1
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Cortese R, Battaglini M, Stromillo ML, Luchetti L, Leoncini M, Gentile G, Gasparini D, Plantone D, Altieri M, D'Ambrosio A, Gallo A, Giannì C, Piervincenzi C, Pantano P, Pagani E, Valsasina P, Preziosa P, Tedone N, Rocca MA, Filippi M, De Stefano N. Regional hippocampal atrophy reflects memory impairment in patients with early relapsing remitting multiple sclerosis. J Neurol 2024; 271:4897-4908. [PMID: 38743090 PMCID: PMC11319433 DOI: 10.1007/s00415-024-12290-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Research work has shown that hippocampal subfields are atrophic to varying extents in multiple sclerosis (MS) patients. However, studies examining the functional implications of subfield-specific hippocampal damage in early MS are limited. We aim to gain insights into the relationship between hippocampal atrophy and memory function by investigating the correlation between global and regional hippocampal atrophy and memory performance in early MS patients. METHODS From the Italian Neuroimaging Network Initiative (INNI) dataset, we selected 3D-T1-weighted brain MRIs of 219 early relapsing remitting (RR)MS and 246 healthy controls (HC) to identify hippocampal atrophic areas. At the time of MRI, patients underwent Selective-Reminding-Test (SRT) and Spatial-Recall-Test (SPART) and were classified as mildly (MMI-MS: n.110) or severely (SMI-MS: n:109) memory impaired, according to recently proposed cognitive phenotypes. RESULTS Early RRMS showed lower hippocampal volumes compared to HC (p < 0.001), while these did not differ between MMI-MS and SMI-MS. In MMI-MS, lower hippocampal volumes correlated with worse memory tests (r = 0.23-0.37, p ≤ 0.01). Atrophic voxels were diffuse in the hippocampus but more prevalent in cornu ammonis (CA, 79%) than in tail (21%). In MMI-MS, decreased subfield volumes correlated with decreases in memory, particularly in the right CA1 (SRT-recall: r = 0.38; SPART: r = 0.34, p < 0.01). No correlations were found in the SMI-MS group. CONCLUSION Hippocampal atrophy spreads from CA to tail from early disease stages. Subfield hippocampal atrophy is associated with memory impairment in MMI-MS, while this correlation is lost in SMI-MS. This plays in favor of a limited capacity for an adaptive functional reorganization of the hippocampi in MS patients.
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Affiliation(s)
- Rosa Cortese
- Department of Medicine, Surgery and Neuroscience, University of Siena, Viale Bracci 2, 53100, Siena, Italy
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Viale Bracci 2, 53100, Siena, Italy
- SIENA Imaging SRL, 53100, Siena, Italy
| | - Maria Laura Stromillo
- Department of Medicine, Surgery and Neuroscience, University of Siena, Viale Bracci 2, 53100, Siena, Italy
| | - Ludovico Luchetti
- Department of Medicine, Surgery and Neuroscience, University of Siena, Viale Bracci 2, 53100, Siena, Italy
- SIENA Imaging SRL, 53100, Siena, Italy
| | - Matteo Leoncini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Viale Bracci 2, 53100, Siena, Italy
- SIENA Imaging SRL, 53100, Siena, Italy
| | - Giordano Gentile
- Department of Medicine, Surgery and Neuroscience, University of Siena, Viale Bracci 2, 53100, Siena, Italy
- SIENA Imaging SRL, 53100, Siena, Italy
| | - Daniele Gasparini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Viale Bracci 2, 53100, Siena, Italy
| | - Domenico Plantone
- Department of Medicine, Surgery and Neuroscience, University of Siena, Viale Bracci 2, 53100, Siena, Italy
| | - Manuela Altieri
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Alessandro D'Ambrosio
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Antonio Gallo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Costanza Giannì
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed, Pozzilli, IS, Italy
| | | | - Patrizia Pantano
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed, Pozzilli, IS, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Nicolo' Tedone
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria Assunta Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Viale Bracci 2, 53100, Siena, Italy.
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2
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De Rosa AP, d'Ambrosio A, Bisecco A, Altieri M, Cirillo M, Gallo A, Esposito F. Functional gradients reveal cortical hierarchy changes in multiple sclerosis. Hum Brain Mapp 2024; 45:e26678. [PMID: 38647001 PMCID: PMC11033924 DOI: 10.1002/hbm.26678] [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: 11/17/2023] [Revised: 02/26/2024] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
Functional gradient (FG) analysis represents an increasingly popular methodological perspective for investigating brain hierarchical organization but whether and how network hierarchy changes concomitant with functional connectivity alterations in multiple sclerosis (MS) has remained elusive. Here, we analyzed FG components to uncover possible alterations in cortical hierarchy using resting-state functional MRI (rs-fMRI) data acquired in 122 MS patients and 97 healthy control (HC) subjects. Cortical hierarchy was assessed by deriving regional FG scores from rs-fMRI connectivity matrices using a functional parcellation of the cerebral cortex. The FG analysis identified a primary (visual-to-sensorimotor) and a secondary (sensory-to-transmodal) component. Results showed a significant alteration in cortical hierarchy as indexed by regional changes in FG scores in MS patients within the sensorimotor network and a compression (i.e., a reduced standard deviation across all cortical parcels) of the sensory-transmodal gradient axis, suggesting disrupted segregation between sensory and cognitive processing. Moreover, FG scores within limbic and default mode networks were significantly correlated (ρ = 0.30 $$ \rho =0.30 $$ , p < .005 after Bonferroni correction for both) with the symbol digit modality test (SDMT) score, a measure of information processing speed commonly used in MS neuropsychological assessments. Finally, leveraging supervised machine learning, we tested the predictive value of network-level FG features, highlighting the prominent role of the FG scores within the default mode network in the accurate prediction of SDMT scores in MS patients (average mean absolute error of 1.22 ± 0.07 points on a hold-out set of 24 patients). Our work provides a comprehensive evaluation of FG alterations in MS, shedding light on the hierarchical organization of the MS brain and suggesting that FG connectivity analysis can be regarded as a valuable approach in rs-fMRI studies across different MS populations.
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Affiliation(s)
- Alessandro Pasquale De Rosa
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Alessandro d'Ambrosio
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Alvino Bisecco
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Manuela Altieri
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Mario Cirillo
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Antonio Gallo
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Fabrizio Esposito
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
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3
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Rocca MA, D’Amore G, Valsasina P, Tedone N, Meani A, Filippi M. 2.5-Year changes of connectivity dynamism are relevant for physical and cognitive deterioration in multiple sclerosis. Mult Scler 2024; 30:546-557. [PMID: 38372039 PMCID: PMC11010569 DOI: 10.1177/13524585241231155] [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: 11/20/2023] [Revised: 01/11/2024] [Accepted: 01/20/2024] [Indexed: 02/20/2024]
Abstract
BACKGROUND In MS, functional connectivity (FC) dynamism may influence disease evolution. OBJECTIVES The objective is to assess time-varying functional connectivity (TVFC) changes over time at 2.5-year follow-up in MS patients according to physical and cognitive worsening. METHODS We collected 3T magnetic resonance imaging (MRI) for TVFC assessment (performed using sliding-window analysis of centrality) and clinical evaluations at baseline and 2.5-year follow-up from 28 healthy controls and 129 MS patients. Of these, 79 underwent baseline and follow-up neuropsychological assessment. At 2.5 years, physical/cognitive worsening was defined according to disability/neuropsychological score changes. RESULTS At follow-up, 25/129 (19.3%) MS patients worsened physically and 14/79 (17.7%) worsened cognitively. At baseline, MS patients showed reduced TVFC versus controls. At 2.5-year follow-up, no TVFC changes were detected in controls. Conversely, TVFC decreased over time in parieto-temporal regions in stable MS patients and in default-mode network in worsened MS. In physically worsened MS, basal ganglia TVFC reductions were also found. Reduced TVFC over time in the putamen in physically worsened and reduced TVFC in the precuneus in cognitively worsened were significant versus stable MS. DISCUSSION At 2.5-year follow-up, default-mode network TVFC reductions were found in worsening MS. Moreover, reduced deep gray matter TVFC characterized physically worsened patients, whereas precuneus involvement characterized cognitively worsened MS patients.
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Affiliation(s)
- Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy/
- Vita-Salute San Raffaele University, Milan, Italy
| | - Giulia D’Amore
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Nicolò Tedone
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alessandro Meani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy/Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
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4
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Ananthavarathan P, Sahi N, Chard DT. An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression. Expert Rev Neurother 2024; 24:201-216. [PMID: 38235594 DOI: 10.1080/14737175.2024.2304116] [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: 11/01/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024]
Abstract
INTRODUCTION While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear. AREAS COVERED The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning. EXPERT OPINION Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.
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Affiliation(s)
- Piriyankan Ananthavarathan
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Nitin Sahi
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Declan T Chard
- Clinical Research Associate & Consultant Neurologist, Institute of Neurology - Queen Square Multiple Sclerosis Centre, London, UK
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5
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Gakis G, Angelopoulos I, Panagoulias I, Mouzaki A. Current knowledge on multiple sclerosis pathophysiology, disability progression assessment and treatment options, and the role of autologous hematopoietic stem cell transplantation. Autoimmun Rev 2024; 23:103480. [PMID: 38008300 DOI: 10.1016/j.autrev.2023.103480] [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: 10/31/2023] [Accepted: 11/20/2023] [Indexed: 11/28/2023]
Abstract
Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) that affects nearly 2.8 million people each year. MS distinguishes three main types: relapsing-remitting MS (RRMS), secondary progressive MS (SPMS) and primary progressive MS (PPMS). RRMS is the most common type, with the majority of patients eventually progressing to SPMS, in which neurological development is constant, whereas PPMS is characterized by a progressive course from disease onset. New or additional insights into the role of effector and regulatory cells of the immune and CNS systems, Epstein-Barr virus (EBV) infection, and the microbiome in the pathophysiology of MS have emerged, which may lead to the development of more targeted therapies that can halt or reverse neurodegeneration. Depending on the type and severity of the disease, various disease-modifying therapies (DMTs) are currently used for RRMS/SPMS and PPMS. As a last resort, and especially in highly active RRMS that does not respond to DMTs, autologous hematopoietic stem cell transplantation (AHSCT) is performed and has shown good results in reducing neuroinflammation. Nevertheless, the question of its potential role in preventing disability progression remains open. The aim of this review is to provide a comprehensive update on MS pathophysiology, assessment of MS disability progression and current treatments, and to examine the potential role of AHSCT in preventing disability progression.
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Affiliation(s)
- Georgios Gakis
- Laboratory of Immunohematology, Medical School, University of Patras, Patras, Greece
| | - Ioannis Angelopoulos
- Laboratory of Immunohematology, Medical School, University of Patras, Patras, Greece
| | - Ioannis Panagoulias
- Laboratory of Immunohematology, Medical School, University of Patras, Patras, Greece
| | - Athanasia Mouzaki
- Laboratory of Immunohematology, Medical School, University of Patras, Patras, Greece.
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6
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Tilsley P, Strohmeyer IA, Heinrich I, Rosenthal F, Patra S, Schulz KH, Rosenkranz SC, Ramien C, Pöttgen J, Heesen C, Has AC, Gold SM, Stellmann JP. Physical fitness moderates the association between brain network impairment and both motor function and cognition in progressive multiple sclerosis. J Neurol 2023; 270:4876-4888. [PMID: 37341806 DOI: 10.1007/s00415-023-11806-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/01/2023] [Accepted: 06/03/2023] [Indexed: 06/22/2023]
Abstract
BACKGROUND Neurodegeneration leads to continuous accumulation of disability in progressive Multiple Sclerosis (MS). Exercise is considered to counteract disease progression, but little is known on the interaction between fitness, brain networks and disability in MS. OBJECTIVE The aim of this study to explore functional and structural brain connectivity and the interaction between fitness and disability based on motor and cognitive functional outcomes in a secondary analysis of a randomised, 3-month, waiting group controlled arm ergometry intervention in progressive MS. METHODS We modelled individual structural and functional brain networks based on magnetic resonance imaging (MRI). We used linear mixed effect models to compare changes in brain networks between the groups and explore the association between fitness, brain connectivity and functional outcomes in the entire cohort. RESULTS We recruited 34 persons with advanced progressive MS (pwMS, mean age 53 years, females 71%, mean disease duration 17 years and an average walking restriction of < 100 m without aid). Functional connectivity increased in highly connected brain regions of the exercise group (p = 0.017), but no structural changes (p = 0.817) were observed. Motor and cognitive task performance correlated positively with nodal structural connectivity but not nodal functional connectivity. We also found that the correlation between fitness and functional outcomes was stronger with lower connectivity. CONCLUSIONS Functional reorganisation seems to be an early indicator of exercise effects on brain networks. Fitness moderates the relationship between network disruption and both motor and cognitive outcomes, with growing importance in more disrupted brain networks. These findings underline the need and opportunities associated with exercise in advanced MS.
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Affiliation(s)
- Penelope Tilsley
- CEMEREM, APHM La Timone, 264 Rue Saint-Pierre, 13385, Marseille, France
- CNRS, CRMBM, UMR 7339, Aix-Marseille Univ, Marseille, France
| | - Isanbert Arun Strohmeyer
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Inga Heinrich
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Neurologische Klinik, Klinikum Aschaffenburg-Alzenau, Aschaffenburg, Germany
| | - Friederike Rosenthal
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Patra
- Universitäres Kompetenzzentrum für Sport- und Bewegungsmedizin (Athleticum) und Institut und Poliklinik für Medizinische Psychologie, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Karl Heinz Schulz
- Universitäres Kompetenzzentrum für Sport- und Bewegungsmedizin (Athleticum) und Institut und Poliklinik für Medizinische Psychologie, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Sina C Rosenkranz
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Caren Ramien
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jana Pöttgen
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph Heesen
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Arzu Ceylan Has
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan M Gold
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
- Division of Psychosomatic Medicine, Medical Department, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Jan-Patrick Stellmann
- CEMEREM, APHM La Timone, 264 Rue Saint-Pierre, 13385, Marseille, France.
- CNRS, CRMBM, UMR 7339, Aix-Marseille Univ, Marseille, France.
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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7
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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] [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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8
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Strik M, Eijlers AJC, Dekker I, Broeders TAA, Douw L, Killestein J, Kolbe SC, Geurts JJG, Schoonheim MM. Sensorimotor network dynamics predict decline in upper and lower limb function in people with multiple sclerosis. Mult Scler 2023; 29:81-91. [PMID: 36177896 PMCID: PMC9896264 DOI: 10.1177/13524585221125372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Upper and lower limb disabilities are hypothesized to have partially independent underlying (network) disturbances in multiple sclerosis (MS). OBJECTIVE This study investigated functional network predictors and longitudinal network changes related to upper and lower limb progression in MS. METHODS Two-hundred fourteen MS patients and 58 controls underwent functional magnetic resonance imaging (fMRI), dexterity (9-Hole Peg Test) and mobility (Timed 25-Foot Walk) measurements (baseline and 5 years). Patients were stratified into progressors (>20% decline) or non-progressors. Functional network efficiency was calculated using static (over entire scan) and dynamic (fluctuations during scan) approaches. Baseline measurements were used to predict progression; significant predictors were explored over time. RESULTS In both limbs, progression was related to supplementary motor area and caudate efficiency (dynamic and static, respectively). Upper limb progression showed additional specific predictors; cortical grey matter volume, putamen static efficiency and posterior associative sensory (PAS) cortex, putamen, primary somatosensory cortex and thalamus dynamic efficiency. Additional lower limb predictors included motor network grey matter volume, caudate (dynamic) and PAS (static). Only the caudate showed a decline in efficiency over time in one group (non-progressors). CONCLUSION Disability progression can be predicted using sensorimotor network measures. Upper and lower limb progression showed unique predictors, possibly indicating different network disturbances underlying these types of progression in MS.
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Affiliation(s)
- Myrte Strik
- M Strik Melbourne Brain Centre Imaging Unit, Department of Radiology, University of Melbourne, Melbourne Medical School, Level 1, Kenneth Myer building, 30 Royal Parade, Melbourne, VIC 3010 Australia.
| | - Anand JC Eijlers
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Iris Dekker
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Tommy AA Broeders
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Linda Douw
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Joep Killestein
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Scott C Kolbe
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Jeroen JG Geurts
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
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9
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Huiskamp M, Yaqub M, van Lingen MR, Pouwels PJW, de Ruiter LRJ, Killestein J, Schwarte LA, Golla SSV, van Berckel BNM, Boellaard R, Geurts JJG, Hulst HE. Cognitive performance in multiple sclerosis: what is the role of the gamma-aminobutyric acid system? Brain Commun 2023; 5:fcad140. [PMID: 37180993 PMCID: PMC10174207 DOI: 10.1093/braincomms/fcad140] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 01/26/2023] [Accepted: 04/28/2023] [Indexed: 05/16/2023] Open
Abstract
Cognitive impairment occurs in 40-65% of persons with multiple sclerosis and may be related to alterations in glutamatergic and GABAergic neurotransmission. Therefore, the aim of this study was to determine how glutamatergic and GABAergic changes relate to cognitive functioning in multiple sclerosis in vivo. Sixty persons with multiple sclerosis (mean age 45.5 ± 9.6 years, 48 females, 51 relapsing-remitting multiple sclerosis) and 22 age-matched healthy controls (45.6 ± 22.0 years, 17 females) underwent neuropsychological testing and MRI. Persons with multiple sclerosis were classified as cognitively impaired when scoring at least 1.5 standard deviations below normative scores on ≥30% of tests. Glutamate and GABA concentrations were determined in the right hippocampus and bilateral thalamus using magnetic resonance spectroscopy. GABA-receptor density was assessed using quantitative [11C]flumazenil positron emission tomography in a subset of participants. Positron emission tomography outcome measures were the influx rate constant (a measure predominantly reflecting perfusion) and volume of distribution, which is a measure of GABA-receptor density. Twenty persons with multiple sclerosis (33%) fulfilled the criteria for cognitive impairment. No differences were observed in glutamate or GABA concentrations between persons with multiple sclerosis and healthy controls, or between cognitively preserved, impaired and healthy control groups. Twenty-two persons with multiple sclerosis (12 cognitively preserved and 10 impaired) and 10 healthy controls successfully underwent [11C]flumazenil positron emission tomography. Persons with multiple sclerosis showed a lower influx rate constant in the thalamus, indicating lower perfusion. For the volume of distribution, persons with multiple sclerosis showed higher values than controls in deep grey matter, reflecting increased GABA-receptor density. When comparing cognitively impaired and preserved patients to controls, the preserved group showed a significantly higher volume of distribution in cortical and deep grey matter and hippocampus. Positive correlations were observed between both positron emission tomography measures and information processing speed in the multiple sclerosis group only. Whereas concentrations of glutamate and GABA did not differ between multiple sclerosis and control nor between cognitively impaired, preserved and control groups, increased GABA-receptor density was observed in preserved persons with multiple sclerosis that was not seen in cognitively impaired patients. In addition, GABA-receptor density correlated to cognition, in particular with information processing speed. This could indicate that GABA-receptor density is upregulated in the cognitively preserved phase of multiple sclerosis as a means to regulate neurotransmission and potentially preserve cognitive functioning.
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Affiliation(s)
- Marijn Huiskamp
- Correspondence to: M. Huiskamp Department of Anatomy & Neurosciences Amsterdam UMC, Location Vrije Universiteit PO Box 7057, 1007 MB Amsterdam, The Netherlands E-mail:
| | - Maqsood Yaqub
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Marike R van Lingen
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Petra J W Pouwels
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Lodewijk R J de Ruiter
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Joep Killestein
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Lothar A Schwarte
- Department of Anesthesiology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Sandeep S V Golla
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Ronald Boellaard
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Jeroen J G Geurts
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Hanneke E Hulst
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, 2333 AK, The Netherlands
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10
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von Schwanenflug N, Koch SP, Krohn S, Broeders TAA, Lydon-Staley DM, Bassett DS, Schoonheim MM, Paul F, Finke C. Increased flexibility of brain dynamics in patients with multiple sclerosis. Brain Commun 2023; 5:fcad143. [PMID: 37188221 PMCID: PMC10176242 DOI: 10.1093/braincomms/fcad143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 03/08/2023] [Accepted: 04/28/2023] [Indexed: 05/17/2023] Open
Abstract
Patients with multiple sclerosis consistently show widespread changes in functional connectivity. Yet, alterations are heterogeneous across studies, underscoring the complexity of functional reorganization in multiple sclerosis. Here, we aim to provide new insights by applying a time-resolved graph-analytical framework to identify a clinically relevant pattern of dynamic functional connectivity reconfigurations in multiple sclerosis. Resting-state data from 75 patients with multiple sclerosis (N = 75, female:male ratio of 3:2, median age: 42.0 ± 11.0 years, median disease duration: 6 ± 11.4 years) and 75 age- and sex-matched controls (N = 75, female:male ratio of 3:2, median age: 40.2 ± 11.8 years) were analysed using multilayer community detection. Local, resting-state functional system and global levels of dynamic functional connectivity reconfiguration were characterized using graph-theoretical measures including flexibility, promiscuity, cohesion, disjointedness and entropy. Moreover, we quantified hypo- and hyper-flexibility of brain regions and derived the flexibility reorganization index as a summary measure of whole-brain reorganization. Lastly, we explored the relationship between clinical disability and altered functional dynamics. Significant increases in global flexibility (t = 2.38, PFDR = 0.024), promiscuity (t = 1.94, PFDR = 0.038), entropy (t = 2.17, PFDR = 0.027) and cohesion (t = 2.45, PFDR = 0.024) were observed in patients and were driven by pericentral, limbic and subcortical regions. Importantly, these graph metrics were correlated with clinical disability such that greater reconfiguration dynamics tracked greater disability. Moreover, patients demonstrate a systematic shift in flexibility from sensorimotor areas to transmodal areas, with the most pronounced increases located in regions with generally low dynamics in controls. Together, these findings reveal a hyperflexible reorganization of brain activity in multiple sclerosis that clusters in pericentral, subcortical and limbic areas. This functional reorganization was linked to clinical disability, providing new evidence that alterations of multilayer temporal dynamics play a role in the manifestation of multiple sclerosis.
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Affiliation(s)
- Nina von Schwanenflug
- Department of Neurology and Experimental Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10098, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin 10117, Germany
| | - Stefan P Koch
- Department of Experimental Neurology, Center for Stroke Research Berlin, Berlin 10117, Germany
- NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Stephan Krohn
- Department of Neurology and Experimental Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10098, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin 10117, Germany
| | - Tommy A A Broeders
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1007 MB, The Netherlands
| | - David M Lydon-Staley
- Annenberg School for Communication, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, PA, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia 19104, PA, USA
| | - Dani S Bassett
- Department of Biological Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, PA, USA
- Santa Fe Institute, Santa Fe 87501, NM, USA
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1007 MB, The Netherlands
| | - Friedemann Paul
- Department of Neurology and Experimental Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10098, Germany
- Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité—Universitätsmedizin Berlin, Berlin 10117, Germany
- NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10017, Germany
| | - Carsten Finke
- Correspondence to: Carsten Finke Charité - Universitätsklinikum Berlin Department of Neurology and Experimental Neurology Campus Mitte, Bonhoeffer Weg 3, 10098 Berlin, Germany E-mail:
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11
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Neurorehabilitation in Multiple Sclerosis-A Review of Present Approaches and Future Considerations. J Clin Med 2022; 11:jcm11237003. [PMID: 36498578 PMCID: PMC9739865 DOI: 10.3390/jcm11237003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022] Open
Abstract
Multiple sclerosis is an increasingly prevalent disease, representing the leading cause of non-traumatic neurological disease in Europe and North America. The most common symptoms include gait deficits, balance and coordination impairments, fatigue, spasticity, dysphagia and an overactive bladder. Neurorehabilitation therapeutic approaches aim to alleviate symptoms and improve the quality of life through promoting positive immunological transformations and neuroplasticity. The purpose of this study is to evaluate the current treatments for the most debilitating symptoms in multiple sclerosis, identify areas for future improvement, and provide a reference guide for practitioners in the field. It analyzes the most cited procedures currently in use for the management of a number of symptoms affecting the majority of patients with multiple sclerosis, from different training routines to cognitive rehabilitation and therapies using physical agents, such as electrostimulation, hydrotherapy, cryotherapy and electromagnetic fields. Furthermore, it investigates the quality of evidence for the aforementioned therapies and the different tests applied in practice to assess their utility. Lastly, the study looks at potential future candidates for the treatment and evaluation of patients with multiple sclerosis and the supposed benefits they could bring in clinical settings.
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12
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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] [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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13
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Combes AJE, O'Grady KP, Rogers BP, Schilling KG, Lawless RD, Visagie M, Houston D, Prock L, Malone S, Satish S, Witt AA, McKnight CD, Bagnato F, Gore JC, Smith SA. Functional connectivity in the dorsal network of the cervical spinal cord is correlated with diffusion tensor imaging indices in relapsing-remitting multiple sclerosis. Neuroimage Clin 2022; 35:103127. [PMID: 35917721 PMCID: PMC9421501 DOI: 10.1016/j.nicl.2022.103127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 06/03/2022] [Accepted: 07/23/2022] [Indexed: 01/12/2023]
Abstract
Focal lesions may affect functional connectivity (FC) of the ventral and dorsal networks in the cervical spinal cord of people with relapsing-remitting multiple sclerosis (RRMS). Resting-state FC can be measured using functional MRI (fMRI) at 3T. This study sought to determine whether alterations in FC may be related to the degree of damage in the normal-appearing tissue. Tissue integrity and FC in the cervical spinal cord were assessed with diffusion tensor imaging (DTI) and resting-state fMRI, respectively, in a group of 26 RRMS participants with high cervical lesion load, low disability, and minimally impaired sensorimotor function, and healthy controls. Lower fractional anisotropy (FA) and higher radial diffusivity (RD) were observed in the normal-appearing white matter in the RRMS group relative to controls. Average FC in ventral and dorsal networks was similar between groups. Significant associations were found between higher FC in the dorsal sensory network and several DTI markers of pathology in the normal-appearing tissue. In the normal-appearing grey matter, dorsal FC was positively correlated with axial diffusivity (AD) (r = 0.46, p = 0.020) and mean diffusivity (MD) (r = 0.43, p = 0.032). In the normal-appearing white matter, dorsal FC was negatively correlated with FA (r = -0.43, p = 0.028) and positively correlated with RD (r = 0.49, p = 0.012), AD (r = 0.42, p = 0.037) and MD (r = 0.53, p = 0.006). These results suggest that increased connectivity, while remaining within the normal range, may represent a compensatory mechanism in response to structural damage in support of preserved sensory function in RRMS.
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Affiliation(s)
- Anna J E Combes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States.
| | - Kristin P O'Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States
| | - Richard D Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, PMB 351826, Nashville, TN 37235-1826, United States
| | - Mereze Visagie
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States
| | - Delaney Houston
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States
| | - Logan Prock
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States
| | - Shekinah Malone
- School of Medicine, Meharry Medical College, 1005 Dr. D. B. Todd, Jr. Blvd., Nashville, TN 37208, United States
| | - Sanjana Satish
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States
| | - Atlee A Witt
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States
| | - Colin D McKnight
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States
| | - Francesca Bagnato
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, 1161 21st Ave. South, A-0118 Medical Center North, Nashville, TN 37232, United States; Department of Neurology, Nashville VA Medical Center, TN Valley Healthcare System, 1310 24th Avenue South, Nashville, TN 37212-2637, United States
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, PMB 351826, Nashville, TN 37235-1826, United States
| | - Seth A Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, PMB 351826, Nashville, TN 37235-1826, United States
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14
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Rocca MA, Schoonheim MM, Valsasina P, Geurts JJG, Filippi M. Task- and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective. Neuroimage Clin 2022; 35:103076. [PMID: 35691253 PMCID: PMC9194954 DOI: 10.1016/j.nicl.2022.103076] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 01/12/2023]
Abstract
Functional MRI is able to detect adaptive and maladaptive abnormalities at different MS stages. Increased fMRI activity is a feature of early MS, while progressive exhaustion of adaptive mechanisms is detected later on in the disease. Collapse of long-range connections and impaired hub integration characterize MS network reorganization. Time-varying connectivity analysis provides useful and complementary pieces of information to static functional connectivity. New perspectives might be the use of multimodal MRI and artificial intelligence.
Multiple sclerosis (MS) is a neurological disorder affecting the central nervous system and features extensive functional brain changes that are poorly understood but relate strongly to clinical impairments. Functional magnetic resonance imaging (fMRI) is a non-invasive, powerful technique able to map activity of brain regions and to assess how such regions interact for an efficient brain network. FMRI has been widely applied to study functional brain changes in MS, allowing to investigate functional plasticity consequent to disease-related structural injury. The first studies in MS using active fMRI tasks mainly aimed to study such plastic changes by identifying abnormal activity in salient brain regions (or systems) involved by the task. In later studies the focus shifted towards resting state (RS) functional connectivity (FC) studies, which aimed to map large-scale functional networks of the brain and to establish how MS pathology impairs functional integration, eventually leading to the hypothesized network collapse as patients clinically progress. This review provides a summary of the main findings from studies using task-based and RS fMRI and illustrates how functional brain alterations relate to clinical disability and cognitive deficits in this condition. We also give an overview of longitudinal studies that used task-based and RS fMRI to monitor disease evolution and effects of motor and cognitive rehabilitation. In addition, we discuss the results of studies using newer technologies involving time-varying FC to investigate abnormal dynamism and flexibility of network configurations in MS. Finally, we show some preliminary results from two recent topics (i.e., multimodal MRI analysis and artificial intelligence) that are receiving increasing attention. Together, these functional studies could provide new (conceptual) insights into disease stage-specific mechanisms underlying progression in MS, with recommendations for future research.
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Affiliation(s)
- Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy.
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
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15
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Sobczak AM, Bohaterewicz B, Ceglarek A, Zyrkowska A, Fafrowicz M, Slowik A, Wnuk M, Marona M, Nowak K, Zur-Wyrozumska K, Marek T. Brain Under Fatigue – Can Perceived Fatigability in Multiple Sclerosis Be Seen on the Level of Functional Brain Network Architecture? Front Hum Neurosci 2022; 16:852981. [PMID: 35620154 PMCID: PMC9128356 DOI: 10.3389/fnhum.2022.852981] [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: 01/11/2022] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Background Fatigue is one of the most common symptoms of multiple sclerosis (MS), significantly affecting the functioning of the patients. However, the neural underpinnings of physical and mental fatigue in MS are still vague. The aim of our study was to investigate the functional architecture of resting-state networks associated with fatigue in patients with MS. Methods The sum of 107 high-functioning patients underwent a resting-state scanning session and filled out the 9-item Fatigue Severity Scale (FSS). Based on the FSS score, we identified patients with different levels of fatigue using the cluster analysis. The low-fatigue group consisted of n = 53 subjects, while the high-fatigue group n = 48. The neuroimaging data were analyzed in terms of functional connectivity (FC) between various resting-state networks as well as amplitude of low-frequency fluctuation (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF). Results Two-sample t-test revealed between-group differences in FC of posterior salience network (SN). No differences occurred in default mode network (DMN) and sensorimotor network (SMN). Moreover, differences in fALFF were shown in the right middle frontal gyrus and right superior frontal gyrus, however, no ALFF differences took place. Conclusion Current study revealed significant functional network (FN) architecture between-group differences associated with fatigue. Present results suggest the higher level of fatigue is related to deficits in awareness as well as higher interoceptive awareness and nociception.
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Affiliation(s)
- Anna Maria Sobczak
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
- *Correspondence: Anna Maria Sobczak,
| | - Bartosz Bohaterewicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
- Department of Psychology of Individual Differences, Psychological Diagnosis, and Psychometrics, Institute of Psychology, University of Social Sciences and Humanities, Warsaw, Poland
| | - Anna Ceglarek
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
| | - Aleksandra Zyrkowska
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
| | - Magdalena Fafrowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Collegium Medicum, Kraków, Poland
- Department of Neurology, University Hospital in Krakow, Kraków, Poland
| | - Marcin Wnuk
- Department of Neurology, Jagiellonian University Collegium Medicum, Kraków, Poland
- Department of Neurology, University Hospital in Krakow, Kraków, Poland
| | - Monika Marona
- Department of Neurology, Jagiellonian University Collegium Medicum, Kraków, Poland
- Department of Neurology, University Hospital in Krakow, Kraków, Poland
| | - Klaudia Nowak
- Department of Neurology, Jagiellonian University Collegium Medicum, Kraków, Poland
- Department of Neurology, University Hospital in Krakow, Kraków, Poland
| | - Kamila Zur-Wyrozumska
- Department of Medical Education, Jagiellonian University Medical College, Kraków, Poland
- Department of Neurology, 5th Military Hospital, Kraków, Poland
| | - Tadeusz Marek
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
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16
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Ravano V, Andelova M, Fartaria MJ, Mahdi MFAW, Maréchal B, Meuli R, Uher T, Krasensky J, Vaneckova M, Horakova D, Kober T, Richiardi J. Validating atlas-based lesion disconnectomics in multiple sclerosis: A retrospective multi-centric study. Neuroimage Clin 2022; 32:102817. [PMID: 34500427 PMCID: PMC8429972 DOI: 10.1016/j.nicl.2021.102817] [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: 04/26/2021] [Revised: 07/30/2021] [Accepted: 08/30/2021] [Indexed: 12/01/2022]
Abstract
Structural disconnectomes can be modelled without diffusion using tractography atlases. Atlas-based and DTI-derived disconnectome topological metrics correlate strongly. MS patient disconnectomes relate to clinical scores.
The translational potential of MR-based connectivity modelling is limited by the need for advanced diffusion imaging, which is not part of clinical protocols for many diseases. In addition, where diffusion data is available, brain connectivity analyses rely on tractography algorithms which imply two major limitations. First, tracking algorithms are known to be sensitive to the presence of white matter lesions and therefore leading to interpretation pitfalls and poor inter-subject comparability in clinical applications such as multiple sclerosis. Second, tractography quality is highly dependent on the acquisition parameters of diffusion sequences, leading to a trade-off between acquisition time and tractography precision. Here, we propose an atlas-based approach to study the interplay between structural disconnectivity and lesions without requiring individual diffusion imaging. In a multi-centric setting involving three distinct multiple sclerosis datasets (containing both 1.5 T and 3 T data), we compare our atlas-based structural disconnectome computation pipeline to disconnectomes extracted from individual tractography and explore its clinical utility for reducing the gap between radiological findings and clinical symptoms in multiple sclerosis. Results using topological graph properties showed that overall, our atlas-based disconnectomes were suitable approximations of individual disconnectomes from diffusion imaging. Small-worldness was found to decrease for larger total lesion volumes thereby suggesting a loss of efficiency in brain connectivity of MS patients. Finally, the global efficiency of the created brain graph, combined with total lesion volume, allowed to stratify patients into subgroups with different clinical scores in all three cohorts.
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Affiliation(s)
- Veronica Ravano
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Michaela Andelova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Mário João Fartaria
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Bénédicte Maréchal
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Reto Meuli
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Tomas Uher
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Jan Krasensky
- MR unit, Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Manuela Vaneckova
- MR unit, Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Dana Horakova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jonas Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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17
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Kulik SD, Nauta IM, Tewarie P, Koubiyr I, van Dellen E, Ruet A, Meijer KA, de Jong BA, Stam CJ, Hillebrand A, Geurts JJG, Douw L, Schoonheim MM. Structure-function coupling as a correlate and potential biomarker of cognitive impairment in multiple sclerosis. Netw Neurosci 2021; 6:339-356. [PMID: 35733434 PMCID: PMC9208024 DOI: 10.1162/netn_a_00226] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/21/2021] [Indexed: 11/04/2022] Open
Abstract
Abstract
Multiple sclerosis (MS) features extensive connectivity changes, but how structural and functional connectivity relate, and whether this relation could be a useful biomarker for cognitive impairment in MS is unclear.
This study included 79 MS patients and 40 healthy controls (HCs). Patients were classified as cognitively impaired (CI) or cognitively preserved (CP). Structural connectivity was determined using diffusion MRI and functional connectivity using resting-state magnetoencephalography (MEG) data (theta, alpha1 and alpha2 bands). Structure-function coupling was assessed by correlating modalities, and further explored in frequency bands that significantly correlated with whole-brain structural connectivity. Functional correlates of short- and long-range structural connections (based on tract length) were then specifically assessed. ROC analyses were performed on coupling values to identify biomarker potential.
Only the theta band showed significant correlations between whole-brain structural and functional connectivity (rho = −0.26, p = 0.023, only in MS). Long-range structure-function coupling was higher in CI patients compared to HCs (p = 0.005). Short-range coupling showed no group differences. Structure-function coupling was not a significant classifier of cognitive impairment for any tract length (short-range AUC = 0.498, p = 0.976, long-range AUC = 0.611, p = 0.095).
Long-range structure-function coupling was higher in CI-MS compared to HC, but more research is needed to further explore this measure as biomarkers in MS.
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Affiliation(s)
- Shanna D. Kulik
- Departments of Anatomy and Neurosciences, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Ilse M. Nauta
- Department of Neurology, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Prejaas Tewarie
- Department of Neurology, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Clinical Neurophysiology and MEG Center, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Ismail Koubiyr
- Univ. Bordeaux, INSERM, Neurocentre Magendie, U1215, Bordeaux, France
| | - Edwin van Dellen
- University Medical Center Utrecht, Psychiatry, Brain Center Rudolf Magnus, Utrecht, Netherlands
| | - Aurelie Ruet
- Univ. Bordeaux, INSERM, Neurocentre Magendie, U1215, Bordeaux, France
- CHU de Bordeaux, Service de Neurologie, Bordeaux, France
| | - Kim A. Meijer
- Departments of Anatomy and Neurosciences, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Brigit A. de Jong
- Department of Neurology, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Cornelis J. Stam
- Department of Neurology, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Clinical Neurophysiology and MEG Center, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Arjan Hillebrand
- Clinical Neurophysiology and MEG Center, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Jeroen J. G. Geurts
- Departments of Anatomy and Neurosciences, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Linda Douw
- Departments of Anatomy and Neurosciences, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Menno M. Schoonheim
- Departments of Anatomy and Neurosciences, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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18
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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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19
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Saadat N, Mayo CD, Lacey C, Gawryluk JR. Functional connectivity pre-post exercise intervention in individuals with relapsing-remitting multiple sclerosis. Neuroreport 2021; 32:1100-1105. [PMID: 34284447 DOI: 10.1097/wnr.0000000000001702] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Exercise interventions have emerged as a promising approach for managing symptoms associated with multiple sclerosis (MS). However, changes in brain function underlying exercise-related improvements in symptoms of MS have not been fully investigated, and in no instances have they been investigated using a graph theory approach. For the first time, the effects of an exercise intervention on functional brain network connectivity were examined using graph theory analyses of resting-state functional MRI (fMRI) data among individuals with relapsing-remitting MS (RRMS). METHODS Resting-state fMRI data were obtained from 10 participants before and after 12 weeks of a speeded walking intervention. Functional connectivity data were preprocessed in Data Processing Assistant for Resting-State fMRI Advanced (DPARSF A version 4.2) and analyzed in GraphVar2.02 to compute global and local graph theory metrics. To examine differences in graph metrics before and after the intervention, one-sample permutation tests were performed. RESULTS There were no significant pre to post exercise intervention changes in global metrics. Changes in local metrics (i.e. clustering coefficient, local efficiency, degree centrality and betweenness centrality) were mixed, with both increases and decreases observed. CONCLUSION Following a 12-week speeded walking exercise intervention, there were no significant increases or decreases in global graph metrics and results at the level of local metrics were equivocal in individuals with RRMS. Further research with experimental designs that include baseline and longitudinal follow-up, as well as larger sample sizes, is needed to understand the underlying mechanisms of symptom improvement following exercise in RRMS.
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Affiliation(s)
| | - Chantel D Mayo
- Department of Psychology
- Institute on Aging and Lifelong Health
| | - Colleen Lacey
- Department of Psychology
- Institute on Aging and Lifelong Health
| | - Jodie R Gawryluk
- Department of Psychology
- Institute on Aging and Lifelong Health
- Division of Medical Sciences, University of Victoria, Victoria, British Columbia
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20
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Rocca MA, Valsasina P, Meani A, Pagani E, Cordani C, Cervellin C, Filippi M. Network Damage Predicts Clinical Worsening in Multiple Sclerosis: A 6.4-Year Study. NEUROLOGY-NEUROIMMUNOLOGY & NEUROINFLAMMATION 2021; 8:8/4/e1006. [PMID: 34021055 PMCID: PMC8143700 DOI: 10.1212/nxi.0000000000001006] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 03/05/2021] [Indexed: 01/06/2023]
Abstract
OBJECTIVE In multiple sclerosis (MS), clinical impairment is likely due to both structural damage and abnormal brain function. We assessed the added value of integrating structural and functional network MRI measures to predict 6.4-year MS clinical disability deterioration. METHODS Baseline 3D T1-weighted and resting-state functional MRI scans were obtained from 233 patients with MS and 77 healthy controls. Patients underwent a neurologic evaluation at baseline and at 6.4-year median follow-up (interquartile range = 5.06-7.51 years). At follow-up, patients were classified as clinically stable/worsened according to disability changes. In relapsing-remitting (RR) MS, secondary progressive (SP) MS conversion was evaluated. Global brain volumetry was obtained. Furthermore, independent component analysis identified the main functional connectivity (FC) and gray matter (GM) network patterns. RESULTS At follow-up, 105/233 (45%) patients were clinically worsened; 26/157 (16%) patients with RRMS evolved to SPMS. The treatment-adjusted random forest model identified normalized GM and brain volumes, decreased FC between default-mode networks, increased FC of the left precentral gyrus in the sensorimotor network (SMN), and GM atrophy in the fronto-parietal network (false discovery rate [FDR]-corrected p = range 0.01-0.09) as predictors of clinical worsening (out-of-bag [OOB] accuracy = 0.74). An expected contribution of baseline disability was also present (FDR-p = 0.01). Baseline disability, normalized GM volume, and GM atrophy in the SMN (FDR-p = range 0.01-0.09) were independently associated with SPMS conversion (OOB accuracy = 0.84). At receiver operating characteristic analysis, including network MRI variables improved disability worsening (p = 0.05) and SPMS conversion (p = 0.02) prediction. CONCLUSIONS Integration of MRI network measures helped determining the relative contributions of global/local GM damage and functional reorganization to clinical deterioration in MS.
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Affiliation(s)
- Maria A Rocca
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Valsasina
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alessandro Meani
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Claudio Cordani
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Chiara Cervellin
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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21
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Shao Y, Yang L, Zhu PW, Su T, Zhou XZ, Li B, Shi WQ, Lin Q, Min YL, Yuan Q, Ye L, Zhou Q. Functional connectivity density alterations in middle-age retinal detachment patients. Brain Behav 2021; 11:e01783. [PMID: 33645927 PMCID: PMC8035440 DOI: 10.1002/brb3.1783] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Revised: 07/02/2020] [Accepted: 07/19/2020] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE Middle-age patients with retinal detachment (RD) exhibit a loss of visual information, and previous studies of functional magnetic resonance imaging (fMRI) have demonstrated abnormal spontaneous activity in the RD brain. Therefore, this study assessed changes in local functional connectivity density (lFCD) and long-range functional connectivity density (longFCD) in middle-age RD patients during resting-state FC. METHODS In total, 32 middle-age patients with RD (18 men and 14 women), and 32 age-, sex-, and education-matched normal controls (NCs) (18 men and 14 women) were recruited and underwent functional magnetic resonance examination in the resting state. Two-sample t test was performed to compare lFCD and longFCD between groups. Receiver operating characteristic (ROC) curves were generated to distinguish middle-age RD patients from NCs. RESULTS Compared with NCs, middle-age RD patients demonstrated increased lFCD values in the right inferior temporal gyrus, and increased longFCD values in the bilateral inferior frontal gyri, left superior and middle frontal gyrus, bilateral inferior temporal gyri, and left cerebellum posterior lobe. Middle-age RD patient exhibited decreased lFCD values in the left cuneus, right lingual gyrus, and left middle frontal gyrus. They also had lower longFCD values in the left lingual gyrus and left inferior occipital gyrus. ROC curve analysis showed excellent accuracy of the specific areas under the curve. CONCLUSION Our results reveal that middle-age RD patients exhibited variations of binarized lFCD and longFCD in specific brain areas, which provides insight into the pathological mechanism of RD patients with acute visual loss.
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Affiliation(s)
- Yi Shao
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lin Yang
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Pei-Wen Zhu
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ting Su
- Eye Institute of Xiamen University, Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Xiamen, Fujian Province, China
| | - Xue-Zhi Zhou
- Department of Ophthalmology, Xiangya Hospital, Central South University, Hunan Province, Changsha, China
| | - Biao Li
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wen-Qing Shi
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qi Lin
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - You-Lan Min
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qing Yuan
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lei Ye
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qiong Zhou
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, China
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22
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Jouzizadeh M, Ghaderi AH, Cheraghmakani H, Baghbanian SM, Khanbabaie R. Resting-State Brain Network Deficits in Multiple Sclerosis Participants: Evidence from Electroencephalography and Graph Theoretical Analysis. Brain Connect 2021; 11:359-367. [PMID: 33780635 DOI: 10.1089/brain.2020.0857] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Multiple sclerosis (MS) is a chronic inflammatory disease leading to demyelination and axonal loss in the central nervous system that causes focal lesions of gray and white matter. However, the functional impairments of brain networks in this disease are still unspecified and need to be clearer. Materials and Methods: In the present study, we investigate the resting-state brain network impairments for MS participants in comparison to a normal group using electroencephalography (EEG) and graph theoretical analysis with a source localization method. Thirty-four age- and gender-matched participants from each MS group and normal group participated in this study. We recorded 5 min of EEG in the resting-state eyes open condition for each participant. One min (15 equal 4-sec artifact-free segments) of the EEG signals were selected for each participant, and the Low-Resolution Electromagnetic Tomography software was employed to calculate the functional connectivity among whole cortical regions in six frequency bands (delta, theta, alpha, beta1, beta2, and beta3). Graph theoretical analysis was used to calculate the clustering coefficient (CL), betweenness centrality (BC), shortest path length (SPL), and small-world propensity (SWP) for weighted connectivity matrices. Nonparametric permutation tests were utilized to compare these measures between groups. Results: Significant differences between the MS group and the normal group in the average of BC and SWP were found in the alpha band. The significant differences in the BC were spread over all lobes. Conclusion: These results suggest that the resting-state brain network for the MS group is disrupted in local and global scales, and EEG has the capability of revealing these impairments.
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Affiliation(s)
- Mojtaba Jouzizadeh
- Department of Physics, Babol Noshirvani University of Technology, Babol, Iran
| | - Amir Hossein Ghaderi
- Department of Psychology and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Hamed Cheraghmakani
- Department of Neurology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | | | - Reza Khanbabaie
- Department of Physics, Babol Noshirvani University of Technology, Babol, Iran.,Department of Physics, I.K. Barber School of Arts and Sciences, University of British Columbia, Kelowna, British Columbia, Canada
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23
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Bagnato F, Gauthier SA, Laule C, Moore GRW, Bove R, Cai Z, Cohen-Adad J, Harrison DM, Klawiter EC, Morrow SA, Öz G, Rooney WD, Smith SA, Calabresi PA, Henry RG, Oh J, Ontaneda D, Pelletier D, Reich DS, Shinohara RT, Sicotte NL. Imaging Mechanisms of Disease Progression in Multiple Sclerosis: Beyond Brain Atrophy. J Neuroimaging 2021; 30:251-266. [PMID: 32418324 DOI: 10.1111/jon.12700] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 02/04/2020] [Accepted: 02/18/2020] [Indexed: 12/11/2022] Open
Abstract
Clinicians involved with different aspects of the care of persons with multiple sclerosis (MS) and scientists with expertise on clinical and imaging techniques convened in Dallas, TX, USA on February 27, 2019 at a North American Imaging in Multiple Sclerosis Cooperative workshop meeting. The aim of the workshop was to discuss cardinal pathobiological mechanisms implicated in the progression of MS and novel imaging techniques, beyond brain atrophy, to unravel these pathologies. Indeed, although brain volume assessment demonstrates changes linked to disease progression, identifying the biological mechanisms leading up to that volume loss are key for understanding disease mechanisms. To this end, the workshop focused on the application of advanced magnetic resonance imaging (MRI) and positron emission tomography (PET) imaging techniques to assess and measure disease progression in both the brain and the spinal cord. Clinical translation of quantitative MRI was recognized as of vital importance, although the need to maintain a relatively short acquisition time mandated by most radiology departments remains the major obstacle toward this effort. Regarding PET, the panel agreed upon its utility to identify ongoing pathological processes. However, due to costs, required expertise, and the use of ionizing radiation, PET was not considered to be a viable option for ongoing care of persons with MS. Collaborative efforts fostering robust study designs and imaging technique standardization across scanners and centers are needed to unravel disease mechanisms leading to progression and discovering medications halting neurodegeneration and/or promoting repair.
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Affiliation(s)
- Francesca Bagnato
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
| | - Susan A Gauthier
- Judith Jaffe Multiple Sclerosis Center, Department of Neurology, Feil Family Brain and Mind Institute, and Department of Radiology, Weill Cornell Medicine, New York, NY
| | - Cornelia Laule
- Department of Radiology, Pathology, and Laboratory Medicine, Department of Physics and Astronomy, and International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada
| | - George R Wayne Moore
- Department of Pathology and Laboratory Medicine, and International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada
| | - Riley Bove
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA
| | - Zhengxin Cai
- Department of Radiology and Biomedical Imaging, PET Center, Yale University, New Haven, CT
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal and Functional Neuroimaging Unit, CRIUGM, University of Montreal, Montreal, Quebec, Canada
| | - Daniel M Harrison
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD
| | - Eric C Klawiter
- Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Sarah A Morrow
- Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, Canada
| | - Gülin Öz
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN
| | - William D Rooney
- Advanced Imaging Research Center, Departments of Biomedical Engineering, Neurology, and Behavioral Neuroscience, Oregon Health & Science University, Portland, OR
| | - Seth A Smith
- Radiology and Radiological Sciences and Vanderbilt University Imaging Institute, Vanderbilt University Medical Center, and Biomedical Engineering, Vanderbilt University, Nashville, TN
| | - Peter A Calabresi
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Roland G Henry
- Departments of Neurology, Radiology and Biomedical Imaging, and the UC San Francisco & Berkeley Bioengineering Graduate Group, University of California San Francisco, San Francisco, CA
| | - Jiwon Oh
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD.,Division of Neurology, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis, Neurological Institute, Cleveland Clinic, Cleveland, OH
| | - Daniel Pelletier
- Department of Neurology, University of Southern California Keck School of Medicine, Los Angeles, CA
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, Bethesda, MD
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA
| | - Nancy L Sicotte
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA
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- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
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24
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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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25
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Strik M, Chard DT, Dekker I, Meijer KA, Eijlers AJ, Pardini M, Uitdehaag BM, Kolbe SC, Geurts JJ, Schoonheim MM. Increased functional sensorimotor network efficiency relates to disability in multiple sclerosis. Mult Scler 2020; 27:1364-1373. [PMID: 33104448 PMCID: PMC8358536 DOI: 10.1177/1352458520966292] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
BACKGROUND Network abnormalities could help explain physical disability in multiple sclerosis (MS), which remains poorly understood. OBJECTIVE This study investigates functional network efficiency changes in the sensorimotor system. METHODS We included 222 MS patients, divided into low disability (LD, Expanded Disability Status Scale (EDSS) ⩽3.5, n = 185) and high disability (HD, EDSS ⩾6, n = 37), and 82 healthy controls (HC). Functional connectivity was assessed between 23 sensorimotor regions. Measures of efficiency were computed and compared between groups using general linear models corrected for age and sex. Binary logistic regression models related disability status to local functional network efficiency (LE), brain volumes and demographics. Functional connectivity patterns of regions important for disability were explored. RESULTS HD patients demonstrated significantly higher LE of the left primary somatosensory cortex (S1) and right pallidum compared to LD and HC, and left premotor cortex compared to HC only. The logistic regression model for disability (R2 = 0.38) included age, deep grey matter volume and left S1 LE. S1 functional connectivity was increased with prefrontal and secondary sensory areas in HD patients, compared to LD and HC. CONCLUSION Clinical disability in MS associates with functional sensorimotor increases in efficiency and connectivity, centred around S1, independent of structural damage.
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Affiliation(s)
- Myrte Strik
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands/Department of Radiology and Medicine, The University of Melbourne, Melbourne, VIC, Australia
| | - Declan T Chard
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK/National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Iris Dekker
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands/Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Kim A Meijer
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anand Jc Eijlers
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Matteo Pardini
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK/Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Genoa, Italy/Ospedale Policlinico San Martino-IRCCS, Genoa, Italy
| | - Bernard Mj Uitdehaag
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Scott C Kolbe
- Department of Radiology and Medicine, The University of Melbourne, Melbourne, VIC, Australia/Department of Neurosciences, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Jeroen Jg Geurts
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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26
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Labbe TP, Zurita M, Montalba C, Ciampi EL, Cruz JP, Vasquez M, Uribe S, Crossley N, Cárcamo C. Social cognition in Multiple Sclerosis is associated to changes in brain connectivity: A resting-state fMRI study. Mult Scler Relat Disord 2020; 45:102333. [DOI: 10.1016/j.msard.2020.102333] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 06/21/2020] [Accepted: 06/22/2020] [Indexed: 12/14/2022]
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27
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Wu L, Huang M, Zhou F, Zeng X, Gong H. Distributed causality in resting-state network connectivity in the acute and remitting phases of RRMS. BMC Neurosci 2020; 21:37. [PMID: 32933478 PMCID: PMC7493168 DOI: 10.1186/s12868-020-00590-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 09/09/2020] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Although previous studies have shown that intra-network abnormalities in brain functional networks are correlated with clinical/cognitive impairment in multiple sclerosis (MS), there is little information regarding the pattern of causal interactions among cognition-related resting-state networks (RSNs) in different disease stages of relapsing-remitting MS (RRMS) patients. We hypothesized that abnormalities of causal interactions among RSNs occurred in RRMS patients in the acute and remitting phases. METHODS Seventeen patients in the acute phases of RRMS, 24 patients in the remitting phases of RRMS, and 23 appropriately matched healthy controls participated in this study. First, we used group independent component analysis to extract the time courses of the spatially independent components from all the subjects. Then, the Granger causality analysis was used to investigate the causal relationships among RSNs in the spectral domain and to identify correlations with clinical indices. RESULTS Compared with the patients in the acute phase of RRMS, patients in the remitting phase of RRMS showed a significantly lower expanded disability status scale, modified fatigue impact scale scores, and significantly higher paced auditory serial addition test (PASAT) scores. Compared with healthy subjects, during the acute phase, RRMS patients had significantly increased driving connectivity from the right executive control network (rECN) to the anterior salience network (aSN), and the causal coefficient was negatively correlated with the PASAT score. During the remitting phase, RRMS patients had significantly increased driving connectivity from the rECN to the aSN and from the rECN to the visuospatial network. CONCLUSIONS Together with the disease duration (mean disease duration < 5 years) and relatively better clinical scores than those in the acute phase, abnormal connections, such as the information flow from the rECN to the aSN and the rECN to the visuospatial network, might provide adaptive compensation in the remitting phase of RRMS.
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Affiliation(s)
- Lin Wu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, People's Republic of China.,Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China
| | - Muhua Huang
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, People's Republic of China.,Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, People's Republic of China. .,Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China.
| | - Xianjun Zeng
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, People's Republic of China.,Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China
| | - Honghan Gong
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, People's Republic of China.,Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China
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28
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Droby A, Fleysher L, Petracca M, Podranski K, Xu J, Fabian M, Marjańska M, Inglese M. Lower cortical gamma-aminobutyric acid level contributes to increased connectivity in sensory-motor regions in progressive MS. Mult Scler Relat Disord 2020; 43:102183. [DOI: 10.1016/j.msard.2020.102183] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 04/30/2020] [Accepted: 05/04/2020] [Indexed: 10/24/2022]
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29
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Stellmann JP, Maarouf A, Schulz KH, Baquet L, Pöttgen J, Patra S, Penner IK, Gellißen S, Ketels G, Besson P, Ranjeva JP, Guye M, Nolte G, Engel AK, Audoin B, Heesen C, Gold SM. Aerobic Exercise Induces Functional and Structural Reorganization of CNS Networks in Multiple Sclerosis: A Randomized Controlled Trial. Front Hum Neurosci 2020; 14:255. [PMID: 32714172 PMCID: PMC7340166 DOI: 10.3389/fnhum.2020.00255] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 06/09/2020] [Indexed: 12/22/2022] Open
Abstract
Objectives: Evidence from animal studies suggests that aerobic exercise may promote neuroplasticity and could, therefore, provide therapeutic benefits for neurological diseases such as multiple sclerosis (MS). However, the effects of exercise in human CNS disorders on the topology of brain networks, which might serve as an outcome at the interface between biology and clinical performance, remain poorly understood. Methods: We investigated functional and structural networks in patients with relapsing-remitting MS in a clinical trial of standardized aerobic exercise. Fifty-seven patients were randomly assigned to moderate-intensity exercise for 3 months or a non-exercise control group. We reconstructed functional networks based on resting-state functional magnetic resonance imaging (MRI) and used probabilistic tractography on diffusion-weighted imaging data for structural networks. Results: At baseline, compared to 30 healthy controls, patients exhibited decreased structural connectivity that was most pronounced in hub regions of the brain. Vice versa, functional connectivity was increased in hubs. After 3 months, we observed hub independent increased functional connectivity in the exercise group while the control group presented a loss of functional hub connectivity. On a structural level, the control group remained unchanged, while the exercise group had also increased connectivity. Increased clustering of hubs indicates a better structural integration and internal connectivity at the top of the network hierarchy. Conclusion: Increased functional connectivity of hubs contrasts a loss of structural connectivity in relapsing-remitting MS. Under an exercise condition, a further hub independent increase of functional connectivity seems to translate in higher structural connectivity of the whole brain.
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Affiliation(s)
- Jan-Patrick Stellmann
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,APHM, Hopital de la Timone, CEMEREM, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Adil Maarouf
- APHM, Hopital de la Timone, CEMEREM, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Karl-Heinz Schulz
- Institut und Poliklinik für Medizinische Psychologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Universitäres Kompetenzzentrum für Sport-und Bewegungsmedizin (Athleticum), Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Lisa Baquet
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Jana Pöttgen
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Patra
- Institut und Poliklinik für Medizinische Psychologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Universitäres Kompetenzzentrum für Sport-und Bewegungsmedizin (Athleticum), Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Iris-Katharina Penner
- Department of Neurology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Susanne Gellißen
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Gesche Ketels
- Department of Physiotherapy, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Pierre Besson
- APHM, Hopital de la Timone, CEMEREM, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Jean-Philippe Ranjeva
- APHM, Hopital de la Timone, CEMEREM, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Maxime Guye
- APHM, Hopital de la Timone, CEMEREM, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Guido Nolte
- Department of Neurophysiology and Pathophysiology, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Andreas K Engel
- Department of Neurophysiology and Pathophysiology, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Bertrand Audoin
- APHM, Hopital de la Timone, CEMEREM, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Christoph Heesen
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan M Gold
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health (BIH), Klinik für Psychiatrie und Psychotherapie, Campus Benjamin Franklin (CBF), Berlin, Germany.,Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health (BIH), Med. Klinik m.S. Psychosomatik, Campus Benjamin Franklin (CBF), Berlin, Germany
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30
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Filippi M, Preziosa P, Langdon D, Lassmann H, Paul F, Rovira À, Schoonheim MM, Solari A, Stankoff B, Rocca MA. Identifying Progression in Multiple Sclerosis: New Perspectives. Ann Neurol 2020; 88:438-452. [PMID: 32506714 DOI: 10.1002/ana.25808] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 05/18/2020] [Accepted: 05/25/2020] [Indexed: 01/10/2023]
Abstract
The identification of progression in multiple sclerosis is typically retrospective. Given the profound burden of progressive multiple sclerosis, and the recent development of effective treatments for these patients, there is a need to establish measures capable of identifying progressive multiple sclerosis early in the disease course. Starting from recent pathological findings, this review assesses the state of the art of potential measures able to predict progressive multiple sclerosis. Future promising biomarkers that might shed light on mechanisms of progression are also discussed. Finally, expansion of the concept of progressive multiple sclerosis, by including an assessment of cognition, patient-reported outcomes, and comorbidities, is considered. ANN NEUROL 2020;88:438-452.
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Affiliation(s)
- Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurophysiology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Paolo Preziosa
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Dawn Langdon
- Royal Holloway, University of London, London, United Kingdom
| | - Hans Lassmann
- Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Friedemann Paul
- NeuroCure Clinical Research Center and Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Àlex Rovira
- Neuroradiology Section, Department of Radiology, Vall d'Hebron University Hospital and Research Institute, Autonomous University of Barcelona, Barcelona, Spain
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, Multiple Sclerosis Center Amsterdam, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Alessandra Solari
- Unit of Neuroepidemiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Bruno Stankoff
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute - ICM, Inserm, CNRS, APHP, Paris, France
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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31
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Cognitive impairment in benign multiple sclerosis: a multiparametric structural and functional MRI study. J Neurol 2020; 267:3508-3517. [DOI: 10.1007/s00415-020-10025-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 06/24/2020] [Accepted: 06/25/2020] [Indexed: 10/23/2022]
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32
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Filip P, Svatkova A, Carpenter AF, Eberly LE, Nestrasil I, Nissi MJ, Michaeli S, Mangia S. Rotating frame MRI relaxations as markers of diffuse white matter abnormalities in multiple sclerosis. NEUROIMAGE-CLINICAL 2020; 26:102234. [PMID: 32272373 PMCID: PMC7139162 DOI: 10.1016/j.nicl.2020.102234] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 02/16/2020] [Accepted: 02/29/2020] [Indexed: 01/04/2023]
Abstract
T1ρ and RAFF4 - MRI protocols sensitive to slow motional regimes – are able to detect changes in T2w-defined normally appearing white matter of patients with multiple sclerosis. RAFF4, T1ρ and T2ρ showed differences in hippocampus in patients with multiple sclerosis despite the absence of alterations in resting-state functional MRI metrics. Hence, RAFF4, T1ρ and T2ρ hold promise as potential non-invasive tools for monitoring MS activity and eventually for the evaluation of therapeutic effects.
Even though MRI visualization of white matter lesions is pivotal for the diagnosis and management of multiple sclerosis (MS), the issue of detecting diffuse brain tissue damage beyond the apparent T2-hyperintense lesions continues to spark considerable interest. Motivated by the notion that rotating frame MRI methods are sensitive to slow motional regimes critical for tissue characterization, here we utilized novel imaging protocols of rotating frame MRI on a clinical 3 Tesla platform, including adiabatic longitudinal, T1ρ, and transverse, T2ρ, relaxation methods, and Relaxation Along a Fictitious Field (RAFF) in the rotating frame of rank 4 (RAFF4), in 10 relapsing-remitting multiple sclerosis patients and 10 sex- and age-matched healthy controls. T1ρ, T2ρ and RAFF4 relaxograms extracted from the whole white matter exhibited a significant shift towards longer relaxation time constants in MS patients as compared to controls. T1ρ and RAFF4 detected alterations even when considering only regions of normally appearing white matter (NAWM), while other MRI metrics such as T1w/T2w ratio and diffusion tensor imaging measures failed to find group differences. In addition, RAFF4, T2ρ and, to a lesser extent, T1ρ showed differences in subcortical grey matter structures, mainly hippocampus, whereas no functional changes in this region were detected in resting-state functional MRI metrics. We conclude that rotating frame MRI techniques are exceptionally sensitive methods for the detection of subtle abnormalities not only in NAWM, but also in deep grey matter in MS, where they surpass even highly sensitive measures of functional changes, which are often suggested to precede detectable structural alterations. Such abnormalities are consistent with a wide spectrum of different, but interconnected pathological features of MS, including the loss of neuronal cells and their axons, decreased levels of myelin even in NAWM, and altered iron content.
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Affiliation(s)
- Pavel Filip
- First Department of Neurology, Faculty of Medicine, Masaryk University and University Hospital of St. Anne, Brno, Czech Republic; Department of Radiology, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Alena Svatkova
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Department of Medicine III, Clinical Division of Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria
| | - Adam F Carpenter
- Department of Neurology, School of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Lynn E Eberly
- Department of Radiology, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Igor Nestrasil
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Mikko J Nissi
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Shalom Michaeli
- Department of Radiology, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Silvia Mangia
- Department of Radiology, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA.
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33
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Fortanier E, Grapperon AM, Le Troter A, Verschueren A, Ridley B, Guye M, Attarian S, Ranjeva JP, Zaaraoui W. Structural Connectivity Alterations in Amyotrophic Lateral Sclerosis: A Graph Theory Based Imaging Study. Front Neurosci 2019; 13:1044. [PMID: 31632235 PMCID: PMC6783612 DOI: 10.3389/fnins.2019.01044] [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: 09/05/2019] [Accepted: 09/17/2019] [Indexed: 12/15/2022] Open
Abstract
Background Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive neurodegenerative disorder. Diffusion magnetic resonance imagining (MRI) studies have consistently showed widespread alterations in both motor and non-motor brain regions. However, connectomics and graph theory based approaches have shown inconsistent results. Hub-centered lesion patterns and their impact on local and large-scale brain networks remain to be established. The objective of this work is to characterize topological properties of structural brain connectivity in ALS using an array of local, global and hub-based network metrics. Materials and Methods Magnetic resonance imagining data were acquired from 25 patients with ALS and 26 age-matched healthy controls. Structural network graphs were constructed from diffusion tensor MRI. Network-based statistics (NBS) and graph theory metrics were used to compare structural networks without a priori regions of interest. Results Patients with ALS exhibited global network alterations with decreased global efficiency (Eglob) (p = 0.03) and a trend of reduced whole brain mean degree (p = 0.05) compared to controls. Six nodes showed significantly decreased mean degree in ALS: left postcentral gyrus, left interparietal and transverse parietal sulcus, left calcarine sulcus, left occipital temporal medial and lingual sulcus, right precentral gyrus and right frontal inferior sulcus (p < 0.01). Hub distribution was comparable between the two groups. There was no selective hub vulnerability or topological reorganization centered on these regions as the hub disruption index (κ) was not significant for the relevant metrics (degree, local efficiency and betweenness centrality). Using NBS, we identified an impaired motor subnetwork of 11 nodes and 10 edges centered on the precentral and the paracentral nodes (p < 0.01). Significant clinical correlations were identified between degree in the frontal area and the disease progression rate of ALS patients (p < 0.01). Conclusion Our study provides evidence that alterations of structural connectivity in ALS are primarily driven by node degree and white matter tract degeneration within an extended network around the precentral and the paracentral areas without hub-centered reorganization.
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Affiliation(s)
- Etienne Fortanier
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital de la Timone, Referral Centre for Neuromuscular Diseases and ALS, Marseille, France.,APHM, Hôpital de la Timone, CEMEREM, Marseille, France
| | - Aude-Marie Grapperon
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital de la Timone, Referral Centre for Neuromuscular Diseases and ALS, Marseille, France.,APHM, Hôpital de la Timone, CEMEREM, Marseille, France
| | - Arnaud Le Troter
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital de la Timone, CEMEREM, Marseille, France
| | - Annie Verschueren
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital de la Timone, Referral Centre for Neuromuscular Diseases and ALS, Marseille, France.,APHM, Hôpital de la Timone, CEMEREM, Marseille, France
| | - Ben Ridley
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital de la Timone, CEMEREM, Marseille, France
| | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital de la Timone, CEMEREM, Marseille, France
| | - Shahram Attarian
- APHM, Hôpital de la Timone, Referral Centre for Neuromuscular Diseases and ALS, Marseille, France.,Aix Marseille Univ, INSERM, GMGF, Marseille, France
| | - Jean-Philippe Ranjeva
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital de la Timone, CEMEREM, Marseille, France
| | - Wafaa Zaaraoui
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital de la Timone, CEMEREM, Marseille, France
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Manca R, Mitolo M, Stabile MR, Bevilacqua F, Sharrack B, Venneri A. Multiple brain networks support processing speed abilities of patients with multiple sclerosis. Postgrad Med 2019; 131:523-532. [PMID: 31478421 DOI: 10.1080/00325481.2019.1663706] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Objectives: Many people affected by multiple sclerosis (MS) experience cognitive impairment, especially decreases in information processing speed (PS). Neural disconnection is thought to represent the neural marker of this symptom, although the role played by alterations of specific functional brain networks still remains unclear. The aim is to investigate and compare patterns of association between PS-demanding cognitive performance and functional connectivity across two MS phenotypes. Methods: Forty patients with relapsing-remitting MS (RRMS) and 25 with secondary progressive MS (SPMS) had neuropsychological and MRI assessments. Multiple regression models were used to investigate the relationship between performance on tests of visuomotor and verbal PS, and on the verbal fluency tests, and functional connectivity of four cognitive networks, i.e. left and right frontoparietal, salience and default-mode, and two control networks, i.e. visual and sensorimotor. Results: Patients with SPMS were older and had longer disease history than patients with RRMS and presented with worse overall clinical conditions: higher disease severity, total lesion volume, and cognitive impairment rates. However, in both patient samples, cognitive performance across tests was negatively correlated with functional connectivity of the salience and default-mode networks, and positively with connectivity of the left frontoparietal network. Only the visuomotor PS scores of the RRMS group were also associated with connectivity of the sensorimotor network. Conclusions: PS-demanding cognitive performance in patients with MS appears mainly associated with strength of functional connectivity of frontal networks involved in the evaluation and manipulation of information, as well as the default mode network. These results are in line with the hypothesis that multiple neural networks are needed to support normal cognitive performance across MS phenotypes. However, different PS measures showed partially different patterns of association with functional connectivity. Therefore, further investigations are needed to clarify the contribution of inter-network communication to specific cognitive deficits due to MS.
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Affiliation(s)
- Riccardo Manca
- Department of Neuroscience, University of Sheffield , Sheffield , UK
| | - Micaela Mitolo
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Diagnostica Funzionale Neuroradiologica , Bologna , Italy
| | | | | | - Basil Sharrack
- Academic Department of Neuroscience, Sheffield Teaching Hospital, NHS Foundation Trust , Sheffield , UK
| | - Annalena Venneri
- Department of Neuroscience, University of Sheffield , Sheffield , UK
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Finke C, Zimmermann H, Pache F, Oertel FC, Chavarro VS, Kramarenko Y, Bellmann-Strobl J, Ruprecht K, Brandt AU, Paul F. Association of Visual Impairment in Neuromyelitis Optica Spectrum Disorder With Visual Network Reorganization. JAMA Neurol 2019; 75:296-303. [PMID: 29297041 DOI: 10.1001/jamaneurol.2017.3890] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Importance Severe visual impairment is one of the major symptoms in neuromyelitis optica spectrum disorder (NMOSD), but functional network reorganization induced by the diminished sensory input has not been investigated thus far. Objective To examine adaptive visual network connectivity changes in NMOSD. Design, Setting, and Participants In this cross-sectional study, data were collected from May 1, 2013, through February 31, 2016, from 31 patients with aquaporin-4 antibody-positive NMOSD and 31 age- and sex-matched healthy control individuals at the Department of Neurology and NeuroCure Clinical Research Center at Charité-Universitätsmedizin Berlin, Berlin, Germany. Main Outcomes and Measures Visual function (high-contrast visual acuity and contrast sensitivity), optical coherence tomography (peripapillary retinal nerve fiber layer and ganglion cell layer thickness), and resting-state functional magnetic resonance imaging (functional connectivity of large-scale brain networks). Results Thirty-one patients with NMOSD (mean [SD] age, 48.2 [13.9] years; 28 women and 3 men) and 31 healthy controls (mean [SD] age, 47.2 [15.3] years; 28 women and 3 men) participated in the study. Patients had a selective and pronounced increase of functional connectivity in the primary and secondary visual networks. Increased primary visual network connectivity correlated with reduced high-contrast visual acuity (r = -0.39, P = .006), reduced low-contrast sensitivity (r = -0.33, P = .03), and more severe retinal damage measured by optical coherence tomography (r = -0.4, P = .01). Furthermore, visual functional connectivity was significantly higher in patients with a history of optic neuritis compared with patients without optic neuritis (mean [SD] regression coefficients, 50.0 [4.3] vs 34.6 [5.6]; P = .04). Conclusions and Relevance Impaired visual function and retinal damage are associated with selective reorganization of the visual network in NMOSD. These findings advance the understanding of visual system dysfunction in NMOSD and, more generally, provide insight into pathophysiologic responses of the visual system to impaired visual input.
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Affiliation(s)
- Carsten Finke
- Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Hanna Zimmermann
- NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Florence Pache
- Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany.,NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Frederike C Oertel
- NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | | | - Judith Bellmann-Strobl
- NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Klemens Ruprecht
- Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Alexander U Brandt
- NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Friedemann Paul
- Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany.,NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité-Universitätsmedizin Berlin, Berlin, Germany
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Farahani FV, Karwowski W, Lighthall NR. Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review. Front Neurosci 2019; 13:585. [PMID: 31249501 PMCID: PMC6582769 DOI: 10.3389/fnins.2019.00585] [Citation(s) in RCA: 281] [Impact Index Per Article: 56.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 05/23/2019] [Indexed: 12/20/2022] Open
Abstract
Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.
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Affiliation(s)
- Farzad V Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Nichole R Lighthall
- Department of Psychology, University of Central Florida, Orlando, FL, United States
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37
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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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38
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Farahani FV, Karwowski W, Lighthall NR. Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review. Front Neurosci 2019. [PMID: 31249501 DOI: 10.3389/fnins.2019.00585/bibtex] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2023] Open
Abstract
Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.
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Affiliation(s)
- Farzad V Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Nichole R Lighthall
- Department of Psychology, University of Central Florida, Orlando, FL, United States
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Khavari R, Elias SN, Boone T, Karmonik C. Similarity of functional connectivity patterns in patients with multiple sclerosis who void spontaneously versus patients with voiding dysfunction. Neurourol Urodyn 2019; 38:239-247. [PMID: 30311665 PMCID: PMC6574088 DOI: 10.1002/nau.23837] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Accepted: 07/08/2018] [Indexed: 12/15/2022]
Abstract
AIM To investigate if Multiple Sclerosis (MS) lesion characteristics affect functional brain connectivity (FC) during bladder voiding. METHODS Twenty-seven ambulatory female patients with MS completed our functional magnetic resonance imaging (fMRI)/urodynamic testing (UDS) platform. Individual fMRI activation maps were generated at initiation of voiding. FC patterns of these regions were calculated and compared. Similarity of the FC pattern from one patient relative to all others was expressed by a parameter FC_sim. A statistical analysis was performed to reveal the relationship of the existence of an enhancing brain lesion, the size of the largest lesion and the ability to void spontaneously to this FC similarity measure. RESULTS FC_sim values were significantly lower for patients with an enhancing MS lesion (11.7 ± 3.1 vs 5.3 ± 2.1 P < 0.001). Lesion size smaller than 20 mm inversely correlated significantly with FC_sim (R = -0.43, P = 0.05). Patients with the ability to void spontaneously had a higher FC_sim value (12.0 ± 2.8 vs 9.3 ± 4.4 s, P = 0.08). Patients that exhibited a decrease of compliance also showed a significantly lower FC_sim value (11.3 ± 3.5 vs 4.7 ± 0.7, P < 1e-5). CONCLUSION FC connectivity analysis derived from an fMRI task-based study including repetitive voiding cycles is able to quantify the heterogeneity of connectivity patterns in the brain of MS patients. FC similarity decreased with maximum lesion size or the presence of enhancing lesions affecting the ability to void spontaneously.
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Affiliation(s)
- Rose Khavari
- Department of Urology, Houston Methodist Hospital, Houston, Texas
| | - Saba N. Elias
- Houston Methodist Research Institute, MRI Core, Huston, Texas
| | - Timothy Boone
- Department of Urology, Houston Methodist Hospital, Houston, Texas
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The Role of fMRI in the Assessment of Neuroplasticity in MS: A Systematic Review. Neural Plast 2018; 2018:3419871. [PMID: 30693023 PMCID: PMC6332922 DOI: 10.1155/2018/3419871] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 11/05/2018] [Indexed: 11/17/2022] Open
Abstract
Neuroplasticity, which is the ability of the brain to adapt to internal and external environmental changes, physiologically occurs during growth and in response to damage. The brain's response to damage is of particular interest in multiple sclerosis, a chronic disease characterized by inflammatory and neurodegenerative damage to the central nervous system. Functional MRI (fMRI) is a tool that allows functional changes related to the disease and to its evolution to be studied in vivo. Several studies have shown that abnormal brain recruitment during the execution of a task starts in the early phases of multiple sclerosis. The increased functional activation during a specific task observed has been interpreted mainly as a mechanism of adaptive plasticity designed to contrast the increase in tissue damage. More recent fMRI studies, which have focused on the activity of brain regions at rest, have yielded nonunivocal results, suggesting that changes in functional brain connections represent mechanisms of either adaptive or maladaptive plasticity. The few longitudinal studies available to date on disease evolution have also yielded discrepant results that are likely to depend on the clinical features considered and the length of the follow-up. Lastly, fMRI has been used in interventional studies to investigate plastic changes induced by pharmacological therapy or rehabilitation, though whether such changes represent a surrogate of neuroplasticity remains unclear. The aim of this paper is to systematically review the existing literature in order to provide an overall description of both the neuroplastic process itself and the evolution in the use of fMRI techniques as a means of assessing neuroplasticity. The quantitative and qualitative approach adopted here ensures an objective analysis of published, peer-reviewed research and yields an overview of up-to-date knowledge.
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41
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Lin S, Vavasour I, Kosaka B, Li DKB, Traboulsee A, MacKay A, McKeown MJ. Education, and the balance between dynamic and stationary functional connectivity jointly support executive functions in relapsing-remitting multiple sclerosis. Hum Brain Mapp 2018; 39:5039-5049. [PMID: 30240533 PMCID: PMC6866468 DOI: 10.1002/hbm.24343] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 07/18/2018] [Accepted: 07/24/2018] [Indexed: 01/20/2023] Open
Abstract
Graphical network characteristics and nonstationary functional connectivity features, both derived from resting-state functional magnetic resonance imaging (rsfMRI) data, have been associated with cognitive performance in healthy subjects. How these features jointly relate to cognition in diseased states has not been investigated. In this study, 46 relapsing-remitting multiple sclerosis subjects underwent rsfMRI scans and a focused cognitive battery. With a sliding window approach, we examined six dynamic network features that indicated how connectivity changed over time as well as six measures derived from graph theory to reflect static network characteristics. Multiset canonical correlation analysis (MCCA) was then carried out to investigate the relations between dynamic network features, stationary network characteristics, cognitive testing, demographic, disease severity, and mood. Multiple sclerosis (MS) subjects demonstrated weaker connectivity strength, decreased network density, reduced global changes, but increased changes in interhemispheric connectivity compared to controls. The MCCA model determined that executive functions and processing speed ability measured by Wechsler Adult Intelligence Scale IV (WAIS-IV) Working Memory Index, WAIS-IV Processing Speed Index, and the Verbal Fluency Test were positively correlated with education, dynamic connectivity, and static connectivity strength; while poor task switching was correlated with disease severity, psychiatric comorbidities such as depression, anxiety, and fatigue, and static network density. Taken together, our results suggest that better executive functioning in MS requires maintenance of a continued coordination between stationary and dynamic functional connectivity as well as the support of education, and dynamic functional connectivity may provide an additional cognitive biomarker of disease severity in the MS population.
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Affiliation(s)
- Sue‐Jin Lin
- Graduate Program in NeuroscienceUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Pacific Parkinson's Research CentreUniversity of British Columbia HospitalVancouverBritish ColumbiaCanada
| | - Irene Vavasour
- Department of RadiologyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Brenda Kosaka
- Department of PsychiatryUniversity of British Columbia HospitalVancouverBritish ColumbiaCanada
| | - David K. B. Li
- Department of RadiologyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Faculty of Medicine, NeurologyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Anthony Traboulsee
- Faculty of Medicine, NeurologyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Alex MacKay
- Department of RadiologyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Department of Physics and AstronomyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Martin J. McKeown
- Graduate Program in NeuroscienceUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Pacific Parkinson's Research CentreUniversity of British Columbia HospitalVancouverBritish ColumbiaCanada
- Faculty of Medicine, NeurologyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
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Koubiyr I, Deloire M, Besson P, Coupé P, Dulau C, Pelletier J, Tourdias T, Audoin B, Brochet B, Ranjeva JP, Ruet A. Longitudinal study of functional brain network reorganization in clinically isolated syndrome. Mult Scler 2018; 26:188-200. [PMID: 30480467 DOI: 10.1177/1352458518813108] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND There is a lack of longitudinal studies exploring the topological organization of functional brain networks at the early stages of multiple sclerosis (MS). OBJECTIVE This study aims to assess potential brain functional reorganization at rest in patients with CIS (PwCIS) after 1 year of evolution and to characterize the dynamics of functional brain networks at the early stage of the disease. METHODS We prospectively included 41 PwCIS and 19 matched healthy controls (HCs). They were scanned at baseline and after 1 year. Using graph theory, topological metrics were calculated for each region. Hub disruption index was computed for each metric. RESULTS Hub disruption indexes of degree and betweenness centrality were negative at baseline in patients (p < 0.05), suggesting brain reorganization. After 1 year, hub disruption indexes for degree and betweenness centrality were still negative (p < 0.00001), but such reorganization appeared more pronounced than at baseline. Different brain regions were driving these alterations. No global efficiency differences were observed between PwCIS and HCs either at baseline or at 1 year. CONCLUSION Dynamic changes in functional brain networks appear at the early stages of MS and are associated with the maintenance of normal global efficiency in the brain, suggesting a compensatory effect.
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Affiliation(s)
- Ismail Koubiyr
- University of Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France
| | | | - Pierre Besson
- Aix-Marseille University, CNRS, CRMBM UMR, Marseille, France/Aix-Marseille University, APHM, Hopital la Timone, CEMEREM, Marseille, France
| | - Pierrick Coupé
- Laboratoire Bordelais de Recherche en Informatique, Talence, France
| | - Cécile Dulau
- CHU Pellegrin, CHU de Bordeaux, Bordeaux, France
| | - Jean Pelletier
- Aix-Marseille University, CNRS, CRMBM UMR, Marseille, France/Aix-Marseille University, APHM, Hopital la Timone, CEMEREM, Marseille, France/APHM, Hopital la Timone, service de Neurologie, Marseille, France
| | - Thomas Tourdias
- University of Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France/CHU Pellegrin, CHU de Bordeaux, Bordeaux, France
| | - Bertrand Audoin
- Aix-Marseille University, CNRS, CRMBM UMR, Marseille, France/Aix-Marseille University, APHM, Hopital la Timone, CEMEREM, Marseille, France/APHM, Hopital la Timone, service de Neurologie, Marseille, France
| | - Bruno Brochet
- University of Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France/CHU Pellegrin, CHU de Bordeaux, Bordeaux, France
| | - Jean-Philippe Ranjeva
- Aix-Marseille University, CNRS, CRMBM UMR, Marseille, France/Aix-Marseille University, APHM, Hopital la Timone, CEMEREM, Marseille, France
| | - Aurélie Ruet
- University of Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France/CHU Pellegrin, CHU de Bordeaux, Bordeaux, France
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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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Castellazzi G, Debernard L, Melzer TR, Dalrymple-Alford JC, D'Angelo E, Miller DH, Gandini Wheeler-Kingshott CAM, Mason DF. Functional Connectivity Alterations Reveal Complex Mechanisms Based on Clinical and Radiological Status in Mild Relapsing Remitting Multiple Sclerosis. Front Neurol 2018; 9:690. [PMID: 30177910 PMCID: PMC6109785 DOI: 10.3389/fneur.2018.00690] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 07/30/2018] [Indexed: 11/13/2022] Open
Abstract
Resting state functional MRI (rs-fMRI) has provided important insights into functional reorganization in subjects with Multiple Sclerosis (MS) at different stage of disease. In this cross-sectional study we first assessed, by means of rs-fMRI, the impact of overall T2 lesion load (T2LL) and MS severity score (MSSS) on resting state networks (RSNs) in 62 relapsing remitting MS (RRMS) patients with mild disability (MSSS < 3). Independent Component Analysis (ICA) followed by dual regression analysis confirmed functional connectivity (FC) alterations of many RSNs in RRMS subjects compared to healthy controls. The anterior default mode network (DMNa) and the superior precuneus network (PNsup) showed the largest areas of decreased FC, while the sensory motor networks area M1 (SMNm1) and the medial visual network (MVN) showed the largest areas of increased FC. In order to better understand the nature of these alterations as well as the mechanisms of functional alterations in MS we proposed a method, based on linear regression, that takes into account FC changes and their correlation with T2LL and MSSS. Depending on the sign of the correlation between FC and T2LL, and furthermore the sign of the correlation with MSSS, we suggested the following possible underlying mechanisms to interpret altered FC: (1) FC reduction driven by MS lesions, (2) "true" functional compensatory mechanism, (3a) functional compensation attempt, (3b) "false" functional compensation, (4a) neurodegeneration, (4b) pre-symptomatic condition (damage precedes MS clinical manifestation). Our data shows areas satisfying 4 of these 6 conditions (i.e., 1,2,3b,4b), supporting the suggestion that increased FC has a complex nature that may exceed the simplistic assumption of an underlying compensatory mechanism attempting to limit the brain damage caused by MS progression. Exploring differences between RRMS subjects with short disease duration (MSshort) and RRMS with similar disability but longer disease duration (MSlong), we found that MSshort and MSlong were characterized by clearly distinct pattern of FC, involving predominantly sensory and cognitive networks respectively. Overall, these results suggest that the analysis of FC alterations in multiple large-scale networks in relation to radiological (T2LL) and clinical (MSSS, disease duration) status may provide new insights into the pathophysiology of relapse onset MS evolution.
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Affiliation(s)
- Gloria Castellazzi
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Institute of Neurology, London, United Kingdom.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Laetitia Debernard
- New Zealand Brain Research Institute, Christchurch, New Zealand.,Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Tracy R Melzer
- New Zealand Brain Research Institute, Christchurch, New Zealand.,Department of Medicine, University of Otago, Christchurch, New Zealand.,Brain Research New Zealand, Auckland, New Zealand
| | - John C Dalrymple-Alford
- New Zealand Brain Research Institute, Christchurch, New Zealand.,Brain Research New Zealand, Auckland, New Zealand.,Department of Psychology, University of Canterbury, Christchurch, New Zealand
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - David H Miller
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Institute of Neurology, London, United Kingdom.,New Zealand Brain Research Institute, Christchurch, New Zealand.,Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Institute of Neurology, London, United Kingdom.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain MRI 3T Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Deborah F Mason
- New Zealand Brain Research Institute, Christchurch, New Zealand.,Department of Medicine, University of Otago, Christchurch, New Zealand.,Department of Neurology, Christchurch Hospital, Christchurch, New Zealand
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Herbert E, Engel-Hills P, Hattingh C, Fouche JP, Kidd M, Lochner C, Kotze MJ, van Rensburg SJ. Fractional anisotropy of white matter, disability and blood iron parameters in multiple sclerosis. Metab Brain Dis 2018; 33:545-557. [PMID: 29396631 DOI: 10.1007/s11011-017-0171-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Accepted: 12/18/2017] [Indexed: 12/13/2022]
Abstract
UNLABELLED Multiple sclerosis (MS) is a disorder related to myelin damage, which can be investigated by neuroimaging techniques such as fractional anisotropy (FA), a measure of microstructural white matter properties. The objectives of this study were to investigate (1) the relationship between FA and disability using an extremes of outcome approach, and (2) whether blood iron parameters were associated with FA and/or disability. Patients diagnosed with MS (n = 107; 14 males and 93 females) had iron parameter tests and disability determinations using the Expanded Disability Status Scale (EDSS). FA was recorded in 48 white matter tracts in 11 of the female patients with MS and 12 female controls. RESULTS In patients with high disability scores the mean FA was significantly lower (0.34 ± 0.067) than in the control group (0.45 ± 0.036; p = 0.04), while patients with low disability had mean FA values (0.44 ± 0.014) similar to controls (p = 0.5). Positive associations were found between FA and the iron parameters serum iron, ferritin and percentage transferrin saturation (%Tfsat) in all the white matter tracts. For % Tfsat, the associations were highly significant in 14 tracts (p < 0.01; r-values 0.74-0.84) and p < 0.001 (r = 0.83) in the superior fronto occipital fasciculus (LH). In the whole patient group a trend was found towards an inverse association between the EDSS and the %Tfsat (r = -0.26, p = 0.05) after excluding male gender and smoking as confounders, suggesting reduced disability in the presence of higher blood iron parameters. Additionally, significant inverse associations between disease duration and haemoglobin (p = 0.04) as well as %Tfsat (p = 0.02) suggested that patients with MS may experience a decrease in blood iron concentrations over time.
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Affiliation(s)
- Estelle Herbert
- Department of Medical Imaging and Therapeutic Sciences, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Cape Town, South Africa.
| | - Penelope Engel-Hills
- Department of Medical Imaging and Therapeutic Sciences, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Cape Town, South Africa
| | - Coenraad Hattingh
- Division of Chemical Pathology, Department of Pathology, National Health Laboratory Service (NHLS) and Stellenbosch University, Cape Town, South Africa
| | - Jean-Paul Fouche
- MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Martin Kidd
- Centre for Statistical Consultation, Stellenbosch University, Cape Town, South Africa
| | - Christine Lochner
- MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
| | - Maritha J Kotze
- Division of Chemical Pathology, Department of Pathology, National Health Laboratory Service (NHLS) and Stellenbosch University, Cape Town, South Africa
| | - Susan J van Rensburg
- Division of Chemical Pathology, Department of Pathology, National Health Laboratory Service (NHLS) and Stellenbosch University, Cape Town, South Africa
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Mahajan KR, Ontaneda D. The Role of Advanced Magnetic Resonance Imaging Techniques in Multiple Sclerosis Clinical Trials. Neurotherapeutics 2017; 14:905-923. [PMID: 28770481 PMCID: PMC5722766 DOI: 10.1007/s13311-017-0561-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Magnetic resonance imaging has been crucial in the development of anti-inflammatory disease-modifying treatments. The current landscape of multiple sclerosis clinical trials is currently expanding to include testing not only of anti-inflammatory agents, but also neuroprotective, remyelinating, neuromodulating, and restorative therapies. This is especially true of therapies targeting progressive forms of the disease where neurodegeneration is a prominent feature. Imaging techniques of the brain and spinal cord have rapidly evolved in the last decade to permit in vivo characterization of tissue microstructural changes, connectivity, metabolic changes, neuronal loss, glial activity, and demyelination. Advanced magnetic resonance imaging techniques hold significant promise for accelerating the development of different treatment modalities targeting a variety of pathways in MS.
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Affiliation(s)
- Kedar R Mahajan
- Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland Clinic, 9500 Euclid Avenue, U-10, Cleveland, OH, 44195, USA
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland Clinic, 9500 Euclid Avenue, U-10, Cleveland, OH, 44195, USA.
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Stellmann JP, Hodecker S, Cheng B, Wanke N, Young KL, Hilgetag C, Gerloff C, Heesen C, Thomalla G, Siemonsen S. Reduced rich-club connectivity is related to disability in primary progressive MS. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2017; 4:e375. [PMID: 28804744 PMCID: PMC5532749 DOI: 10.1212/nxi.0000000000000375] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 05/17/2017] [Indexed: 11/15/2022]
Abstract
OBJECTIVE To investigate whether the structural connectivity of the brain's rich-club organization is altered in patients with primary progressive MS and whether such changes to this fundamental network feature are associated with disability measures. METHODS We recruited 37 patients with primary progressive MS and 21 healthy controls for an observational cohort study. Structural connectomes were reconstructed based on diffusion-weighted imaging data using probabilistic tractography and analyzed with graph theory. RESULTS We observed the same topological organization of brain networks in patients and controls. Consistent with the originally defined rich-club regions, we identified superior frontal, precuneus, superior parietal, and insular cortex in both hemispheres as rich-club nodes. Connectivity within the rich club was significantly reduced in patients with MS (p = 0.039). The extent of reduced rich-club connectivity correlated with clinical measurements of mobility (Kendall rank correlation coefficient τ = -0.20, p = 0.047), hand function (τ = -0.26, p = 0.014), and information processing speed (τ = -0.20, p = 0.049). CONCLUSIONS In patients with primary progressive MS, the fundamental organization of the structural connectome in rich-club and peripheral nodes was preserved and did not differ from healthy controls. The proportion of rich-club connections was altered and correlated with disability measures. Thus, the rich-club organization of the brain may be a promising network phenotype for understanding the patterns and mechanisms of neurodegeneration in MS.
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Affiliation(s)
- Jan-Patrick Stellmann
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS) (J.-P.S., S.H., N.W., K.L.Y., C.G., C. Heesen, S.S.), Klinik und Poliklinik für Neurologie (J.-P.S., S.H., B.C., N.W., K.L.Y., C. Heesen, G.T.), Institute of Computational Neuroscience (C. Hilgetag), and Department of Diagnostic and Interventional Neuroradiology (S.S.), University Medical Center Hamburg-Eppendorf, Germany
| | - Sibylle Hodecker
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS) (J.-P.S., S.H., N.W., K.L.Y., C.G., C. Heesen, S.S.), Klinik und Poliklinik für Neurologie (J.-P.S., S.H., B.C., N.W., K.L.Y., C. Heesen, G.T.), Institute of Computational Neuroscience (C. Hilgetag), and Department of Diagnostic and Interventional Neuroradiology (S.S.), University Medical Center Hamburg-Eppendorf, Germany
| | - Bastian Cheng
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS) (J.-P.S., S.H., N.W., K.L.Y., C.G., C. Heesen, S.S.), Klinik und Poliklinik für Neurologie (J.-P.S., S.H., B.C., N.W., K.L.Y., C. Heesen, G.T.), Institute of Computational Neuroscience (C. Hilgetag), and Department of Diagnostic and Interventional Neuroradiology (S.S.), University Medical Center Hamburg-Eppendorf, Germany
| | - Nadine Wanke
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS) (J.-P.S., S.H., N.W., K.L.Y., C.G., C. Heesen, S.S.), Klinik und Poliklinik für Neurologie (J.-P.S., S.H., B.C., N.W., K.L.Y., C. Heesen, G.T.), Institute of Computational Neuroscience (C. Hilgetag), and Department of Diagnostic and Interventional Neuroradiology (S.S.), University Medical Center Hamburg-Eppendorf, Germany
| | - Kim Lea Young
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS) (J.-P.S., S.H., N.W., K.L.Y., C.G., C. Heesen, S.S.), Klinik und Poliklinik für Neurologie (J.-P.S., S.H., B.C., N.W., K.L.Y., C. Heesen, G.T.), Institute of Computational Neuroscience (C. Hilgetag), and Department of Diagnostic and Interventional Neuroradiology (S.S.), University Medical Center Hamburg-Eppendorf, Germany
| | - Claus Hilgetag
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS) (J.-P.S., S.H., N.W., K.L.Y., C.G., C. Heesen, S.S.), Klinik und Poliklinik für Neurologie (J.-P.S., S.H., B.C., N.W., K.L.Y., C. Heesen, G.T.), Institute of Computational Neuroscience (C. Hilgetag), and Department of Diagnostic and Interventional Neuroradiology (S.S.), University Medical Center Hamburg-Eppendorf, Germany
| | - Christian Gerloff
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS) (J.-P.S., S.H., N.W., K.L.Y., C.G., C. Heesen, S.S.), Klinik und Poliklinik für Neurologie (J.-P.S., S.H., B.C., N.W., K.L.Y., C. Heesen, G.T.), Institute of Computational Neuroscience (C. Hilgetag), and Department of Diagnostic and Interventional Neuroradiology (S.S.), University Medical Center Hamburg-Eppendorf, Germany
| | - Christoph Heesen
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS) (J.-P.S., S.H., N.W., K.L.Y., C.G., C. Heesen, S.S.), Klinik und Poliklinik für Neurologie (J.-P.S., S.H., B.C., N.W., K.L.Y., C. Heesen, G.T.), Institute of Computational Neuroscience (C. Hilgetag), and Department of Diagnostic and Interventional Neuroradiology (S.S.), University Medical Center Hamburg-Eppendorf, Germany
| | - Götz Thomalla
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS) (J.-P.S., S.H., N.W., K.L.Y., C.G., C. Heesen, S.S.), Klinik und Poliklinik für Neurologie (J.-P.S., S.H., B.C., N.W., K.L.Y., C. Heesen, G.T.), Institute of Computational Neuroscience (C. Hilgetag), and Department of Diagnostic and Interventional Neuroradiology (S.S.), University Medical Center Hamburg-Eppendorf, Germany
| | - Susanne Siemonsen
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS) (J.-P.S., S.H., N.W., K.L.Y., C.G., C. Heesen, S.S.), Klinik und Poliklinik für Neurologie (J.-P.S., S.H., B.C., N.W., K.L.Y., C. Heesen, G.T.), Institute of Computational Neuroscience (C. Hilgetag), and Department of Diagnostic and Interventional Neuroradiology (S.S.), University Medical Center Hamburg-Eppendorf, Germany
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Abstract
Recent advances in connectomics have led to a synthesis of perspectives regarding the brain's functional organization that reconciles classical concepts of localized specialization with an appreciation for properties that emerge from interactions across distributed functional networks. This provides a more comprehensive framework for understanding neural mechanisms of normal cognition and disease. Although fMRI has not become a routine clinical tool, research has already had important influences on clinical concepts guiding diagnosis and patient management. Here we review illustrative examples. Studies demonstrating the network plasticity possible in adults and the global consequences of even focal brain injuries or disease both have had substantial impact on modern concepts of disease evolution and expression. Applications of functional connectomics in studies of clinical populations are challenging traditional disease classifications and helping to clarify biological relationships between clinical syndromes (and thus also ways of extending indications for, or "re-purposing," current treatments). Large datasets from prospective, longitudinal studies promise to enable the discovery and validation of functional connectomic biomarkers with the potential to identify people at high risk of disease before clinical onset, at a time when treatments may be most effective. Studies of pain and consciousness have catalyzed reconsiderations of approaches to clinical management, but also have stimulated debate about the clinical meaningfulness of differences in internal perceptual or cognitive states inferred from functional connectomics or other physiological correlates. By way of a closing summary, we offer a personal view of immediate challenges and potential opportunities for clinically relevant applications of fMRI-based functional connectomics.
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Affiliation(s)
- Paul M Matthews
- Division of Brain Sciences, Department of Medicine and Centre for Neurotechnology, Imperial College London, London WC12 0NN, UK.
| | - Adam Hampshire
- Division of Brain Sciences, Department of Medicine and Centre for Neurotechnology, Imperial College London, London WC12 0NN, UK
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49
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Liu Y, Wang H, Duan Y, Huang J, Ren Z, Ye J, Dong H, Shi F, Li K, Wang J. Functional Brain Network Alterations in Clinically Isolated Syndrome and Multiple Sclerosis: A Graph-based Connectome Study. Radiology 2016; 282:534-541. [PMID: 27541686 DOI: 10.1148/radiol.2016152843] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Purpose To investigate the topological organization of functional brain networks in clinically isolated syndrome (CIS) and multiple sclerosis (MS) and examine the clinical relevance. Materials and Methods The institutional review board of Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China, approved the study, and written informed consent was obtained from each participant. Functional brain networks were constructed for 34 patients with MS, 34 patients with CIS, and 36 matched healthy control subjects by using resting-state functional magnetic resonance (MR) imaging data. Graph-based network measures were then calculated, followed by performance of between-group comparison and brain-behavior correlation analysis. Results Decreased whole-brain network efficiency was observed for patients with MS when compared with healthy control subjects, with intermediate values for the patients with CIS (P < .05, corrected). Regionally, both patient groups showed decreased nodal efficiency in the left rolandic operculum and insula and the superior temporal gyrus of the bilateral temporal pole (P < .05, corrected). Moreover, impaired functional connectivity involving the occipital, temporal, and frontal cortices and the insula was identified in MS (P = .007), and a similar but smaller component was observed in CIS (P = .032). The disrupted functional connectivity correlated with disease duration of the patients (r = 0.312, P = .011) and served to distinguish the patients from healthy control subjects with high performance (area under the curve for MS, 0.825 [P < .001]; area under the curve for CIS, 0.789 [P < .001]). These findings were reproducible across several different analytical strategies and were largely independent of white matter lesions and gray matter atrophy. Conclusion The results of this study demonstrate that disrupted network organization already emerges in CIS, with a lesser degree relative to MS. © RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Yaou Liu
- From the Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China (Y.L., Y.D., J.H., Z.R., K.L.); Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands (Y.L.); Department of Neurology and Tianjin Neurologic Institute, Tianjin Medical University General Hospital, Tianjin, People's Republic of China (Y.L., F.S.); Department of Psychology, Hangzhou Normal University, No. 58 Haishu Rd, Cangqian, Yuhang District, Hangzhou 311121, People's Republic of China (H.W., J.W.); Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, People's Republic of China (H.W., J.W.); and Department of Anatomy and Neurosciences, Section of Clinical Neuroscience, VU University Medical Center, Amsterdam, the Netherlands (J.Y., H.D.)
| | - Hao Wang
- From the Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China (Y.L., Y.D., J.H., Z.R., K.L.); Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands (Y.L.); Department of Neurology and Tianjin Neurologic Institute, Tianjin Medical University General Hospital, Tianjin, People's Republic of China (Y.L., F.S.); Department of Psychology, Hangzhou Normal University, No. 58 Haishu Rd, Cangqian, Yuhang District, Hangzhou 311121, People's Republic of China (H.W., J.W.); Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, People's Republic of China (H.W., J.W.); and Department of Anatomy and Neurosciences, Section of Clinical Neuroscience, VU University Medical Center, Amsterdam, the Netherlands (J.Y., H.D.)
| | - Yunyun Duan
- From the Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China (Y.L., Y.D., J.H., Z.R., K.L.); Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands (Y.L.); Department of Neurology and Tianjin Neurologic Institute, Tianjin Medical University General Hospital, Tianjin, People's Republic of China (Y.L., F.S.); Department of Psychology, Hangzhou Normal University, No. 58 Haishu Rd, Cangqian, Yuhang District, Hangzhou 311121, People's Republic of China (H.W., J.W.); Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, People's Republic of China (H.W., J.W.); and Department of Anatomy and Neurosciences, Section of Clinical Neuroscience, VU University Medical Center, Amsterdam, the Netherlands (J.Y., H.D.)
| | - Jing Huang
- From the Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China (Y.L., Y.D., J.H., Z.R., K.L.); Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands (Y.L.); Department of Neurology and Tianjin Neurologic Institute, Tianjin Medical University General Hospital, Tianjin, People's Republic of China (Y.L., F.S.); Department of Psychology, Hangzhou Normal University, No. 58 Haishu Rd, Cangqian, Yuhang District, Hangzhou 311121, People's Republic of China (H.W., J.W.); Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, People's Republic of China (H.W., J.W.); and Department of Anatomy and Neurosciences, Section of Clinical Neuroscience, VU University Medical Center, Amsterdam, the Netherlands (J.Y., H.D.)
| | - Zhuoqiong Ren
- From the Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China (Y.L., Y.D., J.H., Z.R., K.L.); Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands (Y.L.); Department of Neurology and Tianjin Neurologic Institute, Tianjin Medical University General Hospital, Tianjin, People's Republic of China (Y.L., F.S.); Department of Psychology, Hangzhou Normal University, No. 58 Haishu Rd, Cangqian, Yuhang District, Hangzhou 311121, People's Republic of China (H.W., J.W.); Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, People's Republic of China (H.W., J.W.); and Department of Anatomy and Neurosciences, Section of Clinical Neuroscience, VU University Medical Center, Amsterdam, the Netherlands (J.Y., H.D.)
| | - Jing Ye
- From the Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China (Y.L., Y.D., J.H., Z.R., K.L.); Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands (Y.L.); Department of Neurology and Tianjin Neurologic Institute, Tianjin Medical University General Hospital, Tianjin, People's Republic of China (Y.L., F.S.); Department of Psychology, Hangzhou Normal University, No. 58 Haishu Rd, Cangqian, Yuhang District, Hangzhou 311121, People's Republic of China (H.W., J.W.); Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, People's Republic of China (H.W., J.W.); and Department of Anatomy and Neurosciences, Section of Clinical Neuroscience, VU University Medical Center, Amsterdam, the Netherlands (J.Y., H.D.)
| | - Huiqing Dong
- From the Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China (Y.L., Y.D., J.H., Z.R., K.L.); Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands (Y.L.); Department of Neurology and Tianjin Neurologic Institute, Tianjin Medical University General Hospital, Tianjin, People's Republic of China (Y.L., F.S.); Department of Psychology, Hangzhou Normal University, No. 58 Haishu Rd, Cangqian, Yuhang District, Hangzhou 311121, People's Republic of China (H.W., J.W.); Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, People's Republic of China (H.W., J.W.); and Department of Anatomy and Neurosciences, Section of Clinical Neuroscience, VU University Medical Center, Amsterdam, the Netherlands (J.Y., H.D.)
| | - Fudong Shi
- From the Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China (Y.L., Y.D., J.H., Z.R., K.L.); Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands (Y.L.); Department of Neurology and Tianjin Neurologic Institute, Tianjin Medical University General Hospital, Tianjin, People's Republic of China (Y.L., F.S.); Department of Psychology, Hangzhou Normal University, No. 58 Haishu Rd, Cangqian, Yuhang District, Hangzhou 311121, People's Republic of China (H.W., J.W.); Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, People's Republic of China (H.W., J.W.); and Department of Anatomy and Neurosciences, Section of Clinical Neuroscience, VU University Medical Center, Amsterdam, the Netherlands (J.Y., H.D.)
| | - Kuncheng Li
- From the Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China (Y.L., Y.D., J.H., Z.R., K.L.); Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands (Y.L.); Department of Neurology and Tianjin Neurologic Institute, Tianjin Medical University General Hospital, Tianjin, People's Republic of China (Y.L., F.S.); Department of Psychology, Hangzhou Normal University, No. 58 Haishu Rd, Cangqian, Yuhang District, Hangzhou 311121, People's Republic of China (H.W., J.W.); Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, People's Republic of China (H.W., J.W.); and Department of Anatomy and Neurosciences, Section of Clinical Neuroscience, VU University Medical Center, Amsterdam, the Netherlands (J.Y., H.D.)
| | - Jinhui Wang
- From the Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China (Y.L., Y.D., J.H., Z.R., K.L.); Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands (Y.L.); Department of Neurology and Tianjin Neurologic Institute, Tianjin Medical University General Hospital, Tianjin, People's Republic of China (Y.L., F.S.); Department of Psychology, Hangzhou Normal University, No. 58 Haishu Rd, Cangqian, Yuhang District, Hangzhou 311121, People's Republic of China (H.W., J.W.); Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, People's Republic of China (H.W., J.W.); and Department of Anatomy and Neurosciences, Section of Clinical Neuroscience, VU University Medical Center, Amsterdam, the Netherlands (J.Y., H.D.)
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