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4. Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model

Author

Affectieve & Psychotische Med., Brain, Monitoring of the failing circulation, Geneeskunde, Kulik, S D, Douw, L, van Dellen, E, Steenwijk, M D, Geurts, J J G, Stam, C J, Hillebrand, A, Schoonheim, M M, Tewarie, P, Affectieve & Psychotische Med., Brain, Monitoring of the failing circulation, Geneeskunde, Kulik, S D, Douw, L, van Dellen, E, Steenwijk, M D, Geurts, J J G, Stam, C J, Hillebrand, A, Schoonheim, M M, and Tewarie, P

Published
2023

5. A multicentric investigation of the diagnostic accuracy of cortical lesions and central vein sign in multiple sclerosis

Author

Cagol, A., Cortese, R., Barakovic, M., Schaedelin, S., Ruberte, E., Absinta, M., Barkhof, F., Calabrese, M., Marco Castellaro, Ciccarelli, O., Cocozza, S., Stefano, N., Enzinger, C., Filippi, M., Jurynczyk, M., Maggi, P., Mahmoudi, N., Montalban, X., Palace, J., Pontillo, G., Rocca, M. A., Ropele, S., Rovira, A., Schoonheim, M. M., Sowa, P., Strijbis, E., Wattjes, M. P., Wuerfel, J., Kappos, L., and Granziera, C.

Published
2022

7. Cognitive phenotypes and their associated MRI patterns

Author

Bouman, P. M., van Dam, M., Schoonheim, M. M., Steenwijk, M. D., Jonkman, L. E., Geurts, J. J., Hulst, H. E., Anatomy and neurosciences, Amsterdam Neuroscience - Neuroinfection & -inflammation, Amsterdam Neuroscience - Brain Imaging, and Amsterdam Neuroscience - Neurodegeneration

Published
2021

8. Mind the gap: from neurons to networks to outcomes in multiple sclerosis

Author

Chard, D. T., Alahmadi, A. A. S., Audoin, B., Charalambous, T., Enzinger, C., Hulst, H. E., Rocca, M. A., Rovira, A., Sastre-Garriga, J., Schoonheim, M. M., Tijms, B., Tur, C., Gandini Wheeler-Kingshott, C. A. M., Wink, A. M., Ciccarelli, O., Barkhof, F., De Stefano, N., Filippi, M., Frederiksen, J. L., Gasperini, C., Kappos, L., Palace, J., Yousry, T., Vrenken, H., University College of London [London] (UCL), Department of Diagnostic Radiology, Faculty of Applied Medical Science, King Abdulaziz University (KAU), Jeddah, Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), AP-HM, CHU Timone, Pole de Neurosciences Cliniques, Department of Neurology, Marseille, France., Department of Neurology, Research Unit for Neuronal Repair and Plasticity, Medical University of Graz, Graz, Department of Radiology, Division of Neuroradiology, Vascular and Interventional Radiology, Medical University of Graz, Graz, Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Neuroimaging Research Unit and Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Universitat Autònoma de Barcelona (UAB), Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Department of Neurology, Luton and Dunstable University Hospital, Luton, Università degli Studi di Pavia = University of Pavia (UNIPV), Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - AP-HM] (CEMEREM), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Section of Neuroradiology, Department of Radiology Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Servei de Neurologia/Neuroimmunologia, Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Institutes of Neurology and Healthcare Engineering, University College London, London, Chard, Declan T, Alahmadi, Adnan A S, Audoin, Bertrand, Charalambous, Thali, Enzinger, Christian, Hulst, Hanneke E, Rocca, Maria A, Rovira, Àlex, Sastre-Garriga, Jaume, Schoonheim, Menno M, Tijms, Betty, Tur, Carmen, Gandini Wheeler-Kingshott, Claudia A M, Wink, Alle Meije, Ciccarelli, Olga, Barkhof, Frederik, MAGNIMS Study, Group, and Filippi, Massimo

Subjects
Multiple Sclerosis, Grey matter, Network topology, 030218 nuclear medicine & medical imaging, White matter, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Alzheimer Disease, medicine, Humans, Default mode network, ComputingMilieux_MISCELLANEOUS, Inflammation, Neurons, Artificial neural network, business.industry, Multiple sclerosis, [SCCO.NEUR]Cognitive science/Neuroscience, Brain, medicine.disease, medicine.anatomical_structure, Neurology (clinical), Disconnection, Alzheimer's disease, Nerve Net, business, Neuroscience, 030217 neurology & neurosurgery
Abstract

MRI studies have provided valuable insights into the structure and function of neural networks, particularly in health and in classical neurodegenerative conditions such as Alzheimer disease. However, such work is also highly relevant in other diseases of the CNS, including multiple sclerosis (MS). In this Review, we consider the effects of MS pathology on brain networks, as assessed using MRI, and how these changes to brain networks translate into clinical impairments. We also discuss how this knowledge can inform the targeting of MS treatments and the potential future directions for research in this area. Studying MS is challenging as its pathology involves neurodegenerative and focal inflammatory elements, both of which could disrupt neural networks. The disruption of white matter tracts in MS is reflected in changes in network efficiency, an increasingly random grey matter network topology, relative cortical disconnection, and both increases and decreases in connectivity centred around hubs such as the thalamus and the default mode network. The results of initial longitudinal studies suggest that these changes evolve rather than simply increase over time and are linked with clinical features. Studies have also identified a potential role for treatments that functionally modify neural networks as opposed to altering their structure.

Published
2021
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11. Evaluation of the Central Vein Sign as a Diagnostic Imaging Biomarker in Multiple Sclerosis

Author

Sinnecker T., Clarke M. A., Meier D., Enzinger C., Calabrese M., De Stefano N., Pitiot A., Giorgio A., Schoonheim M. M., Paul F., Pawlak M. A., Schmidt R., Kappos L., Montalban X., Rovira A., Evangelou N., Wuerfel J., MAGNIMS Study Group, Filippi M, Rocca M. A., Anatomy and neurosciences, Amsterdam Neuroscience - Neuroinfection & -inflammation, Sinnecker, T., Clarke, M. A., Meier, D., Enzinger, C., Calabrese, M., De Stefano, N., Pitiot, A., Giorgio, A., Schoonheim, M. M., Paul, F., Pawlak, M. A., Schmidt, R., Kappos, L., Montalban, X., Rovira, A., Evangelou, N., Wuerfel, J., MAGNIMS Study, Group, Filippi, M, and Rocca, M. A.

Subjects
Central Nervous System, Male, Imaging biomarker, diagnosis, Cluster Headache, Relapsing-Remitting, multiple sclerosis, 0302 clinical medicine, 80 and over, 030212 general & internal medicine, Aged, 80 and over, Clinically isolated syndrome, medicine.diagnostic_test, Lupus Vasculitis, Central Nervous System, Neuromyelitis Optica, Brain, Middle Aged, Magnetic Resonance Imaging, medicine.anatomical_structure, Biomarker (medicine), biomarker, Female, Radiology, medicine.symptom, Adult, medicine.medical_specialty, Adolescent, Migraine Disorders, Lupus Vasculitis, Sensitivity and Specificity, Lesion, Young Adult, 03 medical and health sciences, Multiple Sclerosis, Relapsing-Remitting, central vein sign, clinical 3T magnetic resonance imaging, medicine, Humans, Vein, Aged, Expanded Disability Status Scale, business.industry, Multiple sclerosis, Correction, Magnetic resonance imaging, Case-Control Studies, Cerebral Veins, Cross-Sectional Studies, Demyelinating Diseases, Multiple Sclerosis, medicine.disease, Neurology (clinical), business, 030217 neurology & neurosurgery
Abstract

Importance: The central vein sign has been proposed as a specific imaging biomarker for distinguishing between multiple sclerosis (MS) and not MS, mainly based on findings from ultrahigh-field magnetic resonance imaging (MRI) studies. The diagnostic value of the central vein sign in a multicenter setting with a variety of clinical 3 tesla (T) MRI protocols, however, remains unknown.Objective: To evaluate the sensitivity and specificity of various central vein sign lesion criteria for differentiating MS from non-MS conditions using 3T brain MRI with various commonly used pulse sequences.Design, Setting, and Participants: This large multicenter, cross-sectional study enrolled participants (n = 648) of ongoing observational studies and patients included in neuroimaging research databases of 8 neuroimaging centers in Europe. Patient enrollment and MRI data collection were performed between January 1, 2010, and November 30, 2016. Data analysis was conducted between January 1, 2016, and April 30, 2018. Investigators were blinded to participant diagnosis by a novel blinding procedure.Main Outcomes and Measures: Occurrence of central vein sign was detected on 3T T2*-weighted or susceptibility-weighted imaging. Sensitivity and specificity were assessed for these MRI sequences and for different central vein sign lesion criteria, which were defined by the proportion of lesions with central vein sign or by absolute numbers of lesions with central vein sign.Results: A total of 606 participants were included in the study after exclusion of 42 participants. Among the 606 participants, 413 (68.2%) were women. Patients with clinically isolated syndrome and relapsing-remitting MS (RRMS) included 235 women (66.6%) and had a median (range) age of 37 (14.7-61.4) years, a median (range) disease duration of 2 (0-33) years, and a median (range) Expanded Disability Status Scale score of 1.5 (0-6.5). Patients without MS included 178 women (70.4%) and had a median (range) age of 54 (18-83) years. A total of 4447 lesions were analyzed in a total of 487 patients: 690 lesions in 98 participants with clinically isolated syndrome, 2815 lesions in 225 participants with RRMS, 54 lesions in 13 participants with neuromyelitis optica spectrum disorder, 54 lesions in 14 participants with systemic lupus erythematosus, 121 lesions in 29 participants with migraine or cluster headache, 240 lesions in 20 participants with diabetes, and 473 lesions in 88 participants with other types of small-vessel disease. The sensitivity was 68.1% and specificity was 82.9% for distinguishing MS from not MS using a 35% central vein sign proportion threshold. The 3 central vein sign lesion criteria had a sensitivity of 61.9% and specificity of 89.0%. Sensitivity was higher when an optimized T2*-weighted sequence was used.Conclusions and Relevance: In this study, use of the central vein sign at 3T MRI yielded a high specificity and a moderate sensitivity in differentiating MS from not MS; international, multicenter studies may be needed to ascertain whether the central vein sign-based criteria can accurately detect MS.

Published
2019
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16. Temporal ordering of multimodal biomarker abnormality in multiple sclerosis

Author

Dekker, I., Schoonheim, M. M., Venkatraghavan, V., Eijlers, A. J. C., Brouwer, I., Wattjes, M. P., Wink, A. M., Geurts, J. J. G., Uitdehaag, B. M. J., Oxtoby, N. P., Vrenken, H., Killestein, J., Barkhof, F., Wottschel, V., Radiology and nuclear medicine, Anatomy and neurosciences, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neuroinfection & -inflammation, Other Research, Amsterdam Neuroscience - Neurodegeneration, Neurology, AII - Inflammatory diseases, and Amsterdam Neuroscience - Cellular & Molecular Mechanisms

Published
2019

17. Specific cerebellar atrophy patterns in cross-sectional and longitudinal physical disability and cognitive impairment in multiple sclerosis

Author

Dekker, I., Ruet, A., Strik, M., Hulst, H. E., Wattjes, M. P., Barkhof, F., Uitdehaag, B. M. J., Killestein, J., Geurts, J. J. G., Schoonheim, M. M., Radiology and nuclear medicine, Anatomy and neurosciences, Other Research, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Amsterdam Neuroscience - Neurodegeneration, Amsterdam Neuroscience - Neuroinfection & -inflammation, Neurology, and AII - Inflammatory diseases

Published
2019

18. Thalamic volume as a measure to discriminate between subjective and objective cognitive complaints in MS: towards clinical application?

Author

Huiskamp, M., de Jong, B. A., Stalman, E. W., Steenwijk, M. D., Nauta, I. M., de Geus-Driessen, S., Reuling, I. E. W., Vrenken, H., de Groot, V., Klein, M., Uitdehaag, B. M. J., Geurts, J. J. G., Schoonheim, M. M., Hulst, H. E., Anatomy and neurosciences, Neurology, Amsterdam Neuroscience - Neuroinfection & -inflammation, Medical psychology, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Radiology and nuclear medicine, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neurodegeneration, Rehabilitation medicine, Amsterdam Movement Sciences - Rehabilitation & Development, APH - Methodology, APH - Societal Participation & Health, Amsterdam Neuroscience - Systems & Network Neuroscience, and APH - Quality of Care

Published
2019

19. Functional imaging biomarkers of cognitive impairment in multiple sclerosis: a resting-state magnetoencephalography study

Author

Kulik, S. D., Nauta, I. M., Lith, B. D. W. T., Strijbis, E. E. M., Tewarie, P., Douw, L., Hillebrand, A., Stam, C. J., Geurts, J. J. G., de Jong, B. A., Schoonheim, M. M., Anatomy and neurosciences, Amsterdam Neuroscience - Neuroinfection & -inflammation, Neurology, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neurodegeneration, and APH - Quality of Care

Published
2019

20. Different network functional connectivity characteristics of responders and non-responders to attention training in MS

Author

Prouskas, S. E., Steenwijk, M. D., Gehring, K., Huiskamp, M., de Jong, B., Geurts, J. J. G., Sitskoorn, M. M., Schoonheim, M. M., Hulst, H. E., Cognitive Neuropsychology, Anatomy and neurosciences, Neurology, Amsterdam Neuroscience - Neuroinfection & -inflammation, Amsterdam Neuroscience - Neurodegeneration, CCA - Imaging and biomarkers, and Amsterdam Neuroscience - Brain Imaging

Abstract

Background: Cognitive rehabilitation has been suggested as a means to improve cognitive function. However, cognitive rehabilitation is only effective in a subset of MS patients. It is therefore imperative to identify characteristics that might influence the ability to respond to rehabilitation of cognitive impairment. While cognitive impairment in MS is related to changes in functional connectivity (FC) of cognitive brain networks such as the default mode network (DMN), it is unknown how functional network characteristics affect the brain's ability to respond to cognitive training. Aim: To investigate whether baseline resting-state FC within and between the DMN, dorsal attention network (DAN) and ventral attention network (VAN), can distinguish responders from non-responders to an attention training in MS. Methods: Patients were randomized into an attention training (home-based computerized C-Car: 7-week, 45 min/week, N=58, age=48.4±10.2 years, 34 women, RRMS=42, median EDSS=4.0) or a waiting-list control group (CG, N=24, age=48.5±9.4 years, 19 women, RRMS=16, median EDSS=4.0). Neuropsychological assessment at baseline and follow-up included tests of attention, memory, information processing speed, and executive functioning. Based on the CG, a reliable change index (RCI) was calculated, adjusted for practice effects. Responders were defined as patients scoring RCI>1.64 (90% CI) on at least two tests. 3D T1 MRI and resting-state fMRI was obtained at baseline. After preprocessing and denoising using ICA-AROMA, a subject-wise fMRI correlation matrix was computed. Within- and between-network measures of FC of the DMN, DAN, and VAN were calculated using relative correlations and the Brainnetome atlas and compared between responders and non-responders. Results: Responders (N=22) and non-responders (N=36) did not differ significantly in age, sex, education, MS subtype, EDSS, disease duration, or baseline cognition. Responders, compared to non-responders, had lower average FC between DMN and DAN (0.85 vs 0.92 respectively; p=0.04) and DMN and VAN (0.90 vs 0.98 respectively; p=0.04). There were no significant group differences in within-network FC. Conclusion: Lower FC between DMN and attention networks seems an indicator of response to attention training in MS, which might reflect a more intact network functioning at baseline.

Published
2019

21. Increased functional efficiency of the sensorimotor network is associated with disability in multiple sclerosis

Author

Strik, M., Chard, D. T., Dekker, I., Pardini, M., Meijer, K. A., Eijlers, A. J. C., Uitdehaag, B. M. J., Kolbe, S. C., Geurts, J. J. G., Schoonheim, M. M., Anatomy and neurosciences, Radiology and nuclear medicine, Amsterdam Neuroscience - Neuroinfection & -inflammation, Neurology, CCA - Imaging and biomarkers, Amsterdam Neuroscience - Neurodegeneration, and Amsterdam Neuroscience - Brain Imaging

Published
2018

23. Hub connectivity is related to cognitive impairment in multiple sclerosis: a magnetoencephalography study

Author

Kulik, S. D., Nauta, I. M., Breedt, L., de Jong, B. A., Tewarie, P., Hillebrand, A., Strijbis, E. M. M., Stam, C. J., Geurts, J. J. G., Douw, L., Schoonheim, M. M., Anatomy and neurosciences, Amsterdam Neuroscience - Neuroinfection & -inflammation, Neurology, APH - Quality of Care, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neurodegeneration, and CCA - Imaging and biomarkers

Published
2018

25. Altered brain network modules in MS identified using magnetoencephalography: a less connected state

Author

Strijbis, E. M. M., Boon, L. I., Kulik, S. D., Hepp, D. H., Fraschini, M., Uitdehaag, B. M. J., Geurts, J. J. G., Hillebrand, A., Schoonheim, M. M., Stam, C. J., Neurology, Anatomy and neurosciences, Amsterdam Neuroscience - Neurodegeneration, Amsterdam Neuroscience - Neuroinfection & -inflammation, CCA - Imaging and biomarkers, and Amsterdam Neuroscience - Brain Imaging

Published
2018

26. Loss of functional brain network hierarchy relates to cognitive impairment in multiple sclerosis: a magnetoencephalography study

Author

Nauta, I. M., Kulik, S. D., Breedt, L. C., Eijlers, A. J. C., Strijbis, E. M. M., Tewarie, P., Hillebrand, A., Stam, C. J., Douw, L., Geurts, J. J. G., Uitdehaag, B. M. J., de Jong, B. A., Schoonheim, M. M., Anatomy and neurosciences, Neurology, Amsterdam Neuroscience - Neuroinfection & -inflammation, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neurodegeneration, CCA - Imaging and biomarkers, and APH - Quality of Care

Published
2018

28. Accelerating cortical atrophy as an explanation for cognitive decline in progressive MS

Author

Eijlers, A. J., Dekker, I., Steenwijk, M. D., Meijer, K. A., Hulst, H. E., Pouwels, Petra, Uitdehaag, B. M., Barkhof, F., Vrenken, H., Schoonheim, M. M., Geurts, J. J., Anatomy and neurosciences, Amsterdam Neuroscience - Neuroinfection & -inflammation, Radiology and nuclear medicine, Amsterdam Neuroscience - Brain Imaging, Neurology, CCA - Imaging and biomarkers, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Amsterdam Neuroscience - Neurodegeneration, and CCA - Cancer biology and immunology

Published
2018

29. Specific patterns of thalamic functional connectivity changes related to disability and cognitive impairment in multiple sclerosis

Author

Schoonheim, M. M., Pinter, D., Meijer, K. A., Eijlers, A. J. C., Pirpamer, L., Khalil, M., Pichler, A., Fuchs, S., Fazekas, F., Uitdehaag, B. M. J., Barkhof, F., Enzinger, C., Geurts, J. J. G., Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neuroinfection & -inflammation, Anatomy and neurosciences, Clinical chemistry, Plastic, Reconstructive and Hand Surgery, Neurology, CCA - Imaging and biomarkers, Radiology and nuclear medicine, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Amsterdam Neuroscience - Neurodegeneration, and CCA - Cancer biology and immunology

Published
2018

33. Structural MRI predictors of cognitive decline in MS

Author

Eijlers, A. J., Dekker, I., Meijer, K. A., Uitdehaag, B. M., Barkhof, F., Geurts, J. J., Schoonheim, M. M., Anatomy and neurosciences, Amsterdam Neuroscience - Neuroinfection & -inflammation, Radiology and nuclear medicine, Neurology, CCA - Imaging and biomarkers, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Amsterdam Neuroscience - Neurodegeneration, and CCA - Cancer biology and immunology

Published
2017

34. Altered default-mode network dynamics in cognitively impaired MS patients

Author

Eijlers, A. J., Wink, A. M., Meijer, K. A., Douw, L., Schoonheim, M. M., Geurts, J. J., Anatomy and neurosciences, Amsterdam Neuroscience - Neuroinfection & -inflammation, Radiology and nuclear medicine, Amsterdam Neuroscience - Brain Imaging, CCA - Imaging and biomarkers, and Amsterdam Neuroscience - Neurodegeneration

Published
2017

36. Predicting clinical progression in multiple sclerosis six and twelve years after onset

Author

Dekker, I., Eijlers, A. J. C., Popescu, V., Balk, L. J., Vrenken, H., Wattjes, M. P., Uitdehaag, B. M. J., Killestein, J., Geurts, J. J. G., Barkhof, F., Schoonheim, M. M., Radiology and nuclear medicine, Amsterdam Neuroscience - Neuroinfection & -inflammation, Anatomy and neurosciences, Neurology, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neurodegeneration, Other Research, CCA - Imaging and biomarkers, AII - Inflammatory diseases, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, and CCA - Cancer biology and immunology

Published
2017

42. Cognitive decline in multiple sclerosis is associated with structural network disruption - a single-subject network approach

Author

Rimkus, Carolina de Medeiros, Schoonheim, M. M., Steenwijk, M. D., Wattjes, M., Killestein, J., Leite, C. C., Barkhof, F., Tijms, B. M., Anatomy and neurosciences, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neuroinfection & -inflammation, Neurology, Radiology and nuclear medicine, and Amsterdam Neuroscience - Neurodegeneration

Published
2016

45. Consistency of grey matter volume measurement in multiple sclerosis: comparison of FSL, FreeSurfer and SPM

Author

Popescu, V., Schoonheim, M. M., Versteeg, A., Chaturvedi, N., Jonker, M., de Menezes, R. Xavier, Gallindo-Garre, F., Uitdehaag, B. M. J., Barkhof, F., Vrenken, H., Radiology and nuclear medicine, Anatomy and neurosciences, NCA - Neuroinflamation, Dermatology, Epidemiology and Data Science, NCA - Brain mechanisms in health and disease, Neurology, NCA - Brain imaging technology, and NCA - neurodegeneration

Published
2015

47. Local increases in metabolite levels predict lesion formation and lesion severity in relapsing-remitting multiple sclerosis

Author

Klauser, A. M., Wiebenga, O. T., Eijlers, A. J. C., Schoonheim, M. M., Uitdehaag, B. M. J., Barkhof, F., Pouwels, P. J. W., Geurts, J. J. G., NCA - Neuroinflamation, Radiology and nuclear medicine, Anatomy and neurosciences, Neurology, NCA - Brain imaging technology, NCA - Brain mechanisms in health and disease, NCA - neurodegeneration, and CCA - Disease profiling

Published
2015

48. Predicting clinical progression in multiple sclerosis after 6 and 12 years.

Author

Popescu, V., Vrenken, H., Dekker, I., Wattjes, M. P., Barkhof, F., Balk, L. J., Uitdehaag, B. M. J., Killestein, J., Eijlers, A. J. C., Geurts, J. J. G., and Schoonheim, M. M.

Subjects
MULTIPLE sclerosis, EDUCATIONAL attainment, NEUROPSYCHOLOGICAL tests, BRAIN damage, DIAGNOSTIC imaging
Abstract

Background and purpose: To predict disability and cognition in multiple sclerosis (MS) after 6 and 12 years, using early clinical and imaging measures. Methods: A total of 115 patients with MS were selected and followed up after 2 and 6 years, with 79 patients also being followed up after 12 years. Disability was measured using the Expanded Disability Status Scale (EDSS); cognition was measured only at follow‐up using neuropsychological testing. Predictors of interest included EDSS score, baseline brain and lesion volumes and their changes over 2 years, baseline age, clinical phenotype, sex and educational level. Results: Higher 6‐year EDSS score was predicted by early EDSS score and whole‐brain volume changes and baseline diagnosis of primary progressive MS (adjusted R2 = 0.56). Predictors for 12‐year EDSS score included larger EDSS score changes and higher T1‐hypointense lesion volumes (adjusted R2 = 0.38). Year 6 cognition was predicted by primary progressive MS phenotype, lower educational level, male sex and early whole‐brain atrophy (adjusted R2 = 0.26); year 12 predictors included male sex, lower educational level and higher baseline T1‐hypointense lesion volumes (adjusted R2 = 0.14). Conclusions: Patients with early signs of neurodegeneration and a progressive disease onset were more prone to develop both disability progression and cognitive dysfunction. Male sex and lower educational level only affected cognitive dysfunction, which remains difficult to predict and probably needs more advanced imaging measures. [ABSTRACT FROM AUTHOR]

Published
2019
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