the theoretical foundations of MWF imaging, including its basis in MR physics and the mathematical modeling underlying its calculation, with an overview of the most adopted MRI methods of MWF imaging. Next, we delve into the clinical and research applications that have been explored to date, highlighting its advantages and limitations. Finally, we explore the potential of MWF to serve as a predictive biomarker for neurological disorders and identify future research directions for optimizing MWF imaging protocols and interpreting MWF in various contexts. By harnessing the power of MWF imaging, we may gain new insights into brain health and disease across the human lifespan, ultimately informing novel diagnostic and therapeutic strategies.
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Affiliation(s)
- Mary E Faulkner
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Zhaoyuan Gong
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Alex Guo
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - John P Laporte
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Jonghyun Bae
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
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Moazamian D, Shaterian Mohammadi H, Athertya JS, Shin SH, Lo J, Chang EY, Du J, Bydder GM, Ma Y. Myelin water quantification in multiple sclerosis using short repetition time adiabatic inversion recovery prepared-fast spin echo (STAIR-FSE) imaging. Quant Imaging Med Surg 2024; 14:1673-1685. [PMID: 38415151 PMCID: PMC10895142 DOI: 10.21037/qims-23-1021] [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: 07/20/2023] [Accepted: 12/01/2023] [Indexed: 02/29/2024]
Abstract
Background Myelin water imaging (MWI) is a myelin-specific technique, which has great potential for the assessment of demyelination and remyelination. This study develops a new MWI method, which employs a short repetition time adiabatic inversion recovery (STAIR) technique in combination with a commonly used fast spin echo (FSE) sequence and provides quantification of myelin water (MW) fractions. Method Whole-brain MWI was performed using the short repetition time adiabatic inversion recovery prepared-fast spin echo (STAIR-FSE) technique on eight healthy volunteers (mean age: 38±14 years, four-males) and seven patients with multiple sclerosis (MS) (mean age: 53.7±8.7 years, two-males) on a 3T clinical magnetic resonance imaging scanner. To facilitate the quantification of apparent myelin water fraction (aMWF), a proton density-weighted FSE was also used during the scans to allow total water imaging. The aMWF measurements of MS lesions and normal-appearing white matter (NAWM) regions in MS patients were compared with those measured in normal white matter (NWM) regions in healthy volunteers. Both the analysis of variance (ANOVA) test and paired comparison were performed for the comparison. Results The MW in the whole-brain was selectively imaged and quantified using the STAIR-FSE technique in all participants. MS lesions showed much lower signal intensities than NAWM in the STAIR-FSE images. ANOVA analysis revealed a significant difference in the aMWF measurements between the three groups. Moreover, the aMWF measurements in MS lesions were significantly lower than those in both NWM of healthy volunteers and NAWM of MS patients. Lower aMWF measurements in NAWM were also found in comparison with those in NWM. Conclusions The STAIR-FSE technique is capable of measuring aMWF values for the indirect detection of myelin loss in MS, thus facilitating clinical translation of whole brain MWI and quantification, which show great potential for the detection and evaluation of changes in myelin in the brain of patients with MS for future larger cohort studies.
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Affiliation(s)
- Dina Moazamian
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | | | - Jiyo S. Athertya
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Soo Hyun Shin
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - James Lo
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Eric Y. Chang
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
- Radiology Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Jiang Du
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Radiology Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Graeme M. Bydder
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Yajun Ma
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
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Shaterian Mohammadi H, Moazamian D, Athertya JS, Shin SH, Lo J, Suprana A, Malhi BS, Ma Y. Quantitative myelin water imaging using short TR adiabatic inversion recovery prepared echo-planar imaging (STAIR-EPI) sequence. FRONTIERS IN RADIOLOGY 2023; 3:1263491. [PMID: 37840897 PMCID: PMC10568074 DOI: 10.3389/fradi.2023.1263491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 09/18/2023] [Indexed: 10/17/2023]
Abstract
Introduction Numerous techniques for myelin water imaging (MWI) have been devised to specifically assess alterations in myelin. The biomarker employed to measure changes in myelin content is known as the myelin water fraction (MWF). The short TR adiabatic inversion recovery (STAIR) sequence has recently been identified as a highly effective method for calculating MWF. The purpose of this study is to develop a new clinical transitional myelin water imaging (MWI) technique that combines STAIR preparation and echo-planar imaging (EPI) (STAIR-EPI) sequence for data acquisition. Methods Myelin water (MW) in the brain has shorter T1 and T2 relaxation times than intracellular and extracellular water. In the proposed STAIR-EPI sequence, a short TR (e.g., ≤300 ms) together with an optimized inversion time enable robust long T1 water suppression with a wide range of T1 values [i.e., (600, 2,000) ms]. The EPI allows fast data acquisition of the remaining MW signals. Seven healthy volunteers and seven patients with multiple sclerosis (MS) were recruited and scanned in this study. The apparent myelin water fraction (aMWF), defined as the signal ratio of MW to total water, was measured in the lesions and normal-appearing white matter (NAWM) in MS patients and compared with those measured in the normal white matter (NWM) in healthy volunteers. Results As seen in the STAIR-EPI images acquired from MS patients, the MS lesions show lower signal intensities than NAWM do. The aMWF measurements for both MS lesions (3.6 ± 1.3%) and NAWM (8.6 ± 1.2%) in MS patients are significantly lower than NWM (10 ± 1.3%) in healthy volunteers (P < 0.001). Discussion The proposed STAIR-EPI technique, which can be implemented in MRI scanners from all vendors, is able to detect myelin loss in both MS lesions and NAWM in MS patients.
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Affiliation(s)
| | | | | | | | | | | | | | - Yajun Ma
- Department of Radiology, University of California San Diego, San Diego, CA, United States
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Stellingwerff MD, Pouwels PJW, Roosendaal SD, Barkhof F, van der Knaap MS. Quantitative MRI in leukodystrophies. Neuroimage Clin 2023; 38:103427. [PMID: 37150021 PMCID: PMC10193020 DOI: 10.1016/j.nicl.2023.103427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 05/09/2023]
Abstract
Leukodystrophies constitute a large and heterogeneous group of genetic diseases primarily affecting the white matter of the central nervous system. Different disorders target different white matter structural components. Leukodystrophies are most often progressive and fatal. In recent years, novel therapies are emerging and for an increasing number of leukodystrophies trials are being developed. Objective and quantitative metrics are needed to serve as outcome measures in trials. Quantitative MRI yields information on microstructural properties, such as myelin or axonal content and condition, and on the chemical composition of white matter, in a noninvasive fashion. By providing information on white matter microstructural involvement, quantitative MRI may contribute to the evaluation and monitoring of leukodystrophies. Many distinct MR techniques are available at different stages of development. While some are already clinically applicable, others are less far developed and have only or mainly been applied in healthy subjects. In this review, we explore the background, current status, potential and challenges of available quantitative MR techniques in the context of leukodystrophies.
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Affiliation(s)
- Menno D Stellingwerff
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Child Neurology, Emma Children's Hospital, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Petra J W Pouwels
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Stefan D Roosendaal
- Amsterdam UMC Location University of Amsterdam, Department of Radiology, Meibergdreef 9, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands; University College London, Institutes of Neurology and Healthcare Engineering, London, UK
| | - Marjo S van der Knaap
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Child Neurology, Emma Children's Hospital, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands; Vrije Universiteit Amsterdam, Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, De Boelelaan 1105, Amsterdam, the Netherlands.
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Milotta G, Corbin N, Lambert C, Lutti A, Mohammadi S, Callaghan MF. Mitigating the impact of flip angle and orientation dependence in single compartment R2* estimates via 2-pool modeling. Magn Reson Med 2023; 89:128-143. [PMID: 36161672 PMCID: PMC9827921 DOI: 10.1002/mrm.29428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 07/08/2022] [Accepted: 08/08/2022] [Indexed: 01/12/2023]
Abstract
PURPOSE The effective transverse relaxation rate (R 2 * $$ {\mathrm{R}}_2^{\ast } $$ ) is influenced by biological features that make it a useful means of probing brain microstructure. However, confounding factors such as dependence on flip angle (α) and fiber orientation with respect to the main field (θ $$ \uptheta $$ ) complicate interpretation. The α- andθ $$ \uptheta $$ -dependence stem from the existence of multiple sub-voxel micro-environments (e.g., myelin and non-myelin water compartments). Ordinarily, it is challenging to quantify these sub-compartments; therefore, neuroscientific studies commonly make the simplifying assumption of a mono-exponential decay obtaining a singleR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimate per voxel. In this work, we investigated how the multi-compartment nature of tissue microstructure affects single compartmentR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates. METHODS We used 2-pool (myelin and non-myelin water) simulations to characterize the bias in single compartmentR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates. Based on our numeric observations, we introduced a linear model that partitionsR 2 * $$ {\mathrm{R}}_2^{\ast } $$ into α-dependent and α-independent components and validated this in vivo at 7T. We investigated the dependence of both components on the sub-compartment properties and assessed their robustness, orientation dependence, and reproducibility empirically. RESULTS R 2 * $$ {\mathrm{R}}_2^{\ast } $$ increased with myelin water fraction and residency time leading to a linear dependence on α. We observed excellent agreement between our numeric and empirical results. Furthermore, the α-independent component of the proposed linear model was robust to the choice of α and reduced dependence on fiber orientation, although it suffered from marginally higher noise sensitivity. CONCLUSION We have demonstrated and validated a simple approach that mitigates flip angle and orientation biases in single-compartmentR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates.
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Affiliation(s)
- Giorgia Milotta
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of NeurologyUniversity College London
LondonUnited Kingdom
| | - Nadège Corbin
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of NeurologyUniversity College London
LondonUnited Kingdom
- Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536CNRS/University BordeauxBordeauxFrance
| | - Christian Lambert
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of NeurologyUniversity College London
LondonUnited Kingdom
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department for Clinical NeuroscienceLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Siawoosh Mohammadi
- Department of Systems NeurosciencesUniversity Medical Center Hamburg‐EppendorfHamburgGermany
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Martina F. Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of NeurologyUniversity College London
LondonUnited Kingdom
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Ma YJ, Jang H, Lombardi AF, Corey-Bloom J, Bydder GM. Myelin water imaging using a short-TR adiabatic inversion-recovery (STAIR) sequence. Magn Reson Med 2022; 88:1156-1169. [PMID: 35613378 PMCID: PMC9867567 DOI: 10.1002/mrm.29287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/21/2022] [Accepted: 04/13/2022] [Indexed: 01/26/2023]
Abstract
PURPOSE To develop a new myelin water imaging (MWI) technique using a short-TR adiabatic inversion-recovery (STAIR) sequence on a clinical 3T MR scanner. METHODS Myelin water (MW) in the brain has both a much shorter T1 and a much shorter T2 * than intracellular/extracellular water. A STAIR sequence with a short TR was designed to efficiently suppress long T1 signals from intracellular/extracellular water, and therefore allow selective imaging of MW, which has a much shorter T1 . Numerical simulation and phantom studies were performed to investigate the effectiveness of long T1 signal suppression. TheT2 * in white matter (WM) was measured with STAIR and compared with T2 * measured with a conventional gradient recall echo in in vivo study. Four healthy volunteers and 4 patients with multiple sclerosis were recruited for qualitative and quantitative MWI. Apparent MW fraction was generated to compare MW in normal WM in volunteers to MW in lesions in patients with multiple sclerosis. RESULTS Both simulation and phantom studies showed that when TR was sufficiently short (eg, 250 ms), the STAIR sequence effectively suppressed long T1 signals from tissues with a broad range of T1 s using a single TR/TI combination. The volunteer study showed a short T2 * of 9.5 ± 1.7 ms in WM, which is similar to reported values for MW. Lesions in patients with multiple sclerosis showed a significantly lower apparent MW fraction (4.5% ± 1.0%) compared with that of normal WM (9.2% ± 1.5%) in healthy volunteers (p < 0.05). CONCLUSIONS The STAIR sequence provides selective MWI in brain and can quantify reductions in MW content in patients with multiple sclerosis.
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Affiliation(s)
- Ya-Jun Ma
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Hyungseok Jang
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Alecio F. Lombardi
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Jody Corey-Bloom
- Department of Neurosciences, University of California San Diego, San Diego, CA, USA
| | - Graeme M. Bydder
- Department of Radiology, University of California San Diego, San Diego, CA, USA
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7
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Edwards AD, Rueckert D, Smith SM, Abo Seada S, Alansary A, Almalbis J, Allsop J, Andersson J, Arichi T, Arulkumaran S, Bastiani M, Batalle D, Baxter L, Bozek J, Braithwaite E, Brandon J, Carney O, Chew A, Christiaens D, Chung R, Colford K, Cordero-Grande L, Counsell SJ, Cullen H, Cupitt J, Curtis C, Davidson A, Deprez M, Dillon L, Dimitrakopoulou K, Dimitrova R, Duff E, Falconer S, Farahibozorg SR, Fitzgibbon SP, Gao J, Gaspar A, Harper N, Harrison SJ, Hughes EJ, Hutter J, Jenkinson M, Jbabdi S, Jones E, Karolis V, Kyriakopoulou V, Lenz G, Makropoulos A, Malik S, Mason L, Mortari F, Nosarti C, Nunes RG, O’Keeffe C, O’Muircheartaigh J, Patel H, Passerat-Palmbach J, Pietsch M, Price AN, Robinson EC, Rutherford MA, Schuh A, Sotiropoulos S, Steinweg J, Teixeira RPAG, Tenev T, Tournier JD, Tusor N, Uus A, Vecchiato K, Williams LZJ, Wright R, Wurie J, Hajnal JV. The Developing Human Connectome Project Neonatal Data Release. Front Neurosci 2022; 16:886772. [PMID: 35677357 PMCID: PMC9169090 DOI: 10.3389/fnins.2022.886772] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/19/2022] [Indexed: 11/24/2022] Open
Abstract
The Developing Human Connectome Project has created a large open science resource which provides researchers with data for investigating typical and atypical brain development across the perinatal period. It has collected 1228 multimodal magnetic resonance imaging (MRI) brain datasets from 1173 fetal and/or neonatal participants, together with collateral demographic, clinical, family, neurocognitive and genomic data from 1173 participants, together with collateral demographic, clinical, family, neurocognitive and genomic data. All subjects were studied in utero and/or soon after birth on a single MRI scanner using specially developed scanning sequences which included novel motion-tolerant imaging methods. Imaging data are complemented by rich demographic, clinical, neurodevelopmental, and genomic information. The project is now releasing a large set of neonatal data; fetal data will be described and released separately. This release includes scans from 783 infants of whom: 583 were healthy infants born at term; as well as preterm infants; and infants at high risk of atypical neurocognitive development. Many infants were imaged more than once to provide longitudinal data, and the total number of datasets being released is 887. We now describe the dHCP image acquisition and processing protocols, summarize the available imaging and collateral data, and provide information on how the data can be accessed.
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Affiliation(s)
- A. David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
- Institute for AI and Informatics in Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Samy Abo Seada
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Amir Alansary
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Jennifer Almalbis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joanna Allsop
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Sophie Arulkumaran
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Luke Baxter
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Eleanor Braithwaite
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Jacqueline Brandon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Olivia Carney
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Raymond Chung
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Kathleen Colford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Serena J. Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Harriet Cullen
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King’s College London, London, United Kingdom
| | - John Cupitt
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Charles Curtis
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Alice Davidson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Louise Dillon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Konstantina Dimitrakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Translational Bioinformatics Platform, NIHR Biomedical Research Centre, Guy’s and St. Thomas’ NHS Foundation Trust and King’s College London, London, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Seyedeh-Rezvan Farahibozorg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Sean P. Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jianliang Gao
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Andreia Gaspar
- Institute for Systems and Robotics (ISR-Lisboa)/LaRSyS, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Nicholas Harper
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Sam J. Harrison
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Emer J. Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Emily Jones
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Vyacheslav Karolis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Antonios Makropoulos
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Shaihan Malik
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Luke Mason
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Rita G. Nunes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Institute for Systems and Robotics (ISR-Lisboa)/LaRSyS, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Camilla O’Keeffe
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Hamel Patel
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Maximillian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Anthony N. Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Emma C. Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Mary A. Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Stamatios Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Rui Pedro Azeredo Gomes Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Logan Z. J. Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Robert Wright
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Julia Wurie
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joseph V. Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
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8
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McDowell AR, Petrova N, Carassiti D, Miquel ME, Thomas DL, Barker GJ, Schmierer K, Wood TC. High-resolution quantitative MRI of multiple sclerosis spinal cord lesions. Magn Reson Med 2022; 87:2914-2921. [PMID: 35014736 PMCID: PMC9208576 DOI: 10.1002/mrm.29152] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 12/19/2021] [Accepted: 12/20/2021] [Indexed: 11/10/2022]
Abstract
PURPOSE Validation of quantitative MR measures for myelin imaging in the postmortem multiple sclerosis spinal cord. METHODS Four fixed spinal cord samples were imaged first with a 3T clinical MR scanner to identify areas of interest for scanning, and then with a 7T small bore scanner using a multicomponent-driven equilibrium single-pulse observation of T1 and T2 protocol to produce apparent proton density, T1 , T2 , myelin water, intracellular water, and free-water fraction maps. After imaging, the cords were sectioned and stained with histological markers (hematoxylin and eosin, myelin basic protein, and neurofilament protein), which were quantitatively compared with the MR maps. RESULTS Excellent correspondence was found between high-resolution MR parameter maps and histology, particularly for apparent proton density MRI and myelin basic protein staining. CONCLUSION High-resolution quantitative MRI of the spinal cord provides biologically meaningful measures, and could be beneficial to diagnose and track multiple sclerosis lesions in the spinal cord.
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Affiliation(s)
- Amy R McDowell
- Queen Square Centre for Neuromuscular Diseases, UCL, London, United Kingdom
| | - Natalia Petrova
- The Blizard Institute (Neuroscience, Surgery & Trauma), Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, United Kingdom
| | - Daniele Carassiti
- The Blizard Institute (Neuroscience, Surgery & Trauma), Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, United Kingdom
| | - Marc E Miquel
- Clinical Physics, Barts Health NHS Trust, London, United Kingdom
| | - David L Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, United Kingdom
| | | | - Klaus Schmierer
- The Blizard Institute (Neuroscience, Surgery & Trauma), Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, United Kingdom.,Clinical Board Medicine (Neuroscience), Barts Health NHS Trust, The Royal London Hospital, London, United Kingdom
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9
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West DJ, Cruz G, Teixeira RPAG, Schneider T, Tournier JD, Hajnal JV, Prieto C, Malik SJ. An MR fingerprinting approach for quantitative inhomogeneous magnetization transfer imaging. Magn Reson Med 2022; 87:220-235. [PMID: 34418151 PMCID: PMC7614010 DOI: 10.1002/mrm.28984] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/15/2021] [Accepted: 08/05/2021] [Indexed: 01/07/2023]
Abstract
PURPOSE Magnetization transfer (MT) and inhomogeneous MT (ihMT) contrasts are used in MRI to provide information about macromolecular tissue content. In particular, MT is sensitive to macromolecules, and ihMT appears to be specific to myelinated tissue. This study proposes a technique to characterize MT and ihMT properties from a single acquisition, producing both semiquantitative contrast ratios and quantitative parameter maps. THEORY AND METHODS Building on previous work that uses multiband RF pulses to efficiently generate ihMT contrast, we propose a cyclic steady-state approach that cycles between multiband and single-band pulses to boost the achieved contrast. Resultant time-variable signals are reminiscent of an MR fingerprinting acquisition, except that the signal fluctuations are entirely mediated by MT effects. A dictionary-based low-rank inversion method is used to reconstruct the resulting images and to produce both semiquantitative MT ratio and ihMT ratio maps, as well as quantitative parameter estimates corresponding to an ihMT tissue model. RESULTS Phantom and in vivo brain data acquired at 1.5 Tesla demonstrate the expected contrast trends, with ihMT ratio maps showing contrast more specific to white matter, as has been reported by others. Quantitative estimation of semisolid fraction and dipolar T1 was also possible and yielded measurements consistent with literature values in the brain. CONCLUSION By cycling between multiband and single-band pulses, an entirely MT-mediated fingerprinting method was demonstrated. This proof-of-concept approach can be used to generate semiquantitative maps and quantitatively estimate some macromolecular-specific tissue parameters.
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Affiliation(s)
- Daniel J. West
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Gastao Cruz
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Rui P. A. G. Teixeira
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | | | - Jacques-Donald Tournier
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Joseph V. Hajnal
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Shaihan J. Malik
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
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10
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Mohammadi S, Callaghan MF. Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging. J Neurosci Methods 2021; 348:108990. [PMID: 33129894 PMCID: PMC7840525 DOI: 10.1016/j.jneumeth.2020.108990] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/21/2020] [Accepted: 10/20/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND The g-ratio, quantifying the comparative thickness of the myelin sheath encasing an axon, is a geometrical invariant that has high functional relevance because of its importance in determining neuronal conduction velocity. Advances in MRI data acquisition and signal modelling have put in vivo mapping of the g-ratio, across the entire white matter, within our reach. This capacity would greatly increase our knowledge of the nervous system: how it functions, and how it is impacted by disease. NEW METHOD This is the second review on the topic of g-ratio mapping using MRI. RESULTS This review summarizes the most recent developments in the field, while also providing methodological background pertinent to aggregate g-ratio weighted mapping, and discussing pitfalls associated with these approaches. COMPARISON WITH EXISTING METHODS Using simulations based on recently published data, this review reveals caveats to the state-of-the-art calibration methods that have been used for in vivo g-ratio mapping. It highlights the need to estimate both the slope and offset of the relationship between these MRI-based markers and the true myelin volume fraction if we are really to achieve the goal of precise, high sensitivity g-ratio mapping in vivo. Other challenges discussed in this review further evidence the need for gold standard measurements of human brain tissue from ex vivo histology. CONCLUSIONS We conclude that the quest to find the most appropriate MRI biomarkers to enable in vivo g-ratio mapping is ongoing, with the full potential of many novel techniques yet to be investigated.
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Affiliation(s)
- Siawoosh Mohammadi
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
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11
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Dvorak AV, Ljungberg E, Vavasour IM, Lee LE, Abel S, Li DKB, Traboulsee A, MacKay AL, Kolind SH. Comparison of multi echo T 2 relaxation and steady state approaches for myelin imaging in the central nervous system. Sci Rep 2021; 11:1369. [PMID: 33446710 PMCID: PMC7809349 DOI: 10.1038/s41598-020-80585-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 12/22/2020] [Indexed: 12/11/2022] Open
Abstract
The traditional approach for measuring myelin-associated water with quantitative magnetic resonance imaging (MRI) uses multi-echo T2 relaxation data to calculate the myelin water fraction (MWF). A fundamentally different approach, abbreviated “mcDESPOT”, uses a more efficient steady-state acquisition to generate an equivalent metric (fM). Although previous studies have demonstrated inherent instability and bias in the complex mcDESPOT analysis procedure, fM has often been used as a surrogate for MWF. We produced and compared multivariate atlases of MWF and fM in healthy human brain and cervical spinal cord (available online) and compared their ability to detect multiple sclerosis pathology. A significant bias was found in all regions (p < 10–5), albeit reversed for spinal cord (fM-MWF = − 3.4%) compared to brain (+ 6.2%). MWF and fM followed an approximately linear relationship for regions with MWF < ~ 10%. For MWF > ~ 10%, the relationship broke down and fM no longer increased in tandem with MWF. For multiple sclerosis patients, MWF and fM Z score maps showed overlapping areas of low Z score and similar trends between patients and brain regions, although those of fM generally had greater spatial extent and magnitude of severity. These results will guide future choice of myelin-sensitive quantitative MRI and improve interpretation of studies using either myelin imaging approach.
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Affiliation(s)
- Adam V Dvorak
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada. .,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada.
| | - Emil Ljungberg
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Irene M Vavasour
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Lisa Eunyoung Lee
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Shawna Abel
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - David K B Li
- Radiology, University of British Columbia, Vancouver, BC, Canada.,Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Anthony Traboulsee
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Alex L MacKay
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Shannon H Kolind
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada.,Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
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12
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Bladt P, den Dekker AJ, Clement P, Achten E, Sijbers J. The costs and benefits of estimating T 1 of tissue alongside cerebral blood flow and arterial transit time in pseudo-continuous arterial spin labeling. NMR IN BIOMEDICINE 2020; 33:e4182. [PMID: 31736223 PMCID: PMC7685117 DOI: 10.1002/nbm.4182] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 07/09/2019] [Accepted: 08/14/2019] [Indexed: 06/10/2023]
Abstract
Multi-post-labeling-delay pseudo-continuous arterial spin labeling (multi-PLD PCASL) allows for absolute quantification of the cerebral blood flow (CBF) as well as the arterial transit time (ATT). Estimating these perfusion parameters from multi-PLD PCASL data is a non-linear inverse problem, which is commonly tackled by fitting the single-compartment model (SCM) for PCASL, with CBF and ATT as free parameters. The longitudinal relaxation time of tissue T1t is an important parameter in this model, as it governs the decay of the perfusion signal entirely upon entry in the imaging voxel. Conventionally, T1t is fixed to a population average. This approach can cause CBF quantification errors, as T1t can vary significantly inter- and intra-subject. This study compares the impact on CBF quantification, in terms of accuracy and precision, of either fixing T1t , the conventional approach, or estimating it alongside CBF and ATT. It is shown that the conventional approach can cause a significant bias in CBF. Indeed, simulation experiments reveal that if T1t is fixed to a value that is 10% off its true value, this may already result in a bias of 15% in CBF. On the other hand, as is shown by both simulation and real data experiments, estimating T1t along with CBF and ATT results in a loss of CBF precision of the same order, even if the experiment design is optimized for the latter estimation problem. Simulation experiments suggest that an optimal balance between accuracy and precision of CBF estimation from multi-PLD PCASL data can be expected when using the two-parameter estimator with a fixed T1t value between population averages of T1t and the longitudinal relaxation time of blood T1b .
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Affiliation(s)
- Piet Bladt
- imec‐Vision Lab, Department of PhysicsUniversity of Antwerp2610AntwerpBelgium
| | - Arnold J. den Dekker
- imec‐Vision Lab, Department of PhysicsUniversity of Antwerp2610AntwerpBelgium
- Delft Center for Systems and ControlDelft University of Technology2628 CDDelftThe Netherlands
| | - Patricia Clement
- Department of Radiology and Nuclear MedicineGhent University9000GhentBelgium
| | - Eric Achten
- Department of Radiology and Nuclear MedicineGhent University9000GhentBelgium
| | - Jan Sijbers
- imec‐Vision Lab, Department of PhysicsUniversity of Antwerp2610AntwerpBelgium
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13
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Qian W, Khattar N, Cortina LE, Spencer RG, Bouhrara M. Nonlinear associations of neurite density and myelin content with age revealed using multicomponent diffusion and relaxometry magnetic resonance imaging. Neuroimage 2020; 223:117369. [PMID: 32931942 PMCID: PMC7775614 DOI: 10.1016/j.neuroimage.2020.117369] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/07/2020] [Accepted: 09/08/2020] [Indexed: 12/18/2022] Open
Abstract
Most magnetic resonance imaging (MRI) studies investigating the relationship between regional brain myelination or axonal density and aging have relied upon nonspecific methods to probe myelin and axonal content, including diffusion tensor imaging and relaxation time mapping. While these studies have provided pivotal insights into changes in cerebral architecture with aging and pathology, details of the underlying microstructural alterations have not been fully elucidated. In the current study, we used the BMC-mcDESPOT analysis, a direct and specific multicomponent relaxometry method for imaging of myelin water fraction (MWF), a marker of myelin content, and NODDI, an emerging multicomponent diffusion technique, for neurite density index (NDI) imaging, a proxy of axonal density. We investigated age-related differences in MWF and NDI in several white matter brain regions in a cohort of cognitively unimpaired participants over a wide age range. Our results indicate a quadratic, inverted U-shape, relationship between MWF and age in all brain regions investigated, suggesting that myelination continues until middle age followed by a decrease at older ages, in agreement with previous work. We found a similarly complex regional association between NDI and age, with several cerebral structures also exhibiting a quadratic, inverted U-shape, relationship. This novel observation suggests an increase in axonal density until the fourth decade of age followed by a rapid loss at older ages. We also observed that these age-related differences in MWF and NDI vary across different brain regions, as expected. Finally, our study indicates no significant association between MWF and NDI in most cerebral structures investigated, although this association approached significance in a limited number of brain regions, indicating the complementary nature of their information and encouraging further investigation. Overall, we find evidence of nonlinear associations between age and myelin or axonal density in a sample of well-characterized adults, using direct myelin and axonal content imaging methods.
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Affiliation(s)
- Wenshu Qian
- Magnetic Resonance Physics of Aging and Dementia Unit, Laboratory of Clinical Investigations, National Institute on Aging, National Institutes of Health, NIA, NIH, 251 Bayview Blvd., Baltimore, MD 21224, USA
| | - Nikkita Khattar
- Magnetic Resonance Physics of Aging and Dementia Unit, Laboratory of Clinical Investigations, National Institute on Aging, National Institutes of Health, NIA, NIH, 251 Bayview Blvd., Baltimore, MD 21224, USA
| | - Luis E Cortina
- Magnetic Resonance Physics of Aging and Dementia Unit, Laboratory of Clinical Investigations, National Institute on Aging, National Institutes of Health, NIA, NIH, 251 Bayview Blvd., Baltimore, MD 21224, USA
| | - Richard G Spencer
- Magnetic Resonance Physics of Aging and Dementia Unit, Laboratory of Clinical Investigations, National Institute on Aging, National Institutes of Health, NIA, NIH, 251 Bayview Blvd., Baltimore, MD 21224, USA
| | - Mustapha Bouhrara
- Magnetic Resonance Physics of Aging and Dementia Unit, Laboratory of Clinical Investigations, National Institute on Aging, National Institutes of Health, NIA, NIH, 251 Bayview Blvd., Baltimore, MD 21224, USA.
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14
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Whitaker ST, Nataraj G, Nielsen JF, Fessler JA. Myelin water fraction estimation using small-tip fast recovery MRI. Magn Reson Med 2020; 84:1977-1990. [PMID: 32281179 PMCID: PMC7478173 DOI: 10.1002/mrm.28259] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 02/05/2020] [Accepted: 02/26/2020] [Indexed: 11/09/2022]
Abstract
PURPOSE To demonstrate the feasibility of an optimized set of small-tip fast recovery (STFR) MRI scans for rapidly estimating myelin water fraction (MWF) in the brain. METHODS We optimized a set of STFR scans to minimize the Cramér-Rao Lower Bound of MWF estimates. We evaluated the RMSE of MWF estimates from the optimized scans in simulation. We compared STFR-based MWF estimates (both modeling exchange and not modeling exchange) to multi-echo spin echo (MESE)-based estimates. We used the optimized scans to acquire in vivo data from which a MWF map was estimated. We computed the STFR-based MWF estimates using PERK, a recently developed kernel regression technique, and the MESE-based MWF estimates using both regularized non-negative least squares (NNLS) and PERK. RESULTS In simulation, the optimized STFR scans led to estimates of MWF with low RMSE across a range of tissue parameters and across white matter and gray matter. The STFR-based MWF estimates that modeled exchange compared well to MESE-based MWF estimates in simulation. When the optimized scans were tested in vivo, the MWF map that was estimated using a 3-compartment model with exchange was closer to the MESE-based MWF map. CONCLUSIONS The optimized STFR scans appear to be well suited for estimating MWF in simulation and in vivo when we model exchange in training. In this case, the STFR-based MWF estimates are close to the MESE-based estimates.
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Affiliation(s)
- Steven T. Whitaker
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA
| | - Gopal Nataraj
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jon-Fredrik Nielsen
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Jeffrey A. Fessler
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA
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15
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Lynn JD, Anand C, Arshad M, Homayouni R, Rosenberg DR, Ofen N, Raz N, Stanley JA. Microstructure of Human Corpus Callosum across the Lifespan: Regional Variations in Axon Caliber, Density, and Myelin Content. Cereb Cortex 2020; 31:1032-1045. [PMID: 32995843 DOI: 10.1093/cercor/bhaa272] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 08/21/2020] [Accepted: 08/21/2020] [Indexed: 12/13/2022] Open
Abstract
The myeloarchitecture of the corpus callosum (CC) is characterized as a mosaic of distinct differences in fiber density of small- and large-diameter axons along the anterior-posterior axis; however, regional and age differences across the lifespan are not fully understood. Using multiecho T2 magnetic resonance imaging combined with multi-T2 fitting, the myelin water fraction (MWF) and geometric-mean of the intra-/extracellular water T2 (geomT2IEW) in 395 individuals (7-85 years; 41% males) were examined. The approach was validated where regional patterns along the CC closely resembled the histology; MWF matched mean axon diameter and geomT2IEW mirrored the density of large-caliber axons. Across the lifespan, MWF exhibited a quadratic association with age in all 10 CC regions with evidence of a positive linear MWF-age relationship among younger participants and minimal age differences in the remainder of the lifespan. Regarding geomT2IEW, a significant linear age × region interaction reflected positive linear age dependence mostly prominent in the regions with the highest density of small-caliber fibers-genu and splenium. In all, these two indicators characterize distinct attributes that are consistent with histology, which is a first. In addition, these results conform to rapid developmental progression of CC myelination leveling in middle age as well as age-related degradation of axon sheaths in older adults.
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Affiliation(s)
- Jonathan D Lynn
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
| | - Chaitali Anand
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
| | - Muzamil Arshad
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
| | - Roya Homayouni
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
- Department of Psychology, Wayne State University, Detroit MI 48201, USA
| | - David R Rosenberg
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
| | - Noa Ofen
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
- Department of Psychology, Wayne State University, Detroit MI 48201, USA
- Lifespan Cognitive Neuroscience, Merrill Palmer Skillman Institute, Wayne State University, Detroit MI 14195, USA
| | - Naftali Raz
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
- Department of Psychology, Wayne State University, Detroit MI 48201, USA
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin 14195, Germany
| | - Jeffrey A Stanley
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
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16
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Piredda GF, Hilbert T, Thiran JP, Kober T. Probing myelin content of the human brain with MRI: A review. Magn Reson Med 2020; 85:627-652. [PMID: 32936494 DOI: 10.1002/mrm.28509] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 08/12/2020] [Accepted: 08/17/2020] [Indexed: 12/11/2022]
Abstract
Rapid and efficient transmission of electric signals among neurons of vertebrates is ensured by myelin-insulating sheaths surrounding axons. Human cognition, sensation, and motor functions rely on the integrity of these layers, and demyelinating diseases often entail serious cognitive and physical impairments. Magnetic resonance imaging radically transformed the way these disorders are monitored, offering an irreplaceable tool to noninvasively examine the brain structure. Several advanced techniques based on MRI have been developed to provide myelin-specific contrasts and a quantitative estimation of myelin density in vivo. Here, the vast offer of acquisition strategies developed to date for this task is reviewed. Advantages and pitfalls of the different approaches are compared and discussed.
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Affiliation(s)
- Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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17
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Jung S, Lee H, Ryu K, Song JE, Park M, Moon WJ, Kim DH. Artificial neural network for multi-echo gradient echo-based myelin water fraction estimation. Magn Reson Med 2020; 85:380-389. [PMID: 32686208 DOI: 10.1002/mrm.28407] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 06/09/2020] [Accepted: 06/09/2020] [Indexed: 02/01/2023]
Abstract
PURPOSE To demonstrate robust myelin water fraction (MWF) mapping using an artificial neural network (ANN) with multi-echo gradient-echo (GRE) signal. METHODS Multi-echo gradient-echo signals simulated with a three-pool exponential model were used to generate the training data set for the ANN, which was designed to yield the MWF. We investigated the performance of our proposed ANN for various conditions using both numerical simulations and in vivo data. Simulations were conducted with various SNRs to investigate the performance of the ANN. In vivo data with high spatial resolutions were applied in the analyses, and results were compared with MWFs derived by the nonlinear least-squares algorithm using a complex three-pool exponential model. RESULTS The network results for the simulations show high accuracies against noise compared with nonlinear least-squares MWFs: RMS-error value of 5.46 for the nonlinear least-squares MWF and 3.56 for the ANN MWF at an SNR of 150 (relative gain = 34.80%). These effects were also found in the in vivo data, with reduced SDs in the region-of-interest analyses. These effects of the ANN demonstrate the feasibility of acquiring high-resolution myelin water images. CONCLUSION The simulation results and in vivo data suggest that the ANN facilitates more robust MWF mapping in multi-echo gradient-echo sequences compared with the conventional nonlinear least-squares method.
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Affiliation(s)
- Soozy Jung
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Hongpyo Lee
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Kanghyun Ryu
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Jae Eun Song
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Mina Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Won-Jin Moon
- Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
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18
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Lee J, Hyun JW, Lee J, Choi EJ, Shin HG, Min K, Nam Y, Kim HJ, Oh SH. So You Want to Image Myelin Using MRI: An Overview and Practical Guide for Myelin Water Imaging. J Magn Reson Imaging 2020; 53:360-373. [PMID: 32009271 DOI: 10.1002/jmri.27059] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/01/2020] [Accepted: 01/02/2020] [Indexed: 12/22/2022] Open
Abstract
Myelin water imaging (MWI) is an MRI imaging biomarker for myelin. This method can generate an in vivo whole-brain myelin water fraction map in approximately 10 minutes. It has been applied in various applications including neurodegenerative disease, neurodevelopmental, and neuroplasticity studies. In this review we start with a brief introduction of myelin biology and discuss the contributions of myelin in conventional MRI contrasts. Then the MRI properties of myelin water and four different MWI methods, which are categorized as T2 -, T2 *-, T1 -, and steady-state-based MWI, are summarized. After that, we cover more practical issues such as availability, interpretation, and validation of these methods. To illustrate the utility of MWI as a clinical research tool, MWI studies for two diseases, multiple sclerosis and neuromyelitis optica, are introduced. Additional topics about imaging myelin in gray matter and non-MWI methods for myelin imaging are also included. Although technical and physiological limitations exist, MWI is a potent surrogate biomarker of myelin that carries valuable and useful information of myelin. Evidence Level: 5 Technical Efficacy: 1 J. MAGN. RESON. IMAGING 2021;53:360-373.
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Affiliation(s)
- Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Jae-Won Hyun
- Department of Neurology, Research Institute and Hospital, National Cancer Center, Goyang-si, Korea
| | - Jieun Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Eun-Jung Choi
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Hyeong-Geol Shin
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Kyeongseon Min
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Yoonho Nam
- Department of Radiology, Seoul Saint Mary's Hospital, College of Medicine, Catholic University of Korea, Seoul, Korea
| | - Ho Jin Kim
- Department of Neurology, Research Institute and Hospital, National Cancer Center, Goyang-si, Korea
| | - Se-Hong Oh
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Korea.,Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
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19
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Drakesmith M, Harms R, Rudrapatna SU, Parker GD, Evans CJ, Jones DK. Estimating axon conduction velocity in vivo from microstructural MRI. Neuroimage 2019; 203:116186. [PMID: 31542512 PMCID: PMC6854468 DOI: 10.1016/j.neuroimage.2019.116186] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 09/09/2019] [Accepted: 09/10/2019] [Indexed: 11/19/2022] Open
Abstract
The conduction velocity (CV) of action potentials along axons is a key neurophysiological property central to neural communication. The ability to estimate CV in humans in vivo from non-invasive MRI methods would therefore represent a significant advance in neuroscience. However, there are two major challenges that this paper aims to address: (1) Much of the complexity of the neurophysiology of action potentials cannot be captured with currently available MRI techniques. Therefore, we seek to establish the variability in CV that can be captured when predicting CV purely from parameters that have been reported to be estimatable from MRI: inner axon diameter (AD) and g-ratio. (2) errors inherent in existing MRI-based biophysical models of tissue will propagate through to estimates of CV, the extent to which is currently unknown. Issue (1) is investigated by performing a sensitivity analysis on a comprehensive model of axon electrophysiology and determining the relative sensitivity to various morphological and electrical parameters. The investigations suggest that 85% of the variance in CV is accounted for by variation in AD and g-ratio. The observed dependency of CV on AD and g-ratio is well characterised by the previously reported model by Rushton. Issue (2) is investigated through simulation of diffusion and relaxometry MRI data for a range of axon morphologies, applying models of restricted diffusion and relaxation processes to derive estimates of axon volume fraction (AVF), AD and g-ratio and estimating CV from the derived parameters. The results show that errors in the AVF have the biggest detrimental impact on estimates of CV, particularly for sparse fibre populations (AVF<0.3). For our equipment set-up and acquisition protocol, CV estimates are most accurate (below 5% error) where AVF is above 0.3, g-ratio is between 0.6 and 0.85 and AD is high (above 4μm). CV estimates are robust to errors in g-ratio estimation but are highly sensitive to errors in AD estimation, particularly where ADs are small. We additionally show CV estimates in human corpus callosum in a small number of subjects. In conclusion, we demonstrate accurate CV estimates are possible in regions of the brain where AD is sufficiently large. Problems with estimating ADs for smaller axons presents a problem for estimating CV across the whole CNS and should be the focus of further study.
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Affiliation(s)
- Mark Drakesmith
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom.
| | - Robbert Harms
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands
| | - Suryanarayana Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Phillips Inovation Campus, Bangalore, India
| | - Greg D Parker
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Experimental MRI Centre (EMRIC), School of Biosciences, Cardiff University, Cardiff, United Kingdom
| | - C John Evans
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; Mary McKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, 3065, Australia
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20
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Quantitative age-dependent differences in human brainstem myelination assessed using high-resolution magnetic resonance mapping. Neuroimage 2019; 206:116307. [PMID: 31669302 DOI: 10.1016/j.neuroimage.2019.116307] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 10/18/2019] [Accepted: 10/21/2019] [Indexed: 12/13/2022] Open
Abstract
Previous in-vivo magnetic resonance imaging (MRI)-based studies of age-related differences in the human brainstem have focused on volumetric morphometry. These investigations have provided pivotal insights into regional brainstem atrophy but have not addressed microstructural age differences. However, growing evidence indicates the sensitivity of quantitative MRI to microstructural tissue changes in the brain. These studies have largely focused on the cerebrum, with very few MR investigations addressing age-dependent differences in the brainstem, in spite of its central role in the regulation of vital functions. Several studies indicate early brainstem alterations in a myriad of neurodegenerative diseases and dementias. The paucity of MR-focused investigations is likely due in part to the challenges imposed by the small structural scale of the brainstem itself as well as of substructures within, requiring accurate high spatial resolution imaging studies. In this work, we applied our recently developed approach to high-resolution myelin water fraction (MWF) mapping, a proxy for myelin content, to investigate myelin differences with normal aging within the brainstem. In this cross-sectional investigation, we studied a large cohort (n = 125) of cognitively unimpaired participants spanning a wide age range (21-94 years) and found a decrease in myelination with age in most brainstem regions studied, with several regions exhibiting a quadratic association between myelin and age. We believe that this study is the first investigation of MWF differences with normative aging in the adult brainstem. Further, our results provide reference MWF values.
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21
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Adult brain aging investigated using BMC-mcDESPOT-based myelin water fraction imaging. Neurobiol Aging 2019; 85:131-139. [PMID: 31735379 DOI: 10.1016/j.neurobiolaging.2019.10.003] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 09/30/2019] [Accepted: 10/07/2019] [Indexed: 01/23/2023]
Abstract
The relationship between regional brain myelination and aging has been the subject of intense study, with magnetic resonance imaging perhaps the most effective modality for elucidating this. However, most of these studies have used nonspecific methods to probe myelin content, including diffusion tensor imaging, magnetization transfer ratio, and relaxation times. In the present study, we used the BMC-mcDESPOT analysis, a direct and specific method for imaging of myelin water fraction (MWF), a surrogate of myelin content. We investigated age-related differences in MWF in several brain regions in a large cohort of cognitively unimpaired participants, spanning a wide age range. Our results indicate a quadratic, inverted U-shape, relationship between MWF and age in all brain regions investigated, suggesting that myelination continues until middle age followed by decreases at older ages. We also observed that these age-related differences vary across different brain regions, as expected. Our results provide evidence for nonlinear associations between age and myelin in a large sample of well-characterized adults, using a direct myelin content imaging method.
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22
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Soustelle L, Antal MC, Lamy J, Rousseau F, Armspach JP, Loureiro de Sousa P. Correlations of quantitative MRI metrics with myelin basic protein (MBP) staining in a murine model of demyelination. NMR IN BIOMEDICINE 2019; 32:e4116. [PMID: 31225675 DOI: 10.1002/nbm.4116] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 04/17/2019] [Accepted: 04/21/2019] [Indexed: 06/09/2023]
Abstract
Myelin imaging in the central nervous system is essential for monitoring pathologies involving white matter alterations. Various quantitative MRI protocols relying on the modeling of the interactions of water protons with myelinated tissues have shown sensitivities in case of myelin disruption. Some extracted model parameters are more sensitive to demyelination, such as the bound pool fraction (f) in quantitative magnetization transfer imaging (qMTI), the radial diffusivity in diffusion tensor imaging (DTI), and the myelin water fraction (MWF) in myelin water imaging (MWI). A 3D ultrashort echo time (UTE) sequence within an appropriate water suppression condition (Diff-UTE) is also considered for the direct visualization of the myelin semi-solid matrix (Diff-UTE normalized signal; rSPF). In this paper, we aimed at assessing the sensitivities and correlations of the parameters mentioned above to an immuno-histological study of the myelin basic protein (MBP) in a murine model of demyelination at 7 T. We demonstrated a high sensitivity of the MRI metrics to demyelination, and strong Spearman correlations in the corpus callosum between histology, macromolecular proton fraction (ρ>0.87) and Diff-UTE signal (ρ>0.76), but moderate ones with radial diffusivity and MWF (|ρ|<0.70).
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Affiliation(s)
- Lucas Soustelle
- Université de Strasbourg, CNRS, ICube, FMTS, Strasbourg, France
| | - Maria C Antal
- Université de Strasbourg, CNRS, ICube, FMTS, Strasbourg, France
| | - Julien Lamy
- Université de Strasbourg, CNRS, ICube, FMTS, Strasbourg, France
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23
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West DJ, Teixeira RPAG, Wood TC, Hajnal JV, Tournier JD, Malik SJ. Inherent and unpredictable bias in multi-component DESPOT myelin water fraction estimation. Neuroimage 2019; 195:78-88. [PMID: 30930311 PMCID: PMC7100802 DOI: 10.1016/j.neuroimage.2019.03.049] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 03/13/2019] [Accepted: 03/21/2019] [Indexed: 01/13/2023] Open
Abstract
Multicomponent driven equilibrium steady-state observation of T1 and T2 (mcDESPOT) aims to quantify the Myelin Water Fraction (MWF) using a two-pool microstructural model. The MWF has been used to track neurodevelopment and neurodegeneration and has been histologically correlated to myelin content. mcDESPOT has a clinically feasible acquisition time and high signal-to-noise ratio (SNR) relative to other MWF techniques. However, disagreement exists in the literature between experimental studies that show MWF maps with plausible grey matter-white matter (GM-WM) contrast and theoretical work that questions the accuracy and precision of mcDESPOT. We demonstrate that mcDESPOT parameter estimation is inaccurate and imprecise if intercompartmental exchange is included in the microstructural model, but that significant bias results if exchange is neglected. The source of apparent MWF contrast is likely due to the complex convergence behaviour of the Stochastic Region Contraction (SRC) method commonly used to fit the mcDESPOT model. mcDESPOT-derived parameter estimates are hence not directly relatable to the underlying microstructural model and are only comparable to others using similar acquisition schemes and fitting constraints.
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Affiliation(s)
- Daniel J West
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom.
| | - Rui P A G Teixeira
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - Tobias C Wood
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, Camberwell, London, SE5 8AB, United Kingdom
| | - Joseph V Hajnal
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - Jacques-Donald Tournier
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - Shaihan J Malik
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom
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24
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West KL, Kelm ND, Carson RP, Gochberg DF, Ess KC, Does MD. Myelin volume fraction imaging with MRI. Neuroimage 2018; 182:511-521. [PMID: 28025129 PMCID: PMC5481512 DOI: 10.1016/j.neuroimage.2016.12.067] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Revised: 11/23/2016] [Accepted: 12/22/2016] [Indexed: 11/20/2022] Open
Abstract
MRI is a valuable tool to assess myelin during development and demyelinating disease processes. While multiexponential T2 and quantitative magnetization transfer measures correlate with myelin content, neither provides the total myelin volume fraction. In many cases correlative measures are adequate; but to assess microstructure of myelin, (e.g. calculate the g-ratio using MRI), an accurate measure of myelin volume fraction is imperative. Using a volumetric model of white matter, we relate MRI measures of myelin to absolute measures of myelin volume fraction and compare them to quantitative histology. We assess our approach in control mice along with two models of hypomyelination and one model of hypermyelination and find strong agreement between MRI and histology amongst models. This work investigates the sensitivities of MRI myelin measures to changes in axon geometry and displays promise for estimating g-ratio from MRI.
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Affiliation(s)
- Kathryn L West
- Department of Biomedical Engineering, Vanderbilt University, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University, USA
| | - Nathaniel D Kelm
- Department of Biomedical Engineering, Vanderbilt University, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University, USA
| | - Robert P Carson
- Department of Pediatrics, Vanderbilt University School of Medicine, USA; Department of Neurology, Vanderbilt University School of Medicine, USA
| | - Daniel F Gochberg
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, USA; Department of Radiology and Radiological Sciences, Vanderbilt University School of Medicine, USA
| | - Kevin C Ess
- Department of Pediatrics, Vanderbilt University School of Medicine, USA; Department of Neurology, Vanderbilt University School of Medicine, USA
| | - Mark D Does
- Department of Biomedical Engineering, Vanderbilt University, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University, USA; Department of Radiology and Radiological Sciences, Vanderbilt University School of Medicine, USA; Department of Electrical Engineering, Vanderbilt University, USA.
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25
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Dortch RD, Bagnato F, Gochberg DF, Gore JC, Smith SA. Optimization of selective inversion recovery magnetization transfer imaging for macromolecular content mapping in the human brain. Magn Reson Med 2018; 80:1824-1835. [PMID: 29573356 DOI: 10.1002/mrm.27174] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 02/01/2018] [Accepted: 02/17/2018] [Indexed: 11/11/2022]
Abstract
PURPOSE To optimize a selective inversion recovery (SIR) sequence for macromolecular content mapping in the human brain at 3.0T. THEORY AND METHODS SIR is a quantitative method for measuring magnetization transfer (qMT) that uses a low-power, on-resonance inversion pulse. This results in a biexponential recovery of free water signal that can be sampled at various inversion/predelay times (tI/ tD ) to estimate a subset of qMT parameters, including the macromolecular-to-free pool-size-ratio (PSR), the R1 of free water (R1f ), and the rate of MT exchange (kmf ). The adoption of SIR has been limited by long acquisition times (≈4 min/slice). Here, we use Cramér-Rao lower bound theory and data reduction strategies to select optimal tI /tD combinations to reduce imaging times. The schemes were experimentally validated in phantoms, and tested in healthy volunteers (N = 4) and a multiple sclerosis patient. RESULTS Two optimal sampling schemes were determined: (i) a 5-point scheme (kmf estimated) and (ii) a 4-point scheme (kmf assumed). In phantoms, the 5/4-point schemes yielded parameter estimates with similar SNRs as our previous 16-point scheme, but with 4.1/6.1-fold shorter scan times. Pair-wise comparisons between schemes did not detect significant differences for any scheme/parameter. In humans, parameter values were consistent with published values, and similar levels of precision were obtained from all schemes. Furthermore, fixing kmf reduced the sensitivity of PSR to partial-volume averaging, yielding more consistent estimates throughout the brain. CONCLUSIONS qMT parameters can be robustly estimated in ≤1 min/slice (without independent measures of ΔB0 , B1+, and T1 ) when optimized tI -tD combinations are selected.
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Affiliation(s)
- Richard D Dortch
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
| | - Francesca Bagnato
- Department of Neurology/Neuroimmunology Division/Neuroimaging Unit, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Daniel F Gochberg
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee
| | - John C Gore
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.,Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee.,Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee
| | - Seth A Smith
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.,Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee
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26
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Gilmore JH, Knickmeyer RC, Gao W. Imaging structural and functional brain development in early childhood. Nat Rev Neurosci 2018; 19:123-137. [PMID: 29449712 PMCID: PMC5987539 DOI: 10.1038/nrn.2018.1] [Citation(s) in RCA: 471] [Impact Index Per Article: 78.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
In humans, the period from term birth to ∼2 years of age is characterized by rapid and dynamic brain development and plays an important role in cognitive development and risk of disorders such as autism and schizophrenia. Recent imaging studies have begun to delineate the growth trajectories of brain structure and function in the first years after birth and their relationship to cognition and risk of neuropsychiatric disorders. This Review discusses the development of grey and white matter and structural and functional networks, as well as genetic and environmental influences on early-childhood brain development. We also discuss initial evidence regarding the usefulness of early imaging biomarkers for predicting cognitive outcomes and risk of neuropsychiatric disorders.
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Affiliation(s)
- John H Gilmore
- Department of Psychiatry, CB# 7160, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Rebecca C Knickmeyer
- Department of Psychiatry, CB# 7160, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Wei Gao
- Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Medicine, University of California, Los Angeles, CA, USA
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27
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Does MD. Inferring brain tissue composition and microstructure via MR relaxometry. Neuroimage 2018; 182:136-148. [PMID: 29305163 DOI: 10.1016/j.neuroimage.2017.12.087] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 12/25/2017] [Accepted: 12/27/2017] [Indexed: 11/28/2022] Open
Abstract
MRI relaxometry is sensitive to a variety of tissue characteristics in a complex manner, which makes it both attractive and challenging for characterizing tissue. This article reviews the most common water proton relaxometry measures, T1, T2, and T2*, and reports on their development and current potential to probe the composition and microstructure of brain tissue. The development of these relaxometry measures is challenged by the need for suitably accurate tissue models, as well as robust acquisition and analysis methodologies. MRI relaxometry has been established as a tool for characterizing neural tissue, particular with respect to myelination, and the potential for further development exists.
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Affiliation(s)
- Mark D Does
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA.
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28
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Heath F, Hurley SA, Johansen-Berg H, Sampaio-Baptista C. Advances in noninvasive myelin imaging. Dev Neurobiol 2017; 78:136-151. [PMID: 29082667 PMCID: PMC5813152 DOI: 10.1002/dneu.22552] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 09/18/2017] [Accepted: 10/24/2017] [Indexed: 12/11/2022]
Abstract
Myelin is important for the normal development and healthy function of the nervous system. Recent developments in MRI acquisition and tissue modeling aim to provide a better characterization and more specific markers for myelin. This allows for specific monitoring of myelination longitudinally and noninvasively in the healthy brain as well as assessment of treatment and intervention efficacy. Here, we offer a nontechnical review of MRI techniques developed to specifically monitor myelin such as magnetization transfer (MT) and myelin water imaging (MWI). We further summarize recent studies that employ these methods to measure myelin in relation to development and aging, learning and experience, and neuropathology and psychiatric disorders. © 2017 The Authors. Developmental Neurobiology Published by Wiley Periodicals, Inc. Develop Neurobiol 78: 136–151, 2018
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Affiliation(s)
- Florence Heath
- Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Samuel A Hurley
- Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, United Kingdom.,Departments of Neuroscience and Radiology, 1111 Highland Ave, University of Wisconsin - Madison, Madison, Wisconsin, 53705
| | - Heidi Johansen-Berg
- Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Cassandra Sampaio-Baptista
- Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, United Kingdom
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29
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Bouhrara M, Spencer RG. Fisher information and Cramér-Rao lower bound for experimental design in parallel imaging. Magn Reson Med 2017; 79:3249-3255. [PMID: 29090485 DOI: 10.1002/mrm.26984] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2017] [Revised: 09/14/2017] [Accepted: 10/04/2017] [Indexed: 01/13/2023]
Abstract
PURPOSE The Cramér-Rao lower bound (CRLB) is widely used in the design of magnetic resonance (MR) experiments for parameter estimation. Previous work has considered only Gaussian or Rician noise distributions in this calculation. However, the noise distribution for multi-coil acquisitions, such as in parallel imaging, obeys the noncentral χ-distribution under many circumstances. The purpose of this paper is to present the CRLB calculation for parameter estimation from multi-coil acquisitions. THEORY AND METHODS We perform explicit calculations of Fisher matrix elements and the associated CRLB for noise distributions following the noncentral χ-distribution. The special case of diffusion kurtosis is examined as an important example. For comparison with analytic results, Monte Carlo (MC) simulations were conducted to evaluate experimental minimum standard deviations (SDs) in the estimation of diffusion kurtosis model parameters. Results were obtained for a range of signal-to-noise ratios (SNRs), and for both the conventional case of Gaussian noise distribution and noncentral χ-distribution with different numbers of coils, m. RESULTS At low-to-moderate SNR, the noncentral χ-distribution deviates substantially from the Gaussian distribution. Our results indicate that this departure is more pronounced for larger values of m. As expected, the minimum SDs (i.e., CRLB) in derived diffusion kurtosis model parameters assuming a noncentral χ-distribution provided a closer match to the MC simulations as compared to the Gaussian results. CONCLUSION Estimates of minimum variance for parameter estimation and experimental design provided by the CRLB must account for the noncentral χ-distribution of noise in multi-coil acquisitions, especially in the low-to-moderate SNR regime. Magn Reson Med 79:3249-3255, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Richard G Spencer
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
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Campbell JSW, Leppert IR, Narayanan S, Boudreau M, Duval T, Cohen-Adad J, Pike GB, Stikov N. Promise and pitfalls of g-ratio estimation with MRI. Neuroimage 2017; 182:80-96. [PMID: 28822750 DOI: 10.1016/j.neuroimage.2017.08.038] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 07/28/2017] [Accepted: 08/12/2017] [Indexed: 12/13/2022] Open
Abstract
The fiber g-ratio is the ratio of the inner to the outer diameter of the myelin sheath of a myelinated axon. It has a limited dynamic range in healthy white matter, as it is optimized for speed of signal conduction, cellular energetics, and spatial constraints. In vivo imaging of the g-ratio in health and disease would greatly increase our knowledge of the nervous system and our ability to diagnose, monitor, and treat disease. MRI based g-ratio imaging was first conceived in 2011, and expanded to be feasible in full brain white matter with preliminary results in 2013. This manuscript reviews the growing g-ratio imaging literature and speculates on future applications. It details the methodology for imaging the g-ratio with MRI, and describes the known pitfalls and challenges in doing so.
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Affiliation(s)
- Jennifer S W Campbell
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada; NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada.
| | - Ilana R Leppert
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sridar Narayanan
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Mathieu Boudreau
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Tanguy Duval
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montréal, QC, Canada
| | | | - Nikola Stikov
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada; Montreal Heart Institute, Université de Montréal, Montréal, QC, Canada
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Mohammadi S, Weiskopf N. [Computational neuroanatomy and microstructure imaging using magnetic resonance imaging]. DER NERVENARZT 2017; 88:839-849. [PMID: 28721539 DOI: 10.1007/s00115-017-0373-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Current computational neuroanatomy focuses on morphological measurements of the brain using standard magnetic resonance imaging (MRI) techniques. In comparison quantitative MRI (qMRI) typically provides a better tissue contrast and also greatly improves the sensitivity and specificity with respect to the microstructural characteristics of tissue. OBJECTIVE Current methodological developments in qMRI are presented, which go beyond morphology because this provides standardized measurements of the microstructure of the brain. The concept of in-vivo histology is introduced, based on biophysical modelling of qMRI data (hMRI) for determination of quantitative histology-like markers of the microstructure. RESULTS The qMRI metrics can be used as direct biomarkers of the microstructural mechanisms driving observed morphological findings. The hMRI metrics utilize biophysical models of the MRI signal in order to determine 3‑dimensional maps of histology-like measurements in the white matter. CONCLUSION Non-invasive brain tissue characterization using qMRI or hMRI has significant implications for both scientific and clinical applications. Both approaches improve the comparability across sites and time points, facilitate multicenter and longitudinal studies as well as standardized diagnostics. The hMRI is expected to shed new light on the relationship between brain microstructure, function and behavior both in health and disease. In the future hMRI will play an indispensable role in the field of computational neuroanatomy.
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Affiliation(s)
- S Mohammadi
- Institut für systemische Neurowissenschaften, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
- Max-Planck-Institut für Kognitions- und Neurowissenschaften, Stephanstr. 1a, 04103, Leipzig, Deutschland
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, Großbritannien
| | - N Weiskopf
- Max-Planck-Institut für Kognitions- und Neurowissenschaften, Stephanstr. 1a, 04103, Leipzig, Deutschland.
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, Großbritannien.
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Bouhrara M, Reiter DA, Sexton KW, Bergeron CM, Zukley LM, Spencer RG. Clinical high-resolution mapping of the proteoglycan-bound water fraction in articular cartilage of the human knee joint. Magn Reson Imaging 2017. [PMID: 28645697 DOI: 10.1016/j.mri.2017.06.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
PURPOSE We applied our recently introduced Bayesian analytic method to achieve clinically-feasible in-vivo mapping of the proteoglycan water fraction (PgWF) of human knee cartilage with improved spatial resolution and stability as compared to existing methods. MATERIALS AND METHODS Multicomponent driven equilibrium single-pulse observation of T1 and T2 (mcDESPOT) datasets were acquired from the knees of two healthy young subjects and one older subject with previous knee injury. Each dataset was processed using Bayesian Monte Carlo (BMC) analysis incorporating a two-component tissue model. We assessed the performance and reproducibility of BMC and of the conventional analysis of stochastic region contraction (SRC) in the estimation of PgWF. Stability of the BMC analysis of PgWF was tested by comparing independent high-resolution (HR) datasets from each of the two young subjects. RESULTS Unlike SRC, the BMC-derived maps from the two HR datasets were essentially identical. Furthermore, SRC maps showed substantial random variation in estimated PgWF, and mean values that differed from those obtained using BMC. In addition, PgWF maps derived from conventional low-resolution (LR) datasets exhibited partial volume and magnetic susceptibility effects. These artifacts were absent in HR PgWF images. Finally, our analysis showed regional variation in PgWF estimates, and substantially higher values in the younger subjects as compared to the older subject. CONCLUSIONS BMC-mcDESPOT permits HR in-vivo mapping of PgWF in human knee cartilage in a clinically-feasible acquisition time. HR mapping reduces the impact of partial volume and magnetic susceptibility artifacts compared to LR mapping. Finally, BMC-mcDESPOT demonstrated excellent reproducibility in the determination of PgWF.
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Affiliation(s)
- Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
| | - David A Reiter
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
| | - Kyle W Sexton
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
| | - Christopher M Bergeron
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
| | - Linda M Zukley
- Clinical Research Core, Office of the Scientific Director, National Institute on Aging, National Institutes of Health, Baltimore, MD 21225, USA.
| | - Richard G Spencer
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
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Björnholm L, Nikkinen J, Kiviniemi V, Nordström T, Niemelä S, Drakesmith M, Evans JC, Pike GB, Veijola J, Paus T. Structural properties of the human corpus callosum: Multimodal assessment and sex differences. Neuroimage 2017; 152:108-118. [DOI: 10.1016/j.neuroimage.2017.02.056] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 02/15/2017] [Accepted: 02/21/2017] [Indexed: 11/17/2022] Open
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Lankford CL, Does MD. Propagation of error from parameter constraints in quantitative MRI: Example application of multiple spin echo T 2 mapping. Magn Reson Med 2017; 79:673-682. [PMID: 28426147 DOI: 10.1002/mrm.26713] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 03/21/2017] [Accepted: 03/23/2017] [Indexed: 12/20/2022]
Abstract
PURPOSE Quantitative MRI may require correcting for nuisance parameters which can or must be constrained to independently measured or assumed values. The noise and/or bias in these constraints propagate to fitted parameters. For example, the case of refocusing pulse flip angle constraint in multiple spin echo T2 mapping is explored. METHODS An analytical expression for the mean-squared error of a parameter of interest was derived as a function of the accuracy and precision of an independent estimate of a nuisance parameter. The expression was validated by simulations and then used to evaluate the effects of flip angle (θ) constraint on the accuracy and precision of T⁁2 for a variety of multi-echo T2 mapping protocols. RESULTS Constraining θ improved T⁁2 precision when the θ-map signal-to-noise ratio was greater than approximately one-half that of the first spin echo image. For many practical scenarios, constrained fitting was calculated to reduce not just the variance but the full mean-squared error of T⁁2, for bias in θ⁁≲6%. CONCLUSION The analytical expression derived in this work can be applied to inform experimental design in quantitative MRI. The example application to T2 mapping provided specific cases, depending on θ⁁ accuracy and precision, in which θ⁁ measurement and constraint would be beneficial to T⁁2 variance or mean-squared error. Magn Reson Med 79:673-682, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Christopher L Lankford
- Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, USA
| | - Mark D Does
- Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, USA.,Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, USA.,Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA
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Metzler-Baddeley C, Foley S, de Santis S, Charron C, Hampshire A, Caeyenberghs K, Jones DK. Dynamics of White Matter Plasticity Underlying Working Memory Training: Multimodal Evidence from Diffusion MRI and Relaxometry. J Cogn Neurosci 2017; 29:1509-1520. [PMID: 28358656 PMCID: PMC5881889 DOI: 10.1162/jocn_a_01127] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Adaptive working memory (WM) training may lead to cognitive benefits that are associated with white matter plasticity in parietofrontal networks, but the underlying mechanisms remain poorly understood. We investigated white matter microstructural changes after adaptive WM training relative to a nonadaptive comparison group. Microstructural changes were studied in the superior longitudinal fasciculus, the main parietofrontal connection, and the cingulum bundle as a comparison pathway. MRI-based metrics were the myelin water fraction and longitudinal relaxation rate R1 from multicomponent relaxometry (captured with the mcDESPOT approach) as proxy metrics of myelin, the restricted volume fraction from the composite hindered and restricted model of diffusion as an estimate of axon morphology, and fractional anisotropy and radial diffusivity from diffusion tensor imaging. PCA was used for dimensionality reduction. Adaptive training was associated with benefits in a “WM capacity” component and increases in a microstructural component (increases in R1, restricted volume fraction, fractional anisotropy, and reduced radial diffusivity) that predominantly loaded on changes in the right dorsolateral superior longitudinal fasciculus and the left parahippocampal cingulum. In contrast, nonadaptive comparison activities were associated with the opposite pattern of reductions in WM capacity and microstructure. No group differences were observed for the myelin water fraction metric suggesting that R1 was a more sensitive “myelin” index. These results demonstrate task complexity and location-specific white matter microstructural changes that are consistent with tissue alterations underlying myelination in response to training.
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Affiliation(s)
| | - Sonya Foley
- Cardiff University, Brain Research Imaging Centre (CUBRIC)
| | | | - Cyril Charron
- Cardiff University, Brain Research Imaging Centre (CUBRIC)
| | | | | | - Derek K Jones
- Cardiff University, Brain Research Imaging Centre (CUBRIC).,Australian Catholic University
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36
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Teixeira RPAG, Malik SJ, Hajnal JV. Joint system relaxometry (JSR) and Crámer-Rao lower bound optimization of sequence parameters: A framework for enhanced precision of DESPOT T 1 and T 2 estimation. Magn Reson Med 2017; 79:234-245. [PMID: 28303617 PMCID: PMC5763350 DOI: 10.1002/mrm.26670] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 01/30/2017] [Accepted: 02/13/2017] [Indexed: 12/13/2022]
Abstract
Purpose This study aims to increase the precision of single‐compartment DESPOT relaxometry by two means: (i) a joint system relaxometry (JSR) approach that estimates parameters in a single step using all available data; and (ii) optimizing acquisition parameters by deploying a robust design tool based on the Crámer‐Rao lower bound (CRLB). Methods Following the development of the analysis and design capabilities, phantom and four in vivo subject experiments were performed to compare directly the precision achieved with DESPOT and JSR estimation using published protocols and protocols designed using a proposed CRLB framework. Results Experimental data demonstrate JSR's ability to decrease relaxometry estimation variance. Phantom results show 72 to 77% improvement using the same data as conventional DESPOT. This is further improved to 81 to 87% using optimal parameters. Both experiments show systematic bias depending on the acquisition parameters used, which are shown to be highly reproducible and to vary with different magnetization transfer conditions. Conclusions Compared with DESPOT, JSR produces reproducible relaxation maps with improved precision. Further improvement was achieved using CRLB as a protocol design tool. With this combined approach, it is possible to achieve submillimeter maps of
ρ,T1,T2, and B0 in an 11‐min examination, making the approach appealing for potential clinical use. Magn Reson Med 79:234–245, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Rui Pedro A G Teixeira
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.,Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Shaihan J Malik
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.,Centre for the Developing Brain, King's College London, London, United Kingdom
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Liu Y, Buck JR, Ikonomidou VN. Generalized min-max bound-based MRI pulse sequence design framework for wide-range T1 relaxometry: A case study on the tissue specific imaging sequence. PLoS One 2017; 12:e0172573. [PMID: 28222197 PMCID: PMC5319767 DOI: 10.1371/journal.pone.0172573] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Accepted: 02/07/2017] [Indexed: 11/18/2022] Open
Abstract
This paper proposes a new design strategy for optimizing MRI pulse sequences for T1 relaxometry. The design strategy optimizes the pulse sequence parameters to minimize the maximum variance of unbiased T1 estimates over a range of T1 values using the Cramér-Rao bound. In contrast to prior sequences optimized for a single nominal T1 value, the optimized sequence using our bound-based strategy achieves improved precision and accuracy for a broad range of T1 estimates within a clinically feasible scan time. The optimization combines the downhill simplex method with a simulated annealing process. To show the effectiveness of the proposed strategy, we optimize the tissue specific imaging (TSI) sequence. Preliminary Monte Carlo simulations demonstrate that the optimized TSI sequence yields improved precision and accuracy over the popular driven-equilibrium single-pulse observation of T1 (DESPOT1) approach for normal brain tissues (estimated T1 700–2000 ms at 3.0T). The relative mean estimation error (MSE) for T1 estimation is less than 1.7% using the optimized TSI sequence, as opposed to less than 7.0% using DESPOT1 for normal brain tissues. The optimized TSI sequence achieves good stability by keeping the MSE under 7.0% over larger T1 values corresponding to different lesion tissues and the cerebrospinal fluid (up to 5000 ms). The T1 estimation accuracy using the new pulse sequence also shows improvement, which is more pronounced in low SNR scenarios.
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Affiliation(s)
- Yang Liu
- Department of Electrical and Computer Engineering, University of Massachusetts Dartmouth, Dartmouth, MA, United States of America
- * E-mail:
| | - John R. Buck
- Department of Electrical and Computer Engineering, University of Massachusetts Dartmouth, Dartmouth, MA, United States of America
| | - Vasiliki N. Ikonomidou
- Department of Bioengineering, George Mason University, Fairfax, VA, United States of America
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Abstract
Myelin is critical for healthy brain function. An accurate in vivo measure of myelin content has important implications for understanding brain plasticity and neurodegenerative diseases. Myelin water imaging is a magnetic resonance imaging method which can be used to visualize myelination in the brain and spinal cord in vivo. This review presents an overview of myelin water imaging data acquisition and analysis, post-mortem validation work, findings in both animal and human studies and a brief discussion about other MR techniques purported to provide in vivo myelin content. Multi-echo T2 relaxation approaches continue to undergo development and whole-brain imaging time now takes less than 10 minutes; the standard analysis method for this type of data acquisition is a non-negative least squares approach. Alternate methods including the multi-flip angle gradient echo mcDESPOT are also being used for myelin water imaging. Histological validation studies in animal and human brain and spinal cord tissue demonstrate high specificity of myelin water imaging for myelin. Potential confounding factors for in vivo myelin water fraction measurement include the presence of myelin debris and magnetization exchange processes. Myelin water imaging has successfully been used to study animal models of injury, applied in healthy human controls and can be used to assess damage and injury in conditions such as multiple sclerosis, neuromyelitis optica, schizophrenia, phenylketonuria, neurofibromatosis, niemann pick’s disease, stroke and concussion. Other quantitative magnetic resonance approaches that are sensitive to, but not specific for, myelin exist including magnetization transfer, diffusion tensor imaging and T1 weighted imaging.
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Affiliation(s)
- Alex L MacKay
- Department of Radiology, University of British Columbia, Vancouver, Canada.,Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
| | - Cornelia Laule
- Department of Radiology, University of British Columbia, Vancouver, Canada.,Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, Canada.,International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada
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Wood TC, Simmons C, Hurley SA, Vernon AC, Torres J, Dell’Acqua F, Williams SC, Cash D. Whole-brain ex-vivo quantitative MRI of the cuprizone mouse model. PeerJ 2016; 4:e2632. [PMID: 27833805 PMCID: PMC5101606 DOI: 10.7717/peerj.2632] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 09/29/2016] [Indexed: 11/20/2022] Open
Abstract
Myelin is a critical component of the nervous system and a major contributor to contrast in Magnetic Resonance (MR) images. However, the precise contribution of myelination to multiple MR modalities is still under debate. The cuprizone mouse is a well-established model of demyelination that has been used in several MR studies, but these have often imaged only a single slice and analysed a small region of interest in the corpus callosum. We imaged and analyzed the whole brain of the cuprizone mouse ex-vivo using high-resolution quantitative MR methods (multi-component relaxometry, Diffusion Tensor Imaging (DTI) and morphometry) and found changes in multiple regions, including the corpus callosum, cerebellum, thalamus and hippocampus. The presence of inflammation, confirmed with histology, presents difficulties in isolating the sensitivity and specificity of these MR methods to demyelination using this model.
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Affiliation(s)
- Tobias C. Wood
- Department of Neuroimaging, IOPPN, King’s College London, London, United Kingdom
| | - Camilla Simmons
- Department of Neuroimaging, IOPPN, King’s College London, London, United Kingdom
| | - Samuel A. Hurley
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, United Kingdom
- Synaptive Medical, Toronto, ON, Canada
| | - Anthony C. Vernon
- Cells and Behaviour Unit, Department of Basic and Clinical Neuroscience, IOPPN, King’s College London, London, United Kingdom
| | - Joel Torres
- Department of Neuroimaging, IOPPN, King’s College London, London, United Kingdom
| | - Flavio Dell’Acqua
- Department of Neuroimaging, IOPPN, King’s College London, London, United Kingdom
- NatBrainLab, Department of Basic and Clinical Neuroscience, IOPPN, King’s College London, London, United Kingdom
| | - Steve C.R. Williams
- Department of Neuroimaging, IOPPN, King’s College London, London, United Kingdom
| | - Diana Cash
- Department of Neuroimaging, IOPPN, King’s College London, London, United Kingdom
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Bouhrara M, Spencer RG. Rapid simultaneous high-resolution mapping of myelin water fraction and relaxation times in human brain using BMC-mcDESPOT. Neuroimage 2016; 147:800-811. [PMID: 27729276 DOI: 10.1016/j.neuroimage.2016.09.064] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2016] [Revised: 08/21/2016] [Accepted: 09/26/2016] [Indexed: 10/20/2022] Open
Abstract
A number of central nervous system (CNS) diseases exhibit changes in myelin content and magnetic resonance longitudinal, T1, and transverse, T2, relaxation times, which therefore represent important biomarkers of CNS pathology. Among the methods applied for measurement of myelin water fraction (MWF) and relaxation times, the multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) approach is of particular interest. mcDESPOT permits whole brain mapping of multicomponent T1 and T2, with data acquisition accomplished within a clinically realistic acquisition time. Unfortunately, previous studies have indicated the limited performance of mcDESPOT in the setting of the modest signal-to-noise range of high-resolution mapping, required for the depiction of small structures and to reduce partial volume effects. Recently, we showed that a new Bayesian Monte Carlo (BMC) analysis substantially improved determination of MWF from mcDESPOT imaging data. However, our previous study was limited in that it did not discuss determination of relaxation times. Here, we extend the BMC analysis to the simultaneous determination of whole-brain MWF and relaxation times using the two-component mcDESPOT signal model. Simulation analyses and in-vivo human brain studies indicate the overall greater performance of this approach compared to the stochastic region contraction (SRC) algorithm, conventionally used to derive parameter estimates from mcDESPOT data. SRC estimates of the transverse relaxation time of the long T2 fraction, T2,l, and the longitudinal relaxation time of the short T1 fraction, T1,s, clustered towards the lower and upper parameter search space limits, respectively, indicating failure of the fitting procedure. We demonstrate that this effect is absent in the BMC analysis. Our results also showed improved parameter estimation for BMC as compared to SRC for high-resolution mapping. Overall we find that the combination of BMC analysis and mcDESPOT, BMC-mcDESPOT, shows excellent performance for accurate high-resolution whole-brain mapping of MWF and bi-component transverse and longitudinal relaxation times within a clinically realistic acquisition time.
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Affiliation(s)
- Mustapha Bouhrara
- Magnetic Resonance Imaging and Spectroscopy Section, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Intramural Research Program, BRC 04B-116, 251 Bayview Boulevard, Baltimore, MD 21224, USA.
| | - Richard G Spencer
- Magnetic Resonance Imaging and Spectroscopy Section, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Intramural Research Program, BRC 04B-116, 251 Bayview Boulevard, Baltimore, MD 21224, USA.
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Assessment of the myelin water fraction in rodent spinal cord using T2-prepared ultrashort echo time MRI. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2016; 29:875-884. [PMID: 27394911 DOI: 10.1007/s10334-016-0579-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Revised: 06/28/2016] [Accepted: 06/29/2016] [Indexed: 10/21/2022]
Abstract
OBJECTIVE Multi-component T2 relaxation allows for assessing the myelin water fraction in nervous tissue, providing a surrogate marker for demyelination. The assessment of the number and distribution of different T2 components for devising exact models of tissue relaxation has been limited by T2 sampling with conventional MR methods. MATERIALS AND METHODS A T2-prepared UTE sequence was used to assess multicomponent T2 relaxation at 9.4 T of fixed mouse and rat spinal cord samples and of mouse spinal cord in vivo. For in vivo scans, a cryogenically cooled probe allowed for 78-µm resolution in 1-mm slices. Voxel-wise non-negative least square analysis was used to assess the number of myelin water-associated T2 components. RESULTS More than one myelin water-associated T2 component was detected in only 12 % of analyzed voxels in rat spinal cords and 6 % in mouse spinal cords, both in vivo and in vitro. However, myelin water-associated T2 values of individual voxels varied between 0.1 and 20 ms. While in fixed samples almost no components below 1 ms were identified, in vivo, these contributed 14 % of the T2 spectrum. No significant differences in MWF were observed in mouse spinal cord in vivo versus ex vivo measurements. CONCLUSION Voxel-wise analysis methods using relaxation models with one myelin water-associated T2 component are appropriate for assessing myelin content of nervous tissue.
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Barkovich AJ, Deon S. Reprint of "Hypomyelinating disorders: An MRI approach. Neurobiol Dis 2016; 92:46-54. [PMID: 27235001 DOI: 10.1016/j.nbd.2015.10.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Revised: 10/10/2015] [Accepted: 10/14/2015] [Indexed: 12/19/2022] Open
Abstract
In recent years, the concept of hypomyelinating disorders has been proposed as a group of disorders with varying systemic manifestations that are identified by MR findings of absence or near absence of the T2 hypointensity that develops in white matter as a result of myelination. Initially proposed as a separate group because they were the largest single category of undiagnosed leukodystrophies, their separation as a distinct group that can be recognized by looking for a specific MRI feature has resulted in a marked increase in their diagnosis and a better understanding of the different causes of hypomyelination. This review will discuss the clinical presentations, imaging findings on standard MRI, and new MRI-related techniques that allow a better understanding of these disorders and proposed methods for quantifying the myelination as a potential means of assessing disease course and the effects of proposed treatments. Disorders with hypomyelination of white matter, or hypomyelinating disorders (HMDs), represent the single largest category among undiagnosed genetic leukoencephalopathies (Schiffmann and van der Knaap, 2009; Steenweg et al., 2010). This group of inborn errors of metabolism is characterized by a magnetic resonance imaging (MRI) appearance of reduced or absent myelin development: delay in the development of T2 hypointensity and, often, T1 hyperintensity in the white matter of the brain. The concept of hypomyelination was first conceptualized by (Schiffmann and van der Knaap, 2009; Steenweg et al., 2010; Schiffmann et al., 1994) in a series of papers that showed that these MRI characteristics were easily recognized, were different from the MRI characteristics of dysmyelinating and demyelinating disorders, and that the combination of these imaging findings with specific other clinical and imaging features could be used to make diagnoses with some confidence. In this manuscript, we will discuss the physiologic and genetic bases of hypomyelinating disorders, as well as their classification, clinical manifestations and imaging characteristics.
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Affiliation(s)
- A James Barkovich
- Neuroradiology Section, Department of Radiology and Biomedical Imaging, UCSF-Benioff Children's Hospital, San Francisco, Q6 CA, United States.
| | - Sean Deon
- University of Colorado Medical Center and Prof. Petra Pouwels, University of Amsterdam
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Dean DC, O'Muircheartaigh J, Dirks H, Travers BG, Adluru N, Alexander AL, Deoni SCL. Mapping an index of the myelin g-ratio in infants using magnetic resonance imaging. Neuroimage 2016; 132:225-237. [PMID: 26908314 PMCID: PMC4851913 DOI: 10.1016/j.neuroimage.2016.02.040] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Revised: 02/07/2016] [Accepted: 02/12/2016] [Indexed: 12/03/2022] Open
Abstract
Optimal myelination of neuronal axons is essential for effective brain and cognitive function. The ratio of the axon diameter to the outer fiber diameter, known as the g-ratio, is a reliable measure to assess axonal myelination and is an important index reflecting the efficiency and maximal conduction velocity of white matter pathways. Although advanced neuroimaging techniques including multicomponent relaxometry (MCR) and diffusion tensor imaging afford insight into the microstructural characteristics of brain tissue, by themselves they do not allow direct analysis of the myelin g-ratio. Here, we show that by combining myelin content information (obtained with mcDESPOT MCR) with neurite density information (obtained through NODDI diffusion imaging) an index of the myelin g-ratio may be estimated. Using this framework, we present the first quantitative study of myelin g-ratio index changes across childhood, examining 18 typically developing children 3months to 7.5years of age. We report a spatio-temporal pattern of maturation that is consistent with histological and developmental MRI studies, as well as theoretical studies of the myelin g-ratio. This work represents the first ever in vivo visualization of the evolution of white matter g-ratio indices throughout early childhood.
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Affiliation(s)
- Douglas C Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA.
| | | | - Holly Dirks
- Advanced Baby Imaging Lab, Brown University School of Engineering, Providence, RI 02912, USA
| | - Brittany G Travers
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA; Department of Kinesiology, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Andrew L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Sean C L Deoni
- Advanced Baby Imaging Lab, Brown University School of Engineering, Providence, RI 02912, USA; Department of Pediatric Radiology, Children's Hospital Colorado, Aurora, CO, USA; Department of Radiology, University of Colorado Denver, Denver, CO, USA
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Thomas AG, Dennis A, Rawlings NB, Stagg CJ, Matthews L, Morris M, Kolind SH, Foxley S, Jenkinson M, Nichols TE, Dawes H, Bandettini PA, Johansen-Berg H. Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise. Neuroimage 2016; 131:162-70. [PMID: 26654786 PMCID: PMC4848119 DOI: 10.1016/j.neuroimage.2015.10.090] [Citation(s) in RCA: 92] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Revised: 09/30/2015] [Accepted: 10/31/2015] [Indexed: 12/15/2022] Open
Abstract
The hippocampus has been shown to demonstrate a remarkable degree of plasticity in response to a variety of tasks and experiences. For example, the size of the human hippocampus has been shown to increase in response to aerobic exercise. However, it is currently unknown what underlies these changes. Here we scanned sedentary, young to middle-aged human adults before and after a six-week exercise intervention using nine different neuroimaging measures of brain structure, vasculature, and diffusion. We then tested two different hypotheses regarding the nature of the underlying changes in the tissue. Surprisingly, we found no evidence of a vascular change as has been previously reported. Rather, the pattern of changes is better explained by an increase in myelination. Finally, we show that hippocampal volume increase is temporary, returning to baseline after an additional six weeks without aerobic exercise. This is the first demonstration of a change in hippocampal volume in early to middle adulthood suggesting that hippocampal volume is modulated by aerobic exercise throughout the lifespan rather than only in the presence of age related atrophy. It is also the first demonstration of hippocampal volume change over a period of only six weeks, suggesting that gross morphometric hippocampal plasticity occurs faster than previously thought.
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Affiliation(s)
- Adam G Thomas
- Section on Functional Imaging Methods, NIMH, NIH, DHHS, Bethesda, MD, USA; FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Andrea Dennis
- FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Nancy B Rawlings
- FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Charlotte J Stagg
- FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Lucy Matthews
- FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Martyn Morris
- Movement Sciences Group, Oxford Brookes University, Oxford, United Kingdom
| | - Shannon H Kolind
- Center for Neuroimaging Sciences, King's College London, London, ON, Canada
| | - Sean Foxley
- FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Mark Jenkinson
- FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Thomas E Nichols
- Department of Statistics, University of Warwick, Coventry, United Kingdom
| | - Helen Dawes
- Department of Statistics, University of Warwick, Coventry, United Kingdom
| | - Peter A Bandettini
- Section on Functional Imaging Methods, NIMH, NIH, DHHS, Bethesda, MD, USA
| | - Heidi Johansen-Berg
- FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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Abstract
PURPOSE OF REVIEW Current computational neuroanatomy based on MRI focuses on morphological measures of the brain. We present recent methodological developments in quantitative MRI (qMRI) that provide standardized measures of the brain, which go beyond morphology. We show how biophysical modelling of qMRI data can provide quantitative histological measures of brain tissue, leading to the emerging field of in-vivo histology using MRI (hMRI). RECENT FINDINGS qMRI has greatly improved the sensitivity and specificity of computational neuroanatomy studies. qMRI metrics can also be used as direct indicators of the mechanisms driving observed morphological findings. For hMRI, biophysical models of the MRI signal are being developed to directly access histological information such as cortical myelination, axonal diameters or axonal g-ratio in white matter. Emerging results indicate promising prospects for the combined study of brain microstructure and function. SUMMARY Non-invasive brain tissue characterization using qMRI or hMRI has significant implications for both research and clinics. Both approaches improve comparability across sites and time points, facilitating multicentre/longitudinal studies and standardized diagnostics. hMRI is expected to shed new light on the relationship between brain microstructure, function and behaviour, both in health and disease, and become an indispensable addition to computational neuroanatomy.
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Liu F, Block WF, Kijowski R, Samsonov A. Rapid multicomponent relaxometry in steady state with correction of magnetization transfer effects. Magn Reson Med 2016; 75:1423-33. [PMID: 25959974 PMCID: PMC4637271 DOI: 10.1002/mrm.25672] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2014] [Revised: 01/26/2015] [Accepted: 02/06/2015] [Indexed: 11/07/2022]
Abstract
PURPOSE To study the effects of magnetization transfer (MT) on multicomponent T2 parameters obtained using mcDESPOT in macromolecule-rich tissues and to propose a new method called mcRISE to correct MT-induced biases. METHODS The two-pool mcDESPOT model was modified by the addition of an exchanging macromolecule proton pool to model the MT effect in cartilage. The mcRISE acquisition scheme was developed to provide sensitivity to all pools. An incremental fitting was applied to estimate MT and relaxometry parameters with minimized coupling. The interaction between MT and relaxometry parameters, efficacy of MT correction, and feasibility of mcRISE in vivo were investigated in simulations and in healthy volunteers. RESULTS The MT effect caused significant errors in multicomponent T1/T2 values and in fast-relaxing water fraction fF , which is consistent with previous experimental observations. fF increased significantly with macromolecule content if MT was ignored. mcRISE resulted in a multifold reduction of MT biases and yielded decoupled multicomponent T1/T2 relaxometry and quantitative MT parameters. CONCLUSION mcRISE is an efficient approach for correcting MT biases in multicomponent relaxometry based on steady state sequences. Improved specificity of mcRISE may help to elucidate the sources of the previously described high sensitivity of noncorrected mcDESPOT parameters to disease-related changes in cartilage and the brain.
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Affiliation(s)
- Fang Liu
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Walter F Block
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Richard Kijowski
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Alexey Samsonov
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
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Bouhrara M, Spencer RG. Improved determination of the myelin water fraction in human brain using magnetic resonance imaging through Bayesian analysis of mcDESPOT. Neuroimage 2016; 127:456-471. [PMID: 26499810 PMCID: PMC4854306 DOI: 10.1016/j.neuroimage.2015.10.034] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Revised: 09/26/2015] [Accepted: 10/14/2015] [Indexed: 10/22/2022] Open
Abstract
Myelin water fraction (MWF) mapping with magnetic resonance imaging has led to the ability to directly observe myelination and demyelination in both the developing brain and in disease. Multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) has been proposed as a rapid approach for multicomponent relaxometry and has been applied to map MWF in the human brain. However, even for the simplest two-pool signal model consisting of myelin-associated and non-myelin-associated water, the dimensionality of the parameter space for obtaining MWF estimates remains high. This renders parameter estimation difficult, especially at low-to-moderate signal-to-noise ratios (SNRs), due to the presence of local minima and the flatness of the fit residual energy surface used for parameter determination using conventional nonlinear least squares (NLLS)-based algorithms. In this study, we introduce three Bayesian approaches for analysis of the mcDESPOT signal model to determine MWF. Given the high-dimensional nature of the mcDESPOT signal model, and, therefore the high-dimensional marginalizations over nuisance parameters needed to derive the posterior probability distribution of the MWF, the Bayesian analyses introduced here use different approaches to reduce the dimensionality of the parameter space. The first approach uses normalization by average signal amplitude, and assumes that noise can be accurately estimated from signal-free regions of the image. The second approach likewise uses average amplitude normalization, but incorporates a full treatment of noise as an unknown variable through marginalization. The third approach does not use amplitude normalization and incorporates marginalization over both noise and signal amplitude. Through extensive Monte Carlo numerical simulations and analysis of in vivo human brain datasets exhibiting a range of SNR and spatial resolution, we demonstrated markedly improved accuracy and precision in the estimation of MWF using these Bayesian methods as compared to the stochastic region contraction (SRC) implementation of NLLS.
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Affiliation(s)
- Mustapha Bouhrara
- Magnetic Resonance Imaging and Spectroscopy Section, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
| | - Richard G Spencer
- Magnetic Resonance Imaging and Spectroscopy Section, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
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Harlow DE, Honce JM, Miravalle AA. Remyelination Therapy in Multiple Sclerosis. Front Neurol 2015; 6:257. [PMID: 26696956 PMCID: PMC4674562 DOI: 10.3389/fneur.2015.00257] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Accepted: 11/23/2015] [Indexed: 01/10/2023] Open
Abstract
Multiple sclerosis (MS) is an immune-mediated disorder of the central nervous system that results in destruction of the myelin sheath that surrounds axons and eventual neurodegeneration. Current treatments approved for the treatment of relapsing forms of MS target the aberrant immune response and successfully reduce the severity of attacks and frequency of relapses. Therapies are still needed that can repair damage particularly for the treatment of progressive forms of MS for which current therapies are relatively ineffective. Remyelination can restore neuronal function and prevent further neuronal loss and clinical disability. Recent advancements in our understanding of the molecular and cellular mechanisms regulating myelination, as well as the development of high-throughput screens to identify agents that enhance myelination, have lead to the identification of many potential remyelination therapies currently in preclinical and early clinical development. One problem that has plagued the development of treatments to promote remyelination is the difficulty in assessing remyelination in patients with current imaging techniques. Powerful new imaging technologies are making it easier to discern remyelination in patients, which is critical for the assessment of these new therapeutic strategies during clinical trials. This review will summarize what is currently known about remyelination failure in MS, strategies to overcome this failure, new therapeutic treatments in the pipeline for promoting remyelination in MS patients, and new imaging technologies for measuring remyelination in patients.
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Affiliation(s)
- Danielle E Harlow
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus , Aurora, CO , USA
| | - Justin M Honce
- Department of Radiology, University of Colorado Anschutz Medical Campus , Aurora, CO , USA
| | - Augusto A Miravalle
- Department of Neurology, University of Colorado Anschutz Medical Campus , Aurora, CO , USA
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Barkovich AJ, Deon S. Hypomyelinating disorders: An MRI approach. Neurobiol Dis 2015; 87:50-8. [PMID: 26477299 DOI: 10.1016/j.nbd.2015.10.015] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Revised: 10/10/2015] [Accepted: 10/14/2015] [Indexed: 12/31/2022] Open
Abstract
In recent years, the concept of hypomyelinating disorders has been proposed as a group of disorders with varying systemic manifestations that are identified by MR findings of absence or near absence of the T2 hypointensity that develops in white matter as a result of myelination. Initially proposed as a separate group because they were the largest single category of undiagnosed leukodystrophies, their separation as a distinct group that can be recognized by looking for a specific MRI feature has resulted in a marked increase in their diagnosis and a better understanding of the different causes of hypomyelination. This review will discuss the clinical presentations, imaging findings on standard MRI, and new MRI-related techniques that allow a better understanding of these disorders and proposed methods for quantifying the myelination as a potential means of assessing disease course and the effects of proposed treatments. Disorders with hypomyelination of white matter, or hypomyelinating disorders (HMDs), represent the single largest category among undiagnosed genetic leukoencephalopathies (Schiffmann and van der Knaap, 2009; Steenweg et al., 2010). This group of inborn errors of metabolism is characterized by a magnetic resonance imaging (MRI) appearance of reduced or absent myelin development: delay in the development of T2 hypointensity and, often, T1 hyperintensity in the white matter of the brain. The concept of hypomyelination was first conceptualized by (Schiffmann and van der Knaap, 2009; Steenweg et al., 2010; Schiffmann et al., 1994) in a series of papers that showed that these MRI characteristics were easily recognized, were different from the MRI characteristics of dysmyelinating and demyelinating disorders, and that the combination of these imaging findings with specific other clinical and imaging features could be used to make diagnoses with some confidence. In this manuscript, we will discuss the physiologic and genetic bases of hypomyelinating disorders, as well as their classification, clinical manifestations and imaging characteristics.
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Affiliation(s)
- A James Barkovich
- Neuroradiology Section, Department of Radiology and Biomedical Imaging, UCSF-Benioff Children's Hospital, San Francisco, Q6 CA, United States.
| | - Sean Deon
- University of Colorado Medical Center and Prof. Petra Pouwels, University of Amsterdam
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Bouhrara M, Reiter DA, Celik H, Fishbein KW, Kijowski R, Spencer RG. Analysis of mcDESPOT- and CPMG-derived parameter estimates for two-component nonexchanging systems. Magn Reson Med 2015; 75:2406-20. [PMID: 26140371 DOI: 10.1002/mrm.25801] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2015] [Revised: 05/06/2015] [Accepted: 05/18/2015] [Indexed: 02/06/2023]
Abstract
PURPOSE To compare the reliability and stability of the multicomponent-driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) and Carl-Purcell-Meiboom-Gill (CPMG) approaches to parameter estimation. METHODS The stability and reliability of mcDESPOT and CPMG-derived parameter estimates were compared through examination of energy surfaces, evaluation of model sloppiness, and Monte Carlo simulations. Comparisons were performed on an equal time basis and assuming a two-component system. Parameter estimation bias, reflecting accuracy, and dispersion, reflecting precision, were derived for a range of signal-to-noise ratios (SNRs) and relaxation parameters. RESULTS The energy surfaces for parameters incorporated into the mcDESPOT signal model exhibit flatness, a complex structure of local minima, and instability to noise to a much greater extent than the corresponding surfaces for CPMG. Although both mcDESPOT and CPMG performed well at high SNR, the CPMG approach yielded parameter estimates of considerably greater accuracy and precision at lower SNR. CONCLUSION mcDESPOT and CPMG both permit high-quality parameter estimates under SNR that are clinically achievable under many circumstances, depending upon available hardware and resolution and acquisition time constraints. At moderate to high SNR, the mcDESPOT approach incorporating two-step phase increments can yield accurate parameter estimates while providing values for longitudinal relaxation times that are not available through CPMG. However, at low SNR, the CPMG approach is more stable and provides superior parameter estimates. Magn Reson Med 75:2406-2420, 2016. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - David A Reiter
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Hasan Celik
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Kenneth W Fishbein
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Richard Kijowski
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
| | - Richard G Spencer
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
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