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Li Y, Dai Y, Chu L. V-ATPase B2 promotes microglial phagocytosis of myelin debris by inactivating the MAPK signaling pathway. Neuropeptides 2024; 106:102436. [PMID: 38733728 DOI: 10.1016/j.npep.2024.102436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/06/2024] [Accepted: 05/06/2024] [Indexed: 05/13/2024]
Abstract
Microglial phagocytosis of myelin debris is a crucial process for promoting myelin regeneration in conditions such as multiple sclerosis (MS). Vacuolar-ATPase B2 (V-ATPase B2) has been implicated in various cellular processes, but its role in microglial phagocytosis and its potential impact on MS-related responses remain unclear. In this study, we employed BV-2 murine microglial cells to investigate the influence of V-ATPase B2 on the phagocytosis of myelin debris by microglia. The results revealed that V-ATPase B2 expression increased in response to myelin debris exposure. Overexpression of V-ATPase B2 significantly enhanced BV-2 phagocytosis of myelin debris. Additionally, V-ATPase B2 overexpression shifted microglial polarization towards an anti-inflammatory M2 phenotype, coupled with decreased lysosomal pH and enhanced lysosome degradation capacity. Moreover, endoplasmic reticulum (ER) stress inhibitor, 4-PBA, reversed the effects of V-ATPase B2 silencing on ER stress, M2 polarization, and lysosomal degradation of BV-2 cells. The MAPK pathway was inhibited upon V-ATPase B2 overexpression, contributing to heightened myelin debris clearance by BV-2 cells. Notably, MAPK pathway inhibition partially attenuated the inhibitory effects of V-ATPase B2 knockdown on myelin debris clearance. In conclusion, our findings reveal a pivotal role for V-ATPase B2 in promoting microglial phagocytosis of myelin debris by regulating microglial polarization and lysosomal function via the MAPK signaling pathway, suggesting that targeting V-ATPase B2 may hold therapeutic potential for enhancing myelin debris clearance and modulating microglial responses in MS and related neuroinflammatory disorders.
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Affiliation(s)
- Yao Li
- School of Clinical Medicine, Guizhou Medical University, Guiyang, China
| | - Yuhan Dai
- School of Clinical Medicine, Guizhou Medical University, Guiyang, China
| | - Lan Chu
- Department of Neurology, Affiliated Hospital of Guizhou Medical University, Guizhou Medical University, Guiyang, China.
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2
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Liang X, Yan Z, Li Y. Exploring subtypes of multiple sclerosis through unsupervised machine learning of automated fiber quantification. Jpn J Radiol 2024; 42:581-589. [PMID: 38409299 DOI: 10.1007/s11604-024-01535-1] [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: 12/03/2023] [Accepted: 01/14/2024] [Indexed: 02/28/2024]
Abstract
PURPOSE This study aimed to subtype multiple sclerosis (MS) patients using unsupervised machine learning on white matter (WM) fiber tracts and investigate the implications for cognitive function and disability outcomes. MATERIALS AND METHODS We utilized the automated fiber quantification (AFQ) method to extract 18 WM fiber tracts from the imaging data of 103 MS patients in total. Unsupervised machine learning techniques were applied to conduct cluster analysis and identify distinct subtypes. Clinical and diffusion tensor imaging (DTI) metrics were compared among the subtypes, and survival analysis was conducted to examine disability progression and cognitive impairment. RESULTS The clustering analysis revealed three distinct subtypes with variations in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Significant differences were observed in clinical and DTI metrics among the subtypes. Subtype 3 showed the fastest disability progression and cognitive decline, while Subtype 2 exhibited a slower rate, and Subtype 1 fell in between. CONCLUSIONS Subtyping MS based on WM fiber tracts using unsupervised machine learning identified distinct subtypes with significant cognitive and disability differences. WM abnormalities may serve as biomarkers for predicting disease outcomes, enabling personalized treatment strategies and prognostic predictions for MS patients.
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Affiliation(s)
- Xueheng Liang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No 1 Youyi Road, Yuzhong District, Chongqing, 40016, China
- Department of Radiology, Banan Hospital of Chongqing Medical University, Chongqing, China
| | - Zichun Yan
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No 1 Youyi Road, Yuzhong District, Chongqing, 40016, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No 1 Youyi Road, Yuzhong District, Chongqing, 40016, China.
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3
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Zhang HQ, Lee JCY, Wang L, Cao P, Chan KH, Mak HKF. Dynamic Changes in Long-Standing Multiple Sclerosis Revealed by Longitudinal Structural Network Analysis Using Diffusion Tensor Imaging. AJNR Am J Neuroradiol 2024; 45:305-311. [PMID: 38302198 DOI: 10.3174/ajnr.a8115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/27/2023] [Indexed: 02/03/2024]
Abstract
BACKGROUND AND PURPOSE DTI can be used to derive conventional diffusion measurements, which can measure WM abnormalities in multiple sclerosis. DTI can also be used to construct structural brain networks and derive network measurements. However, few studies have compared their sensitivity in detecting brain alterations, especially in longitudinal studies. Therefore, in this study, we aimed to determine which type of measurement is more sensitive in tracking the dynamic changes over time in MS. MATERIALS AND METHODS Eighteen patients with MS were recruited at baseline and followed up at 6 and 12 months. All patients underwent MR imaging and clinical evaluation at 3 time points. Diffusion and network measurements were derived, and their brain changes were evaluated. RESULTS None of the conventional DTI measurements displayed statistically significant changes during the follow-up period; however, the nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part showed significant longitudinal changes between baseline and at 12 months, respectively. CONCLUSIONS The nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part may be used to monitor brain changes over time in MS.
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Affiliation(s)
- Hui-Qin Zhang
- From the Department of Diagnostic Radiology (H.-Q.Z.), National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Diagnostic Radiology (H.-Q.Z., P.C., H.K.-F.M.), Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China
| | - Jacky Chi-Yan Lee
- Department of Medicine (J.C.-Y.L., K.-H.C.), Queen Mary Hospital, Hong Kong SAR, China
| | - Lu Wang
- Department of Health Technology and Informatics (L.W.), Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Peng Cao
- Department of Diagnostic Radiology (H.-Q.Z., P.C., H.K.-F.M.), Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China
| | - Koon-Ho Chan
- Department of Medicine (J.C.-Y.L., K.-H.C.), Queen Mary Hospital, Hong Kong SAR, China
- Alzheimer's Disease Research Network (H.K.-F.M., K.-H.C.), University of Hong Kong, Hong Kong SAR, China
| | - Henry Ka-Fung Mak
- Department of Diagnostic Radiology (H.-Q.Z., P.C., H.K.-F.M.), Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China
- Alzheimer's Disease Research Network (H.K.-F.M., K.-H.C.), University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Brain and Cognitive Sciences (H.K.-F.M.), University of Hong Kong, Hong Kong SAR, China
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4
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Cerdán Cerdá A, Toschi N, Treaba CA, Barletta V, Herranz E, Mehndiratta A, Gomez-Sanchez JA, Mainero C, De Santis S. A translational MRI approach to validate acute axonal damage detection as an early event in multiple sclerosis. eLife 2024; 13:e79169. [PMID: 38192199 PMCID: PMC10776086 DOI: 10.7554/elife.79169] [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: 04/01/2022] [Accepted: 12/05/2023] [Indexed: 01/10/2024] Open
Abstract
Axonal degeneration is a central pathological feature of multiple sclerosis and is closely associated with irreversible clinical disability. Current noninvasive methods to detect axonal damage in vivo are limited in their specificity and clinical applicability, and by the lack of proper validation. We aimed to validate an MRI framework based on multicompartment modeling of the diffusion signal (AxCaliber) in rats in the presence of axonal pathology, achieved through injection of a neurotoxin damaging the neuronal terminal of axons. We then applied the same MRI protocol to map axonal integrity in the brain of multiple sclerosis relapsing-remitting patients and age-matched healthy controls. AxCaliber is sensitive to acute axonal damage in rats, as demonstrated by a significant increase in the mean axonal caliber along the targeted tract, which correlated with neurofilament staining. Electron microscopy confirmed that increased mean axonal diameter is associated with acute axonal pathology. In humans with multiple sclerosis, we uncovered a diffuse increase in mean axonal caliber in most areas of the normal-appearing white matter, preferentially affecting patients with short disease duration. Our results demonstrate that MRI-based axonal diameter mapping is a sensitive and specific imaging biomarker that links noninvasive imaging contrasts with the underlying biological substrate, uncovering generalized axonal damage in multiple sclerosis as an early event.
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Affiliation(s)
| | - Nicola Toschi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
- Department of Biomedicine and Prevention, University of Rome Tor VergataRomeItaly
| | - Constantina A Treaba
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
| | - Valeria Barletta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
| | - Elena Herranz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
| | - Ambica Mehndiratta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
| | - Jose A Gomez-Sanchez
- Instituto de Neurociencias de Alicante, CSIC-UMHSan Juan de AlicanteSpain
- Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL)AlicanteSpain
- Millennium Nucleus for the Study of Pain (MiNuSPain)SantiagoChile
| | - Caterina Mainero
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
| | - Silvia De Santis
- Instituto de Neurociencias de Alicante, CSIC-UMHSan Juan de AlicanteSpain
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5
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Jung Y, Damoiseaux JS. The potential of blood neurofilament light as a marker of neurodegeneration for Alzheimer's disease. Brain 2024; 147:12-25. [PMID: 37540027 DOI: 10.1093/brain/awad267] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 07/22/2023] [Accepted: 07/28/2023] [Indexed: 08/05/2023] Open
Abstract
Over the past several years, there has been a surge in blood biomarker studies examining the value of plasma or serum neurofilament light (NfL) as a biomarker of neurodegeneration for Alzheimer's disease. However, there have been limited efforts to combine existing findings to assess the utility of blood NfL as a biomarker of neurodegeneration for Alzheimer's disease. In addition, we still need better insight into the specific aspects of neurodegeneration that are reflected by the elevated plasma or serum concentration of NfL. In this review, we survey the literature on the cross-sectional and longitudinal relationships between blood-based NfL levels and other, neuroimaging-based, indices of neurodegeneration in individuals on the Alzheimer's continuum. Then, based on the biomarker classification established by the FDA-NIH Biomarker Working group, we determine the utility of blood-based NfL as a marker for monitoring the disease status (i.e. monitoring biomarker) and predicting the severity of neurodegeneration in older adults with and without cognitive decline (i.e. a prognostic or a risk/susceptibility biomarker). The current findings suggest that blood NfL exhibits great promise as a monitoring biomarker because an increased NfL level in plasma or serum appears to reflect the current severity of atrophy, hypometabolism and the decline of white matter integrity, particularly in the brain regions typically affected by Alzheimer's disease. Longitudinal evidence indicates that blood NfL can be useful not only as a prognostic biomarker for predicting the progression of neurodegeneration in patients with Alzheimer's disease but also as a susceptibility/risk biomarker predicting the likelihood of abnormal alterations in brain structure and function in cognitively unimpaired individuals with a higher risk of developing Alzheimer's disease (e.g. those with a higher amyloid-β). There are still limitations to current research, as discussed in this review. Nevertheless, the extant literature strongly suggests that blood NfL can serve as a valuable prognostic and susceptibility biomarker for Alzheimer's disease-related neurodegeneration in clinical settings, as well as in research settings.
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Affiliation(s)
- Youjin Jung
- Department of Psychology, Wayne State University, Detroit, MI 48202, USA
- Institute of Gerontology, Wayne State University, Detroit, MI 48202, USA
| | - Jessica S Damoiseaux
- Department of Psychology, Wayne State University, Detroit, MI 48202, USA
- Institute of Gerontology, Wayne State University, Detroit, MI 48202, USA
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6
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Snoussi H, Cohen‐Adad J, Combès B, Bannier É, Tounekti S, Kerbrat A, Barillot C, Caruyer E. Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord. Brain Behav 2023; 13:e3159. [PMID: 37775975 PMCID: PMC10636413 DOI: 10.1002/brb3.3159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 07/03/2023] [Accepted: 07/06/2023] [Indexed: 10/01/2023] Open
Abstract
INTRODUCTION Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. Although conventional magnetic resonance imaging (MRI) is widely used for MS diagnosis and clinical follow-up, quantitative MRI has the potential to provide valuable intrinsic values of tissue properties that can enhance accuracy. In this study, we investigate the efficacy of diffusion MRI in distinguishing MS lesions within the cervical spinal cord, using a combination of metrics extracted from diffusion tensor imaging and Ball-and-Stick models. METHODS We analyzed spinal cord data acquired from multiple hospitals and extracted average diffusion MRI metrics per vertebral level using a collection of image processing methods and an atlas-based approach. We then performed a statistical analysis to evaluate the feasibility of these metrics for detecting lesions, exploring the usefulness of combining different metrics to improve accuracy. RESULTS Our study demonstrates the sensitivity of each metric to underlying microstructure changes in MS patients. We show that selecting a specific subset of metrics, which provide complementary information, significantly improves the prediction score of lesion presence in the cervical spinal cord. Furthermore, the Ball-and-Stick model has the potential to provide novel information about the microstructure of damaged tissue. CONCLUSION Our results suggest that diffusion measures, particularly combined measures, are sensitive in discriminating abnormal from healthy cervical vertebral levels in patients. This information could aid in improving MS diagnosis and clinical follow-up. Our study highlights the potential of the Ball-and-Stick model in providing additional insights into the microstructure of the damaged tissue.
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Affiliation(s)
- Haykel Snoussi
- Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, FranceUniversité de Rennes, CNRS, Inria, Inserm, IRISA UMR 6074RennesFrance
- Department of RadiologyBoston Children's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Julien Cohen‐Adad
- NeuroPoly LabInstitute of Biomedical Engineering, Polytechnique MontrealMontrealQuebecCanada
- Functional Neuroimaging UnitCRIUGM, Université de MontréalMontréalQuebecCanada
- Mila – Quebec AI InstituteMontréalQuebecCanada
| | - Benoît Combès
- Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, FranceUniversité de Rennes, CNRS, Inria, Inserm, IRISA UMR 6074RennesFrance
| | - Élise Bannier
- Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, FranceUniversité de Rennes, CNRS, Inria, Inserm, IRISA UMR 6074RennesFrance
- Department of RadiologyRennes University HospitalRennesFrance
| | - Slimane Tounekti
- Department of RadiologyThomas Jefferson UniversityPhiladelphiaPennsylvaniaUSA
| | - Anne Kerbrat
- Departement of NeurologyRennes University HospitalRennesFrance
| | - Christian Barillot
- Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, FranceUniversité de Rennes, CNRS, Inria, Inserm, IRISA UMR 6074RennesFrance
| | - Emmanuel Caruyer
- Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, FranceUniversité de Rennes, CNRS, Inria, Inserm, IRISA UMR 6074RennesFrance
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7
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Weaver JM, DiPiero M, Rodrigues PG, Cordash H, Davidson RJ, Planalp EM, Dean DC. Automated motion artifact detection in early pediatric diffusion MRI using a convolutional neural network. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:10.1162/imag_a_00023. [PMID: 38344118 PMCID: PMC10854394 DOI: 10.1162/imag_a_00023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Diffusion MRI (dMRI) is a widely used method to investigate the microstructure of the brain. Quality control (QC) of dMRI data is an important processing step that is performed prior to analysis using models such as diffusion tensor imaging (DTI) or neurite orientation dispersion and density imaging (NODDI). When processing dMRI data from infants and young children, where intra-scan motion is common, the identification and removal of motion artifacts is of the utmost importance. Manual QC of dMRI data is (1) time-consuming due to the large number of diffusion directions, (2) expensive, and (3) prone to subjective errors and observer variability. Prior techniques for automated dMRI QC have mostly been limited to adults or school-age children. Here, we propose a deep learning-based motion artifact detection tool for dMRI data acquired from infants and toddlers. The proposed framework uses a simple three-dimensional convolutional neural network (3DCNN) trained and tested on an early pediatric dataset of 2,276 dMRI volumes from 121 exams acquired at 1 month and 24 months of age. An average classification accuracy of 95% was achieved following four-fold cross-validation. A second dataset with different acquisition parameters and ages ranging from 2-36 months (consisting of 2,349 dMRI volumes from 26 exams) was used to test network generalizability, achieving 98% classification accuracy. Finally, to demonstrate the importance of motion artifact volume removal in a dMRI processing pipeline, the dMRI data were fit to the DTI and NODDI models and the parameter maps were compared with and without motion artifact removal.
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Affiliation(s)
- Jayse Merle Weaver
- Department of Medical Physics, University of Wisconsin–Madison, Madison, WI, United States
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
| | - Marissa DiPiero
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin–Madison, Madison, WI, United States
| | | | - Hassan Cordash
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
| | - Richard J. Davidson
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, United States
- Center for Healthy Minds, University of Wisconsin–Madison, Madison WI, United States
- Department of Psychiatry, University of Wisconsin–Madison, Madison, WI, United States
| | - Elizabeth M. Planalp
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Department of Medicine, University of Wisconsin–Madison, Madison, WI, United States
| | - Douglas C. Dean
- Department of Medical Physics, University of Wisconsin–Madison, Madison, WI, United States
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Department of Pediatrics, University of Wisconsin–Madison, Madison, WI, United States
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8
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Kim E, Carreira Figueiredo I, Simmons C, Randall K, Rojo Gonzalez L, Wood T, Ranieri B, Sureda-Gibert P, Howes O, Pariante C, Nima Consortium, Pasternak O, Dell'Acqua F, Turkheimer F, Cash D. Mapping acute neuroinflammation in vivo with diffusion-MRI in rats given a systemic lipopolysaccharide challenge. Brain Behav Immun 2023; 113:289-301. [PMID: 37482203 DOI: 10.1016/j.bbi.2023.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 06/19/2023] [Accepted: 07/17/2023] [Indexed: 07/25/2023] Open
Abstract
It is becoming increasingly apparent that neuroinflammation plays a critical role in an array of neurological and psychiatric disorders. Recent studies have demonstrated the potential of diffusion MRI (dMRI) to characterize changes in microglial density and morphology associated with neuroinflammation, but these were conducted mostly ex vivo and/or in extreme, non-physiological animal models. Here, we build upon these studies by investigating the utility of well-established dMRI methods to detect neuroinflammation in vivo in a more clinically relevant animal model of sickness behavior. We show that diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) indicate widespread increases in diffusivity in the brains of rats given a systemic lipopolysaccharide challenge (n = 20) vs. vehicle-treated controls (n = 12). These diffusivity changes correlated with histologically measured changes in microglial morphology, confirming the sensitivity of dMRI to neuroinflammatory processes. This study marks a further step towards establishing a noninvasive indicator of neuroinflammation, which would greatly facilitate early diagnosis and treatment monitoring in various neurological and psychiatric diseases.
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Affiliation(s)
- Eugene Kim
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
| | - Ines Carreira Figueiredo
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
| | - Camilla Simmons
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Karen Randall
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Loreto Rojo Gonzalez
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Tobias Wood
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
| | - Brigida Ranieri
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Paula Sureda-Gibert
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
| | - Oliver Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Carmine Pariante
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
| | - Nima Consortium
- The Wellcome Trust Consortium for the Neuroimmunology of Mood Disorders and Alzheimer's Disease (NIMA), United Kingdom
| | - Ofer Pasternak
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Flavio Dell'Acqua
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
| | - Diana Cash
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
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9
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Bhuiyan EH, Chowdhury MEH, Glover PM. Feasibility of tracking involuntary head movement for MRI using a coil as a magnetic dipole in a time-varying gradient. Magn Reson Imaging 2023; 101:76-89. [PMID: 37044168 DOI: 10.1016/j.mri.2023.03.021] [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: 02/01/2023] [Accepted: 03/29/2023] [Indexed: 04/14/2023]
Abstract
Accurate tracking involuntary head movements is fairly a challenging problem in MR imaging of the brain. Though there are few techniques available to monitor the head movement of the subject for a prospective motion correction, it is still an unsolved problem in MRI. In this theoretical study, we aim to describe an analytical investigation to track head movement inside an MR scanner by calculating the change in induced voltage in the head-mounted coils during the execution of time-varying gradients. We derive an expression to calculate the change in induced voltage in a coil placed in a time-varying gradient. We also derive a general equation to investigate the changes in the induced voltage in a set of coils mounted onto the head for the planar position and orientation of the coils. Each coil is considered as a magnetic dipole with location and sensitivity vectors. The changes of the vectors can track the head movement in the MR scanner by measuring the changes in the induced voltage in the coils. The dipole concept is valid for a wide range of coils. The changes in induced voltage in the coils are linear due to small changes in pose of the head. Movement parameters are estimated from the induced voltage changes. If the random noise voltage is less than 100 μV, it does not significantly affect movement parameters because the change in induced voltage in the coils dominates over the small noise voltage. This method and array of the coils may provide a real-life solution to the long-standing problem of head motion during MRI.
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Affiliation(s)
- E H Bhuiyan
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK; Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - M E H Chowdhury
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK; Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - P M Glover
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK
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10
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Lopez-Soley E, Martinez-Heras E, Solana E, Solanes A, Radua J, Vivo F, Prados F, Sepulveda M, Cabrera-Maqueda JM, Fonseca E, Blanco Y, Alba-Arbalat S, Martinez-Lapiscina EH, Villoslada P, Saiz A, Llufriu S. Diffusion tensor imaging metrics associated with future disability in multiple sclerosis. Sci Rep 2023; 13:3565. [PMID: 36864113 PMCID: PMC9981711 DOI: 10.1038/s41598-023-30502-5] [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: 08/31/2022] [Accepted: 02/24/2023] [Indexed: 03/04/2023] Open
Abstract
The relationship between brain diffusion microstructural changes and disability in multiple sclerosis (MS) remains poorly understood. We aimed to explore the predictive value of microstructural properties in white (WM) and grey matter (GM), and identify areas associated with mid-term disability in MS patients. We studied 185 patients (71% female; 86% RRMS) with the Expanded Disability Status Scale (EDSS), timed 25-foot walk (T25FW), nine-hole peg test (9HPT), and Symbol Digit Modalities Test (SDMT) at two time-points. We used Lasso regression to analyse the predictive value of baseline WM fractional anisotropy and GM mean diffusivity, and to identify areas related to each outcome at 4.1 years follow-up. Motor performance was associated with WM (T25FW: RMSE = 0.524, R2 = 0.304; 9HPT dominant hand: RMSE = 0.662, R2 = 0.062; 9HPT non-dominant hand: RMSE = 0.649, R2 = 0.139), and SDMT with GM diffusion metrics (RMSE = 0.772, R2 = 0.186). Cingulum, longitudinal fasciculus, optic radiation, forceps minor and frontal aslant were the WM tracts most closely linked to motor dysfunction, and temporal and frontal cortex were relevant for cognition. Regional specificity related to clinical outcomes provide valuable information that can be used to develop more accurate predictive models that could improve therapeutic strategies.
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Affiliation(s)
- E Lopez-Soley
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Calle Villarroel 170, 08036, Barcelona, Spain
| | - E Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Calle Villarroel 170, 08036, Barcelona, Spain.
| | - E Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Calle Villarroel 170, 08036, Barcelona, Spain.
| | - A Solanes
- Imaging of Mood- and Anxiety-Related Disorders Group, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, and CIBERSAM, Barcelona, Spain
| | - J Radua
- Imaging of Mood- and Anxiety-Related Disorders Group, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, and CIBERSAM, Barcelona, Spain
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Early Psychosis Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - F Vivo
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Calle Villarroel 170, 08036, Barcelona, Spain
| | - F Prados
- E-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - M Sepulveda
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Calle Villarroel 170, 08036, Barcelona, Spain
| | - J M Cabrera-Maqueda
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Calle Villarroel 170, 08036, Barcelona, Spain
| | - E Fonseca
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Calle Villarroel 170, 08036, Barcelona, Spain
- Department of Neurology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile
| | - Y Blanco
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Calle Villarroel 170, 08036, Barcelona, Spain
| | - S Alba-Arbalat
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Calle Villarroel 170, 08036, Barcelona, Spain
| | - E H Martinez-Lapiscina
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Calle Villarroel 170, 08036, Barcelona, Spain
| | - P Villoslada
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Calle Villarroel 170, 08036, Barcelona, Spain
| | - A Saiz
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Calle Villarroel 170, 08036, Barcelona, Spain
| | - S Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Calle Villarroel 170, 08036, Barcelona, Spain
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11
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Palotai M, Pintye D, Glanz B, Chitnis T, Guttmann CRG. Fronto-striatal damage may contribute to resistance to fatigue-lowering medications in multiple sclerosis. J Neuroimaging 2023; 33:269-278. [PMID: 36746670 DOI: 10.1111/jon.13082] [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: 10/20/2022] [Revised: 12/12/2022] [Accepted: 12/28/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND AND PURPOSE Commonly used fatigue-lowering medications have not been proven effective in treating multiple sclerosis (MS)-related fatigue. A neuroanatomical basis for treatment-resistant fatigue in MS has not been explored. The aim of this study was to investigate the association between brain diffusion abnormality patterns and resistance to fatigue-lowering treatment. METHODS Retrospective patient stratification: 1. treatment-resistant (n = 22) received anti-fatigue and/or anti-depressant and/or anxiolytic medication and the latest two Modified Fatigue Impact Scale (MFIS) score≥38; 2. responder (n = 16): received anti-fatigue and/or antidepressant and/or anxiolytic medication while the latest MFIS was <38, and minimum one previous MFIS was ≥38; 3. non-treated never-fatigued (n = 26): received none of the above-mentioned medications and MFIS was always<38 (over minimum four years assessed with MFIS every 1-2 years). 3T brain MRI was used to perform a cross-sectional voxel-wise comparison of fractional anisotropy (FA) between the groups. RESULTS Treatment-resistant versus responder patients showed more extensive brain damage (ie, lower FA) favoring the fronto-striatal pathways. Both groups showed more widespread brain damage than non-treated never-fatigued patients. A mean fronto-striatal FA value of 0.26 could perfectly predict response to modafinil/armodafinil. CONCLUSION Fronto-striatal damage may play a role in the development of resistance to fatigue-lowering treatment. Fronto-striatal FA may serve as a biomarker to predict response to fatigue-lowering medications in MS.
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Affiliation(s)
- Miklos Palotai
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Diana Pintye
- Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Bonnie Glanz
- Partners Multiple Sclerosis Center, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tanuja Chitnis
- Partners Multiple Sclerosis Center, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Charles R G Guttmann
- Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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12
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Arezza NJJ, Santini T, Omer M, Baron CA. Estimation of free water-corrected microscopic fractional anisotropy. Front Neurosci 2023; 17:1074730. [PMID: 36960165 PMCID: PMC10027922 DOI: 10.3389/fnins.2023.1074730] [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: 10/19/2022] [Accepted: 02/16/2023] [Indexed: 03/09/2023] Open
Abstract
Water diffusion anisotropy MRI is sensitive to microstructural changes in the brain that are hallmarks of various neurological conditions. However, conventional metrics like fractional anisotropy are confounded by neuron fiber orientation dispersion, and the relatively low resolution of diffusion-weighted MRI gives rise to significant free water partial volume effects in many brain regions that are adjacent to cerebrospinal fluid. Microscopic fractional anisotropy is a recent metric that can report water diffusion anisotropy independent of neuron fiber orientation dispersion but is still susceptible to free water contamination. In this paper, we present a free water elimination (FWE) technique to estimate microscopic fractional anisotropy and other related diffusion indices by implementing a signal representation in which the MRI signal within a voxel is assumed to come from two distinct sources: a tissue compartment and a free water compartment. A two-part algorithm is proposed to rapidly fit a set of diffusion-weighted MRI volumes containing both linear- and spherical-tensor encoding acquisitions to the representation. Simulations and in vivo acquisitions with four healthy volunteers indicated that the FWE method may be a feasible technique for measuring microscopic fractional anisotropy and other indices with greater specificity to neural tissue characteristics than conventional methods.
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Affiliation(s)
- Nico J. J. Arezza
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, Western University, London, ON, Canada
- *Correspondence: Nico J. J. Arezza,
| | - Tales Santini
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, Western University, London, ON, Canada
| | - Mohammad Omer
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Corey A. Baron
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, Western University, London, ON, Canada
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13
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Chen X, Schädelin S, Lu PJ, Ocampo-Pineda M, Weigel M, Barakovic M, Ruberte E, Cagol A, Marechal B, Kober T, Kuhle J, Kappos L, Melie-Garcia L, Granziera C. Personalized maps of T1 relaxometry abnormalities provide correlates of disability in multiple sclerosis patients. Neuroimage Clin 2023; 37:103349. [PMID: 36801600 PMCID: PMC9958406 DOI: 10.1016/j.nicl.2023.103349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 02/08/2023] [Accepted: 02/10/2023] [Indexed: 02/16/2023]
Abstract
OBJECTIVES AND AIMS Quantitative MRI (qMRI) has greatly improved the sensitivity and specificity of microstructural brain pathology in multiple sclerosis (MS) when compared to conventional MRI (cMRI). More than cMRI, qMRI also provides means to assess pathology within the normal-appearing and lesion tissue. In this work, we further developed a method providing personalized quantitative T1 (qT1) abnormality maps in individual MS patients by modeling the age dependence of qT1 alterations. In addition, we assessed the relationship between qT1 abnormality maps and patients' disability, in order to evaluate the potential value of this measurement in clinical practice. METHODS We included 119 MS patients (64 relapsing-remitting MS (RRMS), 34 secondary progressive MS (SPMS), 21 primary progressive MS (PPMS)), and 98 Healthy Controls (HC). All individuals underwent 3T MRI examinations, including Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) for qT1 maps and High-Resolution 3D Fluid Attenuated Inversion Recovery (FLAIR) imaging. To calculate personalized qT1 abnormality maps, we compared qT1 in each brain voxel in MS patients to the average qT1 obtained in the same tissue (grey/white matter) and region of interest (ROI) in healthy controls, hereby providing individual voxel-based Z-score maps. The age dependence of qT1 in HC was modeled using linear polynomial regression. We computed the average qT1 Z-scores in white matter lesions (WMLs), normal-appearing white matter (NAWM), cortical grey matter lesions (GMcLs) and normal-appearing cortical grey matter (NAcGM). Lastly, a multiple linear regression (MLR) model with the backward selection including age, sex, disease duration, phenotype, lesion number, lesion volume and average Z-score (NAWM/NAcGM/WMLs/GMcLs) was used to assess the relationship between qT1 measures and clinical disability (evaluated with EDSS). RESULTS The average qT1 Z-score was higher in WMLs than in NAWM. (WMLs: 1.366 ± 0.409, NAWM: -0.133 ± 0.288, [mean ± SD], p < 0.001). The average Z-score in NAWM in RRMS patients was significantly lower than in PPMS patients (p = 0.010). The MLR model showed a strong association between average qT1 Z-scores in white matter lesions (WMLs) and EDSS (R2 = 0.549, β = 0.178, 97.5 % CI = 0.030 to 0.326, p = 0.019). Specifically, we measured a 26.9 % increase in EDSS per unit of qT1 Z-score in WMLs in RRMS patients (R2 = 0.099, β = 0.269, 97.5 % CI = 0.078 to 0.461, p = 0.007). CONCLUSIONS We showed that personalized qT1 abnormality maps in MS patients provide measures related to clinical disability, supporting the use of those maps in clinical practice.
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Affiliation(s)
- Xinjie Chen
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Sabine Schädelin
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Mario Ocampo-Pineda
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland; Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Esther Ruberte
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Alessandro Cagol
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Benedicte Marechal
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Jens Kuhle
- Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
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14
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Alshehri A, Al-iedani O, Arm J, Gholizadeh N, Billiet T, Lea R, Lechner-Scott J, Ramadan S. Neural diffusion tensor imaging metrics correlate with clinical measures in people with relapsing-remitting MS. Neuroradiol J 2022; 35:592-599. [PMID: 35118885 PMCID: PMC9513917 DOI: 10.1177/19714009211067400] [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] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND PURPOSE Diffusion tensor imaging (DTI) can detect microstructural changes of white matter in multiple sclerosis (MS) and might clarify mechanisms responsible for disability. Thus, we aimed to compare DTI metrics in relapsing-remitting MS patients (RRMS) with healthy controls (HCs), and explore the correlations between DTI metrics, total brain white matter (TBWM) and white matter lesion (WML) with clinical parameters compared to volumetric measures. MATERIAL AND METHODS 37 RRMS patients and 19 age/sex-matched HCs were included. All participants had clinical assessments, structural and diffusion scans on a 3T MRI. Volumetric and white matter DTI metrics; fractional anisotropy (FA), mean, radial and axial diffusivities (MD, RD and AD) were estimated and correlated with clinical parameters. The mean group differences were calculated using t-tests, and univariate correlations with Pearson correlation coefficients. RESULTS Compared to HCs, statistically significant increases in MD (+3.6%), RD (+4.8%), AD (+2.7%) and a decrease in FA (-4.3%) for TBWM in RRMS was observed (p < .01). MD and RD in TBWM and AD in WML correlated moderately with disability status. Volumetric segmentation indicated a decrease in the total brain volume, GM and WM(-5%) with a reciprocal increase in CSF(+26%) in RRMS(p < .01). Importantly, DTI parameters showed a medium correlation with cognitive domains in contrast to white matter-related volumetric measurements in RRMS(Pearson correlation, p < .05). CONCLUSIONS Our study shows a correlation of DTI metrics with clinical symptoms of MS, in particular cognition. More generally, these findings indicated that DTI is a useful and unique technique for evaluating the clinical features of white matter disease and warrants further investigation into its clinical role.
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Affiliation(s)
- Abdulaziz Alshehri
- School of Health Sciences, College
of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research
Institute, New Lambton Heights, NSW, Australia
- Department of Radiology, King Fahad
University Hospital, Imam Abdulrahman Bin Faisal
University, Dammam, Saudi Arabia
| | - Oun Al-iedani
- School of Health Sciences, College
of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research
Institute, New Lambton Heights, NSW, Australia
| | - Jameen Arm
- School of Health Sciences, College
of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research
Institute, New Lambton Heights, NSW, Australia
| | - Neda Gholizadeh
- School of Mathematical and Physical
Science, Faculty of Science, University of Newcastle, Callaghan, NSW, Australia
| | - Thibo Billiet
- Research and Development
Department, Icometrix, Leuven, Belgium
| | - Rodney Lea
- Hunter Medical Research
Institute, New Lambton Heights, NSW, Australia
| | - Jeannette Lechner-Scott
- Hunter Medical Research
Institute, New Lambton Heights, NSW, Australia
- Department of Neurology, John Hunter Hospital, New Lambton Heights, NSW, Australia
- School of Medicine and Public
Health, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
| | - Saadallah Ramadan
- School of Health Sciences, College
of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research
Institute, New Lambton Heights, NSW, Australia
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15
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Baxi M, Cetin-Karayumak S, Papadimitriou G, Makris N, van der Kouwe A, Jenkins B, Moore TL, Rosene DL, Kubicki M, Rathi Y. Investigating the contribution of cytoarchitecture to diffusion MRI measures in gray matter using histology. FRONTIERS IN NEUROIMAGING 2022; 1:947526. [PMID: 37555179 PMCID: PMC10406256 DOI: 10.3389/fnimg.2022.947526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 07/19/2022] [Indexed: 08/10/2023]
Abstract
Postmortem studies are currently considered a gold standard for investigating brain structure at the cellular level. To investigate cellular changes in the context of human development, aging, or disease treatment, non-invasive in-vivo imaging methods such as diffusion MRI (dMRI) are needed. However, dMRI measures are only indirect measures and require validation in gray matter (GM) in the context of their sensitivity to the underlying cytoarchitecture, which has been lacking. Therefore, in this study we conducted direct comparisons between in-vivo dMRI measures and histology acquired from the same four rhesus monkeys. Average and heterogeneity of fractional anisotropy and trace from diffusion tensor imaging and mean squared displacement (MSD) and return-to-origin-probability from biexponential model were calculated in nine cytoarchitectonically different GM regions using dMRI data. DMRI measures were compared with corresponding histology measures of regional average and heterogeneity in cell area density. Results show that both average and heterogeneity in trace and MSD measures are sensitive to the underlying cytoarchitecture (cell area density) and capture different aspects of cell composition and organization. Trace and MSD thus would prove valuable as non-invasive imaging biomarkers in future studies investigating GM cytoarchitectural changes related to development and aging as well as abnormal cellular pathologies in clinical studies.
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Affiliation(s)
- Madhura Baxi
- Graduate Program for Neuroscience, Boston University, Boston, MA, United States
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Suheyla Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - George Papadimitriou
- Center for Morphometric Analysis, Massachusetts General Hospital, Charlestown, MA, United States
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Andre van der Kouwe
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Bruce Jenkins
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Tara L. Moore
- Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
- Center for Systems Neuroscience, Boston, MA, United States
| | - Douglas L. Rosene
- Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
- Center for Systems Neuroscience, Boston, MA, United States
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
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16
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Syed Nasser N, Rajan S, Venugopal VK, Lasič S, Mahajan V, Mahajan H. A review on investigation of the basic contrast mechanism underlying multidimensional diffusion MRI in assessment of neurological disorders. J Clin Neurosci 2022; 102:26-35. [PMID: 35696817 DOI: 10.1016/j.jocn.2022.05.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 05/20/2022] [Accepted: 05/30/2022] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Multidimensional diffusion MRI (MDD MRI) is a novel diffusion technique that uses advanced gradient waveforms for microstructural tissue characterization to provide information about average rate, anisotropy and orientation of the diffusion and to disentangle the signal fraction from specific cell types i.e., elongated cells, isotropic cells and free water. AIM To review the diagnostic potential of MDD MRI in the clinical setting for microstructural tissue characterization in patients with neurological disorders to aid in patient care and treatment. METHOD A scoping review on the clinical applications of MDD MRI was conducted from original articles published in PubMed and Scopus from 2015 to 2021 using the keywords "Multidimensional diffusion MRI" OR "diffusion tensor distribution" OR "Tensor-Valued Diffusion" OR "b-tensor encoding" OR "microscopic diffusion anisotropy" OR "microscopic anisotropy" OR "microscopic fractional anisotropy" OR "double diffusion encoding" OR "triple diffusion encoding" OR "double pulsed field gradients" OR "double wave vector" OR "correlation tensor imaging" AND "brain" OR "axons". RESULTS Initially 145 articles were screened and after applying inclusion and exclusion criteria, nine articles were included in the final analysis. In most of these studies, microscopic diffusion anisotropy within the lesion showed deviation from the normal-appearing tissue. CONCLUSION Multidimensional diffusion MRI can provide better quantification and visualization of tissue microstructure than conventional diffusion MRI and can be used in the clinical setting for diagnosis of neurological disorders.
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Affiliation(s)
| | - Sriram Rajan
- Department of Radiology, Mahajan Imaging, New Delhi, India
| | | | | | | | - Harsh Mahajan
- CARPL.ai, New Delhi, India; Department of Radiology, Mahajan Imaging, New Delhi, India
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17
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Jandric D, Parker GJM, Haroon H, Tomassini V, Muhlert N, Lipp I. A tractometry principal component analysis of white matter tract network structure and relationships with cognitive function in relapsing-remitting multiple sclerosis. Neuroimage Clin 2022; 34:102995. [PMID: 35349892 PMCID: PMC8958271 DOI: 10.1016/j.nicl.2022.102995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 03/04/2022] [Accepted: 03/23/2022] [Indexed: 10/25/2022]
Abstract
Understanding the brain changes underlying cognitive dysfunction is a key priority in multiple sclerosis (MS) to improve monitoring and treatment of this debilitating symptom. Functional connectivity network changes are associated with cognitive dysfunction, but it is less well understood how changes in normal appearing white matter relate to cognitive symptoms. If white matter tracts have network structure it would be expected that tracts within a network share susceptibility to MS pathology. In the present study, we used a tractometry approach to explore patterns of variance in white matter metrics across white matter (WM) tracts, and assessed how such patterns relate to neuropsychological test performance across cognitive domains. A sample of 102 relapsing-remitting MS patients and 27 healthy controls underwent MRI and neuropsychological testing. Tractography was performed on diffusion MRI data to extract 40 WM tracts and microstructural measures were extracted from each tract. Principal component analysis (PCA) was used to decompose metrics from all tracts to assess the presence of any co-variance structure among the tracts. Similarly, PCA was applied to cognitive test scores to identify the main cognitive domains. Finally, we assessed the ability of tract co-variance patterns to predict test performance across cognitive domains. We found that a single co-variance pattern which captured microstructure across all tracts explained the most variance (65% variance explained) and that there was little evidence for separate, smaller network patterns of pathology. Variance in this pattern was explained by effects related to lesions, but one main co-variance pattern persisted after this effect was regressed out. This main WM tract co-variance pattern contributed to explaining a modest degree of variance in one of our four cognitive domains in MS. These findings highlight the need to investigate the relationship between the normal appearing white matter and cognitive impairment further and on a more granular level, to improve the understanding of the network structure of the brain in MS.
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Affiliation(s)
- Danka Jandric
- Division of Neuroscience & Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Geoff J M Parker
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London, London, UK; Bioxydyn Limited, Manchester, UK
| | - Hamied Haroon
- Division of Neuroscience & Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Valentina Tomassini
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK; Institute for Advanced Biomedical Technologies (ITAB), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; Multiple Sclerosis Centre, Department of Neurology, SS. Annunziata University Hospital, Chieti, Italy
| | - Nils Muhlert
- Division of Neuroscience & Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Ilona Lipp
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK; Department of Neurophysics, Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany.
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18
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Kornaropoulos EN, Winzeck S, Rumetshofer T, Wikstrom A, Knutsson L, Correia MM, Sundgren PC, Nilsson M. Sensitivity of Diffusion MRI to White Matter Pathology: Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus. Front Neurol 2022; 13:837385. [PMID: 35557624 PMCID: PMC9087851 DOI: 10.3389/fneur.2022.837385] [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: 12/16/2021] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Abstract
There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes.
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Affiliation(s)
- Evgenios N. Kornaropoulos
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
| | - Stefan Winzeck
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
- BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom
| | | | - Anna Wikstrom
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Marta M. Correia
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Pia C. Sundgren
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
- Lund University BioImaging Center, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Markus Nilsson
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
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Riemenschneider M, Hvid LG, Ringgaard S, Nygaard MKE, Eskildsen SF, Gaemelke T, Magyari M, Jensen HB, Nielsen HH, Kant M, Falah M, Petersen T, Stenager E, Dalgas U. Investigating the potential disease-modifying and neuroprotective efficacy of exercise therapy early in the disease course of multiple sclerosis: The Early Multiple Sclerosis Exercise Study (EMSES). Mult Scler 2022; 28:1620-1629. [PMID: 35296183 DOI: 10.1177/13524585221079200] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Potential supplemental disease-modifying and neuroprotective treatment strategies are warranted in multiple sclerosis (MS). Exercise is a promising non-pharmacological approach, and an uninvestigated 'window of opportunity' exists early in the disease course. OBJECTIVE To investigate the effect of early exercise on relapse rate, global brain atrophy and secondary magnetic resonance imaging (MRI) outcomes. METHODS This randomized controlled trial (n = 84, disease duration <2 years) included 48 weeks of supervised aerobic exercise or control condition. Population-based control data (Danish MS Registry) was included (n = 850, disease duration <2 years). Relapse rates were obtained from medical records, and patients underwent structural and diffusion-kurtosis MRI at baseline, 24 and 48 weeks. RESULTS No between-group differences were observed for primary outcomes, relapse rate (incidence-rate-ratio exercise relative to control: (0.49 (0.15; 1.66), p = 0.25) and global brain atrophy rate (-0.04 (-0.48; 0.40)%, p = 0.87), or secondary measures of lesion load. Aerobic fitness increased in favour of the exercise group. Microstructural integrity was higher in four of eight a priori defined motor-related tracts and nuclei in the exercise group compared with the control (thalamus, corticospinal tract, globus pallidus, cingulate gyrus) at 48 weeks. CONCLUSION Early supervised aerobic exercise did not reduce relapse rate or global brain atrophy, but does positively affect the microstructural integrity of important motor-related tracts and nuclei.
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Affiliation(s)
| | - Lars G Hvid
- Exercise Biology, Department of Public Health, Aarhus University, Aarhus C, Denmark/MS Hospitals in Denmark, The Danish MS Hospitals, Ry and Haslev, Denmark
| | - Steffen Ringgaard
- The MR Research Centre, Aarhus University Hospital, Aarhus N, Denmark
| | - Mikkel Karl Emil Nygaard
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus C, Denmark
| | - Simon Fristed Eskildsen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus C, Denmark
| | - Tobias Gaemelke
- Exercise Biology, Department of Public Health, Aarhus University, Aarhus C, Denmark
| | - Melinda Magyari
- The Danish Multiple Sclerosis Registry, Department of Neurology, University Hospital Rigshospitalet, Glostrup, Denmark
| | - Henrik Boye Jensen
- Brain and Nerve Diseases, Lillebaelt Hospital, Kolding, Denmark/Department of Regional Health Research, University of Southern Denmark, Odense M, Denmark
| | | | - Matthias Kant
- MS-Clinics of Southern Jutland (Sønderborg, Esbjerg and Kolding), Department of Neurology, Sønderborg, Denmark
| | - Masoud Falah
- MS-Clinic Hospital Unit of Western Denmark, Department of Neurology, Holstebro, Denmark
| | - Thor Petersen
- The Multiple Sclerosis Clinic, Department of Neurology, Aarhus University Hospital, Aarhus N, Denmark
| | - Egon Stenager
- Department of Regional Health Research, University of Southern Denmark, Odense M, Denmark/MS-Clinics of Southern Jutland (Sønderborg, Esbjerg and Kolding), Department of Neurology, Sønderborg, Denmark
| | - Ulrik Dalgas
- Exercise Biology, Department of Public Health, Aarhus University, Aarhus C, Denmark
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20
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Pang Y, Tan Z, Mo W, Chen X, Wei J, Guo Q, Zhong Q, Zhong J. A pilot study of combined optical coherence tomography and diffusion tensor imaging method for evaluating microstructural change in the visual pathway of pituitary adenoma patients. BMC Ophthalmol 2022; 22:115. [PMID: 35279128 PMCID: PMC8917617 DOI: 10.1186/s12886-022-02320-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 02/19/2022] [Indexed: 02/06/2023] Open
Abstract
Background RNFL thickness measured by optical coherence tomography (OCT) and visual pathway measured by diffusion tensor imaging (DTI) can be used to predict visual field recovery, respectively. However, the relationship between RNFL thickness and visual pathway injury in patients with pituitary adenoma (PA) remains unclear. This study aims to evaluate the combining DTI and OCT methods in observing the microstructural change in the visual pathway in patients with PA. Methods Twenty-nine patients who were diagnosed with PA were included in the study group, and 29 healthy subjects were included as the control group. OCT detected the thickness of circumpapillary retinal nerve fiber layer (CP-RNFL) and ganglion cell layer (GCL). DTI measured the values of fractional anisotropy (FA) and apparent diffusion coefficient (ADC). Correlation between CP-RNFL and GCL thickness and FA and ADC values was analyzed in the study group. Results Compared with the control group, the FA values of the bilateral optic nerve, chiasma, bilateral optic tract, and left optic radiation in the study group were reduced, and the ADC values of the bilateral optic nerve and optic chiasma were increased. Correlation analysis showed that the FA value of the optic chiasma was positively correlated with the average thickness of RNFL, the CP-RNFL thickness in the nasal and temporal retinal quadrants in both eyes, as well as the thickness of macular ring GCL in the nasal, supra, and inferior quadrants. The FA values of the optic nerve, optic chiasma, optic tract, and optic radiation were positively correlated with CP-RNFL thickness in the nasal and temporal quadrants. Conclusion Combined DTI and OCT can provide a comprehensive understanding of the microscopic changes in the structure and function of the whole visual pathway in patients with PA.
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21
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Predictive MRI Biomarkers in MS—A Critical Review. Medicina (B Aires) 2022; 58:medicina58030377. [PMID: 35334554 PMCID: PMC8949449 DOI: 10.3390/medicina58030377] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 02/12/2022] [Accepted: 02/21/2022] [Indexed: 11/16/2022] Open
Abstract
Background and Objectives: In this critical review, we explore the potential use of MRI measurements as prognostic biomarkers in multiple sclerosis (MS) patients, for both conventional measurements and more novel techniques such as magnetization transfer, diffusion tensor, and proton spectroscopy MRI. Materials and Methods: All authors individually and comprehensively reviewed each of the aspects listed below in PubMed, Medline, and Google Scholar. Results: There are numerous MRI metrics that have been proven by clinical studies to hold important prognostic value for MS patients, most of which can be readily obtained from standard 1.5T MRI scans. Conclusions: While some of these parameters have passed the test of time and seem to be associated with a reliable predictive power, some are still better interpreted with caution. We hope this will serve as a reminder of how vast a resource we have on our hands in this versatile tool—it is up to us to make use of it.
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22
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Hori M, Maekawa T, Kamiya K, Hagiwara A, Goto M, Takemura MY, Fujita S, Andica C, Kamagata K, Cohen-Adad J, Aoki S. Advanced Diffusion MR Imaging for Multiple Sclerosis in the Brain and Spinal Cord. Magn Reson Med Sci 2022; 21:58-70. [PMID: 35173096 PMCID: PMC9199983 DOI: 10.2463/mrms.rev.2021-0091] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Diffusion tensor imaging (DTI) has been established its usefulness in evaluating normal-appearing white matter (NAWM) and other lesions that are difficult to evaluate with routine clinical MRI in the evaluation of the brain and spinal cord lesions in multiple sclerosis (MS), a demyelinating disease. With the recent advances in the software and hardware of MRI systems, increasingly complex and sophisticated MRI and analysis methods, such as q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, white matter tract integrity, and multiple diffusion encoding, referred to as advanced diffusion MRI, have been proposed. These are capable of capturing in vivo microstructural changes in the brain and spinal cord in normal and pathological states in greater detail than DTI. This paper reviews the current status of recent advanced diffusion MRI for assessing MS in vivo as part of an issue celebrating two decades of magnetic resonance in medical sciences (MRMS), an official journal of the Japanese Society of Magnetic Resonance in Medicine.
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Affiliation(s)
- Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center.,Department of Radiology, Juntendo University School of Medicine
| | - Tomoko Maekawa
- Department of Radiology, Juntendo University School of Medicine
| | - Kouhei Kamiya
- Department of Radiology, Toho University Omori Medical Center.,Department of Radiology, Juntendo University School of Medicine
| | | | - Masami Goto
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
| | | | - Shohei Fujita
- Department of Radiology, Juntendo University School of Medicine
| | | | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine
| | | | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine
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23
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Mohamed AAB, Algahalan HA, Thabit MN. Correlation between functional MRI techniques and early disability in ambulatory patients with relapsing–remitting MS. THE EGYPTIAN JOURNAL OF NEUROLOGY, PSYCHIATRY AND NEUROSURGERY 2022. [DOI: 10.1186/s41983-022-00457-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
Abstract
Background
Multiple sclerosis (MS) is a common neurological disorder which can lead to an occasional damage to the central nervous system. Conventional magnetic resonance imaging (cMRI) is an important modality in the diagnosis of MS; however, correlation between cMRI findings and clinical impairment is weak. Non-conventional MRI techniques including apparent diffusion coefficient (ADC) and magnetic resonance spectroscopy (MRS) investigate the metabolic changes over the course of MS and overcome the limits of cMRI.
A total of 80 patients with MS and 20 age and sex-matched healthy control subjects were enrolled in this cross-sectional study. Ambulatory patients with relapsing–remitting MS (RRMS) were recruited. Expanded Disability Status Scale (EDSS) was used to assess the disability and the patients were categorized into three groups “no disability”, “minimal disability” and “moderate disability”. All patients underwent cMRI techniques. ADC was measured in MS plaques and in normal appearing white matter (NAWM) adjacent and around the plaque. All metabolites concentrations were expressed as ratios including N-acetyl-aspartate/creatine (NAA/Cr), choline/N-acetyl-aspartate (Cho/NAA) and choline/creatine (Cho/Cr). ADC and metabolite concentrations were measured in the normal white matter of 20 healthy control subjects.
Results
The study was carried on 80 MS patients [36 males (45%) and 44 females (55%)] and 20 healthy control [8 males (40%) and 12 females (60%)]. The ADC values and MRS parameters in NAWM of patients with MS were significantly different from those of the control group. The number of the plaques on T2 images and black holes were significantly higher at “Minimal disability” group. Most of the enhanced plaques were at the “Moderate disability” group with P value < 0.001. The mean of ADC in the group 1, 2 and 3 of disability was 1.12 ± 0.19, 1.50 ± 0.35, 1.51 ± 0.36, respectively, with P value < 0. 001. In the group 1, 2 and 3 of disability, the mean of NAA/Cr ratio at the plaque was 1.34 ± 0.44, 1.59 ± 0.51 and 1.11 ± 0.15, respectively, with P value equal 0.001.
Conclusion
The non-conventional quantitative MRI techniques are useful tools for detection of early disability in MS patients.
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Richmond SB, Peterson DS, Fling BW. Bridging the callosal gap in gait: corpus callosum white matter integrity's role in lower limb coordination. Brain Imaging Behav 2022; 16:1552-1562. [PMID: 35088352 DOI: 10.1007/s11682-021-00612-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/29/2021] [Indexed: 11/30/2022]
Abstract
Bilateral coordination of the lower extremities is an essential component of mobility. The corpus callosum bridges the two hemispheres of the brain and is integral for the coordination of such complex movements. The aim of this project was to assess structural integrity of the transcallosal sensorimotor fiber tracts and identify their associations with gait coordination using novel methods of ecologically valid mobility assessments in persons with multiple sclerosis and age-/gender-matched neurotypical adults. Neurotypical adults (n = 29) and persons with multiple sclerosis (n = 27) underwent gait and diffusion tensor imaging assessments; the lower limb coordination via Phase Coordination Index, and radial diffusivity, an indirect marker of myelination, were applied as the primary outcome measures. Persons with multiple sclerosis possessed poorer transcallosal white matter microstructural integrity of sensorimotor fiber tracts compared to the neurotypical adults. Further, persons with multiple sclerosis demonstrated significantly poorer bilateral coordination of the lower limbs during over-ground walking in comparison to an age and gender-matched neurotypical cohort. Finally, bilateral coordination of the lower limbs was significantly associated with white matter microstructural integrity of the dorsal premotor and primary motor fiber bundles in persons with multiple sclerosis, but not in neurotypical adults. This analysis revealed that persons with multiple sclerosis exhibit poorer transcallosal microstructural integrity than neurotypical peers. Furthermore, these structural deficits were correlated to poorer consistency and accuracy of gait in those with multiple sclerosis. Together, these results, emphasize the importance of transcallosal communication for gait coordination in those with multiple sclerosis.
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Affiliation(s)
- Sutton B Richmond
- College of Health and Human Sciences, Department of Health and Exercise Science, Colorado State University, Room B220 Moby Complex B Wing, 951 Plum Street, Fort Collins, CO, 80523-1582, USA.
| | - Daniel S Peterson
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA.,Phoenix V.A. Health Care System, 650 Indian School Rd., Phoenix, AZ, USA
| | - Brett W Fling
- College of Health and Human Sciences, Department of Health and Exercise Science, Colorado State University, Room B220 Moby Complex B Wing, 951 Plum Street, Fort Collins, CO, 80523-1582, USA.,Molecular, Cellular and Integrative Neurosciences Program, Colorado State University, 1675 Campus Delivery, Fort Collins, CO, 80523, USA
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25
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Influence of Pain on Cognitive Dysfunction and Emotion Dysregulation in Chiari Malformation Type I. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1378:155-178. [DOI: 10.1007/978-3-030-99550-8_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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26
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Guo ZJ, Xu Q, Bai ZM, Liu Y, Lin Q, Zhao BH, Liu HT. Factors associated with brain white matter damage in type 2 diabetes mellitus: a tract-based spatial statistics study. Acta Radiol 2021; 63:1678-1688. [PMID: 34851138 DOI: 10.1177/02841851211056471] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The pathogenesis and related factors of central nervous system abnormality in patients with type 2 diabetes mellitus (T2DM) have always been the focus of clinical research. PURPOSE To compare and analyze the area of white matter (WM) damage in patients with T2DM based on their level of hemoglobin A1C (HBA1c) and discuss any related factors. MATERIAL AND METHODS Based on their levels of HBA1c, 87 patients with T2DM were divided into three groups (Group B, C, or D), of which 29 non-diabetic volunteers served as the control group (Group A). DTI data analysis was based on tract-based spatial statistics (TBSS). The obtained parameters were compared among each group and the relevant clinical factors were analyzed. RESULTS For age, sex, mini-mental state examination (MMSE), and Montreal Cognitive Assessment (MoCA) scores, there were no statistically significant differences among groups. For fractional anisotropy (FA) and radial diffusivity (RD) of WM, there were statistically significant differences (P < 0.05, two-tailed, FWE corrected) in the local area of corpus callosum, corona radiate, superior longitudinal fasciculus, etc. Most of these were significantly correlated with body mass index (BMI), left systolic blood pressure (SBP_L), and β2 microglobulin. CONCLUSION Before the cognitive function was obviously impaired, abnormalities of FA and RD had been found in the corpus callosum, corona radiate, and upper fasciculus in patients with T2DM, which suggested that the damage mainly occurred in the myelin sheath of WM and may be related to systemic vascular damage.
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Affiliation(s)
- Zhi-Jun Guo
- Department of Radiology, Huabei Petroleum General Hospital, Renqiu, Hebei, PR China
| | - Qian Xu
- Department of Radiology, Huabei Petroleum General Hospital, Renqiu, Hebei, PR China
| | - Ze-Mei Bai
- Department of medical administration, Huabei Petroleum Health Bureau, Renqiu, Hebei, PR China
| | - Yan Liu
- School of computer science and technology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Qiang Lin
- Department of oncology, Huabei Petroleum General Hospital, Renqiu, Hebei, PR China
| | - Bao-Hong Zhao
- Department of Radiology, Huabei Petroleum General Hospital, Renqiu, Hebei, PR China
| | - Hai-Tao Liu
- Department of respiratory medicine, Huabei Petroleum General Hospital, Renqiu, Hebei, PR China
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27
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Homos MD. Can white matter lesion burden predict involvement of normal appearing thalami in multiple sclerosis? Study using 3D FLAIR and DTI. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2021. [DOI: 10.1186/s43055-021-00406-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Multiple sclerosis is a chronic demyelinating disease that affects the white and grey matter. The thalamus is responsible for many neurological functions, and it is liable to damage in multiple sclerosis in the absence of MRI-detectable thalamic lesions. Standardized imaging protocol for multiple sclerosis includes 3D FLAIR sequence that is highly sensitive in detecting white matter lesions. Owing to the thalamic functional importance, we aim in this study to show to what extent the standardized imaging protocol (3D FLAIR) can predict microscopic damage of normal appearing thalami, depending on DTI metrics (ADC and FA) as indicators of the microscopic damage.
Results
We examined 42 multiple sclerosis patients, 16 males and 26 females, with mean age 29 ± 6 years using 3D FLAIR sequence to delineate the white matter lesions and calculate their total areas and using DTI to calculate the average ADC and FA values of the thalami. Spearman’s correlation coefficient (r) was used to correlate between the white matter lesion burden and the thalamic diffusivity (ADC and FA).
Moderate correlation was found between average ADC values of the thalami and the total white matter lesion areas (r = 0.5, p = 0.03).
Very weak correlation was found between average FA values of the thalami and the total white matter lesion areas (r = − 0.1, p = 0.6)
Conclusion
White matter lesion burden detected using the highly sensitive 3D FLAIR sequence does not always correlate with the microstructural damage in normal appearing thalami. DTI needs to be added to the examination protocol if damage of normal appearing thalami is of concern.
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28
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Jandric D, Lipp I, Paling D, Rog D, Castellazzi G, Haroon H, Parkes L, Parker GJM, Tomassini V, Muhlert N. Mechanisms of Network Changes in Cognitive Impairment in Multiple Sclerosis. Neurology 2021; 97:e1886-e1897. [PMID: 34649879 PMCID: PMC8601205 DOI: 10.1212/wnl.0000000000012834] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 09/13/2021] [Indexed: 12/14/2022] Open
Abstract
Background and Objectives Cognitive impairment in multiple sclerosis (MS) is associated with functional connectivity abnormalities. While there have been calls to use functional connectivity measures as biomarkers, there remains to be a full understanding of why they are affected in MS. In this cross-sectional study, we tested the hypothesis that functional network regions may be susceptible to disease-related “wear and tear” and that this can be observable on co-occurring abnormalities on other magnetic resonance metrics. We tested whether functional connectivity abnormalities in cognitively impaired patients with MS co-occur with (1) overlapping, (2) local, or (3) distal changes in anatomic connectivity and cerebral blood flow abnormalities. Methods Multimodal 3T MRI and assessment with the Brief Repeatable Battery of Neuropsychological tests were performed in 102 patients with relapsing-remitting MS and 27 healthy controls. Patients with MS were classified as cognitively impaired if they scored ≥1.5 SDs below the control mean on ≥2 tests (n = 55) or as cognitively preserved (n = 47). Functional connectivity was assessed with Independent Component Analysis and dual regression of resting-state fMRI images. Cerebral blood flow maps were estimated, and anatomic connectivity was assessed with anatomic connectivity mapping and fractional anisotropy of diffusion-weighted MRI. Changes in cerebral blood flow and anatomic connectivity were assessed within resting-state networks that showed functional connectivity abnormalities in cognitively impaired patients with MS. Results Functional connectivity was significantly decreased in the anterior and posterior default mode networks and significantly increased in the right and left frontoparietal networks in cognitively impaired relative to cognitively preserved patients with MS (threshold-free cluster enhancement corrected at p ≤ 0.05, 2 sided). Networks showing functional abnormalities showed altered cerebral blood flow and anatomic connectivity locally and distally but not in overlapping locations. Discussion We provide the first evidence that functional connectivity abnormalities are accompanied by local cerebral blood flow and structural connectivity abnormalities but also demonstrate that these effects do not occur in exactly the same location. Our findings suggest a possibly shared pathologic mechanism for altered functional connectivity in brain networks in MS.
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Affiliation(s)
- Danka Jandric
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - Ilona Lipp
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - David Paling
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - David Rog
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - Gloria Castellazzi
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - Hamied Haroon
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - Laura Parkes
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - Geoff J M Parker
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - Valentina Tomassini
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - Nils Muhlert
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy.
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Quantification of normal-appearing white matter damage in early relapse-onset multiple sclerosis through neurite orientation dispersion and density imaging. Mult Scler Relat Disord 2021; 58:103396. [DOI: 10.1016/j.msard.2021.103396] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/02/2021] [Accepted: 11/08/2021] [Indexed: 11/23/2022]
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De Luca A, Ianus A, Leemans A, Palombo M, Shemesh N, Zhang H, Alexander DC, Nilsson M, Froeling M, Biessels GJ, Zucchelli M, Frigo M, Albay E, Sedlar S, Alimi A, Deslauriers-Gauthier S, Deriche R, Fick R, Afzali M, Pieciak T, Bogusz F, Aja-Fernández S, Özarslan E, Jones DK, Chen H, Jin M, Zhang Z, Wang F, Nath V, Parvathaneni P, Morez J, Sijbers J, Jeurissen B, Fadnavis S, Endres S, Rokem A, Garyfallidis E, Sanchez I, Prchkovska V, Rodrigues P, Landman BA, Schilling KG. On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge. Neuroimage 2021; 240:118367. [PMID: 34237442 PMCID: PMC7615259 DOI: 10.1016/j.neuroimage.2021.118367] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/09/2021] [Accepted: 07/04/2021] [Indexed: 12/29/2022] Open
Abstract
Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
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Affiliation(s)
- Alberto De Luca
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Hui Zhang
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | - Martijn Froeling
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Geert-Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mauro Zucchelli
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Matteo Frigo
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Enes Albay
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France; Istanbul Technical University, Istanbul, Turkey
| | - Sara Sedlar
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Abib Alimi
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | | | - Rachid Deriche
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | | | - Maryam Afzali
- Cardiff University Brain Research, Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Fabian Bogusz
- AGH University of Science and Technology, Kraków, Poland
| | | | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Derek K Jones
- Cardiff University Brain Research, Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Haoze Chen
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | - Mingwu Jin
- Department of Physics, University of Texas at Arlington, Arlington, USA
| | - Zhijie Zhang
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | - Fengxiang Wang
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | | | | | - Jan Morez
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Jan Sijbers
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Shreyas Fadnavis
- Intelligent Systems Engineering, Indiana University Bloomington, Indiana, USA
| | - Stefan Endres
- Leibniz Institute for Materials Engineering - IWT, Faculty of Production Engineering, University of Bremen, Bremen, Germany
| | - Ariel Rokem
- Department of Psychology and the eScience Institute, University of Washington, Seattle, WA USA
| | | | | | | | | | - Bennet A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, USA; Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, USA
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Lin TY, Chien C, Lu A, Paul F, Zimmermann HG. Retinal optical coherence tomography and magnetic resonance imaging in neuromyelitis optica spectrum disorders and MOG-antibody associated disorders: an updated review. Expert Rev Neurother 2021; 21:1101-1123. [PMID: 34551653 DOI: 10.1080/14737175.2021.1982697] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Neuromyelitis optica spectrum disorders (NMOSD) and myelin oligodendrocyte glycoprotein IgG antibody-associated disorders (MOGAD) comprise two groups of rare neuroinflammatory diseases that cause attack-related damage to the central nervous system (CNS). Clinical attacks are often characterized by optic neuritis, transverse myelitis, and to a lesser extent, brainstem encephalitis/area postrema syndrome. Retinal optical coherence tomography (OCT) is a non-invasive technique that allows for in vivo thickness quantification of the retinal layers. Apart from OCT, magnetic resonance imaging (MRI) plays an increasingly important role in NMOSD and MOGAD diagnosis based on the current international diagnostic criteria. Retinal OCT and brain/spinal cord/optic nerve MRI can help to distinguish NMOSD and MOGAD from other neuroinflammatory diseases, particularly from multiple sclerosis, and to monitor disease-associated CNS-damage. AREAS COVERED This article summarizes the current status of imaging research in NMOSD and MOGAD, and reviews the clinical relevance of OCT, MRI and other relevant imaging techniques for differential diagnosis, screening and monitoring of the disease course. EXPERT OPINION Retinal OCT and MRI can visualize and quantify CNS damage in vivo, improving our understanding of NMOSD and MOGAD pathology. Further efforts on the standardization of these imaging techniques are essential for implementation into clinical practice and as outcome parameters in clinical trials.
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Affiliation(s)
- Ting-Yi Lin
- Experimental and Clinical Research Center, Max-Delbrück Center for Molecular Medicine and Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Corporate Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Claudia Chien
- Experimental and Clinical Research Center, Max-Delbrück Center for Molecular Medicine and Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Corporate Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Angelo Lu
- Experimental and Clinical Research Center, Max-Delbrück Center for Molecular Medicine and Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Corporate Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Friedemann Paul
- Experimental and Clinical Research Center, Max-Delbrück Center for Molecular Medicine and Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Corporate Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,Department of Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Hanna G Zimmermann
- Experimental and Clinical Research Center, Max-Delbrück Center for Molecular Medicine and Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Corporate Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
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Investigating Microstructural Changes in White Matter in Multiple Sclerosis: A Systematic Review and Meta-Analysis of Neurite Orientation Dispersion and Density Imaging. Brain Sci 2021; 11:brainsci11091151. [PMID: 34573172 PMCID: PMC8469792 DOI: 10.3390/brainsci11091151] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 08/24/2021] [Accepted: 08/27/2021] [Indexed: 11/17/2022] Open
Abstract
Multiple sclerosis (MS) is characterised by widespread damage of the central nervous system that includes alterations in normal-appearing white matter (NAWM) and demyelinating white matter (WM) lesions. Neurite orientation dispersion and density imaging (NODDI) has been proposed to provide a precise characterisation of WM microstructures. NODDI maps can be calculated for the Neurite Density Index (NDI) and Orientation Dispersion Index (ODI), which estimate orientation dispersion and neurite density. Although NODDI has not been widely applied in MS, this technique is promising in investigating the complexity of MS pathology, as it is more specific than diffusion tensor imaging (DTI) in capturing microstructural alterations. We conducted a meta-analysis of studies using NODDI metrics to assess brain microstructural changes and neuroaxonal pathology in WM lesions and NAWM in patients with MS. Three reviewers conducted a literature search of four electronic databases. We performed a random-effect meta-analysis and the extent of between-study heterogeneity was assessed with the I2 statistic. Funnel plots and Egger’s tests were used to assess publication bias. We identified seven studies analysing 374 participants (202 MS and 172 controls). The NDI in WM lesions and NAWM were significantly reduced compared to healthy WM and the standardised mean difference of each was −3.08 (95%CI −4.22 to (−1.95), p ≤ 0.00001, I2 = 88%) and −0.70 (95%CI −0.99 to (−0.40), p ≤ 0.00001, I2 = 35%), respectively. There was no statistically significant difference of the ODI in MS WM lesions and NAWM compared to healthy controls. This systematic review and meta-analysis confirmed that the NDI is significantly reduced in MS lesions and NAWM than in WM from healthy participants, corresponding to reduced intracellular signal fraction, which may reflect underlying damage or loss of neurites.
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Govindarajan ST, Liu Y, Parra Corral MA, Bangiyev L, Krupp L, Charvet L, Duong TQ. White matter correlates of slowed information processing speed in unimpaired multiple sclerosis patients with young age onset. Brain Imaging Behav 2021; 15:1460-1468. [PMID: 32748319 DOI: 10.1007/s11682-020-00345-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Slowed information processing speed is among the earliest markers of cognitive impairment in multiple sclerosis (MS) and has been associated with white matter (WM) structural integrity. Localization of WM tracts associated with slowing, but not significant impairment, on specific cognitive tasks in pediatric and young age onset MS can facilitate early and effective therapeutic intervention. Diffusion tensor imaging data were collected on 25 MS patients and 24 controls who also underwent the Symbol Digit Modalities Test (SDMT) and the computer-based Cogstate simple and choice reaction time tests. Fractional anisotropy (FA), mean (MD), radial (RD) and axial (AD) diffusivities were correlated voxel-wise with processing speed measures. All DTI metrics of several white matter tracts were significantly different between groups (p < 0.05). Notably, higher MD, RD, and AD, but not FA, in the corpus callosum correlated with lower scores on both SDMT and simple reaction time. Additionally, all diffusivity metrics in the left corticospinal tract correlated negatively with SDMT scores, whereas only MD in the right superior fronto-occipital fasciculus correlated with simple reaction time. In conclusion, subtle slowing of processing speed is correlated with WM damage in the visual-motor processing pathways in patients with young age of MS onset.
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Affiliation(s)
| | - Yilin Liu
- Department of Radiology, Stony Brook University School of Medicine, Stony Brook, NY, USA
| | | | - Lev Bangiyev
- Department of Neurology, New York University Grossman School of Medicine, New York, NY, USA
| | - Lauren Krupp
- Department of Neurology, New York University Grossman School of Medicine, New York, NY, USA
| | - Leigh Charvet
- Department of Neurology, New York University Grossman School of Medicine, New York, NY, USA
| | - Tim Q Duong
- Department of Radiology, Stony Brook University School of Medicine, Stony Brook, NY, USA.
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34
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Karakatsani ME, Pouliopoulos AN, Liu M, Jambawalikar SR, Konofagou EE. Contrast-Free Detection of Focused Ultrasound-Induced Blood-Brain Barrier Opening Using Diffusion Tensor Imaging. IEEE Trans Biomed Eng 2021; 68:2499-2508. [PMID: 33360980 DOI: 10.1109/tbme.2020.3047575] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
OBJECTIVE Focused ultrasound (FUS) has emerged as a non-invasive technique to locally and reversibly disrupt the blood-brain barrier (BBB). Here, we investigate the use of diffusion tensor imaging (DTI) as a means of detecting FUS-induced BBB opening at the absence of an MRI contrast agent. A non-human primate (NHP) was repeatedly treated with FUS and preformed circulating microbubbles to transiently disrupt the BBB (n = 4). T1- and diffusion-weighted MRI scans were acquired after the ultrasound treatment, with and without gadolinium-based contrast agent, respectively. Both scans were registered with a high-resolution T1-weighted scan of the NHP to investigate signal correlations. DTI detected an increase in fractional anisotropy from 0.21 ± 0.02 to 0.38 ± 0.03 (82.6 ± 5.2% change) within the targeted area one hour after BBB opening. Enhanced DTI contrast overlapped by 77.22 ± 9.2% with hyper-intense areas of gadolinium-enhanced T1-weighted scans, indicating diffusion anisotropy enhancement only within the BBB opening volume. Diffusion was highly anisotropic and unidirectional within the treated brain region, as indicated by the direction of the principal diffusion eigenvectors. Polar and azimuthal angle ranges decreased by 35.6% and 82.4%, respectively, following BBB opening. Evaluation of the detection methodology on a second NHP (n = 1) confirmed the across-animal feasibility of the technique. In conclusion, DTI may be used as a contrast-free MR imaging modality in lieu of contrast-enhanced T1 mapping for detecting BBB opening during focused-ultrasound treatment or evaluating BBB integrity in brain-related pathologies.
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35
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Cai LY, Yang Q, Hansen CB, Nath V, Ramadass K, Johnson GW, Conrad BN, Boyd BD, Begnoche JP, Beason-Held LL, Shafer AT, Resnick SM, Taylor WD, Price GR, Morgan VL, Rogers BP, Schilling KG, Landman BA. PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images. Magn Reson Med 2021; 86:456-470. [PMID: 33533094 PMCID: PMC8387107 DOI: 10.1002/mrm.28678] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 12/19/2020] [Accepted: 12/22/2020] [Indexed: 12/31/2022]
Abstract
PURPOSE Diffusion weighted MRI imaging (DWI) is often subject to low signal-to-noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document. METHODS The proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter-scan intensity normalization; susceptibility-, eddy current-, and motion-induced artifact correction; and slice-wise signal drop-out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness-of-fit and fractional anisotropy analyses. RESULTS Raw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility-, eddy current-, and motion-induced artifacts; imputed signal-lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file-type conversion errors and was shown to be effective on externally available datasets. CONCLUSIONS The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA.
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Affiliation(s)
- Leon Y. Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Qi Yang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Colin B. Hansen
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Vishwesh Nath
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Graham W. Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Benjamin N. Conrad
- Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, TN, USA
| | - Brian D. Boyd
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John P. Begnoche
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lori L. Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Andrea T. Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Warren D. Taylor
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Gavin R. Price
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, TN, USA
| | - Victoria L. Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Baxter P. Rogers
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Kurt G. Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Bennett A. Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
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36
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Houston JR, Hughes ML, Bennett IJ, Allen PA, Rogers JM, Lien MC, Stoltz H, Sakaie K, Loth F, Maleki J, Vorster SJ, Luciano MG. Evidence of Neural Microstructure Abnormalities in Type I Chiari Malformation: Associations Among Fiber Tract Integrity, Pain, and Cognitive Dysfunction. PAIN MEDICINE 2021; 21:2323-2335. [PMID: 32388548 DOI: 10.1093/pm/pnaa094] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND Previous case-control investigations of type I Chiari malformation (CMI) have reported cognitive deficits and microstructural white matter abnormalities, as measured by diffusion tensor imaging (DTI). CMI is also typically associated with pain, including occipital headache, but the relationship between pain symptoms and microstructure is not known. METHODS Eighteen CMI patients and 18 adult age- and education-matched control participants underwent DTI, were tested using digit symbol coding and digit span tasks, and completed a self-report measure of chronic pain. Tissue microstructure indices were used to examine microstructural abnormalities in CMI as compared with healthy controls. Group differences in DTI parameters were then reassessed after controlling for self-reported pain. Finally, DTI parameters were correlated with performance on the digit symbol coding and digit span tasks within each group. RESULTS CMI patients exhibited greater fractional anisotropy (FA), lower radial diffusivity, and lower mean diffusivity in multiple brain regions compared with controls in diffuse white matter regions. Group differences no longer existed after controlling for self-reported pain. A significant correlation between FA and the Repeatable Battery for the Assessment of Neuropsychological Status coding performance was observed for controls but not for the CMI group. CONCLUSIONS Diffuse microstructural abnormalities appear to be a feature of CMI, manifesting predominantly as greater FA and less diffusivity on DTI sequences. These white matter changes are associated with the subjective pain experience of CMI patients and may reflect reactivity to neuroinflammatory responses. However, this hypothesis will require further deliberate testing in future studies.
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Affiliation(s)
- James R Houston
- Department of Psychology, Middle Tennessee State University, Murfreesboro, Tennessee
| | | | - Ilana J Bennett
- Department of Psychology, University of California, Riverside, California, USA
| | - Philip A Allen
- Department of Psychology, University of Akron, Akron, Ohio
| | - Jeffrey M Rogers
- Faculty of Health Sciences, University of Sydney, Sydney, Australia
| | - Mei-Ching Lien
- School of Psychological Science, Oregon State University, Corvallis, Oregon
| | - Haylie Stoltz
- Department of Psychology, Middle Tennessee State University, Murfreesboro, Tennessee
| | - Ken Sakaie
- Department of Diagnostic Radiology, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Francis Loth
- Department of Mechanical Engineering, University of Akron, Akron, Ohio
| | - Jahangir Maleki
- Center for Neuro-Restoration, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Sarel J Vorster
- Department of Neurological Surgery, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Mark G Luciano
- Department of Neurosurgery, Johns Hopkins Medical Center, Baltimore, Maryland, USA
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37
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Oladosu O, Liu WQ, Pike BG, Koch M, Metz LM, Zhang Y. Advanced Analysis of Diffusion Tensor Imaging Along With Machine Learning Provides New Sensitive Measures of Tissue Pathology and Intra-Lesion Activity in Multiple Sclerosis. Front Neurosci 2021; 15:634063. [PMID: 34025338 PMCID: PMC8138061 DOI: 10.3389/fnins.2021.634063] [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: 11/26/2020] [Accepted: 04/15/2021] [Indexed: 12/02/2022] Open
Abstract
Tissue pathology in multiple sclerosis (MS) is highly complex, requiring multi-dimensional analysis. In this study, our goal was to test the feasibility of obtaining high angular resolution diffusion imaging (HARDI) metrics through single-shell modeling of diffusion tensor imaging (DTI) data, and investigate how advanced measures from single-shell HARDI and DTI tractography perform relative to classical DTI metrics in assessing MS pathology. We examined 52 relapsing-remitting MS patients who had 3T anatomical brain MRI and DTI. Single-shell HARDI modeling yielded 5 sub-voxel-based metrics, totalling 11 diffusion measures including 4 DTI and 2 tractography metrics. Based on machine learning of 3-dimensional regions of interest, we evaluated the importance of the measures through several tissue classification tasks. These included two within-subject comparisons: lesion versus normal appearing white matter (NAWM); and lesion core versus shell. Further, by stratifying patients as having high (above 75%ile) and low (below 25%ile) number of MS lesions, we also performed 2 classifications between subjects for lesions and NAWM respectively. Results showed that in lesion-NAWM analysis, HARDI orientation distribution function (ODF) energy, DTI fractional anisotropy (FA), and HARDI orientation dispersion index were the top three metrics, which together achieved 65.2% accuracy and 0.71 area under the receiver operating characteristic curve (AUROC). In core-shell analysis, DTI mean diffusivity (MD), radial diffusivity, and FA were the top three metrics, and MD dominated the classification, which achieved 59.3% accuracy and 0.59 AUROC alone. Between patients, FA was the leading feature in lesion comparisons, while ODF energy was the best in NAWM separation. Collectively, single-shell modeling of common diffusion data can provide robust orientation measures of lesion and NAWM pathology, and DTI metrics are most sensitive to intra-lesion abnormality. Combined analysis of both advanced and classical diffusion measures may be critical for improved understanding of MS pathology.
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Affiliation(s)
- Olayinka Oladosu
- Department of Neuroscience, Faculty of Graduate Studies, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Wei-Qiao Liu
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Bruce G Pike
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Marcus Koch
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Luanne M Metz
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yunyan Zhang
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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38
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Arezza NJJ, Tse DHY, Baron CA. Rapid microscopic fractional anisotropy imaging via an optimized linear regression formulation. Magn Reson Imaging 2021; 80:132-143. [PMID: 33945859 DOI: 10.1016/j.mri.2021.04.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/01/2021] [Accepted: 04/29/2021] [Indexed: 02/06/2023]
Abstract
Water diffusion anisotropy in the human brain is affected by disease, trauma, and development. Microscopic fractional anisotropy (μFA) is a diffusion MRI (dMRI) metric that can quantify water diffusion anisotropy independent of neuron fiber orientation dispersion. However, there are several different techniques to estimate μFA and few have demonstrated full brain imaging capabilities within clinically viable scan times and resolutions. Here, we present an optimized spherical tensor encoding (STE) technique to acquire μFA directly from the 2nd order cumulant expansion of the powder averaged dMRI signal obtained from direct linear regression (i.e. diffusion kurtosis) which requires fewer powder-averaged signals than other STE fitting techniques and can be rapidly computed. We found that the optimal dMRI parameters for white matter μFA imaging were a maximum b-value of 2000 s/mm2 and a ratio of STE to LTE tensor encoded acquisitions of 1.7 for our system specifications. We then compared two implementations of the direct regression approach to the well-established gamma model in 4 healthy volunteers on a 3 Tesla system. One implementation used mean diffusivity (D) obtained from a 2nd order fit of the cumulant expansion, while the other used a linear estimation of D from the low b-values. Both implementations of the direct regression approach showed strong linear correlations with the gamma model (ρ = 0.97 and ρ = 0.90) but mean biases of -0.11 and - 0.02 relative to the gamma model were also observed, respectively. All three μFA measurements showed good test-retest reliability (ρ ≥ 0.79 and bias = 0). To demonstrate the potential scan time advantage of the direct approach, 2 mm isotropic resolution μFA was demonstrated over a 10 cm slab using a subsampled data set with fewer powder-averaged signals that would correspond to a 3.3-min scan. Accordingly, our results introduce an optimization procedure that has enabled nearly full brain μFA in only several minutes.
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Affiliation(s)
- N J J Arezza
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada.
| | - D H Y Tse
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Canada
| | - C A Baron
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
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39
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Rahmanzadeh R, Lu PJ, Barakovic M, Weigel M, Maggi P, Nguyen TD, Schiavi S, Daducci A, La Rosa F, Schaedelin S, Absinta M, Reich DS, Sati P, Wang Y, Bach Cuadra M, Radue EW, Kuhle J, Kappos L, Granziera C. Myelin and axon pathology in multiple sclerosis assessed by myelin water and multi-shell diffusion imaging. Brain 2021; 144:1684-1696. [PMID: 33693571 PMCID: PMC8374972 DOI: 10.1093/brain/awab088] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 12/29/2020] [Accepted: 01/03/2021] [Indexed: 12/25/2022] Open
Abstract
Damage to the myelin sheath and the neuroaxonal unit is a cardinal feature of multiple sclerosis; however, a detailed characterization of the interaction between myelin and axon damage in vivo remains challenging. We applied myelin water and multi-shell diffusion imaging to quantify the relative damage to myelin and axons (i) among different lesion types; (ii) in normal-appearing tissue; and (iii) across multiple sclerosis clinical subtypes and healthy controls. We also assessed the relation of focal myelin/axon damage with disability and serum neurofilament light chain as a global biological measure of neuroaxonal damage. Ninety-one multiple sclerosis patients (62 relapsing-remitting, 29 progressive) and 72 healthy controls were enrolled in the study. Differences in myelin water fraction and neurite density index were substantial when lesions were compared to healthy control subjects and normal-appearing multiple sclerosis tissue: both white matter and cortical lesions exhibited a decreased myelin water fraction and neurite density index compared with healthy (P < 0.0001) and peri-plaque white matter (P < 0.0001). Periventricular lesions showed decreased myelin water fraction and neurite density index compared with lesions in the juxtacortical region (P < 0.0001 and P < 0.05). Similarly, lesions with paramagnetic rims showed decreased myelin water fraction and neurite density index relative to lesions without a rim (P < 0.0001). Also, in 75% of white matter lesions, the reduction in neurite density index was higher than the reduction in the myelin water fraction. Besides, normal-appearing white and grey matter revealed diffuse reduction of myelin water fraction and neurite density index in multiple sclerosis compared to healthy controls (P < 0.01). Further, a more extensive reduction in myelin water fraction and neurite density index in normal-appearing cortex was observed in progressive versus relapsing-remitting participants. Neurite density index in white matter lesions correlated with disability in patients with clinical deficits (P < 0.01, beta = -10.00); and neurite density index and myelin water fraction in white matter lesions were associated to serum neurofilament light chain in the entire patient cohort (P < 0.01, beta = -3.60 and P < 0.01, beta = 0.13, respectively). These findings suggest that (i) myelin and axon pathology in multiple sclerosis is extensive in both lesions and normal-appearing tissue; (ii) particular types of lesions exhibit more damage to myelin and axons than others; (iii) progressive patients differ from relapsing-remitting patients because of more extensive axon/myelin damage in the cortex; and (iv) myelin and axon pathology in lesions is related to disability in patients with clinical deficits and global measures of neuroaxonal damage.
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Affiliation(s)
- Reza Rahmanzadeh
- Department of Medicine and Biomedical Engineering, Translational Imaging in Neurology Basel, University Hospital Basel and University of Basel, Basel, Switzerland.,Departments of Medicine, Clinical Research and Biomedical Engineering Neurologic Clinic and Policlinic, Switzerland, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Department of Medicine and Biomedical Engineering, Translational Imaging in Neurology Basel, University Hospital Basel and University of Basel, Basel, Switzerland.,Departments of Medicine, Clinical Research and Biomedical Engineering Neurologic Clinic and Policlinic, Switzerland, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Department of Medicine and Biomedical Engineering, Translational Imaging in Neurology Basel, University Hospital Basel and University of Basel, Basel, Switzerland.,Departments of Medicine, Clinical Research and Biomedical Engineering Neurologic Clinic and Policlinic, Switzerland, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Department of Medicine and Biomedical Engineering, Translational Imaging in Neurology Basel, University Hospital Basel and University of Basel, Basel, Switzerland.,Departments of Medicine, Clinical Research and Biomedical Engineering Neurologic Clinic and Policlinic, Switzerland, University Hospital Basel and University of Basel, Basel, Switzerland.,Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Pietro Maggi
- Department of Neurology, Lausanne University Hospital, Lausanne, Switzerland.,Cliniques universitaires Saint Luc, Université catholique de Louvain, Brussel, Belgium
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | | | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Radiology Department, Center for Biomedical Imaging (CIBM), Lausanne University and University Hospital, Lausanne, Switzerland
| | - Sabine Schaedelin
- Department of Medicine and Biomedical Engineering, Translational Imaging in Neurology Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Martina Absinta
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA.,Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA.,Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Radiology Department, Center for Biomedical Imaging (CIBM), Lausanne University and University Hospital, Lausanne, Switzerland
| | - Ernst-Wilhelm Radue
- Department of Medicine and Biomedical Engineering, Translational Imaging in Neurology Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Departments of Medicine, Clinical Research and Biomedical Engineering Neurologic Clinic and Policlinic, Switzerland, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Departments of Medicine, Clinical Research and Biomedical Engineering Neurologic Clinic and Policlinic, Switzerland, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Cristina Granziera
- Department of Medicine and Biomedical Engineering, Translational Imaging in Neurology Basel, University Hospital Basel and University of Basel, Basel, Switzerland.,Departments of Medicine, Clinical Research and Biomedical Engineering Neurologic Clinic and Policlinic, Switzerland, University Hospital Basel and University of Basel, Basel, Switzerland
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40
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Thaler C, Kyselyova AA, Faizy TD, Nawka MT, Jespersen S, Hansen B, Stellmann JP, Heesen C, Stürner KH, Stark M, Fiehler J, Bester M, Gellißen S. Heterogeneity of multiple sclerosis lesions in fast diffusional kurtosis imaging. PLoS One 2021; 16:e0245844. [PMID: 33539364 PMCID: PMC7861404 DOI: 10.1371/journal.pone.0245844] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 01/09/2021] [Indexed: 12/14/2022] Open
Abstract
Background Mean kurtosis (MK), one of the parameters derived from diffusion kurtosis imaging (DKI), has shown increased sensitivity to tissue microstructure damage in several neurological disorders. Methods Thirty-seven patients with relapsing-remitting MS and eleven healthy controls (HC) received brain imaging on a 3T MR scanner, including a fast DKI sequence. MK and mean diffusivity (MD) were measured in the white matter of HC, normal-appearing white matter (NAWM) of MS patients, contrast-enhancing lesions (CE-L), FLAIR lesions (FLAIR-L) and black holes (BH). Results Overall 1529 lesions were analyzed, including 30 CE-L, 832 FLAIR-L and 667 BH. Highest MK values were obtained in the white matter of HC (0.814 ± 0.129), followed by NAWM (0.724 ± 0.137), CE-L (0.619 ± 0.096), FLAIR-L (0.565 ± 0.123) and BH (0.549 ± 0.12). Lowest MD values were obtained in the white matter of HC (0.747 ± 0.068 10−3mm2/sec), followed by NAWM (0.808 ± 0.163 10−3mm2/sec), CE-L (0.853 ± 0.211 10−3mm2/sec), BH (0.957 ± 0.304 10−3mm2/sec) and FLAIR-L (0.976 ± 0.35 10−3mm2/sec). While MK differed significantly between CE-L and non-enhancing lesions, MD did not. Conclusion MK adds predictive value to differentiate between MS lesions and might provide further information about diffuse white matter injury and lesion microstructure.
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Affiliation(s)
- Christian Thaler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- * E-mail:
| | - Anna A. Kyselyova
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias D. Faizy
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marie T. Nawka
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Sune Jespersen
- Department of Clinical Medicine - Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | - Brian Hansen
- Department of Clinical Medicine - Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | - Jan-Patrick Stellmann
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute for Neuroimmunology and Clinical MS Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- APHM, Hospital de la Timone, CEMEREM, Marseille, France
- Aix Marseille University, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Christoph Heesen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute for Neuroimmunology and Clinical MS Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Klarissa H. Stürner
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute for Neuroimmunology and Clinical MS Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Maria Stark
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Maxim Bester
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Susanne Gellißen
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Quantitative evaluation of callosal abnormalities in relapsing-remitting multiple sclerosis using diffusion tensor imaging: A systemic review and meta-analysis. Clin Neurol Neurosurg 2021; 201:106442. [DOI: 10.1016/j.clineuro.2020.106442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 12/12/2020] [Accepted: 12/14/2020] [Indexed: 01/13/2023]
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42
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Bernitsas E, Kopinsky H, Lichtman-Mikol S, Razmjou S, Santiago-Martinez C, Yarraguntla K, Bao F. Multimodal MRI Response to Fingolimod in Multiple Sclerosis: A Nonrandomized, Single Arm, Observational Study. J Neuroimaging 2020; 31:379-387. [PMID: 33368776 DOI: 10.1111/jon.12824] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 11/22/2020] [Accepted: 11/30/2020] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE Fingolimod has a favorable effect on conventional MRI measures; however, its neuroprotective effect is not clear. We aim to investigate changes of conventional and advanced MRI measures in lesions and normal-appearing white matter (NAWM) over 2 years in fingolimod-treated patients. METHODS Fifty relapsing-remitting multiple sclerosis patients and 27 healthy controls were enrolled in the study and underwent baseline, 1-year, and 2-year 3T MRI scans. T2 lesion volume, whole brain volume, cortical gray matter volume, white matter volume, corpus callosum area, percentage brain volume change (PBVC), Expanded Disability Status Scale, gadolinium-enhancing lesions, PBVC, magnetization transfer ratio (MTR), and diffusion tensor imaging metrics (fractional anisotropy [FA] and median diffusivity [MD]) in lesions and NAWM were calculated. Longitudinal changes were examined using one-way repeated measures ANOVA. Bonferroni correction for multiple testing was used when appropriate. RESULTS Conventional MRI measures were unchanged in both groups. Lesion MTR increased significantly (P < .001), but NAWM-MTR remained unchanged. Lesion FA improved significantly in year 1 (P = .003) and over the study duration (P = .05). Lesion MD changed significantly from baseline to year 1 (P < .001) and remained stable over 2 years. NAWM-FA was significant from baseline to year 1 (P = .002) and from baseline to year 2 (P < .001). NAWM-MD was significant only from baseline to year 1 (P = .001). CONCLUSIONS These findings suggest a possible neuroreparative effect of fingolimod on the MS lesions and NAWM. Larger and longer randomized studies are required to confirm these results.
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Affiliation(s)
- Evanthia Bernitsas
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI
| | - Hannah Kopinsky
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI
| | | | - Sarah Razmjou
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI
| | | | - Kalyan Yarraguntla
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI
| | - Fen Bao
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI
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A cross-sectional comparison of performance, neurophysiological and MRI outcomes of responders and non-responders to fampridine treatment in multiple sclerosis - An explorative study. J Clin Neurosci 2020; 82:179-185. [PMID: 33317729 DOI: 10.1016/j.jocn.2020.10.034] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 08/10/2020] [Accepted: 10/18/2020] [Indexed: 01/18/2023]
Abstract
OBJECTIVE To compare baseline physical and cognitive performance, neurophysiological, and magnetic resonance imaging (MRI) outcomes and examinetheir interrelationship inparticipants with Multiple Sclerosis (MS), already established aseither responder or non-responder to Fampridine treatment, andto examine associationswiththe expanded disability status scale (EDSS) and 12-item MS walking scale (MSWS-12). METHODS Baseline data from an explorative longitudinal observational study were analyzed. Participants underwent the Timed 25-Foot Walk Test (T25FW), Six Spot Step Test (SSST), Nine-Hole Peg Test, Five Times Sit-to-Stand Test, Symbol Digit Modalities Test (SDMT), neurophysiological testing, including central motor conduction time (CMCT), peripheral motor conduction time (PMCT), motor evoked potential (MEP) amplitudesand electroneuronographyof the lower extremities, and brain MRI (brain volume, number and volume of T2-weighted lesions and lesion load normalized to brain volume). RESULTS 41 responders and 8 non-responders were examined. There were no intergroup differences inphysical performance, cognitive, neurophysiological, andMRI outcomes (p > 0.05).CMCT was associated withT25FW, SSST, EDSS, and MSWS-12,(p < 0.05). SDMT was associated with the number and volume of T2-weighted lesions, and lesion load normalized to brain volume (p < 0.05). CONCLUSION No differences were identified between responders and non-responders to Fampridine treatment regarding physical and cognitive performance, neurophysiological or MRI outcomes. The results call for cautious interpretation and further large-scale studies are needed to expand ourunderstanding of underlying mechanisms discriminating Fampridine responders and non-responders.CMCT may be used as a marker of disability and walking impairment, while SDMT was associated with white matter lesions estimated by MRI. ClinicalTrials.gov identifier: NCT03401307.
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44
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Gatto RG, Weissmann C. Diffusion Tensor Imaging in Preclinical and Human Studies of Huntington's Disease: What Have we Learned so Far? Curr Med Imaging 2020; 15:521-542. [PMID: 32008561 DOI: 10.2174/1573405614666181115113400] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 10/23/2018] [Accepted: 10/26/2018] [Indexed: 12/13/2022]
Abstract
BACKGROUND Huntington's Disease is an irreversible neurodegenerative disease characterized by the progressive deterioration of specific brain nerve cells. The current evaluation of cellular and physiological events in patients with HD relies on the development of transgenic animal models. To explore such events in vivo, diffusion tensor imaging has been developed to examine the early macro and microstructural changes in brain tissue. However, the gap in diffusion tensor imaging findings between animal models and clinical studies and the lack of microstructural confirmation by histological methods has questioned the validity of this method. OBJECTIVE This review explores white and grey matter ultrastructural changes associated to diffusion tensor imaging, as well as similarities and differences between preclinical and clinical Huntington's Disease studies. METHODS A comprehensive review of the literature using online-resources was performed (Pub- Med search). RESULTS Similar changes in fractional anisotropy as well as axial, radial and mean diffusivities were observed in white matter tracts across clinical and animal studies. However, comparative diffusion alterations in different grey matter structures were inconsistent between clinical and animal studies. CONCLUSION Diffusion tensor imaging can be related to specific structural anomalies in specific cellular populations. However, some differences between animal and clinical studies could derive from the contrasting neuroanatomy or connectivity across species. Such differences should be considered before generalizing preclinical results into the clinical practice. Moreover, current limitations of this technique to accurately represent complex multicellular events at the single micro scale are real. Future work applying complex diffusion models should be considered.
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Affiliation(s)
- Rodolfo Gabriel Gatto
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, 60607, United States
| | - Carina Weissmann
- Insituto de Fisiología Biologia Molecular y Neurociencias-IFIBYNE-CONICET, University of Buenos Aires, Buenos Aires, Argentina
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Martínez-Heras E, Solana E, Prados F, Andorrà M, Solanes A, López-Soley E, Montejo C, Pulido-Valdeolivas I, Alba-Arbalat S, Sola-Valls N, Sepúlveda M, Blanco Y, Saiz A, Radua J, Llufriu S. Characterization of multiple sclerosis lesions with distinct clinical correlates through quantitative diffusion MRI. NEUROIMAGE-CLINICAL 2020; 28:102411. [PMID: 32950904 PMCID: PMC7502564 DOI: 10.1016/j.nicl.2020.102411] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 08/24/2020] [Accepted: 09/01/2020] [Indexed: 01/26/2023]
Abstract
Diffusion magnetic resonance imaging can reveal quantitative information about the tissue changes in multiple sclerosis. The recently developed multi-compartment spherical mean technique can map different microscopic properties based only on local diffusion signals, and it may provide specific information on the underlying microstructural modifications that arise in multiple sclerosis. Given that the lesions in multiple sclerosis may reflect different degrees of damage, we hypothesized that quantitative diffusion maps may help characterize the severity of lesions "in vivo" and correlate these to an individual's clinical profile. We evaluated this in a cohort of 59 multiple sclerosis patients (62% female, mean age 44.7 years), for whom demographic and disease information was obtained, and who underwent a comprehensive physical and cognitive evaluation. The magnetic resonance imaging protocol included conventional sequences to define focal lesions, and multi-shell diffusion imaging was used with b-values of 1000, 2000 and 3000 s/mm2 in 180 encoding directions. Quantitative diffusion properties on a macro- and micro-scale were used to discriminate distinct types of lesions through a k-means clustering algorithm, and the number and volume of those lesion types were correlated with parameters of the disease. The combination of diffusion tensor imaging metrics (fractional anisotropy and radial diffusivity) and multi-compartment spherical mean technique values (microscopic fractional anisotropy and intra-neurite volume fraction) differentiated two type of lesions, with a prediction strength of 0.931. The B-type lesions had larger diffusion changes compared to the A-type lesions, irrespective of their location (P < 0.001). The number of A and B type lesions was similar, although in juxtacortical areas B-type lesions predominated (60%, P < 0.001). Also, the percentage of B-type lesion volume was higher (64%, P < 0.001), indicating that these lesions were larger. The number and volume of B-type lesions was related to the severity of disease evolution, clinical disability and cognitive decline (P = 0.004, Bonferroni correction). Specifically, more and larger B-type lesions were correlated with a worse Multiple Sclerosis Severity Score, cerebellar function and cognitive performance. Thus, by combining several microscopic and macroscopic diffusion properties, the severity of damage within focal lesions can be characterized, further contributing to our understanding of the mechanisms that drive disease evolution. Accordingly, the classification of lesion types has the potential to permit more specific and better-targeted treatment of patients with multiple sclerosis.
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Affiliation(s)
- Eloy Martínez-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
| | - 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
| | - Ferran Prados
- E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain; Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, UK
| | - Magí Andorrà
- 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
| | - Aleix Solanes
- Imaging of Mood- and Anxiety-related Disorders (IMARD) Group, IDIBAPS and CIBERSAM, Barcelona, Spain
| | - Elisabet López-Soley
- 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
| | - Carmen Montejo
- 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
| | - Irene Pulido-Valdeolivas
- 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
| | - Salut Alba-Arbalat
- 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
| | - Nuria Sola-Valls
- 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
| | - Maria Sepúlveda
- 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
| | - Yolanda Blanco
- 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
| | - Albert Saiz
- 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
| | - Joaquim Radua
- Imaging of Mood- and Anxiety-related Disorders (IMARD) Group, IDIBAPS and CIBERSAM, Barcelona, Spain; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - 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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Abel S, Vavasour I, Lee LE, Johnson P, Ristow S, Ackermans N, Chan J, Cross H, Laule C, Dvorak A, Schabas A, Hernández-Torres E, Tam R, Kuan AJ, Morrow SA, Wilken J, Rauscher A, Bhan V, Sayao AL, Devonshire V, Li DKB, Carruthers R, Traboulsee A, Kolind SH. Associations Between Findings From Myelin Water Imaging and Cognitive Performance Among Individuals With Multiple Sclerosis. JAMA Netw Open 2020; 3:e2014220. [PMID: 32990740 PMCID: PMC7525360 DOI: 10.1001/jamanetworkopen.2020.14220] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE Cognitive impairment is a debilitating symptom of multiple sclerosis (MS) that affects up to 70% of patients. An improved understanding of the underlying pathology of MS-related cognitive impairment would provide considerable benefit to patients and clinicians. OBJECTIVE To determine whether there is an association between myelin damage in tissue that appears completely normal on standard clinical imaging, but can be detected by myelin water imaging (MWI), with cognitive performance in MS. DESIGN, SETTING, AND PARTICIPANTS In this cross-sectional study, participants with MS and controls underwent cognitive testing and magnetic resonance imaging (MRI) from August 23, 2017, to February 20, 2019. Participants were recruited through the University of British Columbia Hospital MS clinic and via online recruitment advertisements on local health authority websites. Cognitive testing was performed in the MS clinic, and MRI was performed at the adjacent academic research neuroimaging center. Seventy-three participants with clinically definite MS fulfilling the 2017 revised McDonald criteria for diagnosis and 22 age-, sex-, and education-matched healthy volunteers without neurological disease were included in the study. Data analysis was performed from March to November 2019. EXPOSURES MWI was performed at 3 T with a 48-echo, 3-dimensional, gradient and spin-echo (GRASE) sequence. Cognitive testing was performed with assessments drawn from cognitive batteries validated for use in MS. MAIN OUTCOMES AND MEASURES The association between myelin water measures, a measurement of the T2 relaxation signal from water in the myelin bilayers providing a specific marker for myelin, and cognitive test scores was assessed using Pearson correlation. Three white matter regions of interest-the cingulum, superior longitudinal fasciculus (SLF), and corpus callosum-were selected a priori according to their known involvement in MS-related cognitive impairment. RESULTS For the 95 total participants, the mean (SD) age was 49.33 (11.44) years. The mean (SD) age was 50.2 (10.7) years for the 73 participants with MS and 46.4 (13.5) for the 22 controls. Forty-eight participants with MS (66%) and 14 controls (64%) were women. The mean (SD) years of education were 14.7 (2.2) for patients and 15.8 (2.5) years for controls. In MS, significant associations were observed between myelin water measures and scores on the Symbol Digit Modalities Test (SLF, r = -0.490; 95% CI, -0.697 to -0.284; P < .001; corpus callosum, r = -0.471; 95% CI, -0.680 to -0.262; P < .001; and cingulum, r = -0.419; 95% CI, -0.634 to -0.205; P < .001), Selective Reminding Test (SLF, r = -0.444; 95% CI, -0.660 to -0.217; P < .001; corpus callosum, r = -0.411; 95% CI, -0.630 to -0.181; P = .001; and cingulum, r = -0.361; 95% CI, -0.602 to -0.130; P = .003), and Controlled Oral Word Association Test (SLF, r = -0.317; 95% CI, -0.549 to -0.078; P = .01; and cingulum, r = -0.335; 95% CI, -0.658 to -0.113; P = .006). No significant associations were found in controls. CONCLUSIONS AND RELEVANCE This study used MWI to demonstrate that otherwise normal-appearing brain tissue is diffusely damaged in MS, and the findings suggest that myelin water measures are associated with cognitive performance. MWI offers an in vivo biomarker feasible for use in clinical trials investigating cognition, providing a means for monitoring changes in myelination and its association with symptom worsening or improvement.
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Affiliation(s)
- Shawna Abel
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
| | - Irene Vavasour
- Department of Radiology, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Lisa Eunyoung Lee
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
| | - Poljanka Johnson
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
| | - Stephen Ristow
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
| | - Nathalie Ackermans
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
| | - Jillian Chan
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
| | - Helen Cross
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
| | - Cornelia Laule
- Department of Radiology, The University of British Columbia, Vancouver, British Columbia, Canada
- Department of Pathology & Laboratory Medicine, The University of British Columbia, Vancouver, British Columbia, Canada
- Department of Physics & Astronomy, The University of British Columbia, Vancouver, British Columbia, Canada
- International Collaboration on Repair Discoveries, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Adam Dvorak
- Department of Physics & Astronomy, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Alice Schabas
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
| | - Enedino Hernández-Torres
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
| | - Roger Tam
- Department of Radiology, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Annie J. Kuan
- Department of Psychiatry, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Sarah A. Morrow
- Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada
| | - Jeffrey Wilken
- Department of Neurology, Georgetown University Hospital, Washington, DC
- Washington Neuropsychology Research Group LLC, Fairfax, Virginia
| | - Alexander Rauscher
- Department of Radiology, The University of British Columbia, Vancouver, British Columbia, Canada
- Department of Physics & Astronomy, The University of British Columbia, Vancouver, British Columbia, Canada
- Department of Pediatrics, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Virender Bhan
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
| | - Ana-Luiza Sayao
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
| | - Virginia Devonshire
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
| | - David K. B. Li
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
- Department of Radiology, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Robert Carruthers
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
| | - Anthony Traboulsee
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
| | - Shannon H. Kolind
- Department of Medicine (Neurology), The University of British Columbia, Vancouver, British Columbia, Canada
- Department of Radiology, The University of British Columbia, Vancouver, British Columbia, Canada
- Department of Physics & Astronomy, The University of British Columbia, Vancouver, British Columbia, Canada
- International Collaboration on Repair Discoveries, The University of British Columbia, Vancouver, British Columbia, Canada
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Ramli N, Lim CH, Rajagopal R, Tan LK, Seow P, Ariffin H. Assessing changes in microstructural integrity of white matter tracts in children with leukaemia following exposure to chemotherapy. Pediatr Radiol 2020; 50:1277-1283. [PMID: 32591982 DOI: 10.1007/s00247-020-04717-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 03/21/2020] [Accepted: 05/12/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Intrathecal and intravenous chemotherapy, specifically methotrexate, might contribute to neural microstructural damage. OBJECTIVE To assess, by diffusion tensor imaging, microstructural integrity of white matter in paediatric patients with acute lymphoblastic leukaemia (ALL) following intrathecal and intravenous chemotherapy. MATERIALS AND METHODS Eleven children diagnosed with de novo ALL underwent MRI scans of the brain with diffusion tensor imaging (DTI) prior to commencement of chemotherapy and at 12 months after diagnosis, using a 3-tesla (T) MRI scanner. We investigated the changes in DTI parameters in white matter tracts before and after chemotherapy using tract-based spatial statistics overlaid on the International Consortium of Brain Mapping DTI-81 atlas. All of the children underwent formal neurodevelopmental assessment at the two study time points. RESULTS Whole-brain DTI analysis showed significant changes between the two time points, affecting several white matter tracts. The tracts demonstrated longitudinal changes of decreasing mean and radial diffusivity. The neurodevelopment of the children was near compatible for age at the end of ALL treatment. CONCLUSION The quantification of white matter tracts changes in children undergoing chemotherapy showed improving longitudinal values in DTI metrics (stable fractional anisotropy, decreasing mean and radial diffusivity), which are incompatible with deterioration of microstructural integrity in these children.
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Affiliation(s)
- Norlisah Ramli
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Jalan Universiti, 50603, Kuala Lumpur, Malaysia.
| | - Chuin Hoong Lim
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Jalan Universiti, 50603, Kuala Lumpur, Malaysia
| | - Revathi Rajagopal
- Department of Paediatrics, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Li Kuo Tan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Jalan Universiti, 50603, Kuala Lumpur, Malaysia
| | - Pohchoo Seow
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Jalan Universiti, 50603, Kuala Lumpur, Malaysia.,Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
| | - Hany Ariffin
- Department of Paediatrics, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
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48
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Moon PK, Qian JZ, McKenna E, Xi K, Rowe NC, Ng NN, Zheng J, Tam LT, MacEachern SJ, Ahmad I, Cheng AG, Forkert ND, Yeom KW. Cerebral volume and diffusion MRI changes in children with sensorineural hearing loss. NEUROIMAGE-CLINICAL 2020; 27:102328. [PMID: 32622314 PMCID: PMC7334366 DOI: 10.1016/j.nicl.2020.102328] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 06/19/2020] [Accepted: 06/20/2020] [Indexed: 12/11/2022]
Abstract
Microstructural and macrostructural changes in sensorineural hearing loss. Magnetic resonance imaging as tool to assess cerebral volume and diffusion. Greater diffusion in cortex, thalamus, caudate, brainstem with hearing loss. Smaller brainstem volume with hearing loss. Connexin 26, Pendrin mutations show diffusion changes in brainstem and thalamus.
Purpose Sensorineural hearing loss (SNHL) is the most prevalent congenital sensory deficit in children. Information regarding underlying brain microstructure could offer insight into neural development in deaf children and potentially guide therapies that optimize language development. We sought to quantitatively evaluate MRI-based cerebral volume and gray matter microstructure children with SNHL. Methods & Materials We conducted a retrospective study of children with SNHL who obtained brain MRI at 3 T. The study cohort comprised 63 children with congenital SNHL without known focal brain lesion or structural abnormality (33 males; mean age 5.3 years; age range 1 to 11.8 years) and 64 age-matched controls without neurological, developmental, or MRI-based brain macrostructure abnormality. An atlas-based analysis was used to extract quantitative volume and median diffusivity (ADC) in the following brain regions: cerebral cortex, thalamus, caudate, putamen, globus pallidus, hippocampus, amygdala, nucleus accumbens, brain stem, and cerebral white matter. SNHL patients were further stratified by severity scores and hearing loss etiology. Results Children with SNHL showed higher median ADC of the cortex (p = .019), thalamus (p < .001), caudate (p = .005), and brainstem (p = .003) and smaller brainstem volumes (p = .007) compared to controls. Patients with profound bilateral SNHL did not show any significant differences compared to patients with milder bilateral SNHL, but both cohorts independently had smaller brainstem volumes compared to controls. Children with unilateral SNHL showed greater amygdala volumes compared to controls (p = .021), but no differences were found comparing unilateral SNHL to bilateral SNHL. Based on etiology for SNHL, patients with Pendrin mutations showed higher ADC values in the brainstem (p = .029, respectively); patients with Connexin 26 showed higher ADC values in both the thalamus (p < .001) and brainstem (p < .001) compared to controls. Conclusion SNHL patients showed significant differences in diffusion and volume in brain subregions, with region-specific findings for patients with Connexin 26 and Pendrin mutations. Future longitudinal studies could examine macro- and microstructure changes in children with SNHL over development and potential predictive role for MRI after interventions including cochlear implant outcome.
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Affiliation(s)
- Peter K Moon
- Stanford University School of Medicine, Stanford, CA, USA
| | - Jason Z Qian
- Department of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA
| | - Emily McKenna
- Department of Radiology, Lucile Packard Children's Hospital, Stanford University, Stanford, CA, USA
| | - Kevin Xi
- Department of Radiology, Lucile Packard Children's Hospital, Stanford University, Stanford, CA, USA
| | - Nathan C Rowe
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Nathan N Ng
- Stanford University School of Medicine, Stanford, CA, USA
| | - Jimmy Zheng
- Stanford University School of Medicine, Stanford, CA, USA
| | - Lydia T Tam
- Stanford University School of Medicine, Stanford, CA, USA
| | - Sarah J MacEachern
- Department of Pediatrics, University of Calgary, Calgary, Alberta, Canada
| | - Iram Ahmad
- Department of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA
| | - Alan G Cheng
- Department of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA
| | - Nils D Forkert
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Kristen W Yeom
- Department of Radiology, Lucile Packard Children's Hospital, Stanford University, Stanford, CA, USA.
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Tractography in the presence of multiple sclerosis lesions. Neuroimage 2019; 209:116471. [PMID: 31877372 PMCID: PMC7613131 DOI: 10.1016/j.neuroimage.2019.116471] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 12/13/2019] [Accepted: 12/16/2019] [Indexed: 12/11/2022] Open
Abstract
Accurate anatomical localisation of specific white matter tracts and the quantification of their tract-specific microstructural damage in conditions such as multiple sclerosis (MS) can contribute to a better understanding of symptomatology, disease evolution and intervention effects. Diffusion MRI-based tractography is being used increasingly to segment white matter tracts as regions-of-interest for subsequent quantitative analysis. Since MS lesions can interrupt the tractography algorithm’s tract reconstruction, clinical studies frequently resort to atlas-based approaches, which are convenient but ignorant to individual variability in tract size and shape. Here, we revisit the problem of individual tractography in MS, comparing tractography algorithms using: (i) The diffusion tensor framework; (ii) constrained spherical deconvolution (CSD); and (iii) damped Richardson-Lucy (dRL) deconvolution. Firstly, using simulated and in vivo data from 29 MS patients and 19 healthy controls, we show that the three tracking algorithms respond differentially to MS pathology. While the tensor-based approach is unable to deal with crossing fibres, CSD produces spurious streamlines, in particular in tissue with high fibre loss and low diffusion anisotropy. With dRL, streamlines are increasingly interrupted in pathological tissue. Secondly, we demonstrate that despite the effects of lesions on the fibre orientation reconstruction algorithms, fibre tracking algorithms are still able to segment tracts that pass through areas with a high prevalence of lesions. Combining dRL-based tractography with an automated tract segmentation tool on data from 131 MS patients, the corticospinal tracts and arcuate fasciculi could be reconstructed in more than 90% of individuals. Comparing tract-specific microstructural parameters (fractional anisotropy, radial diffusivity and magnetisation transfer ratio) in individually segmented tracts to those from a tract probability map, we show that there is no systematic disease-related bias in the individually reconstructed tracts, suggesting that lesions and otherwise damaged parts are not systematically omitted during tractography. Thirdly, we demonstrate modest anatomical correspondence between the individual and tract probability-based approach, with a spatial overlap between 35 and 55%. Correlations between tract-averaged microstructural parameters in individually segmented tracts and the probability-map approach ranged between r = .53 (p < .001) for radial diffusivity in the right cortico-spinal tract and r = .97 (p < .001) for magnetisation transfer ratio in the arcuate fasciculi. Our results show that MS white matter lesions impact fibre orientation reconstructions but this does not appear to hinder the ability to anatomically reconstruct white matter tracts in MS. Individual tract segmentation in MS is feasible on a large scale and could prove a powerful tool for investigating diagnostic and prognostic markers.
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50
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Chuhutin A, Hansen B, Wlodarczyk A, Owens T, Shemesh N, Jespersen SN. Diffusion Kurtosis Imaging maps neural damage in the EAE model of multiple sclerosis. Neuroimage 2019; 208:116406. [PMID: 31830588 PMCID: PMC9358435 DOI: 10.1016/j.neuroimage.2019.116406] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 11/20/2019] [Accepted: 11/25/2019] [Indexed: 01/22/2023] Open
Abstract
Diffusion kurtosis imaging (DKI) is an imaging modality that yields novel
disease biomarkers and in combination with nervous tissue modeling, provides
access to microstructural parameters. Recently, DKI and subsequent estimation of
microstructural model parameters has been used for assessment of tissue changes
in neurodegenerative diseases and associated animal models. In this study, mouse
spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of
multiple sclerosis (MS) were investigated for the first time using DKI in
combination with biophysical modeling to study the relationship between
microstructural metrics and degree of animal dysfunction. Thirteen spinal cords
were extracted from animals with varied grades of disability and scanned in a
high-field MRI scanner along with five control specimen. Diffusion weighted data
were acquired together with high resolution T2*
images. Diffusion data were fit to estimate diffusion and kurtosis tensors and
white matter modeling parameters, which were all used for subsequent statistical
analysis using a linear mixed effects model. T2*
images were used to delineate focal demyelination/inflammation. Our results
reveal a strong relationship between disability and measured microstructural
parameters in normal appearing white matter and gray matter. Relationships
between disability and mean of the kurtosis tensor, radial kurtosis, radial
diffusivity were similar to what has been found in other hypomyelinating MS
models, and in patients. However, the changes in biophysical modeling parameters
and in particular in extra-axonal axial diffusivity were clearly different from
previous studies employing other animal models of MS. In conclusion, our data
suggest that DKI and microstructural modeling can provide a unique contrast
capable of detecting EAE-specific changes correlating with clinical
disability.
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Affiliation(s)
| | | | - Agnieszka Wlodarczyk
- Department of Neurobiology Research, Institute for Molecular Medicine,University of South Denmark, Odense, Denmark
| | - Trevor Owens
- Department of Neurobiology Research, Institute for Molecular Medicine,University of South Denmark, Odense, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Sune Nørhøj Jespersen
- CFIN, Aarhus University, Aarhus, Denmark; Department of Physics, Aarhus University, Aarhus, Denmark
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