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Colato E, Prados F, Stutters J, Bianchi A, Narayanan S, Arnold DL, Wheeler-Kingshott C, Barkhof F, Ciccarelli O, Chard DT, Eshaghi A. Networks of microstructural damage predict disability in multiple sclerosis. J Neurol Neurosurg Psychiatry 2023; 94:992-1003. [PMID: 37468305 DOI: 10.1136/jnnp-2022-330203] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 06/13/2023] [Indexed: 07/21/2023]
Abstract
BACKGROUND Network-based measures are emerging MRI markers in multiple sclerosis (MS). We aimed to identify networks of white (WM) and grey matter (GM) damage that predict disability progression and cognitive worsening using data-driven methods. METHODS We analysed data from 1836 participants with different MS phenotypes (843 in a discovery cohort and 842 in a replication cohort). We calculated standardised T1-weighted/T2-weighted (sT1w/T2w) ratio maps in brain GM and WM, and applied spatial independent component analysis to identify networks of covarying microstructural damage. Clinical outcomes were Expanded Disability Status Scale worsening confirmed at 24 weeks (24-week confirmed disability progression (CDP)) and time to cognitive worsening assessed by the Symbol Digit Modalities Test (SDMT). We used Cox proportional hazard models to calculate predictive value of network measures. RESULTS We identified 8 WM and 7 GM sT1w/T2w networks (of regional covariation in sT1w/T2w measures) in both cohorts. Network loading represents the degree of covariation in regional T1/T2 ratio within a given network. The loading factor in the anterior corona radiata and temporo-parieto-frontal components were associated with higher risks of developing CDP both in the discovery (HR=0.85, p<0.05 and HR=0.83, p<0.05, respectively) and replication cohorts (HR=0.84, p<0.05 and HR=0.80, p<0.005, respectively). The decreasing or increasing loading factor in the arcuate fasciculus, corpus callosum, deep GM, cortico-cerebellar patterns and lesion load were associated with a higher risk of developing SDMT worsening both in the discovery (HR=0.82, p<0.01; HR=0.87, p<0.05; HR=0.75, p<0.001; HR=0.86, p<0.05 and HR=1.27, p<0.0001) and replication cohorts (HR=0.82, p<0.005; HR=0.73, p<0.0001; HR=0.80, p<0.005; HR=0.85, p<0.01 and HR=1.26, p<0.0001). CONCLUSIONS GM and WM networks of microstructural changes predict disability and cognitive worsening in MS. Our approach may be used to identify patients at greater risk of disability worsening and stratify cohorts in treatment trials.
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Affiliation(s)
- Elisa Colato
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Ferran Prados
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, UK
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Jonathan Stutters
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Alessia Bianchi
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Sridar Narayanan
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Douglas L Arnold
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Claudia Wheeler-Kingshott
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Brain Connectivity Centre, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Frederik Barkhof
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, location Vrije Universiteit, Amsterdam, Netherlands
- Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK
| | - Olga Ciccarelli
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK
| | - Declan T Chard
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK
| | - Arman Eshaghi
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
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Nakamura K, Zheng Y, Mahajan KR, Cohen JA, Fox RJ, Ontaneda D. Effect of ibudilast on thalamic magnetization transfer ratio and volume in progressive multiple sclerosis. Mult Scler 2023; 29:1257-1265. [PMID: 37537928 PMCID: PMC11130979 DOI: 10.1177/13524585231187289] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
BACKGROUND Thalamic volume (TV) is a sensitive biomarker of disease burden of injury in multiple sclerosis (MS) and appears to reflect overall lesion loads. Ibudilast showed significant treatment effect on brain atrophy and magnetization transfer ratio (MTR) of normal-appearing brain tissue but not in new/enlarging T2 lesion in the SPRINT-MS randomized clinical trial. OBJECTIVE To evaluate the effect of ibudilast on thalamic tissue integrity and volume in the SPRINT-MS. METHODS A total of 255 participants with progressive MS were randomized to oral ibudilast or placebo, and thalamic MTR and normalized TV over 96 weeks were quantified. Mixed-effect modeling assessed treatment effects on the thalamic MTR and TV, separately. Similarly, the measures were compared between the participants with confirmed disability progression (CDP). RESULTS Ibudilast's treatment effect was observed compared to placebo for thalamic MTR (p = 0.03) but not for TV (p = 0.68) while TV correlated with T2 lesion volume (p < 0.001). CDP associated with thalamic MTR (p = 0.04) but not with TV (p = 0.7). CONCLUSION Ibudilast showed an effect on thalamic MTR, which was associated with CDP, suggesting a clinically relevant effect on thalamic tissue integrity. However, the treatment effect was not observed in TV, suggesting that thalamic atrophy is more closely associated with global inflammatory activity than local tissue integrity. CLINICALTRIALS.GOV NCT01982942.
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Manning AR, Beck ES, Schindler MK, Nair G, Clark KA, Parvathaneni P, Reich DS, Shinohara RT, Solomon AJ. T 1 /T 2 ratio from 3T MRI improves multiple sclerosis cortical lesion contrast. J Neuroimaging 2023; 33:434-445. [PMID: 36715449 PMCID: PMC10175128 DOI: 10.1111/jon.13088] [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: 09/15/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND AND PURPOSE Cortical demyelinated lesions are prevalent in multiple sclerosis (MS), associated with disability, and have recently been incorporated into MS diagnostic criteria. Presently, advanced and ultrahigh-field MRIs-not routinely available in clinical practice-are the most sensitive methods for detection of cortical lesions. Approaches utilizing MRI sequences obtainable in routine clinical practice remain an unmet need. We plan to assess the sensitivity of the ratio of T1 -weighted and T2 -weighted (T1 /T2 ) signal intensity for focal cortical lesions in comparison to other high-field imaging methods. METHODS 3-Tesla and 7-Tesla MRI collected from 10 adults with MS were included in the study. T1 /T2 images were calculated by dividing 3T T1 -weighted (T1 w) images by 3T T2 -weighted (T2 w) fluid-attenuated inversion recovery images for each participant. A total of 614 cortical lesions were identified using 7T T2 *w and T1 w images and corresponding voxels were assessed on registered 3T images. Signal intensities were compared across 3T imaging sequences, including T1 /T2 , T1 w, T2 w, and inversion recovery susceptibility-weighted imaging with enhanced T2 weighting (IR-SWIET) images. RESULTS T1 /T2 images demonstrated a larger contrast between median lesional and nonlesional cortical signal intensity (median ratio = 1.29, range: 1.19-1.38) when compared to T1 w (1.01, 0.97-1.10, p < .002), T2 w (1.17, 1.07-1.26, p < .002), and IR-SWIET (1.21, 1.01-1.29, p < .03). CONCLUSION T1 /T2 images are sensitive to cortical lesions. Approaches incorporating T1 /T2 could improve the accessibility of cortical lesion detection in research settings and clinical practice.
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Affiliation(s)
- Abigail R Manning
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Erin S Beck
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Matthew K Schindler
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Govind Nair
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Kelly A Clark
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Prasanna Parvathaneni
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Andrew J Solomon
- Department of Neurological Sciences, Larner College of Medicine at The University of Vermont, Burlington, Vermont, USA
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Fernandez-Alvarez M, Atienza M, Cantero JL. Effects of non-modifiable risk factors of Alzheimer's disease on intracortical myelin content. Alzheimers Res Ther 2022; 14:202. [PMID: 36587227 PMCID: PMC9805254 DOI: 10.1186/s13195-022-01152-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 12/25/2022] [Indexed: 01/01/2023]
Abstract
BACKGROUND Non-modifiable risk factors of Alzheimer's disease (AD) have lifelong effects on cortical integrity that could be mitigated if identified at early stages. However, it remains unknown whether cortical microstructure is affected in older individuals with non-modifiable AD risk factors and whether altered cortical tissue integrity produces abnormalities in brain functional networks in this AD-risk population. METHODS Using relative T1w/T2w (rT1w/T2w) ratio maps, we have compared tissue integrity of normal-appearing cortical GM between controls and cognitively normal older adults with either APOE4 (N = 50), with a first-degree family history (FH) of AD (N = 52), or with the co-occurrence of both AD risk factors (APOE4+FH) (N = 35). Additionally, individuals with only one risk factor (APOE4 or FH) were combined into one group (N = 102) and compared with controls. The same number of controls matched in age, sex, and years of education was employed for each of these comparisons. Group differences in resting state functional connectivity (rs-FC) patterns were also investigated, using as FC seeds those cortical regions showing significant changes in rT1w/T2w ratios. RESULTS Overall, individuals with non-modifiable AD risk factors exhibited significant variations in rT1w/T2w ratios compared to controls, being APOE4 and APOE4+FH at opposite ends of a continuum. The co-occurrence of APOE4 and FH was further accompanied by altered patterns of rs-FC. CONCLUSIONS These findings may have practical implications for early detection of cortical abnormalities in older populations with APOE4 and/or FH of AD and open new avenues to monitor changes in cortical tissue integrity associated with non-modifiable AD risk factors.
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Affiliation(s)
- Marina Fernandez-Alvarez
- grid.15449.3d0000 0001 2200 2355Laboratory of Functional Neuroscience, Pablo de Olavide University, Ctra. de Utrera Km 1, 41013 Seville, Spain ,grid.418264.d0000 0004 1762 4012CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, Madrid, Spain
| | - Mercedes Atienza
- grid.15449.3d0000 0001 2200 2355Laboratory of Functional Neuroscience, Pablo de Olavide University, Ctra. de Utrera Km 1, 41013 Seville, Spain ,grid.418264.d0000 0004 1762 4012CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, Madrid, Spain
| | - Jose L. Cantero
- grid.15449.3d0000 0001 2200 2355Laboratory of Functional Neuroscience, Pablo de Olavide University, Ctra. de Utrera Km 1, 41013 Seville, Spain ,grid.418264.d0000 0004 1762 4012CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, Madrid, Spain
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Combes AJ, Clarke MA, O'Grady KP, Schilling KG, Smith SA. Advanced spinal cord MRI in multiple sclerosis: Current techniques and future directions. Neuroimage Clin 2022; 36:103244. [PMID: 36306717 PMCID: PMC9668663 DOI: 10.1016/j.nicl.2022.103244] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 09/02/2022] [Accepted: 10/19/2022] [Indexed: 11/11/2022]
Abstract
Spinal cord magnetic resonance imaging (MRI) has a central role in multiple sclerosis (MS) clinical practice for diagnosis and disease monitoring. Advanced MRI sequences capable of visualizing and quantifying tissue macro- and microstructure and reflecting different pathological disease processes have been used in MS research; however, the spinal cord remains under-explored, partly due to technical obstacles inherent to imaging this structure. We propose that the study of the spinal cord merits equal ambition in overcoming technical challenges, and that there is much information to be exploited to make valuable contributions to our understanding of MS. We present a narrative review on the latest progress in advanced spinal cord MRI in MS, covering in the first part structural, functional, metabolic and vascular imaging methods. We focus on recent studies of MS and those making significant technical steps, noting the challenges that remain to be addressed and what stands to be gained from such advances. Throughout we also refer to other works that presend more in-depth review on specific themes. In the second part, we present several topics that, in our view, hold particular potential. The need for better imaging of gray matter is discussed. We stress the importance of developing imaging beyond the cervical spinal cord, and explore the use of ultra-high field MRI. Finally, some recommendations are given for future research, from study design to newer developments in analysis, and the need for harmonization of sequences and methods within the field. This review is aimed at researchers and clinicians with an interest in gaining an overview of the current state of advanced MRI research in this field and what is primed to be the future of spinal cord imaging in MS research.
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Affiliation(s)
- Anna J.E. Combes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States,Corresponding author at: 1161 21st Ave S, MCN AA1105, Nashville, TN 37232, USA.
| | - Margareta A. Clarke
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States
| | - Kristin P. O'Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States,Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, PMB 351826, Nashville, TN 37235-1826, United States
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States
| | - Seth A. Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States,Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, PMB 351826, Nashville, TN 37235-1826, United States
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