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Rocca MA, Romanò F, Tedone N, Filippi M. Advanced neuroimaging techniques to explore the effects of motor and cognitive rehabilitation in multiple sclerosis. J Neurol 2024; 271:3806-3848. [PMID: 38691168 DOI: 10.1007/s00415-024-12395-0] [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: 01/31/2024] [Revised: 04/17/2024] [Accepted: 04/17/2024] [Indexed: 05/03/2024]
Abstract
INTRODUCTION Progress in magnetic resonance imaging (MRI) technology and analyses is improving our comprehension of multiple sclerosis (MS) pathophysiology. These advancements, which enable the evaluation of atrophy, microstructural tissue abnormalities, and functional plasticity, are broadening our insights into the effectiveness and working mechanisms of motor and cognitive rehabilitative treatments. AREAS COVERED This narrative review with selected studies discusses findings derived from the application of advanced MRI techniques to evaluate structural and functional neuroplasticity modifications underlying the effects of motor and cognitive rehabilitative treatments in people with MS (PwMS). Current applications as outcome measure in longitudinal trials and observational studies, their interpretation and possible pitfalls and limitations in their use are covered. Finally, we examine how the use of these techniques could evolve in the future to improve monitoring of motor and cognitive rehabilitative treatments. EXPERT COMMENTARY Despite substantial variability in study design and participant characteristics in rehabilitative studies for PwMS, improvements in motor and cognitive functions accompanied by structural and functional brain modifications induced by rehabilitation can be observed. However, significant enhancements to refine rehabilitation strategies are needed. Future studies in this field should strive to implement standardized methodologies regarding MRI acquisition and processing, possibly integrating multimodal measures. This will help identifying relevant markers of treatment response in PwMS, thus improving the use of rehabilitative interventions at individual level. The combination of motor and cognitive strategies, longer periods of treatment, as well as adequate follow-up assessments will contribute to enhance the quality of evidence in support of their routine use.
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Affiliation(s)
- Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
| | - Francesco Romanò
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Nicolò Tedone
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
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Uddin MN, Singh MV, Faiyaz A, Szczepankiewicz F, Nilsson M, Boodoo ZD, Sutton KR, Tivarus ME, Zhong J, Wang L, Qiu X, Weber MT, Schifitto G. Tensor-valued diffusion MRI detects brain microstructure changes in HIV infected individuals with cognitive impairment. RESEARCH SQUARE 2024:rs.3.rs-4482269. [PMID: 38946952 PMCID: PMC11213220 DOI: 10.21203/rs.3.rs-4482269/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Despite advancements, the prevalence of HIV-associated neurocognitive impairment remains at approximately 40%, attributed to factors like pre-cART (combination antiretroviral therapy) irreversible brain injury. People with HIV (PWH) treated with cART do not show significant neurocognitive changes over relatively short follow-up periods. However, quantitative neuroimaging may be able to detect ongoing subtle microstructural changes. This study aimed to investigate the sensitivity of tensor-valued diffusion encoding in detecting such changes in brain microstructural integrity in cART-treated PWH. Additionally, it explored relationships between these metrics, neurocognitive scores, and plasma levels of neurofilament light (NFL) chain and glial fibrillary acidic protein (GFAP). Using MRI at 3T, 24 PWH and 31 healthy controls underwent cross-sectional examination. The results revealed significant variations in b-tensor encoding metrics across white matter regions, with associations observed between these metrics, cognitive performance, and blood markers of neuronal and glial injury (NFL and GFAP). Moreover, a significant interaction between HIV status and imaging metrics was observed, particularly impacting total cognitive scores in both gray and white matter. These findings suggest that b-tensor encoding metrics offer heightened sensitivity in detecting subtle changes associated with axonal injury in HIV infection, underscoring their potential clinical relevance in understanding neurocognitive impairment in PWH.
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Srinivasan VS, Krishna R, Munirathinam BR. The Interaural Time Difference for High-Pass Filtered Noise and Its Relationship With Brainstem Dysfunction and Disability in Multiple Sclerosis. Am J Audiol 2023; 32:853-864. [PMID: 37678147 DOI: 10.1044/2023_aja-22-00184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023] Open
Abstract
PURPOSE Just noticeable difference for interaural time difference (JND-ITD) is a sensitive test to detect silent lesions and neural asynchrony along the auditory pathways among individuals with multiple sclerosis (MS), but it has not been studied with brainstem functional system scores (BFSS) and expanded disability status scale (EDSS). The study aims to assess the usefulness of JND-ITD thresholds in individuals with MS and relate to brainstem magnetic resonance imaging (MRI) lesions, BFSS, and disability (EDSS). METHOD Standard group comparison design was adapted to compare the JND-ITD thresholds between individuals with MS (n = 45) and age and gender-matched healthy participants (n = 45). All participants underwent case history, neurological examination including BFSS and EDSS scoring, MRI brain imaging, minimental state examination, routine audiological evaluation, and ITD testing for high-pass filtered noise stimuli. RESULTS Of the 36 MS participants with abnormal JND-ITD thresholds, 22 (48.9%) participants could not identify maximum JND-ITD values (1,280 μs) in the ITD task. Abnormal JND-ITDs thresholds (139-1,280 μs) were obtained in 14 (31.11%) participants with MS. The JND-ITD thresholds were significantly different between the healthy and MS group. No significant association was found between the presence of ITD abnormality with the presence of brainstem lesions (MRI) and brainstem dysfunction (BFSS). Also, this study did not find any relationship between JND-ITD thresholds with disability (EDSS). CONCLUSIONS This study supports the findings that JND-ITD for high-pass filtered noise is a sensitive test to detect lesions along the auditory system. Even though JND-ITD thresholds did not relate with BFSS and EDSS scores, JND-ITD abnormalities can be of great value in identifying lesions along the auditory system, especially in the early stages of MS, when clinical neurological examination does not show any signs of brainstem dysfunction, disability, and MRI without any lesions in the brain.
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Affiliation(s)
| | - Rajalakshmi Krishna
- Department of Audiology, School of Rehabilitation and Behavioral Sciences, Vinayaka Mission's Research Foundation, Aarupadai Veedu Medical College and Hospital Campus, Puducherry, India
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Stellingwerff MD, Pouwels PJW, Roosendaal SD, Barkhof F, van der Knaap MS. Quantitative MRI in leukodystrophies. Neuroimage Clin 2023; 38:103427. [PMID: 37150021 PMCID: PMC10193020 DOI: 10.1016/j.nicl.2023.103427] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 05/09/2023]
Abstract
Leukodystrophies constitute a large and heterogeneous group of genetic diseases primarily affecting the white matter of the central nervous system. Different disorders target different white matter structural components. Leukodystrophies are most often progressive and fatal. In recent years, novel therapies are emerging and for an increasing number of leukodystrophies trials are being developed. Objective and quantitative metrics are needed to serve as outcome measures in trials. Quantitative MRI yields information on microstructural properties, such as myelin or axonal content and condition, and on the chemical composition of white matter, in a noninvasive fashion. By providing information on white matter microstructural involvement, quantitative MRI may contribute to the evaluation and monitoring of leukodystrophies. Many distinct MR techniques are available at different stages of development. While some are already clinically applicable, others are less far developed and have only or mainly been applied in healthy subjects. In this review, we explore the background, current status, potential and challenges of available quantitative MR techniques in the context of leukodystrophies.
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Affiliation(s)
- Menno D Stellingwerff
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Child Neurology, Emma Children's Hospital, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Petra J W Pouwels
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Stefan D Roosendaal
- Amsterdam UMC Location University of Amsterdam, Department of Radiology, Meibergdreef 9, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands; University College London, Institutes of Neurology and Healthcare Engineering, London, UK
| | - Marjo S van der Knaap
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Child Neurology, Emma Children's Hospital, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands; Vrije Universiteit Amsterdam, Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, De Boelelaan 1105, Amsterdam, the Netherlands.
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Margoni M, Pagani E, Preziosa P, Gueye M, Azzimonti M, Rocca MA, Filippi M. Unraveling the heterogeneous pathological substrates of relapse-onset multiple sclerosis: a multiparametric voxel-wise 3 T MRI study. J Neurol 2023:10.1007/s00415-023-11736-9. [PMID: 37093395 DOI: 10.1007/s00415-023-11736-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 04/17/2023] [Accepted: 04/17/2023] [Indexed: 04/25/2023]
Abstract
BACKGROUND In multiple sclerosis (MS), pathological processes affecting brain gray (GM) and white matter (WM) are heterogeneous. OBJECTIVE To apply a multimodal MRI approach to investigate the regional distribution of the different pathological processes occurring in the brain WM and GM of relapse-onset MS patients. METHODS Fifty-seven MS patients (forty-two relapsing remitting [RR], fifteen secondary progressive [SP]) and forty-seven age- and sex-matched healthy controls (HC) underwent a multimodal 3 T MRI acquisition. Between-group voxel-wise differences of brain WM and GM volumes, magnetization transfer ratio (MTR), T1-weighted(w)/T2w ratio, intracellular volume fraction (ICV_f), and quantitative susceptibility mapping (QSM) maps were investigated. RESULTS Compared to HC, RRMS showed significant WM, deep GM and cortical atrophy, significantly lower MTR and T1w/T2w ratio of periventricular and infratentorial WM, deep GM and several cortical areas, lower ICV_f in supratentorial and cerebellar WM and in some cortical areas, and lower QSM values in bilateral periventricular WM (p < 0.001). Compared to RRMS, SPMS patients showed significant deep GM and widespread cortical atrophy, significantly lower MTR of periventricular WM, deep GM and cerebellum, lower T1w/T2w ratio of fronto-temporal WM regions, lower ICV_f of some fronto-tempo-occipital WM and cortical areas. They also had increased QSM and T1w/T2w ratio in the pallidum, bilaterally (p < 0.001). CONCLUSION A periventricular pattern of demyelination and widespread GM and WM neuro-axonal loss are detectable in RRMS and are more severe in SPMS. Higher T1w/T2w ratio and QSM in the pallidum, possibly reflecting iron accumulation and neurodegeneration, may represent a relevant MRI marker to differentiate SPMS from RRMS.
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Affiliation(s)
- Monica Margoni
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Mor Gueye
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Matteo Azzimonti
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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Correspondence among gray matter atrophy and atlas-based neurotransmitter maps is clinically relevant in multiple sclerosis. Mol Psychiatry 2023; 28:1770-1782. [PMID: 36658334 DOI: 10.1038/s41380-023-01943-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 12/29/2022] [Accepted: 01/06/2023] [Indexed: 01/20/2023]
Abstract
In multiple sclerosis (MS), gray matter (GM) atrophy progresses in a non-random manner, possibly in regions with a high distribution of specific neurotransmitters involved in several relevant central nervous system functions. We investigated the associations among regional GM atrophy, atlas-based neurotransmitter distributions and clinical manifestations in a large MS patients' group. Brain 3 T MRI scans, neurological examinations and neuropsychological evaluations were obtained from 286 MS patients and 172 healthy controls (HC). Spatial correlations among regional GM volume differences and atlas-based nuclear imaging-derived neurotransmitter maps, and their associations with MS clinical features were investigated using voxel-based morphometry and JuSpace toolbox. Compared to HC, MS patients showed widespread GM atrophy being spatially correlated with the majority of neurotransmitter maps (false discovery rate [FDR]-p ≤ 0.004). Patients with a disease duration ≥ 5 vs < 5 years had significant cortical, subcortical and cerebellar atrophy, being spatially correlated with a higher distribution of serotoninergic and dopaminergic receptors (FDR-p ≤ 0.03). Compared to mildly-disabled patients, those with Expanded Disability Status Scale ≥ 3.0 or ≥ 4.0 had significant cortical, subcortical and cerebellar atrophy being associated with serotonergic, dopaminergic, opioid and cholinergic maps (FDR-p ≤ 0.04). Cognitively impaired vs cognitively preserved patients had widespread GM atrophy being spatially associated with serotonergic, dopaminergic, noradrenergic, cholinergic and glutamatergic maps (FDR-p ≤ 0.04). Fatigued vs non-fatigued MS patients had significant cortical, subcortical and cerebellar atrophy, not associated with neurotransmitter maps. No significant association between GM atrophy and neurotransmitter maps was found for depression. Regional GM atrophy with specific neurotransmitter systems may explain part of MS clinical manifestations, including locomotor disability, cognitive impairment and fatigue.
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Margoni M, Pagani E, Preziosa P, Palombo M, Gueye M, Azzimonti M, Filippi M, Rocca MA. In vivo quantification of brain soma and neurite density abnormalities in multiple sclerosis. J Neurol 2023; 270:433-445. [PMID: 36153468 DOI: 10.1007/s00415-022-11386-3] [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: 07/25/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND Soma and neurite density imaging (SANDI) is a new biophysical model that incorporates soma in addition to neurite density, thus possibly providing more specific information about the complex pathological processes of multiple sclerosis (MS). PURPOSE To discriminate the pathological abnormalities of MS white matter (WM) lesions, normal-appearing (NA) WM and cortex and to evaluate the associations among SANDI-derived measures, clinical disability, and conventional MRI variables. METHODS Twenty healthy controls (HC) and 23 MS underwent a 3 T brain MRI. Using SANDI on diffusion-weighted sequence, the fractions of neurite (fneurite) and soma (fsoma) were assessed in WM lesions, NAWM, and cortex. RESULTS Compared to HC WM, MS NAWM showed lower fneurite (false discovery rate [FDR]-p = 0.011). In MS patients, WM lesions showed lower fneurite and fsoma compared to both HC and MS NAWM (FDR-p < 0.001 for all). In the cortex, MS patients had lower fneurite and fsoma compared to HC (FDR-p ≤ 0.009). Compared to both HC and RRMS, PMS patients had lower fneurite in NAWM (vs HC: FDR-p < 0.001; vs RRMS: FDR-p = 0.003) and cortex (vs HC: FDR-p < 0.001; vs RRMS: p = 0.031, not surviving FDR correction), and lower cortical fsoma (vs HC: FDR-p < 0.001; vs RRMS: FDR-p = 0.009). Compared to HC, PMS also showed a higher fsoma in NAWM (FDR-p = 0.015). Fneurite and fsoma in the different brain compartments were correlated with age, phenotype, disease duration, disability, WM lesion volumes, normalized brain, cortical, and WM volumes (r from - 0.761 to 0.821, FDR-p ≤ 0.4). CONCLUSIONS SANDI may represent a clinically relevant model to discriminate different neurodegenerative phenomena that gradually accumulate through MS disease course.
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Affiliation(s)
- Monica Margoni
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Mor Gueye
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Matteo Azzimonti
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Maria Assunta Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
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Hosseinpour Z, Jonkman L, Oladosu O, Pridham G, Pike GB, Inglese M, Geurts JJ, Zhang Y. Texture analysis in brain T2 and diffusion MRI differentiates histology-verified grey and white matter pathology types in multiple sclerosis. J Neurosci Methods 2022; 379:109671. [PMID: 35820450 DOI: 10.1016/j.jneumeth.2022.109671] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 06/19/2022] [Accepted: 07/07/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Multiple sclerosis (MS) is a co mplex disease of the central nervous system involving several types of brain pathology that are difficult to characterize using conventional imaging methods. NEW METHOD We originated novel texture analysis and machine learning approaches for classifying MS pathology subtypes as compared with 2 common advanced MRI measures: magnetization transfer ratio (MTR) and fractional anisotropy (FA). Texture analysis used an optimized grey level co-occurrence matrix method with histology-informed 7T T2-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) from 15 MS and 12 control brain specimens. DTI analysis took an innovative approach that assessed the texture across diffusion directions upsampled from 30 to 90. Tissue types included de- and re-myelinated lesions and normal-appearing areas in both grey and white matter, and diffusely abnormal white matter. Data analyses were stepwise, including: (1) group-wise classification using random forest algorithms based on all or individual imaging parameters; (2) parameter importance ranking; and (3) pairwise analysis using top-ranked features. RESULTS Texture analysis performed better than MTR and FA, with T2 texture performed the best. T2 texture measures ranked the highest in classifying most grey and white matter tissue types, including de- versus re-myelinated lesions and among grey matter lesion subtypes (accuracy=0.86-0.59; kappa=0.60-0.41). Diffusion texture best differentiated normal appearing and control white matter. COMPARISON WITH EXISTING METHODS There is no established method in imaging for differentiating MS pathology subtypes. In combined texture analysis and machine learning studies, there is also no direct evidence comparing conventional with advanced MRI measures for assessing MS pathology. Further, this study is unique in conducting innovative texture analysis with DTI following data-augmentation using robust methods. CONCLUSIONS T2 and diffusion MRI texture analysis integrated with machine learning may be valuable approaches for characterizing MS pathology.
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Affiliation(s)
- Zahra Hosseinpour
- Biomedical Engineering Graduate Program, University of Calgary, Alberta T2N 4N, Canada; Hotchkiss Brain Institute, University of Calgary, Alberta T2N 4N1, Canada
| | - Laura Jonkman
- Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Olayinka Oladosu
- Department of Neuroscience, University of Calgary, Alberta T2N 4N1, Canada; Hotchkiss Brain Institute, University of Calgary, Alberta T2N 4N1, Canada
| | - Glen Pridham
- Department of Clinical Neurosciences, University of Calgary, Alberta T2N 4N1, Canada; Hotchkiss Brain Institute, University of Calgary, Alberta T2N 4N1, Canada
| | - G Bruce Pike
- Department of Clinical Neurosciences, University of Calgary, Alberta T2N 4N1, Canada; Department of Radiology, University of Calgary, Alberta T2N 4N1, Canada; Hotchkiss Brain Institute, University of Calgary, Alberta T2N 4N1, Canada
| | - Matilde Inglese
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA 10029; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI) and Center of Excellence for Biomedical Research (CEBR), University of Genoa, Genoa, Italy
| | - Jeroen J Geurts
- Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Yunyan Zhang
- Department of Clinical Neurosciences, University of Calgary, Alberta T2N 4N1, Canada; Department of Radiology, University of Calgary, Alberta T2N 4N1, Canada; Hotchkiss Brain Institute, University of Calgary, Alberta T2N 4N1, Canada.
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Rocca MA, Schoonheim MM, Valsasina P, Geurts JJG, Filippi M. Task- and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective. Neuroimage Clin 2022; 35:103076. [PMID: 35691253 PMCID: PMC9194954 DOI: 10.1016/j.nicl.2022.103076] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 01/12/2023]
Abstract
Functional MRI is able to detect adaptive and maladaptive abnormalities at different MS stages. Increased fMRI activity is a feature of early MS, while progressive exhaustion of adaptive mechanisms is detected later on in the disease. Collapse of long-range connections and impaired hub integration characterize MS network reorganization. Time-varying connectivity analysis provides useful and complementary pieces of information to static functional connectivity. New perspectives might be the use of multimodal MRI and artificial intelligence.
Multiple sclerosis (MS) is a neurological disorder affecting the central nervous system and features extensive functional brain changes that are poorly understood but relate strongly to clinical impairments. Functional magnetic resonance imaging (fMRI) is a non-invasive, powerful technique able to map activity of brain regions and to assess how such regions interact for an efficient brain network. FMRI has been widely applied to study functional brain changes in MS, allowing to investigate functional plasticity consequent to disease-related structural injury. The first studies in MS using active fMRI tasks mainly aimed to study such plastic changes by identifying abnormal activity in salient brain regions (or systems) involved by the task. In later studies the focus shifted towards resting state (RS) functional connectivity (FC) studies, which aimed to map large-scale functional networks of the brain and to establish how MS pathology impairs functional integration, eventually leading to the hypothesized network collapse as patients clinically progress. This review provides a summary of the main findings from studies using task-based and RS fMRI and illustrates how functional brain alterations relate to clinical disability and cognitive deficits in this condition. We also give an overview of longitudinal studies that used task-based and RS fMRI to monitor disease evolution and effects of motor and cognitive rehabilitation. In addition, we discuss the results of studies using newer technologies involving time-varying FC to investigate abnormal dynamism and flexibility of network configurations in MS. Finally, we show some preliminary results from two recent topics (i.e., multimodal MRI analysis and artificial intelligence) that are receiving increasing attention. Together, these functional studies could provide new (conceptual) insights into disease stage-specific mechanisms underlying progression in MS, with recommendations for future research.
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Affiliation(s)
- Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy.
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
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Circulating miRNAs as Potential Biomarkers Distinguishing Relapsing-Remitting from Secondary Progressive Multiple Sclerosis. A Review. Int J Mol Sci 2021; 22:ijms222111887. [PMID: 34769314 PMCID: PMC8584709 DOI: 10.3390/ijms222111887] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/30/2021] [Accepted: 10/31/2021] [Indexed: 12/18/2022] Open
Abstract
Multiple sclerosis (MS) is a debilitating neurodegenerative, highly heterogeneous disease with a variable course. The most common MS subtype is relapsing–remitting (RR), having interchanging periods of worsening and relative stabilization. After a decade, in most RR patients, it alters into the secondary progressive (SP) phase, the most debilitating one with no clear remissions, leading to progressive disability deterioration. Among the greatest challenges for clinicians is understanding disease progression molecular mechanisms, since RR is mainly characterized by inflammatory processes, while in SP, the neurodegeneration prevails. This is especially important because distinguishing RR from the SP subtype early will enable faster implementation of appropriate treatment. Currently, the MS course is not well-correlated with the biomarkers routinely used in clinical practice. Despite many studies, there are still no reliable indicators correlating with the disease stage and its activity degree. Circulating microRNAs (miRNAs) may be considered valuable molecules for the MS diagnosis and, presumably, helpful in predicting disease subtype. MiRNA expression dysregulation is commonly observed in the MS course. Moreover, knowledge of diverse miRNA panel expression between RRMS and SPMS may allow for deterring disability progression through successful treatment. Therefore, in this review, we address the current state of research on differences in miRNA panel expression between the phases.
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Vavasour IM, Sun P, Graf C, Yik JT, Kolind SH, Li DK, Tam R, Sayao AL, Schabas A, Devonshire V, Carruthers R, Traboulsee A, Moore GW, Song SK, Laule C. Characterization of multiple sclerosis neuroinflammation and neurodegeneration with relaxation and diffusion basis spectrum imaging. Mult Scler 2021; 28:418-428. [PMID: 34132126 DOI: 10.1177/13524585211023345] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Advanced magnetic resonance imaging (MRI) methods can provide more specific information about various microstructural tissue changes in multiple sclerosis (MS) brain. Quantitative measurement of T1 and T2 relaxation, and diffusion basis spectrum imaging (DBSI) yield metrics related to the pathology of neuroinflammation and neurodegeneration that occurs across the spectrum of MS. OBJECTIVE To use relaxation and DBSI MRI metrics to describe measures of neuroinflammation, myelin and axons in different MS subtypes. METHODS 103 participants (20 clinically isolated syndrome (CIS), 33 relapsing-remitting MS (RRMS), 30 secondary progressive MS and 20 primary progressive MS) underwent quantitative T1, T2, DBSI and conventional 3T MRI. Whole brain, normal-appearing white matter, lesion and corpus callosum MRI metrics were compared across MS subtypes. RESULTS A gradation of MRI metric values was seen from CIS to RRMS to progressive MS. RRMS demonstrated large oedema-related differences, while progressive MS had the most extensive abnormalities in myelin and axonal measures. CONCLUSION Relaxation and DBSI-derived MRI measures show differences between MS subtypes related to the severity and composition of underlying tissue damage. RRMS showed oedema, demyelination and axonal loss compared with CIS. Progressive MS had even more evidence of increased oedema, demyelination and axonal loss compared with CIS and RRMS.
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Affiliation(s)
- Irene M Vavasour
- Department of Radiology, The University of British Columbia, UBC Hospital, Vancouver, BC, Canada/International Collaboration on Repair Discoveries (ICORD), The University of British Columbia, Vancouver, BC, Canada
| | - Peng Sun
- Department of Radiology, Washington University, St. Louis, MO, USA
| | - Carina Graf
- Department of Physics & Astronomy, The University of British Columbia, Vancouver, BC, Canada
| | - Jackie T Yik
- Department of Physics & Astronomy, The University of British Columbia, Vancouver, BC, Canada
| | - Shannon H Kolind
- Department of Radiology, The University of British Columbia, Vancouver, BC, Canada/International Collaboration on Repair Discoveries (ICORD), The University of British Columbia, Vancouver, BC, Canada/Department of Physics & Astronomy, The University of British Columbia, Vancouver, BC, Canada/Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - David Kb Li
- Department of Radiology, The University of British Columbia, Vancouver, BC, Canada/Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Roger Tam
- Department of Radiology, The University of British Columbia, Vancouver, BC, Canada/School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Ana-Luiza Sayao
- Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Alice Schabas
- Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Virginia Devonshire
- Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Robert Carruthers
- Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Anthony Traboulsee
- Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Gr Wayne Moore
- Department of Medicine, The University of British Columbia, Vancouver, BC, Canada/Department of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Sheng-Kwei Song
- Department of Radiology, Washington University, St. Louis, MO, USA
| | - Cornelia Laule
- Department of Radiology, The University of British Columbia, Vancouver, BC, Canada/International Collaboration on Repair Discoveries (ICORD), The University of British Columbia, Vancouver, BC, Canada/Department of Physics & Astronomy, The University of British Columbia, Vancouver, BC, Canada/Department of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, BC, Canada
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Preziosa P, Storelli L, Meani A, Moiola L, Rodegher M, Filippi M, Rocca MA. Effects of Fingolimod and Natalizumab on Brain T1-/T2-Weighted and Magnetization Transfer Ratios: a 2-Year Study. Neurotherapeutics 2021; 18:878-888. [PMID: 33483938 PMCID: PMC8423925 DOI: 10.1007/s13311-020-00997-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/18/2020] [Indexed: 11/26/2022] Open
Abstract
Fingolimod and natalizumab significantly reduce disease activity in relapsing-remitting multiple sclerosis (RRMS) and could promote tissue repair and neuroprotection. The ratio between conventional T1- and T2-weighted sequences (T1w/T2w-ratio) and magnetization transfer ratio (MTR) allow to quantify brain microstructural tissue abnormalities. Here, we compared fingolimod and natalizumab effects on brain T1w/T2w-ratio and MTR in RRMS over 2 years of treatment. RRMS patients starting fingolimod (n = 25) or natalizumab (n = 30) underwent 3T brain MRI scans at baseline (T0), month 6 (M6), month 12 (M12), and month 24 (M24). White matter (WM) lesions, normal-appearing (NA) WM, and gray matter (GM) T1w/T2w-ratio and MTR were estimated and compared between groups using linear mixed models. No baseline demographic, clinical, and MRI difference was found between groups. In natalizumab patients, lesion T1w/T2w-ratio and MTR significantly increased at M6 vs. T0 (p ≤ 0.035) and decreased at subsequent timepoints (p ≤ 0.037). In fingolimod patients, lesion T1w/T2w-ratio increased at M12 vs. T0 (p = 0.010), while MTR gradually increased at subsequent timepoints vs. T0 (p ≤ 0.027). Natalizumab stabilized NAWM and GM T1w/T2w-ratio and MTR. In fingolimod patients, NAWM T1w/T2w-ratio and MTR significantly increased at M24 vs. M12 (p ≤ 0.001). A significant GM T1w/T2w-ratio decrease at M6 vs. T0 (p = 0.014) and increase at M24 vs. M6 (p = 0.008) occurred, whereas GM MTR was significantly higher at M24 vs. previous timepoints (p ≤ 0.017) with significant between-group differences (p ≤ 0.034). Natalizumab may promote an early recovery of lesional damage and prevent microstructural damage accumulation in NAWM and GM during the first 2 years of treatment. Fingolimod enhances tissue damage recovery being visible after 6 months in lesions and after 2 years in NAWM and GM.
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Affiliation(s)
- Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Loredana Storelli
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alessandro Meani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Lucia Moiola
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Via Olgettina, 60, 20132, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Via Olgettina, 60, 20132, Milan, Italy.
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Moccia M, van de Pavert S, Eshaghi A, Haider L, Pichat J, Yiannakas M, Ourselin S, Wang Y, Wheeler-Kingshott C, Thompson A, Barkhof F, Ciccarelli O. Pathologic correlates of the magnetization transfer ratio in multiple sclerosis. Neurology 2020; 95:e2965-e2976. [PMID: 32938787 DOI: 10.1212/wnl.0000000000010909] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 07/22/2020] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To identify pathologic correlates of magnetization transfer ratio (MTR) in multiple sclerosis (MS) in an MRI-pathology study. METHODS We acquired MTR maps at 3T from 16 fixed MS brains and 4 controls, and immunostained 100 tissue blocks for neuronal neurofilaments, myelin (SMI94), tissue macrophages (CD68), microglia (IBA1), B-lymphocytes, T-lymphocytes, cytotoxic T-lymphocytes, astrocytes (glial fibrillary acidic protein), and mitochondrial damage (COX4, VDAC). We defined regions of interest in lesions, normal-appearing white matter (NAWM), and cortical normal-appearing gray matter (NAGM). Associations between MTR and immunostaining intensities were explored using linear mixed-effects models (with cassettes nested within patients) and interaction terms (for differences between regions of interest and between cases and controls); a multivariate linear mixed-effects model identified the best pathologic correlates of MTR. RESULTS MTR was the lowest in white matter (WM) lesions (23.4 ± 9.4%) and the highest in NAWM (38.1 ± 8.7%). In MS brains, lower MTR was associated with lower immunostaining intensity for myelin (coefficient 0.31; 95% confidence interval [CI] 0.07-0.55), macrophages (coefficient 0.03; 95% CI 0.01-0.07), and astrocytes (coefficient 0.51; 95% CI 0.02-1.00), and with greater mitochondrial damage (coefficient 0.31; 95% CI 0.07-0.55). Based on interaction terms, MTR was more strongly associated with myelin in WM (coefficient 1.58; 95% CI 1.09-2.08) and gray matter (GM) lesions (coefficient 0.66; 95% CI 0.13-1.20), and with macrophages (coefficient 1.40; 95% CI 0.56-2.25), astrocytes (coefficient 2.66; 95% CI 1.31-4.01), and mitochondrial damage (coefficient -12.59; 95% CI -23.16 to -2.02) in MS brains than controls. In the multivariate model, myelin immunostaining intensity was the best correlate of MTR (coefficient 0.31; 95% CI 0.09-0.52; p = 0.004). CONCLUSIONS Myelin was the strongest correlate of MTR, especially in WM and cortical GM lesions, but additional correlates should be kept in mind when designing and interpreting MTR observational and experimental studies in MS.
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Affiliation(s)
- Marcello Moccia
- From the Department of Neuroinflammation, Queen Square MS Centre, NMR Research Unit, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences (M.M., S.v.d.P., A.E., L.H., M.Y., Y.W., C.W.-K., A.T., F.B., O.C.), Centre for Medical Image Computing, Department of Medical Physics and Bioengineering (J.P., S.O.), and Translational Imaging Group, UCL Institute of Healthcare Engineering (F.B.), University College London, UK; Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences (M.M.), Federico II University, Naples, Italy; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam, the Netherlands; and National Institute for Health Research University College London Hospitals Biomedical Research Centre (A.T., F.B., O.C.), UK
| | - Steven van de Pavert
- From the Department of Neuroinflammation, Queen Square MS Centre, NMR Research Unit, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences (M.M., S.v.d.P., A.E., L.H., M.Y., Y.W., C.W.-K., A.T., F.B., O.C.), Centre for Medical Image Computing, Department of Medical Physics and Bioengineering (J.P., S.O.), and Translational Imaging Group, UCL Institute of Healthcare Engineering (F.B.), University College London, UK; Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences (M.M.), Federico II University, Naples, Italy; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam, the Netherlands; and National Institute for Health Research University College London Hospitals Biomedical Research Centre (A.T., F.B., O.C.), UK
| | - Arman Eshaghi
- From the Department of Neuroinflammation, Queen Square MS Centre, NMR Research Unit, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences (M.M., S.v.d.P., A.E., L.H., M.Y., Y.W., C.W.-K., A.T., F.B., O.C.), Centre for Medical Image Computing, Department of Medical Physics and Bioengineering (J.P., S.O.), and Translational Imaging Group, UCL Institute of Healthcare Engineering (F.B.), University College London, UK; Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences (M.M.), Federico II University, Naples, Italy; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam, the Netherlands; and National Institute for Health Research University College London Hospitals Biomedical Research Centre (A.T., F.B., O.C.), UK
| | - Lukas Haider
- From the Department of Neuroinflammation, Queen Square MS Centre, NMR Research Unit, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences (M.M., S.v.d.P., A.E., L.H., M.Y., Y.W., C.W.-K., A.T., F.B., O.C.), Centre for Medical Image Computing, Department of Medical Physics and Bioengineering (J.P., S.O.), and Translational Imaging Group, UCL Institute of Healthcare Engineering (F.B.), University College London, UK; Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences (M.M.), Federico II University, Naples, Italy; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam, the Netherlands; and National Institute for Health Research University College London Hospitals Biomedical Research Centre (A.T., F.B., O.C.), UK
| | - Jonas Pichat
- From the Department of Neuroinflammation, Queen Square MS Centre, NMR Research Unit, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences (M.M., S.v.d.P., A.E., L.H., M.Y., Y.W., C.W.-K., A.T., F.B., O.C.), Centre for Medical Image Computing, Department of Medical Physics and Bioengineering (J.P., S.O.), and Translational Imaging Group, UCL Institute of Healthcare Engineering (F.B.), University College London, UK; Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences (M.M.), Federico II University, Naples, Italy; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam, the Netherlands; and National Institute for Health Research University College London Hospitals Biomedical Research Centre (A.T., F.B., O.C.), UK
| | - Marios Yiannakas
- From the Department of Neuroinflammation, Queen Square MS Centre, NMR Research Unit, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences (M.M., S.v.d.P., A.E., L.H., M.Y., Y.W., C.W.-K., A.T., F.B., O.C.), Centre for Medical Image Computing, Department of Medical Physics and Bioengineering (J.P., S.O.), and Translational Imaging Group, UCL Institute of Healthcare Engineering (F.B.), University College London, UK; Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences (M.M.), Federico II University, Naples, Italy; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam, the Netherlands; and National Institute for Health Research University College London Hospitals Biomedical Research Centre (A.T., F.B., O.C.), UK
| | - Sebastien Ourselin
- From the Department of Neuroinflammation, Queen Square MS Centre, NMR Research Unit, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences (M.M., S.v.d.P., A.E., L.H., M.Y., Y.W., C.W.-K., A.T., F.B., O.C.), Centre for Medical Image Computing, Department of Medical Physics and Bioengineering (J.P., S.O.), and Translational Imaging Group, UCL Institute of Healthcare Engineering (F.B.), University College London, UK; Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences (M.M.), Federico II University, Naples, Italy; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam, the Netherlands; and National Institute for Health Research University College London Hospitals Biomedical Research Centre (A.T., F.B., O.C.), UK
| | - Yi Wang
- From the Department of Neuroinflammation, Queen Square MS Centre, NMR Research Unit, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences (M.M., S.v.d.P., A.E., L.H., M.Y., Y.W., C.W.-K., A.T., F.B., O.C.), Centre for Medical Image Computing, Department of Medical Physics and Bioengineering (J.P., S.O.), and Translational Imaging Group, UCL Institute of Healthcare Engineering (F.B.), University College London, UK; Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences (M.M.), Federico II University, Naples, Italy; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam, the Netherlands; and National Institute for Health Research University College London Hospitals Biomedical Research Centre (A.T., F.B., O.C.), UK
| | - Claudia Wheeler-Kingshott
- From the Department of Neuroinflammation, Queen Square MS Centre, NMR Research Unit, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences (M.M., S.v.d.P., A.E., L.H., M.Y., Y.W., C.W.-K., A.T., F.B., O.C.), Centre for Medical Image Computing, Department of Medical Physics and Bioengineering (J.P., S.O.), and Translational Imaging Group, UCL Institute of Healthcare Engineering (F.B.), University College London, UK; Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences (M.M.), Federico II University, Naples, Italy; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam, the Netherlands; and National Institute for Health Research University College London Hospitals Biomedical Research Centre (A.T., F.B., O.C.), UK
| | - Alan Thompson
- From the Department of Neuroinflammation, Queen Square MS Centre, NMR Research Unit, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences (M.M., S.v.d.P., A.E., L.H., M.Y., Y.W., C.W.-K., A.T., F.B., O.C.), Centre for Medical Image Computing, Department of Medical Physics and Bioengineering (J.P., S.O.), and Translational Imaging Group, UCL Institute of Healthcare Engineering (F.B.), University College London, UK; Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences (M.M.), Federico II University, Naples, Italy; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam, the Netherlands; and National Institute for Health Research University College London Hospitals Biomedical Research Centre (A.T., F.B., O.C.), UK
| | - Frederik Barkhof
- From the Department of Neuroinflammation, Queen Square MS Centre, NMR Research Unit, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences (M.M., S.v.d.P., A.E., L.H., M.Y., Y.W., C.W.-K., A.T., F.B., O.C.), Centre for Medical Image Computing, Department of Medical Physics and Bioengineering (J.P., S.O.), and Translational Imaging Group, UCL Institute of Healthcare Engineering (F.B.), University College London, UK; Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences (M.M.), Federico II University, Naples, Italy; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam, the Netherlands; and National Institute for Health Research University College London Hospitals Biomedical Research Centre (A.T., F.B., O.C.), UK
| | - Olga Ciccarelli
- From the Department of Neuroinflammation, Queen Square MS Centre, NMR Research Unit, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences (M.M., S.v.d.P., A.E., L.H., M.Y., Y.W., C.W.-K., A.T., F.B., O.C.), Centre for Medical Image Computing, Department of Medical Physics and Bioengineering (J.P., S.O.), and Translational Imaging Group, UCL Institute of Healthcare Engineering (F.B.), University College London, UK; Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences (M.M.), Federico II University, Naples, Italy; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam, the Netherlands; and National Institute for Health Research University College London Hospitals Biomedical Research Centre (A.T., F.B., O.C.), UK.
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Filippi M, Preziosa P, Langdon D, Lassmann H, Paul F, Rovira À, Schoonheim MM, Solari A, Stankoff B, Rocca MA. Identifying Progression in Multiple Sclerosis: New Perspectives. Ann Neurol 2020; 88:438-452. [PMID: 32506714 DOI: 10.1002/ana.25808] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 05/18/2020] [Accepted: 05/25/2020] [Indexed: 01/10/2023]
Abstract
The identification of progression in multiple sclerosis is typically retrospective. Given the profound burden of progressive multiple sclerosis, and the recent development of effective treatments for these patients, there is a need to establish measures capable of identifying progressive multiple sclerosis early in the disease course. Starting from recent pathological findings, this review assesses the state of the art of potential measures able to predict progressive multiple sclerosis. Future promising biomarkers that might shed light on mechanisms of progression are also discussed. Finally, expansion of the concept of progressive multiple sclerosis, by including an assessment of cognition, patient-reported outcomes, and comorbidities, is considered. ANN NEUROL 2020;88:438-452.
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Affiliation(s)
- Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurophysiology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Paolo Preziosa
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Dawn Langdon
- Royal Holloway, University of London, London, United Kingdom
| | - Hans Lassmann
- Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Friedemann Paul
- NeuroCure Clinical Research Center and Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Àlex Rovira
- Neuroradiology Section, Department of Radiology, Vall d'Hebron University Hospital and Research Institute, Autonomous University of Barcelona, Barcelona, Spain
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, Multiple Sclerosis Center Amsterdam, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Alessandra Solari
- Unit of Neuroepidemiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Bruno Stankoff
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute - ICM, Inserm, CNRS, APHP, Paris, France
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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15
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Bonacchi R, Pagani E, Meani A, Cacciaguerra L, Preziosa P, De Meo E, Filippi M, Rocca MA. Clinical Relevance of Multiparametric MRI Assessment of Cervical Cord Damage in Multiple Sclerosis. Radiology 2020; 296:605-615. [PMID: 32573387 DOI: 10.1148/radiol.2020200430] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background In multiple sclerosis (MS), knowledge about how spinal cord abnormalities translate into clinical manifestations is incomplete. Comprehensive, multiparametric MRI studies are useful in this perspective, but studies for the spinal cord are lacking. Purpose To identify MRI features of cervical spinal cord damage that could help predict disability and disease course in MS by using a comprehensive, multiparametric MRI approach. Materials and Methods In this retrospective hypothesis-driven analysis of longitudinally acquired data between June 2017 and April 2019, 120 patients with MS (58 with relapsing-remitting MS [RRMS] and 62 with progressive MS [PMS]) and 30 age- and sex-matched healthy control participants underwent 3.0-T MRI of the brain and cervical spinal cord. Cervical spinal cord MRI was performed with three-dimensional (3D) T1-weighted, T2-weighted, and diffusion-weighted imaging; sagittal two-dimensional (2D) short inversion time inversion-recovery imaging; and axial 2D phase-sensitive inversion-recovery imaging at the C2-C3 level. Brain MRI was performed with 3D T1-weighted, fluid-attenuated inversion-recovery and T2-weighted sequences. Associations between MRI variables and disability were explored with age-, sex- and phenotype-adjusted linear models. Results In patients with MS, multivariable analysis identified phenotype, cervical spinal cord gray matter (GM) cross-sectional area (CSA), lateral funiculi fractional anisotropy (FA), and brain GM volume as independent predictors of Expanded Disability Status Scale (EDSS) score (R2 = 0.86). The independent predictors of EDSS score in RRMS were lateral funiculi FA, normalized brain volume, and cervical spinal cord GM T2 lesion volume (R2 = 0.51). The independent predictors of EDSS score in PMS were cervical spinal cord GM CSA and brain GM volume (R2 = 0.44). Logistic regression analysis identified cervical spinal cord GM CSA and T2 lesion volume as independent predictors of phenotype (area under the receiver operating characteristic curve = 0.95). An optimal cervical spinal cord GM CSA cut-off value of 11.1 mm2 was found to enable accurate differentiation of patients with PMS, having values below the threshold, from those with RRMS (sensitivity = 90% [56 of 62], specificity = 91% [53 of 58]). Conclusion Cervical spinal cord MRI involvement has a central role in explaining disability in multiple sclerosis (MS): Lesion-induced damage in the lateral funiculi and gray matter (GM) in relapsing-remitting MS and GM atrophy in patients with progressive MS are the most relevant variables. Cervical spinal cord GM atrophy is an accurate predictor of progressive phenotype. Cervical spinal cord GM lesions may subsequently cause GM atrophy, which may contribute to evolution to PMS. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Zivadinov and Bergsland in this issue.
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Affiliation(s)
- Raffaello Bonacchi
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| | - Alessandro Meani
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| | - Laura Cacciaguerra
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| | - Paolo Preziosa
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| | - Ermelinda De Meo
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| | - Massimo Filippi
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| | - Maria A Rocca
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
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16
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Ferrazzano G, Crisafulli SG, Baione V, Tartaglia M, Cortese A, Frontoni M, Altieri M, Pauri F, Millefiorini E, Conte A. Early diagnosis of secondary progressive multiple sclerosis: focus on fluid and neurophysiological biomarkers. J Neurol 2020; 268:3626-3645. [PMID: 32504180 DOI: 10.1007/s00415-020-09964-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 05/28/2020] [Accepted: 05/30/2020] [Indexed: 01/19/2023]
Abstract
BACKGROUND AND AIMS Most patients with multiple sclerosis presenting with a relapsing-remitting disease course at diagnosis transition to secondary progressive multiple sclerosis (SPMS) 1-2 decades after onset. SPMS is characterized by predominant neurodegeneration and atrophy. These pathogenic hallmarks result in unsatisfactory treatment response in SPMS patients. Therefore, early diagnosis of SPMS is necessary for prompt treatment decisions. The aim of this review was to assess neurophysiological and fluid biomarkers that have the potential to monitor disease progression and support early SPMS diagnosis. METHODS We performed a systematic review of studies that analyzed the role of neurophysiological techniques and fluid biomarkers in supporting SPMS diagnosis using the preferred reporting items for systematic reviews and meta-analyses statement. RESULTS From our initial search, we selected 24 relevant articles on neurophysiological biomarkers and 55 articles on fluid biomarkers. CONCLUSION To date, no neurophysiological or fluid biomarker is sufficiently validated to support the early diagnosis of SPMS. Neurophysiological measurements, including short interval intracortical inhibition and somatosensory temporal discrimination threshold, and the neurofilament light chain fluid biomarker seem to be the most promising. Cross-sectional studies on an adequate number of patients followed by longitudinal studies are needed to confirm the diagnostic and prognostic value of these biomarkers. A combination of neurophysiological and fluid biomarkers may be more sensitive in detecting SPMS conversion.
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Affiliation(s)
- Gina Ferrazzano
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | | | - Viola Baione
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Matteo Tartaglia
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Antonio Cortese
- Multiple Sclerosis Center, San Filippo Neri Hospital, Rome, Italy
| | - Marco Frontoni
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Marta Altieri
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Flavia Pauri
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | | | - Antonella Conte
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy. .,IRCCS Neuromed, Pozzilli, IS, Italy.
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17
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Preziosa P, Kiljan S, Steenwijk MD, Meani A, van de Berg WDJ, Schenk GJ, Rocca MA, Filippi M, Geurts JJG, Jonkman LE. Axonal degeneration as substrate of fractional anisotropy abnormalities in multiple sclerosis cortex. Brain 2020; 142:1921-1937. [PMID: 31168614 DOI: 10.1093/brain/awz143] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 03/14/2019] [Accepted: 04/09/2019] [Indexed: 12/11/2022] Open
Abstract
Cortical microstructural abnormalities are associated with clinical and cognitive deterioration in multiple sclerosis. Using diffusion tensor MRI, a higher fractional anisotropy has been found in cortical lesions versus normal-appearing cortex in multiple sclerosis. The pathological substrates of this finding have yet to be definitively elucidated. By performing a combined post-mortem diffusion tensor MRI and histopathology study, we aimed to define the histopathological substrates of diffusivity abnormalities in multiple sclerosis cortex. Sixteen subjects with multiple sclerosis and 10 age- and sex-matched non-neurological control donors underwent post-mortem in situ at 3 T MRI, followed by brain dissection. One hundred and ten paraffin-embedded tissue blocks (54 from multiple sclerosis patients, 56 from non-neurological controls) were matched to the diffusion tensor sequence to obtain regional diffusivity measures. Using immunohistochemistry and silver staining, cortical density of myelin, microglia, astrocytes and axons, and density and volume of neurons and glial cells were evaluated. Correlates of diffusivity abnormalities with histological markers were assessed through linear mixed-effects models. Cortical lesions (77% subpial) were found in 27/54 (50%) multiple sclerosis cortical regions. Multiple sclerosis normal-appearing cortex had a significantly lower fractional anisotropy compared to cortex from non-neurological controls (P = 0.047), whereas fractional anisotropy in demyelinated cortex was significantly higher than in multiple sclerosis normal-appearing cortex (P = 0.012) but not different from non-neurological control cortex (P = 0.420). Compared to non-neurological control cortex, both multiple sclerosis normal-appearing and demyelinated cortices showed a lower density of axons perpendicular to the cortical surface (P = 0.012 for both) and of total axons (parallel and perpendicular to cortical surface) (P = 0.028 and 0.012). In multiple sclerosis, demyelinated cortex had a lower density of myelin (P = 0.004), parallel (P = 0.018) and total axons (P = 0.029) versus normal-appearing cortex. Regarding the pathological substrate, in non-neurological controls, cortical fractional anisotropy was positively associated with density of perpendicular, parallel, and total axons (P = 0.031 for all). In multiple sclerosis, normal-appearing cortex fractional anisotropy was positively associated with perpendicular and total axon density (P = 0.031 for both), while associations with myelin, glial and total cells and parallel axons did not survive multiple comparison correction. Demyelinated cortex fractional anisotropy was positively associated with density of neurons, and total cells and negatively with microglia density, without surviving multiple comparison correction. Our results suggest that a reduction of perpendicular axons in normal-appearing cortex and of both perpendicular and parallel axons in demyelinated cortex may underlie the substrate influencing cortical microstructural coherence and being responsible for the different patterns of fractional anisotropy changes occurring in multiple sclerosis cortex.
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Affiliation(s)
- Paolo Preziosa
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.,Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Svenja Kiljan
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Martijn D Steenwijk
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Alessandro Meani
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Wilma D J van de Berg
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Geert J Schenk
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.,Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.,Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Laura E Jonkman
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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18
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Preziosa P, Rocca MA, Filippi M. Current state-of-art of the application of serum neurofilaments in multiple sclerosis diagnosis and monitoring. Expert Rev Neurother 2020; 20:747-769. [DOI: 10.1080/14737175.2020.1760846] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Paolo Preziosa
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria A. Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
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19
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MRI quality control for the Italian Neuroimaging Network Initiative: moving towards big data in multiple sclerosis. J Neurol 2019; 266:2848-2858. [PMID: 31422457 DOI: 10.1007/s00415-019-09509-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 08/12/2019] [Accepted: 08/13/2019] [Indexed: 01/19/2023]
Abstract
The Italian Neuroimaging Network Initiative (INNI) supports the creation of a repository, where MRI, clinical, and neuropsychological data from multiple sclerosis (MS) patients and healthy controls are collected from Italian Research Centers with internationally recognized expertise in MRI applied to MS. However, multicenter MRI data integration needs standardization and quality control (QC). This study aimed to implement quantitative measures for characterizing the standardization and quality of MRI collected within INNI. MRI scans of 423 MS patients, including 3D T1- and T2-weighted, were obtained from INNI repository (from Centers A, B, C, and D). QC measures were implemented to characterize: (1) head positioning relative to the magnet isocenter; (2) intensity inhomogeneity; (3) relative image contrast between brain tissues; and (4) image artefacts. Centers A and D showed the most accurate subject positioning within the MR scanner (median z-offsets = - 2.6 ± 1.7 cm and - 1.1 ± 2 cm). A low, but significantly different, intensity inhomogeneity on 3D T1-weighted MRI was found between all centers (p < 0.05), except for Centers A and C that showed comparable image bias fields. Center D showed the highest relative contrast between gray and normal appearing white matter (NAWM) on 3D T1-weighed MRI (0.63 ± 0.04), while Center B showed the highest relative contrast between NAWM and MS lesions on FLAIR (0.21 ± 0.06). Image artefacts were mainly due to brain movement (60%) and ghosting (35%). The implemented QC procedure ensured systematic data quality assessment within INNI, thus making available a huge amount of high-quality MRI to better investigate pathophysiological substrates and validate novel MRI biomarkers in MS.
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20
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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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21
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Rocca MA, Preziosa P, Filippi M. Application of advanced MRI techniques to monitor pharmacologic and rehabilitative treatment in multiple sclerosis: current status and future perspectives. Expert Rev Neurother 2018; 19:835-866. [PMID: 30500303 DOI: 10.1080/14737175.2019.1555038] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Introduction: Advances in magnetic resonance imaging (MRI) technology and analyses are improving our understanding of the pathophysiology of multiple sclerosis (MS). Due to their ability to grade the presence of irreversible tissue loss, microstructural tissue abnormalities, metabolic changes and functional plasticity, the application of these techniques is also expanding our knowledge on the efficacy and mechanisms of action of different pharmacological and rehabilitative treatments. Areas covered: This review discusses recent findings derived from the application of advanced MRI techniques to evaluate the structural and functional substrates underlying the effects of pharmacologic and rehabilitative treatments in patients with MS. Current applications as outcome in clinical trials and observational studies, their interpretation and possible pitfalls in their use are discussed. Finally, how these techniques could evolve in the future to improve monitoring of disease progression and treatment response is examined. Expert commentary: The number of treatments currently available for MS is increasing. The application of advanced MRI techniques is providing reliable and specific measures to better understand the targets of different treatments, including neuroprotection, tissue repair, and brain plasticity. This is a fundamental progress to move toward personalized medicine and individual treatment selection.
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Affiliation(s)
- Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University , Milan , Italy.,Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University , Milan , Italy
| | - Paolo Preziosa
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University , Milan , Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University , Milan , Italy.,Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University , Milan , Italy
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22
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Neeb H, Schenk J. Multivariate prediction of multiple sclerosis using robust quantitative MR-based image metrics. Z Med Phys 2018; 29:262-271. [PMID: 30442457 DOI: 10.1016/j.zemedi.2018.10.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 08/25/2018] [Accepted: 10/14/2018] [Indexed: 02/02/2023]
Abstract
OBJECTIVES The current work investigates the performance of different multivariate supervised machine learning models to predict the presence or absence of multiple sclerosis (MS) based on features derived from quantitative MRI acquisitions. The performance of these models was evaluated for images which are significantly degraded due to subject motion, a problem which is often observed in clinical routine diagnostics. Finally, the difference between a true multivariate analysis and the corresponding univariate analysis based on single parameters alone was addressed. MATERIALS AND METHODS 52 MS patients and 45 healthy controls where scanned on a 3T system. The datasets showed variable degrees of motion-associated artefacts. For each dataset, the average of T1, T2*, total and myelin bound water content was determined in white and grey matter. Based on these parameters, different multivariate models were trained and their cross-validated performance to predict the presence of MS was evaluated. Furthermore, the univariate distributions of each quantitative parameter were employed to define optimised cut-offs that differentiate MS patients from healthy controls. RESULTS For data not affected by motion, 83.7% of all subjects were correctly classified using a crossvalidated multivariate model. Inclusion of data with significant artefacts reduces the rate of correct classification to 74.5%. T1 in grey and myelin water content in white matter where the most discriminating variables in the multivariate analysis. In contrast, the total water content in white matter and the ratio of white and grey matter total water content each resulted in 77% correct classifications in a univariate regression analysis. CONCLUSION The results demonstrate that even simple quantitative MRI-based measures allow for an automated prediction of the presence/absence of multiple sclerosis with good specificity. Importantly, even highly degraded datasets due to motion-artefacts could be correctly classified, especially when pooling features derived from grey and white matter. Finally, the advantage of a multivariate over a univariate analysis of quantitative MR data was shown.
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Affiliation(s)
- Heiko Neeb
- Multimodal Imaging Physics Group, University of Applied Sciences Koblenz, RheinAhrCampus Remagen, 53424 Remagen, Germany; Institute for Medical Engineering and Information Processing - MTI Mittelrhein, University of Koblenz, 56070 Koblenz, Germany.
| | - Jochen Schenk
- Radiologisches Institut Hohenzollernstrasse, 56068 Koblenz, Germany
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23
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Zivadinov R, Tavazzi E, Hagemeier J, Carl E, Hojnacki D, Kolb C, Weinstock-Guttman B. The Effect of Glatiramer Acetate on Retinal Nerve Fiber Layer Thickness in Patients with Relapsing-Remitting Multiple Sclerosis: A Longitudinal Optical Coherence Tomography Study. CNS Drugs 2018; 32:763-770. [PMID: 29767815 DOI: 10.1007/s40263-018-0521-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
BACKGROUND Optical coherence tomography (OCT) is a technique that allows for the assessment of retinal nerve fiber layer thickness (RNFLT) and total macular volume (TMV), which reflect neuroaxonal integrity within the retina. As such it has been used in multiple sclerosis (MS) to study neurodegeneration. Glatiramer acetate (GA) is a widely used treatment for MS, which is suggested to have a possible neuroprotective role. OBJECTIVE The aim of this study was to assess RFNLT and TMV changes in relapsing-remitting MS (RRMS) patients who started treatment with GA and were followed for a 24-month period. METHODS A cohort of 60 RRMS patients and 40 healthy controls (HCs) were imaged with OCT at baseline and follow-up. All subjects also underwent clinical and neurological examination. Measurements were compared between the RRMS patients and HCs as well as between optic neuritis (ON)-affected and ON-unaffected eyes. RESULTS At baseline, MS patients showed lower average RNFLT (p = 0.046) and TMV (p = 0.013) when compared with HCs. No significant differences in the evolution of OCT measures were detected over the follow-up between MS patients and HCs. MS patients with both affected and unaffected eyes showed significantly lower average RNFLT, temporal inferior RNFLT, and TMV at baseline, compared with HCs. No significant differences between ON-affected and ON-unaffected eyes in MS patients were detected over the follow-up, except for the nasal superior RNFLT (p = 0.019). CONCLUSIONS This study suggests a beneficial role of GA on retinal axonal degeneration in MS, and further confirms the utility of OCT to monitor the neuroprotective effect of disease-modifying treatment.
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Affiliation(s)
- Robert Zivadinov
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo Neuroimaging Analysis Center, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY, 14203, USA. .,Center for Biomedical Imaging at Clinical and Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA.
| | - Eleonora Tavazzi
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo Neuroimaging Analysis Center, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY, 14203, USA
| | - Jesper Hagemeier
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo Neuroimaging Analysis Center, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY, 14203, USA
| | - Ellen Carl
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo Neuroimaging Analysis Center, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY, 14203, USA
| | - David Hojnacki
- Department of Neurology, School of Medicine and Biomedical Sciences, Jacobs Multiple Sclerosis Center, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Channa Kolb
- Department of Neurology, School of Medicine and Biomedical Sciences, Jacobs Multiple Sclerosis Center, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Bianca Weinstock-Guttman
- Department of Neurology, School of Medicine and Biomedical Sciences, Jacobs Multiple Sclerosis Center, University at Buffalo, State University of New York, Buffalo, NY, USA
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Keegan BM, Kaufmann TJ, Weinshenker BG, Kantarci OH, Schmalstieg WF, Paz Soldan MM, Flanagan EP. Progressive motor impairment from a critically located lesion in highly restricted CNS-demyelinating disease. Mult Scler 2018; 24:1445-1452. [DOI: 10.1177/1352458518781979] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Objective: To report progressive motor impairment from a critically located central nervous system (CNS) demyelinating lesion in patients with restricted magnetic resonance imaging (MRI)-lesion burden. Methods: We identified 38 patients with progressive upper motor-neuron impairment for >1 year, 2–5 MRI CNS-demyelinating lesions, with one seemingly anatomically responsible for progressive motor impairment. Patients with any alternative etiology for progressive motor impairment were excluded. A neuroradiologist blinded to clinical evaluation reviewed multiple brain and spinal-cord MRI, selecting a candidate critically located demyelinating lesion. Lesion characteristics were determined and subsequently compared with clinical course. Results: Median onset age was 47.5 years (24–64); 23 (61%) women. Median follow-up was 94 months (18–442); median Expanded Disability Status Scale Score (EDSS) at last follow-up was 4.5 (2–10). Clinical presentations were progressive: hemiparesis/monoparesis 31; quadriparesis 5; and paraparesis 2; 27 patients had progression from onset; 11 progression post-relapse. Total MRI lesions were 2 ( n = 8), 3 ( n = 12), 4 ( n = 12), and 5 ( n = 6). Critical lesions were located on corticospinal tracts, chronically atrophic in 26/38 (68%) and involved cervical spinal cord in 27, cervicomedullary/brainstem region in 6, thoracic spinal cord in 4, and subcortical white matter in 1. Conclusion: Progressive motor impairment may ascribe to a critically located CNS-demyelinating lesion in patients with highly restricted MRI burden. Motor progression from a specific demyelinating lesion has implications for understanding multiple sclerosis (MS) progression.
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Affiliation(s)
- B Mark Keegan
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
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Wang Y, Gu C, Huo Y, Han W, Yu J, Ding C, Zhao X, Meng Y, Li C. Diffusion tensor imaging for evaluating perianal fistula: Feasibility study. Medicine (Baltimore) 2018; 97:e11570. [PMID: 30024560 PMCID: PMC6086465 DOI: 10.1097/md.0000000000011570] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
To explore the feasibility of using diffusion tensor imaging (DTI) in the diagnosis of anal fistula and evaluating its activity.Thirty-four patients with perianal fistulas were examined with DTI on a 3.0 T magnetic resonance imaging (MRI) before undergoing surgery. Based on the surgery requirement and preoperative examinations, the lesions fell into 2 groups: the positive inflammation activity (PIA) group and the negative inflammation activity (NIA) group. Each lesion was divided into 3 regions of interest (ROIs) (i.e., the fistula area, edema area, and distant normal-appearing area). Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were calculated and analyzed.There were statistically significant differences in FA and ADC values of the fistula area, edema area, and distant normal-appearing area. The FA values of the fistula area, edema area, and distant normal-appearing area in PIA were 0.134 ± 0.046, 0.225 ± 0.060, 0.343 ± 0.070, respectively. The ADC values (×10 mm/s) of the fistula area, edema area, and distant normal-appearing area in PIA were 0.979 ± 0.441, 1.542 ± 0.274, 1.864 ± 0.336, respectively. The FA values of the fistula area, edema area, and distant normal-appearing area in NIA were 0.183 ± 0.057, 0.286 ± 0.059, 0.382 ± 0.084, respectively. The ADC values (×10 mm/s) of the fistula area, edema area, and distant normal-appearing area in NIA were 1.393 ± 0.256, 1.518 ± 0.274, 1.703 ± 0.432, respectively. Regarding the activity, the FA and ADC values of the PIA group were lower than those of the NIA group in the fistula area, and the differences were statistically significant (P = .009, .004). The FA values of the edema area in the PIA group were lower than those in the NIA group, and the difference was statistically significant. The ADC values of the edema area, and both the FA and ADC values of the distant normal-appearing area all exhibited no statistically significant differences between the 2 groups.DTI parameters may reflect microstructures of perianal fiatulas via quantitative information. FA and ADC values were instrumental in evaluating the activity of perianal fistulas.
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Affiliation(s)
- Yu Wang
- Shandong Medical Imaging Research Institute, Shandong University
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine
| | - Chao Gu
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine
| | - Yongjun Huo
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine
| | - Weiwei Han
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine
| | - Jinfen Yu
- Traditional Chinese Medical Hospital, Zhangqiu
| | - Chengzong Ding
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine
| | - Xiuyu Zhao
- Shandong Medical Imaging Research Institute, Shandong University
| | - Yunfang Meng
- Shandong Provincial Hospital, Jinan, Shandong, China
| | - Chuanting Li
- Shandong Medical Imaging Research Institute, Shandong University
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Sinnecker T, Granziera C, Wuerfel J, Schlaeger R. Future Brain and Spinal Cord Volumetric Imaging in the Clinic for Monitoring Treatment Response in MS. Curr Treat Options Neurol 2018; 20:17. [PMID: 29679165 DOI: 10.1007/s11940-018-0504-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
PURPOSE OF REVIEW Volumetric analysis of brain imaging has emerged as a standard approach used in clinical research, e.g., in the field of multiple sclerosis (MS), but its application in individual disease course monitoring is still hampered by biological and technical limitations. This review summarizes novel developments in volumetric imaging on the road towards clinical application to eventually monitor treatment response in patients with MS. RECENT FINDINGS In addition to the assessment of whole-brain volume changes, recent work was focused on the volumetry of specific compartments and substructures of the central nervous system (CNS) in MS. This included volumetric imaging of the deep brain structures and of the spinal cord white and gray matter. Volume changes of the latter indeed independently correlate with clinical outcome measures especially in progressive MS. Ultrahigh field MRI and quantitative MRI added to this trend by providing a better visualization of small compartments on highly resolving MR images as well as microstructural information. New developments in volumetric imaging have the potential to improve sensitivity as well as specificity in detecting and hence monitoring disease-related CNS volume changes in MS.
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Affiliation(s)
- Tim Sinnecker
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Medical Image Analysis Center Basel AG, Basel, Switzerland
- NeuroCure Clinical Research Center, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Cristina Granziera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Wuerfel
- Medical Image Analysis Center Basel AG, Basel, Switzerland
- NeuroCure Clinical Research Center, Charité Universitätsmedizin Berlin, Berlin, Germany
- Berlin Ultrahigh Field Facility, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Regina Schlaeger
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
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Genc B, Bozan HR, Genc S, Genc K. Stem Cell Therapy for Multiple Sclerosis. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1084:145-174. [PMID: 30039439 DOI: 10.1007/5584_2018_247] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Multiple sclerosis (MS) is a chronic inflammatory, autoimmune, and neurodegenerative disease of the central nervous system (CNS). It is characterized by demyelination and neuronal loss that is induced by attack of autoreactive T cells to the myelin sheath and endogenous remyelination failure, eventually leading to functional neurological disability. Although recent evidence suggests that MS relapses are induced by environmental and exogenous triggers such as viral infections in a genetic background, its very complex pathogenesis is not completely understood. Therefore, the efficiency of current immunosuppression-based therapies of MS is too low, and emerging disease-modifying immunomodulatory agents such as fingolimod and dimethyl fumarate cannot stop progressive neurodegenerative process. Thus, the cell replacement therapy approach that aims to overcome neuronal cell loss and remyelination failure and to increase endogenous myelin repair capacity is considered as an alternative treatment option. A wide variety of preclinical studies, using experimental autoimmune encephalomyelitis model of MS, have recently shown that grafted cells with different origins including mesenchymal stem cells (MSCs), neural precursor and stem cells, and induced-pluripotent stem cells have the ability to repair CNS lesions and to recover functional neurological deficits. The results of ongoing autologous hematopoietic stem cell therapy studies, with the advantage of peripheral administration to the patients, have suggested that cell replacement therapy is also a feasible option for immunomodulatory treatment of MS. In this chapter, we overview cell sources and applications of the stem cell therapy for treatment of MS. We also discuss challenges including those associated with administration route, immune responses to grafted cells, integration of these cells to existing neural circuits, and risk of tumor growth. Finally, future prospects of stem cell therapy for MS are addressed.
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Affiliation(s)
- Bilgesu Genc
- Department of Molecular Biology and Genetics, Izmir Institute of Technology, Izmir, Turkey
| | - Hemdem Rodi Bozan
- School of Medicine, Dokuz Eylul University Health Campus, Izmir, Turkey
| | - Sermin Genc
- Izmir Biomedicine and Genome Center, Dokuz Eylul University Health Campus, Izmir, Turkey.,Department of Neuroscience, Institute of Health Sciences, Dokuz Eylul University Health Campus, Izmir, Turkey
| | - Kursad Genc
- Department of Neuroscience, Institute of Health Sciences, Dokuz Eylul University Health Campus, Izmir, Turkey.
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Abstract
Understanding the clinico-radiological paradox is important in the search for more sensitive and specific surrogates of relapses and disability progression (such that they can be used to inform treatment choices in individual people with multiple sclerosis) and to gain a better understanding of the pathophysiological basis of disability in multiple sclerosis (to identify and assess key therapeutic targets). In this brief review, we will consider themes and issues underlying the clinico-radiological paradox and recent advances in its resolution.
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Affiliation(s)
- Declan Chard
- National Institute for Health Research (NIHR) University College London Hospitals (UCLH), Biomedical Research Centre, London, UK.,NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, London, UK
| | - S Anand Trip
- National Institute for Health Research (NIHR) University College London Hospitals (UCLH), Biomedical Research Centre, London, UK.,NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, London, UK
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Bonnier G, Maréchal B, Fartaria MJ, Falkowskiy P, Marques JP, Simioni S, Schluep M, Du Pasquier R, Thiran JP, Krueger G, Granziera C. The Combined Quantification and Interpretation of Multiple Quantitative Magnetic Resonance Imaging Metrics Enlightens Longitudinal Changes Compatible with Brain Repair in Relapsing-Remitting Multiple Sclerosis Patients. Front Neurol 2017; 8:506. [PMID: 29021778 PMCID: PMC5623825 DOI: 10.3389/fneur.2017.00506] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 09/08/2017] [Indexed: 12/25/2022] Open
Abstract
Objective Quantitative and semi-quantitative MRI (qMRI) metrics provide complementary specificity and differential sensitivity to pathological brain changes compatible with brain inflammation, degeneration, and repair. Moreover, advanced magnetic resonance imaging (MRI) metrics with overlapping elements amplify the true tissue-related information and limit measurement noise. In this work, we combined multiple advanced MRI parameters to assess focal and diffuse brain changes over 2 years in a group of early-stage relapsing-remitting MS patients. Methods Thirty relapsing-remitting MS patients with less than 5 years disease duration and nine healthy subjects underwent 3T MRI at baseline and after 2 years including T1, T2, T2* relaxometry, and magnetization transfer imaging. To assess longitudinal changes in normal-appearing (NA) tissue and lesions, we used analyses of variance and Bonferroni correction for multiple comparisons. Multivariate linear regression was used to assess the correlation between clinical outcome and multiparametric MRI changes in lesions and NA tissue. Results In patients, we measured a significant longitudinal decrease of mean T2 relaxation times in NA white matter (p = 0.005) and a decrease of T1 relaxation times in the pallidum (p < 0.05), which are compatible with edema reabsorption and/or iron deposition. No longitudinal changes in qMRI metrics were observed in controls. In MS lesions, we measured a decrease in T1 relaxation time (p-value < 2.2e−16) and a significant increase in MTR (p-value < 1e−6), suggesting repair mechanisms, such as remyelination, increased axonal density, and/or a gliosis. Last, the evolution of advanced MRI metrics—and not changes in lesions or brain volume—were correlated to motor and cognitive tests scores evolution (Adj-R2 > 0.4, p < 0.05). In summary, the combination of multiple advanced MRI provided evidence of changes compatible with focal and diffuse brain repair at early MS stages as suggested by histopathological studies.
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Affiliation(s)
- Guillaume Bonnier
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Benedicte Maréchal
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare, Lausanne, Switzerland.,Signal Processing Laboratory 5 LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Mário João Fartaria
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare, Lausanne, Switzerland.,Signal Processing Laboratory 5 LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Pavel Falkowskiy
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare, Lausanne, Switzerland.,Signal Processing Laboratory 5 LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - José P Marques
- Donders Centre for Cognitive Neuroimaging, Radbound University, Nijmegen, Netherlands
| | - Samanta Simioni
- Neuropsychology, Institution de Lavigny, Denens, Switzerland
| | - Myriam Schluep
- Neurology Service and Neuroimmunology Laboratory, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Renaud Du Pasquier
- Neurology Service and Neuroimmunology Laboratory, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Gunnar Krueger
- Siemens Medical Solutions USA IM MR COL NEZ, Burlington, MA, United States
| | - Cristina Granziera
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States.,Neurology Service and Neuroimmunology Laboratory, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
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