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Stam CJ. Hub overload and failure as a final common pathway in neurological brain network disorders. Netw Neurosci 2024; 8:1-23. [PMID: 38562292 PMCID: PMC10861166 DOI: 10.1162/netn_a_00339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 09/26/2023] [Indexed: 04/04/2024] Open
Abstract
Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.
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Affiliation(s)
- Cornelis Jan Stam
- Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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2
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Martinez-Heras E, Solana E, Vivó F, Lopez-Soley E, Calvi A, Alba-Arbalat S, Schoonheim MM, Strijbis EM, Vrenken H, Barkhof F, Rocca MA, Filippi M, Pagani E, Groppa S, Fleischer V, Dineen RA, Bellenberg B, Lukas C, Pareto D, Rovira A, Sastre-Garriga J, Collorone S, Prados F, Toosy A, Ciccarelli O, Saiz A, Blanco Y, Llufriu S. Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes. J Neurol Neurosurg Psychiatry 2023; 94:916-923. [PMID: 37321841 DOI: 10.1136/jnnp-2023-331531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND We aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct MS phenotypes. METHODS Clinical information and brain MRIs were collected from 221 healthy individuals and 823 people with MS at 8 MAGNIMS centres. The patients were divided into four clinical phenotypes: clinically isolated syndrome, relapsing-remitting, secondary progressive and primary progressive. Advanced tractography methods were used to obtain connectivity matrices. Then, differences in whole-brain and nodal graph-derived measures, and in the fractional anisotropy of connections between groups were analysed. Support vector machine algorithms were used to classify groups. RESULTS Clinically isolated syndrome and relapsing-remitting patients shared similar network changes relative to controls. However, most global and local network properties differed in secondary progressive patients compared with the other groups, with lower fractional anisotropy in most connections. Primary progressive participants had fewer differences in global and local graph measures compared with clinically isolated syndrome and relapsing-remitting patients, and reductions in fractional anisotropy were only evident for a few connections. The accuracy of support vector machine to discriminate patients from healthy controls based on connection was 81%, and ranged between 64% and 74% in distinguishing among the clinical phenotypes. CONCLUSIONS In conclusion, brain connectivity is disrupted in MS and has differential patterns according to the phenotype. Secondary progressive is associated with more widespread changes in connectivity. Additionally, classification tasks can distinguish between MS types, with subcortical connections being the most important factor.
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Affiliation(s)
- Eloy Martinez-Heras
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Elisabeth Solana
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Francesc Vivó
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Elisabet Lopez-Soley
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Alberto Calvi
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Salut Alba-Arbalat
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Eva M Strijbis
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Sergiu Groppa
- Department of Neurology, Neurostimulation and Neuroimaging, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Vinzenz Fleischer
- Department of Neurology, Neurostimulation and Neuroimaging, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Robert A Dineen
- Mental Health and Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK; and NIHR Nottingham Biomedical Research Centre, Nottingham, UK
| | - Barbara Bellenberg
- Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Carsten Lukas
- Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Vall d'Hebron University Hospital and Research Institute (VHIR), Barcelona, Spain
| | - Alex Rovira
- Section of Neuroradiology, Department of Radiology, Vall d'Hebron University Hospital and Research Institute (VHIR), Barcelona, Spain
| | - Jaume Sastre-Garriga
- Neurology-Neuroimmunology Department, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Sara Collorone
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
| | - Ferran Prados
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Ahmed Toosy
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
| | - Olga Ciccarelli
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
| | - Albert Saiz
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Yolanda Blanco
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Sara Llufriu
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
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Hejazi S, Karwowski W, Farahani FV, Marek T, Hancock PA. Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review. Brain Sci 2023; 13:brainsci13020246. [PMID: 36831789 PMCID: PMC9953947 DOI: 10.3390/brainsci13020246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 02/04/2023] Open
Abstract
(1) Background: Multiple sclerosis (MS) is an immune system disease in which myelin in the nervous system is affected. This abnormal immune system mechanism causes physical disabilities and cognitive impairment. Functional magnetic resonance imaging (fMRI) is a common neuroimaging technique used in studying MS. Computational methods have recently been applied for disease detection, notably graph theory, which helps researchers understand the entire brain network and functional connectivity. (2) Methods: Relevant databases were searched to identify articles published since 2000 that applied graph theory to study functional brain connectivity in patients with MS based on fMRI. (3) Results: A total of 24 articles were included in the review. In recent years, the application of graph theory in the MS field received increased attention from computational scientists. The graph-theoretical approach was frequently combined with fMRI in studies of functional brain connectivity in MS. Lower EDSSs of MS stage were the criteria for most of the studies (4) Conclusions: This review provides insights into the role of graph theory as a computational method for studying functional brain connectivity in MS. Graph theory is useful in the detection and prediction of MS and can play a significant role in identifying cognitive impairment associated with MS.
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Affiliation(s)
- Sara Hejazi
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
- Correspondence:
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
| | - Farzad V. Farahani
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Tadeusz Marek
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Kraków, Poland
| | - P. A. Hancock
- Department of Psychology, University of Central Florida, Orlando, FL 32816, USA
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4
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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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 (MS) can be considered as a network disorder. This review discusses network concepts in order to understand progression in MS. Damage is hypothesized to lead to a “network collapse” and clinical progression. New concepts are discussed that will likely influence the field in the near future. These include brain wiring, how regions communicate and robustness to damage.
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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Afzal HMR, Luo S, Ramadan S, Khari M, Chaudhary G, Lechner-Scott J. Prediction of Conversion from CIS to Clinically Definite Multiple Sclerosis Using Convolutional Neural Networks. Comput Math Methods Med 2022; 2022:5154896. [PMID: 35872945 PMCID: PMC9307372 DOI: 10.1155/2022/5154896] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 06/13/2022] [Indexed: 11/18/2022]
Abstract
Multiple sclerosis (MS) is a chronic neurological disease of the central nervous system (CNS). Early diagnosis of MS is highly desirable as treatments are more effective in preventing MS-related disability when given in the early stages of the disease. The main aim of this research is to predict the occurrence of a second MS-related clinical event, which indicates the conversion of clinically isolated syndrome (CIS) to clinically definite MS (CDMS). In this study, we apply a branch of artificial intelligence known as deep learning and develop a fully automated algorithm primed with convolutional neural network (CNN) that has the ability to learn from MRI scan features. The basic architecture of our algorithm is that of the VGG16 CNN model, but amended such that it can handle MRI DICOM images. A dataset comprised of scans acquired using two different scanners was used for the purposes of verification of the algorithm. A group of 49 patients had volumetric MRI scans taken at onset of the disease and then again one year later using one of the two scanners. In total, this yielded 7360 images which were then used for training, validation, and testing of the algorithm. Initially, these raw images were taken through 4 steps of preprocessing. In order to boost the efficiency of the process, we pretrained our algorithm using the publicly available ADNI dataset used to classify Alzheimer's disease. Finally, we used our preprocessed dataset to train and test the algorithm. Clinical evaluation conducted a year after the first time point revealed that 26 of the 49 patients had converted to CDMS, while the remaining 23 had not. Results of testing showed that our algorithm was able to predict the clinical results with an accuracy of 88.8% and with an area under the curve (AUC) of 91%. A highly accurate algorithm was developed using CNN approach to reliably predict conversion of patients with CIS to CDMS using MRI data from two different scanners.
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Affiliation(s)
- H. M. Rehan Afzal
- School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW 2308, Australia
| | - Suhuai Luo
- School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW 2308, Australia
| | - Saadallah Ramadan
- Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia
| | - Manju Khari
- Jawaharlal Nehru University, New Delhi, India
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Koubiyr I, Deloire M, Brochet B, Besson P, Charré-Morin J, Saubusse A, Tourdias T, Ruet A. Structural constraints of functional connectivity drive cognitive impairment in the early stages of multiple sclerosis. Mult Scler 2020; 27:559-567. [PMID: 33283582 DOI: 10.1177/1352458520971807] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The relationship between structural and functional deficits in multiple sclerosis (MS) is unclear. OBJECTIVE This study explored structure-function relationships during the 5 years following a clinically isolated syndrome and their role in cognitive performance. METHODS Thirty-two patients were enrolled after their first neurological episode suggestive of MS and followed for 5 years, along with 10 matched healthy controls. We assessed structural (using diffusion tensor imaging) and functional (using resting-state functional magnetic resonance imaging (fMRI)) brain network metrics, clinical and cognitive scores at each follow-up visit. Structural-functional coupling, calculated as the correlation coefficient between strengths of structural and functional networks, was used to assess structure-function relationships. RESULTS Structural clustering coefficient was significantly increased after 5 years, whereas characteristic path length decreased. Structural connections decreased after 1 year and increased after 5 years. Functional connections and related path lengths were decreased after 5 years. Structural-functional coupling had increased significantly after 5 years. This structural-functional coupling was associated with cognitive and clinical evolution, with stronger coupling associated with a decline in both domains. CONCLUSION Our findings provide novel biological evidence that MS leads to a more constrained anatomical-dependant functional connectivity. The collapse of this network seems to lead to both cognitive worsening and clinical disability.
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Affiliation(s)
- Ismail Koubiyr
- University of Bordeaux, Bordeaux, France; Inserm U1215 - Neurocentre Magendie, Bordeaux, France
| | | | - Bruno Brochet
- University of Bordeaux, Bordeaux, France; Inserm U1215 - Neurocentre Magendie, Bordeaux, France; CHU Pellegrin Bordeaux, 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, Bordeaux, France; Inserm U1215 - Neurocentre Magendie, Bordeaux, France; CHU Pellegrin Bordeaux, Bordeaux, France
| | - Aurélie Ruet
- University of Bordeaux, Bordeaux, France; Inserm U1215 - Neurocentre Magendie, Bordeaux, France; CHU Pellegrin Bordeaux, Bordeaux, France
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Rosca EC, Simu M. Montreal cognitive assessment for evaluating cognitive impairment in multiple sclerosis: a systematic review. Acta Neurol Belg 2020; 120:1307-1321. [PMID: 32996098 DOI: 10.1007/s13760-020-01509-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 09/19/2020] [Indexed: 12/13/2022]
Abstract
This study aims to systematically review the evidence on the accuracy of the Montreal Cognitive Assessment (MoCA) test for evaluating the presence of cognitive impairment in patients with multiple sclerosis (MS) and to outline the quality and quantity of research evidence available about the use of MoCA in this population. We conducted a systematic literature review, searching five databases from inception until May 2020. We identified fourteen studies that met the inclusion criteria: three cross-sectional studies and two case - control studies comparing MoCA to a battery of tests, one study comparing MoCA to Mini-Mental State Examination (MMSE), and eight studies estimating the prevalence of cognitive impairment in individuals with MS. Publication period ranged from 2012 to 2020. Although the MoCA test demonstrated good sensitivity and specificity when used at the recommended threshold of 26, a lower threshold than the original cut-off was also reported to be useful for optimal screening, as it lowers false positive rates and improves diagnostic accuracy. Furthermore, in MS patients without subjective cognitive complaints, a cutoff of 27 could provide a better balance between the sensitivity and the specificity of the test. In patients with MS, the MoCA provides information on general cognitive functions disturbances. Nonetheless, more studies are required to examine the optimum cut-off score for detecting cognitive impairments in MS patients.
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Affiliation(s)
- Elena Cecilia Rosca
- Department of Neurology, University of Medicine and Pharmacy "Victor Babes" Timisoara, Timisoara, Romania.
- Department of Neurology, Clinical Emergency County Hospital, Bd. Iosif Bulbuca nr. 10, 300736, Timisoara, Romania.
| | - Mihaela Simu
- Department of Neurology, University of Medicine and Pharmacy "Victor Babes" Timisoara, Timisoara, Romania
- Department of Neurology, Clinical Emergency County Hospital, Bd. Iosif Bulbuca nr. 10, 300736, Timisoara, Romania
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Huang S, Wakaizumi K, Wu B, Shen B, Wu B, Fan L, Baliki MN, Zhan G, Apkarian AV, Huang L. Whole-brain functional network disruption in chronic pain with disk herniation. Pain 2019; 160:2829-40. [PMID: 31408051 DOI: 10.1097/j.pain.0000000000001674] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Brain functional network properties are globally disrupted in multiple musculoskeletal chronic pain conditions. Back pain with lumbar disk herniation (LDH) is highly prevalent and a major route for progression to chronic back pain. However, brain functional network properties remain unknown in such patients. Here, we examined resting-state functional magnetic resonance imaging-based functional connectivity networks in chronic back pain patients with clear evidence for LDH (LDH-chronic pain n = 146), in comparison to healthy controls (HCs, n = 165). The study was conducted in China, thus providing the opportunity to also examine the influence of culture on brain functional reorganization with chronic pain. The data were equally subdivided into discovery and validation subgroups (n = 68 LDH-chronic pain and n = 68 HC, for each subgroup), and contrasted to an off-site data set (n = 272, NITRC 1000). Graph disruption indices derived from 3 network topological measurements, degree, clustering coefficient, and efficiency, which respectively represent network hubness, segregation, and integration, were significantly decreased compared with HC, across all predefined link densities, in both discovery and validation groups. However, global mean clustering coefficient and betweenness centrality were decreased in the discovery group and showed trend in the validation group. The relationship between pain and graph disruption indices was limited to males with high education. These results deviate somewhat from recent similar analysis for other musculoskeletal chronic pain conditions, yet we cannot determine whether the differences are due to types of pain or also to cultural differences between patients studied in China and the United States.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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12
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Stampanoni Bassi M, Iezzi E, Pavone L, Mandolesi G, Musella A, Gentile A, Gilio L, Centonze D, Buttari F. Modeling Resilience to Damage in Multiple Sclerosis: Plasticity Meets Connectivity. Int J Mol Sci 2019; 21:E143. [PMID: 31878257 DOI: 10.3390/ijms21010143] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 12/05/2019] [Accepted: 12/20/2019] [Indexed: 02/03/2023] Open
Abstract
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) characterized by demyelinating white matter lesions and neurodegeneration, with a variable clinical course. Brain network architecture provides efficient information processing and resilience to damage. The peculiar organization characterized by a low number of highly connected nodes (hubs) confers high resistance to random damage. Anti-homeostatic synaptic plasticity, in particular long-term potentiation (LTP), represents one of the main physiological mechanisms underlying clinical recovery after brain damage. Different types of synaptic plasticity, including both anti-homeostatic and homeostatic mechanisms (synaptic scaling), contribute to shape brain networks. In MS, altered synaptic functioning induced by inflammatory mediators may represent a further cause of brain network collapse in addition to demyelination and grey matter atrophy. We propose that impaired LTP expression and pathologically enhanced upscaling may contribute to disrupting brain network topology in MS, weakening resilience to damage and negatively influencing the disease course.
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Brochet B, Ruet A. Cognitive Impairment in Multiple Sclerosis With Regards to Disease Duration and Clinical Phenotypes. Front Neurol 2019; 10:261. [PMID: 30949122 PMCID: PMC6435517 DOI: 10.3389/fneur.2019.00261] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 02/27/2019] [Indexed: 12/26/2022] Open
Abstract
The relationships between cognitive impairment that exist during the clinical course of multiple sclerosis (MS) remain poorly described. The effect of disease duration has been studied in a few longitudinal cohorts and some cross-sectional studies that suggest that cognitive deficits tend to extend with disease duration. However, the effect of disease duration seems to be confounded by the effect of age. At the pre-clinical stage, cognitive deficits have been observed in patients with radiologically isolated syndromes, and their profile is similar than in clinically isolated syndromes (CIS) and relapsing-remitting MS (RRMS). The frequency of cognitive impairment tends to be higher in RRMS than in CIS. In these phenotypes, slowness of information processing speed (IPS) and episodic verbal and visuo-spatial memory deficits are frequently observed, but executive functions, and in particular verbal fluency, could also be impaired. More frequent and severe deficits are reported in SPMS than in RRMS with more severe deficits for memory tests, working memory and IPS. Similarly to what is observed in SPMS, patients with primary progressive MS (PPMS) present with a wide range of cognitive deficits in IPS, attention, working memory, executive functions, and verbal episodic memory with more tests and domains impaired than RRMS patients. Altogether these data suggested that not only the duration of the disease and age play an important role in the cognitive profile of patients, but also the phenotype itself, probably because of its specific pathological mechanism.
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Affiliation(s)
- Bruno Brochet
- Service de Neurologie, CHU de Bordeaux, Bordeaux, France.,Team Glia-Neuron Interactions, Neurocentre Magendie, INSERM U1215, Université de Bordeaux, Bordeaux, France
| | - Aurélie Ruet
- Service de Neurologie, CHU de Bordeaux, Bordeaux, France.,Team Glia-Neuron Interactions, Neurocentre Magendie, INSERM U1215, Université de Bordeaux, Bordeaux, France
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15
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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: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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