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Ter Telgte A, Duering M. Cerebral Small Vessel Disease: Advancing Knowledge With Neuroimaging. Stroke 2024; 55:1686-1688. [PMID: 38328947 DOI: 10.1161/strokeaha.123.044294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Affiliation(s)
- Annemieke Ter Telgte
- VASCage-Center on Clinical Stroke Research, Innsbruck, Austria (A.t.T.)
- Department of Neurology, Medical University of Innsbruck, Austria (A.t.T.)
| | - Marco Duering
- Institute for Stroke and Dementia Research, LMU University Hospital, Munich, Germany (M.D.)
- Medical Image Analysis Center and Department of Biomedical Engineering, University of Basel, Switzerland (M.D.)
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2
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Schipper MR, Vlegels N, van Harten TW, Rasing I, Koemans EA, Voigt S, de Luca A, Kaushik K, van Etten ES, van Zwet EW, Terwindt GM, Biessels GJ, van Osch MJP, van Walderveen MAA, Wermer MJH. Microstructural white matter integrity in relation to vascular reactivity in Dutch-type hereditary cerebral amyloid angiopathy. J Cereb Blood Flow Metab 2023; 43:2144-2155. [PMID: 37708241 PMCID: PMC10925868 DOI: 10.1177/0271678x231200425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 09/16/2023]
Abstract
Cerebral Amyloid Angiopathy (CAA) is characterized by cerebrovascular amyloid-β accumulation leading to hallmark cortical MRI markers, such as vascular reactivity, but white matter is also affected. By studying the relationship in different disease stages of Dutch-type CAA (D-CAA), we tested the relation between vascular reactivity and microstructural white matter integrity loss. In a cross-sectional study in D-CAA, 3 T MRI was performed with Blood-Oxygen-Level-Dependent (BOLD) fMRI upon visual activation to assess vascular reactivity and diffusion tensor imaging to assess microstructural white matter integrity through Peak Width of Skeletonized Mean Diffusivity (PSMD). We assessed the relationship between BOLD parameters - amplitude, time-to-peak (TTP), and time-to-baseline (TTB) - and PSMD, with linear and quadratic regression modeling. In total, 25 participants were included (15/10 pre-symptomatic/symptomatic; mean age 36/59 y). A lowered BOLD amplitude (unstandardized β = 0.64, 95%CI [0.10, 1.18], p = 0.02, Adjusted R2 = 0.48), was quadratically associated with increased PSMD levels. A delayed BOLD response, with prolonged TTP (β = 8.34 × 10-6, 95%CI [1.84 × 10-6, 1.48 × 10-5], p = 0.02, Adj. R2 = 0.25) and TTB (β = 6.57 × 10-6, 95%CI [1.92 × 10-6, 1.12 × 10-5], p = 0.008, Adj. R2 = 0.29), was linearly associated with increased PSMD. In D-CAA subjects, predominantly in the symptomatic stage, impaired cerebrovascular reactivity is related to microstructural white matter integrity loss. Future longitudinal studies are needed to investigate whether this relation is causal.
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Affiliation(s)
- Manon R Schipper
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Naomi Vlegels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Thijs W van Harten
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ingeborg Rasing
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Emma A Koemans
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Sabine Voigt
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Alberto de Luca
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Kanishk Kaushik
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ellis S van Etten
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Erik W van Zwet
- Department of Biostatistics, Leiden University Medical Center, Leiden, The Netherlands
| | - Gisela M Terwindt
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Matthias JP van Osch
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Marieke JH Wermer
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Neurology, University Medical Center Groningen, Groningen, The Netherlands
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3
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Li K, Wang S, Luo X, Zeng Q, Liu X, Hong L, Li J, Hong H, Xu X, Zhang Y, Jiaerken Y, Zhang R, Xie L, Xu S, Zhang X, Chen Y, Liu Z, Zhang M, Huang P. Associations of Alzheimer's Disease Pathology and Small Vessel Disease With Cerebral White Matter Degeneration: A Tract-Based MR Diffusion Imaging Study. J Magn Reson Imaging 2023. [PMID: 37737474 DOI: 10.1002/jmri.29022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 09/02/2023] [Accepted: 09/05/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND White matter (WM) degeneration is a key feature of Alzheimer's disease (AD). However, the underlying mechanism remains unclear. PURPOSE To investigate how amyloid-β (Aβ), tau, and small vascular disease (SVD) jointly affect WM degeneration in subjects along AD continuum. STUDY TYPE Retrospective. SUBJECTS 152 non-demented participants (age: 55.8-91.6, male/female: 66/86) from the ADNI database were included, classified into three groups using the A (Aβ)/T (tau)/N pathological scheme (Group 1: A-T-; Group 2: A+T-; Group 3: A+T+) based on positron emission tomography data. FIELD STRENGTH/SEQUENCE 3T; T1-weighted images, T2-weighted fluid-attenuated inversion recovery images, T2*-weighted images, diffusion-weighted spin-echo echo-planar imaging sequence (54 diffusion directions). ASSESSMENT Free-water diffusion model (generated parameters: free water, FW; tissue fractional anisotropy, FAt; tissue mean diffusivity, MDt); SVD total score; Neuropsychological tests. STATISTICAL TESTS Linear regression analysis was performed to investigate the independent contribution of AD (Aβ and tau) and SVD pathologies to diffusion parameters in each fiber tract, first in the entire population and then in each subgroup. We also investigated associations between diffusion parameters and cognitive functions. The level of statistical significance was set at p < 0.05 (false discovery rate corrected). RESULTS In the entire population, we found that: 1) Increased FW was significantly associated with SVD and tau, while FAt and MDt were significantly associated with Aβ and tau; 2) The spatial pattern of fiber tracts related to a certain pathological marker is consistent with the known distribution of that pathology; 3) Subgroup analysis showed that Group 2 and 3 had more alterations of FAt and MDt associated with Aβ and tau; 4) Diffusion imaging indices showed significant associations with cognitive score in all domains except memory. DATA CONCLUSION WM microstructural injury was associated with both AD and SVD pathologies, showing compartment-specific, tract-specific, and stage-specific WM patterns. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Shuyue Wang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Luo
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Luwei Hong
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jixuan Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Hui Hong
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaopei Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yao Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yeerfan Jiaerken
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Ruiting Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Linyun Xie
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Shan Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xinyi Zhang
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yanxing Chen
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhirong Liu
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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Klimecki OM, Liebscher M, Gaubert M, Hayek D, Zarucha A, Dyrba M, Bartels C, Buerger K, Butryn M, Dechent P, Dobisch L, Ewers M, Fliessbach K, Freiesleben SD, Glanz W, Hetzer S, Janowitz D, Kilimann I, Kleineidam L, Laske C, Maier F, Munk MH, Perneczky R, Peters O, Priller J, Rauchmann BS, Roy N, Scheffler K, Schneider A, Spruth EJ, Spottke A, Teipel SJ, Wiltfang J, Wolfsgruber S, Yakupov R, Düzel E, Jessen F, Wagner M, Roeske S, Wirth M. Long-term environmental enrichment is associated with better fornix microstructure in older adults. Front Aging Neurosci 2023; 15:1170879. [PMID: 37711996 PMCID: PMC10498282 DOI: 10.3389/fnagi.2023.1170879] [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: 02/21/2023] [Accepted: 08/04/2023] [Indexed: 09/16/2023] Open
Abstract
Background Sustained environmental enrichment (EE) through a variety of leisure activities may decrease the risk of developing Alzheimer's disease. This cross-sectional cohort study investigated the association between long-term EE in young adulthood through middle life and microstructure of fiber tracts associated with the memory system in older adults. Methods N = 201 cognitively unimpaired participants (≥ 60 years of age) from the DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) baseline cohort were included. Two groups of participants with higher (n = 104) or lower (n = 97) long-term EE were identified, using the self-reported frequency of diverse physical, intellectual, and social leisure activities between the ages 13 to 65. White matter (WM) microstructure was measured by fractional anisotropy (FA) and mean diffusivity (MD) in the fornix, uncinate fasciculus, and parahippocampal cingulum using diffusion tensor imaging. Long-term EE groups (lower/higher) were compared with adjustment for potential confounders, such as education, crystallized intelligence, and socio-economic status. Results Reported participation in higher long-term EE was associated with greater fornix microstructure, as indicated by higher FA (standardized β = 0.117, p = 0.033) and lower MD (β = -0.147, p = 0.015). Greater fornix microstructure was indirectly associated (FA: unstandardized B = 0.619, p = 0.038; MD: B = -0.035, p = 0.026) with better memory function through higher long-term EE. No significant effects were found for the other WM tracts. Conclusion Our findings suggest that sustained participation in a greater variety of leisure activities relates to preserved WM microstructure in the memory system in older adults. This could be facilitated by the multimodal stimulation associated with the engagement in a physically, intellectually, and socially enriched lifestyle. Longitudinal studies will be needed to support this assumption.
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Affiliation(s)
- Olga M Klimecki
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Maxie Liebscher
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Malo Gaubert
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
- Department of Neuroradiology, Rennes University Hospital Centre Hospitalier Universitaire (CHU), Rennes, France
| | - Dayana Hayek
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Alexis Zarucha
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Claudia Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Göttingen, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Michaela Butryn
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Peter Dechent
- Magnetic Resonance (MR)-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Goettingen, Göttingen, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Silka Dawn Freiesleben
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Department of Psychiatry and Psychotherapy, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Franziska Maier
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, United Kingdom
| | - Oliver Peters
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany
- University of Edinburgh and United Kingdom Dementia Research Institute (UK DRI), Edinburgh, United Kingdom
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Eike Jakob Spruth
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Göttingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Neurosciences and Signaling Group, Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne, Cologne, Germany
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Sandra Roeske
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Miranka Wirth
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
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5
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Duering M, Biessels GJ, Brodtmann A, Chen C, Cordonnier C, de Leeuw FE, Debette S, Frayne R, Jouvent E, Rost NS, Ter Telgte A, Al-Shahi Salman R, Backes WH, Bae HJ, Brown R, Chabriat H, De Luca A, deCarli C, Dewenter A, Doubal FN, Ewers M, Field TS, Ganesh A, Greenberg S, Helmer KG, Hilal S, Jochems ACC, Jokinen H, Kuijf H, Lam BYK, Lebenberg J, MacIntosh BJ, Maillard P, Mok VCT, Pantoni L, Rudilosso S, Satizabal CL, Schirmer MD, Schmidt R, Smith C, Staals J, Thrippleton MJ, van Veluw SJ, Vemuri P, Wang Y, Werring D, Zedde M, Akinyemi RO, Del Brutto OH, Markus HS, Zhu YC, Smith EE, Dichgans M, Wardlaw JM. Neuroimaging standards for research into small vessel disease-advances since 2013. Lancet Neurol 2023; 22:602-618. [PMID: 37236211 DOI: 10.1016/s1474-4422(23)00131-x] [Citation(s) in RCA: 80] [Impact Index Per Article: 80.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/03/2023] [Accepted: 03/28/2023] [Indexed: 05/28/2023]
Abstract
Cerebral small vessel disease (SVD) is common during ageing and can present as stroke, cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive and other symptoms and affect activities of daily living. Standards for Reporting Vascular Changes on Neuroimaging 1 (STRIVE-1) categorised and standardised the diverse features of SVD that are visible on structural MRI. Since then, new information on these established SVD markers and novel MRI sequences and imaging features have emerged. As the effect of combined SVD imaging features becomes clearer, a key role for quantitative imaging biomarkers to determine sub-visible tissue damage, subtle abnormalities visible at high-field strength MRI, and lesion-symptom patterns, is also apparent. Together with rapidly emerging machine learning methods, these metrics can more comprehensively capture the effect of SVD on the brain than the structural MRI features alone and serve as intermediary outcomes in clinical trials and future routine practice. Using a similar approach to that adopted in STRIVE-1, we updated the guidance on neuroimaging of vascular changes in studies of ageing and neurodegeneration to create STRIVE-2.
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Affiliation(s)
- Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany; Medical Image Analysis Center, University of Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
| | - Geert Jan Biessels
- Department of Neurology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Amy Brodtmann
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia; Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | - Christopher Chen
- Department of Pharmacology, Memory Aging and Cognition Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Psychological Medicine, Memory Aging and Cognition Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Charlotte Cordonnier
- Université de Lille, INSERM, CHU Lille, U1172-Lille Neuroscience and Cognition (LilNCog), Lille, France
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Center for Medical Neuroscience, Radboudumc, Nijmegen, Netherlands
| | - Stéphanie Debette
- Bordeaux Population Health Research Center, University of Bordeaux, INSERM, UMR 1219, Bordeaux, France; Department of Neurology, Institute for Neurodegenerative Diseases, CHU de Bordeaux, Bordeaux, France
| | - Richard Frayne
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Radiology, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, University of Calgary, Calgary, AB, Canada
| | - Eric Jouvent
- AP-HP, Lariboisière Hospital, Translational Neurovascular Centre, FHU NeuroVasc, Université Paris Cité, Paris, France; Université Paris Cité, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Natalia S Rost
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Walter H Backes
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, Netherlands; School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, Netherlands
| | - Hee-Joon Bae
- Department of Neurology, Seoul National University College of Medicine, Seoul, South Korea; Cerebrovascular Disease Center, Seoul National University Bundang Hospital, Seongn-si, South Korea
| | - Rosalind Brown
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Hugues Chabriat
- Centre Neurovasculaire Translationnel, CERVCO, INSERM U1141, FHU NeuroVasc, Université Paris Cité, Paris, France
| | - Alberto De Luca
- Image Sciences Institute, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Charles deCarli
- Department of Neurology and Center for Neuroscience, University of California, Davis, CA, USA
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Fergus N Doubal
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Thalia S Field
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada; Vancouver Stroke Program, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Aravind Ganesh
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
| | - Steven Greenberg
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Karl G Helmer
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Athinoula A Martinos Center for Biomedical Imaging, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Saima Hilal
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Angela C C Jochems
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Hanna Jokinen
- Division of Neuropsychology, HUS Neurocenter, Helsinki University Hospital, University of Helsinki, Helsinki, Finland; Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Hugo Kuijf
- Image Sciences Institute, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Bonnie Y K Lam
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Margaret KL Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Jessica Lebenberg
- AP-HP, Lariboisière Hospital, Translational Neurovascular Centre, FHU NeuroVasc, Université Paris Cité, Paris, France; Université Paris Cité, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Bradley J MacIntosh
- Sandra E Black Centre for Brain Resilience and Repair, Hurvitz Brain Sciences, Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Computational Radiology and Artificial Intelligence Unit, Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Pauline Maillard
- Department of Neurology and Center for Neuroscience, University of California, Davis, CA, USA
| | - Vincent C T Mok
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Margaret KL Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Leonardo Pantoni
- Department of Biomedical and Clinical Science, University of Milan, Milan, Italy
| | - Salvatore Rudilosso
- Comprehensive Stroke Center, Department of Neuroscience, Hospital Clinic and August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Neurology, Boston University Medical Center, Boston, MA, USA; Framingham Heart Study, Framingham, MA, USA
| | - Markus D Schirmer
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Colin Smith
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Julie Staals
- School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, Netherlands; Department of Neurology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | | | | | - Yilong Wang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - David Werring
- Stroke Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Marialuisa Zedde
- Neurology Unit, Stroke Unit, Department of Neuromotor Physiology and Rehabilitation, Azienda Unità Sanitaria-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Rufus O Akinyemi
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oscar H Del Brutto
- School of Medicine and Research Center, Universidad de Especialidades Espiritu Santo, Ecuador
| | - Hugh S Markus
- Stroke Research Group, Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK
| | - Yi-Cheng Zhu
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Eric E Smith
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada; Department of Radiology, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; German Centre for Cardiovascular Research (DZHK), Munich, Germany
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK.
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6
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Zanon Zotin MC, Yilmaz P, Sveikata L, Schoemaker D, van Veluw SJ, Etherton MR, Charidimou A, Greenberg SM, Duering M, Viswanathan A. Peak Width of Skeletonized Mean Diffusivity: A Neuroimaging Marker for White Matter Injury. Radiology 2023; 306:e212780. [PMID: 36692402 PMCID: PMC9968775 DOI: 10.1148/radiol.212780] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 10/01/2022] [Accepted: 10/14/2022] [Indexed: 01/25/2023]
Abstract
A leading cause of white matter (WM) injury in older individuals is cerebral small vessel disease (SVD). Cerebral SVD is the most prevalent vascular contributor to cognitive impairment and dementia. Therapeutic progress for cerebral SVD and other WM disorders depends on the development and validation of neuroimaging markers suitable as outcome measures in future interventional trials. Diffusion-tensor imaging (DTI) is one of the best-suited MRI techniques for assessing the extent of WM damage in the brain. But the optimal method to analyze individual DTI data remains hindered by labor-intensive and time-consuming processes. Peak width of skeletonized mean diffusivity (PSMD), a recently developed fast, fully automated DTI marker, was designed to quantify the WM damage secondary to cerebral SVD and reflect related cognitive impairment. Despite its promising results, knowledge about PSMD is still limited in the radiologic community. This focused review provides an overview of the technical details of PSMD while synthesizing the available data on its clinical and neuroimaging associations. From a critical expert viewpoint, the authors discuss the limitations of PSMD and its current validation status as a neuroimaging marker for vascular cognitive impairment. Finally, they point out the gaps to be addressed to further advance the field.
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Affiliation(s)
| | | | - Lukas Sveikata
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Dorothee Schoemaker
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Susanne J. van Veluw
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Mark R. Etherton
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Andreas Charidimou
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Steven M. Greenberg
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Marco Duering
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Anand Viswanathan
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
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7
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Abstract
Cerebral small vessel disease (cSVD) is a major cause of stroke and dementia. This review summarizes recent developments in advanced neuroimaging of cSVD with a focus on clinical and research applications. In the first section, we highlight how advanced structural imaging techniques, including diffusion magnetic resonance imaging (MRI), enable improved detection of tissue damage, including characterization of tissue appearing normal on conventional MRI. These techniques enable progression to be monitored and may be useful as surrogate endpoint in clinical trials. Quantitative MRI, including iron and myelin imaging, provides insights into tissue composition on the molecular level. In the second section, we cover how advanced MRI techniques can demonstrate functional or dynamic abnormalities of the blood vessels, which could be targeted in mechanistic research and early-stage intervention trials. Such techniques include the use of dynamic contrast enhanced MRI to measure blood-brain barrier permeability, and MRI methods to assess cerebrovascular reactivity. In the third section, we discuss how the increased spatial resolution provided by ultrahigh field MRI at 7 T allows imaging of perforating arteries, and flow velocity and pulsatility within them. The advanced MRI techniques we describe are providing novel pathophysiological insights in cSVD and allow improved quantification of disease burden and progression. They have application in clinical trials, both in assessing novel therapeutic mechanisms, and as a sensitive endpoint to assess efficacy of interventions on parenchymal tissue damage. We also discuss challenges of these advanced techniques and suggest future directions for research.
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Affiliation(s)
- Hilde van den Brink
- Department of Neurology and
Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University,
Utrecht, The Netherlands
| | - Fergus N Doubal
- Centre for Clinical Brain Sciences, UK
Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Marco Duering
- Medical Image Analysis Center (MIAC AG)
and qbig, Department of Biomedical Engineering, University of Basel, Basel,
Switzerland,Marco Duering, Medical Image Analysis
Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of
Basel, Marktgasse 8, Basel, CH-4051, Switzerland.
; @MarcoDuering
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8
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Zhang LL, Zhao YJ, Zhang L, Wang XP. Experience of diagnosis and managements for patients with primary progressive freezing of gait. JOURNAL OF NEURORESTORATOLOGY 2022. [DOI: 10.1016/j.jnrt.2022.100039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
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9
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Zanon Zotin MC, Schoemaker D, Raposo N, Perosa V, Bretzner M, Sveikata L, Li Q, van Veluw SJ, Horn MJ, Etherton MR, Charidimou A, Gurol ME, Greenberg SM, Duering M, dos Santos AC, Pontes-Neto OM, Viswanathan A. Peak width of skeletonized mean diffusivity in cerebral amyloid angiopathy: Spatial signature, cognitive, and neuroimaging associations. Front Neurosci 2022; 16:1051038. [PMID: 36440281 PMCID: PMC9693722 DOI: 10.3389/fnins.2022.1051038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 10/25/2022] [Indexed: 11/13/2022] Open
Abstract
Background Peak width of skeletonized mean diffusivity (PSMD) is a promising diffusion tensor imaging (DTI) marker that shows consistent and strong cognitive associations in the context of different cerebral small vessel diseases (cSVD). Purpose Investigate whether PSMD (1) is higher in patients with Cerebral Amyloid Angiopathy (CAA) than those with arteriolosclerosis; (2) can capture the anteroposterior distribution of CAA-related abnormalities; (3) shows similar neuroimaging and cognitive associations in comparison to other classical DTI markers, such as average mean diffusivity (MD) and fractional anisotropy (FA). Materials and methods We analyzed cross-sectional neuroimaging and neuropsychological data from 90 non-demented memory-clinic subjects from a single center. Based on MRI findings, we classified them into probable-CAA (those that fulfilled the modified Boston criteria), subjects with MRI markers of cSVD not attributable to CAA (presumed arteriolosclerosis; cSVD), and subjects without evidence of cSVD on MRI (non-cSVD). We compared total and lobe-specific (frontal and occipital) DTI metrics values across the groups. We used linear regression models to investigate how PSMD, MD, and FA correlate with conventional neuroimaging markers of cSVD and cognitive scores in CAA. Results PSMD was comparable in probable-CAA (median 4.06 × 10–4 mm2/s) and cSVD (4.07 × 10–4 mm2/s) patients, but higher than in non-cSVD (3.30 × 10–4 mm2/s; p < 0.001) subjects. Occipital-frontal PSMD gradients were higher in probable-CAA patients, and we observed a significant interaction between diagnosis and region on PSMD values [F(2, 87) = 3.887, p = 0.024]. PSMD was mainly associated with white matter hyperintensity volume, whereas MD and FA were also associated with other markers, especially with the burden of perivascular spaces. PSMD correlated with worse executive function (β = −0.581, p < 0.001) and processing speed (β = −0.463, p = 0.003), explaining more variance than other MRI markers. MD and FA were not associated with performance in any cognitive domain. Conclusion PSMD is a promising biomarker of cognitive impairment in CAA that outperforms other conventional and DTI-based neuroimaging markers. Although global PSMD is similarly increased in different forms of cSVD, PSMD’s spatial variations could potentially provide insights into the predominant type of underlying microvascular pathology.
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Affiliation(s)
- Maria Clara Zanon Zotin
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Center for Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
- *Correspondence: Maria Clara Zanon Zotin, ,
| | - Dorothee Schoemaker
- Department of Psychiatry, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States
| | - Nicolas Raposo
- Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France
| | | | - Martin Bretzner
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog (JPARC) - Lille Neurosciences & Cognition, Lille, France
| | - Lukas Sveikata
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland
- Institute of Cardiology, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Qi Li
- The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Susanne J. van Veluw
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Mitchell J. Horn
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Mark R. Etherton
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Andreas Charidimou
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Department of Neurology, Boston University School of Medicine, Boston University Medical Center, Boston, MA, United States
| | - M. Edip Gurol
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Steven M. Greenberg
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Marco Duering
- Department of Biomedical Engineering, Medical Imaging Analysis Center (MIAC), University of Basel, Basel, Switzerland
| | - Antonio Carlos dos Santos
- Center for Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Octavio M. Pontes-Neto
- Department of Neuroscience and Behavioral Sciences, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Anand Viswanathan
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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10
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Tregub PP, Averchuk AS, Baranich TI, Ryazanova MV, Salmina AB. Physiological and Pathological Remodeling of Cerebral Microvessels. Int J Mol Sci 2022; 23:ijms232012683. [PMID: 36293539 PMCID: PMC9603917 DOI: 10.3390/ijms232012683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/10/2022] [Accepted: 10/18/2022] [Indexed: 11/13/2022] Open
Abstract
There is growing evidence that the remodeling of cerebral microvessels plays an important role in plastic changes in the brain associated with development, experience, learning, and memory consolidation. At the same time, abnormal neoangiogenesis, and deregulated regulation of microvascular regression, or pruning, could contribute to the pathogenesis of neurodevelopmental diseases, stroke, and neurodegeneration. Aberrant remodeling of microvesselsis associated with blood-brain barrier breakdown, development of neuroinflammation, inadequate microcirculation in active brain regions, and leads to the dysfunction of the neurovascular unit and progressive neurological deficits. In this review, we summarize current data on the mechanisms of blood vessel regression and pruning in brain plasticity and in Alzheimer's-type neurodegeneration. We discuss some novel approaches to modulating cerebral remodeling and preventing degeneration-coupled aberrant microvascular activity in chronic neurodegeneration.
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11
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Eikenes L, Visser E, Vangberg T, Håberg AK. Both brain size and biological sex contribute to variation in white matter microstructure in middle-aged healthy adults. Hum Brain Mapp 2022; 44:691-709. [PMID: 36189786 PMCID: PMC9842919 DOI: 10.1002/hbm.26093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/04/2022] [Accepted: 07/07/2022] [Indexed: 01/25/2023] Open
Abstract
Whether head size and/or biological sex influence proxies of white matter (WM) microstructure such as fractional anisotropy (FA) and mean diffusivity (MD) remains controversial. Diffusion tensor imaging (DTI) indices are also associated with age, but there are large discrepancies in the spatial distribution and timeline of age-related differences reported. The aim of this study was to evaluate the associations between intracranial volume (ICV), sex, and age and DTI indices from WM in a population-based study of healthy individuals (n = 812) aged 50-66 in the Nord-Trøndelag health survey. Semiautomated tractography and tract-based spatial statistics (TBSS) analyses were performed on the entire sample and in an ICV-matched sample of men and women. The tractography results showed a similar positive association between ICV and FA in all major WM tracts in men and women. Associations between ICV and MD, radial diffusivity and axial diffusivity were also found, but to a lesser extent than FA. The TBSS results showed that both men and women had areas of higher and lower FA when controlling for age, but after controlling for age and ICV only women had areas with higher FA. The ICV matched analysis also demonstrated that only women had areas of higher FA. Age was negatively associated with FA across the entire WM skeleton in the TBSS analysis, independent of both sex and ICV. Combined, these findings demonstrated that both ICV and sex contributed to variation in DTI indices and emphasized the importance of considering ICV as a covariate in DTI analysis.
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Affiliation(s)
- Live Eikenes
- Department of Circulation and Medical ImagingNorwegian University of Science and TechnologyTrondheimNorway
| | - Eelke Visser
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK,Donders InstituteRadboud University Nijmegen Medical CentreNijmegenThe Netherlands
| | - Torgil Vangberg
- Department of Clinical MedicineUiT The Arctic University of NorwayTromsøNorway,PET CenterUniversity Hospital North NorwayTromsøNorway
| | - Asta K. Håberg
- Department of NeuroscienceNorwegian University of Science and TechnologyTrondheimNorway,Department of Diagnostic Imaging, MR‐CenterSt. Olav's University HospitalTrondheimNorway
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12
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Multimodal tract-based MRI metrics outperform whole brain markers in determining cognitive impact of small vessel disease-related brain injury. Brain Struct Funct 2022; 227:2553-2567. [PMID: 35994115 PMCID: PMC9418106 DOI: 10.1007/s00429-022-02546-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/27/2022] [Indexed: 01/04/2023]
Abstract
In cerebral small vessel disease (cSVD), whole brain MRI markers of cSVD-related brain injury explain limited variance to support individualized prediction. Here, we investigate whether considering abnormalities in brain tracts by integrating multimodal metrics from diffusion MRI (dMRI) and structural MRI (sMRI), can better capture cognitive performance in cSVD patients than established approaches based on whole brain markers. We selected 102 patients (73.7 ± 10.2 years old, 59 males) with MRI-visible SVD lesions and both sMRI and dMRI. Conventional linear models using demographics and established whole brain markers were used as benchmark of predicting individual cognitive scores. Multi-modal metrics of 73 major brain tracts were derived from dMRI and sMRI, and used together with established markers as input of a feed-forward artificial neural network (ANN) to predict individual cognitive scores. A feature selection strategy was implemented to reduce the risk of overfitting. Prediction was performed with leave-one-out cross-validation and evaluated with the R2 of the correlation between measured and predicted cognitive scores. Linear models predicted memory and processing speed with R2 = 0.26 and R2 = 0.38, respectively. With ANN, feature selection resulted in 13 tract-specific metrics and 5 whole brain markers for predicting processing speed, and 28 tract-specific metrics and 4 whole brain markers for predicting memory. Leave-one-out ANN prediction with the selected features achieved R2 = 0.49 and R2 = 0.40 for processing speed and memory, respectively. Our results show proof-of-concept that combining tract-specific multimodal MRI metrics can improve the prediction of cognitive performance in cSVD by leveraging tract-specific multi-modal metrics.
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13
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da Silva PHR, Paschoal AM, Secchinatto KF, Zotin MCZ, Dos Santos AC, Viswanathan A, Pontes-Neto OM, Leoni RF. Contrast agent-free state-of-the-art magnetic resonance imaging on cerebral small vessel disease - Part 2: Diffusion tensor imaging and functional magnetic resonance imaging. NMR IN BIOMEDICINE 2022; 35:e4743. [PMID: 35429070 DOI: 10.1002/nbm.4743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 04/04/2022] [Indexed: 06/14/2023]
Abstract
Cerebral small vessel disease (cSVD) has been widely studied using conventional magnetic resonance imaging (MRI) methods, although the association between MRI findings and clinical features of cSVD is not always concordant. We assessed the additional contribution of contrast agent-free, state-of-the-art MRI techniques, particularly diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), to understand brain damage and structural and functional connectivity impairment related to cSVD. We performed a review following the PICOS worksheet and Search Strategy, including 152 original papers in English, published from 2000 to 2022. For each MRI method, we extracted information about their contributions regarding the origins, pathology, markers, and clinical outcomes in cSVD. In general, DTI studies have shown that changes in mean, radial, and axial diffusivity measures are related to the presence of cSVD. In addition to the classical deficit in executive functions and processing speed, fMRI studies indicate connectivity dysfunctions in other domains, such as sensorimotor, memory, and attention. Neuroimaging metrics have been correlated with the diagnosis, prognosis, and rehabilitation of patients with cSVD. In short, the application of contrast agent-free, state-of-the-art MRI techniques has provided a complete picture of cSVD markers and tools to explore questions that have not yet been clarified about this clinical condition. Longitudinal studies are desirable to look for causal relationships between image biomarkers and clinical outcomes.
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Affiliation(s)
| | - André Monteiro Paschoal
- Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | | | - Maria Clara Zanon Zotin
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
- J Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Antônio Carlos Dos Santos
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Anand Viswanathan
- J Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Octavio M Pontes-Neto
- Department of Neurosciences and Behavioral Science, Ribeirão Preto Medical School, University of Sao Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Renata Ferranti Leoni
- Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
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14
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Dewenter A, Jacob MA, Cai M, Gesierich B, Hager P, Kopczak A, Biel D, Ewers M, Tuladhar AM, de Leeuw FE, Dichgans M, Franzmeier N, Duering M. Disentangling the effects of Alzheimer's and small vessel disease on white matter fibre tracts. Brain 2022; 146:678-689. [PMID: 35859352 PMCID: PMC9924910 DOI: 10.1093/brain/awac265] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/30/2022] [Accepted: 06/25/2022] [Indexed: 12/27/2022] Open
Abstract
Alzheimer's disease and cerebral small vessel disease are the two leading causes of cognitive decline and dementia and coexist in most memory clinic patients. White matter damage as assessed by diffusion MRI is a key feature in both Alzheimer's and cerebral small vessel disease. However, disease-specific biomarkers of white matter alterations are missing. Recent advances in diffusion MRI operating on the fixel level (fibre population within a voxel) promise to advance our understanding of disease-related white matter alterations. Fixel-based analysis allows derivation of measures of both white matter microstructure, measured by fibre density, and macrostructure, measured by fibre-bundle cross-section. Here, we evaluated the capacity of these state-of-the-art fixel metrics to disentangle the effects of cerebral small vessel disease and Alzheimer's disease on white matter integrity. We included three independent samples (total n = 387) covering genetically defined cerebral small vessel disease and age-matched controls, the full spectrum of biomarker-confirmed Alzheimer's disease including amyloid- and tau-PET negative controls and a validation sample with presumed mixed pathology. In this cross-sectional analysis, we performed group comparisons between patients and controls and assessed associations between fixel metrics within main white matter tracts and imaging hallmarks of cerebral small vessel disease (white matter hyperintensity volume, lacune and cerebral microbleed count) and Alzheimer's disease (amyloid- and tau-PET), age and a measure of neurodegeneration (brain volume). Our results showed that (i) fibre density was reduced in genetically defined cerebral small vessel disease and strongly associated with cerebral small vessel disease imaging hallmarks; (ii) fibre-bundle cross-section was mainly associated with brain volume; and (iii) both fibre density and fibre-bundle cross-section were reduced in the presence of amyloid, but not further exacerbated by abnormal tau deposition. Fixel metrics were only weakly associated with amyloid- and tau-PET. Taken together, our results in three independent samples suggest that fibre density captures the effect of cerebral small vessel disease, while fibre-bundle cross-section is largely determined by neurodegeneration. The ability of fixel-based imaging markers to capture distinct effects on white matter integrity can propel future applications in the context of precision medicine.
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Affiliation(s)
- Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
| | - Mina A Jacob
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mengfei Cai
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Benno Gesierich
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
- Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Paul Hager
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
- Institute for AI and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Anna Kopczak
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
| | - Davina Biel
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
- German Center for Neurodegenerative Disease (DZNE), Munich, Germany
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
- German Center for Neurodegenerative Disease (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | | | - Marco Duering
- Correspondence to: Marco Duering Medical Image Analysis Center (MIAC AG) Marktgasse 8 CH-4051 Basel Switzerland E-mail:
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15
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Wang S, Zhang F, Huang P, Hong H, Jiaerken Y, Yu X, Zhang R, Zeng Q, Zhang Y, Kikinis R, Rathi Y, Makris N, Lou M, Pasternak O, Zhang M, O'Donnell LJ. Superficial white matter microstructure affects processing speed in cerebral small vessel disease. Hum Brain Mapp 2022; 43:5310-5325. [PMID: 35822593 PMCID: PMC9812245 DOI: 10.1002/hbm.26004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/10/2022] [Accepted: 06/15/2022] [Indexed: 01/15/2023] Open
Abstract
White matter hyperintensities (WMH) are a typical feature of cerebral small vessel disease (CSVD), which contributes to about 50% of dementias worldwide. Microstructural alterations in deep white matter (DWM) have been widely examined in CSVD. However, little is known about abnormalities in superficial white matter (SWM) and their relevance for processing speed, the main cognitive deficit in CSVD. In 141 CSVD patients, processing speed was assessed using Trail Making Test Part A. White matter abnormalities were assessed by WMH burden (volume on T2-FLAIR) and diffusion MRI measures. SWM imaging measures had a large contribution to processing speed, despite a relatively low SWM WMH burden. Across all imaging measures, SWM free water (FW) had the strongest association with processing speed, followed by SWM mean diffusivity (MD). SWM FW was the only marker to significantly increase between two subgroups with the lowest WMH burdens. When comparing two subgroups with the highest WMH burdens, the involvement of WMH in the SWM was accompanied by significant differences in processing speed and white matter microstructure. Mediation analysis revealed that SWM FW fully mediated the association between WMH volume and processing speed, while no mediation effect of MD or DWM FW was observed. Overall, results suggest that the SWM has an important contribution to processing speed, while SWM FW is a sensitive imaging marker associated with cognition in CSVD. This study extends the current understanding of CSVD-related dysfunction and suggests that the SWM, as an understudied region, can be a potential target for monitoring pathophysiological processes.
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Affiliation(s)
- Shuyue Wang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina,Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Peiyu Huang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Hui Hong
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Yeerfan Jiaerken
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Xinfeng Yu
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ruiting Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Qingze Zeng
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Yao Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ron Kikinis
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA,Center for Morphometric AnalysisMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Min Lou
- Department of Neurologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ofer Pasternak
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Minming Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
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16
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Vlegels N, Ossenkoppele R, van der Flier WM, Koek HL, Reijmer YD, Wisse LEM, Biessels GJ. Does Loss of Integrity of the Cingulum Bundle Link Amyloid-β Accumulation and Neurodegeneration in Alzheimer’s Disease? J Alzheimers Dis 2022; 89:39-49. [DOI: 10.3233/jad-220024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Alzheimer’s disease is characterized by the accumulation of amyloid-β (Aβ) into plaques, aggregation of tau into neurofibrillary tangles, and neurodegenerative processes including atrophy. However, there is a poorly understood spatial discordance between initial Aβ deposition and local neurodegeneration. Objective: Here, we test the hypothesis that the cingulum bundle links Aβ deposition in the cingulate cortex to medial temporal lobe (MTL) atrophy. Methods: 21 participants with mild cognitive impairment (MCI) from the UMC Utrecht memory clinic (UMCU, discovery sample) and 37 participants with MCI from Alzheimer’s Disease Neuroimaging Initiative (ADNI, replication sample) with available Aβ-PET scan, T1-weighted and diffusion-weighted MRI were included. Aβ load of the cingulate cortex was measured by the standardized uptake value ratio (SUVR), white matter integrity of the cingulum bundle was assessed by mean diffusivity and atrophy of the MTL by normalized MTL volume. Relationships were tested with linear mixed models, to accommodate multiple measures for each participant. Results: We found at most a weak association between cingulate Aβ and MTL volume (added R2 <0.06), primarily for the posterior hippocampus. In neither sample, white matter integrity of the cingulum bundle was associated with cingulate Aβ or MTL volume (added R2 <0.01). Various sensitivity analyses (Aβ-positive individuals only, posterior cingulate SUVR, MTL sub region volume) provided similar results. Conclusion: These findings, consistent in two independent cohorts, do not support our hypothesis that loss of white matter integrity of the cingulum is a connecting factor between cingulate gyrus Aβ deposition and MTL atrophy.
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Affiliation(s)
- Naomi Vlegels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Epidemiology and Data Science, VU University Medical Center, Amsterdam, The Netherlands
| | - Huiberdina L. Koek
- Department of Geriatrics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D. Reijmer
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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17
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Dewenter A, Gesierich B, Ter Telgte A, Wiegertjes K, Cai M, Jacob MA, Marques JP, Norris DG, Franzmeier N, de Leeuw FE, Tuladhar AM, Duering M. Systematic validation of structural brain networks in cerebral small vessel disease. J Cereb Blood Flow Metab 2022; 42:1020-1032. [PMID: 34929104 PMCID: PMC9125482 DOI: 10.1177/0271678x211069228] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Cerebral small vessel disease (SVD) is considered a disconnection syndrome, which can be quantified using structural brain network analysis obtained from diffusion MRI. Network analysis is a demanding analysis approach and the added benefit over simpler diffusion MRI analysis is largely unexplored in SVD. In this pre-registered study, we assessed the clinical and technical validity of network analysis in two non-overlapping samples of SVD patients from the RUN DMC study (n = 52 for exploration and longitudinal analysis and n = 105 for validation). We compared two connectome pipelines utilizing single-shell or multi-shell diffusion MRI, while also systematically comparing different node and edge definitions. For clinical validation, we assessed the added benefit of network analysis in explaining processing speed and in detecting short-term disease progression. For technical validation, we determined test-retest repeatability.Our findings in clinical validation show that structural brain networks provide only a small added benefit over simpler global white matter diffusion metrics and do not capture short-term disease progression. Test-retest reliability was excellent for most brain networks. Our findings question the added value of brain network analysis in clinical applications in SVD and highlight the utility of simpler diffusion MRI based markers.
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Affiliation(s)
- Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Benno Gesierich
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Annemieke Ter Telgte
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.,VASCage - Research Centre on Vascular Ageing and Stroke, Innsbruck, Austria
| | - Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mengfei Cai
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mina A Jacob
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - David G Norris
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.,Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.,Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
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18
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Berger M, Pirpamer L, Hofer E, Ropele S, Duering M, Gesierich B, Pasternak O, Enzinger C, Schmidt R, Koini M. Free water diffusion MRI and executive function with a speed component in healthy aging. Neuroimage 2022; 257:119303. [PMID: 35568345 PMCID: PMC9465649 DOI: 10.1016/j.neuroimage.2022.119303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 11/12/2022] Open
Abstract
Extracellular free water (FW) increases are suggested to better provide pathophysiological information in brain aging than conventional biomarkers such as fractional anisotropy. The aim of the present study was to determine the relationship between conventional biomarkers, FW in white matter hyperintensities (WMH), FW in normal appearing white matter (NAWM) and in white matter tracts and executive functions (EF) with a speed component in elderly persons. We examined 226 healthy elderly participants (median age 69.83 years, IQR: 56.99–74.42) who underwent brain MRI and neuropsychological examination. FW in WMH and in NAWM as well as FW corrected diffusion metrics and measures derived from conventional MRI (white matter hyperintensities, brain volume, lacunes) were used in partial correlation (adjusted for age) to assess their correlation with EF with a speed component. Random forest analysis was used to assess the relative importance of these variables as determinants. Lastly, linear regression analyses of FW in white matter tracts corrected for risk factors of cognitive and white matter deterioration, were used to examine the role of specific tracts on EF with a speed component, which were then ranked with random forest regression. Partial correlation analyses revealed that almost all imaging metrics showed a significant association with EF with a speed component (r = −0.213 – 0.266). Random forest regression highlighted FW in WMH and in NAWM as most important among all diffusion and structural MRI metrics. The fornix (R2=0.421, p = 0.018) and the corpus callosum (genu (R2 = 0.418, p = 0.021), prefrontal (R2 = 0.416, p = 0.026), premotor (R2 = 0.418, p = 0.021)) were associated with EF with a speed component in tract based regression analyses and had highest variables importance. In a normal aging population FW in WMH and NAWM is more closely related to EF with a speed component than standard DTI and brain structural measures. Higher amounts of FW in the fornix and the frontal part of the corpus callosum leads to deteriorating EF with a speed component.
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Affiliation(s)
- Martin Berger
- Department of Neurology, Division of Neurogeriatrics, Medical University of Graz, Auenbruggerplatz 22, Graz 8036, Austria
| | - Lukas Pirpamer
- Department of Neurology, Division of Neurogeriatrics, Medical University of Graz, Auenbruggerplatz 22, Graz 8036, Austria
| | - Edith Hofer
- Department of Neurology, Division of Neurogeriatrics, Medical University of Graz, Auenbruggerplatz 22, Graz 8036, Austria; Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Stefan Ropele
- Department of Neurology, Division of Neurogeriatrics, Medical University of Graz, Auenbruggerplatz 22, Graz 8036, Austria
| | - Marco Duering
- Medical Image Analysis Center (MIAC AG) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Benno Gesierich
- Institute for Stroke and Dementia Research (ISD), University Hospital, Munich, Germany
| | - Ofer Pasternak
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Reinhold Schmidt
- Department of Neurology, Division of Neurogeriatrics, Medical University of Graz, Auenbruggerplatz 22, Graz 8036, Austria.
| | - Marisa Koini
- Department of Neurology, Division of Neurogeriatrics, Medical University of Graz, Auenbruggerplatz 22, Graz 8036, Austria
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19
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Yuan J, Chen L, Wang J, Xia S, Huang J, Zhou L, Feng C, Hu X, Zhou Z, Ran H. Adenosine A2A Receptor Suppressed Astrocyte-Mediated Inflammation Through the Inhibition of STAT3/YKL-40 Axis in Mice With Chronic Cerebral Hypoperfusion-induced White Matter Lesions. Front Immunol 2022; 13:841290. [PMID: 35237278 PMCID: PMC8882648 DOI: 10.3389/fimmu.2022.841290] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/24/2022] [Indexed: 11/13/2022] Open
Abstract
White matter lesions are an important pathological manifestation of cerebral small vessel disease, with inflammation playing a pivotal role in their development. The adenosine A2a receptor (ADORA2A) is known to inhibit the inflammation mediated by microglia, but its effect on astrocytes is unknown. Additionally, although the level of YKL-40 (expressed mainly in astrocytes) has been shown to be elevated in the model of white matter lesions induced by chronic cerebral hypoperfusion, the specific regulatory mechanism involved is not clear. In this study, we established in vivo and in vitro chronic cerebral hypoperfusion models to explore whether the ADORA2A regulated astrocyte-mediated inflammation through STAT3/YKL-40 axis and using immunohistochemical, western blotting, ELISA, PCR, and other techniques to verify the effect of astrocytes ADORA2A on the white matter injury. The in vivo experiments showed that activation of the ADORA2A decreased the expression of both STAT3 and YKL-40 in the astrocytes and alleviated the white matter injury, whereas its inhibition had the opposite effects. Similarly, ADORA2A inhibition significantly increased the expression of STAT3 and YKL-40 in astrocytes in vitro, with more proinflammatory cytokines being released by astrocytes. STAT3 inhibition enhanced the inhibitory effect of ADORA2A on YKL-40 synthesis, whereas its activation reversed the phenomenon. These results suggest that the activation of ADORA2A in astrocytes can inhibit the inflammation mediated by the STAT3/YKL-40 axis and thereby reduce white matter injury in cerebral small vessel disease.
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Affiliation(s)
- Jichao Yuan
- Department of Neurology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Lin Chen
- Department of Neurology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jie Wang
- Department of Neurology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Simin Xia
- Department of Neurology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jialu Huang
- Department of Neurology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Linke Zhou
- Department of Neurology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Chengjian Feng
- Department of Medical Engineering, 958th Hospital of the People’s Liberation Army, Chongqing, China
| | - Xiaofei Hu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
- *Correspondence: Hong Ran, ; Zhenhua Zhou, ; Xiaofei Hu,
| | - Zhenhua Zhou
- Department of Neurology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
- *Correspondence: Hong Ran, ; Zhenhua Zhou, ; Xiaofei Hu,
| | - Hong Ran
- Department of Neurology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
- *Correspondence: Hong Ran, ; Zhenhua Zhou, ; Xiaofei Hu,
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20
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Zhu Z, Zeng Q, Zhang R, Luo X, Li K, Xu X, Zhang M, Yang Y, Huang P. White Matter Free Water Outperforms Cerebral Small Vessel Disease Total Score in Predicting Cognitive Decline in Persons with Mild Cognitive Impairment. J Alzheimers Dis 2022; 86:741-751. [PMID: 35124653 DOI: 10.3233/jad-215541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Vascular pathology is an important partner of Alzheimer's disease (AD). Both total cerebral small vessel disease (CSVD) score and white matter free water (FW) are useful markers that could reflect cerebral vascular injury. OBJECTIVE We aim to investigate the efficacy of these two metrics in predicting cognitive declines in patients with mild cognitive impairment (MCI). METHODS We enrolled 126 MCI subjects with 3D T1-weighted images, fluid-attenuated inversion recovery images, T2 * images, diffusion tensor imaging images, cerebrospinal fluid biomarkers and neuropsychological tests from the Alzheimer's Disease Neuroimaging Initiative database. The total CSVD score and FW values were calculated. Simple and multiple linear regression analyses were applied to explore the association between vascular and cognitive impairments. Linear mixed effect models were constructed to investigate the efficacy of total CSVD score and FW on predicting cognitive decline. RESULTS FW was associated with baseline cognition and could predict the decline of executive and language functions in MCI subjects, while no association was found between total CSVD score and cognitive declines. CONCLUSION FW is a promising imaging marker for investigating the effect of CSVD on AD progression.
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Affiliation(s)
- Zili Zhu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Shangcheng District, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Ouhai District, Wenzhou, China
| | - Qingze Zeng
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Shangcheng District, Hangzhou, China
| | - Ruiting Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Shangcheng District, Hangzhou, China
| | - Xiao Luo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Shangcheng District, Hangzhou, China
| | - Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Shangcheng District, Hangzhou, China
| | - Xiaopei Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Shangcheng District, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Shangcheng District, Hangzhou, China
| | - Yunjun Yang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Ouhai District, Wenzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Shangcheng District, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Ouhai District, Wenzhou, China
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21
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Vemuri P, Decarli CS, Duering M. Imaging Markers of Vascular Brain Health: Quantification, Clinical Implications, and Future Directions. Stroke 2022; 53:416-426. [PMID: 35000423 PMCID: PMC8830603 DOI: 10.1161/strokeaha.120.032611] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Cerebrovascular disease (CVD) manifests through a broad spectrum of mechanisms that negatively impact brain and cognitive health. Oftentimes, CVD changes (excluding acute stroke) are insufficiently considered in aging and dementia studies which can lead to an incomplete picture of the etiologies contributing to the burden of cognitive impairment. Our goal with this focused review is 3-fold. First, we provide a research update on the current magnetic resonance imaging methods that can measure CVD lesions as well as early CVD-related brain injury specifically related to small vessel disease. Second, we discuss the clinical implications and relevance of these CVD imaging markers for cognitive decline, incident dementia, and disease progression in Alzheimer disease, and Alzheimer-related dementias. Finally, we present our perspective on the outlook and challenges that remain in the field. With the increased research interest in this area, we believe that reliable CVD imaging biomarkers for aging and dementia studies are on the horizon.
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Affiliation(s)
| | - Charles S. Decarli
- Departments of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, California, USA
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
- Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Switzerland
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22
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van Veluw SJ, Arfanakis K, Schneider JA. Neuropathology of Vascular Brain Health: Insights From Ex Vivo Magnetic Resonance Imaging-Histopathology Studies in Cerebral Small Vessel Disease. Stroke 2022; 53:404-415. [PMID: 35000425 PMCID: PMC8830602 DOI: 10.1161/strokeaha.121.032608] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Sporadic cerebral small vessel disease (SVD) is a major contributor to vascular cognitive impairment and dementia in the aging human brain. On neuropathology, sporadic SVD is characterized by abnormalities to the small vessels of the brain predominantly in the form of cerebral amyloid angiopathy and arteriolosclerosis. These pathologies frequently coexist with Alzheimer disease changes, such as plaques and tangles, in a single brain. Conversely, during life, magnetic resonance imaging (MRI) only captures the larger manifestations of SVD in the form of parenchymal brain abnormalities. There appears to be a major knowledge gap regarding the underlying neuropathology of individual MRI-detectable SVD abnormalities. Ex vivo MRI in postmortem human brain tissue is a powerful tool to bridge this gap. This review summarizes current insights into the histopathologic correlations of MRI manifestations of SVD, their underlying cause, presumed pathophysiology, and associated secondary tissue injury. Moreover, we discuss the advantages and limitations of ex vivo MRI-guided histopathologic investigations and make recommendations for future studies.
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Affiliation(s)
- Susanne J. van Veluw
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA,MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital, Charlestown, MA, USA,Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA,Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Julie A. Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA,Departments of Pathology and Neurological Sciences, Rush University Medical Center, Chicago IL, USA
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23
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de Brito Robalo BM, Biessels GJ, Chen C, Dewenter A, Duering M, Hilal S, Koek HL, Kopczak A, Yin Ka Lam B, Leemans A, Mok V, Onkenhout LP, van den Brink H, de Luca A. Diffusion MRI harmonization enables joint-analysis of multicentre data of patients with cerebral small vessel disease. Neuroimage Clin 2021; 32:102886. [PMID: 34911192 PMCID: PMC8609094 DOI: 10.1016/j.nicl.2021.102886] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 11/16/2021] [Indexed: 01/10/2023]
Abstract
OBJECTIVES Acquisition-related differences in diffusion magnetic resonance imaging (dMRI) hamper pooling of multicentre data to achieve large sample sizes. A promising solution is to harmonize the raw diffusion signal using rotation invariant spherical harmonic (RISH) features, but this has not been tested in elderly subjects. Here we aimed to establish if RISH harmonization effectively removes acquisition-related differences in multicentre dMRI of elderly subjects with cerebral small vessel disease (SVD), while preserving sensitivity to disease effects. METHODS Five cohorts of patients with SVD (N = 397) and elderly controls (N = 175) with 3 Tesla MRI on different systems were included. First, to establish effectiveness of harmonization, the RISH method was trained with data of 13 to 15 age and sex-matched controls from each site. Fractional anisotropy (FA) and mean diffusivity (MD) were compared in matched controls between sites using tract-based spatial statistics (TBSS) and voxel-wise analysis, before and after harmonization. Second, to assess sensitivity to disease effects, we examined whether the contrast (effect sizes of FA, MD and peak width of skeletonized MD - PSMD) between patients and controls within each site remained unaffected by harmonization. Finally, we evaluated the association between white matter hyperintensity (WMH) burden, FA, MD and PSMD using linear regression analyses both within individual cohorts as well as with pooled scans from multiple sites, before and after harmonization. RESULTS Before harmonization, significant differences in FA and MD were observed between matched controls of different sites (p < 0.05). After harmonization these site-differences were removed. Within each site, RISH harmonization did not alter the effect sizes of FA, MD and PSMD between patients and controls (relative change in Cohen's d = 4 %) nor the strength of association with WMH volume (relative change in R2 = 2.8 %). After harmonization, patient data of all sites could be aggregated in a single analysis to infer the association between WMH volume and FA (R2 = 0.62), MD (R2 = 0.64), and PSMD (R2 = 0.60). CONCLUSIONS We showed that RISH harmonization effectively removes acquisition-related differences in dMRI of elderly subjects while preserving sensitivity to SVD-related effects. This study provides proof of concept for future multicentre SVD studies with pooled datasets.
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Affiliation(s)
- Bruno M de Brito Robalo
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Christopher Chen
- Memory, Aging and Cognition Center, Department of Pharmacology, National University of Singapore, Singapore.
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany; Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
| | - Saima Hilal
- Memory, Aging and Cognition Center, Department of Pharmacology, National University of Singapore, Singapore.
| | - Huiberdina L Koek
- Department of Geriatric Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Anna Kopczak
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.
| | - Bonnie Yin Ka Lam
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region.
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Vincent Mok
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region.
| | - Laurien P Onkenhout
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Hilde van den Brink
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Alberto de Luca
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
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24
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Chen Y, Wang X, Guan L, Wang Y. Role of White Matter Hyperintensities and Related Risk Factors in Vascular Cognitive Impairment: A Review. Biomolecules 2021; 11:biom11081102. [PMID: 34439769 PMCID: PMC8391787 DOI: 10.3390/biom11081102] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 07/24/2021] [Accepted: 07/25/2021] [Indexed: 02/06/2023] Open
Abstract
White matter hyperintensities (WMHs) of presumed vascular origin are one of the imaging markers of cerebral small-vessel disease, which is prevalent in older individuals and closely associated with the occurrence and development of cognitive impairment. The heterogeneous nature of the imaging manifestations of WMHs creates difficulties for early detection and diagnosis of vascular cognitive impairment (VCI) associated with WMHs. Because the underlying pathological processes and biomarkers of WMHs and their development in cognitive impairment remain uncertain, progress in prevention and treatment is lagging. For this reason, this paper reviews the status of research on the features of WMHs related to VCI, as well as mediators associated with both WMHs and VCI, and summarizes potential treatment strategies for the prevention and intervention in WMHs associated with VCI.
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Affiliation(s)
- Yiyi Chen
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China; (Y.C.); (X.W.)
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100070, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing 100070, China
| | - Xing Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China; (Y.C.); (X.W.)
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100070, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing 100070, China
- Department of Neurology, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing 400000, China
| | - Ling Guan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China; (Y.C.); (X.W.)
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100070, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing 100070, China
- Correspondence: (L.G.); (Y.W.)
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China; (Y.C.); (X.W.)
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100070, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing 100070, China
- Correspondence: (L.G.); (Y.W.)
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25
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Stone DB, Ryman SG, Hartman AP, Wertz CJ, Vakhtin AA. Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer's Disease. Front Aging Neurosci 2021; 13:711579. [PMID: 34366830 PMCID: PMC8343075 DOI: 10.3389/fnagi.2021.711579] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 06/28/2021] [Indexed: 11/24/2022] Open
Abstract
Identifying biomarkers that can assess the risk of developing Alzheimer's Disease (AD) remains a significant challenge. In this study, we investigated the integrity levels of brain white matter in 34 patients with mild cognitive impairment (MCI) who later converted to AD and 53 stable MCI patients. We used diffusion tensor imaging (DTI) and automated fiber quantification to obtain the diffusion properties of 20 major white matter tracts. To identify which tracts and diffusion measures are most relevant to AD conversion, we used support vector machines (SVMs) to classify the AD conversion and non-conversion MCI patients based on the diffusion properties of each tract individually. We found that diffusivity measures from seven white matter tracts were predictive of AD conversion with axial diffusivity being the most predictive diffusion measure. Additional analyses revealed that white matter changes in the central and parahippocampal terminal regions of the right cingulate hippocampal bundle, central regions of the right inferior frontal occipital fasciculus, and posterior and anterior regions of the left inferior longitudinal fasciculus were the best predictors of conversion from MCI to AD. An SVM based on these white matter tract regions achieved an accuracy of 0.75. These findings provide additional potential biomarkers of AD risk in MCI patients.
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26
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Raghavan S, Reid RI, Przybelski SA, Lesnick TG, Graff-Radford J, Schwarz CG, Knopman DS, Mielke MM, Machulda MM, Petersen RC, Jack CR, Vemuri P. Diffusion models reveal white matter microstructural changes with ageing, pathology and cognition. Brain Commun 2021; 3:fcab106. [PMID: 34136811 PMCID: PMC8202149 DOI: 10.1093/braincomms/fcab106] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/24/2021] [Accepted: 04/12/2021] [Indexed: 01/20/2023] Open
Abstract
White matter microstructure undergoes progressive changes during the lifespan, but the neurobiological underpinnings related to ageing and disease remains unclear. We used an advanced diffusion MRI, Neurite Orientation Dispersion and Density Imaging, to investigate the microstructural alterations due to demographics, common age-related pathological processes (amyloid, tau and white matter hyperintensities) and cognition. We also compared Neurite Orientation Dispersion and Density Imaging findings to the older Diffusion Tensor Imaging model-based findings. Three hundred and twenty-eight participants (264 cognitively unimpaired, 57 mild cognitive impairment and 7 dementia with a mean age of 68.3 ± 13.1 years) from the Mayo Clinic Study of Aging with multi-shell diffusion imaging, fluid attenuated inversion recovery MRI as well as amyloid and tau PET scans were included in this study. White matter tract level diffusion measures were calculated from Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging. Pearson correlation and multiple linear regression analyses were performed with diffusion measures as the outcome and age, sex, education/occupation, white matter hyperintensities, amyloid and tau as predictors. Analyses were also performed with each diffusion MRI measure as a predictor of cognitive outcomes. Age and white matter hyperintensities were the strongest predictors of all white matter diffusion measures with low associations with amyloid and tau. However, neurite density decrease from Neurite Orientation Dispersion and Density Imaging was observed with amyloidosis specifically in the temporal lobes. White matter integrity (mean diffusivity and free water) in the corpus callosum showed the greatest associations with cognitive measures. All diffusion measures provided information about white matter ageing and white matter changes due to age-related pathological processes and were associated with cognition. Neurite orientation dispersion and density imaging and diffusion tensor imaging are two different diffusion models that provide distinct information about variation in white matter microstructural integrity. Neurite Orientation Dispersion and Density Imaging provides additional information about synaptic density, organization and free water content which may aid in providing mechanistic insights into disease progression.
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Affiliation(s)
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Timothy G Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Michelle M Mielke
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA.,Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Mary M Machulda
- Department of Psychology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
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27
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Pichet Binette A, Theaud G, Rheault F, Roy M, Collins DL, Levin J, Mori H, Lee JH, Farlow MR, Schofield P, Chhatwal JP, Masters CL, Benzinger T, Morris J, Bateman R, Breitner JC, Poirier J, Gonneaud J, Descoteaux M, Villeneuve S. Bundle-specific associations between white matter microstructure and Aβ and tau pathology in preclinical Alzheimer's disease. eLife 2021; 10:62929. [PMID: 33983116 PMCID: PMC8169107 DOI: 10.7554/elife.62929] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 05/12/2021] [Indexed: 12/12/2022] Open
Abstract
Beta-amyloid (Aβ) and tau proteins, the pathological hallmarks of Alzheimer's disease (AD), are believed to spread through connected regions of the brain. Combining diffusion imaging and positron emission tomography, we investigated associations between white matter microstructure specifically in bundles connecting regions where Aβ or tau accumulates and pathology. We focused on free-water-corrected diffusion measures in the anterior cingulum, posterior cingulum, and uncinate fasciculus in cognitively normal older adults at risk of sporadic AD and presymptomatic mutation carriers of autosomal dominant AD. In Aβ-positive or tau-positive groups, lower tissue fractional anisotropy and higher mean diffusivity related to greater Aβ and tau burden in both cohorts. Associations were found in the posterior cingulum and uncinate fasciculus in preclinical sporadic AD, and in the anterior and posterior cingulum in presymptomatic mutation carriers. These results suggest that microstructural alterations accompany pathological accumulation as early as the preclinical stage of both sporadic and autosomal dominant AD.
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Affiliation(s)
- Alexa Pichet Binette
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Canada.,Douglas Mental Health University Institute, Montreal, Canada
| | - Guillaume Theaud
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, Canada
| | - François Rheault
- Electrical Engineering, Vanderbilt University, Nashville, United States
| | - Maggie Roy
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany.,German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Hiroshi Mori
- Department of Clinical Neuroscience, Osaka City University Medical School, Osaka, Japan
| | - Jae Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | | | - Peter Schofield
- Neuroscience Research Australia, Sydney, Australia.,School of Medical Sciences, UNSW Sydney, Sydney, Australia
| | - Jasmeer P Chhatwal
- Harvard Medical School, Massachusetts General Hospital, Boston, United States
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Australia
| | - Tammie Benzinger
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, United States.,Department of Neurology, Washington University School of Medicine, St. Louis, United States
| | - John Morris
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, United States.,Department of Neurology, Washington University School of Medicine, St. Louis, United States
| | - Randall Bateman
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, United States.,Department of Neurology, Washington University School of Medicine, St. Louis, United States
| | - John Cs Breitner
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Canada.,Douglas Mental Health University Institute, Montreal, Canada
| | - Judes Poirier
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Canada.,Douglas Mental Health University Institute, Montreal, Canada
| | - Julie Gonneaud
- Douglas Mental Health University Institute, Montreal, Canada.,Normandie Univ, UNICAEN, INSERM, U1237, Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, Canada
| | - Sylvia Villeneuve
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Canada.,Douglas Mental Health University Institute, Montreal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
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28
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Luo X, Wang S, Jiaerken Y, Li K, Zeng Q, Zhang R, Wang C, Xu X, Wu D, Huang P, Zhang M. Distinct fiber-specific white matter reductions pattern in early- and late-onset Alzheimer's disease. Aging (Albany NY) 2021; 13:12410-12430. [PMID: 33930871 PMCID: PMC8148465 DOI: 10.18632/aging.202702] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 02/08/2021] [Indexed: 01/31/2023]
Abstract
Background: The underlying white matter impairment in patients with early and late-onset Alzheimer’s disease (EOAD and LOAD) is still unclear, and this might due to the complex AD pathology. Methods: We included 31 EOAD, 45 LOAD, and 64 younger, 46 elder controls in our study to undergo MRI examinations. Fiber density (FD) and fiber bundle cross-section (FC) were measured using fixel-based analysis based on diffusion weighted images. On whole brain and tract-based level, we compared these parameters among different groups (p<0.05, FWE corrected). Moreover, we verified our results in another independent dataset using the same analyses. Results: Compared to young healthy controls, EOAD had significantly lower FD in the splenium of corpus callosum, limbic tracts, cingulum bundles, and posterior thalamic radiation, and higher FC in the splenium of corpus callosum, dorsal cingulum and posterior thalamic radiation. On the other hand, LOAD had lower FD and FC as well. Importantly, a similar pattern was found in the independent validation dataset. Among all groups, both the FD and FC were associated with cognitive function. Furthermore, FD of fornix column and body, and FC of ventral cingulum were associated with composite amyloid and tau level (r=-0.34 and -0.53, p<0.001) respectively. Conclusions: EOAD and LOAD were characterized by distinct white matter impairment patterns, which may be attributable to their different neuropathologies.
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Affiliation(s)
- Xiao Luo
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Shuyue Wang
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yeerfan Jiaerken
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Kaicheng Li
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Ruiting Zhang
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Chao Wang
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaopei Xu
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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29
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Zanon Zotin MC, Sveikata L, Viswanathan A, Yilmaz P. Cerebral small vessel disease and vascular cognitive impairment: from diagnosis to management. Curr Opin Neurol 2021; 34:246-257. [PMID: 33630769 PMCID: PMC7984766 DOI: 10.1097/wco.0000000000000913] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE OF REVIEW We present recent developments in the field of small vessel disease (SVD)-related vascular cognitive impairment, including pathological mechanisms, updated diagnostic criteria, cognitive profile, neuroimaging markers and risk factors. We further address available management and therapeutic strategies. RECENT FINDINGS Vascular and neurodegenerative pathologies often co-occur and share similar risk factors. The updated consensus criteria aim to standardize vascular cognitive impairment (VCI) diagnosis, relying strongly on cognitive profile and MRI findings. Aggressive blood pressure control and multidomain lifestyle interventions are associated with decreased risk of cognitive impairment, but disease-modifying treatments are still lacking. Recent research has led to a better understanding of mechanisms leading to SVD-related cognitive decline, such as blood-brain barrier dysfunction, reduced cerebrovascular reactivity and impaired perivascular clearance. SUMMARY SVD is the leading cause of VCI and is associated with substantial morbidity. Tackling cardiovascular risk factors is currently the most effective approach to prevent cognitive decline in the elderly. Advanced imaging techniques provide tools for early diagnosis and may play an important role as surrogate markers for cognitive endpoints in clinical trials. Designing and testing disease-modifying interventions for VCI remains a key priority in healthcare.
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Affiliation(s)
- Maria Clara Zanon Zotin
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States
- Center for Imaging Sciences and Medical Physics. Department of Medical Imaging, Hematology and Clinical Oncology. Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Lukas Sveikata
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States
- Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Institute of Cardiology, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Anand Viswanathan
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Pinar Yilmaz
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States
- Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
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30
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Raposo N, Zanon Zotin MC, Schoemaker D, Xiong L, Fotiadis P, Charidimou A, Pasi M, Boulouis G, Schwab K, Schirmer MD, Etherton MR, Gurol ME, Greenberg SM, Duering M, Viswanathan A. Peak Width of Skeletonized Mean Diffusivity as Neuroimaging Biomarker in Cerebral Amyloid Angiopathy. AJNR Am J Neuroradiol 2021; 42:875-881. [PMID: 33664113 DOI: 10.3174/ajnr.a7042] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 11/20/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND AND PURPOSE Whole-brain network connectivity has been shown to be a useful biomarker of cerebral amyloid angiopathy and related cognitive impairment. We evaluated an automated DTI-based method, peak width of skeletonized mean diffusivity, in cerebral amyloid angiopathy, together with its association with conventional MRI markers and cognitive functions. MATERIALS AND METHODS We included 24 subjects (mean age, 74.7 [SD, 6.0] years) with probable cerebral amyloid angiopathy and mild cognitive impairment and 62 patients with MCI not attributable to cerebral amyloid angiopathy (non-cerebral amyloid angiopathy-mild cognitive impairment). We compared peak width of skeletonized mean diffusivity between subjects with cerebral amyloid angiopathy-mild cognitive impairment and non-cerebral amyloid angiopathy-mild cognitive impairment and explored its associations with cognitive functions and conventional markers of cerebral small-vessel disease, using linear regression models. RESULTS Subjects with Cerebral amyloid angiopathy-mild cognitive impairment showed increased peak width of skeletonized mean diffusivity in comparison to those with non-cerebral amyloid angiopathy-mild cognitive impairment (P < .001). Peak width of skeletonized mean diffusivity values were correlated with the volume of white matter hyperintensities in both groups. Higher peak width of skeletonized mean diffusivity was associated with worse performance in processing speed among patients with cerebral amyloid angiopathy, after adjusting for other MRI markers of cerebral small vessel disease. The peak width of skeletonized mean diffusivity did not correlate with cognitive functions among those with non-cerebral amyloid angiopathy-mild cognitive impairment. CONCLUSIONS Peak width of skeletonized mean diffusivity is altered in cerebral amyloid angiopathy and is associated with performance in processing speed. This DTI-based method may reflect the degree of white matter structural disruption in cerebral amyloid angiopathy and could be a useful biomarker for cognition in this population.
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Affiliation(s)
- N Raposo
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts .,Department of Neurology (N.R.), Centre Hospitalier Universitaire de Toulouse, Toulouse, France.,Toulouse NeuroImaging Center (N.R.), Université de Toulouse, Institut National de la Santé et de la Recherche Médicale, Toulouse, UPS, France
| | - M C Zanon Zotin
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.,Center for Imaging Sciences and Medical Physics (M.C.Z.Z.). Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil;, Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, São Paulo, Brazil
| | - D Schoemaker
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - L Xiong
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - P Fotiadis
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - A Charidimou
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - M Pasi
- Department of Neurology (M.P.), Centre Hospitalier Universitaire de Lille, Lille, France
| | - G Boulouis
- Department of Neuroradiology (G.B.), Centre Hospitalier Sainte-Anne, Université Paris-Descartes, Paris, France
| | - K Schwab
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - M D Schirmer
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.,Computer Science and Artificial Intelligence Lab (M.D.S.), Massachusetts Institute of Technology, Boston, Massachusetts.,Department of Population Health Sciences (M.D.S.), German Center for Neurodegenerative Diseases, Bonn, Germany
| | - M R Etherton
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - M E Gurol
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - S M Greenberg
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - M Duering
- Medical Image Analysis Center and Quantitative Biomedical Imaging Group (M.D.), Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - A Viswanathan
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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31
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Du L, Zhao Z, Xu B, Gao W, Liu X, Chen Y, Wang Y, Liu J, Liu B, Sun S, Ma G, Gao J. Anisotropy of Anomalous Diffusion Improves the Accuracy of Differentiating and Grading Alzheimer's Disease Using Novel Fractional Motion Model. Front Aging Neurosci 2020; 12:602510. [PMID: 33328977 PMCID: PMC7710869 DOI: 10.3389/fnagi.2020.602510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 10/19/2020] [Indexed: 11/13/2022] Open
Abstract
Background and Purpose: Recent evidence shows that the fractional motion (FM) model may be a more appropriate model for describing the complex diffusion process of water in brain tissue and has shown to be beneficial in clinical applications of Alzheimer's disease (AD). However, the FM model averaged the anomalous diffusion parameter values, which omitted the impacts of anisotropy. This study aimed to investigate the potential feasibility of anisotropy of anomalous diffusion using the FM model for distinguishing and grading AD patients. Methods: Twenty-four patients with AD and 11 matched healthy controls were recruited, diffusion MRI was obtained from all participants and analyzed using the FM model. Generalized fractional anisotropy (gFA), an anisotropy metric, was introduced and the gFA values of FM-related parameters, Noah exponent (α) and the Hurst exponent (H), were calculated and compared between the healthy group and AD group and between the mild AD group and moderate AD group. The receiver-operating characteristic (ROC) analysis and the multivariate logistic regression analysis were used to assess the diagnostic performances of the anisotropy values and the directionally averaged values. Results: The gFA(α) and gFA(H) values of the moderate AD group were higher than those of the mild AD group in left hippocampus. The gFA(α) value of the moderate AD group was significantly higher than that of the healthy control group in both the left and right hippocampus. The gFA(ADC) values of the moderate AD group were significantly lower than those of the mild AD group and healthy control group in the right hippocampus. Compared with the gFA(α), gFA(H), α, and H, the ROC analysis showed larger areas under the curves for combination of α + gFA(α) and the combination of H + gFA(H) in differentiating the mild AD and moderate AD groups, and larger area under the curves for combination of α + gFA(α) in differentiating the healthy controls and AD groups. Conclusion: The anisotropy of anomalous diffusion could significantly differentiate and grade patients with AD, and the diagnostic performance was improved when the anisotropy metric was combined with commonly used directionally averaged values. The utility of anisotropic anomalous diffusion may provide novel insights to profoundly understand the neuropathology of AD.
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Affiliation(s)
- Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zifang Zhao
- Department of Anesthesiology, Peking University First Hospital, Peking University, Beijing, China
| | - Boyan Xu
- Beijing Intelligent Brain Cloud Inc., Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Jian Liu
- Department of Ultrasound Diagnosis, China-Japan Friendship Hospital, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Shilong Sun
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiahong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
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