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Han Y, Chen H, Cao X, Yin X, Zhang J. A novel perspective for exploring the relationship between cerebral small vessel disease and deep medullary veins with automatic segmentation. Clin Radiol 2024; 79:e933-e940. [PMID: 38670919 DOI: 10.1016/j.crad.2024.03.014] [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: 07/20/2023] [Revised: 03/26/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND This study aimed to establish an intelligent segmentation algorithm to count the number of deep medullary veins (DMVs) and analyze the relationship between DMVs and imaging markers of cerebral small vessel disease (CSVD). METHODS DMVs on magnetic resonance imaging (MRI) of patients with CSVD were counted by intelligent segmentation and manual counting. The dice coefficient and intraclass correlation coefficient (ICC) were used to evaluate their consistency and correlation. Structural MR images were used to assess imaging markers and total burden of CSVD. A multivariate linear regression model was used to evaluate the correlation between the number of DMVs counted by intelligent segmentation and imaging markers of CSVD, including white matter hyperintensities of the presumed vascular origin, lacune, perivascular spaces, cerebral microbleeds, and total CSVD burden. RESULTS A total of 305 patients with CSVD were enrolled. An intelligent segmentation algorithm was established to calculate the number of DMVs, and it was validated and tested. The number of DMVs counted intelligently significantly correlated with the manual counting method (r = 0.761, P< 0.001). The number of smart-counted DMVs negatively correlated with the imaging markers and total burden of CSVD (P< 0.001), and the correlation remained after adjusting for age and hypertension (P< 0.05). CONCLUSIONS The proposed intelligent segmentation algorithm, which was established to count DMVs, can provide objective and quantitative imaging information for the follow-up of patients with CSVD. DMVs are involved in CSVD pathogenesis and a likely new imaging marker for CSVD.
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Affiliation(s)
- Y Han
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China
| | - H Chen
- Academy for Engineering and Technology, Fudan University, Shanghai 200040, China
| | - X Cao
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China; National Center for Neurological Disorders, 12 Wulumuqi Middle Road, Shanghai 200040, China
| | - X Yin
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China
| | - J Zhang
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China; National Center for Neurological Disorders, 12 Wulumuqi Middle Road, Shanghai 200040, China.
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2
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You TY, Dong Q, Cui M. Emerging Links between Cerebral Blood Flow Regulation and Cognitive Decline: A Role for Brain Microvascular Pericytes. Aging Dis 2023:AD.2022.1204. [PMID: 37163446 PMCID: PMC10389833 DOI: 10.14336/ad.2022.1204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 12/04/2022] [Indexed: 05/12/2023] Open
Abstract
Cognitive impairment associated with vascular etiology has been of considerable interest in the development of dementia. Recent studies have started to uncover cerebral blood flow deficits in initiating cognitive deterioration. Brain microvascular pericytes, the only type of contractile cells in capillaries, are involved in the precise modulation of vascular hemodynamics due to their ability to regulate resistance in the capillaries. They exhibit potential in maintaining the capillary network geometry and basal vascular tone. In addition, pericytes can facilitate better blood flow supply in response to neurovascular coupling. Their dysfunction is thought to disturb cerebral blood flow causing metabolic imbalances or structural injuries, leading to consequent cognitive decline. In this review, we summarize the characteristics of microvascular pericytes in brain blood flow regulation and outline the framework of a two-hit hypothesis in cognitive decline, where we emphasize how pericytes serve as targets of cerebral blood flow dysregulation that occurs with neurological challenges, ranging from genetic factors, aging, and pathological proteins to ischemic stress.
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Affiliation(s)
- Tong-Yao You
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Mei Cui
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
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3
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Garnier-Crussard A, Cotton F, Krolak-Salmon P, Chételat G. White matter hyperintensities in Alzheimer's disease: Beyond vascular contribution. Alzheimers Dement 2023; 19:3738-3748. [PMID: 37027506 DOI: 10.1002/alz.13057] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/15/2023] [Accepted: 03/03/2023] [Indexed: 04/09/2023]
Abstract
White matter hyperintensities (WMH), frequently seen in older adults, are usually considered vascular lesions, and participate in the vascular contribution to cognitive impairment and dementia. However, emerging evidence highlights the heterogeneity of WMH pathophysiology, suggesting that non-vascular mechanisms could also be involved, notably in Alzheimer's disease (AD). This led to the alternative hypothesis that in AD, part of WMH may be secondary to AD-related processes. The current perspective brings together the arguments from different fields of research, including neuropathology, neuroimaging and fluid biomarkers, and genetics, in favor of this alternative hypothesis. Possible underlying mechanisms leading to AD-related WMH, such as AD-related neurodegeneration or neuroinflammation, are discussed, as well as implications for diagnostic criteria and management of AD. We finally discuss ways to test this hypothesis and remaining challenges. Acknowledging the heterogeneity of WMH and the existence of AD-related WMH may improve personalized diagnosis and care of patients.
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Affiliation(s)
- Antoine Garnier-Crussard
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders," Neuropresage Team, Cyceron, Caen, France
- Clinical and Research Memory Center of Lyon, Lyon Institute For Aging, Hospices Civils de Lyon, Villeurbanne, France
- Eduwell team, Lyon Neuroscience Research Center (CRNL), INSERM U1028, CNRS UMR5292, UCBL1, Lyon, France
| | - François Cotton
- Radiology Department, Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, Pierre-Bénite, France
- CREATIS, INSERM U1044, CNRS UMR 5220, UCBL1, Villeurbanne, France
| | - Pierre Krolak-Salmon
- Clinical and Research Memory Center of Lyon, Lyon Institute For Aging, Hospices Civils de Lyon, Villeurbanne, France
- Eduwell team, Lyon Neuroscience Research Center (CRNL), INSERM U1028, CNRS UMR5292, UCBL1, Lyon, France
| | - Gaël Chételat
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders," Neuropresage Team, Cyceron, Caen, France
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4
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Haddad SMH, Scott CJM, Ozzoude M, Berezuk C, Holmes M, Adamo S, Ramirez J, Arnott SR, Nanayakkara ND, Binns M, Beaton D, Lou W, Sunderland K, Sujanthan S, Lawrence J, Kwan D, Tan B, Casaubon L, Mandzia J, Sahlas D, Saposnik G, Hassan A, Levine B, McLaughlin P, Orange JB, Roberts A, Troyer A, Black SE, Dowlatshahi D, Strother SC, Swartz RH, Symons S, Montero-Odasso M, ONDRI Investigators, Bartha R. Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity. Int J Biomed Imaging 2022; 2022:5860364. [PMID: 36313789 PMCID: PMC9616672 DOI: 10.1155/2022/5860364] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 04/21/2022] [Accepted: 06/01/2022] [Indexed: 11/13/2023] Open
Abstract
Alterations in tissue microstructure in normal-appearing white matter (NAWM), specifically measured by diffusion tensor imaging (DTI) fractional anisotropy (FA), have been associated with cognitive outcomes following stroke. The purpose of this study was to comprehensively compare conventional DTI measures of tissue microstructure in NAWM to diverse vascular brain lesions in people with cerebrovascular disease (CVD) and to examine associations between FA in NAWM and cerebrovascular risk factors. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured in cerebral tissues and cerebrovascular anomalies from 152 people with CVD participating in the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Ten cerebral tissue types were segmented including NAWM, and vascular lesions including stroke, periventricular and deep white matter hyperintensities, periventricular and deep lacunar infarcts, and perivascular spaces (PVS) using T1-weighted, proton density-weighted, T2-weighted, and fluid attenuated inversion recovery MRI scans. Mean DTI metrics were measured in each tissue region using a previously developed DTI processing pipeline and compared between tissues using multivariate analysis of covariance. Associations between FA in NAWM and several CVD risk factors were also examined. DTI metrics in vascular lesions differed significantly from healthy tissue. Specifically, all tissue types had significantly different MD values, while FA was also found to be different in most tissue types. FA in NAWM was inversely related to hypertension and modified Rankin scale (mRS). This study demonstrated the differences between conventional DTI metrics, FA, MD, AD, and RD, in cerebral vascular lesions and healthy tissue types. Therefore, incorporating DTI to characterize the integrity of the tissue microstructure could help to define the extent and severity of various brain vascular anomalies. The association between FA within NAWM and clinical evaluation of hypertension and disability provides further evidence that white matter microstructural integrity is impacted by cerebrovascular function.
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Affiliation(s)
- Seyyed M. H. Haddad
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
| | - Christopher J. M. Scott
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | - Miracle Ozzoude
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | | | - Melissa Holmes
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | - Sabrina Adamo
- Clinical Neurosciences, University of Toronto, Toronto, Canada
| | - Joel Ramirez
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | - Stephen R. Arnott
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Nuwan D. Nanayakkara
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
| | - Malcolm Binns
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Wendy Lou
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Kelly Sunderland
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | | | - Jane Lawrence
- Thunder Bay Regional Health Research Institute, Thunder Bay, Canada
| | | | - Brian Tan
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Leanne Casaubon
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Jennifer Mandzia
- Department of Medicine, Division of Neurology, University of Western Ontario, London, Canada
| | - Demetrios Sahlas
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | | | - Ayman Hassan
- Thunder Bay Regional Research Institute, Thunder Bay, Canada
| | - Brian Levine
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | | | - J. B. Orange
- School of Communication Sciences and Disorders, Western University, London, Canada
| | - Angela Roberts
- Roxelyn and Richard Pepper Department of Communication Sciences and Disorder, Northwestern University, Evanston, USA
| | - Angela Troyer
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Sandra E. Black
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
- Sunnybrook Health Sciences Centre, University of Toronto, Stroke Research Program, Toronto, Canada
| | | | - Stephen C. Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Richard H. Swartz
- Sunnybrook Health Sciences Centre, University of Toronto, Stroke Research Program, Toronto, Canada
| | - Sean Symons
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Manuel Montero-Odasso
- Department of Medicine, Division of Geriatric Medicine, Parkwood Hospital, St. Joseph's Health Care London, London, Canada
| | - ONDRI Investigators
- Ontario Neurodegenerative Disease Initiative, Ontario Brain Institute, Toronto, Canada
| | - Robert Bartha
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
- Department of Medical Biophysics, University of Western Ontario, London, Canada
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5
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Keuss SE, Coath W, Nicholas JM, Poole T, Barnes J, Cash DM, Lane CA, Parker TD, Keshavan A, Buchanan SM, Wagen AZ, Storey M, Harris M, Malone IB, Sudre CH, Lu K, James SN, Street R, Thomas DL, Dickson JC, Murray-Smith H, Wong A, Freiberger T, Crutch S, Richards M, Fox NC, Schott JM. Associations of β-Amyloid and Vascular Burden With Rates of Neurodegeneration in Cognitively Normal Members of the 1946 British Birth Cohort. Neurology 2022; 99:e129-e141. [PMID: 35410910 PMCID: PMC9280996 DOI: 10.1212/wnl.0000000000200524] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 03/01/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The goals of this work were to quantify the independent and interactive associations of β-amyloid (Aβ) and white matter hyperintensity volume (WMHV), a marker of presumed cerebrovascular disease (CVD), with rates of neurodegeneration and to examine the contributions of APOE ε4 and vascular risk measured at different stages of adulthood in cognitively normal members of the 1946 British Birth Cohort. METHODS Participants underwent brain MRI and florbetapir-Aβ PET as part of Insight 46, an observational population-based study. Changes in whole-brain, ventricular, and hippocampal volume were directly measured from baseline and repeat volumetric T1 MRI with the boundary shift integral. Linear regression was used to test associations with baseline Aβ deposition, baseline WMHV, APOE ε4, and office-based Framingham Heart Study Cardiovascular Risk Score (FHS-CVS) and systolic blood pressure (BP) at ages 36, 53, and 69 years. RESULTS Three hundred forty-six cognitively normal participants (mean [SD] age at baseline scan 70.5 [0.6] years; 48% female) had high-quality T1 MRI data from both time points (mean [SD] scan interval 2.4 [0.2] years). Being Aβ positive at baseline was associated with 0.87-mL/y faster whole-brain atrophy (95% CI 0.03, 1.72), 0.39-mL/y greater ventricular expansion (95% CI 0.16, 0.64), and 0.016-mL/y faster hippocampal atrophy (95% CI 0.004, 0.027), while each 10-mL additional WMHV at baseline was associated with 1.07-mL/y faster whole-brain atrophy (95% CI 0.47, 1.67), 0.31-mL/y greater ventricular expansion (95% CI 0.13, 0.60), and 0.014-mL/y faster hippocampal atrophy (95% CI 0.006, 0.022). These contributions were independent, and there was no evidence that Aβ and WMHV interacted in their effects. There were no independent associations of APOE ε4 with rates of neurodegeneration after adjustment for Aβ status and WMHV, no clear relationships between FHS-CVS or systolic BP and rates of neurodegeneration when assessed across the whole sample, and no evidence that FHS-CVS or systolic BP acted synergistically with Aβ. DISCUSSION Aβ and presumed CVD have distinct and additive effects on rates of neurodegeneration in cognitively normal elderly. These findings have implications for the use of MRI measures as biomarkers of neurodegeneration and emphasize the importance of risk management and early intervention targeting both pathways.
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Affiliation(s)
- Sarah E Keuss
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - William Coath
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Jennifer M Nicholas
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Teresa Poole
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Josephine Barnes
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - David M Cash
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Christopher A Lane
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Thomas D Parker
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Ashvini Keshavan
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Sarah M Buchanan
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Aaron Z Wagen
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Mathew Storey
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Matthew Harris
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Ian B Malone
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Carole H Sudre
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Kirsty Lu
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Sarah-Naomi James
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Rebecca Street
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - David L Thomas
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - John C Dickson
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Heidi Murray-Smith
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Andrew Wong
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Tamar Freiberger
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Sebastian Crutch
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Marcus Richards
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Nick C Fox
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Jonathan M Schott
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK.
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Liu ZY, Zhai FF, Ao DH, Han F, Li ML, Zhou L, Ni J, Yao M, Zhang SY, Cui LY, Jin ZY, Zhu YC. Deep medullary veins are associated with widespread brain structural abnormalities. J Cereb Blood Flow Metab 2022; 42:997-1006. [PMID: 34855528 PMCID: PMC9125483 DOI: 10.1177/0271678x211065210] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 10/13/2021] [Accepted: 10/20/2021] [Indexed: 10/19/2022]
Abstract
Our aim is to investigate the association of cerebral deep medullary veins (DMVs) with white matter microstructural integrity and regional brain atrophy in MRI. In a community-based cohort of 979 participants (mean age 55.4 years), DMVs were identified on susceptibility-weighted imaging. Brain structural measurements including gray matter and hippocampus volumes, as well as diffusion tensor metrics, were evaluated. The mean (SD)number of DMVs was 19.0 (1.7). A fewer number of DMVs was related to lower fractional anisotropy and higher mean diffusivity in multiple voxels on the white matter skeleton (threshold-free cluster enhancement corrected p < 0.05, adjusted for age and sex). Also, fewer DMVs were significantly related to a lower gray matter fraction and a hippocampal fraction (0.10 and 0.11 per DMV, respectively; SE, 0.03 for both; p < 0.001 for both). A significant correlation between DMVs' reduction and cortical atrophy was observed in the bilateral occipital lobes, temporal lobes, hippocampus, and frontal lobes (p < 0.001, adjusted for age, sex, and total intracranial volume). Our results provided evidence that cerebral small venules disease play a role in brain parenchymal lesions and neurodegenerative processes.
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Affiliation(s)
- Zi-Yue Liu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fei-Fei Zhai
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dong-Hui Ao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Neurology, Wu Han Tong Ji Hospital, Wuhan, China
| | - Fei Han
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming-Li Li
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lixin Zhou
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jun Ni
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Yao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shu-Yang Zhang
- Department of Cardiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Li-Ying Cui
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zheng-Yu Jin
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yi-Cheng Zhu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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7
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Koncz R, Wen W, Makkar SR, Lam BCP, Crawford JD, Rowe CC, Sachdev P. The Interaction Between Vascular Risk Factors, Cerebral Small Vessel Disease, and Amyloid Burden in Older Adults. J Alzheimers Dis 2022; 86:1617-1628. [PMID: 35213365 DOI: 10.3233/jad-210358] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Cerebral small vessel disease (SVD) and Alzheimer's disease pathology, namely amyloid-β (Aβ) deposition, commonly co-occur. Exactly how they interact remains uncertain. OBJECTIVE Using participants from the Alzheimer's Disease Neuroimaging Initiative (n = 216; mean age 73.29±7.08 years, 91 (42.1%) females), we examined whether the presence of vascular risk factors and/or baseline cerebral SVD was related to a greater burden of Aβ cross-sectionally, and at 24 months follow-up. METHOD Amyloid burden, assessed using 18F-florbetapir PET, was quantified as the global standardized uptake value ratio (SUVR). Multimodal imaging was used to strengthen the quantification of baseline SVD as a composite variable, which included white matter hyperintensity volume using MRI, and peak width of skeletonized mean diffusivity using diffusion tensor imaging. Structural equation modelling was used to analyze the associations between demographic factors, Apolipoprotein E ɛ4 carrier status, vascular risk factors, SVD burden and cerebral amyloid. RESULTS SVD burden had a direct association with Aβ burden cross-sectionally (coeff. = 0.229, p = 0.004), and an indirect effect over time (indirect coeff. = 0.235, p = 0.004). Of the vascular risk factors, a history of hypertension (coeff. = 0.094, p = 0.032) and a lower fasting glucose at baseline (coeff. = -0.027, p = 0.014) had a direct effect on Aβ burden at 24 months, but only the direct effect of glucose persisted after regularization. CONCLUSION While Aβ and SVD burden have an association cross-sectionally, SVD does not appear to directly influence the accumulation of Aβ longitudinally. Glucose regulation may be an important modifiable risk factor for Aβ accrual over time.
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Affiliation(s)
- Rebecca Koncz
- Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, UNSW Sydney, NSW, Australia.,The University of Sydney Specialty of Psychiatry, Faculty of Medicine and Health, Concord, NSW, Australia.,Concord Repatriation General Hospital, Sydney Local Health District, Concord, NSW, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, UNSW Sydney, NSW, Australia
| | - Steve R Makkar
- Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, UNSW Sydney, NSW, Australia
| | - Ben C P Lam
- Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, UNSW Sydney, NSW, Australia
| | - John D Crawford
- Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, UNSW Sydney, NSW, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, Victoria, Australia.,Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, UNSW Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW, Australia
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Pietroboni AM, Colombi A, Carandini T, Sacchi L, Fenoglio C, Marotta G, Arighi A, De Riz MA, Fumagalli GG, Castellani M, Bozzali M, Scarpini E, Galimberti D. Amyloid PET imaging and dementias: potential applications in detecting and quantifying early white matter damage. Alzheimers Res Ther 2022; 14:33. [PMID: 35151361 PMCID: PMC8841045 DOI: 10.1186/s13195-021-00933-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 11/04/2021] [Indexed: 11/11/2022]
Abstract
Purpose Positron emission tomography (PET) with amyloid tracers (amy-PET) allows the quantification of pathological amyloid deposition in the brain tissues, including the white matter (WM). Here, we evaluate amy-PET uptake in WM lesions (WML) and in the normal-appearing WM (NAWM) of patients with Alzheimer’s disease (AD) and non-AD type of dementia. Methods Thirty-three cognitively impaired subjects underwent brain magnetic resonance imaging (MRI), Aβ1-42 (Aβ) determination in the cerebrospinal fluid (CSF) and amy-PET. Twenty-three patients exhibiting concordant results in both CSF analysis and amy-PET for cortical amyloid deposition were recruited and divided into two groups, amyloid positive (A+) and negative (A−). WML quantification and brain volumes’ segmentation were performed. Standardized uptake values ratios (SUVR) were calculated in the grey matter (GM), NAWM and WML on amy-PET coregistered to MRI images. Results A+ compared to A− showed a higher WML load (p = 0.049) alongside higher SUVR in all brain tissues (p < 0.01). No correlations between CSF Aβ levels and WML and NAWM SUVR were found in A+, while, in A−, CSF Aβ levels were directly correlated to NAWM SUVR (p = 0.04). CSF Aβ concentration was the only predictor of NAWM SUVR (adj R2 = 0.91; p = 0.04) in A−. In A+ but not in A− direct correlations were identified between WM and GM SUVR (p < 0.01). Conclusions Our data provide evidence on the role of amy-PET in the assessment of microstructural WM injury in non-AD dementia, whereas amy-PET seems less suitable to assess WM damage in AD patients due to a plausible amyloid accrual therein.
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Affiliation(s)
- Anna M Pietroboni
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy. .,University of Milan, Milan, Italy. .,Dino Ferrari Center, Milan, Italy.
| | - Annalisa Colombi
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| | - Tiziana Carandini
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| | - Luca Sacchi
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| | | | - Giorgio Marotta
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy
| | - Andrea Arighi
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| | - Milena A De Riz
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| | - Giorgio G Fumagalli
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| | - Massimo Castellani
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy
| | - Marco Bozzali
- 'Rita Levi Montalcini' Department of Neuroscience, University of Torino, Turin, Italy.,Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK
| | - Elio Scarpini
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
| | - Daniela Galimberti
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122, Milan, Italy.,University of Milan, Milan, Italy.,Dino Ferrari Center, Milan, Italy
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9
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Chen Y, Wang X, Guan L, Wang Y. Role of White Matter Hyperintensities and Related Risk Factors in Vascular Cognitive Impairment: A Review. Biomolecules 2021; 11:biom11081102. [PMID: 34439769 PMCID: PMC8391787 DOI: 10.3390/biom11081102] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 07/24/2021] [Accepted: 07/25/2021] [Indexed: 02/06/2023] Open
Abstract
White matter hyperintensities (WMHs) of presumed vascular origin are one of the imaging markers of cerebral small-vessel disease, which is prevalent in older individuals and closely associated with the occurrence and development of cognitive impairment. The heterogeneous nature of the imaging manifestations of WMHs creates difficulties for early detection and diagnosis of vascular cognitive impairment (VCI) associated with WMHs. Because the underlying pathological processes and biomarkers of WMHs and their development in cognitive impairment remain uncertain, progress in prevention and treatment is lagging. For this reason, this paper reviews the status of research on the features of WMHs related to VCI, as well as mediators associated with both WMHs and VCI, and summarizes potential treatment strategies for the prevention and intervention in WMHs associated with VCI.
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Affiliation(s)
- Yiyi Chen
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China; (Y.C.); (X.W.)
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100070, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing 100070, China
| | - Xing Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China; (Y.C.); (X.W.)
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100070, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing 100070, China
- Department of Neurology, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing 400000, China
| | - Ling Guan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China; (Y.C.); (X.W.)
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100070, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing 100070, China
- Correspondence: (L.G.); (Y.W.)
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China; (Y.C.); (X.W.)
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100070, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing 100070, China
- Correspondence: (L.G.); (Y.W.)
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10
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Wang YJ, Hu H, Yang YX, Zuo CT, Tan L, Yu JT. Regional Amyloid Accumulation and White Matter Integrity in Cognitively Normal Individuals. J Alzheimers Dis 2021; 74:1261-1270. [PMID: 32176644 DOI: 10.3233/jad-191350] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Recent studies have shown that amyloid-β (Aβ) burden influenced white matter (WM) integrity before the onset of dementia. OBJECTIVE To assess whether the effects of Aβ burden on WM integrity in cognitively normal (CN) individuals were regionally specific. METHODS Our cohort consisted of 71 CNs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database who underwent both AV45 amyloid-PET and diffusion tensor imaging. Standardized uptake value ratio (SUVR) was computed across four bilateral regions of interest (ROIs) corresponding to four stages of in vivo amyloid staging model (Amyloid stages I-IV). Linear regression models were conducted in entire CN group and between APOEɛ4 carriers and non-carriers. RESULTS Our results indicated that higher global Aβ-SUVR was associated with higher mean diffusivity (MD) in the entire CN group (p = 0.023), and with both higher MD (p = 0.015) and lower fractional anisotropy (FA) (p = 0.026) in APOEɛ4 carriers. Subregion analysis showed that higher Amyloid stage I-II Aβ-SUVRs were associated with higher MD (Stage-1: p = 0.030; Stage-2: p = 0.016) in the entire CN group, and with both higher MD (Stage-1: p = 0.004; Stage-2: p = 0.010) and lower FA (Stage-1: p = 0.022; Stage-2: p = 0.014) in APOEɛ4 carriers. No associations were found in APOEɛ4 non-carriers and in Amyloid stage III-IV ROIs. CONCLUSIONS Our results indicated that the effects of Aβ burden on WM integrity in CNs might be regionally specific, particularly in Amyloid stage I-II ROIs, and modulated by APOEɛ4 status.
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Affiliation(s)
- Ya-Juan Wang
- Department of Neurology, Qingdao Municipal Hospital, Dalian Medical University, China
| | - Hao Hu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, China
| | - Yu-Xiang Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chuan-Tao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Dalian Medical University, China.,Department of Neurology, Qingdao Municipal Hospital, Qingdao University, China
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
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11
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Pålhaugen L, Sudre CH, Tecelao S, Nakling A, Almdahl IS, Kalheim LF, Cardoso MJ, Johnsen SH, Rongve A, Aarsland D, Bjørnerud A, Selnes P, Fladby T. Brain amyloid and vascular risk are related to distinct white matter hyperintensity patterns. J Cereb Blood Flow Metab 2021; 41:1162-1174. [PMID: 32955960 PMCID: PMC8054718 DOI: 10.1177/0271678x20957604] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
White matter hyperintensities (WMHs) are associated with vascular risk and Alzheimer's disease. In this study, we examined relations between WMH load and distribution, amyloid pathology and vascular risk in 339 controls and cases with either subjective (SCD) or mild cognitive impairment (MCI). Regional deep (DWMH) and periventricular (PWMH) WMH loads were determined using an automated algorithm. We stratified on Aβ1-42 pathology (Aβ+/-) and analyzed group differences, as well as associations with Framingham Risk Score for cardiovascular disease (FRS-CVD) and age. Occipital PWMH (p = 0.001) and occipital DWMH (p = 0.003) loads were increased in SCD-Aβ+ compared with Aβ- controls. In MCI-Aβ+ compared with Aβ- controls, there were differences in global WMH (p = 0.003), as well as occipital DWMH (p = 0.001) and temporal DWMH (p = 0.002) loads. FRS-CVD was associated with frontal PWMHs (p = 0.003) and frontal DWMHs (p = 0.005), after adjusting for age. There were associations between global and all regional WMH loads and age. In summary, posterior WMH loads were increased in SCD-Aβ+ and MCI-Aβ+ cases, whereas frontal WMHs were associated with vascular risk. The differences in WMH topography support the use of regional WMH load as an early-stage marker of etiology.
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Affiliation(s)
- Lene Pålhaugen
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Carole H Sudre
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Dementia Research Centre, Institute of Neurology, University College London, London, UK.,Department of Medical Physics, University College London, London, UK
| | - Sandra Tecelao
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | | | - Ina S Almdahl
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Geriatric Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Lisa F Kalheim
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Dementia Research Centre, Institute of Neurology, University College London, London, UK.,Department of Medical Physics, University College London, London, UK
| | - Stein H Johnsen
- Department of Neurology, University Hospital of North Norway, Tromsø, Norway.,Department of Clinical Medicine, Brain and Circulation Research Group, UiT The Arctic University of Norway, Tromsø, Norway
| | - Arvid Rongve
- Department of Research and Innovation, Haugesund Hospital, Haugesund, Norway.,Department of Clinical Medicine (K1), University of Bergen, Bergen, Norway
| | - Dag Aarsland
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway.,Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Center for Age-Related Diseases, Stavanger University Hospital, Stavanger, Norway
| | - Atle Bjørnerud
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway.,Department of Physics, University of Oslo, Oslo, Norway
| | - Per Selnes
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tormod Fladby
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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12
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Pietroboni AM, Colombi A, Carandini T, Scarpini E, Galimberti D, Bozzali M. The Role of Amyloid-β in White Matter Damage: Possible Common Pathogenetic Mechanisms in Neurodegenerative and Demyelinating Diseases. J Alzheimers Dis 2020; 78:13-22. [PMID: 32925075 DOI: 10.3233/jad-200868] [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: 01/23/2023]
Abstract
Just as multiple sclerosis (MS) has long been primarily considered a white matter (WM) disease, Alzheimer's disease (AD) has for decades been regarded only as a grey matter disorder. However, convergent evidences have suggested that WM abnormalities are also important components of AD, at the same extent as axonal and neuronal loss is critically involved in MS pathophysiology since early clinical stages. These observations have motivated a more thorough investigation about the possible mechanisms that could link neuroinflammation and neurodegeneration, focusing on amyloid-β (Aβ). Neuroimaging studies have found that patients with AD have widespread WM abnormalities already at the earliest disease stages and prior to the presence of Aβ plaques. Moreover, a correlation between cerebrospinal fluid (CSF) Aβ levels and WM lesion load was found. On the other hand, recent studies suggest a predictive role for CSF Aβ levels in MS, possibly due in the first instance to the reduced capacity for remyelination, consequently to a higher risk of WM damage progression, and ultimately to neuronal loss. We undertook a review of the recent findings concerning the involvement of CSF Aβ levels in the MS disease course and of the latest evidence of AD related WM abnormalities, with the aim to discuss the potential causes that may connect WM damage and amyloid pathology.
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Affiliation(s)
- Anna M Pietroboni
- Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.,University of Milan, Dino Ferrari Centre, Milan, Italy
| | | | - Tiziana Carandini
- Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.,University of Milan, Dino Ferrari Centre, Milan, Italy
| | - Elio Scarpini
- Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.,University of Milan, Dino Ferrari Centre, Milan, Italy
| | - Daniela Galimberti
- Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.,University of Milan, Dino Ferrari Centre, Milan, Italy
| | - Marco Bozzali
- Department of Neuroscience 'Rita Levi Montalcini', University of Torino, Turin, Italy.,Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK
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13
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Knapstad MK, Steihaug OM, Aaslund MK, Nakling A, Naterstad IF, Fladby T, Aarsland D, Giil LM. Reduced Walking Speed in Subjective and Mild Cognitive Impairment: A Cross-Sectional Study. J Geriatr Phys Ther 2020; 42:E122-E128. [PMID: 29298174 DOI: 10.1519/jpt.0000000000000157] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
BACKGROUND AND PURPOSE Walking speed is reduced in people with dementia, but less is known about predementia conditions. We, therefore, studied the relationship between walking speed, cognition, and cerebrospinal fluid biomarkers in persons with subjective (SCI) and mild cognitive impairment (MCI). METHODS We conducted a cross-sectional study of 22 healthy controls, 30 SCI and 17 MCI (N = 69). Walking speed was measured by a 10-m gait test at usual and fast pace. We analyzed the association between walking speed and the ordered categories of controls, SCI, and MCI in a generalized proportional odds model. Neuropsychological tests, Consortium to Establish a Registry for Alzheimer's Disease (delayed recall), and Trail Making (TMT) A and B, were analyzed by negative binomial, linear, and robust regression for association with walking speed. RESULTS Walking speed at usual pace was slower moving from controls to SCI (odds ratio: 0.46, P = 0.031) and MCI (odds ratio: 0.44, P = .019) on an ordinal scale. In MCI, walking speed was reduced at fast speed (odds ratio: 0.46, P = 0.04). There were significant associations between walking speeds and neuropsychological test performance. Usual walking speed was associated with slower test performance on TMT-A (β: -.02, P = .04) and fast pace with slower performance on TMT-B (β: -.01, P = .03). There were no associations between cerebrospinal fluid biomarkers and walking speeds. CONCLUSION Usual walking speed is reduced in a graded fashion with the early symptoms of cognitive impairment. Our results suggest that reduced walking speed at both usual and fast speed is associated with impaired cognitive function, and that walking speed could be affected at very early stages of neurodegeneration.
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Affiliation(s)
- Mari Kalland Knapstad
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.,Department of Clinical Medicine, University of Bergen, Bergen, Norway.,National Centre for Vestibular Disorders, Department of Otorhinolaryngology/Head and Neck Surgery, Haukeland University Hospital, Bergen, Norway
| | - Ole Martin Steihaug
- Department of Internal Medicine, Haraldsplass Deaconess Hospital, Bergen, Norway
| | - Mona Kristin Aaslund
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.,Department of Physiotherapy, Haukeland University Hospital, Bergen, Norway
| | - Arne Nakling
- Department of Clinical Medicine, University of Bergen, Bergen, Norway.,Betanien Diaconal Hospital, Bergen, Norway
| | | | - Tormod Fladby
- Department of Neurology, Akershus University Hospital, Loerenskog, Norway.,Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
| | - Dag Aarsland
- Center for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England, United Kingdom
| | - Lasse Melvaer Giil
- Department of Internal Medicine, Haraldsplass Deaconess Hospital, Bergen, Norway
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14
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Walsh P, Sudre CH, Fiford CM, Ryan NS, Lashley T, Frost C, Barnes J. CSF amyloid is a consistent predictor of white matter hyperintensities across the disease course from aging to Alzheimer's disease. Neurobiol Aging 2020; 91:5-14. [PMID: 32305782 DOI: 10.1016/j.neurobiolaging.2020.03.008] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 03/05/2020] [Accepted: 03/10/2020] [Indexed: 01/06/2023]
Abstract
This study investigated the relationship between white matter hyperintensities (WMH) and cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers. Subjects included 180 controls, 107 individuals with a significant memory concern, 320 individuals with early mild cognitive impairment, 171 individuals with late mild cognitive impairment, and 151 individuals with AD, with 3T MRI and CSF Aβ1-42, total tau (t-tau), and phosphorylated tau (p-tau) data. Multiple linear regression models assessed the relationship between WMH and CSF Aβ1-42, t-tau, and p-tau. Directionally, a higher WMH burden was associated with lower CSF Aβ1-42 within each diagnostic group, with no evidence for a difference in the slope of the association across diagnostic groups (p = 0.4). Pooling all participants, this association was statistically significant after adjustment for t-tau, p-tau, age, diagnostic group, and APOE-ε4 status (p < 0.001). Age was the strongest predictor of WMH (partial R2~16%) compared with CSF Aβ1-42 (partial R2~5%). There was no evidence for an association with WMH and either t-tau or p-tau. These data are supportive of a link between amyloid burden and presumed vascular pathology.
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Affiliation(s)
- Phoebe Walsh
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK.
| | - Carole H Sudre
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK; Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Centre for Medical Image Computing, University College London, London, UK
| | - Cassidy M Fiford
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Natalie S Ryan
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Tammaryn Lashley
- Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK; Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Chris Frost
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK; Department of Medical Statistics, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Josephine Barnes
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
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15
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Decreased visible deep medullary veins is a novel imaging marker for cerebral small vessel disease. Neurol Sci 2020; 41:1497-1506. [PMID: 31955350 DOI: 10.1007/s10072-019-04203-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 12/16/2019] [Indexed: 12/15/2022]
Abstract
PURPOSE Visibility of deep medullary veins (DMVs) seen at SWI is predictive of poor prognosis in ischemic stroke. Few attentions have been paid to DMVs in atherosclerotic cerebral small vessel disease (aCSVD) which is attributed to long-term imbalanced microhemodynamics. We conducted this retrospective study to explore the association between DMVs profiles and aCSVD risk factors, neuroimaging markers. METHODS Two hundred and two patients identified as aCSVD from January 2017 to March 2019 were included in the study. Their demographic, clinical, laboratory, and neuroimaging data were reviewed. The quantity and morphology of DMVs were assessed with a 5-grade (range 0~4) visual rating scale. Total CSVD burden was calculated with an ordinal "SVD score" (range 0~4). Spearman rank correlation and multivariable logistic regression analysis were performed to determine the association between DMV scale and CSVD markers. RESULTS DMV scale showed strong positive correlation with CSVD burden (rs = 0.629, P < 0.001). Age (OR 1.078, 95% CI 1.015-1.145, P = 0.015) and hypertension (OR 2.629, 95% CI 1.024-6.749, P = 0.045) were two demographic risk factors for high DMV scale. Among CSVD neuroimaging markers, periventricular WMH (OR 2.925, 95% CI 1.464-5.845, P = 0.002), deep WMH (OR 2.872, 95% CI 1.174-7.022, P = 0.021), lacunae (OR 1.961, 95% CI 1.181-3.254, P = 0.009), and cerebral atrophy (OR 2.046, 95% CI 1.079-3.880, P = 0.028) were associated with high DMV scale after adjusting for clinical and metabolic confounders. CONCLUSION Multifactorial association between DMV scale and epidemiological, radiological contributors of aCSVD suggests DMV's involved pathomechanism may participate in aCSVD development. Attach importance to DMV radiological profile in aCSVD will provide more neuroimaging information for diagnosis and prognosis.
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16
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Wang Z, Williams VJ, Stephens KA, Kim CM, Bai L, Zhang M, Salat DH. The effect of white matter signal abnormalities on default mode network connectivity in mild cognitive impairment. Hum Brain Mapp 2019; 41:1237-1248. [PMID: 31742814 PMCID: PMC7267894 DOI: 10.1002/hbm.24871] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 10/04/2019] [Accepted: 11/12/2019] [Indexed: 01/18/2023] Open
Abstract
Regions within the default mode network (DMN) are particularly vulnerable to Alzheimer's disease pathology and mechanisms of DMN disruption in mild cognitive impairment (MCI) are still unclear. White matter lesions are presumed to be mechanistically linked to vascular dysfunction whereas cortical atrophy may be related to neurodegeneration. We examined associations between DMN seed‐based connectivity, white matter lesion load, and cortical atrophy in MCI and cognitively healthy controls. MCI showed decreased functional connectivity (FC) between the precuneus‐seed and bilateral lateral temporal cortex (LTC), medial prefrontal cortex (mPFC), posterior cingulate cortex, and inferior parietal lobe compared to those with controls. When controlling for white matter lesion volume, DMN connectivity differences between groups were diminished within bilateral LTC, although were significantly increased in the mPFC explained by significant regional associations between white matter lesion volume and DMN connectivity only in the MCI group. When controlling for cortical thickness, DMN FC was similarly decreased across both groups. These findings suggest that white matter lesions and cortical atrophy are differentially associated with alterations in FC patterns in MCI. Associations between white matter lesions and DMN connectivity in MCI further support at least a partial but important vascular contribution to age‐associated neural and cognitive impairment.
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Affiliation(s)
- Zhuonan Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Victoria J Williams
- Alzheimer's Clinical and Translational Research Unit, Department of Neurology, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Kimberly A Stephens
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Chan-Mi Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Lijun Bai
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Ming Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts.,Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, Massachusetts
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Gomez G, Beason-Held LL, Bilgel M, An Y, Wong DF, Studenski S, Ferrucci L, Resnick SM. Metabolic Syndrome and Amyloid Accumulation in the Aging Brain. J Alzheimers Dis 2019; 65:629-639. [PMID: 30103324 DOI: 10.3233/jad-180297] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Recent studies show links between metabolic syndrome and Alzheimer's disease (AD) neuropathology. Understanding the link between vascular-related health conditions and dementia will help target at risk populations and inform clinical strategies for early detection and prevention of AD. OBJECTIVE To determine whether metabolic syndrome is associated with global cerebral amyloid-β (Aβ) positivity and longitudinal Aβ accumulation. METHODS Prospective study of 165 participants who underwent (11)C-Pittsburgh compound B (PiB) PET neuroimaging to measure Aβ, from June 2005 to May 2016. Metabolic syndrome was defined using the revised Third Adults Treatment Panel of the National Cholesterol Education Program criteria. Participants were classified as PiB+/-. Linear mixed effects models assessed the relationships between baseline metabolic syndrome and PiB status and regional Aβ change over time. RESULTS A total of 165 cognitively normal participants of the Baltimore Longitudinal Study of Aging (BLSA) Neuroimaging substudy, aged 55-92 years (mean baseline age = 76.4 years, 85 participants were male), received an average of 2.5 PET-PiB scans over an average interval of 2.6 (3.08 SD) years between first and last visits. Metabolic syndrome was not associated with baseline PiB positivity or concurrent regional Aβ. Metabolic syndrome was associated with increased rates of Aβ accumulation in superior parietal and precuneus regions over time in the PiB+ group. Elevated fasting glucose and blood pressure showed individual associations with accelerated Aβ accumulation. CONCLUSION Metabolic syndrome was associated with accelerated Aβ accumulation in PiB+ individuals and may be an important factor in the progression of AD pathology.
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Affiliation(s)
- Gabriela Gomez
- Intramural Research Program, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Lori L Beason-Held
- Intramural Research Program, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Murat Bilgel
- Intramural Research Program, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Yang An
- Intramural Research Program, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Dean F Wong
- Department of Radiology, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Stephanie Studenski
- Intramural Research Program, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Luigi Ferrucci
- Intramural Research Program, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Susan M Resnick
- Intramural Research Program, National Institute on Aging, NIH, Baltimore, MD, USA
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18
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Kalheim LF, Fladby T, Coello C, Bjørnerud A, Selnes P. [18F]-Flutemetamol Uptake in Cortex and White Matter: Comparison with Cerebrospinal Fluid Biomarkers and [18F]-Fludeoxyglucose. J Alzheimers Dis 2019; 62:1595-1607. [PMID: 29504529 PMCID: PMC6218124 DOI: 10.3233/jad-170582] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Flutemetamol (18F-Flut) is an [18F]-labelled amyloid PET tracer with increasing availability. The main objectives of this study were to investigate 1) cerebrospinal fluid (CSF) Aβ 1-42 (Aβ42) concentrations associated with regional 18F-Flut uptake, 2) associations between cortical 18F-Flut and [18F]-fludeoxyglucose (18F-FDG)-PET, and 3) the potential use of 18F-Flut in WM pathology. Cognitively impaired, nondemented subjects were recruited (n = 44). CSF was drawn, and 18F-Flut-PET, 18F-FDG-PET, and MRI performed. Our main findings were: 1) Different Alzheimer’s disease predilection areas showed increased 18F-Flut retention at different CSF Aβ42 concentrations (posterior regions were involved at higher concentrations). 2) There were strong negative correlations between regional cortical 18F-Flut and 18F-FDG uptake. 3) Increased 18F-Flut uptake were observed in multiple subcortical regions in amyloid positive subjects, including investigated reference regions. However, WM hyperintensity 18F-Flut standardized uptake value ratios (SUVr) were not significantly different, thus we cannot definitely conclude that the higher uptake in 18F-Flut(+) is due to amyloid deposition. In conclusion, our findings support clinical use of CSF Aβ42, putatively relate decreasing CSF Aβ42 concentrations to a sequence of regional amyloid deposition, and associate amyloid pathology to cortical hypometabolism. However, we cannot conclude that 18F-Flut-PET is a suitable marker for WM pathology due to high aberrant WM uptake.
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Affiliation(s)
- Lisa Flem Kalheim
- Department of Neurology, Akershus University Hospital, L-renskog, Norway.,Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
| | - Tormod Fladby
- Department of Neurology, Akershus University Hospital, L-renskog, Norway.,Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
| | - Christopher Coello
- Preclinical PET/CT, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Atle Bjørnerud
- The Intervention Centre, Oslo University Hospital, Oslo, Norway
| | - Per Selnes
- Department of Neurology, Akershus University Hospital, L-renskog, Norway.,Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
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19
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Pietroboni AM, Scarioni M, Carandini T, Basilico P, Cadioli M, Giulietti G, Arighi A, Caprioli M, Serra L, Sina C, Fenoglio C, Ghezzi L, Fumagalli GG, De Riz MA, Calvi A, Triulzi F, Bozzali M, Scarpini E, Galimberti D. CSF β-amyloid and white matter damage: a new perspective on Alzheimer's disease. J Neurol Neurosurg Psychiatry 2018; 89:352-357. [PMID: 29054920 DOI: 10.1136/jnnp-2017-316603] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 09/13/2017] [Accepted: 09/28/2017] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To assess the connection between amyloid pathology and white matter (WM) macrostructural and microstructural damage in demented patients compared with controls. METHODS Eighty-five participants were recruited: 65 with newly diagnosed Alzheimer's disease (AD), non-AD dementia or mild cognitive impairment and 20 age-matched and sex-matched healthy controls. β-amyloid1-42 (Aβ) levels were determined in cerebrospinal fluid (CSF) samples from all patients and five controls. Among patients, 42 had pathological CSF Aβ levels (Aβ(+)), while 23 had normal CSF Aβ levels (Aβ(-)). All participants underwent neurological examination, neuropsychological testing and brain MRI. We used T2-weighted scans to quantify WM lesion loads (LLs) and diffusion-weighted images to assess their microstructural substrate. Non-parametric statistical tests were used for between-group comparisons and multiple regression analyses. RESULTS We found an increased WM-LL in Aβ(+) compared with both, healthy controls (p=0.003) and Aβ(-) patients (p=0.02). Interestingly, CSF Aβ concentration was the best predictor of patients' WM-LL (r=-0.30, p<0.05) when using age as a covariate. Lesion apparent diffusion coefficient value was higher in all patients than in controls (p=0.0001) and correlated with WM-LL (r=0.41, p=0.001). In Aβ(+), WM-LL correlated with WM microstructural damage in the left peritrigonal WM (p<0.0001). CONCLUSIONS WM damage is crucial in AD pathogenesis. The correlation between CSF Aβ levels and WM-LL suggests a direct link between amyloid pathology and WM macrostructural and microstructural damage.
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Affiliation(s)
- Anna M Pietroboni
- Department of Pathophysiology and Transplantation, Neurodegenerative Disease Unit, University of Milan, Centro Dino Ferrari, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Marta Scarioni
- Department of Pathophysiology and Transplantation, Neurodegenerative Disease Unit, University of Milan, Centro Dino Ferrari, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Tiziana Carandini
- Department of Pathophysiology and Transplantation, Neurodegenerative Disease Unit, University of Milan, Centro Dino Ferrari, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Paola Basilico
- Department of Pathophysiology and Transplantation, Neurodegenerative Disease Unit, University of Milan, Centro Dino Ferrari, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Marcello Cadioli
- Department of Pathophysiology and Transplantation, Neuroradiology Unit, University of Milan, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Andrea Arighi
- Department of Pathophysiology and Transplantation, Neurodegenerative Disease Unit, University of Milan, Centro Dino Ferrari, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Michela Caprioli
- Department of Pathophysiology and Transplantation, Neurodegenerative Disease Unit, University of Milan, Centro Dino Ferrari, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Laura Serra
- Neuroimaging Laboratory, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Clara Sina
- Department of Pathophysiology and Transplantation, Neuroradiology Unit, University of Milan, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Chiara Fenoglio
- Department of Pathophysiology and Transplantation, Neurodegenerative Disease Unit, University of Milan, Centro Dino Ferrari, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Laura Ghezzi
- Department of Pathophysiology and Transplantation, Neurodegenerative Disease Unit, University of Milan, Centro Dino Ferrari, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Giorgio G Fumagalli
- Department of Pathophysiology and Transplantation, Neurodegenerative Disease Unit, University of Milan, Centro Dino Ferrari, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Milena A De Riz
- Department of Pathophysiology and Transplantation, Neurodegenerative Disease Unit, University of Milan, Centro Dino Ferrari, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Alberto Calvi
- Department of Pathophysiology and Transplantation, Neurodegenerative Disease Unit, University of Milan, Centro Dino Ferrari, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Fabio Triulzi
- Department of Pathophysiology and Transplantation, Neuroradiology Unit, University of Milan, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Marco Bozzali
- Neuroimaging Laboratory, Santa Lucia Foundation IRCCS, Rome, Italy.,Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK
| | - Elio Scarpini
- Department of Pathophysiology and Transplantation, Neurodegenerative Disease Unit, University of Milan, Centro Dino Ferrari, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Daniela Galimberti
- Department of Pathophysiology and Transplantation, Neurodegenerative Disease Unit, University of Milan, Centro Dino Ferrari, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
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Abstract
PURPOSE OF REVIEW Alzheimer's disease and cerebrovascular disease (CVD) commonly co-occur. Whether CVD promotes the progression of Alzheimer's disease pathology remains a source of great interest. Recent technological developments have enabled us to examine their inter-relationship using quantifiable, biomarker-based approaches. We provide an overview of advances in understanding the relationship between vascular and Alzheimer's disease pathologies, with particular emphasis on β-amyloid and tau as measured by positron emission tomography and cerebrospinal fluid (CSF) concentration, and magnetic resonance imaging markers of small vessel disease (SVD). RECENT FINDINGS The relationship between cerebral β-amyloid and various markers of SVD has been widely studied, albeit with somewhat mixed results. Significant associations have been elucidated, particularly between β-amyloid burden and white matter hyperintensities (WMH), as well as lobar cerebral microbleeds (CMB), with additive effects on cognition. There is preliminary evidence for an association between SVD and tau burden in vivo, although compared with β-amyloid, fewer studies have examined this relationship. SUMMARY The overlap between Alzheimer's disease and cerebrovascular pathologies is now being increasingly supported by imaging and CSF biomarkers, indicating a synergistic effect of these co-pathologies on cognition. The association of WMH and CMB with Alzheimer's disease pathology does not establish direction of causality, for which long-term longitudinal studies are needed.
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21
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Chong JSX, Liu S, Loke YM, Hilal S, Ikram MK, Xu X, Tan BY, Venketasubramanian N, Chen CLH, Zhou J. Influence of cerebrovascular disease on brain networks in prodromal and clinical Alzheimer's disease. Brain 2017; 140:3012-3022. [PMID: 29053778 PMCID: PMC5841199 DOI: 10.1093/brain/awx224] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 07/12/2017] [Indexed: 01/16/2023] Open
Abstract
Network-sensitive neuroimaging methods have been used to characterize large-scale brain network degeneration in Alzheimer’s disease and its prodrome. However, few studies have investigated the combined effect of Alzheimer’s disease and cerebrovascular disease on brain network degeneration. Our study sought to examine the intrinsic functional connectivity and structural covariance network changes in 235 prodromal and clinical Alzheimer’s disease patients with and without cerebrovascular disease. We focused particularly on two higher-order cognitive networks—the default mode network and the executive control network. We found divergent functional connectivity and structural covariance patterns in Alzheimer’s disease patients with and without cerebrovascular disease. Alzheimer’s disease patients without cerebrovascular disease, but not Alzheimer’s disease patients with cerebrovascular disease, showed reductions in posterior default mode network functional connectivity. By comparison, while both groups exhibited parietal reductions in executive control network functional connectivity, only Alzheimer’s disease patients with cerebrovascular disease showed increases in frontal executive control network connectivity. Importantly, these distinct executive control network changes were recapitulated in prodromal Alzheimer’s disease patients with and without cerebrovascular disease. Across Alzheimer’s disease patients with and without cerebrovascular disease, higher default mode network functional connectivity z-scores correlated with greater hippocampal volumes while higher executive control network functional connectivity z-scores correlated with greater white matter changes. In parallel, only Alzheimer’s disease patients without cerebrovascular disease showed increased default mode network structural covariance, while only Alzheimer’s disease patients with cerebrovascular disease showed increased executive control network structural covariance compared to controls. Our findings demonstrate the differential neural network structural and functional changes in Alzheimer’s disease with and without cerebrovascular disease, suggesting that the underlying pathology of Alzheimer’s disease patients with cerebrovascular disease might differ from those without cerebrovascular disease and reflect a combination of more severe cerebrovascular disease and less severe Alzheimer’s disease network degeneration phenotype.
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Affiliation(s)
- Joanna Su Xian Chong
- Centre for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore Medical School, Singapore
| | - Siwei Liu
- Centre for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore Medical School, Singapore
| | - Yng Miin Loke
- Centre for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore Medical School, Singapore
| | - Saima Hilal
- Department of Pharmacology, Clinical Research Centre, National University Health System, National University of Singapore, Singapore.,Memory Ageing and Cognition Centre, National University Health System, Singapore
| | - Mohammad Kamran Ikram
- Memory Ageing and Cognition Centre, National University Health System, Singapore.,Duke-National University of Singapore Medical School, Singapore
| | - Xin Xu
- Department of Pharmacology, Clinical Research Centre, National University Health System, National University of Singapore, Singapore.,Memory Ageing and Cognition Centre, National University Health System, Singapore
| | | | | | - Christopher Li-Hsian Chen
- Department of Pharmacology, Clinical Research Centre, National University Health System, National University of Singapore, Singapore.,Memory Ageing and Cognition Centre, National University Health System, Singapore
| | - Juan Zhou
- Centre for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore Medical School, Singapore.,Clinical Imaging Research Centre, The Agency for Science, Technology and Research and National University of Singapore, Singapore
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22
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Blair GW, Hernandez MV, Thrippleton MJ, Doubal FN, Wardlaw JM. Advanced Neuroimaging of Cerebral Small Vessel Disease. CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2017. [PMID: 28620783 PMCID: PMC5486578 DOI: 10.1007/s11936-017-0555-1] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Cerebral small vessel disease (SVD) is characterised by damage to deep grey and white matter structures of the brain and is responsible for a diverse range of clinical problems that include stroke and dementia. In this review, we describe advances in neuroimaging published since January 2015, mainly with magnetic resonance imaging (MRI), that, in general, are improving quantification, observation and investigation of SVD focussing on three areas: quantifying the total SVD burden, imaging brain microstructural integrity and imaging vascular malfunction. Methods to capture ‘whole brain SVD burden’ across the spectrum of SVD imaging changes will be useful for patient stratification in clinical trials, an approach that we are already testing. More sophisticated imaging measures of SVD microstructural damage are allowing the disease to be studied at earlier stages, will help identify specific factors that are important in development of overt SVD imaging features and in understanding why specific clinical consequences may occur. Imaging vascular function will help establish the precise blood vessel and blood flow alterations at early disease stages and, together with microstructural integrity measures, may provide important surrogate endpoints in clinical trials testing new interventions. Better knowledge of SVD pathophysiology will help identify new treatment targets, improve patient stratification and may in future increase efficiency of clinical trials through smaller sample sizes or shorter follow-up periods. However, most of these methods are not yet sufficiently mature to use with confidence in clinical trials, although rapid advances in the field suggest that reliable quantification of SVD lesion burden, tissue microstructural integrity and vascular dysfunction are imminent.
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Affiliation(s)
- Gordon W Blair
- Brain Research Imaging Centres, Centre for Clinical Brain Sciences, University of Edinburgh, 49 Little France Crescent, Chancellor's Building, Edinburgh, EH16 4SB, UK
| | - Maria Valdez Hernandez
- Brain Research Imaging Centres, Centre for Clinical Brain Sciences, University of Edinburgh, 49 Little France Crescent, Chancellor's Building, Edinburgh, EH16 4SB, UK
| | - Michael J Thrippleton
- Brain Research Imaging Centres, Centre for Clinical Brain Sciences, University of Edinburgh, 49 Little France Crescent, Chancellor's Building, Edinburgh, EH16 4SB, UK
| | - Fergus N Doubal
- Brain Research Imaging Centres, Centre for Clinical Brain Sciences, University of Edinburgh, 49 Little France Crescent, Chancellor's Building, Edinburgh, EH16 4SB, UK
| | - Joanna M Wardlaw
- Brain Research Imaging Centres, Centre for Clinical Brain Sciences, University of Edinburgh, 49 Little France Crescent, Chancellor's Building, Edinburgh, EH16 4SB, UK.
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23
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Almdahl IS, Lauridsen C, Selnes P, Kalheim LF, Coello C, Gajdzik B, Møller I, Wettergreen M, Grambaite R, Bjørnerud A, Bråthen G, Sando SB, White LR, Fladby T. Cerebrospinal Fluid Levels of Amyloid Beta 1-43 Mirror 1-42 in Relation to Imaging Biomarkers of Alzheimer's Disease. Front Aging Neurosci 2017; 9:9. [PMID: 28223932 PMCID: PMC5293760 DOI: 10.3389/fnagi.2017.00009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 01/12/2017] [Indexed: 11/24/2022] Open
Abstract
Introduction: Amyloid beta 1-43 (Aβ43), with its additional C-terminal threonine residue, is hypothesized to play a role in early Alzheimer’s disease pathology possibly different from that of amyloid beta 1-42 (Aβ42). Cerebrospinal fluid (CSF) Aβ43 has been suggested as a potential novel biomarker for predicting conversion from mild cognitive impairment (MCI) to dementia in Alzheimer’s disease. However, the relationship between CSF Aβ43 and established imaging biomarkers of Alzheimer’s disease has never been assessed. Materials and Methods: In this observational study, CSF Aβ43 was measured with ELISA in 89 subjects; 34 with subjective cognitive decline (SCD), 51 with MCI, and four with resolution of previous cognitive complaints. All subjects underwent structural MRI; 40 subjects on a 3T and 50 on a 1.5T scanner. Forty subjects, including 24 with SCD and 12 with MCI, underwent 18F-Flutemetamol PET. Seventy-eight subjects were assessed with 18F-fluorodeoxyglucose PET (21 SCD/7 MCI and 11 SCD/39 MCI on two different scanners). Ten subjects with SCD and 39 with MCI also underwent diffusion tensor imaging. Results: Cerebrospinal fluid Aβ43 was both alone and together with p-tau a significant predictor of the distinction between SCD and MCI. There was a marked difference in CSF Aβ43 between subjects with 18F-Flutemetamol PET scans visually interpreted as negative (37 pg/ml, n = 27) and positive (15 pg/ml, n = 9), p < 0.001. Both CSF Aβ43 and Aβ42 were negatively correlated with standardized uptake value ratios for all analyzed regions; CSF Aβ43 average rho -0.73, Aβ42 -0.74. Both CSF Aβ peptides correlated significantly with hippocampal volume, inferior parietal and frontal cortical thickness and axial diffusivity in the corticospinal tract. There was a trend toward CSF Aβ42 being better correlated with cortical glucose metabolism. None of the studied correlations between CSF Aβ43/42 and imaging biomarkers were significantly different for the two Aβ peptides when controlling for multiple testing. Conclusion: Cerebrospinal fluid Aβ43 appears to be strongly correlated with cerebral amyloid deposits in the same way as Aβ42, even in non-demented patients with only subjective cognitive complaints. Regarding imaging biomarkers, there is no evidence from the present study that CSF Aβ43 performs better than the classical CSF biomarker Aβ42 for distinguishing SCD and MCI.
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Affiliation(s)
- Ina S Almdahl
- Division of Medicine and Laboratory Sciences, Institute of Clinical Medicine, Faculty of Medicine, University of OsloOslo, Norway; Department of Neurology, Akershus University HospitalLørenskog, Norway
| | - Camilla Lauridsen
- Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and Technology Trondheim, Norway
| | - Per Selnes
- Division of Medicine and Laboratory Sciences, Institute of Clinical Medicine, Faculty of Medicine, University of OsloOslo, Norway; Department of Neurology, Akershus University HospitalLørenskog, Norway
| | - Lisa F Kalheim
- Division of Medicine and Laboratory Sciences, Institute of Clinical Medicine, Faculty of Medicine, University of OsloOslo, Norway; Department of Neurology, Akershus University HospitalLørenskog, Norway
| | - Christopher Coello
- Preclinical PET/CT, Institute of Basic Medical Sciences, University of Oslo Oslo, Norway
| | | | - Ina Møller
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim Trondheim, Norway
| | - Marianne Wettergreen
- Department of Neurology, Akershus University HospitalLørenskog, Norway; Department of Clinical Molecular Biology (EpiGen), Institute of Clinical Medicine, University of Oslo - Akershus University HospitalLørenskog, Norway
| | - Ramune Grambaite
- Department of Neurology, Akershus University Hospital Lørenskog, Norway
| | - Atle Bjørnerud
- The Intervention Centre, Oslo University Hospital Oslo, Norway
| | - Geir Bråthen
- Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and TechnologyTrondheim, Norway; Department of Neurology and Clinical Neurophysiology, University Hospital of TrondheimTrondheim, Norway
| | - Sigrid B Sando
- Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and TechnologyTrondheim, Norway; Department of Neurology and Clinical Neurophysiology, University Hospital of TrondheimTrondheim, Norway
| | - Linda R White
- Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and TechnologyTrondheim, Norway; Department of Neurology and Clinical Neurophysiology, University Hospital of TrondheimTrondheim, Norway
| | - Tormod Fladby
- Division of Medicine and Laboratory Sciences, Institute of Clinical Medicine, Faculty of Medicine, University of OsloOslo, Norway; Department of Neurology, Akershus University HospitalLørenskog, Norway
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24
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Kalheim LF, Selnes P, Bjørnerud A, Coello C, Vegge K, Fladby T. Amyloid Dysmetabolism Relates to Reduced Glucose Uptake in White Matter Hyperintensities. Front Neurol 2016; 7:209. [PMID: 27917152 PMCID: PMC5116462 DOI: 10.3389/fneur.2016.00209] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Accepted: 11/08/2016] [Indexed: 12/25/2022] Open
Abstract
Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and cause of dementia and is characterized by amyloid plaques and neurofibrillary tangles. AD has traditionally been considered to primarily affect gray matter, but multiple lines of evidence also indicate white matter (WM) pathology and associated small-vessel cerebrovascular disease. WM glucose delivery and metabolism may have implications for local tissue integrity, and [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) may be helpful to assess neuroglial and axonal function in WM. Hypothesizing that affection of oligodendroglia will be associated with loss of glucose uptake, we aimed to investigate glucose metabolism in magnetic resonance imaging (MRI) white matter hyperintensities (WMHs) and normal-appearing WM in patients with and without evidence of amyloid plaques. Subjects with mild cognitive impairment or subjective cognitive decline were included and dichotomized according to pathological (Aβ+) or normal (Aβ−) concentrations of cerebrospinal fluid amyloid-β 1–42. A total of 50 subjects were included, of whom 30 subjects were classified as Aβ(+) and 20 subjects as Aβ(−). All subjects were assessed with MRI and FDG-PET. FDG-PET images were corrected for effects of partial voluming and normalized to cerebellar WM, before determining WMH FDG-uptake. Although there were no significant differences between the groups in terms of age, WMH volume, number of individual WMHs, or WMH distribution, we found significantly lower (p = 0.021) FDG-uptake in WMHs in Aβ(+) subjects (mean = 0.662, SD = 0.113) compared to Aβ(−) subjects (mean = 0.596, SD = 0.073). There were no significant group differences in the FDG-uptake in normal-appearing WM. Similar results were obtained without correction for effects of partial voluming. Our findings add to the evidence for a link between Aβ dysmetabolism and WM pathology in AD.
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Affiliation(s)
- Lisa Flem Kalheim
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Per Selnes
- Department of Neurology, Akershus University Hospital , Lørenskog , Norway
| | - Atle Bjørnerud
- The Intervention Centre, Oslo University Hospital , Oslo , Norway
| | - Christopher Coello
- Preclinical PET/CT, Institute of Basic Medical Sciences, University of Oslo , Oslo , Norway
| | - Kjetil Vegge
- Department of Radiology, Akershus University Hospital , Lørenskog , Norway
| | - Tormod Fladby
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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