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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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2
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Ferreira JA, Pinto N, Maricoto T, Pato MV. Relationship between event-related potentials and cognitive dysfunction in multiple sclerosis: A systematic review. Clin Neurophysiol 2024; 163:174-184. [PMID: 38759513 DOI: 10.1016/j.clinph.2024.04.024] [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: 02/08/2024] [Revised: 04/15/2024] [Accepted: 04/30/2024] [Indexed: 05/19/2024]
Abstract
OBJECTIVE This systematic review aimed to evaluate if event-related potentials (ERPs) can be a relevant tool for cognitive dysfunction diagnosis in Multiple Sclerosis (MS). METHODS Four databases were consulted (PubMed, Embase, Scielo, and Web of Science). The included studies should include adults with clear MS diagnoses, independently of having cognitive complaints, and all should have been submitted to ERPs (P300, N400 or mismatch negativity (MMN)). The main outcomes evaluated were ERPs' amplitude and/or latency. RESULTS 425 studies were obtained initially from all databases, with 26 studies fulfilling the eligibility criteria. P300 was the most used ERP (25 studies), showing a reduced amplitude or an increased latency in 84% of those. N400 was evaluated in one study, showing also abnormal results. MMN was addressed in two studies with inconsistent findings. Some studies further suggest that ERPs may show earlier abnormal results compared with neuropsychological tests. CONCLUSIONS Most MS patient groups revealed ERP abnormalities, suggesting that these neurophysiological tests may be a relevant and appropriate diagnostic aid method for cognitive impairment in MS. SIGNIFICANCE The use of ERPs in MS patients seems able to demonstrate cognitive impairment and its use should be considered as part of the regular patient evaluation.
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Affiliation(s)
- Joana Almeida Ferreira
- Faculty of Health Sciences, University of Beira Interior, Covilhã, Portugal; Neurology Department, Hospital Garcia de Orta, Almada, Portugal
| | - Nuno Pinto
- Faculty of Health Sciences, University of Beira Interior, Covilhã, Portugal; CICS-UBI - Health Sciences Research Centre, University of Beira Interior, Covilhã, Portugal; GRUBI - University of Beira Interior Systematic Reviews Group, Covilhã, Portugal.
| | - Tiago Maricoto
- Faculty of Health Sciences, University of Beira Interior, Covilhã, Portugal; CICS-UBI - Health Sciences Research Centre, University of Beira Interior, Covilhã, Portugal; GRUBI - University of Beira Interior Systematic Reviews Group, Covilhã, Portugal; Beira Ria Health Center, Aveiro Health Centre, Ílhavo, Portugal
| | - Maria Vaz Pato
- Faculty of Health Sciences, University of Beira Interior, Covilhã, Portugal; CICS-UBI - Health Sciences Research Centre, University of Beira Interior, Covilhã, Portugal; GRUBI - University of Beira Interior Systematic Reviews Group, Covilhã, Portugal
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3
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Baller EB, Sweeney EM, Cieslak M, Robert-Fitzgerald T, Covitz SC, Martin ML, Schindler MK, Bar-Or A, Elahi A, Larsen BS, Manning AR, Markowitz CE, Perrone CM, Rautman V, Seitz MM, Detre JA, Fox MD, Shinohara RT, Satterthwaite TD. Mapping the Relationship of White Matter Lesions to Depression in Multiple Sclerosis. Biol Psychiatry 2024; 95:1072-1080. [PMID: 37981178 PMCID: PMC11101593 DOI: 10.1016/j.biopsych.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/27/2023] [Accepted: 11/11/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND Multiple sclerosis (MS) is an immune-mediated neurological disorder, and up to 50% of patients experience depression. We investigated how white matter network disruption is related to depression in MS. METHODS Using electronic health records, 380 participants with MS were identified. Depressed individuals (MS+Depression group; n = 232) included persons who had an ICD-10 depression diagnosis, had a prescription for antidepressant medication, or screened positive via Patient Health Questionnaire (PHQ)-2 or PHQ-9. Age- and sex-matched nondepressed individuals with MS (MS-Depression group; n = 148) included persons who had no prior depression diagnosis, had no psychiatric medication prescriptions, and were asymptomatic on PHQ-2 or PHQ-9. Research-quality 3T structural magnetic resonance imaging was obtained as part of routine care. We first evaluated whether lesions were preferentially located within the depression network compared with other brain regions. Next, we examined if MS+Depression patients had greater lesion burden and if this was driven by lesions in the depression network. Primary outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain. RESULTS MS lesions preferentially affected fascicles within versus outside the depression network (β = 0.09, 95% CI = 0.08 to 0.10, p < .001). MS+Depression patients had more lesion burden (β = 0.06, 95% CI = 0.01 to 0.10, p = .015); this was driven by lesions within the depression network (β = 0.02, 95% CI = 0.003 to 0.040, p = .020). CONCLUSIONS We demonstrated that lesion location and burden may contribute to depression comorbidity in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression patients had more disease than MS-Depression patients, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.
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Affiliation(s)
- Erica B Baller
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Elizabeth M Sweeney
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Timothy Robert-Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sydney C Covitz
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Melissa L Martin
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Matthew K Schindler
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Amit Bar-Or
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ameena Elahi
- Department of Information Services, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Bart S Larsen
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Abigail R Manning
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Clyde E Markowitz
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christopher M Perrone
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Victoria Rautman
- Department of Information Services, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Madeleine M Seitz
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania.
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Irastorza-Valera L, Soria-Gómez E, Benitez JM, Montáns FJ, Saucedo-Mora L. Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM). Biomimetics (Basel) 2024; 9:362. [PMID: 38921242 PMCID: PMC11202129 DOI: 10.3390/biomimetics9060362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 05/28/2024] [Accepted: 06/05/2024] [Indexed: 06/27/2024] Open
Abstract
The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.
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Affiliation(s)
- Luis Irastorza-Valera
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- PIMM Laboratory, ENSAM–Arts et Métiers ParisTech, 151 Bd de l’Hôpital, 75013 Paris, France
| | - Edgar Soria-Gómez
- Achúcarro Basque Center for Neuroscience, Barrio Sarriena, s/n, 48940 Leioa, Spain;
- Ikerbasque, Basque Foundation for Science, Plaza Euskadi, 5, 48009 Bilbao, Spain
- Department of Neurosciences, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940 Leioa, Spain
| | - José María Benitez
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
| | - Francisco J. Montáns
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Luis Saucedo-Mora
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology (MIT), 77 Massachusetts Ave, Cambridge, MA 02139, USA
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5
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Sandroff BM, Motl RW, Salter A. Focusing on neural mechanisms of exercise training benefits in multiple sclerosis. Mult Scler Relat Disord 2024; 86:105633. [PMID: 38643688 DOI: 10.1016/j.msard.2024.105633] [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: 02/23/2024] [Accepted: 04/15/2024] [Indexed: 04/23/2024]
Abstract
Exercise training (ET) should be a mainstay of comprehensive care in multiple sclerosis (MS), yet there is reluctance regarding its implementation among healthcare providers. This reluctance has its roots in the lack of evidence from randomized controlled trials (RCTs) that provide a neurobiological mechanism for beneficial outcomes. We argue that ET training is efficacious for improving mobility and cognitive dysfunction as hallmark consequences of MS, and propose an experimental medicine framework for guiding research focusing on CNS mechanisms-of-action for ET benefits. The framework establishes three hierarchical pathways as preconditions for applying a mediation framework in a fourth pathway for fully testing mechanistic research questions using a RCT. This paper describes the conceptual basis of the pathways, and then reviews the existing evidence within the pathways of the framework for ET effects on the CNS, mobility, and cognition in MS. Lastly, this paper discusses future directions and challenges for testing the pathways of the framework for advancing the evidence regarding CNS mechanisms of ET benefits in this population. Our objective is guiding the field toward evidence that facilitates the prescription and promotion of ET as a neurorestorative approach within MS clinical practice.
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Affiliation(s)
- Brian M Sandroff
- Center for Neuropsychology and Neuroscience Research, Kessler Foundation, West Orange, NJ, USA; Department of Physical Medicine & Rehabilitation, Rutgers NJ Medical School, Newark, NJ, USA.
| | - Robert W Motl
- Department of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, IL, USA
| | - Amber Salter
- Department of Neurology, University of Texas Southwestern, Dallas, TX, USA
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6
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Gaetani L, Salvadori N, Brachelente G, Sperandei S, Di Sabatino E, Fiacca A, Mancini A, Villa A, De Stefano N, Parnetti L, Di Filippo M. Intrathecal B cell activation and memory impairment in multiple sclerosis. Mult Scler Relat Disord 2024; 85:105548. [PMID: 38513467 DOI: 10.1016/j.msard.2024.105548] [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: 03/04/2024] [Accepted: 03/07/2024] [Indexed: 03/23/2024]
Abstract
BACKGROUND Cognitive impairment (CI) is a common and disabling feature of people with multiple sclerosis (pwMS), but its underlying mechanisms are heterogenous and not fully understood. A role of infiltrating immune cells in the meninges and brain parenchyma has been hypothesized. This study aimed to explore the hypothesis that intrathecal B cells might influence cognitive performance in pwMS. METHODS A retrospective study was performed on 39 newly diagnosed pwMS who underwent cerebrospinal fluid (CSF) analysis. Kappa (κ)-index was measured as a biomarker of intrathecal B cell activation. Cognitive performance was assessed using the Brief Repeatable Battery of Neuropsychological Tests (BRBN). Brain T2 lesions number (T2LN) and volume (T2LV) together with brain, cortical grey matter, thalamic and hippocampal volumes were calculated to account for MRI-visible damage. RESULTS κ-index was higher in pwMS with verbal memory impairment (median 99.6, range 58.5-195.2 vs. median 37.2, range 2.3-396.9, p < 0.001), and it was negatively associated with BRBN tests exploring verbal memory and information processing speed. In multivariate models, higher κ-index was confirmed to be independently associated with worse scores of BRBN tests exploring verbal memory and with a higher probability of verbal memory impairment. CONCLUSION Intrathecal B cells might drive memory impairment in pwMS independently of brain damage visible on MRI scans.
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Affiliation(s)
- Lorenzo Gaetani
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Piazzale Severi 8, Perugia 06132, Italy..
| | - Nicola Salvadori
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Piazzale Severi 8, Perugia 06132, Italy
| | - Giovanni Brachelente
- Clinical Pathology Laboratory, University Hospital S. Maria della Misericordia, Perugia, Italy
| | - Silvia Sperandei
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Piazzale Severi 8, Perugia 06132, Italy
| | - Elena Di Sabatino
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Piazzale Severi 8, Perugia 06132, Italy
| | - Andrea Fiacca
- Section of Neuroradiology, University Hospital S. Maria della Misericordia, Perugia, Italy
| | - Andrea Mancini
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Piazzale Severi 8, Perugia 06132, Italy
| | - Alfredo Villa
- Clinical Pathology Laboratory, University Hospital S. Maria della Misericordia, Perugia, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Lucilla Parnetti
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Piazzale Severi 8, Perugia 06132, Italy
| | - Massimiliano Di Filippo
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Piazzale Severi 8, Perugia 06132, Italy
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Zivadinov R, Bergsland N, Jakimovski D, Weinstock-Guttman B, Lorefice L, Schoonheim MM, Morrow SA, Ann Picone M, Pardo G, Zarif M, Gudesblatt M, Nicholas JA, Smith A, Hunter S, Newman S, AbdelRazek MA, Hoti I, Riolo J, Silva D, Fuchs TA, Dwyer MG, Hb Benedict R. Thalamic atrophy and dysconnectivity are associated with cognitive impairment in a multi-center, clinical routine, real-word study of people with relapsing-remitting multiple sclerosis. Neuroimage Clin 2024; 42:103609. [PMID: 38718640 PMCID: PMC11098945 DOI: 10.1016/j.nicl.2024.103609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/29/2024] [Accepted: 04/22/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND Prior research has established a link between thalamic pathology and cognitive impairment (CI) in people with multiple sclerosis (pwMS). However, the translation of these findings to pwMS in everyday clinical settings has been insufficient. OBJECTIVE To assess which global and/or thalamic imaging biomarkers can be used to identify pwMS at risk for CI and cognitive worsening (CW) in a real-world setting. METHODS This was an international, multi-center (11 centers), longitudinal, retrospective, real-word study of people with relapsing-remitting MS (pwRRMS). Brain MRI exams acquired at baseline and follow-up were collected. Cognitive status was evaluated using the Symbol Digit Modalities Test (SDMT). Thalamic volume (TV) measurement was performed on T2-FLAIR, as well as on T1-WI, when available. Thalamic dysconnectivity, T2-lesion volume (T2-LV), and volumes of gray matter (GM), whole brain (WB) and lateral ventricles (LVV) were also assessed. RESULTS 332 pwMS were followed for an average of 2.8 years. At baseline, T2-LV, LVV, TV and thalamic dysconnectivity on T2-FLAIR (p < 0.016), and WB, GM and TV volumes on T1-WI (p < 0.039) were significantly worse in 90 (27.1 %) CI vs. 242 (62.9 %) non-CI pwRRMS. Greater SDMT decline over the follow-up was associated with lower baseline TV on T2-FLAIR (standardized β = 0.203, p = 0.002) and greater thalamic dysconnectivity (standardized β = -0.14, p = 0.028) in a linear regression model. CONCLUSIONS PwRRMS with thalamic atrophy and worse thalamic dysconnectivity present more frequently with CI and experience greater CW over mid-term follow-up in a real-world setting.
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Affiliation(s)
- Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, NY, United States; Center for Biomedical Imaging at Clinical and Translational Science Institute, University of Buffalo, State University of New York, NY, United States.
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, NY, United States
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, NY, United States
| | - Bianca Weinstock-Guttman
- Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York and Kaleida Health, BGH, Buffalo, NY, United States
| | - Lorena Lorefice
- Department of Medical Sciences and Public Health, Multiple Sclerosis Center, Binaghi Hospital, ASL Cagliari, University of Cagliari, Cagliari, Italy
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy & Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Sarah A Morrow
- Schulich School of Medicine and Dentistry, London Health Sciences Centre, University Hospital, London, Ontario, CA, Canada; Department of Clinical Neurological Sciences, Hotchkiss Brain Institute, University of Calgary, Canada
| | | | - Gabriel Pardo
- Oklahoma Medical Research Foundation, Oklahoma City, OK, United States
| | - Myassar Zarif
- South Shore Neurologic Associates NYU Langone, Patchogue, NY, United States
| | - Mark Gudesblatt
- South Shore Neurologic Associates NYU Langone, Patchogue, NY, United States
| | | | - Andrew Smith
- OhioHealth MS Center, Riverside Methodist Hospital, Columbus, OH, United States
| | - Samuel Hunter
- Advanced Neurosciences Institute, Franklin, TN, United States
| | - Stephen Newman
- Island Neurological Association, Plainview, NY, United States
| | - Mahmoud A AbdelRazek
- Mount Auburn Hospital, Harvard Medical School, United States; Atrium Health Neurosciences Institute, Wake Forest University School of Medicine, United States
| | - Ina Hoti
- Mount Auburn Hospital, Harvard Medical School, United States
| | - Jon Riolo
- Bristol Myers Squibb, Summit, NJ, United States
| | - Diego Silva
- Bristol Myers Squibb, Summit, NJ, United States
| | - Tom A Fuchs
- MS Center Amsterdam, Anatomy & Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, NY, United States; Center for Biomedical Imaging at Clinical and Translational Science Institute, University of Buffalo, State University of New York, NY, United States
| | - Ralph Hb Benedict
- Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York and Kaleida Health, BGH, Buffalo, NY, United States
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8
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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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Nauta IM, Kessels RPC, Bertens D, Stam CJ, Strijbis EEM, Hillebrand A, Fasotti L, Uitdehaag BMJ, Hulst HE, Speckens AEM, Schoonheim MM, de Jong BA. Neurophysiological brain function predicts response to cognitive rehabilitation and mindfulness in multiple sclerosis: a randomized trial. J Neurol 2024; 271:1649-1662. [PMID: 38278979 PMCID: PMC10972975 DOI: 10.1007/s00415-024-12183-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 12/07/2023] [Accepted: 12/30/2023] [Indexed: 01/28/2024]
Abstract
BACKGROUND Cognitive treatment response varies highly in people with multiple sclerosis (PwMS). Identification of mechanisms is essential for predicting response. OBJECTIVES This study aimed to investigate whether brain network function predicts response to cognitive rehabilitation therapy (CRT) and mindfulness-based cognitive therapy (MBCT). METHODS PwMS with cognitive complaints completed CRT, MBCT, or enhanced treatment as usual (ETAU) and performed three measurements (baseline, post-treatment, 6-month follow-up). Baseline magnetoencephalography (MEG) measures were used to predict treatment effects on cognitive complaints, personalized cognitive goals, and information processing speed (IPS) using mixed models (secondary analysis REMIND-MS study). RESULTS We included 105 PwMS (96 included in prediction analyses; 32 CRT, 31 MBCT, 33 ETAU), and 56 healthy controls with baseline MEG. MEG did not predict reductions in complaints. Higher connectivity predicted better goal achievement after MBCT (p = 0.010) and CRT (p = 0.018). Lower gamma power (p = 0.006) and higher connectivity (p = 0.020) predicted larger IPS benefits after MBCT. These MEG predictors indicated worse brain function compared to healthy controls (p < 0.05). CONCLUSIONS Brain network function predicted better cognitive goal achievement after MBCT and CRT, and IPS improvements after MBCT. PwMS with neuronal slowing and hyperconnectivity were most prone to show treatment response, making network function a promising tool for personalized treatment recommendations. TRIAL REGISTRATION The REMIND-MS study was prospectively registered in the Dutch Trial registry (NL6285; https://trialsearch.who.int/Trial2.aspx?TrialID=NTR6459 ).
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Affiliation(s)
- Ilse M Nauta
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands.
| | - Roy P C Kessels
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Klimmendaal Rehabilitation Center, Arnhem, The Netherlands
- Vincent Van Gogh Institute for Psychiatry, Venray, The Netherlands
- Department of Medical Psychology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dirk Bertens
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Klimmendaal Rehabilitation Center, Arnhem, The Netherlands
| | - Cornelis J Stam
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- MEG Center, Clinical Neurophysiology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Eva E M Strijbis
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- MEG Center, Clinical Neurophysiology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Luciano Fasotti
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Klimmendaal Rehabilitation Center, Arnhem, The Netherlands
| | - Bernard M J Uitdehaag
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Hanneke E Hulst
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Anne E M Speckens
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Brigit A de Jong
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
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10
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Kopetzky SJ, Li Y, Kaiser M, Butz-Ostendorf M. Predictability of intelligence and age from structural connectomes. PLoS One 2024; 19:e0301599. [PMID: 38557681 PMCID: PMC10984540 DOI: 10.1371/journal.pone.0301599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
In this study, structural images of 1048 healthy subjects from the Human Connectome Project Young Adult study and 94 from ADNI-3 study were processed by an in-house tractography pipeline and analyzed together with pre-processed data of the same subjects from braingraph.org. Whole brain structural connectome features were used to build a simple correlation-based regression machine learning model to predict intelligence and age of healthy subjects. Our results showed that different forms of intelligence as well as age are predictable to a certain degree from diffusion tensor imaging detecting anatomical fiber tracts in the living human brain. Though we did not identify significant differences in the prediction capability for the investigated features depending on the imaging feature extraction method, we did find that crystallized intelligence was consistently better predictable than fluid intelligence from structural connectivity data through all datasets. Our findings suggest a practical and scalable processing and analysis framework to explore broader research topics employing brain MR imaging.
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Affiliation(s)
- Sebastian J. Kopetzky
- Labvantage—Biomax GmbH, Planegg, Germany
- School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Yong Li
- Labvantage—Biomax GmbH, Planegg, Germany
| | - Marcus Kaiser
- Precision Imaging Beacon, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Department of Functional Neurosurgery, Rui Jin Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Markus Butz-Ostendorf
- Labvantage—Biomax GmbH, Planegg, Germany
- Laboratory for Parallel Programming, Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany
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11
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Hindriks R, Broeders TAA, Schoonheim MM, Douw L, Santos F, van Wieringen W, Tewarie PKB. Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants. Hum Brain Mapp 2024; 45:e26663. [PMID: 38520377 PMCID: PMC10960559 DOI: 10.1002/hbm.26663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 02/12/2024] [Accepted: 03/08/2024] [Indexed: 03/25/2024] Open
Abstract
Blood-level oxygenation-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most common modality to study functional connectivity in the human brain. Most research to date has focused on connectivity between pairs of brain regions. However, attention has recently turned towards connectivity involving more than two regions, that is, higher-order connectivity. It is not yet clear how higher-order connectivity can best be quantified. The measures that are currently in use cannot distinguish between pairwise (i.e., second-order) and higher-order connectivity. We show that genuine higher-order connectivity can be quantified by using multivariate cumulants. We explore the use of multivariate cumulants for quantifying higher-order connectivity and the performance of block bootstrapping for statistical inference. In particular, we formulate a generative model for fMRI signals exhibiting higher-order connectivity and use it to assess bias, standard errors, and detection probabilities. Application to resting-state fMRI data from the Human Connectome Project demonstrates that spontaneous fMRI signals are organized into higher-order networks that are distinct from second-order resting-state networks. Application to a clinical cohort of patients with multiple sclerosis further demonstrates that cumulants can be used to classify disease groups and explain behavioral variability. Hence, we present a novel framework to reliably estimate genuine higher-order connectivity in fMRI data which can be used for constructing hyperedges, and finally, which can readily be applied to fMRI data from populations with neuropsychiatric disease or cognitive neuroscientific experiments.
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Affiliation(s)
- Rikkert Hindriks
- Department of Mathematics, Faculty of ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Tommy A. A. Broeders
- Department of Anatomy and Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Menno M. Schoonheim
- Department of Anatomy and Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Fernando Santos
- Dutch Institute for Emergent Phenomena (DIEP)Institute for Advanced Studies, University of AmsterdamAmsterdamThe Netherlands
- Korteweg de Vries Institute for MathematicsUniversity of AmsterdamAmsterdamthe Netherlands
| | - Wessel van Wieringen
- Department of Epidemiology and BiostatisticsAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Prejaas K. B. Tewarie
- Sir Peter Mansfield Imaging CenterSchool of Physics, University of NottinghamNottinghamUnited Kingdom
- Clinical Neurophysiology GroupUniversity of TwenteEnschedeThe Netherlands
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12
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Stam CJ. Hub overload and failure as a final common pathway in neurological brain network disorders. Netw Neurosci 2024; 8:1-23. [PMID: 38562292 PMCID: PMC10861166 DOI: 10.1162/netn_a_00339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 09/26/2023] [Indexed: 04/04/2024] Open
Abstract
Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.
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Affiliation(s)
- Cornelis Jan Stam
- Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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13
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Ananthavarathan P, Sahi N, Chard DT. An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression. Expert Rev Neurother 2024; 24:201-216. [PMID: 38235594 DOI: 10.1080/14737175.2024.2304116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024]
Abstract
INTRODUCTION While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear. AREAS COVERED The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning. EXPERT OPINION Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.
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Affiliation(s)
- Piriyankan Ananthavarathan
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Nitin Sahi
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Declan T Chard
- Clinical Research Associate & Consultant Neurologist, Institute of Neurology - Queen Square Multiple Sclerosis Centre, London, UK
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14
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Zhang C, Zhang K, Hu X, Cai X, Chen Y, Gao F, Wang G. Regional GABA levels modulate abnormal resting-state network functional connectivity and cognitive impairment in multiple sclerosis. Cereb Cortex 2024; 34:bhad535. [PMID: 38271282 DOI: 10.1093/cercor/bhad535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/18/2023] [Accepted: 12/19/2023] [Indexed: 01/27/2024] Open
Abstract
More evidence shows that changes in functional connectivity with regard to brain networks and neurometabolite levels correlated to cognitive impairment in multiple sclerosis. However, the neurological basis underlying the relationship among neurometabolite levels, functional connectivity, and cognitive impairment remains unclear. For this purpose, we used a combination of magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging to study gamma-aminobutyric acid and glutamate concentrations in the posterior cingulate cortex, medial prefrontal cortex and left hippocampus, and inter-network functional connectivity in 29 relapsing-remitting multiple sclerosis patients and 34 matched healthy controls. Neuropsychological tests were used to evaluate the cognitive function. We found that relapsing-remitting multiple sclerosis patients demonstrated significantly reduced gamma-aminobutyric acid and glutamate concentrations and aberrant functional connectivity involving cognitive-related networks compared to healthy controls, and both alterations were associated with specific cognition decline. Moreover, mediation analyses indicated that decremented hippocampus gamma-aminobutyric acid levels in relapsing-remitting multiple sclerosis patients mediated the association between inter-network functional connectivity in various components of default mode network and verbal memory deficits. In summary, our findings shed new lights on the essential function of GABAergic system abnormalities in regulating network dysconnectivity and functional connectivity in relapsing-remitting multiple sclerosis patients, suggesting potential novel approach to treatment.
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Affiliation(s)
- Chao Zhang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan 250021, China
| | - Kaihua Zhang
- School of Psychology, Shandong Normal University, Jinan 250358, China
| | - Xin Hu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Xianyun Cai
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Yufan Chen
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Fei Gao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan 250021, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
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15
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Fleischer V, Gonzalez-Escamilla G, Pareto D, Rovira A, Sastre-Garriga J, Sowa P, Høgestøl EA, Harbo HF, Bellenberg B, Lukas C, Ruggieri S, Gasperini C, Uher T, Vaneckova M, Bittner S, Othman AE, Collorone S, Toosy AT, Meuth SG, Zipp F, Barkhof F, Ciccarelli O, Groppa S. Prognostic value of single-subject grey matter networks in early multiple sclerosis. Brain 2024; 147:135-146. [PMID: 37642541 PMCID: PMC10766234 DOI: 10.1093/brain/awad288] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/17/2023] [Accepted: 08/02/2023] [Indexed: 08/31/2023] Open
Abstract
The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for 5 years (mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35.0 ± 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30.0, P < 0.001] and global efficiency [H(2) = 31.3, P < 0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1.5, P = 0.474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P-values < 0.05). Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors.
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Affiliation(s)
- 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, 55131 Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- 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, 55131 Mainz, Germany
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain
| | - Alex Rovira
- Section of Neuroradiology, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain
| | - Jaume Sastre-Garriga
- Department of Neurology/Neuroimmunology, Multiple Sclerosis Centre of Catalonia, Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain
| | - Piotr Sowa
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, 0424 Oslo, Norway
| | - Einar A Høgestøl
- Institute of Clinical Medicine, University of Oslo, NO-0316 Oslo, Norway
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
| | - Hanne F Harbo
- Institute of Clinical Medicine, University of Oslo, NO-0316 Oslo, Norway
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
| | - Barbara Bellenberg
- Institute of Neuroradiology, St Josef Hospital, Ruhr-University Bochum, 44791 Bochum, Germany
| | - Carsten Lukas
- Institute of Neuroradiology, St Josef Hospital, Ruhr-University Bochum, 44791 Bochum, Germany
| | - Serena Ruggieri
- Department of Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Claudio Gasperini
- Department of Neurosciences, San Camillo-Forlanini Hospital, 00152 Rome, Italy
| | - Tomas Uher
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, 121 08 Prague, Czech Republic
| | - Manuela Vaneckova
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, 121 08 Prague, Czech Republic
| | - 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, 55131 Mainz, Germany
| | - Ahmed E Othman
- Department of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Sara Collorone
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
| | - Ahmed T Toosy
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
| | - Sven G Meuth
- Department of Neurology, Medical Faculty, Heinrich-Heine-University, 40225 Düsseldorf, 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, 55131 Mainz, Germany
| | - Frederik Barkhof
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, 1100 DD Amsterdam, Netherlands
| | - Olga Ciccarelli
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
| | - 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, 55131 Mainz, Germany
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16
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Coupé P, Planche V, Mansencal B, Kamroui RA, Koubiyr I, Manjòn JV, Tourdias T. Lifespan neurodegeneration of the human brain in multiple sclerosis. Hum Brain Mapp 2023; 44:5602-5611. [PMID: 37615064 PMCID: PMC10619394 DOI: 10.1002/hbm.26464] [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: 03/14/2023] [Revised: 07/17/2023] [Accepted: 08/08/2023] [Indexed: 08/25/2023] Open
Abstract
Atrophy related to multiple sclerosis (MS) has been found at the early stages of the disease. However, the archetype dynamic trajectories of the neurodegenerative process, even prior to clinical diagnosis, remain unknown. We modeled the volumetric trajectories of brain structures across the entire lifespan using 40,944 subjects (38,295 healthy controls and 2649 MS patients). Then, we estimated the chronological progression of MS by assessing the divergence of lifespan trajectories between normal brain charts and MS brain charts. Chronologically, the first affected structure was the thalamus, then the putamen and the pallidum (around 4 years later), followed by the ventral diencephalon (around 7 years after thalamus) and finally the brainstem (around 9 years after thalamus). To a lesser extent, the anterior cingulate gyrus, insular cortex, occipital pole, caudate and hippocampus were impacted. Finally, the precuneus and accumbens nuclei exhibited a limited atrophy pattern. Subcortical atrophy was more pronounced than cortical atrophy. The thalamus was the most impacted structure with a very early divergence in life. Our experiments showed that lifespan models of most impacted structures could be an important tool for future preclinical/prodromal prognosis and monitoring of MS.
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Affiliation(s)
| | - Vincent Planche
- Univ. Bordeaux, CNRSBordeauxFrance
- Centre Mémoire Ressources Recherches, Pôle de Neurosciences Cliniques, CHU de BordeauxBordeauxFrance
| | | | | | - Ismail Koubiyr
- Inserm U1215 ‐ Neurocentre MagendieBordeauxFrance
- Service de Neuroimagerie diagnostique et thérapeutique, CHU de BordeauxBordeauxFrance
| | - José V. Manjòn
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de ValènciaValenciaSpain
| | - Thomas Tourdias
- Inserm U1215 ‐ Neurocentre MagendieBordeauxFrance
- Service de Neuroimagerie diagnostique et thérapeutique, CHU de BordeauxBordeauxFrance
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17
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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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18
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Baller EB, Sweeney EM, Cieslak MC, Robert-Fitzgerald T, Covitz SC, Martin ML, Schindler MK, Bar-Or A, Elahi A, Larsen BS, Manning AR, Markowitz CE, Perrone CM, Rautman V, Seitz MM, Detre JA, Fox MD, Shinohara RT, Satterthwaite TD. Mapping the relationship of white matter lesions to depression in multiple sclerosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.09.23291080. [PMID: 37398183 PMCID: PMC10312888 DOI: 10.1101/2023.06.09.23291080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Importance Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects nearly one million people in the United States. Up to 50% of patients with MS experience depression. Objective To investigate how white matter network disruption is related to depression in MS. Design Retrospective case-control study of participants who received research-quality 3-tesla neuroimaging as part of MS clinical care from 2010-2018. Analyses were performed from May 1 to September 30, 2022. Setting Single-center academic medical specialty MS clinic. Participants Participants with MS were identified via the electronic health record (EHR). All participants were diagnosed by an MS specialist and completed research-quality MRI at 3T. After excluding participants with poor image quality, 783 were included. Inclusion in the depression group (MS+Depression) required either: 1) ICD-10 depression diagnosis (F32-F34.*); 2) prescription of antidepressant medication; or 3) screening positive via Patient Health Questionnaire-2 (PHQ-2) or -9 (PHQ-9). Age- and sex-matched nondepressed comparators (MS-Depression) included persons with no depression diagnosis, no psychiatric medications, and were asymptomatic on PHQ-2/9. Exposure Depression diagnosis. Main Outcomes and Measures We first evaluated if lesions were preferentially located within the depression network compared to other brain regions. Next, we examined if MS+Depression patients had greater lesion burden, and if this was driven by lesions specifically in the depression network. Outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain. Secondary measures included between-diagnosis lesion burden, stratified by brain network. Linear mixed-effects models were employed. Results Three hundred-eighty participants met inclusion criteria, (232 MS+Depression: age[SD]=49[12], %females=86; 148 MS-Depression: age[SD]=47[13], %females=79). MS lesions preferentially affected fascicles within versus outside the depression network (β=0.09, 95% CI=0.08-0.10, P<0.001). MS+Depression had more white matter lesion burden (β=0.06, 95% CI=0.01-0.10, P=0.015); this was driven by lesions within the depression network (β=0.02, 95% CI 0.003-0.040, P=0.020). Conclusions and Relevance We provide new evidence supporting a relationship between white matter lesions and depression in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression had more disease than MS-Depression, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.
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Affiliation(s)
- Erica B Baller
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Elizabeth M Sweeney
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Matthew C Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Timothy Robert-Fitzgerald
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Sydney C Covitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Melissa L Martin
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Matthew K Schindler
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Amit Bar-Or
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Ameena Elahi
- Department of Information Services, University of Pennsylvania, Philadelphia, PA USA
| | - Bart S Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Abigail R Manning
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Clyde E Markowitz
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Christopher M Perrone
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Victoria Rautman
- Department of Information Services, University of Pennsylvania, Philadelphia, PA USA
| | - Madeleine M Seitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA USA
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19
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Sandroff BM, Rafizadeh CM, Motl RW. Neuroimaging Technology in Exercise Neurorehabilitation Research in Persons with MS: A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094530. [PMID: 37177732 PMCID: PMC10181711 DOI: 10.3390/s23094530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/15/2023]
Abstract
There is increasing interest in the application of neuroimaging technology in exercise neurorehabilitation research among persons with multiple sclerosis (MS). The inclusion and focus on neuroimaging outcomes in MS exercise training research is critical for establishing a biological basis for improvements in functioning and elevating exercise within the neurologist's clinical armamentarium alongside disease modifying therapies as an approach for treating the disease and its consequences. Indeed, the inclusion of selective neuroimaging approaches and sensor-based technology among physical activity, mobility, and balance outcomes in such MS research might further allow for detecting specific links between the brain and real-world behavior. This paper provided a scoping review on the application of neuroimaging in exercise training research among persons with MS based on searches conducted in PubMed, Web of Science, and Scopus. We identified 60 studies on neuroimaging-technology-based (primarily MRI, which involved a variety of sequences and approaches) correlates of functions, based on multiple sensor-based measures, which are typically targets for exercise training trials in MS. We further identified 12 randomized controlled trials of exercise training effects on neuroimaging outcomes in MS. Overall, there was a large degree of heterogeneity whereby we could not identify definitive conclusions regarding a consistent neuroimaging biomarker of MS-related dysfunction or singular sensor-based measure, or consistent neural adaptation for exercise training in MS. Nevertheless, the present review provides a first step for better linking correlational and randomized controlled trial research for the development of high-quality exercise training studies on the brain in persons with MS, and this is timely given the substantial interest in exercise as a potential disease-modifying and/or neuroplasticity-inducing behavior in this population.
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Affiliation(s)
- Brian M Sandroff
- Center for Neuropsychology and Neuroscience Research, Kessler Foundation, 1199 Pleasant Valley Way, West Orange, NJ 07052, USA
- Department of Physical Medicine and Rehabilitation, Rutgers New Jersey Medical School, Newark, NJ 07103, USA
| | - Caroline M Rafizadeh
- Center for Neuropsychology and Neuroscience Research, Kessler Foundation, 1199 Pleasant Valley Way, West Orange, NJ 07052, USA
| | - Robert W Motl
- Department of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, IL 60607, USA
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20
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Coupé P, Planche V, Mansencal B, Kamroui RA, Koubiyr I, Manjon JV, Tourdias T. Lifespan Neurodegeneration Of The Human Brain In Multiple Sclerosis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.14.532535. [PMID: 36993352 PMCID: PMC10055083 DOI: 10.1101/2023.03.14.532535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Background Atrophy related to Multiple Sclerosis (MS) has been found at the early stages of the disease. However, the archetype dynamic trajectories of the neurodegenerative process, even prior to clinical diagnosis, remain unknown. Methods We modeled the volumetric trajectories of brain structures across the entire lifespan using 40944 subjects (38295 healthy controls and 2649 MS patients). Then, we estimated the chronological progression of MS by assessing the divergence of lifespan trajectories between normal brain charts and MS brain charts. Results Chronologically, the first affected structure was the thalamus, then the putamen and the pallidum (3 years later), followed by the ventral diencephalon (7 years after thalamus) and finally the brainstem (9 years after thalamus). To a lesser extent, the anterior cingulate gyrus, insular cortex, occipital pole, caudate and hippocampus were impacted. Finally, the precuneus and accumbens nuclei exhibited a limited atrophy pattern. Conclusion Subcortical atrophy was more pronounced than cortical atrophy. The thalamus was the most impacted structure with a very early divergence in life. It paves the way toward utilization of these lifespan models for future preclinical/prodromal prognosis and monitoring of MS.
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Affiliation(s)
- Pierrick Coupé
- CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, F-33400 Talence, France
| | - Vincent Planche
- Univ. Bordeaux, CNRS, UMR 5293, Institut des Maladies Neurodégénératives, F-33000 Bordeaux, France
- Centre Mémoire Ressources Recherches, Pôle de Neurosciences Cliniques, CHU de Bordeaux, F-33000 Bordeaux, France
| | - Boris Mansencal
- CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, F-33400 Talence, France
| | - Reda A. Kamroui
- CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, F-33400 Talence, France
| | - Ismail Koubiyr
- Inserm U1215 - Neurocentre Magendie, Bordeaux F-33000, France
- Service de Neuroimagerie diagnostique et thérapeutique, CHU de Bordeaux, F-33000 Bordeaux, France
| | - José V. Manjon
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain
| | - Thomas Tourdias
- Inserm U1215 - Neurocentre Magendie, Bordeaux F-33000, France
- Service de Neuroimagerie diagnostique et thérapeutique, CHU de Bordeaux, F-33000 Bordeaux, France
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21
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Dobryakova E, Hafiz R, Iosipchuk O, Sandry J, Biswal B. ALFF response interaction with learning during feedback in individuals with multiple sclerosis. Mult Scler Relat Disord 2023; 70:104510. [PMID: 36706463 DOI: 10.1016/j.msard.2023.104510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 12/06/2022] [Accepted: 01/05/2023] [Indexed: 01/07/2023]
Abstract
Amplitude of low-frequency fluctuations (ALFF) is defined as changes of BOLD signal during resting state (RS) brain activity. Previous studies identified differences in RS activation between healthy and multiple sclerosis (MS) participants. However, no research has investigated the relationship between ALFF and learning in MS. We thus examine this here. Twenty-five MS and nineteen healthy participants performed a paired-associate word learning task where participants were presented with extrinsic or intrinsic performance feedback. Compared to healthy participants, MS participants showed higher local brain activation in the right thalamus. We also observed a positive correlation in the MS group between ALFF and extrinsic feedback within the left inferior frontal gyrus, and within the left superior temporal gyrus in association with intrinsic feedback. Healthy participants showed a positive correlation in the right fusiform gyrus between ALFF and extrinsic feedback. Findings suggest that while MS participants do not show a feedback learning impairment compared to the healthy participants, ALFF differences might suggest a general maladaptive pattern of task unrelated thalamic activation and adaptive activation in frontal and temporal regions. Results indicate that ALFF can be successfully used at capturing pathophysiological changes in local brain activation in MS in association with learning through feedback.
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Affiliation(s)
- Ekaterina Dobryakova
- Center for Traumatic Brain Injury Research, Kessler Foundation, 120 Eagle Rock Ave., East Hanover, NJ, USA
| | | | - Olesya Iosipchuk
- Center for Traumatic Brain Injury Research, Kessler Foundation, 120 Eagle Rock Ave., East Hanover, NJ, USA.
| | - Joshua Sandry
- Psychology Department, Montclair State University, 1 Normal Ave., Montclair, NJ, USA
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22
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Gaetani L, Schoonheim MM. Serum neurofilament light chain predicts cognitive worsening in secondary progressive multiple sclerosis better than brain MRI measures. Mult Scler 2022; 28:1831-1833. [PMID: 36124836 PMCID: PMC9493404 DOI: 10.1177/13524585221122916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Lorenzo Gaetani
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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