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Simani L, Molaeipour L, Kian S, Leavitt VM. Correlation between cognitive changes and neuroradiological changes over time in multiple sclerosis: a systematic review and meta-analysis. J Neurol 2024; 271:5498-5518. [PMID: 38890188 DOI: 10.1007/s00415-024-12517-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 06/01/2024] [Accepted: 06/10/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND While many studies have examined relationships of neuroimaging variables to cognitive measures in multiple sclerosis (MS), longitudinal studies are lacking. The relationship of cognitive changes to neuroradiological changes in MS is thus incompletely understood. The present study systematically reviews all studies reporting a relationship between MRI changes and cognitive changes after at least one year of follow-up. METHOD An extensive and methodical search of online databases was conducted to identify qualified studies until August 2023. Among various cognitive tests and magnetic resonance imaging (MRI) measures, Symbol Digit Modalities Test (SDMT), Paced Auditory Serial Addition Test (PASAT), verbal fluency, T2 lesion volume (T2LV), white matter lesion volume (WML), and grey matter volume (GMV) qualified for inclusion in a meta-analysis investigating the association of cognitive changes to neuroradiological changes. RESULTS We identified 35 studies that explored the link between MRI changes and changes in cognitive outcomes. Of these, twenty studies (57.14%) investigated the association between SDMT/PASAT and MRI metrics. Eleven studies (31.42%) focused on the relationship between MRI metrics and verbal learning and memory, while ten studies (28.57%) reported associations with visuospatial learning and memory. Furthermore, eight studies (22.85%) analyzed the correlation between verbal fluency and MRI measures. Only 5 were eligible for inclusion in the meta-analysis. The meta-analysis evaluated correlations between SDMT/PASAT and GMV (rs = 0.67, 95% CI 0.44-0.91), and verbal fluency and T2LV (rs = 0.35, 95% CI 0.09-0.60). CONCLUSION In this rigorously conducted systematic review, we found a significant association of cognitive changes, specifically SDMT/PASAT and verbal fluency, to changes in T2LV and atrophy in individuals with MS. Findings should be interpreted cautiously due to the limited amount of high-quality research, small sample sizes, and variability in study methodologies.
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Affiliation(s)
- Leila Simani
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Leila Molaeipour
- Department of Biostatistics and Epidemiology, School of Health, Guilan University of Medical Sciences, Rasht, Iran
| | - Saeid Kian
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Victoria M Leavitt
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA.
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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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Zhang HQ, Lee JCY, Wang L, Cao P, Chan KH, Mak HKF. Dynamic Changes in Long-Standing Multiple Sclerosis Revealed by Longitudinal Structural Network Analysis Using Diffusion Tensor Imaging. AJNR Am J Neuroradiol 2024; 45:305-311. [PMID: 38302198 DOI: 10.3174/ajnr.a8115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/27/2023] [Indexed: 02/03/2024]
Abstract
BACKGROUND AND PURPOSE DTI can be used to derive conventional diffusion measurements, which can measure WM abnormalities in multiple sclerosis. DTI can also be used to construct structural brain networks and derive network measurements. However, few studies have compared their sensitivity in detecting brain alterations, especially in longitudinal studies. Therefore, in this study, we aimed to determine which type of measurement is more sensitive in tracking the dynamic changes over time in MS. MATERIALS AND METHODS Eighteen patients with MS were recruited at baseline and followed up at 6 and 12 months. All patients underwent MR imaging and clinical evaluation at 3 time points. Diffusion and network measurements were derived, and their brain changes were evaluated. RESULTS None of the conventional DTI measurements displayed statistically significant changes during the follow-up period; however, the nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part showed significant longitudinal changes between baseline and at 12 months, respectively. CONCLUSIONS The nodal degree, nodal efficiency, and nodal path length of the left middle frontal gyrus and bilateral inferior frontal gyrus, opercular part may be used to monitor brain changes over time in MS.
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Affiliation(s)
- Hui-Qin Zhang
- From the Department of Diagnostic Radiology (H.-Q.Z.), National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Diagnostic Radiology (H.-Q.Z., P.C., H.K.-F.M.), Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China
| | - Jacky Chi-Yan Lee
- Department of Medicine (J.C.-Y.L., K.-H.C.), Queen Mary Hospital, Hong Kong SAR, China
| | - Lu Wang
- Department of Health Technology and Informatics (L.W.), Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Peng Cao
- Department of Diagnostic Radiology (H.-Q.Z., P.C., H.K.-F.M.), Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China
| | - Koon-Ho Chan
- Department of Medicine (J.C.-Y.L., K.-H.C.), Queen Mary Hospital, Hong Kong SAR, China
- Alzheimer's Disease Research Network (H.K.-F.M., K.-H.C.), University of Hong Kong, Hong Kong SAR, China
| | - Henry Ka-Fung Mak
- Department of Diagnostic Radiology (H.-Q.Z., P.C., H.K.-F.M.), Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China
- Alzheimer's Disease Research Network (H.K.-F.M., K.-H.C.), University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Brain and Cognitive Sciences (H.K.-F.M.), University of Hong Kong, Hong Kong SAR, China
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Huang RR, Wu JJ, Shen J, Xing XX, Hua XY, Zheng MX, Xiao LB, Xu JG. Limbic system plasticity after electroacupuncture intervention in knee osteoarthritis rats. Neurosci Lett 2024; 820:137580. [PMID: 38072028 DOI: 10.1016/j.neulet.2023.137580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/16/2023] [Accepted: 12/05/2023] [Indexed: 12/25/2023]
Abstract
Knee osteoarthritis (KOA) is characterized by debilitating pain. Electroacupuncture (EA), a traditional Chinese medical therapy, has shown promise in KOA pain management. This study investigated the therapeutic potential of EA in KOA and its impact on limbic system neural plasticity. Sixteen rats were randomly assigned into two groups: EA group and sham-EA group. EA or sham-EA interventions were administered at acupoints ST32 (Futu) and ST36 (Zusanli) for three weeks. Post-intervention resting-state fMRI was scanned, assessing parameters including Amplitude of low frequency fluctuations (ALFF), regional homogeneity (ReHo), functional connectivity (FC) and nodal characterizations of network within limbic system. The results showed that EA was strategically directed towards the limbic system, resulting in discernible alterations in neural activity, FC, and network characteristics. Our findings demonstrate that EA had a significant impact on the limbic system neural plasticity in rats with KOA, presenting a novel nonpharmacological approach for KOA treatment.
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Affiliation(s)
- Rong-Rong Huang
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China; School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Jun Shen
- Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Arthritis Institute of Integrated Traditional Chinese and Western Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Xiang-Xin Xing
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Lian-Bo Xiao
- Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Arthritis Institute of Integrated Traditional Chinese and Western Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Jian-Guang Xu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China; School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China.
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Shahbodaghy F, Shafaghi L, Rostampour M, Rostampour A, Kolivand P, Gharaylou Z. Symmetry differences of structural connectivity in multiple sclerosis and healthy state. Brain Res Bull 2023; 205:110816. [PMID: 37972899 DOI: 10.1016/j.brainresbull.2023.110816] [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: 04/13/2023] [Revised: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 11/19/2023]
Abstract
Focal and diffuse cerebral damages occur in Multiple Sclerosis (MS) that promotes profound shifts in local and global structural connectivity parameters, mainly derived from diffusion tensor imaging. Most of the reconstruction analyses have applied conventional tracking algorithms largely based on the controversial streamline count. For a more credible explanation of the diffusion MRI signal, we used convex optimization modeling for the microstructure-informed tractography2 (COMMIT2) framework. All multi-shell diffusion data from 40 healthy controls (HCs) and 40 relapsing-remitting MS (RRMS) patients were transformed into COMMIT2-weighted matrices based on the Schefer-200 parcels atlas (7 networks) and 14 bilateral subcortical regions. The success of the classification process between MS and healthy state was efficiently predicted by the left DMN-related structures and visual network-associated pathways. Additionally, the lesion volume and age of onset were remarkably correlated with the components of the left DMN. Using complementary approaches such as global metrics revealed differences in WM microstructural integrity between MS and HCs (efficiency, strength). Our findings demonstrated that the cutting-edge diffusion MRI biomarkers could hold the potential for interpreting brain abnormalities in a more distinctive way.
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Affiliation(s)
- Fatemeh Shahbodaghy
- Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran
| | - Lida Shafaghi
- Department of Neuroscience, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Massoumeh Rostampour
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Ali Rostampour
- Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran
| | - Pirhossein Kolivand
- Department of Health Economics, School of Medicine, Shahed University, Tehran, Iran
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Azarmi F, Shalbaf A, Miri Ashtiani SN, Behnam H, Daliri MR. Early MS Identification Using Non-linear Functional Connectivity and Graph-theoretic Measures of Cognitive Task-fMRI Data. Basic Clin Neurosci 2023; 14:787-804. [PMID: 39070191 PMCID: PMC11273198 DOI: 10.32598/bcn.14.6.2034.4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 01/05/2023] [Accepted: 03/24/2023] [Indexed: 07/30/2024] Open
Abstract
Introduction Functional neuroimaging has developed a fundamental ground for understanding the physical basis of the brain. Recent studies have extracted invaluable information from the underlying substrate of the brain. However, cognitive deficiency has insufficiently been assessed by researchers in multiple sclerosis (MS). Therefore, extracting the brain network differences among relapsing-remitting MS (RRMS) patients and healthy controls as biomarkers of cognitive task functional magnetic resonance imaging (fMRI) data and evaluating such biomarkers using machine learning were the aims of this study. Methods In order to activate cognitive functions of the brain, blood-oxygen-level-dependent (BOLD) data were collected throughout the application of a cognitive task. Accordingly, a nonlinear-based brain network was established using kernel mutual information based on the automated anatomical labeling atlas (AAL). Subsequently, a statistical test was carried out to determine the variation in brain network measures between the two groups on binary adjacency matrices. We also found the prominent graph features by merging the Wilcoxon rank-sum test with the Fisher score as a hybrid feature selection method. Results The results of the classification performance measures showed that the construction of a brain network using a new nonlinear connectivity measure in task-fMRI performs better than the linear connectivity measures in terms of classification. The Wilcoxon rank-sum test also demonstrated a superior result for clinical applications. Conclusion We believe that non-linear connectivity measures, like KMI, outperform linear connectivity measures, like correlation coefficient in finding the biomarkers of MS disease according to classification performance metrics. Highlights The performance of some brain regions (the hippocampus, parahippocampus, cuneus, pallidum, and two segments of the cerebellum) is different between healthy and MS people.Non-linear connectivity measures, such as Kernel mutual information, perform better than linear connectivity measures, such as correlation coefficient, in finding the biomarkers of MS disease. Plain Language Summary Multiple sclerosis (MS) can disrupt the function of the central nervous system. The function of brain network is impaired in these patients. In this study, we evaluated the change in brain network based on a non-linear connectivity measure using cognitive task-based fMRI data between MS patients and healthy controls. We used Kernel mutual information (KMI) and designed a graph network based on the results of connectivity analysis. The the paced auditory serial addition test was used to activate cognitive functions of the brain. The classification was employed for the results using different decision tree -based technique and support vector machine. KMI can be considered a valid measure of connectivity over linear measures, like the correlation coefficient. KMI does not have the drawbacks of mutual information technique. However, further studies should be implemented on brain data of MS patients to draw more definite conclusions.
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Affiliation(s)
- Farzad Azarmi
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyedeh Naghmeh Miri Ashtiani
- Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran
| | - Hamid Behnam
- Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran
| | - Mohammad Reza Daliri
- Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran
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Carter SL, Patel R, Fisk JD, Figley CR, Marrie RA, Mazerolle EL, Uddin MN, Wong K, Graff LA, Bolton JM, Marriott JJ, Bernstein CN, Kornelsen J. Differences in resting state functional connectivity relative to multiple sclerosis and impaired information processing speed. Front Neurol 2023; 14:1250894. [PMID: 37928146 PMCID: PMC10625423 DOI: 10.3389/fneur.2023.1250894] [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: 06/30/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Background Fifty-one percent of individuals with multiple sclerosis (MS) develop cognitive impairment (CI) in information processing speed (IPS). Although IPS scores are associated with health and well-being, neural changes that underlie IPS impairments in MS are not understood. Resting state fMRI can provide insight into brain function changes underlying impairment in persons with MS. Objectives We aimed to assess functional connectivity (FC) differences in (i) persons with MS compared to healthy controls (HC), (ii) persons with both MS and CI (MS-CI) compared to HC, (iii) persons with MS that are cognitively preserved (MS-CP) compared to HC, (iv) MS-CI compared to MS-CP, and (v) in relation to cognition within the MS group. Methods We included 107 participants with MS (age 49.5 ± 12.9, 82% women), and 94 controls (age 37.9 ± 15.4, 66% women). Each participant was administered the Symbol Digit Modalities Test (SDMT) and underwent a resting state fMRI scan. The MS-CI group was created by applying a z-score cut-off of ≤ -1.5 to locally normalized SDMT scores. The MS-CP group was created by applying a z-score of ≥0. Control groups (HCMS-CI and HCMS-CP) were based on the nearest age-matched HC participants. A whole-brain ROI-to-ROI analysis was performed followed by specific contrasts and a regression analysis. Results Individuals with MS showed FC differences compared to HC that involved the cerebellum, visual and language-associated brain regions, and the thalamus, hippocampus, and basal ganglia. The MS-CI showed FC differences compared to HCMS-CI that involved the cerebellum, visual and language-associated areas, thalamus, and caudate. SDMT scores were correlated with FC between the cerebellum and lateral occipital cortex in MS. No differences were observed between the MS-CP and HCMS-CP or MS-CI and MS-CP groups. Conclusion Our findings emphasize FC changes of cerebellar, visual, and language-associated areas in persons with MS. These differences were apparent for (i) all MS participants compared to HC, (ii) MS-CI subgroup and their matched controls, and (iii) the association between FC and SDMT scores within the MS group. Our findings strongly suggest that future work that examines the associations between FC and IPS impairments in MS should focus on the involvement of these regions.
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Affiliation(s)
- Sean L. Carter
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg Health Sciences Centre, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Winnipeg Health Sciences Centre, Winnipeg, MB, Canada
| | - Ronak Patel
- Department of Clinical Health Psychology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - John D. Fisk
- Nova Scotia Health and the Departments of Psychiatry, Psychology & Neuroscience, and Medicine, Dalhousie University, Halifax, NS, Canada
| | - Chase R. Figley
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg Health Sciences Centre, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Winnipeg Health Sciences Centre, Winnipeg, MB, Canada
- Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Departments of Physiology and Pathophysiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Ruth Ann Marrie
- Department of Internal Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Erin L. Mazerolle
- Department of Psychology, St. Francis Xavier University, Antigonish, NS, Canada
| | - Md Nasir Uddin
- Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Department of Neurology, School of Medicine & Dentistry, University of Rochester, Rochester, NY, United States
- Department of Biomedical Engineering, Hajim School of Engineering & Applied Sciences, University of Rochester, Rochester, NY, United States
| | - Kaihim Wong
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg Health Sciences Centre, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Winnipeg Health Sciences Centre, Winnipeg, MB, Canada
- Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Lesley A. Graff
- Department of Clinical Health Psychology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - James M. Bolton
- Department of Psychiatry, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - James J. Marriott
- Department of Internal Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Charles N. Bernstein
- Department of Internal Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Jennifer Kornelsen
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg Health Sciences Centre, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Winnipeg Health Sciences Centre, Winnipeg, MB, Canada
- Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Departments of Physiology and Pathophysiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
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9
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Wenger AL, Barakovic M, Bosticardo S, Schaedelin S, Daducci A, Schiavi S, Weigel M, Rahmanzadeh R, Lu PJ, Cagol A, Kappos L, Kuhle J, Calabrese P, Granziera C. An investigation of the association between focal damage and global network properties in cognitively impaired and cognitively preserved patients with multiple sclerosis. Front Neurosci 2023; 17:1007580. [PMID: 36824214 PMCID: PMC9941549 DOI: 10.3389/fnins.2023.1007580] [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: 01/19/2023] [Indexed: 02/09/2023] Open
Abstract
Introduction The presence of focal cortical and white matter damage in patients with multiple sclerosis (pwMS) might lead to specific alterations in brain networks that are associated with cognitive impairment. We applied microstructure-weighted connectomes to investigate (i) the relationship between global network metrics and information processing speed in pwMS, and (ii) whether the disruption provoked by focal lesions on global network metrics is associated to patients' information processing speed. Materials and methods Sixty-eight pwMS and 92 healthy controls (HC) underwent neuropsychological examination and 3T brain MRI including multishell diffusion (dMRI), 3D FLAIR, and MP2RAGE. Whole-brain deterministic tractography and connectometry were performed on dMRI. Connectomes were obtained using the Spherical Mean Technique and were weighted for the intracellular fraction. We identified white matter lesions and cortical lesions on 3D FLAIR and MP2RAGE images, respectively. PwMS were subdivided into cognitively preserved (CPMS) and cognitively impaired (CIMS) using the Symbol Digit Modalities Test (SDMT) z-score at cut-off value of -1.5 standard deviations. Statistical analyses were performed using robust linear models with age, gender, and years of education as covariates, followed by correction for multiple testing. Results Out of 68 pwMS, 18 were CIMS and 50 were CPMS. We found significant changes in all global network metrics in pwMS vs HC (p < 0.05), except for modularity. All global network metrics were positively correlated with SDMT, except for modularity which showed an inverse correlation. Cortical, leukocortical, and periventricular lesion volumes significantly influenced the relationship between (i) network density and information processing speed and (ii) modularity and information processing speed in pwMS. Interestingly, this was not the case, when an exploratory analysis was performed in the subgroup of CIMS patients. Discussion Our study showed that cortical (especially leukocortical) and periventricular lesions affect the relationship between global network metrics and information processing speed in pwMS. Our data also suggest that in CIMS patients increased focal cortical and periventricular damage does not linearly affect the relationship between network properties and SDMT, suggesting that other mechanisms (e.g. disruption of local networks, loss of compensatory processes) might be responsible for the development of processing speed deficits.
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Affiliation(s)
- A. L. Wenger
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel, University of Basel, Basel, Switzerland,Interdisciplinary Platform, Psychiatry, and Psychology, Division of Molecular and Cognitive Neuroscience, Neuropsychology, and Behavioral Neurology Unit, University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel, University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland
| | - Sara Bosticardo
- Department of Computer Science, University of Verona, Verona, Italy
| | - Sabine Schaedelin
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel, University of Basel, Basel, Switzerland,Clinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | | | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel, University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland,Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Reza Rahmanzadeh
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel, University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel, University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland
| | - Alessandro Cagol
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel, University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel, University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland
| | - Pasquale Calabrese
- Interdisciplinary Platform, Psychiatry, and Psychology, Division of Molecular and Cognitive Neuroscience, Neuropsychology, and Behavioral Neurology Unit, University of Basel, Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel, University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland,*Correspondence: Cristina Granziera, ;
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10
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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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11
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Has Silemek AC, Nolte G, Pöttgen J, Engel AK, Heesen C, Gold SM, Stellmann JP. Topological reorganization of brain network might contribute to the resilience of cognitive functioning in mildly disabled relapsing remitting multiple sclerosis. J Neurosci Res 2023; 101:143-161. [PMID: 36263462 DOI: 10.1002/jnr.25135] [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: 10/18/2021] [Revised: 09/28/2022] [Accepted: 10/05/2022] [Indexed: 11/08/2022]
Abstract
Multiple sclerosis (MS) is an inflammatory and demyelinating disease which leads to impairment in several functional systems including cognition. Alteration of brain networks is linked to disability and its progression. However, results are mostly cross-sectional and yet contradictory as putative adaptive and maladaptive mechanisms were found. Here, we aimed to explore longitudinal reorganization of brain networks over 2-years by combining diffusion tensor imaging (DTI), resting-state functional MRI (fMRI), magnetoencephalography (MEG), and a comprehensive neuropsychological-battery. In 37 relapsing-remitting MS (RRMS) and 39 healthy-controls, cognition remained stable over-time. We reconstructed network models based on the three modalities and analyzed connectivity in relation to the hierarchical topology and functional subnetworks. Network models were compared across modalities and in their association with cognition using linear-mixed-effect-regression models. Loss of hub connectivity and global reduction was observed on a structural level over-years (p < .010), which was similar for functional MEG-networks but not for fMRI-networks. Structural hub connectivity increased in controls (p = .044), suggesting a physiological mechanism of healthy aging. Despite a general loss in structural connectivity in RRMS, hub connectivity was preserved (p = .002) over-time in default-mode-network (DMN). MEG-networks were similar to DTI and weakly correlated with fMRI in MS (p < .050). Lower structural (β between .23-.33) and both lower (β between .40-.59) and higher functional connectivity (β = -.54) in DMN was associated with poorer performance in attention and memory in RRMS (p < .001). MEG-networks involved no association with cognition. Here, cognitive stability despite ongoing neurodegeneration might indicate a resilience mechanism of DMN hubs mimicking a physiological reorganization observed in healthy aging.
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Affiliation(s)
- Arzu Ceylan Has Silemek
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Guido Nolte
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jana Pöttgen
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Andreas K Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph Heesen
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan M Gold
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health (BIH), Klinik für Psychiatrie & Psychotherapie und Medizinische Klinik m.S. Psychosomatik, Campus Benjamin Franklin (CBF), Berlin, Germany
| | - Jan-Patrick Stellmann
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,APHM, Hopital de la Timone, CEMEREM, Marseille, France.,Aix-Marseille Université, CNRS, CRMBM, UMR 7339, Marseille, France
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12
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Schoonheim MM, Broeders TAA, Geurts JJG. The network collapse in multiple sclerosis: An overview of novel concepts to address disease dynamics. Neuroimage Clin 2022; 35:103108. [PMID: 35917719 PMCID: PMC9421449 DOI: 10.1016/j.nicl.2022.103108] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/01/2022] [Accepted: 07/10/2022] [Indexed: 11/16/2022]
Abstract
Multiple sclerosis (MS) can be considered as a network disorder. This review discusses network concepts in order to understand progression in MS. Damage is hypothesized to lead to a “network collapse” and clinical progression. New concepts are discussed that will likely influence the field in the near future. These include brain wiring, how regions communicate and robustness to damage.
Multiple sclerosis is a neuroinflammatory and neurodegenerative disorder of the central nervous system that can be considered a network disorder. In MS, lesional pathology continuously disconnects structural pathways in the brain, forming a disconnection syndrome. Complex functional network changes then occur that are poorly understood but closely follow clinical status. Studying these structural and functional network changes has been and remains crucial to further decipher complex symptoms like cognitive impairment and physical disability. Recent insights especially implicate the importance of monitoring network hubs in MS, like the thalamus and default-mode network which seem especially hit hard. Such network insights in MS have led to the hypothesis that as the network continues to become disconnected and dysfunctional, exceeding a certain threshold of network efficiency loss leads to a “network collapse”. After this collapse, crucial network hubs become rigid and overloaded, and at the same time a faster neurodegeneration and accelerated clinical (and cognitive) progression can be seen. As network neuroscience has evolved, the MS field can now move towards a clearer classification of the network collapse itself and specific milestone events leading up to it. Such an updated network-focused conceptual framework of MS could directly impact clinical decision making as well as the design of network-tailored rehabilitation strategies. This review therefore provides an overview of recent network concepts that have enhanced our understanding of clinical progression in MS, especially focusing on cognition, as well as new concepts that will likely move the field forward in the near future.
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Affiliation(s)
- Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Tommy A A Broeders
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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13
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Manglani HR, Fountain-Zaragoza S, Shankar A, Nicholas JA, Prakash RS. Employing Connectome-Based Models to Predict Working Memory in Multiple Sclerosis. Brain Connect 2022; 12:502-514. [PMID: 34309408 PMCID: PMC10039278 DOI: 10.1089/brain.2021.0037] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Introduction: Individuals with multiple sclerosis (MS) are vulnerable to deficits in working memory (WM), but the search for neural correlates of WM within circumscribed areas has been inconclusive. Given the widespread neural alterations observed in MS, predictive modeling approaches that capitalize on whole-brain connectivity may better capture individual differences in WM. Materials and Methods: We applied connectome-based predictive modeling to functional magnetic resonance imaging data from WM tasks in two independent samples with relapsing-remitting MS. In the internal sample (ninternal = 36), cross-validation was used to train a model to predict accuracy on the Paced Visual Serial Addition Test from functional connectivity. We hypothesized that this MS-specific model would successfully predict performance on the N-back task in the validation cohort (nvalidation = 36). In addition, we assessed the generalizability of existing WM networks derived in healthy young adults to these samples, and we explored anatomical differences between the healthy and MS networks. Results: We successfully derived an MS-specific predictive model of WM in the internal sample (full: rs = 0.47, permuted p = 0.011), but the predictions were not significant in the validation cohort (rs = -0.047; p = 0.78, mean squared error [MSE] = 0.006, R2 = -2.21%). In contrast, the healthy networks successfully predicted WM in both MS samples (internal: rs = 0.33 p = 0.049, MSE = 0.009, R2 = 13.4%; validation cohort: rs = 0.46, p = 0.005, MSE = 0.005, R2 = 16.9%), demonstrating their translational potential. Discussion: Functional networks identified in a large sample of healthy individuals predicted significant variance in WM in MS. Networks derived in small samples of people with MS may have limited generalizability, potentially due to disease-related heterogeneity. The robustness of models derived in large clinical samples warrants further investigation. ClinicalTrials.gov ID: NCT03244696.
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Affiliation(s)
- Heena R Manglani
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, USA
| | - Stephanie Fountain-Zaragoza
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, USA
| | - Anita Shankar
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, USA
| | | | - Ruchika Shaurya Prakash
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, USA
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14
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Brummer T, Muthuraman M, Steffen F, Uphaus T, Minch L, Person M, Zipp F, Groppa S, Bittner S, Fleischer V. Improved prediction of early cognitive impairment in multiple sclerosis combining blood and imaging biomarkers. Brain Commun 2022; 4:fcac153. [PMID: 35813883 PMCID: PMC9263885 DOI: 10.1093/braincomms/fcac153] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 02/28/2022] [Accepted: 06/17/2022] [Indexed: 12/30/2022] Open
Abstract
Disability in multiple sclerosis is generally classified by sensory and motor symptoms, yet cognitive impairment has been identified as a frequent manifestation already in the early disease stages. Imaging- and more recently blood-based biomarkers have become increasingly important for understanding cognitive decline associated with multiple sclerosis. Thus, we sought to determine the prognostic utility of serum neurofilament light chain levels alone and in combination with MRI markers by examining their ability to predict cognitive impairment in early multiple sclerosis. A comprehensive and detailed assessment of 152 early multiple sclerosis patients (Expanded Disability Status Scale: 1.3 ± 1.2, mean age: 33.0 ± 10.0 years) was performed, which included serum neurofilament light chain measurement, MRI markers (i.e. T2-hyperintense lesion volume and grey matter volume) acquisition and completion of a set of cognitive tests (Symbol Digits Modalities Test, Paced Auditory Serial Addition Test, Verbal Learning and Memory Test) and mood questionnaires (Hospital Anxiety and Depression scale, Fatigue Scale for Motor and Cognitive Functions). Support vector regression, a branch of unsupervised machine learning, was applied to test serum neurofilament light chain and combination models of biomarkers for the prediction of neuropsychological test performance. The support vector regression results were validated in a replication cohort of 101 early multiple sclerosis patients (Expanded Disability Status Scale: 1.1 ± 1.2, mean age: 34.4 ± 10.6 years). Higher serum neurofilament light chain levels were associated with worse Symbol Digits Modalities Test scores after adjusting for age, sex Expanded Disability Status Scale, disease duration and disease-modifying therapy (B = −0.561; SE = 0.192; P = 0.004; 95% CI = −0.940 to −0.182). Besides this association, serum neurofilament light chain levels were not linked to any other cognitive or mood measures (all P-values > 0.05). The tripartite combination of serum neurofilament light chain levels, lesion volume and grey matter volume showed a cross-validated accuracy of 88.7% (90.8% in the replication cohort) in predicting Symbol Digits Modalities Test performance in the support vector regression approach, and outperformed each single biomarker (accuracy range: 68.6–75.6% and 68.9–77.8% in the replication cohort), as well as the dual biomarker combinations (accuracy range: 71.8–82.3% and 72.6–85.6% in the replication cohort). Taken together, early neuro-axonal loss reflects worse information processing speed, the key deficit underlying cognitive dysfunction in multiple sclerosis. Our findings demonstrate that combining blood and imaging measures improves the accuracy of predicting cognitive impairment, highlighting the clinical utility of cross-modal biomarkers in multiple sclerosis.
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Affiliation(s)
- Tobias Brummer
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz , Langenbeckstr, 1, Mainz 55131 , Germany
| | - Muthuraman Muthuraman
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz , Langenbeckstr, 1, Mainz 55131 , Germany
| | - Falk Steffen
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz , Langenbeckstr, 1, Mainz 55131 , Germany
| | - Timo Uphaus
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz , Langenbeckstr, 1, Mainz 55131 , Germany
| | - Lena Minch
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz , Langenbeckstr, 1, Mainz 55131 , Germany
| | - Maren Person
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz , Langenbeckstr, 1, Mainz 55131 , Germany
| | - Frauke Zipp
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz , Langenbeckstr, 1, Mainz 55131 , Germany
| | - Sergiu Groppa
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz , Langenbeckstr, 1, Mainz 55131 , Germany
| | - Stefan Bittner
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz , Langenbeckstr, 1, Mainz 55131 , Germany
| | - Vinzenz Fleischer
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz , Langenbeckstr, 1, Mainz 55131 , Germany
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15
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Pang L, Fan B, Chen Z, Chen Z, Lv C, Zheng J. Disruption of Cerebellar–Cerebral Functional Connectivity in Temporal Lobe Epilepsy and the Connection to Language and Cognitive Functions. Front Neurosci 2022; 16:871128. [PMID: 35837122 PMCID: PMC9273908 DOI: 10.3389/fnins.2022.871128] [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: 02/07/2022] [Accepted: 05/27/2022] [Indexed: 01/26/2023] Open
Abstract
ObjectiveTo investigate the changes in the cerebellar-cerebral language network in temporal lobe epilepsy (TLE) patients from the cerebellar perspective, the research analyzes the changes of language and cognitive network in terms of functional connectivity (FC), as well as their efficiency of the reorganization were evaluated basing on relationship between the network metrics and neuropsychological scale scores.Methods30 TLE patients and 30 healthy controls were recruited. Brain activity was evaluated by voxel-mirrored homotopic connectivity analysis (VMHC). Two groups were analyzed and compared in terms of language FC using the following methods: Seed-to-Voxel analysis, pairwise correlations [region of interest(ROI)-to-ROI] and graph theory. Correlation analysis was performed between network properties and neuropsychological score.ResultsCompared with healthy participants, VMHC values in the Cerebellum Anterior Lobe, Frontal Lobe, Frontal_Sup_R/L, Cingulum_Ant_R/L, and Cingulum_Mid_R/L were decreased in TLE patients. Decreased FC was observed from the Cerebelum_10_R to the left inferior frontal gyrus, from the Cerebelum_6_R to the left Lingual Gyrus, from the Cerebelum_4_5_R to left Lingual Gyrus, left Cuneal Cortex and Precuneous Cortex, from the Cerebelum_3_R to Brain-Stem, and from the Cerebelum_Crus1_L to Cerebelum_6_R in TLE patients. The FC was enhanced between bilateral Cingulum_Mid and angular gyrus and frontoparietal insular cranium, between Frontal_Sup_Med L and left/right superior temporal gyrus (pSTG l/r), while it was decreased between left middle temporal gyrus and pSTG l/r. Compared with controls, the Betweenness Centrality (BC) of the right superior marginal gyrus (SMG), Temporal_Pole_Mid_R and Temporal_Mid_L as well as the Degree Centrality (DC) and Nodal Efficiency (NE) of the right SMG were lower in TLE patients. Further analysis showed that decreased VMHC in bilateral Cerebellum Anterior Lobe was positively correlated with the Boston Naming Test score in TLE patients, but it was negatively correlated with the Verbal Fluency Test score. The NE and DC of SMG_R were both negatively correlated with visual perception score in Montreal Cognitive Assessment.ConclusionOur results suggest that presence of abnormalities in the static functional connectivity and the language and cognitive network of TLE patients. Cerebellum potentially represents an intervention target for delaying or improving language and cognitive deficits in patients with TLE.
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16
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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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17
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Leavitt VM, Dworkin JD, Buyukturkoglu K, Riley CS, Ritchey M. Summary metrics of memory subnetwork functional connectivity alterations in multiple sclerosis. Mult Scler 2022; 28:1963-1972. [PMID: 35658737 DOI: 10.1177/13524585221099169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Memory dysfunction is common in multiple sclerosis (MS); mechanistic understanding of its causes is lacking. Large-scale network resting-state functional connectivity (RSFC) is sensitive to memory dysfunction. OBJECTIVE We derived and tested summary metrics of memory network RSFC. METHODS Cognitive data and 3T magnetic resonance imaging (MRI) scans were collected from 235 MS patients and 35 healthy controls (HCs). Index scores were calculated as RSFC within (anteriority index, AntI) and between (integration index, IntI) dorsomedial anterior temporal and medial temporal memory subnetworks. Group differences in index expression were evaluated. Associations between index scores and memory/non-memory cognition were evaluated; relationships between T2 lesion volume (T2LV) and index scores were assessed. RESULTS Index scores were related to memory and T2LV in MS patients, who showed marginally elevated AntI relative to HC (p = 0.06); no group differences were found for IntI. Better memory was associated with higher AntI (β = 0.15, p = 0.018) and IntI (β = 0.16, p = 0.014). No associations were found for non-memory cognition. Higher T2LV was associated with higher AntI and IntI; exploratory mediation analysis revealed significant inconsistent mediation, that is, higher index scores partially suppressed the negative association between T2LV and memory. CONCLUSION Summary, within-subject metrics permit replication and circumvent challenges of traditional (incommensurate) RSFC variables to advance development of mechanistic models of memory dysfunction in MS.
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Affiliation(s)
- Victoria M Leavitt
- Translational Cognitive Neuroscience Laboratory, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA/Multiple Sclerosis Center, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jordan D Dworkin
- Department of Psychiatry, Columbia University and the New York State Psychiatric Institute, New York, NY, USA
| | - Korhan Buyukturkoglu
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Claire S Riley
- Multiple Sclerosis Center, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA/Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Maureen Ritchey
- Department of Psychology, Boston College, Chestnut Hill, MA, USA
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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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Disrupted structural network of inferomedial temporal regions in relapsing-remitting multiple sclerosis compared with neuromyelitis optica spectrum disorder. Sci Rep 2022; 12:5152. [PMID: 35338192 PMCID: PMC8956623 DOI: 10.1038/s41598-022-09065-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 03/09/2022] [Indexed: 11/08/2022] Open
Abstract
Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are two representative chronic inflammatory demyelinating disorders of the central nervous system. We aimed to determine and compare the alterations of white matter (WM) connectivity between MS, NMOSD, and healthy controls (HC). This study included 68 patients with relapsing–remitting MS, 50 with NMOSD, and 26 HC. A network-based statistics method was used to assess disrupted patterns in WM networks. Topological characteristics of the three groups were compared and their associations with clinical parameters were examined. WM network analysis indicated that the MS and NMOSD groups had lower total strength, clustering coefficient, global efficiency, and local efficiency and had longer characteristic path length than HC, but there were no differences between the MS and NMOSD groups. At the nodal level, the MS group had more brain regions with altered network topologies than did the NMOSD group when compared with the HC group. Network alterations were correlated with Expanded Disability Status Scale score and disease duration in both MS and NMOSD groups. Two distinct subnetworks that characterized the disease groups were also identified. When compared with NMOSD, the most discriminative connectivity changes in MS were located between the thalamus, hippocampus, parahippocampal gyrus, amygdala, fusiform gyrus, and inferior and superior temporal gyri. In conclusion, MS patients had greater network dysfunction compared to NMOSD and altered short connections within the thalamus and inferomedial temporal regions were relatively spared in NMOSD compared with MS.
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20
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Gregorich M, Melograna F, Sunqvist M, Michiels S, Van Steen K, Heinze G. Individual-specific networks for prediction modelling – A scoping review of methods. BMC Med Res Methodol 2022; 22:62. [PMID: 35249534 PMCID: PMC8898441 DOI: 10.1186/s12874-022-01544-6] [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: 07/27/2021] [Accepted: 02/11/2022] [Indexed: 11/10/2022] Open
Abstract
Background Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space. Methods We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000–2020 in the electronic databases PubMed, Scopus and Embase. Results Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual’s contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction. Conclusion The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01544-6.
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21
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Bosticardo S, Schiavi S, Schaedelin S, Lu PJ, Barakovic M, Weigel M, Kappos L, Kuhle J, Daducci A, Granziera C. Microstructure-Weighted Connectomics in Multiple Sclerosis. Brain Connect 2021; 12:6-17. [PMID: 34210167 PMCID: PMC8867108 DOI: 10.1089/brain.2021.0047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Introduction: Graph theory has been applied to study the pathophysiology of multiple sclerosis (MS) since it provides global and focal measures of brain network properties that are affected by MS. Typically, the connection strength and, consequently, the network properties are computed by counting the number of streamlines (NOS) connecting couples of gray matter regions. However, recent studies have shown that this method is not quantitative. Methods: We evaluated diffusion-based microstructural measures extracted from three different models to assess the network properties in a group of 66 MS patients and 64 healthy subjects. Besides, we assessed their correlation with patients' disability and with a biological measure of neuroaxonal damage. Results: Graph metrics extracted from connectomes weighted by intra-axonal microstructural components were the most sensitive to MS pathology and the most related to clinical disability. In contrast, measures of network segregation extracted from the connectomes weighted by maps describing extracellular diffusivity were the most related to serum concentration of neurofilament light chain. Network properties assessed with NOS were neither sensitive to MS pathology nor correlated with clinical and pathological measures of disease impact in MS patients. Conclusion: Using tractometry-derived graph measures in MS patients, we identified a set of metrics based on microstructural components that are highly sensitive to the disease and that provide sensitive correlates of clinical and biological deterioration in MS patients.
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Affiliation(s)
- Sara Bosticardo
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Simona Schiavi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Sabine Schaedelin
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Ludwig Kappos
- Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Alessandro Daducci
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Cristina Granziera
- Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland
- Address correspondence to: Cristina Granziera, Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Gewerbestrasse 16, 4123 Allschwil, BL, Switzerland
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22
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Jandric D, Doshi A, Scott R, Paling D, Rog D, Chataway J, Schoonheim M, Parker G, Muhlert N. A systematic review of resting state functional MRI connectivity changes and cognitive impairment in multiple sclerosis. Brain Connect 2021; 12:112-133. [PMID: 34382408 DOI: 10.1089/brain.2021.0104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Cognitive impairment in multiple sclerosis (MS) is increasingly being investigated with resting state functional MRI (rs-fMRI) functional connectivity (FC) . However, results remain difficult to interpret, showing both high and low FC associated with cognitive impairment. We conducted a systematic review of rs-fMRI studies in MS to understand whether the direction of FC change relates to cognitive dysfunction, and how this may be influenced by the choice of methodology. METHODS Embase, Medline and PsycINFO were searched for studies assessing cognitive function and rs-fMRI FC in adults with MS. RESULTS Fifty-seven studies were included in a narrative synthesis. Of these, 50 found an association between cognitive impairment and FC abnormalities. Worse cognition was linked to high FC in 18 studies, and to low FC in 17 studies. Nine studies found patterns of both high and low FC related to poor cognitive performance, in different regions or for different MR metrics. There was no clear link to increased FC during early stages of MS and reduced FC in later stages, as predicted by common models of MS pathology. Throughout, we found substantial heterogeneity in study methodology, and carefully consider how this may impact on the observed findings. DISCUSSION These results indicate an urgent need for greater standardisation in the field - in terms of the choice of MRI analysis and the definition of cognitive impairment. This will allow us to use rs-fMRI FC as a biomarker in future clinical studies, and as a tool to understand mechanisms underpinning cognitive symptoms in MS.
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Affiliation(s)
- Danka Jandric
- The University of Manchester, 5292, Oxford Road, Manchester, United Kingdom of Great Britain and Northern Ireland, M13 9PL;
| | - Anisha Doshi
- University College London, 4919, London, London, United Kingdom of Great Britain and Northern Ireland;
| | - Richelle Scott
- The University of Manchester, 5292, Manchester, United Kingdom of Great Britain and Northern Ireland;
| | - David Paling
- Royal Hallamshire Hospital, 105629, Sheffield, Sheffield, United Kingdom of Great Britain and Northern Ireland;
| | - David Rog
- Salford Royal Hospital, 105621, Salford, Salford, United Kingdom of Great Britain and Northern Ireland;
| | - Jeremy Chataway
- University College London, 4919, London, London, United Kingdom of Great Britain and Northern Ireland;
| | - Menno Schoonheim
- Amsterdam UMC Locatie VUmc, 1209, Anatomy & Neurosciences, Amsterdam, Noord-Holland, Netherlands;
| | - Geoff Parker
- University College London, 4919, London, London, United Kingdom of Great Britain and Northern Ireland.,The University of Manchester, 5292, Manchester, United Kingdom of Great Britain and Northern Ireland;
| | - Nils Muhlert
- The University of Manchester, 5292, Manchester, United Kingdom of Great Britain and Northern Ireland;
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23
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Meng D, Welton T, Elsarraj A, Morgan PS, das Nair R, Constantinescu CS, Evangelou N, Auer DP, Dineen RA. Dorsolateral prefrontal circuit effective connectivity mediates the relationship between white matter structure and PASAT-3 performance in multiple sclerosis. Hum Brain Mapp 2021; 42:495-509. [PMID: 33073920 PMCID: PMC7776003 DOI: 10.1002/hbm.25239] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 09/21/2020] [Accepted: 09/29/2020] [Indexed: 11/15/2022] Open
Abstract
Three decades ago a series of parallel circuits were described involving the frontal cortex and deep grey matter structures, with putative roles in control of motor and oculomotor function, cognition, behaviour and emotion. The circuit comprising the dorsolateral prefrontal cortex, caudate, globus pallidus and thalamus has a putative role in regulating executive functions. The aim of this study is to investigate effective connectivity (EC) of the dorsolateral-prefrontal circuit and its association with PASAT-3 performance in people with multiple sclerosis(MS). We use Granger causality analysis of resting-state functional MRI from 52 people with MS and 36 healthy people to infer that reduced EC in the afferent limb of the dorsolateral prefrontal circuit occurs in the people with MS with cognitive dysfunction (left: p = .006; right: p = .029), with bilateral EC reductions in this circuit resulting in more severe cognitive dysfunction than unilateral reductions alone (p = .002). We show that reduced EC in the afferent limb of the dorsolateral prefrontal circuit mediates the relationship between cognitive performance and macrostrucutral and microstructural alterations of white matter tracts in components of the circuit. Specificity is shown by the absence of any relationship between cognition and EC in the analogous and anatomically proximal motor circuit. We demonstrate good stability of the EC measures in people with MS over an interval averaging 8-months. Key positive and negative results are replicated in an independent cohort of people with MS. Our findings identify the dorsolateral prefrontal circuit as a potential target for therapeutic strategies aimed at improving cognition in people with MS.
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Affiliation(s)
- Dewen Meng
- Radiological Sciences, Division of Clinical Neuroscience, School of MedicineUniversity of NottinghamNottinghamUK
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
- NIHR Nottingham Biomedical Research Centre, Queen's Medical CentreUniversity of NottinghamNottinghamUK
| | - Thomas Welton
- Radiological Sciences, Division of Clinical Neuroscience, School of MedicineUniversity of NottinghamNottinghamUK
- National Neuroscience InstituteTan Tock Seng HospitalSingaporeSingapore
| | - Afaf Elsarraj
- Radiological Sciences, Division of Clinical Neuroscience, School of MedicineUniversity of NottinghamNottinghamUK
| | - Paul S. Morgan
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
- NIHR Nottingham Biomedical Research Centre, Queen's Medical CentreUniversity of NottinghamNottinghamUK
- Medical Physics and Clinical EngineeringNottingham University Hospitals NHS TrustNottinghamUK
| | - Roshan das Nair
- Institute of Mental HealthUniversity of NottinghamNottinghamUK
- Division of Psychiatry & Applied Psychology, School of MedicineUniversity of NottinghamNottinghamUK
| | - Cris S. Constantinescu
- Clinical Neurology, Division of Clinical Neuroscience, School of MedicineUniversity of NottinghamNottinghamUK
| | - Nikos Evangelou
- Clinical Neurology, Division of Clinical Neuroscience, School of MedicineUniversity of NottinghamNottinghamUK
| | - Dorothee P. Auer
- Radiological Sciences, Division of Clinical Neuroscience, School of MedicineUniversity of NottinghamNottinghamUK
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
- NIHR Nottingham Biomedical Research Centre, Queen's Medical CentreUniversity of NottinghamNottinghamUK
| | - Rob A. Dineen
- Radiological Sciences, Division of Clinical Neuroscience, School of MedicineUniversity of NottinghamNottinghamUK
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
- NIHR Nottingham Biomedical Research Centre, Queen's Medical CentreUniversity of NottinghamNottinghamUK
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Gray Matter Morphometry Correlates with Attentional Efficiency in Young-Adult Multiple Sclerosis. Brain Sci 2021; 11:brainsci11010080. [PMID: 33435314 PMCID: PMC7826940 DOI: 10.3390/brainsci11010080] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/02/2021] [Accepted: 01/07/2021] [Indexed: 01/27/2023] Open
Abstract
Slowed processing on the alerting, orienting and executive control components of attention measured using the Attention Network Test-Interactions (ANT-I) have been widely reported in multiple sclerosis (MS). Despite the assumption that these components correspond to specific neuroanatomical networks in the brain, little is known about gray matter changes that occur in MS and their association with ANT-I performance. We investigated vertex-wise cortical thickness changes and deep gray matter volumetric changes in young MS participants (N = 21, age range: 18-35) with pediatric or young-adult onset and mild disease severity. ANT-I scores and cortical thickness were not significantly different between MS participants and healthy volunteers (N = 19, age range: 18-35), but thalamic volumes were significantly lower in MS. Slowed reaction times on the alerting component in MS correlated significantly with reduced volume of the right pallidum in MS. Slowed reaction times on executive control component correlated significantly with reduced thickness in the frontal, parietal and visual cortical areas and with reduced volume of the left putamen in MS. These findings demonstrate associations between gray matter changes and attentional performance even in the absence of widespread atrophy or slowed attentional processes.
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