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Pontillo G, Cepas MB, Broeders TAA, Koubiyr I, Schoonheim MM. Network Analysis in Multiple Sclerosis and Related Disorders. Neuroimaging Clin N Am 2024; 34:375-384. [PMID: 38942522 DOI: 10.1016/j.nic.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease of the central nervous system, commonly featuring disability and cognitive impairment. The pathologic hallmark of MS lies in demyelination and hence impaired structural and functional neuronal pathways. Recent studies have shown that MS shows extensive structural disconnection of key network hub areas like the thalamus, combined with a functional network reorganization that can mostly be related to poorer clinical functioning. As MS can, therefore, be considered a network disorder, this review outlines recent innovations in the field of network neuroscience in MS.
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Affiliation(s)
- Giuseppe Pontillo
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands; MS Center Amsterdam, Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands.
| | - Mar Barrantes Cepas
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Tommy A A Broeders
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Ismail Koubiyr
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
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Caporale AS, Chiarelli AM, Biondetti E, Villani A, Lipp I, Di Censo D, Tomassini V, Wise RG. Changes of brain parenchyma free water fraction reflect tissue damage and impaired processing speed in multiple sclerosis. Hum Brain Mapp 2024; 45:e26761. [PMID: 38895882 PMCID: PMC11187860 DOI: 10.1002/hbm.26761] [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: 06/30/2023] [Revised: 05/13/2024] [Accepted: 06/02/2024] [Indexed: 06/21/2024] Open
Abstract
Free water fraction (FWF) represents the amount of water per unit volume of brain parenchyma, which is not bound to macromolecules. Its excess in multiple sclerosis (MS) is related to increased tissue loss. The use of mcDESPOT (multicomponent driven single pulse observation of T1 and T2), a 3D imaging method which exploits both the T1 and T2 contrasts, allows FWF to be derived in clinically feasible times. However, this method has not been used to quantify changes of FWF and their potential clinical impact in MS. The aim of this study is to investigate the changes in FWF in MS patients and their relationship with tissue damage and cognition, under the hypothesis that FWF is a proxy of clinically meaningful tissue loss. To this aim, we tested the relationship between FWF, MS lesion burden and information processing speed, evaluated via the Symbol Digit Modalities Test (SDMT). In addition to standard sequences, used for T1- and T2-weighted lesion delineation, the mcDESPOT sequence with 1.7 mm isotropic resolution and a diffusion weighted imaging protocol (b = 0, 1200 s/mm2, 40 diffusion directions) were employed at 3 T. The fractional anisotropy map derived from diffusion data was used to define a subject-specific white matter (WM) atlas. Brain parenchyma segmentation returned masks of gray matter (GM) and WM, and normal-appearing WM (NAWM), in addition to the T1 and T2 lesion masks (T1L and T2L, respectively). Ninety-nine relapsing-remitting MS patients (age = 43.3 ± 9.9 years, disease duration 12.3 ± 7.7 years) were studied, together with twenty-five healthy controls (HC, age = 38.8 ± 11.0 years). FWF was higher in GM and NAWM of MS patients, compared to GM and WM of HC (both p < .001). In MS patients, FWF was the highest in the T1L and GM, followed by T2L and NAWM, respectively. FWF increased significantly with T1L and T2L volume (ρ ranging from 0.40 to 0.58, p < .001). FWF in T2L was strongly related to both T1L volume and the volume ratio T1L/T2L (ρ = 0.73, p < .001). MS patients performed worse than HC in the processing speed test (mean ± SD: 54.1 ± 10.3 for MS, 63.8 ± 10.8 for HC). FWF in GM, T2L, perilesional tissue and NAWM increased with SDMT score reduction (ρ = -0.30, -0.29, -0.33 respectively and r = -.30 for T2L, all with p < .005). A regional analysis, conducted to determine which NAWM regions were of particular importance to explain the relationship between FWF and cognitive impairment, revealed that FWF spatial variance was negatively related to SDMT score in the corpus callosum and the superior longitudinal fasciculus, WM structures known to be associated with cognitive impairment, in addition to the left corticospinal tract, the sagittal stratum, the right anterior limb of internal capsule. In conclusion, we found excess free water in brain parenchyma of MS patients, an alteration that involved not only MS lesions, but also the GM and NAWM, impinging on brain function and negatively associated with cognitive processing speed. We suggest that the FWF metric, derived from noninvasive, rapid MRI acquisitions and bearing good biological interpretability, may prove valuable as an MRI biomarker of tissue damage and associated cognitive impairment in MS.
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Affiliation(s)
- Alessandra Stella Caporale
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Antonio Maria Chiarelli
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Emma Biondetti
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Alessandro Villani
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Ilona Lipp
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Davide Di Censo
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Valentina Tomassini
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- MS Centre, Department of Clinical Neurology‘SS. Annunziata’ University HospitalChietiItaly
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
| | - Richard Geoffrey Wise
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
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Mishra S, Bapuraj J, Srinivasan A. Multiple Sclerosis Part 2: Advanced Imaging and Emerging Techniques. Magn Reson Imaging Clin N Am 2024; 32:221-231. [PMID: 38555138 DOI: 10.1016/j.mric.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Multiple advanced imaging methods for multiple sclerosis (MS) have been in investigation to identify new imaging biomarkers for early disease detection, predicting disease prognosis, and clinical trial endpoints. Multiple techniques probing different aspects of tissue microstructure (ie, advanced diffusion imaging, magnetization transfer, myelin water imaging, magnetic resonance spectroscopy, glymphatic imaging, and perfusion) support the notion that MS is a global disease with microstructural changes evident in normal-appearing white and gray matter. These global changes are likely better predictors of disability compared with lesion load alone. Emerging techniques in glymphatic and molecular imaging may improve understanding of pathophysiology and emerging treatments.
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Affiliation(s)
- Shruti Mishra
- Department of Radiology, University of Michigan, 1500 East Medical Center Drive, UH B2A209, Ann Arbor, MI 48109-5030, USA.
| | - Jayapalli Bapuraj
- Department of Radiology, University of Michigan, 1500 East Medical Center Drive, UH B2A209, Ann Arbor, MI 48109-5030, USA
| | - Ashok Srinivasan
- Department of Radiology, University of Michigan, 1500 East Medical Center Drive, UH B2A209, Ann Arbor, MI 48109-5030, USA
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Friesen E, Hari K, Sheft M, Thiessen JD, Martin M. Magnetic resonance metrics for identification of cuprizone-induced demyelination in the mouse model of neurodegeneration: a review. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01160-z. [PMID: 38635150 DOI: 10.1007/s10334-024-01160-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 03/17/2024] [Accepted: 03/26/2024] [Indexed: 04/19/2024]
Abstract
Neurodegenerative disorders, including Multiple Sclerosis (MS), are heterogenous disorders which affect the myelin sheath of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) provides a non-invasive method for studying, diagnosing, and monitoring disease progression. As an emerging research area, many studies have attempted to connect MR metrics to underlying pathophysiological presentations of heterogenous neurodegeneration. Most commonly, small animal models are used, including Experimental Autoimmune Encephalomyelitis (EAE), Theiler's Murine Encephalomyelitis (TMEV), and toxin models including cuprizone (CPZ), lysolecithin, and ethidium bromide (EtBr). A contrast and comparison of these models is presented, with focus on the cuprizone model, followed by a review of literature studying neurodegeneration using MRI and the cuprizone model. Conventional MRI methods including T1 Weighted (T1W) and T2 Weighted (T2W) Imaging are mentioned. Quantitative MRI methods which are sensitive to diffusion, magnetization transfer, susceptibility, relaxation, and chemical composition are discussed in relation to studying the CPZ model. Overall, additional studies are needed to improve both the sensitivity and specificity of MRI metrics for underlying pathophysiology of neurodegeneration and the relationships in attempts to clear the clinico-radiological paradox. We therefore propose a multiparametric approach for the investigation of MR metrics for underlying pathophysiology.
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Affiliation(s)
- Emma Friesen
- Chemistry, University of Winnipeg, Winnipeg, Canada.
| | - Kamya Hari
- Physics, University of Winnipeg, Winnipeg, Canada
- Electronics and Communication Engineering, SSN College of Engineering, Chennai, India
| | - Maxina Sheft
- Physics, University of Winnipeg, Winnipeg, Canada
- Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, USA
| | - Jonathan D Thiessen
- Imaging Program, Lawson Health Research Institute, London, Canada
- Medical Biophysics, Western University, London, Canada
- Medical Imaging, Western University, London, Canada
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Michel LC, McCormick EM, Kievit RA. Gray and White Matter Metrics Demonstrate Distinct and Complementary Prediction of Differences in Cognitive Performance in Children: Findings from ABCD ( N = 11,876). J Neurosci 2024; 44:e0465232023. [PMID: 38388427 PMCID: PMC10957209 DOI: 10.1523/jneurosci.0465-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 11/01/2023] [Accepted: 11/03/2023] [Indexed: 02/24/2024] Open
Abstract
Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either gray or white matter metrics in humans, leaving open the key question as to whether gray or white matter microstructure plays distinct or complementary roles supporting cognitive performance. To compare the role of gray and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with gray and white matter measures. Specifically, we compared how gray matter (volume, cortical thickness, and surface area) and white matter measures (volume, fractional anisotropy, and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study; 5,680 female, 6,196 male) at 10 years old. We found that gray and white matter metrics bring partly nonoverlapping information to predict cognitive performance. The models with only gray or white matter explained respectively 15.4 and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in, we additionally found that different metrics within gray and white matter had different predictive power and that the tracts/regions that were most predictive of cognitive performance differed across metrics. These results show that studies focusing on a single metric in either gray or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation.
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Affiliation(s)
- Lea C Michel
- Cognitive Neuroscience Department, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Ethan M McCormick
- Cognitive Neuroscience Department, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
- Methodology and Statistics, Institute of Psychology, Leiden University, Leiden 2333 AK, The Netherlands
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, North Carolina 27599-3270
| | - Rogier A Kievit
- Cognitive Neuroscience Department, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
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Bontempi P, Rozzanigo U, Marangoni S, Fogazzi E, Ravanelli D, Cazzoletti L, Giometto B, Farace P. Non-lesional white matter in relapsing-remitting multiple sclerosis assessed by multicomponent T2 relaxation. Brain Behav 2023; 13:e3334. [PMID: 38041516 PMCID: PMC10726908 DOI: 10.1002/brb3.3334] [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: 08/29/2023] [Revised: 10/31/2023] [Accepted: 11/02/2023] [Indexed: 12/03/2023] Open
Abstract
INTRODUCTION The purpose of the study is to investigate, by T2 relaxation, non-lesional white matter (WM) in relapsing-remitting (RR) multiple sclerosis (MS). METHODS Twenty stable RR MS patients underwent 1.5T Magnetic Resonance Imaging (MRI) with 3D Fluid-Attenuated Inversion-Recovery (FLAIR), 3D-T1-weighted, and T2-relaxation multi-echo sequences. The Lesion Segmentation Tool processed FLAIR images to identify focal lesions (FLs), whereas T1 images were segmented to identify WM and FL sub-volumes with T1 hypo-intensity. Non-lesional WM was obtained as the segmented WM, excluding FL volumes. The multi-echo sequence allowed decomposition into myelin water, intra-extracellular water, and free water (Fw), which were evaluated on the segmented non-lesional WM. Correlation analysis was performed between the non-lesional WM relaxation parameters and Expanded Disability Status Scale (EDSS), disease duration, patient age, and T1 hypo-intense FL volumes. RESULTS The T1 hypo-intense FL volumes correlated with EDSS. On the non-lesional WM, the median Fw correlated with EDSS, disease duration, age, and T1 hypo-intense FL volumes. Bivariate EDSS correlation of FL volumes and WM T2-relaxation parameters did not improve significance. CONCLUSION T2 relaxation allowed identifying subtle WM alterations, which significantly correlated with EDSS, disease duration, and age but do not seem to be EDSS-predictors independent from FL sub-volumes in stable RR patients. Particularly, the increase in the Fw component is suggestive of an uninvestigated prodromal phenomenon in brain degeneration.
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Affiliation(s)
- Pietro Bontempi
- Department of Engineering for Innovation MedicineUniversity of VeronaVeronaItaly
| | - Umberto Rozzanigo
- Neuro‐radiology Unit, Hospital of TrentoAzienda Provinciale per i Servizi Sanitari (APSS)TrentoItaly
| | - Sabrina Marangoni
- Neurology Unit, Hospital of TrentoAzienda Provinciale per i Servizi Sanitari (APSS)TrentoItaly
| | - Elena Fogazzi
- Physics departmentUniversity of TrentoPovoTrentoItaly
| | - Daniele Ravanelli
- Medical Physics Department, Hospital of TrentoAzienda Provinciale per i Servizi Sanitari (APSS)TrentoItaly
| | - Lucia Cazzoletti
- Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public HealthUniversity of VeronaVeronaItaly
| | - Bruno Giometto
- Neurology Unit, Hospital of TrentoAzienda Provinciale per i Servizi Sanitari (APSS)TrentoItaly
| | - Paolo Farace
- Medical Physics Department, Hospital of TrentoAzienda Provinciale per i Servizi Sanitari (APSS)TrentoItaly
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Hosseinpour Z, Oladosu O, Liu WQ, Pike GB, Yong VW, Metz LM, Zhang Y. Distinct characteristics and severity of brain magnetic resonance imaging lesions in women and men with multiple sclerosis assessed using verified texture analysis measures. Front Neurol 2023; 14:1213377. [PMID: 37638198 PMCID: PMC10449451 DOI: 10.3389/fneur.2023.1213377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023] Open
Abstract
Background and goal In vivo characterization of brain lesion types in multiple sclerosis (MS) has been an ongoing challenge. Based on verified texture analysis measures from clinical magnetic resonance imaging (MRI), this study aimed to develop a method to identify two extremes of brain MS lesions that were approximately severely demyelinated (sDEM) and highly remyelinated (hREM), and compare them in terms of common clinical variables. Method Texture analysis used an optimized gray-level co-occurrence matrix (GLCM) method based on FLAIR MRI from 200 relapsing-remitting MS participants. Two top-performing metrics were calculated: texture contrast and dissimilarity. Lesion identification applied a percentile approach according to texture values calculated: ≤ 25 percentile for hREM and ≥75 percentile for sDEM. Results The sDEM had a greater total normalized volume yet smaller average size, and worse MRI texture than hREM. In lesion distribution mapping, the two lesion types appeared to overlap largely in location and were present the most in the corpus callosum and periventricular regions. Further, in sDEM, the normalized volume was greater and in hREM, the average size was smaller in men than women. There were no other significant results in clinical variable-associated analyses. Conclusion Percentile statistics of competitive MRI texture measures may be a promising method for probing select types of brain MS lesion pathology. Associated findings can provide another useful dimension for improved measurement and monitoring of disease activity in MS. The different characteristics of sDEM and hREM between men and women likely adds new information to the literature, deserving further confirmation.
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Affiliation(s)
- Zahra Hosseinpour
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Olayinka Oladosu
- Department of Neuroscience, Faculty of Graduate Studies, University of Calgary, Calgary, AB, Canada
| | - Wei-qiao Liu
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - G. Bruce Pike
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - V. Wee Yong
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Luanne M. Metz
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yunyan Zhang
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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Mascali D, Villani A, Chiarelli AM, Biondetti E, Lipp I, Digiovanni A, Pozzilli V, Caporale AS, Rispoli MG, Ajdinaj P, D'Apolito M, Grasso E, Sensi SL, Murphy K, Tomassini V, Wise RG. Pathophysiology of multiple sclerosis damage and repair: Linking cerebral hypoperfusion to the development of irreversible tissue loss in multiple sclerosis using magnetic resonance imaging. Eur J Neurol 2023; 30:2348-2356. [PMID: 37154298 PMCID: PMC7615142 DOI: 10.1111/ene.15827] [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: 12/23/2022] [Revised: 03/10/2023] [Accepted: 05/01/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND AND PURPOSE Reduced cerebral perfusion has been observed in multiple sclerosis (MS) and may contribute to tissue loss both acutely and chronically. Here, we test the hypothesis that hypoperfusion occurs in MS and relates to the presence of irreversible tissue damage. METHODS In 91 patients with relapsing MS and 26 healthy controls (HC), gray matter (GM) cerebral blood flow (CBF) was assessed using pulsed arterial spin labeling. GM volume, T1 hypointense and T2 hyperintense lesion volumes (T1LV and T2LV, respectively), and the proportion of T2-hyperintense lesion volume that appears hypointense on T1-weighted magnetic resonance imaging (T1LV/T2LV) were quantified. GM CBF and GM volume were evaluated globally, as well as regionally, using an atlas-based approach. RESULTS Global GM CBF was lower in patients (56.9 ± 12.3 mL/100 g/min) than in HC (67.7 ± 10.0 mL/100 g/min; p < 0.001), a difference that was widespread across brain regions. Although total GM volume was comparable between groups, significant reductions were observed in a subset of subcortical structures. GM CBF negatively correlated with T1LV (r = -0.43, p = 0.0002) and T1LV/T2LV (r = -0.37, p = 0.0004), but not with T2LV. CONCLUSIONS GM hypoperfusion occurs in MS and is associated with irreversible white matter damage, thus suggesting that cerebral hypoperfusion may actively contribute and possibly precede neurodegeneration by hampering tissue repair abilities in MS.
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Affiliation(s)
- Daniele Mascali
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
| | - Alessandro Villani
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
| | - Antonio M. Chiarelli
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
| | - Emma Biondetti
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
| | - Ilona Lipp
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Anna Digiovanni
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- MS Centre, Department of Clinical NeurologySS. Annunziata University HospitalChietiItaly
| | - Valeria Pozzilli
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- MS Centre, Department of Clinical NeurologySS. Annunziata University HospitalChietiItaly
| | - Alessandra S. Caporale
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
| | - Marianna G. Rispoli
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- MS Centre, Department of Clinical NeurologySS. Annunziata University HospitalChietiItaly
| | - Paola Ajdinaj
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- MS Centre, Department of Clinical NeurologySS. Annunziata University HospitalChietiItaly
| | - Maria D'Apolito
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- MS Centre, Department of Clinical NeurologySS. Annunziata University HospitalChietiItaly
| | - Eleonora Grasso
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Department of PaediatricsSS. Annunziata University HospitalChietiItaly
| | - Stefano L. Sensi
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Behavioral Neurology and Molecular Neurology Units, Centre for Advanced Studies and TechnologyG. d'Annunzio University of Chieti‐PescaraChietiItaly
| | - Kevin Murphy
- Cardiff University Brain Research Imaging Centre, School of Physics and AstronomyCardiff UniversityCardiffUK
| | - Valentina Tomassini
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
- MS Centre, Department of Clinical NeurologySS. Annunziata University HospitalChietiItaly
| | - Richard G. Wise
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
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Radial diffusivity reflects general decline rather than specific cognitive deterioration in multiple sclerosis. Sci Rep 2022; 12:21771. [PMID: 36526708 PMCID: PMC9758146 DOI: 10.1038/s41598-022-26204-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
Advanced structural brain imaging techniques, such as diffusion tensor imaging (DTI), have been used to study the relationship between DTI-parameters and cognitive scores in multiple sclerosis (MS). In this study, we assessed cognitive function in 61 individuals with MS and a control group of 35 healthy individuals with the Symbol Digit Modalities Test, the California Verbal Learning Test-II, the Brief Visuospatial Memory Test-Revised, the Controlled Oral Word Association Test, and Stroop-test. We also acquired diffusion-weighted images (b = 1000; 32 directions), which were processed to obtain the following DTI scalars: fractional anisotropy, mean, axial, and radial diffusivity. The relation between DTI scalars and cognitive parameters was assessed through permutations. Although fractional anisotropy and axial diffusivity did not correlate with any of the cognitive tests, mean and radial diffusivity were negatively correlated with all of these tests. However, this effect was not specific to any specific white matter tract or cognitive test and demonstrated a general effect with only low to moderate individual voxel-based correlations of <0.6. Similarly, lesion and white matter volume show a general effect with medium to high voxel-based correlations of 0.5-0.8. In conclusion, radial diffusivity is strongly related to cognitive impairment in MS. However, the strong associations of radial diffusivity with both cognition and whole brain lesion volume suggest that it is a surrogate marker for general decline in MS, rather than a marker for specific cognitive functions.
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Zhu Q, Zheng Q, Luo D, Peng Y, Yan Z, Wang X, Chen X, Li Y. The Application of Diffusion Kurtosis Imaging on the Heterogeneous White Matter in Relapsing-Remitting Multiple Sclerosis. Front Neurosci 2022; 16:849425. [PMID: 35360163 PMCID: PMC8960252 DOI: 10.3389/fnins.2022.849425] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 01/31/2022] [Indexed: 12/21/2022] Open
Abstract
Objectives To evaluate the microstructural damage in the heterogeneity of different white matter areas in relapsing-remitting multiple sclerosis (RRMS) patients by using diffusion kurtosis imaging (DKI) and its correlation with clinical and cognitive status. Materials and Methods Kurtosis fractional anisotropy (KFA), fractional anisotropy (FA), mean kurtosis (MK), and mean diffusivity (MD) in T1-hypointense lesions (T1Ls), pure T2-hyperintense lesions (pure-T2Ls), normal-appearing white matter (NAWM), and white matter in healthy controls (WM in HCs) were measured in 48 RRMS patients and 26 sex- and age-matched HCs. All the participants were assessed with the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the Symbol Digit Modalities Test (SDMT) scores as the cognitive status. The Kurtzke Expanded Disability Status Scale (EDSS) scores were used to evaluate the clinical status in RRMS patients. Results The lowest KFA, FA, and MK values and the highest MD values were found in T1Ls, followed by pure-T2Ls, NAWM, and WM in HCs. The T1Ls and pure-T2Ls were significantly different in FA (p = 0.002) and MK (p = 0.013), while the NAWM and WM in HCs were significantly different in KFA, FA, and MK (p < 0.001; p < 0.001; p = 0.001). The KFA, FA, MK, and MD values in NAWM (r = 0.360, p = 0.014; r = 0.415, p = 0.004; r = 0.369, p = 0.012; r = −0.531, p < 0.001) were correlated with the MMSE scores and the FA, MK, and MD values in NAWM (r = 0.423, p = 0.003; r = 0.427, p = 0.003; r = −0.359, p = 0.014) were correlated with the SDMT scores. Conclusion Applying DKI to the imaging-based white matter classification has the potential to reflect the white matter damage and is correlated with cognitive impairment.
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Myelin imaging measures as predictors of cognitive impairment in MS patients: A hybrid PET-MRI study. Mult Scler Relat Disord 2022; 57:103331. [PMID: 35158445 DOI: 10.1016/j.msard.2021.103331] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/01/2021] [Accepted: 10/11/2021] [Indexed: 12/27/2022]
Abstract
BACKGROUND Cognitive impairment is one of the concerns of Multiple Sclerosis (MS) and has been related to myelin loss. Different neuroimaging methods have been used to quantify myelin and relate it to cognitive dysfunctions, among them Magnetization Transfer Ratio (MTR), Diffusion Tensor Imaging (DTI), and, more recently, Positron Emission Tomography (PET) with 11C-PIB. OBJECTIVE To investigate different myelin imaging modalities as predictors of cognitive dysfunction. METHODS Fifty-one MS patients and 24 healthy controls underwent clinical and neuropsychological assessment and MTR, DTI (Axial Diffusion-AD and Fractional Anisotropy-FA maps), and 11C-PIB PET images in a PET/MR hybrid system. RESULTS MTR and DTI(FA) differed in patients with or without cognitive impairment. There was an association of DTI(FA) and DTI(AD) with cognition and psychomotor speed for progressive MS, and of 11C-PIB uptake and MTR for relapsing-remitting MS. MTR in the Thalamus (β= -0.51, p = 0.021) and Corpus Callosum (β= -0.24, p = 0.033) were predictive of cognitive impairment. DTI-FA in the Caudate (β= -26.93, p = 0.006) presented abnormal predictive result. CONCLUSION Lower myelin content by 11C-PIB uptake was associated with worse cognitive status. MTR was predictive of cognitive impairment in MS.
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12
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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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York EN, Martin SJ, Meijboom R, Thrippleton MJ, Bastin ME, Carter E, Overell J, Connick P, Chandran S, Waldman AD, Hunt DPJ. MRI-derived g-ratio and lesion severity in newly diagnosed multiple sclerosis. Brain Commun 2021; 3:fcab249. [PMID: 34877533 PMCID: PMC8643503 DOI: 10.1093/braincomms/fcab249] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 08/24/2021] [Accepted: 08/30/2021] [Indexed: 01/19/2023] Open
Abstract
Myelin loss is associated with axonal damage in established multiple sclerosis. This relationship is challenging to study in vivo in early disease. Here, we ask whether myelin loss is associated with axonal damage at diagnosis by combining non-invasive neuroimaging and blood biomarkers. We performed quantitative microstructural MRI and single-molecule ELISA plasma neurofilament measurement in 73 patients with newly diagnosed, immunotherapy naïve relapsing-remitting multiple sclerosis. Myelin integrity was evaluated using aggregate g-ratios, derived from magnetization transfer saturation and neurite orientation dispersion and density imaging diffusion data. We found significantly higher g-ratios within cerebral white matter lesions (suggesting myelin loss) compared with normal-appearing white matter (0.61 versus 0.57, difference 0.036, 95% CI: 0.029-0.043, P < 0.001). Lesion volume (Spearman's rho rs= 0.38, P < 0.001) and g-ratio (rs= 0.24, P < 0.05) correlated independently with plasma neurofilament. In patients with substantial lesion load (n = 38), those with higher g-ratio (defined as greater than median) were more likely to have abnormally elevated plasma neurofilament than those with normal g-ratio (defined as less than median) [11/23 (48%) versus 2/15 (13%), P < 0.05]. These data suggest that, even at multiple sclerosis diagnosis, reduced myelin integrity is associated with axonal damage. MRI-derived g-ratio may provide useful additional information regarding lesion severity and help to identify individuals with a high degree of axonal damage at disease onset.
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Affiliation(s)
- Elizabeth N York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Sarah-Jane Martin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
- Department of Neurosciences, University of Glasgow, Glasgow G51 4LB, UK
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | | | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Edwin Carter
- Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - James Overell
- Department of Neurosciences, University of Glasgow, Glasgow G51 4LB, UK
| | - Peter Connick
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh EH16 4SB, UK
- Anne Rowling Clinic, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
- Anne Rowling Clinic, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - David P J Hunt
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh EH16 4SB, UK
- Anne Rowling Clinic, University of Edinburgh, Edinburgh EH16 4SB, UK
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14
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Beaudoin AM, Rheault F, Theaud G, Laberge F, Whittingstall K, Lamontagne A, Descoteaux M. Modern Technology in Multi-Shell Diffusion MRI Reveals Diffuse White Matter Changes in Young Adults With Relapsing-Remitting Multiple Sclerosis. Front Neurosci 2021; 15:665017. [PMID: 34447292 PMCID: PMC8383891 DOI: 10.3389/fnins.2021.665017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 07/20/2021] [Indexed: 11/13/2022] Open
Abstract
Objective To characterize microstructural white matter changes related to relapsing-remitting multiple sclerosis using advanced diffusion MRI modeling and tractography. The association between imaging data and patient’s cognitive performance, fatigue severity and depressive symptoms is also explored. Methods In this cross-sectional study, 24 relapsing-remitting multiple sclerosis patients and 11 healthy controls were compared using high angular resolution diffusion imaging (HARDI). The imaging method includes a multi-shell scheme, free water correction to obtain tissue-specific measurements, probabilistic tracking algorithm robust to crossing fibers and white matter lesions, automatic streamlines and bundle dissection and tract-profiling with tractometry. The neuropsychological evaluation included the Symbol Digit Modalities Test, Paced Auditory Serial Addition Test, Modified Fatigue Impact Scale and Beck Depression Inventory-II. Results Bundle-wise analysis by tractometry revealed a difference between patients and controls for 11 of the 14 preselected white matter bundles. In patients, free water corrected fractional anisotropy was significantly reduced while radial and mean diffusivities were increased, consistent with diffuse demyelination. The fornix and left inferior fronto-occipital fasciculus exhibited a higher free water fraction. Eight bundles showed an increase in total apparent fiber density and four bundles had a higher number of fiber orientations, suggesting axonal swelling and increased organization complexity, respectively. In the association study, depressive symptoms were associated with diffusion abnormalities in the right superior longitudinal fasciculus. Conclusion Tissue-specific diffusion measures showed abnormalities along multiple cerebral white matter bundles in patients with relapsing-remitting multiple sclerosis. The proposed methodology combines free-water imaging, advanced bundle dissection and tractometry, which is a novel approach to investigate cerebral pathology in multiple sclerosis. It opens a new window of use for HARDI-derived measures and free water corrected diffusion measures. Advanced diffusion MRI provides a better insight into cerebral white matter changes in relapsing-remitting multiple sclerosis, namely diffuse demyelination, edema and increased fiber density and complexity.
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Affiliation(s)
- Ann-Marie Beaudoin
- Department of Neurology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - François Rheault
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Guillaume Theaud
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Frédéric Laberge
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Kevin Whittingstall
- Department of Radiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Albert Lamontagne
- Department of Neurology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
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15
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Vavasour IM, Chang KL, Combes AJE, Meyers SM, Kolind SH, Rauscher A, Li DKB, Traboulsee A, MacKay AL, Laule C. Water content changes in new multiple sclerosis lesions have a minimal effect on the determination of myelin water fraction values. J Neuroimaging 2021; 31:1119-1125. [PMID: 34310789 DOI: 10.1111/jon.12908] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 07/07/2021] [Accepted: 07/07/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND AND PURPOSE Myelin water fraction (MWF) is a histopathologically validated in vivo myelin marker. As MWF is the proportion of water with a short T2 relative to the total water, increases in water from edema and inflammation may confound MWF determination in multiple sclerosis (MS) lesions. Total water content (TWC) measurement enables calculation of absolute myelin water content (MWC) and can be used to distinguish edema/inflammation from demyelination. We assessed what influence changes in total water might have on MWF by calculating MWC values in new MS lesions. METHODS 3T 32-echo T2 relaxation data were collected monthly for 6 months from six relapsing-remitting MS participants. TWC was determined and multiplied with MWF images to calculate corrected MWC images. The effect of this water content correction was examined in 20 new lesions by comparing mean MWF and MWC over time. RESULTS On average, at lesion first appearance, lesion TWC increased by 6.4% (p = .003; range: -1% to +21%), MWF decreased by 24% (p = .006; range: -70% to +12%), and MWC decreased by 20% (p = .026; range: -68% to +21%), relative to prelesion values. Average TWC in lesions then gradually decreased, whereas MWF and MWC remained low. The shape of the MWF and MWC lesion evolution curves was nearly identical, differing only by an offset. CONCLUSION MWF mirrors MWC and is able to monitor myelin in new lesions. Even after taking into account water content increases, MWC still decreased at lesion first appearance attributed to demyelination.
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Affiliation(s)
- Irene M Vavasour
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada
| | - Kimberley L Chang
- Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Anna J E Combes
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sandra M Meyers
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Radiation Medicine and Applied Sciences, University of California, San Diego, California, USA
| | - Shannon H Kolind
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada.,Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alexander Rauscher
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Pediatrics, University of British Columbia, Vancouver, British Columbia, Canada
| | - David K B Li
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Anthony Traboulsee
- Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alex L MacKay
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Cornelia Laule
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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16
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Lazari A, Lipp I. Can MRI measure myelin? Systematic review, qualitative assessment, and meta-analysis of studies validating microstructural imaging with myelin histology. Neuroimage 2021; 230:117744. [PMID: 33524576 PMCID: PMC8063174 DOI: 10.1016/j.neuroimage.2021.117744] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 01/05/2021] [Accepted: 01/09/2021] [Indexed: 12/16/2022] Open
Abstract
Recent years have seen an increased understanding of the importance of myelination in healthy brain function and neuropsychiatric diseases. Non-invasive microstructural magnetic resonance imaging (MRI) holds the potential to expand and translate these insights to basic and clinical human research, but the sensitivity and specificity of different MR markers to myelination is a subject of debate. To consolidate current knowledge on the topic, we perform a systematic review and meta-analysis of studies that validate microstructural imaging by combining it with myelin histology. We find meta-analytic evidence for correlations between various myelin histology metrics and markers from different MRI modalities, including fractional anisotropy, radial diffusivity, macromolecular pool, magnetization transfer ratio, susceptibility and longitudinal relaxation rate, but not mean diffusivity. Meta-analytic correlation effect sizes range widely, between R2 = 0.26 and R2 = 0.82. However, formal comparisons between MRI-based myelin markers are limited by methodological variability, inconsistent reporting and potential for publication bias, thus preventing the establishment of a single most sensitive strategy to measure myelin with MRI. To facilitate further progress, we provide a detailed characterisation of the evaluated studies as an online resource. We also share a set of 12 recommendations for future studies validating putative MR-based myelin markers and deploying them in vivo in humans.
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Affiliation(s)
- Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Ilona Lipp
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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17
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Petracca M, Pontillo G, Moccia M, Carotenuto A, Cocozza S, Lanzillo R, Brunetti A, Brescia Morra V. Neuroimaging Correlates of Cognitive Dysfunction in Adults with Multiple Sclerosis. Brain Sci 2021; 11:346. [PMID: 33803287 PMCID: PMC8000635 DOI: 10.3390/brainsci11030346] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 03/02/2021] [Accepted: 03/04/2021] [Indexed: 02/06/2023] Open
Abstract
Cognitive impairment is a frequent and meaningful symptom in multiple sclerosis (MS), caused by the accrual of brain structural damage only partially counteracted by effective functional reorganization. As both these aspects can be successfully investigated through the application of advanced neuroimaging, here, we offer an up-to-date overview of the latest findings on structural, functional and metabolic correlates of cognitive impairment in adults with MS, focusing on the mechanisms sustaining damage accrual and on the identification of useful imaging markers of cognitive decline.
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Affiliation(s)
- Maria Petracca
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (M.P.); (M.M.); (A.C.); (V.B.M.)
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (G.P.); (S.C.); (A.B.)
- Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80125 Naples, Italy
| | - Marcello Moccia
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (M.P.); (M.M.); (A.C.); (V.B.M.)
| | - Antonio Carotenuto
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (M.P.); (M.M.); (A.C.); (V.B.M.)
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (G.P.); (S.C.); (A.B.)
| | - Roberta Lanzillo
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (M.P.); (M.M.); (A.C.); (V.B.M.)
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (G.P.); (S.C.); (A.B.)
| | - Vincenzo Brescia Morra
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (M.P.); (M.M.); (A.C.); (V.B.M.)
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18
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Meng D, Welton T, Elsarraj A, Morgan PS, das Nair R, Constantinescu CS, Evangelou N, Auer DP, Dineen RA. Dorsolateral prefrontal circuit effective connectivity mediates the relationship between white matter structure and PASAT-3 performance in multiple sclerosis. Hum Brain Mapp 2021; 42:495-509. [PMID: 33073920 PMCID: PMC7776003 DOI: 10.1002/hbm.25239] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 09/21/2020] [Accepted: 09/29/2020] [Indexed: 11/15/2022] Open
Abstract
Three decades ago a series of parallel circuits were described involving the frontal cortex and deep grey matter structures, with putative roles in control of motor and oculomotor function, cognition, behaviour and emotion. The circuit comprising the dorsolateral prefrontal cortex, caudate, globus pallidus and thalamus has a putative role in regulating executive functions. The aim of this study is to investigate effective connectivity (EC) of the dorsolateral-prefrontal circuit and its association with PASAT-3 performance in people with multiple sclerosis(MS). We use Granger causality analysis of resting-state functional MRI from 52 people with MS and 36 healthy people to infer that reduced EC in the afferent limb of the dorsolateral prefrontal circuit occurs in the people with MS with cognitive dysfunction (left: p = .006; right: p = .029), with bilateral EC reductions in this circuit resulting in more severe cognitive dysfunction than unilateral reductions alone (p = .002). We show that reduced EC in the afferent limb of the dorsolateral prefrontal circuit mediates the relationship between cognitive performance and macrostrucutral and microstructural alterations of white matter tracts in components of the circuit. Specificity is shown by the absence of any relationship between cognition and EC in the analogous and anatomically proximal motor circuit. We demonstrate good stability of the EC measures in people with MS over an interval averaging 8-months. Key positive and negative results are replicated in an independent cohort of people with MS. Our findings identify the dorsolateral prefrontal circuit as a potential target for therapeutic strategies aimed at improving cognition in people with MS.
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Affiliation(s)
- Dewen Meng
- Radiological Sciences, Division of Clinical Neuroscience, School of MedicineUniversity of NottinghamNottinghamUK
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
- NIHR Nottingham Biomedical Research Centre, Queen's Medical CentreUniversity of NottinghamNottinghamUK
| | - Thomas Welton
- Radiological Sciences, Division of Clinical Neuroscience, School of MedicineUniversity of NottinghamNottinghamUK
- National Neuroscience InstituteTan Tock Seng HospitalSingaporeSingapore
| | - Afaf Elsarraj
- Radiological Sciences, Division of Clinical Neuroscience, School of MedicineUniversity of NottinghamNottinghamUK
| | - Paul S. Morgan
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
- NIHR Nottingham Biomedical Research Centre, Queen's Medical CentreUniversity of NottinghamNottinghamUK
- Medical Physics and Clinical EngineeringNottingham University Hospitals NHS TrustNottinghamUK
| | - Roshan das Nair
- Institute of Mental HealthUniversity of NottinghamNottinghamUK
- Division of Psychiatry & Applied Psychology, School of MedicineUniversity of NottinghamNottinghamUK
| | - Cris S. Constantinescu
- Clinical Neurology, Division of Clinical Neuroscience, School of MedicineUniversity of NottinghamNottinghamUK
| | - Nikos Evangelou
- Clinical Neurology, Division of Clinical Neuroscience, School of MedicineUniversity of NottinghamNottinghamUK
| | - Dorothee P. Auer
- Radiological Sciences, Division of Clinical Neuroscience, School of MedicineUniversity of NottinghamNottinghamUK
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
- NIHR Nottingham Biomedical Research Centre, Queen's Medical CentreUniversity of NottinghamNottinghamUK
| | - Rob A. Dineen
- Radiological Sciences, Division of Clinical Neuroscience, School of MedicineUniversity of NottinghamNottinghamUK
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
- NIHR Nottingham Biomedical Research Centre, Queen's Medical CentreUniversity of NottinghamNottinghamUK
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19
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Myelin Measurement Using Quantitative Magnetic Resonance Imaging: A Correlation Study Comparing Various Imaging Techniques in Patients with Multiple Sclerosis. Cells 2020; 9:cells9020393. [PMID: 32046340 PMCID: PMC7072333 DOI: 10.3390/cells9020393] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 01/28/2020] [Accepted: 02/06/2020] [Indexed: 12/13/2022] Open
Abstract
Evaluation of myelin by magnetic resonance imaging (MRI) is a difficult challenge, but holds promise in demyelinating diseases, such as multiple sclerosis (MS). Although multiple techniques have been developed, no gold standard has been established. This study aims to evaluate the correlation between synthetic MRI myelin volume fraction (SyMRIMVF) and myelin fraction estimated by other techniques, i.e., magnetization transfer saturation (MTsat), T1-weighted images divided by T2-weighted images (T1w/T2w), and radial diffusivity (RD) in patients with MS. We also compared the sensitivities of these techniques for detecting MS-related myelin damage. SyMRIMVF, MTsat, T1w/T2w, and RD were averaged on plaque, periplaque white matter, and normal-appearing white matter (NAWM). Pairwise correlation was calculated using Spearman’s correlation analysis. For all segmented regions, strong correlations were found between SyMRIMVF and T1w/T2w (Rho = 0.89), MTsat (Rho = 0.82), or RD (Rho = −0.75). For each technique, the average estimated myelin differed significantly among regions, but the percentage change of NAWM from both periplaque white matter and plaque were highest in SyMRIMVF. SyMRIMVF might be suitable for myelin evaluation in MS patients, with relevant results as compared to other well-studied techniques. Moreover, it presented better sensitivity for the detection of the difference between plaque or periplaque white matter and NAWM.
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20
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Lee J, Hyun JW, Lee J, Choi EJ, Shin HG, Min K, Nam Y, Kim HJ, Oh SH. So You Want to Image Myelin Using MRI: An Overview and Practical Guide for Myelin Water Imaging. J Magn Reson Imaging 2020; 53:360-373. [PMID: 32009271 DOI: 10.1002/jmri.27059] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/01/2020] [Accepted: 01/02/2020] [Indexed: 12/22/2022] Open
Abstract
Myelin water imaging (MWI) is an MRI imaging biomarker for myelin. This method can generate an in vivo whole-brain myelin water fraction map in approximately 10 minutes. It has been applied in various applications including neurodegenerative disease, neurodevelopmental, and neuroplasticity studies. In this review we start with a brief introduction of myelin biology and discuss the contributions of myelin in conventional MRI contrasts. Then the MRI properties of myelin water and four different MWI methods, which are categorized as T2 -, T2 *-, T1 -, and steady-state-based MWI, are summarized. After that, we cover more practical issues such as availability, interpretation, and validation of these methods. To illustrate the utility of MWI as a clinical research tool, MWI studies for two diseases, multiple sclerosis and neuromyelitis optica, are introduced. Additional topics about imaging myelin in gray matter and non-MWI methods for myelin imaging are also included. Although technical and physiological limitations exist, MWI is a potent surrogate biomarker of myelin that carries valuable and useful information of myelin. Evidence Level: 5 Technical Efficacy: 1 J. MAGN. RESON. IMAGING 2021;53:360-373.
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Affiliation(s)
- Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Jae-Won Hyun
- Department of Neurology, Research Institute and Hospital, National Cancer Center, Goyang-si, Korea
| | - Jieun Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Eun-Jung Choi
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Hyeong-Geol Shin
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Kyeongseon Min
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Yoonho Nam
- Department of Radiology, Seoul Saint Mary's Hospital, College of Medicine, Catholic University of Korea, Seoul, Korea
| | - Ho Jin Kim
- Department of Neurology, Research Institute and Hospital, National Cancer Center, Goyang-si, Korea
| | - Se-Hong Oh
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Korea.,Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
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21
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Liu H, Ljungberg E, Dvorak AV, Lee LE, Yik JT, MacMillan EL, Barlow L, Li DKB, Traboulsee A, Kolind SH, Kramer JLK, Laule C. Myelin Water Fraction and Intra/Extracellular Water Geometric Mean T 2 Normative Atlases for the Cervical Spinal Cord from 3T MRI. J Neuroimaging 2019; 30:50-57. [PMID: 31407400 DOI: 10.1111/jon.12659] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 07/19/2019] [Accepted: 07/23/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND AND PURPOSE Acquiring and interpreting quantitative myelin-specific MRI data at an individual level is challenging because of technical difficulties and natural myelin variation in the population. To overcome these challenges, we used multiecho T2 myelin water imaging (MWI) to create T2 metric healthy population atlases that depict the mean and variation of myelin water fraction (MWF), and intra- and extracellular water mobility as described by geometric mean T2 (IEGMT2 ). METHODS Cervical cord MWI was performed at 3T on 20 healthy individuals (10M/10F, mean age: 36 years) and 3 relapsing remitting multiple sclerosis (RRMS) participants (1M/2F, age: 39/42/37 years). Anatomical data were collected for the purpose of image segmentation and registration. Atlases were created by coregistering and averaging T2 metrics from all controls. Voxel-wise z-score maps from 3 RRMS participants were produced to demonstrate the preliminary utility of the MWF and IEGMT2 atlases. RESULTS The average MWF atlas provides a representation of myelin in the spinal cord consistent with well-known spinal cord anatomical characteristics. The IEGMT2 atlas also depicted structural variations in the spinal cord. Z-score analysis illustrated distinct abnormalities in MWF and IEGMT2 in the 3 RRMS cases. CONCLUSIONS Our findings highlight the potential for using a quantitative T2 relaxation metric atlas to visualize and detect pathology in spinal cord. Our MWF and IEGMT2 atlases (URL: https://sourceforge.net/projects/mwi-spinal-cord-atlases/) can serve as normative references in the cervical spinal cord for other studies.
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Affiliation(s)
- Hanwen Liu
- Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada.,International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada
| | - Emil Ljungberg
- Department of Neuroimaging, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Adam V Dvorak
- Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada.,International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada
| | - Lisa Eunyoung Lee
- Department of Medicine, University of British Columbia, Vancouver, Canada
| | - Jackie T Yik
- Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada.,International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada
| | - Erin L MacMillan
- Philips, Markham, Canada.,School of Mechatronic Systems Engineering, Simon Fraser University, Canada.,Department of Radiology, University of British Columbia, Vancouver, Canada
| | | | - David K B Li
- Department of Medicine, University of British Columbia, Vancouver, Canada.,Department of Radiology, University of British Columbia, Vancouver, Canada
| | - Anthony Traboulsee
- Department of Medicine, University of British Columbia, Vancouver, Canada
| | - Shannon H Kolind
- Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada.,International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada.,Department of Medicine, University of British Columbia, Vancouver, Canada.,Department of Radiology, University of British Columbia, Vancouver, Canada
| | - John L K Kramer
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada.,School of Kinesiology, University of British Columbia, Vancouver, Canada
| | - Cornelia Laule
- Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada.,International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada.,Department of Radiology, University of British Columbia, Vancouver, Canada.,Pathology & Laboratory Medicine, University of British Columbia, Vancouver, Canada
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22
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Lipp I, Jones DK, Bells S, Sgarlata E, Foster C, Stickland R, Davidson AE, Tallantyre EC, Robertson NP, Wise RG, Tomassini V. Comparing MRI metrics to quantify white matter microstructural damage in multiple sclerosis. Hum Brain Mapp 2019; 40:2917-2932. [PMID: 30891838 PMCID: PMC6563497 DOI: 10.1002/hbm.24568] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 02/10/2019] [Accepted: 03/01/2019] [Indexed: 12/12/2022] Open
Abstract
Quantifying white matter damage in vivo is becoming increasingly important for investigating the effects of neuroprotective and repair strategies in multiple sclerosis (MS). While various approaches are available, the relationship between MRI‐based metrics of white matter microstructure in the disease, that is, to what extent the metrics provide complementary versus redundant information, remains largely unexplored. We obtained four microstructural metrics from 123 MS patients: fractional anisotropy (FA), radial diffusivity (RD), myelin water fraction (MWF), and magnetisation transfer ratio (MTR). Coregistration of maps of these four indices allowed quantification of microstructural damage through voxel‐wise damage scores relative to healthy tissue, as assessed in a group of 27 controls. We considered three white matter tissue‐states, which were expected to vary in microstructural damage: normal appearing white matter (NAWM), T2‐weighted hyperintense lesional tissue without T1‐weighted hypointensity (T2L), and T1‐weighted hypointense lesional tissue with corresponding T2‐weighted hyperintensity (T1L). All MRI indices suggested significant damage in all three tissue‐states, the greatest damage being in T1L. The correlations between indices ranged from r = 0.18 to r = 0.87. MWF was most sensitive when differentiating T2L from NAWM, while MTR was most sensitive when differentiating T1L from NAWM and from T2L. Combining the four metrics into one, through a principal component analysis, did not yield a measure more sensitive to damage than any single measure. Our findings suggest that the metrics are (at least partially) correlated with each other, but sensitive to the different aspects of pathology. Leveraging these differences could be beneficial in clinical trials testing the effects of therapeutic interventions.
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Affiliation(s)
- Ilona Lipp
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, UK.,Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff, UK.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff, UK.,Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia
| | - Sonya Bells
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff, UK.,Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Canada
| | - Eleonora Sgarlata
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, UK.,Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Catherine Foster
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff, UK
| | - Rachael Stickland
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff, UK
| | - Alison E Davidson
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, UK.,Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff, UK
| | - Emma C Tallantyre
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, UK.,Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, UK
| | - Neil P Robertson
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, UK.,Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, UK
| | - Richard G Wise
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff, UK
| | - Valentina Tomassini
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, UK.,Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff, UK.,Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, UK
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