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Hayden MR. Cerebral Microbleeds Associate with Brain Endothelial Cell Activation-Dysfunction and Blood-Brain Barrier Dysfunction/Disruption with Increased Risk of Hemorrhagic and Ischemic Stroke. Biomedicines 2024; 12:1463. [PMID: 39062035 PMCID: PMC11274519 DOI: 10.3390/biomedicines12071463] [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: 05/08/2024] [Revised: 06/25/2024] [Accepted: 06/27/2024] [Indexed: 07/28/2024] Open
Abstract
Globally, cerebral microbleeds (CMBs) are increasingly being viewed not only as a marker for cerebral small vessel disease (SVD) but also as having an increased risk for the development of stroke (hemorrhagic/ischemic) and aging-related dementia. Recently, brain endothelial cell activation and dysfunction and blood-brain barrier dysfunction and/or disruption have been shown to be associated with SVD, enlarged perivascular spaces, and the development and evolution of CMBs. CMBs are a known disorder of cerebral microvessels that are visualized as 3-5 mm, smooth, round, or oval, and hypointense (black) lesions seen only on T2*-weighted gradient recall echo or susceptibility-weighted sequences MRI images. CMBs are known to occur with high prevalence in community-dwelling older individuals. Since our current global population is the oldest recorded in history and is only expected to continue to grow, we can expect the healthcare burdens associated with CMBs to also grow. Increased numbers (≥10) of CMBs should raise a red flag regarding the increased risk of large symptomatic neurologic intracerebral hemorrhages. Importantly, CMBs are also currently regarded as markers of diffuse vascular and neurodegenerative brain damage. Herein author highlights that it is essential to learn as much as we can about CMB development, evolution, and their relation to impaired cognition, dementia, and the exacerbation of neurodegeneration.
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Affiliation(s)
- Melvin R Hayden
- Department of Internal Medicine, Endocrinology Diabetes and Metabolism, Diabetes and Cardiovascular Disease Center, University of Missouri School of Medicine, One Hospital Drive, Columbia, MO 65211, USA
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Stipho F, Malek-Ahmadi M. Meta-Analysis of White Matter Hyperintensity Volume Differences Between APOE ε4 Carriers and Noncarriers. Alzheimer Dis Assoc Disord 2024; 38:208-212. [PMID: 38748617 PMCID: PMC11141236 DOI: 10.1097/wad.0000000000000620] [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: 02/27/2024] [Accepted: 04/07/2024] [Indexed: 05/31/2024]
Abstract
Several studies have suggested that white matter hyperintensity volume (WMHV) is increased among apolipoprotein E (APOE) ε4 carriers while others have reported contradictory findings. Although APOE ε4 carriage is associated with greater AD pathology, it remains unclear whether cerebrovascular damage is also associated with APOE ε4 carriage. The aim of this meta-analysis was to determine whether WMHV is associated with APOE ε4 carrier status. 12 studies that were included yielded a total sample size of 16,738 adult subjects (ε4 carrier n = 4,721; ε4 noncarrier n = 12,017). There were no significant differences in WMHV between ε4 carriers and noncarriers (Hedge's g = 0.07; 95% CI (-0.01 to 0.15), P = 0.09). Subgroup analysis of community-based studies (n = 8) indicated a small effect size where ε4 carriers had greater WMHV relative to noncarriers (Hedge's g = 0.09 95% CI (0.02 to 0.16), P = 0.008). Among clinic-based studies (n = 3) there was no significant difference in WMHV by APOE ε4 carrier status (Hedge's g = -0.09, 95% CI (-0.60 to 0.41), P = 0.70). Observed APOE ε4-associated WMHV differences may be context-dependent and may also be confounded by a lack of standardization for WMHV segmentation.
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Affiliation(s)
- Faissal Stipho
- University of Arizona College of Medicine-Tucson, Tucson, AZ
| | - Michael Malek-Ahmadi
- Banner Alzheimer’s Institute, Phoenix, AZ
- University of Arizona College of Medicine-Phoenix, Dept. of Biomedical Informatics, Phoenix, AZ
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Vilor‐Tejedor N, Genius P, Rodríguez‐Fernández B, Minguillón C, Sadeghi I, González‐Escalante A, Crous‐Bou M, Suárez‐Calvet M, Grau‐Rivera O, Brugulat‐Serrat A, Sánchez‐Benavides G, Esteller M, Fauria K, Molinuevo JL, Navarro A, Gispert JD. Genetic characterization of the ALFA study: Uncovering genetic profiles in the Alzheimer's continuum. Alzheimers Dement 2024; 20:1703-1715. [PMID: 38088508 PMCID: PMC10984507 DOI: 10.1002/alz.13537] [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: 05/24/2023] [Revised: 09/12/2023] [Accepted: 10/11/2023] [Indexed: 03/16/2024]
Abstract
INTRODUCTION In 2013, the ALzheimer's and FAmilies (ALFA) project was established to investigate pathophysiological changes in preclinical Alzheimer's disease (AD), and to foster research on early detection and preventive interventions. METHODS We conducted a comprehensive genetic characterization of ALFA participants with respect to neurodegenerative/cerebrovascular diseases, AD biomarkers, brain endophenotypes, risk factors and aging biomarkers. We placed particular emphasis on amyloid/tau status and assessed gender differences. Multiple polygenic risk scores were computed to capture different aspects of genetic predisposition. We additionally compared AD risk in ALFA to that across the full disease spectrum from the Alzheimer's Disease Neuroimaging Initiative (ADNI). RESULTS Results show that the ALFA project has been successful at establishing a cohort of cognitively unimpaired individuals at high genetic predisposition of AD. DISCUSSION It is, therefore, well-suited to study early pathophysiological changes in the preclinical AD continuum. Highlights Prevalence of ε4 carriers in ALzheimer and FAmilies (ALFA) is higher than in the general European population The ALFA study is highly enriched in Alzheimer's disease (AD) genetic risk factors beyond APOE AD genetic profiles in ALFA are similar to clinical groups along the continuum ALFA has succeeded in establishing a cohort of cognitively unimpaired individuals at high genetic AD risk ALFA is well suited to study pathogenic events/early pathophysiological changes in AD.
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Affiliation(s)
- Natalia Vilor‐Tejedor
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Centre for Genomic Regulation (CRG)The Barcelona Institute for Science and TechnologyBarcelonaSpain
- Department of Clinical GeneticsErasmus University Medical CenterRotterdamNetherlands
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
| | - Patricia Genius
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Centre for Genomic Regulation (CRG)The Barcelona Institute for Science and TechnologyBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
| | - Blanca Rodríguez‐Fernández
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Centre for Genomic Regulation (CRG)The Barcelona Institute for Science and TechnologyBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
| | - Carolina Minguillón
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBER‐FES)Instituto de Salud Carlos IIIMadridSpain
| | - Iman Sadeghi
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Centre for Genomic Regulation (CRG)The Barcelona Institute for Science and TechnologyBarcelonaSpain
| | - Armand González‐Escalante
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
- Department of Medicine and Life SciencesUniversitat Pompeu FabraBarcelonaSpain
| | - Marta Crous‐Bou
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Department of EpidemiologyHarvard T.H. Chan School of Public Health. School of Public Health 2BostonMassachusettsUSA
- Catalan Institute of Oncology (ICO)‐Bellvitge Biomedical Research Center (IDIBELL)Hospital Duran i ReynalsBarcelonaSpain
| | - Marc Suárez‐Calvet
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBER‐FES)Instituto de Salud Carlos IIIMadridSpain
- Servei de NeurologiaHospital del MarBarcelonaSpain
| | - Oriol Grau‐Rivera
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBER‐FES)Instituto de Salud Carlos IIIMadridSpain
- Servei de NeurologiaHospital del MarBarcelonaSpain
| | - Anna Brugulat‐Serrat
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBER‐FES)Instituto de Salud Carlos IIIMadridSpain
- Global Brain Health InstituteSan FranciscoCaliforniaUSA
| | - Gonzalo Sánchez‐Benavides
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBER‐FES)Instituto de Salud Carlos IIIMadridSpain
| | - Manel Esteller
- Cancer Epigenetics, Josep Carreras Leukaemia Research Institute (IJC)BarcelonaSpain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), Instituto de Salud Carlos IIIMadridSpain
- Integrated Pharmacology and Systems NeurosciencesIMIM‐Hospital del Mar Medical Research InstituteBarcelonaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain
- Physiological Sciences DepartmentSchool of Medicine and Health SciencesUniversity of Barcelona (UB)BarcelonaSpain
| | - Karine Fauria
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBER‐FES)Instituto de Salud Carlos IIIMadridSpain
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Experimental Medicine, H. Lundbeck A/SKøbenhavnDenmark
| | - Arcadi Navarro
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Centre for Genomic Regulation (CRG)The Barcelona Institute for Science and TechnologyBarcelonaSpain
- Department of Medicine and Life SciencesUniversitat Pompeu FabraBarcelonaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain
- Department of Experimental and Health SciencesInstitute of Evolutionary Biology (CSIC‐UPF)BarcelonaSpain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC)Pasqual Maragall FoundationBarcelonaSpain
- Neurosciences Programme, IMIM ‐ Hospital del Mar Medical Research InstituteBarcelonaSpain
- Department of Medicine and Life SciencesUniversitat Pompeu FabraBarcelonaSpain
- Centro de Investigación Biomédica en Red BioingenieríaBiomateriales y Nanomedicina. Instituto de Salud carlos IIIMadridSpain
- Centro Nacional de Investigaciones Cardiovasculares (CNIC)MadridSpain
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Sudre CH, Van Wijnen K, Dubost F, Adams H, Atkinson D, Barkhof F, Birhanu MA, Bron EE, Camarasa R, Chaturvedi N, Chen Y, Chen Z, Chen S, Dou Q, Evans T, Ezhov I, Gao H, Girones Sanguesa M, Gispert JD, Gomez Anson B, Hughes AD, Ikram MA, Ingala S, Jaeger HR, Kofler F, Kuijf HJ, Kutnar D, Lee M, Li B, Lorenzini L, Menze B, Molinuevo JL, Pan Y, Puybareau E, Rehwald R, Su R, Shi P, Smith L, Tillin T, Tochon G, Urien H, van der Velden BHM, van der Velpen IF, Wiestler B, Wolters FJ, Yilmaz P, de Groot M, Vernooij MW, de Bruijne M. Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021. Med Image Anal 2024; 91:103029. [PMID: 37988921 DOI: 10.1016/j.media.2023.103029] [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: 07/01/2022] [Revised: 08/09/2023] [Accepted: 11/13/2023] [Indexed: 11/23/2023]
Abstract
Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.
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Affiliation(s)
- Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, United Kingdom; Centre for Medical Image Computing, University College London, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - Kimberlin Van Wijnen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Florian Dubost
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Hieab Adams
- Department of Clinical Genetics and Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - David Atkinson
- Centre for Medical Imaging, University College London, London, United Kingdom
| | - Frederik Barkhof
- Centre for Medical Image Computing, University College London, London, United Kingdom; Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Mahlet A Birhanu
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Esther E Bron
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Robin Camarasa
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Nish Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, United Kingdom
| | - Yuan Chen
- Department of Radiology, University of Massachusetts Medical School, Worcester, USA
| | - Zihao Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shuai Chen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Qi Dou
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, China
| | - Tavia Evans
- Department of Clinical Genetics and Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Ivan Ezhov
- Department of Informatics, Technische Universitat Munchen, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany
| | - Haojun Gao
- Department of Radiology, Zhejiang University, Hangzhou, China
| | | | - Juan Domingo Gispert
- Barcelonaß Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain; Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Barcelona, Spain
| | | | - Alun D Hughes
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, United Kingdom
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - H Rolf Jaeger
- Institute of Neurology, University College London, London, United Kingdom
| | - Florian Kofler
- Department of Informatics, Technische Universitat Munchen, Munich, Germany; Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Denis Kutnar
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Bo Li
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Luigi Lorenzini
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Bjoern Menze
- Department of Informatics, Technische Universitat Munchen, Munich, Germany; Department of Quantitative Biomedicine, University of Zurich, Switzerland
| | - Jose Luis Molinuevo
- Barcelonaß Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; H. Lundbeck A/S, Copenhagen, Denmark
| | - Yiwei Pan
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | | | - Rafael Rehwald
- Institute of Neurology, University College London, London, United Kingdom
| | - Ruisheng Su
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Pengcheng Shi
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | | | - Therese Tillin
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, United Kingdom
| | | | - Hélène Urien
- ISEP-Institut Supérieur d'Électronique de Paris, Issy-les-Moulineaux, France
| | | | - Isabelle F van der Velpen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany
| | - Frank J Wolters
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Pinar Yilmaz
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Marius de Groot
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; GlaxoSmithKline Research, Stevenage, United Kingdom
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
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Moutzouri E, Glutz M, Abolhassani N, Feller M, Adam L, Gencer B, Del Giovane C, Bétrisey S, Paladini RE, Hennings E, Aeschbacher S, Beer JH, Moschovitis G, Seiffge D, De Marchis GM, Coslovsky M, Reichlin T, Conte G, Sinnecker T, Schwenkglenks M, Bonati LH, Kastner P, Aujesky D, Kühne M, Osswald S, Fischer U, Conen D, Rodondi N. Association of statin use and lipid levels with cerebral microbleeds and intracranial hemorrhage in patients with atrial fibrillation: A prospective cohort study. Int J Stroke 2023; 18:1219-1227. [PMID: 37243540 PMCID: PMC10676039 DOI: 10.1177/17474930231181010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 05/02/2023] [Indexed: 05/29/2023]
Abstract
BACKGROUND An increased risk of intracranial hemorrhage (ICH) associated with statins has been reported, but data on the relationship between statin use and cerebral microbleeds (CMBs) in patients with atrial fibrillation (AF), a population at high bleeding and cardiovascular risk, are lacking. AIMS To explore the association between statin use and blood lipid levels with the prevalence and progression of CMBs in patients with AF with a particular focus on anticoagulated patients. METHODS Data of Swiss-AF, a prospective cohort of patients with established AF, were analyzed. Statin use was assessed during baseline and throughout follow-up. Lipid values were measured at baseline. CMBs were assessed using magnetic resonance imagining (MRI) at baseline and at 2 years follow-up. Imaging data were centrally assessed by blinded investigators. Associations of statin use and low-density lipoprotein (LDL) levels with CMB prevalence at baseline or CMB progression (at least one additional or new CMB on follow-up MRI at 2 years compared with baseline) were assessed using logistic regression models; the association with ICH was assessed using flexible parametric survival models. Models were adjusted for hypertension, smoking, body mass index, diabetes, stroke/transient ischemic attack, coronary heart disease, antiplatelet use, anticoagulant use, and education. RESULTS Of the 1693 patients with CMB data at baseline MRI (mean ± SD age 72.5 ± 8.4 years, 27.6% women, 90.1% on oral anticoagulants), 802 patients (47.4%) were statin users. The multivariable adjusted odds ratio (adjOR) for CMBs prevalence at baseline for statin users was 1.10 (95% CI = 0.83-1.45). AdjOR for 1 unit increase in LDL levels was 0.95 (95% CI = 0.82-1.10). At 2 years, 1188 patients had follow-up MRI. CMBs progression was observed in 44 (8.0%) statin users and 47 (7.4%) non-statin users. Of these patients, 64 (70.3%) developed a single new CMB, 14 (15.4%) developed 2 CMBs, and 13 developed more than 3 CMBs. The multivariable adjOR for statin users was 1.09 (95% CI = 0.66-1.80). There was no association between LDL levels and CMB progression (adjOR 1.02, 95% CI = 0.79-1.32). At follow-up 14 (1.2%) statin users had ICH versus 16 (1.3%) non-users. The age and sex adjusted hazard ratio (adjHR) was 0.75 (95% CI = 0.36-1.55). The results remained robust in sensitivity analyses excluding participants without anticoagulants. CONCLUSIONS In this prospective cohort of patients with AF, a population at increased hemorrhagic risk due to anticoagulation, the use of statins was not associated with an increased risk of CMBs.
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Affiliation(s)
- Elisavet Moutzouri
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
- Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department of Hematology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Matthias Glutz
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
| | - Nazanin Abolhassani
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
- Department of Epidemiology and Health Systems, Center for Primary Care and Public Health, (Unisanté), University of Lausanne, Lausanne, Switzerland
| | - Martin Feller
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
| | - Luise Adam
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
| | - Baris Gencer
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
- Department of Cardiology, HUG, University Hospital Geneva, Geneva, Switzerland
| | - Cinzia Del Giovane
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
| | - Sylvain Bétrisey
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
| | - Rebecca E Paladini
- Cardiovascular Research Institute Basel, Basel University Hospital, University of Basel, Basel, Switzerland
| | - Elisa Hennings
- Cardiovascular Research Institute Basel, Basel University Hospital, University of Basel, Basel, Switzerland
- Cardiology Division, Department of Medicine, Basel University Hospital, University of Basel, Basel, Switzerland
| | - Stefanie Aeschbacher
- Cardiovascular Research Institute Basel, Basel University Hospital, University of Basel, Basel, Switzerland
- Cardiology Division, Department of Medicine, Basel University Hospital, University of Basel, Basel, Switzerland
| | - Jürg H Beer
- Department of Medicine, Cantonal Hospital of Baden and Center for Molecular Cardiology, University Hospital of Zurich, Zurich, Switzerland
| | - Giorgio Moschovitis
- Cardiology Division, Regional Hospital of Lugano, Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - David Seiffge
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Gian Marco De Marchis
- Department of Neurology and Stroke Center, Basel University Hospital, University of Basel, Basel, Switzerland
| | - Michael Coslovsky
- Cardiovascular Research Institute Basel, Basel University Hospital, University of Basel, Basel, Switzerland
- Department Clinical Research, Basel University Hospital, University of Basel, Basel, Switzerland
| | - Tobias Reichlin
- Division of Cardiology, Department of Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Giulio Conte
- Cardiocentro Ticino Institute, Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - Tim Sinnecker
- Department of Neurology and Stroke Center, Basel University Hospital, University of Basel, Basel, Switzerland
| | - Matthias Schwenkglenks
- Epidemiology, Biostatistics and Prevention Institute (EBPI), University of Zurich, Zurich, Switzerland
- Institute of Pharmaceutical Medicine (ECPM), University of Basel, Basel, Switzerland
| | - Leo H Bonati
- Department of Neurology and Stroke Center, Basel University Hospital, University of Basel, Basel, Switzerland
| | | | - Drahomir Aujesky
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
- Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Michael Kühne
- Cardiovascular Research Institute Basel, Basel University Hospital, University of Basel, Basel, Switzerland
- Cardiology Division, Department of Medicine, Basel University Hospital, University of Basel, Basel, Switzerland
| | - Stefan Osswald
- Cardiovascular Research Institute Basel, Basel University Hospital, University of Basel, Basel, Switzerland
- Cardiology Division, Department of Medicine, Basel University Hospital, University of Basel, Basel, Switzerland
| | - Urs Fischer
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department of Neurology and Stroke Center, Basel University Hospital, University of Basel, Basel, Switzerland
| | - David Conen
- Division of Cardiology, Department of Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Population Health Research Institute, McMaster University, Hamilton, ON, Canada
| | - Nicolas Rodondi
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
- Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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Lv K, Liu Y, Chen Y, Buch S, Wang Y, Yu Z, Wang H, Zhao C, Fu D, Wang H, Wang B, Zhang S, Luo Y, Haacke EM, Shen W, Chai C, Xia S. The iron burden of cerebral microbleeds contributes to brain atrophy through the mediating effect of white matter hyperintensity. Neuroimage 2023; 281:120370. [PMID: 37716591 DOI: 10.1016/j.neuroimage.2023.120370] [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: 11/11/2022] [Revised: 05/04/2023] [Accepted: 09/08/2023] [Indexed: 09/18/2023] Open
Abstract
The goal of this work was to explore the total iron burden of cerebral microbleeds (CMBs) using a semi-automatic quantitative susceptibility mapping and to establish its effect on brain atrophy through the mediating effect of white matter hyperintensities (WMH). A total of 95 community-dwelling people were enrolled. Quantitative susceptibility mapping (QSM) combined with a dynamic programming algorithm (DPA) was used to measure the characteristics of 1309 CMBs. WMH were evaluated according to the Fazekas scale, and brain atrophy was assessed using a 2D linear measurement method. Histogram analysis was used to explore the distribution of CMBs susceptibility, volume, and total iron burden, while a correlation analysis was used to explore the relationship between volume and susceptibility. Stepwise regression analysis was used to analyze the risk factors for CMBs and their contribution to brain atrophy. Mediation analysis was used to explore the interrelationship between CMBs and brain atrophy. We found that the frequency distribution of susceptibility of the CMBs was Gaussian in nature with a mean of 201 ppb and a standard deviation of 84 ppb; however, the volume and total iron burden of CMBs were more Rician in nature. A weak but significant correlation between the susceptibility and volume of CMBs was found (r = -0.113, P < 0.001). The periventricular WMH (PVWMH) was a risk factor for the presence of CMBs (number: β = 0.251, P = 0.014; volume: β = 0.237, P = 0.042; total iron burden: β = 0.238, P = 0.020) and was a risk factor for brain atrophy (third ventricle width: β = 0.325, P = 0.001; Evans's index: β = 0.323, P = 0.001). PVWMH had a significant mediating effect on the correlation between CMBs and brain atrophy. In conclusion, QSM along with the DPA can measure the total iron burden of CMBs. PVWMH might be a risk factor for CMBs and may mediate the effect of CMBs on brain atrophy.
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Affiliation(s)
- Ke Lv
- Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Yanzhen Liu
- Department of Radiology, Tianjin Chest Hospital, Tianjin, China
| | - Yongsheng Chen
- Department of Neurology, Wayne State University, Detroit, MI, USA
| | - Sagar Buch
- Department of Neurology, Wayne State University, Detroit, MI, USA
| | - Ying Wang
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA; Department of Radiology, Wayne State University, Detroit, MI, USA
| | - Zhuo Yu
- Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Huiying Wang
- The School of Medicine, Nankai University, Tianjin, China
| | - Chenxi Zhao
- Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Dingwei Fu
- Department of Radiology, The Second Affiliated Hospital of Wannan Medical College, Wuhu, Anhui Province, China
| | - Huapeng Wang
- Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Beini Wang
- Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China
| | | | - Yu Luo
- Department of Radiology, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, China
| | - E Mark Haacke
- Department of Neurology, Wayne State University, Detroit, MI, USA; Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA; Department of Radiology, Wayne State University, Detroit, MI, USA; Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA
| | - Wen Shen
- Department of Radiology, Tianjin Institute of Imaging Medicine, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Chao Chai
- Department of Radiology, Tianjin Institute of Imaging Medicine, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Shuang Xia
- Department of Radiology, Tianjin Institute of Imaging Medicine, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
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Chouliaras L, O'Brien JT. The use of neuroimaging techniques in the early and differential diagnosis of dementia. Mol Psychiatry 2023; 28:4084-4097. [PMID: 37608222 PMCID: PMC10827668 DOI: 10.1038/s41380-023-02215-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 07/27/2023] [Accepted: 08/03/2023] [Indexed: 08/24/2023]
Abstract
Dementia is a leading cause of disability and death worldwide. At present there is no disease modifying treatment for any of the most common types of dementia such as Alzheimer's disease (AD), Vascular dementia, Lewy Body Dementia (LBD) and Frontotemporal dementia (FTD). Early and accurate diagnosis of dementia subtype is critical to improving clinical care and developing better treatments. Structural and molecular imaging has contributed to a better understanding of the pathophysiology of neurodegenerative dementias and is increasingly being adopted into clinical practice for early and accurate diagnosis. In this review we summarise the contribution imaging has made with particular focus on multimodal magnetic resonance imaging (MRI) and positron emission tomography imaging (PET). Structural MRI is widely used in clinical practice and can help exclude reversible causes of memory problems but has relatively low sensitivity for the early and differential diagnosis of dementia subtypes. 18F-fluorodeoxyglucose PET has high sensitivity and specificity for AD and FTD, while PET with ligands for amyloid and tau can improve the differential diagnosis of AD and non-AD dementias, including recognition at prodromal stages. Dopaminergic imaging can assist with the diagnosis of LBD. The lack of a validated tracer for α-synuclein or TAR DNA-binding protein 43 (TDP-43) imaging remain notable gaps, though work is ongoing. Emerging PET tracers such as 11C-UCB-J for synaptic imaging may be sensitive early markers but overall larger longitudinal multi-centre cross diagnostic imaging studies are needed.
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Affiliation(s)
- Leonidas Chouliaras
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Specialist Dementia and Frailty Service, Essex Partnership University NHS Foundation Trust, St Margaret's Hospital, Epping, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, UK.
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
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Jensen PN, Rashid T, Ware JB, Cui Y, Sitlani CM, Austin TR, Longstreth W, Bertoni AG, Mamourian E, Bryan RN, Nasrallah IM, Habes M, Heckbert SR. Association of brain microbleeds with risk factors, cognition, and MRI markers in MESA. Alzheimers Dement 2023; 19:4139-4149. [PMID: 37289978 PMCID: PMC11163989 DOI: 10.1002/alz.13346] [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: 03/09/2023] [Revised: 05/11/2023] [Accepted: 05/17/2023] [Indexed: 06/10/2023]
Abstract
INTRODUCTION Little is known about the epidemiology of brain microbleeds in racially/ethnically diverse populations. METHODS In the Multi-Ethnic Study of Atherosclerosis, brain microbleeds were identified from 3T magnetic resonance imaging susceptibility-weighted imaging sequences using deep learning models followed by radiologist review. RESULTS Among 1016 participants without prior stroke (25% Black, 15% Chinese, 19% Hispanic, 41% White, mean age 72), microbleed prevalence was 20% at age 60 to 64.9 and 45% at ≥85 years. Deep microbleeds were associated with older age, hypertension, higher body mass index, and atrial fibrillation, and lobar microbleeds with male sex and atrial fibrillation. Overall, microbleeds were associated with greater white matter hyperintensity volume and lower total white matter fractional anisotropy. DISCUSSION Results suggest differing associations for lobar versus deep locations. Sensitive microbleed quantification will facilitate future longitudinal studies of their potential role as an early indicator of vascular pathology.
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Affiliation(s)
- Paul N. Jensen
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington 98195, USA
- Department of Medicine, University of Washington, Seattle, Washington 98195, USA
| | - Tanweer Rashid
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio, Texas 78229, USA
| | - Jeffrey B. Ware
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19103, USA
| | - Yuhan Cui
- Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Colleen M. Sitlani
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington 98195, USA
- Department of Medicine, University of Washington, Seattle, Washington 98195, USA
| | - Thomas R. Austin
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington 98195, USA
- Department of Epidemiology, University of Washington, Seattle, Washington 98195, USA
| | - W.T. Longstreth
- Department of Epidemiology, University of Washington, Seattle, Washington 98195, USA
- Department of Neurology, University of Washington, Seattle, Washington 98195, USA
| | - Alain G. Bertoni
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA
| | - Elizabeth Mamourian
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19103, USA
- Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - R. Nick Bryan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19103, USA
| | - Ilya M. Nasrallah
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19103, USA
- Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio, Texas 78229, USA
- Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington 98195, USA
- Department of Epidemiology, University of Washington, Seattle, Washington 98195, USA
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Li Y, Chang J, Chen X, Liu J, Zhao L. Advances in the Study of APOE and Innate Immunity in Alzheimer's Disease. J Alzheimers Dis 2023:JAD230179. [PMID: 37182889 DOI: 10.3233/jad-230179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Alzheimer's disease (AD) is a progressive degenerative disease of the nervous system (CNS) with an insidious onset. Clinically, it is characterized by a full range of dementia manifestations including memory impairment, aphasia, loss of speech, loss of use, loss of recognition, impairment of visuospatial skills, and impairment of executive function, as well as changes in personality and behavior. The exact cause of AD has not yet been identified. Nevertheless, modern research indicates that genetic factors contribute to 70% of human's risk of AD. Apolipoprotein (APOE) accounts for up to 90% of the genetic predisposition. APOE is a crucial gene that cannot be overstated. In addition, innate immunity plays a significant role in the etiology and treatment of AD. Understanding the different subtypes of APOE and their interconnections is of paramount importance. APOE and innate immunity, along with their relationship to AD, are primary research motivators for in-depth research and clinical trials. The exploration of novel technologies has led to an increasing trend in the study of AD at the cellular and molecular levels and continues to make more breakthroughs and progress. As of today, there is no effective treatment available for AD around the world. This paper aims to summarize and analyze the role of APOE and innate immunity, as well as development trends in recent years. It is anticipated that APOE and innate immunity will provide a breakthrough for humans to hinder AD progression in the near future.
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Affiliation(s)
- Yujiao Li
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Jun Chang
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Xi Chen
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Jianwei Liu
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Lan Zhao
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
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10
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Wan H, Chen H, Zhang M, Feng T, Wang Y. Cerebral microbleeds is associated with dementia in Parkinson's disease. Acta Neurol Belg 2023; 123:407-413. [PMID: 35672560 DOI: 10.1007/s13760-022-01918-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 03/07/2022] [Indexed: 11/01/2022]
Abstract
INTRODUCTION Emerging evidence suggests that cerebral small vessel disease (CSVD) may worsen cognitive functions in Parkinson's disease (PD). However, the effect of microbleeds on cognitive function in patients with PD remains unknown. This study explored the association between the presence, number and location of microbleeds with dementia in PD patients. METHODS This cross-sectional study included 431 patients with PD from Beijing Tiantan Hospital from May 2016 to August 2019. Cognition assessments (MMSE, MoCA) were performed for these patients. MRI imaging sequences were obtained and reviewed independently by two well-trained readers who were blind to all clinical data. Spearman's correlation analysis and logistic regression model analysis were further used for the assessments. RESULTS An association between cerebral microbleeds with cognitive ability and dementia in PD patients was revealed. A significance was observed between the total number of microbleeds and two widely used scores of cognitive assessments (Spearman R = - 0.120 to MMSE with a p = 0.016, and - 0.117 to MoCA with a p = 0.020). In detail, infratentorial microbleeds were associated with the level of cognition in PD patients (Spearman R = - 0.099 to MMSE with a p = 0.049, and - 0.116 to MoCA with a p = 0.021). Furthermore, logistic regression analysis results also confirmed such correlations between the number of microbleeds and cognitive ability after adjusting for age, cholesterol level, Hamilton Anxiety Scale, Hamilton Depression Scale, and white matter hyperintensity Fazekas score (OR 3.28, p = 0.035, 95% CI 1.090-9.892). CONCLUSIONS The occurrence of microbleeds, especially in the infratentorial locations, may worsen the cognitive function of PD patients and result in dementia. Management of cerebral vascular disease could be beneficial to patients with PD.
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Affiliation(s)
- Huijuan Wan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases (NCRC-ND), Beijing, China
- Advanced Innovation Center for Human Brain Projection, Capital Medical University, Beijing, China
- Department of Neurology, First Affiliated Hospital, Xiamen University, Xiamen, China
| | - Huimin Chen
- Department of Neurology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Meimei Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases (NCRC-ND), Beijing, China
- Advanced Innovation Center for Human Brain Projection, Capital Medical University, Beijing, China
| | - Tao Feng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
- China National Clinical Research Center for Neurological Diseases (NCRC-ND), Beijing, China.
- Advanced Innovation Center for Human Brain Projection, Capital Medical University, Beijing, China.
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
- China National Clinical Research Center for Neurological Diseases (NCRC-ND), Beijing, China.
- Advanced Innovation Center for Human Brain Projection, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China.
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11
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Elsaid AF, Fahmi RM, Shehta N, Ramadan BM. Machine learning approach for hemorrhagic transformation prediction: Capturing predictors' interaction. Front Neurol 2022; 13:951401. [PMID: 36504664 PMCID: PMC9731336 DOI: 10.3389/fneur.2022.951401] [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: 05/24/2022] [Accepted: 10/28/2022] [Indexed: 11/25/2022] Open
Abstract
Background and purpose Patients with ischemic stroke frequently develop hemorrhagic transformation (HT), which could potentially worsen the prognosis. The objectives of the current study were to determine the incidence and predictors of HT, to evaluate predictor interaction, and to identify the optimal predicting models. Methods A prospective study included 360 patients with ischemic stroke, of whom 354 successfully continued the study. Patients were subjected to thorough general and neurological examination and T2 diffusion-weighted MRI, at admission and 1 week later to determine the incidence of HT. HT predictors were selected by a filter-based minimum redundancy maximum relevance (mRMR) algorithm independent of model performance. Several machine learning algorithms including multivariable logistic regression classifier (LRC), support vector classifier (SVC), random forest classifier (RFC), gradient boosting classifier (GBC), and multilayer perceptron classifier (MLPC) were optimized for HT prediction in a randomly selected half of the sample (training set) and tested in the other half of the sample (testing set). The model predictive performance was evaluated using receiver operator characteristic (ROC) and visualized by observing case distribution relative to the models' predicted three-dimensional (3D) hypothesis spaces within the testing dataset true feature space. The interaction between predictors was investigated using generalized additive modeling (GAM). Results The incidence of HT in patients with ischemic stroke was 19.8%. Infarction size, cerebral microbleeds (CMB), and the National Institute of Health stroke scale (NIHSS) were identified as the best HT predictors. RFC (AUC: 0.91, 95% CI: 0.85-0.95) and GBC (AUC: 0.91, 95% CI: 0.86-0.95) demonstrated significantly superior performance compared to LRC (AUC: 0.85, 95% CI: 0.79-0.91) and MLPC (AUC: 0.85, 95% CI: 0.78-0.92). SVC (AUC: 0.90, 95% CI: 0.85-0.94) outperformed LRC and MLPC but did not reach statistical significance. LRC and MLPC did not show significant differences. The best models' 3D hypothesis spaces demonstrated non-linear decision boundaries suggesting an interaction between predictor variables. GAM analysis demonstrated a linear and non-linear significant interaction between NIHSS and CMB and between NIHSS and infarction size, respectively. Conclusion Cerebral microbleeds, NIHSS, and infarction size were identified as HT predictors. The best predicting models were RFC and GBC capable of capturing nonlinear interaction between predictors. Predictor interaction suggests a dynamic, rather than, fixed cutoff risk value for any of these predictors.
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Affiliation(s)
- Ahmed F. Elsaid
- Department of Public Health and Community Medicine, Zagazig University, Zagazig, Egypt,*Correspondence: Ahmed F. Elsaid ;
| | - Rasha M. Fahmi
- Neurology Department, Faculty of Medicine, Zagazig University, Zagazig, Egypt
| | - Nahed Shehta
- Neurology Department, Faculty of Medicine, Zagazig University, Zagazig, Egypt
| | - Bothina M. Ramadan
- Neurology Department, Faculty of Medicine, Zagazig University, Zagazig, Egypt
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12
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Low A, Prats-Sedano MA, Stefaniak JD, McKiernan EF, Carter SF, Douvani ME, Mak E, Su L, Stupart O, Muniz G, Ritchie K, Ritchie CW, Markus HS, O'Brien JT. CAIDE dementia risk score relates to severity and progression of cerebral small vessel disease in healthy midlife adults: the PREVENT-Dementia study. J Neurol Neurosurg Psychiatry 2022; 93:481-490. [PMID: 35135868 PMCID: PMC9016254 DOI: 10.1136/jnnp-2021-327462] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 12/27/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Markers of cerebrovascular disease are common in dementia, and may be present before dementia onset. However, their clinical relevance in midlife adults at risk of future dementia remains unclear. We investigated whether the Cardiovascular Risk Factors, Ageing and Dementia (CAIDE) risk score was associated with markers of cerebral small vessel disease (SVD), and if it predicted future progression of SVD. We also determined its relationship to systemic inflammation, which has been additionally implicated in dementia and SVD. METHODS Cognitively healthy midlife participants were assessed at baseline (n=185) and 2-year follow-up (n=158). To assess SVD, we quantified white matter hyperintensities (WMH), enlarged perivascular spaces (EPVS), microbleeds and lacunes. We derived composite scores of SVD burden, and subtypes of hypertensive arteriopathy and cerebral amyloid angiopathy. Inflammation was quantified using serum C-reactive protein (CRP) and fibrinogen. RESULTS At baseline, higher CAIDE scores were associated with all markers of SVD and inflammation. Longitudinally, CAIDE scores predicted greater total (p<0.001), periventricular (p<0.001) and deep (p=0.012) WMH progression, and increased CRP (p=0.017). Assessment of individual CAIDE components suggested that markers were driven by different risk factors (WMH/EPVS: age/hypertension, lacunes/deep microbleeds: hypertension/obesity). Interaction analyses demonstrated that higher CAIDE scores amplified the effect of age on SVD, and the effect of WMH on poorer memory. CONCLUSION Higher CAIDE scores, indicating greater risk of dementia, predicts future progression of both WMH and systemic inflammation. Findings highlight the CAIDE score's potential as both a prognostic and predictive marker in the context of cerebrovascular disease, identifying at-risk individuals who might benefit most from managing modifiable risk.
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Affiliation(s)
- Audrey Low
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Maria A Prats-Sedano
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - James D Stefaniak
- Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | | | - Stephen F Carter
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Maria-Eleni Douvani
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Elijah Mak
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Li Su
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Neuroscience, The University of Sheffield, Sheffield, UK
| | - Olivia Stupart
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Graciela Muniz
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh, UK
| | - Karen Ritchie
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh, UK
- INSERM, Montpellier, France
| | - Craig W Ritchie
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh, UK
| | - Hugh S Markus
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - John Tiernan O'Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
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Resistance to developing brain pathology due to vascular risk factors: the role of educational attainment. Neurobiol Aging 2021; 106:197-206. [PMID: 34298318 DOI: 10.1016/j.neurobiolaging.2021.06.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 04/19/2021] [Accepted: 06/10/2021] [Indexed: 11/22/2022]
Abstract
Brain pathology develops at different rates between individuals with similar burden of risk factors, possibly explained by brain resistance. We examined if education contributes to brain resistance by studying its influence on the association between vascular risk factors and brain pathology. In 4111 stroke-free and dementia-free community-dwelling participants (62.9 ± 10.7 years), we explored the association between vascular risk factors (hypertension and the Framingham Stroke Risk Profile [FRSP]) and imaging markers of brain pathology (markers of cerebral small vessel disease and brain volumetry), stratified by educational attainment level. Associations of hypertension and FSRP with markers of brain pathology were not significantly different between levels of educational attainment. Certain associations appeared weaker in those with higher compared to lower educational attainment, particularly for white matter hyperintensities (WMH). Supplementary residual analyses showed significant associations between higher educational attainment and stronger resistance to WMH among others. Our results suggest a role for educational attainment in resistance to vascular brain pathology. Yet, further research is needed to better characterize determinants of brain resistance.
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Sala-Vila A, Arenaza-Urquijo EM, Sánchez-Benavides G, Suárez-Calvet M, Milà-Alomà M, Grau-Rivera O, González-de-Echávarri JM, Crous-Bou M, Minguillón C, Fauria K, Operto G, Falcón C, Salvadó G, Cacciaglia R, Ingala S, Barkhof F, Schröder H, Scarmeas N, Gispert JD, Molinuevo JL. DHA intake relates to better cerebrovascular and neurodegeneration neuroimaging phenotypes in middle-aged adults at increased genetic risk of Alzheimer disease. Am J Clin Nutr 2021; 113:1627-1635. [PMID: 33733657 PMCID: PMC8168359 DOI: 10.1093/ajcn/nqab016] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 01/11/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The number of APOE-ε4 alleles is a major nonmodifiable risk factor for sporadic Alzheimer disease (AD). There is increasing evidence on the benefits of dietary DHA (22:6n-3) before the onset of AD symptoms, particularly in APOE-ε4 carriers. Brain alterations in the preclinical stage can be detected by structural MRI. OBJECTIVES We aimed, in middle-aged cognitively unimpaired individuals at increased risk of AD, to cross-sectionally investigate whether dietary DHA intake relates to cognitive performance and to MRI-based markers of cerebral small vessel disease and AD-related neurodegeneration, exploring the effect modification by APOE-ε4 status. METHODS In 340 participants of the ALFA (ALzheimer and FAmilies) study, which is enriched for APOE-ε4 carriership (n = 122, noncarriers; n = 157, 1 allele; n = 61, 2 alleles), we assessed self-reported DHA intake through an FFQ. We measured cognitive performance by administering episodic memory and executive function tests. We performed high-resolution structural MRI to assess cerebral small vessel disease [white matter hyperintensities (WMHs) and cerebral microbleeds (CMBs)] and AD-related brain atrophy (cortical thickness in an AD signature). We constructed regression models adjusted for potential confounders, exploring the interaction DHA × APOE-ε4. RESULTS We observed no significant associations between DHA and cognitive performance or WMH burden. We observed a nonsignificant inverse association between DHA and prevalence of lobar CMBs (OR: 0.446; 95% CI: 0.195, 1.018; P = 0.055). DHA was found to be significantly related to greater cortical thickness in the AD signature in homozygotes but not in nonhomozygotes (P-interaction = 0.045). The association strengthened when analyzing homozygotes and nonhomozygotes matched for risk factors. CONCLUSIONS In cognitively unimpaired APOE-ε4 homozygotes, dietary DHA intake related to structural patterns that may result in greater resilience to AD pathology. This is consistent with the current hypothesis that those subjects at highest risk would obtain the largest benefits from DHA supplementation in the preclinical stage.This trial was registered at clinicaltrials.gov as NCT01835717.
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Affiliation(s)
| | - Eider M Arenaza-Urquijo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Center for Biomedical Research Network on Frailty and Healthy Aging (CIBERFES), Madrid, Spain
| | - Gonzalo Sánchez-Benavides
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Center for Biomedical Research Network on Frailty and Healthy Aging (CIBERFES), Madrid, Spain
| | - Marc Suárez-Calvet
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Center for Biomedical Research Network on Frailty and Healthy Aging (CIBERFES), Madrid, Spain,Neurology Service, Hospital del Mar, Barcelona, Spain
| | - Marta Milà-Alomà
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Center for Biomedical Research Network on Frailty and Healthy Aging (CIBERFES), Madrid, Spain
| | - Oriol Grau-Rivera
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Neurology Service, Hospital del Mar, Barcelona, Spain
| | - José M González-de-Echávarri
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Center for Biomedical Research Network on Frailty and Healthy Aging (CIBERFES), Madrid, Spain
| | - Marta Crous-Bou
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain,Center for Biomedical Research Network on Frailty and Healthy Aging (CIBERFES), Madrid, Spain,Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA,Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO)–Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Carolina Minguillón
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Karine Fauria
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Grégory Operto
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Center for Biomedical Research Network on Frailty and Healthy Aging (CIBERFES), Madrid, Spain
| | - Carles Falcón
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Center for Biomedical Research Network on Bioengineering, Biomaterials, and Nanomedicine (CIBERBBN), Madrid, Spain,Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Center for Biomedical Research Network on Frailty and Healthy Aging (CIBERFES), Madrid, Spain
| | - Raffaele Cacciaglia
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Center for Biomedical Research Network on Frailty and Healthy Aging (CIBERFES), Madrid, Spain
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands,Institute of Neurology, University College London, London, United Kingdom,Institute of Healthcare Engineering, University College London, London, United Kingdom
| | - Helmut Schröder
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Center for Biomedical Research Network on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Nikolaos Scarmeas
- 1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece,Department of Neurology, The Gertrude H Sergievsky Center, Taub Institute for Research in Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA
| | - Juan-Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain,Center for Biomedical Research Network on Bioengineering, Biomaterials, and Nanomedicine (CIBERBBN), Madrid, Spain,Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain
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15
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Dekker I, Schoonheim MM, Venkatraghavan V, Eijlers AJC, Brouwer I, Bron EE, Klein S, Wattjes MP, Wink AM, Geurts JJG, Uitdehaag BMJ, Oxtoby NP, Alexander DC, Vrenken H, Killestein J, Barkhof F, Wottschel V. The sequence of structural, functional and cognitive changes in multiple sclerosis. Neuroimage Clin 2020; 29:102550. [PMID: 33418173 PMCID: PMC7804841 DOI: 10.1016/j.nicl.2020.102550] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/09/2020] [Accepted: 12/20/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND As disease progression remains poorly understood in multiple sclerosis (MS), we aim to investigate the sequence in which different disease milestones occur using a novel data-driven approach. METHODS We analysed a cohort of 295 relapse-onset MS patients and 96 healthy controls, and considered 28 features, capturing information on T2-lesion load, regional brain and spinal cord volumes, resting-state functional centrality ("hubness"), microstructural tissue integrity of major white matter (WM) tracts and performance on multiple cognitive tests. We used a discriminative event-based model to estimate the sequence of biomarker abnormality in MS progression in general, as well as specific models for worsening physical disability and cognitive impairment. RESULTS We demonstrated that grey matter (GM) atrophy of the cerebellum, thalamus, and changes in corticospinal tracts are early events in MS pathology, whereas other WM tracts as well as the cognitive domains of working memory, attention, and executive function are consistently late events. The models for disability and cognition show early functional changes of the default-mode network and earlier changes in spinal cord volume compared to the general MS population. Overall, GM atrophy seems crucial due to its early involvement in the disease course, whereas WM tract integrity appears to be affected relatively late despite the early onset of WM lesions. CONCLUSION Data-driven modelling revealed the relative occurrence of both imaging and non-imaging events as MS progresses, providing insights into disease propagation mechanisms, and allowing fine-grained staging of patients for monitoring purposes.
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Affiliation(s)
- Iris Dekker
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands; Neurology, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Vikram Venkatraghavan
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Anand J C Eijlers
- Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Iman Brouwer
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Esther E Bron
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Mike P Wattjes
- Dept. of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Alle Meije Wink
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Jeroen J G Geurts
- Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Bernard M J Uitdehaag
- Neurology, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK
| | - Hugo Vrenken
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Joep Killestein
- Neurology, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands; Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK; Institute of Neurology, UCL, London, UK
| | - Viktor Wottschel
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands.
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