1
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Kleban E, Jones DK, Tax CM. The impact of head orientation with respect to B 0 on diffusion tensor MRI measures. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:1-17. [PMID: 38405373 PMCID: PMC10884544 DOI: 10.1162/imag_a_00012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 07/27/2023] [Indexed: 02/27/2024]
Abstract
Diffusion tensor MRI (DT-MRI) remains the most commonly used approach to characterise white matter (WM) anisotropy. However, DT estimates may be affected by tissue orientation w.r.t. B → 0 due to local gradients and intrinsic T 2 orientation dependence induced by the microstructure. This work aimed to investigate whether and how diffusion tensor MRI-derived measures depend on the orientation of the head with respect to the static magnetic field, B → 0 . By simulating WM as two compartments, we demonstrated that compartmental T 2 anisotropy can induce the dependence of diffusion tensor measures on the angle between WM fibres and the magnetic field. In in vivo experiments, reduced radial diffusivity and increased axial diffusivity were observed in white matter fibres perpendicular to B → 0 compared to those parallel to B → 0 . Fractional anisotropy varied by up to 20 % as a function of the angle between WM fibres and the orientation of the main magnetic field. To conclude, fibre orientation w.r.t. B → 0 is responsible for up to 7 % variance in diffusion tensor measures across the whole brain white matter from all subjects and head orientations. Fibre orientation w.r.t. B → 0 may introduce additional variance in clinical research studies using diffusion tensor imaging, particularly when it is difficult to control for (e.g., fetal or neonatal imaging, or when the trajectories of fibres change due to, e.g., space occupying lesions).
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Affiliation(s)
- Elena Kleban
- CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom
- Inselspital, University of Bern, Bern, Switzerland
| | - Derek K. Jones
- CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom
- MMIHR, Faculty of Health Sciences, Australian Catholic University, Melbourne, Australia
| | - Chantal M.W. Tax
- CUBRIC, School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
- UMC Utrecht, Utrecht University, Utrecht, The Netherlands
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2
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Olesen JL, Ianus A, Østergaard L, Shemesh N, Jespersen SN. Tensor denoising of multidimensional MRI data. Magn Reson Med 2023; 89:1160-1172. [PMID: 36219475 PMCID: PMC10092037 DOI: 10.1002/mrm.29478] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/17/2022] [Accepted: 09/15/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE To develop a denoising strategy leveraging redundancy in high-dimensional data. THEORY AND METHODS The SNR fundamentally limits the information accessible by MRI. This limitation has been addressed by a host of denoising techniques, recently including the so-called MPPCA: principal component analysis of the signal followed by automated rank estimation, exploiting the Marchenko-Pastur distribution of noise singular values. Operating on matrices comprised of data patches, this popular approach objectively identifies noise components and, ideally, allows noise to be removed without introducing artifacts such as image blurring, or nonlocal averaging. The MPPCA rank estimation, however, relies on a large number of noise singular values relative to the number of signal components to avoid such ill effects. This condition is unlikely to be met when data patches and therefore matrices are small, for example due to spatially varying noise. Here, we introduce tensor MPPCA (tMPPCA) for the purpose of denoising multidimensional data, such as from multicontrast acquisitions. Rather than combining dimensions in matrices, tMPPCA uses each dimension of the multidimensional data's inherent tensor-structure to better characterize noise, and to recursively estimate signal components. RESULTS Relative to matrix-based MPPCA, tMPPCA requires no additional assumptions, and comparing the two in a numerical phantom and a multi-TE diffusion MRI data set, tMPPCA dramatically improves denoising performance. This is particularly true for small data patches, suggesting that tMPPCA can be especially beneficial in such cases. CONCLUSIONS The MPPCA denoising technique can be extended to high-dimensional data with improved performance for smaller patch sizes.
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Affiliation(s)
- Jonas L Olesen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Leif Østergaard
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Sune N Jespersen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
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3
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Sandgaard AD, Shemesh N, Kiselev VG, Jespersen SN. Larmor frequency shift from magnetized cylinders with arbitrary orientation distribution. NMR IN BIOMEDICINE 2023; 36:e4859. [PMID: 36285793 PMCID: PMC10078263 DOI: 10.1002/nbm.4859] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 10/22/2022] [Accepted: 10/23/2022] [Indexed: 06/01/2023]
Abstract
The magnetic susceptibility of tissue can provide valuable information about its chemical composition and microstructural organization. However, the relation between the magnetic microstructure and the measurable Larmor frequency shift is understood only for a few idealized cases. Here we analyze the microstructure formed by magnetized, NMR-invisible infinite cylinders suspended in an NMR-reporting fluid. Through simulations, we scrutinize various geometries of mesoscopic Lorentz cavities and inclusions, and show that the cavity size should be approximately one order of magnitude larger than the width of the inclusions. We also analytically derive the Larmor frequency shift for a population of cylinders with arbitrary orientation dispersion and show that it is determined by the l = 2 Laplace expansion coefficients p 2 m of the cylinders' orientation distribution function. Our work underscores the need to account for microstructural organization when estimating magnetic tissue properties.
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Affiliation(s)
- Anders Dyhr Sandgaard
- Center for Functionally Integrative Neuroscience, Department of Clinical MedicineAarhus UniversityDenmark
| | - Noam Shemesh
- Champalimaud ResearchChampalimaud Centre for the UnknownLisbonPortugal
| | - Valerij G. Kiselev
- Division of Medical Physics, Department of RadiologyUniversity Medical Center FreiburgFreiburgGermany
| | - Sune Nørhøj Jespersen
- Center for Functionally Integrative Neuroscience, Department of Clinical MedicineAarhus UniversityDenmark
- Department of Physics and AstronomyAarhus UniversityDenmark
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4
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Howard AF, Cottaar M, Drakesmith M, Fan Q, Huang SY, Jones DK, Lange FJ, Mollink J, Rudrapatna SU, Tian Q, Miller KL, Jbabdi S. Estimating axial diffusivity in the NODDI model. Neuroimage 2022; 262:119535. [PMID: 35931306 PMCID: PMC9802007 DOI: 10.1016/j.neuroimage.2022.119535] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/20/2022] [Accepted: 08/01/2022] [Indexed: 01/03/2023] Open
Abstract
To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d∥=1.7μm2/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼2-2.5μm2/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.
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Affiliation(s)
- Amy Fd Howard
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Michiel Cottaar
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Mark Drakesmith
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin, China
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - 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
| | - Frederik J Lange
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jeroen Mollink
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Suryanarayana Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Philips Innovation Campus, Bangalore, India
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States
| | - Karla L Miller
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Saad Jbabdi
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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Casella C, Chamberland M, Laguna PL, Parker GD, Rosser AE, Coulthard E, Rickards H, Berry SC, Jones DK, Metzler‐Baddeley C. Mutation-related magnetization-transfer, not axon density, drives white matter differences in premanifest Huntington disease: Evidence from in vivo ultra-strong gradient MRI. Hum Brain Mapp 2022; 43:3439-3460. [PMID: 35396899 PMCID: PMC9248323 DOI: 10.1002/hbm.25859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 03/07/2022] [Accepted: 03/27/2022] [Indexed: 11/10/2022] Open
Abstract
White matter (WM) alterations have been observed in Huntington disease (HD) but their role in the disease-pathophysiology remains unknown. We assessed WM changes in premanifest HD by exploiting ultra-strong-gradient magnetic resonance imaging (MRI). This allowed to separately quantify magnetization transfer ratio (MTR) and hindered and restricted diffusion-weighted signal fractions, and assess how they drove WM microstructure differences between patients and controls. We used tractometry to investigate region-specific alterations across callosal segments with well-characterized early- and late-myelinating axon populations, while brain-wise differences were explored with tract-based cluster analysis (TBCA). Behavioral measures were included to explore disease-associated brain-function relationships. We detected lower MTR in patients' callosal rostrum (tractometry: p = .03; TBCA: p = .03), but higher MTR in their splenium (tractometry: p = .02). Importantly, patients' mutation-size and MTR were positively correlated (all p-values < .01), indicating that MTR alterations may directly result from the mutation. Further, MTR was higher in younger, but lower in older patients relative to controls (p = .003), suggesting that MTR increases are detrimental later in the disease. Finally, patients showed higher restricted diffusion signal fraction (FR) from the composite hindered and restricted model of diffusion (CHARMED) in the cortico-spinal tract (p = .03), which correlated positively with MTR in the posterior callosum (p = .033), potentially reflecting compensatory mechanisms. In summary, this first comprehensive, ultra-strong gradient MRI study in HD provides novel evidence of mutation-driven MTR alterations at the premanifest disease stage which may reflect neurodevelopmental changes in iron, myelin, or a combination of these.
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Affiliation(s)
- Chiara Casella
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
- Department of Perinatal Imaging and Health, School of Biomedical Engineering & Imaging SciencesKing's College London, St Thomas' HospitalLondonUK
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
- Donders Institute for Brain, Cognition and BehaviorRadboud UniversityNijmegenThe Netherlands
| | - Pedro L. Laguna
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
| | - Greg D. Parker
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
| | - Anne E. Rosser
- Department of Neurology and Psychological MedicineHayden Ellis BuildingCardiffUK
- School of BiosciencesCardiff UniversityCardiffUK
| | | | - Hugh Rickards
- Birmingham and Solihull Mental Health NHS Foundation TrustBirminghamUK
- Institute of Clinical Sciences, College of Medical and Dental SciencesUniversity of BirminghamBirminghamUK
| | - Samuel C. Berry
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
| | - Claudia Metzler‐Baddeley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
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6
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Molendowska M, Fasano F, Rudrapatna U, Kimmlingen R, Jones DK, Kusmia S, Tax CMW, John Evans C. Physiological effects of human body imaging with 300 mT/m gradients. Magn Reson Med 2022; 87:2512-2520. [PMID: 34932236 PMCID: PMC7615249 DOI: 10.1002/mrm.29118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 11/19/2021] [Accepted: 11/21/2021] [Indexed: 11/06/2022]
Abstract
PURPOSE The use of high-performance gradient systems (i.e., high gradient strength and/or high slew rate) for human MRI is limited by physiological effects (including the elicitation of magnetophosphenes and peripheral nerve stimulation (PNS)). These effects, in turn, depend on the interaction between time-varying magnetic fields and the body, and thus on the participant's position with respect to the scanner's isocenter. This study investigated the occurrence of magnetophosphenes and PNS when scanning participants on a high-gradient (300 mT/m) system, for different gradient amplitudes, ramp times, and participant positions. METHODS Using a whole-body 300 mT/m gradient MRI system, a cohort of participants was scanned with the head, heart, and prostate at magnet isocenter and a train of trapezoidal bipolar gradient pulses, with ramp times from 0.88 to 4.20 ms and gradient amplitudes from 60 to 300 mT/m. Reports of magnetophosphenes and incidental reports of PNS were obtained. A questionnaire was used to record any additional subjective effects. RESULTS Magnetophosphenes were strongly dependent on participant position in the scanner. 87% of participants reported the effect with the heart at isocenter, 33% with the head at isocenter, and only 7% with the prostate at isocenter. PNS was most widely reported by participants for the vertical gradient axis (67% of participants), and was the dominant physiological effect for ramp times below 2 ms. CONCLUSION This study evaluates the probability of eliciting magnetophosphenes during whole-body imaging using an ultra-strong gradient MRI system. It provides empirical guidance on the use of high-performance gradient systems for whole-body human MRI.
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Affiliation(s)
- Malwina Molendowska
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Fabrizio Fasano
- Siemens Healthcare Ltd, Camberley, United Kingdom
- Siemens Healthcare Gmbh, Erlangen, Germany
| | - Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | | | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- Faculty of Health Sciences, Mary McKillop Institute For Health Research, Australian Catholic University, Melbourne, Australia
| | - Slawomir Kusmia
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Chantal M. W. Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht Imaging Division, Utrecht, The Netherlands
| | - C. John Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
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7
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Fan Q, Eichner C, Afzali M, Mueller L, Tax CMW, Davids M, Mahmutovic M, Keil B, Bilgic B, Setsompop K, Lee HH, Tian Q, Maffei C, Ramos-Llordén G, Nummenmaa A, Witzel T, Yendiki A, Song YQ, Huang CC, Lin CP, Weiskopf N, Anwander A, Jones DK, Rosen BR, Wald LL, Huang SY. Mapping the Human Connectome using Diffusion MRI at 300 mT/m Gradient Strength: Methodological Advances and Scientific Impact. Neuroimage 2022; 254:118958. [PMID: 35217204 DOI: 10.1016/j.neuroimage.2022.118958] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 12/20/2022] Open
Abstract
Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in Continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength dMRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for dMRI and where the field is headed in the coming years.
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Affiliation(s)
- Qiuyun Fan
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Cornelius Eichner
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, LS2 9JT, UK
| | - Lars Mueller
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, LS2 9JT, UK
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Image Sciences Institute, University Medical Center (UMC) Utrecht, Utrecht, Netherlands
| | - Mathias Davids
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mirsad Mahmutovic
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | | | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Yi-Qiao Song
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA USA
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Shanghai Changning Mental Health Center, Shanghai, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Alfred Anwander
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States.
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8
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What's New and What's Next in Diffusion MRI Preprocessing. Neuroimage 2021; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing.
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9
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Slator PJ, Palombo M, Miller KL, Westin C, Laun F, Kim D, Haldar JP, Benjamini D, Lemberskiy G, de Almeida Martins JP, Hutter J. Combined diffusion-relaxometry microstructure imaging: Current status and future prospects. Magn Reson Med 2021; 86:2987-3011. [PMID: 34411331 PMCID: PMC8568657 DOI: 10.1002/mrm.28963] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 06/25/2021] [Accepted: 07/20/2021] [Indexed: 12/15/2022]
Abstract
Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.
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Affiliation(s)
- Paddy J. Slator
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Marco Palombo
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Carl‐Fredrik Westin
- Department of RadiologyBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA
| | - Frederik Laun
- Institute of RadiologyUniversity Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Daeun Kim
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBethesdaMDUSA
- The Center for Neuroscience and Regenerative MedicineUniformed Service University of the Health SciencesBethesdaMDUSA
| | | | - Joao P. de Almeida Martins
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Department of Radiology and Nuclear MedicineSt. Olav’s University HospitalTrondheimNorway
| | - Jana Hutter
- Centre for Biomedical EngineeringSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
- Centre for the Developing BrainSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
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10
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Cottaar M, Wu W, Tendler BC, Nagy Z, Miller K, Jbabdi S. Quantifying myelin in crossing fibers using diffusion-prepared phase imaging: Theory and simulations. Magn Reson Med 2021; 86:2618-2634. [PMID: 34254349 PMCID: PMC8581995 DOI: 10.1002/mrm.28907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 06/03/2021] [Accepted: 06/08/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE Myelin has long been the target of neuroimaging research. However, most available techniques can only provide a voxel-averaged estimate of myelin content. In the human brain, white matter fiber pathways connecting different brain areas and carrying different functions often cross each other in the same voxel. A measure that can differentiate the degree of myelination of crossing fibers would provide a more specific marker of myelination. THEORY AND METHODS One MRI signal property that is sensitive to myelin is the phase accumulation. This sensitivity is used by measuring the phase accumulation of the signal remaining after diffusion-weighting, which is called diffusion-prepared phase imaging (DIPPI). Including diffusion-weighting before estimating the phase accumulation has two distinct advantages for estimating the degree of myelination: (1) It increases the relative contribution of intra-axonal water, whose phase is related linearly to the thickness of the surrounding myelin (in particular the log g-ratio); and (2) it gives directional information, which can be used to distinguish between crossing fibers. Here the DIPPI sequence is described, an approach is proposed to estimate the log g-ratio, and simulations are used and DIPPI data acquired in an isotropic phantom to quantify other sources of phase accumulation. RESULTS The expected bias is estimated in the log g-ratio for reasonable in vivo acquisition parameters caused by eddy currents (~4%-10%), remaining extra-axonal signal (~15%), and gradients in the bulk off-resonance field (<10% for most of the brain). CONCLUSION This new sequence may provide a g-ratio estimate per fiber population crossing within a voxel.
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Affiliation(s)
- Michiel Cottaar
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
| | - Benjamin C. Tendler
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
| | - Zoltan Nagy
- Laboratory for Social and Neural Systems ResearchUniversity of ZurichZurichSwitzerland
| | - Karla Miller
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
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11
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Martinez-Heras E, Grussu F, Prados F, Solana E, Llufriu S. Diffusion-Weighted Imaging: Recent Advances and Applications. Semin Ultrasound CT MR 2021; 42:490-506. [PMID: 34537117 DOI: 10.1053/j.sult.2021.07.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain "in vivo", and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications.
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Affiliation(s)
- Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain.
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain; Queen Square MS Center, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Ferran Prados
- Queen Square MS Center, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Center for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; E-health Center, Universitat Oberta de Catalunya. Barcelona. Spain
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain
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12
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Tax CMW, Kleban E, Chamberland M, Baraković M, Rudrapatna U, Jones DK. Measuring compartmental T 2-orientational dependence in human brain white matter using a tiltable RF coil and diffusion-T 2 correlation MRI. Neuroimage 2021; 236:117967. [PMID: 33845062 PMCID: PMC8270891 DOI: 10.1016/j.neuroimage.2021.117967] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 02/15/2021] [Accepted: 03/08/2021] [Indexed: 02/08/2023] Open
Abstract
The anisotropy of brain white matter microstructure manifests itself in orientational-dependence of various MRI contrasts, and can result in significant quantification biases if ignored. Understanding the origins of this orientation-dependence could enhance the interpretation of MRI signal changes in development, ageing and disease and ultimately improve clinical diagnosis. Using a novel experimental setup, this work studies the contributions of the intra- and extra-axonal water to the orientation-dependence of one of the most clinically-studied parameters, apparent transverse relaxation T2. Specifically, a tiltable receive coil is interfaced with an ultra-strong gradient MRI scanner to acquire multidimensional MRI data with an unprecedented range of acquisition parameters. Using this setup, compartmental T2 can be disentangled based on differences in diffusional-anisotropy, and its orientation-dependence further elucidated by re-orienting the head with respect to the main magnetic field B→0. A dependence of (compartmental) T2 on the fibre orientation w.r.t. B→0 was observed, and further quantified using characteristic representations for susceptibility- and magic angle effects. Across white matter, anisotropy effects were dominated by the extra-axonal water signal, while the intra-axonal water signal decay varied less with fibre-orientation. Moreover, the results suggest that the stronger extra-axonal T2 orientation-dependence is dominated by magnetic susceptibility effects (presumably from the myelin sheath) while the weaker intra-axonal T2 orientation-dependence may be driven by a combination of microstructural effects. Even though the current design of the tiltable coil only offers a modest range of angles, the results demonstrate an overall effect of tilt and serve as a proof-of-concept motivating further hardware development to facilitate experiments that explore orientational anisotropy. These observations have the potential to lead to white matter microstructural models with increased compartmental sensitivity to disease, and can have direct consequences for longitudinal and group-wise T2- and diffusion-MRI data analysis, where the effect of head-orientation in the scanner is commonly ignored.
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Affiliation(s)
- Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, UK; University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
| | - Elena Kleban
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Muhamed Baraković
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK; Signal Processing Laboratory 5, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland; Translational Imaging in Neurology Basel, Department of Biomedical Engineering, University Hospital Basel, Basel, Switzerland
| | - Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK; Mary MacKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Australia
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13
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Casella C, Kleban E, Rosser AE, Coulthard E, Rickards H, Fasano F, Metzler-Baddeley C, Jones DK. Multi-compartment analysis of the complex gradient-echo signal quantifies myelin breakdown in premanifest Huntington's disease. Neuroimage Clin 2021; 30:102658. [PMID: 33865029 PMCID: PMC8079666 DOI: 10.1016/j.nicl.2021.102658] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 03/25/2021] [Accepted: 03/30/2021] [Indexed: 12/04/2022]
Abstract
White matter (WM) alterations have been identified as a relevant pathological feature of Huntington's disease (HD). Increasing evidence suggests that WM changes in this disorder are due to alterations in myelin-associated biological processes. Multi-compartmental analysis of the complex gradient-echo MRI signal evolution in WM has been shown to quantify myelin in vivo, therefore pointing to the potential of this technique for the study of WM myelin changes in health and disease. This study first characterized the reproducibility of metrics derived from the complex multi-echo gradient-recalled echo (mGRE) signal across the corpus callosum in healthy participants, finding highest reproducibility in the posterior callosal segment. Subsequently, the same analysis pipeline was applied in this callosal region in a sample of premanifest HD patients (n = 19) and age, sex and education matched healthy controls (n = 21). In particular, we focused on two myelin-associated derivatives: i. the myelin water signal fraction (fm), a parameter dependent on myelin content; and ii. The difference in frequency between myelin and intra-axonal water pools (Δω), a parameter dependent on the ratio between the inner and the outer axonal radii. fm was found to be lower in HD patients (β = -0.13, p = 0.03), while Δω did not show a group effect. Performance in tests of working memory, executive function, social cognition and movement was also assessed, and a greater age-related decline in executive function was detected in HD patients (β = -0.06, p = 0.006), replicating previous evidence of executive dysfunction in HD. Finally, the correlation between fm, executive function, and proximity to disease onset was explored in patients, and a positive correlation between executive function and fm was detected (r = 0.542; p = 0.02). This study emphasises the potential of complex mGRE signal analysis for aiding understanding of HD pathogenesis and progression. Moreover, expanding on evidence from pathology and animal studies, it provides novel in vivo evidence supporting myelin breakdown as an early feature of HD.
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Affiliation(s)
- Chiara Casella
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF 24 4HQ, UK.
| | - Elena Kleban
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF 24 4HQ, UK
| | - Anne E Rosser
- Department of Neurology and Psychological Medicine, Hayden Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK; School of Biosciences, Cardiff University, Museum Avenue, Cardiff CF10 3AX, UK
| | | | - Hugh Rickards
- Birmingham and Solihull Mental Health NHS Foundation Trust, 50 Summer Hill Road, Birmingham B1 3RB, UK; Institute of Clinical Sciences, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Fabrizio Fasano
- Siemens Healthcare Ltd, Camberly, UK; Siemens Healthcare GmbH, Erlangen, Germany
| | - Claudia Metzler-Baddeley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF 24 4HQ, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF 24 4HQ, UK
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14
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Bryant L, McKinnon ET, Taylor JA, Jensen JH, Bonilha L, de Bezenac C, Kreilkamp BAK, Adan G, Wieshmann UC, Biswas S, Marson AG, Keller SS. Fiber ball white matter modeling in focal epilepsy. Hum Brain Mapp 2021; 42:2490-2507. [PMID: 33605514 PMCID: PMC8090772 DOI: 10.1002/hbm.25382] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 02/09/2021] [Accepted: 02/10/2021] [Indexed: 12/15/2022] Open
Abstract
Multicompartment diffusion magnetic resonance imaging (MRI) approaches are increasingly being applied to estimate intra‐axonal and extra‐axonal diffusion characteristics in the human brain. Fiber ball imaging (FBI) and its extension fiber ball white matter modeling (FBWM) are such recently described multicompartment approaches. However, these particular approaches have yet to be applied in clinical cohorts. The modeling of several diffusion parameters with interpretable biological meaning may offer the development of new, noninvasive biomarkers of pharmacoresistance in epilepsy. In the present study, we used FBI and FBWM to evaluate intra‐axonal and extra‐axonal diffusion properties of white matter tracts in patients with longstanding focal epilepsy. FBI/FBWM diffusion parameters were calculated along the length of 50 white matter tract bundles and statistically compared between patients with refractory epilepsy, nonrefractory epilepsy and controls. We report that patients with chronic epilepsy had a widespread distribution of extra‐axonal diffusivity relative to controls, particularly in circumscribed regions along white matter tracts projecting to cerebral cortex from thalamic, striatal, brainstem, and peduncular regions. Patients with refractory epilepsy had significantly greater markers of extra‐axonal diffusivity compared to those with nonrefractory epilepsy. The extra‐axonal diffusivity alterations in patients with epilepsy observed in the present study could be markers of neuroinflammatory processes or a reflection of reduced axonal density, both of which have been histologically demonstrated in focal epilepsy. FBI is a clinically feasible MRI approach that provides the basis for more interpretive conclusions about the microstructural environment of the brain and may represent a unique biomarker of pharmacoresistance in epilepsy.
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Affiliation(s)
- Lorna Bryant
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK
| | - Emilie T McKinnon
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
| | - James A Taylor
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Jens H Jensen
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Christophe de Bezenac
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK
| | - Barbara A K Kreilkamp
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK.,Department of Clinical Neurophysiology, University Medicine Göttingen, Göttingen, Germany
| | - Guleed Adan
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK.,The Walton Centre NHS Foundation Trust, Liverpool, UK
| | | | | | - Anthony G Marson
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK.,The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK.,The Walton Centre NHS Foundation Trust, Liverpool, UK
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15
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Eichner C, Paquette M, Mildner T, Schlumm T, Pléh K, Samuni L, Crockford C, Wittig RM, Jäger C, Möller HE, Friederici AD, Anwander A. Increased sensitivity and signal-to-noise ratio in diffusion-weighted MRI using multi-echo acquisitions. Neuroimage 2020; 221:117172. [DOI: 10.1016/j.neuroimage.2020.117172] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 07/10/2020] [Accepted: 07/13/2020] [Indexed: 11/29/2022] Open
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16
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Novikov DS. The present and the future of microstructure MRI: From a paradigm shift to normal science. J Neurosci Methods 2020; 351:108947. [PMID: 33096152 DOI: 10.1016/j.jneumeth.2020.108947] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/29/2020] [Accepted: 09/10/2020] [Indexed: 12/29/2022]
Abstract
The aspiration of imaging tissue microstructure with MRI is to uncover micrometer-scale tissue features within millimeter-scale imaging voxels, in vivo. This kind of super-resolution has fueled a paradigm shift within the biomedical imaging community. However, what feels like an ongoing revolution in MRI, has been conceptually experienced in physics decades ago; from this point of view, our current developments can be seen as Thomas Kuhn's "normal science" stage of progress. While the concept of model-based quantification below the nominal imaging resolution is not new, its possibilities in neuroscience and neuroradiology are only beginning to be widely appreciated. This disconnect calls for communicating the progress of tissue microstructure MR imaging to its potential users. Here, a number of recent research developments are outlined in terms of the overarching concept of coarse-graining the tissue structure over an increasing diffusion length. A variety of diffusion models and phenomena are summarized on the phase diagram of diffusion MRI, with the unresolved problems and future directions corresponding to its unexplored domains.
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Affiliation(s)
- Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.
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