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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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2
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Hindriks R, Broeders TAA, Schoonheim MM, Douw L, Santos F, van Wieringen W, Tewarie PKB. Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants. Hum Brain Mapp 2024; 45:e26663. [PMID: 38520377 PMCID: PMC10960559 DOI: 10.1002/hbm.26663] [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: 05/25/2023] [Revised: 02/12/2024] [Accepted: 03/08/2024] [Indexed: 03/25/2024] Open
Abstract
Blood-level oxygenation-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most common modality to study functional connectivity in the human brain. Most research to date has focused on connectivity between pairs of brain regions. However, attention has recently turned towards connectivity involving more than two regions, that is, higher-order connectivity. It is not yet clear how higher-order connectivity can best be quantified. The measures that are currently in use cannot distinguish between pairwise (i.e., second-order) and higher-order connectivity. We show that genuine higher-order connectivity can be quantified by using multivariate cumulants. We explore the use of multivariate cumulants for quantifying higher-order connectivity and the performance of block bootstrapping for statistical inference. In particular, we formulate a generative model for fMRI signals exhibiting higher-order connectivity and use it to assess bias, standard errors, and detection probabilities. Application to resting-state fMRI data from the Human Connectome Project demonstrates that spontaneous fMRI signals are organized into higher-order networks that are distinct from second-order resting-state networks. Application to a clinical cohort of patients with multiple sclerosis further demonstrates that cumulants can be used to classify disease groups and explain behavioral variability. Hence, we present a novel framework to reliably estimate genuine higher-order connectivity in fMRI data which can be used for constructing hyperedges, and finally, which can readily be applied to fMRI data from populations with neuropsychiatric disease or cognitive neuroscientific experiments.
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Affiliation(s)
- Rikkert Hindriks
- Department of Mathematics, Faculty of ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Tommy A. A. Broeders
- Department of Anatomy and Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Menno M. Schoonheim
- Department of Anatomy and Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Fernando Santos
- Dutch Institute for Emergent Phenomena (DIEP)Institute for Advanced Studies, University of AmsterdamAmsterdamThe Netherlands
- Korteweg de Vries Institute for MathematicsUniversity of AmsterdamAmsterdamthe Netherlands
| | - Wessel van Wieringen
- Department of Epidemiology and BiostatisticsAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Prejaas K. B. Tewarie
- Sir Peter Mansfield Imaging CenterSchool of Physics, University of NottinghamNottinghamUnited Kingdom
- Clinical Neurophysiology GroupUniversity of TwenteEnschedeThe Netherlands
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3
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Aarts J, Saddal SRD, Bosmans JE, de Groot V, de Jong BA, Klein M, Ruitenberg MFL, Schaafsma FG, Schippers ECF, Schoonheim MM, Uitdehaag BMJ, van der Veen S, Waskowiak PT, Widdershoven GAM, van der Hiele K, Hulst HE. Don't be late! Postponing cognitive decline and preventing early unemployment in people with multiple sclerosis: a study protocol. BMC Neurol 2024; 24:28. [PMID: 38225561 PMCID: PMC10789039 DOI: 10.1186/s12883-023-03513-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 12/17/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Up to 65% of people with multiple sclerosis (PwMS) develop cognitive deficits, which hampers their ability to work, participating in day-to-day life and ultimately reducing quality of life (QoL). Early cognitive symptoms are often less tangible to PwMS and their direct environment and are noticed only when symptoms and work functioning problems become more advanced, i.e., when (brain) damage is already advanced. Treatment of symptoms at a late stage can lead to cognitive impairment and unemployment, highlighting the need for preventative interventions in PwMS. AIMS This study aims to evaluate the (cost-) effectiveness of two innovative preventative interventions, aimed at postponing cognitive decline and work functioning problems, compared to enhanced usual care in improving health-related QoL (HRQoL). METHODS Randomised controlled trial including 270 PwMS with mild cognitive impairment, who have paid employment ≥ 12 h per week and are able to participate in physical exercise (Expanded Disability Status Scale < 6.0). Participants are randomised across three study arms: 1) 'strengthening the brain' - a lifestyle intervention combining personal fitness, mental coaching, dietary advice, and cognitive training; 2) 'strengthening the mind' - a work-focused intervention combining the capability approach and the participatory approach in one-on-one coaching by trained work coaches who have MS themselves; 3) Control group-receiving general information about cognitive impairment in MS and receiving care as usual. Intervention duration is four months, with short-term and long-term follow-up measurements at 10 and 16 months, respectively. The primary outcome measure of the Don't be late! intervention study will be HRQoL as measured with the 36-item Short Form. Secondary outcomes include cognition, work related outcomes, physical functioning, structural and functional brain changes, psychological functioning, and societal costs. Semi-structured interviews and focus groups with stakeholders will be organised to qualitatively reflect on the process and outcome of the interventions. DISCUSSION This study seeks to prevent (further) cognitive decline and job loss due to MS by introducing tailor-made interventions at an early stage of cognitive symptoms, thereby maintaining or improving HRQoL. Qualitative analyses will be performed to allow successful implementation into clinical practice. TRIAL REGISTRATION Retrospectively registered at ClinicalTrials.gov with reference number NCT06068582 on 10 October 2023.
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Affiliation(s)
- Jip Aarts
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Faculty of Social Sciences, Leiden University, Wassenaarseweg 52, Leiden, 2333 AK, The Netherlands.
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands.
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands.
| | - Shalina R D Saddal
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Faculty of Social Sciences, Leiden University, Wassenaarseweg 52, Leiden, 2333 AK, The Netherlands
- MS Center Amsterdam, Public and Occupational Health, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Judith E Bosmans
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Vincent de Groot
- MS Center Amsterdam, Rehabilitation Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Brigit A de Jong
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Martin Klein
- Medical Psychology, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Marit F L Ruitenberg
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Faculty of Social Sciences, Leiden University, Wassenaarseweg 52, Leiden, 2333 AK, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Frederieke G Schaafsma
- MS Center Amsterdam, Public and Occupational Health, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Esther C F Schippers
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Faculty of Social Sciences, Leiden University, Wassenaarseweg 52, Leiden, 2333 AK, The Netherlands
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Bernard M J Uitdehaag
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Sabina van der Veen
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Faculty of Social Sciences, Leiden University, Wassenaarseweg 52, Leiden, 2333 AK, The Netherlands
| | - Pauline T Waskowiak
- Medical Psychology, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Guy A M Widdershoven
- Ethics, Law & Medical Humanities, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Karin van der Hiele
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Faculty of Social Sciences, Leiden University, Wassenaarseweg 52, Leiden, 2333 AK, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Hanneke E Hulst
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Faculty of Social Sciences, Leiden University, Wassenaarseweg 52, Leiden, 2333 AK, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
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Wang Y, Duan Y, Wu Y, Zhuo Z, Zhang N, Han X, Zeng C, Chen X, Huang M, Zhu Y, Li H, Cao G, Sun J, Li Y, Zhou F, Li Y. Male and female are not the same: a multicenter study of static and dynamic functional connectivity in relapse-remitting multiple sclerosis in China. Front Immunol 2023; 14:1216310. [PMID: 37885895 PMCID: PMC10597802 DOI: 10.3389/fimmu.2023.1216310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 08/30/2023] [Indexed: 10/28/2023] Open
Abstract
Background Sex-related effects have been observed in relapsing-remitting multiple sclerosis (RRMS), but their impact on functional networks remains unclear. Objective To investigate the sex-related differences in connectivity strength and time variability within large-scale networks in RRMS. Methods This is a multi-center retrospective study. A total of 208 RRMS patients (135 females; 37.55 ± 11.47 years old) and 228 healthy controls (123 females; 36.94 ± 12.17 years old) were included. All participants underwent clinical and MRI assessments. Independent component analysis was used to extract resting-state networks (RSNs). We assessed the connectivity strength using spatial maps (SMs) and static functional network connectivity (sFNC), evaluated temporal properties and dynamic functional network connectivity (dFNC) patterns of RSNs using dFNC, and investigated their associations with structural damage or clinical variables. Results For static connectivity, only male RRMS patients displayed decreased SMs in the attention network and reduced sFNC between the sensorimotor network and visual or frontoparietal networks compared with healthy controls [P<0.05, false discovery rate (FDR) corrected]. For dynamic connectivity, three recurring states were identified for all participants: State 1 (sparse connected state; 42%), State 2 (middle-high connected state; 36%), and State 3 (high connected state; 16%). dFNC analyses suggested that altered temporal properties and dFNC patterns only occurred in females: female patients showed a higher fractional time (P<0.001) and more dwell time in State 1 (P<0.001) with higher transitions (P=0.004) compared with healthy females. Receiver operating characteristic curves revealed that the fraction time and mean dwell time of State 1 could significantly distinguish female patients from controls (area under the curve: 0.838-0.896). In addition, female patients with RRMS also mainly showed decreased dFNC in all states, particularly within cognitive networks such as the default mode, frontoparietal, and visual networks compared with healthy females (P < 0.05, FDR corrected). Conclusion Our results observed alterations in connectivity strength only in male patients and time variability in female patients, suggesting that sex-related effects may play an important role in the functional impairment and reorganization of RRMS.
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Affiliation(s)
- Yao Wang
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, Jiangxi, China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuling Wu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, Jiangxi, China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ningnannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xuemei Han
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, Jilin, China
| | - Chun Zeng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaoya Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Muhua Huang
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, Jiangxi, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, Jiangxi, China
| | - Haiqing Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Guanmei Cao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jie Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, Jiangxi, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
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5
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Yulug B, Ayyıldız B, Ayyıldız S, Sayman D, Salar AB, Cankaya S, Ozdemir Oktem E, Ozsimsek A, Kurt CC, Lakadamyalı H, Akturk A, Altay Ö, Hanoglu L, Velioglu HA, Mardinoglu A. Infection with COVID-19 is no longer a public emergency: But what about degenerative dementia? J Med Virol 2023; 95:e29072. [PMID: 37724347 DOI: 10.1002/jmv.29072] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/16/2023] [Accepted: 08/23/2023] [Indexed: 09/20/2023]
Abstract
Although no longer considered a public health threat, post-COVID cognitive syndrome continues to impact on a considerable proportion of individuals who were infected with COVID-19. Recent studies have also suggested that COVID may be represent a critical risk factor for the development of Alzheimer's disease (AD). We compared 17 COVID patients with 20 controls and evaluated the effects of COVID-19 on general cognitive performance, hippocampal volume, and connections using structural and seed-based connectivity analysis. We showed that COVID patients exhibited considerably worse cognitive functioning and increased hippocampal connectivity supported by the strong correlation between hippocampal connectivity and cognitive scores. Our findings of higher hippocampal connectivity with no observable hippocampal morphological changes even in mild COVID cases may be represent evidence of a prestructural compensatory mechanism for stimulating additional neuronal resources to combat cognitive dysfunction as recently shown for the prodromal stages of degenerative cognitive disorders. Our findings may be also important in light of recent data showing that other viral infections as well as COVID may constitute a critical risk factor for the development of AD. To our knowledge, this is the first study that investigated network differences in COVID patients, with a particular focus on compensatory hippocampal connectivity.
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Affiliation(s)
- Burak Yulug
- Department of Neurology and Neuroscience, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Behçet Ayyıldız
- Anatomy PhD Program, Graduate School of Health Sciences, Kocaeli University, Istanbul, Turkey
| | - Sevilay Ayyıldız
- Anatomy PhD Program, Graduate School of Health Sciences, Kocaeli University, Istanbul, Turkey
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-NIC Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Dila Sayman
- Department of Neurology and Neuroscience, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Ali Behram Salar
- Department of Neuroscience, Faculty of Medicine, Istanbul Medipol University, Istanbul, Türkiye
| | - Seyda Cankaya
- Department of Neurology and Neuroscience, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Ece Ozdemir Oktem
- Department of Neurology and Neuroscience, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Ahmet Ozsimsek
- Department of Neurology and Neuroscience, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Cagla Ceren Kurt
- Department of Physiotherapy, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Hatice Lakadamyalı
- Department of Radiology, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Aynur Akturk
- Department of Neurology and Neuroscience, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Özlem Altay
- KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Lutfu Hanoglu
- Department of Neurology, Istanbul Medipol University, Istanbul, Turkey
| | - Halil Aziz Velioglu
- Department of Neuroscience, Faculty of Medicine, Istanbul Medipol University, Istanbul, Türkiye
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York, USA
| | - Adil Mardinoglu
- KTH-Royal Institute of Technology, Stockholm, Sweden
- King's College London, Faculty of Dentistry, London, UK
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6
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De Rosa AP, Esposito F, Valsasina P, d'Ambrosio A, Bisecco A, Rocca MA, Tommasin S, Marzi C, De Stefano N, Battaglini M, Pantano P, Cirillo M, Tedeschi G, Filippi M, Gallo A. Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative. J Neurol 2023; 270:1047-1066. [PMID: 36350401 PMCID: PMC9886598 DOI: 10.1007/s00415-022-11479-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/11/2022]
Abstract
The Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates.
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Affiliation(s)
- Alessandro Pasquale De Rosa
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy.
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Alessandro d'Ambrosio
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Alvino Bisecco
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Silvia Tommasin
- Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università, 30, 00185, Rome, Italy
| | - Chiara Marzi
- Institute of Applied Physics "Nello Cararra" (IFAC), National Research Council (CNR), Via Madonna del Piano, 10, Sesto Fiorentino, 50019, Florence, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Patrizia Pantano
- Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università, 30, 00185, Rome, Italy
| | - Mario Cirillo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Gioacchino Tedeschi
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Antonio Gallo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
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7
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Strik M, Eijlers AJC, Dekker I, Broeders TAA, Douw L, Killestein J, Kolbe SC, Geurts JJG, Schoonheim MM. Sensorimotor network dynamics predict decline in upper and lower limb function in people with multiple sclerosis. Mult Scler 2023; 29:81-91. [PMID: 36177896 PMCID: PMC9896264 DOI: 10.1177/13524585221125372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Upper and lower limb disabilities are hypothesized to have partially independent underlying (network) disturbances in multiple sclerosis (MS). OBJECTIVE This study investigated functional network predictors and longitudinal network changes related to upper and lower limb progression in MS. METHODS Two-hundred fourteen MS patients and 58 controls underwent functional magnetic resonance imaging (fMRI), dexterity (9-Hole Peg Test) and mobility (Timed 25-Foot Walk) measurements (baseline and 5 years). Patients were stratified into progressors (>20% decline) or non-progressors. Functional network efficiency was calculated using static (over entire scan) and dynamic (fluctuations during scan) approaches. Baseline measurements were used to predict progression; significant predictors were explored over time. RESULTS In both limbs, progression was related to supplementary motor area and caudate efficiency (dynamic and static, respectively). Upper limb progression showed additional specific predictors; cortical grey matter volume, putamen static efficiency and posterior associative sensory (PAS) cortex, putamen, primary somatosensory cortex and thalamus dynamic efficiency. Additional lower limb predictors included motor network grey matter volume, caudate (dynamic) and PAS (static). Only the caudate showed a decline in efficiency over time in one group (non-progressors). CONCLUSION Disability progression can be predicted using sensorimotor network measures. Upper and lower limb progression showed unique predictors, possibly indicating different network disturbances underlying these types of progression in MS.
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Affiliation(s)
- Myrte Strik
- M Strik Melbourne Brain Centre Imaging Unit, Department of Radiology, University of Melbourne, Melbourne Medical School, Level 1, Kenneth Myer building, 30 Royal Parade, Melbourne, VIC 3010 Australia.
| | - Anand JC Eijlers
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Iris Dekker
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Tommy AA Broeders
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Linda Douw
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Joep Killestein
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Scott C Kolbe
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Jeroen JG Geurts
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
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8
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von Schwanenflug N, Koch SP, Krohn S, Broeders TAA, Lydon-Staley DM, Bassett DS, Schoonheim MM, Paul F, Finke C. Increased flexibility of brain dynamics in patients with multiple sclerosis. Brain Commun 2023; 5:fcad143. [PMID: 37188221 PMCID: PMC10176242 DOI: 10.1093/braincomms/fcad143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 03/08/2023] [Accepted: 04/28/2023] [Indexed: 05/17/2023] Open
Abstract
Patients with multiple sclerosis consistently show widespread changes in functional connectivity. Yet, alterations are heterogeneous across studies, underscoring the complexity of functional reorganization in multiple sclerosis. Here, we aim to provide new insights by applying a time-resolved graph-analytical framework to identify a clinically relevant pattern of dynamic functional connectivity reconfigurations in multiple sclerosis. Resting-state data from 75 patients with multiple sclerosis (N = 75, female:male ratio of 3:2, median age: 42.0 ± 11.0 years, median disease duration: 6 ± 11.4 years) and 75 age- and sex-matched controls (N = 75, female:male ratio of 3:2, median age: 40.2 ± 11.8 years) were analysed using multilayer community detection. Local, resting-state functional system and global levels of dynamic functional connectivity reconfiguration were characterized using graph-theoretical measures including flexibility, promiscuity, cohesion, disjointedness and entropy. Moreover, we quantified hypo- and hyper-flexibility of brain regions and derived the flexibility reorganization index as a summary measure of whole-brain reorganization. Lastly, we explored the relationship between clinical disability and altered functional dynamics. Significant increases in global flexibility (t = 2.38, PFDR = 0.024), promiscuity (t = 1.94, PFDR = 0.038), entropy (t = 2.17, PFDR = 0.027) and cohesion (t = 2.45, PFDR = 0.024) were observed in patients and were driven by pericentral, limbic and subcortical regions. Importantly, these graph metrics were correlated with clinical disability such that greater reconfiguration dynamics tracked greater disability. Moreover, patients demonstrate a systematic shift in flexibility from sensorimotor areas to transmodal areas, with the most pronounced increases located in regions with generally low dynamics in controls. Together, these findings reveal a hyperflexible reorganization of brain activity in multiple sclerosis that clusters in pericentral, subcortical and limbic areas. This functional reorganization was linked to clinical disability, providing new evidence that alterations of multilayer temporal dynamics play a role in the manifestation of multiple sclerosis.
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Affiliation(s)
- Nina von Schwanenflug
- Department of Neurology and Experimental Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10098, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin 10117, Germany
| | - Stefan P Koch
- Department of Experimental Neurology, Center for Stroke Research Berlin, Berlin 10117, Germany
- NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Stephan Krohn
- Department of Neurology and Experimental Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10098, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin 10117, Germany
| | - Tommy A A Broeders
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1007 MB, The Netherlands
| | - David M Lydon-Staley
- Annenberg School for Communication, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, PA, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia 19104, PA, USA
| | - Dani S Bassett
- Department of Biological Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, PA, USA
- Santa Fe Institute, Santa Fe 87501, NM, USA
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1007 MB, The Netherlands
| | - Friedemann Paul
- Department of Neurology and Experimental Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10098, Germany
- Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité—Universitätsmedizin Berlin, Berlin 10117, Germany
- NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10017, Germany
| | - Carsten Finke
- Correspondence to: Carsten Finke Charité - Universitätsklinikum Berlin Department of Neurology and Experimental Neurology Campus Mitte, Bonhoeffer Weg 3, 10098 Berlin, Germany E-mail:
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9
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Combes AJE, O'Grady KP, Rogers BP, Schilling KG, Lawless RD, Visagie M, Houston D, Prock L, Malone S, Satish S, Witt AA, McKnight CD, Bagnato F, Gore JC, Smith SA. Functional connectivity in the dorsal network of the cervical spinal cord is correlated with diffusion tensor imaging indices in relapsing-remitting multiple sclerosis. Neuroimage Clin 2022; 35:103127. [PMID: 35917721 PMCID: PMC9421501 DOI: 10.1016/j.nicl.2022.103127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 06/03/2022] [Accepted: 07/23/2022] [Indexed: 01/12/2023]
Abstract
Focal lesions may affect functional connectivity (FC) of the ventral and dorsal networks in the cervical spinal cord of people with relapsing-remitting multiple sclerosis (RRMS). Resting-state FC can be measured using functional MRI (fMRI) at 3T. This study sought to determine whether alterations in FC may be related to the degree of damage in the normal-appearing tissue. Tissue integrity and FC in the cervical spinal cord were assessed with diffusion tensor imaging (DTI) and resting-state fMRI, respectively, in a group of 26 RRMS participants with high cervical lesion load, low disability, and minimally impaired sensorimotor function, and healthy controls. Lower fractional anisotropy (FA) and higher radial diffusivity (RD) were observed in the normal-appearing white matter in the RRMS group relative to controls. Average FC in ventral and dorsal networks was similar between groups. Significant associations were found between higher FC in the dorsal sensory network and several DTI markers of pathology in the normal-appearing tissue. In the normal-appearing grey matter, dorsal FC was positively correlated with axial diffusivity (AD) (r = 0.46, p = 0.020) and mean diffusivity (MD) (r = 0.43, p = 0.032). In the normal-appearing white matter, dorsal FC was negatively correlated with FA (r = -0.43, p = 0.028) and positively correlated with RD (r = 0.49, p = 0.012), AD (r = 0.42, p = 0.037) and MD (r = 0.53, p = 0.006). These results suggest that increased connectivity, while remaining within the normal range, may represent a compensatory mechanism in response to structural damage in support of preserved sensory function in RRMS.
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Affiliation(s)
- Anna J E Combes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States.
| | - Kristin P O'Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States
| | - Richard D Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, PMB 351826, Nashville, TN 37235-1826, United States
| | - Mereze Visagie
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States
| | - Delaney Houston
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States
| | - Logan Prock
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States
| | - Shekinah Malone
- School of Medicine, Meharry Medical College, 1005 Dr. D. B. Todd, Jr. Blvd., Nashville, TN 37208, United States
| | - Sanjana Satish
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States
| | - Atlee A Witt
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States
| | - Colin D McKnight
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States
| | - Francesca Bagnato
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, 1161 21st Ave. South, A-0118 Medical Center North, Nashville, TN 37232, United States; Department of Neurology, Nashville VA Medical Center, TN Valley Healthcare System, 1310 24th Avenue South, Nashville, TN 37212-2637, United States
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, PMB 351826, Nashville, TN 37235-1826, United States
| | - Seth A Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States; Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, PMB 351826, Nashville, TN 37235-1826, United States
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10
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Schoonheim MM, Broeders TAA, Geurts JJG. The network collapse in multiple sclerosis: An overview of novel concepts to address disease dynamics. Neuroimage Clin 2022; 35:103108. [PMID: 35917719 PMCID: PMC9421449 DOI: 10.1016/j.nicl.2022.103108] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/01/2022] [Accepted: 07/10/2022] [Indexed: 11/16/2022]
Abstract
Multiple sclerosis is a neuroinflammatory and neurodegenerative disorder of the central nervous system that can be considered a network disorder. In MS, lesional pathology continuously disconnects structural pathways in the brain, forming a disconnection syndrome. Complex functional network changes then occur that are poorly understood but closely follow clinical status. Studying these structural and functional network changes has been and remains crucial to further decipher complex symptoms like cognitive impairment and physical disability. Recent insights especially implicate the importance of monitoring network hubs in MS, like the thalamus and default-mode network which seem especially hit hard. Such network insights in MS have led to the hypothesis that as the network continues to become disconnected and dysfunctional, exceeding a certain threshold of network efficiency loss leads to a "network collapse". After this collapse, crucial network hubs become rigid and overloaded, and at the same time a faster neurodegeneration and accelerated clinical (and cognitive) progression can be seen. As network neuroscience has evolved, the MS field can now move towards a clearer classification of the network collapse itself and specific milestone events leading up to it. Such an updated network-focused conceptual framework of MS could directly impact clinical decision making as well as the design of network-tailored rehabilitation strategies. This review therefore provides an overview of recent network concepts that have enhanced our understanding of clinical progression in MS, especially focusing on cognition, as well as new concepts that will likely move the field forward in the near future.
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Affiliation(s)
- Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Tommy A A Broeders
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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11
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James Bates RE, Browne E, Schalks R, Jacobs H, Tan L, Parekh P, Magliozzi R, Calabrese M, Mazarakis ND, Reynolds R. Lymphotoxin-alpha expression in the meninges causes lymphoid tissue formation and neurodegeneration. Brain 2022; 145:4287-4307. [PMID: 35776111 DOI: 10.1093/brain/awac232] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 05/24/2022] [Accepted: 06/17/2022] [Indexed: 11/14/2022] Open
Abstract
Organised meningeal immune cell infiltrates are suggested to play an important role in cortical grey matter pathology in the multiple sclerosis brain, but the mechanisms involved are as yet unresolved. Lymphotoxin-alpha plays a key role in lymphoid organ development and cellular cytotoxicity in the immune system and its expression is increased in the cerebrospinal fluid of naïve and progressive multiple sclerosis patients and post-mortem meningeal tissue. Here we show that persistently increased levels of lymphotoxin alpha in the cerebral meninges can give rise to lymphoid-like structures and underlying multiple sclerosis-like cortical pathology. Stereotaxic injections of recombinant lymphotoxin-alpha into the rat meninges led to acute meningeal inflammation and subpial demyelination that resolved after 28 days, with demyelination being dependent on prior sub-clinical immunisation with myelin oligodendrocyte glycoprotein. Injection of a lymphotoxin-alpha lentiviral vector into the cortical meningeal space, to produce chronic localised over-expression of the cytokine, induced extensive lymphoid-like immune cell aggregates, maintained over 3 months, including T-cell rich zones containing podoplanin+ fibroblastic reticular stromal cells and B-cell rich zones with a network of follicular dendritic cells, together with expression of lymphoid chemokines and their receptors. Extensive microglial and astroglial activation, subpial demyelination and marked neuronal loss occurred in the underlying cortical parenchyma. Whereas subpial demyelination was partially dependent on prior myelin oligodendrocyte glycoprotein immunisation, the neuronal loss was present irrespective of immunisation. Conditioned medium from LTα treated microglia was able to induce a reactive phenotype in astrocytes. Our results show that chronic lymphotoxin-alpha overexpression alone is sufficient to induce formation of meningeal lymphoid-like structures and subsequent neurodegeneration, similar to that seen in the progressive multiple sclerosis brain.
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Affiliation(s)
- Rachel E James Bates
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, Hammersmith, Hospital Campus, UK
| | - Eleanor Browne
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, Hammersmith, Hospital Campus, UK
| | - Renee Schalks
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, Hammersmith, Hospital Campus, UK
| | - Heather Jacobs
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, Hammersmith, Hospital Campus, UK
| | - Li Tan
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, Hammersmith, Hospital Campus, UK
| | - Puja Parekh
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, Hammersmith, Hospital Campus, UK
| | - Roberta Magliozzi
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, Hammersmith, Hospital Campus, UK.,Neurology Section, Department of Neurological and Movement Sciences, University of Verona, Verona 37134, Italy
| | - Massimiliano Calabrese
- Neurology Section, Department of Neurological and Movement Sciences, University of Verona, Verona 37134, Italy
| | - Nicholas D Mazarakis
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, Hammersmith, Hospital Campus, UK
| | - Richard Reynolds
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, Hammersmith, Hospital Campus, UK.,Centre for Molecular Neuropathology, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
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12
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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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13
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Broeders TA, Douw L, Eijlers AJ, Dekker I, Uitdehaag BM, Barkhof F, Hulst HE, Vinkers CH, Geurts JJ, Schoonheim MM. A more unstable resting-state functional network in cognitively declining multiple sclerosis. Brain Commun 2022; 4:fcac095. [PMID: 35620116 PMCID: PMC9128379 DOI: 10.1093/braincomms/fcac095] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/14/2022] [Accepted: 04/11/2022] [Indexed: 11/24/2022] Open
Abstract
Cognitive impairment is common in people with multiple sclerosis and strongly
affects their daily functioning. Reports have linked disturbed cognitive
functioning in multiple sclerosis to changes in the organization of the
functional network. In a healthy brain, communication between brain regions and
which network a region belongs to is continuously and dynamically adapted to
enable adequate cognitive function. However, this dynamic network adaptation has
not been investigated in multiple sclerosis, and longitudinal network data
remain particularly rare. Therefore, the aim of this study was to longitudinally
identify patterns of dynamic network reconfigurations that are related to the
worsening of cognitive decline in multiple sclerosis. Resting-state functional
MRI and cognitive scores (expanded Brief Repeatable Battery of
Neuropsychological tests) were acquired in 230 patients with multiple sclerosis
and 59 matched healthy controls, at baseline (mean disease duration: 15 years)
and at 5-year follow-up. A sliding-window approach was used for functional MRI
analyses, where brain regions were dynamically assigned to one of seven
literature-based subnetworks. Dynamic reconfigurations of subnetworks were
characterized using measures of promiscuity (number of subnetworks switched to),
flexibility (number of switches), cohesion (mutual switches) and disjointedness
(independent switches). Cross-sectional differences between cognitive groups and
longitudinal changes were assessed, as well as relations with structural damage
and performance on specific cognitive domains. At baseline, 23% of
patients were cognitively impaired (≥2/7 domains
Z < −2) and 18% were mildly
impaired (≥2/7 domains
Z < −1.5). Longitudinally,
28% of patients declined over time (0.25 yearly change on ≥2/7
domains based on reliable change index). Cognitively impaired patients displayed
more dynamic network reconfigurations across the whole brain compared with
cognitively preserved patients and controls, i.e. showing higher promiscuity
(P = 0.047), flexibility
(P = 0.008) and cohesion
(P = 0.008). Over time, cognitively
declining patients showed a further increase in cohesion
(P = 0.004), which was not seen in stable
patients (P = 0.544). More cohesion was
related to more severe structural damage (average
r = 0.166,
P = 0.015) and worse verbal memory
(r = −0.156,
P = 0.022), information processing speed
(r = −0.202,
P = 0.003) and working memory
(r = −0.163,
P = 0.017). Cognitively impaired multiple
sclerosis patients exhibited a more unstable network reconfiguration compared to
preserved patients, i.e. brain regions switched between subnetworks more often,
which was related to structural damage. This shift to more unstable network
reconfigurations was also demonstrated longitudinally in patients that showed
cognitive decline only. These results indicate the potential relevance of a
progressive destabilization of network topology for understanding cognitive
decline in multiple sclerosis.
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Affiliation(s)
- Tommy A.A. Broeders
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Linda Douw
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anand J.C. Eijlers
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Iris Dekker
- Departments of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bernard M.J. Uitdehaag
- Departments of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, UK
| | - Hanneke E. Hulst
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Christiaan H. Vinkers
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Departments of Psychiatry, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeroen J.G. Geurts
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno M. Schoonheim
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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14
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Backner Y, Zamir S, Petrou P, Paul F, Karussis D, Levin N. Anatomical and functional visual network patterns in progressive multiple sclerosis. Hum Brain Mapp 2021; 43:1590-1597. [PMID: 34931352 PMCID: PMC8886643 DOI: 10.1002/hbm.25744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/29/2021] [Accepted: 11/30/2021] [Indexed: 11/09/2022] Open
Abstract
The gradual accrual of disability over time in progressive multiple sclerosis is believed to be driven by widespread degeneration. Yet another facet of the problem may reside in the loss of the brain's ability to adapt to the damage incurred as the disease progresses. In this study, we attempted to examine whether changes associated with optic neuritis in the structural and functional visual networks can still be discerned in progressive patients even years after the acute insult. Forty-eight progressive multiple sclerosis patients, 21 with and 27 without prior optic neuritis, underwent structural and functional MRI, including DTI and resting state fMRI. Anatomical and functional visual networks were analyzed using graph theory-based methods. While no functional metrics were significantly different between the two groups, anatomical global efficiency and density were significantly lower in the optic neuritis group, despite no significant difference in lesion load between the groups. We conclude that long-standing distal damage to the optic nerve causes trans-synaptic effects and the early ability of the cortex to adapt may be altered, or possibly nullified. We suggest that this limited ability of the brain to compensate should be considered when attempting to explain the accumulation of disability in progressive multiple sclerosis patients.
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Affiliation(s)
- Yael Backner
- The fMRI Unit, Department of Neurology, Hadassah Medical Organization, Jerusalem, Israel.,The Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Sol Zamir
- The fMRI Unit, Department of Neurology, Hadassah Medical Organization, Jerusalem, Israel.,The Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Panayiota Petrou
- Multiple Sclerosis Center, Department of Neurology, Hadassah Medical Organization, Jerusalem, Israel
| | - Friedemann Paul
- NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Dimitrios Karussis
- Multiple Sclerosis Center, Department of Neurology, Hadassah Medical Organization, Jerusalem, Israel
| | - Netta Levin
- The fMRI Unit, Department of Neurology, Hadassah Medical Organization, Jerusalem, Israel.,The Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
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Motor Imagery of Walking in People Living with and without Multiple Sclerosis: A Cross-Sectional Comparison of Mental Chronometry. Brain Sci 2021; 11:brainsci11091131. [PMID: 34573154 PMCID: PMC8466525 DOI: 10.3390/brainsci11091131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 08/19/2021] [Accepted: 08/24/2021] [Indexed: 11/21/2022] Open
Abstract
Motor imagery represents the ability to simulate anticipated movements mentally prior to their actual execution and has been proposed as a tool to assess both individuals’ perception of task difficulty as well as their perception of their own abilities. People with multiple sclerosis (pwMS) often present with motor and cognitive dysfunction, which may negatively affect motor imagery. In this cross-sectional study, we explored differences in motor imagery of walking performance between pwMS (n = 20, age = 57.1 (SD = 8.6) years, 55% female) and age- and sex-matched healthy controls (n = 20, age = 58.1 (SD = 7.0) years, 60% female). Participants underwent mental chronometry assessments, a subset of motor imagery, which evaluated the difference between imagined and actual walking times across four walking tasks of increasing difficulty (i.e., large/narrow-width walkway with/without obstacles). Raw and absolute mental chronometry (A-MC) measures were recorded in single- (ST) and dual-task (DT) conditions. In ST conditions, pwMS had higher A-MC scores across all walking conditions (p ≤ 0.031, η2 ≥ 0.119), indicating lower motor imagery ability compared to healthy controls. During DT, all participants tended to underestimate their walking ability (3.38 ± 6.72 to 5.63 ± 9.17 s). However, after physical practice, pwMS were less able to adjust their imagined walking performance compared to healthy controls. In pwMS, A-MC scores were correlated with measures of balance confidence (ρ = −0.629, p < 0.01) and the self-reported expanded disability status scale (ρ = 0.747, p < 0.01). While the current study revealed that pwMS have lower motor imagery of walking performance compared to healthy individuals, further work is necessary to examine how the disassociation between mental chronometry and actual performance relates to quality of life and well-being.
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