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Ottoy J, Ozzoude M, Zukotynski K, Kang MS, Adamo S, Scott C, Ramirez J, Swardfager W, Lam B, Bhan A, Mojiri P, Kiss A, Strother S, Bocti C, Borrie M, Chertkow H, Frayne R, Hsiung R, Laforce RJ, Noseworthy MD, Prato FS, Sahlas DJ, Smith EE, Kuo PH, Chad JA, Pasternak O, Sossi V, Thiel A, Soucy JP, Tardif JC, Black SE, Goubran M. Amyloid-PET of the white matter: Relationship to free water, fiber integrity, and cognition in patients with dementia and small vessel disease. J Cereb Blood Flow Metab 2023; 43:921-936. [PMID: 36695071 DOI: 10.1177/0271678x231152001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
White matter (WM) injury is frequently observed along with dementia. Positron emission tomography with amyloid-ligands (Aβ-PET) recently gained interest for detecting WM injury. Yet, little is understood about the origin of the altered Aβ-PET signal in WM regions. Here, we investigated the relative contributions of diffusion MRI-based microstructural alterations, including free water and tissue-specific properties, to Aβ-PET in WM and to cognition. We included a unique cohort of 115 participants covering the spectrum of low-to-severe white matter hyperintensity (WMH) burden and cognitively normal to dementia. We applied a bi-tensor diffusion-MRI model that differentiates between (i) the extracellular WM compartment (represented via free water), and (ii) the fiber-specific compartment (via free water-adjusted fractional anisotropy [FA]). We observed that, in regions of WMH, a decrease in Aβ-PET related most closely to higher free water and higher WMH volume. In contrast, in normal-appearing WM, an increase in Aβ-PET related more closely to higher cortical Aβ (together with lower free water-adjusted FA). In relation to cognitive impairment, we observed a closer relationship with higher free water than with either free water-adjusted FA or WM PET. Our findings support free water and Aβ-PET as markers of WM abnormalities in patients with mixed dementia, and contribute to a better understanding of processes giving rise to the WM PET signal.
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Affiliation(s)
- Julie Ottoy
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Miracle Ozzoude
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Katherine Zukotynski
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Departments of Medicine and Radiology, McMaster University, Hamilton, ON, Canada.,Department of Medical Imaging, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.,Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Min Su Kang
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sabrina Adamo
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Christopher Scott
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Joel Ramirez
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Walter Swardfager
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada
| | - Benjamin Lam
- Department of Medicine (Division of Neurology), Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Aparna Bhan
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Parisa Mojiri
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Alex Kiss
- Department of Research Design and Biostatistics, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Stephen Strother
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,The Rotman Research Institute Baycrest, University of Toronto, Toronto, ON, Canada
| | - Christian Bocti
- Service de Neurologie, Département de Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Michael Borrie
- Lawson Health Research Institute, Western University, London, ON, Canada
| | - Howard Chertkow
- Jewish General Hospital and Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Richard Frayne
- Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Robin Hsiung
- Physics and Astronomy Department and DM Center for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Robert Jr Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, Université Laval, Québec, QC, Canada
| | - Michael D Noseworthy
- Departments of Medicine and Radiology, McMaster University, Hamilton, ON, Canada.,Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
| | - Frank S Prato
- Lawson Health Research Institute, Western University, London, ON, Canada
| | | | - Eric E Smith
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Phillip H Kuo
- Department of Medical Imaging, Medicine, and Biomedical Engineering, University of Arizona, Tucson, AZ, USA
| | - Jordan A Chad
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,The Rotman Research Institute Baycrest, University of Toronto, Toronto, ON, Canada
| | - Ofer Pasternak
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Vesna Sossi
- Physics and Astronomy Department and DM Center for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Alexander Thiel
- Jewish General Hospital and Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jean-Paul Soucy
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | | | - Sandra E Black
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medicine (Division of Neurology), Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Maged Goubran
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
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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: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Zhang M, Ni Y, Zhou Q, He L, Meng H, Gao Y, Huang X, Meng H, Li P, Chen M, Wang D, Hu J, Huang Q, Li Y, Chauveau F, Li B, Chen S. 18F-florbetapir PET/MRI for quantitatively monitoring myelin loss and recovery in patients with multiple sclerosis: A longitudinal study. EClinicalMedicine 2021; 37:100982. [PMID: 34195586 PMCID: PMC8234356 DOI: 10.1016/j.eclinm.2021.100982] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 05/19/2021] [Accepted: 06/02/2021] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Amyloid positron emission tomography (PET) can measure in-vivo demyelination in patients with multiple sclerosis (MS). However, the value of 18F-labeled amyloid PET tracer, 18F-florbetapir in the longitudinal study for monitoring myelin loss and recovery has not been confirmed. METHODS From March 2019 to September 2020, twenty-three patients with MS and nine healthy controls (HCs) underwent a hybrid PET/MRI at baseline and expanded disability status scale (EDSS) assessment, and eight of 23 patients further underwent follow-up PET/MRI. The distribution volume ratio (DVR) and standard uptake value ratio (SUVR) of 18F-florbetapir in damaged white matter (DWM) and normal-appearance white matter (NAWM) were obtained from dynamic and static PET acquisition. Diffusion tensor imaging-derived parameters were also calculated. Data were expressed as mean ± standard deviation with 99% confidence interval (99%CI). FINDING The mean DVR (1.08 ± 0.12, 99%CI [1.02 ~ 1.14]) but not the mean SUVR of DWM lesions was lower than that of NAWM in patients with MS (1.25 ± 0.10, 99%CI [1.20 ~ 1.31]) and HCs (1.29 ± 0.08, 99%CI [1.23 ~ 1.36]). A trend toward lower mean fractional anisotropy (374.95 ± 45.30 vs. 419.07 ± 4.83) and higher mean radial diffusivity (0.45 ± 0.05 vs. 0.40 ± 0.01) of NAWM in patients with MS than those in HCs was found. DVR decreased in DWM lesions with higher MD (rho = -0.261, 99%CI [-0.362 ~ -0.144]), higher AD (rho = -0.200, 99%CI [-0.318 ~ -0.070]) and higher RD (rho = -0.198, 99%CI [-0.313 ~ -0.075]). Patients' EDSS scores were reduced (B = 0.04, 99%CI [-0.005 ~ 0.084]) with decreased index of global demyelination in the longitudinal study. INTERPRETATION Our exploratory study suggests that dynamic 18F-florbetapir PET/MRI may be a very promising tool for quantitatively monitoring myelin loss and recovery in patients with MS. FUNDING Shanghai Pujiang Program, Shanghai Municipal Key Clinical Specialty, Shanghai Shuguang Plan Project, Shanghai Health and Family Planning Commission Research Project, Clinical Research Plan of SHDC, French-Chinese program "Xu Guangqi".
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Affiliation(s)
- Min Zhang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - You Ni
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai 200025, China
| | - Qinming Zhou
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai 200025, China
| | - Lu He
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai 200025, China
| | - Huanyu Meng
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai 200025, China
| | - Yining Gao
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai 200025, China
| | - Xinyun Huang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongping Meng
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Peihan Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Meidi Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Danni Wang
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jingyi Hu
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qiu Huang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Fabien Chauveau
- Univ Lyon, Lyon Neuroscience research Center, CNRS UMR5292, INSERM U1028, Univ Lyon 1, Lyon, France
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sheng Chen
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai 200025, China
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Liu H, Nai YH, Saridin F, Tanaka T, O' Doherty J, Hilal S, Gyanwali B, Chen CP, Robins EG, Reilhac A. Improved amyloid burden quantification with nonspecific estimates using deep learning. Eur J Nucl Med Mol Imaging 2021; 48:1842-1853. [PMID: 33415430 PMCID: PMC8113180 DOI: 10.1007/s00259-020-05131-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 11/18/2020] [Indexed: 11/28/2022]
Abstract
PURPOSE Standardized uptake value ratio (SUVr) used to quantify amyloid-β burden from amyloid-PET scans can be biased by variations in the tracer's nonspecific (NS) binding caused by the presence of cerebrovascular disease (CeVD). In this work, we propose a novel amyloid-PET quantification approach that harnesses the intermodal image translation capability of convolutional networks to remove this undesirable source of variability. METHODS Paired MR and PET images exhibiting very low specific uptake were selected from a Singaporean amyloid-PET study involving 172 participants with different severities of CeVD. Two convolutional neural networks (CNN), ScaleNet and HighRes3DNet, and one conditional generative adversarial network (cGAN) were trained to map structural MR to NS PET images. NS estimates generated for all subjects using the most promising network were then subtracted from SUVr images to determine specific amyloid load only (SAβL). Associations of SAβL with various cognitive and functional test scores were then computed and compared to results using conventional SUVr. RESULTS Multimodal ScaleNet outperformed other networks in predicting the NS content in cortical gray matter with a mean relative error below 2%. Compared to SUVr, SAβL showed increased association with cognitive and functional test scores by up to 67%. CONCLUSION Removing the undesirable NS uptake from the amyloid load measurement is possible using deep learning and substantially improves its accuracy. This novel analysis approach opens a new window of opportunity for improved data modeling in Alzheimer's disease and for other neurodegenerative diseases that utilize PET imaging.
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Affiliation(s)
- Haohui Liu
- Raffles Institution, Singapore, Singapore
| | - Ying-Hwey Nai
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Translational Medicine (MD6), 14 Medical Drive, #B1-01, Singapore, 117599, Singapore.
| | - Francis Saridin
- Memory Aging and Cognition Centre, National University Health System, Singapore, Singapore
| | - Tomotaka Tanaka
- Department of Neurology, National Cerebral and Cardiovascular Center, Osaka, Japan
| | - Jim O' Doherty
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Translational Medicine (MD6), 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - Saima Hilal
- Memory Aging and Cognition Centre, National University Health System, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Bibek Gyanwali
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Christopher P Chen
- Memory Aging and Cognition Centre, National University Health System, Singapore, Singapore
| | - Edward G Robins
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Translational Medicine (MD6), 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
- Singapore BioImaging Consortium (SBIC), Agency for Science, Technology and Research (A*Star), Singapore, Singapore
| | - Anthonin Reilhac
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Translational Medicine (MD6), 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
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Moscoso A, Grothe MJ, Schöll M; Alzheimer’s Disease Neuroimaging Initiative. Reduced [ 18F]flortaucipir retention in white matter hyperintensities compared to normal-appearing white matter. Eur J Nucl Med Mol Imaging 2021; 48:2283-94. [PMID: 33475761 DOI: 10.1007/s00259-021-05195-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 01/04/2021] [Indexed: 01/18/2023]
Abstract
PURPOSE Recent research has suggested the use of white matter (WM) reference regions for longitudinal tau-PET imaging. However, tau tracers display affinity for the β-sheet structure formed by myelin, and thus WM lesions might influence tracer retention. Here, we explored whether the tau-sensitive tracer [18F]flortaucipir shows reduced retention in WM hyperintensities (WMH) and how this retention changes over time. METHODS We included 707 participants from the Alzheimer's Disease Neuroimaging Initiative with available [18F]flortaucipir-PET and structural and FLAIR MRI scans. WM segments and WMH were automatically delineated in the structural MRI and FLAIR scans, respectively. [18F]flortaucipir standardized uptake value ratios (SUVR) of WMH and normal-appearing WM (NAWM) were calculated using the inferior cerebellar grey matter as reference region, and a 3-mm erosion was applied to the combined NAWM and WMH masks to avoid partial volume effects. Longitudinal [18F]flortaucipir SUVR changes in NAWM and WMH were estimated using linear mixed models. The percent variance of WM-referenced cortical [18F]flortaucipir SUVRs explained by longitudinal changes in the WM reference region was estimated with the R2 coefficient. RESULTS Compared to NAWM, WMH areas displayed significantly reduced [18F]flortaucipir SUVR, independent of cognitive impairment or Aβ status (mean difference = 0.14 SUVR, p < 0.001). Older age was associated with lower [18F]flortaucipir SUVR in both NAWM (- 0.002 SUVR/year, p = 0.005) and WMH (- 0.004 SUVR/year, p < 0.001). Longitudinally, [18F]flortaucipir SUVR decreased in NAWM (- 0.008 SUVR/year, p = 0.03) and even more so in WMH (- 0.02 SUVR/year, p < 0.001). Between 17% and 66% of the variance of longitudinal changes in cortical WM-referenced [18F]flortaucipir SUVRs were explained by longitudinal changes in the reference region. CONCLUSIONS [18F]flortaucipir retention in the WM decreases over time and is influenced by the presence of WMH, supporting the hypothesis that [18F]flortaucipir retention in the WM is partially myelin-dependent. These findings have implications for the use of WM reference regions for [18F]flortaucipir-PET imaging.
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Wang ML, Yu MM, Li WB, Li YH. Longitudinal Association between White Matter Hyperintensities and White Matter Beta-Amyloid Deposition in Cognitively Unimpaired Elderly. Curr Alzheimer Res 2021; 18:8-13. [PMID: 33761854 DOI: 10.2174/1567205018666210324125116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 01/29/2021] [Accepted: 03/15/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND White matter (WM) beta-amyloid uptake has been used as a reference region to calculate the cortical standard uptake value ratio (SUVr). However, white matter hyperintensities (WMH) may have an influence on WM beta-amyloid uptake. Our study aimed to investigate the associations between WMH and WM beta-amyloid deposition in cognitively unimpaired elderly. METHODS Data from 83 cognitively unimpaired individuals in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset were analyzed. All participants had complete baseline and four-year follow-up information about WMH volume, WM 18F-AV-45 SUVr, and cognitive function, including ADNI-Memory (ADNI-Mem) and ADNI-Executive function (ADNI-EF) scores. Cross-sectional and longitudinal linear regression analyses were used to determine the associations between WMH and WM SUVr and cognitive measures. RESULTS Lower WM 18F-AV-45 SUVr at baseline was associated with younger age (β=0.01, P=0.037) and larger WMH volume (β=-0.049, P=0.048). The longitudinal analysis found an annual increase in WM 18F-AV-45 SUVr was associated with an annual decrease in WMH volume (β=-0.016, P=0.041). An annual decrease in the ADNI-Mem score was associated with an annual increase in WMH volume (β=-0.070, P=0.001), an annual decrease in WM 18F-AV-45 SUVr (β=0.559, P=0.030), and fewer years of education (β=0.011, P=0.044). There was no significant association between WM 18F-AV-45 SUVr and ADNI-EF (P>0.05). CONCLUSION Reduced beta-amyloid deposition in WM was associated with higher WMH load and memory decline in cognitively unimpaired elderly. WMH volume should be considered when WM 18F-AV-45 SUVr is used as a reference for evaluating cortical 18F-AV-45 SUVr.
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Affiliation(s)
- Ming-Liang Wang
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200233,China
| | - Meng-Meng Yu
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200233,China
| | - Wen-Bin Li
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200233,China
| | - Yue-Hua Li
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200233,China
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Stojić-Vukanić Z, Hadžibegović S, Nicole O, Nacka-Aleksić M, Leštarević S, Leposavić G. CD8+ T Cell-Mediated Mechanisms Contribute to the Progression of Neurocognitive Impairment in Both Multiple Sclerosis and Alzheimer's Disease? Front Immunol 2020; 11:566225. [PMID: 33329528 PMCID: PMC7710704 DOI: 10.3389/fimmu.2020.566225] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 08/17/2020] [Indexed: 12/20/2022] Open
Abstract
Neurocognitive impairment (NCI) is one of the most relevant clinical manifestations of multiple sclerosis (MS). The profile of NCI and the structural and functional changes in the brain structures relevant for cognition in MS share some similarities to those in Alzheimer's disease (AD), the most common cause of neurocognitive disorders. Additionally, despite clear etiopathological differences between MS and AD, an accumulation of effector/memory CD8+ T cells and CD8+ tissue-resident memory T (Trm) cells in cognitively relevant brain structures of MS/AD patients, and higher frequency of effector/memory CD8+ T cells re-expressing CD45RA (TEMRA) with high capacity to secrete cytotoxic molecules and proinflammatory cytokines in their blood, were found. Thus, an active pathogenetic role of CD8+ T cells in the progression of MS and AD may be assumed. In this mini-review, findings supporting the putative role of CD8+ T cells in the pathogenesis of MS and AD are displayed, and putative mechanisms underlying their pathogenetic action are discussed. A special effort was made to identify the gaps in the current knowledge about the role of CD8+ T cells in the development of NCI to "catalyze" translational research leading to new feasible therapeutic interventions.
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Affiliation(s)
- Zorica Stojić-Vukanić
- Department of Microbiology and Immunology, University of Belgrade-Faculty of Pharmacy, Belgrade, Serbia
| | - Senka Hadžibegović
- Institut des Maladies Neurodégénératives, CNRS, UMR5293, Bordeaux, France.,Institut des Maladies Neurodégénératives, Université de Bordeaux, UMR5293, Bordeaux, France
| | - Olivier Nicole
- Institut des Maladies Neurodégénératives, CNRS, UMR5293, Bordeaux, France.,Institut des Maladies Neurodégénératives, Université de Bordeaux, UMR5293, Bordeaux, France
| | - Mirjana Nacka-Aleksić
- Department of Pathobiology, University of Belgrade-Faculty of Pharmacy, Belgrade, Serbia
| | - Sanja Leštarević
- Department of Pathobiology, University of Belgrade-Faculty of Pharmacy, Belgrade, Serbia
| | - Gordana Leposavić
- Department of Pathobiology, University of Belgrade-Faculty of Pharmacy, Belgrade, Serbia
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8
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Zlokovic BV, Gottesman RF, Bernstein KE, Seshadri S, McKee A, Snyder H, Greenberg SM, Yaffe K, Schaffer CB, Yuan C, Hughes TM, Daemen MJ, Williamson JD, González HM, Schneider J, Wellington CL, Katusic ZS, Stoeckel L, Koenig JI, Corriveau RA, Fine L, Galis ZS, Reis J, Wright JD, Chen J. Vascular contributions to cognitive impairment and dementia (VCID): A report from the 2018 National Heart, Lung, and Blood Institute and National Institute of Neurological Disorders and Stroke Workshop. Alzheimers Dement 2020; 16:1714-1733. [PMID: 33030307 DOI: 10.1002/alz.12157] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 06/30/2020] [Accepted: 06/30/2020] [Indexed: 12/14/2022]
Abstract
Vascular contributions to cognitive impairment and dementia (VCID) are characterized by the aging neurovascular unit being confronted with and failing to cope with biological insults due to systemic and cerebral vascular disease, proteinopathy including Alzheimer's biology, metabolic disease, or immune response, resulting in cognitive decline. This report summarizes the discussion and recommendations from a working group convened by the National Heart, Lung, and Blood Institute and the National Institute of Neurological Disorders and Stroke to evaluate the state of the field in VCID research, identify research priorities, and foster collaborations. As discussed in this report, advances in understanding the biological mechanisms of VCID across the wide spectrum of pathologies, chronic systemic comorbidities, and other risk factors may lead to potential prevention and new treatment strategies to decrease the burden of dementia. Better understanding of the social determinants of health that affect risks for both vascular disease and VCID could provide insight into strategies to reduce racial and ethnic disparities in VCID.
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Affiliation(s)
| | | | | | - Sudha Seshadri
- University of Texas Health Science Center, San Antonio and Boston University, San Antonio, Texas, USA
| | - Ann McKee
- VA Boston Healthcare System and Boston University, Boston, Massachusetts, USA
| | | | - Steven M Greenberg
- Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Kristine Yaffe
- University of California, San Francisco, San Francisco, California, USA
| | | | - Chun Yuan
- University of Washington, Seattle, Washington, USA
| | - Timothy M Hughes
- Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Mat J Daemen
- Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | | | | | | | | | | | - Luke Stoeckel
- National Institute on Aging, Bethesda, Maryland, USA
| | - James I Koenig
- National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA
| | - Roderick A Corriveau
- National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA
| | - Lawrence Fine
- National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA
| | - Zorina S Galis
- National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA
| | - Jared Reis
- National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA
| | | | - Jue Chen
- National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA
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9
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Kasahara H, Ikeda M, Nagashima K, Fujita Y, Makioka K, Tsukagoshi S, Yamazaki T, Takai E, Sanada E, Kobayashi A, Kishi K, Suto T, Higuchi T, Tsushima Y, Ikeda Y. Deep White Matter Lesions Are Associated with Early Recognition of Dementia in Alzheimer's Disease. J Alzheimers Dis 2020; 68:797-808. [PMID: 30775989 DOI: 10.3233/jad-180939] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Neuroimages of cerebral amyloid-β (Aβ) accumulation and small vessel disease (SVD) were examined in patients with various types of cognitive disorders using 11C-labeled Pittsburgh Compound B-positron emission tomography (PiB-PET) and magnetic resonance imaging (MRI). The mean cortical standardized uptake value ratio (mcSUVR) was applied for a quantitative analysis of PiB-PET data. The severity of white matter lesions (WML) and enlarged perivascular spaces (EPVS) on MRI were assessed to evaluate complicating cerebral SVD using semiquantitative scales. In homozygous apolipoprotein E ɛ3/ɛ3 carriers, the incidence of more severe WML and EPVS was higher in PiB-positive than PiB-negative patients, indicating that WML and EPVS might be associated with enhanced Aβ accumulation. An association study between PiB-PET and MRI findings revealed that higher WML grades significantly correlate with lower mcSUVRs, especially in the frontal area, indicating that more severe ischemic MRI findings are associated with milder Aβ accumulation among patients with Alzheimer's disease. In these patients SVD may accelerate the occurrence of cognitive decline and facilitate early recognition of dementia.
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Affiliation(s)
- Hiroo Kasahara
- Department of Neurology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Masaki Ikeda
- Department of Neurology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Kazuaki Nagashima
- Department of Neurology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Yukio Fujita
- Department of Neurology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Kouki Makioka
- Department of Neurology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Setsuki Tsukagoshi
- Department of Neurology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Tsuneo Yamazaki
- Department of Rehabilitation, Gunma University Graduate School of Health Sciences, Maebashi, Japan
| | - Eriko Takai
- Department of Neurology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Etsuko Sanada
- Department of Neurology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Ayumi Kobayashi
- Department of Neurology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Kazuhiro Kishi
- Department of Radiology, Gunma University Hospital, Maebashi, Japan
| | - Takayuki Suto
- Department of Radiology, Gunma University Hospital, Maebashi, Japan
| | - Tetsuya Higuchi
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Yoshito Tsushima
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Yoshio Ikeda
- Department of Neurology, Gunma University Graduate School of Medicine, Maebashi, Japan
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10
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Pytel V, Matias-Guiu JA, Matías-Guiu J, Cortés-Martínez A, Montero P, Moreno-Ramos T, Arrazola J, Carreras JL, Cabrera-Martín MN. Amyloid PET findings in multiple sclerosis are associated with cognitive decline at 18 months. Mult Scler Relat Disord 2020; 39:101926. [PMID: 31918239 DOI: 10.1016/j.msard.2020.101926] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 12/16/2019] [Accepted: 01/01/2020] [Indexed: 12/29/2022]
Abstract
OBJECTIVE To study the clinical, cognitive, and radiological progression of a cohort of patients with MS, taking into account the amyloid PET with 18F-florbetaben analyses. METHODS Twenty-nine patients with MS were assessed with longitudinal structural MRI and a clinical and comprehensive neuropsychological protocol, with a mean interval between assessments of 18 ± 3.31 months. 18F-florbetaben PET was performed at baseline. Uptake was analysed in demyelinating plaques (DWM) and normal-appearing white matter (NAWM). Results were correlated with clinical, cognitive and MRI data. RESULTS Patients with cognitive decline over the follow-up period showed a lower standardised uptake value ratio in NAWM and lower thalamic volume and a higher lesion load in the baseline MRI. Myelin status was correlated with EDSS and cognitive tests mainly evaluating visuospatial function and working memory. Lower uptake in NAWM at baseline was also associated with a growth in white matter lesion volume over time. CONCLUSIONS Lower white matter uptake in amyloid PET is associated with cognitive decline and an increase in white matter lesion volume during the follow-up. Our study suggests that 18F-florbetaben may be a useful biomarker in assessing myelin status in MS, understanding MS pathophysiology, and predicting cognitive outcomes.
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Affiliation(s)
- Vanesa Pytel
- Department of Neurology, Hospital Clínico San Carlos. San Carlos Health Research Institute (IdISSC) Complutense University of Madrid. Calle Prof. Martín Lagos s/n. 28040. Madrid, Spain
| | - Jordi A Matias-Guiu
- Department of Neurology, Hospital Clínico San Carlos. San Carlos Health Research Institute (IdISSC) Complutense University of Madrid. Calle Prof. Martín Lagos s/n. 28040. Madrid, Spain.
| | - Jorge Matías-Guiu
- Department of Neurology, Hospital Clínico San Carlos. San Carlos Health Research Institute (IdISSC) Complutense University of Madrid. Calle Prof. Martín Lagos s/n. 28040. Madrid, Spain
| | - Ana Cortés-Martínez
- Department of Neurology, Hospital Clínico San Carlos. San Carlos Health Research Institute (IdISSC) Complutense University of Madrid. Calle Prof. Martín Lagos s/n. 28040. Madrid, Spain
| | - Paloma Montero
- Department of Neurology, Hospital Clínico San Carlos. San Carlos Health Research Institute (IdISSC) Complutense University of Madrid. Calle Prof. Martín Lagos s/n. 28040. Madrid, Spain
| | - Teresa Moreno-Ramos
- Department of Neurology, Hospital Clínico San Carlos. San Carlos Health Research Institute (IdISSC) Complutense University of Madrid. Calle Prof. Martín Lagos s/n. 28040. Madrid, Spain
| | - Juan Arrazola
- Department of Radiology, Hospital Clínico San Carlos. San Carlos Health Research Institute (IdISSC) Complutense University of Madrid. Calle Prof. Martín Lagos s/n. 28040. Madrid, Spain
| | - José Luis Carreras
- Department of Nuclear Medicine, Hospital Clínico San Carlos. San Carlos Health Research Institute (IdISSC) Complutense University of Madrid. Calle Prof. Martín Lagos s/n. 28040. Madrid, Spain
| | - María Nieves Cabrera-Martín
- Department of Nuclear Medicine, Hospital Clínico San Carlos. San Carlos Health Research Institute (IdISSC) Complutense University of Madrid. Calle Prof. Martín Lagos s/n. 28040. Madrid, Spain
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11
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Tanaka T, Stephenson MC, Nai YH, Khor D, Saridin FN, Hilal S, Villaraza S, Gyanwali B, Ihara M, Vrooman H, Weekes AA, Totman JJ, Robins EG, Chen CP, Reilhac A. Improved quantification of amyloid burden and associated biomarker cut-off points: results from the first amyloid Singaporean cohort with overlapping cerebrovascular disease. Eur J Nucl Med Mol Imaging 2019; 47:319-331. [PMID: 31863136 DOI: 10.1007/s00259-019-04642-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 11/26/2019] [Indexed: 12/19/2022]
Abstract
PURPOSE The analysis of the [11C]PiB-PET amyloid images of a unique Asian cohort of 186 participants featuring overlapping vascular diseases raised the question about the validity of current standards for amyloid quantification under abnormal conditions. In this work, we implemented a novel pipeline for improved amyloid PET quantification of this atypical cohort. METHODS The investigated data correction and amyloid quantification methods included motion correction, standardized uptake value ratio (SUVr) quantification using the parcellated MRI (standard method) and SUVr quantification without MRI. We introduced a novel amyloid analysis method yielding 2 biomarkers: AβL which quantifies the global Aβ burden and ns that characterizes the non-specific uptake. Cut-off points were first determined using visual assessment as ground truth and then using unsupervised classification techniques. RESULTS Subject's motion impacts the accuracy of the measurement outcome but has however a limited effect on the visual rating and cut-off point determination. SUVr computation can be reliably performed for all the subjects without MRI parcellation while, when required, the parcellation failed or was of mediocre quality in 10% of the cases. The novel biomarker AβL showed an association increase of 29.5% with the cognitive tests and increased effect size between positive and negative scans compared with SUVr. ns was found sensitive to cerebral microbleeds, white matter hyperintensity, volume, and age. The cut-off points for SUVr using parcellated MRI, SUVr without parcellation, and AβL were 1.56, 1.39, and 25.5. Finally, k-means produced valid cut-off points without the requirement of visual assessment. CONCLUSION The optimal processing for the amyloid quantification of this atypical cohort allows the quantification of all the subjects, producing SUVr values and two novel biomarkers: AβL, showing important increased in their association with various cognitive tests, and ns, a parameter sensitive to non-specific retention variations caused by age and cerebrovascular diseases.
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Affiliation(s)
- Tomotaka Tanaka
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore. .,Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Blk MD3, 16 Medical Drive. Level 4, #04-01, Singapore, 117600, Singapore. .,Department of Neurology, National Cerebral and Cardiovascular Center, 5-7-1 Fujishiro-dai, Suita, Osaka, 565-8565, Japan.
| | - Mary C Stephenson
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - Ying-Hwey Nai
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - Damian Khor
- Department of Diagnostic Imaging, National Cancer Institute of Singapore, 11 Hospital Drive, Singapore, 169610, Singapore
| | - Francis N Saridin
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Blk MD3, 16 Medical Drive. Level 4, #04-01, Singapore, 117600, Singapore
| | - Saima Hilal
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Blk MD3, 16 Medical Drive. Level 4, #04-01, Singapore, 117600, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Steven Villaraza
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Blk MD3, 16 Medical Drive. Level 4, #04-01, Singapore, 117600, Singapore
| | - Bibek Gyanwali
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Blk MD3, 16 Medical Drive. Level 4, #04-01, Singapore, 117600, Singapore
| | - Masafumi Ihara
- Department of Neurology, National Cerebral and Cardiovascular Center, 5-7-1 Fujishiro-dai, Suita, Osaka, 565-8565, Japan
| | - Henri Vrooman
- Biomedical Imaging group Rotterdam, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, Netherlands
| | - Ashley A Weekes
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - John J Totman
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - Edward G Robins
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore.,Singapore Bioimaging Consortium, Agency for Science, A*Star,1Fusionopolis way, #20-10 Connexis North Tower, Singapore, 138632, Singapore
| | - Christopher P Chen
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Blk MD3, 16 Medical Drive. Level 4, #04-01, Singapore, 117600, Singapore
| | - Anthonin Reilhac
- Clinical Imaging Research Centre, National University of Singapore, 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
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12
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Zhang M, Liu J, Li B, Chen S. 18F-florbetapir PET/MRI for quantitatively monitoring demyelination and remyelination in acute disseminated encephalomyelitis. EJNMMI Res 2019; 9:96. [PMID: 31720882 PMCID: PMC6851275 DOI: 10.1186/s13550-019-0568-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Accepted: 10/16/2019] [Indexed: 01/31/2023] Open
Affiliation(s)
- Min Zhang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Liu
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sheng Chen
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China.
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13
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Zhang M, Hugon G, Bouillot C, Bolbos R, Langlois JB, Billard T, Bonnefoi F, Li B, Zimmer L, Chauveau F. Evaluation of Myelin Radiotracers in the Lysolecithin Rat Model of Focal Demyelination: Beware of Pitfalls! Contrast Media Mol Imaging 2019; 2019:9294586. [PMID: 31281236 DOI: 10.1155/2019/9294586] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 02/06/2019] [Accepted: 02/21/2019] [Indexed: 11/29/2022]
Abstract
The observation that amyloid radiotracers developed for Alzheimer's disease bind to cerebral white matter paved the road to nuclear imaging of myelin in multiple sclerosis. The lysolecithin (lysophosphatidylcholine (LPC)) rat model of demyelination proved useful in evaluating and comparing candidate radiotracers to target myelin. Focal demyelination following stereotaxic LPC injection is larger than lesions observed in experimental autoimmune encephalitis models and is followed by spontaneous progressive remyelination. Moreover, the contralateral hemisphere may serve as an internal control in a given animal. However, demyelination can be accompanied by concurrent focal necrosis and/or adjacent ventricle dilation. The influence of these side effects on imaging findings has never been carefully assessed. The present study describes an optimization of the LPC model and highlights the use of MRI for controlling the variability and pitfalls of the model. The prototypical amyloid radiotracer [11C]PIB was used to show that in vivo PET does not provide sufficient sensitivity to reliably track myelin changes and may be sensitive to LPC side effects instead of demyelination as such. Ex vivo autoradiography with a fluorine radiotracer should be preferred, to adequately evaluate and compare radiotracers for the assessment of myelin content.
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14
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Caunca MR, De Leon-Benedetti A, Latour L, Leigh R, Wright CB. Neuroimaging of Cerebral Small Vessel Disease and Age-Related Cognitive Changes. Front Aging Neurosci 2019; 11:145. [PMID: 31316367 PMCID: PMC6610261 DOI: 10.3389/fnagi.2019.00145] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 05/31/2019] [Indexed: 01/04/2023] Open
Abstract
Subclinical cerebrovascular disease is frequently identified in neuroimaging studies and is thought to play a role in the pathogenesis of cognitive disorders. Identifying the etiologies of different types of lesions may help investigators differentiate between age-related and pathological cerebrovascular damage in cognitive aging. In this review article, we aim to describe the epidemiology and etiology of various brain magnetic resonance imaging (MRI) measures of vascular damage in cognitively normal, older adult populations. We focus here on population-based prospective cohort studies of cognitively unimpaired older adults, as well as discuss the heterogeneity of MRI findings and their relationships with cognition. This review article emphasizes the need for a better understanding of subclinical cerebrovascular disease in cognitively normal populations, in order to more effectively identify and prevent cognitive decline in our rapidly aging population.
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Affiliation(s)
- Michelle R Caunca
- Division of Epidemiology and Population Health Sciences, Department of Public Health Sciences, Leonard M. Miller School of Medicine, Evelyn F. McKnight Brain Institute, University of Miami, Miami, FL, United States.,Department of Neurology, Leonard M. Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Andres De Leon-Benedetti
- Department of Neurology, Leonard M. Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Lawrence Latour
- National Institute of Neurological Diseases and Stroke (NINDS), National Institutes of Health, Bethesda, MD, United States
| | - Richard Leigh
- National Institute of Neurological Diseases and Stroke (NINDS), National Institutes of Health, Bethesda, MD, United States
| | - Clinton B Wright
- National Institute of Neurological Diseases and Stroke (NINDS), National Institutes of Health, Bethesda, MD, United States
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15
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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16
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Zeydan B, Schwarz CG, Lowe VJ, Reid RI, Przybelski SA, Lesnick TG, Kremers WK, Senjem ML, Gunter JL, Min H, Vemuri P, Knopman DS, Petersen RC, Jack CR, Kantarci OH, Kantarci K. Investigation of white matter PiB uptake as a marker of white matter integrity. Ann Clin Transl Neurol 2019; 6:678-688. [PMID: 31019992 PMCID: PMC6469255 DOI: 10.1002/acn3.741] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 01/14/2019] [Accepted: 02/03/2019] [Indexed: 01/20/2023] Open
Abstract
OBJECTIVE To investigate the associations of Pittsburgh compound-B (PiB) uptake in white matter hyperintensities (WMH) and normal appearing white matter (NAWM) with white matter (WM) integrity measured with DTI and cognitive function in cognitively unimpaired older adults. METHODS Cognitively unimpaired older adults from the population-based Mayo Clinic Study of Aging (n = 537, age 65-95) who underwent both PiB PET and DTI were included. The associations of WM PiB standard uptake value ratio (SUVr) with fractional anisotropy (FA) and mean diffusivity (MD) in the WMH and NAWM were tested after adjusting for age. The associations of PiB SUVr with cognitive function z-scores were tested after adjusting for age and global cortical PiB SUVr. RESULTS The WMH PiB SUVr was lower than NAWM PiB SUVr (P < 0.001). In the WMH, lower PiB SUVr correlated with lower FA (r = 0.21, P < 0.001), and higher MD (r = -0.31, P < 0.001). In the NAWM, lower PiB SUVr only correlated with higher MD (r = -0.10, P = 0.02). Both in the WMH and NAWM, lower PiB SUVr was associated with lower memory, language, and global cognitive function z-scores after adjusting for age and global cortical PiB SUVr. INTERPRETATION Reduced PiB uptake in the WMH is associated with a loss of WM integrity and cognitive function after accounting for the global cortical PiB uptake, suggesting that WM PiB uptake may be an early biomarker of WM integrity that precedes cognitive impairment in older adults. When using WM as a reference region in cross-sectional analysis of PiB SUVr, individual variability in WMH volume as well as age should be considered.
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Affiliation(s)
- Burcu Zeydan
- Department of RadiologyMayo ClinicRochesterMinnesota
- Department of NeurologyMayo ClinicRochesterMinnesota
- Center for Multiple Sclerosis and Autoimmune NeurologyMayo ClinicRochesterMinnesota
| | | | - Val J. Lowe
- Department of RadiologyMayo ClinicRochesterMinnesota
| | - Robert I. Reid
- Department of Information TechnologyMayo ClinicRochesterMinnesota
| | | | | | | | - Matthew L. Senjem
- Department of RadiologyMayo ClinicRochesterMinnesota
- Department of Information TechnologyMayo ClinicRochesterMinnesota
| | - Jeffrey L. Gunter
- Department of RadiologyMayo ClinicRochesterMinnesota
- Department of Information TechnologyMayo ClinicRochesterMinnesota
| | - Hoon‐Ki Min
- Department of RadiologyMayo ClinicRochesterMinnesota
| | | | | | | | | | - Orhun H. Kantarci
- Department of NeurologyMayo ClinicRochesterMinnesota
- Center for Multiple Sclerosis and Autoimmune NeurologyMayo ClinicRochesterMinnesota
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17
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Liu Y, Braidy N, Poljak A, Chan DKY, Sachdev P. Cerebral small vessel disease and the risk of Alzheimer's disease: A systematic review. Ageing Res Rev 2018; 47:41-48. [PMID: 29898422 DOI: 10.1016/j.arr.2018.06.002] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 05/10/2018] [Accepted: 06/05/2018] [Indexed: 12/20/2022]
Abstract
BACKGROUND Cerebral small vessel disease (CSVD) comprises a variety of disorders affecting small arteries and microvessels of the brain, manifesting as white matter hyperintensities (WMHs), cerebral microbleeds (CMBs), and deep brain infarcts. In addition to its contribution to vascular dementia (VaD), it has also been suggested to contribute to the pathogenesis of Alzheimer's disease (AD). METHOD A systematic review of the literature available on Medline, Embase and Pubmed was undertaken, whereby CSVD was divided into WMHs, CMBs and deep brain infarcts. Biomarkers of AD pathology in the cerebrospinal fluid or plasma, or positron emission tomographic imaging for amyloid and/or tau deposition were used for AD pathology. RESULTS A total of 4117 articles were identified and 41 articles met criteria for inclusion. These consisted of 17 articles on vascular risk factors for clinical AD, 21 articles on Aβ pathology and 15 articles on tau pathology, permitting ten meta-analyses. CMBs or lobar CMBs were associated with pooled relative risk (RR) of AD at 1.546, (95%CI 0.842-2.838, z = 1.41 p = 0.160) and 1.526(95%CI 0.760-3.063, z = 1.19, p = 0.235) respectively, both non-significant. Microinfarcts were associated with significantly increased AD risk, with pooled odds ratio OR at 1.203(95%CI 1.014-1.428, 2.12 p = 0.034). Aβ pathology was significantly associated with WMHs in AD patients but not in normal age-matched controls. The pooled β (linear regression) for total WMHs with CSF Aβ42 in AD patients was -0.19(95%CI -0.26-0.11, z = 4.83 p = 0.000) and the pooled r (correlation coefficient) for WMHs and PiB in the normal population was -0.10 (95%CI -0.11-0.30, 0.93 p = 0.351). CMBs were significantly associated with Aβ pathology in AD patients. The pooled standardized mean difference (SMD) was -0.453, 95%CI -0.697- -0.208, z = 3.63 p = 0.000. There was no significant relationship between the incidence of lacunes and levels of CSFAβ, with a pooled β of 0.057 (95%CI -0.050-0.163, z = 1.05 p = 0.295). No significant relationship was found between CMBs and the levels of CSFt-tau/CSFp-tau in AD patients (-0.014, 95%CI -0.556-0.529, z = 0.05 p = 0.960; -0.058, 95%CI -0.630-0.515, z = 0.20 p = 0.844) and cortical CMBs and CSF p-tau in the normal population (0.000, 95%CI -0.706-0.706, z = 0.00 p = 0.999). CONCLUSIONS Some CSVD markers were significantly associated with clinical AD pathology and may be associated with Aβ/tau pathology. WMHs and microinfarcts were associated with increased risk of AD. It remains unclear whether they precede or follow AD pathology.
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Affiliation(s)
- Yue Liu
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Nady Braidy
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia.
| | - Anne Poljak
- Mark Wainwright Analytical Centre, University of New South Wales, Sydney, Australia; School of Medical Sciences, University of New South Wales, Sydney, Australia
| | - Daniel K Y Chan
- Department of Aged Care and Rehabilitation, Bankstown Hospital, Bankstown, NSW, Australia
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia; Neuropsychiatric Institute, Euroa Centre, Prince of Wales Hospital, Sydney, Australia
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Lowe VJ, Lundt ES, Senjem ML, Schwarz CG, Min HK, Przybelski SA, Kantarci K, Knopman D, Petersen RC, Jack CR. White Matter Reference Region in PET Studies of 11C-Pittsburgh Compound B Uptake: Effects of Age and Amyloid-β Deposition. J Nucl Med 2018; 59:1583-1589. [PMID: 29674420 PMCID: PMC6167534 DOI: 10.2967/jnumed.117.204271] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 04/04/2018] [Indexed: 02/06/2023] Open
Abstract
Amyloid-β (Aβ) deposition as seen on PET using an Aβ-binding agent is a critical diagnostic biomarker for Alzheimer disease (AD). Some reports suggest using white matter (WM) as a reference region for quantification of serial Aβ PET studies; however, nonspecific WM retention in Aβ PET in people with dementia or cognitively unimpaired (CU) has been widely reported and is poorly understood. Methods: To investigate the suitability of WM as a reference region and the factors affecting WM 11C-Pittsburgh compound B (11C-PiB) uptake variability, we conducted a retrospective study on 2 large datasets: a longitudinal study of participants (n = 577) who were CU, had mild cognitive impairment, or had dementia likely due to AD; and a cross-sectional study of single-scan PET imaging in CU subjects (n = 1,349). In the longitudinal study, annual changes in WM 11C-PiB uptake were assessed, and in the cross-sectional study, WM 11C-PiB uptake was assessed relative to subject age. Results: Overall, we found that WM 11C-PiB uptake showed age-related increases, which varied with the WM regions selected. Further, variable annual WM 11C-PiB uptake changes were seen with different gray matter (GM) 11C-PiB baseline uptake levels. Conclusion: WM binding increases with age and varies with GM 11C-PiB. These correlations should be considered when using WM for normalization in 11C-PiB PET studies. The cerebellar crus1+crus2 showed no increase with age and cerebellar GM+WM showed minimal increase, supporting their use as reference regions for cross-sectional studies comparing wide age spans. In longitudinal studies, the increase in WM uptake may be minimal in the short-term and thus using WM as a reference region in these studies seems reasonable. However, as participants age, the findings may be affected by changes in WM uptake. Changes in WM 11C-PiB uptake may relate to disease progression, warranting examination of the causes of WM 11C-PiB uptake.
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Affiliation(s)
- Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Emily S Lundt
- Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Information Technology, Mayo Clinic, Rochester, Minnesota; and
| | | | - Hoon-Ki Min
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Scott A Przybelski
- Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - David Knopman
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
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Zeydan B, Lowe VJ, Schwarz CG, Przybelski SA, Tosakulwong N, Zuk SM, Senjem ML, Gunter JL, Roberts RO, Mielke MM, Benarroch EE, Rodriguez M, Machulda MM, Lesnick TG, Knopman DS, Petersen RC, Jack CR, Kantarci K, Kantarci OH. Pittsburgh compound-B PET white matter imaging and cognitive function in late multiple sclerosis. Mult Scler 2018; 24:739-749. [PMID: 28474977 PMCID: PMC5665724 DOI: 10.1177/1352458517707346] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
BACKGROUND There is growing interest in white matter (WM) imaging with positron emission tomography (PET). OBJECTIVES We studied the association of cognitive function in late multiple sclerosis (MS) with cortical and WM Pittsburgh compound-B PET (PiB-PET) binding. METHODS In the population-based Mayo Clinic Study of Aging, 24 of 4869 participants had MS (12 underwent PiB-PET). Controls were age and sex matched (5:1). We used automated or semi-automated processing for quantitative image analyses and conditional logistic regression for group differences. RESULTS MS patients had lower memory ( p = 0.03) and language ( p = 0.02) performance; smaller thalamic volumes ( p = 0.003); and thinner temporal ( p = 0.001) and frontal ( p = 0.045) cortices on magnetic resonance imaging (MRI) than controls. There was no difference in global cortical PiB standardized uptake value ratios between MS and controls ( p = 0.35). PiB uptake was lower in areas of WM hyperintensities compared to normal-appearing white matter (NAWM) in MS ( p = 0.0002). Reduced PiB uptake in both the areas of WM hyperintensities ( r = 0.65; p = 0.02) and NAWM ( r = 0.69; p = 0.01) was associated with decreased visuospatial performance in MS. CONCLUSION PiB uptake in the cortex in late MS is not different from normal age-matched controls. PiB uptake in the WM in late MS may be a marker of the large network structures' integrity such as those involved in visuospatial performance.
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Affiliation(s)
- Burcu Zeydan
- Mayo Clinic College of Medicine, Department of Neurology, Rochester, Minnesota, United States of America
- Mayo Clinic College of Medicine, Department of Radiology, Rochester, Minnesota, United States of America
| | - Val J. Lowe
- Mayo Clinic College of Medicine, Department of Radiology, Rochester, Minnesota, United States of America
| | - Christopher G. Schwarz
- Mayo Clinic College of Medicine, Department of Radiology, Rochester, Minnesota, United States of America
| | - Scott A. Przybelski
- Mayo Clinic College of Medicine, Department of Health Sciences Research, Rochester, Minnesota, United States of America
| | - Nirubol Tosakulwong
- Mayo Clinic College of Medicine, Department of Health Sciences Research, Rochester, Minnesota, United States of America
| | - Samantha M. Zuk
- Mayo Clinic College of Medicine, Department of Radiology, Rochester, Minnesota, United States of America
| | - Matthew L. Senjem
- Mayo Clinic College of Medicine, Department of Radiology, Rochester, Minnesota, United States of America
- Mayo Clinic College of Medicine, Department of Information Technology, Rochester, Minnesota, United States of America
| | - Jeffrey L. Gunter
- Mayo Clinic College of Medicine, Department of Information Technology, Rochester, Minnesota, United States of America
| | - Rosebud O. Roberts
- Mayo Clinic College of Medicine, Department of Neurology, Rochester, Minnesota, United States of America
- Mayo Clinic College of Medicine, Department of Health Sciences Research, Rochester, Minnesota, United States of America
| | - Michelle M. Mielke
- Mayo Clinic College of Medicine, Department of Neurology, Rochester, Minnesota, United States of America
- Mayo Clinic College of Medicine, Department of Health Sciences Research, Rochester, Minnesota, United States of America
| | - Eduardo E. Benarroch
- Mayo Clinic College of Medicine, Department of Neurology, Rochester, Minnesota, United States of America
| | - Moses Rodriguez
- Mayo Clinic College of Medicine, Department of Neurology, Rochester, Minnesota, United States of America
| | - Mary M. Machulda
- Mayo Clinic College of Medicine, Department of Psychiatry and Psychology, Rochester, Minnesota, United States of America
| | - Timothy G. Lesnick
- Mayo Clinic College of Medicine, Department of Health Sciences Research, Rochester, Minnesota, United States of America
| | - David S. Knopman
- Mayo Clinic College of Medicine, Department of Neurology, Rochester, Minnesota, United States of America
| | - Ronald C. Petersen
- Mayo Clinic College of Medicine, Department of Neurology, Rochester, Minnesota, United States of America
| | - Clifford R. Jack
- Mayo Clinic College of Medicine, Department of Radiology, Rochester, Minnesota, United States of America
| | - Kejal Kantarci
- Mayo Clinic College of Medicine, Department of Radiology, Rochester, Minnesota, United States of America
| | - Orhun H. Kantarci
- Mayo Clinic College of Medicine, Department of Neurology, Rochester, Minnesota, United States of America
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Jiménez-Bonilla J, Quirce R, de Arcocha-Torres M, Martínez-Rodríguez I, Martínez-Amador N, Sánchez-Juan P, Pozueta A, Martín-Láez R, Banzo I, Rodríguez-Rodríguez E. Patrones de retención de 11 C-PIB en la sustancia blanca y en la sustancia gris cerebral de pacientes con hidrocefalia a presión normal idiopática. Un análisis visual. Rev Esp Med Nucl Imagen Mol 2018. [DOI: 10.1016/j.remnie.2017.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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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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22
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Yun HJ, Moon SH, Kim HJ, Lockhart SN, Choe YS, Lee KH, Na DL, Lee JM, Seo SW. Centiloid method evaluation for amyloid PET of subcortical vascular dementia. Sci Rep 2017; 7:16322. [PMID: 29176753 PMCID: PMC5701176 DOI: 10.1038/s41598-017-16236-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 10/27/2017] [Indexed: 11/08/2022] Open
Abstract
Reference region selection is important for proper amyloid PET analysis, especially in subcortical vascular dementia (SVaD) patients. We investigated reference region differences between SVaD and Alzheimer's disease (AD) using Centiloid scores. In 57 [C-11] Pittsburgh compound B (PiB) positive (+) AD and 23 PiB (+) SVaD patients, we assessed standardized PiB uptake and Centiloid scores in disease-specific cortical regions, with several reference regions: cerebellar gray (CG), whole cerebellum (WC), WC with brainstem (WC + B), pons, and white matter (WM). We calculated disease group differences from young controls (YC) and YC variance according to reference region. SVaD patients showed large effect sizes (Cohen's d > 0.8) using all reference regions. WM and pons showed larger YC variances than other regions. Findings were similar for AD patients. CG, WC, and WC + B, but not WM or pons, are reliable reference regions for amyloid imaging analysis in SVaD.
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Affiliation(s)
- Hyuk Jin Yun
- Department of Biomedical Engineering, Hanyang University, Seoul, 04763, Korea
- Fetal Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, 02115, MA, USA
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Korea
| | - Samuel N Lockhart
- Helen Wills Neuroscience Institute, University of California, Berkeley, 94720, CA, USA
- Department of Internal Medicine, Division of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA
| | - Yearn Seong Choe
- Department of Nuclear Medicine, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Kyung Han Lee
- Department of Nuclear Medicine, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Korea
- Department of Health Sciences and Technology, Sungkyunkwan University, Seoul, 06351, Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, 04763, Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea.
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Korea.
- Department of Health Sciences and Technology, Sungkyunkwan University, Seoul, 06351, Korea.
- Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, 06351, Korea.
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23
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Jiménez-Bonilla JF, Quirce R, de Arcocha-Torres M, Martínez-Rodríguez I, Martínez-Amador N, Sánchez-Juan P, Pozueta A, Martín-Láez R, Banzo I, Rodríguez-Rodríguez E. 11C-PIB retention patterns in white and grey cerebral matter in idiopathic normal pressure hydrocephalus patients. A visual analysis. Rev Esp Med Nucl Imagen Mol 2018; 37:87-93. [PMID: 28869176 DOI: 10.1016/j.remn.2017.07.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 07/10/2017] [Accepted: 07/11/2017] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Cortical cerebral amyloid disease, a hallmark of Alzheimer's disease, has also been observed in idiopathic normal pressure hydrocephalus (iNPH). The aim of this study was to compare the 11C-PIB PET/CT retention pattern in iNPH patients and healthy subjects. MATERIAL AND METHODS A comparison was made of the 11C-PIB PET/CT retention pattern in 13 iNPH patients selected for surgical deviation, compared to a normal control population. Images were visually analyzed and scored for gray matter and white matter (WM) from 1 to 4 (slight to very high PIB retention). The scoring was analyzed in both groups separately for infra- and supra-tentorial regions. A comprehensive clinical report was presented in terms of positive, negative, or equivocal. RESULTS 11C-PIB PET/CT scan were reported as negative in 8, positive in 3, and equivocal in 2. Five of 13 patients showed at least one cortical area with PIB retention with an intensity higher than that observed in the control group. Overall, white matter (WM) PIB retention of iNPH scored lower than in the control group, showing a statistically significant difference in the infratentorial WM (92/104 vs 54/56; p<.05) and a tendency to be lower in the supratentorial regions (70/84 vs 122/156, p=.327), in particular in the upper periventricular region (25/28 vs 40/52; p=.134). CONCLUSIONS The PIB retention pattern seems to be different in NPH, compared to normal subjects. PIB retention in WM of NPH appears less intense than in healthy subjects, and they show a higher degree of PIB retention in cortical regions. This deserves to be taken it into account.
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