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Kakucs Z, Illes Z, Hayden Z, Berki T, Orsi G. Osteopontin predicts late-time salience network-related functional connectivity in multiple sclerosis. PLoS One 2024; 19:e0309563. [PMID: 39208261 PMCID: PMC11361605 DOI: 10.1371/journal.pone.0309563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 08/13/2024] [Indexed: 09/04/2024] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely utilized to investigate plasticity mechanisms and functional reorganization in multiple sclerosis (MS). Among many resting state (RS) networks, a significant role is played by the salience network (SN, ventral attention network). Previous reports have demonstrated the involvement of osteopontin (OPN) in the pathogenesis of MS, which acts as a proinflammatory cytokine ultimately leading to neurodegeneration. Concentration of serum OPN was related to MRI findings 10.22±2.84 years later in 44 patients with MS. Local and interhemispheric correlations (LCOR, IHC), ROI-to-ROI and seed-based connectivity analyses were performed using serum OPN levels as independent variable along with age and gender as nuisance variables. We found significant associations between OPN levels and local correlation in right and left clusters encompassing the central opercular- and insular cortices (p-FDR = 0.0018 and p-FDR = 0.0205, respectively). Moreover, a significant association was identified between OPN concentration and interhemispheric correlation between central opercular- and insular cortices (p-FDR = 0.00015). Significant positive associations were found between OPN concentration and functional connectivity (FC) within the SN (FC strength between the anterior insula ventral division and 3 other insular regions, F(2,13) = 7.84, p-FDR = 0.0117). Seed-based connectivity analysis using the seven nodes of the SN resulted in several positive and inverse associations with OPN level. Serum OPN level may predict FC alterations within the SN in 10 years.
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Affiliation(s)
- Zsofia Kakucs
- Department of Medical Imaging, Medical School, University of Pecs, Pecs, Hungary
- Department of Radiology and Medical Imaging, Mures County Emergency Clinical Hospital of Targu Mures, Targu Mures, Romania
| | - Zsolt Illes
- Department of Neurology, Medical School, University of Pecs, Pecs, Hungary
- Department of Neurology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Zsofia Hayden
- Department of Neurology, Medical School, University of Pecs, Pecs, Hungary
| | - Timea Berki
- Department of Immunology and Biotechnology, Medical School, University of Pecs, Pecs, Hungary
| | - Gergely Orsi
- Department of Neurology, Medical School, University of Pecs, Pecs, Hungary
- HUN-REN-PTE Clinical Neuroscience MR Research Group, Hungarian Research Network, Pecs, Hungary
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2
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Antonopoulos G, More S, Raimondo F, Eickhoff SB, Hoffstaedter F, Patil KR. A systematic comparison of VBM pipelines and their application to age prediction. Neuroimage 2023; 279:120292. [PMID: 37572766 PMCID: PMC10529438 DOI: 10.1016/j.neuroimage.2023.120292] [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: 02/05/2023] [Revised: 06/23/2023] [Accepted: 07/21/2023] [Indexed: 08/14/2023] Open
Abstract
Voxel-based morphometry (VBM) analysis is commonly used for localized quantification of gray matter volume (GMV). Several alternatives exist to implement a VBM pipeline. However, how these alternatives compare and their utility in applications, such as the estimation of aging effects, remain largely unclear. This leaves researchers wondering which VBM pipeline they should use for their project. In this study, we took a user-centric perspective and systematically compared five VBM pipelines, together with registration to either a general or a study-specific template, utilizing three large datasets (n>500 each). Considering the known effect of aging on GMV, we first compared the pipelines in their ability of individual-level age prediction and found markedly varied results. To examine whether these results arise from systematic differences between the pipelines, we classified them based on their GMVs, resulting in near-perfect accuracy. To gain deeper insights, we examined the impact of different VBM steps using the region-wise similarity between pipelines. The results revealed marked differences, largely driven by segmentation and registration steps. We observed large variability in subject-identification accuracies, highlighting the interpipeline differences in individual-level quantification of GMV. As a biologically meaningful criterion we correlated regional GMV with age. The results were in line with the age-prediction analysis, and two pipelines, CAT and the combination of fMRIPrep for tissue characterization with FSL for registration, reflected age information better.
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Affiliation(s)
- Georgios Antonopoulos
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Shammi More
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Federico Raimondo
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
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3
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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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4
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Gentile G, Mattiesing RM, Brouwer I, van Schijndel RA, Uitdehaag BMJ, Twisk JWR, Kappos L, Freedman MS, Comi G, Jack D, Barkhof F, De Stefano N, Vrenken H, Battaglini M. The spatio-temporal relationship between concurrent lesion and brain atrophy changes in early multiple sclerosis: A post-hoc analysis of the REFLEXION study. Neuroimage Clin 2023; 38:103397. [PMID: 37086648 PMCID: PMC10300577 DOI: 10.1016/j.nicl.2023.103397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/30/2023] [Accepted: 04/02/2023] [Indexed: 04/08/2023]
Abstract
BACKGROUND White matter (WM) lesions and brain atrophy are present early in multiple sclerosis (MS). However, their spatio-temporal relationship remains unclear. METHODS Yearly magnetic resonance images were analysed in 387 patients with a first clinical demyelinating event (FCDE) from the 5-year REFLEXION study. Patients received early (from baseline; N = 258; ET) or delayed treatment (from month-24; N = 129; DT) with subcutaneous interferon beta-1a. FSL-SIENA/VIENA were used to provide yearly percentage volume change of brain (PBVC) and ventricles (PVVC). Yearly total lesion volume change (TLVC) was determined by a semi-automated method. Using linear mixed models and voxel-wise analyses, we firstly investigated the overall relationship between TLVC and PBVC and between TLVC and PVVC in the same follow-up period. Analyses were then separately performed for: the untreated period of DT patients (first two years), the first year of treatment (year 1 for ET and year 3 for DT), and a period where patients had received at least 1 year of treatment (stable treatment; ET: years 2, 3, 4, and 5; DT: years 4 and 5). RESULTS Whole brain: across the whole study period, lower TLVC was related to faster atrophy (PBVC: B = 0.046, SE = 0.013, p < 0.001; PVVC: B = -0.466, SE = 0.118, p < 0.001). Within the untreated period of DT patients, lower TLVC was related to faster atrophy (PBVC: B = 0.072, SE = 0.029, p = 0.013; PVVC: B = -0.917, SE = 0.306, p = 0.003). A similar relationship was found within the first year of treatment of ET patients (PBVC: B = 0.081, SE = 0.027, p = 0.003; PVVC: B = -1.08, SE = 0.284, p < 0.001), consistent with resolving oedema and pseudo-atrophy. Voxel-wise: overall, higher TLVC was related to faster ventricular enlargement. Lower TLVC was related to faster widespread atrophy in year 1 in both ET (first year of treatment) and DT (untreated) patients. In the second untreated year of DT patients and within the stable treatment period of ET patients (year 4), faster periventricular and occipital lobe atrophy was associated with higher TLVC. CONCLUSIONS WM lesion changes and atrophy occurred simultaneously in early MS. Spatio-temporal correspondence of these two processes involved mostly the periventricular area. Within the first year of the study, in both treatment groups, faster atrophy was linked to lower lesion volume changes, consistent with higher shrinking and disappearing lesion activity. This might reflect the pseudo-atrophy phenomenon that is probably related to the therapy driven (only in ET patients, as they received treatment from baseline) and "natural" (both ET and DT patients entered the study after a FCDE) resolution of oedema. In an untreated period and later on during stable treatment, (real) atrophy was related to higher lesion volume changes, consistent with increased new and enlarging lesion activity.
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Affiliation(s)
- Giordano Gentile
- Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy; Siena Imaging SRL, 53100 Siena, Italy.
| | - Rozemarijn M Mattiesing
- MS Center Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC location VUmc, De Boelelaan 1118, 1081 HZ Amsterdam, the Netherlands
| | - Iman Brouwer
- MS Center Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC location VUmc, De Boelelaan 1118, 1081 HZ Amsterdam, the Netherlands
| | - Ronald A van Schijndel
- MS Center Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC location VUmc, De Boelelaan 1118, 1081 HZ Amsterdam, the Netherlands
| | - Bernard M J Uitdehaag
- MS Center Amsterdam, Neurology, Amsterdam Neuroscience, Amsterdam UMC location VUmc, De Boelelaan 1118, 1081 HZ Amsterdam, the Netherlands
| | - Jos W R Twisk
- Epidemiology and Data Science, Amsterdam UMC location VUmc, De Boelelaan 1118, 1081 HZ Amsterdam, the Netherlands
| | - Ludwig Kappos
- Research Center for Clinical Neuroimmunology, and Neuroscience Basel (RC2NB), University Hospital Basel, CH-4031 Basel, Switzerland; Neurology Departments of Head, Spine and Neuromedicine, Biomedical Engineering and Clinical Research, University of Basel, Basel, Switzerland
| | - Mark S Freedman
- Department of Medicine, University of Ottawa, Ottawa ON, K1N 6N5, Ontario, Canada; Ottawa Hospital Research Institute, Ottawa ON, K1H 8L6, Ontario, Canada
| | - Giancarlo Comi
- Università Vita Salute San Raffaele, Casa di Cura del Policlinico, 20132 Milan, Italy
| | - Dominic Jack
- Merck Serono Ltd, Feltham, TW14 8HD, UK, an affiliate of Merck KGaA
| | - Frederik Barkhof
- MS Center Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC location VUmc, De Boelelaan 1118, 1081 HZ Amsterdam, the Netherlands; UCL Institutes of Neurology and Healthcare Engineering, London, WC1E 6BT, UK
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy
| | - Hugo Vrenken
- MS Center Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC location VUmc, De Boelelaan 1118, 1081 HZ Amsterdam, the Netherlands
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy; Siena Imaging SRL, 53100 Siena, Italy
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5
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Vrenken H, Battaglini M, de Vos ML, Nagtegaal GJ, Teixeira BCA, Seitzinger A, Jack D, Sormani MP, Uitdehaag BMJ, Versteeg A, Comi G, Kappos L, De Stefano N, Barkhof F. Temporal evolution of new T1-weighted hypo-intense lesions and central brain atrophy in patients with a first clinical demyelinating event treated with subcutaneous interferon β-1a. J Neurol 2023; 270:2271-2282. [PMID: 36723685 PMCID: PMC10025187 DOI: 10.1007/s00415-022-11554-5] [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: 12/09/2021] [Revised: 12/23/2022] [Accepted: 12/27/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Evaluate the effect of subcutaneous interferon β-1a (sc IFN β-1a) versus placebo on the evolution of T1-weighted MRI lesions and central brain atrophy in in patients with a first clinical demyelinating event (FCDE). METHODS Post hoc analysis of baseline-to-24 month MRI data from patients with an FCDE who received sc IFN β-1a 44 μg once- (qw) or three-times-weekly (tiw), or placebo, in REFLEX. Patients were grouped according to treatment regimen or conversion to clinically definite MS (CDMS) status. The intensity of new lesions on unenhanced T1-weighted images was classified as T1 iso- or hypo-intense (black holes) and percentage ventricular volume change (PVVC) was assessed throughout the study. RESULTS In patients not converting to CDMS, sc IFN β-1a tiw or qw, versus placebo, reduced the overall number of new lesions (P < 0.001 and P = 0.005) and new T1 iso-intense lesions (P < 0.001 and P = 0.002) after 24 months; only sc IFN β-1a tiw was associated with fewer T1 hypo-intense lesions versus placebo (P < 0.001). PVVC findings in patients treated with sc IFN β-1a suggested pseudo-atrophy that was ~ fivefold greater versus placebo in the first year of treatment (placebo 1.11%; qw 4.28%; tiw 6.76%; P < 001); similar findings were apparent for non-converting patients. CONCLUSIONS In patients with an FCDE, treatment with sc IFN β-1a tiw for 24 months reduced the number of new lesions evolving into black holes.
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Affiliation(s)
- H Vrenken
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.
| | - M Battaglini
- Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy
| | - M L de Vos
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - G J Nagtegaal
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - B C A Teixeira
- Department of Radiology, Federal University of Paraná, Curitiba, Paraná, Brazil
- Neuroradiology Department, Neurological Institute of Curitiba (INC/CETAC), Curitiba, Paraná, Brazil
| | - A Seitzinger
- Global Biostatistics, Merck Healthcare KGaA, Darmstadt, Germany
| | - D Jack
- Global Medical Affairs, Merck Serono Ltd, (an affiliate of Merck KGaA), Feltham, UK
| | - M P Sormani
- Department of Health Sciences, University of Genoa and Ospedale Policlinico San Martino IRCCS, Genoa, Italy
| | - B M J Uitdehaag
- Department of Neurology, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - A Versteeg
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Biomedical Imaging Group Rotterdam, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - G Comi
- Università Vita-Salute San Raffaele, Casa di Cura Privata del Policlinico, Milan, Italy
| | - L Kappos
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) and Neurology Departments of Head, Spine and Neuromedicine, Biomedical Engineering and Clinical Research, University Hospital, University of Basel, Basel, Switzerland
| | - N De Stefano
- Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy
| | - F Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
- UCL Institutes of Neurology and Healthcare Engineering, London, UK
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6
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Conti A, Treaba CA, Mehndiratta A, Barletta VT, Mainero C, Toschi N. An Interpretable Machine Learning Model to Predict Cortical Atrophy in Multiple Sclerosis. Brain Sci 2023; 13:brainsci13020198. [PMID: 36831740 PMCID: PMC9954500 DOI: 10.3390/brainsci13020198] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/14/2023] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
To date, the relationship between central hallmarks of multiple sclerosis (MS), such as white matter (WM)/cortical demyelinated lesions and cortical gray matter atrophy, remains unclear. We investigated the interplay between cortical atrophy and individual lesion-type patterns that have recently emerged as new radiological markers of MS disease progression. We employed a machine learning model to predict mean cortical thinning in whole-brain and single hemispheres in 150 cortical regions using demographic and lesion-related characteristics, evaluated via an ultrahigh field (7 Tesla) MRI. We found that (i) volume and rimless (i.e., without a "rim" of iron-laden immune cells) WM lesions, patient age, and volume of intracortical lesions have the most predictive power; (ii) WM lesions are more important for prediction when their load is small, while cortical lesion load becomes more important as it increases; (iii) WM lesions play a greater role in the progression of atrophy during the latest stages of the disease. Our results highlight the intricacy of MS pathology across the whole brain. In turn, this calls for multivariate statistical analyses and mechanistic modeling techniques to understand the etiopathogenesis of lesions.
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Affiliation(s)
- Allegra Conti
- Department of Biomedicine and Prevention, University of Rome ‘Tor Vergata’, Via Montpellier 1, 00133 Rome, Italy
- Correspondence: ; Tel.: +39-06-72596393
| | - Constantina Andrada Treaba
- Massachusetts General Hospital, Boston, MA 02114, USA
- A. A. Martinos Center for Biomedical Imaging, Boston, MA 02129, USA
| | - Ambica Mehndiratta
- Massachusetts General Hospital, Boston, MA 02114, USA
- A. A. Martinos Center for Biomedical Imaging, Boston, MA 02129, USA
| | - Valeria Teresa Barletta
- Massachusetts General Hospital, Boston, MA 02114, USA
- A. A. Martinos Center for Biomedical Imaging, Boston, MA 02129, USA
| | - Caterina Mainero
- Massachusetts General Hospital, Boston, MA 02114, USA
- A. A. Martinos Center for Biomedical Imaging, Boston, MA 02129, USA
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome ‘Tor Vergata’, Via Montpellier 1, 00133 Rome, Italy
- Massachusetts General Hospital, Boston, MA 02114, USA
- A. A. Martinos Center for Biomedical Imaging, Boston, MA 02129, USA
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7
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De Stefano N, Battaglini M, Pareto D, Cortese R, Zhang J, Oesingmann N, Prados F, Rocca MA, Valsasina P, Vrenken H, Gandini Wheeler-Kingshott CAM, Filippi M, Barkhof F, Rovira À. MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies. Neuroimage Clin 2022; 34:102972. [PMID: 35245791 PMCID: PMC8892169 DOI: 10.1016/j.nicl.2022.102972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 11/24/2022]
Abstract
Sharing data from cooperative studies is essential to develop new biomarkers in MS. Differences in MRI acquisition, analysis, storage represent a substantial constraint. We review the state of the art and developments in the harmonization of MRI. We provide recommendations to harmonize large MRI datasets in the MS field.
There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.
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Affiliation(s)
- Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Rosa Cortese
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Jian Zhang
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | | | - Ferran Prados
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Hugo Vrenken
- Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Brain MRI 3T Research Center, C. Mondino National Neurological Institute, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy; Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Frederik Barkhof
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
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8
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Ahmed SM, Fransen NL, Touil H, Michailidou I, Huitinga I, Gommerman JL, Bar-Or A, Ramaglia V. Accumulation of meningeal lymphocytes correlates with white matter lesion activity in progressive multiple sclerosis. JCI Insight 2022; 7:151683. [PMID: 35104246 PMCID: PMC8983127 DOI: 10.1172/jci.insight.151683] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 01/28/2022] [Indexed: 11/17/2022] Open
Abstract
Subpial cortical demyelination is an important component of multiple sclerosis (MS) pathology contributing to disease progression, yet mechanism(s) underlying its development remain unclear. Compartmentalized inflammation involving the meninges may drive this type of injury. Given recent findings identifying substantial white matter (WM) lesion activity in patients with progressive MS, elucidating whether and how WM lesional activity relates to meningeal inflammation and subpial cortical injury is of interest. Using postmortem FFPE tissue blocks (range, 5-72 blocks; median, 30 blocks) for each of 27 patients with progressive MS, we assessed the relationship between meningeal inflammation, the extent of subpial cortical demyelination, and the state of subcortical WM lesional activity. Meningeal accumulations of T cells and B cells, but not myeloid cells, were spatially adjacent to subpial cortical lesions, and greater immune cell accumulation was associated with larger subpial lesion areas. Patients with a higher extent of meningeal inflammation harbored a greater proportion of active and mixed active/inactive WM lesions and an overall lower proportion of inactive and remyelinated WM lesions. Our findings support the involvement of meningeal lymphocytes in subpial cortical injury and point to a potential link between inflammatory subpial cortical demyelination and pathological mechanisms occurring in the subcortical WM.
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Affiliation(s)
- Shanzeh M. Ahmed
- Department of Immunology, University of Toronto, Toronto, Ontario, Canada
| | - Nina L. Fransen
- Department of Neuroimmunology, Netherlands Institute for Neuroscience, Meibergdreef, Amsterdam, Netherlands.,Department of Pathology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Hanane Touil
- Department of Neurology and Center for Neuroinflammation and Neurotherapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Iliana Michailidou
- Department of Clinical Genetics, Leiden University Medical Center, Einthovenweg, Leiden, Netherlands
| | - Inge Huitinga
- Department of Neuroimmunology, Netherlands Institute for Neuroscience, Meibergdreef, Amsterdam, Netherlands.,Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | | | - Amit Bar-Or
- Department of Neurology and Center for Neuroinflammation and Neurotherapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Valeria Ramaglia
- Department of Immunology, University of Toronto, Toronto, Ontario, Canada
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9
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Age-related changes in multiple sclerosis and experimental autoimmune encephalomyelitis. Semin Immunol 2022; 59:101631. [PMID: 35752572 DOI: 10.1016/j.smim.2022.101631] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 06/03/2022] [Accepted: 06/13/2022] [Indexed: 01/15/2023]
Abstract
A better understanding of the pathological mechanisms that drive neurodegeneration in people living with multiple sclerosis (MS) is needed to design effective therapies to treat and/or prevent disease progression. We propose that CNS-intrinsic inflammation and re-modelling of the sub-arachnoid space of the leptomeninges sets the stage for neurodegeneration from the earliest stages of MS. While neurodegenerative processes are clinically silent early in disease, ageing results in neurodegenerative changes that become clinically manifest as progressive disability. Here we review pathological correlates of MS disease progression, highlight emerging mouse models that mimic key progressive changes in MS, and provide new perspectives on therapeutic approaches to protect against MS-associated neurodegeneration.
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10
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Treaba CA, Herranz E, Barletta VT, Mehndiratta A, Ouellette R, Sloane JA, Klawiter EC, Kinkel RP, Mainero C. The relevance of multiple sclerosis cortical lesions on cortical thinning and their clinical impact as assessed by 7.0-T MRI. J Neurol 2021; 268:2473-2481. [PMID: 33523256 DOI: 10.1007/s00415-021-10400-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/04/2021] [Accepted: 01/05/2021] [Indexed: 10/22/2022]
Abstract
OBJECTIVE This study aimed to investigate at 7.0-T MRI a) the role of multiple sclerosis (MS) cortical lesions in cortical tissue loss b) their relation to neurological disability. METHODS In 76 relapsing remitting and 26 secondary progressive MS patients (N = 102) and 56 healthy subjects 7.0-T T2*-weighted images were acquired for lesion segmentation; 3.0-T T1-weighted structural scans for cortical surface reconstruction/cortical thickness estimation. Patients were dichotomized based on the median cortical lesion volume in low and high cortical lesion load groups that differed by age, MS phenotype and degree of neurological disability. Group differences in cortical thickness were tested on reconstructed cortical surface. Patients were evaluated clinically by means of the Expanded Disability Status Scale (EDSS). RESULTS Cortical lesions were detected in 96% of patients. White matter lesion load was greater in the high than in the low cortical lesion load MS group (p = 0.01). Both MS groups disclosed clusters (prevalently parietal) of cortical thinning relative to healthy subjects, though these regions did not show the highest cortical lesion density, which predominantly involved frontal regions. Cortical thickness decreased on average by 0.37 mm, (p = 0.002) in MS patients for each unit standard deviation change in white matter lesion volume. The odds of having a higher EDSS were associated with cortical lesion volume (1.78, p = 0.01) and disease duration (1.15, p < 0.001). CONCLUSION Cortical thinning in MS is not directly related to cortical lesion load but rather with white matter lesion volume. Neurological disability in MS is better explained by cortical lesion volume assessment.
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Affiliation(s)
- Constantina A Treaba
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Elena Herranz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Valeria T Barletta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Ambica Mehndiratta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Russell Ouellette
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Jacob A Sloane
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Eric C Klawiter
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Caterina Mainero
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA. .,Harvard Medical School, Boston, MA, USA.
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11
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Bizzo BC, Arruda-Sanchez T, Tobyne SM, Bireley JD, Lev MH, Gasparetto EL, Klawiter EC. Anterior Insular Resting-State Functional Connectivity is Related to Cognitive Reserve in Multiple Sclerosis. J Neuroimaging 2020; 31:98-102. [PMID: 32857919 DOI: 10.1111/jon.12779] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 08/07/2020] [Accepted: 08/15/2020] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND AND PURPOSE Cognitive dysfunction is common in multiple sclerosis (MS). The dorsal anterior insula (dAI) is a key hub of the salience network (SN) orchestrating access to critical cognitive brain regions. The aim of this study was to assess whole-brain dAI intrinsic functional connectivity (iFC) using resting-state functional MRI (rs-fMRI) in people with MS and healthy controls (HC) and test the relationship between cognitive reserve (CR) and dAI iFC in people with MS. METHODS We studied 28 people with relapsing-remitting MS and 28 HC. CR index was quantified by combining premorbid IQ, leisure activities, and education level. For whole-brain iFC analyses, the bilateral dAI were used as seeds. Individual subject correlation maps were entered into general linear models for group comparison and to analyze the effect of CR index on dAI iFC, controlling for multiple comparisons. The correlation between CR index and iFC was assessed using a linear regression model. RESULTS rs-fMRI analyses revealed a negative relationship between CR index and iFC within the left dAI and a left occipital cluster in people with MS including regions of the cuneus, superior occipital gyrus, and parieto-occipital sulcus. The regression analysis showed that people with MS and a higher CR index had a statistically significantly reduced iFC within the left dAI and the cluster. CONCLUSIONS CR is relevant to functional connectivity within one of the main nodes of the SN, the dAI, and occipital regions in MS. These results have implications for how CR may modulate the susceptibility to cognitive dysfunction in MS.
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Affiliation(s)
- Bernardo Canedo Bizzo
- Department of Radiology, Faculty of Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.,A.A. Martinos Center for Biomedical Imaging, Charlestown, MA
| | - Tiago Arruda-Sanchez
- Department of Radiology, Faculty of Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Sean M Tobyne
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - John Daniel Bireley
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Michael Howard Lev
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Emerson Leandro Gasparetto
- Department of Radiology, Faculty of Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Eric C Klawiter
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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12
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Central nervous system targeted autoimmunity causes regional atrophy: a 9.4T MRI study of the EAE mouse model of Multiple Sclerosis. Sci Rep 2019; 9:8488. [PMID: 31186441 PMCID: PMC6560061 DOI: 10.1038/s41598-019-44682-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 05/02/2019] [Indexed: 12/28/2022] Open
Abstract
Atrophy has become a clinically relevant marker of progressive neurodegeneration in multiple sclerosis (MS). To better understand atrophy, mouse models that feature atrophy along with other aspects of MS are needed. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to determine the extent of atrophy in a model of inflammation-associated central nervous system pathology. High-resolution magnetic resonance imaging (MRI) and atlas-based volumetric analysis were performed to measure brain regional volumes in EAE mice. EAE brains were larger at peak clinical disease (days 14–16) compared to controls, with affected regions including the cerebellum, hippocampus, and corpus callosum. Following peak clinical disease, EAE mice exhibited significant loss of volume at chronic long-term disease duration (day 66+). Atrophy was identified in both white and grey matter regions including the cerebral cortex, cerebellum, hippocampus, corpus callosum, basal forebrain, midbrain, optic tract, and colliculus. Histological analysis of the atrophied cortex, cerebellum, and hippocampus showed demyelination, and axonal/neuronal loss. We hypothesize this atrophy could be a result of inflammatory associated neurodegenerative processes, which may also be involved in MS. Using MRI and atlas-based volumetrics, EAE has the potential to be a test bed for treatments aimed at reducing progressive neurological deterioration in MS.
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13
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MacKenzie‐Graham A, Brook J, Kurth F, Itoh Y, Meyer C, Montag MJ, Wang H, Elashoff R, Voskuhl RR. Estriol-mediated neuroprotection in multiple sclerosis localized by voxel-based morphometry. Brain Behav 2018; 8:e01086. [PMID: 30144306 PMCID: PMC6160650 DOI: 10.1002/brb3.1086] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 07/05/2018] [Accepted: 07/08/2018] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION Progressive gray matter (GM) atrophy is a hallmark of multiple sclerosis (MS). Cognitive impairment has been observed in 40%-70% of MS patients and has been linked to GM atrophy. In a phase 2 trial of estriol treatment in women with relapsing-remitting MS (RRMS), higher estriol levels correlated with greater improvement on the paced auditory serial addition test (PASAT) and imaging revealed sparing of localized GM in estriol-treated compared to placebo-treated patients. To better understand the significance of this GM sparing, the current study explored the relationships between the GM sparing and traditional MRI measures and clinical outcomes. METHODS Sixty-two estriol- and forty-nine placebo-treated RRMS patients underwent clinical evaluations and brain MRI. Voxel-based morphometry (VBM) was used to evaluate voxelwise GM sparing from high-resolution T1-weighted scans. RESULTS A region of treatment-induced sparing (TIS) was defined as the areas where GM was spared in estriol- as compared to placebo-treated groups, localized primarily within the frontal and parietal cortices. We observed that TIS volume was directly correlated with improvement on the PASAT. Next, a longitudinal cognitive disability-specific atlas (DSA) was defined by correlating voxelwise GM volumes with PASAT scores, that is, areas where less GM correlated with less improvement in PASAT scores. Finally, overlap between the TIS and the longitudinal cognitive DSA revealed a specific region of cortical GM that was preserved in estriol-treated subjects that was associated with better performance on the PASAT. CONCLUSIONS Discovery of this region of overlap was biology driven, not based on an a priori structure of interest. It included the medial frontal cortex, an area previously implicated in problem solving and attention. These findings indicate that localized GM sparing during estriol treatment was associated with improvement in cognitive testing, suggesting a clinically relevant, disability-specific biomarker for clinical trials of candidate neuroprotective treatments in MS.
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Affiliation(s)
- Allan MacKenzie‐Graham
- Department of NeurologyAhmanson‐Lovelace Brain Mapping CenterDavid Geffen School of Medicine at UCLALos AngelesCalifornia
- UCLA Multiple Sclerosis ProgramDepartment of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCalifornia
| | - Jenny Brook
- Department of BiomathematicsDavid Geffen School of Medicine at UCLALos AngelesCalifornia
| | - Florian Kurth
- Department of NeurologyAhmanson‐Lovelace Brain Mapping CenterDavid Geffen School of Medicine at UCLALos AngelesCalifornia
- UCLA Multiple Sclerosis ProgramDepartment of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCalifornia
| | - Yuichiro Itoh
- UCLA Multiple Sclerosis ProgramDepartment of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCalifornia
| | - Cassandra Meyer
- Department of NeurologyAhmanson‐Lovelace Brain Mapping CenterDavid Geffen School of Medicine at UCLALos AngelesCalifornia
- UCLA Multiple Sclerosis ProgramDepartment of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCalifornia
| | - Michael J. Montag
- UCLA Multiple Sclerosis ProgramDepartment of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCalifornia
| | - He‐Jing Wang
- Department of BiomathematicsDavid Geffen School of Medicine at UCLALos AngelesCalifornia
| | - Robert Elashoff
- Department of BiomathematicsDavid Geffen School of Medicine at UCLALos AngelesCalifornia
| | - Rhonda R. Voskuhl
- UCLA Multiple Sclerosis ProgramDepartment of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCalifornia
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14
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Amiri H, de Sitter A, Bendfeldt K, Battaglini M, Gandini Wheeler-Kingshott CAM, Calabrese M, Geurts JJG, Rocca MA, Sastre-Garriga J, Enzinger C, de Stefano N, Filippi M, Rovira Á, Barkhof F, Vrenken H. Urgent challenges in quantification and interpretation of brain grey matter atrophy in individual MS patients using MRI. Neuroimage Clin 2018; 19:466-475. [PMID: 29984155 PMCID: PMC6030805 DOI: 10.1016/j.nicl.2018.04.023] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 03/28/2018] [Accepted: 04/22/2018] [Indexed: 01/18/2023]
Abstract
Atrophy of the brain grey matter (GM) is an accepted and important feature of multiple sclerosis (MS). However, its accurate measurement is hampered by various technical, pathological and physiological factors. As a consequence, it is challenging to investigate the role of GM atrophy in the disease process as well as the effect of treatments that aim to reduce neurodegeneration. In this paper we discuss the most important challenges currently hampering the measurement and interpretation of GM atrophy in MS. The focus is on measurements that are obtained in individual patients rather than on group analysis methods, because of their importance in clinical trials and ultimately in clinical care. We discuss the sources and possible solutions of the current challenges, and provide recommendations to achieve reliable measurement and interpretation of brain GM atrophy in MS.
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Key Words
- BET, brain extraction tool
- Brain atrophy
- CNS, central nervous system
- CTh, cortical thickness
- DGM, deep grey matter
- DTI, diffusion tensor imaging
- FA, fractional anisotropy
- GM, grey matter
- Grey matter
- MRI, magnetic resonance imaging
- MS, multiple sclerosis
- Magnetic resonance imaging
- Multiple sclerosis
- TE, echo time
- TI, inversion time
- TR, repetition time
- VBM, voxel-based morphometry
- WM, white matter
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Affiliation(s)
- Houshang Amiri
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Alexandra de Sitter
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands.
| | | | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | | | - Massimiliano Calabrese
- Multiple Sclerosis Centre, Neurology Section, Department of Neurosciences, Biomedicine and Movements, University of Verona, Italy
| | - Jeroen J G Geurts
- Anatomy & Neurosciences, VU University Medical Center, Amsterdam Neuroscience, 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
| | - Jaume Sastre-Garriga
- Servei de Neurologia/Neuroimmunologia, Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Christian Enzinger
- Department of Neurology & Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University of Graz, Austria
| | - Nicola de Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Álex Rovira
- Unitat de Ressonància Magnètica (Servei de Radiologia), Hospital universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands; Institutes of Neurology and Healthcare Engineering, UCL, London, UK
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
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15
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Giorgio A, De Stefano N. Effective Utilization of MRI in the Diagnosis and Management of Multiple Sclerosis. Neurol Clin 2018; 36:27-34. [DOI: 10.1016/j.ncl.2017.08.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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16
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Favaretto A, Lazzarotto A, Margoni M, Poggiali D, Gallo P. Effects of disease modifying therapies on brain and grey matter atrophy in relapsing remitting multiple sclerosis. ACTA ACUST UNITED AC 2018. [DOI: 10.1186/s40893-017-0033-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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17
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Peterson DS, Fling BW. How changes in brain activity and connectivity are associated with motor performance in people with MS. Neuroimage Clin 2017; 17:153-162. [PMID: 29071209 PMCID: PMC5651557 DOI: 10.1016/j.nicl.2017.09.019] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Revised: 09/22/2017] [Accepted: 09/25/2017] [Indexed: 01/18/2023]
Abstract
People with multiple sclerosis (MS) exhibit pronounced changes in brain structure, activity, and connectivity. While considerable work has begun to elucidate how these neural changes contribute to behavior, the heterogeneity of symptoms and diagnoses makes interpretation of findings and application to clinical practice challenging. In particular, whether MS related changes in brain activity or brain connectivity protect against or contribute to worsening motor symptoms is unclear. With the recent emergence of neuromodulatory techniques that can alter neural activity in specific brain regions, it is critical to establish whether localized brain activation patterns are contributing to (i.e. maladaptive) or protecting against (i.e. adaptive) progression of motor symptoms. In this manuscript, we consolidate recent findings regarding changes in supraspinal structure and activity in people with MS and how these changes may contribute to motor performance. Furthermore, we discuss a hypothesis suggesting that increased neural activity during movement may be either adaptive or maladaptive depending on where in the brain this increase is observed. Specifically, we outline preliminary evidence suggesting sensorimotor cortex activity in the ipsilateral cortices may be maladaptive in people with MS. We also discuss future work that could supply data to support or refute this hypothesis, thus improving our understanding of this important topic.
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Affiliation(s)
- Daniel S Peterson
- Arizona State University, Tempe, AZ, USA; Veterans Affairs Phoenix Medical Center Phoenix, AZ, USA.
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18
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Preziosa P, Pagani E, Mesaros S, Riccitelli GC, Dackovic J, Drulovic J, Filippi M, Rocca MA. Progression of regional atrophy in the left hemisphere contributes to clinical and cognitive deterioration in multiple sclerosis: A 5-year study. Hum Brain Mapp 2017; 38:5648-5665. [PMID: 28792103 DOI: 10.1002/hbm.23755] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 06/22/2017] [Accepted: 07/25/2017] [Indexed: 01/18/2023] Open
Abstract
In this longitudinal study, we investigated the regional patterns of focal lesions accumulation, and gray (GM) and white matter (WM) atrophy progression over a five-year follow-up (FU) in multiple sclerosis (MS) patients and their association with clinical and cognitive deterioration. Neurological, neuropsychological and brain MRI (dual-echo and 3D T1-weighted sequences) assessments were prospectively performed at baseline (T0) and after a median FU of 4.9 years from 66 MS patients (including relapse-onset and primary progressive MS) and 16 matched controls. Lesion probability maps were obtained. Longitudinal changes of GM and WM volumes and their association with clinical and cognitive deterioration were assessed using tensor-based morphometry and SPM12. At FU, 36/66 (54.5%) MS patients showed a significant disability worsening, 14/66 (21.2%) evolved to a worse clinical phenotype, and 18/63 (28.6%) developed cognitive deterioration. At T0, compared to controls, MS patients showed a widespread pattern of GM atrophy, involving cortex, deep GM and cerebellum, and atrophy of the majority of WM tracts, which further progressed at FU (P < 0.001, uncorrected). Compared to stable patients, those with clinical and cognitive worsening showed a left-lateralized pattern of GM and WM atrophy, involving deep GM, fronto-temporo-parieto-occipital regions, cerebellum, and several WM tracts (P < 0.001, uncorrected).GM and WM atrophy of relevant brain regions occur in MS after 5 years. A different vulnerability of the two brain hemispheres to irreversible structural damage may be among the factors contributing to clinical and cognitive worsening in these patients. Hum Brain Mapp 38:5648-5665, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Paolo Preziosa
- 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
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Sarlota Mesaros
- Neurology Clinic, Clinical Centre of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Gianna C Riccitelli
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Jelena Dackovic
- Neurology Clinic, Clinical Centre of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Jelena Drulovic
- Neurology Clinic, Clinical Centre of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - 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
| | - 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
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19
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MacKenzie-Graham A, Kurth F, Itoh Y, Wang HJ, Montag MJ, Elashoff R, Voskuhl RR. Disability-Specific Atlases of Gray Matter Loss in Relapsing-Remitting Multiple Sclerosis. JAMA Neurol 2017; 73:944-53. [PMID: 27294295 DOI: 10.1001/jamaneurol.2016.0966] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
IMPORTANCE Multiple sclerosis (MS) is characterized by progressive gray matter (GM) atrophy that strongly correlates with clinical disability. However, whether localized GM atrophy correlates with specific disabilities in patients with MS remains unknown. OBJECTIVE To understand the association between localized GM atrophy and clinical disability in a biology-driven analysis of MS. DESIGN, SETTING, AND PARTICIPANTS In this cross-sectional study, magnetic resonance images were acquired from 133 women with relapsing-remitting MS and analyzed using voxel-based morphometry and volumetry. A regression analysis was used to determine whether voxelwise GM atrophy was associated with specific clinical deficits. Data were collected from June 28, 2007, to January 9, 2014. MAIN OUTCOMES AND MEASURES Voxelwise correlation of GM change with clinical outcome measures (Expanded Disability Status Scale and Multiple Sclerosis Functional Composite scores). RESULTS Among the 133 female patients (mean [SD] age, 37.4 [7.5] years), worse performance on the Multiple Sclerosis Functional Composite correlated with voxelwise GM volume loss in the middle cingulate cortex (P < .001) and a cluster in the precentral gyrus bilaterally (P = .004). In addition, worse performance on the Paced Auditory Serial Addition Test correlated with volume loss in the auditory and premotor cortices (P < .001), whereas worse performance on the 9-Hole Peg Test correlated with GM volume loss in Brodmann area 44 (Broca area; P = .02). Finally, voxelwise GM loss in the right paracentral lobulus correlated with bowel and bladder disability (P = .03). Thus, deficits in specific clinical test results were directly associated with localized GM loss in clinically eloquent locations. CONCLUSIONS AND RELEVANCE These biology-driven data indicate that specific disabilities in MS are associated with voxelwise GM loss in distinct locations. This approach may be used to develop disability-specific biomarkers for use in future clinical trials of neuroprotective treatments in MS.
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Affiliation(s)
- Allan MacKenzie-Graham
- Department of Neurology, David Geffen School of Medicine at University of California, Los Angeles
| | - Florian Kurth
- Department of Neurology, David Geffen School of Medicine at University of California, Los Angeles
| | - Yuichiro Itoh
- Department of Neurology, David Geffen School of Medicine at University of California, Los Angeles
| | - He-Jing Wang
- Department of Biomathematics, David Geffen School of Medicine at University of California, Los Angeles
| | - Michael J Montag
- Department of Neurology, David Geffen School of Medicine at University of California, Los Angeles
| | - Robert Elashoff
- Department of Biomathematics, David Geffen School of Medicine at University of California, Los Angeles
| | - Rhonda R Voskuhl
- Department of Neurology, David Geffen School of Medicine at University of California, Los Angeles
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20
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Ma AY, Vitorino RC, Hojjat SP, Mulholland AD, Zhang L, Lee L, Carroll TJ, Cantrell CG, Figley CR, Aviv RI. The relationship between white matter fiber damage and gray matter perfusion in large-scale functionally defined networks in multiple sclerosis. Mult Scler 2017; 23:1884-1892. [PMID: 28178867 DOI: 10.1177/1352458517691149] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Recent studies utilizing perfusion as a surrogate of cortical integrity show promise for overall cognition, but the association between white matter (WM) damage and gray matter (GM) integrity in specific functional networks is not previously studied. OBJECTIVE To investigate the relationship between WM fiber integrity and GM node perfusion within six functional networks of relapsing-remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS) patients. METHODS Magnetic resonance imaging (MRI) and neurocognitive testing were performed on 19 healthy controls (HC), 39 RRMS, and 45 SPMS patients. WM damage extent and severity were quantified with T2-hyper/T1-hypointense (T2h/T1h) lesion volume and degree of perfusion reduction in lesional and normal-appearing white matter (NAWM), respectively. A two-step linear regression corrected for confounders was employed. RESULTS Cognitive impairment was present in 20/39 (51%) RRMS and 25/45 (53%) SPMS patients. GM node perfusion was associated with WM fiber damage severity (WM hypoperfusion) within each network-including both NAWM ( R2 = 0.67-0.89, p < 0.0001) and T2h ( R2 = 0.39-0.62, p < 0.0001) WM regions-but was not significantly associated ( p > 0.01) with WM fiber damage extent (i.e. T2h/T1h lesion volumes). CONCLUSION Overall, GM node perfusion was associated with severity rather than extent of WM network damage, supporting a primary etiology of GM hypoperfusion.
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Affiliation(s)
- Ashley Y Ma
- Department of Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Rita C Vitorino
- Department of Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Seyed-Parsa Hojjat
- Department of Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Alannah D Mulholland
- Department of Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Liying Zhang
- Department of Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Liesly Lee
- Department of Neurology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Timothy J Carroll
- Department of Biomedical Engineering and Radiology, Northwestern University, Chicago, IL, USA
| | - Charles G Cantrell
- Department of Biomedical Engineering, Northwestern University, Chicago, IL, USA
| | - Chase R Figley
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada/Neuroscience Research Program and Division of Diagnostic Imaging, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, MB, Canada
| | - Richard I Aviv
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada/Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
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21
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Han XM, Tian HJ, Han Z, Zhang C, Liu Y, Gu JB, Bakshi R, Cao X. Correlation between white matter damage and gray matter lesions in multiple sclerosis patients. Neural Regen Res 2017; 12:787-794. [PMID: 28616036 PMCID: PMC5461617 DOI: 10.4103/1673-5374.206650] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
We observed the characteristics of white matter fibers and gray matter in multiple sclerosis patients, to identify changes in diffusion tensor imaging fractional anisotropy values following white matter fiber injury. We analyzed the correlation between fractional anisotropy values and changes in whole-brain gray matter volume. The participants included 20 patients with relapsing-remitting multiple sclerosis and 20 healthy volunteers as controls. All subjects underwent head magnetic resonance imaging and diffusion tensor imaging. Our results revealed that fractional anisotropy values decreased and gray matter volumes were reduced in the genu and splenium of corpus callosum, left anterior thalamic radiation, hippocampus, uncinate fasciculus, right corticospinal tract, bilateral cingulate gyri, and inferior longitudinal fasciculus in multiple sclerosis patients. Gray matter volumes were significantly different between the two groups in the right frontal lobe (superior frontal, middle frontal, precentral, and orbital gyri), right parietal lobe (postcentral and inferior parietal gyri), right temporal lobe (caudate nucleus), right occipital lobe (middle occipital gyrus), right insula, right parahippocampal gyrus, and left cingulate gyrus. The voxel sizes of atrophic gray matter positively correlated with fractional anisotropy values in white matter association fibers in the patient group. These findings suggest that white matter fiber bundles are extensively injured in multiple sclerosis patients. The main areas of gray matter atrophy in multiple sclerosis are the frontal lobe, parietal lobe, caudate nucleus, parahippocampal gyrus, and cingulate gyrus. Gray matter atrophy is strongly associated with white matter injury in multiple sclerosis patients, particularly with injury to association fibers.
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Affiliation(s)
- Xue-Mei Han
- First Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Hong-Ji Tian
- First Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Zheng Han
- First Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Ce Zhang
- Department of Clinical Medicine (Grade 2013 Student), School of Clinical Medicine, Jilin University, Changchun, Jilin Province, China
| | - Ying Liu
- First Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Jie-Bing Gu
- First Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Rohit Bakshi
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA, USA
| | - Xia Cao
- School of Pharmaceutical Sciences, Jilin University, Changchun, Jilin Province, China
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22
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Contribution of Gray and White Matter Abnormalities to Cognitive Impairment in Multiple Sclerosis. Int J Mol Sci 2016; 18:ijms18010046. [PMID: 28035997 PMCID: PMC5297681 DOI: 10.3390/ijms18010046] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 12/10/2016] [Accepted: 12/16/2016] [Indexed: 02/05/2023] Open
Abstract
Patients with multiple sclerosis (MS) commonly exhibit cognitive impairments (CI). However, the neural mechanisms underlying CI remain unclear. The current study applied diffusion tensor imaging (DTI) and voxel-based morphometric (VBM) magnetic resonance imaging (MRI) techniques to evaluate differences in white matter (WM) integrity and gray matter (GM) volume between MS patients with CI and MS patients with cognitive preservation (CP). Neuropsychological assessment and MRI were obtained from 39 relapsing-remitting MS (RRMS) patients and 29 healthy controls (HCs). Patients were classified as CI or CP according to cognitive ability, and demographic characteristics and MRI images were compared. Compared with HCs, MS patients exhibited widespread damage in WM integrity, and GM loss in several regions. Compared with CP patients, CI patients exhibited more extensive WM impairments, particularly in the corpus callosum, cerebellar peduncle, corona radiata, optic radiation, superior longitudinal fasciculus, anterior limb of the internal capsule, and cingulate, as well as decreased GM volume in the bilateral caudate, left insula and right temporal lobe. MS patients with CI exhibited more significant structural abnormalities than those with CP. Widespread impairments of WM integrity and selective GM atrophy both appear to be associated with impaired cognition in RRMS.
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23
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Rocca MA, Battaglini M, Benedict RHB, De Stefano N, Geurts JJG, Henry RG, Horsfield MA, Jenkinson M, Pagani E, Filippi M. Brain MRI atrophy quantification in MS: From methods to clinical application. Neurology 2016; 88:403-413. [PMID: 27986875 DOI: 10.1212/wnl.0000000000003542] [Citation(s) in RCA: 162] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 10/18/2016] [Indexed: 01/06/2023] Open
Abstract
Patients with the main clinical phenotypes of multiple sclerosis (MS) manifest varying degrees of brain atrophy beyond that of normal aging. Assessment of atrophy helps to distinguish clinically and cognitively deteriorating patients and predicts those who will have a less-favorable clinical outcome over the long term. Atrophy can be measured from brain MRI scans, and many technological improvements have been made over the last few years. Several software tools, with differing requirements on technical ability and levels of operator intervention, are currently available and have already been applied in research or clinical trial settings. Despite this, the measurement of atrophy in routine clinical practice remains an unmet need. After a short summary of the pathologic substrates of brain atrophy in MS, this review attempts to guide the clinician towards a better understanding of the methods currently used for quantifying brain atrophy in this condition. Important physiologic factors that affect brain volume measures are also considered. Finally, the most recent research on brain atrophy in MS is summarized, including whole brain and various compartments thereof (i.e., white matter, gray matter, selected CNS structures). Current methods provide sufficient precision for cohort studies, but are not adequate for confidently assessing changes in individual patients over the scale of months or a few years.
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Affiliation(s)
- Maria A Rocca
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Marco Battaglini
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Ralph H B Benedict
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Nicola De Stefano
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Jeroen J G Geurts
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Roland G Henry
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Mark A Horsfield
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Mark Jenkinson
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Massimo Filippi
- From the Neuroimaging Research Unit (M.A.R., E.P., M.F.), Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan; Department of Medicine, Surgery and Neuroscience (M.B., N.D.S.), University of Siena, Italy; Department of Neurology (R.H.B.B.), Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York; Department of Anatomy and Neuroscience (J.J.G.G.), Section of Clinical Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, the Netherlands; Department of Neurology (R.G.H.), University of California, San Francisco; Xinapse Systems Ltd. (M.A.H.), Colchester, Essex, UK; and FMRIB Centre (M.J.), Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
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Qian W, Chan KH, Hui ES, Lee CY, Hu Y, Mak HKF. Application of diffusional kurtosis imaging to detect occult brain damage in multiple sclerosis and neuromyelitis optica. NMR IN BIOMEDICINE 2016; 29:1536-1545. [PMID: 27602543 DOI: 10.1002/nbm.3607] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Revised: 07/21/2016] [Accepted: 07/25/2016] [Indexed: 06/06/2023]
Abstract
Multiple sclerosis (MS) and neuromyelitis optica (NMO) are two common types of inflammatory demyelinating disease of the central nervous system. Early distinction of NMO from MS is crucial but quite challenging. In this study, 13 NMO spectrum disorder patients (Expanded Disability Status Scale (EDSS) of 3.0 ± 1.7, ranging from 2 to 6.5; disease duration of 5.3 ± 4.7 years), 17 relapsing-remitting MS patients (EDSS of 2.6 ± 1.4, ranging from 1 to 5.5; disease duration of 7.9 ± 7.8 years) and 18 healthy volunteers were recruited. Diffusional kurtosis imaging was employed to discriminate NMO and MS patients at the early or stable stage from each other, and from healthy volunteers. The presence of alterations in diffusion and diffusional kurtosis metrics in normal-appearing white matter (NAWM) and diffusely increased mean diffusivity (MD) in the cortical normal-appearing gray matter (NAGM) favors the diagnosis of MS rather than NMO. Meanwhile, normal diffusivities and kurtosis metrics in all NAWM as well as increases in MD in the frontal and temporal NAGM suggest NMO. Our results suggest that diffusion and diffusional kurtosis metrics may well aid in discriminating the two diseases.
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Affiliation(s)
- Wenshu Qian
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong
| | - Koon Ho Chan
- Department of Medicine, The University of Hong Kong, Hong Kong.
| | - Edward S Hui
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong
| | - Chi Yan Lee
- Department of Medicine, The University of Hong Kong, Hong Kong
| | - Yong Hu
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong
| | - Henry Ka-Fung Mak
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong.
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25
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Meijer K, Cercignani M, Muhlert N, Sethi V, Chard D, Geurts J, Ciccarelli O. Patterns of white matter damage are non-random and associated with cognitive function in secondary progressive multiple sclerosis. Neuroimage Clin 2016; 12:123-31. [PMID: 27408797 PMCID: PMC4932616 DOI: 10.1016/j.nicl.2016.06.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 05/15/2016] [Accepted: 06/11/2016] [Indexed: 01/12/2023]
Abstract
In multiple sclerosis (MS), white matter damage is thought to contribute to cognitive dysfunction, which is especially prominent in secondary progressive MS (SPMS). While studies in healthy subjects have revealed patterns of correlated fractional anisotropy (FA) across white matter tracts, little is known about the underlying patterns of white matter damage in MS. In the present study, we aimed to map the SPMS-related covariance patterns of microstructural white matter changes, and investigated whether or not these patterns were associated with cognitive dysfunction. Diffusion MRI was acquired from 30 SPMS patients and 32 healthy controls (HC). A tensor model was fitted and FA maps were processed using tract-based spatial statistics (TBSS) in order to obtain a skeletonised map for each subject. The skeletonised FA maps of patients only were decomposed into 18 spatially independent components (ICs) using independent component analysis. Comprehensive cognitive assessment was conducted to evaluate five cognitive domains. Correlations between cognitive performance and (1) severity of FA abnormalities of the extracted ICs (i.e. z-scores relative to FA values of HC) and (2) IC load (i.e. FA covariance of a particular IC) were examined. SPMS patients showed lower FA values of all examined patterns of correlated FA (i.e. spatially independent components) than HC (p < 0.01). Tracts visually assigned to the supratentorial commissural class were most severely damaged (z = - 3.54; p < 0.001). Reduced FA was significantly correlated with reduced IC load (i.e. FA covariance) (r = 0.441; p < 0.05). Lower mean FA and component load of the supratentorial projection tracts and limbic association tracts classes were associated with worse cognitive function, including executive function, working memory and verbal memory. Despite the presence of white matter damage, it was possible to reveal patterns of FA covariance across SPMS patients. This could indicate that white matter tracts belonging to the same cluster, and thus with similar characteristics, tend to follow similar trends during neurodegeneration. Furthermore, these underlying FA patterns might help to explain cognitive dysfunction in SPMS.
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Affiliation(s)
- K.A. Meijer
- Department of Anatomy and Neurosciences, VU Medical Centre, Amsterdam, The Netherlands
- NMR Research Unit, Queen Square MS Centre, University College London Institute of Neurology, London, United Kingdom
| | - M. Cercignani
- Clinical Imaging Centre, Brighton and Sussex Medical School, Brighton, United Kingdom
- Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy
| | - N. Muhlert
- School of Psychological Sciences, University of Manchester, Manchester, United Kingdom
| | - V. Sethi
- NMR Research Unit, Queen Square MS Centre, University College London Institute of Neurology, London, United Kingdom
- School of Psychology and Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - D. Chard
- NMR Research Unit, Queen Square MS Centre, University College London Institute of Neurology, London, United Kingdom
- NIHR University College London Hospitals Biomedical Research Centre, London, United Kingdom
| | - J.J.G. Geurts
- Department of Anatomy and Neurosciences, VU Medical Centre, Amsterdam, The Netherlands
| | - O. Ciccarelli
- NMR Research Unit, Queen Square MS Centre, University College London Institute of Neurology, London, United Kingdom
- NIHR University College London Hospitals Biomedical Research Centre, London, United Kingdom
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26
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Roy S, Carass A, Pacheco J, Bilgel M, Resnick SM, Prince JL, Pham DL. Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation. Neuroimage Clin 2016; 11:264-275. [PMID: 26958465 PMCID: PMC4773508 DOI: 10.1016/j.nicl.2016.02.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Revised: 01/13/2016] [Accepted: 02/12/2016] [Indexed: 01/13/2023]
Abstract
Longitudinal analysis of magnetic resonance images of the human brain provides knowledge of brain changes during both normal aging as well as the progression of many diseases. Previous longitudinal segmentation methods have either ignored temporal information or have incorporated temporal consistency constraints within the algorithm. In this work, we assume that some anatomical brain changes can be explained by temporal transitions in image intensities. Once the images are aligned in the same space, the intensities of each scan at the same voxel constitute a temporal (or 4D) intensity trend at that voxel. Temporal intensity variations due to noise or other artifacts are corrected by a 4D intensity-based filter that smooths the intensity values where appropriate, while preserving real anatomical changes such as atrophy. Here smoothing refers to removal of sudden changes or discontinuities in intensities. Images processed with the 4D filter can be used as a pre-processing step to any segmentation method. We show that such a longitudinal pre-processing step produces robust and consistent longitudinal segmentation results, even when applying 3D segmentation algorithms. We compare with state-of-the-art 4D segmentation algorithms. Specifically, we experimented on three longitudinal datasets containing 4-12 time-points, and showed that the 4D temporal filter is more robust and has more power in distinguishing between healthy subjects and those with dementia, mild cognitive impairment, as well as different phenotypes of multiple sclerosis.
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Affiliation(s)
- Snehashis Roy
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation for the Advancement of Military Medicine, United States,Corresponding author.
| | - Aaron Carass
- Image Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, United States,Department of Computer Science, Johns Hopkins University, United States
| | - Jennifer Pacheco
- Laboratory of Behavioral Neuroscience, National Institute on Aging, United States
| | - Murat Bilgel
- Image Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, United States,Laboratory of Behavioral Neuroscience, National Institute on Aging, United States
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, United States
| | - Jerry L. Prince
- Image Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, United States
| | - Dzung L. Pham
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation for the Advancement of Military Medicine, United States
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27
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Popescu V, Schoonheim MM, Versteeg A, Chaturvedi N, Jonker M, Xavier de Menezes R, Gallindo Garre F, Uitdehaag BMJ, Barkhof F, Vrenken H. Grey Matter Atrophy in Multiple Sclerosis: Clinical Interpretation Depends on Choice of Analysis Method. PLoS One 2016; 11:e0143942. [PMID: 26745873 PMCID: PMC4706325 DOI: 10.1371/journal.pone.0143942] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Accepted: 11/11/2015] [Indexed: 11/27/2022] Open
Abstract
Background Studies disagree on the location of grey matter (GM) atrophy in the multiple sclerosis (MS) brain. Aim To examine the consistency between FSL, FreeSurfer, SPM for GM atrophy measurement (for volumes, patient/control discrimination, and correlations with cognition). Materials and Methods 127 MS patients and 50 controls were included and cortical and deep grey matter (DGM) volumetrics were performed. Consistency of volumes was assessed with Intraclass Correlation Coefficient/ICC. Consistency of patients/controls discrimination was assessed with Cohen’s d, t-tests, MANOVA and a penalized double-loop logistic classifier. Consistency of association with cognition was assessed with Pearson correlation coefficient and ANOVA. Voxel-based morphometry (SPM-VBM and FSL-VBM) and vertex-wise FreeSurfer were used for group-level comparisons. Results The highest volumetry ICC were between SPM and FreeSurfer for cortical regions, and the lowest between SPM and FreeSurfer for DGM. The caudate nucleus and temporal lobes had high consistency between all software, while amygdala had lowest volumetric consistency. Consistency of patients/controls discrimination was largest in the DGM for all software, especially for thalamus and pallidum. The penalized double-loop logistic classifier most often selected the thalamus, pallidum and amygdala for all software. FSL yielded the largest number of significant correlations. DGM yielded stronger correlations with cognition than cortical volumes. Bilateral putamen and left insula volumes correlated with cognition using all methods. Conclusion GM volumes from FreeSurfer, FSL and SPM are different, especially for cortical regions. While group-level separation between MS and controls is comparable, correlations between regional GM volumes and clinical/cognitive variables in MS should be cautiously interpreted.
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Affiliation(s)
- Veronica Popescu
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam (NCA), VU University Medical Center, Amsterdam, The Netherlands
| | - Menno M. Schoonheim
- Department of Anatomy and Neurosciences, Neuroscience Campus Amsterdam (NCA), VU University Medical Center, Amsterdam, The Netherlands
| | - Adriaan Versteeg
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam (NCA), VU University Medical Center, Amsterdam, The Netherlands
| | - Nimisha Chaturvedi
- Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands
| | - Marianne Jonker
- Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands
| | - Renee Xavier de Menezes
- Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands
| | - Francisca Gallindo Garre
- Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands
| | - Bernard M. J. Uitdehaag
- Department of Neurology, Neuroscience Campus Amsterdam (NCA), VU University Medical Center, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam (NCA), VU University Medical Center, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam (NCA), VU University Medical Center, Amsterdam, The Netherlands
- Department of Physics and Medical Technology, Neuroscience Campus Amsterdam (NCA), VU University Medical Center, Amsterdam, The Netherlands
- * E-mail:
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28
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Guizard N, Nakamura K, Coupé P, Fonov VS, Arnold DL, Collins DL. Non-Local Means Inpainting of MS Lesions in Longitudinal Image Processing. Front Neurosci 2015; 9:456. [PMID: 26696815 PMCID: PMC4678220 DOI: 10.3389/fnins.2015.00456] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 11/16/2015] [Indexed: 12/26/2022] Open
Abstract
In medical imaging, multiple sclerosis (MS) lesions can lead to confounding effects in automatic morphometric processing tools such as registration, segmentation and cortical extraction, and subsequently alter individual longitudinal measurements. Multiple magnetic resonance imaging (MRI) inpainting techniques have been proposed to decrease the impact of MS lesions in medical image processing, however, most of these methods make the assumption that lesions only affect white matter. Here, we propose a method to fill lesion regions using the patch-based non-local mean (NLM) strategy. The method consists of a hierarchical concentric filling strategy after identification of the lesion region. The lesion is filled iteratively, based on the surrounding tissue intensity, using an onion peel strategy. This concentric technique presents the advantage of preserving the local information and therefore the continuity of the anatomy and does not require identification of any a priori normal brain tissues. The method is first evaluated on 20 healthy subjects with simulated artificial MS lesions where we assessed our technique by measuring the peak signal-to-noise ratio (PSNR) of the images with inpainted lesion and the original healthy images. Second, in order to assess the impact of lesion filling on longitudinal image analyses, we performed a power analysis with sample size estimation to evaluate brain atrophy and ventricular growth in patients with MS. The method was compared to two different publicly available methods (FSL lesion fill and Lesion LEAP) and a more classic method, which fills the region with intensities similar to that of the surrounding healthy white matter tissue or mask the lesions. The proposed method was shown to exceed the other methods in reproducing the fidelity of healthy subject images where the lesions were inpainted. The method also improved the power to detect brain atrophy or ventricular growth by decreasing the sample size by 25% in the presence of MS lesions.
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Affiliation(s)
- Nicolas Guizard
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC Canada
| | - Kunio Nakamura
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC Canada ; Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic Cleveland, OH, USA
| | - Pierrick Coupé
- Pictura Research Group, Unité Mixte de Recherche Centre National de la Recherche Scientifique, Laboratoire Bordelais de Recherche en Informatique Talence, France
| | - Vladimir S Fonov
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC Canada
| | - Douglas L Arnold
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC Canada
| | - D Louis Collins
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC Canada
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29
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Steenwijk MD, Geurts JJG, Daams M, Tijms BM, Wink AM, Balk LJ, Tewarie PK, Uitdehaag BMJ, Barkhof F, Vrenken H, Pouwels PJW. Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant. Brain 2015; 139:115-26. [DOI: 10.1093/brain/awv337] [Citation(s) in RCA: 175] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Accepted: 09/29/2015] [Indexed: 01/07/2023] Open
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30
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Ota M, Sato N, Okamoto T, Noda T, Araki M, Yamamura T, Kunugi H. Neuromyelitis optica spectrum disorder and multiple sclerosis: Differentiation by a multimodal approach. Mult Scler Relat Disord 2015; 4:515-20. [DOI: 10.1016/j.msard.2015.08.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Revised: 07/28/2015] [Accepted: 08/17/2015] [Indexed: 11/26/2022]
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31
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Galego O, Gouveia A, Batista S, Moura C, Machado E. Brain atrophy and physical disability in primary progressive multiple sclerosis: A volumetric study. Neuroradiol J 2015; 28:354-8. [PMID: 26246109 DOI: 10.1177/1971400915594984] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
UNLABELLED Grey matter atrophy has been shown in primary progressive multiple sclerosis (PPMS), but its association with physical incapacity is unclear. We submitted 19 patients with PPMS to a neurological evaluation and brain magnetic resonance imaging (MRI) with volumetric analysis using FreeSurfer. We found no relation between the Expanded Disability Status Scale or disease duration and the grey matter or white matter structures analysed. Lesion load was negatively correlated with cortical and subcortical grey matter volumes, but not with total white matter volume. We concluded that physical disability in PPMS is not directly related to brain atrophy and that focal inflammatory white matter lesions may contribute to progressive neuronal degeneration. INTRODUCTION Primary progressive multiple sclerosis (PPMS) is characterized by chronic progression since onset, with predominant involvement of the spinal cord and prominent neurodegeneration. Grey matter atrophy has been shown in patients with PPMS, but its association with clinical incapacity is uncertain. We investigated the relationship between regional brain atrophy and physical disability in patients with PPMS. METHODS Patients with an established diagnosis of PPMS underwent a neurological evaluation followed by brain MRI at 1.5 T. Volumetric analysis was performed with FreeSurfer software, and evaluated the neocortex, total white matter, total subcortical grey matter, putamen, caudate, globus pallidus, thalamus, hippocampus, brainstem, corpus callosum and pre-central gyrus volumes. Clinical data obtained included physical disability as measured by the Expanded Disability Status Scale (EDSS). RESULTS Nineteen patients were included, 14 female (73.7%), mean age of 55.7 (SD 7.6) and mean disease duration of 13.0 years (SD 8.8). Median EDSS score was 6.0 (3.5-8.0). The average T1 lesion load (4.9 cm³, SD 3.4) and T2 load (10.5 cm³, SD 9.9) did not relate to disease duration. There was no significant correlation between EDSS score or disease duration and the cortical grey matter, deep grey matter or white matter structures analysed. Lesion load was negatively correlated with cortical and subcortical grey matter volumes (p < 0.05), but not with total white matter volume. CONCLUSIONS Physical disability in PPMS is not directly related to brain volume loss. Grey matter atrophy correlates with lesion load in patients with PPMS, indicating that focal inflammatory white matter lesions may contribute to progressive neuronal degeneration.
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Affiliation(s)
- Orlando Galego
- Coimbra Hospital and University Centre, Department of Neuroradiology, Coimbra, Portugal
| | - Ana Gouveia
- Coimbra Hospital and University Centre, Department of Neurology, Coimbra, Portugal
| | - Sónia Batista
- Coimbra Hospital and University Centre, Department of Neurology, Coimbra, Portugal
| | - Cristina Moura
- Coimbra Hospital and University Centre, Department of Neuroradiology, Coimbra, Portugal
| | - Egídio Machado
- Coimbra Hospital and University Centre, Department of Neuroradiology, Coimbra, Portugal
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32
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Tillema JM, Hulst HE, Rocca MA, Vrenken H, Steenwijk MD, Damjanovic D, Enzinger C, Ropele S, Tedeschi G, Gallo A, Ciccarelli O, Rovira A, Montalban X, de Stefano N, Stromillo ML, Filippi M, Barkhof F. Regional cortical thinning in multiple sclerosis and its relation with cognitive impairment: A multicenter study. Mult Scler 2015; 22:901-9. [DOI: 10.1177/1352458515607650] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Accepted: 08/17/2015] [Indexed: 11/17/2022]
Abstract
Objectives: The objectives of this paper are to compare in a multicenter setting patterns of regional cortical thickness in patients with relapsing–remitting multiple sclerosis (RRMS) and cognitive impairment (CI) and those cognitively preserved (CP), and explore the relationship between cortical thinning and cognitive performance. Methods: T1-weighted isotropic brain scans were collected at 3T from seven European centers in 60 RRMS patients and 65 healthy controls (HCs). Patients underwent clinical and neuropsychological examinations. Cortical thickness (CTh) measures were calculated using FreeSurfer (failing in four) and both lobar and vertex-based general linear model (GLM) analyses were compared between study groups. Results: Twenty (36%) MS patients were classified as CI. Mean global CTh was smaller in RRMS patients compared to HCs (left 2.43 vs. 2.53 mm, right 2.44 vs. 2.54 mm, p < 0.001). Multivariate GLM regional analysis showed significantly more temporal thinning in CI compared to CP patients. Verbal memory scores correlated to regional cortical thinning in the insula whereas visual memory scores correlated to parietal thinning. Conclusions: This multicenter study showed mild global cortical thinning in RRMS. The extent of thinning is less pronounced than previously reported. Only subtle regional differences between CI and CP patients were observed, some of which related to specific cognitive domains.
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Affiliation(s)
- JM Tillema
- Department of Neurology, Mayo Clinic, USA/Department of Radiology and Nuclear Medicine, VU University Medical Center, The Netherlands
| | - HE Hulst
- Department of Anatomy and Neurosciences, VU University Medical Center, The Netherlands
| | - MA Rocca
- Neuroimaging Research Unit, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Italy/Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Italy
| | - H Vrenken
- Department of Radiology and Nuclear Medicine, VU University Medical Center, The Netherlands/Department of Physics and Medical Technology, VU University Medical Center, The Netherlands
| | - MD Steenwijk
- Department of Radiology and Nuclear Medicine, VU University Medical Center, The Netherlands
| | - D Damjanovic
- Neuroimaging Research Unit, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Italy
| | - C Enzinger
- Department of Neurology and Division of Neuroradiology, Department of Radiology, Medical University of Graz, Austria
| | - S Ropele
- Department of Neurology and Division of Neuroradiology, Department of Radiology, Medical University of Graz, Austria
| | - G Tedeschi
- MRI Center “SUN-FISM,” Second University of Naples and Institute of Diagnosis and Care “Hermitage-Capodimonte,” Italy/Division of Neurology, Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, Second University of Naples, Italy
| | - A Gallo
- MRI Center “SUN-FISM,” Second University of Naples and Institute of Diagnosis and Care “Hermitage-Capodimonte,” Italy/Division of Neurology, Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, Second University of Naples, Italy
| | - O Ciccarelli
- NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, UK/National Institute for Health Research (NIHR) University College London Hospital (UCLH) Biomedical Research Centre (BRC)
| | - A Rovira
- Magnetic Resonance Unit, Department of Radiology, Hospital Universitari Vall d’Hebron, Spain/Unitat de Neuroimmunologia Clinica, CEM-Cat, Hospital Universitari Vall d’Hebron, Spain
| | - X Montalban
- Unitat de Neuroimmunologia Clinica, CEM-Cat, Hospital Universitari Vall d’Hebron, Spain
| | - N de Stefano
- Department of Medicine, Surgery and Neurosciences, University of Siena, Italy
| | - ML Stromillo
- Department of Medicine, Surgery and Neurosciences, University of Siena, Italy
| | - M Filippi
- Neuroimaging Research Unit, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Italy/Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Italy
| | - F Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, The Netherlands
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33
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van Munster CE, Jonkman LE, Weinstein HC, Uitdehaag BM, Geurts JJ. Gray matter damage in multiple sclerosis: Impact on clinical symptoms. Neuroscience 2015; 303:446-61. [DOI: 10.1016/j.neuroscience.2015.07.006] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2014] [Revised: 06/29/2015] [Accepted: 07/01/2015] [Indexed: 01/12/2023]
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Calabrese M, Magliozzi R, Ciccarelli O, Geurts JJG, Reynolds R, Martin R. Exploring the origins of grey matter damage in multiple sclerosis. Nat Rev Neurosci 2015; 16:147-58. [PMID: 25697158 DOI: 10.1038/nrn3900] [Citation(s) in RCA: 277] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Multiple sclerosis is characterized at the gross pathological level by the presence of widespread focal demyelinating lesions of the myelin-rich white matter. However, it is becoming clear that grey matter is not spared, even during the earliest phases of the disease. Furthermore, grey matter damage may have an important role both in physical and cognitive disability. Grey matter pathology involves both inflammatory and neurodegenerative mechanisms, but the relationship between the two is unclear. Histological, immunological and neuroimaging studies have provided new insight in this rapidly expanding field, and form the basis of the most recent hypotheses on the pathogenesis of grey matter damage.
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Affiliation(s)
- Massimiliano Calabrese
- Advanced Neuroimaging Laboratory of Neurology B, Department of Neurological and Movement Sciences, University Hospital Verona, Piazzale Ludovico Antonio Scuro 10, 37134, Verona, Italy
| | - Roberta Magliozzi
- 1] Division of Brain Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital, London W12 0NN, UK. [2] Department of Cell Biology and Neurosciences, Istituto Superiore di Sanità, Viale Regina Elena 299, Rome, Italy
| | - Olga Ciccarelli
- 1] National Institute for Health Research, University College London/University College London Hospitals NHS Foundation Trust (NIHR UCL/UCLH) Biomedical Research Centre, 149 Tottenham Court Road, London W1T 7DN, UK. [2] Queen Square Multiple Sclerosis Centre, University College London, Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Jeroen J G Geurts
- Section of Clinical Neuroscience, Department of Anatomy and Neurosciences, VU University Medical Center, van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands
| | - Richard Reynolds
- Division of Brain Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital, London W12 0NN, UK
| | - Roland Martin
- Neuroimmunology and Multiple Sclerosis Research Section, Department of Neurology, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 26, 8091 Zurich, Switzerland
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35
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Rocca MA, Amato MP, De Stefano N, Enzinger C, Geurts JJ, Penner IK, Rovira A, Sumowski JF, Valsasina P, Filippi M. Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis. Lancet Neurol 2015; 14:302-17. [PMID: 25662900 DOI: 10.1016/s1474-4422(14)70250-9] [Citation(s) in RCA: 368] [Impact Index Per Article: 40.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
In patients with multiple sclerosis (MS), grey matter damage is widespread and might underlie many of the clinical symptoms, especially cognitive impairment. This relation between grey matter damage and cognitive impairment has been lent support by findings from clinical and MRI studies. However, many aspects of cognitive impairment in patients with MS still need to be characterised. Standardised neuropsychological tests that are easy to administer and sensitive to disease-related abnormalities are needed to gain a better understanding of the factors affecting cognitive performance in patients with MS than exists at present. Imaging measures of the grey matter are necessary, but not sufficient to fully characterise cognitive decline in MS. Imaging measures of both lesioned and normal-appearing white matter lend support to the hypothesis of the existence of an underlying disconnection syndrome that causes clinical symptoms to trigger. Findings on cortical reorganisation support the contribution of brain plasticity and cognitive reserve in limiting cognitive deficits. The development of clinical and imaging biomarkers that can monitor disease development and treatment response is crucial to allow early identification of patients with MS who are at risk of cognitive impairment.
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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
| | - Maria P Amato
- Department of Neurofarba, Section of Neurosciences, University of Florence, Florence, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | | | - Jeroen J Geurts
- Department of Anatomy and Neuroscience, Section of Clinical Neuroscience, VU University Medical Centre, VUmc Multiple Sclerosis Centre Amsterdam, Amsterdam, Netherlands
| | - Iris-K Penner
- University and University Children's Hospital Basel, Cognitive Psychology and Methodology and Division of Paediatric Neurology and Developmental Medicine, Basel, Switzerland
| | - Alex Rovira
- Magnetic Resonance Unit, Department of Radiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - James F Sumowski
- Neuropsychology and Neuroscience, Kessler Foundation Research Center, West Orange, NJ, USA
| | - Paola Valsasina
- 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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Steenwijk MD, Daams M, Pouwels PJW, J Balk L, Tewarie PK, Geurts JJG, Barkhof F, Vrenken H. Unraveling the relationship between regional gray matter atrophy and pathology in connected white matter tracts in long-standing multiple sclerosis. Hum Brain Mapp 2015; 36:1796-807. [PMID: 25627545 DOI: 10.1002/hbm.22738] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 11/23/2014] [Accepted: 01/06/2015] [Indexed: 01/07/2023] Open
Abstract
INTRODUCTION Gray matter (GM) atrophy is common in multiple sclerosis (MS), but the relationship with white matter (WM) pathology is largely unknown. Some studies found a co-occurrence in specific systems, but a regional analysis across the brain in different clinical phenotypes is necessary to further understand the disease mechanism underlying GM atrophy in MS. Therefore, we investigated the association between regional GM atrophy and pathology in anatomically connected WM tracts. METHODS Conventional and diffusion tensor imaging was performed at 3T in 208 patients with long-standing MS and 60 healthy controls. Deep and cortical GM regions were segmented and quantified, and both lesion volumes and average normal appearing WM fractional anisotropy of their associated tracts were derived using an atlas obtained by probabilistic tractography in the controls. Linear regression was then performed to quantify the amount of regional GM atrophy that can be explained by WM pathology in the connected tract. RESULTS MS patients showed extensive deep and cortical GM atrophy. Cortical atrophy was particularly present in frontal and temporal regions. Pathology in connected WM tracts statistically explained both regional deep and cortical GM atrophy in relapsing-remitting (RR) patients, but only deep GM atrophy in secondary-progressive (SP) patients. CONCLUSION In RRMS patients, both deep and cortical GM atrophy were associated with pathology in connected WM tracts. In SPMS patients, only regional deep GM atrophy could be explained by pathology in connected WM tracts. This suggests that in SPMS patients cortical GM atrophy and WM damage are (at least partly) independent disease processes.
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Affiliation(s)
- Martijn D Steenwijk
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, The Netherlands
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Steenwijk MD, Daams M, Pouwels PJW, Balk LJ, Tewarie PK, Killestein J, Uitdehaag BMJ, Geurts JJG, Barkhof F, Vrenken H. What Explains Gray Matter Atrophy in Long-standing Multiple Sclerosis? Radiology 2014; 272:832-42. [PMID: 24761837 DOI: 10.1148/radiol.14132708] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Martijn D Steenwijk
- From the Departments of Radiology and Nuclear Medicine (M.D.S., M.D., F.B., H.V.), Anatomy and Neurosciences (M.D., J.J.G.G.), Physics and Medical Technology (P.J.W.P., H.V.), and Neurology (L.J.B., P.K.T., J.K., B.M.J.U.), Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
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Dwyer MG, Bergsland N, Zivadinov R. Improved longitudinal gray and white matter atrophy assessment via application of a 4-dimensional hidden Markov random field model. Neuroimage 2014; 90:207-17. [DOI: 10.1016/j.neuroimage.2013.12.004] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Revised: 12/01/2013] [Accepted: 12/03/2013] [Indexed: 10/25/2022] Open
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Diffusion Tensor Imaging in NAWM and NADGM in MS and CIS: Association with Candidate Biomarkers in Sera. Mult Scler Int 2013; 2013:265259. [PMID: 24455265 PMCID: PMC3877634 DOI: 10.1155/2013/265259] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2013] [Revised: 10/09/2013] [Accepted: 10/09/2013] [Indexed: 12/31/2022] Open
Abstract
The aim of this study was to evaluate diffusion tensor imaging (DTI) indices in the corpus callosum and pyramidal tract in normal-appearing white matter (NAWM) and the caudate nucleus and thalamus in deep grey matter (NADGM) in all MS subtypes and clinically isolated syndrome (CIS). Furthermore, it was determined whether these metrics are associated with clinical measures and the serum levels of candidate immune biomarkers. Apparent diffusion coefficients (ADC) values were significantly higher than in controls in all six studied NAWM regions in SPMS, 4/6 regions in RRMS and PPMS and 2/6 regions in CIS. In contrast, decreased fractional anisotropy (FA) values in comparison to controls were detected in 2/6 NAWM regions in SPMS and 1/6 in RRMS and PPMS. In RRMS, the level of neurological disability correlated with thalamic FA values (r = 0.479, P = 0.004). In chronic progressive subtypes and CIS, ADC values of NAWM and NADGM were associated with the levels of MIF, sFas, and sTNF-α. Our data indicate that DTI may be useful in detecting pathological changes in NAWM and NADGM in MS patients and that these changes are related to neurological disability.
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Eshaghi A, Bodini B, Ridgway GR, García-Lorenzo D, Tozer DJ, Sahraian MA, Thompson AJ, Ciccarelli O. Temporal and spatial evolution of grey matter atrophy in primary progressive multiple sclerosis. Neuroimage 2013; 86:257-64. [PMID: 24099844 PMCID: PMC3898881 DOI: 10.1016/j.neuroimage.2013.09.059] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2013] [Revised: 09/03/2013] [Accepted: 09/23/2013] [Indexed: 01/13/2023] Open
Abstract
Grey matter (GM) atrophy occurs early in primary progressive MS (PPMS), but it is unknown whether its progression involves different brain regions at different rates, as is seen in other neurodegenerative diseases. We aimed to investigate the temporal and regional evolution of GM volume loss over 5 years and its relationship with disability progression in early PPMS. We studied 36 patients with PPMS within five years from onset and 19 age and gender-matched healthy controls with clinical and imaging assessments at study entry and yearly for 3 years and then at 5 years. Patients were scored on the expanded disability status scale (EDSS) and MS Functional Composite (MSFC) at each time-point. An unbiased longitudinal voxel-based morphometry approach, based on high-dimensional spatial alignment within-subject, was applied to the serial imaging data. The rate of local (voxel-wise) volume change per year was compared between groups and its relationship with clinical outcomes was assessed. Patients deteriorated significantly during the five years follow-up. Patients showed a greater decline of GM volume (p < 0.05, FWE-corrected) bilaterally in the cingulate cortex, thalamus, putamen, precentral gyrus, insula and cerebellum when compared to healthy controls over five years, although the rate of volume loss varied across the brain, and was the fastest in the cingulate cortex. Significant (p < 0.05, FWE-corrected) volume loss was detected in the left insula, left precuneus, and right cingulate cortex in patients at three years, as compared to baseline, whilst the bilateral putamen and the left superior temporal gyrus showed volume loss at five years. In patients, there was a relationship between a higher rate of volume loss in the bilateral cingulate cortex and greater clinical disability, as measured by the MSFC, at five years (Pearson's r = 0.49, p = 0.003). Longitudinal VBM demonstrated that the progression of GM atrophy in PPMS occurs at different rates in different regions across the brain. The involvement of the cingulate cortex occurs early in the disease course, continues at a steady rate throughout the follow-up period and is associated with patient outcome. These findings provide new insights into the characteristics of GM atrophy across the brain in MS, and have potential consequences for the selection of brain atrophy as an outcome measure in neuroprotective clinical trials. Longitudinal VBM and TBM can be used in longitudinal studies in multiple sclerosis. GM loss is a dynamic process in primary progressive multiple sclerosis. The highest rate of GM reduction is seen in the cingulate gyri. GM atrophy may be used as an outcome measure for neuroprotective clinical trials.
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Affiliation(s)
- Arman Eshaghi
- NMR research Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square MS Centre, London, UK; MS Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran.
| | - Benedetta Bodini
- NMR research Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square MS Centre, London, UK; Brain and Spine institute, ICM, Paris, France; Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière, Université Pierre et Marie Curie, Inserm U975, Paris, France
| | - Gerard R Ridgway
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK
| | | | - Daniel J Tozer
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
| | - Mohammad Ali Sahraian
- MS Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Alan J Thompson
- NMR research Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square MS Centre, London, UK; National Institute of Health Research (NIHR), UCLH, Biomedical Research Centre (BRC), London, UK
| | - Olga Ciccarelli
- NMR research Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square MS Centre, London, UK; National Institute of Health Research (NIHR), UCLH, Biomedical Research Centre (BRC), London, UK
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Lansley J, Mataix-Cols D, Grau M, Radua J, Sastre-Garriga J. Localized grey matter atrophy in multiple sclerosis: A meta-analysis of voxel-based morphometry studies and associations with functional disability. Neurosci Biobehav Rev 2013; 37:819-30. [DOI: 10.1016/j.neubiorev.2013.03.006] [Citation(s) in RCA: 71] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2012] [Revised: 02/27/2013] [Accepted: 03/11/2013] [Indexed: 01/01/2023]
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Giorgio A, De Stefano N. Clinical use of brain volumetry. J Magn Reson Imaging 2013; 37:1-14. [PMID: 23255412 DOI: 10.1002/jmri.23671] [Citation(s) in RCA: 82] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2011] [Accepted: 03/12/2012] [Indexed: 12/13/2022] Open
Abstract
Magnetic resonance imaging (MRI)-based brain volumetry is increasingly being used in the clinical setting to assess brain volume changes from structural MR images in a range of neurologic conditions. Measures of brain volumes have been shown to be valid biomarkers of the clinical state and progression by offering high reliability and robust inferences on the underlying disease-related mechanisms. This review critically examines the different scenarios of the application of MRI-based brain volumetry in neurology: 1) supporting disease diagnosis, 2) understanding mechanisms and tracking clinical progression of disease, and 3) monitoring treatment effect. These aspects will be discussed in a wide range of neurologic conditions, with particular emphasis on Alzheimer's disease and multiple sclerosis.
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Affiliation(s)
- Antonio Giorgio
- Department of Neurological and Behavioral Sciences, University of Siena, Italy
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43
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Jehna M, Langkammer C, Khalil M, Fuchs S, Reishofer G, Fazekas F, Ebner F, Enzinger C. An exploratory study on the spatial relationship between regional cortical volume changes and white matter integrity in multiple sclerosis. Brain Connect 2013; 3:255-64. [PMID: 23573900 DOI: 10.1089/brain.2012.0108] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Multiple sclerosis (MS) is a chronic inflammatory central nervous system disorder with a neurodegenerative component. While in the past, MS has been predominantly viewed as a white matter (WM) disease, gray matter (GM) pathology receives increasing attention in MS research. In this study, we tested hypothesis-free for a possible spatial relationship between cortical volume changes and disturbed integrity of projecting WM tracts. We used voxel-based morphometry (VBM), lesion probability maps (LPM), and probabilistic tractography to compare brain magnetic resonance imaging (MRI) scans obtained at 3 Tesla of 15 low disabled MS patients with 15 matched healthy controls (HCs). Areas of decreased cortical volume in the patients identified by VBM were used as seeds for tractography. Volume in two cortical areas in the left inferior frontal gyrus (IFG) and the left lateral occipital cortex (LOC) was reduced in patients compared to HCs. Starting from the IFG-region, tractography suggested impaired connections between left and right portions of the frontal lobe in the patients. Using the LOC as a seed, in patients, the left inferior longitudinal and fronto-occipital pathways appeared disintegrated compared to HCs. Swapping the seeds to homologous contralateral areas showed similar results for frontal, but different results for occipital brain areas. This at least partly could be explained by differential interference with WM lesions. These findings suggest a regional dependence between cortical GM and WM tract alterations in MS patients. While confirmation in larger and more heterogenic samples is needed, this study indicates that combining several MRI methods (VBM, LPM, and Probabilistic Tractography) may provide important insights into interacting processes related to the fiber tract and GM changes in MS.
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Affiliation(s)
- Margit Jehna
- Division of Neuroradiology, Department of Radiology, Medical University of Graz, Graz, Austria.
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44
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Vrenken H, Jenkinson M, Horsfield MA, Battaglini M, van Schijndel RA, Rostrup E, Geurts JJG, Fisher E, Zijdenbos A, Ashburner J, Miller DH, Filippi M, Fazekas F, Rovaris M, Rovira A, Barkhof F, de Stefano N. Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis. J Neurol 2012; 260:2458-71. [PMID: 23263472 PMCID: PMC3824277 DOI: 10.1007/s00415-012-6762-5] [Citation(s) in RCA: 83] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2012] [Accepted: 11/12/2012] [Indexed: 01/14/2023]
Abstract
Focal lesions and brain atrophy are the most extensively studied aspects of multiple sclerosis (MS), but the image acquisition and analysis techniques used can be further improved, especially those for studying within-patient changes of lesion load and atrophy longitudinally. Improved accuracy and sensitivity will reduce the numbers of patients required to detect a given treatment effect in a trial, and ultimately, will allow reliable characterization of individual patients for personalized treatment. Based on open issues in the field of MS research, and the current state of the art in magnetic resonance image analysis methods for assessing brain lesion load and atrophy, this paper makes recommendations to improve these measures for longitudinal studies of MS. Briefly, they are (1) images should be acquired using 3D pulse sequences, with near-isotropic spatial resolution and multiple image contrasts to allow more comprehensive analyses of lesion load and atrophy, across timepoints. Image artifacts need special attention given their effects on image analysis results. (2) Automated image segmentation methods integrating the assessment of lesion load and atrophy are desirable. (3) A standard dataset with benchmark results should be set up to facilitate development, calibration, and objective evaluation of image analysis methods for MS.
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Affiliation(s)
- H Vrenken
- Department of Radiology, VU University Medical Center, Amsterdam, The Netherlands,
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Ceccarelli A, Jackson JS, Tauhid S, Arora A, Gorky J, Dell'Oglio E, Bakshi A, Chitnis T, Khoury SJ, Weiner HL, Guttmann CRG, Bakshi R, Neema M. The impact of lesion in-painting and registration methods on voxel-based morphometry in detecting regional cerebral gray matter atrophy in multiple sclerosis. AJNR Am J Neuroradiol 2012; 33:1579-85. [PMID: 22460341 DOI: 10.3174/ajnr.a3083] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE VBM has been widely used to study GM atrophy in MS. MS lesions lead to segmentation and registration errors that may affect the reliability of VBM results. Improved segmentation and registration have been demonstrated by WM LI before segmentation. DARTEL appears to improve registration versus the USM. Our aim was to compare the performance of VBM-DARTEL versus VBM-USM and the effect of LI in the regional analysis of GM atrophy in MS. MATERIALS AND METHODS 3T T1 MR imaging scans were acquired from 26 patients with RRMS and 28 age-matched NC. LI replaced WM lesions with normal-appearing WM intensities before image segmentation. VBM analysis was performed in SPM8 by using DARTEL and USM with and without LI, allowing the comparison of 4 VBM methods (DARTEL + LI, DARTEL - LI, USM + LI, and USM - LI). Accuracy of VBM was assessed by using NMI, CC, and a simulation analysis. RESULTS Overall, DARTEL + LI yielded the most accurate GM maps among the 4 methods (highest NMI and CC, P < .001). DARTEL + LI showed significant GM loss in the bilateral thalami and caudate nuclei in patients with RRMS versus NC. The other 3 methods overestimated the number of regions of GM loss in RRMS versus NC. LI improved the accuracy of both VBM methods. Simulated data suggested the accuracy of the results provided from patient MR imaging analysis. CONCLUSIONS We introduce a pipeline that shows promise in limiting segmentation and registration errors in VBM analysis in MS.
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Affiliation(s)
- A Ceccarelli
- Department of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research, Partners MS Center, Harvard Medical School, Boston, MA 02445, USA
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46
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Popescu V, Battaglini M, Hoogstrate W, Verfaillie S, Sluimer I, van Schijndel R, van Dijk B, Cover K, Knol D, Jenkinson M, Barkhof F, de Stefano N, Vrenken H. Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis. Neuroimage 2012; 61:1484-94. [PMID: 22484407 DOI: 10.1016/j.neuroimage.2012.03.074] [Citation(s) in RCA: 113] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2011] [Revised: 02/28/2012] [Accepted: 03/19/2012] [Indexed: 10/28/2022] Open
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Lai CH, Lane HY, Tsai GE. Clinical and cerebral volumetric effects of sodium benzoate, a D-amino acid oxidase inhibitor, in a drug-naïve patient with major depression. Biol Psychiatry 2012; 71:e9-e10. [PMID: 22177737 DOI: 10.1016/j.biopsych.2011.10.034] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2011] [Revised: 10/07/2011] [Accepted: 10/09/2011] [Indexed: 01/04/2023]
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48
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Multivariate pattern classification of gray matter pathology in multiple sclerosis. Neuroimage 2012; 60:400-8. [PMID: 22245259 DOI: 10.1016/j.neuroimage.2011.12.070] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2011] [Revised: 12/22/2011] [Accepted: 12/24/2011] [Indexed: 11/21/2022] Open
Abstract
Univariate analyses have identified gray matter (GM) alterations in different groups of MS patients. While these methods detect differences on the basis of the single voxel or cluster, multivariate methods like support vector machines (SVM) identify the complex neuroanatomical patterns of GM differences. Using multivariate linear SVM analysis and leave-one-out cross-validation, we aimed at identifying neuroanatomical GM patterns relevant for individual classification of MS patients. We used SVM to separate GM segmentations of T1-weighted three-dimensional magnetic resonance (MR) imaging scans within different age- and sex-matched groups of MS patients with either early (n=17) or late MS (n=17) (contrast I), low (n=20) or high (n=20) white matter lesion load (contrast II), and benign MS (BMS, n=13) or non-benign MS (NBMS, n=13) (contrast III) scanned on a single 1.5 T MR scanner. GM patterns most relevant for individual separation of MS patients comprised cortical areas of all the cerebral lobes as well as deep GM structures, including the thalamus and caudate. The patterns detected were sufficiently informative to separate individuals of the respective groups with high sensitivity and specificity in 85% (contrast I), 83% (contrast II) and 77% (contrast III) of cases. The study demonstrates that neuroanatomical spatial patterns of GM segmentations contain information sufficient for correct classification of MS patients at the single case level, thus making multivariate SVM analysis a promising clinical application.
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Battaglini M, Jenkinson M, De Stefano N. Evaluating and reducing the impact of white matter lesions on brain volume measurements. Hum Brain Mapp 2011; 33:2062-71. [PMID: 21882300 DOI: 10.1002/hbm.21344] [Citation(s) in RCA: 243] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2010] [Revised: 03/06/2011] [Accepted: 04/11/2011] [Indexed: 11/06/2022] Open
Abstract
MR-based measurements of brain volumes may be affected by the presence of white matter (WM) lesions. Here, we assessed how and to what extent this may happen for WM lesions of various sizes and intensities. After inserting WM lesions of different sizes and intensities into T1-W brain images of healthy subjects, we assessed the effect on two widely used automatic methods for brain volume measurement such as SIENAX (segmentation-based) and SIENA (registration-based). To explore the relevance of partial volume (PV) estimation, we performed the experiments with two different PV models, implemented by the same segmentation algorithm (FAST) of SIENAX and SIENA. Finally, we tested potential solutions to this issue. The presence of WM lesions did not bias measurements for registration-based method such as SIENA. By contrast, the presence of WM lesions affected segmentation-based brain volume measurements such as SIENAx. The misclassification of both gray matter (GM) and WM volumes varied considerably with lesion size and intensity, especially when the lesion intensity was similar to that of the GM/WM interface. The extent to which the presence of WM lesions could affect tissue-class measures was clearly driven by the PV modeling used, with the mixel-type PV model giving a lower error in the presence of WM lesions. The tissue misclassification due to WM lesions was still present when they were masked out. By contrast, refilling the lesions with intensities matching the surrounding normal-appearing WM ensured accurate tissue-class measurements and thus represents a promising approach for accurate tissue classification and brain volume measurements.
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Affiliation(s)
- Marco Battaglini
- Department of Neurological and Behavioral Sciences, University of Siena, Italy
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50
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Bendfeldt K, Blumhagen JO, Egger H, Loetscher P, Denier N, Kuster P, Traud S, Mueller-Lenke N, Naegelin Y, Gass A, Hirsch J, Kappos L, Nichols TE, Radue EW, Borgwardt SJ. Spatiotemporal distribution pattern of white matter lesion volumes and their association with regional grey matter volume reductions in relapsing-remitting multiple sclerosis. Hum Brain Mapp 2011; 31:1542-55. [PMID: 20108225 DOI: 10.1002/hbm.20951] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The association of white matter (WM) lesions and grey matter (GM) atrophy is a feature in relapsing-remitting multiple sclerosis (RRMS). The spatiotemporal distribution pattern of WM lesions, their relations to regional GM changes and the underlying dynamics are unclear. Here we combined parametric and non-parametric voxel-based morphometry (VBM) to clarify these issues. MRI data from RRMS patients with progressive (PLV, n = 45) and non-progressive WM lesion volumes (NPLV, n = 44) followed up for 12 months were analysed. Cross-sectionally, the spatial WM lesion distribution was compared using lesion probability maps (LPMs). Longitudinally, WM lesions and GM volumes were studied using FSL-VBM and SPM5-VBM, respectively. WM lesions clustered around the lateral ventricles and in the centrum semiovale with a more widespread pattern in the PLV than in the NPLV group. The maximum local probabilities were similar in both groups and higher for T2 lesions (PLV: 27%, NPLV: 25%) than for T1 lesions (PLV: 15%, NPLV 14%). Significant WM lesion changes accompanied by cortical GM volume reductions occurred in the corpus callosum and optic radiations (P = 0.01 corrected), and more liberally tested (uncorrected P < 0.01) in the inferior fronto-occipital and longitudinal fasciculi, and corona radiata in the PLV group. Not any WM or GM changes were found in the NPLV group. In the PLV group, WM lesion distribution and development in fibres, was associated with regional GM volume loss. The different spatiotemporal distribution patterns of patients with progressive compared to patients with non-progressive WM lesions suggest differences in the dynamics of pathogenesis.
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Affiliation(s)
- Kerstin Bendfeldt
- Medical Image Analysis Center, University Hospital Basel, CH-4031 Basel, Switzerland
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