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Seehafer S, Larsen N, Aludin S, Jansen O, Schmill LPA. Perivascular spaces and where to find them - MR imaging and evaluation methods. ROFO-FORTSCHR RONTG 2024; 196:1029-1036. [PMID: 38408476 DOI: 10.1055/a-2254-5651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
BACKGROUND Perivascular spaces (synonym: Virchow-Robin spaces) were first described over 150 years ago. They are defined as the fluid-filled spaces surrounding the small penetrating cerebral vessels. They gained growing scientific interest especially with the postulation of the so-called glymphatic system and their possible role in neurodegenerative and neuroinflammatory diseases. METHODS PubMed was used for a systematic search with a focus on literature regarding MRI imaging and evaluation methods of perivascular spaces. Studies on human in-vivo imaging were included with a focus on studies involving healthy populations. No time frame was set. The nomenclature in the literature is very heterogeneous with terms like "large", "dilated", "enlarged" perivascular spaces whereas borders and definitions often remain unclear. This work generally talks about perivascular spaces. RESULTS This review article discusses the morphologic MRI characteristics in different sequences. With the continual improvement of image quality, more and tinier structures can be depicted in detail. Visual analysis and semi or fully automated segmentation methods are briefly discussed. CONCLUSION If they are looked for, perivascular spaces are apparent in basically every cranial MRI examination. Their physiologic or pathologic value is still under debate. KEY POINTS · Perivascular spaces can be seen in basically every cranial MRI examination.. · Primarily T2-weighend sequences are used for visual analysis. Additional sequences are helpful for distinction from their differential diagnoses.. · There are promising approaches for the semi or fully automated segmentation of perivascular spaces with the possibility to collect more quantitative parameters.. CITATION FORMAT · Seehafer S, Larsen N, Aludin S et al. Perivascular spaces and where to find them - MRI imaging and evaluation methods. Fortschr Röntgenstr 2024; 196: 1029 - 1036.
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Affiliation(s)
- Svea Seehafer
- Clinic for Radiology and Neuroradiology, University Hospital Schleswig-Holstein - Campus Kiel, Germany
| | - Naomi Larsen
- Clinic for Radiology and Neuroradiology, University Hospital Schleswig-Holstein - Campus Kiel, Germany
| | - Schekeb Aludin
- Clinic for Radiology and Neuroradiology, University Hospital Schleswig-Holstein - Campus Kiel, Germany
| | - Olav Jansen
- Clinic for Radiology and Neuroradiology, University Hospital Schleswig-Holstein - Campus Kiel, Germany
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Cai D, Pan M, Liu C, He W, Ge X, Lin J, Li R, Liu M, Xia J. Deep-learning-based segmentation of perivascular spaces on T2-Weighted 3T magnetic resonance images. Front Aging Neurosci 2024; 16:1457405. [PMID: 39267720 PMCID: PMC11390432 DOI: 10.3389/fnagi.2024.1457405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Accepted: 08/12/2024] [Indexed: 09/15/2024] Open
Abstract
Purpose Studying perivascular spaces (PVSs) is important for understanding the pathogenesis and pathological changes of neurological disorders. Although some methods for automated segmentation of PVSs have been proposed, most of them were based on 7T MR images that were majorly acquired in healthy young people. Notably, 7T MR imaging is rarely used in clinical practice. Herein, we propose a deep-learning-based method that enables automatic segmentation of PVSs on T2-weighted 3T MR images. Method Twenty patients with Parkinson's disease (age range, 42-79 years) participated in this study. Specifically, we introduced a multi-scale supervised dense nested attention network designed to segment the PVSs. This model fosters progressive interactions between high-level and low-level features. Simultaneously, it utilizes multi-scale foreground content for deep supervision, aiding in refining segmentation results at various levels. Result Our method achieved the best segmentation results compared with the four other deep-learning-based methods, achieving a dice similarity coefficient (DSC) of 0.702. The results of the visual count of the PVSs in our model correlated extremely well with the expert scoring results on the T2-weighted images (basal ganglia: rs = 0.845, P < 0.001; rs = 0.868, P < 0.001; centrum semiovale: rs = 0.845, P < 0.001; rs = 0.823, P < 0.001 for raters 1 and 2, respectively). Experimental results show that the proposed method performs well in the segmentation of PVSs. Conclusion The proposed method can accurately segment PVSs; it will facilitate practical clinical applications and is expected to replace the method of visual counting directly on T1-weighted images or T2-weighted images.
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Affiliation(s)
- Die Cai
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China
| | - Minmin Pan
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Chenyuan Liu
- Five-Year Clinical Medicine, Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Wenjie He
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China
| | - Xinting Ge
- School of Information Science and Engineering, Shandong Normal University, Shandong, China
| | - Jiaying Lin
- Department of Biomedical Engineering, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Rui Li
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China
| | - Mengting Liu
- Department of Biomedical Engineering, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Jun Xia
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China
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Barisano G, Iv M, Choupan J, Hayden-Gephart M. Cerebral perivascular spaces as predictors of dementia risk and accelerated brain atrophy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.25.24306324. [PMID: 38712073 PMCID: PMC11071547 DOI: 10.1101/2024.04.25.24306324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Cerebral small vessel disease, an important risk factor for dementia, lacks robust, in vivo measurement methods. Perivascular spaces (PVS) on brain MRI are surrogates for small parenchymal blood vessels and their perivascular compartment, and may relate to brain health. We developed a novel, robust algorithm to automatically assess PVS count and size on MRI, and investigated their relationship with dementia risk and brain atrophy. We analyzed 46,478 clinical measurements of cognitive functioning and 20,845 brain MRI scans from 10,004 participants (71.1±9.7 years-old, 56.6% women). Fewer PVS and larger PVS diameter at baseline were associated with higher dementia risk and accelerated brain atrophy. Longitudinal trajectories of PVS markers were significantly different in non-demented individuals who converted to dementia compared with non-converters. In simulated placebo-controlled trials for treatments targeting cognitive decline, screening out participants less likely to develop dementia based on our PVS markers enhanced the power of the trial. These novel radiographic cerebrovascular markers may improve risk-stratification of individuals, potentially reducing cost and increasing throughput of clinical trials to combat dementia.
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Affiliation(s)
| | - Michael Iv
- Department of Radiology, Stanford University, Stanford, CA, USA
| | | | - Jeiran Choupan
- Laboratory of Neuro Imaging, University of Southern California, Los Angeles, CA, USA
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Parillo M, Vaccarino F, Di Gennaro G, Kumar S, Van Goethem J, Beomonte Zobel B, Quattrocchi CC, Parizel PM, Mallio CA. Overview of the Current Knowledge and Conventional MRI Characteristics of Peri- and Para-Vascular Spaces. Brain Sci 2024; 14:138. [PMID: 38391713 PMCID: PMC10886993 DOI: 10.3390/brainsci14020138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 01/10/2024] [Accepted: 01/26/2024] [Indexed: 02/24/2024] Open
Abstract
Brain spaces around (perivascular spaces) and alongside (paravascular or Virchow-Robin spaces) vessels have gained significant attention in recent years due to the advancements of in vivo imaging tools and to their crucial role in maintaining brain health, contributing to the anatomic foundation of the glymphatic system. In fact, it is widely accepted that peri- and para-vascular spaces function as waste clearance pathways for the brain for materials such as ß-amyloid by allowing exchange between cerebrospinal fluid and interstitial fluid. Visible brain spaces on magnetic resonance imaging are often a normal finding, but they have also been associated with a wide range of neurological and systemic conditions, suggesting their potential as early indicators of intracranial pressure and neurofluid imbalance. Nonetheless, several aspects of these spaces are still controversial. This article offers an overview of the current knowledge and magnetic resonance imaging characteristics of peri- and para-vascular spaces, which can help in daily clinical practice image description and interpretation. This paper is organized into different sections, including the microscopic anatomy of peri- and para-vascular spaces, their associations with pathological and physiological events, and their differential diagnosis.
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Affiliation(s)
- Marco Parillo
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Federica Vaccarino
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Gianfranco Di Gennaro
- Department of Health Sciences, Chair of Medical Statistics, University of Catanzaro "Magna Græcia", 88100 Catanzaro, Italy
| | - Sumeet Kumar
- Department of Neuroradiology, National Neuroscience Institute, Singapore 308433, Singapore
- Duke-National University of Singapore Medical School, Singapore 169857, Singapore
| | - Johan Van Goethem
- Department of Radiology, Antwerp University Hospital, 2650 Edegem, Belgium
| | - Bruno Beomonte Zobel
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Carlo Cosimo Quattrocchi
- Centre for Medical Sciences-CISMed, University of Trento, Via S. Maria Maddalena 1, 38122 Trento, Italy
| | - Paul M Parizel
- Royal Perth Hospital & University of Western Australia, Perth, WA 6000, Australia
- Medical School, University of Western Australia, Perth, WA 6009, Australia
| | - Carlo Augusto Mallio
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
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Okar SV, Hu F, Shinohara RT, Beck ES, Reich DS, Ineichen BV. The etiology and evolution of magnetic resonance imaging-visible perivascular spaces: Systematic review and meta-analysis. Front Neurosci 2023; 17:1038011. [PMID: 37065926 PMCID: PMC10098201 DOI: 10.3389/fnins.2023.1038011] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 03/15/2023] [Indexed: 04/03/2023] Open
Abstract
ObjectivesPerivascular spaces have been involved in neuroinflammatory and neurodegenerative diseases. Upon a certain size, these spaces can become visible on magnetic resonance imaging (MRI), referred to as enlarged perivascular spaces (EPVS) or MRI-visible perivascular spaces (MVPVS). However, the lack of systematic evidence on etiology and temporal dynamics of MVPVS hampers their diagnostic utility as MRI biomarker. Thus, the goal of this systematic review was to summarize potential etiologies and evolution of MVPVS.MethodsIn a comprehensive literature search, out of 1,488 unique publications, 140 records assessing etiopathogenesis and dynamics of MVPVS were eligible for a qualitative summary. 6 records were included in a meta-analysis to assess the association between MVPVS and brain atrophy.ResultsFour overarching and partly overlapping etiologies of MVPVS have been proposed: (1) Impairment of interstitial fluid circulation, (2) Spiral elongation of arteries, (3) Brain atrophy and/or perivascular myelin loss, and (4) Immune cell accumulation in the perivascular space. The meta-analysis in patients with neuroinflammatory diseases did not support an association between MVPVS and brain volume measures [R: −0.15 (95%-CI −0.40–0.11)]. Based on few and mostly small studies in tumefactive MVPVS and in vascular and neuroinflammatory diseases, temporal evolution of MVPVS is slow.ConclusionCollectively, this study provides high-grade evidence for MVPVS etiopathogenesis and temporal dynamics. Although several potential etiologies for MVPVS emergence have been proposed, they are only partially supported by data. Advanced MRI methods should be employed to further dissect etiopathogenesis and evolution of MVPVS. This can benefit their implementation as an imaging biomarker.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=346564, identifier CRD42022346564.
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Affiliation(s)
- Serhat V. Okar
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Fengling Hu
- Department of Biostatistics, Epidemiology, and Informatics, Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, United States
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, United States
| | - Erin S. Beck
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Daniel S. Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Benjamin V. Ineichen
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Center for Reproducible Science, University of Zurich, Zurich, Switzerland
- *Correspondence: Benjamin V. Ineichen, , ; orcid.org/0000-0003-1362-4819
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Moses J, Sinclair B, Law M, O'Brien TJ, Vivash L. Automated Methods for Detecting and Quantitation of Enlarged Perivascular spaces on MRI. J Magn Reson Imaging 2023; 57:11-24. [PMID: 35866259 PMCID: PMC10083963 DOI: 10.1002/jmri.28369] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/06/2022] [Accepted: 07/08/2022] [Indexed: 02/03/2023] Open
Abstract
The brain's glymphatic system is a network of intracerebral vessels that function to remove "waste products" such as degraded proteins from the brain. It comprises of the vasculature, perivascular spaces (PVS), and astrocytes. Poor glymphatic function has been implicated in numerous diseases; however, its contribution is still unknown. Efforts have been made to image the glymphatic system to further assess its role in the pathogenesis of different diseases. Numerous imaging modalities have been utilized including two-photon microscopy and contrast-enhanced magnetic resonance imaging (MRI). However, these are associated with limitations for clinical use. PVS form a part of the glymphatic system and can be visualized on standard MRI sequences when enlarged. It is thought that PVS become enlarged secondary to poor glymphatic drainage of metabolites. Thus, quantitating PVS could be a good surrogate marker for glymphatic function. Numerous manual rating scales have been developed to measure the PVS number and size on MRI scans; however, these are associated with many limitations. Instead, automated methods have been created to measure PVS more accurately in different diseases. In this review, we discuss the imaging techniques currently available to visualize the glymphatic system as well as the automated methods currently available to measure PVS, and the strengths and limitations associated with each technique. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Jasmine Moses
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne, Australia
| | - Ben Sinclair
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne, Australia.,Department of Neurology, Alfred Hospital, Melbourne, Australia.,Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia
| | - Meng Law
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne, Australia.,Department of Radiology, Alfred Health, Melbourne, Victoria, Australia.,Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Victoria, Australia
| | - Terence J O'Brien
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne, Australia.,Department of Neurology, Alfred Hospital, Melbourne, Australia.,Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia.,Department of Neurology, Royal Melbourne Hospital, University of Melbourne, Victoria, Australia
| | - Lucy Vivash
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne, Australia.,Department of Neurology, Alfred Hospital, Melbourne, Australia.,Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia.,Department of Neurology, Royal Melbourne Hospital, University of Melbourne, Victoria, Australia
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Pham W, Lynch M, Spitz G, O’Brien T, Vivash L, Sinclair B, Law M. A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging. Front Neurosci 2022; 16:1021311. [PMID: 36590285 PMCID: PMC9795229 DOI: 10.3389/fnins.2022.1021311] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 11/16/2022] [Indexed: 12/15/2022] Open
Abstract
The glymphatic system is responsible for waste clearance in the brain. It is comprised of perivascular spaces (PVS) that surround penetrating blood vessels. These spaces are filled with cerebrospinal fluid and interstitial fluid, and can be seen with magnetic resonance imaging. Various algorithms have been developed to automatically label these spaces in MRI. This has enabled volumetric and morphological analyses of PVS in healthy and disease cohorts. However, there remain inconsistencies between PVS measures reported by different methods of automated segmentation. The present review emphasizes that importance of voxel-wise evaluation of model performance, mainly with the Sørensen Dice similarity coefficient. Conventional count correlations for model validation are inadequate if the goal is to assess volumetric or morphological measures of PVS. The downside of voxel-wise evaluation is that it requires manual segmentations that require large amounts of time to produce. One possible solution is to derive these semi-automatically. Additionally, recommendations are made to facilitate rigorous development and validation of automated PVS segmentation models. In the application of automated PVS segmentation tools, publication of image quality metrics, such as the contrast-to-noise ratio, alongside descriptive statistics of PVS volumes and counts will facilitate comparability between studies. Lastly, a head-to-head comparison between two algorithms, applied to two cohorts of astronauts reveals how results can differ substantially between techniques.
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Affiliation(s)
- William Pham
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Miranda Lynch
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Gershon Spitz
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Terence O’Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Neurology, Alfred Hospital, Melbourne, VIC, Australia
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia
- Department of Neurology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Neurology, Alfred Hospital, Melbourne, VIC, Australia
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia
- Department of Neurology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia
| | - Benjamin Sinclair
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Neurology, Alfred Hospital, Melbourne, VIC, Australia
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia
| | - Meng Law
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Radiology, Alfred Health Hospital, Melbourne, VIC, Australia
- Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia
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Ineichen BV, Okar SV, Proulx ST, Engelhardt B, Lassmann H, Reich DS. Perivascular spaces and their role in neuroinflammation. Neuron 2022; 110:3566-3581. [PMID: 36327898 PMCID: PMC9905791 DOI: 10.1016/j.neuron.2022.10.024] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 08/17/2022] [Accepted: 10/13/2022] [Indexed: 11/19/2022]
Abstract
It is uncontested that perivascular spaces play critical roles in maintaining homeostasis and priming neuroinflammation. However, despite more than a century of intense research on perivascular spaces, many open questions remain about the anatomical compartment surrounding blood vessels within the CNS. The goal of this comprehensive review is to summarize the literature on perivascular spaces in human neuroinflammation and associated animal disease models. We describe the cell types taking part in the morphological and functional aspects of perivascular spaces and how those spaces can be visualized. Based on this, we propose a model of the cascade of events occurring during neuroinflammatory pathology. We also discuss current knowledge gaps and limitations of the available evidence. An improved understanding of perivascular spaces could advance our comprehension of the pathophysiology of neuroinflammation and open a new therapeutic window for neuroinflammatory diseases such as multiple sclerosis.
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Affiliation(s)
- Benjamin V Ineichen
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA; Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Center for Reproducible Science, University of Zurich, Zurich, Switzerland.
| | - Serhat V Okar
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Steven T Proulx
- Theodor Kocher Institute, University of Bern, Bern, Switzerland
| | | | - Hans Lassmann
- Department of Neuroimmunology, Center for Brain Research, Medical University of Vienna, Spitalgasse 4, 1090 Vienna, Austria
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
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Barisano G, Lynch KM, Sibilia F, Lan H, Shih NC, Sepehrband F, Choupan J. Imaging perivascular space structure and function using brain MRI. Neuroimage 2022; 257:119329. [PMID: 35609770 PMCID: PMC9233116 DOI: 10.1016/j.neuroimage.2022.119329] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/04/2022] [Accepted: 05/19/2022] [Indexed: 12/03/2022] Open
Abstract
In this article, we provide an overview of current neuroimaging methods for studying perivascular spaces (PVS) in humans using brain MRI. In recent years, an increasing number of studies highlighted the role of PVS in cerebrospinal/interstial fluid circulation and clearance of cerebral waste products and their association with neurological diseases. Novel strategies and techniques have been introduced to improve the quantification of PVS and to investigate their function and morphological features in physiological and pathological conditions. After a brief introduction on the anatomy and physiology of PVS, we examine the latest technological developments to quantitatively analyze the structure and function of PVS in humans with MRI. We describe the applications, advantages, and limitations of these methods, providing guidance and suggestions on the acquisition protocols and analysis techniques that can be applied to study PVS in vivo. Finally, we review the human neuroimaging studies on PVS across the normative lifespan and in the context of neurological disorders.
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Affiliation(s)
- Giuseppe Barisano
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA; Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA.
| | - Kirsten M Lynch
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA
| | - Francesca Sibilia
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA
| | - Haoyu Lan
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA; Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - Nien-Chu Shih
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA
| | - Farshid Sepehrband
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA
| | - Jeiran Choupan
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA
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Reply to Wostyn et al.: Potential models for perivascular space (PVS) enlargement and spaceflight-associated neuro-ocular syndrome (SANS). Proc Natl Acad Sci U S A 2022; 119:e2208241119. [PMID: 35858379 PMCID: PMC9371741 DOI: 10.1073/pnas.2208241119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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