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Pontillo G, Cepas MB, Broeders TAA, Koubiyr I, Schoonheim MM. Network Analysis in Multiple Sclerosis and Related Disorders. Neuroimaging Clin N Am 2024; 34:375-384. [PMID: 38942522 DOI: 10.1016/j.nic.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease of the central nervous system, commonly featuring disability and cognitive impairment. The pathologic hallmark of MS lies in demyelination and hence impaired structural and functional neuronal pathways. Recent studies have shown that MS shows extensive structural disconnection of key network hub areas like the thalamus, combined with a functional network reorganization that can mostly be related to poorer clinical functioning. As MS can, therefore, be considered a network disorder, this review outlines recent innovations in the field of network neuroscience in MS.
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Affiliation(s)
- Giuseppe Pontillo
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands; MS Center Amsterdam, Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands.
| | - Mar Barrantes Cepas
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Tommy A A Broeders
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Ismail Koubiyr
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, Postbus 7057, 1007 MB, Amsterdam, The Netherlands
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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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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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Liang L, Li S, Huang Y, Zhou J, Xiong D, Li S, Li H, Zhu B, Li X, Ning Y, Hou X, Wu F, Wu K. Relationships among the gut microbiome, brain networks, and symptom severity in schizophrenia patients: A mediation analysis. Neuroimage Clin 2024; 41:103567. [PMID: 38271852 PMCID: PMC10835015 DOI: 10.1016/j.nicl.2024.103567] [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: 10/03/2023] [Revised: 01/12/2024] [Accepted: 01/12/2024] [Indexed: 01/27/2024]
Abstract
The microbiome-gut-brain axis (MGBA) plays a critical role in schizophrenia (SZ). However, the underlying mechanisms of the interactions among the gut microbiome, brain networks, and symptom severity in SZ patients remain largely unknown. Fecal samples, structural and functional magnetic resonance imaging (MRI) data, and Positive and Negative Syndrome Scale (PANSS) scores were collected from 38 SZ patients and 38 normal controls, respectively. The data of 16S rRNA gene sequencing were used to analyze the abundance of gut microbiome and the analysis of human brain networks was applied to compute the nodal properties of 90 brain regions. A total of 1,691,280 mediation models were constructed based on 261 gut bacterial, 810 nodal properties, and 4 PANSS scores in SZ patients. A strong correlation between the gut microbiome and brain networks (r = 0.89, false discovery rate (FDR) -corrected p < 0.05) was identified. Importantly, the PANSS scores were linearly correlated with both the gut microbiome (r = 0.5, FDR-corrected p < 0.05) and brain networks (r = 0.59, FDR-corrected p < 0.05). The abundance of genus Sellimonas significantly affected the PANSS negative scores of SZ patients via the betweenness centrality of white matter networks in the inferior frontal gyrus and amygdala. Moreover, 19 significant mediation models demonstrated that the nodal properties of 7 brain regions, predominately from the systems of visual, language, and control of action, showed significant mediating effects on the PANSS scores with the gut microbiome as mediators. Together, our findings indicated the tripartite relationships among the gut microbiome, brain networks, and PANSS scores and suggested their potential role in the neuropathology of SZ.
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Affiliation(s)
- Liqin Liang
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China
| | - Shijia Li
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China; Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, Amsterdam, The Netherlands
| | - Yuanyuan Huang
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Jing Zhou
- School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China
| | - Dongsheng Xiong
- School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China
| | - Shaochuan Li
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China; Realmeta Technology (Guangzhou) Co., Ltd, Guangzhou 510535, China
| | - Hehua Li
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Baoyuan Zhu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Yuping Ning
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Xiaohui Hou
- Guangdong Provincial Key Laboratory of Physical Activity and Health Promotion, Guangzhou Sport University, Guangzhou 510500, China.
| | - Fengchun Wu
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China.
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China; Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan.
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Wu H, Fan S, Yan C, Wang H. Cortical microstructural brain network mediates the association between personality trait of agreeableness and life satisfaction. Cereb Cortex 2024; 34:bhad410. [PMID: 37948663 DOI: 10.1093/cercor/bhad410] [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: 09/12/2022] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/12/2023] Open
Abstract
Personality traits are commonly regarded as relatively stable, whereas life satisfaction can fluctuate with time and circumstances, shaped by external influences and personal encounters. The correlation between personality traits and life satisfaction is well-established, yet the underlying neural mechanisms of the myelin-based microstructural brain network connecting them remain unclear. Here, we constructed individual-level whole-brain myelin microstructural networks from the MRI data of 1,043 healthy adults and performed correlation analysis to detect significant personality trait-related and life satisfaction-related subnetworks. A mediation analysis was used to verify whether the shared structural basis of personality traits and life satisfaction significantly mediated their association. The results showed that agreeableness positively correlated with life satisfaction. We identified a shared structural basis of the personality trait of agreeableness and life satisfaction. The regions comprising this overlapping network include the superior parietal lobule, inferior parietal lobule, and temporoparietal junction. Moreover, the shared microstructural connections mediate the association between the personality trait of agreeableness and life satisfaction. This large-scale neuroimaging investigation substantiates a mediation framework for understanding the microstructural connections between personality and life satisfaction, offering potential targets for assessment and interventions to promote human well-being.
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Affiliation(s)
- Huijun Wu
- School of Media & Communication, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shijia Fan
- School of Psychological and Cognitive Sciences & Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China
| | - Chuyao Yan
- School of Psychology, Nanjing Normal University, Nanjing 200097, China
| | - Hao Wang
- School of Physics and Optoelectronic Engineering, Hainan University, Haikou 570228, China
- G. Oppenheimer Center for Neurobiology of Stress & Resilience, UCLA, Los Angeles, CA 90095, United States
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Yamin MA, Valsasina P, Tessadori J, Filippi M, Murino V, Rocca MA, Sona D. Discovering functional connectivity features characterizing multiple sclerosis phenotypes using explainable artificial intelligence. Hum Brain Mapp 2023; 44:2294-2306. [PMID: 36715247 PMCID: PMC10028625 DOI: 10.1002/hbm.26210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 12/14/2022] [Accepted: 01/02/2023] [Indexed: 01/31/2023] Open
Abstract
Multiple sclerosis (MS) is a neurological condition characterized by severe structural brain damage and by functional reorganization of the main brain networks that try to limit the clinical consequences of structural burden. Resting-state (RS) functional connectivity (FC) abnormalities found in this condition were shown to be variable across different MS phases, according to the severity of clinical manifestations. The article describes a system exploiting machine learning on RS FC matrices to discriminate different MS phenotypes and to identify relevant functional connections for MS stage characterization. To this end, the system exploits some mathematical properties of covariance-based RS FC representation, which can be described by a Riemannian manifold. The classification performance of the proposed framework was significantly above the chance level for all MS phenotypes. Moreover, the proposed system was successful in identifying relevant RS FC alterations contributing to an accurate phenotype classification.
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Affiliation(s)
- Muhammad Abubakar Yamin
- Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy
- Center for Autism Research, Kessler Foundation, East Hanover, New Jersey, USA
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Jacopo Tessadori
- Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy
- Dipartimento di Informatica, University of Verona, Verona, 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
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita Salute San Raffaele University, Milan, Italy
| | - Vittorio Murino
- Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy
- Dipartimento di Informatica, University of Verona, Verona, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita Salute San Raffaele University, Milan, Italy
| | - Diego Sona
- Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy
- Data Science for Health, Center for Digital Health and Wellbeing, Fondazione Bruno Kessler, Trento, Italy
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Nair G, Nair SS, Arun KM, Camacho P, Bava E, Ajayaghosh P, Menon RN, Nair M, Kesavadas C, Anteraper SA. Resting-State Functional Connectivity in Relapsing-Remitting Multiple Sclerosis with Mild Disability: A Data-Driven, Whole-Brain Multivoxel Pattern Analysis Study. Brain Connect 2023; 13:89-96. [PMID: 36006365 DOI: 10.1089/brain.2021.0182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Multivoxel pattern analysis (MVPA) has emerged as a powerful unbiased approach for generating seed regions of interest (ROIs) in resting-state functional connectivity (RSFC) analysis in a data-driven manner. Studies exploring RSFC in multiple sclerosis have produced diverse and often incongruent results. Objectives: The aim of the present study was to investigate RSFC differences between people with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC). Methods: We performed a whole-brain connectome-wide MVPA in 50 RRMS patients with expanded disability status scale ≤4 and 50 age and gender-matched HCs. Results: Significant group differences were noted in RSFC in three clusters distributed in the following regions: anterior cingulate gyrus, right middle frontal gyrus, and frontal medial cortex. Whole-brain seed-to-voxel RSFC characterization of these clusters as seed ROIs revealed network-specific abnormalities, specifically in the anterior cingulate cortex and the default mode network. Conclusions: The network-wide RSFC abnormalities we report agree with the previous findings in RRMS, the cognitive and clinical implications of which are discussed herein. Impact statement This study investigated resting-state functional connectivity (RSFC) in relapsing-remitting multiple sclerosis (RRMS) people with mild disability (expanded disability status scale ≤4). Whole-brain connectome-wide multivoxel pattern analysis was used for assessing RSFC. Compared with healthy controls, we were able to identify three regions of interest for significant differences in connectivity patterns, which were then extracted as a mask for whole-brain seed-to-voxel analysis. A reduced connectivity was noted in the RRMS group, particularly in the anterior cingulate cortex and the default mode network regions, providing insights into the RSFC abnormalities in RRMS.
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Affiliation(s)
- Gowthami Nair
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Sruthi S Nair
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Karumattu Manattu Arun
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Paul Camacho
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Bioengineering, Interdisciplinary Health Science Institute, Urbana, Illinois, USA
| | - Elshal Bava
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Priya Ajayaghosh
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Ramshekhar N Menon
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Muralidharan Nair
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Chandrasekharan Kesavadas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
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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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Chen X, Peng Y, Zheng Q, Luo D, Han Y, Luo Q, Zhu Q, Luo T, Li Y. The different damage patterns of short-, middle- and long-range connections between patients with relapse-remitting multiple sclerosis and neuromyelitis optica spectrum disorder. Front Immunol 2022; 13:1007335. [PMID: 36532033 PMCID: PMC9755727 DOI: 10.3389/fimmu.2022.1007335] [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: 07/30/2022] [Accepted: 11/16/2022] [Indexed: 12/04/2022] Open
Abstract
Objective To investigate the differences in short-, middle- and long-range connections between patients with relapse-remitting multiple sclerosis (RRMS) and neuromyelitis optica spectrum disorder (NMOSD), and their correlation with brain tissue volume, structural and functional network parameters. Methods A total of 51 RRMS, 42 NMOSD and 56 health controls (HC) were recruited. Of these 25 RRMS (median: 1.37 years) and 20 NMOSD (median: 1.25 years) patients were also studied at follow-up. The whole-brain fiber connection was divided into three groups according to the trisected lengths of the tract in HC group, including short-, middle- and long-range connections. The brain tissue features (including total brain tissue and deep grey matter volumes) and parameters of DTI and functional networks (including the shortest path, clustering coefficient, local efficiency and global efficiency) were calculated. The differences in fiber number (FN) and average fractional anisotropy (FA) were compared between RRMS and NMOSD by the One-way ANOVA and post hoc tests. The correlation between the FN or FA and the brain tissue volume, DTI and functional network parameters were further analyzed by Pearson analysis. Results Compared to HC and NMOSD, the total number of fibers in RRMS was decreased, including the reduced FN of middle- and long-range connections, but increased FN of short-range connections. Compared to HC, the FA of three fibers in RRMS and NMOSD were reduced significantly, and the decrease of FA in RRMS was greater than in NMOSD. There were correlations between the FN of short-, and long-range connections and the atrophy of whole brain tissue in two diseases and structural network topological parameters in RRMS. Additionally, there was no significant difference of FN and FA in short-, middle- and long-range connections between the baseline and follow-up in two diseases. Conclusions RRMS and NMOSD patients have different patterns of fiber connection damage. The FN of different lengths in RRMS and NMOSD patients may be associated with brain atrophy. The FN and FA of different lengths may explain the decreased efficiency of the structural network in RRMS patients. In the short-term follow-up, neither has worsened damage of different fibers in two diseases.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Yongmei Li
- *Correspondence: Tianyou Luo, ; Yongmei Li,
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Tahedl M, Levine SM, Weissert R, Kohl Z, Lee DH, Linker RA, Schwarzbach JV. Early remission in multiple sclerosis is linked to altered coherence of the Cerebellar Network. J Transl Med 2022; 20:488. [PMID: 36303221 PMCID: PMC9615296 DOI: 10.1186/s12967-022-03576-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 08/06/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND The development of permanent disability in multiple sclerosis (MS) is highly variable among patients, and the exact mechanisms that contribute to this disability remain unknown. METHODS Following the idea that the brain has intrinsic network organization, we investigated changes of functional networks in MS patients to identify possible links between network reorganization and remission from clinical episodes in MS. Eighteen relapsing-remitting MS patients (RRMS) in their first clinical manifestation underwent resting-state functional MRI and again during remission. We used ten template networks, identified from independent component analysis, to compare changes in network coherence for each patient compared to those of 44 healthy controls from the Human Connectome Project test-retest dataset (two-sample t-test of pre-post differences). Combining a binomial test with Monte Carlo procedures, we tested four models of how functional coherence might change between the first clinical episode and remission: a network can change its coherence (a) with itself ("one-with-self"), (b) with another network ("one-with-other"), or (c) with a set of other networks ("one-with-many"), or (d) multiple networks can change their coherence with respect to one common network ("many-with-one"). RESULTS We found evidence supporting two of these hypotheses: coherence decreased between the Executive Control Network and several other networks ("one-with-many" hypothesis), and a set of networks altered their coherence with the Cerebellar Network ("many-with-one" hypothesis). CONCLUSION Given the unexpected commonality of the Cerebellar Network's altered coherence with other networks (a finding present in more than 70% of the patients, despite their clinical heterogeneity), we conclude that remission in MS may result from learning processes mediated by the Cerebellar Network.
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Affiliation(s)
- Marlene Tahedl
- grid.7727.50000 0001 2190 5763Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany ,grid.7727.50000 0001 2190 5763Institute for Psychology, University of Regensburg, 93053 Regensburg, Germany
| | - Seth M. Levine
- grid.5252.00000 0004 1936 973XDepartment of Psychology, LMU Munich, 80802 Munich, Germany ,grid.411095.80000 0004 0477 2585NeuroImaging Core Unit Munich (NICUM), University Hospital LMU, 80336 Munich, Germany
| | - Robert Weissert
- grid.7727.50000 0001 2190 5763Department of Neurology, University of Regensburg, 93053 Regensburg, Germany
| | - Zacharias Kohl
- grid.7727.50000 0001 2190 5763Department of Neurology, University of Regensburg, 93053 Regensburg, Germany
| | - De-Hyung Lee
- grid.7727.50000 0001 2190 5763Department of Neurology, University of Regensburg, 93053 Regensburg, Germany
| | - Ralf A. Linker
- grid.7727.50000 0001 2190 5763Department of Neurology, University of Regensburg, 93053 Regensburg, Germany
| | - Jens V. Schwarzbach
- grid.7727.50000 0001 2190 5763Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany
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11
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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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12
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Riedmann U, Fink A, Weber B, Koschutnig K. Functional Connectivity as an Index of Brain Changes Following a Unicycle Intervention: A Graph-Theoretical Network Analysis. Brain Sci 2022; 12:brainsci12081092. [PMID: 36009155 PMCID: PMC9405869 DOI: 10.3390/brainsci12081092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 07/24/2022] [Accepted: 08/02/2022] [Indexed: 11/16/2022] Open
Abstract
Grey matter volume reductions in the right superior temporal gyrus (rSTG) were observed in young adults who learned to ride a unicycle. As these decreases were correlated with the acquired ability in unicycling, the authors interpreted the change as a brain tissue reorganization to increase postural control’s automated and efficient coordination. The current study aims to further corroborate this interpretation by looking at changes in the functional brain network in the very same sample of participants. For this reason, we applied graph theory, a mathematics field used to study network structure functionality. Four global and two local graph-theoretical parameters were calculated to measure whole brain and rSTG specific changes in functional network activity following the three-week-unicycle training. Findings revealed that the Local Efficiency of the rSTG was significantly elevated after the intervention indicating an increase in fault tolerance of the rSTG, possibly reflecting decentralisation of rSTG specific functions to neighbouring nodes. Thus, the increased Local Efficiency may indicate increased task efficiency by decentralising the processing of functions crucial for balance.
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13
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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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14
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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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15
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Wang J, Ke P, Zang J, Wu F, Wu K. Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study. Front Neurosci 2022; 15:785595. [PMID: 35087373 PMCID: PMC8787107 DOI: 10.3389/fnins.2021.785595] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 12/01/2021] [Indexed: 12/12/2022] Open
Abstract
Interest in the application of machine learning (ML) techniques to multimodal magnetic resonance imaging (MRI) data for the diagnosis of schizophrenia (SZ) at the individual level is growing. However, a few studies have applied the features of structural and functional brain networks derived from multimodal MRI data to the discriminative analysis of SZ patients at different clinical stages. In this study, 205 normal controls (NCs), 61 first-episode drug-naive SZ (FESZ) patients, and 79 chronic SZ (CSZ) patients were recruited. We acquired their structural MRI, diffusion tensor imaging, and resting-state functional MRI data and constructed brain networks for each participant, including the gray matter network (GMN), white matter network (WMN), and functional brain network (FBN). We then calculated 3 nodal properties for each brain network, including degree centrality, nodal efficiency, and betweenness centrality. Two classifications (SZ vs. NC and FESZ vs. CSZ) were performed using five ML algorithms. We found that the SVM classifier with the input features of the combination of nodal properties of both the GMN and FBN achieved the best performance to discriminate SZ patients from NCs [accuracy, 81.2%; area under the receiver operating characteristic curve (AUC), 85.2%; p < 0.05]. Moreover, the SVM classifier with the input features of the combination of the nodal properties of both the GMN and WMN achieved the best performance to discriminate FESZ from CSZ patients (accuracy, 86.2%; AUC, 92.3%; p < 0.05). Furthermore, the brain areas in the subcortical/cerebellum network and the frontoparietal network showed significant importance in both classifications. Together, our findings provide new insights to understand the neuropathology of SZ and further highlight the potential advantages of multimodal network properties for identifying SZ patients at different clinical stages.
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Affiliation(s)
- Jing Wang
- School of Biomedical Engineering, Guangzhou Xinhua University, Guangzhou, China
| | - Pengfei Ke
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Jinyu Zang
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- *Correspondence: Fengchun Wu,
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
- Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou, China
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Kai Wu,
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16
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Ren S, Huang Q, Bao W, Jiang D, Xiao J, Li J, Xie F, Guan Y, Feng R, Hua F. Metabolic Brain Network and Surgical Outcome in Temporal Lobe Epilepsy: A Graph Theoretical Study Based on 18F-fluorodeoxyglucose PET. Neuroscience 2021; 478:39-48. [PMID: 34687794 DOI: 10.1016/j.neuroscience.2021.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 10/07/2021] [Accepted: 10/11/2021] [Indexed: 10/20/2022]
Abstract
Drug-resistant temporal lobe epilepsy (TLE) is a potential candidate for surgery; however, nearly one-third subjects had a poor surgical prognosis. We studied the underlying neuromechanism related to the surgical prognosis using graph theory based on metabolic brain network. Sixty-four unilateral TLE subjects with preoperative 18F-fluorodeoxyglucose (FDG) PET scanning were retrospectively enrolled and divided into Ia (Engel class Ia, n = 32) and non-Ia (Engel class Ib-IV, n = 32) groups according to more than 3-year follow-up after unilateral anterior temporal lobectomy (ATL). The metabolic brain network was constructed and the changed metabolic connectivity of Ia and non-Ia was detected compared with 15 matched healthy controls (HCs). Further, the network properties, including small-worldness and global efficiency, were calculated and hub nodes were also identified for the 3 groups respectively. Non-Ia group exhibited increased connectivity between contralateral fusiform gyrus and contralateral lingual gyrus; while Ia showed decreased connectivity mainly among bilateral frontal, temporal and parietal cortex. Graph theoretical analysis revealed that non-Ia group showed increased small-worldness (35%<s < 55%, P ≤ 0.05) compared to HCs; and elevated global efficiency (P = 0.05) and decreased Lp (P = 0.05) compared to Ia group. Ia group showed reduced Cp (55%<s < 63%, P < 0.05) and increased small-worldness (35%<s < 37%, P < 0.05) compared to HCs; Furthermore, disrupted hub nodes distribution pattern with the midcingulate gyrus disappeared, was also found in non-Ia group compared with the Ia group. All those results revealed that elevated network integration and metabolic connectivity, redistributed hub nodes pattern is associated with ongoing postoperative seizures in subjects with intractable TLE.
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Affiliation(s)
- Shuhua Ren
- PET Center, Huashan Hospital, Fudan University, Shanghai 200235, China
| | - Qi Huang
- PET Center, Huashan Hospital, Fudan University, Shanghai 200235, China
| | - Weiqi Bao
- PET Center, Huashan Hospital, Fudan University, Shanghai 200235, China
| | - Donglang Jiang
- PET Center, Huashan Hospital, Fudan University, Shanghai 200235, China
| | - Jianfei Xiao
- PET Center, Huashan Hospital, Fudan University, Shanghai 200235, China
| | - Junpeng Li
- PET Center, Huashan Hospital, Fudan University, Shanghai 200235, China
| | - Fang Xie
- PET Center, Huashan Hospital, Fudan University, Shanghai 200235, China
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, Shanghai 200235, China.
| | - Rui Feng
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China.
| | - Fengchun Hua
- PET Center, Huashan Hospital, Fudan University, Shanghai 200235, China; Department of Nuclear Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China.
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17
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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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18
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Moody JF, Adluru N, Alexander AL, Field AS. The Connectomes: Methods of White Matter Tractography and Contributions of Resting State fMRI. Semin Ultrasound CT MR 2021; 42:507-522. [PMID: 34537118 DOI: 10.1053/j.sult.2021.07.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
A comprehensive mapping of the structural and functional circuitry of the brain is a major unresolved problem in contemporary neuroimaging research. Diffusion-weighted and functional MRI have provided investigators with the capability to assess structural and functional connectivity in-vivo, driven primarily by methods of white matter tractography and resting-state fMRI, respectively. These techniques have paved the way for the construction of the functional and structural connectomes, which are quantitative representations of brain architecture as neural networks, comprised of nodes and edges. The connectomes, typically depicted as matrices or graphs, possess topological properties that inherently characterize the strength, efficiency, and organization of the connections between distinct brain regions. Graph theory, a general mathematical framework for analyzing networks, can be implemented to derive metrics from the connectomes that are sensitive to changes in brain connectivity associated with age, sex, cognitive function, and disease. These quantities can be assessed at either the global (whole brain) or local levels, allowing for the identification of distinct regional connectivity hubs and associated localized brain networks, which together serve crucial roles in establishing the structural and functional architecture of the brain. As a result, structural and functional connectomes have each been employed to study the brain circuitry underlying early brain development, neuroplasticity, developmental disorders, psychopathology, epilepsy, aging, neurodegenerative disorders, and traumatic brain injury. While these studies have yielded important insights into brain structure, function, and pathology, a precise description of the innate relationship between functional and structural networks across the brain remains unachieved. To date, connectome research has merely scratched the surface of potential clinical applications and related characterizations of brain-wide connectivity. Continued advances in diffusion and functional MRI acquisition, the delineation of functional and structural networks, and the quantification of neural network properties in specific brain regions, will be invaluable to future progress in neuroimaging science.
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Affiliation(s)
- Jason F Moody
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI; Waisman Center, University of Wisconsin-Madison, Madison, WI
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI; Department of Radiology, University of Wisconsin-Madison, Madison, WI
| | - Andrew L Alexander
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI; Waisman Center, University of Wisconsin-Madison, Madison, WI
| | - Aaron S Field
- Department of Radiology, University of Wisconsin-Madison, Madison, WI.
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19
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Hidalgo de la Cruz M, Valsasina P, Mesaros S, Meani A, Ivanovic J, Martinovic V, Drulovic J, Filippi M, Rocca MA. Clinical predictivity of thalamic sub-regional connectivity in clinically isolated syndrome: a 7-year study. Mol Psychiatry 2021; 26:2163-2174. [PMID: 32322087 DOI: 10.1038/s41380-020-0726-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 03/12/2020] [Accepted: 04/01/2020] [Indexed: 02/07/2023]
Abstract
Here, we explored trajectories of sub-regional thalamic resting state (RS) functional connectivity (FC) modifications occurring in clinically isolated syndrome (CIS) patients early after their first clinical episode, and assessed their relationship with disability over 7 years. RS fMRI and clinical data were prospectively acquired from 59 CIS patients and 13 healthy controls (HC) over 2 years. A clinical re-assessment was performed in 53 (89%) patients after 7 years. Using a structural connectivity-based atlas, five thalamic sub-regions (frontal, motor, postcentral, occipital, and temporal) were used for seed-based RS FC. Thalamic RS FC abnormalities and their longitudinal changes were correlated with disability. Thirty-nine (66.1%) patients suffered a second clinical relapse, but the median EDSS remained stable over time. At baseline, CIS patients vs HC showed reduced RS FC (p < 0.001, uncorrected) with: (1) frontal cortices, for the whole thalamus, occipital, postcentral, and temporal thalamic sub-regions, (2) occipital cortices, for the occipital thalamic sub-region. In CIS, the longitudinal analysis revealed at year 2 vs baseline: (1) no significant whole-thalamic RS FC changes; (2) reduction of motor, postcentral, and temporal sub-regional RS FC with occipital cortices (p < 0.05, corrected); (3) an increase (p < 0.001, uncorrected) of postcentral and occipital sub-regional thalamic RS FC with frontal cortices, left putamen, and ipsi- and contralateral thalamus, this latter correlating with less severe clinical disability at year 7. Thalamo-cortical disconnections were present in CIS mainly in thalamic sub-regions closer to the third ventricle early after the demyelinating event, evolved in the subsequent 2 years, and were associated with long-term clinical disability.
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Affiliation(s)
- Milagros Hidalgo de la Cruz
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Sarlota Mesaros
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Alessandro Meani
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Jovana Ivanovic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Vanja Martinovic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Jelena Drulovic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - 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
| | - 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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20
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Baijot J, Denissen S, Costers L, Gielen J, Cambron M, D'Haeseleer M, D'hooghe MB, Vanbinst AM, De Mey J, Nagels G, Van Schependom J. Signal quality as Achilles' heel of graph theory in functional magnetic resonance imaging in multiple sclerosis. Sci Rep 2021; 11:7376. [PMID: 33795779 PMCID: PMC8016888 DOI: 10.1038/s41598-021-86792-0] [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: 03/27/2020] [Accepted: 03/16/2021] [Indexed: 11/29/2022] Open
Abstract
Graph-theoretical analysis is a novel tool to understand the organisation of the brain. We assessed whether altered graph theoretical parameters, as observed in multiple sclerosis (MS), reflect pathology-induced restructuring of the brain's functioning or result from a reduced signal quality in functional MRI (fMRI). In a cohort of 49 people with MS and a matched group of 25 healthy subjects (HS), we performed a cognitive evaluation and acquired fMRI. From the fMRI measurement, Pearson correlation-based networks were calculated and graph theoretical parameters reflecting global and local brain organisation were obtained. Additionally, we assessed metrics of scanning quality (signal to noise ratio (SNR)) and fMRI signal quality (temporal SNR and contrast to noise ratio (CNR)). In accordance with the literature, we found that the network parameters were altered in MS compared to HS. However, no significant link was found with cognition. Scanning quality (SNR) did not differ between both cohorts. In contrast, measures of fMRI signal quality were significantly different and explained the observed differences in GTA parameters. Our results suggest that differences in network parameters between MS and HS in fMRI do not reflect a functional reorganisation of the brain, but rather occur due to reduced fMRI signal quality.
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Affiliation(s)
- Johan Baijot
- Center For Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium. .,, Ke.2.13; Pleinlaan 2, 1050, Elsene, Belgium.
| | - Stijn Denissen
- Center For Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Lars Costers
- Center For Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Jeroen Gielen
- Center For Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Melissa Cambron
- Center For Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,AZ Sint-Jan, Brugge, Belgium
| | - Miguel D'Haeseleer
- Center For Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center Melsbroek, Melsbroek, Belgium
| | - Marie B D'hooghe
- Center For Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center Melsbroek, Melsbroek, Belgium
| | | | - Johan De Mey
- Department of Radiology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Guy Nagels
- Center For Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center Melsbroek, Melsbroek, Belgium.,St Edmund Hall, University of Oxford, Oxford, Great Britain and Northern Ireland, UK
| | - Jeroen Van Schependom
- Center For Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,Department of Radiology, Vrije Universiteit Brussel, Brussels, Belgium.,Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, Belgium
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21
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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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22
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Welton T, Constantinescu CS, Auer DP, Dineen RA. Graph Theoretic Analysis of Brain Connectomics in Multiple Sclerosis: Reliability and Relationship with Cognition. Brain Connect 2020; 10:95-104. [PMID: 32079409 PMCID: PMC7196369 DOI: 10.1089/brain.2019.0717] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Research suggests that disruption of brain networks might explain cognitive deficits in multiple sclerosis (MS). The reliability and effectiveness of graph theoretic network metrics as measures of cognitive performance were tested in 37 people with MS and 23 controls. Specifically, relationships with cognitive performance (linear regression against the paced auditory serial addition test-3 seconds [PASAT-3], symbol digit modalities test [SDMT], and attention network test) and 1-month reliability (using the intraclass correlation coefficient [ICC]) of network metrics were measured using both resting-state functional and diffusion magnetic resonance imaging data. Cognitive impairment was directly related to measures of brain network segregation and inversely related to network integration (prediction of PASAT-3 by small worldness, modularity, characteristic path length, R2 = 0.55; prediction of SDMT by small worldness, global efficiency, and characteristic path length, R2 = 0.60). Reliability of the measures for 1 month in a subset of nine participants was mostly rated as good (ICC >0.6) for both controls and MS patients in both functional and diffusion data, but was highly dependent on the chosen parcellation and graph density, with the 0.2–0.5 density range being the most reliable. This suggests that disrupted network organization predicts cognitive impairment in MS and its measurement is reliable for a 1-month period. These new findings support the hypothesis of network disruption as a major determinant of cognitive deficits in MS and the future possibility of the application of derived metrics as surrogate outcomes in trials of therapies for cognitive impairment.
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Affiliation(s)
- Thomas Welton
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom.,Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom.,Sydney Translational Imaging Laboratory, Heart Research Institute, University of Sydney, Camperdown, Australia
| | - Cris S Constantinescu
- Clinical Neurology, Division of Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom
| | - Dorothee P Auer
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom.,Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom.,NIHR Nottingham Biomedical Research Centre, Nottingham, United Kingdom
| | - Rob A Dineen
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom.,Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom.,NIHR Nottingham Biomedical Research Centre, Nottingham, United Kingdom
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23
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Bizzo BC, Arruda-Sanchez T, Tobyne SM, Bireley JD, Lev MH, Gasparetto EL, Klawiter EC. Anterior Insular Resting-State Functional Connectivity is Related to Cognitive Reserve in Multiple Sclerosis. J Neuroimaging 2020; 31:98-102. [PMID: 32857919 DOI: 10.1111/jon.12779] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 08/07/2020] [Accepted: 08/15/2020] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND AND PURPOSE Cognitive dysfunction is common in multiple sclerosis (MS). The dorsal anterior insula (dAI) is a key hub of the salience network (SN) orchestrating access to critical cognitive brain regions. The aim of this study was to assess whole-brain dAI intrinsic functional connectivity (iFC) using resting-state functional MRI (rs-fMRI) in people with MS and healthy controls (HC) and test the relationship between cognitive reserve (CR) and dAI iFC in people with MS. METHODS We studied 28 people with relapsing-remitting MS and 28 HC. CR index was quantified by combining premorbid IQ, leisure activities, and education level. For whole-brain iFC analyses, the bilateral dAI were used as seeds. Individual subject correlation maps were entered into general linear models for group comparison and to analyze the effect of CR index on dAI iFC, controlling for multiple comparisons. The correlation between CR index and iFC was assessed using a linear regression model. RESULTS rs-fMRI analyses revealed a negative relationship between CR index and iFC within the left dAI and a left occipital cluster in people with MS including regions of the cuneus, superior occipital gyrus, and parieto-occipital sulcus. The regression analysis showed that people with MS and a higher CR index had a statistically significantly reduced iFC within the left dAI and the cluster. CONCLUSIONS CR is relevant to functional connectivity within one of the main nodes of the SN, the dAI, and occipital regions in MS. These results have implications for how CR may modulate the susceptibility to cognitive dysfunction in MS.
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Affiliation(s)
- Bernardo Canedo Bizzo
- Department of Radiology, Faculty of Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.,A.A. Martinos Center for Biomedical Imaging, Charlestown, MA
| | - Tiago Arruda-Sanchez
- Department of Radiology, Faculty of Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Sean M Tobyne
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - John Daniel Bireley
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Michael Howard Lev
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Emerson Leandro Gasparetto
- Department of Radiology, Faculty of Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Eric C Klawiter
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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24
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Naser Moghadasi A. The role of the brain in the treatment of multiple sclerosis as a connectomopathy. Med Hypotheses 2020; 143:110090. [PMID: 32679428 DOI: 10.1016/j.mehy.2020.110090] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 06/18/2020] [Accepted: 07/05/2020] [Indexed: 12/14/2022]
Abstract
Multiple sclerosis (MS) is an inflammatory disease of the central nervous system (CNS) causing a variety of symptoms. Although MS is recognized by the demyelinating process, the axonal injury can occur from the start of the disease and lead to neurodegenerative process in the disease. Although MS appears to damage the brain locally, the progressive and neurodegenerative nature of the disease indicate the general and global brain damage. Various studies have indicated this global damage at all areas of white and gray matter. Moreover, the earlier stages of mentioned disease can affect the structural and functional brain connections. Demyelinating lesions, which are local at first glance, lead to a global damage to the functional connections of the brain. Therefore, it seems that the brain network or brain connectome are broadly affected by this disease; therefore, MS can be referred as a connectomopathy. The drugs used in this disease all seek to suppress or regulate the immune system, and the human brain has always been considered as a therapeutic target. However, if the brain is generally involved in the disease, so the treatment should be general. In fact, the treatment process should target the connectomopathy. One of the methods that can be used to achieve the mentioned goal is attending to the role of the brain in its treatment.
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Affiliation(s)
- Abdorreza Naser Moghadasi
- Multiple Sclerosis Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran.
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25
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Yin G, Li H, Tan S, Yao R, Cui X, Zhao L. Synchronization Stability Model of Complex Brain Networks: An EEG Study. Front Psychiatry 2020; 11:571068. [PMID: 33343416 PMCID: PMC7746829 DOI: 10.3389/fpsyt.2020.571068] [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: 06/09/2020] [Accepted: 10/16/2020] [Indexed: 11/16/2022] Open
Abstract
In this paper, from the perspective of complex network dynamics we investigated the formation of the synchronization state of the brain networks. Based on the Lyapunov stability theory of complex networks, a synchronous steady-state model suitable for application to complex dynamic brain networks was proposed. The synchronization stability problem of brain network state equation was transformed into a convex optimization problem with Block Coordinate Descent (BCD) method. By using Random Apollo Network (RAN) method as a node selection rule, the brain network constructs its subnet work dynamically. We also analyzes the change of the synchronous stable state of the subnet work constructed by this method with the increase of the size of the network. Simulation EEG data from alcohol addicts patients and Real experiment EEG data from schizophrenia patients were used to verify the robustness and validity of the proposed model. Differences in the synchronization characteristics of the brain networks between normal and alcoholic patients were analyzed, so as differences between normal and schizophrenia patients. The experimental results indicated that the establishment of a synchronous steady state model in this paper could be used to verify the synchronization of complex dynamic brain networks and potentially be of great value in the further study of the pathogenic mechanisms of mental illness.
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Affiliation(s)
- Guimei Yin
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.,Department of Computer Science, Taiyuan Normal University, Jinzhong, China
| | - Haifang Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Shuping Tan
- Center for Psychiatric Research, Beijing Huilongguan Hospital, Beijing, China
| | - Rong Yao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Lun Zhao
- School of Educational Sciences, Liaocheng University, Liaocheng, China
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26
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Modeling Resilience to Damage in Multiple Sclerosis: Plasticity Meets Connectivity. Int J Mol Sci 2019; 21:ijms21010143. [PMID: 31878257 PMCID: PMC6981966 DOI: 10.3390/ijms21010143] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 12/05/2019] [Accepted: 12/20/2019] [Indexed: 02/03/2023] Open
Abstract
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) characterized by demyelinating white matter lesions and neurodegeneration, with a variable clinical course. Brain network architecture provides efficient information processing and resilience to damage. The peculiar organization characterized by a low number of highly connected nodes (hubs) confers high resistance to random damage. Anti-homeostatic synaptic plasticity, in particular long-term potentiation (LTP), represents one of the main physiological mechanisms underlying clinical recovery after brain damage. Different types of synaptic plasticity, including both anti-homeostatic and homeostatic mechanisms (synaptic scaling), contribute to shape brain networks. In MS, altered synaptic functioning induced by inflammatory mediators may represent a further cause of brain network collapse in addition to demyelination and grey matter atrophy. We propose that impaired LTP expression and pathologically enhanced upscaling may contribute to disrupting brain network topology in MS, weakening resilience to damage and negatively influencing the disease course.
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27
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Van Schependom J, Guldolf K, D'hooghe MB, Nagels G, D'haeseleer M. Detecting neurodegenerative pathology in multiple sclerosis before irreversible brain tissue loss sets in. Transl Neurodegener 2019; 8:37. [PMID: 31827784 PMCID: PMC6900860 DOI: 10.1186/s40035-019-0178-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 11/07/2019] [Indexed: 12/29/2022] Open
Abstract
Background Multiple sclerosis (MS) is a complex chronic inflammatory and degenerative disorder of the central nervous system. Accelerated brain volume loss, or also termed atrophy, is currently emerging as a popular imaging marker of neurodegeneration in affected patients, but, unfortunately, can only be reliably interpreted at the time when irreversible tissue damage likely has already occurred. Timing of treatment decisions based on brain atrophy may therefore be viewed as suboptimal. Main body This Narrative Review focuses on alternative techniques with the potential of detecting neurodegenerative events in the brain of subjects with MS prior to the atrophic stage. First, metabolic and molecular imaging provide the opportunity to identify early subcellular changes associated with energy dysfunction, which is an assumed core mechanism of axonal degeneration in MS. Second, cerebral hypoperfusion has been observed throughout the entire clinical spectrum of the disorder but it remains an open question whether this serves as an alternative marker of reduced metabolic activity, or exists as an independent contributing process, mediated by endothelin-1 hyperexpression. Third, both metabolic and perfusion alterations may lead to repercussions at the level of network performance and structural connectivity, respectively assessable by functional and diffusion tensor imaging. Fourth and finally, elevated body fluid levels of neurofilaments are gaining interest as a biochemical mirror of axonal damage in a wide range of neurological conditions, with early rises in patients with MS appearing to be predictive of future brain atrophy. Conclusions Recent findings from the fields of advanced neuroradiology and neurochemistry provide the promising prospect of demonstrating degenerative brain pathology in patients with MS before atrophy has installed. Although the overall level of evidence on the presented topic is still preliminary, this Review may pave the way for further longitudinal and multimodal studies exploring the relationships between the abovementioned measures, possibly leading to novel insights in early disease mechanisms and therapeutic intervention strategies.
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Affiliation(s)
- Jeroen Van Schependom
- 1Neurology Department, Universitair Ziekenhuis Brussel; Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussel, Belgium.,2Radiology Department Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Kaat Guldolf
- 1Neurology Department, Universitair Ziekenhuis Brussel; Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussel, Belgium
| | - Marie Béatrice D'hooghe
- 1Neurology Department, Universitair Ziekenhuis Brussel; Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussel, Belgium.,Nationaal Multiple Sclerose Centrum, Melsbroek, Belgium
| | - Guy Nagels
- 1Neurology Department, Universitair Ziekenhuis Brussel; Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussel, Belgium.,Nationaal Multiple Sclerose Centrum, Melsbroek, Belgium
| | - Miguel D'haeseleer
- 1Neurology Department, Universitair Ziekenhuis Brussel; Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussel, Belgium.,Nationaal Multiple Sclerose Centrum, Melsbroek, Belgium
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28
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Eijlers AJC, Wink AM, Meijer KA, Douw L, Geurts JJG, Schoonheim MM. Reduced Network Dynamics on Functional MRI Signals Cognitive Impairment in Multiple Sclerosis. Radiology 2019; 292:449-457. [PMID: 31237498 DOI: 10.1148/radiol.2019182623] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Background Previous studies have demonstrated extensive functional network disturbances in patients with multiple sclerosis (MS), showing a less efficient brain network. Recent studies indicate that the dynamic properties of the brain network show a strong correlation with cognitive function. Purpose To investigate network dynamics on functional MRI in cognitively impaired patients with MS. Materials and Methods In secondary analysis of prospectively acquired data, with imaging performed between 2008 and 2012, differences in regional functional network dynamics (ie, eigenvector centrality dynamics) between cognitively impaired and cognitively preserved participants with MS were investigated. Functional network dynamics were computed on images from functional MRI (3 T) by using a sliding-window approach. Cognitively impaired and preserved groups were compared by using a clusterwise permutation-based method. Results The study included 96 healthy control subjects and 332 participants with MS (including 226 women and 106 men; median age, 48.1 years ± 11.0). Among the 332 participants with MS, 87 were cognitively impaired and 180 had preserved cognitive function; mildly impaired patients (n = 65) were excluded. The cognitively impaired group included a higher proportion of men compared with the cognitively preserved group (35 of 87 [40%] vs 48 of 180 [27%], respectively; P = .02) and had a higher mean age (51.1 years vs 46.3 years, respectively; P < .01). The clusterwise permutation-based comparison at P less than .05 showed reduced centrality dynamics in default-mode, frontoparietal, and visual network regions on functional MRI in cognitively impaired participants versus cognitively preserved participants. A subsequent correlation and hierarchical clustering analysis revealed that the default-mode and visual networks normally demonstrate negatively correlated fluctuations in functional importance (r = -0.23 in healthy control subjects), with an almost complete loss of this negative correlation in cognitively impaired participants compared with cognitively preserved participants (r = -0.04 vs r = -0.14; corrected P = .02). Conclusion As shown on functional MRI, cognitively impaired patients with multiple sclerosis not only demonstrate reduced dynamics in default-mode, frontoparietal, and visual networks, but also show a loss of interplay between default-mode and visual networks. © RSNA, 2019 Online supplemental material is available for this article. See also the article by Eijlers et al and the editorial by Zivadinov and Dwyer in this issue.
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Affiliation(s)
- Anand J C Eijlers
- From the Departments of Anatomy and Neurosciences (A.J.C.E., K.A.M., L.D., J.J.G.G., M.M.S.) and Radiology and Nuclear Medicine (A.M.W.), MS Center Amsterdam, Amsterdam UMC, Locatie VUmc, Amsterdam Neuroscience, De Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, the Netherlands
| | - Alle Meije Wink
- From the Departments of Anatomy and Neurosciences (A.J.C.E., K.A.M., L.D., J.J.G.G., M.M.S.) and Radiology and Nuclear Medicine (A.M.W.), MS Center Amsterdam, Amsterdam UMC, Locatie VUmc, Amsterdam Neuroscience, De Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, the Netherlands
| | - Kim A Meijer
- From the Departments of Anatomy and Neurosciences (A.J.C.E., K.A.M., L.D., J.J.G.G., M.M.S.) and Radiology and Nuclear Medicine (A.M.W.), MS Center Amsterdam, Amsterdam UMC, Locatie VUmc, Amsterdam Neuroscience, De Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, the Netherlands
| | - Linda Douw
- From the Departments of Anatomy and Neurosciences (A.J.C.E., K.A.M., L.D., J.J.G.G., M.M.S.) and Radiology and Nuclear Medicine (A.M.W.), MS Center Amsterdam, Amsterdam UMC, Locatie VUmc, Amsterdam Neuroscience, De Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, the Netherlands
| | - Jeroen J G Geurts
- From the Departments of Anatomy and Neurosciences (A.J.C.E., K.A.M., L.D., J.J.G.G., M.M.S.) and Radiology and Nuclear Medicine (A.M.W.), MS Center Amsterdam, Amsterdam UMC, Locatie VUmc, Amsterdam Neuroscience, De Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, the Netherlands
| | - Menno M Schoonheim
- From the Departments of Anatomy and Neurosciences (A.J.C.E., K.A.M., L.D., J.J.G.G., M.M.S.) and Radiology and Nuclear Medicine (A.M.W.), MS Center Amsterdam, Amsterdam UMC, Locatie VUmc, Amsterdam Neuroscience, De Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, the Netherlands
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29
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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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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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Foschi M, Rizzo G, Liguori R, Avoni P, Mancinelli L, Lugaresi A, Ferini-Strambi L. Sleep-related disorders and their relationship with MRI findings in multiple sclerosis. Sleep Med 2019; 56:90-97. [DOI: 10.1016/j.sleep.2019.01.010] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Revised: 01/08/2019] [Accepted: 01/10/2019] [Indexed: 12/23/2022]
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Rocca MA, Hidalgo de La Cruz M, Valsasina P, Mesaros S, Martinovic V, Ivanovic J, Drulovic J, Filippi M. Two-year dynamic functional network connectivity in clinically isolated syndrome. Mult Scler 2019; 26:645-658. [DOI: 10.1177/1352458519837704] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Background: The features of functional network connectivity reorganization at the earliest stages of MS have not been investigated yet. Objective: To combine static and dynamic analysis of resting state (RS) functional connectivity (FC) to identify mechanisms of clinical dysfunction and recovery occurring in clinically isolated syndrome (CIS) patients. Methods: RS functional magnetic resonance imaging (fMRI) and clinical data were prospectively acquired from 50 CIS patients and 13 healthy controls (HC) at baseline, month 12 and month 24. Between-group differences and longitudinal evolution of network FC were analysed across 41 functionally relevant networks. Results: At follow-up, 47 patients developed MS. Disability remained stable (and relatively low). CIS and HC exhibited two recurring RS FC states (states 1 and 2, showing low and high internetwork connectivity, respectively). At baseline, patients showed reduced state 2 connectivity strength in the default-mode and cerebellar networks, and no differences in global dynamism versus HC. A selective FC reduction in networks affected by the clinical attack was also detected. At follow-up, increased state 2 connectivity strength and global connectivity dynamism was observed in patients versus HC. Conclusion: Longitudinal FC modifications occurring relatively early in the course of multiple sclerosis may represent a protective mechanism contributing to preserve clinical function over time.
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Affiliation(s)
- Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy/Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Milagros Hidalgo de La Cruz
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Sarlota Mesaros
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Vanja Martinovic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Jovana Ivanovic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Jelena Drulovic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy/Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
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Abstract
PURPOSE OF REVIEW To summarize recent findings from the application of MRI in the diagnostic work-up of patients with suspected multiple sclerosis (MS), and to review the insights into disease pathophysiology and the utility of MRI for monitoring treatment response. RECENT FINDINGS New evidence from the application of MRI in patients with clinically isolated syndromes has guided the 2017 revision of the McDonald criteria for MS diagnosis, which has simplified their clinical use while preserving accuracy. Other MRI measures (e.g., cortical lesions and central vein signs) may improve diagnostic specificity, but their assessment still needs to be standardized, and their reliability confirmed. Novel MRI techniques are providing fundamental insights into the pathological substrates of the disease and are helping to give a better understanding of its clinical manifestations. Combined clinical-MRI measures of disease activity and progression, together with the use of clinically relevant MRI measures (e.g., brain atrophy) might improve treatment monitoring, but these are still not ready for the clinical setting. SUMMARY Advances in MRI technology are improving the diagnostic work-up and monitoring of MS, even in the earliest phases of the disease, and are providing MRI measures that are more specific and sensitive to disease pathological substrates.
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Affiliation(s)
- Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
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Charalambous T, Tur C, Prados F, Kanber B, Chard DT, Ourselin S, Clayden JD, A M Gandini Wheeler-Kingshott C, Thompson AJ, Toosy AT. Structural network disruption markers explain disability in multiple sclerosis. J Neurol Neurosurg Psychiatry 2019; 90:219-226. [PMID: 30467210 PMCID: PMC6518973 DOI: 10.1136/jnnp-2018-318440] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 07/26/2018] [Accepted: 08/28/2018] [Indexed: 12/13/2022]
Abstract
OBJECTIVE To evaluate whether structural brain network metrics correlate better with clinical impairment and information processing speed in multiple sclerosis (MS) beyond atrophy measures and white matter lesions. METHODS This cross-sectional study included 51 healthy controls and 122 patients comprising 58 relapsing-remitting, 28 primary progressive and 36 secondary progressive. Structural brain networks were reconstructed from diffusion-weighted MRIs and standard metrics reflecting network density, efficiency and clustering coefficient were derived and compared between subjects' groups. Stepwise linear regression analyses were used to investigate the contribution of network measures that explain clinical disability (Expanded Disability Status Scale (EDSS)) and information processing speed (Symbol Digit Modalities Test (SDMT)) compared with conventional MRI metrics alone and to determine the best statistical model that explains better EDSS and SDMT. RESULTS Compared with controls, network efficiency and clustering coefficient were reduced in MS while these measures were also reduced in secondary progressive relative to relapsing-remitting patients. Structural network metrics increase the variance explained by the statistical models for clinical and information processing dysfunction. The best model for EDSS showed that reduced network density and global efficiency and increased age were associated with increased clinical disability. The best model for SDMT showed that lower deep grey matter volume, reduced efficiency and male gender were associated with worse information processing speed. CONCLUSIONS Structural topological changes exist between subjects' groups. Network density and global efficiency explained disability above non-network measures, highlighting that network metrics can provide clinically relevant information about MS pathology.
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Affiliation(s)
- Thalis Charalambous
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
| | - Carmen Tur
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
| | - Ferran Prados
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Baris Kanber
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Declan T Chard
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
| | - Sebastian Ourselin
- Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Jonathan D Clayden
- UCL GOS Institute of Child Health, University College London, London, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
- Brain MRI 3T Research Center, C. Mondino National Neurological Institute, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Alan J Thompson
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
| | - Ahmed T Toosy
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square MS Centre, London, UK
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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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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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Bede P, Finegan E, Chipika RH, Li Hi Shing S, Lambe J, Meaney J, Redmond J. Occulomotor Neural Integrator Dysfunction in Multiple Sclerosis: Insights From Neuroimaging. Front Neurol 2018; 9:691. [PMID: 30190700 PMCID: PMC6116658 DOI: 10.3389/fneur.2018.00691] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 07/31/2018] [Indexed: 02/03/2023] Open
Abstract
Background: Magnetic resonance imaging is a key diagnostic and monitoring tool in multiple Sclerosis (MS). While the substrates of motor and neuropsychological symptoms in MS have been extensively investigated, nystagmus-associated imaging signatures are relatively under studied. Accordingly, the objective of this study is the comprehensive characterisation of cortical, subcortical, and brainstem involvement in a cohort of MS patients with gaze-evoked nystagmus. Methods: Patients were recruited from a specialist MS clinic and underwent multimodal neuroimaging including high-resolution structural and diffusion tensor data acquisitions. Morphometric analyses were carried out to evaluate patterns of cortical, subcortical, brainstem, and cerebellar gray matter pathology. Volumetric analyses were also performed to further characterize subcortical gray matter degeneration. White matter integrity was evaluated using axial-, mean-, and radial diffusivity as well as fractional anisotropy. Results: Whole-brain morphometry highlighted considerable brainstem and cerebellar gray matter atrophy, and the tract-wise evaluation of white matter metrics revealed widespread pathology in frontotemporal and parietal regions. Nystagmus-associated gray matter degeneration was identified in medial cerebellar, posterior medullar, central pontine, and superior collicular regions. Volume reductions were identified in the putamen, thalamus and hippocampus. Conclusions: Multiple sclerosis is associated with widespread gray matter pathology which is not limited to cortical regions but involves striatal, thalamic, cerebellar, and hippocampal foci. The imaging signature of gaze-evoked nystagmus in MS confirms the degeneration of key structures of the neural integrator network.
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Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group, Academic Unit of Neurology, Trinity College Dublin, Dublin, Ireland.,Laboratoire d'Imagerie Biomédicale, Sorbonne University, CNRS, INSERM, Paris, France.,Department of Neurology, St James's Hospital, Dublin, Ireland
| | - Eoin Finegan
- Computational Neuroimaging Group, Academic Unit of Neurology, Trinity College Dublin, Dublin, Ireland.,Department of Neurology, St James's Hospital, Dublin, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group, Academic Unit of Neurology, Trinity College Dublin, Dublin, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Academic Unit of Neurology, Trinity College Dublin, Dublin, Ireland
| | - Jeffrey Lambe
- Department of Neurology, St James's Hospital, Dublin, Ireland
| | - James Meaney
- Centre for Advanced Medical Imaging (CAMI), St James's Hospital, Dublin, Ireland.,School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Janice Redmond
- Department of Neurology, St James's Hospital, Dublin, Ireland
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Solana E, Martinez-Heras E, Martinez-Lapiscina EH, Sepulveda M, Sola-Valls N, Bargalló N, Berenguer J, Blanco Y, Andorra M, Pulido-Valdeolivas I, Zubizarreta I, Saiz A, Llufriu S. Magnetic resonance markers of tissue damage related to connectivity disruption in multiple sclerosis. NEUROIMAGE-CLINICAL 2018; 20:161-168. [PMID: 30094165 PMCID: PMC6072676 DOI: 10.1016/j.nicl.2018.07.012] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 06/19/2018] [Accepted: 07/11/2018] [Indexed: 01/16/2023]
Abstract
Patients with multiple sclerosis (MS) display reduced structural connectivity among brain regions, but the pathogenic mechanisms underlying network disruption are still unknown. We aimed to investigate the association between the loss of diffusion-based structural connectivity, measured with graph theory metrics, and magnetic resonance (MR) markers of microstructural damage. Moreover, we evaluated the cognitive consequences of connectivity changes. We analysed the frontoparietal network in 102 MS participants and 25 healthy volunteers (HV). MR measures included radial diffusivity (RD), as marker of demyelination, and ratios of myo-inositol, N-acetylaspartate and glutamate+glutamine with creatine in white (WM) and grey matter as markers of astrogliosis, neuroaxonal integrity and glutamatergic neurotoxicity. Patients showed decreased global and local efficiency, and increased assortativity (p < 0.01) of the network, as well as increased RD and myo-inositol, and decreased N-acetylaspartate in WM compared with HV (p < 0.05). In patients, the age-adjusted OR of presenting abnormal global and local efficiency was increased for each increment of 0.01 points in RD and myo-inositol, while it was decreased for each increment of 0.01 points in N-acetylaspartate (the increase of N-acetylaspartate reduced the risk of having abnormal connectivity), all in WM. In a multiple logistic regression analysis, the OR of presenting abnormal global efficiency was 0.95 (95% confidence interval, CI: 0.91–0.99, p = 0.011) for each 0.01 increase in N-acetylaspartate, and the OR of presenting abnormal local efficiency was 1.39 (95% CI: 1.14–1.71, p = 0.001) for each 0.01 increase in RD. Patients with abnormal efficiency had worse performance in attention, working memory and processing speed (p < 0.05). In conclusion, patients with MS exhibit decreased structural network efficiency driven by diffuse microstructural impairment of the WM, probably related to demyelination, astroglial and neuroaxonal damage. The accumulation of neuroaxonal pathological burden seems to magnify the risk of global network collapse, while demyelination may contribute to the regional disorganization. These network modifications have negative consequences on cognition. The pathogenic mechanisms underlying structural network disruption in MS are unknown. The microstructural damage of the white matter contributes to network impairment. The worsening of global efficiency is associated with markers of neuroaxonal burden. Local efficiency impairment is related to MR measures of demyelination. The described network modifications contribute to cognitive dysfunction.
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Affiliation(s)
- Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Barcelona, Spain
| | - Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Barcelona, Spain
| | - Elena H Martinez-Lapiscina
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Barcelona, Spain
| | - Maria Sepulveda
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Barcelona, Spain
| | - Nuria Sola-Valls
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Barcelona, Spain
| | - Nuria Bargalló
- Magnetic Resonance Image Core Facility, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Joan Berenguer
- Magnetic Resonance Image Core Facility, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Yolanda Blanco
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Barcelona, Spain
| | - Magi Andorra
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Barcelona, Spain
| | - Irene Pulido-Valdeolivas
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Barcelona, Spain
| | - Irati Zubizarreta
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Barcelona, Spain
| | - Albert Saiz
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Barcelona, Spain
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Barcelona, Spain.
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41
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Liu Y, Duan Y, Dong H, Barkhof F, Li K, Shu N. Disrupted Module Efficiency of Structural and Functional Brain Connectomes in Clinically Isolated Syndrome and Multiple Sclerosis. Front Hum Neurosci 2018; 12:138. [PMID: 29692717 PMCID: PMC5902485 DOI: 10.3389/fnhum.2018.00138] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 03/27/2018] [Indexed: 12/22/2022] Open
Abstract
Recent studies have demonstrated disrupted topological organization of brain connectome in multiple sclerosis (MS). However, whether the communication efficiency between different functional systems is affected in the early stage of MS remained largely unknown. In this study, we constructed the structural connectivity (SC) and functional connectivity (FC) networks in 41 patients with clinically isolated syndrome (CIS), 32 MS patients and 35 healthy controls (HC) based on diffusion and resting-state functional MRI. To quantify the communication efficiency within and between different functional systems, we proposed two measures called intra- and inter-module efficiency. Based on the module parcellation of functional backbone network, the intra- and inter-module efficiency of SC and FC networks was calculated for each participant. For the SC network, CIS showed decreased inter-module efficiency between the sensory-motor network (SMN), the visual network (VN), the default-mode network (DMN) and the fronto-parietal network (FPN) compared with HC, while MS showed more widespread decreased module efficiency both within and between modules relative to HC and CIS. For the FC network, no differences were found between CIS and HC, and a decreased inter-module efficiency between SMN and FPN and between VN and FPN was identified in MS, compared with HC and CIS. Moreover, both intra- and inter-module efficiency of SC network were correlated with the disability and cognitive scores in MS. Therefore, our results demonstrated early SC changes between modules in CIS, and more widespread SC alterations and inter-module FC changes were observed in MS, which were further associated with cognitive impairment and physical disability.
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Affiliation(s)
- Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China.,Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China.,Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Huiqing Dong
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands.,Institute of Neurology and Healthcare Engineering, University College London, London, United Kingdom
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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Shu N, Duan Y, Huang J, Ren Z, Liu Z, Dong H, Barkhof F, Li K, Liu Y. Progressive brain rich-club network disruption from clinically isolated syndrome towards multiple sclerosis. NEUROIMAGE-CLINICAL 2018; 19:232-239. [PMID: 30035017 PMCID: PMC6051763 DOI: 10.1016/j.nicl.2018.03.034] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 03/14/2018] [Accepted: 03/26/2018] [Indexed: 12/19/2022]
Abstract
Objective To investigate the rich-club organization in clinically isolated syndrome (CIS) and multiple sclerosis (MS), and to characterize its relationships with physical disabilities and cognitive impairments. Methods We constructed high-resolution white matter (WM) structural networks in 41 CIS, 32 MS and 35 healthy controls (HCs) using diffusion MRI and deterministic tractography. Group differences in rich-club organization, global and local network metrics were investigated. The relationship between the altered network metrics, brain lesions and clinical variables including EDSS, MMSE, PASAT, disease duration were calculated. Additionally, reproducibility analysis was performed using different parcellation schemes. Results Compared with HCs, MS patients exhibited a decreased strength in all types of connections (rich-club: p < 0.0001; feeder: p = 0.0004; and local: p = 0.0026). CIS patients showed intermediate values between MS patients and HCs and exhibited a decreased strength in feeder and local connections (feeder: p = 0.019; and local: p = 0.031) but not in rich-club connections. Compared with CIS patients, MS patients showed significant reductions in rich-club connections (p = 0.0004). The reduced strength of rich-club and feeder connections was correlated with cognitive impairments in the MS group. These results were independent of lesion distribution and reproducible across different brain parcellation schemes. Conclusion The rich-club organization was disrupted in MS patients and relatively preserved in CIS. The disrupted rich-club connectivity was correlated with cognitive impairment in MS. These findings suggest that impaired rich-club connectivity is an essential feature of progressive structural network disruption, heralding the development of clinical disability in MS. The rich-club organization was disrupted in MS patients and preserved in CIS. The disrupted rich-club connectivity correlated with cognitive impairment in MS. The rich-club results are reproducible across data analysis methods.
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Affiliation(s)
- Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jing Huang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zhuoqiong Ren
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zheng Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Huiqing Dong
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands; Institute of Neurology and Healthcare Engineering, University College London, England
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China; Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
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43
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Tacchino A, Saiote C, Brichetto G, Bommarito G, Roccatagliata L, Cordano C, Battaglia MA, Mancardi GL, Inglese M. Motor Imagery as a Function of Disease Severity in Multiple Sclerosis: An fMRI Study. Front Hum Neurosci 2018; 11:628. [PMID: 29375340 PMCID: PMC5768615 DOI: 10.3389/fnhum.2017.00628] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 12/08/2017] [Indexed: 11/13/2022] Open
Abstract
Motor imagery (MI) is defined as mental execution without any actual movement. While healthy adults usually show temporal equivalence, i.e., isochrony, between the mental simulation of an action and its actual performance, neurological disorders are associated with anisochrony. Unlike in patients with stroke and Parkinson disease, only a few studies have investigated differences of MI ability in multiple sclerosis (MS). However, the relationship among disease severity, anisochrony and brain activation patterns during MI has not been investigated yet. Here, we propose to investigate MI in MS patients using fMRI during a behavioral task executed with dominant/non-dominant hand and to evaluate whether anisochrony is associated with disease severity. Thirty-seven right-handed MS patients, 17 with clinically isolated syndrome (CIS) suggestive of MS and 20 with relapsing-remitting MS (RR-MS) and 20 right-handed healthy controls (HC) underwent fMRI during a motor task consisting in the actual or imaged movement of squeezing a foam ball with the dominant and non-dominant hand. The same tasks were performed outside the MRI room to record the number of actual and imagined ball squeezes, and calculate an Index of performance (IP) based on the ratio between actual and imagined movements. IP showed that a progressive loss of ability in simulating actions (i.e., anisochrony) more pronounced for non-dominant hand, was found as function of the disease course. Moreover, anisochrony was associated with activation of occipito-parieto-frontal areas that were more extensive at the early stages of the disease, probably in order to counteract the changes due to MS. However, the neural engagement of compensatory brain areas becomes more difficult with more challenging tasks, i.e., dominant vs. non-dominant hand, with a consequent deficit in behavioral performance. These results show a strict association between MI performance and disease severity, suggesting that, at early stages of the disease, anisochrony in MI could be considered as surrogate behavioral marker of MS severity.
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Affiliation(s)
- Andrea Tacchino
- Scientific Research Area, Italian MS Foundation (FISM), Genoa, Italy
| | - Catarina Saiote
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | | | - Giulia Bommarito
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Luca Roccatagliata
- Department of Health Sciences (DISSAL), IRCCS San Martino University Hospital and IST, Genoa, Italy.,Neuroradiology Department, IRCCS San Martino University Hospital and IST, Genoa, Italy
| | - Christian Cordano
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | | | - Gian L Mancardi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Matilde Inglese
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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Oliveira B, Ehrenreich H. Pursuing functional connectivity in NMDAR1 autoantibody carriers. Lancet Psychiatry 2018; 5:21-22. [PMID: 29277208 DOI: 10.1016/s2215-0366(17)30465-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 10/29/2017] [Accepted: 10/30/2017] [Indexed: 11/25/2022]
Affiliation(s)
- Bárbara Oliveira
- Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Hermann-Rein-Strasse 3, 37075 Goettingen, Germany
| | - Hannelore Ehrenreich
- Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Hermann-Rein-Strasse 3, 37075 Goettingen, Germany.
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45
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Huang M, Zhou F, Wu L, Wang B, Wan H, Li F, Zeng X, Gong H. Synchronization within, and interactions between, the default mode and dorsal attention networks in relapsing-remitting multiple sclerosis. Neuropsychiatr Dis Treat 2018; 14:1241-1252. [PMID: 29795982 PMCID: PMC5957478 DOI: 10.2147/ndt.s155478] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND PURPOSE The effects of the interactions between the default mode network (DMN) and the dorsal attention network (DAN), which present anticorrelated behaviors, in relapsing-remitting multiple sclerosis (RRMS) are poorly understood. This study used resting-state functional connectivity (FC) and the Granger causality test (GCT) to examine changes in the undirected and effective functional network connectivity (FNC) between the two networks during the remitting phase in RRMS patients. PATIENTS AND METHODS Thirty-three patients experiencing a clinically diagnosed remitting phase of RRMS and 33 well-matched healthy control subjects participated in this study. First, an independent component (IC) analysis was performed to preprocess the functional magnetic resonance imaging data and select resting-state networks. Then, an FNC analysis and the GCT were combined to examine the temporal correlations between the ICs of the DMN and DAN and to identify correlations with clinical markers. RESULTS Compared with the healthy subjects, the RRMS patients in the remitting phase showed the following: 1) significantly decreased FC within the DAN in the postcentral gyrus and decreased FC within the DMN in several regions except the parahippocampal gyrus, where increased FC was observed; 2) a relatively stable interaction between the two anticorrelated networks as well as a driving connectivity from the DAN to DMN (IC15); and 3) significantly positive correlations between the connectivity coefficient of the right superior temporal gyrus and the Modified Fatigue Impact Scale score (ρ = 0.379, p = 0.036). CONCLUSION Adaptive mechanisms that maintain stable interactions might occur between the DMN and DAN during the remitting phase in RRMS patients.
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Affiliation(s)
- Muhua Huang
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, People's Republic of China.,Neuroradiology Laboratory, Jiangxi Province Medical Imaging Research Institute, Nanchang, People's Republic of China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, People's Republic of China.,Neuroradiology Laboratory, Jiangxi Province Medical Imaging Research Institute, Nanchang, People's Republic of China
| | - Lin Wu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, People's Republic of China.,Neuroradiology Laboratory, Jiangxi Province Medical Imaging Research Institute, Nanchang, People's Republic of China
| | - Bo Wang
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, People's Republic of China.,Neuroradiology Laboratory, Jiangxi Province Medical Imaging Research Institute, Nanchang, People's Republic of China
| | - Hui Wan
- Department of Neurology, The First Affiliated Hospital, Nanchang University, Nanchang, People's Republic of China
| | - Fangjun Li
- Department of Neurology, The First Affiliated Hospital, Nanchang University, Nanchang, People's Republic of China
| | - Xianjun Zeng
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, People's Republic of China.,Neuroradiology Laboratory, Jiangxi Province Medical Imaging Research Institute, Nanchang, People's Republic of China
| | - Honghan Gong
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, People's Republic of China.,Neuroradiology Laboratory, Jiangxi Province Medical Imaging Research Institute, Nanchang, People's Republic of China
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Stampanoni Bassi M, Gilio L, Buttari F, Maffei P, Marfia GA, Restivo DA, Centonze D, Iezzi E. Remodeling Functional Connectivity in Multiple Sclerosis: A Challenging Therapeutic Approach. Front Neurosci 2017; 11:710. [PMID: 29321723 PMCID: PMC5733539 DOI: 10.3389/fnins.2017.00710] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 12/04/2017] [Indexed: 11/13/2022] Open
Abstract
Neurons in the central nervous system are organized in functional units interconnected to form complex networks. Acute and chronic brain damage disrupts brain connectivity producing neurological signs and/or symptoms. In several neurological diseases, particularly in Multiple Sclerosis (MS), structural imaging studies cannot always demonstrate a clear association between lesion site and clinical disability, originating the "clinico-radiological paradox." The discrepancy between structural damage and disability can be explained by a complex network perspective. Both brain networks architecture and synaptic plasticity may play important roles in modulating brain networks efficiency after brain damage. In particular, long-term potentiation (LTP) may occur in surviving neurons to compensate network disconnection. In MS, inflammatory cytokines dramatically interfere with synaptic transmission and plasticity. Importantly, in addition to acute and chronic structural damage, inflammation could contribute to reduce brain networks efficiency in MS leading to worse clinical recovery after a relapse and worse disease progression. These evidence suggest that removing inflammation should represent the main therapeutic target in MS; moreover, as synaptic plasticity is particularly altered by inflammation, specific strategies aimed at promoting LTP mechanisms could be effective for enhancing clinical recovery. Modulation of plasticity with different non-invasive brain stimulation (NIBS) techniques has been used to promote recovery of MS symptoms. Better knowledge of features inducing brain disconnection in MS is crucial to design specific strategies to promote recovery and use NIBS with an increasingly tailored approach.
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Affiliation(s)
- Mario Stampanoni Bassi
- Unit of Neurology & Unit of Neurorehabilitation, IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy.,Multiple Sclerosis Research Unit, Department of Systems Medicine, Tor Vergata University, Rome, Italy
| | - Luana Gilio
- Unit of Neurology & Unit of Neurorehabilitation, IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy.,Multiple Sclerosis Research Unit, Department of Systems Medicine, Tor Vergata University, Rome, Italy
| | - Fabio Buttari
- Unit of Neurology & Unit of Neurorehabilitation, IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy.,Multiple Sclerosis Research Unit, Department of Systems Medicine, Tor Vergata University, Rome, Italy
| | - Pierpaolo Maffei
- Unit of Neurology & Unit of Neurorehabilitation, IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy
| | - Girolama A Marfia
- Multiple Sclerosis Research Unit, Department of Systems Medicine, Tor Vergata University, Rome, Italy
| | | | - Diego Centonze
- Unit of Neurology & Unit of Neurorehabilitation, IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy.,Multiple Sclerosis Research Unit, Department of Systems Medicine, Tor Vergata University, Rome, Italy
| | - Ennio Iezzi
- Unit of Neurology & Unit of Neurorehabilitation, IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy
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