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Tang J, Pahlavian SH, Joe E, Gamez MT, Zhao T, Ma S, Jin J, Cen SY, Chui H, Yan L. Assessment of arterial pulsatility of cerebral perforating arteries using 7T high-resolution dual-VENC phase-contrast MRI. Magn Reson Med 2024; 92:605-617. [PMID: 38440807 PMCID: PMC11186522 DOI: 10.1002/mrm.30073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/24/2024] [Accepted: 02/14/2024] [Indexed: 03/06/2024]
Abstract
PURPOSE Directly imaging the function of cerebral perforating arteries could provide valuable insight into the pathology of cerebral small vessel diseases (cSVD). Arterial pulsatility has been identified as a useful biomarker for assessing vascular dysfunction. In this study, we investigate the feasibility and reliability of using dual velocity encoding (VENC) phase-contrast MRI (PC-MRI) to measure the pulsatility of cerebral perforating arteries at 7 T. METHODS Twenty participants, including 12 young volunteers and 8 elder adults, underwent high-resolution 2D PC-MRI scans with VENCs of 20 cm/s and 40 cm/s at 7T. The sensitivity of perforator detection and the reliability of pulsatility measurement of cerebral perforating arteries using dual-VENC PC-MRI were evaluated by comparison with the single-VENC data. The effects of temporal resolution in the PC-MRI acquisition and aging on the pulsatility measurements were investigated. RESULTS Compared to the single VENCs, dual-VENC PC-MRI provided improved sensitivity of perforator detection and more reliable pulsatility measurements. Temporal resolution impacted the pulsatility measurements, as decreasing temporal resolution led to an underestimation of pulsatility. Elderly adults had elevated pulsatility in cerebral perforating arteries compared to young adults, but there was no difference in the number of detected perforators between the two age groups. CONCLUSION Dual-VENC PC-MRI is a reliable imaging method for the assessment of pulsatility of cerebral perforating arteries, which could be useful as a potential imaging biomarker of aging and cSVD.
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Affiliation(s)
- Jianing Tang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, United States
| | - Soroush Heidari Pahlavian
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, United States
| | - Elizabeth Joe
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States
| | - Maria Tereza Gamez
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
| | - Tianrui Zhao
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, United States
| | - Samantha Ma
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, United States
- Siemens Medical Solutions USA, Los Angeles, California, United States
| | - Jin Jin
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, United States
- Siemens Medical Solutions USA, Los Angeles, California, United States
| | - Steven Yong Cen
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Helena Chui
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States
| | - Lirong Yan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, United States
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, United States
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States
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Mendyk-Bordet AM, Ouk T, Muhr-Tailleux A, Pétrault M, Vallez E, Gelé P, Dondaine T, Labreuche J, Deplanque D, Bordet R. Endothelial Dysfunction and Pre-Existing Cognitive Disorders in Stroke Patients. Biomolecules 2024; 14:721. [PMID: 38927124 PMCID: PMC11202150 DOI: 10.3390/biom14060721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/01/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND The origin of pre-existing cognitive impairment in stroke patients remains controversial, with a vascular or a degenerative hypothesis. OBJECTIVE To determine whether endothelial dysfunction is associated with pre-existing cognitive problems, lesion load and biological anomalies in stroke patients. METHODS Patients originated from the prospective STROKDEM study. The baseline cognitive state, assessed using the IQ-CODE, and risk factors for stroke were recorded at inclusion. Patients with an IQ-CODE score >64 were excluded. Endothelial function was determined 72 h after stroke symptom onset by non-invasive digital measurement of endothelium-dependent flow-mediated dilation and calculation of the reactive hyperemia index (RHI). RHI ≤ 1.67 indicated endothelial dysfunction. Different biomarkers of endothelial dysfunction were analysed in blood or plasma. All patients underwent MRI 72 h after stroke symptom onset. RESULTS A total of 86 patients were included (52 males; mean age 63.5 ± 11.5 years). Patients with abnormal RHI have hypertension or antihypertensive treatment more often. The baseline IQ-CODE was abnormal in 33 (38.4%) patients, indicating a pre-existing cognitive problem. Baseline IQ-CODE > 48 was observed in 15 patients (28.3%) with normal RHI and in 18 patients (54.6%) with abnormal RHI (p = 0.016). The RHI median was significantly lower in patients with abnormal IQ-CODE. Abnormal RHI was associated with a significantly higher median FAZEKAS score (2.5 vs. 2; p = 0.008), a significantly higher frequency of periventricular lesions (p = 0.015), more white matter lesions (p = 0.007) and a significantly higher cerebral atrophy score (p < 0.001) on MRI. Vascular biomarkers significantly associated with abnormal RHI were MCP-1 (p = 0.009), MIP_1a (p = 0.042), and homocysteinemia (p < 0.05). CONCLUSIONS A vascular mechanism may be responsible for cognitive problems pre-existing stroke. The measurement of endothelial dysfunction after stroke could become an important element of follow-up, providing an indication of the functional and cognitive prognosis of stroke patients.
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Affiliation(s)
| | - Thavarak Ouk
- Univ. Lille, CHU Lille, Inserm, Lille Neuroscience and Cognition, F-59000 Lille, France
| | - Anne Muhr-Tailleux
- Univ. Lille, CHU Lille, Inserm, Institut Pasteur de Lille, Nuclear Receptor, Metabolic and Cardiovascular Diseases, F-59000 Lille, France; (A.M.-T.); (E.V.)
| | - Maud Pétrault
- Univ. Lille, CHU Lille, Inserm, Lille Neuroscience and Cognition, F-59000 Lille, France
| | - Emmanuelle Vallez
- Univ. Lille, CHU Lille, Inserm, Institut Pasteur de Lille, Nuclear Receptor, Metabolic and Cardiovascular Diseases, F-59000 Lille, France; (A.M.-T.); (E.V.)
| | - Patrick Gelé
- Univ. Lille, CHU Lille, Inserm, Lille Neuroscience and Cognition, F-59000 Lille, France
| | - Thibaut Dondaine
- Univ. Lille, CHU Lille, Inserm, Lille Neuroscience and Cognition, F-59000 Lille, France
| | - Julien Labreuche
- Univ. Lille, CHU Lille, Inserm, Biostatistic Platform, F-59000 Lille, France
| | - Dominique Deplanque
- Univ. Lille, CHU Lille, Inserm, Lille Neuroscience and Cognition, F-59000 Lille, France
| | - Régis Bordet
- Univ. Lille, CHU Lille, Inserm, Lille Neuroscience and Cognition, F-59000 Lille, France
- Univ. Lille, CHU Lille, Inserm, Department of Medical Pharmacology, F-59000 Lille, France
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Nyúl-Tóth Á, Patai R, Csiszar A, Ungvari A, Gulej R, Mukli P, Yabluchanskiy A, Benyo Z, Sotonyi P, Prodan CI, Liotta EM, Toth P, Elahi F, Barsi P, Maurovich-Horvat P, Sorond FA, Tarantini S, Ungvari Z. Linking peripheral atherosclerosis to blood-brain barrier disruption: elucidating its role as a manifestation of cerebral small vessel disease in vascular cognitive impairment. GeroScience 2024:10.1007/s11357-024-01194-0. [PMID: 38831182 DOI: 10.1007/s11357-024-01194-0] [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: 04/10/2024] [Accepted: 05/06/2024] [Indexed: 06/05/2024] Open
Abstract
Aging plays a pivotal role in the pathogenesis of cerebral small vessel disease (CSVD), contributing to the onset and progression of vascular cognitive impairment and dementia (VCID). In older adults, CSVD often leads to significant pathological outcomes, including blood-brain barrier (BBB) disruption, which in turn triggers neuroinflammation and white matter damage. This damage is frequently observed as white matter hyperintensities (WMHs) in neuroimaging studies. There is mounting evidence that older adults with atherosclerotic vascular diseases, such as peripheral artery disease, ischemic heart disease, and carotid artery stenosis, face a heightened risk of developing CSVD and VCID. This review explores the complex relationship between peripheral atherosclerosis, the pathogenesis of CSVD, and BBB disruption. It explores the continuum of vascular aging, emphasizing the shared pathomechanisms that underlie atherosclerosis in large arteries and BBB disruption in the cerebral microcirculation, exacerbating both CSVD and VCID. By reviewing current evidence, this paper discusses the impact of endothelial dysfunction, cellular senescence, inflammation, and oxidative stress on vascular and neurovascular health. This review aims to enhance understanding of these complex interactions and advocate for integrated approaches to manage vascular health, thereby mitigating the risk and progression of CSVD and VCID.
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Affiliation(s)
- Ádám Nyúl-Tóth
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Public Health, Semmelweis University, Semmelweis University, Budapest, Hungary
| | - Roland Patai
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Anna Csiszar
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK, USA
| | - Anna Ungvari
- Department of Public Health, Semmelweis University, Semmelweis University, Budapest, Hungary.
| | - Rafal Gulej
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Peter Mukli
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Public Health, Semmelweis University, Semmelweis University, Budapest, Hungary
| | - Andriy Yabluchanskiy
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK, USA
- Department of Health Promotion Sciences, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Doctoral College/Department of Public Health, International Training Program in Geroscience, Semmelweis University, Budapest, Hungary
| | - Zoltan Benyo
- Institute of Translational Medicine, Semmelweis University, 1094, Budapest, Hungary
- Cerebrovascular and Neurocognitive Disorders Research Group, HUN-REN, Semmelweis University, 1094, Budapest, Hungary
| | - Peter Sotonyi
- Department of Vascular and Endovascular Surgery, Heart and Vascular Centre, Semmelweis University, 1122, Budapest, Hungary
| | - Calin I Prodan
- Veterans Affairs Medical Center, Oklahoma City, OK, USA
- Department of Neurology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Eric M Liotta
- Doctoral College/Department of Public Health, International Training Program in Geroscience, Semmelweis University, Budapest, Hungary
- Department of Neurology, Division of Stroke and Neurocritical Care, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Peter Toth
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Public Health, Semmelweis University, Semmelweis University, Budapest, Hungary
- Department of Neurosurgery, Medical School, University of Pecs, Pecs, Hungary
- Neurotrauma Research Group, Szentagothai Research Centre, University of Pecs, Pecs, Hungary
- ELKH-PTE Clinical Neuroscience MR Research Group, University of Pecs, Pecs, Hungary
| | - Fanny Elahi
- Departments of Neurology and Neuroscience Ronald M. Loeb Center for Alzheimer's Disease Friedman Brain Institute Icahn School of Medicine at Mount Sinai, New York, NY, USA
- James J. Peters VA Medical Center, Bronx, NY, USA
| | - Péter Barsi
- ELKH-SE Cardiovascular Imaging Research Group, Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Pál Maurovich-Horvat
- ELKH-SE Cardiovascular Imaging Research Group, Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Farzaneh A Sorond
- Department of Neurology, Division of Stroke and Neurocritical Care, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Stefano Tarantini
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK, USA
- Department of Health Promotion Sciences, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Doctoral College/Department of Public Health, International Training Program in Geroscience, Semmelweis University, Budapest, Hungary
| | - Zoltan Ungvari
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK, USA
- Department of Health Promotion Sciences, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Doctoral College/Department of Public Health, International Training Program in Geroscience, Semmelweis University, Budapest, Hungary
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Agbonon R, Forestier G, Bricout N, Benhassen W, Turc G, Bretzner M, Pasi M, Benzakoun J, Seners P, Derraz I, Legrand L, Trystram D, Rodriguez-Regent C, Charidimou A, Rost NS, Bracard S, Cordonnier C, Eker OF, Oppenheim C, Naggara O, Henon H, Boulouis G. Cerebral microbleeds and risk of symptomatic hemorrhagic transformation following mechanical thrombectomy for large vessel ischemic stroke. J Neurol 2024; 271:2631-2638. [PMID: 38355868 DOI: 10.1007/s00415-024-12205-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/08/2024] [Accepted: 01/16/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND AND PURPOSE In patients with acute ischemic stroke (AIS) treated with endovascular therapy (EVT), the association of pre-existing cerebral small vessel disease (cSVD) with symptomatic intracerebral hemorrhage (sICH) remains controversial. We tested the hypothesis that the presence of cerebral microbleeds (CMBs) and their burden would be associated with sICH after EVT of AIS. METHODS We conducted a retrospective study combining cohorts of patients that underwent EVT between January 1st 2015 and January 1st 2020. CMB presence, burden, and other cSVD markers were assessed on a pre-treatment MRI, evaluated independently by two observers. Primary outcome was the occurrence of sICH. RESULTS 445 patients with pretreatment MRI were included, of which 70 (15.7%) demonstrated CMBs on baseline MRI. sICH occurred in 36 (7.6%) of all patients. Univariate analysis did not demonstrate an association between CMB and the occurrence of sICH (7.5% in CMB+ group vs 8.6% in CMB group, p = 0.805). In multivariable models, CMBs' presence was not significantly associated with increased odds for sICH (-aOR- 1.19; 95% CI [0.43-3.27], p = 0.73). Only ASPECTs (aOR 0.71 per point increase; 95% CI [0.60-0.85], p < 0.001) and collaterals status (aOR 0.22 for adequate versus poor collaterals; 95% CI [0.06-0.93], p 0.019) were independently associated with sICH. CONCLUSION CMB presence and burden is not associated with increased occurrence of sICH after EVT. This result incites not to exclude patients with CMBs from EVT. The risk of sICH after EVT in patients with more than10 CMBs will require further investigation. REGISTRATION Registration-URL: http://www. CLINICALTRIALS gov ; Unique identifier: NCT01062698.
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Affiliation(s)
- Rémi Agbonon
- Neuroradiology Department, GHU Paris Psychiatry and Neurosciences, Sainte-Anne Hospital, Paris, France
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), UMR_S1266, INSERM, IMA-BRAIN INSERM U1266, Université de Paris, Paris, France
| | - Géraud Forestier
- Neuroradiology Department, GHU Paris Psychiatry and Neurosciences, Sainte-Anne Hospital, Paris, France.
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), UMR_S1266, INSERM, IMA-BRAIN INSERM U1266, Université de Paris, Paris, France.
- Neuroradiology Department, Limoges University Hospital, 2 avenue Martin Luther-King, 87042, Limoges, France.
| | - Nicolas Bricout
- Neuroradiology Department, Univ. Lille, Inserm, CHU Lille, U1172-LilNCog (JPARC)-Lille Neurosciences & Cognition, 59000, Lille, France
| | - Wagih Benhassen
- Neuroradiology Department, GHU Paris Psychiatry and Neurosciences, Sainte-Anne Hospital, Paris, France
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), UMR_S1266, INSERM, IMA-BRAIN INSERM U1266, Université de Paris, Paris, France
| | - Guillaume Turc
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), UMR_S1266, INSERM, IMA-BRAIN INSERM U1266, Université de Paris, Paris, France
- Neurology Department, GHU Paris Psychiatry and Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Martin Bretzner
- Neuroradiology Department, Univ. Lille, Inserm, CHU Lille, U1172-LilNCog (JPARC)-Lille Neurosciences & Cognition, 59000, Lille, France
| | - Marco Pasi
- Univ. Lille, Inserm, CHU Lille, U1172-LilNCog-Lille Neuroscience & Cognition, 59000, Lille, France
| | - Joseph Benzakoun
- Neuroradiology Department, GHU Paris Psychiatry and Neurosciences, Sainte-Anne Hospital, Paris, France
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), UMR_S1266, INSERM, IMA-BRAIN INSERM U1266, Université de Paris, Paris, France
| | - Pierre Seners
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), UMR_S1266, INSERM, IMA-BRAIN INSERM U1266, Université de Paris, Paris, France
- Neurology Department, GHU Paris Psychiatry and Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Imad Derraz
- Department of Neuroradiology, Hôpital Gui de Chauliac, Montpellier University Medical Center, Montpellier, France
| | - Laurence Legrand
- Neuroradiology Department, GHU Paris Psychiatry and Neurosciences, Sainte-Anne Hospital, Paris, France
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), UMR_S1266, INSERM, IMA-BRAIN INSERM U1266, Université de Paris, Paris, France
| | - Denis Trystram
- Neuroradiology Department, GHU Paris Psychiatry and Neurosciences, Sainte-Anne Hospital, Paris, France
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), UMR_S1266, INSERM, IMA-BRAIN INSERM U1266, Université de Paris, Paris, France
| | - Christine Rodriguez-Regent
- Neuroradiology Department, GHU Paris Psychiatry and Neurosciences, Sainte-Anne Hospital, Paris, France
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), UMR_S1266, INSERM, IMA-BRAIN INSERM U1266, Université de Paris, Paris, France
| | - Andreas Charidimou
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Natalia S Rost
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Serge Bracard
- Neuroradiology Department, Lorraine University, INSERM U1254 CHRU Nancy, Nancy, France
| | - Charlotte Cordonnier
- Univ. Lille, Inserm, CHU Lille, U1172-LilNCog-Lille Neuroscience & Cognition, 59000, Lille, France
| | - Omer F Eker
- Department of Neuroradiology of Pierre Wertheimer Hospital, Hospices Civils de Lyon, Lyon, France
| | - Catherine Oppenheim
- Neuroradiology Department, GHU Paris Psychiatry and Neurosciences, Sainte-Anne Hospital, Paris, France
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), UMR_S1266, INSERM, IMA-BRAIN INSERM U1266, Université de Paris, Paris, France
| | - Olivier Naggara
- Neuroradiology Department, GHU Paris Psychiatry and Neurosciences, Sainte-Anne Hospital, Paris, France
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), UMR_S1266, INSERM, IMA-BRAIN INSERM U1266, Université de Paris, Paris, France
| | - Hilde Henon
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), UMR_S1266, INSERM, IMA-BRAIN INSERM U1266, Université de Paris, Paris, France
- Neurology Department, GHU Paris Psychiatry and Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Grégoire Boulouis
- Neuroradiology Department, GHU Paris Psychiatry and Neurosciences, Sainte-Anne Hospital, Paris, France
- Institut de Psychiatrie et Neurosciences de Paris (IPNP), UMR_S1266, INSERM, IMA-BRAIN INSERM U1266, Université de Paris, Paris, France
- Neuroradiology Department, CHU de Tours, Centre Val de Loire Region, Tours, France
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Chen S, Huang R, Zhang M, Huang X, Ling S, Liu S, Yang N. Altered brain spontaneous activity in patients with cerebral small vessel disease using the amplitude of low-frequency fluctuation of different frequency bands. Front Neurosci 2023; 17:1282496. [PMID: 38033542 PMCID: PMC10687154 DOI: 10.3389/fnins.2023.1282496] [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: 08/24/2023] [Accepted: 10/16/2023] [Indexed: 12/02/2023] Open
Abstract
Background Previous studies showed that cerebral small vessel disease (cSVD) is a leading cause of cognitive decline in elderly people and the development of Alzheimer's disease. Although brain structural changes of cSVD have been documented well, it remains unclear about the properties of brain intrinsic spontaneous activity in patients with cSVD. Methods We collected resting-state fMRI (rs-fMRI) and T1-weighted 3D high-resolution brain structural images from 41 cSVD patients and 32 healthy controls (HC). By estimating the amplitude of low-frequency fluctuation (ALFF) under three different frequency bands (typical band: 0.01-0.1 Hz; slow-4: 0.027-0.073 Hz; and slow-5: 0.01-0.027 Hz) in the whole-brain, we analyzed band-specific ALFF differences between the cSVD patients and controls. Results The cSVD patients showed uniformly lower ALFF than the healthy controls in the typical and slow-4 bands (pFWE < 0.05). In the typical band, cSVD patients showed lower ALFF involving voxels of the fusiform, hippocampus, inferior occipital cortex, middle occipital cortex, insula, inferior frontal cortex, rolandic operculum, and cerebellum compared with the controls. In the slow-4 band, cSVD patients showed lower ALFF involving voxels of the cerebellum, hippocampus, occipital, and fusiform compared with the controls. However, there is no significant between-group difference of ALFF in the slow-5 band. Moreover, we found significant "group × frequency" interactions in the left precuneus. Conclusion Our results suggested that brain intrinsic spontaneous activity of cSVD patients was abnormal and showed a frequency-specific characteristic. The ALFF in the slow-4 band may be more sensitive to detecting a malfunction in cSVD patients.
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Affiliation(s)
- Sina Chen
- Zhongshan Hospital of Traditional Chinese Medicine, Zhongshan, Guangdong, China
| | - Ruiwang Huang
- Center for Study of Applied Psychology, School of Psychology, South China Normal University, Guangzhou, Guangdong, China
| | - Mingxian Zhang
- Center for Study of Applied Psychology, School of Psychology, South China Normal University, Guangzhou, Guangdong, China
| | - Xiaohuang Huang
- Zhongshan Hospital of Traditional Chinese Medicine, Zhongshan, Guangdong, China
| | - Shuiqiao Ling
- Zhongshan Hospital of Traditional Chinese Medicine, Zhongshan, Guangdong, China
| | - Shuxue Liu
- Zhongshan Hospital of Traditional Chinese Medicine, Zhongshan, Guangdong, China
| | - Nan Yang
- Zhongshan Hospital of Traditional Chinese Medicine, Zhongshan, Guangdong, China
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6
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Richter S, Winzeck S, Correia MM, Kornaropoulos EN, Manktelow A, Outtrim J, Chatfield D, Posti JP, Tenovuo O, Williams GB, Menon DK, Newcombe VF. Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort. NEUROIMAGE. REPORTS 2022; 2:None. [PMID: 36507071 PMCID: PMC9726680 DOI: 10.1016/j.ynirp.2022.100136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 09/07/2022] [Accepted: 09/14/2022] [Indexed: 11/05/2022]
Abstract
Background The growth in multi-center neuroimaging studies generated a need for methods that mitigate the differences in hardware and acquisition protocols across sites i.e., scanner effects. ComBat harmonization methods have shown promise but have not yet been tested on all the data types commonly studied with magnetic resonance imaging (MRI). This study aimed to validate neuroCombat, longCombat and gamCombat on both structural and diffusion metrics in both cross-sectional and longitudinal data. Methods We used a travelling subject design whereby 73 healthy volunteers contributed 161 scans across two sites and four machines using one T1 and five diffusion MRI protocols. Scanner was defined as a composite of site, machine and protocol. A common pipeline extracted two structural metrics (volumes and cortical thickness) and two diffusion tensor imaging metrics (mean diffusivity and fractional anisotropy) for seven regions of interest including gray and (except for cortical thickness) white matter regions. Results Structural data exhibited no significant scanner effect and therefore did not benefit from harmonization in our particular cohort. Indeed, attempting harmonization obscured the true biological effect for some regions of interest. Diffusion data contained marked scanner effects and was successfully harmonized by all methods, resulting in smaller scanner effects and better detection of true biological effects. LongCombat less effectively reduced the scanner effect for cross-sectional white matter data but had a slightly lower probability of incorrectly finding group differences in simulations, compared to neuroCombat and gamCombat. False positive rates for all methods and all metrics did not significantly exceed 5%. Conclusions Statistical harmonization of structural data is not always necessary and harmonization in the absence of a scanner effect may be harmful. Harmonization of diffusion MRI data is highly recommended with neuroCombat, longCombat and gamCombat performing well in cross-sectional and longitudinal settings.
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Affiliation(s)
- Sophie Richter
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
- Corresponding author.
| | - Stefan Winzeck
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
- BioMedIA Group, Department of Computing, Imperial College London, London, UK
| | - Marta M. Correia
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | | | - Anne Manktelow
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Joanne Outtrim
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Doris Chatfield
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Jussi P. Posti
- Turku Brain Injury Center, Turku University Hospital & University of Turku, Turku, Finland
- Department of Neurosurgery, Turku University Hospital, Turku, Finland
| | - Olli Tenovuo
- Turku Brain Injury Center, Turku University Hospital & University of Turku, Turku, Finland
| | - Guy B. Williams
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - David K. Menon
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
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7
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Markus HS, van Der Flier WM, Smith EE, Bath P, Biessels GJ, Briceno E, Brodtman A, Chabriat H, Chen C, de Leeuw FE, Egle M, Ganesh A, Georgakis MK, Gottesman RF, Kwon S, Launer L, Mok V, O'Brien J, Ottenhoff L, Pendlebury S, Richard E, Sachdev P, Schmidt R, Springer M, Tiedt S, Wardlaw JM, Verdelho A, Webb A, Werring D, Duering M, Levine D, Dichgans M. Framework for Clinical Trials in Cerebral Small Vessel Disease (FINESSE): A Review. JAMA Neurol 2022; 79:1187-1198. [PMID: 35969390 PMCID: PMC11036410 DOI: 10.1001/jamaneurol.2022.2262] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Cerebral small vessel disease (SVD) causes a quarter of strokes and is the most common pathology underlying vascular cognitive impairment and dementia. An important step to developing new treatments is better trial methodology. Disease mechanisms in SVD differ from other stroke etiologies; therefore, treatments need to be evaluated in cohorts in which SVD has been well characterized. Furthermore, SVD itself can be caused by a number of different pathologies, the most common of which are arteriosclerosis and cerebral amyloid angiopathy. To date, there have been few sufficiently powered high-quality randomized clinical trials in SVD, and inconsistent trial methodology has made interpretation of some findings difficult. Observations To address these issues and develop guidelines for optimizing design of clinical trials in SVD, the Framework for Clinical Trials in Cerebral Small Vessel Disease (FINESSE) was created under the auspices of the International Society of Vascular Behavioral and Cognitive Disorders. Experts in relevant aspects of SVD trial methodology were convened, and a structured Delphi consensus process was used to develop recommendations. Areas in which recommendations were developed included optimal choice of study populations, choice of clinical end points, use of brain imaging as a surrogate outcome measure, use of circulating biomarkers for participant selection and as surrogate markers, novel trial designs, and prioritization of therapeutic agents using genetic data via Mendelian randomization. Conclusions and Relevance The FINESSE provides recommendations for trial design in SVD for which there are currently few effective treatments. However, new insights into understanding disease pathogenesis, particularly from recent genetic studies, provide novel pathways that could be therapeutically targeted. In addition, whether other currently available cardiovascular interventions are specifically effective in SVD, as opposed to other subtypes of stroke, remains uncertain. FINESSE provides a framework for design of trials examining such therapeutic approaches.
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Affiliation(s)
- Hugh S Markus
- Alzheimer Center Amsterdam, Department of Neurology, Epidemiology and Data Science, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Wiesje M van Der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Epidemiology and Data Science, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Eric E Smith
- Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Philip Bath
- Stroke Trials Unit, Mental Health & Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom
| | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Emily Briceno
- Department of Physical Medicine & Rehabilitation, University of Michigan Medical School, Ann Arbor
| | - Amy Brodtman
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- University of Melbourne, Melbourne, Victoria, Australia
- Monash University, Melbourne, Victoria, Australia
| | - Hugues Chabriat
- Department of Neurology, FHU NeuroVasc, APHP, University of Paris, Paris, France
| | - Christopher Chen
- Memory Aging and Cognition Centre, Departments of Pharmacology and Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijimegen, the Netherlands
| | - Marco Egle
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Aravind Ganesh
- Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Marios K Georgakis
- Institute for Stroke and Dementia Research (ISD), LMU University Hospital, Munich, Germany
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Rebecca F Gottesman
- Now with National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, Maryland
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Sun Kwon
- University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Lenore Launer
- Intramural Research Program, National Institute on Aging, Baltimore, Maryland
| | - Vincent Mok
- Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, Margaret K.L. Cheung Research Centre for Management of Parkinsonism, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - John O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Lois Ottenhoff
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam and the Netherlands and Brain Research Center Amsterdam, the Netherlands
| | - Sarah Pendlebury
- Wolfson Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, NIHR Oxford Biomedical Research Centre, Departments of General (internal) Medicine and Geratology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Edo Richard
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijimegen, the Netherlands
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, New South Wales, Australia
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | | | - Stefan Tiedt
- Institute for Stroke and Dementia Research (ISD), LMU University Hospital, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, United Kingdom
| | - Ana Verdelho
- Faculdade de Medicina, Department of Neurosciences and Mental Health, CHULN-Hospital de Santa Maria Instituto de Medicina Molecular (IMM) e Instituto de Saúde Ambiental (ISAMB), University of Lisbon, Lisbon, Portugal
| | - Alastair Webb
- Wolfson Centre for Prevention of Stroke and Dementia, Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - David Werring
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology and the National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), LMU University Hospital, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical Imaging Group, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Deborah Levine
- Departments of Internal Medicine and Neurology, University of Michigan, Ann Arbor
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), LMU University Hospital, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
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8
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Zhu R, Li Y, Chen L, Wang Y, Cai G, Chen X, Ye Q, Chen Y. Total Burden of Cerebral Small Vessel Disease on MRI May Predict Cognitive Impairment in Parkinson’s Disease. J Clin Med 2022; 11:jcm11185381. [PMID: 36143028 PMCID: PMC9501874 DOI: 10.3390/jcm11185381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/17/2022] [Accepted: 08/25/2022] [Indexed: 11/25/2022] Open
Abstract
(1) Objective: to investigate the association between the total burden of cerebral small vessel disease (CSVD) and cognitive function in Parkinson’s disease (PD). (2) Methods: this retrospective study compared clinical and neuroimaging characteristics of 122 PD patients to determine the association between cognitive decline and total burden of CSVD in PD. All patients underwent brain MRI examinations, and their total CSVD burden scores were evaluated by silent lacunar infarction (SLI), cerebral microbleeds (CMB), white matter hyperintensities (WMH), and enlarged perivascular spaces (EPVS). The cognitive function was assessed by administering Mini-Mental State Examination (MMSE). Receiver-operating characteristic (ROC) curve and the area under the ROC curve (AUC) were performed to quantify the accuracy of the total burden of CSVD and PVH in discriminating PD patients with or without cognitive impairment. (3) Results: the PD patients with cognitive impairment had a significantly higher SLI, CMB, periventricular hyperintensities (PVH), deep white matter hyperintensities (DWMH), enlarged perivascular spaces of basal ganglia (BG-EPVS), and the total CSVD score compared with no cognitive impairment. Total CSVD score and MMSE had a significant negative correlation (r = −0. 483). Furthermore, total burden of CSVD and PVH were the independent risk factors of cognitive impairment in PD, and their good accuracy in discriminating PD patients with cognitive impairment from those with no cognitive impairment was confirmed by the results of ROC curves. (4) Conclusions: total burden of CSVD tightly linked to cognitive impairment in PD patients. The total burden of CSVD or PVH may predict the cognitive impairment in PD.
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Affiliation(s)
- Ruihan Zhu
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Department of Neurology, The Second Affiliated Hospital, Xiamen Medical College, Xiamen 361021, China
- Fujian Key Laboratory of Molecular Neurology, Institute of Neuroscience, Fujian Medical University, Fuzhou 350004, China
- Institute of Clinical Neurology, Fujian Medical University, Fuzhou 350004, China
| | - Yunjing Li
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Key Laboratory of Molecular Neurology, Institute of Neuroscience, Fujian Medical University, Fuzhou 350004, China
- Institute of Clinical Neurology, Fujian Medical University, Fuzhou 350004, China
| | - Lina Chen
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Key Laboratory of Molecular Neurology, Institute of Neuroscience, Fujian Medical University, Fuzhou 350004, China
- Institute of Clinical Neurology, Fujian Medical University, Fuzhou 350004, China
| | - Yingqing Wang
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Key Laboratory of Molecular Neurology, Institute of Neuroscience, Fujian Medical University, Fuzhou 350004, China
- Institute of Clinical Neurology, Fujian Medical University, Fuzhou 350004, China
| | - Guoen Cai
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Key Laboratory of Molecular Neurology, Institute of Neuroscience, Fujian Medical University, Fuzhou 350004, China
- Institute of Clinical Neurology, Fujian Medical University, Fuzhou 350004, China
| | - Xiaochun Chen
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Key Laboratory of Molecular Neurology, Institute of Neuroscience, Fujian Medical University, Fuzhou 350004, China
- Institute of Clinical Neurology, Fujian Medical University, Fuzhou 350004, China
| | - Qinyong Ye
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Key Laboratory of Molecular Neurology, Institute of Neuroscience, Fujian Medical University, Fuzhou 350004, China
- Institute of Clinical Neurology, Fujian Medical University, Fuzhou 350004, China
- Correspondence: (Q.Y.); (Y.C.)
| | - Ying Chen
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Key Laboratory of Molecular Neurology, Institute of Neuroscience, Fujian Medical University, Fuzhou 350004, China
- Institute of Clinical Neurology, Fujian Medical University, Fuzhou 350004, China
- Correspondence: (Q.Y.); (Y.C.)
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9
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Salluzzi M, McCreary CR, Gobbi DG, Lauzon ML, Frayne R. Short-term repeatability and long-term reproducibility of quantitative MR imaging biomarkers in a single centre longitudinal study. Neuroimage 2022; 260:119488. [PMID: 35878725 DOI: 10.1016/j.neuroimage.2022.119488] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 06/21/2022] [Accepted: 07/14/2022] [Indexed: 10/16/2022] Open
Abstract
Quantitative imaging biomarkers (QIBs) can be defined as objective measures that are sensitive and specific to changes in tissue physiology. Provided the acquired QIBs are not affected by scanner changes, they could play an important role in disease diagnosis, prognosis, management, and treatment monitoring. The precision of selected QIBs was assessed from data collected on a 3-T scanner in four healthy participants over a 5-year period. Inevitable scanner changes and acquisition protocol revisions occurred during this time. Standard and custom processing pipelines were used to calculate regional brain volume, cortical thickness, T2, T2*, quantitative susceptibility, cerebral blood flow, axial, radial and mean diffusivity, peak width of skeletonized mean diffusivity, and fractional anisotropy from the acquired images. Coefficient of variation (CoV) and intra-class correlation (ICC) indices were determined in the short-term (i.e., repeatable over three acquisitions within 4 weeks) and in the long-term (i.e., reproducible over four acquisition sessions in 5 years). Precision indices varied based on acquisition technique, processing pipeline, and anatomical region. Good repeatability (average CoV=2.40% and ICC=0.78) and reproducibility (average CoV=8.86 % and ICC=0.72) were found over all QIBs. The best performance indices were obtained for diffusion derived biomarkers (CoV∼0.96% and ICCs=0.87); conversely, the poorest indices were found for the cerebral blood flow biomarker (CoV>10% and ICC<0.5). These results demonstrate that changes in protocol, along with hardware and software upgrades, did not affect the estimates of the selected biomarkers and their precision. Further characterization of the QIB is necessary to understand meaningful changes in the biomarkers in longitudinal studies of normal brain aging and translation to clinical research.
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Affiliation(s)
- Marina Salluzzi
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Calgary Image Processing and Analysis Centre (CIPAC), Foothills Medical Centre, Calgary, Alberta, Canada.
| | - Cheryl R McCreary
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - David G Gobbi
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Calgary Image Processing and Analysis Centre (CIPAC), Foothills Medical Centre, Calgary, Alberta, Canada
| | - Michel Louis Lauzon
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Richard Frayne
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada; Calgary Image Processing and Analysis Centre (CIPAC), Foothills Medical Centre, Calgary, Alberta, Canada
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10
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Blood-brain barrier leakage in Alzheimer's disease: From discovery to clinical relevance. Pharmacol Ther 2022; 234:108119. [PMID: 35108575 PMCID: PMC9107516 DOI: 10.1016/j.pharmthera.2022.108119] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 12/16/2022]
Abstract
Alzheimer's disease (AD) is the most common form of dementia. AD brain pathology starts decades before the onset of clinical symptoms. One early pathological hallmark is blood-brain barrier dysfunction characterized by barrier leakage and associated with cognitive decline. In this review, we summarize the existing literature on the extent and clinical relevance of barrier leakage in AD. First, we focus on AD animal models and their susceptibility to barrier leakage based on age and genetic background. Second, we re-examine barrier dysfunction in clinical and postmortem studies, summarize changes that lead to barrier leakage in patients and highlight the clinical relevance of barrier leakage in AD. Third, we summarize signaling mechanisms that link barrier leakage to neurodegeneration and cognitive decline in AD. Finally, we discuss clinical relevance and potential therapeutic strategies and provide future perspectives on investigating barrier leakage in AD. Identifying mechanistic steps underlying barrier leakage has the potential to unravel new targets that can be used to develop novel therapeutic strategies to repair barrier leakage and slow cognitive decline in AD and AD-related dementias.
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11
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Lebenberg J, Guichard JP, Guillonnet A, Hervé D, Alili N, Taleb A, Dias-Gastellier N, Chabriat H, Jouvent E. The Epidermal Growth Factor Domain of the Mutation Does Not Appear to Influence Disease Progression in CADASIL When Brain Volume and Sex Are Taken into Account. AJNR Am J Neuroradiol 2022; 43:715-720. [PMID: 35487587 PMCID: PMC9089269 DOI: 10.3174/ajnr.a7499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 03/13/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE By studying the evolution of brain volume across the life span in male and female patients, we aimed to understand how sex, brain volume, and the epidermal growth factor repeat domain of the mutation, the 3 major determinants of disability in CADASIL, interact in driving disease evolution. MATERIALS AND METHODS We used validated methods to model the evolution of normalized brain volume with age in male and female patients using nonparametric regression in a large, monocentric cohort with prospectively collected clinical and high-resolution MR imaging data. We used k-means clustering to test for the presence of different clinical course profiles. RESULTS We included 229 patients (mean age, 53 [SD, 12] years; 130 women). Brain volume was larger in women (mean size, 1024 [SD, 62] cm3 versus 979 [SD, 50] cm3; P < .001) and decreased regularly. In men, the relationship between brain volume and age unexpectedly suggested an increase in brain volume around midlife. Cluster analyses showed that this finding was related to the presence of a group of older male patients with milder symptoms and larger brain volumes, similar to findings of age-matched women. This group did not show specific epidermal growth factor repeat domain distribution. CONCLUSIONS Our results demonstrate a detrimental effect of male sex on brain volume throughout life in CADASIL. We identified a subgroup of male patients whose brain volume and clinical outcomes were similar to those of age-matched women. They did not have a specific distribution of the epidermal growth factor repeat domain, suggesting that yet-unidentified predictors may interact with sex and brain volume in driving disease evolution.
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Affiliation(s)
- J Lebenberg
- the Centre de Neurologie Vasculaire Translationel (J.L., D.H., N.A., A.T., N.D.-G., H.C.), Assistance Publique-Hôpitaux de Paris, Hôpital Lariboisiere, Paris, France; L'Institut National de la Santé et de la RechercheMédicale INSERM U1141, Université Paris Cité, Paris, France
- Federation Hospitalo-Universitaire NeuroVasc (J.L., N.D.-G., D.H., H.C., E.J.), Paris, France
| | | | | | - D Hervé
- the Centre de Neurologie Vasculaire Translationel (J.L., D.H., N.A., A.T., N.D.-G., H.C.), Assistance Publique-Hôpitaux de Paris, Hôpital Lariboisiere, Paris, France; L'Institut National de la Santé et de la RechercheMédicale INSERM U1141, Université Paris Cité, Paris, France
- Federation Hospitalo-Universitaire NeuroVasc (J.L., N.D.-G., D.H., H.C., E.J.), Paris, France
| | - N Alili
- the Centre de Neurologie Vasculaire Translationel (J.L., D.H., N.A., A.T., N.D.-G., H.C.), Assistance Publique-Hôpitaux de Paris, Hôpital Lariboisiere, Paris, France; L'Institut National de la Santé et de la RechercheMédicale INSERM U1141, Université Paris Cité, Paris, France
| | - A Taleb
- the Centre de Neurologie Vasculaire Translationel (J.L., D.H., N.A., A.T., N.D.-G., H.C.), Assistance Publique-Hôpitaux de Paris, Hôpital Lariboisiere, Paris, France; L'Institut National de la Santé et de la RechercheMédicale INSERM U1141, Université Paris Cité, Paris, France
| | - N Dias-Gastellier
- the Centre de Neurologie Vasculaire Translationel (J.L., D.H., N.A., A.T., N.D.-G., H.C.), Assistance Publique-Hôpitaux de Paris, Hôpital Lariboisiere, Paris, France; L'Institut National de la Santé et de la RechercheMédicale INSERM U1141, Université Paris Cité, Paris, France
- Federation Hospitalo-Universitaire NeuroVasc (J.L., N.D.-G., D.H., H.C., E.J.), Paris, France
| | - H Chabriat
- the Centre de Neurologie Vasculaire Translationel (J.L., D.H., N.A., A.T., N.D.-G., H.C.), Assistance Publique-Hôpitaux de Paris, Hôpital Lariboisiere, Paris, France; L'Institut National de la Santé et de la RechercheMédicale INSERM U1141, Université Paris Cité, Paris, France
- Federation Hospitalo-Universitaire NeuroVasc (J.L., N.D.-G., D.H., H.C., E.J.), Paris, France
| | - E Jouvent
- From the Department of Neurology (E.J.)
- Federation Hospitalo-Universitaire NeuroVasc (J.L., N.D.-G., D.H., H.C., E.J.), Paris, France
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12
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Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2022; 49:2820-2835. [PMID: 34455593 PMCID: PMC8882689 DOI: 10.1002/mp.15195] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/17/2021] [Accepted: 08/17/2021] [Indexed: 01/31/2023] Open
Abstract
Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.
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Affiliation(s)
- Kathryn E. Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Jana G. Delfino
- Center for Devices and Radiological Health, US Food and Drug Administration, 10993 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Kalina V. Jordanova
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Megan E. Poorman
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Prathyush Chirra
- Dept of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Akshay S. Chaudhari
- Department of Radiology, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
| | - Bettina Baessler
- University Hospital of Zurich and University of Zurich, Raemistrasse 100, 8091 Zurich, Switzerland
| | - Jessica Winfield
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
| | - Satish E. Viswanath
- Dept of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Nandita M. deSouza
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
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13
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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: 15] [Impact Index Per Article: 7.5] [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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14
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Bento M, Fantini I, Park J, Rittner L, Frayne R. Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets. Front Neuroinform 2022; 15:805669. [PMID: 35126080 PMCID: PMC8811356 DOI: 10.3389/fninf.2021.805669] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 12/27/2021] [Indexed: 12/22/2022] Open
Abstract
Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic resonance (MR) image-based diagnostic and treatment monitoring approaches. When assembling a number of smaller datasets to form a larger dataset, understanding the underlying variability between different acquisition and processing protocols across the aggregated dataset (termed “batch effects”) is critical. The presence of variation in the training dataset is important as it more closely reflects the true underlying data distribution and, thus, may enhance the overall generalizability of the tool. However, the impact of batch effects must be carefully evaluated in order to avoid undesirable effects that, for example, may reduce performance measures. Batch effects can result from many sources, including differences in acquisition equipment, imaging technique and parameters, as well as applied processing methodologies. Their impact, both beneficial and adversarial, must be considered when developing tools to ensure that their outputs are related to the proposed clinical or research question (i.e., actual disease-related or pathological changes) and are not simply due to the peculiarities of underlying batch effects in the aggregated dataset. We reviewed applications of DL in structural brain MR imaging that aggregated images from neuroimaging datasets, typically acquired at multiple sites. We examined datasets containing both healthy control participants and patients that were acquired using varying acquisition protocols. First, we discussed issues around Data Access and enumerated the key characteristics of some commonly used publicly available brain datasets. Then we reviewed methods for correcting batch effects by exploring the two main classes of approaches: Data Harmonization that uses data standardization, quality control protocols or other similar algorithms and procedures to explicitly understand and minimize unwanted batch effects; and Domain Adaptation that develops DL tools that implicitly handle the batch effects by using approaches to achieve reliable and robust results. In this narrative review, we highlighted the advantages and disadvantages of both classes of DL approaches, and described key challenges to be addressed in future studies.
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Affiliation(s)
- Mariana Bento
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- *Correspondence: Mariana Bento
| | - Irene Fantini
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Justin Park
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- Radiology and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Leticia Rittner
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Richard Frayne
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- Radiology and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada
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Clancy U, Ramirez J, Chappell FM, Doubal FN, Wardlaw JM, Black SE. Neuropsychiatric symptoms as a sign of small vessel disease progression in cognitive impairment. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2022; 3:100041. [PMID: 36324402 PMCID: PMC9616231 DOI: 10.1016/j.cccb.2022.100041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/12/2022] [Accepted: 01/16/2022] [Indexed: 11/27/2022]
Abstract
Background Neuropsychiatric symptoms associate cross-sectionally with cerebral small vessel disease but it is not clear whether these symptoms could act as early clinical markers of small vessel disease progression. We investigated whether longitudinal change in Neuropsychiatric Inventory (NPI) scores associated with white matter hyperintensity (WMH) progression in a memory clinic population. Material and methods We included participants from the prospective Sunnybrook Dementia Study with Alzheimer's disease and vascular subtypes of mild cognitive impairment and dementia with two MRI and ≥ 1 NPI. We conducted linear mixed-effects analyses, adjusting for age, atrophy, vascular risk factors, cognition, function, and interscan interval. Results At baseline (n=124), greater atrophy, age, vascular risk factors and total NPI score were associated with higher baseline WMH volume, while longitudinally, all but vascular risk factors were associated. Change in total NPI score was associated with change in WMH volume, χ2 = 7.18, p = 0.007, whereby a one-point change in NPI score from baseline to follow-up was associated with a 0.0017 change in normalized WMH volume [expressed as cube root of (WMH volume cm³ as % intracranial volume)], after adjusting for age, atrophy, vascular risk factors and interscan interval. Conclusions In memory clinic patients, WMH progression over 1-2 years associated with worsening neuropsychiatric symptoms, while WMH volume remained unchanged in those with stable NPI scores in this population with low background WMH burden.
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Affiliation(s)
- Una Clancy
- Brain Research Imaging Centre, Division of Neuroimaging Sciences, Centre for Clinical Brain Sciences, UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Joel Ramirez
- Dr. Sandra Black Centre for Brain Resilience & Recovery, LC Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, 2075 Bayview Avenue, Room A4 21, Toronto, ON M4N 3M5, Canada,Corresponding author.
| | - Francesca M. Chappell
- Brain Research Imaging Centre, Division of Neuroimaging Sciences, Centre for Clinical Brain Sciences, UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Fergus N. Doubal
- Brain Research Imaging Centre, Division of Neuroimaging Sciences, Centre for Clinical Brain Sciences, UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Joanna M. Wardlaw
- Brain Research Imaging Centre, Division of Neuroimaging Sciences, Centre for Clinical Brain Sciences, UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Sandra E. Black
- Dr. Sandra Black Centre for Brain Resilience & Recovery, LC Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, 2075 Bayview Avenue, Room A4 21, Toronto, ON M4N 3M5, Canada,Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
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16
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Wiegertjes K, Jansen MG, Jolink WM, Duering M, Koemans EA, Schreuder FH, Tuladhar AM, Wermer MJ, Klijn CJ, de Leeuw FE. Differences in cerebral small vessel disease magnetic resonance imaging markers between lacunar stroke and non-Lobar intracerebral hemorrhage. Eur Stroke J 2021; 6:236-244. [PMID: 34746419 PMCID: PMC8564151 DOI: 10.1177/23969873211031753] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/21/2021] [Indexed: 11/16/2022] Open
Abstract
Introduction It is unclear why cerebral small vessel disease (SVD) leads to lacunar stroke
in some and to non–lobar intracerebral hemorrhage (ICH) in others. We
investigated differences in MRI markers of SVD in patients with lacunar
stroke or non–lobar ICH. Patients and methods We included patients from two prospective cohort studies with either lacunar
stroke (RUN DMC) or non–lobar ICH (FETCH). Differences in SVD markers (white
matter hyperintensities [WMH], lacunes, cerebral microbleeds [CMB]) between
groups were investigated with univariable tests; multivariable logistic
regression analysis, adjusted for age, sex, and vascular risk factors;
spatial correlation analysis and voxel–wise lesion symptom mapping. Results We included 82 patients with lacunar stroke (median age 63, IQR 57–72) and 54
with non-lobar ICH (66, 59–75). WMH volumes and distribution were not
different between groups. Lacunes were more frequent in patients with a
lacunar stroke (44% vs. 17%, adjusted odds ratio [aOR] 5.69, 95% CI
[1.66–22.75]) compared to patients with a non–lobar ICH. CMB were more
frequent in patients with a non–lobar ICH (71% vs. 23%, aOR for lacunar
stroke vs non–lobar ICH 0.08 95% CI [0.02–0.26]), and more often located in
non–lobar regions compared to CMB in lacunar stroke. Discussion Although we obserd different types of MRI markers of SVD within the same
patient, ischemic markers of SVD were more frequent in the ischemic type of
lacunar stroke, and hemorrhagic markers were more prevalent in the
hemorrhagic phenotype of non-lobar ICH. Conclusion There are differences between MRI markers of SVD between patients with a
lacunar stroke and those with a non-lobar ICH.
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Affiliation(s)
- Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Michelle G Jansen
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Wilmar Mt Jolink
- Department of Neurology and Neurosurgery, University Medical Center Utrecht, Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Marco Duering
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands.,Institute for Stroke and Dementia Research, LMU University Hospital Munich, Munich, Germany.,Munich Cluster for Systems Neurology, Munich, Germany
| | - Emma A Koemans
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Floris Hbm Schreuder
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marieke Jh Wermer
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Catharina Jm Klijn
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
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Imaging neurovascular, endothelial and structural integrity in preparation to treat small vessel diseases. The INVESTIGATE-SVDs study protocol. Part of the SVDs@Target project. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2021; 2:100020. [PMID: 36324725 PMCID: PMC9616332 DOI: 10.1016/j.cccb.2021.100020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 04/25/2021] [Accepted: 06/20/2021] [Indexed: 12/30/2022]
Abstract
Background Sporadic cerebral small vessel disease (SVD) and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) share clinical and neuroimaging features and possibly vascular dysfunction(s). However few studies have included both conditions, assessed more than one vascular dysfunction simultaneously, or included more than one centre. The INVESTIGATE-SVDs study will assess several cerebrovascular dysfunctions with MRI in participants with sporadic SVD or CADASIL at three European centres. Methods We will recruit participants with sporadic SVDs (ischaemic stroke or vascular cognitive impairment) and CADASIL in Edinburgh, Maastricht and Munich. We will perform detailed clinical and neuropsychological phenotyping of the participants, and neuroimaging including structural MRI, cerebrovascular reactivity MRI (CVR: using carbon dioxide challenge), phase contrast MRI (arterial, venous and CSF flow and pulsatility), dynamic contrast-enhanced MRI (blood brain barrier (BBB) leakage) and multishell diffusion imaging. Participants will measure their blood pressure (BP) and its variability over seven days using a telemetric device. Discussion INVESTIGATE-SVDs will assess the relationships of BBB integrity, CVR, pulsatility and CSF flow in sporadic SVD and CADASIL using a multisite, multimodal MRI protocol. We aim to establish associations between these measures of vascular function, risk factors particularly BP and its variability, and brain parenchymal lesions in these two SVD phenotypes. Additionally we will test feasibility of complex multisite MRI, provide reliable intermediary outcome measures and sample size estimates for future trials.
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Key Words
- BBB, blood brain barrier
- BOLD, blood oxygen level dependent
- BP, blood pressure
- BPv, blood pressure variability
- Blood-brain barrier permeability
- CADASIL
- CADASIL, cerebral autosomal dominant arteriopathy with leukoencephalopathy and subcortical infarcts
- CBF, cerebral blood flow
- CERAD+, consortium to establish a disease registry for Alzheimer's disease plus battery
- CO2, carbon dioxide
- CSF, cerebrospinal fluid
- CVR, cerebrovascular reactivity
- Cerebral small vessel disease
- Cerebrovascular reactivity
- DCE, dynamic contrast enhanced
- EtCO2, end-tidal carbon dioxide
- GM, grey matter
- MMSE, mini-mental state examination
- MRI
- MoCA, Montreal cognitive exam
- NIHSS, national institute for health stroke scale
- PI, pulsatility index
- PVS, perivascular space
- RSSI, recent small subcortical infarct
- SVDs, small vessel diseases
- WM, white matter
- WMH, white matter hyperintensity
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18
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Paolini Paoletti F, Simoni S, Parnetti L, Gaetani L. The Contribution of Small Vessel Disease to Neurodegeneration: Focus on Alzheimer's Disease, Parkinson's Disease and Multiple Sclerosis. Int J Mol Sci 2021; 22:ijms22094958. [PMID: 34066951 PMCID: PMC8125719 DOI: 10.3390/ijms22094958] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 05/04/2021] [Accepted: 05/05/2021] [Indexed: 01/18/2023] Open
Abstract
Brain small vessel disease (SVD) refers to a variety of structural and functional changes affecting small arteries and micro vessels, and manifesting as white matter changes, microbleeds and lacunar infarcts. Growing evidence indicates that SVD might play a significant role in the neurobiology of central nervous system (CNS) neurodegenerative disorders, namely Alzheimer's disease (AD) and Parkinson's disease (PD), and neuroinflammatory diseases, such as multiple sclerosis (MS). These disorders share different pathophysiological pathways and molecular mechanisms (i.e., protein misfolding, derangement of cellular clearance systems, mitochondrial impairment and immune system activation) having neurodegeneration as biological outcome. In these diseases, the actual contribution of SVD to the clinical picture, and its impact on response to pharmacological treatments, is not known yet. Due to the high frequency of SVD in adult-aged patients, it is important to address this issue. In this review, we report preclinical and clinical data on the impact of SVD in AD, PD and MS, with the main aim of clarifying the predictability of SVD on clinical manifestations and treatment response.
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19
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Melazzini L, Mackay CE, Bordin V, Suri S, Zsoldos E, Filippini N, Mahmood A, Sundaresan V, Codari M, Duff E, Singh-Manoux A, Kivimäki M, Ebmeier KP, Jenkinson M, Sardanelli F, Griffanti L. White matter hyperintensities classified according to intensity and spatial location reveal specific associations with cognitive performance. Neuroimage Clin 2021; 30:102616. [PMID: 33743476 PMCID: PMC7995650 DOI: 10.1016/j.nicl.2021.102616] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 02/26/2021] [Accepted: 02/27/2021] [Indexed: 12/19/2022]
Abstract
White matter hyperintensities (WMHs) on T2-weighted images are radiological signs of cerebral small vessel disease. As their total volume is variably associated with cognition, a new approach that integrates multiple radiological criteria is warranted. Location may matter, as periventricular WMHs have been shown to be associated with cognitive impairments. WMHs that appear as hypointense in T1-weighted images (T1w) may also indicate the most severe component of WMHs. We developed an automatic method that sub-classifies WMHs into four categories (periventricular/deep and T1w-hypointense/nonT1w-hypointense) using MRI data from 684 community-dwelling older adults from the Whitehall II study. To test if location and intensity information can impact cognition, we derived two general linear models using either overall or subdivided volumes. Results showed that periventricular T1w-hypointense WMHs were significantly associated with poorer performance in the trail making A (p = 0.011), digit symbol (p = 0.028) and digit coding (p = 0.009) tests. We found no association between total WMH volume and cognition. These findings suggest that sub-classifying WMHs according to both location and intensity in T1w reveals specific associations with cognitive performance.
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Affiliation(s)
- Luca Melazzini
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy; Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Clare E Mackay
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Oxford, UK
| | - Valentina Bordin
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Sana Suri
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK; Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Enikő Zsoldos
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK; Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Nicola Filippini
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Abda Mahmood
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Vaanathi Sundaresan
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Marina Codari
- Department of Radiology, Stanford University School of Medicine, Stanford, USA
| | - Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Paediatrics, University of Oxford, Oxford, UK
| | - Archana Singh-Manoux
- INSERM U1153, Epidemiology of Ageing and Neurodegenerative Diseases, Université de Paris, Paris, France; Department of Epidemiology and Public Health, University College London, London, UK
| | - Mika Kivimäki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Klaus P Ebmeier
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy; Department of Radiology, IRCCS Policlinico San Donato, Milan, Italy
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK
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20
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Puy L, Pasi M, Rodrigues M, van Veluw SJ, Tsivgoulis G, Shoamanesh A, Cordonnier C. Cerebral microbleeds: from depiction to interpretation. J Neurol Neurosurg Psychiatry 2021; 92:jnnp-2020-323951. [PMID: 33563804 DOI: 10.1136/jnnp-2020-323951] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/22/2020] [Accepted: 01/04/2021] [Indexed: 11/04/2022]
Abstract
Cerebral microbleeds (CMBs) are defined as hypointense foci visible on T2*-weighted and susceptible-weighted MRI sequences. CMBs are increasingly recognised with the widespread use of MRI in healthy individuals as well as in the context of cerebrovascular disease or dementia. They can also be encountered in major critical medical conditions such as in patients requiring extracorporeal mechanical oxygenation. The advent of MRI-guided postmortem neuropathological examinations confirmed that, in the context of cerebrovascular disease, the vast majority of CMBs correspond to recent or old microhaemorrhages. Detection of CMBs is highly influenced by MRI parameters, in particular field strength, postprocessing methods used to enhance T2* contrast and three dimensional sequences. Despite recent progress, harmonising imaging parameters across research studies remains necessary to improve cross-study comparisons. CMBs are helpful markers to identify the nature and the severity of the underlying chronic small vessel disease. In daily clinical practice, presence and numbers of CMBs often trigger uncertainty for clinicians especially when antithrombotic treatments and acute reperfusion therapies are discussed. In the present review, we discuss those clinical dilemmas and address the value of CMBs as diagnostic and prognostic markers for future vascular events.
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Affiliation(s)
- Laurent Puy
- Department of Neurology, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ. Lille, Inserm, CHU Lille, F-59000 Lille, France
| | - Marco Pasi
- Department of Neurology, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ. Lille, Inserm, CHU Lille, F-59000 Lille, France
| | - Mark Rodrigues
- Centre for Clinical Brain Sciences, The University of Edinburgh College of Medicine and Veterinary Medicine, Edinburgh, Midlothian, UK
| | - Susanne J van Veluw
- Neurology Department, Hemorrhagic Stroke Research Program, Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Georgios Tsivgoulis
- Second Department of Neurology, "Attikon" University Hospital, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece
| | - Ashkan Shoamanesh
- Department of Medicine (Neurology), McMaster University and Population Health Research Institute, Hamilton, Ontario, Canada
| | - Charlotte Cordonnier
- Department of Neurology, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ. Lille, Inserm, CHU Lille, F-59000 Lille, France
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Jurek L, Dorey JM, Nourredine M, Galvao F, Brunelin J. Impact of vascular risk factors on clinical outcome in elderly patients with depression receiving electroconvulsive therapy. J Affect Disord 2021; 279:308-315. [PMID: 33096329 DOI: 10.1016/j.jad.2020.10.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 08/29/2020] [Accepted: 10/11/2020] [Indexed: 11/24/2022]
Abstract
BACKGROUND Although electroconvulsive therapy (ECT) is a highly effective, safe, and well-tolerated antidepressant treatment for late-life depression (LLD), there is large variability in response rates across individuals. We hypothesized that these variations would be in part explained by the level of vascular risk in this population. We therefore compared response rates to ECT in patients with LLD presenting with or without vascular risk factors (VRF). METHODS 52 patients with LLD (age > 55) who received a course of ECT were separated into 2 groups according to the presence of VRF (n = 20) or not (n = 32). Framingham score (10-year risk for developing a coronary heart disease) was calculated for each patient. Our primary outcome was the number of responders to ECT in each group (defined as at least 50% decrease of the Montgomery-Åsberg Depression Rating Scale score following ECT course). Scores at the self-rated Beck Depression Inventory are also reported. RESULTS Patients with VRF presented significant lower response rates to ECT (12 out of 20; 60%) than patients without VRF (30 out of 32; 94%; p = 0.004). A negative correlation was found between Framingham score and changes in depression scores pre/post ECT (r = -0.42; p = 0.0039). LIMITATIONS Our study was limited by sample size and retrospective design. CONCLUSION Patients with LLD and VRF showed lower response rates to ECT than those without VRF. The more the VRF increased, the less the antidepressant effect of ECT was observed. Results are discussed in light of the role of apathy in clinical response to ECT.
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Affiliation(s)
- Lucie Jurek
- University Lyon 1, Villeurbanne F-69000, France; Centre Hospitalier Le Vinatier, Bron, France.
| | - Jean-Michel Dorey
- Centre Hospitalier Le Vinatier, Bron, France; Brain Dynamics and Cognition, Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR 5292, Lyon, France
| | - Mikaïl Nourredine
- University Lyon 1, Villeurbanne F-69000, France; INSERM, Lyon Neuroscience Research Center, Psychiatric Disorders: from Resistance to Response, U1028; CNRS, UMR5292, PSYR2 Team, Lyon F-69000, France
| | - Filipe Galvao
- University Lyon 1, Villeurbanne F-69000, France; INSERM, Lyon Neuroscience Research Center, Psychiatric Disorders: from Resistance to Response, U1028; CNRS, UMR5292, PSYR2 Team, Lyon F-69000, France; Centre Hospitalier Le Vinatier, Bron, France
| | - Jérome Brunelin
- University Lyon 1, Villeurbanne F-69000, France; INSERM, Lyon Neuroscience Research Center, Psychiatric Disorders: from Resistance to Response, U1028; CNRS, UMR5292, PSYR2 Team, Lyon F-69000, France; Centre Hospitalier Le Vinatier, Bron, France
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22
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Melazzini L, Vitali P, Olivieri E, Bolchini M, Zanardo M, Savoldi F, Di Leo G, Griffanti L, Baselli G, Sardanelli F, Codari M. White Matter Hyperintensities Quantification in Healthy Adults: A Systematic Review and Meta-Analysis. J Magn Reson Imaging 2020; 53:1732-1743. [PMID: 33345393 DOI: 10.1002/jmri.27479] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 12/02/2020] [Accepted: 12/03/2020] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Although white matter hyperintensities (WMH) volumetric assessment is now customary in research studies, inconsistent WMH measures among homogenous populations may prevent the clinical usability of this biomarker. PURPOSE To determine whether a point estimate and reference standard for WMH volume in the healthy aging population could be determined. STUDY TYPE Systematic review and meta-analysis. POPULATION In all, 9716 adult subjects from 38 studies reporting WMH volume were retrieved following a systematic search on EMBASE. FIELD STRENGTH/SEQUENCE 1.0T, 1.5T, or 3.0T/fluid-attenuated inversion recovery (FLAIR) and/or proton density/T2 -weighted fast spin echo sequences or gradient echo T1 -weighted sequences. ASSESSMENT After a literature search, sample size, demographics, magnetic field strength, MRI sequences, level of automation in WMH assessment, study population, and WMH volume were extracted. STATISTICAL TESTS The pooled WMH volume with 95% confidence interval (CI) was calculated using the random-effect model. The I2 statistic was calculated as a measure of heterogeneity across studies. Meta-regression analysis of WMH volume on age was performed. RESULTS Of the 38 studies analyzed, 17 reported WMH volume as the mean and standard deviation (SD) and were included in the meta-analysis. Mean and SD of age was 66.11 ± 10.92 years (percentage of men 50.45% ± 21.48%). Heterogeneity was very high (I2 = 99%). The pooled WMH volume was 4.70 cm3 (95% CI: 3.88-5.53 cm3 ). At meta-regression analysis, WMH volume was positively associated with subjects' age (β = 0.358 cm3 per year, P < 0.05, R2 = 0.27). DATA CONCLUSION The lack of standardization in the definition of WMH together with the high technical variability in assessment may explain a large component of the observed heterogeneity. Currently, volumes of WMH in healthy subjects are not comparable between studies and an estimate and reference interval could not be determined. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Luca Melazzini
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| | - Paolo Vitali
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Emanuele Olivieri
- Medicine and Surgery Medical School, Università degli Studi di Milano, Milano, Italy
| | - Marco Bolchini
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy
| | - Moreno Zanardo
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Filippo Savoldi
- Postgraduate School in Radiology, Università degli Studi di Milano, Milano, Italy
| | - Giovanni Di Leo
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Ludovica Griffanti
- Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging (WIN), University of Oxford, Oxford, UK
| | - Giuseppe Baselli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy.,Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Marina Codari
- Department of Radiology, Stanford University School of Medicine, Stanford, California, USA
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Benveniste H, Elkin R, Heerdt PM, Koundal S, Xue Y, Lee H, Wardlaw J, Tannenbaum A. The glymphatic system and its role in cerebral homeostasis. J Appl Physiol (1985) 2020; 129:1330-1340. [PMID: 33002383 DOI: 10.1152/japplphysiol.00852.2019] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The brain's high bioenergetic state is paralleled by high metabolic waste production. Authentic lymphatic vasculature is lacking in brain parenchyma. Cerebrospinal fluid (CSF) flow has long been thought to facilitate central nervous system detoxification in place of lymphatics, but the exact processes involved in toxic waste clearance from the brain remain incompletely understood. Over the past 8 yr, novel data in animals and humans have begun to shed new light on these processes in the form of the "glymphatic system," a brain-wide perivascular transit passageway dedicated to CSF transport and interstitial fluid exchange that facilitates metabolic waste drainage from the brain. Here we will discuss glymphatic system anatomy and methods to visualize and quantify glymphatic system (GS) transport in the brain and also discuss physiological drivers of its function in normal brain and in neurodegeneration.
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Affiliation(s)
- Helene Benveniste
- Department of Anesthesiology, Yale School of Medicine, New Haven, Connecticut
| | - Rena Elkin
- Departments of Computer Science and Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York
| | - Paul M Heerdt
- Department of Anesthesiology, Yale School of Medicine, New Haven, Connecticut
| | - Sunil Koundal
- Department of Anesthesiology, Yale School of Medicine, New Haven, Connecticut
| | - Yuechuan Xue
- Department of Anesthesiology, Yale School of Medicine, New Haven, Connecticut
| | - Hedok Lee
- Department of Anesthesiology, Yale School of Medicine, New Haven, Connecticut
| | - Joanna Wardlaw
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, Dementia Research Institute at the University of Edinburgh, Edinburgh, United Kingdom
| | - Allen Tannenbaum
- Departments of Computer Science and Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York
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24
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Stringer MS, Lee H, Huuskonen MT, MacIntosh BJ, Brown R, Montagne A, Atwi S, Ramirez J, Jansen MA, Marshall I, Black SE, Zlokovic BV, Benveniste H, Wardlaw JM. A Review of Translational Magnetic Resonance Imaging in Human and Rodent Experimental Models of Small Vessel Disease. Transl Stroke Res 2020; 12:15-30. [PMID: 32936435 PMCID: PMC7803876 DOI: 10.1007/s12975-020-00843-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 08/16/2020] [Accepted: 08/19/2020] [Indexed: 12/29/2022]
Abstract
Cerebral small vessel disease (SVD) is a major health burden, yet the pathophysiology remains poorly understood with no effective treatment. Since much of SVD develops silently and insidiously, non-invasive neuroimaging such as MRI is fundamental to detecting and understanding SVD in humans. Several relevant SVD rodent models are established for which MRI can monitor in vivo changes over time prior to histological examination. Here, we critically review the MRI methods pertaining to salient rodent models and evaluate synergies with human SVD MRI methods. We found few relevant publications, but argue there is considerable scope for greater use of MRI in rodent models, and opportunities for harmonisation of the rodent-human methods to increase the translational potential of models to understand SVD in humans. We summarise current MR techniques used in SVD research, provide recommendations and examples and highlight practicalities for use of MRI SVD imaging protocols in pre-selected, relevant rodent models.
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Affiliation(s)
- Michael S Stringer
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK
| | - Hedok Lee
- Department of Anesthesiology, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Mikko T Huuskonen
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.,Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Bradley J MacIntosh
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Rosalind Brown
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK
| | - Axel Montagne
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.,Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarah Atwi
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Joel Ramirez
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Maurits A Jansen
- Edinburgh Preclinical Imaging, Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Ian Marshall
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK
| | - Sandra E Black
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada.,Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada
| | - Berislav V Zlokovic
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.,Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Helene Benveniste
- Department of Anesthesiology, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Joanna M Wardlaw
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK. .,UK Dementia Research Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK.
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25
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McCreary CR, Salluzzi M, Andersen LB, Gobbi D, Lauzon L, Saad F, Smith EE, Frayne R. Calgary Normative Study: design of a prospective longitudinal study to characterise potential quantitative MR biomarkers of neurodegeneration over the adult lifespan. BMJ Open 2020; 10:e038120. [PMID: 32792445 PMCID: PMC7430487 DOI: 10.1136/bmjopen-2020-038120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
INTRODUCTION A number of MRI methods have been proposed to be useful, quantitative biomarkers of neurodegeneration in ageing. The Calgary Normative Study (CNS) is an ongoing single-centre, prospective, longitudinal study that seeks to develop, test and assess quantitative magnetic resonance (MR) methods as potential biomarkers of neurodegeneration. The CNS has three objectives: first and foremost, to evaluate and characterise the dependence of the selected quantitative neuroimaging biomarkers on age over the adult lifespan; second, to evaluate the precision, variability and repeatability of quantitative neuroimaging biomarkers as part of biomarker validation providing proof-of-concept and proof-of-principle; and third, provide a shared repository of normative data for comparison to various disease cohorts. METHODS AND ANALYSIS Quantitative MR mapping of the brain including longitudinal relaxation time (T1), transverse relaxation time (T2), T2*, magnetic susceptibility (QSM), diffusion and perfusion measurements, as well as morphological assessments are performed. The Montreal Cognitive Assessment (MoCA) and a brief, self-report medical history will be collected. Mixed regression models will be used to characterise changes in quantitative MR biomarker measures over the adult lifespan. In this report, we describe the study design, strategies to recruit and perform changes to the acquisition protocol from inception to 31 December 2018, planned statistical approach and data sharing procedures for the study. ETHICS AND DISSEMINATION Participants provide signed informed consent. Changes in quantitative MR biomarkers measured over the adult lifespan as well as estimates of measurement variance and repeatability will be disseminated through peer-reviewed scientific publication.
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Affiliation(s)
- Cheryl R McCreary
- Departments of Clinical Neurosciences and Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, University of Calgary, Calgary, Alberta, Canada
| | - Marina Salluzzi
- Departments of Clinical Neurosciences and Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Calgary Image Analysis and Processing Centre, University of Calgary, Calgary, Alberta, Canada
| | - Linda B Andersen
- Departments of Clinical Neurosciences and Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - David Gobbi
- Departments of Clinical Neurosciences and Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Calgary Image Analysis and Processing Centre, University of Calgary, Calgary, Alberta, Canada
| | - Louis Lauzon
- Departments of Clinical Neurosciences and Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, University of Calgary, Calgary, Alberta, Canada
| | - Feryal Saad
- Departments of Clinical Neurosciences and Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Eric E Smith
- Departments of Clinical Neurosciences and Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, University of Calgary, Calgary, Alberta, Canada
| | - Richard Frayne
- Departments of Clinical Neurosciences and Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, University of Calgary, Calgary, Alberta, Canada
- Calgary Image Analysis and Processing Centre, University of Calgary, Calgary, Alberta, Canada
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26
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Alonso J, Pareto D, Alberich M, Kober T, Maréchal B, Lladó X, Rovira A. Assessment of brain volumes obtained from MP-RAGE and MP2RAGE images, quantified using different segmentation methods. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2020; 33:757-767. [DOI: 10.1007/s10334-020-00854-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 05/14/2020] [Accepted: 05/19/2020] [Indexed: 11/30/2022]
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Abstract
Lacunes on magnetic resonance imaging (MRI) are considered as a key hallmark for evaluating the progression and severity of cerebral small vessel diseases. We aimed to review the MRI diagnostic criteria, frequency, predictors and clinical impact of incident lacunes in the largest longitudinal studies. Analyses were restricted to cohort studies of more than 50 individuals that investigated incident lacunes over a duration of at least one year. We observed that: (1) MRI parameters and definition of lacunes are inconsistent across studies, (2) the frequency of incident lacunes is strongly related to the previous clinical and MRI status at individual level, (3) both age and hypertension diagnosed at onset predict incident lacunes but the exact impact of blood pressure level during follow-up remains undetermined, (4) the clinical correlates of these lesions on cognition are repeatedly observed but the exact consequences on motor or gait performances are not always evaluated. Homogenization of imaging techniques, the use of strict diagnostic criteria and a broader clinical assessment considering motor and gait performances should be recommended in future longitudinal studies of incident lacunes including clinical trials testing preventative treatments in cerebral small vessel diseases.
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Affiliation(s)
- Yifeng Ling
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hugues Chabriat
- Department of Neurology, Groupe Hospitalier Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris (APHP), Université Denis Diderot and DHU NeuroVasc Sorbonne Paris-Cité (INSERM U1161), Paris, France
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28
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Pongpitakmetha T, Fotiadis P, Pasi M, Boulouis G, Xiong L, Warren AD, Schwab KM, Rosand J, Gurol ME, Greenberg SM, Viswanathan A, Charidimou A. Cortical superficial siderosis progression in cerebral amyloid angiopathy: Prospective MRI study. Neurology 2020; 94:e1853-e1865. [PMID: 32284360 DOI: 10.1212/wnl.0000000000009321] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Accepted: 11/26/2019] [Indexed: 12/27/2022] Open
Abstract
OBJECTIVE To investigate the prevalence, predictors, and clinical relevance of cortical superficial siderosis (cSS) progression in cerebral amyloid angiopathy (CAA). METHODS Consecutive patients with symptomatic CAA meeting Boston criteria in a prospective cohort underwent baseline and follow-up MRI within 1 year. cSS progression was evaluated on an ordinal scale and categorized into mild (score 1-2 = cSS extension within an already present cSS focus or appearance of 1 new cSS focus) and severe progression (score 3-4 = appearance of ≥2 new cSS foci). Binominal and ordinal multivariable logistic regression were used to determine cSS progression predictors. We investigated future lobar intracerebral hemorrhage (ICH) risk in survival analysis models. RESULTS We included 79 patients with CAA (mean age, 69.2 years), 56 (71%) with lobar ICH at baseline. cSS progression was detected in 23 (29%) patients: 15 (19%) patients had mild and 8 (10%) severe progression. In binominal multivariable logistic regression, ICH presence (odds ratio [OR], 7.54; 95% confidence interval [CI], 1.75-53.52; p = 0.016) and baseline cSS (OR, 10.41; 95% CI, 2.84-52.83; p = 0.001) were independent predictors of cSS progression. In similar models, presence of disseminated (but not focal) cSS at baseline (OR, 5.58; 95% CI, 1.81-19.41; p = 0.004) was an independent predictor of cSS progression. Results were similar in ordinal multivariable logistic regression models. In multivariable Cox regression analysis, severe cSS progression was independently associated with increased future ICH risk (HR, 5.90; 95% CI, 1.30-26.68; p = 0.021). CONCLUSIONS cSS evolution on MRI is common in patients with symptomatic CAA and might be a potential biomarker for assessing disease severity and future ICH risk. External validation of these findings is warranted.
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Affiliation(s)
- Thanakit Pongpitakmetha
- From the Hemorrhagic Stroke Research Program, J. Philip Kistler Stroke Research Center, Department of Neurology (T.P., P.F., M.P., G.B., L.X., A.D.W., K.M.S., J.R., M.E.G., S.M.G., A.V., A.C.), and Division of Neurocritical Care and Emergency Neurology (J.R.), Massachusetts General Hospital, and MIND Informatics, Massachusetts General Hospital Biomedical Informatics Core (J.R.), Harvard Medical School, Boston; and Department of Pharmacology, Faculty of Medicine (T.P.), Chulalongkorn University, Bangkok, Thailand
| | - Panagiotis Fotiadis
- From the Hemorrhagic Stroke Research Program, J. Philip Kistler Stroke Research Center, Department of Neurology (T.P., P.F., M.P., G.B., L.X., A.D.W., K.M.S., J.R., M.E.G., S.M.G., A.V., A.C.), and Division of Neurocritical Care and Emergency Neurology (J.R.), Massachusetts General Hospital, and MIND Informatics, Massachusetts General Hospital Biomedical Informatics Core (J.R.), Harvard Medical School, Boston; and Department of Pharmacology, Faculty of Medicine (T.P.), Chulalongkorn University, Bangkok, Thailand
| | - Marco Pasi
- From the Hemorrhagic Stroke Research Program, J. Philip Kistler Stroke Research Center, Department of Neurology (T.P., P.F., M.P., G.B., L.X., A.D.W., K.M.S., J.R., M.E.G., S.M.G., A.V., A.C.), and Division of Neurocritical Care and Emergency Neurology (J.R.), Massachusetts General Hospital, and MIND Informatics, Massachusetts General Hospital Biomedical Informatics Core (J.R.), Harvard Medical School, Boston; and Department of Pharmacology, Faculty of Medicine (T.P.), Chulalongkorn University, Bangkok, Thailand
| | - Gregoire Boulouis
- From the Hemorrhagic Stroke Research Program, J. Philip Kistler Stroke Research Center, Department of Neurology (T.P., P.F., M.P., G.B., L.X., A.D.W., K.M.S., J.R., M.E.G., S.M.G., A.V., A.C.), and Division of Neurocritical Care and Emergency Neurology (J.R.), Massachusetts General Hospital, and MIND Informatics, Massachusetts General Hospital Biomedical Informatics Core (J.R.), Harvard Medical School, Boston; and Department of Pharmacology, Faculty of Medicine (T.P.), Chulalongkorn University, Bangkok, Thailand
| | - Li Xiong
- From the Hemorrhagic Stroke Research Program, J. Philip Kistler Stroke Research Center, Department of Neurology (T.P., P.F., M.P., G.B., L.X., A.D.W., K.M.S., J.R., M.E.G., S.M.G., A.V., A.C.), and Division of Neurocritical Care and Emergency Neurology (J.R.), Massachusetts General Hospital, and MIND Informatics, Massachusetts General Hospital Biomedical Informatics Core (J.R.), Harvard Medical School, Boston; and Department of Pharmacology, Faculty of Medicine (T.P.), Chulalongkorn University, Bangkok, Thailand
| | - Andrew D Warren
- From the Hemorrhagic Stroke Research Program, J. Philip Kistler Stroke Research Center, Department of Neurology (T.P., P.F., M.P., G.B., L.X., A.D.W., K.M.S., J.R., M.E.G., S.M.G., A.V., A.C.), and Division of Neurocritical Care and Emergency Neurology (J.R.), Massachusetts General Hospital, and MIND Informatics, Massachusetts General Hospital Biomedical Informatics Core (J.R.), Harvard Medical School, Boston; and Department of Pharmacology, Faculty of Medicine (T.P.), Chulalongkorn University, Bangkok, Thailand
| | - Kristin M Schwab
- From the Hemorrhagic Stroke Research Program, J. Philip Kistler Stroke Research Center, Department of Neurology (T.P., P.F., M.P., G.B., L.X., A.D.W., K.M.S., J.R., M.E.G., S.M.G., A.V., A.C.), and Division of Neurocritical Care and Emergency Neurology (J.R.), Massachusetts General Hospital, and MIND Informatics, Massachusetts General Hospital Biomedical Informatics Core (J.R.), Harvard Medical School, Boston; and Department of Pharmacology, Faculty of Medicine (T.P.), Chulalongkorn University, Bangkok, Thailand
| | - Jonathan Rosand
- From the Hemorrhagic Stroke Research Program, J. Philip Kistler Stroke Research Center, Department of Neurology (T.P., P.F., M.P., G.B., L.X., A.D.W., K.M.S., J.R., M.E.G., S.M.G., A.V., A.C.), and Division of Neurocritical Care and Emergency Neurology (J.R.), Massachusetts General Hospital, and MIND Informatics, Massachusetts General Hospital Biomedical Informatics Core (J.R.), Harvard Medical School, Boston; and Department of Pharmacology, Faculty of Medicine (T.P.), Chulalongkorn University, Bangkok, Thailand
| | - M Edip Gurol
- From the Hemorrhagic Stroke Research Program, J. Philip Kistler Stroke Research Center, Department of Neurology (T.P., P.F., M.P., G.B., L.X., A.D.W., K.M.S., J.R., M.E.G., S.M.G., A.V., A.C.), and Division of Neurocritical Care and Emergency Neurology (J.R.), Massachusetts General Hospital, and MIND Informatics, Massachusetts General Hospital Biomedical Informatics Core (J.R.), Harvard Medical School, Boston; and Department of Pharmacology, Faculty of Medicine (T.P.), Chulalongkorn University, Bangkok, Thailand
| | - Steven M Greenberg
- From the Hemorrhagic Stroke Research Program, J. Philip Kistler Stroke Research Center, Department of Neurology (T.P., P.F., M.P., G.B., L.X., A.D.W., K.M.S., J.R., M.E.G., S.M.G., A.V., A.C.), and Division of Neurocritical Care and Emergency Neurology (J.R.), Massachusetts General Hospital, and MIND Informatics, Massachusetts General Hospital Biomedical Informatics Core (J.R.), Harvard Medical School, Boston; and Department of Pharmacology, Faculty of Medicine (T.P.), Chulalongkorn University, Bangkok, Thailand
| | - Anand Viswanathan
- From the Hemorrhagic Stroke Research Program, J. Philip Kistler Stroke Research Center, Department of Neurology (T.P., P.F., M.P., G.B., L.X., A.D.W., K.M.S., J.R., M.E.G., S.M.G., A.V., A.C.), and Division of Neurocritical Care and Emergency Neurology (J.R.), Massachusetts General Hospital, and MIND Informatics, Massachusetts General Hospital Biomedical Informatics Core (J.R.), Harvard Medical School, Boston; and Department of Pharmacology, Faculty of Medicine (T.P.), Chulalongkorn University, Bangkok, Thailand
| | - Andreas Charidimou
- From the Hemorrhagic Stroke Research Program, J. Philip Kistler Stroke Research Center, Department of Neurology (T.P., P.F., M.P., G.B., L.X., A.D.W., K.M.S., J.R., M.E.G., S.M.G., A.V., A.C.), and Division of Neurocritical Care and Emergency Neurology (J.R.), Massachusetts General Hospital, and MIND Informatics, Massachusetts General Hospital Biomedical Informatics Core (J.R.), Harvard Medical School, Boston; and Department of Pharmacology, Faculty of Medicine (T.P.), Chulalongkorn University, Bangkok, Thailand.
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29
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Brown EM, Pierce ME, Clark DC, Fischl BR, Iglesias JE, Milberg WP, McGlinchey RE, Salat DH. Test-retest reliability of FreeSurfer automated hippocampal subfield segmentation within and across scanners. Neuroimage 2020; 210:116563. [DOI: 10.1016/j.neuroimage.2020.116563] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 01/13/2020] [Accepted: 01/15/2020] [Indexed: 11/26/2022] Open
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30
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Chabriat H, Jouvent E. Imaging of the aging brain and development of MRI signal abnormalities. Rev Neurol (Paris) 2020; 176:661-669. [PMID: 32229042 DOI: 10.1016/j.neurol.2019.12.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Revised: 12/14/2019] [Accepted: 12/16/2019] [Indexed: 02/04/2023]
Abstract
Major changes occur at the cerebral level with aging. Cerebral atrophy develops progressively. Multiple lesions related to small-vessel diseases are detected in association with cerebral atrophy including white-matter hyperintensities, lacunes, microbleeds, dilated perivascular spaces and cerebral, including cortex, atrophy. The clinical impact and predictive value of these Imaging makers were examined.
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Affiliation(s)
- H Chabriat
- Inserm U1161 and DHU NeuroVasc, department of neurology, Paris University, Lariboisiere Hospital,Assistance Publique-Hopitaux de Paris, Paris, France.
| | - E Jouvent
- Inserm U1161 and DHU NeuroVasc, department of neurology, Paris University, Lariboisiere Hospital,Assistance Publique-Hopitaux de Paris, Paris, France
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31
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De Guio F, Duering M, Fazekas F, De Leeuw FE, Greenberg SM, Pantoni L, Aghetti A, Smith EE, Wardlaw J, Jouvent E. Brain atrophy in cerebral small vessel diseases: Extent, consequences, technical limitations and perspectives: The HARNESS initiative. J Cereb Blood Flow Metab 2020; 40:231-245. [PMID: 31744377 PMCID: PMC7370623 DOI: 10.1177/0271678x19888967] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Brain atrophy is increasingly evaluated in cerebral small vessel diseases. We aim at systematically reviewing the available data regarding its extent, correlates and cognitive consequences. Given that in this context, brain atrophy measures might be biased, the first part of the review focuses on technical aspects. Thereafter, data from the literature are analyzed in light of these potential limitations, to better understand the relationships between brain atrophy and other MRI markers of cerebral small vessel diseases. In the last part, we review the links between brain atrophy and cognitive alterations in patients with cerebral small vessel diseases.
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Affiliation(s)
- François De Guio
- Department of Neurology and Referral Center for Rare Vascular Diseases of the Brain and Retina (CERVCO), APHP, Lariboisière Hospital, Paris, DHU NeuroVasc, Univ Paris Diderot, and U1141 INSERM, France
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Franz Fazekas
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Frank-Erik De Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Centre for Neuroscience, Radboud University, Nijmegen, The Netherlands
| | - Steven M Greenberg
- Department of Neurology, Stroke Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Leonardo Pantoni
- "Luigi Sacco" Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Agnès Aghetti
- Department of Neurology and Referral Center for Rare Vascular Diseases of the Brain and Retina (CERVCO), APHP, Lariboisière Hospital, Paris, DHU NeuroVasc, Univ Paris Diderot, and U1141 INSERM, France
| | - Eric E Smith
- Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, Edinburgh Imaging, UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Eric Jouvent
- Department of Neurology and Referral Center for Rare Vascular Diseases of the Brain and Retina (CERVCO), APHP, Lariboisière Hospital, Paris, DHU NeuroVasc, Univ Paris Diderot, and U1141 INSERM, France
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32
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Joutel A. Prospects for Diminishing the Impact of Nonamyloid Small-Vessel Diseases of the Brain. Annu Rev Pharmacol Toxicol 2020; 60:437-456. [PMID: 31425001 DOI: 10.1146/annurev-pharmtox-010818-021712] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Small-vessel diseases (SVDs) of the brain are involved in about one-fourth of ischemic strokes and a vast majority of intracerebral hemorrhages and are responsible for nearly half of dementia cases in the elderly. SVDs are a heavy burden for society, a burden that is expected to increase further in the absence of significant therapeutic advances, given the aging population. Here, we provide a critical appraisal of currently available therapeutic approaches for nonamyloid sporadic SVDs that are largely based on targeting modifiable risk factors. We review what is known about the pathogenic mechanisms of vascular risk factor-related SVDs and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), the most frequent hereditary SVD, and elaborate on two mechanism-based therapeutic approaches worth exploring in sporadic SVD and CADASIL. We conclude by discussing opportunities and challenges that need to be tackled if efforts to achieve significant therapeutic advances for these diseases are to be successful.
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Affiliation(s)
- Anne Joutel
- Institute of Psychiatry and Neurosciences of Paris, INSERM UMR1266, Paris Descartes University, 75014 Paris, France; .,DHU NeuroVasc, Sorbonne Paris Cité, 75010 Paris, France.,Department of Pharmacology, College of Medicine, University of Vermont, Burlington, Vermont 05405, USA
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Kamran S, Khan A, Salam A, Akhtar N, Petropoulos I, Ponirakis G, Babu B, George P, Shuaib A, Malik RA. Cornea: A Window to White Matter Changes in Stroke; Corneal Confocal Microscopy a Surrogate Marker for the Presence and Severity of White Matter Hyperintensities in Ischemic Stroke. J Stroke Cerebrovasc Dis 2020; 29:104543. [PMID: 31902645 DOI: 10.1016/j.jstrokecerebrovasdis.2019.104543] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 10/14/2019] [Accepted: 11/18/2019] [Indexed: 12/13/2022] Open
Abstract
PURPOSE The presence of white matter hyperintensities (WMH) on MRI imaging confers an increased risk of stroke, dementia, and death. Corneal confocal microscopy (CCM) can detect nerve injury non-invasively and may be a useful surrogate marker for WMH. The objective is to determine whether corneal nerve pathology identified using CCM is associated with the presence of WMH in patients with acute ischemic stroke. METHODS This is a cross-sectional study where 196 consecutive individuals with acute ischemic stroke were enrolled and underwent neurological examination, MRI brain imaging and CCM. Participants underwent blinded quantification of WMH and corneal nerve fiber density (CNFD), corneal nerve branch density (CNBD) and corneal nerve fiber length (CNFL). RESULTS The prevalence of hypertension [P = .013] was significantly higher and CNFD [P = .031] was significantly lower in patients with WMH compared to those without WMH. CNFD and CNFL were significantly lower in patients with DM without WMH [P = .008, P = .019] and in patients with DM and WMH [P = .042, P = .024] compared to patients without DM or WMH, respectively. In a multivariate model, a 1-unit decrease in the CNFD increased the risk of WMH by 6%, after adjusting for age, DM, gender, dyslipidemia, metabolic syndrome, smoking, and HbA1c. DM was associated with a decrease in all CCM parameters but was not a significant independent factor associated with WMH. CONCLUSIONS CCM demonstrates corneal nerve pathology, which is associated with the presence of WMH in participants with acute ischemic stroke. CCM may be a useful surrogate imaging marker for the presence and severity of WMHs.
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Affiliation(s)
- Saadat Kamran
- Neuroscience Institute, Hamad General Hospital, Doha, Qatar; Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar.
| | - Adnan Khan
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar
| | - Abdul Salam
- Neuroscience Institute, Hamad General Hospital, Doha, Qatar
| | - Naveed Akhtar
- Neuroscience Institute, Hamad General Hospital, Doha, Qatar; Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar
| | | | - Georgios Ponirakis
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar
| | - Blessy Babu
- Neuroscience Institute, Hamad General Hospital, Doha, Qatar
| | - Pooja George
- Neuroscience Institute, Hamad General Hospital, Doha, Qatar
| | - Ashfaq Shuaib
- Neuroscience Institute, Hamad General Hospital, Doha, Qatar
| | - Rayaz A Malik
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar
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Cognitive dysfunction and brain atrophy in Susac syndrome. J Neurol 2019; 267:994-1003. [DOI: 10.1007/s00415-019-09664-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 11/30/2019] [Accepted: 12/03/2019] [Indexed: 10/25/2022]
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Smith EE, Biessels GJ, De Guio F, de Leeuw FE, Duchesne S, Düring M, Frayne R, Ikram MA, Jouvent E, MacIntosh BJ, Thrippleton MJ, Vernooij MW, Adams H, Backes WH, Ballerini L, Black SE, Chen C, Corriveau R, DeCarli C, Greenberg SM, Gurol ME, Ingrisch M, Job D, Lam BY, Launer LJ, Linn J, McCreary CR, Mok VC, Pantoni L, Pike GB, Ramirez J, Reijmer YD, Romero JR, Ropele S, Rost NS, Sachdev PS, Scott CJ, Seshadri S, Sharma M, Sourbron S, Steketee RM, Swartz RH, van Oostenbrugge R, van Osch M, van Rooden S, Viswanathan A, Werring D, Dichgans M, Wardlaw JM. Harmonizing brain magnetic resonance imaging methods for vascular contributions to neurodegeneration. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2019; 11:191-204. [PMID: 30859119 PMCID: PMC6396326 DOI: 10.1016/j.dadm.2019.01.002] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Many consequences of cerebrovascular disease are identifiable by magnetic resonance imaging (MRI), but variation in methods limits multicenter studies and pooling of data. The European Union Joint Program on Neurodegenerative Diseases (EU JPND) funded the HARmoNizing Brain Imaging MEthodS for VaScular Contributions to Neurodegeneration (HARNESS) initiative, with a focus on cerebral small vessel disease. METHODS Surveys, teleconferences, and an in-person workshop were used to identify gaps in knowledge and to develop tools for harmonizing imaging and analysis. RESULTS A framework for neuroimaging biomarker development was developed based on validating repeatability and reproducibility, biological principles, and feasibility of implementation. The status of current MRI biomarkers was reviewed. A website was created at www.harness-neuroimaging.org with acquisition protocols, a software database, rating scales and case report forms, and a deidentified MRI repository. CONCLUSIONS The HARNESS initiative provides resources to reduce variability in measurement in MRI studies of cerebral small vessel disease.
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Affiliation(s)
- Eric E. Smith
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Alberta, Canada
| | - Geert Jan Biessels
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - François De Guio
- Department of Neurology, Lariboisière Hospital, University Paris Diderot, Paris, France
| | - Frank Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Center for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Simon Duchesne
- CERVO Research Center, Quebec Mental Health Institute, Québec, Canada
- Radiology Department, Université Laval, Québec, Canada
| | - Marco Düring
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig-Maximilians-Universität LMU, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Richard Frayne
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Alberta, Canada
- Seaman Family MR Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Eric Jouvent
- Department of Neurology, Lariboisière Hospital, University Paris Diderot, Paris, France
| | - Bradley J. MacIntosh
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Ontario, Canada
| | - Michael J. Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Meike W. Vernooij
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Hieab Adams
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Walter H. Backes
- Department of Radiology & Nuclear Medicine, School for Mental Health & Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Lucia Ballerini
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Sandra E. Black
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Christopher Chen
- Memory Aging and Cognition Centre, Department of Pharmacology, National University of Singapore, Singapore
| | - Rod Corriveau
- National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Charles DeCarli
- Department of Neurology and Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Steven M. Greenberg
- J. Philip Kistler Stroke Research Center, Stroke Service and Memory Disorders Unit, Massachusetts General Hospital, Boston, MA, USA
| | - M. Edip Gurol
- J. Philip Kistler Stroke Research Center, Stroke Service and Memory Disorders Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Michael Ingrisch
- Department of Radiology, Ludwig-Maximilians-University Hospital Munich, Munich, Germany
| | - Dominic Job
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Bonnie Y.K. Lam
- Therese Pei Fong Chow Research Centre for Prevention of Dementia, Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong
| | - Lenore J. Launer
- National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer Linn
- Institute of Neuroradiology, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Cheryl R. McCreary
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Vincent C.T. Mok
- Therese Pei Fong Chow Research Centre for Prevention of Dementia, Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong
| | - Leonardo Pantoni
- Luigi Sacco Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - G. Bruce Pike
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Alberta, Canada
| | - Joel Ramirez
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Ontario, Canada
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Yael D. Reijmer
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Jose Rafael Romero
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Natalia S. Rost
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing, University of New South Wales, Sydney, Australia
| | - Christopher J.M. Scott
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Ontario, Canada
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Mukul Sharma
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Medicine (Neurology) McMaster University, Hamilton, Ontario, Canada
| | - Steven Sourbron
- Imaging Biomarkers Group, Department of Biomedical Imaging Sciences, University of Leeds, Leeds, UK
| | - Rebecca M.E. Steketee
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Richard H. Swartz
- Department of Medicine (Neurology), University of Toronto, Toronto, Canada
- Hurvitz Brain Sciences Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Robert van Oostenbrugge
- Department of Neurology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Matthias van Osch
- C.J. Gorter Center for high field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Sanneke van Rooden
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Anand Viswanathan
- J. Philip Kistler Stroke Research Center, Stroke Service and Memory Disorders Unit, Massachusetts General Hospital, Boston, MA, USA
| | - David Werring
- University College London Queen Square institute of Neurology, London, UK
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig-Maximilians-Universität LMU, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Joanna M. Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
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Jouvent E, Duering M, Chabriat H. Cerebral Autosomal Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalopathy: Lessons From Neuroimaging. Stroke 2019; 51:21-28. [PMID: 31752612 DOI: 10.1161/strokeaha.119.024152] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Eric Jouvent
- From the Department of Neurology and Referral Center for Rare Vascular Diseases of the Brain and Retina (CERVCO), APHP, Lariboisière Hospital, F-75475 Paris, France (E.J., H.C.).,DHU NeuroVasc, University Paris Diderot (E.J., H.C.).,U1141 INSERM, Paris, France (E.J., H.C.)
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany (M.D.).,Munich Cluster for Systems Neurology (SyNergy), Germany (M.D.)
| | - Hugues Chabriat
- From the Department of Neurology and Referral Center for Rare Vascular Diseases of the Brain and Retina (CERVCO), APHP, Lariboisière Hospital, F-75475 Paris, France (E.J., H.C.).,DHU NeuroVasc, University Paris Diderot (E.J., H.C.).,U1141 INSERM, Paris, France (E.J., H.C.)
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Heinen R, Steenwijk MD, Barkhof F, Biesbroek JM, van der Flier WM, Kuijf HJ, Prins ND, Vrenken H, Biessels GJ, de Bresser J. Performance of five automated white matter hyperintensity segmentation methods in a multicenter dataset. Sci Rep 2019; 9:16742. [PMID: 31727919 PMCID: PMC6856351 DOI: 10.1038/s41598-019-52966-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 10/22/2019] [Indexed: 11/23/2022] Open
Abstract
White matter hyperintensities (WMHs) are a common manifestation of cerebral small vessel disease, that is increasingly studied with large, pooled multicenter datasets. This data pooling increases statistical power, but poses challenges for automated WMH segmentation. Although there is extensive literature on the evaluation of automated WMH segmentation methods, such evaluations in a multicenter setting are lacking. We performed WMH segmentations in sixty patients scanned on six different magnetic resonance imaging (MRI) scanners (10 patients per scanner) using five freely available and fully-automated WMH segmentation methods (Cascade, kNN-TTP, Lesion-TOADS, LST-LGA and LST-LPA). Different MRI scanner vendors and field strengths were included. We compared these automated WMH segmentations with manual WMH segmentations as a reference. Performance of each method both within and across scanners was assessed using spatial and volumetric correspondence with the reference segmentations by Dice's similarity coefficient (DSC) and intra-class correlation coefficient (ICC) respectively. We found the best performance, both within and across scanners, for kNN-TTP, followed by LST-LPA and LST-LGA, with worse performance for Lesion-TOADS and Cascade. Our findings can serve as a guide for choosing a method and also highlight the importance to further improve and evaluate consistency of methods in a multicenter setting.
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Affiliation(s)
- Rutger Heinen
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Martijn D Steenwijk
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Institutes of Neurology & Healthcare Engineering, University College London (UCL), London, United Kingdom
| | - J Matthijs Biesbroek
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center & Department of Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Niels D Prins
- Alzheimer Center & Department of Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jeroen de Bresser
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
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Álvarez‐Torres M, Juan‐Albarracín J, Fuster‐Garcia E, Bellvís‐Bataller F, Lorente D, Reynés G, Font de Mora J, Aparici‐Robles F, Botella C, Muñoz‐Langa J, Faubel R, Asensio‐Cuesta S, García‐Ferrando GA, Chelebian E, Auger C, Pineda J, Rovira A, Oleaga L, Mollà‐Olmos E, Revert AJ, Tshibanda L, Crisi G, Emblem KE, Martin D, Due‐Tønnessen P, Meling TR, Filice S, Sáez C, García‐Gómez JM. Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study. J Magn Reson Imaging 2019; 51:1478-1486. [DOI: 10.1002/jmri.26958] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 09/19/2019] [Indexed: 02/03/2023] Open
Affiliation(s)
- María Álvarez‐Torres
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
| | - Javier Juan‐Albarracín
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
| | | | - Fuensanta Bellvís‐Bataller
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
| | - David Lorente
- Hospital Provincial de Castellón, Department of Medical Oncology Castellón de la Plana Castellón de la Plana Spain
| | - Gaspar Reynés
- Health Research Institute Hospital La FeCancer Research Group Valencia Spain
| | - Jaime Font de Mora
- Instituto de Investigación Sanitaria La Fe, Laboratory of Cellular and Molecular Biology Valencia Spain
| | | | - Carlos Botella
- Hospital Universitari i Politècnic La Fe, Área Clínica de Neurociencias Valencia Spain
| | - Jose Muñoz‐Langa
- Health Research Institute Hospital La FeCancer Research Group Valencia Spain
| | - Raquel Faubel
- Universitat de València, Departament de Fisioteràpia Valencia Spain
| | - Sabina Asensio‐Cuesta
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
| | - Germán A. García‐Ferrando
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
| | - Eduard Chelebian
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
| | - Cristina Auger
- Hospital Vall d'Hebron, Universitat Autònoma de Barcelona, Magnetic Resonance Unit, Department of Radiology Barcelona Spain
| | - Jose Pineda
- Hospital Clinic de Barcelona Barcelona Spain
| | - Alex Rovira
- Hospital Vall d'Hebron, Universitat Autònoma de Barcelona, Magnetic Resonance Unit, Department of Radiology Barcelona Spain
| | | | - Enrique Mollà‐Olmos
- Hospital Universitario de la Ribera, Departamento de Radiodiagnóstico Alzira Valencia Spain
| | | | - Luaba Tshibanda
- Centre Hospitalier Universitaire de Liège, Service médical de Radiodiagnostic Liège Belgium
| | - Girolamo Crisi
- Azienda Ospedaliero‐Universitaria di Parma, Neuroradiology Parma Italy
| | - Kyrre E. Emblem
- Oslo University Hospital, Department of Diagnostic Physics Oslo Norway
| | - Didier Martin
- Centre Hospitalier Universitaire de Liege, Service de Neurochirurugie Liège Belgium
| | | | - Torstein R. Meling
- Oslo University Hospital, Department of Neurosurgery Oslo Norway
- Geneva University Hospital, Department of Neurosurgery Geneva Switzerland
| | - Silvano Filice
- Azienda Ospedaliero‐Universitaria di Parma, Medical Physics Parma Italy
| | - Carlos Sáez
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
| | - Juan M. García‐Gómez
- Universitat Politècnica de València, BDSLab, Instituto Universitarios de Tecnologías de la Información y Comunicaciones (ITACA) Valencia, Spain
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Zhang Y, Zhang Y, Wu R, Gao F, Zang P, Hu X, Gu C. Effect of cerebral small vessel disease on cognitive function and TLR4 expression in hippocampus. J Clin Neurosci 2019; 67:210-214. [DOI: 10.1016/j.jocn.2019.06.038] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 03/11/2019] [Accepted: 06/21/2019] [Indexed: 02/02/2023]
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Ter Telgte A, van Leijsen EMC, Wiegertjes K, Klijn CJM, Tuladhar AM, de Leeuw FE. Cerebral small vessel disease: from a focal to a global perspective. Nat Rev Neurol 2019; 14:387-398. [PMID: 29802354 DOI: 10.1038/s41582-018-0014-y] [Citation(s) in RCA: 290] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Cerebral small vessel disease (SVD) is commonly observed on neuroimaging among elderly individuals and is recognized as a major vascular contributor to dementia, cognitive decline, gait impairment, mood disturbance and stroke. However, clinical symptoms are often highly inconsistent in nature and severity among patients with similar degrees of SVD on brain imaging. Here, we provide a new framework based on new advances in structural and functional neuroimaging that aims to explain the remarkable clinical variation in SVD. First, we discuss the heterogeneous pathology present in SVD lesions despite an identical appearance on imaging and the perilesional and remote effects of these lesions. We review effects of SVD on structural and functional connectivity in the brain, and we discuss how network disruption by SVD can lead to clinical deficits. We address reserve and compensatory mechanisms in SVD and discuss the part played by other age-related pathologies. Finally, we conclude that SVD should be considered a global rather than a focal disease, as the classically recognized focal lesions affect remote brain structures and structural and functional network connections. The large variability in clinical symptoms among patients with SVD can probably be understood by taking into account the heterogeneity of SVD lesions, the effects of SVD beyond the focal lesions, the contribution of neurodegenerative pathologies other than SVD, and the interaction with reserve mechanisms and compensatory mechanisms.
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Affiliation(s)
- Annemieke Ter Telgte
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Esther M C van Leijsen
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Catharina J M Klijn
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands.
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41
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Pantoni L, Marzi C, Poggesi A, Giorgio A, De Stefano N, Mascalchi M, Inzitari D, Salvadori E, Diciotti S. Fractal dimension of cerebral white matter: A consistent feature for prediction of the cognitive performance in patients with small vessel disease and mild cognitive impairment. NEUROIMAGE-CLINICAL 2019; 24:101990. [PMID: 31491677 PMCID: PMC6731209 DOI: 10.1016/j.nicl.2019.101990] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 08/01/2019] [Accepted: 08/19/2019] [Indexed: 11/17/2022]
Abstract
Patients with cerebral small vessel disease (SVD) frequently show decline in cognitive performance. However, neuroimaging in SVD patients discloses a wide range of brain lesions and alterations so that it is often difficult to understand which of these changes are the most relevant for cognitive decline. It has also become evident that visually-rated alterations do not fully explain the neuroimaging correlates of cognitive decline in SVD. Fractal dimension (FD), a unitless feature of structural complexity that can be computed from high-resolution T1-weighted images, has been recently applied to the neuroimaging evaluation of the human brain. Indeed, white matter (WM) and cortical gray matter (GM) exhibit an inherent structural complexity that can be measured through the FD. In our study, we included 64 patients (mean age ± standard deviation, 74.6 ± 6.9, education 7.9 ± 4.2 years, 53% males) with SVD and mild cognitive impairment (MCI), and a control group of 24 healthy subjects (mean age ± standard deviation, 72.3 ± 4.4 years, 50% males). With the aim of assessing whether the FD values of cerebral WM (WM FD) and cortical GM (GM FD) could be valuable structural predictors of cognitive performance in patients with SVD and MCI, we employed a machine learning strategy based on LASSO (least absolute shrinkage and selection operator) regression applied on a set of standard and advanced neuroimaging features in a nested cross-validation (CV) loop. This approach was aimed at 1) choosing the best predictive models, able to reliably predict the individual neuropsychological scores sensitive to attention and executive dysfunctions (prominent features of subcortical vascular cognitive impairment) and 2) identifying a features ranking according to their importance in the model through the assessment of the out-of-sample error. For each neuropsychological test, using 1000 repetitions of LASSO regression and 5000 random permutations, we found that the statistically significant models were those for the Montreal Cognitive Assessment scores (p-value = .039), Symbol Digit Modalities Test scores (p-value = .039), and Trail Making Test Part A scores (p-value = .025). Significant prediction of these scores was obtained using different sets of neuroimaging features in which the WM FD was the most frequently selected feature. In conclusion, we showed that a machine learning approach could be useful in SVD research field using standard and advanced neuroimaging features. Our study results raise the possibility that FD may represent a consistent feature in predicting cognitive decline in SVD that can complement standard imaging. White matter fractal dimension is altered in small vessel disease patients with MCI. White matter complexity's decrease consistently predicts worse cognitive performance. Fractal dimension may be a new marker of white matter damage in small vessel disease.
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Affiliation(s)
- Leonardo Pantoni
- 'L. Sacco' Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy.
| | - Chiara Marzi
- Department of Electrical, Electronic, and Information Engineering 'Guglielmo Marconi', University of Bologna, Cesena, Italy
| | - Anna Poggesi
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Antonio Giorgio
- Department of Medicine, Surgery, and Neuroscience, University of Siena, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery, and Neuroscience, University of Siena, Italy
| | - Mario Mascalchi
- 'Mario Serio' Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Domenico Inzitari
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Emilia Salvadori
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering 'Guglielmo Marconi', University of Bologna, Cesena, Italy
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Bahrani AA, Al-Janabi OM, Abner EL, Bardach SH, Kryscio RJ, Wilcock DM, Smith CD, Jicha GA. Post-acquisition processing confounds in brain volumetric quantification of white matter hyperintensities. J Neurosci Methods 2019; 327:108391. [PMID: 31408649 DOI: 10.1016/j.jneumeth.2019.108391] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 08/03/2019] [Accepted: 08/03/2019] [Indexed: 01/18/2023]
Abstract
BACKGROUND Disparate research sites using identical or near-identical magnetic resonance imaging (MRI) acquisition techniques often produce results that demonstrate significant variability regarding volumetric quantification of white matter hyperintensities (WMH) in the aging population. The sources of such variability have not previously been fully explored. NEW METHOD 3D FLAIR sequences from a group of randomly selected aged subjects were analyzed to identify sources-of-variability in post-acquisition processing that can be problematic when comparing WMH volumetric data across disparate sites. The methods developed focused on standardizing post-acquisition protocol processing methods to develop a protocol with less than 0.5% inter-rater variance. RESULTS A series of experiments using standard MRI acquisition sequences explored post-acquisition sources-of-variability in the quantification of WMH volumetric data. Sources-of-variability included: the choice of image center, software suite and version, thresholding selection, and manual editing procedures (when used). Controlling for the identified sources-of-variability led to a protocol with less than 0.5% variability between independent raters in post-acquisition WMH volumetric quantification. COMPARISON WITH EXISTING METHOD(S) Post-acquisition processing techniques can introduce an average variance approaching 15% in WMH volume quantification despite identical scan acquisitions. Understanding and controlling for such sources-of-variability can reduce post-acquisition quantitative image processing variance to less than 0.5%. DISCUSSION Considerations of potential sources-of-variability in MRI volume quantification techniques and reduction in such variability is imperative to allow for reliable cross-site and cross-study comparisons.
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Affiliation(s)
- Ahmed A Bahrani
- Department of Biomedical Engineering, College of Engineering, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
| | - Omar M Al-Janabi
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Erin L Abner
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Shoshana H Bardach
- Department of Gerontology, College of Public Health, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Richard J Kryscio
- Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY, 40506, United States; Department of Statistics, College of Arts and Science, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Donna M Wilcock
- Department of Physiology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Charles D Smith
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Magnetic Resonance Imaging and Spectroscopy Center (MRISC), College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Gregory A Jicha
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States.
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Prohl AK, Scherrer B, Tomas-Fernandez X, Filip-Dhima R, Kapur K, Velasco-Annis C, Clancy S, Carmody E, Dean M, Valle M, Prabhu SP, Peters JM, Bebin EM, Krueger DA, Northrup H, Wu JY, Sahin M, Warfield SK. Reproducibility of Structural and Diffusion Tensor Imaging in the TACERN Multi-Center Study. Front Integr Neurosci 2019; 13:24. [PMID: 31417372 PMCID: PMC6650594 DOI: 10.3389/fnint.2019.00024] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 06/24/2019] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Multi-site MRI studies are often necessary for recruiting sufficiently sized samples when studying rare conditions. However, they require pooling data from multiple scanners into a single data set, and therefore it is critical to evaluate the variability of quantitative MRI measures within and across scanners used in multi-site studies. The aim of this study was to evaluate the reproducibility of structural and diffusion weighted (DW) MRI measurements acquired on seven scanners at five medical centers as part of the Tuberous Sclerosis Complex Autism Center of Excellence Research Network (TACERN) multisite study. METHODS The American College of Radiology (ACR) phantom was imaged monthly to measure reproducibility of signal intensity and uniformity within and across seven 3T scanners from General Electric, Philips, and Siemens vendors. One healthy adult male volunteer was imaged repeatedly on all seven scanners under the TACERN structural and DW protocol (5 b = 0 s/mm2 and 30 b = 1000 s/mm2) over a period of 5 years (age 22-27 years). Reproducibility of inter- and intra-scanner brain segmentation volumes and diffusion tensor imaging metrics fractional anisotropy (FA) and mean diffusivity (MD) within white matter regions was quantified with coefficient of variation. RESULTS The American College of Radiology Phantom signal intensity and uniformity were similar across scanners and changed little over time, with a mean intra-scanner coefficient of variation of 3.6 and 1.8%, respectively. The mean inter- and intra-scanner coefficients of variation of brain structure volumes derived from T1-weighted (T1w) images of the human phantom were 3.3 and 1.1%, respectively. The mean inter- and intra-scanner coefficients of variation of FA in white matter regions were 4.5 and 2.5%, while the mean inter- and intra-scanner coefficients of variation of MD in white matter regions were 5.4 and 1.5%. CONCLUSION Our results suggest that volumetric and diffusion tensor imaging (DTI) measurements are highly reproducible between and within scanners and provide typical variation amplitudes that can be used as references to interpret future findings in the TACERN network.
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Affiliation(s)
- Anna K. Prohl
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Benoit Scherrer
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Xavier Tomas-Fernandez
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Rajna Filip-Dhima
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Kush Kapur
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Sean Clancy
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Erin Carmody
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Meghan Dean
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Molly Valle
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Sanjay P. Prabhu
- Division of Neuroradiology, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Jurriaan M. Peters
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - E. Martina Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Darcy A. Krueger
- Department of Neurology and Rehabilitation Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Hope Northrup
- Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Joyce Y. Wu
- Division of Pediatric Neurology, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Mustafa Sahin
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
- F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Simon K. Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
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Ling Y, De Guio F, Jouvent E, Duering M, Hervé D, Guichard JP, Godin O, Dichgans M, Chabriat H. Clinical correlates of longitudinal MRI changes in CADASIL. J Cereb Blood Flow Metab 2019; 39:1299-1305. [PMID: 29400120 PMCID: PMC6668524 DOI: 10.1177/0271678x18757875] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Previous studies showed that various types of cerebral lesions, as assessed on MRI, largely contribute to the clinical severity of CADASIL. However, the clinical impact of longitudinal changes of classical markers of small vessel disease on conventional MRI has been only poorly investigated. One hundred sixty NOTCH3 mutation carriers (mean age ± SD, 49.8 ± 10.9 years) were followed over three years. Validated methods were used to determine the percent brain volume change (PBVC), number of incident lacunes, change of volume of white matter hyperintensities and change of number of cerebral microbleeds. Multivariable logistic regression analyses were performed to assess the independent association between changes of these MRI markers and incident clinical events. Mixed-effect multiple linear regression analyses were used to assess their association with changes of clinical scales. Over a mean period of 3.1 ± 0.2 years, incident lacunes are found independently associated with incident stroke and change of Trail Making Test Part B. PBVC is independently associated with all incident events and clinical scale changes except the modified Rankin Scale at three years. Our results suggest that, on conventional MRI, PBVC and the number of incident lacunes are the most sensitive and independent correlates of clinical worsening over three years in CADASIL.
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Affiliation(s)
- Yifeng Ling
- 1 INSERM, U1161 Paris, France.,2 Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - François De Guio
- 1 INSERM, U1161 Paris, France.,3 Department of Neurology, Groupe Hospitalier Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris (APHP), Université Paris Denis Diderot and DHU NeuroVasc Sorbonne Paris-Cité, Paris, France
| | - Eric Jouvent
- 1 INSERM, U1161 Paris, France.,3 Department of Neurology, Groupe Hospitalier Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris (APHP), Université Paris Denis Diderot and DHU NeuroVasc Sorbonne Paris-Cité, Paris, France
| | - Marco Duering
- 4 Institute for Stroke and Dementia Research, Klinikum der Universitaüt Muünchen, Ludwig-Maximilians-University, Munich, Germany
| | - Dominique Hervé
- 1 INSERM, U1161 Paris, France.,3 Department of Neurology, Groupe Hospitalier Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris (APHP), Université Paris Denis Diderot and DHU NeuroVasc Sorbonne Paris-Cité, Paris, France
| | | | - Ophélia Godin
- 3 Department of Neurology, Groupe Hospitalier Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris (APHP), Université Paris Denis Diderot and DHU NeuroVasc Sorbonne Paris-Cité, Paris, France
| | - Martin Dichgans
- 4 Institute for Stroke and Dementia Research, Klinikum der Universitaüt Muünchen, Ludwig-Maximilians-University, Munich, Germany.,5 Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Hugues Chabriat
- 1 INSERM, U1161 Paris, France.,3 Department of Neurology, Groupe Hospitalier Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris (APHP), Université Paris Denis Diderot and DHU NeuroVasc Sorbonne Paris-Cité, Paris, France
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45
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Caunca MR, De Leon-Benedetti A, Latour L, Leigh R, Wright CB. Neuroimaging of Cerebral Small Vessel Disease and Age-Related Cognitive Changes. Front Aging Neurosci 2019; 11:145. [PMID: 31316367 PMCID: PMC6610261 DOI: 10.3389/fnagi.2019.00145] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 05/31/2019] [Indexed: 01/04/2023] Open
Abstract
Subclinical cerebrovascular disease is frequently identified in neuroimaging studies and is thought to play a role in the pathogenesis of cognitive disorders. Identifying the etiologies of different types of lesions may help investigators differentiate between age-related and pathological cerebrovascular damage in cognitive aging. In this review article, we aim to describe the epidemiology and etiology of various brain magnetic resonance imaging (MRI) measures of vascular damage in cognitively normal, older adult populations. We focus here on population-based prospective cohort studies of cognitively unimpaired older adults, as well as discuss the heterogeneity of MRI findings and their relationships with cognition. This review article emphasizes the need for a better understanding of subclinical cerebrovascular disease in cognitively normal populations, in order to more effectively identify and prevent cognitive decline in our rapidly aging population.
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Affiliation(s)
- Michelle R Caunca
- Division of Epidemiology and Population Health Sciences, Department of Public Health Sciences, Leonard M. Miller School of Medicine, Evelyn F. McKnight Brain Institute, University of Miami, Miami, FL, United States.,Department of Neurology, Leonard M. Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Andres De Leon-Benedetti
- Department of Neurology, Leonard M. Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Lawrence Latour
- National Institute of Neurological Diseases and Stroke (NINDS), National Institutes of Health, Bethesda, MD, United States
| | - Richard Leigh
- National Institute of Neurological Diseases and Stroke (NINDS), National Institutes of Health, Bethesda, MD, United States
| | - Clinton B Wright
- National Institute of Neurological Diseases and Stroke (NINDS), National Institutes of Health, Bethesda, MD, United States
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46
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Valdés Hernández MDC, Case T, Chappell FM, Glatz A, Makin S, Doubal F, Wardlaw JM. Association between Striatal Brain Iron Deposition, Microbleeds and Cognition 1 Year After a Minor Ischaemic Stroke. Int J Mol Sci 2019; 20:ijms20061293. [PMID: 30875807 PMCID: PMC6470500 DOI: 10.3390/ijms20061293] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 03/08/2019] [Accepted: 03/11/2019] [Indexed: 01/02/2023] Open
Abstract
Brain iron deposits (IDs) are inversely associated with cognitive function in community-dwelling older people, but their association with cognition after ischemic stroke, and whether that differs from microbleeds, is unknown. We quantified basal ganglia IDs (BGID) and microbleeds (BMBs) semi-automatically on brain magnetic resonance images from patients with minor stroke (NIHSS < 7), at presentation and 12 months after stroke. We administered the National Adult Reading Test (NART, estimates premorbid or peak adult cognition) and the Revised Addenbrooke's Cognitive Examination (ACE-R; current cognition) at 1 and 12 months after stroke. We adjusted analyses for baseline cognition, age, gender, white matter hyperintensity (WMH) volume and vascular risk factors. In 200 patients, mean age 65 years, striatal IDs and BMBs volumes did not change over the 12 months. Baseline BGID volumes correlated positively with NART scores at both times (ρ = 0.19, p < 0.01). Baseline and follow-up BGID volumes correlated positively with age (ρ = 0.248, p < 0.001 and ρ = 0.271, p < 0.001 respectively), but only baseline (and not follow-up) BMB volume correlated with age (ρ = 0.129, p < 0.05). Both smoking and baseline WMH burden predicted verbal fluency and visuospatial abilities scores (B = -1.13, p < 0.02 and B = -0.22, p = 0.001 respectively) at 12 months after stroke. BGIDs and BMBs are associated differently with cognition post-stroke; studies of imaging and post-stroke cognition should adjust for premorbid cognition. The positive correlation of BGID with NART may reflect the lower premorbid cognition in patients with stroke at younger vs older ages.
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Affiliation(s)
- Maria Del C Valdés Hernández
- College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH16 4SB, UK.
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK.
- Dementia Research Institute, University of Edinburgh, Edinburgh EH16 4SB, UK.
| | - Tessa Case
- Row Fogo Centre for Ageing and the Brain, University of Edinburgh, Edinburgh EH16 4SB, UK.
| | - Francesca M Chappell
- College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH16 4SB, UK.
- Dementia Research Institute, University of Edinburgh, Edinburgh EH16 4SB, UK.
| | - Andreas Glatz
- College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH16 4SB, UK.
| | - Stephen Makin
- College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH16 4SB, UK.
| | - Fergus Doubal
- College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH16 4SB, UK.
| | - Joanna M Wardlaw
- College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH16 4SB, UK.
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK.
- Dementia Research Institute, University of Edinburgh, Edinburgh EH16 4SB, UK.
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Validation and Optimization of BIANCA for the Segmentation of Extensive White Matter Hyperintensities. Neuroinformatics 2019; 16:269-281. [PMID: 29594711 DOI: 10.1007/s12021-018-9372-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
White matter hyperintensities (WMH) are a hallmark of small vessel diseases (SVD). Yet, no automated segmentation method is readily and widely used, especially in patients with extensive WMH where lesions are close to the cerebral cortex. BIANCA (Brain Intensity AbNormality Classification Algorithm) is a new fully automated, supervised method for WMH segmentation. In this study, we optimized and compared BIANCA against a reference method with manual editing in a cohort of patients with extensive WMH. This was achieved in two datasets: a clinical protocol with 90 patients having 2-dimensional FLAIR and an advanced protocol with 66 patients having 3-dimensional FLAIR. We first determined simultaneously which input modalities (FLAIR alone or FLAIR + T1) and which training sets were better compared to the reference. Three strategies for the selection of the threshold that is applied to the probabilistic output of BIANCA were then evaluated: chosen at the group level, based on Fazekas score or determined individually. Accuracy of the segmentation was assessed through measures of spatial agreement and volumetric correspondence with respect to reference segmentation. Based on all our tests, we identified multimodal inputs (FLAIR + T1), mixed WMH load training set and individual threshold selection as the best conditions to automatically segment WMH in our cohort. A median Dice similarity index of 0.80 (0.80) and an intraclass correlation coefficient of 0.97 (0.98) were obtained for the clinical (advanced) protocol. However, Bland-Altman plots identified a difference with the reference method that was linearly related to the total burden of WMH. Our results suggest that BIANCA is a reliable and fast segmentation method to extract masks of WMH in patients with extensive lesions.
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Donahue MJ, Hendrikse J. Improved detection of cerebrovascular disease processes: Introduction to the Journal of Cerebral Blood Flow and Metabolism special issue on cerebrovascular disease. J Cereb Blood Flow Metab 2018; 38:1387-1390. [PMID: 30160626 PMCID: PMC6125974 DOI: 10.1177/0271678x17739802] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Approximately 15 million individuals suffer a stroke worldwide each year, and stroke results in death or permanent disability in two-thirds of these individuals. Due to increased knowledge and management of modifiable risk factors, stroke incidence in developed countries is declining, however remains high at just under 1 million patients per year in the United States alone. Further improving management of patients with cerebrovascular disease (CVD) ultimately will require development and clinical adoption of sensitive markers of hemodynamic and metabolic failure, as well as trials that evaluate how to interpret these markers to optimize therapies. Realizing this goal and reducing the complete burden of CVD is dependent on an improved understanding of the pathophysiological processes that underlie CVD in all stages, including sub-clinical disease processes, acute stroke, and post-stroke recovery mechanisms. This document serves as an introduction to the Journal of Cerebral Blood Flow and Metabolism special issue on cerebrovascular diseases, which is comprised of contributions from experts in each of the above stages of CVD, and outlines current standards for patient management and emerging directions that have potential for improving patient care over the next decade.
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Affiliation(s)
- Manus J Donahue
- 1 Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.,2 Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA.,3 Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, USA.,4 Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, USA
| | - Jeroen Hendrikse
- 5 Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
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De Guio F, Vignaud A, Chabriat H, Jouvent E. Different types of white matter hyperintensities in CADASIL: Insights from 7-Tesla MRI. J Cereb Blood Flow Metab 2018; 38:1654-1663. [PMID: 28128022 PMCID: PMC6125962 DOI: 10.1177/0271678x17690164] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
In Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL), by contrast to sporadic cerebral small vessel disease related to age and hypertension, white matter hyperintensities (WMH) are frequently observed in the white matter of anterior temporal poles, external capsules, and superior frontal regions. Whether these WMH (specific WMH) differ from those observed in other white matter areas (nonspecific WMH) remains unknown. Twenty patients were scanned to compare specific and nonspecific WMH using high-resolution images and analyses of relaxation times (T1R: longitudinal relaxation time and T2*R: effective transversal relaxation time). Specific WMH were characterized by significantly longer T1R and T2*R (T1R: 2309 ± 120 ms versus 2145 ± 138 ms; T2*R: 40 ± 5 ms versus 35 ± 5 ms, p < 0.001). These results were not explained by the presence of dilated perivascular spaces found in the close vicinity of specific WMH. They were not either explained by the normal regional variability of T1R and T2*R in the white matter nor by systematic imaging artifacts as shown by the study of 17 age- and sex-matched healthy controls. Our results suggest large differences in water content between specific and nonspecific WMH in CADASIL, supporting that mechanisms underlying WMH may differ according to their location.
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Affiliation(s)
- François De Guio
- 1 University Paris Diderot, Sorbonne Paris Cité, UMR-S 1161 INSERM, Paris, France.,2 DHU NeuroVasc Sorbonne Paris Cité, Paris, France
| | | | - Hugues Chabriat
- 1 University Paris Diderot, Sorbonne Paris Cité, UMR-S 1161 INSERM, Paris, France.,2 DHU NeuroVasc Sorbonne Paris Cité, Paris, France.,4 AP-PH, Lariboisière Hosp., Department of neurology, F-75475, Paris, France
| | - Eric Jouvent
- 1 University Paris Diderot, Sorbonne Paris Cité, UMR-S 1161 INSERM, Paris, France.,2 DHU NeuroVasc Sorbonne Paris Cité, Paris, France.,4 AP-PH, Lariboisière Hosp., Department of neurology, F-75475, Paris, France.,5 UNIACT, NeuroSpin, I2BM/DSV, CEA, Gif-sur-Yvette, France
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50
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Donahue MJ, Achten E, Cogswell PM, De Leeuw FE, Derdeyn CP, Dijkhuizen RM, Fan AP, Ghaznawi R, Heit JJ, Ikram MA, Jezzard P, Jordan LC, Jouvent E, Knutsson L, Leigh R, Liebeskind DS, Lin W, Okell TW, Qureshi AI, Stagg CJ, van Osch MJP, van Zijl PCM, Watchmaker JM, Wintermark M, Wu O, Zaharchuk G, Zhou J, Hendrikse J. Consensus statement on current and emerging methods for the diagnosis and evaluation of cerebrovascular disease. J Cereb Blood Flow Metab 2018; 38:1391-1417. [PMID: 28816594 PMCID: PMC6125970 DOI: 10.1177/0271678x17721830] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 05/26/2017] [Accepted: 06/10/2017] [Indexed: 01/04/2023]
Abstract
Cerebrovascular disease (CVD) remains a leading cause of death and the leading cause of adult disability in most developed countries. This work summarizes state-of-the-art, and possible future, diagnostic and evaluation approaches in multiple stages of CVD, including (i) visualization of sub-clinical disease processes, (ii) acute stroke theranostics, and (iii) characterization of post-stroke recovery mechanisms. Underlying pathophysiology as it relates to large vessel steno-occlusive disease and the impact of this macrovascular disease on tissue-level viability, hemodynamics (cerebral blood flow, cerebral blood volume, and mean transit time), and metabolism (cerebral metabolic rate of oxygen consumption and pH) are also discussed in the context of emerging neuroimaging protocols with sensitivity to these factors. The overall purpose is to highlight advancements in stroke care and diagnostics and to provide a general overview of emerging research topics that have potential for reducing morbidity in multiple areas of CVD.
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Affiliation(s)
- Manus J Donahue
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, USA
| | - Eric Achten
- Department of Radiology and Nuclear Medicine, Universiteit Gent, Gent, Belgium
| | - Petrice M Cogswell
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Frank-Erik De Leeuw
- Radboud University, Nijmegen Medical Center, Donders Institute Brain Cognition & Behaviour, Center for Neuroscience, Department of Neurology, Nijmegen, The Netherlands
| | - Colin P Derdeyn
- Department of Radiology and Neurology, University of Iowa, Iowa City, IA, USA
| | - Rick M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Audrey P Fan
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Rashid Ghaznawi
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jeremy J Heit
- Department of Radiology, Neuroimaging and Neurointervention Division, Stanford University, CA, USA
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Peter Jezzard
- Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Lori C Jordan
- Department of Pediatrics, Division of Pediatric Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric Jouvent
- Department of Neurology, AP-HP, Lariboisière Hospital, Paris, France
| | - Linda Knutsson
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Richard Leigh
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | | | - Weili Lin
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Thomas W Okell
- Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Adnan I Qureshi
- Department of Neurology, Zeenat Qureshi Stroke Institute, St. Cloud, MN, USA
| | - Charlotte J Stagg
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK
| | | | - Peter CM van Zijl
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jennifer M Watchmaker
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Max Wintermark
- Department of Radiology, Neuroimaging and Neurointervention Division, Stanford University, CA, USA
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Greg Zaharchuk
- Department of Radiology, Neuroimaging and Neurointervention Division, Stanford University, CA, USA
| | - Jinyuan Zhou
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jeroen Hendrikse
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
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