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Wang X, Bao L. Comparison of ocular changes in multiple sclerosis and neuromyelitis optica spectrum disorder patients. Front Neurol 2024; 15:1417814. [PMID: 39224886 PMCID: PMC11366610 DOI: 10.3389/fneur.2024.1417814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 08/05/2024] [Indexed: 09/04/2024] Open
Abstract
Purpose To explore ocular changes in patients with MS and NMOSD via SD-OCT and PVEP analysis. Methods From August 2020 to July 2021, 82 patients (164 eyes) diagnosed with MS, 59 patients (118 eyes) diagnosed with NMOSD and 50 healthy controls (100 eyes) were retrospectively selected. SD-OCT and PVEP were performed to compare retinal nerve fibre layer (RNFL) thickness around the optic disc, ganglion cell inner plexiform layer (GCIPL) thickness in the macula and P100 latency and amplitude between the disease groups and the control group. Results In the NMOSD and MS groups, the thickness of the GCIPL quadrants in eyes with optic neuritis was thinner than that in eyes without optic neuritis, and the amplitude of the P100 wave decreased. In addition, in eyes with optic neuritis, patients with NMOSD have thinner RNFL thicknesses in the temporal and superior quadrants than patients with MS, and the thickness of the GCIPL is thinner in each region. In eyes without optic neuritis, patients with MS have thinner nasal RNFL than do those with NMOSD. Conclusion SD-OCT and VEP may be useful for monitoring and distinguishing pathological changes in MS and NMOSD patients.
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Affiliation(s)
- Xiaoyue Wang
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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2
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Hua T, Fan H, Duan Y, Tian D, Chen Z, Xu X, Bai Y, Li Y, Zhang N, Sun J, Li H, Li Y, Li Y, Zeng C, Han X, Zhou F, Huang M, Xu S, Jin Y, Li H, Zhuo Z, Zhang X, Liu Y. Spinal cord and brain atrophy patterns in neuromyelitis optica spectrum disorder and multiple sclerosis. J Neurol 2024; 271:3595-3609. [PMID: 38558149 DOI: 10.1007/s00415-024-12281-9] [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: 10/17/2023] [Revised: 02/25/2024] [Accepted: 02/26/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND Spinal cord and brain atrophy are common in neuromyelitis optica spectrum disorder (NMOSD) and relapsing-remitting multiple sclerosis (RRMS) but harbor distinct patterns accounting for disability and cognitive impairment. METHODS This study included 209 NMOSD and 304 RRMS patients and 436 healthy controls. Non-negative matrix factorization was used to parse differences in spinal cord and brain atrophy at subject level into distinct patterns based on structural MRI. The weights of patterns were obtained using a linear regression model and associated with Expanded Disability Status Scale (EDSS) and cognitive scores. Additionally, patients were divided into cognitive impairment (CI) and cognitive preservation (CP) groups. RESULTS Three patterns were observed in NMOSD: (1) Spinal Cord-Deep Grey Matter (SC-DGM) pattern was associated with high EDSS scores and decline of visuospatial memory function; (2) Frontal-Temporal pattern was associated with decline of language learning function; and (3) Cerebellum-Brainstem pattern had no observed association. Patients with CI had higher weights of SC-DGM pattern than CP group. Three patterns were observed in RRMS: (1) DGM pattern was associated with high EDSS scores, decreased information processing speed, and decreased language learning and visuospatial memory functions; (2) Frontal-Temporal pattern was associated with overall cognitive decline; and (3) Occipital pattern had no observed association. Patients with CI trended to have higher weights of DGM and Frontal-Temporal patterns than CP group. CONCLUSION This study estimated the heterogeneity of spinal cord and brain atrophy patterns in NMOSD and RRMS patients at individual level, and evaluated the clinical relevance of these patterns, which may contribute to stratifying participants for targeted therapy.
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Affiliation(s)
- Tiantian Hua
- Department of Radiology, Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Houyou Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Decai Tian
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, People's Republic of China
| | - Zhenpeng Chen
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Xiaolu Xu
- Department of Radiology, Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Yutong Bai
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuna Li
- Department of Radiology, Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Ningnannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jie Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Haiqing Li
- Department of Radiology, Huashan Hospital Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital Fudan University, Shanghai, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chun Zeng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xuemei Han
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Muhua Huang
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Siyao Xu
- Department of Radiology, Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Ying Jin
- Department of Radiology, Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Hongfang Li
- Department of Radiology, Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
| | - Xinghu Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, People's Republic of China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
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3
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Tahedl M, Wiltgen T, Voon CC, Berthele A, Kirschke JS, Hemmer B, Mühlau M, Zimmer C, Wiestler B. Cortical Thin Patch Fraction Reflects Disease Burden in MS: The Mosaic Approach. AJNR Am J Neuroradiol 2023; 45:82-89. [PMID: 38164526 PMCID: PMC10756581 DOI: 10.3174/ajnr.a8064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/18/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND AND PURPOSE GM pathology plays an essential role in MS disability progression, emphasizing the importance of neuroradiologic biomarkers to capture the heterogeneity of cortical disease burden. This study aimed to assess the validity of a patch-wise, individual interpretation of cortical thickness data to identify GM pathology, the "mosaic approach," which was previously suggested as a biomarker for assessing and localizing atrophy. MATERIALS AND METHODS We investigated the mosaic approach in a cohort of 501 patients with MS with respect to 89 internal and 651 external controls. The resulting metric of the mosaic approach is the so-called thin patch fraction, which is an estimate of overall cortical disease burden per patient. We evaluated the mosaic approach with respect to the following: 1) discrimination between patients with MS and controls, 2) classification between different MS phenotypes, and 3) association with established biomarkers reflecting MS disease burden, using general linear modeling. RESULTS The thin patch fraction varied significantly between patients with MS and healthy controls and discriminated among MS phenotypes. Furthermore, the thin patch fraction was associated with disease burden, including the Expanded Disability Status Scale, cognitive and fatigue scores, and lesion volume. CONCLUSIONS This study demonstrates the validity of the mosaic approach as a neuroradiologic biomarker in MS. The output of the mosaic approach, namely the thin patch fraction, is a candidate biomarker for assessing and localizing cortical GM pathology. The mosaic approach can furthermore enhance the development of a personalized cortical MS biomarker, given that the thin patch fraction provides a feature on which artificial intelligence methods can be trained. Most important, we showed the validity of the mosaic approach when referencing data with respect to external control MR imaging repositories.
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Affiliation(s)
- Marlene Tahedl
- From the Department of Neuroradiology (M.T., J.S.K., C.Z., B.W.), School of Medicine, Technical University of Munich, Munich, Germany
| | - Tun Wiltgen
- Department of Neurology (T.W., C.C.V., A.B., B.H., M.M.), School of Medicine, Technical University of Munich, Munich, Germany
| | - Cui Ci Voon
- Department of Neurology (T.W., C.C.V., A.B., B.H., M.M.), School of Medicine, Technical University of Munich, Munich, Germany
| | - Achim Berthele
- Department of Neurology (T.W., C.C.V., A.B., B.H., M.M.), School of Medicine, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- From the Department of Neuroradiology (M.T., J.S.K., C.Z., B.W.), School of Medicine, Technical University of Munich, Munich, Germany
| | - Bernhard Hemmer
- Department of Neurology (T.W., C.C.V., A.B., B.H., M.M.), School of Medicine, Technical University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (B.H.), Munich, Germany
| | - Mark Mühlau
- Department of Neurology (T.W., C.C.V., A.B., B.H., M.M.), School of Medicine, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- From the Department of Neuroradiology (M.T., J.S.K., C.Z., B.W.), School of Medicine, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- From the Department of Neuroradiology (M.T., J.S.K., C.Z., B.W.), School of Medicine, Technical University of Munich, Munich, Germany
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4
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Rocca MA, Margoni M, Battaglini M, Eshaghi A, Iliff J, Pagani E, Preziosa P, Storelli L, Taoka T, Valsasina P, Filippi M. Emerging Perspectives on MRI Application in Multiple Sclerosis: Moving from Pathophysiology to Clinical Practice. Radiology 2023; 307:e221512. [PMID: 37278626 PMCID: PMC10315528 DOI: 10.1148/radiol.221512] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 11/28/2022] [Accepted: 01/17/2023] [Indexed: 06/07/2023]
Abstract
MRI plays a central role in the diagnosis of multiple sclerosis (MS) and in the monitoring of disease course and treatment response. Advanced MRI techniques have shed light on MS biology and facilitated the search for neuroimaging markers that may be applicable in clinical practice. MRI has led to improvements in the accuracy of MS diagnosis and a deeper understanding of disease progression. This has also resulted in a plethora of potential MRI markers, the importance and validity of which remain to be proven. Here, five recent emerging perspectives arising from the use of MRI in MS, from pathophysiology to clinical application, will be discussed. These are the feasibility of noninvasive MRI-based approaches to measure glymphatic function and its impairment; T1-weighted to T2-weighted intensity ratio to quantify myelin content; classification of MS phenotypes based on their MRI features rather than on their clinical features; clinical relevance of gray matter atrophy versus white matter atrophy; and time-varying versus static resting-state functional connectivity in evaluating brain functional organization. These topics are critically discussed, which may guide future applications in the field.
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Affiliation(s)
- Maria Assunta Rocca
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Monica Margoni
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Marco Battaglini
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Arman Eshaghi
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Jeffrey Iliff
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Paolo Preziosa
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Loredana Storelli
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Toshiaki Taoka
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Paola Valsasina
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
| | - Massimo Filippi
- From the Neuroimaging Research Unit, Division of Neuroscience
(M.A.R., M.M., E.P., P.P., L.S., P.V., M.F.), Neurology Unit (M.A.R., M.M.,
P.P., M.F.), Neurorehabilitation Unit (M.F.), and Neurophysiology Service
(M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan,
Italy; Vita-Salute San Raffaele University, Milan, Italy (M.A.R., P.P., M.F.);
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena,
Italy (M.B.); Queen Square Multiple Sclerosis Centre, Department of
Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain
Sciences, University College London, London, UK (A.E.); Centre for Medical Image
Computing, Department of Computer Science, University College London, London, UK
(A.E.); VISN20 NW Mental Illness Research, Education, and Clinical Center, VA
Puget Sound Healthcare System, Seattle, Wash (J.I.); Department of Psychiatry
and Behavioral Sciences and Department of Neurology, University of Washington
School of Medicine, Seattle, Wash (J.I.); and Department of Innovative
Biomedical Visualization (iBMV), Department of Radiology, Nagoya University
Graduate School of Medicine, Aichi, Japan (T.T.)
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5
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Kuhlmann T, Moccia M, Coetzee T, Cohen JA, Correale J, Graves J, Marrie RA, Montalban X, Yong VW, Thompson AJ, Reich DS. Multiple sclerosis progression: time for a new mechanism-driven framework. Lancet Neurol 2023; 22:78-88. [PMID: 36410373 PMCID: PMC10463558 DOI: 10.1016/s1474-4422(22)00289-7] [Citation(s) in RCA: 178] [Impact Index Per Article: 178.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 05/29/2022] [Accepted: 06/29/2022] [Indexed: 11/20/2022]
Abstract
Traditionally, multiple sclerosis has been categorised by distinct clinical descriptors-relapsing-remitting, secondary progressive, and primary progressive-for patient care, research, and regulatory approval of medications. Accumulating evidence suggests that the clinical course of multiple sclerosis is better considered as a continuum, with contributions from concurrent pathophysiological processes that vary across individuals and over time. The apparent evolution to a progressive course reflects a partial shift from predominantly localised acute injury to widespread inflammation and neurodegeneration, coupled with failure of compensatory mechanisms, such as neuroplasticity and remyelination. Ageing increases neural susceptibility to injury and decreases resilience. These observations encourage a new consideration of the course of multiple sclerosis as a spectrum defined by the relative contributions of overlapping pathological and reparative or compensatory processes. New understanding of key mechanisms underlying progression and measures to quantify progressive pathology will potentially have important and beneficial implications for clinical care, treatment targets, and regulatory decision-making.
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Affiliation(s)
- Tanja Kuhlmann
- Institute of Neuropathology, University Hospital Münster, Münster, Germany; Neuroimmunology Unit, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
| | - Marcello Moccia
- Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences, Federico II University of Naples, Naples, Italy
| | - Timothy Coetzee
- National Multiple Sclerosis Society (USA), New York, NY, USA
| | - Jeffrey A Cohen
- Department of Neurology, Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jorge Correale
- Fleni, Department of Neurology, Buenos Aires, Argentina; Institute of Biological Chemistry and Biophysics (IQUIFIB), CONICET/UBA, Buenos Aires, Argentina
| | - Jennifer Graves
- Department of Neurosciences, University of California, San Diego, CA, USA
| | - Ruth Ann Marrie
- Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Xavier Montalban
- Multiple Sclerosis Centre of Catalonia and Department of Neurology-Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - V Wee Yong
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Alan J Thompson
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, NIHR University College London Hospitals Biomedical Research Centre, Faculty of Brain Sciences, University College London, London, UK
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
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6
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Rebsamen M, Friedli C, Radojewski P, Diem L, Chan A, Wiest R, Salmen A, Rummel C, Hoepner R. Multiple sclerosis as a model to investigate SARS-CoV-2 effect on brain atrophy. CNS Neurosci Ther 2022; 29:538-543. [PMID: 36479826 PMCID: PMC9873510 DOI: 10.1111/cns.14050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 11/20/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION Data on structural brain changes after infection with SARS-CoV-2 is sparse. We postulate multiple sclerosis as a model to study the effects of SARS-CoV-2 on brain atrophy due to the unique availability of longitudinal imaging data in this patient group, enabling assessment of intraindividual brain atrophy rates. METHODS Global and regional cortical gray matter volumes were derived from structural MRIs using FreeSurfer. A linear model was fitted to the measures of the matching pre-SARS-CoV-2 images with age as an explanatory variable. The residuals were used to determine whether the post-SARS-CoV-2 volumes differed significantly from the baseline. RESULTS Fourteen RRMS patients with a total of 113 longitudinal magnetic resonance images were retrospectively analyzed. We found no acceleration of brain atrophy after infection with SARS-CoV-2 for global gray matter volume (p = 0.17). However, on the regional level, parahippocampal gyri showed a tendency toward volume reduction (p = 0.0076), suggesting accelerated atrophy during or after infection. CONCLUSIONS Our results illustrate the opportunity of using longitudinal MRIs from existing MS registries to study brain changes associated with SARS-CoV-2 infections. We would like to address the global MS community with a call for action to use the available cohorts, reproduce the proposed analysis, and pool the results.
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Affiliation(s)
- Michael Rebsamen
- Support Center for Advanced Neuroimaging (SCAN)University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of BernBernSwitzerland,Graduate School for Cellular and Biomedical SciencesUniversity of BernBernSwitzerland
| | - Christoph Friedli
- Department of NeurologyInselspital, Bern University Hospital and University of BernBernSwitzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging (SCAN)University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of BernBernSwitzerland,Swiss Institute for Translational and Entrepreneurial Medicine, sitem‐inselBernSwitzerland
| | - Lara Diem
- Department of NeurologyInselspital, Bern University Hospital and University of BernBernSwitzerland
| | - Andrew Chan
- Department of NeurologyInselspital, Bern University Hospital and University of BernBernSwitzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN)University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of BernBernSwitzerland,Swiss Institute for Translational and Entrepreneurial Medicine, sitem‐inselBernSwitzerland
| | - Anke Salmen
- Department of NeurologyInselspital, Bern University Hospital and University of BernBernSwitzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN)University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of BernBernSwitzerland
| | - Robert Hoepner
- Department of NeurologyInselspital, Bern University Hospital and University of BernBernSwitzerland
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7
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Andelova M, Vodehnalova K, Krasensky J, Hardubejova E, Hrnciarova T, Srpova B, Uher T, Menkyova I, Stastna D, Friedova L, Motyl J, Lizrova Preiningerova J, Kubala Havrdova E, Maréchal B, Fartaria MJ, Kober T, Horakova D, Vaneckova M. Brainstem lesions are associated with diffuse spinal cord involvement in early multiple sclerosis. BMC Neurol 2022; 22:270. [PMID: 35854235 PMCID: PMC9297663 DOI: 10.1186/s12883-022-02778-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 06/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background Early infratentorial and focal spinal cord lesions on magnetic resonance imaging (MRI) are associated with a higher risk of long-term disability in patients with multiple sclerosis (MS). The role of diffuse spinal cord lesions remains less understood. The purpose of this study was to evaluate focal and especially diffuse spinal cord lesions in patients with early relapsing-remitting MS and their association with intracranial lesion topography, global and regional brain volume, and spinal cord volume. Methods We investigated 58 MS patients with short disease duration (< 5 years) from a large academic MS center and 58 healthy controls matched for age and sex. Brain, spinal cord, and intracranial lesion volumes were compared among patients with- and without diffuse spinal cord lesions and controls. Binary logistic regression models were used to analyse the association between the volume and topology of intracranial lesions and the presence of focal and diffuse spinal cord lesions. Results We found spinal cord involvement in 75% of the patients (43/58), including diffuse changes in 41.4% (24/58). Patients with diffuse spinal cord changes exhibited higher volumes of brainstem lesion volume (p = 0.008). The presence of at least one brainstem lesion was associated with a higher probability of the presence of diffuse spinal cord lesions (odds ratio 47.1; 95% confidence interval 6.9–321.6 p < 0.001) as opposed to focal spinal cord lesions (odds ratio 0.22; p = 0.320). Patients with diffuse spinal cord lesions had a lower thalamus volume compared to patients without diffuse spinal cord lesions (p = 0.007) or healthy controls (p = 0.002). Conclusions Diffuse spinal cord lesions are associated with the presence of brainstem lesions and with a lower volume of the thalamus. This association was not found in patients with focal spinal cord lesions. If confirmed, thalamic atrophy in patients with diffuse lesions could increase our knowledge on the worse prognosis in patients with infratentorial and SC lesions. Supplementary Information The online version contains supplementary material available at 10.1186/s12883-022-02778-z.
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Affiliation(s)
- Michaela Andelova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Katerinska 30, Praha 2, Prague, Czech Republic.
| | - Karolina Vodehnalova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Katerinska 30, Praha 2, Prague, Czech Republic
| | - Jan Krasensky
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Eliska Hardubejova
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Tereza Hrnciarova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Katerinska 30, Praha 2, Prague, Czech Republic
| | - Barbora Srpova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Katerinska 30, Praha 2, Prague, Czech Republic
| | - Tomas Uher
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Katerinska 30, Praha 2, Prague, Czech Republic
| | - Ingrid Menkyova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Katerinska 30, Praha 2, Prague, Czech Republic.,2nd Department of Neurology, Faculty of Medicine, Comenius University, Bratislava, Slovakia
| | - Dominika Stastna
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Katerinska 30, Praha 2, Prague, Czech Republic
| | - Lucie Friedova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Katerinska 30, Praha 2, Prague, Czech Republic
| | - Jiri Motyl
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Katerinska 30, Praha 2, Prague, Czech Republic
| | - Jana Lizrova Preiningerova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Katerinska 30, Praha 2, Prague, Czech Republic
| | - Eva Kubala Havrdova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Katerinska 30, Praha 2, Prague, Czech Republic
| | - Bénédicte Maréchal
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mário João Fartaria
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Dana Horakova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Katerinska 30, Praha 2, Prague, Czech Republic
| | - Manuela Vaneckova
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
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8
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Lie IA, Kaçar S, Wesnes K, Brouwer I, Kvistad SS, Wergeland S, Holmøy T, Midgard R, Bru A, Edland A, Eikeland R, Gosal S, Harbo HF, Kleveland G, Sørenes YS, Øksendal N, Varhaug KN, Vedeler CA, Barkhof F, Teunissen CE, Bø L, Torkildsen Ø, Myhr KM, Vrenken H. Serum neurofilament as a predictor of 10-year grey matter atrophy and clinical disability in multiple sclerosis: a longitudinal study. J Neurol Neurosurg Psychiatry 2022; 93:jnnp-2021-328568. [PMID: 35649699 PMCID: PMC9304101 DOI: 10.1136/jnnp-2021-328568] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 05/18/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND The predictive value of serum neurofilament light chain (sNfL) on long-term prognosis in multiple sclerosis (MS) is still unclear. OBJECTIVE Investigate the relation between sNfL levels over a 2-year period in patients with relapsing-remitting MS, and clinical disability and grey matter (GM) atrophy after 10 years. METHODS 85 patients, originally enrolled in a multicentre, randomised trial of ω-3 fatty acids, participated in a 10-year follow-up visit. sNfL levels were measured by Simoa quarterly until month 12, and then at month 24. The appearance of new gadolinium-enhancing (Gd+) lesions was assessed monthly between baseline and month 9, and then at months 12 and 24. At the 10-year follow-up visit, brain atrophy measures were obtained using FreeSurfer. RESULTS Higher mean sNfL levels during early periods of active inflammation (Gd+ lesions present or recently present) predicted lower total (β=-0.399, p=0.040) and deep (β=-0.556, p=0.010) GM volume, lower mean cortical thickness (β=-0.581, p=0.010) and higher T2 lesion count (β=0.498, p=0.018). Of the clinical outcomes, higher inflammatory sNfL levels were associated with higher disability measured by the dominant hand Nine-Hole Peg Test (β=0.593, p=0.004). Mean sNfL levels during periods of remission (no Gd+ lesions present or recently present) did not predict GM atrophy or disability progression. CONCLUSION Higher sNfL levels during periods of active inflammation predicted more GM atrophy and specific aspects of clinical disability 10 years later. The findings suggest that subsequent long-term GM atrophy is mainly due to neuroaxonal degradation within new lesions.
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Affiliation(s)
- Ingrid Anne Lie
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Sezgi Kaçar
- Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, The Netherlands
| | - Kristin Wesnes
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway
- Department of Neurology, St. Olav's University Hospital, Trondheim, Norway
| | - Iman Brouwer
- Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, The Netherlands
| | - Silje S Kvistad
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway
- Department of Immunology and Transfusion Medicine, Haukeland University Hospital, Bergen, Norway
| | - Stig Wergeland
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway
- Norwegian Multiple Sclerosis Registry and Biobank, Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Trygve Holmøy
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Akershus University Hospital, Lorenskog, Norway
| | - Rune Midgard
- Department of Neurology, Molde Hospital, Molde, Norway
| | - Alla Bru
- Department of Neurology, Stavanger University Hospital, Stavanger, Norway
| | - Astrid Edland
- Department of Neurology, Vestre Viken Hospital Trust, Drammen, Norway
| | - Randi Eikeland
- Department of Research and Education, Sørlandet Hospital Trust, Kristiansand, Norway
- Faculty of Health and Sport Science, University of Agder, Grimstad, Norway
| | - Sonia Gosal
- Department of Neurology, Østfold Hospital Kalnes, Grålum, Norway
| | - Hanne F Harbo
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Grethe Kleveland
- Department of Neurology, Innlandet Hospital Trust, Lillehammer, Norway
| | | | - Nina Øksendal
- Department of Neurology, Nordland Hospital Trust, Bodø, Norway
| | - Kristin N Varhaug
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Christian A Vedeler
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, The Netherlands
- Institutes of Neurology and Healthcare Engineering, UCL, London, UK
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Lars Bø
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Øivind Torkildsen
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Kjell-Morten Myhr
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, The Netherlands
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9
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Bonacchi R, Meani A, Pagani E, Marchesi O, Filippi M, Rocca MA. The role of cerebellar damage in explaining disability and cognition in multiple sclerosis phenotypes: a multiparametric MRI study. J Neurol 2022; 269:3841-3857. [PMID: 35230471 DOI: 10.1007/s00415-022-11021-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 02/07/2022] [Accepted: 02/09/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Cerebellar involvement is not comprehensively studied from an MRI point of view in multiple sclerosis (MS). We aimed to quantify cerebellar damage and identify predictors of physical disability and cognitive dysfunction in MS patients, and to characterize patients with cerebellar disability. METHODS In this prospective study, 164 (89 relapsing-remitting and 75 progressive) MS patients and 53 healthy controls were enrolled. Subjects underwent 3T MRI with sequences for assessing lesions and atrophy in cerebellum, supratentorial brain, brainstem and cervical cord. Cerebellar peduncle diffusion-tensor metrics were also derived. Random forest models identified MRI predictors of Expanded Disability Status Scale (EDSS) score and cognition z-score. Hierarchical clustering was applied on MRI metrics in patients with cerebellar disability. RESULTS In MS patients, predictors of higher EDSS score (out-of-bag-R2 = 0.83) were: lower cord grey matter (GM) and global areas, brain volume, GM volume (GMV), cortical GMV, cerebellum lobules I-IV and vermis GMV; and higher cord GM and brainstem lesion volume (LV). Predictors of lower cognition z-score (out-of-bag-R2 = 0.25) were: higher supratentorial and superior cerebellar peduncle LV; and lower brain, thalamus and basal ganglia volumes, GMV, cerebellum lobule VIIIb and Crus II GMV. In patients with cerebellar disability, we found three clusters with homogenous MRI metrics: patients with high brain lesion volumes (including cerebellar peduncles), those with marked cerebellum GM atrophy and patients with severe cord damage. CONCLUSIONS Damage to cerebellum GM and connecting structures has a relevant role in explaining cognitive dysfunction and physical disability in MS. Data-driven MRI clustering might improve our knowledge of MRI-clinical correlations.
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Affiliation(s)
- Raffaello Bonacchi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alessandro Meani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Olga Marchesi
- 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
| | - 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.
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10
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Tsagkas C, Geiter E, Gaetano L, Naegelin Y, Amann M, Parmar K, Papadopoulou A, Wuerfel J, Kappos L, Sprenger T, Granziera C, Mallar Chakravarty M, Magon S. Longitudinal changes of deep gray matter shape in multiple sclerosis. NEUROIMAGE: CLINICAL 2022; 35:103137. [PMID: 36002960 PMCID: PMC9421532 DOI: 10.1016/j.nicl.2022.103137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/28/2022] [Accepted: 07/27/2022] [Indexed: 01/18/2023] Open
Abstract
Specific shape changes over time occur at the bilateral ventrolateral pallidal and the left posterolateral striatal surface in relapse-onset multiple sclerosis. These shape changes over time were not associated with disease progression. The average shape of deep gray matter structures was associated with the patients’ average disease severity as well as white matter lesion-load.
Objective This study aimed to investigate longitudinal deep gray matter (DGM) shape changes and their relationship with measures of clinical disability and white matter lesion-load in a large multiple sclerosis (MS) cohort. Materials and Methods A total of 230 MS patients (179 relapsing-remitting, 51 secondary progressive; baseline age 44.5 ± 11.3 years; baseline disease duration 12.99 ± 9.18) underwent annual clinical and MRI examinations over a maximum of 6 years (mean 4.32 ± 2.07 years). The DGM structures were segmented on the T1-weighted images using the “Multiple Automatically Generated Templates” brain algorithm. White matter lesion-load was measured on T2-weighted MRI. Clinical examination included the expanded disability status scale, 9-hole peg test, timed 25-foot walk test, symbol digit modalities test and paced auditory serial addition test. Vertex‐wise longitudinal analysis of DGM shapes was performed using linear mixed effect models and evaluated the association between average/temporal changes of DGM shapes with average/temporal changes of clinical measurements, respectively. Results A significant shrinkage over time of the bilateral ventrolateral pallidal and the left posterolateral striatal surface was observed, whereas no significant shape changes over time were observed at the bilateral thalamic and right striatal surfaces. Higher average lesion-load was associated with an average inwards displacement of the global thalamic surface with relative sparing on the posterior side (slight left-side predominance), the antero-dorso-lateral striatal surfaces bilaterally (symmetric on both sides) and the antero-lateral pallidal surface (left-side predominance). There was also an association between shrinkage of large lateral DGM surfaces with higher clinical motor and cognitive disease severity. However, there was no correlation between any DGM shape changes over time and measurements of clinical progression or lesion-load changes over time. Conclusions This study showed specific shape change of DGM structures occurring over time in relapse-onset MS. Although these shape changes over time were not associated with disease progression, we demonstrated a link between DGM shape and the patients’ average disease severity as well as white matter lesion-load.
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11
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Tsagkas C, Parmar K, Pezold S, Barro C, Chakravarty MM, Gaetano L, Naegelin Y, Amann M, Papadopoulou A, Wuerfel J, Kappos L, Kuhle J, Sprenger T, Granziera C, Magon S. Classification of multiple sclerosis based on patterns of CNS regional atrophy covariance. Hum Brain Mapp 2021; 42:2399-2415. [PMID: 33624390 PMCID: PMC8090784 DOI: 10.1002/hbm.25375] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 02/04/2021] [Accepted: 02/07/2021] [Indexed: 01/18/2023] Open
Abstract
There is evidence that multiple sclerosis (MS) pathology leads to distinct patterns of volume loss over time (VLOT) in different central nervous system (CNS) structures. We aimed to use such patterns to identify patient subgroups. MS patients of all classical disease phenotypes underwent annual clinical, blood, and MRI examinations over 6 years. Spinal, striatal, pallidal, thalamic, cortical, white matter, and T2‐weighted lesion volumes as well as serum neurofilament light chain (sNfL) were quantified. CNS VLOT patterns were identified using principal component analysis and patients were classified using hierarchical cluster analysis. 225 MS patients were classified into four distinct Groups A, B, C, and D including 14, 59, 141, and 11 patients, respectively). These groups did not differ in baseline demographics, disease duration, disease phenotype distribution, and lesion‐load expansion. Interestingly, Group A showed pronounced spinothalamic VLOT, Group B marked pallidal VLOT, Group C small between‐structure VLOT differences, and Group D myelocortical volume increase and pronounced white matter VLOT. Neurologic deficits were more severe and progressed faster in Group A that also had higher mean sNfL levels than all other groups. Group B experienced more frequent relapses than Group C. In conclusion, there are distinct patterns of VLOT across the CNS in MS patients, which do not overlap with clinical MS subtypes and are independent of disease duration and lesion‐load but are partially associated to sNfL levels, relapse rates, and clinical worsening. Our findings support the need for a more biologic classification of MS subtypes including volumetric and body‐fluid markers.
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Affiliation(s)
- Charidimos Tsagkas
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, 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 AG, Basel, Switzerland
| | - Katrin Parmar
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Simon Pezold
- Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Christian Barro
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Mallar M Chakravarty
- Department of Psychiatry, McGill University, Montreal, QC, Canada.,Cerebral Imaging Centre-Douglas Mental Health University Institute, Verdun, QC, Canada.,Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | | | - Yvonne Naegelin
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Michael Amann
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Medical Image Analysis Center AG, Basel, Switzerland.,Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Athina Papadopoulou
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Jens Wuerfel
- Medical Image Analysis Center AG, Basel, Switzerland.,Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland.,NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Till Sprenger
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Department of Neurology, DKD HELIOS Klinik Wiesbaden, Germany
| | - Cristina Granziera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Stefano Magon
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
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