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Pontillo G, Cepas MB, Broeders TAA, Koubiyr I, Schoonheim MM. Network Analysis in Multiple Sclerosis and Related Disorders. Neuroimaging Clin N Am 2024; 34:375-384. [PMID: 38942522 DOI: 10.1016/j.nic.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease of the central nervous system, commonly featuring disability and cognitive impairment. The pathologic hallmark of MS lies in demyelination and hence impaired structural and functional neuronal pathways. Recent studies have shown that MS shows extensive structural disconnection of key network hub areas like the thalamus, combined with a functional network reorganization that can mostly be related to poorer clinical functioning. As MS can, therefore, be considered a network disorder, this review outlines recent innovations in the field of network neuroscience in MS.
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Affiliation(s)
- Giuseppe Pontillo
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands; MS Center Amsterdam, Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands.
| | - Mar Barrantes Cepas
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Tommy A A Broeders
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Ismail Koubiyr
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
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2
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Molina Galindo LS, Gonzalez-Escamilla G, Fleischer V, Grotegerd D, Meinert S, Ciolac D, Person M, Stein F, Brosch K, Nenadić I, Alexander N, Kircher T, Hahn T, Winter Y, Othman AE, Bittner S, Zipp F, Dannlowski U, Groppa S. Concurrent inflammation-related brain reorganization in multiple sclerosis and depression. Brain Behav Immun 2024; 119:978-988. [PMID: 38761819 DOI: 10.1016/j.bbi.2024.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 05/02/2024] [Accepted: 05/12/2024] [Indexed: 05/20/2024] Open
Abstract
BACKGROUND Neuroinflammation affects brain tissue integrity in multiple sclerosis (MS) and may have a role in major depressive disorder (MDD). Whether advanced magnetic resonance imaging characteristics of the gray-to-white matter border serve as proxy of neuroinflammatory activity in MDD and MS remain unknown. METHODS We included 684 participants (132 MDD patients with recurrent depressive episodes (RDE), 70 MDD patients with a single depressive episode (SDE), 222 MS patients without depressive symptoms (nMS), 58 MS patients with depressive symptoms (dMS), and 202 healthy controls (HC)). 3 T-T1w MRI-derived gray-to-white matter contrast (GWc) was used to reconstruct and characterize connectivity alterations of GWc-covariance networks by means of modularity, clustering coefficient, and degree. A cross-validated support vector machine was used to test the ability of GWc to stratify groups according to their depression symptoms, measured with BDI, at the single-subject level in MS and MDD independently. FINDINGS MS and MDD patients showed increased modularity (ANOVA partial-η2 = 0.3) and clustering (partial-η2 = 0.1) compared to HC. In the subgroups, a linear trend analysis attested a gradient of modularity increases in the form: HC, dMS, nMS, SDE, and RDE (ANOVA partial-η2 = 0.28, p < 0.001) while this trend was less evident for clustering coefficient. Reduced morphological integrity (GWc) was seen in patients with increased depressive symptoms (partial-η2 = 0.42, P < 0.001) and was associated with depression scores across patient groups (r = -0.2, P < 0.001). Depressive symptoms in MS were robustly classified (88 %). CONCLUSIONS Similar structural network alterations in MDD and MS exist, suggesting possible common inflammatory events like demyelination, neuroinflammation that are caught by GWc analyses. These alterations may vary depending on the severity of symptoms and in the case of MS may elucidate the occurrence of comorbid depression.
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Affiliation(s)
- Lara S Molina Galindo
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn(2)), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn(2)), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Vinzenz Fleischer
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn(2)), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dumitru Ciolac
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn(2)), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Maren Person
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn(2)), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Frederike Stein
- Klinik für Psychiatrie und Psychotherapie, Philipps-Universität Marburg, Marburg, Germany
| | - Katharina Brosch
- Klinik für Psychiatrie und Psychotherapie, Philipps-Universität Marburg, Marburg, Germany
| | - Igor Nenadić
- Klinik für Psychiatrie und Psychotherapie, Philipps-Universität Marburg, Marburg, Germany
| | - Nina Alexander
- Klinik für Psychiatrie und Psychotherapie, Philipps-Universität Marburg, Marburg, Germany
| | - Tilo Kircher
- Klinik für Psychiatrie und Psychotherapie, Philipps-Universität Marburg, Marburg, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Yaroslav Winter
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn(2)), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Ahmed E Othman
- Department of Neuroradiology, Rhine-Main Neuroscience Network (rmn(2)), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Stefan Bittner
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn(2)), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Frauke Zipp
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn(2)), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Sergiu Groppa
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn(2)), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany.
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Foster MA, Prados F, Collorone S, Kanber B, Cawley N, Davagnanam I, Yiannakas MC, Ogunbowale L, Burke A, Barkhof F, Wheeler-Kingshott CAMG, Ciccarelli O, Brownlee W, Toosy AT. Improving explanation of motor disability with diffusion-based graph metrics at onset of the first demyelinating event. Mult Scler 2024; 30:800-811. [PMID: 38751221 PMCID: PMC11134971 DOI: 10.1177/13524585241247785] [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: 01/10/2024] [Revised: 03/20/2024] [Accepted: 03/31/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Conventional magnetic resonance imaging (MRI) does not account for all disability in multiple sclerosis. OBJECTIVE The objective was to assess the ability of graph metrics from diffusion-based structural connectomes to explain motor function beyond conventional MRI in early demyelinating clinically isolated syndrome (CIS). METHODS A total of 73 people with CIS underwent conventional MRI, diffusion-weighted imaging and clinical assessment within 3 months from onset. A total of 28 healthy controls underwent MRI. Structural connectomes were produced. Differences between patients and controls were explored; clinical associations were assessed in patients. Linear regression models were compared to establish relevance of graph metrics over conventional MRI. RESULTS Local efficiency (p = 0.045), clustering (p = 0.034) and transitivity (p = 0.036) were reduced in patients. Higher assortativity was associated with higher Expanded Disability Status Scale (EDSS) (β = 74.9, p = 0.026) scores. Faster timed 25-foot walk (T25FW) was associated with higher assortativity (β = 5.39, p = 0.026), local efficiency (β = 27.1, p = 0.041) and clustering (β = 36.1, p = 0.032) and lower small-worldness (β = -3.27, p = 0.015). Adding graph metrics to conventional MRI improved EDSS (p = 0.045, ΔR2 = 4) and T25FW (p < 0.001, ΔR2 = 13.6) prediction. CONCLUSION Graph metrics are relevant early in demyelination. They show differences between patients and controls and have relationships with clinical outcomes. Segregation (local efficiency, clustering, transitivity) was particularly relevant. Combining graph metrics with conventional MRI better explained disability.
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Affiliation(s)
- Michael A Foster
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Ferran Prados
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Imaging Computing, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Science, University College London, London, UK
- Universitat Oberta de Catalunya, Barcelona, Spain
| | - Sara Collorone
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Baris Kanber
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Imaging Computing, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Science, University College London, London, UK
| | - Niamh Cawley
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Indran Davagnanam
- Department of Brain Repair & Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Marios C Yiannakas
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Lola Ogunbowale
- Strabismus and Neuro-Ophthalmology Service, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Ailbhe Burke
- Strabismus and Neuro-Ophthalmology Service, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Frederik Barkhof
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Imaging Computing, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Science, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Claudia AM Gandini Wheeler-Kingshott
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Olga Ciccarelli
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Wallace Brownlee
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Ahmed T Toosy
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
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Wainberg M, Forde NJ, Mansour S, Kerrebijn I, Medland SE, Hawco C, Tripathy SJ. Genetic architecture of the structural connectome. Nat Commun 2024; 15:1962. [PMID: 38438384 PMCID: PMC10912129 DOI: 10.1038/s41467-024-46023-2] [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: 09/13/2022] [Accepted: 02/12/2024] [Indexed: 03/06/2024] Open
Abstract
Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.
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Affiliation(s)
- Michael Wainberg
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
| | - Natalie J Forde
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Salim Mansour
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Isabel Kerrebijn
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Psychology, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Colin Hawco
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.
| | - Shreejoy J Tripathy
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
- Department of Physiology, University of Toronto, Toronto, ON, Canada.
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Ma H, Zhu Y, Liang X, Wu L, Wang Y, Li X, Qian L, Cheung GL, Zhou F. In patients with mild disability NMOSD: is the alteration in the cortical morphological or functional network topological properties more significant. Front Immunol 2024; 15:1345843. [PMID: 38375481 PMCID: PMC10875087 DOI: 10.3389/fimmu.2024.1345843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/15/2024] [Indexed: 02/21/2024] Open
Abstract
Objective To assess the alteration of individual brain morphological and functional network topological properties and their clinical significance in patients with neuromyelitis optica spectrum disorder (NMOSD). Materials and methods Eighteen patients with NMOSD and twenty-two healthy controls (HCs) were included. The clinical assessment of NMOSD patients involved evaluations of disability status, cognitive function, and fatigue impact. For each participant, brain images, including high-resolution T1-weighted images for individual morphological brain networks (MBNs) and resting-state functional MR images for functional brain networks (FBNs) were obtained. Topological properties were calculated and compared for both MBNs and FBNs. Then, partial correlation analysis was performed to investigate the relationships between the altered network properties and clinical variables. Finally, the altered network topological properties were used to classify NMOSD patients from HCs and to analyses time- to-progression of the patients. Results The average Expanded Disability Status Scale score of NMOSD patients was 1.05 (range from 0 to 2), indicating mild disability. Compared to HCs, NMOSD patients exhibited a higher normalized characteristic path length (λ) in their MBNs (P = 0.0118, FDR corrected) but showed no significant differences in the global properties of FBNs (p: 0.405-0.488). Network-based statistical analysis revealed that MBNs had more significantly altered connections (P< 0.01, NBS corrected) than FBNs. Altered nodal properties of MBNs were correlated with disease duration or fatigue scores (P< 0.05/6 with Bonferroni correction). Using the altered nodal properties of MBNs, the accuracy of classification of NMOSD patients versus HCs was 96.4%, with a sensitivity of 93.3% and a specificity of 100%. This accuracy was better than that achieved using the altered nodal properties of FBNs. Nodal properties of MBN significantly predicted Expanded Disability Status Scale worsening in patients with NMOSD. Conclusion The results indicated that patients with mild disability NMOSD exhibited compensatory increases in local network properties to maintain overall stability. Furthermore, the alterations in the morphological network nodal properties of NMOSD patients not only had better relevance for clinical assessments compared with functional network nodal properties, but also exhibited predictive values of EDSS worsening.
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Affiliation(s)
- Haotian Ma
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Queen Mary College, Nanchang University, Nanchang, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Xiao Liang
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Lin Wu
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Yao Wang
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Xiaoxing Li
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Long Qian
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China
| | | | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Neuroimaging Laboratory, Jiangxi Medical Imaging Research Institute, Nanchang, China
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Fleischer V, Gonzalez-Escamilla G, Pareto D, Rovira A, Sastre-Garriga J, Sowa P, Høgestøl EA, Harbo HF, Bellenberg B, Lukas C, Ruggieri S, Gasperini C, Uher T, Vaneckova M, Bittner S, Othman AE, Collorone S, Toosy AT, Meuth SG, Zipp F, Barkhof F, Ciccarelli O, Groppa S. Prognostic value of single-subject grey matter networks in early multiple sclerosis. Brain 2024; 147:135-146. [PMID: 37642541 PMCID: PMC10766234 DOI: 10.1093/brain/awad288] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/17/2023] [Accepted: 08/02/2023] [Indexed: 08/31/2023] Open
Abstract
The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for 5 years (mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35.0 ± 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30.0, P < 0.001] and global efficiency [H(2) = 31.3, P < 0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1.5, P = 0.474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P-values < 0.05). Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors.
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Affiliation(s)
- Vinzenz Fleischer
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain
| | - Alex Rovira
- Section of Neuroradiology, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain
| | - Jaume Sastre-Garriga
- Department of Neurology/Neuroimmunology, Multiple Sclerosis Centre of Catalonia, Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain
| | - Piotr Sowa
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, 0424 Oslo, Norway
| | - Einar A Høgestøl
- Institute of Clinical Medicine, University of Oslo, NO-0316 Oslo, Norway
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
| | - Hanne F Harbo
- Institute of Clinical Medicine, University of Oslo, NO-0316 Oslo, Norway
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
| | - Barbara Bellenberg
- Institute of Neuroradiology, St Josef Hospital, Ruhr-University Bochum, 44791 Bochum, Germany
| | - Carsten Lukas
- Institute of Neuroradiology, St Josef Hospital, Ruhr-University Bochum, 44791 Bochum, Germany
| | - Serena Ruggieri
- Department of Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Claudio Gasperini
- Department of Neurosciences, San Camillo-Forlanini Hospital, 00152 Rome, Italy
| | - Tomas Uher
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, 121 08 Prague, Czech Republic
| | - Manuela Vaneckova
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, 121 08 Prague, Czech Republic
| | - Stefan Bittner
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Ahmed E Othman
- Department of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Sara Collorone
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
| | - Ahmed T Toosy
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
| | - Sven G Meuth
- Department of Neurology, Medical Faculty, Heinrich-Heine-University, 40225 Düsseldorf, Germany
| | - Frauke Zipp
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Frederik Barkhof
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, 1100 DD Amsterdam, Netherlands
| | - Olga Ciccarelli
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
| | - Sergiu Groppa
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
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7
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Lee H, Jung JH, Chung S, Ju G, Kim S, Son JW, Shin CJ, Lee SI, Lee J. Graph Theoretical Analysis of Brain Structural Connectivity in Patients with Alcohol Dependence. Exp Neurobiol 2023; 32:362-369. [PMID: 37927134 PMCID: PMC10628861 DOI: 10.5607/en23026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/19/2023] [Accepted: 10/26/2023] [Indexed: 11/07/2023] Open
Abstract
This study aimed to compare brain structural connectivity using graph theory between patients with alcohol dependence and social drinkers. The participants were divided into two groups; the alcohol group (N=23) consisting of patients who had been hospitalized and had abstained from alcohol for at least three months and the control group (N=22) recruited through advertisements and were social drinkers. All participants were evaluated using 3T magnetic resonance imaging. A total of 1000 repeated whole-brain tractographies with random parameters were performed using DSI Studio. Four hundred functionally defined cortical regions of interest (ROIs) were parcellated using FreeSurfer based on the Schaefer Atlas. The ROIs were overlaid on the tractography results to generate 1000 structural connectivity matrices per person, and 1000 matrices were averaged into a single matrix per subject. Graph analysis was performed through igraph R package. Graph measures were compared between the two groups using analysis of covariance, considering the effects of age and smoking pack years. The alcohol group showed lower local efficiency than the control group in the whole-brain (F=5.824, p=0.020), somato-motor (F=5.963, p=0.019), and default mode networks (F=4.422, p=0.042). The alcohol group showed a lower global efficiency (F=5.736, p=0.021) in the control network. The transitivity of the alcohol group in the dorsal attention network was higher than that of the control (F=4.257, p=0.046). Our results imply that structural stability of the whole-brain network is affected in patients with alcohol dependence, which can lead to ineffective information processing in cases of local node failure.
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Affiliation(s)
- Hyunjung Lee
- Department of Psychiatry, Chungbuk National University Hospital, Cheongju 28644, Korea
| | - Joon Hyung Jung
- Department of Psychiatry, Chungbuk National University Hospital, Cheongju 28644, Korea
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul 03080, Korea
| | - Seungwon Chung
- Department of Psychiatry, Chungbuk National University Hospital, Cheongju 28644, Korea
- Department of Psychiatry, College of Medicine, Chungbuk National University, Cheongju 28644, Korea
| | - Gawon Ju
- Department of Psychiatry, Chungbuk National University Hospital, Cheongju 28644, Korea
- Department of Psychiatry, College of Medicine, Chungbuk National University, Cheongju 28644, Korea
| | - Siekyeong Kim
- Department of Psychiatry, Chungbuk National University Hospital, Cheongju 28644, Korea
- Department of Psychiatry, College of Medicine, Chungbuk National University, Cheongju 28644, Korea
| | - Jung-Woo Son
- Department of Psychiatry, Chungbuk National University Hospital, Cheongju 28644, Korea
- Department of Psychiatry, College of Medicine, Chungbuk National University, Cheongju 28644, Korea
| | - Chul-Jin Shin
- Department of Psychiatry, Chungbuk National University Hospital, Cheongju 28644, Korea
- Department of Psychiatry, College of Medicine, Chungbuk National University, Cheongju 28644, Korea
| | - Sang Ick Lee
- Department of Psychiatry, Chungbuk National University Hospital, Cheongju 28644, Korea
- Department of Psychiatry, College of Medicine, Chungbuk National University, Cheongju 28644, Korea
| | - Jeonghwan Lee
- Department of Psychiatry, Chungbuk National University Hospital, Cheongju 28644, Korea
- Department of Psychiatry, College of Medicine, Chungbuk National University, Cheongju 28644, Korea
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8
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Brasanac J, Chien C. A review on multiple sclerosis prognostic findings from imaging, inflammation, and mental health studies. Front Hum Neurosci 2023; 17:1151531. [PMID: 37250694 PMCID: PMC10213782 DOI: 10.3389/fnhum.2023.1151531] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 04/21/2023] [Indexed: 05/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) of the brain is commonly used to detect where chronic and active lesions are in multiple sclerosis (MS). MRI is also extensively used as a tool to calculate and extrapolate brain health by way of volumetric analysis or advanced imaging techniques. In MS patients, psychiatric symptoms are common comorbidities, with depression being the main one. Even though these symptoms are a major determinant of quality of life in MS, they are often overlooked and undertreated. There has been evidence of bidirectional interactions between the course of MS and comorbid psychiatric symptoms. In order to mitigate disability progression in MS, treating psychiatric comorbidities should be investigated and optimized. New research for the prediction of disease states or phenotypes of disability have advanced, primarily due to new technologies and a better understanding of the aging brain.
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Affiliation(s)
- Jelena Brasanac
- Charité – Universitätsmedizin Berlin, Klinik für Psychiatrie und Psychotherapie, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Medizinische Klinik m.S. Psychosomatik, Berlin, Germany
| | - Claudia Chien
- Charité – Universitätsmedizin Berlin, Klinik für Psychiatrie und Psychotherapie, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Medizinische Klinik m.S. Psychosomatik, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Experimental and Clinical Research Center, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Neuroscience Clinical Research Center, Berlin, Germany
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9
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Feng J, Men W, Yu X, Liu W, Zhang S, Liu J, Ma L. High-altitude exposure duration dependent global and regional gray matter volume decrease in healthy immigrants: a cross-sectional study. Acta Radiol 2023; 64:751-759. [PMID: 35369766 DOI: 10.1177/02841851221091674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The correlation between brain injury and high-altitude (HA) exposure duration (Dur_HA) as well as peripheral oxygen saturation (SpO2) remains unclear. PURPOSE To evaluate the global and regional brain volume differences between HA immigrants and sea-level residents, and the relationship between brain volume with Dur_HA and SpO2. MATERIAL AND METHODS Structural magnetic resonance imaging (MRI) scans were acquired in 33 healthy male HA immigrants (HA group) and 33 matched sea-level male residents (SL group). Differences in global gray matter volume (GMV), white matter volume (WMV), brain parenchyma volume (BV), total intracranial volume (TIV), and the volume-fraction (the ratio of GMV/TIV, WMV/TIV, BV/TIV) were assessed. Regional gray matter differences were investigated using voxel-based morphology analysis. The volume of clusters with GM loss were calculated as the volume of volume of interest (V_VOI). Student's t-test and partial correlation were adopted for statistic calculation. RESULTS Compared to the SL group, the HA immigrants had larger WMV (P = 0.015), smaller ratio of GMV/WMV (P = 0.022), and regional gray matter loss in bilateral basal ganglion, limbic system, midbrain, and vermis (cluster size >100 voxels, family-wise error corrected at P = 0.01). The global GMV, BV, and V_VOI confined to vermis had negative correlations with the Dur_HA (r = -0.369, P = 0.049; r = -0.380, P = 0.042; and r = -0.471, P = 0.010. Neither global nor regional brain volume correlated with SpO2. CONCLUSION Global and regional brain are affected by long-term HA exposure, and global and regional gray matter have a time-dependent volume loss.
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Affiliation(s)
- Jie Feng
- 104607Medical School of Chinese People's Liberation Army, Beijing, PR China
- Department of Radiology, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing, PR China
- Department of Radiology, Corps Hospital of Shanxi Province of Chinese People's Armed Police Force, Taiyuan, PR China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, PR China
| | - Xiao Yu
- 104607Medical School of Chinese People's Liberation Army, Beijing, PR China
- Department of Radiology, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing, PR China
| | - Wenjia Liu
- Department of Radiology, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing, PR China
| | - Shiyu Zhang
- 104607Medical School of Chinese People's Liberation Army, Beijing, PR China
- Department of Radiology, Beijing Friendship Hospital, 535066Capital Medical University, Beijing, PR China
| | - Jie Liu
- Department of Radiology, General Hospital of Tibet Military Region, Lhasa, Tibet, PR China
| | - Lin Ma
- 104607Medical School of Chinese People's Liberation Army, Beijing, PR China
- Department of Radiology, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing, PR China
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10
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Abdolalizadeh A, Ohadi MAD, Ershadi ASB, Aarabi MH. Graph theoretical approach to brain remodeling in multiple sclerosis. Netw Neurosci 2023; 7:148-159. [PMID: 37334009 PMCID: PMC10270718 DOI: 10.1162/netn_a_00276] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 09/05/2022] [Indexed: 03/21/2024] Open
Abstract
Multiple sclerosis (MS) is a neuroinflammatory disorder damaging structural connectivity. Natural remodeling processes of the nervous system can, to some extent, restore the damage caused. However, there is a lack of biomarkers to evaluate remodeling in MS. Our objective is to evaluate graph theory metrics (especially modularity) as a biomarker of remodeling and cognition in MS. We recruited 60 relapsing-remitting MS and 26 healthy controls. Structural and diffusion MRI, plus cognitive and disability evaluations, were done. We calculated modularity and global efficiency from the tractography-derived connectivity matrices. Association of graph metrics with T2 lesion load, cognition, and disability was evaluated using general linear models adjusting for age, gender, and disease duration wherever applicable. We showed that MS subjects had higher modularity and lower global efficiency compared with controls. In the MS group, modularity was inversely associated with cognitive performance but positively associated with T2 lesion load. Our results indicate that modularity increase is due to the disruption of intermodular connections in MS because of the lesions, with no improvement or preserving of cognitive functions.
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Affiliation(s)
- AmirHussein Abdolalizadeh
- Students’ Scientific Research Program, Tehran University of Medical Sciences, Tehran, Iran
- Interdisciplinary Neuroscience Research Program, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Amin Dabbagh Ohadi
- Students’ Scientific Research Program, Tehran University of Medical Sciences, Tehran, Iran
- Interdisciplinary Neuroscience Research Program, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Sasan Bayani Ershadi
- Students’ Scientific Research Program, Tehran University of Medical Sciences, Tehran, Iran
- Interdisciplinary Neuroscience Research Program, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Hadi Aarabi
- Department of Neuroscience, Padova Neuroscience Center, University of Padova, Padova, Italy
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11
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Ganzetti M, Graves JS, Holm SP, Dondelinger F, Midaglia L, Gaetano L, Craveiro L, Lipsmeier F, Bernasconi C, Montalban X, Hauser SL, Lindemann M. Neural correlates of digital measures shown by structural MRI: a post-hoc analysis of a smartphone-based remote assessment feasibility study in multiple sclerosis. J Neurol 2023; 270:1624-1636. [PMID: 36469103 PMCID: PMC9970954 DOI: 10.1007/s00415-022-11494-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND A study was undertaken to evaluate remote monitoring via smartphone sensor-based tests in people with multiple sclerosis (PwMS). This analysis aimed to explore regional neural correlates of digital measures derived from these tests. METHODS In a 24-week, non-randomized, interventional, feasibility study (NCT02952911), sensor-based tests on the Floodlight Proof-of-Concept app were used to assess cognition (smartphone-based electronic Symbol Digit Modalities Test), upper extremity function (Draw a Shape Test, Pinching Test), and gait and balance (Static Balance Test, Two-Minute Walk Test, U-Turn Test). In this post-hoc analysis, digital measures and standard clinical measures (e.g., Nine-Hole Peg Test [9HPT]) were correlated against regional structural magnetic resonance imaging outcomes. Seventy-six PwMS aged 18-55 years with an Expanded Disability Status Scale score of 0.0-5.5 were enrolled from two different sites (USA and Spain). Sixty-two PwMS were included in this analysis. RESULTS Worse performance on digital and clinical measures was associated with smaller regional brain volumes and larger ventricular volumes. Whereas digital and clinical measures had many neural correlates in common (e.g., putamen, globus pallidus, caudate nucleus, lateral occipital cortex), some were observed only for digital measures. For example, Draw a Shape Test and Pinching Test measures, but not 9HPT score, correlated with volume of the hippocampus (r = 0.37 [drawing accuracy over time on the Draw a Shape Test]/ - 0.45 [touching asynchrony on the Pinching Test]), thalamus (r = 0.38/ - 0.41), and pons (r = 0.35/ - 0.35). CONCLUSIONS Multiple neural correlates were identified for the digital measures in a cohort of people with early MS. Digital measures showed associations with brain regions that clinical measures were unable to demonstrate, thus providing potential novel information on functional ability compared with standard clinical assessments.
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Affiliation(s)
- Marco Ganzetti
- grid.417570.00000 0004 0374 1269F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Jennifer S. Graves
- grid.266100.30000 0001 2107 4242Department of Neurosciences, University of California San Diego, San Diego, CA USA
| | - Sven P. Holm
- grid.417570.00000 0004 0374 1269F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Frank Dondelinger
- grid.417570.00000 0004 0374 1269F. Hoffmann-La Roche Ltd, Basel, Switzerland ,grid.419481.10000 0001 1515 9979Present Address: Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Luciana Midaglia
- grid.411083.f0000 0001 0675 8654Department of Neurology-Neuroimmunology, Centre d’Esclerosi Múltiple de Catalunya (Cemcat), Hospital Universitari Vall d’Hebron, Barcelona, Spain ,grid.7080.f0000 0001 2296 0625Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - Laura Gaetano
- grid.417570.00000 0004 0374 1269F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Licinio Craveiro
- grid.417570.00000 0004 0374 1269F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | | | - Corrado Bernasconi
- grid.417570.00000 0004 0374 1269F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Xavier Montalban
- grid.411083.f0000 0001 0675 8654Department of Neurology-Neuroimmunology, Centre d’Esclerosi Múltiple de Catalunya (Cemcat), Hospital Universitari Vall d’Hebron, Barcelona, Spain ,grid.7080.f0000 0001 2296 0625Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - Stephen L. Hauser
- grid.266102.10000 0001 2297 6811Department of Neurology, University of California San Francisco, San Francisco, CA USA
| | - Michael Lindemann
- grid.417570.00000 0004 0374 1269F. Hoffmann-La Roche Ltd, Basel, Switzerland
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12
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Predicting overall survival in diffuse glioma from the presurgical connectome. Sci Rep 2022; 12:18783. [PMID: 36335224 PMCID: PMC9637134 DOI: 10.1038/s41598-022-22387-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022] Open
Abstract
Diffuse gliomas are incurable brain tumors, yet there is significant heterogeneity in patient survival. Advanced computational techniques such as radiomics show potential for presurgical prediction of survival and other outcomes from neuroimaging. However, these techniques ignore non-lesioned brain features that could be essential for improving prediction accuracy. Gray matter covariance network (connectome) features were retrospectively identified from the T1-weighted MRIs of 305 adult patients diagnosed with diffuse glioma. These features were entered into a Cox proportional hazards model to predict overall survival with 10-folds cross-validation. The mean time-dependent area under the curve (AUC) of the connectome model was compared with the mean AUCs of clinical and radiomic models using a pairwise t-test with Bonferroni correction. One clinical model included only features that are known presurgery (clinical) and another included an advantaged set of features that are not typically known presurgery (clinical +). The median survival time for all patients was 134.2 months. The connectome model (AUC 0.88 ± 0.01) demonstrated superior performance (P < 0.001, corrected) compared to the clinical (AUC 0.61 ± 0.02), clinical + (AUC 0.79 ± 0.01) and radiomic models (AUC 0.75 ± 0.02). These findings indicate that the connectome is a feasible and reliable early biomarker for predicting survival in patients with diffuse glioma. Connectome and other whole-brain models could be valuable tools for precision medicine by informing patient risk stratification and treatment decision-making.
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13
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Barile B, Ashtari P, Stamile C, Marzullo A, Maes F, Durand-Dubief F, Van Huffel S, Sappey-Marinier D. Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome. Front Robot AI 2022; 9:926255. [PMID: 36313252 PMCID: PMC9608344 DOI: 10.3389/frobt.2022.926255] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/18/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose: The main goal of this study is to investigate the discrimination power of Grey Matter (GM) thickness connectome data between Multiple Sclerosis (MS) clinical profiles using statistical and Machine Learning (ML) methods. Materials and Methods: A dataset composed of 90 MS patients acquired at the MS clinic of Lyon Neurological Hospital was used for the analysis. Four MS profiles were considered, corresponding to Clinical Isolated Syndrome (CIS), Relapsing-Remitting MS (RRMS), Secondary Progressive MS (SPMS), and Primary Progressive MS (PPMS). Each patient was classified in one of these profiles by our neurologist and underwent longitudinal MRI examinations including T1-weighted image acquisition at each examination, from which the GM tissue was segmented and the cortical GM thickness measured. Following the GM parcellation using two different atlases (FSAverage and Glasser 2016), the morphological connectome was built and six global metrics (Betweenness Centrality (BC), Assortativity (r), Transitivity (T), Efficiency (E g ), Modularity (Q) and Density (D)) were extracted. Based on their connectivity metrics, MS profiles were first statistically compared and second, classified using four different learning machines (Logistic Regression, Random Forest, Support Vector Machine and AdaBoost), combined in a higher level ensemble model by majority voting. Finally, the impact of the GM spatial resolution on the MS clinical profiles classification was analyzed. Results: Using binary comparisons between the four MS clinical profiles, statistical differences and classification performances higher than 0.7 were observed. Good performances were obtained when comparing the two early clinical forms, RRMS and PPMS (F1 score of 0.86), and the two neurodegenerative profiles, PPMS and SPMS (F1 score of 0.72). When comparing the two atlases, slightly better performances were obtained with the Glasser 2016 atlas, especially between RRMS with PPMS (F1 score of 0.83), compared to the FSAverage atlas (F1 score of 0.69). Also, the thresholding value for graph binarization was investigated suggesting more informative graph properties in the percentile range between 0.6 and 0.8. Conclusion: An automated pipeline was proposed for the classification of MS clinical profiles using six global graph metrics extracted from the GM morphological connectome of MS patients. This work demonstrated that GM morphological connectivity data could provide good classification performances by combining four simple ML models, without the cost of long and complex MR techniques, such as MR diffusion, and/or deep learning architectures.
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Affiliation(s)
- Berardino Barile
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - Pooya Ashtari
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | | | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
| | - Frederik Maes
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - Françoise Durand-Dubief
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- Hôpital Neurologique, Service de Neurologie, Hospices Civils de Lyon, Bron, France
| | | | - Dominique Sappey-Marinier
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- CERMEP–Imagerie du Vivant, Université de Lyon, Lyon, France
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14
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Schoonheim MM, Broeders TAA, Geurts JJG. The network collapse in multiple sclerosis: An overview of novel concepts to address disease dynamics. Neuroimage Clin 2022; 35:103108. [PMID: 35917719 PMCID: PMC9421449 DOI: 10.1016/j.nicl.2022.103108] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/01/2022] [Accepted: 07/10/2022] [Indexed: 11/16/2022]
Abstract
Multiple sclerosis is a neuroinflammatory and neurodegenerative disorder of the central nervous system that can be considered a network disorder. In MS, lesional pathology continuously disconnects structural pathways in the brain, forming a disconnection syndrome. Complex functional network changes then occur that are poorly understood but closely follow clinical status. Studying these structural and functional network changes has been and remains crucial to further decipher complex symptoms like cognitive impairment and physical disability. Recent insights especially implicate the importance of monitoring network hubs in MS, like the thalamus and default-mode network which seem especially hit hard. Such network insights in MS have led to the hypothesis that as the network continues to become disconnected and dysfunctional, exceeding a certain threshold of network efficiency loss leads to a "network collapse". After this collapse, crucial network hubs become rigid and overloaded, and at the same time a faster neurodegeneration and accelerated clinical (and cognitive) progression can be seen. As network neuroscience has evolved, the MS field can now move towards a clearer classification of the network collapse itself and specific milestone events leading up to it. Such an updated network-focused conceptual framework of MS could directly impact clinical decision making as well as the design of network-tailored rehabilitation strategies. This review therefore provides an overview of recent network concepts that have enhanced our understanding of clinical progression in MS, especially focusing on cognition, as well as new concepts that will likely move the field forward in the near future.
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Affiliation(s)
- Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Tommy A A Broeders
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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15
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Ciolac D, Gonzalez-Escamilla G, Winter Y, Melzer N, Luessi F, Radetz A, Fleischer V, Groppa SA, Kirsch M, Bittner S, Zipp F, Muthuraman M, Meuth SG, Grothe M, Groppa S. Altered grey matter integrity and network vulnerability relate to epilepsy occurrence in patients with multiple sclerosis. Eur J Neurol 2022; 29:2309-2320. [PMID: 35582936 DOI: 10.1111/ene.15405] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 03/22/2022] [Accepted: 05/13/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND To investigate the relevance of compartmentalized grey matter (GM) pathology and network reorganization in MS patients with concomitant epilepsy. METHODS From 3T MRI scans of 30 MS patients with epilepsy (MSE; age 41±15 years, 21 females, disease duration 8±6 years, median Expanded Disability Status Scale (EDSS) 3), 60 MS patients without epilepsy (MS; age 41±12 years, 35 females, disease duration 6±4 years, EDSS 2), and 60 healthy subjects (HS; age 40±13 years, 27 females) regional volumes of GM lesions and of cortical, subcortical, and hippocampal structures were quantified. Network topology and vulnerability were modeled within the graph theoretical framework. The receiver operating characteristic (ROC) analysis was applied to assess the accuracy of GM pathology measures to discriminate between MSE and MS patients. RESULTS Higher lesion volumes within the hippocampus, mesiotemporal cortex, and amygdala were detected in MSE compared to MS (all p<0.05). MSE displayed lower cortical volumes mainly in temporal and parietal areas compared to MS and HS (all p<0.05). Lower volumes of hippocampal tail and presubiculum were identified in both MSE and MS patients compared to HS (all p<0.05). Network topology in MSE was characterized by higher transitivity and assortativity, and higher vulnerability compared to MS and HS (all p<0.05). Hippocampal lesion volume yielded the highest accuracy (area under the ROC curve 0.80 [0.67-0.91]) in discriminating between MSE and MS patients. CONCLUSIONS High lesion load, altered integrity of mesiotemporal GM structures, and network reorganization are associated with a greater propensity of epilepsy occurrence in MS.
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Affiliation(s)
- Dumitru Ciolac
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.,Nicolae Testemitanu State University of Medicine and Pharmacy, Chisinau, Republic of Moldova.,Department of Neurology, Institute of Emergency Medicine, Chisinau, Republic of Moldova
| | - Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Yaroslav Winter
- Mainz Comprehensive Epilepsy and Sleep Medicine Center, Department of Neurology, Johannes Gutenberg University Mainz, Mainz, Germany.,Department of Neurology, Philipps-University, Marburg, Germany
| | - Nico Melzer
- Department of Neurology, Heinrich Heine University, Düsseldorf, Germany
| | - Felix Luessi
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Angela Radetz
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Vinzenz Fleischer
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Stanislav A Groppa
- Nicolae Testemitanu State University of Medicine and Pharmacy, Chisinau, Republic of Moldova.,Department of Neurology, Institute of Emergency Medicine, Chisinau, Republic of Moldova
| | - Michael Kirsch
- Institute for Diagnostic Radiology and Neuroradiology, University Medicine of Greifswald, Germany
| | - Stefan Bittner
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Frauke Zipp
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Sven G Meuth
- Department of Neurology, Heinrich Heine University, Düsseldorf, Germany
| | - Matthias Grothe
- Department of Neurology, University Medicine of Greifswald, Greifswald, Germany
| | - Sergiu Groppa
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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16
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Broeders TA, Douw L, Eijlers AJ, Dekker I, Uitdehaag BM, Barkhof F, Hulst HE, Vinkers CH, Geurts JJ, Schoonheim MM. A more unstable resting-state functional network in cognitively declining multiple sclerosis. Brain Commun 2022; 4:fcac095. [PMID: 35620116 PMCID: PMC9128379 DOI: 10.1093/braincomms/fcac095] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/14/2022] [Accepted: 04/11/2022] [Indexed: 11/24/2022] Open
Abstract
Cognitive impairment is common in people with multiple sclerosis and strongly
affects their daily functioning. Reports have linked disturbed cognitive
functioning in multiple sclerosis to changes in the organization of the
functional network. In a healthy brain, communication between brain regions and
which network a region belongs to is continuously and dynamically adapted to
enable adequate cognitive function. However, this dynamic network adaptation has
not been investigated in multiple sclerosis, and longitudinal network data
remain particularly rare. Therefore, the aim of this study was to longitudinally
identify patterns of dynamic network reconfigurations that are related to the
worsening of cognitive decline in multiple sclerosis. Resting-state functional
MRI and cognitive scores (expanded Brief Repeatable Battery of
Neuropsychological tests) were acquired in 230 patients with multiple sclerosis
and 59 matched healthy controls, at baseline (mean disease duration: 15 years)
and at 5-year follow-up. A sliding-window approach was used for functional MRI
analyses, where brain regions were dynamically assigned to one of seven
literature-based subnetworks. Dynamic reconfigurations of subnetworks were
characterized using measures of promiscuity (number of subnetworks switched to),
flexibility (number of switches), cohesion (mutual switches) and disjointedness
(independent switches). Cross-sectional differences between cognitive groups and
longitudinal changes were assessed, as well as relations with structural damage
and performance on specific cognitive domains. At baseline, 23% of
patients were cognitively impaired (≥2/7 domains
Z < −2) and 18% were mildly
impaired (≥2/7 domains
Z < −1.5). Longitudinally,
28% of patients declined over time (0.25 yearly change on ≥2/7
domains based on reliable change index). Cognitively impaired patients displayed
more dynamic network reconfigurations across the whole brain compared with
cognitively preserved patients and controls, i.e. showing higher promiscuity
(P = 0.047), flexibility
(P = 0.008) and cohesion
(P = 0.008). Over time, cognitively
declining patients showed a further increase in cohesion
(P = 0.004), which was not seen in stable
patients (P = 0.544). More cohesion was
related to more severe structural damage (average
r = 0.166,
P = 0.015) and worse verbal memory
(r = −0.156,
P = 0.022), information processing speed
(r = −0.202,
P = 0.003) and working memory
(r = −0.163,
P = 0.017). Cognitively impaired multiple
sclerosis patients exhibited a more unstable network reconfiguration compared to
preserved patients, i.e. brain regions switched between subnetworks more often,
which was related to structural damage. This shift to more unstable network
reconfigurations was also demonstrated longitudinally in patients that showed
cognitive decline only. These results indicate the potential relevance of a
progressive destabilization of network topology for understanding cognitive
decline in multiple sclerosis.
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Affiliation(s)
- Tommy A.A. Broeders
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Linda Douw
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anand J.C. Eijlers
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Iris Dekker
- Departments of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bernard M.J. Uitdehaag
- Departments of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, UK
| | - Hanneke E. Hulst
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Christiaan H. Vinkers
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Departments of Psychiatry, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeroen J.G. Geurts
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno M. Schoonheim
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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17
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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18
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van der Weijden CWJ, Pitombeira MS, Haveman YRA, Sanchez-Catasus CA, Campanholo KR, Kolinger GD, Rimkus CM, Buchpiguel CA, Dierckx RAJO, Renken RJ, Meilof JF, de Vries EFJ, de Paula Faria D. The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging. Insights Imaging 2022; 13:63. [PMID: 35347460 PMCID: PMC8960512 DOI: 10.1186/s13244-022-01198-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/22/2022] [Indexed: 12/03/2022] Open
Abstract
Background Graph theoretical network analysis with structural magnetic resonance imaging (MRI) of multiple sclerosis (MS) patients can be used to assess subtle changes in brain networks. However, the presence of multiple focal brain lesions might impair the accuracy of automatic tissue segmentation methods, and hamper the performance of graph theoretical network analysis. Applying “lesion filling” by substituting the voxel intensities of a lesion with the voxel intensities of nearby voxels, thus creating an image devoid of lesions, might improve segmentation and graph theoretical network analysis. This study aims to determine if brain networks are different between MS subtypes and healthy controls (HC) and if the assessment of these differences is affected by lesion filling. Methods The study included 49 MS patients and 19 HC that underwent a T1w, and T2w-FLAIR MRI scan. Graph theoretical network analysis was performed from grey matter fractions extracted from the original T1w-images and T1w-images after lesion filling. Results Artefacts in lesion-filled T1w images correlated positively with total lesion volume (r = 0.84, p < 0.001) and had a major impact on grey matter segmentation accuracy. Differences in sensitivity for network alterations were observed between original T1w data and after application of lesion filling: graph theoretical network analysis obtained from lesion-filled T1w images produced more differences in network organization in MS patients. Conclusion Lesion filling might reduce variability across subjects resulting in an increased detection rate of network alterations in MS, but also induces significant artefacts, and therefore should be applied cautiously especially in individuals with higher lesions loads. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01198-4.
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19
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Frieske J, Pareto D, García-Vidal A, Cuypers K, Meesen RL, Alonso J, Arévalo MJ, Galán I, Renom M, Vidal-Jordana Á, Auger C, Montalban X, Rovira À, Sastre-Garriga J. Can cognitive training reignite compensatory mechanisms in advanced multiple sclerosis patients? An explorative morphological network approach. Neuroscience 2022; 495:86-96. [DOI: 10.1016/j.neuroscience.2022.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/22/2022] [Accepted: 03/24/2022] [Indexed: 10/18/2022]
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20
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Cherkasova MV, Fu JF, Jarrett M, Johnson P, Abel S, Tam R, Rauscher A, Sossi V, Kolind S, Li DKB, Sadovnick AD, Machan L, Girard JM, Emond F, Vosoughi R, Traboulsee A, Stoessl AJ. Cortical morphology predicts placebo response in multiple sclerosis. Sci Rep 2022; 12:732. [PMID: 35031632 PMCID: PMC8760243 DOI: 10.1038/s41598-021-04462-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 12/22/2021] [Indexed: 11/27/2022] Open
Abstract
Despite significant insights into the neural mechanisms of acute placebo responses, less is known about longer-term placebo responses, such as those seen in clinical trials, or their interactions with brain disease. We examined brain correlates of placebo responses in a randomized trial of a then controversial and now disproved endovascular treatment for multiple sclerosis. Patients received either balloon or sham extracranial venoplasty and were followed for 48 weeks. Venoplasty had no therapeutic effect, but a subset of both venoplasty- and sham-treated patients reported a transient improvement in health-related quality of life, suggesting a placebo response. Placebo responders did not differ from non-responders in total MRI T2 lesion load, count or location, nor were there differences in normalized brain volume, regional grey or white matter volume or cortical thickness (CT). However, responders had higher lesion activity. Graph theoretical analysis of CT covariance showed that non-responders had a more small-world-like CT architecture. In non-responders, lesion load was inversely associated with CT in somatosensory, motor and association areas, precuneus, and insula, primarily in the right hemisphere. In responders, lesion load was unrelated to CT. The neuropathological process in MS may produce in some a cortical configuration less capable of generating sustained placebo responses.
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Affiliation(s)
- Mariya V Cherkasova
- Department of Psychology, University of British Columbia, Vancouver, Canada. .,Department of Psychology, West Virginia University, 2128 Life Science Building, Morgantown, WV, 26506, USA.
| | - Jessie F Fu
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Michael Jarrett
- Population Data BC, University of British Columbia, Vancouver, BC, Canada
| | - Poljanka Johnson
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shawna Abel
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Roger Tam
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada.,School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Alexander Rauscher
- Depatment of Pediatrics (Division of Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Vesna Sossi
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Shannon Kolind
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - David K B Li
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.,Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - A Dessa Sadovnick
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Lindsay Machan
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - J Marc Girard
- Centre Hospitalier de L'Université de Montréal, Montréal, QC, Canada
| | - Francois Emond
- CHU de Québec-Université Laval, Hôpital de L'Enfant-Jésus, Québec, Canada
| | - Reza Vosoughi
- Department of Internal Medicine (Neurology), University of Manitoba, Winnipeg, Canada
| | - Anthony Traboulsee
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - A Jon Stoessl
- Department of Medicine (Division of Neurology), Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
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21
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Groppa S, Gonzalez-Escamilla G, Eshaghi A, Meuth SG, Ciccarelli O. Linking immune-mediated damage to neurodegeneration in multiple sclerosis: could network-based MRI help? Brain Commun 2021; 3:fcab237. [PMID: 34729480 PMCID: PMC8557667 DOI: 10.1093/braincomms/fcab237] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/21/2021] [Indexed: 01/04/2023] Open
Abstract
Inflammatory demyelination characterizes the initial stages of multiple sclerosis, while progressive axonal and neuronal loss are coexisting and significantly contribute to the long-term physical and cognitive impairment. There is an unmet need for a conceptual shift from a dualistic view of multiple sclerosis pathology, involving either inflammatory demyelination or neurodegeneration, to integrative dynamic models of brain reorganization, where, glia-neuron interactions, synaptic alterations and grey matter pathology are longitudinally envisaged at the whole-brain level. Functional and structural MRI can delineate network hallmarks for relapses, remissions or disease progression, which can be linked to the pathophysiology behind inflammatory attacks, repair and neurodegeneration. Here, we aim to unify recent findings of grey matter circuits dynamics in multiple sclerosis within the framework of molecular and pathophysiological hallmarks combined with disease-related network reorganization, while highlighting advances from animal models (in vivo and ex vivo) and human clinical data (imaging and histological). We propose that MRI-based brain networks characterization is essential for better delineating ongoing pathology and elaboration of particular mechanisms that may serve for accurate modelling and prediction of disease courses throughout disease stages.
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Affiliation(s)
- Sergiu Groppa
- Imaging and Neurostimulation, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Gabriel Gonzalez-Escamilla
- Imaging and Neurostimulation, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Arman Eshaghi
- Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London WC1E 6BT, UK.,Department of Computer Science, Centre for Medical Image Computing (CMIC), University College London, London WC1E 6BT, UK
| | - Sven G Meuth
- Department of Neurology, Medical Faculty, Heinrich Heine University, Düsseldorf 40225, Germany
| | - Olga Ciccarelli
- Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London WC1E 6BT, UK
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22
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Ciolac D, Gonzalez-Escamilla G, Radetz A, Fleischer V, Person M, Johnen A, Landmeyer NC, Krämer J, Muthuraman M, Meuth SG, Groppa S. Sex-specific signatures of intrinsic hippocampal networks and regional integrity underlying cognitive status in multiple sclerosis. Brain Commun 2021; 3:fcab198. [PMID: 34514402 PMCID: PMC8417841 DOI: 10.1093/braincomms/fcab198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/27/2021] [Accepted: 07/15/2021] [Indexed: 11/24/2022] Open
Abstract
The hippocampus is an anatomically compartmentalized structure embedded in highly wired networks that are essential for cognitive functions. The hippocampal vulnerability has been postulated in acute and chronic neuroinflammation in multiple sclerosis, while the patterns of occurring inflammation, neurodegeneration or compensation have not yet been described. Besides focal damage to hippocampal tissue, network disruption is an important contributor to cognitive decline in multiple sclerosis patients. We postulate sex-specific trajectories in hippocampal network reorganization and regional integrity and address their relationship to markers of neuroinflammation, cognitive/memory performance and clinical severity. In a large cohort of multiple sclerosis patients (n = 476; 337 females, age 35 ± 10 years, disease duration 16 ± 14 months) and healthy subjects (n = 110, 54 females; age 34 ± 15 years), we utilized MRI at baseline and at 2-year follow-up to quantify regional hippocampal volumetry and reconstruct single-subject hippocampal networks. Through graph analytical tools we assessed the clustered topology of the hippocampal networks. Mixed-effects analyses served to model sex-based differences in hippocampal network and subfield integrity between multiple sclerosis patients and healthy subjects at both time points and longitudinally. Afterwards, hippocampal network and subfield integrity were related to clinical and radiological variables in dependency of sex attribution. We found a more clustered network architecture in both female and male patients compared to their healthy counterparts. At both time points, female patients displayed a more clustered network topology in comparison to male patients. Over time, multiple sclerosis patients developed an even more clustered network architecture, though with a greater magnitude in females. We detected reduced regional volumes in most of the addressed hippocampal subfields in both female and male patients compared to healthy subjects. Compared to male patients, females displayed lower volumes of para- and presubiculum but higher volumes of the molecular layer. Longitudinally, volumetric alterations were more pronounced in female patients, which showed a more extensive regional tissue loss. Despite a comparable cognitive/memory performance between female and male patients over the follow-up period, we identified a strong interrelation between hippocampal network properties and cognitive/memory performance only in female patients. Our findings evidence a more clustered hippocampal network topology in female patients with a more extensive subfield volume loss over time. A stronger relation between cognitive/memory performance and the network topology in female patients suggests greater entrainment of the brain’s reserve. These results may serve to adapt sex-targeted neuropsychological interventions.
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Affiliation(s)
- Dumitru Ciolac
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany.,Department of Neurology, Institute of Emergency Medicine, Chisinau 2004, Moldova.,Laboratory of Neurobiology and Medical Genetics, Nicolae Testemitanu State University of Medicine and Pharmacy, Chisinau 2004, Moldova
| | - Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Angela Radetz
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Vinzenz Fleischer
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Maren Person
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Andreas Johnen
- Department of Neurology with Institute of Translational Neurology, University Hospital of Münster, Münster 48149, Germany
| | - Nils C Landmeyer
- Department of Neurology with Institute of Translational Neurology, University Hospital of Münster, Münster 48149, Germany
| | - Julia Krämer
- Department of Neurology with Institute of Translational Neurology, University Hospital of Münster, Münster 48149, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Sven G Meuth
- Department of Neurology, Heinrich Heine University, Düsseldorf 40225, Germany
| | - Sergiu Groppa
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
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23
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Badji A, de la Colina AN, Boshkovski T, Sabra D, Karakuzu A, Robitaille-Grou MC, Gros C, Joubert S, Bherer L, Lamarre-Cliche M, Stikov N, Gauthier CJ, Cohen-Adad J, Girouard H. A Cross-Sectional Study on the Impact of Arterial Stiffness on the Corpus Callosum, a Key White Matter Tract Implicated in Alzheimer's Disease. J Alzheimers Dis 2021; 77:591-605. [PMID: 32741837 DOI: 10.3233/jad-200668] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Vascular risk factors such as arterial stiffness play an important role in the etiology of Alzheimer's disease (AD), presumably due to the emergence of white matter lesions. However, the impact of arterial stiffness to white matter structure involved in the etiology of AD, including the corpus callosum remains poorly understood. OBJECTIVE The aims of the study are to better understand the relationship between arterial stiffness, white matter microstructure, and perfusion of the corpus callosum in older adults. METHODS Arterial stiffness was estimated using the gold standard measure of carotid-femoral pulse wave velocity (cfPWV). Cognitive performance was evaluated with the Trail Making Test part B-A. Neurite orientation dispersion and density imaging was used to obtain microstructural information such as neurite density and extracellular water diffusion. The cerebral blood flow was estimated using arterial spin labelling. RESULTS cfPWV better predicts the microstructural integrity of the corpus callosum when compared with other index of vascular aging (the augmentation index, the systolic blood pressure, and the pulse pressure). In particular, significant associations were found between the cfPWV, an alteration of the extracellular water diffusion, and a neuronal density increase in the body of the corpus callosum which was also correlated with the performance in cognitive flexibility. CONCLUSION Our results suggest that arterial stiffness is associated with an alteration of brain integrity which impacts cognitive function in older adults.
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Affiliation(s)
- Atef Badji
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.,Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, QC, Canada.,Department of Neurosciences, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada.,Groupe de Recherche sur le Système Nerveux Central (GRSNC), Université de Montréal, Montreal, QC, Canada.,Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Université de Montréal, Montreal, QC, Canada
| | - Adrián Noriega de la Colina
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, QC, Canada.,Department of Biomedical Sciences, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada.,Groupe de Recherche sur le Système Nerveux Central (GRSNC), Université de Montréal, Montreal, QC, Canada.,Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Université de Montréal, Montreal, QC, Canada
| | - Tommy Boshkovski
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Dalia Sabra
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, QC, Canada.,Department of Biomedical Sciences, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada.,Montreal Heart Institute, Montreal, QC, Canada.,PERFORM Centre, Concordia University, Montreal, QC, Canada
| | - Agah Karakuzu
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.,Montreal Heart Institute, Montreal, QC, Canada
| | | | - Charley Gros
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Sven Joubert
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, QC, Canada.,Department of Psychology, Faculty of Arts and Sciences, Université de Montréal, Montreal, QC, Canada
| | - Louis Bherer
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, QC, Canada.,Montreal Heart Institute, Montreal, QC, Canada.,Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Maxime Lamarre-Cliche
- Institut de Recherches Cliniques de Montréal, Université de Montréal, Montreal, QC, Canada
| | - Nikola Stikov
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.,Montreal Heart Institute, Montreal, QC, Canada
| | - Claudine J Gauthier
- Montreal Heart Institute, Montreal, QC, Canada.,Physics Department, Concordia University, Montreal, QC, Canada.,PERFORM Centre, Concordia University, Montreal, QC, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.,Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, QC, Canada.,Functional Neuroimaging Unit, Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Université de Montréal, Montreal, QC, Canada
| | - Hélène Girouard
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, QC, Canada.,Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada.,Groupe de Recherche sur le Système Nerveux Central (GRSNC), Université de Montréal, Montreal, QC, Canada.,Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Université de Montréal, Montreal, QC, Canada
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24
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Boscheron J, Ruet A, Deloire M, Charré-Morin J, Saubusse A, Brochet B, Tourdias T, Koubiyr I. Insights on the Relationship Between Hippocampal Connectivity and Memory Performances at the Early Stage of Multiple Sclerosis. Front Neurol 2021; 12:667531. [PMID: 34093415 PMCID: PMC8170471 DOI: 10.3389/fneur.2021.667531] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 04/19/2021] [Indexed: 11/13/2022] Open
Abstract
While memory impairment in multiple sclerosis (MS) is known to be associated with hippocampal alterations, whether hippocampal networks could dynamically reorganize as a compensation mechanism is still a matter of debate. In this context, our aim was to identify the patterns of structural and functional connectivity between the hippocampus and the rest of the brain and their possible relevance to memory performances in early MS. Thirty-two patients with a first episode suggestive of MS together with 10 matched healthy controls were prospectively explored at baseline, 1 and 5 years follow up. They were scanned with MRI and underwent a neuropsychological battery of tests that included the Selective Reminding Test and the Brief Visual Memory Test Revised to assess verbal and visuo-spatial memory, respectively. Hippocampal volume was computed together with four graph theory metrics to study the structural and functional connectivity of both hippocampi with the rest of the brain. Associations between network parameters and memory performances were assessed using linear mixed-effects (LME) models. Considering cognitive abilities, verbal memory performances of patients decreased over time while visuo-spatial memory performances were maintained. In parallel, hippocampal volumes decreased significantly while structural and functional connectivity metrics were modified, with an increase in hippocampal connections over time. More precisely, these modifications were indicating a reinforcement of hippocampal short-distance connections. LME models revealed that the drop in verbal memory performances was associated with hippocampal volume loss, while the preservation of visuo-spatial memory performances was linked to decreased hippocampal functional shortest path length. In conclusion, we demonstrated a differential impairment in memory performances in the early stages of MS and an important interplay between hippocampal-related structural and functional networks and those performances. As the structural damage increases, functional reorganization seems to be able to maintain visuo-spatial memory performances with strengthened short-distance connections.
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Affiliation(s)
| | - Aurélie Ruet
- Univ. Bordeaux, INSERM, Neurocentre Magendie, U1215, Bordeaux, France.,CHU de Bordeaux, Service de Neurologie, Bordeaux, France
| | | | | | | | - Bruno Brochet
- Univ. Bordeaux, INSERM, Neurocentre Magendie, U1215, Bordeaux, France
| | - Thomas Tourdias
- Univ. Bordeaux, INSERM, Neurocentre Magendie, U1215, Bordeaux, France.,CHU de Bordeaux, Neuroimagerie diagnostique et thérapeutique, Bordeaux, France
| | - Ismail Koubiyr
- Univ. Bordeaux, INSERM, Neurocentre Magendie, U1215, Bordeaux, France
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25
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Michels L, Koirala N, Groppa S, Luechinger R, Gantenbein AR, Sandor PS, Kollias S, Riederer F, Muthuraman M. Structural brain network characteristics in patients with episodic and chronic migraine. J Headache Pain 2021; 22:8. [PMID: 33657996 PMCID: PMC7927231 DOI: 10.1186/s10194-021-01216-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 01/28/2021] [Indexed: 12/28/2022] Open
Abstract
Background Migraine is a primary headache disorder that can be classified into an episodic (EM) and a chronic form (CM). Network analysis within the graph-theoretical framework based on connectivity patterns provides an approach to observe large-scale structural integrity. We test the hypothesis that migraineurs are characterized by a segregated network. Methods 19 healthy controls (HC), 17 EM patients and 12 CM patients were included. Cortical thickness and subcortical volumes were computed, and topology was analyzed using a graph theory analytical framework and network-based statistics. We further used support vector machines regression (SVR) to identify whether these network measures were able to predict clinical parameters. Results Network based statistics revealed significantly lower interregional connectivity strength between anatomical compartments including the fronto-temporal, parietal and visual areas in EM and CM when compared to HC. Higher assortativity was seen in both patients’ group, with higher modularity for CM and higher transitivity for EM compared to HC. For subcortical networks, higher assortativity and transitivity were observed for both patients’ group with higher modularity for CM. SVR revealed that network measures could robustly predict clinical parameters for migraineurs. Conclusion We found global network disruption for EM and CM indicated by highly segregated network in migraine patients compared to HC. Higher modularity but lower clustering coefficient in CM is suggestive of more segregation in this group compared to EM. The presence of a segregated network could be a sign of maladaptive reorganization of headache related brain circuits, leading to migraine attacks or secondary alterations to pain. Supplementary Information The online version contains supplementary material available at 10.1186/s10194-021-01216-8.
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Affiliation(s)
- Lars Michels
- Department of Neuroradiology, University Hospital Zurich, Sternwartstr. 6, CH-8091, Zurich, Switzerland.
| | - Nabin Koirala
- Haskins Laboratories, New Haven, Connecticut, USA.,Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Roger Luechinger
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Andreas R Gantenbein
- Department of Neurology and Neurorehabilitation, RehaClinic, Bad Zurzach, CH-5330, Switzerland.,Department of Neurology, University Hospital Zurich, CH-8091, Zurich, Switzerland
| | - Peter S Sandor
- Department of Neurology and Neurorehabilitation, RehaClinic, Bad Zurzach, CH-5330, Switzerland.,Department of Neurology, University Hospital Zurich, CH-8091, Zurich, Switzerland
| | - Spyros Kollias
- Department of Neuroradiology, University Hospital Zurich, Sternwartstr. 6, CH-8091, Zurich, Switzerland
| | - Franz Riederer
- Department of Neurology, Clinic Hietzing and Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Wolkerssbergenstrasse 1, AT-1130, Vienna, Austria.,University of Zurich, Faculty of Medicine, Rämistrasse 100, CH-8091, Zurich, Switzerland
| | - Muthuraman Muthuraman
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
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26
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Chard DT, Alahmadi AAS, Audoin B, Charalambous T, Enzinger C, Hulst HE, Rocca MA, Rovira À, Sastre-Garriga J, Schoonheim MM, Tijms B, Tur C, Gandini Wheeler-Kingshott CAM, Wink AM, Ciccarelli O, Barkhof F. Mind the gap: from neurons to networks to outcomes in multiple sclerosis. Nat Rev Neurol 2021; 17:173-184. [PMID: 33437067 DOI: 10.1038/s41582-020-00439-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/20/2020] [Indexed: 12/21/2022]
Abstract
MRI studies have provided valuable insights into the structure and function of neural networks, particularly in health and in classical neurodegenerative conditions such as Alzheimer disease. However, such work is also highly relevant in other diseases of the CNS, including multiple sclerosis (MS). In this Review, we consider the effects of MS pathology on brain networks, as assessed using MRI, and how these changes to brain networks translate into clinical impairments. We also discuss how this knowledge can inform the targeting of MS treatments and the potential future directions for research in this area. Studying MS is challenging as its pathology involves neurodegenerative and focal inflammatory elements, both of which could disrupt neural networks. The disruption of white matter tracts in MS is reflected in changes in network efficiency, an increasingly random grey matter network topology, relative cortical disconnection, and both increases and decreases in connectivity centred around hubs such as the thalamus and the default mode network. The results of initial longitudinal studies suggest that these changes evolve rather than simply increase over time and are linked with clinical features. Studies have also identified a potential role for treatments that functionally modify neural networks as opposed to altering their structure.
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Affiliation(s)
- Declan T Chard
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK. .,National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, UK.
| | - Adnan A S Alahmadi
- Department of Diagnostic Radiology, Faculty of Applied Medical Science, King Abdulaziz University (KAU), Jeddah, Saudi Arabia
| | - Bertrand Audoin
- Aix-Marseille University, CNRS, CRMBM, Marseille, France.,AP-HM, University Hospital Timone, Department of Neurology, Marseille, France
| | - Thalis Charalambous
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Christian Enzinger
- Department of Neurology, Research Unit for Neuronal Repair and Plasticity, Medical University of Graz, Graz, Austria.,Department of Radiology, Division of Neuroradiology, Vascular and Interventional Radiology, Medical University of Graz, Graz, Austria
| | - Hanneke E Hulst
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jaume Sastre-Garriga
- Servei de Neurologia/Neuroimmunologia, Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Betty Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Carmen Tur
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Department of Neurology, Luton and Dunstable University Hospital, Luton, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Alle Meije Wink
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Olga Ciccarelli
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, UK
| | - Frederik Barkhof
- National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, UK.,Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Institutes of Neurology and Healthcare Engineering, University College London, London, UK
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27
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Zheng Q, Chen X, Xie M, Fu J, Han Y, Wang J, Zeng C, Li Y. Altered structural networks in neuromyelitis optica spectrum disorder related with cognition impairment and clinical features. Mult Scler Relat Disord 2020; 48:102714. [PMID: 33422915 DOI: 10.1016/j.msard.2020.102714] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 11/19/2020] [Accepted: 12/19/2020] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To investigate the topological properties alterations of white matter (WM) network in neuromyelitis optica spectrum disorder (NMOSD) patients and its correlation with clinical and cognitive performance. METHODS Forty-eight NMOSD patients and fifty healthy controls (HC) underwent DTI and 3D-T1 scan on a 3.0 T MRI and clinical data and cognitive scales were collected. Structural networks were constructed and analyzed by using graph theory. The network metrics between-group comparisons were examined by using GRETNA. Differences in network parameters between two groups and grouped patients according to disease duration (DD) were compared to examine the impact of DD on WM network. The relationships between the network characteristics and clinical data and cognitive performances were also analyzed by partial correlation analysis. RESULTS The NMOSD patients exhibited decreased global and local network efficiency and increased characteristic path length, which were pronounced more in long DD patients. Furthermore, altered nodal efficiencies were observed in several brain regions, which were mainly distributed in default mode and visual systems. The Expanded Disability Status Scale was positively related to nodal shortest path. NMOSD patients showed decreased cognitive performance in attention, short-term memory and verbal memory, which were associated with significantly decreased degree centrality, nodal efficiency and increased nodal shortest path of several brain regions (all p<0.05). CONCLUSIONS This study illustrated the relationship between WM disruption and cognitive impairment in NMOSD patients, which advance the understanding of disrupted WM networks and provide insight into subtle WM pathology to cognitive impairment in NMOSD.
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Affiliation(s)
- Qiao Zheng
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
| | - Xiaoya Chen
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
| | - Min Xie
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
| | - Jialiang Fu
- Department of Radiology, Chongqing Health Center for Women and Children, 120 Longshan Road, Yubei District, Chongqing 401120, China
| | - Yongliang Han
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
| | - Jingjie Wang
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
| | - Chun Zeng
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China.
| | - Yongmei Li
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China.
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28
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Welton T, Constantinescu CS, Auer DP, Dineen RA. Graph Theoretic Analysis of Brain Connectomics in Multiple Sclerosis: Reliability and Relationship with Cognition. Brain Connect 2020; 10:95-104. [PMID: 32079409 PMCID: PMC7196369 DOI: 10.1089/brain.2019.0717] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Research suggests that disruption of brain networks might explain cognitive deficits in multiple sclerosis (MS). The reliability and effectiveness of graph theoretic network metrics as measures of cognitive performance were tested in 37 people with MS and 23 controls. Specifically, relationships with cognitive performance (linear regression against the paced auditory serial addition test-3 seconds [PASAT-3], symbol digit modalities test [SDMT], and attention network test) and 1-month reliability (using the intraclass correlation coefficient [ICC]) of network metrics were measured using both resting-state functional and diffusion magnetic resonance imaging data. Cognitive impairment was directly related to measures of brain network segregation and inversely related to network integration (prediction of PASAT-3 by small worldness, modularity, characteristic path length, R2 = 0.55; prediction of SDMT by small worldness, global efficiency, and characteristic path length, R2 = 0.60). Reliability of the measures for 1 month in a subset of nine participants was mostly rated as good (ICC >0.6) for both controls and MS patients in both functional and diffusion data, but was highly dependent on the chosen parcellation and graph density, with the 0.2–0.5 density range being the most reliable. This suggests that disrupted network organization predicts cognitive impairment in MS and its measurement is reliable for a 1-month period. These new findings support the hypothesis of network disruption as a major determinant of cognitive deficits in MS and the future possibility of the application of derived metrics as surrogate outcomes in trials of therapies for cognitive impairment.
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Affiliation(s)
- Thomas Welton
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom.,Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom.,Sydney Translational Imaging Laboratory, Heart Research Institute, University of Sydney, Camperdown, Australia
| | - Cris S Constantinescu
- Clinical Neurology, Division of Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom
| | - Dorothee P Auer
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom.,Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom.,NIHR Nottingham Biomedical Research Centre, Nottingham, United Kingdom
| | - Rob A Dineen
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom.,Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom.,NIHR Nottingham Biomedical Research Centre, Nottingham, United Kingdom
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29
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Cerina M, Muthuraman M, Gallus M, Koirala N, Dik A, Wachsmuth L, Hundehege P, Schiffler P, Tenberge JG, Fleischer V, Gonzalez-Escamilla G, Narayanan V, Krämer J, Faber C, Budde T, Groppa S, Meuth SG. Myelination- and immune-mediated MR-based brain network correlates. J Neuroinflammation 2020; 17:186. [PMID: 32532336 PMCID: PMC7293122 DOI: 10.1186/s12974-020-01827-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 04/24/2020] [Indexed: 11/23/2022] Open
Abstract
Background Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS), characterized by inflammatory and neurodegenerative processes. Despite demyelination being a hallmark of the disease, how it relates to neurodegeneration has still not been completely unraveled, and research is still ongoing into how these processes can be tracked non-invasively. Magnetic resonance imaging (MRI) derived brain network characteristics, which closely mirror disease processes and relate to functional impairment, recently became important variables for characterizing immune-mediated neurodegeneration; however, their histopathological basis remains unclear. Methods In order to determine the MRI-derived correlates of myelin dynamics and to test if brain network characteristics derived from diffusion tensor imaging reflect microstructural tissue reorganization, we took advantage of the cuprizone model of general demyelination in mice and performed longitudinal histological and imaging analyses with behavioral tests. By introducing cuprizone into the diet, we induced targeted and consistent demyelination of oligodendrocytes, over a period of 5 weeks. Subsequent myelin synthesis was enabled by reintroduction of normal food. Results Using specific immune-histological markers, we demonstrated that 2 weeks of cuprizone diet induced a 52% reduction of myelin content in the corpus callosum (CC) and a 35% reduction in the neocortex. An extended cuprizone diet increased myelin loss in the CC, while remyelination commenced in the neocortex. These histologically determined dynamics were reflected by MRI measurements from diffusion tensor imaging. Demyelination was associated with decreased fractional anisotropy (FA) values and increased modularity and clustering at the network level. MRI-derived modularization of the brain network and FA reduction in key anatomical regions, including the hippocampus, thalamus, and analyzed cortical areas, were closely related to impaired memory function and anxiety-like behavior. Conclusion Network-specific remyelination, shown by histology and MRI metrics, determined amelioration of functional performance and neuropsychiatric symptoms. Taken together, we illustrate the histological basis for the MRI-driven network responses to demyelination, where increased modularity leads to evolving damage and abnormal behavior in MS. Quantitative information about in vivo myelination processes is mirrored by diffusion-based imaging of microstructural integrity and network characteristics.
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Affiliation(s)
- Manuela Cerina
- Department of Neurology with Institute of Translational Neurology, Münster University Hospital, Münster, Germany
| | - Muthuraman Muthuraman
- Movement Disorders, Imaging and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany.
| | - Marco Gallus
- Department of Neurology with Institute of Translational Neurology, Münster University Hospital, Münster, Germany
| | - Nabin Koirala
- Movement Disorders, Imaging and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Andre Dik
- Department of Neurology with Institute of Translational Neurology, Münster University Hospital, Münster, Germany
| | - Lydia Wachsmuth
- Departement of Radiology, University of Münster, Münster, Langenbeckstrasse 1, 55131, Mainz, Germany
| | - Petra Hundehege
- Department of Neurology with Institute of Translational Neurology, Münster University Hospital, Münster, Germany
| | - Patrick Schiffler
- Department of Neurology with Institute of Translational Neurology, Münster University Hospital, Münster, Germany
| | - Jan-Gerd Tenberge
- Department of Neurology with Institute of Translational Neurology, Münster University Hospital, Münster, Germany
| | - Vinzenz Fleischer
- Movement Disorders, Imaging and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- Movement Disorders, Imaging and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Venu Narayanan
- Department of Neurology with Institute of Translational Neurology, Münster University Hospital, Münster, Germany
| | - Julia Krämer
- Department of Neurology with Institute of Translational Neurology, Münster University Hospital, Münster, Germany
| | - Cornelius Faber
- Departement of Radiology, University of Münster, Münster, Langenbeckstrasse 1, 55131, Mainz, Germany
| | - Thomas Budde
- Institute of Physiology I, University of Münster, Münster, Germany
| | - Sergiu Groppa
- Movement Disorders, Imaging and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Sven G Meuth
- Department of Neurology with Institute of Translational Neurology, Münster University Hospital, Münster, Germany
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30
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Koubiyr I, Besson P, Deloire M, Charre-Morin J, Saubusse A, Tourdias T, Brochet B, Ruet A. Dynamic modular-level alterations of structural-functional coupling in clinically isolated syndrome. Brain 2020; 142:3428-3439. [PMID: 31504228 DOI: 10.1093/brain/awz270] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 06/24/2019] [Accepted: 07/11/2019] [Indexed: 11/14/2022] Open
Abstract
Structural and functional connectivity abnormalities have been reported previously in multiple sclerosis. However, little is known about how each modality evolution relates to the other. Recent studies in other neurological disorders have suggested that structural-functional coupling may be more sensitive in detecting brain alterations than any single modality. Accordingly, this study aimed to investigate the longitudinal evolution of structural-functional coupling, both at the global and modular levels, in the first year following clinically isolated syndrome. We hypothesized that during the course of multiple sclerosis, patients exhibit a decoupling between functional and structural connectivity due to the disruptive nature of the disease. Forty-one consecutive patients with clinically isolated syndrome were prospectively enrolled in this study, along with 19 age-, sex- and educational level-matched healthy control subjects. These participants were followed for 1 year and underwent resting-state functional MRI and diffusion tensor imaging at each time point, along with an extensive neuropsychological assessment. Graph theory analysis revealed structural reorganization at baseline that appeared as an increase in the clustering coefficient in patients compared to controls (P < 0.05), as well as modular-specific alterations. After 1 year of follow-up, both structural and functional reorganization was depicted with abnormal modular-specific connectivity and an increase of the functional betweenness centrality in patients compared to controls (P < 0.01). More importantly, structural-functional decoupling was observed in the salience, visual and somatomotor networks. These alterations were present along with preserved cognitive performance at this stage. These results depict structural damage preceding functional reorganization at a global and modular level during the first year following clinically isolated syndrome along with normal cognitive performance, suggesting a compensation mechanism at this stage of the disease. Principally, structural-functional decoupling observed for the first time in multiple sclerosis suggests that functional reorganization occurs along indirect anatomical pathways.
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Affiliation(s)
- Ismail Koubiyr
- University of Bordeaux, F Bordeaux, France.,Inserm U1215 - Neurocentre Magendie, F Bordeaux, France
| | - Pierre Besson
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA.,Department of Neurological Surgery, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | | | | | | | - Thomas Tourdias
- University of Bordeaux, F Bordeaux, France.,Inserm U1215 - Neurocentre Magendie, F Bordeaux, France.,CHU de Bordeaux, F Bordeaux, France
| | - Bruno Brochet
- University of Bordeaux, F Bordeaux, France.,Inserm U1215 - Neurocentre Magendie, F Bordeaux, France.,CHU de Bordeaux, F Bordeaux, France
| | - Aurélie Ruet
- University of Bordeaux, F Bordeaux, France.,Inserm U1215 - Neurocentre Magendie, F Bordeaux, France.,CHU de Bordeaux, F Bordeaux, France
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31
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Nasios G, Bakirtzis C, Messinis L. Cognitive Impairment and Brain Reorganization in MS: Underlying Mechanisms and the Role of Neurorehabilitation. Front Neurol 2020; 11:147. [PMID: 32210905 PMCID: PMC7068711 DOI: 10.3389/fneur.2020.00147] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 02/14/2020] [Indexed: 12/29/2022] Open
Abstract
Multiple sclerosis (MS) is a chronic, immune-mediated, inflammatory, and degenerative disease of the central nervous system (CNS) that affects both white and gray matter. Various mechanisms throughout its course, mainly regarding gray matter lesions and brain atrophy, result in cognitive network dysfunction and can cause clinically significant cognitive impairment in roughly half the persons living with MS. Altered cognition is responsible for many negative aspects of patients' lives, independently of physical disability, such as higher unemployment and divorce rates, reduced social activities, and an overall decrease in quality of life. Despite its devastating impact it is not included in clinical ratings and decision making in the way it should be. It is interesting that only half the persons with MS exhibit cognitive dysfunction, as this implies that the other half remain cognitively intact. It appears that a dynamic balance between brain destruction and brain reorganization is taking place. This balance acts in favor of keeping brain systems functioning effectively, but this is not so in all cases, and the effect does not last forever. When these systems collapse, functional brain reorganization is not effective anymore, and clinically apparent impairments are evident. It is therefore important to reveal which factors could make provision for the subpopulation of patients in whom cognitive impairment occurs. Even if we manage to detect this subpopulation earlier, effective pharmaceutical treatments will still be lacking. Nevertheless, recent evidence shows that cognitive rehabilitation and neuromodulation, using non-invasive techniques such as transcranial magnetic or direct current stimulation, could be effective in cognitively impaired patients with MS. In this Mini Review, we discuss the mechanisms underlying cognitive impairment in MS. We also focus on mechanisms of reorganization of cognitive networks, which occur throughout the disease course. Finally, we review theoretical and practical issues of neurorehabilitation and neuromodulation for cognition in MS as well as factors that influence them and prevent them from being widely applied in clinical settings.
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Affiliation(s)
- Grigorios Nasios
- Department of Speech and Language Therapy, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | - Christos Bakirtzis
- Department of Neurology, The Multiple Sclerosis Center, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Lambros Messinis
- Neuropsychology Section, Departments of Neurology and Psychiatry, University of Patras Medical School, Patras, Greece
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32
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Charalambous T, Clayden JD, Powell E, Prados F, Tur C, Kanber B, Chard D, Ourselin S, Wheeler-Kingshott CAMG, Thompson AJ, Toosy AT. Disrupted principal network organisation in multiple sclerosis relates to disability. Sci Rep 2020; 10:3620. [PMID: 32108146 PMCID: PMC7046772 DOI: 10.1038/s41598-020-60611-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 02/13/2020] [Indexed: 01/15/2023] Open
Abstract
Structural network-based approaches can assess white matter connections revealing topological alterations in multiple sclerosis (MS). However, principal network (PN) organisation and its clinical relevance in MS has not been explored yet. Here, structural networks were reconstructed from diffusion data in 58 relapsing-remitting MS (RRMS), 28 primary progressive MS (PPMS), 36 secondary progressive (SPMS) and 51 healthy controls (HCs). Network hubs' strengths were compared with HCs. Then, PN analysis was performed in each clinical subtype. Regression analysis was applied to investigate the associations between nodal strength derived from the first and second PNs (PN1 and PN2) in MS, with clinical disability. Compared with HCs, MS patients had preserved hub number, but some hubs exhibited reduced strength. PN1 comprised 10 hubs in HCs, RRMS and PPMS but did not include the right thalamus in SPMS. PN2 comprised 10 hub regions with intra-hemispheric connections in HCs. In MS, this subnetwork did not include the right putamen whilst in SPMS the right thalamus was also not included. Decreased nodal strength of the right thalamus and putamen from the PNs correlated strongly with higher clinical disability. These PN analyses suggest distinct patterns of disruptions in MS subtypes which are clinically relevant.
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Affiliation(s)
- Thalis Charalambous
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Jonathan D Clayden
- UCL GOS Institute of Child Health, University College London, London, UK
| | - Elizabeth Powell
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Ferran Prados
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, UK
- eHealth Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Carmen Tur
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Baris Kanber
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, UK
| | - Declan Chard
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Sebastien Ourselin
- Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Brain MRI 3T Research Center, C. Mondino National Neurological Institute, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Alan J Thompson
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Ahmed T Toosy
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
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Hawkins R, Shatil AS, Lee L, Sengupta A, Zhang L, Morrow S, Aviv RI. Reduced Global Efficiency and Random Network Features in Patients with Relapsing-Remitting Multiple Sclerosis with Cognitive Impairment. AJNR Am J Neuroradiol 2020; 41:449-455. [PMID: 32079601 DOI: 10.3174/ajnr.a6435] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 01/11/2020] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Graph theory uses structural similarity to analyze cortical structural connectivity. We used a voxel-based definition of cortical covariance networks to quantify and assess the relationship of network characteristics to cognition in a cohort of patients with relapsing-remitting MS with and without cognitive impairment. MATERIALS AND METHODS We compared subject-specific structural gray matter network properties of 18 healthy controls, 25 patients with MS with cognitive impairment, and 55 patients with MS without cognitive impairment. Network parameters were compared, and predictive value for cognition was assessed, adjusting for confounders (sex, education, gray matter volume, network size and degree, and T1 and T2 lesion load). Backward stepwise multivariable regression quantified predictive factors for 5 neurocognitive domain test scores. RESULTS Greater path length (r = -0.28, P < .0057) and lower normalized path length (r = 0.36, P < .0004) demonstrated a correlation with average cognition when comparing healthy controls with patients with MS. Similarly, MS with cognitive impairment demonstrated a correlation between lower normalized path length (r = 0.40, P < .001) and reduced average cognition. Increased normalized path length was associated with better performance for processing (P < .001), learning (P < .001), and executive domain function (P = .0235), while reduced path length was associated with better executive (P = .0031) and visual domains. Normalized path length improved prediction for processing (R 2 = 43.6%, G2 = 20.9; P < .0001) and learning (R 2 = 40.4%, G2 = 26.1; P < .0001) over a null model comprising confounders. Similarly, higher normalized path length improved prediction of average z scores (G2 = 21.3; P < .0001) and, combined with WM volume, explained 52% of average cognition variance. CONCLUSIONS Patients with MS and cognitive impairment demonstrate more random network features and reduced global efficiency, impacting multiple cognitive domains. A model of normalized path length with normal-appearing white matter volume improved average cognitive z score prediction, explaining 52% of variance.
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Affiliation(s)
- R Hawkins
- From the Department of Medical Imaging (R.H., A.S.S., A.S., L.Z.)
| | - A S Shatil
- From the Department of Medical Imaging (R.H., A.S.S., A.S., L.Z.)
| | - L Lee
- Division of Neurology (L.L.), Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - A Sengupta
- From the Department of Medical Imaging (R.H., A.S.S., A.S., L.Z.)
| | - L Zhang
- From the Department of Medical Imaging (R.H., A.S.S., A.S., L.Z.)
| | - S Morrow
- Division of Neurology (S.M.), Lawson Health Research Institute, London Health Sciences Centre, University Hospital, London, Ontario, Canada
| | - R I Aviv
- Institute of Biomaterials and Biomedical Engineering (R.I.A.), University of Toronto, Toronto, Ontario, Canada .,Department of Radiology (R.I.A.), University of Ottawa, and Division of Neuroradiology, The Ottawa Hospital, Ottawa, Ontario, Canada
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Muthuraman M, Fleischer V, Kroth J, Ciolac D, Radetz A, Koirala N, Gonzalez-Escamilla G, Wiendl H, Meuth SG, Zipp F, Groppa S. Covarying patterns of white matter lesions and cortical atrophy predict progression in early MS. NEUROLOGY-NEUROIMMUNOLOGY & NEUROINFLAMMATION 2020; 7:7/3/e681. [PMID: 32024782 PMCID: PMC7051213 DOI: 10.1212/nxi.0000000000000681] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 01/07/2020] [Indexed: 01/04/2023]
Abstract
Objective We applied longitudinal 3T MRI and advanced computational models in 2 independent cohorts of patients with early MS to investigate how white matter (WM) lesion distribution and cortical atrophy topographically interrelate and affect functional disability. Methods Clinical disability was measured using the Expanded Disability Status Scale Score at baseline and at 1-year follow-up in a cohort of 119 patients with early relapsing-remitting MS and in a replication cohort of 81 patients. Covarying patterns of cortical atrophy and baseline lesion distribution were extracted by parallel independent component analysis. Predictive power of covarying patterns for disability progression was tested by receiver operating characteristic analysis at the group level and support vector machine for individual patient outcome. Results In the study cohort, we identified 3 distinct distribution types of WM lesions (cerebellar, bihemispheric, and left lateralized) that were associated with characteristic cortical atrophy distributions. The cerebellar and left-lateralized patterns were reproducibly detected in the second cohort. Each of the patterns predicted to different extents, short-term disability progression, whereas the cerebellar pattern was associated with the highest risk of clinical worsening, predicting individual disability progression with an accuracy of 88% (study cohort) and 89% (replication cohort), respectively. Conclusion These findings highlight the role of distinct spatial distribution of cortical atrophy and WM lesions predicting disability. The cerebellar involvement is shown as a key determinant of rapid clinical deterioration.
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Affiliation(s)
- Muthuraman Muthuraman
- From the Department of Neurology (M.M., V.F., J.K., D.C., A.R., N.K., G.G.-E., F.Z., S.G.), Focus Program Translational Neuroscience (FTN), Research Center for Immunotherapy (FZI), Rhine-Main Neuroscience Network (rmn), University Medical Center of the Johannes Gutenberg University Mainz; and Department of Neurology with Institute of Translational Neurology (H.W., S.G.M.), University of Munster, Germany
| | - Vinzenz Fleischer
- From the Department of Neurology (M.M., V.F., J.K., D.C., A.R., N.K., G.G.-E., F.Z., S.G.), Focus Program Translational Neuroscience (FTN), Research Center for Immunotherapy (FZI), Rhine-Main Neuroscience Network (rmn), University Medical Center of the Johannes Gutenberg University Mainz; and Department of Neurology with Institute of Translational Neurology (H.W., S.G.M.), University of Munster, Germany
| | - Julia Kroth
- From the Department of Neurology (M.M., V.F., J.K., D.C., A.R., N.K., G.G.-E., F.Z., S.G.), Focus Program Translational Neuroscience (FTN), Research Center for Immunotherapy (FZI), Rhine-Main Neuroscience Network (rmn), University Medical Center of the Johannes Gutenberg University Mainz; and Department of Neurology with Institute of Translational Neurology (H.W., S.G.M.), University of Munster, Germany
| | - Dumitru Ciolac
- From the Department of Neurology (M.M., V.F., J.K., D.C., A.R., N.K., G.G.-E., F.Z., S.G.), Focus Program Translational Neuroscience (FTN), Research Center for Immunotherapy (FZI), Rhine-Main Neuroscience Network (rmn), University Medical Center of the Johannes Gutenberg University Mainz; and Department of Neurology with Institute of Translational Neurology (H.W., S.G.M.), University of Munster, Germany
| | - Angela Radetz
- From the Department of Neurology (M.M., V.F., J.K., D.C., A.R., N.K., G.G.-E., F.Z., S.G.), Focus Program Translational Neuroscience (FTN), Research Center for Immunotherapy (FZI), Rhine-Main Neuroscience Network (rmn), University Medical Center of the Johannes Gutenberg University Mainz; and Department of Neurology with Institute of Translational Neurology (H.W., S.G.M.), University of Munster, Germany
| | - Nabin Koirala
- From the Department of Neurology (M.M., V.F., J.K., D.C., A.R., N.K., G.G.-E., F.Z., S.G.), Focus Program Translational Neuroscience (FTN), Research Center for Immunotherapy (FZI), Rhine-Main Neuroscience Network (rmn), University Medical Center of the Johannes Gutenberg University Mainz; and Department of Neurology with Institute of Translational Neurology (H.W., S.G.M.), University of Munster, Germany
| | - Gabriel Gonzalez-Escamilla
- From the Department of Neurology (M.M., V.F., J.K., D.C., A.R., N.K., G.G.-E., F.Z., S.G.), Focus Program Translational Neuroscience (FTN), Research Center for Immunotherapy (FZI), Rhine-Main Neuroscience Network (rmn), University Medical Center of the Johannes Gutenberg University Mainz; and Department of Neurology with Institute of Translational Neurology (H.W., S.G.M.), University of Munster, Germany
| | - Heinz Wiendl
- From the Department of Neurology (M.M., V.F., J.K., D.C., A.R., N.K., G.G.-E., F.Z., S.G.), Focus Program Translational Neuroscience (FTN), Research Center for Immunotherapy (FZI), Rhine-Main Neuroscience Network (rmn), University Medical Center of the Johannes Gutenberg University Mainz; and Department of Neurology with Institute of Translational Neurology (H.W., S.G.M.), University of Munster, Germany
| | - Sven G Meuth
- From the Department of Neurology (M.M., V.F., J.K., D.C., A.R., N.K., G.G.-E., F.Z., S.G.), Focus Program Translational Neuroscience (FTN), Research Center for Immunotherapy (FZI), Rhine-Main Neuroscience Network (rmn), University Medical Center of the Johannes Gutenberg University Mainz; and Department of Neurology with Institute of Translational Neurology (H.W., S.G.M.), University of Munster, Germany
| | - Frauke Zipp
- From the Department of Neurology (M.M., V.F., J.K., D.C., A.R., N.K., G.G.-E., F.Z., S.G.), Focus Program Translational Neuroscience (FTN), Research Center for Immunotherapy (FZI), Rhine-Main Neuroscience Network (rmn), University Medical Center of the Johannes Gutenberg University Mainz; and Department of Neurology with Institute of Translational Neurology (H.W., S.G.M.), University of Munster, Germany
| | - Sergiu Groppa
- From the Department of Neurology (M.M., V.F., J.K., D.C., A.R., N.K., G.G.-E., F.Z., S.G.), Focus Program Translational Neuroscience (FTN), Research Center for Immunotherapy (FZI), Rhine-Main Neuroscience Network (rmn), University Medical Center of the Johannes Gutenberg University Mainz; and Department of Neurology with Institute of Translational Neurology (H.W., S.G.M.), University of Munster, Germany.
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Solana E, Martinez-Heras E, Casas-Roma J, Calvet L, Lopez-Soley E, Sepulveda M, Sola-Valls N, Montejo C, Blanco Y, Pulido-Valdeolivas I, Andorra M, Saiz A, Prados F, Llufriu S. Modified connectivity of vulnerable brain nodes in multiple sclerosis, their impact on cognition and their discriminative value. Sci Rep 2019; 9:20172. [PMID: 31882922 PMCID: PMC6934774 DOI: 10.1038/s41598-019-56806-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 11/25/2019] [Indexed: 12/30/2022] Open
Abstract
Brain structural network modifications in multiple sclerosis (MS) seem to be clinically relevant. The discriminative ability of those changes to identify MS patients or their cognitive status remains unknown. Therefore, this study aimed to investigate connectivity changes in MS patients related to their cognitive status, and to define an automatic classification method to classify subjects as patients and healthy volunteers (HV) or as cognitively preserved (CP) and impaired (CI) patients. We analysed structural brain connectivity in 45 HV and 188 MS patients (104 CP and 84 CI). A support vector machine with k-fold cross-validation was built using the graph metrics features that best differentiate the groups (p < 0.05). Local efficiency (LE) and node strength (NS) network properties showed the largest differences: 100% and 69.7% of nodes had reduced LE and NS in CP patients compared to HV. Moreover, 55.3% and 57.9% of nodes had decreased LE and NS in CI compared to CP patients, in associative multimodal areas. The classification method achieved an accuracy of 74.8–77.2% to differentiate patients from HV, and 59.9–60.8% to discriminate CI from CP patients. Structural network integrity is widely reduced and worsens as cognitive function declines. Central network properties of vulnerable nodes can be useful to classify MS patients.
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Affiliation(s)
- Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Jordi Casas-Roma
- E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Laura Calvet
- E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Elisabet Lopez-Soley
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Maria Sepulveda
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Nuria Sola-Valls
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Carmen Montejo
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Yolanda Blanco
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Irene Pulido-Valdeolivas
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Magi Andorra
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Albert Saiz
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Ferran Prados
- E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain.,Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.,NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, UK
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain.
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Thalamo-cortical dysfunction contributes to fatigability in multiple sclerosis patients: A neurophysiological study. Mult Scler Relat Disord 2019; 39:101897. [PMID: 31869598 DOI: 10.1016/j.msard.2019.101897] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Revised: 11/30/2019] [Accepted: 12/16/2019] [Indexed: 11/21/2022]
Abstract
BACKGROUND Fatigue and fatigability are common symptoms reported by patients affected by Multiple Sclerosis (MS). The pathogenic mechanisms of such symptoms are currently unknown, but increasing evidence suggests that thalamus could play a key-role. High-frequency oscillations (HFOs) are a neurophysiological measure reflecting the activity of thalamo-cortical network. In particular, the early component is generated from thalamic axons while the late part results from neurons located in somatosensory cortex. OBJECTIVE To investigate the effect of a fatigue-inducing exercise on HFOs and on strength performances in MS patients and healthy controls (HCs). METHODS Fifteen patients and fifteen HCs participated in this study. We recorded HFOs from median nerve somatosensory evoked potentials and assessed strength performances, before and after a fatigue-inducing exercise of hand muscles. RESULTS Compared to HCs, after repeated fatiguing tasks, patients showed a significant reduction of early component of HFOs area and a significant increase of late component of HFOs duration. Strength performance declined both in patients and in HCs but remained lower in patients at all time-points. CONCLUSIONS HFOs, a neurophysiological marker of thalamo-cortical pathway, are significantly modified by fatiguing tasks in MS patients, in particular the early component that refers to the functionality of thalamic axons.
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37
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Chirumamilla VC, Dresel C, Koirala N, Gonzalez-Escamilla G, Deuschl G, Zeuner KE, Muthuraman M, Groppa S. Structural brain network fingerprints of focal dystonia. Ther Adv Neurol Disord 2019; 12:1756286419880664. [PMID: 31798688 PMCID: PMC6859688 DOI: 10.1177/1756286419880664] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 09/10/2019] [Indexed: 01/02/2023] Open
Abstract
Background: Focal dystonias are severe and disabling movement disorders of a still unclear origin. The structural brain networks associated with focal dystonia have not been well characterized. Here, we investigated structural brain network fingerprints in patients with blepharospasm (BSP) compared with those with hemifacial spasm (HFS), and healthy controls (HC). The patients were also examined following treatment with botulinum neurotoxin (BoNT). Methods: This study included matched groups of 13 BSP patients, 13 HFS patients, and 13 HC. We measured patients using structural-magnetic resonance imaging (MRI) at baseline and after one month BoNT treatment, at time points of maximal and minimal clinical symptom representation, and HC at baseline. Group regional cross-correlation matrices calculated based on grey matter volume were included in graph-based network analysis. We used these to quantify global network measures of segregation and integration, and also looked at local connectivity properties of different brain regions. Results: The networks in patients with BSP were more segregated than in patients with HFS and HC (p < 0.001). BSP patients had increased connectivity in frontal and temporal cortices, including sensorimotor cortex, and reduced connectivity in the cerebellum, relative to both HFS patients and HC (p < 0.05). Compared with HC, HFS patients showed increased connectivity in temporal and parietal cortices and a decreased connectivity in the frontal cortex (p < 0.05). In BSP patients, the connectivity of the frontal cortex diminished after BoNT treatment (p < 0.05). In contrast, HFS patients showed increased connectivity in the temporal cortex and reduced connectivity in cerebellum after BoNT treatment (p < 0.05). Conclusions: Our results show that BSP patients display alterations in both segregation and integration in the brain at the network level. The regional differences identified in the sensorimotor cortex and cerebellum of these patients may play a role in the pathophysiology of focal dystonia. Moreover, symptomatic reduction of hyperkinesia by BoNT treatment was associated with different brain network fingerprints in both BSP and HFS patients.
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Affiliation(s)
- Venkata C Chirumamilla
- Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Christian Dresel
- Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Nabin Koirala
- Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Günther Deuschl
- Department of Neurology, University Hospital Schleswig-Holstein, University of Kiel, Kiel, Schleswig-Holstein, Germany
| | - Kirsten E Zeuner
- Department of Neurology, University Hospital Schleswig-Holstein, University of Kiel, Kiel, Schleswig-Holstein, Germany
| | - Muthuraman Muthuraman
- Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience network (rmn), Johannes-Gutenberg-University Mainz, Langenbeckstr. 1, Mainz, 55131, Germany
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38
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Radetz A, Koirala N, Krämer J, Johnen A, Fleischer V, Gonzalez-Escamilla G, Cerina M, Muthuraman M, Meuth SG, Groppa S. Gray matter integrity predicts white matter network reorganization in multiple sclerosis. Hum Brain Mapp 2019; 41:917-927. [PMID: 32026599 PMCID: PMC7268008 DOI: 10.1002/hbm.24849] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 10/16/2019] [Accepted: 10/17/2019] [Indexed: 01/19/2023] Open
Abstract
Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease leading to gray matter atrophy and brain network reconfiguration as a response to increasing tissue damage. We evaluated whether white matter network reconfiguration appears subsequently to gray matter damage, or whether the gray matter degenerates following alterations in white matter networks. MRI data from 83 patients with clinically isolated syndrome and early relapsing-remitting MS were acquired at two time points with a follow-up after 1 year. White matter network integrity was assessed based on probabilistic tractography performed on diffusion-weighted data using graph theoretical analyses. We evaluated gray matter integrity by computing cortical thickness and deep gray matter volume in 94 regions at both time points. The thickness of middle temporal cortex and the volume of deep gray matter regions including thalamus, caudate, putamen, and brain stem showed significant atrophy between baseline and follow-up. White matter network dynamics, as defined by modularity and distance measure changes over time, were predicted by deep gray matter volume of the atrophying anatomical structures. Initial white matter network properties, on the other hand, did not predict atrophy. Furthermore, gray matter integrity at baseline significantly predicted physical disability at 1-year follow-up. In a sub-analysis, deep gray matter volume was significantly related to cognitive performance at baseline. Hence, we postulate that atrophy of deep gray matter structures drives the adaptation of white matter networks. Moreover, deep gray matter volumes are highly predictive for disability progression and cognitive performance.
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Affiliation(s)
- Angela Radetz
- Department of Neurology and Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Nabin Koirala
- Department of Neurology and Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Julia Krämer
- Department of Neurology, University of Münster, Münster, Germany
| | - Andreas Johnen
- Department of Neurology, University of Münster, Münster, Germany
| | - Vinzenz Fleischer
- Department of Neurology and Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- Department of Neurology and Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Manuela Cerina
- Department of Neurology, University of Münster, Münster, Germany
| | - Muthuraman Muthuraman
- Department of Neurology and Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Sven G Meuth
- Department of Neurology, University of Münster, Münster, Germany
| | - Sergiu Groppa
- Department of Neurology and Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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Clarke MA, Samaraweera APR, Falah Y, Pitiot A, Allen CM, Dineen RA, Tench CR, Morgan PS, Evangelou N. Single Test to ARrive at Multiple Sclerosis (STAR-MS) diagnosis: A prospective pilot study assessing the accuracy of the central vein sign in predicting multiple sclerosis in cases of diagnostic uncertainty. Mult Scler 2019; 26:433-441. [DOI: 10.1177/1352458519882282] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Misdiagnosis is common in multiple sclerosis (MS) as a proportion of patients present with atypical clinical/magnetic resonance imaging (MRI) findings. The central vein sign has the potential to be a non-invasive, MS-specific biomarker. Objective: To test the accuracy of the central vein sign in predicting a diagnosis of MS in patients with diagnostic uncertainty at disease presentation using T2*-weighted, 3 T MRI. Methods: In this prospective pilot study, we recruited individuals with symptoms unusual for MS but with brain MRI consistent with the disease, and those with a typical clinical presentation of MS whose MRI did not suggest MS. We calculated the proportion of lesions with central veins for each patient and compared the results to the eventual clinical diagnoses. The optimal central vein threshold for diagnosis was established. Results: Thirty-eight patients were scanned, 35 of whom have received a clinical diagnosis. Median percentage of lesions with central veins was 51% in MS and 28% in non-MS. A threshold of 40.7% lesions with central veins resulted in 100% sensitivity and 73.9% specificity. Conclusion: The central vein sign assessed with a clinically available T2* scan can successfully diagnose MS in cases of diagnostic uncertainty. The central vein sign should be considered as a diagnostic biomarker in MS.
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Affiliation(s)
- Margareta A Clarke
- School of Psychology, University of Nottingham, Nottingham, UK/Department of Clinical Neurology, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | | | - Yasser Falah
- Department of Neurology, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Alain Pitiot
- Laboratory of Image & Data Analysis, Ilixa Ltd., Nottingham, UK
| | | | - Robert A Dineen
- Radiological Sciences, University of Nottingham, Nottingham, UK/National Institute of Health Research Nottingham Biomedical Research Centre, Nottingham, UK/Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
| | - Chris R Tench
- Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Paul S Morgan
- Radiological Sciences, University of Nottingham, Nottingham, UK/Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK/Medical Physics & Clinical Engineering, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Nikos Evangelou
- Department of Clinical Neurology, Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK
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Ciolac D, Luessi F, Gonzalez-Escamilla G, Koirala N, Riedel C, Fleischer V, Bittner S, Krämer J, Meuth SG, Muthuraman M, Groppa S. Selective Brain Network and Cellular Responses Upon Dimethyl Fumarate Immunomodulation in Multiple Sclerosis. Front Immunol 2019; 10:1779. [PMID: 31417557 PMCID: PMC6682686 DOI: 10.3389/fimmu.2019.01779] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 07/15/2019] [Indexed: 11/13/2022] Open
Abstract
Background: Efficient personalized therapy paradigms are needed to modify the disease course and halt gray (GM) and white matter (WM) damage in patients with multiple sclerosis (MS). Presently, promising disease-modifying drugs show impressive efficiency, however, tailored markers of therapy responses are required. Here, we aimed to detect in a real-world setting patients with a more favorable brain network response and immune cell dynamics upon dimethyl fumarate (DMF) treatment. Methods: In a cohort of 78 MS patients we identified two thoroughly matched groups, based on age, disease duration, disability status and lesion volume, receiving DMF (n = 42) and NAT (n = 36) and followed them over 16 months. The rate of cortical atrophy and deep GM volumes were quantified. GM and WM network responses were characterized by brain modularization as a marker of regional and global structural alterations. In the DMF group, lymphocyte subsets were analyzed by flow cytometry and related to clinical and MRI parameters. Results: Sixty percent (25 patients) of the DMF and 36% (13 patients) of the NAT group had disease activity during the study period. The rate of cortical atrophy was higher in the DMF group (-2.4%) compared to NAT (-2.1%, p < 0.05) group. GM and WM network dynamics presented increased modularization in both groups. When dividing the DMF-treated cohort into patients free of disease activity (n = 17, DMFR) and patients with disease activity (n = 25, DMFNR) these groups differed significantly in CD8+ cell depletion counts (DMFR: 197.7 ± 97.1/μl; DMFNR: 298.4 ± 190.6/μl, p = 0.03) and also in cortical atrophy (DMFR: -1.7%; DMFNR: -3.2%, p = 0.01). DMFR presented reduced longitudinal GM and WM modularization and less atrophy as markers of preserved structural global network integrity in comparison to DMFNR and even NAT patients. Conclusions: NAT treatment contributes to a reduced rate of cortical atrophy compared to DMF therapy. However, patients under DMF treatment with a stronger CD8+ T cell depletion present a more favorable response in terms of cortical integrity and GM and WM network responses. Our findings may serve as basis for the development of personalized treatment paradigms.
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Affiliation(s)
- Dumitru Ciolac
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.,Department of Neurology, Institute of Emergency Medicine, Chisinau, Moldova.,Laboratory of Neurobiology and Medical Genetics, Nicolae Testemiţanu State University of Medicine and Pharmacy, Chisinau, Moldova
| | - Felix Luessi
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Nabin Koirala
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | | | - Vinzenz Fleischer
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Stefan Bittner
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Julia Krämer
- Department of Neurology With Institute of Translational Neurology, University of Münster, Münster, Germany
| | - Sven G Meuth
- Department of Neurology With Institute of Translational Neurology, University of Münster, Münster, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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41
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Fleischer V, Koirala N, Droby A, Gracien RM, Deichmann R, Ziemann U, Meuth SG, Muthuraman M, Zipp F, Groppa S. Longitudinal cortical network reorganization in early relapsing-remitting multiple sclerosis. Ther Adv Neurol Disord 2019; 12:1756286419838673. [PMID: 31040880 PMCID: PMC6482642 DOI: 10.1177/1756286419838673] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 02/09/2019] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Network science provides powerful access to essential organizational principles of the brain. The aim of this study was to investigate longitudinal evolution of gray matter networks in early relapsing-remitting MS (RRMS) compared with healthy controls (HCs) and contrast network dynamics with conventional atrophy measurements. METHODS For our longitudinal study, we investigated structural cortical networks over 1 year derived from 3T MRI in 203 individuals (92 early RRMS patients with mean disease duration of 12.1 ± 14.5 months and 101 HCs). Brain networks were computed based on cortical thickness inter-regional correlations and fed into graph theoretical analysis. Network connectivity measures (modularity, clustering coefficient, local efficiency, and transitivity) were compared between patients and HCs, and between patients with and without disease activity. Moreover, we calculated longitudinal brain volume changes and cortical atrophy patterns. RESULTS Our analyses revealed strengthening of local network properties shown by increased modularity, clustering coefficient, local efficiency, and transitivity over time. These network dynamics were not detectable in the cortex of HCs over the same period and occurred independently of patients' disease activity. Most notably, the described network reorganization was evident beyond detectable atrophy as characterized by conventional morphometric methods. CONCLUSION In conclusion, our findings provide evidence for gray matter network reorganization subsequent to clinical disease manifestation in patients with early RRMS. An adaptive cortical response with increased local network characteristics favoring network segregation could play a primordial role for maintaining brain function in response to neuroinflammation.
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Affiliation(s)
- Vinzenz Fleischer
- Department of Neurology and Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Nabin Koirala
- Department of Neurology and Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Amgad Droby
- Department of Neurology and Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - René-Maxime Gracien
- Department of Neurology, and Brain Imaging Center, Goethe University, Frankfurt/Main, Frankfurt am Main, Germany
| | - Ralf Deichmann
- Brain Imaging Center, Goethe University, Frankfurt/Main, Frankfurt am Main, Germany
| | - Ulf Ziemann
- Department of Neurology and Stroke, and Hertie Institute for Clinical Brain Research, Eberhard-Karls University, Tübingen, Germany
| | - Sven G Meuth
- Department of Neurology, University of Muenster, Muenster, Germany
| | - Muthuraman Muthuraman
- Department of Neurology and Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Frauke Zipp
- Department of Neurology and Neuroimaging Center (NIC) of the Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Neuroimaging and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main-Neuroscience Network (rmn), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr.1, 55131 Mainz, Germany
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42
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Graph Theoretical Framework of Brain Networks in Multiple Sclerosis: A Review of Concepts. Neuroscience 2019; 403:35-53. [DOI: 10.1016/j.neuroscience.2017.10.033] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 10/22/2017] [Accepted: 10/24/2017] [Indexed: 12/11/2022]
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43
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Filippi M, Preziosa P, Rocca MA. Brain mapping in multiple sclerosis: Lessons learned about the human brain. Neuroimage 2019; 190:32-45. [DOI: 10.1016/j.neuroimage.2017.09.021] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 09/07/2017] [Accepted: 09/09/2017] [Indexed: 02/07/2023] Open
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44
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Abstract
PURPOSE OF REVIEW To summarize recent findings from the application of MRI in the diagnostic work-up of patients with suspected multiple sclerosis (MS), and to review the insights into disease pathophysiology and the utility of MRI for monitoring treatment response. RECENT FINDINGS New evidence from the application of MRI in patients with clinically isolated syndromes has guided the 2017 revision of the McDonald criteria for MS diagnosis, which has simplified their clinical use while preserving accuracy. Other MRI measures (e.g., cortical lesions and central vein signs) may improve diagnostic specificity, but their assessment still needs to be standardized, and their reliability confirmed. Novel MRI techniques are providing fundamental insights into the pathological substrates of the disease and are helping to give a better understanding of its clinical manifestations. Combined clinical-MRI measures of disease activity and progression, together with the use of clinically relevant MRI measures (e.g., brain atrophy) might improve treatment monitoring, but these are still not ready for the clinical setting. SUMMARY Advances in MRI technology are improving the diagnostic work-up and monitoring of MS, even in the earliest phases of the disease, and are providing MRI measures that are more specific and sensitive to disease pathological substrates.
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Affiliation(s)
- Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
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45
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Charalambous T, Tur C, Prados F, Kanber B, Chard DT, Ourselin S, Clayden JD, A M Gandini Wheeler-Kingshott C, Thompson AJ, Toosy AT. Structural network disruption markers explain disability in multiple sclerosis. J Neurol Neurosurg Psychiatry 2019; 90:219-226. [PMID: 30467210 PMCID: PMC6518973 DOI: 10.1136/jnnp-2018-318440] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 07/26/2018] [Accepted: 08/28/2018] [Indexed: 12/13/2022]
Abstract
OBJECTIVE To evaluate whether structural brain network metrics correlate better with clinical impairment and information processing speed in multiple sclerosis (MS) beyond atrophy measures and white matter lesions. METHODS This cross-sectional study included 51 healthy controls and 122 patients comprising 58 relapsing-remitting, 28 primary progressive and 36 secondary progressive. Structural brain networks were reconstructed from diffusion-weighted MRIs and standard metrics reflecting network density, efficiency and clustering coefficient were derived and compared between subjects' groups. Stepwise linear regression analyses were used to investigate the contribution of network measures that explain clinical disability (Expanded Disability Status Scale (EDSS)) and information processing speed (Symbol Digit Modalities Test (SDMT)) compared with conventional MRI metrics alone and to determine the best statistical model that explains better EDSS and SDMT. RESULTS Compared with controls, network efficiency and clustering coefficient were reduced in MS while these measures were also reduced in secondary progressive relative to relapsing-remitting patients. Structural network metrics increase the variance explained by the statistical models for clinical and information processing dysfunction. The best model for EDSS showed that reduced network density and global efficiency and increased age were associated with increased clinical disability. The best model for SDMT showed that lower deep grey matter volume, reduced efficiency and male gender were associated with worse information processing speed. CONCLUSIONS Structural topological changes exist between subjects' groups. Network density and global efficiency explained disability above non-network measures, highlighting that network metrics can provide clinically relevant information about MS pathology.
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Affiliation(s)
- Thalis Charalambous
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
| | - Carmen Tur
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
| | - Ferran Prados
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Baris Kanber
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Declan T Chard
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
| | - Sebastian Ourselin
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Jonathan D Clayden
- UCL GOS Institute of Child Health, University College London, London, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
- Brain MRI 3T Research Center, C. Mondino National Neurological Institute, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Alan J Thompson
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
| | - Ahmed T Toosy
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
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Bigaut K, Achard S, Hemmert C, Baloglu S, Kremer L, Collongues N, De Sèze J, Kremer S. Resting-state functional MRI demonstrates brain network reorganization in neuromyelitis optica spectrum disorder (NMOSD). PLoS One 2019; 14:e0211465. [PMID: 30695058 PMCID: PMC6351056 DOI: 10.1371/journal.pone.0211465] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 01/15/2019] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND The relation between brain functional connectivity of patients with neuromyelitis optica spectrum disorder (NMOSD) and the degree of disability remains unclear. OBJECTIVE Compare brain functional connectivity of patients with NMOSD to healthy subjects in resting-state functional MRI (rs-fMRI). METHODS We compared the rs-fMRI connectivity in 12 NMOSD patients with 20 healthy subjects matched for age and sex. Graph theory analysis was used to quantify the role of each node using a set of metrics: degree, global efficiency, clustering and modularity. To summarize the abnormal connectivity profile of brain regions in patients compared to healthy subjects, we defined a hub disruption index κ. RESULTS Concerning the global organization of networks in NMOSD, a small-world topology was preserved without significant modification concerning all average metrics. However, visual networks and the sensorimotor network showed decreased connectivity with high interindividual variability. The hub disruption index κ was correlated to the Expanded Disability Status Scale (EDSS). CONCLUSION These results demonstrate a correlation between disability according to the EDSS and neuronal reorganization using the rs-fMRI graph methodology. The conservation of a normal global topological structure despite local modifications in functional connectivity seems to show brain plasticity in response to the disability.
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Affiliation(s)
- Kévin Bigaut
- Department of neurology, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Sophie Achard
- Centre National de la Recherche Scientifique, Grenoble Image Parole Signal Automatique, Grenoble, France
| | - Céline Hemmert
- Department of radiology, Groupe Hospitalier Régional Mulhouse Sud-Alsace, Mulhouse, France
| | - Seyyid Baloglu
- Department of radiology, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Laurent Kremer
- Department of neurology, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Nicolas Collongues
- Department of neurology, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Jérôme De Sèze
- Department of neurology, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Stéphane Kremer
- Department of radiology, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
- * E-mail:
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47
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Kiljan S, Meijer KA, Steenwijk MD, Pouwels PJW, Schoonheim MM, Schenk GJ, Geurts JJG, Douw L. Structural network topology relates to tissue properties in multiple sclerosis. J Neurol 2018; 266:212-222. [PMID: 30467603 PMCID: PMC6342882 DOI: 10.1007/s00415-018-9130-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 11/14/2018] [Accepted: 11/16/2018] [Indexed: 11/01/2022]
Abstract
OBJECTIVE Abnormalities in segregative and integrative properties of brain networks have been observed in multiple sclerosis (MS) and are related to clinical functioning. This study aims to investigate the micro-scale correlates of macro-scale network measures of segregation and integration in MS. METHODS Eight MS patients underwent post-mortem in situ whole-brain diffusion tensor (DT) imaging and subsequent brain dissection. Macro-scale structural network topology was derived from DT data using graph theory. Clustering coefficient and mean white matter (WM) fiber length were measures of nodal segregation and integration. Thirty-three tissue blocks were collected from five cortical brain regions. Using immunohistochemistry micro-scale tissue properties were evaluated, including, neuronal size, neuronal density, axonal density and total cell density. Nodal network properties and tissue properties were correlated. RESULTS A negative correlation between clustering coefficient and WM fiber length was found. Higher clustering coefficient was associated with smaller neuronal size and lower axonal density, and vice versa for fiber length. Higher whole-brain WM lesion load was associated with higher whole-brain clustering, shorter whole-brain fiber length, lower neuronal size and axonal density. CONCLUSION Structural network properties on MRI associate with neuronal size and axonal density, suggesting that macro-scale network measures may grasp cortical neuroaxonal degeneration in MS.
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Affiliation(s)
- Svenja Kiljan
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, De Boelelaan 1108, 1081 HZ, Amsterdam, The Netherlands.
| | - Kim A Meijer
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, De Boelelaan 1108, 1081 HZ, Amsterdam, The Netherlands
| | - Martijn D Steenwijk
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, De Boelelaan 1108, 1081 HZ, Amsterdam, The Netherlands.,Department of Neurology, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, Amsterdam, The Netherlands
| | - Petra J W Pouwels
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, De Boelelaan 1108, 1081 HZ, Amsterdam, The Netherlands
| | - Geert J Schenk
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, De Boelelaan 1108, 1081 HZ, Amsterdam, The Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, De Boelelaan 1108, 1081 HZ, Amsterdam, The Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, De Boelelaan 1108, 1081 HZ, Amsterdam, The Netherlands.,Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
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Gonzalez-Escamilla G, Muthuraman M, Chirumamilla VC, Vogt J, Groppa S. Brain Networks Reorganization During Maturation and Healthy Aging-Emphases for Resilience. Front Psychiatry 2018; 9:601. [PMID: 30519196 PMCID: PMC6258799 DOI: 10.3389/fpsyt.2018.00601] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 10/29/2018] [Indexed: 12/31/2022] Open
Abstract
Maturation and aging are important life periods that are linked to drastic brain reorganization processes which are essential for mental health. However, the development of generalized theories for delimiting physiological and pathological brain remodeling through life periods linked to healthy states and resilience on one side or mental dysfunction on the other remains a challenge. Furthermore, important processes of preservation and compensation of brain function occur continuously in the cerebral brain networks and drive physiological responses to life events. Here, we review research on brain reorganization processes across the lifespan, demonstrating brain circuits remodeling at the structural and functional level that support mental health and are parallelized by physiological trajectories during maturation and healthy aging. We show evidence that aberrations leading to mental disorders result from the specific alterations of cerebral networks and their pathological dynamics leading to distinct excitability patterns. We discuss how these series of large-scale responses of brain circuits can be viewed as protective or malfunctioning mechanisms for the maintenance of mental health and resilience.
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Affiliation(s)
| | - Muthuraman Muthuraman
- Department of Neurology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Venkata C. Chirumamilla
- Department of Neurology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Johannes Vogt
- Institute for Microscopic Anatomy and Neurobiology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Department of Neurology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
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49
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Tahedl M, Levine SM, Greenlee MW, Weissert R, Schwarzbach JV. Functional Connectivity in Multiple Sclerosis: Recent Findings and Future Directions. Front Neurol 2018; 9:828. [PMID: 30364281 PMCID: PMC6193088 DOI: 10.3389/fneur.2018.00828] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 09/14/2018] [Indexed: 02/03/2023] Open
Abstract
Multiple sclerosis is a debilitating disorder resulting from scattered lesions in the central nervous system. Because of the high variability of the lesion patterns between patients, it is difficult to relate existing biomarkers to symptoms and their progression. The scattered nature of lesions in multiple sclerosis offers itself to be studied through the lens of network analyses. Recent research into multiple sclerosis has taken such a network approach by making use of functional connectivity. In this review, we briefly introduce measures of functional connectivity and how to compute them. We then identify several common observations resulting from this approach: (a) high likelihood of altered connectivity in deep-gray matter regions, (b) decrease of brain modularity, (c) hemispheric asymmetries in connectivity alterations, and (d) correspondence of behavioral symptoms with task-related and task-unrelated networks. We propose incorporating such connectivity analyses into longitudinal studies in order to improve our understanding of the underlying mechanisms affected by multiple sclerosis, which can consequently offer a promising route to individualizing imaging-related biomarkers for multiple sclerosis.
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Affiliation(s)
- Marlene Tahedl
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
- Institute for Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Seth M. Levine
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Mark W. Greenlee
- Institute for Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Robert Weissert
- Department of Neurology, University of Regensburg, Regensburg, Germany
| | - Jens V. Schwarzbach
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
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50
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Llufriu S, Rocca MA, Pagani E, Riccitelli GC, Solana E, Colombo B, Rodegher M, Falini A, Comi G, Filippi M. Hippocampal-related memory network in multiple sclerosis: A structural connectivity analysis. Mult Scler 2018; 25:801-810. [DOI: 10.1177/1352458518771838] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Background: We used graph theoretical analysis to quantify structural connectivity of the hippocampal-related episodic memory network and its association with memory performance in multiple sclerosis (MS) patients. Methods: Brain diffusion and T1-weighted sequences were obtained from 71 MS patients and 50 healthy controls (HCs). A total of 30 gray matter regions (selected a priori) were used as seeds to perform probabilistic tractography and create connectivity matrices. Global, nodal, and edge graph theoretical properties were calculated. In patients, verbal and visuospatial memory was assessed. Results: MS patients showed decreased network strength, assortativity, transitivity, global efficiency, and increased average path length. Several nodes had decreased strength and communicability in patients, whereas insula and left temporo-occipital cortex increased communicability. Patients had widespread decreased streamline count (SC) and communicability of edges, although a few ones increased their connectivity. Worse memory performance was associated with reduced network efficiency, decreased right hippocampus strength, and reduced SC and communicability of edges related to medial temporal lobe, thalamus, insula, and occipital cortex. Conclusion: Impaired structural connectivity occurs in the hippocampal-related memory network, decreasing the efficiency of information transmission. Network connectivity measures correlate with episodic memory, supporting the relevance of structural integrity in preserving memory processes in MS.
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Affiliation(s)
- Sara Llufriu
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy/Center of Neuroimmunology, Service of Neurology, Hospital Clinic and Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Casanova, Barcelona, Spain
| | - 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, 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
| | - Gianna C Riccitelli
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Elisabeth Solana
- Center of Neuroimmunology, Service of Neurology, Hospital Clinic and Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Casanova, Barcelona, Spain
| | - Bruno Colombo
- Department of Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Mariaemma Rodegher
- Department of Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Andrea Falini
- Department of Neuroradiology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Giancarlo Comi
- Department of Neurology, 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, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
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