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Chen YY, Chang CJ, Liang YW, Tseng HY, Li SJ, Chang CW, Wu YT, Shao HH, Chen PC, Lai ML, Deng WC, Hsu R, Lo YC. Utilizing diffusion tensor imaging as an image biomarker in exploring the therapeutic efficacy of forniceal deep brain stimulation in a mice model of Alzheimer's disease. J Neural Eng 2024; 21:056003. [PMID: 39230033 DOI: 10.1088/1741-2552/ad7322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 08/15/2024] [Indexed: 09/05/2024]
Abstract
Objective.With prolonged life expectancy, the incidence of memory deficits, especially in Alzheimer's disease (AD), has increased. Although multiple treatments have been evaluated, no promising treatment has been found to date. Deep brain stimulation (DBS) of the fornix area was explored as a possible treatment because the fornix is intimately connected to memory-related areas that are vulnerable in AD; however, a proper imaging biomarker for assessing the therapeutic efficiency of forniceal DBS in AD has not been established.Approach.This study assessed the efficacy and safety of DBS by estimating the optimal intersection volume between the volume of tissue activated and the fornix. Utilizing a gold-electroplating process, the microelectrode's surface area on the neural probe was increased, enhancing charge transfer performance within potential water window limits. Bilateral fornix implantation was conducted in triple-transgenic AD mice (3 × Tg-AD) and wild-type mice (strain: B6129SF1/J), with forniceal DBS administered exclusively to 3 × Tg-AD mice in the DBS-on group. Behavioral tasks, diffusion tensor imaging (DTI), and immunohistochemistry (IHC) were performed in all mice to assess the therapeutic efficacy of forniceal DBS.Main results.The results illustrated that memory deficits and increased anxiety-like behavior in 3 × Tg-AD mice were rescued by forniceal DBS. Furthermore, forniceal DBS positively altered DTI indices, such as increasing fractional anisotropy (FA) and decreasing mean diffusivity (MD), together with reducing microglial cell and astrocyte counts, suggesting a potential causal relationship between revised FA/MD and reduced cell counts in the anterior cingulate cortex, hippocampus, fornix, amygdala, and entorhinal cortex of 3 × Tg-AD mice following forniceal DBS.Significance.The efficacy of forniceal DBS in AD can be indicated by alterations in DTI-based biomarkers reflecting the decreased activation of glial cells, suggesting reduced neural inflammation as evidenced by improvements in memory and anxiety-like behavior.
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Affiliation(s)
- You-Yin Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No.155, Sec.2, Linong St., Taipei 11221, Taiwan, Republic of China
- Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, 12F., Education & Research Building, Shuang-Ho Campus, No. 301, Yuantong Rd., New Taipei City 23564, Taiwan, Republic of China
| | - Chih-Ju Chang
- Department of Neurosurgery, Cathay General Hospital, No. 280, Sec. 4, Renai Rd., Taipei 10629, Taiwan, Republic of China
- School of Medicine, Fu Jen Catholic University, No.510, Zhongzheng Rd., New Taipei City 242062, Taiwan, Republic of China
| | - Yao-Wen Liang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No.155, Sec.2, Linong St., Taipei 11221, Taiwan, Republic of China
| | - Hsin-Yi Tseng
- Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University and National Health Research Institutes, 12F., Education & Research Building, Shuang-Ho Campus, No. 301, Yuantong Rd., New Taipei City 23564, Taiwan, Republic of China
| | - Ssu-Ju Li
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No.155, Sec.2, Linong St., Taipei 11221, Taiwan, Republic of China
| | - Ching-Wen Chang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No.155, Sec.2, Linong St., Taipei 11221, Taiwan, Republic of China
| | - Yen-Ting Wu
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No.155, Sec.2, Linong St., Taipei 11221, Taiwan, Republic of China
| | - Huai-Hsuan Shao
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No.155, Sec.2, Linong St., Taipei 11221, Taiwan, Republic of China
| | - Po-Chun Chen
- Department of Materials and Mineral Resources Engineering, National Taipei University of Technology, No. 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan, Republic of China
| | - Ming-Liang Lai
- Graduate Institute of Intellectual Property, National Taipei University of Technology, No. 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan, Republic of China
| | - Wen-Chun Deng
- Departments of Neurosurgery, Keelung Chang Gung Memorial Hospital, Chang Gung University, No.222, Maijin Rd., Keelung 20400, Taiwan, Republic of China
| | - RuSiou Hsu
- Department of Ophthalmology, Stanford University, 1651 Page Mill Rd., Palo Alto, CA 94304, United States of America
| | - Yu-Chun Lo
- Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, 12F., Education & Research Building, Shuang-Ho Campus, No. 301, Yuantong Rd., New Taipei City 23564, Taiwan, Republic of China
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Rivier CA, Singh S, Senff J, Tack RW, Marini S, Clocchiatti-Tuozzo S, Huo S, Renedo D, Papier K, Conroy M, Littlejohns TJ, Chemali Z, Kourkoulis C, Payabvash S, Newhouse A, Westover MB, Lazar RM, Pikula A, Ibrahim S, Howard VJ, Howard G, Brouwers HB, Van Duijn CM, Fricchione G, Tanzi RE, Yechoor N, Sheth KN, Anderson CD, Rosand J, Falcone GJ. Brain Care Score and Neuroimaging Markers of Brain Health in Asymptomatic Middle-Age Persons. Neurology 2024; 103:e209687. [PMID: 39052961 DOI: 10.1212/wnl.0000000000209687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024] Open
Abstract
OBJECTIVES To investigate associations between health-related behaviors as measured using the Brain Care Score (BCS) and neuroimaging markers of white matter injury. METHODS This prospective cohort study in the UK Biobank assessed the BCS, a novel tool designed to empower patients to address 12 dementia and stroke risk factors. The BCS ranges from 0 to 21, with higher scores suggesting better brain care. Outcomes included white matter hyperintensities (WMH) volume, fractional anisotropy (FA), and mean diffusivity (MD) obtained during 2 imaging assessments, as well as their progression between assessments, using multivariable linear regression adjusted for age and sex. RESULTS We included 34,509 participants (average age 55 years, 53% female) with no stroke or dementia history. At first and repeat imaging assessments, every 5-point increase in baseline BCS was linked to significantly lower WMH volumes (25% 95% CI [23%-27%] first, 33% [27%-39%] repeat) and higher FA (18% [16%-20%] first, 22% [15%-28%] repeat), with a decrease in MD (9% [7%-11%] first, 10% [4%-16%] repeat). In addition, a higher baseline BCS was associated with a 10% [3%-17%] reduction in WMH progression and FA decline over time. DISCUSSION This study extends the impact of the BCS to neuroimaging markers of clinically silent cerebrovascular disease. Our results suggest that improving one's BCS could be a valuable intervention to prevent early brain health decline.
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Affiliation(s)
- Cyprien A Rivier
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Sanjula Singh
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Jasper Senff
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Reinier W Tack
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Sandro Marini
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Santiago Clocchiatti-Tuozzo
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Shufan Huo
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Daniela Renedo
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Keren Papier
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Megan Conroy
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Thomas J Littlejohns
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Zeina Chemali
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Christina Kourkoulis
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Seyedmehdi Payabvash
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Amy Newhouse
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - M Brandon Westover
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Ronald M Lazar
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Aleksandra Pikula
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Sarah Ibrahim
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Virginia J Howard
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - George Howard
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - H Bart Brouwers
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Cornelia M Van Duijn
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Gregory Fricchione
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Rudolph E Tanzi
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Nirupama Yechoor
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Kevin N Sheth
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Christopher D Anderson
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Jonathan Rosand
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
| | - Guido J Falcone
- From the Department of Neurology (C.A.R., S.C.-T., S.H., D.R., K.N.S., G.J.F.), Yale School of Medicine, New Haven, CT; Yale Center for Brain and Mind Health (C.A.R., S.C.-T., S.H., D.R., S.P., K.N.S., G.J.F.); Henry and Allison McCance Center for Brain Health (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., G.F., R.E.T., N.Y., C.D.A., J.R.); Departments of Neurology (S.S., J.S., R.W.T., S.M., Z.C., C.K., M.B.W., N.Y., C.D.A., J.R.), Psychiatry (A.N.), and Medicine (A.N.), Massachusetts General Hospital, Boston; Broad Institute of MIT and Harvard (S.S., J.S., R.W.T., S.M., C.K., N.Y., C.D.A., J.R.), Cambridge, MA; Department of Neurology (S.S., J.S., R.W.T., H.B.B.), Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, the Netherlands; Cancer Epidemiology Unit (K.P.), Nuffield Department of Population Health (M.C., T.J.L., C.M.V.D.), Big Data Institute, University of Oxford, United Kingdom; UAB McKnight Brain Institute (R.M.L.), Department of Neurology, UAB Heersink School of Medicine, University of Alabama at Birmingham; Department of Medicine (Neurology) (A.P.), University of Toronto; Krembil Brain Institute (A.P.), Toronto, Ontario, Canada; Program for Health System and Technology Evaluation (S.I.); Toronto General Hospital Research Institute; The Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health (A.P., S.I.), University Health Network, Toronto; Centre for Advancing Collaborative Healthcare & Education (CACHE) and Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health (S.I.), University of Toronto, Ontario, Canada; Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; Department of Neurosurgery (H.B.B.), Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands; Benson-Henry Institute for Mind Body Medicine (G.F.), Massachusetts General Hospital; and Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA
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Charisis S, Short MI, Bernal R, Kautz TF, Treviño HA, Mathews J, Dediós AGV, Muhammad JAS, Luckey AM, Aslam A, Himali JJ, Shipp EL, Habes M, Beiser AS, DeCarli C, Scarmeas N, Ramachandran VS, Seshadri S, Maillard P, Satizabal CL. Leptin bioavailability and markers of brain atrophy and vascular injury in the middle age. Alzheimers Dement 2024. [PMID: 39132759 DOI: 10.1002/alz.13879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 03/01/2024] [Accepted: 03/24/2024] [Indexed: 08/13/2024]
Abstract
INTRODUCTION We investigated the associations of leptin markers with cognitive function and magnetic resonance imaging (MRI) measures of brain atrophy and vascular injury in healthy middle-aged adults. METHODS We included 2262 cognitively healthy participants from the Framingham Heart Study with neuropsychological evaluation; of these, 2028 also had available brain MRI. Concentrations of leptin, soluble leptin receptor (sOB-R), and their ratio (free leptin index [FLI]), indicating leptin bioavailability, were measured using enzyme-linked immunosorbent assays. Cognitive and MRI measures were derived using standardized protocols. RESULTS Higher sOB-R was associated with lower fractional anisotropy (FA, β = -0.114 ± 0.02, p < 0.001), and higher free water (FW, β = 0.091 ± 0.022, p < 0.001) and peak-width skeletonized mean diffusivity (PSMD, β = 0.078 ± 0.021, p < 0.001). Correspondingly, higher FLI was associated with higher FA (β = 0.115 ± 0.027, p < 0.001) and lower FW (β = -0.096 ± 0.029, p = 0.001) and PSMD (β = -0.085 ± 0.028, p = 0.002). DISCUSSION Higher leptin bioavailability was associated with better white matter (WM) integrity in healthy middle-aged adults, supporting the putative neuroprotective role of leptin in late-life dementia risk. HIGHLIGHTS Higher leptin bioavailability was related to better preservation of white matter microstructure. Higher leptin bioavailability during midlife might confer protection against dementia. Potential benefits might be even stronger for individuals with visceral obesity. DTI measures might be sensitive surrogate markers of subclinical neuropathology.
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Affiliation(s)
- Sokratis Charisis
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Meghan I Short
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USA
| | - Rebecca Bernal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Tiffany F Kautz
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Hector A Treviño
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Julia Mathews
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Angel Gabriel Velarde Dediós
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Jazmyn A S Muhammad
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Alison M Luckey
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Asra Aslam
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Jayandra J Himali
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Eric L Shipp
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Alexa S Beiser
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Charles DeCarli
- Department of Neurology, University of California, Davis, Sacramento, California, USA
| | - Nikolaos Scarmeas
- 1st Department of Neurology, National and Kapodistrian University of Athens, Athens, Greece
- Taub Institute for Research in Alzheimer's Disease and the Aging Brain, the Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA
| | - Vasan S Ramachandran
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Pauline Maillard
- Department of Neurology, University of California, Davis, Sacramento, California, USA
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
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Daniels AJ, McDade E, Llibre-Guerra JJ, Xiong C, Perrin RJ, Ibanez L, Supnet-Bell C, Cruchaga C, Goate A, Renton AE, Benzinger TL, Gordon BA, Hassenstab J, Karch C, Popp B, Levey A, Morris J, Buckles V, Allegri RF, Chrem P, Berman SB, Chhatwal JP, Farlow MR, Fox NC, Day GS, Ikeuchi T, Jucker M, Lee JH, Levin J, Lopera F, Takada L, Sosa AL, Martins R, Mori H, Noble JM, Salloway S, Huey E, Rosa-Neto P, Sánchez-Valle R, Schofield PR, Roh JH, Bateman RJ. 15 Years of Longitudinal Genetic, Clinical, Cognitive, Imaging, and Biochemical Measures in DIAN. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.08.24311689. [PMID: 39148846 PMCID: PMC11326320 DOI: 10.1101/2024.08.08.24311689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
This manuscript describes and summarizes the Dominantly Inherited Alzheimer Network Observational Study (DIAN Obs), highlighting the wealth of longitudinal data, samples, and results from this human cohort study of brain aging and a rare monogenic form of Alzheimer's disease (AD). DIAN Obs is an international collaborative longitudinal study initiated in 2008 with support from the National Institute on Aging (NIA), designed to obtain comprehensive and uniform data on brain biology and function in individuals at risk for autosomal dominant AD (ADAD). ADAD gene mutations in the amyloid protein precursor (APP), presenilin 1 (PSEN1), or presenilin 2 (PSEN2) genes are deterministic causes of ADAD, with virtually full penetrance, and a predictable age at symptomatic onset. Data and specimens collected are derived from full clinical assessments, including neurologic and physical examinations, extensive cognitive batteries, structural and functional neuro-imaging, amyloid and tau pathological measures using positron emission tomography (PET), flurordeoxyglucose (FDG) PET, cerebrospinal fluid and blood collection (plasma, serum, and whole blood), extensive genetic and multi-omic analyses, and brain donation upon death. This comprehensive evaluation of the human nervous system is performed longitudinally in both mutation carriers and family non-carriers, providing one of the deepest and broadest evaluations of the human brain across decades and through AD progression. These extensive data sets and samples are available for researchers to address scientific questions on the human brain, aging, and AD.
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Affiliation(s)
- Alisha J. Daniels
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Eric McDade
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | | | - Chengjie Xiong
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Richard J. Perrin
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Laura Ibanez
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | | | - Carlos Cruchaga
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Alison Goate
- Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Alan E. Renton
- Icahn School of Medicine at Mount Sinai, New York, NY USA
| | | | - Brian A. Gordon
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Jason Hassenstab
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Celeste Karch
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Brent Popp
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Allan Levey
- Goizueta Alzheimer’s Disease Research Center, Emory University, Atlanta, GA, USA
| | - John Morris
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | - Virginia Buckles
- Washington University School of Medicine, St Louis, St Louis, MO, USA
| | | | - Patricio Chrem
- Institute of Neurological Research FLENI, Buenos Aires, Argentina
| | | | - Jasmeer P. Chhatwal
- Massachusetts General and Brigham & Women’s Hospitals, Harvard Medical School, Boston MA, USA
| | | | - Nick C. Fox
- UK Dementia Research Institute at University College London, London, United Kingdom
- University College London, London, United Kingdom
| | | | - Takeshi Ikeuchi
- Brain Research Institute, Niigata University, Niigata, Japan
| | - Mathias Jucker
- Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany
| | | | - Johannes Levin
- DZNE, German Center for Neurodegenerative Diseases, Munich, Germany
- Ludwig-Maximilians-Universität München, Munich, Germany
| | | | | | - Ana Luisa Sosa
- Instituto Nacional de Neurologia y Neurocirugla Innn, Mexico City, Mexico
| | - Ralph Martins
- Edith Cowan University, Western Australia, Australia
| | | | - James M. Noble
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, and GH Sergievsky Center, Columbia University Irving Medical Center, New York, NY, USA
| | | | - Edward Huey
- Brown University, Butler Hospital, Providence, RI, USA
| | - Pedro Rosa-Neto
- Centre de Recherche de L’hopital Douglas and McGill University, Montreal, Quebec
| | - Raquel Sánchez-Valle
- Hospital Clínic de Barcelona. IDIBAPS. University of Barcelona, Barcelona, Spain
| | - Peter R. Schofield
- Neuroscience Research Australia, Sydney, NSW, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Jee Hoon Roh
- Korea University, Korea University Anam Hospital, Seoul, South Korea
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Naveed K, Rashidi-Ranjbar N, Kumar S, Zomorrodi R, Blumberger DM, Fischer CE, Sanches M, Mulsant BH, Pollock BG, Voineskos AN, Rajji TK. Effect of dorsolateral prefrontal cortex structural measures on neuroplasticity and response to paired-associative stimulation in Alzheimer's dementia. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230233. [PMID: 38853564 PMCID: PMC11343312 DOI: 10.1098/rstb.2023.0233] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/04/2023] [Accepted: 01/15/2024] [Indexed: 06/11/2024] Open
Abstract
Long-term potentiation (LTP)-like activity can be induced by stimulation protocols such as paired associative stimulation (PAS). We aimed to determine whether PAS-induced LTP-like activity (PAS-LTP) of the dorsolateral prefrontal cortex (DLPFC) is associated with cortical thickness and other structural measures impaired in Alzheimer's dementia (AD). We also explored longitudinal relationships between these brain structures and PAS-LTP response after a repetitive PAS (rPAS) intervention. Mediation and regression analyses were conducted using data from randomized controlled trials with AD and healthy control participants. PAS-electroencephalography assessed DLPFC PAS-LTP. DLPFC thickness and surface area were acquired from T1-weighted magnetic resonance imaging. Fractional anisotropy and mean diffusivity (MD) of the superior longitudinal fasciculus (SLF)-a tract important to induce PAS-LTP-were measured with diffusion-weighted imaging. AD participants exhibited reduced DLPFC thickness and increased SLF MD. There was also some evidence that reduction in DLPFC thickness mediates DLPFC PAS-LTP impairment. Longitudinal analyses showed preliminary evidence that SLF MD, and to a lesser extent DLPFC thickness, is associated with DLPFC PAS-LTP response to active rPAS. This study expands our understanding of the relationships between brain structural changes and neuroplasticity. It provides promising evidence for a structural predictor to improving neuroplasticity in AD with neurostimulation. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.
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Affiliation(s)
- K. Naveed
- Temerty Faculty of Medicine, University of Toronto, 1 King’s College Cir, Toronto, OntarioM5S 1A8, Canada
- Toronto Dementia Research Alliance, University of Toronto, 250 College Street, Toronto, OntarioM5T 1R8, Canada
| | - N. Rashidi-Ranjbar
- Temerty Faculty of Medicine, University of Toronto, 1 King’s College Cir, Toronto, OntarioM5S 1A8, Canada
- Toronto Dementia Research Alliance, University of Toronto, 250 College Street, Toronto, OntarioM5T 1R8, Canada
- Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, 209 Victoria Street, Toronto, OntarioM5B 1T8, Canada
| | - S. Kumar
- Temerty Faculty of Medicine, University of Toronto, 1 King’s College Cir, Toronto, OntarioM5S 1A8, Canada
- Toronto Dementia Research Alliance, University of Toronto, 250 College Street, Toronto, OntarioM5T 1R8, Canada
- Campbell Family Mental Health Research Institute, CAMH, 479 Spadina Avenue, Toronto, OntarioM5S 2S1, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, 250 College Street, Toronto, OntarioM5T 1R8, Canada
| | - R. Zomorrodi
- Temerty Faculty of Medicine, University of Toronto, 1 King’s College Cir, Toronto, OntarioM5S 1A8, Canada
- Campbell Family Mental Health Research Institute, CAMH, 479 Spadina Avenue, Toronto, OntarioM5S 2S1, Canada
| | - D. M. Blumberger
- Temerty Faculty of Medicine, University of Toronto, 1 King’s College Cir, Toronto, OntarioM5S 1A8, Canada
- Campbell Family Mental Health Research Institute, CAMH, 479 Spadina Avenue, Toronto, OntarioM5S 2S1, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, 250 College Street, Toronto, OntarioM5T 1R8, Canada
| | - C. E. Fischer
- Temerty Faculty of Medicine, University of Toronto, 1 King’s College Cir, Toronto, OntarioM5S 1A8, Canada
- Toronto Dementia Research Alliance, University of Toronto, 250 College Street, Toronto, OntarioM5T 1R8, Canada
- Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, 209 Victoria Street, Toronto, OntarioM5B 1T8, Canada
| | - M. Sanches
- Biostatistics Core, Centre for Addiction and Mental Health, 60 White Squirrel Way, Toronto, OntarioM6J 1H4, Canada
| | - B. H. Mulsant
- Temerty Faculty of Medicine, University of Toronto, 1 King’s College Cir, Toronto, OntarioM5S 1A8, Canada
- Toronto Dementia Research Alliance, University of Toronto, 250 College Street, Toronto, OntarioM5T 1R8, Canada
- Campbell Family Mental Health Research Institute, CAMH, 479 Spadina Avenue, Toronto, OntarioM5S 2S1, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, 250 College Street, Toronto, OntarioM5T 1R8, Canada
| | - B. G. Pollock
- Temerty Faculty of Medicine, University of Toronto, 1 King’s College Cir, Toronto, OntarioM5S 1A8, Canada
- Campbell Family Mental Health Research Institute, CAMH, 479 Spadina Avenue, Toronto, OntarioM5S 2S1, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, 250 College Street, Toronto, OntarioM5T 1R8, Canada
| | - A. N. Voineskos
- Temerty Faculty of Medicine, University of Toronto, 1 King’s College Cir, Toronto, OntarioM5S 1A8, Canada
- Campbell Family Mental Health Research Institute, CAMH, 479 Spadina Avenue, Toronto, OntarioM5S 2S1, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, 250 College Street, Toronto, OntarioM5T 1R8, Canada
| | - T. K. Rajji
- Temerty Faculty of Medicine, University of Toronto, 1 King’s College Cir, Toronto, OntarioM5S 1A8, Canada
- Toronto Dementia Research Alliance, University of Toronto, 250 College Street, Toronto, OntarioM5T 1R8, Canada
- Campbell Family Mental Health Research Institute, CAMH, 479 Spadina Avenue, Toronto, OntarioM5S 2S1, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, 250 College Street, Toronto, OntarioM5T 1R8, Canada
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Korbmacher M, van der Meer D, Beck D, Askeland-Gjerde DE, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Distinct Longitudinal Brain White Matter Microstructure Changes and Associated Polygenic Risk of Common Psychiatric Disorders and Alzheimer's Disease in the UK Biobank. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100323. [PMID: 39132576 PMCID: PMC11313202 DOI: 10.1016/j.bpsgos.2024.100323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 03/24/2024] [Accepted: 04/16/2024] [Indexed: 08/13/2024] Open
Abstract
Background During the course of adulthood and aging, white matter (WM) structure and organization are characterized by slow degradation processes such as demyelination and shrinkage. An acceleration of such aging processes has been linked to the development of a range of diseases. Thus, an accurate description of healthy brain maturation, particularly in terms of WM features, is fundamental to the understanding of aging. Methods We used longitudinal diffusion magnetic resonance imaging to provide an overview of WM changes at different spatial and temporal scales in the UK Biobank (UKB) (n = 2678; agescan 1 = 62.38 ± 7.23 years; agescan 2 = 64.81 ± 7.1 years). To examine the genetic overlap between WM structure and common clinical conditions, we tested the associations between WM structure and polygenic risk scores for the most common neurodegenerative disorder, Alzheimer's disease, and common psychiatric disorders (unipolar and bipolar depression, anxiety, obsessive-compulsive disorder, autism, schizophrenia, attention-deficit/hyperactivity disorder) in longitudinal (n = 2329) and cross-sectional (n = 31,056) UKB validation data. Results Our findings indicate spatially distributed WM changes across the brain, as well as distributed associations of polygenic risk scores with WM. Importantly, brain longitudinal changes reflected genetic risk for disorder development better than the utilized cross-sectional measures, with regional differences giving more specific insights into gene-brain change associations than global averages. Conclusions We extend recent findings by providing a detailed overview of WM microstructure degeneration on different spatial levels, helping to understand fundamental brain aging processes. Further longitudinal research is warranted to examine aging-related gene-brain associations.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Dennis van der Meer
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Dani Beck
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Daniel E. Askeland-Gjerde
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I. Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
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Qiu T, Liu Z, Rheault F, Legarreta JH, Valcourt Caron A, St‐Onge F, Strikwerda‐Brown C, Metz A, Dadar M, Soucy J, Pichet Binette A, Spreng RN, Descoteaux M, Villeneuve S. Structural white matter properties and cognitive resilience to tau pathology. Alzheimers Dement 2024; 20:3364-3377. [PMID: 38561254 PMCID: PMC11095478 DOI: 10.1002/alz.13776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 01/11/2024] [Accepted: 02/07/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION We assessed whether macro- and/or micro-structural white matter properties are associated with cognitive resilience to Alzheimer's disease pathology years prior to clinical onset. METHODS We examined whether global efficiency, an indicator of communication efficiency in brain networks, and diffusion measurements within the limbic network and default mode network moderate the association between amyloid-β/tau pathology and cognitive decline. We also investigated whether demographic and health/risk factors are associated with white matter properties. RESULTS Higher global efficiency of the limbic network, as well as free-water corrected diffusion measures within the tracts of both networks, attenuated the impact of tau pathology on memory decline. Education, age, sex, white matter hyperintensities, and vascular risk factors were associated with white matter properties of both networks. DISCUSSION White matter can influence cognitive resilience against tau pathology, and promoting education and vascular health may enhance optimal white matter properties. HIGHLIGHTS Aβ and tau were associated with longitudinal memory change over ∼7.5 years. White matter properties attenuated the impact of tau pathology on memory change. Health/risk factors were associated with white matter properties.
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Affiliation(s)
- Ting Qiu
- Douglas Mental Health University InstituteMontrealCanada
| | - Zhen‐Qi Liu
- Montreal Neurological InstituteDepartment of Neurology and NeurosurgeryMcGill UniversityMontrealCanada
| | - François Rheault
- Medical Imaging and NeuroInformatics LabUniversité de SherbrookeSherbrookeCanada
| | - Jon Haitz Legarreta
- Department of RadiologyBrigham and Women's HospitalMass General Brigham/Harvard Medical SchoolBostonMassachusettsUSA
| | - Alex Valcourt Caron
- Sherbrooke Connectivity Imaging LaboratoryUniversité de SherbrookeSherbrookeCanada
| | | | - Cherie Strikwerda‐Brown
- Douglas Mental Health University InstituteMontrealCanada
- School of Psychological ScienceThe University of Western AustraliaPerthAustralia
| | - Amelie Metz
- Douglas Mental Health University InstituteMontrealCanada
| | - Mahsa Dadar
- Douglas Mental Health University InstituteMontrealCanada
- Department of PsychiatryMcGill UniversityMontrealCanada
| | - Jean‐Paul Soucy
- Montreal Neurological InstituteDepartment of Neurology and NeurosurgeryMcGill UniversityMontrealCanada
| | | | - R. Nathan Spreng
- Douglas Mental Health University InstituteMontrealCanada
- Montreal Neurological InstituteDepartment of Neurology and NeurosurgeryMcGill UniversityMontrealCanada
- Department of PsychiatryMcGill UniversityMontrealCanada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging LaboratoryUniversité de SherbrookeSherbrookeCanada
| | - Sylvia Villeneuve
- Douglas Mental Health University InstituteMontrealCanada
- Department of PsychiatryMcGill UniversityMontrealCanada
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Phillips JS, Adluru N, Chung MK, Radhakrishnan H, Olm CA, Cook PA, Gee JC, Cousins KAQ, Arezoumandan S, Wolk DA, McMillan CT, Grossman M, Irwin DJ. Greater white matter degeneration and lower structural connectivity in non-amnestic vs. amnestic Alzheimer's disease. Front Neurosci 2024; 18:1353306. [PMID: 38567286 PMCID: PMC10986184 DOI: 10.3389/fnins.2024.1353306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Multimodal evidence indicates Alzheimer's disease (AD) is characterized by early white matter (WM) changes that precede overt cognitive impairment. WM changes have overwhelmingly been investigated in typical, amnestic mild cognitive impairment and AD; fewer studies have addressed WM change in atypical, non-amnestic syndromes. We hypothesized each non-amnestic AD syndrome would exhibit WM differences from amnestic and other non-amnestic syndromes. Materials and methods Participants included 45 cognitively normal (CN) individuals; 41 amnestic AD patients; and 67 patients with non-amnestic AD syndromes including logopenic-variant primary progressive aphasia (lvPPA, n = 32), posterior cortical atrophy (PCA, n = 17), behavioral variant AD (bvAD, n = 10), and corticobasal syndrome (CBS, n = 8). All had T1-weighted MRI and 30-direction diffusion-weighted imaging (DWI). We performed whole-brain deterministic tractography between 148 cortical and subcortical regions; connection strength was quantified by tractwise mean generalized fractional anisotropy. Regression models assessed effects of group and phenotype as well as associations with grey matter volume. Topological analyses assessed differences in persistent homology (numbers of graph components and cycles). Additionally, we tested associations of topological metrics with global cognition, disease duration, and DWI microstructural metrics. Results Both amnestic and non-amnestic patients exhibited lower WM connection strength than CN participants in corpus callosum, cingulum, and inferior and superior longitudinal fasciculi. Overall, non-amnestic patients had more WM disease than amnestic patients. LvPPA patients had left-lateralized WM degeneration; PCA patients had reductions in connections to bilateral posterior parietal, occipital, and temporal areas. Topological analysis showed the non-amnestic but not the amnestic group had more connected components than controls, indicating persistently lower connectivity. Longer disease duration and cognitive impairment were associated with more connected components and fewer cycles in individuals' brain graphs. Discussion We have previously reported syndromic differences in GM degeneration and tau accumulation between AD syndromes; here we find corresponding differences in WM tracts connecting syndrome-specific epicenters. Determining the reasons for selective WM degeneration in non-amnestic AD is a research priority that will require integration of knowledge from neuroimaging, biomarker, autopsy, and functional genetic studies. Furthermore, longitudinal studies to determine the chronology of WM vs. GM degeneration will be key to assessing evidence for WM-mediated tau spread.
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Affiliation(s)
- Jeffrey S. Phillips
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Moo K. Chung
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Hamsanandini Radhakrishnan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Christopher A. Olm
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Philip A. Cook
- Penn Image Computing and Science Laboratory, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - James C. Gee
- Penn Image Computing and Science Laboratory, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Katheryn A. Q. Cousins
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Sanaz Arezoumandan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Memory Center, University of Pennsylvania Health System, Philadelphia, PA, United States
| | - Corey T. McMillan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David J. Irwin
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Yang DX, Sun Z, Yu MM, Zou QQ, Li PY, Zhang JK, Wu X, Li YH, Wang ML. Associations of MRI-Derived Glymphatic System Impairment With Global White Matter Damage and Cognitive Impairment in Mild Traumatic Brain Injury: A DTI-ALPS Study. J Magn Reson Imaging 2024; 59:639-647. [PMID: 37276070 DOI: 10.1002/jmri.28797] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/08/2023] [Accepted: 05/09/2023] [Indexed: 06/07/2023] Open
Abstract
BACKGROUND Assessing the glymphatic function using diffusion tensor image analysis along the perivascular space (DTI-ALPS) may be helpful for mild traumatic brain injury (mTBI) management. PURPOSE To assess glymphatic function using DTI-ALPS and its associations with global white matter damage and cognitive impairment in mTBI. STUDY TYPE Prospective. POPULATION Thirty-four controls (44.1% female, mean age 49.2 years) and 58 mTBI subjects (43.1% female, mean age 48.7 years), including uncomplicated mTBI (N = 32) and complicated mTBI (N = 26). FIELD STRENGTH/SEQUENCE 3-T, single-shot echo-planar imaging sequence. ASSESSMENT Magnetic resonance imaging (MRI) was done within 1 month since injury. DTI-ALPS was performed to assess glymphatic function, and peak width of skeletonized mean diffusivity (PSMD) was used to assess global white matter damage. Cognitive tests included Auditory Verbal Learning Test and Digit Span Test (forward and backward). STATISTICAL TESTS Neuroimaging findings comparisons were done between mTBI and control groups. Partial correlation and multivariable linear regression assessed the associations between DTI-ALPS, PSMD, and cognitive impairment. Mediation effects of PSMD on the relationship between DTI-ALPS and cognitive impairment were explored. P-value <0.05 was considered statistically significant, except for cognitive correlational analyses with a Bonferroni-corrected P-value set at 0.05/3 ≈ 0.017. RESULTS mTBI showed lower DTI-ALPS and higher PSMD, especially in complicated mTBI. DTI-ALPS was significantly correlated with verbal memory (r = 0.566), attention abilities (r = 0.792), executive function (r = 0.618), and PSMD (r = -0.533). DTI-ALPS was associated with verbal memory (β = 8.77, 95% confidence interval [CI] 5.00, 12.54), attention abilities (β = 5.67, 95% CI 4.56, 6.97), executive function (β = 2.34, 95% CI 1.49, 3.20), and PSMD (β = -0.79, 95% CI -1.15, -0.43). PSMD mediated 46.29%, 20.46%, and 24.36% of the effects for the relationship between DTI-ALPS and verbal memory, attention abilities, and executive function. DATA CONCLUSION Glymphatic function may be impaired in mTBI reflected by DTI-ALPS. Glymphatic dysfunction may cause cognitive impairment related to global white matter damage after mTBI. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Dian-Xu Yang
- Department of Neurosurgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zheng Sun
- Department of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Meng-Meng Yu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China
| | - Qiao-Qiao Zou
- Department of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Peng-Yang Li
- Division of Cardiology, Pauley Heart Center, Virginia Commonwealth, University, Richmond, Virginia, USA
| | - Jing-Kun Zhang
- Cardiovascular Research Institute, University of California San Francisco, San Francisco, California, USA
| | - Xue Wu
- Institute for Global Health Sciences, University of California San Francisco, San Francisco, California, USA
| | - Yue-Hua Li
- Department of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ming-Liang Wang
- Department of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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10
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Ruiz-Rizzo AL, Finke K, Archila-Meléndez ME. Diffusion Tensor Imaging in Alzheimer's Studies. Methods Mol Biol 2024; 2785:105-113. [PMID: 38427191 DOI: 10.1007/978-1-0716-3774-6_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
In this chapter, we describe the use of quantitative metrics of white matter obtained from the diffusion tensor model based on diffusion-weighted imaging in Alzheimer's disease (AD). Our description synthesizes insights not only from patient populations with AD dementia but also from participants at risk for AD dementia (e.g., amnestic mild cognitive impairment, subjective cognitive decline, or familial AD mutation carriers). A reference to studies examining correlations with behavioral variables is also included. Our main message is to caution against the overinterpretation of diffusion metrics and to favor analyses that focus on regions of interest or major white matter tracts for biomarker studies in AD.
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Affiliation(s)
| | - Kathrin Finke
- Department of Neurology, Jena University Hospital, Jena, Germany
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11
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Groechel RC, Alosco ML, Dixon D, Tripodis Y, Mez J, Goldstein L, Budson AE, Qiu WQ, Killiany RJ. Associations between white matter integrity of the cingulum bundle, surrounding gray matter regions, and cognition across the dementia continuum. J Comp Neurol 2023; 531:2162-2171. [PMID: 38010204 PMCID: PMC10841586 DOI: 10.1002/cne.25564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 10/19/2023] [Accepted: 10/21/2023] [Indexed: 11/29/2023]
Abstract
INTRODUCTION Previous Alzheimer's disease and related dementias (AD/ADRD) research studies have illustrated the significance of studying alterations in white matter (WM). Fewer studies have examined how WM integrity, measured with diffusion tensor imaging (DTI), is associated with volume of gray matter (GM) regions and measures of cognitive function in aged participants spanning the dementia continuum. METHODS Magnetic resonance imaging and cognitive data were collected from 241 Boston University Alzheimer's Disease Research Center participants who spanned from cognitively normal controls to amnestic mild cognitive impairment to having dementia. Primary DTI tracts of interest were the cingulum ventral (CV) and cingulum dorsal (CD) pathways. GM regions of interest (ROIs) were in the medial temporal lobe (MTL), prefrontal cortex, and retrosplenial cortex. Analyses of covariance models were used to assess differences in WM integrity across groups (control, amnestic mild cognitive impairment, and dementia). Multiple linear regression models were used to assess associations between WM integrity and GM volume, and with measures of memory and executive function. RESULTS Differences in WM integrity were shown in both cingulum pathways in participants across the dementia continuum. Associations between WM integrity of both cingulum pathways and volume of selected GM ROIs were widespread. Functionally significant associations were found between WM of the CV pathway and memory, independent of MTL GM volume. DISCUSSION Differences in WM integrity of the cingulum bundle and surrounding GM ROI are likely related to the progression of AD/ADRD. Such differences should continue to be studied, particularly in association with memory performance.
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Affiliation(s)
- Renée C. Groechel
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine
- National Institute of Neurological Disorders & Stroke Intramural Research Program
| | - Michael L. Alosco
- Boston University Alzheimer’s Disease Research Center
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine
- Boston University Chronic Traumatic Encephalopathy Center
| | - Diane Dixon
- Boston University Alzheimer’s Disease Research Center
- Biostatistics and Epidemiology Data Analytics Center, Boston University School of Public Health
| | - Yorghos Tripodis
- Boston University Alzheimer’s Disease Research Center
- Department of Biostatistics, Boston University School of Public Health
| | - Jesse Mez
- Boston University Alzheimer’s Disease Research Center
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine
- Boston University Chronic Traumatic Encephalopathy Center
| | - Lee Goldstein
- Boston University Alzheimer’s Disease Research Center
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine
| | - Andrew E. Budson
- Boston University Alzheimer’s Disease Research Center
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine
- Neurology Service, VA Boston Healthcare System
| | - Wei Qiao Qiu
- Boston University Alzheimer’s Disease Research Center
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine
| | - Ronald J. Killiany
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine
- Boston University Alzheimer’s Disease Research Center
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine
- Department of Environmental Health, Boston University School of Public Health
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12
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Shirzadi Z, Schultz SA, Yau WYW, Joseph-Mathurin N, Fitzpatrick CD, Levin R, Kantarci K, Preboske GM, Jack CR, Farlow MR, Hassenstab J, Jucker M, Morris JC, Xiong C, Karch CM, Levey AI, Gordon BA, Schofield PR, Salloway SP, Perrin RJ, McDade E, Levin J, Cruchaga C, Allegri RF, Fox NC, Goate A, Day GS, Koeppe R, Chui HC, Berman S, Mori H, Sanchez-Valle R, Lee JH, Rosa-Neto P, Ruthirakuhan M, Wu CY, Swardfager W, Benzinger TLS, Sohrabi HR, Martins RN, Bateman RJ, Johnson KA, Sperling RA, Greenberg SM, Schultz AP, Chhatwal JP. Etiology of White Matter Hyperintensities in Autosomal Dominant and Sporadic Alzheimer Disease. JAMA Neurol 2023; 80:1353-1363. [PMID: 37843849 PMCID: PMC10580156 DOI: 10.1001/jamaneurol.2023.3618] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 06/26/2023] [Indexed: 10/17/2023]
Abstract
Importance Increased white matter hyperintensity (WMH) volume is a common magnetic resonance imaging (MRI) finding in both autosomal dominant Alzheimer disease (ADAD) and late-onset Alzheimer disease (LOAD), but it remains unclear whether increased WMH along the AD continuum is reflective of AD-intrinsic processes or secondary to elevated systemic vascular risk factors. Objective To estimate the associations of neurodegeneration and parenchymal and vessel amyloidosis with WMH accumulation and investigate whether systemic vascular risk is associated with WMH beyond these AD-intrinsic processes. Design, Setting, and Participants This cohort study used data from 3 longitudinal cohort studies conducted in tertiary and community-based medical centers-the Dominantly Inherited Alzheimer Network (DIAN; February 2010 to March 2020), the Alzheimer's Disease Neuroimaging Initiative (ADNI; July 2007 to September 2021), and the Harvard Aging Brain Study (HABS; September 2010 to December 2019). Main Outcome and Measures The main outcomes were the independent associations of neurodegeneration (decreases in gray matter volume), parenchymal amyloidosis (assessed by amyloid positron emission tomography), and vessel amyloidosis (evidenced by cerebral microbleeds [CMBs]) with cross-sectional and longitudinal WMH. Results Data from 3960 MRI sessions among 1141 participants were included: 252 pathogenic variant carriers from DIAN (mean [SD] age, 38.4 [11.2] years; 137 [54%] female), 571 older adults from ADNI (mean [SD] age, 72.8 [7.3] years; 274 [48%] female), and 318 older adults from HABS (mean [SD] age, 72.4 [7.6] years; 194 [61%] female). Longitudinal increases in WMH volume were greater in individuals with CMBs compared with those without (DIAN: t = 3.2 [P = .001]; ADNI: t = 2.7 [P = .008]), associated with longitudinal decreases in gray matter volume (DIAN: t = -3.1 [P = .002]; ADNI: t = -5.6 [P < .001]; HABS: t = -2.2 [P = .03]), greater in older individuals (DIAN: t = 6.8 [P < .001]; ADNI: t = 9.1 [P < .001]; HABS: t = 5.4 [P < .001]), and not associated with systemic vascular risk (DIAN: t = 0.7 [P = .40]; ADNI: t = 0.6 [P = .50]; HABS: t = 1.8 [P = .06]) in individuals with ADAD and LOAD after accounting for age, gray matter volume, CMB presence, and amyloid burden. In older adults without CMBs at baseline, greater WMH volume was associated with CMB development during longitudinal follow-up (Cox proportional hazards regression model hazard ratio, 2.63; 95% CI, 1.72-4.03; P < .001). Conclusions and Relevance The findings suggest that increased WMH volume in AD is associated with neurodegeneration and parenchymal and vessel amyloidosis but not with elevated systemic vascular risk. Additionally, increased WMH volume may represent an early sign of vessel amyloidosis preceding the emergence of CMBs.
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Affiliation(s)
- Zahra Shirzadi
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Stephanie A. Schultz
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Wai-Ying W. Yau
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | | | - Colleen D. Fitzpatrick
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Raina Levin
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | | | | | - Jason Hassenstab
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Mathias Jucker
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Tübingen, Germany
| | - John C. Morris
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Chengjie Xiong
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Celeste M. Karch
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | | | - Brian A. Gordon
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Peter R. Schofield
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | | | - Richard J. Perrin
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Eric McDade
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-Universität München, German Center for Neurodegenerative Diseases, site Munich, Munich Cluster for Systems Neurology, Munich, Germany
| | - Carlos Cruchaga
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | | | - Nick C. Fox
- UK Dementia Research Institute, University College London, London, United Kingdom
| | - Alison Goate
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor
| | - Helena C. Chui
- Keck School of Medicine, University of Southern California, Los Angeles
| | - Sarah Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Hiroshi Mori
- Osaka Metropolitan University Medical School, Osaka, Nagaoka Sutoku University, Osaka City, Niigata, Japan
| | | | - Jae-Hong Lee
- Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Pedro Rosa-Neto
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Myuri Ruthirakuhan
- Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Che-Yuan Wu
- Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Walter Swardfager
- Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | | | - Hamid R. Sohrabi
- Centre for Healthy Ageing, School of Psychology, Health Future Institute, Murdoch University, Perth, Western Australia, Australia
| | - Ralph N. Martins
- School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia
| | - Randall J. Bateman
- Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Keith A. Johnson
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Reisa A. Sperling
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Steven M. Greenberg
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Aaron P. Schultz
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
| | - Jasmeer P. Chhatwal
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston
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13
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Diao Y, Lanz B, Jelescu IO. Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer's using machine learning. Alzheimers Res Ther 2023; 15:193. [PMID: 37936236 PMCID: PMC10629161 DOI: 10.1186/s13195-023-01328-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 10/09/2023] [Indexed: 11/09/2023]
Abstract
BACKGROUND The pathological process of Alzheimer's disease (AD) typically takes decades from onset to clinical symptoms. Early brain changes in AD include MRI-measurable features such as altered functional connectivity (FC) and white matter degeneration. The ability of these features to discriminate between subjects without a diagnosis, or their prognostic value, is however not established. METHODS The main trigger mechanism of AD is still debated, although impaired brain glucose metabolism is taking an increasingly central role. Here, we used a rat model of sporadic AD, based on impaired brain glucose metabolism induced by an intracerebroventricular injection of streptozotocin (STZ). We characterized alterations in FC and white matter microstructure longitudinally using functional and diffusion MRI. Those MRI-derived measures were used to classify STZ from control rats using machine learning, and the importance of each individual measure was quantified using explainable artificial intelligence methods. RESULTS Overall, combining all the FC and white matter metrics in an ensemble way was the best strategy to discriminate STZ rats, with a consistent accuracy over 0.85. However, the best accuracy early on was achieved using white matter microstructure features, and later on using FC. This suggests that consistent damage in white matter in the STZ group might precede FC. For cross-timepoint prediction, microstructure features also had the highest performance while, in contrast, that of FC was reduced by its dynamic pattern which shifted from early hyperconnectivity to late hypoconnectivity. CONCLUSIONS Our study highlights the MRI-derived measures that best discriminate STZ vs control rats early in the course of the disease, with potential translation to humans.
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Affiliation(s)
- Yujian Diao
- Animal Imaging and Technology Section, CIBM Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Bernard Lanz
- Animal Imaging and Technology Section, CIBM Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Ileana Ozana Jelescu
- Animal Imaging and Technology Section, CIBM Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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14
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Schlepckow K, Morenas-Rodríguez E, Hong S, Haass C. Stimulation of TREM2 with agonistic antibodies-an emerging therapeutic option for Alzheimer's disease. Lancet Neurol 2023; 22:1048-1060. [PMID: 37863592 DOI: 10.1016/s1474-4422(23)00247-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/29/2023] [Accepted: 06/29/2023] [Indexed: 10/22/2023]
Abstract
Neurodegenerative disorders, including Alzheimer's disease, are associated with microgliosis. Microglia have long been considered to have detrimental roles in Alzheimer's disease. However, functional analyses of genes encoding risk factors that are linked to late-onset Alzheimer's disease, and that are enriched or exclusively expressed in microglia, have revealed unexpected protective functions. One of the major risk genes for Alzheimer's disease is TREM2. Risk variants of TREM2 are loss-of-function mutations affecting chemotaxis, phagocytosis, lipid and energy metabolism, and survival and proliferation. Agonistic anti-TREM2 antibodies have been developed to boost these protective functions in patients with intact TREM2 alleles. Several anti-TREM2 antibodies are in early clinical trials, and current efforts aim to achieve more efficient transport of these antibodies across the blood-brain barrier. PET imaging could be used to monitor target engagement. Data from animal models, and biomarker studies in patients, further support a rationale for boosting TREM2 functions during the preclinical stage of Alzheimer's disease.
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Affiliation(s)
- Kai Schlepckow
- German Centre for Neurodegenerative Diseases, Munich, Germany
| | - Estrella Morenas-Rodríguez
- Memory Unit, Department of Neurology, Hospital Universitario 12 de Octubre, Madrid, Spain; Group of Neurogenerative Diseases, Hospital Universitario 12 de Octubre Research Institute (imas12), Madrid, Spain
| | - Soyon Hong
- UK Dementia Research Institute, Institute of Neurology, University College London, London, UK
| | - Christian Haass
- German Centre for Neurodegenerative Diseases, Munich, Germany; Metabolic Biochemistry, Biomedical Centre, Faculty of Medicine, Ludwig-Maximilians University, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
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15
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Chen H, Fan L, Guo Q, Wong MY, Yu F, Foxe N, Wang W, Nessim A, Carling G, Liu B, Lopez-Lee C, Huang Y, Amin S, Patel T, Mok SA, Song WM, Zhang B, Ma Q, Fu H, Gan L, Luo W. DAP12 deficiency alters microglia-oligodendrocyte communication and enhances resilience against tau toxicity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.26.563970. [PMID: 37961594 PMCID: PMC10634844 DOI: 10.1101/2023.10.26.563970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Pathogenic tau accumulation fuels neurodegeneration in Alzheimer's disease (AD). Enhancing aging brain's resilience to tau pathology would lead to novel therapeutic strategies. DAP12 (DNAX-activation protein 12) is critically involved in microglial immune responses. Previous studies have showed that mice lacking DAP12 in tauopathy mice exhibit higher tau pathology but are protected from tau-induced cognitive deficits. However, the exact mechanism remains elusive. Our current study uncovers a novel resilience mechanism via microglial interaction with oligodendrocytes. Despite higher tau inclusions, Dap12 deletion curbs tau-induced brain inflammation and ameliorates myelin and synapse loss. Specifically, removal of Dap12 abolished tau-induced disease-associated clusters in microglia (MG) and intermediate oligodendrocytes (iOli), which are spatially correlated with tau pathology in AD brains. Our study highlights the critical role of interactions between microglia and oligodendrocytes in tau toxicity and DAP12 signaling as a promising target for enhancing resilience in AD.
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Affiliation(s)
- Hao Chen
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Li Fan
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Qi Guo
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Man Ying Wong
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Fangmin Yu
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Nessa Foxe
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | | | - Aviram Nessim
- The State University of New York at Stony Brook, Long Island, New York, USA
| | - Gillian Carling
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Bangyan Liu
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Chloe Lopez-Lee
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Yige Huang
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Sadaf Amin
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Tark Patel
- Department of Biochemistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB Canada
| | - Sue-Ann Mok
- Department of Biochemistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB Canada
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Hongjun Fu
- Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Li Gan
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Millburn High School, New Jersey, NJ, USA
| | - Wenjie Luo
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
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16
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Chen H, Fan L, Guo Q, Wong MY, Yu F, Foxe N, Wang W, Nessim A, Carling G, Liu B, Lopez-Lee C, Huang Y, Amin S, Mok SA, Song WM, Zhang B, Ma Q, Fu H, Gan L, Luo W. DAP12 deficiency alters microglia-oligodendrocyte communication and enhances resilience against tau toxicity. RESEARCH SQUARE 2023:rs.3.rs-3454358. [PMID: 37961627 PMCID: PMC10635319 DOI: 10.21203/rs.3.rs-3454358/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Pathogenic tau accumulation fuels neurodegeneration in Alzheimer's disease (AD). Enhancing aging brain's resilience to tau pathology would lead to novel therapeutic strategies. DAP12 (DNAX-activation protein 12) is critically involved in microglial immune responses. Previous studies have showed that mice lacking DAP12 in tauopathy mice exhibit higher tau pathology but are protected from tau-induced cognitive deficits. However, the exact mechanism remains elusive. Our current study uncovers a novel resilience mechanism via microglial interaction with oligodendrocytes. Despite higher tau inclusions, Dap12 deletion curbs tau-induced brain inflammation and ameliorates myelin and synapse loss. Specifically, removal of Dap12 abolished tau-induced disease-associated clusters in microglia (MG) and intermediate oligodendrocytes (iOli), which are spatially correlated with tau pathology in AD brains. Our study highlights the critical role of interactions between microglia and oligodendrocytes in tau toxicity and DAP12 signaling as a promising target for enhancing resilience in AD.
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Affiliation(s)
- Hao Chen
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Li Fan
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Qi Guo
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Man Ying Wong
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Fangmin Yu
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Nessa Foxe
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | | | - Aviram Nessim
- The State University of New York at Stony Brook, Long Island, New York, USA
| | - Gillian Carling
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Bangyan Liu
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Chloe Lopez-Lee
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Yige Huang
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Program of Neuroscience, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Sadaf Amin
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Sue-Ann Mok
- Department of Biochemistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB Canada
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Hongjun Fu
- Department of Neuroscience, College of Medicine, Ohio State University, Columbus, OH 43210 USA
| | - Li Gan
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Millburn High School, New Jersey, NJ, USA
| | - Wenjie Luo
- Helen and Robert Appel Alzheimer Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
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17
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Cerneckis J, Shi Y. Myelin organoids for the study of Alzheimer's disease. Front Neurosci 2023; 17:1283742. [PMID: 37942133 PMCID: PMC10628225 DOI: 10.3389/fnins.2023.1283742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 10/09/2023] [Indexed: 11/10/2023] Open
Affiliation(s)
- Jonas Cerneckis
- Department of Neurodegenerative Diseases, Beckman Research Institute of City of Hope, Duarte, CA, United States
- Irell and Manella Graduate School of Biological Sciences, Beckman Research Institute of City of Hope, Duarte, CA, United States
| | - Yanhong Shi
- Department of Neurodegenerative Diseases, Beckman Research Institute of City of Hope, Duarte, CA, United States
- Irell and Manella Graduate School of Biological Sciences, Beckman Research Institute of City of Hope, Duarte, CA, United States
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18
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Gallagher RL, Koscik RL, Moody JF, Vogt NM, Adluru N, Kecskemeti SR, Van Hulle CA, Chin NA, Asthana S, Kollmorgen G, Suridjan I, Carlsson CM, Johnson SC, Dean DC, Zetterberg H, Blennow K, Alexander AL, Bendlin BB. Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status. Alzheimers Res Ther 2023; 15:180. [PMID: 37848950 PMCID: PMC10583332 DOI: 10.1186/s13195-023-01281-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 07/27/2023] [Indexed: 10/19/2023]
Abstract
BACKGROUND Alzheimer's disease involves accumulating amyloid (A) and tau (T) pathology, and progressive neurodegeneration (N), leading to the development of the AD clinical syndrome. While several markers of N have been proposed, efforts to define normal vs. abnormal neurodegeneration based on neuroimaging have been limited. Sensitive markers that may account for or predict cognitive dysfunction for individuals in early disease stages are critical. METHODS Participants (n = 296) defined on A and T status and spanning the AD-clinical continuum underwent multi-shell diffusion-weighted magnetic resonance imaging to generate Neurite Orientation Dispersion and Density Imaging (NODDI) metrics, which were tested as markers of N. To better define N, we developed age- and sex-adjusted robust z-score values to quantify normal and AD-associated (abnormal) neurodegeneration in both cortical gray matter and subcortical white matter regions of interest. We used general logistic regression with receiver operating characteristic (ROC) and area under the curve (AUC) analysis to test whether NODDI metrics improved diagnostic accuracy compared to models that only relied on cerebrospinal fluid (CSF) A and T status (alone and in combination). RESULTS Using internal robust norms, we found that NODDI metrics correlate with worsening cognitive status and that NODDI captures early, AD neurodegenerative pathology in the gray matter of cognitively unimpaired, but A/T biomarker-positive, individuals. NODDI metrics utilized together with A and T status improved diagnostic prediction accuracy of AD clinical status, compared with models using CSF A and T status alone. CONCLUSION Using a robust norms approach, we show that abnormal AD-related neurodegeneration can be detected among cognitively unimpaired individuals. Metrics derived from diffusion-weighted imaging are potential sensitive markers of N and could be considered for trial enrichment and as outcomes in clinical trials. However, given the small sample sizes, the exploratory nature of the work must be acknowledged.
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Affiliation(s)
- Rigina L Gallagher
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Medical Scientist Training Program, University of Wisconsin-Madison, Madison, WI, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
| | - Rebecca Langhough Koscik
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
- Wisconsin Alzheimer's Institute, Madison, WI, USA
| | - Jason F Moody
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
- Wisconsin Alzheimer's Institute, Madison, WI, USA
| | - Nicholas M Vogt
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Medical Scientist Training Program, University of Wisconsin-Madison, Madison, WI, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
| | - Nagesh Adluru
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
- Waisman Research Center, Madison, WI, USA
| | | | - Carol A Van Hulle
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
| | - Nathaniel A Chin
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
| | - Sanjay Asthana
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
- Veterans Administration, Madison, WI, USA
| | | | | | - Cynthia M Carlsson
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
- Wisconsin Alzheimer's Institute, Madison, WI, USA
- Veterans Administration, Madison, WI, USA
| | - Sterling C Johnson
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
- Wisconsin Alzheimer's Institute, Madison, WI, USA
- Veterans Administration, Madison, WI, USA
| | - Douglas C Dean
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Waisman Research Center, Madison, WI, USA
| | - Henrik Zetterberg
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Andrew L Alexander
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Waisman Research Center, Madison, WI, USA
| | - Barbara B Bendlin
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA.
- Wisconsin Alzheimer's Institute, Madison, WI, USA.
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19
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Ali DG, Bahrani AA, El Khouli RH, Gold BT, Jiang Y, Zachariou V, Wilcock DM, Jicha GA. White matter hyperintensities influence distal cortical β-amyloid accumulation in default mode network pathways. Brain Behav 2023; 13:e3209. [PMID: 37534614 PMCID: PMC10570488 DOI: 10.1002/brb3.3209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 07/19/2023] [Accepted: 07/22/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND AND PURPOSE Cerebral small vessel disease (SVD) has been suggested to contribute to the pathogenesis of Alzheimer's disease (AD). Yet, the role of SVD in potentially contributing to AD pathology is unclear. The main objective of this study was to test the hypothesis that WMHs influence amyloid β (Aβ) levels within connected default mode network (DMN) tracts and cortical regions in cognitively unimpaired older adults. METHODS Regional standard uptake value ratios (SUVr) from Aβ-PET and white matter hyperintensity (WMH) volumes from three-dimensional magnetic resonance imaging FLAIR images were analyzed across a sample of 72 clinically unimpaired (mini-mental state examination ≥26), older adults (mean age 74.96 and standard deviation 8.13) from the Alzheimer's Disease Neuroimaging Initiative (ADNI3). The association of WMH volumes in major fiber tracts projecting from cortical DMN regions and Aβ-PET SUVr in the connected cortical DMN regions was analyzed using linear regression models adjusted for age, sex, ApoE, and total brain volumes. RESULTS The regression analyses demonstrate that increased WMH volumes in the superior longitudinal fasciculus were associated with increased regional SUVr in the inferior parietal lobule (p = .011). CONCLUSION The findings suggest that the relation between Aβ in parietal cortex is associated with SVD in downstream white matter (WM) pathways in preclinical AD. The biological relationships and interplay between Aβ and WM microstructure alterations that precede overt WMH development across the continuum of AD progression warrant further study.
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Affiliation(s)
- Doaa G. Ali
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Behavioral Science, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Ahmed A. Bahrani
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Neurology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Riham H. El Khouli
- Department of Radiology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Brian T. Gold
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Neuroscience, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Yang Jiang
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Behavioral Science, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Valentinos Zachariou
- Department of Neuroscience, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Donna M. Wilcock
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Physiology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Gregory A. Jicha
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Behavioral Science, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Neurology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
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20
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Qu Y, Wang P, Yao H, Wang D, Song C, Yang H, Zhang Z, Chen P, Kang X, Du K, Fan L, Zhou B, Han T, Yu C, Zhang X, Zuo N, Jiang T, Zhou Y, Liu B, Han Y, Lu J, Liu Y. Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer's Disease. Neurosci Bull 2023; 39:1533-1543. [PMID: 37014553 PMCID: PMC10533766 DOI: 10.1007/s12264-023-01041-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 11/29/2022] [Indexed: 04/05/2023] Open
Abstract
Alzheimer's disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.
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Affiliation(s)
- Yida Qu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Hongxiang Yao
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Dawei Wang
- Department of Radiology, Department of Epidemiology and Health Statistics, School of Public Health, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Zengqiang Zhang
- Branch of Chinese, PLA General Hospital, Sanya, 572022, China
| | - Pindong Chen
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaopeng Kang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kai Du
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bo Zhou
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100089, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xi Zhang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100089, China
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Bing Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Lab of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, 100091, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- Beijing Institute of Geriatrics, Beijing, 100053, China
- National Clinical Research Center for Geriatric Disorders, Beijing, 100053, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
| | - Yong Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
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21
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Lopez-Lee C, Kodama L, Fan L, Wong MY, Foxe NR, Jiaz L, Yu F, Ye P, Zhu J, Norman K, Torres ER, Kim RD, Mousa GA, Dubal D, Liddelow S, Luo W, Gan L. Sex Chromosomes and Gonads Shape the Sex-Biased Transcriptomic Landscape in Tlr7-Mediated Demyelination During Aging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.19.558439. [PMID: 37781600 PMCID: PMC10541118 DOI: 10.1101/2023.09.19.558439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Demyelination occurs in aging and associated diseases, including Alzheimer's disease. Several of these diseases exhibit sex differences in prevalence and severity. Biological sex primarily stems from sex chromosomes and gonads releasing sex hormones. To dissect mechanisms underlying sex differences in demyelination of aging brains, we constructed a transcriptomic atlas of cell type-specific responses to illustrate how sex chromosomes, gonads, and their interaction shape responses to demyelination. We found that sex-biased oligodendrocyte and microglial responses are driven by interaction of sex chromosomes and gonads prior to myelin loss. Post demyelination, sex chromosomes mainly guide microglial responses, while gonadal composition influences oligodendrocyte signaling. Significantly, ablation of the X-linked gene Toll-like receptor 7 (Tlr7), which exhibited sex-biased expression during demyelination, abolished the sex-biased responses and protected against demyelination.
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Affiliation(s)
- Chloe Lopez-Lee
- Helen and Robert Appel Institute for Alzheimer’s Disease Research, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
- Neuroscience Graduate Program, Weill Cornell Medicine, New York, NY
| | - Lay Kodama
- Helen and Robert Appel Institute for Alzheimer’s Disease Research, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
- Neuroscience Graduate Program, University of California San Francisco, San Francisco, CA
| | - Li Fan
- Helen and Robert Appel Institute for Alzheimer’s Disease Research, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
| | - Man Ying Wong
- Helen and Robert Appel Institute for Alzheimer’s Disease Research, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
| | - Nessa R. Foxe
- Helen and Robert Appel Institute for Alzheimer’s Disease Research, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
| | - Laraib Jiaz
- Helen and Robert Appel Institute for Alzheimer’s Disease Research, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
| | - Fangmin Yu
- Helen and Robert Appel Institute for Alzheimer’s Disease Research, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
| | - Pearly Ye
- Helen and Robert Appel Institute for Alzheimer’s Disease Research, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
| | - Jingjie Zhu
- Helen and Robert Appel Institute for Alzheimer’s Disease Research, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
| | - Kendra Norman
- Helen and Robert Appel Institute for Alzheimer’s Disease Research, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
| | - Eileen Ruth Torres
- Helen and Robert Appel Institute for Alzheimer’s Disease Research, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
| | - Rachel D. Kim
- Neuroscience Institute, NYU Grossman School of Medicine, New York, NY
| | - Gergey Alzaem Mousa
- Helen and Robert Appel Institute for Alzheimer’s Disease Research, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
| | - Dena Dubal
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA
| | - Shane Liddelow
- Neuroscience Institute, NYU Grossman School of Medicine, New York, NY
- Department of Neuroscience & Physiology, NYU Grossman School of Medicine, New York, NY
- Department of Ophthalmology, NYU Grossman School of Medicine, New York, NY
- Parekh Center for Interdisciplinary Neurology, NYU Grossman School of Medicine, New York, NY
| | - Wenjie Luo
- Helen and Robert Appel Institute for Alzheimer’s Disease Research, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
| | - Li Gan
- Helen and Robert Appel Institute for Alzheimer’s Disease Research, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
- Neuroscience Graduate Program, Weill Cornell Medicine, New York, NY
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22
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Yang S, Park JH, Lu HC. Axonal energy metabolism, and the effects in aging and neurodegenerative diseases. Mol Neurodegener 2023; 18:49. [PMID: 37475056 PMCID: PMC10357692 DOI: 10.1186/s13024-023-00634-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 06/08/2023] [Indexed: 07/22/2023] Open
Abstract
Human studies consistently identify bioenergetic maladaptations in brains upon aging and neurodegenerative disorders of aging (NDAs), such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and Amyotrophic lateral sclerosis. Glucose is the major brain fuel and glucose hypometabolism has been observed in brain regions vulnerable to aging and NDAs. Many neurodegenerative susceptible regions are in the topological central hub of the brain connectome, linked by densely interconnected long-range axons. Axons, key components of the connectome, have high metabolic needs to support neurotransmission and other essential activities. Long-range axons are particularly vulnerable to injury, neurotoxin exposure, protein stress, lysosomal dysfunction, etc. Axonopathy is often an early sign of neurodegeneration. Recent studies ascribe axonal maintenance failures to local bioenergetic dysregulation. With this review, we aim to stimulate research in exploring metabolically oriented neuroprotection strategies to enhance or normalize bioenergetics in NDA models. Here we start by summarizing evidence from human patients and animal models to reveal the correlation between glucose hypometabolism and connectomic disintegration upon aging/NDAs. To encourage mechanistic investigations on how axonal bioenergetic dysregulation occurs during aging/NDAs, we first review the current literature on axonal bioenergetics in distinct axonal subdomains: axon initial segments, myelinated axonal segments, and axonal arbors harboring pre-synaptic boutons. In each subdomain, we focus on the organization, activity-dependent regulation of the bioenergetic system, and external glial support. Second, we review the mechanisms regulating axonal nicotinamide adenine dinucleotide (NAD+) homeostasis, an essential molecule for energy metabolism processes, including NAD+ biosynthetic, recycling, and consuming pathways. Third, we highlight the innate metabolic vulnerability of the brain connectome and discuss its perturbation during aging and NDAs. As axonal bioenergetic deficits are developing into NDAs, especially in asymptomatic phase, they are likely exaggerated further by impaired NAD+ homeostasis, the high energetic cost of neural network hyperactivity, and glial pathology. Future research in interrogating the causal relationship between metabolic vulnerability, axonopathy, amyloid/tau pathology, and cognitive decline will provide fundamental knowledge for developing therapeutic interventions.
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Affiliation(s)
- Sen Yang
- The Linda and Jack Gill Center for Biomolecular Sciences, Indiana University, Bloomington, IN, 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA
| | - Jung Hyun Park
- The Linda and Jack Gill Center for Biomolecular Sciences, Indiana University, Bloomington, IN, 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA
| | - Hui-Chen Lu
- The Linda and Jack Gill Center for Biomolecular Sciences, Indiana University, Bloomington, IN, 47405, USA.
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA.
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23
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Liu H, Cai H, Yang D, Zhu W, Wu G, Chen J. Learning pyramidal multi-scale harmonic wavelets for identifying the neuropathology propagation patterns of Alzheimer's disease. Med Image Anal 2023; 87:102812. [PMID: 37196535 PMCID: PMC10503391 DOI: 10.1016/j.media.2023.102812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 02/25/2023] [Accepted: 04/07/2023] [Indexed: 05/19/2023]
Abstract
Previous studies have established that neurodegenerative disease such as Alzheimer's disease (AD) is a disconnection syndrome, where the neuropathological burdens often propagate across the brain network to interfere with the structural and functional connections. In this context, identifying the propagation patterns of neuropathological burdens sheds new light on understanding the pathophysiological mechanism of AD progression. However, little attention has been paid to propagation pattern identification by fully considering the intrinsic properties of brain-network organization, which plays an important role in improving the interpretability of the identified propagation pathways. To this end, we propose a novel harmonic wavelet analysis approach to construct a set of region-specific pyramidal multi-scale harmonic wavelets, it allows us to characterize the propagation patterns of neuropathological burdens from multiple hierarchical modules across the brain network. Specifically, we first extract underlying hub nodes through a series of network centrality measurements on the common brain network reference generated from a population of minimum spanning tree (MST) brain networks. Then, we propose a manifold learning method to identify the region-specific pyramidal multi-scale harmonic wavelets corresponding to hub nodes by seamlessly integrating the hierarchically modular property of the brain network. We estimate the statistical power of our proposed harmonic wavelet analysis approach on synthetic data and large-scale neuroimaging data from ADNI. Compared with the other harmonic analysis techniques, our proposed method not only effectively predicts the early stage of AD but also provides a new window to capture the underlying hub nodes and the propagation pathways of neuropathological burdens in AD.
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Affiliation(s)
- Huan Liu
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guandong 510006, China
| | - Hongmin Cai
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guandong 510006, China
| | - Defu Yang
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Wentao Zhu
- Zhejiang Lab, Hangzhou, Zhejiang 311121, China
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jiazhou Chen
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guandong 510006, China.
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24
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Byun MS, Chang M, Yi D, Ahn H, Han D, Jeon S, Jang H, Lee DY, Oh SH. Association of Central Auditory Processing Dysfunction With Preclinical Alzheimer's Disease. Otolaryngol Head Neck Surg 2023; 169:112-119. [PMID: 36939433 PMCID: PMC10846842 DOI: 10.1002/ohn.228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/13/2022] [Accepted: 11/21/2022] [Indexed: 01/30/2023]
Abstract
OBJECTIVE To investigate whether central auditory processing dysfunction measured by the dichotic digit test-1 digit (DDT1) is present in preclinical Alzheimer's disease (AD) individuals who are cognitively normal (CN) older adults with the cerebral beta-amyloid (Aβ) deposition and to explore the potential of the DDT1 as a screening test for preclinical AD. STUDY DESIGN Cross-sectional design. SETTING A prospective observational cohort study. METHODS CN older adults with a global clinical dementia rating score of 0 were included. The hearing test battery including pure-tone audiometry, speech audiometry, distortion product otoacoustic emission, and DDT1 was administered to participants. RESULTS Fifty CN older adults were included. Among them, 38 individuals were included in the Aβ deposition negative (AN) group and 12 were included in the Aβ deposition positive (AP) group. The DDT1 scores of both the better and worse ears were significantly lower in the AP group than in the AN group (p = .008 and p = .015, respectively). No significant differences were observed between the groups in tests of the peripheral auditory pathways. In multivariable logistic regression analysis adjusted for apolipoprotein E4 positivity, the DDT1 better ear score predicted the AP group (p = .036, odds ratio = 0.892, 95% confidence interval: 0.780-0.985) with relatively high diagnostic accuracy. CONCLUSION Our findings suggest that Aβ deposition may affect the central auditory pathway even before cognitive decline appears. DDT1, which can easily be applied to the old-age population, may have the potential as a screening tool for preclinical AD.
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Affiliation(s)
- Min Soo Byun
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
| | - Munyoung Chang
- Department of Otolaryngology–Head and Neck Surgery, Chung-Ang University College of Medicine, Seoul, South Korea
- Department of Otolaryngology–Head and Neck Surgery, Chung-Ang University Hospital, Seoul, South Korea
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Centre, Seoul National University, Seoul, South Korea
| | - Hyejin Ahn
- Interdisciplinary Program of Cognitive Science, Seoul National University College of Humanities, Seoul, South Korea
| | - Dongkyun Han
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
| | - Seulki Jeon
- Department of Otolaryngology–Head and Neck Surgery, Seoul National University Hospital, Seoul, South Korea
| | - Hyunsook Jang
- Division of Speech Pathology and Audiology, Research Institute of Audiology & Speech Pathology, Hallym University, Chuncheon-si, Gangwon-do, South Korea
| | - Dong Young Lee
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
- Institute of Human Behavioral Medicine, Medical Research Centre, Seoul National University, Seoul, South Korea
| | - Seung Ha Oh
- Department of Otolaryngology–Head and Neck Surgery, Seoul National University Hospital, Seoul, South Korea
- Department of Otolaryngology–Head and Neck Surgery, Seoul National University College of Medicine, Seoul, South Korea
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25
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Depp C, Sun T, Sasmita AO, Spieth L, Berghoff SA, Nazarenko T, Overhoff K, Steixner-Kumar AA, Subramanian S, Arinrad S, Ruhwedel T, Möbius W, Göbbels S, Saher G, Werner HB, Damkou A, Zampar S, Wirths O, Thalmann M, Simons M, Saito T, Saido T, Krueger-Burg D, Kawaguchi R, Willem M, Haass C, Geschwind D, Ehrenreich H, Stassart R, Nave KA. Myelin dysfunction drives amyloid-β deposition in models of Alzheimer's disease. Nature 2023; 618:349-357. [PMID: 37258678 PMCID: PMC10247380 DOI: 10.1038/s41586-023-06120-6] [Citation(s) in RCA: 78] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 04/21/2023] [Indexed: 06/02/2023]
Abstract
The incidence of Alzheimer's disease (AD), the leading cause of dementia, increases rapidly with age, but why age constitutes the main risk factor is still poorly understood. Brain ageing affects oligodendrocytes and the structural integrity of myelin sheaths1, the latter of which is associated with secondary neuroinflammation2,3. As oligodendrocytes support axonal energy metabolism and neuronal health4-7, we hypothesized that loss of myelin integrity could be an upstream risk factor for neuronal amyloid-β (Aβ) deposition, the central neuropathological hallmark of AD. Here we identify genetic pathways of myelin dysfunction and demyelinating injuries as potent drivers of amyloid deposition in mouse models of AD. Mechanistically, myelin dysfunction causes the accumulation of the Aβ-producing machinery within axonal swellings and increases the cleavage of cortical amyloid precursor protein. Suprisingly, AD mice with dysfunctional myelin lack plaque-corralling microglia despite an overall increase in their numbers. Bulk and single-cell transcriptomics of AD mouse models with myelin defects show that there is a concomitant induction of highly similar but distinct disease-associated microglia signatures specific to myelin damage and amyloid plaques, respectively. Despite successful induction, amyloid disease-associated microglia (DAM) that usually clear amyloid plaques are apparently distracted to nearby myelin damage. Our data suggest a working model whereby age-dependent structural defects of myelin promote Aβ plaque formation directly and indirectly and are therefore an upstream AD risk factor. Improving oligodendrocyte health and myelin integrity could be a promising target to delay development and slow progression of AD.
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Affiliation(s)
- Constanze Depp
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
| | - Ting Sun
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Andrew Octavian Sasmita
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Lena Spieth
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
| | - Stefan A Berghoff
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
| | - Taisiia Nazarenko
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Katharina Overhoff
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Agnes A Steixner-Kumar
- Clinical Neuroscience, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Swati Subramanian
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Sahab Arinrad
- Clinical Neuroscience, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Torben Ruhwedel
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Wiebke Möbius
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Sandra Göbbels
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Gesine Saher
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Hauke B Werner
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Alkmini Damkou
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
| | - Silvia Zampar
- Department of Psychiatry and Psychotherapy, University Medical Center, Georg-August University, Göttingen, Germany
| | - Oliver Wirths
- Department of Psychiatry and Psychotherapy, University Medical Center, Georg-August University, Göttingen, Germany
| | - Maik Thalmann
- Department of German Philology, Georg-August University, Göttingen, Germany
| | - Mikael Simons
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Takashi Saito
- Department of Neurocognitive Science, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, Japan
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Takaomi Saido
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Dilja Krueger-Burg
- Department of Psychiatry and Psychotherapy, University Medical Center, Georg-August University, Göttingen, Germany
- Department of Molecular Neurobiology, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael Willem
- German Center for Neurodegenerative Diseases, Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
- Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians University of Munich, Munich, Germany
| | - Christian Haass
- German Center for Neurodegenerative Diseases, Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
- Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians University of Munich, Munich, Germany
| | - Daniel Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Hannelore Ehrenreich
- Clinical Neuroscience, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Ruth Stassart
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Paul-Flechsig-Institute of Neuropathology, University Clinic Leipzig, Leipzig, Germany
| | - Klaus-Armin Nave
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
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26
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Zanon Zotin MC, Yilmaz P, Sveikata L, Schoemaker D, van Veluw SJ, Etherton MR, Charidimou A, Greenberg SM, Duering M, Viswanathan A. Peak Width of Skeletonized Mean Diffusivity: A Neuroimaging Marker for White Matter Injury. Radiology 2023; 306:e212780. [PMID: 36692402 PMCID: PMC9968775 DOI: 10.1148/radiol.212780] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 10/01/2022] [Accepted: 10/14/2022] [Indexed: 01/25/2023]
Abstract
A leading cause of white matter (WM) injury in older individuals is cerebral small vessel disease (SVD). Cerebral SVD is the most prevalent vascular contributor to cognitive impairment and dementia. Therapeutic progress for cerebral SVD and other WM disorders depends on the development and validation of neuroimaging markers suitable as outcome measures in future interventional trials. Diffusion-tensor imaging (DTI) is one of the best-suited MRI techniques for assessing the extent of WM damage in the brain. But the optimal method to analyze individual DTI data remains hindered by labor-intensive and time-consuming processes. Peak width of skeletonized mean diffusivity (PSMD), a recently developed fast, fully automated DTI marker, was designed to quantify the WM damage secondary to cerebral SVD and reflect related cognitive impairment. Despite its promising results, knowledge about PSMD is still limited in the radiologic community. This focused review provides an overview of the technical details of PSMD while synthesizing the available data on its clinical and neuroimaging associations. From a critical expert viewpoint, the authors discuss the limitations of PSMD and its current validation status as a neuroimaging marker for vascular cognitive impairment. Finally, they point out the gaps to be addressed to further advance the field.
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Affiliation(s)
| | | | - Lukas Sveikata
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Dorothee Schoemaker
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Susanne J. van Veluw
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Mark R. Etherton
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Andreas Charidimou
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Steven M. Greenberg
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Marco Duering
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Anand Viswanathan
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
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27
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Quan M, Wang Q, Qin W, Wang W, Li F, Zhao T, Li T, Qiu Q, Cao S, Wang S, Wang Y, Jin H, Zhou A, Fang J, Jia L, Jia J. Shared and unique effects of ApoEε4 and pathogenic gene mutation on cognition and imaging in preclinical familial Alzheimer's disease. Alzheimers Res Ther 2023; 15:40. [PMID: 36850008 PMCID: PMC9972804 DOI: 10.1186/s13195-023-01192-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 02/13/2023] [Indexed: 03/01/2023]
Abstract
BACKGROUND Neuropsychology and imaging changes have been reported in the preclinical stage of familial Alzheimer's disease (FAD). This study investigated the effects of APOEε4 and known pathogenic gene mutation on different cognitive domains and circuit imaging markers in preclinical FAD. METHODS One hundred thirty-nine asymptomatic subjects in FAD families, including 26 APOEε4 carriers, 17 APP and 20 PS1 mutation carriers, and 76 control subjects, went through a series of neuropsychological tests and MRI scanning. Test scores and imaging measures including volumes, diffusion indices, and functional connectivity (FC) of frontostriatal and hippocampus to posterior cingulate cortex pathways were compared between groups and analyzed for correlation. RESULTS Compared with controls, the APOEε4 group showed increased hippocampal volume and decreased FC of fronto-caudate pathway. The APP group showed increased recall scores in auditory verbal learning test, decreased fiber number, and increased radial diffusivity and FC of frontostriatal pathway. All three genetic groups showed decreased fractional anisotropy of hippocampus to posterior cingulate cortex pathway. These neuropsychological and imaging measures were able to discriminate genetic groups from controls, with areas under the curve from 0.733 to 0.837. Circuit imaging measures are differentially associated with scores in various cognitive scales in control and genetic groups. CONCLUSIONS There are neuropsychological and imaging changes in the preclinical stage of FAD, some of which are shared by APOEε4 and known pathogenic gene mutation, while some are unique to different genetic groups. These findings are helpful for the early identification of Alzheimer's disease and for developing generalized and individualized prevention and intervention strategies.
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Affiliation(s)
- Meina Quan
- grid.413259.80000 0004 0632 3337Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China ,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China ,grid.24696.3f0000 0004 0369 153XClinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China ,grid.24696.3f0000 0004 0369 153XBeijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China ,grid.24696.3f0000 0004 0369 153XCenter of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China
| | - Qi Wang
- grid.413259.80000 0004 0632 3337Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China ,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China ,grid.24696.3f0000 0004 0369 153XClinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China ,grid.24696.3f0000 0004 0369 153XBeijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China ,grid.24696.3f0000 0004 0369 153XCenter of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China
| | - Wei Qin
- grid.413259.80000 0004 0632 3337Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China ,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China ,grid.24696.3f0000 0004 0369 153XClinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China ,grid.24696.3f0000 0004 0369 153XBeijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China ,grid.24696.3f0000 0004 0369 153XCenter of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China
| | - Wei Wang
- grid.413259.80000 0004 0632 3337Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China ,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China ,grid.24696.3f0000 0004 0369 153XClinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China ,grid.24696.3f0000 0004 0369 153XBeijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China ,grid.24696.3f0000 0004 0369 153XCenter of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China
| | - Fangyu Li
- grid.413259.80000 0004 0632 3337Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China ,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China ,grid.24696.3f0000 0004 0369 153XClinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China ,grid.24696.3f0000 0004 0369 153XBeijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China ,grid.24696.3f0000 0004 0369 153XCenter of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China
| | - Tan Zhao
- grid.413259.80000 0004 0632 3337Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China ,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Tingting Li
- grid.413259.80000 0004 0632 3337Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China ,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China ,grid.24696.3f0000 0004 0369 153XClinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China ,grid.24696.3f0000 0004 0369 153XBeijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China ,grid.24696.3f0000 0004 0369 153XCenter of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China
| | - Qiongqiong Qiu
- grid.413259.80000 0004 0632 3337Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China ,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China ,grid.24696.3f0000 0004 0369 153XClinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China ,grid.24696.3f0000 0004 0369 153XBeijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China ,grid.24696.3f0000 0004 0369 153XCenter of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China
| | - Shuman Cao
- grid.413259.80000 0004 0632 3337Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China ,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China ,grid.24696.3f0000 0004 0369 153XClinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China ,grid.24696.3f0000 0004 0369 153XBeijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China ,grid.24696.3f0000 0004 0369 153XCenter of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China
| | - Shiyuan Wang
- grid.413259.80000 0004 0632 3337Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China ,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China ,grid.24696.3f0000 0004 0369 153XClinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China ,grid.24696.3f0000 0004 0369 153XBeijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China ,grid.24696.3f0000 0004 0369 153XCenter of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China
| | - Yan Wang
- grid.413259.80000 0004 0632 3337Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China ,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China ,grid.24696.3f0000 0004 0369 153XClinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China ,grid.24696.3f0000 0004 0369 153XBeijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China ,grid.24696.3f0000 0004 0369 153XCenter of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China
| | - Hongmei Jin
- grid.413259.80000 0004 0632 3337Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China ,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China ,grid.24696.3f0000 0004 0369 153XClinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China ,grid.24696.3f0000 0004 0369 153XBeijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China ,grid.24696.3f0000 0004 0369 153XCenter of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China
| | - Aihong Zhou
- grid.413259.80000 0004 0632 3337Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China ,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China ,grid.24696.3f0000 0004 0369 153XClinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China ,grid.24696.3f0000 0004 0369 153XBeijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China ,grid.24696.3f0000 0004 0369 153XCenter of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China
| | - Jiliang Fang
- grid.464297.aGuang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Longfei Jia
- grid.413259.80000 0004 0632 3337Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China ,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China ,grid.24696.3f0000 0004 0369 153XClinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China ,grid.24696.3f0000 0004 0369 153XBeijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China ,grid.24696.3f0000 0004 0369 153XCenter of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China
| | - Jianping Jia
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China. .,National Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, China. .,Clinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China. .,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China. .,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China. .,Key Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China.
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Sharp FR, DeCarli CS, Jin LW, Zhan X. White matter injury, cholesterol dysmetabolism, and APP/Abeta dysmetabolism interact to produce Alzheimer's disease (AD) neuropathology: A hypothesis and review. Front Aging Neurosci 2023; 15:1096206. [PMID: 36845656 PMCID: PMC9950279 DOI: 10.3389/fnagi.2023.1096206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 01/30/2023] [Indexed: 02/12/2023] Open
Abstract
We postulate that myelin injury contributes to cholesterol release from myelin and cholesterol dysmetabolism which contributes to Abeta dysmetabolism, and combined with genetic and AD risk factors, leads to increased Abeta and amyloid plaques. Increased Abeta damages myelin to form a vicious injury cycle. Thus, white matter injury, cholesterol dysmetabolism and Abeta dysmetabolism interact to produce or worsen AD neuropathology. The amyloid cascade is the leading hypothesis for the cause of Alzheimer's disease (AD). The failure of clinical trials based on this hypothesis has raised other possibilities. Even with a possible new success (Lecanemab), it is not clear whether this is a cause or a result of the disease. With the discovery in 1993 that the apolipoprotein E type 4 allele (APOE4) was the major risk factor for sporadic, late-onset AD (LOAD), there has been increasing interest in cholesterol in AD since APOE is a major cholesterol transporter. Recent studies show that cholesterol metabolism is intricately involved with Abeta (Aβ)/amyloid transport and metabolism, with cholesterol down-regulating the Aβ LRP1 transporter and upregulating the Aβ RAGE receptor, both of which would increase brain Aβ. Moreover, manipulating cholesterol transport and metabolism in rodent AD models can ameliorate pathology and cognitive deficits, or worsen them depending upon the manipulation. Though white matter (WM) injury has been noted in AD brain since Alzheimer's initial observations, recent studies have shown abnormal white matter in every AD brain. Moreover, there is age-related WM injury in normal individuals that occurs earlier and is worse with the APOE4 genotype. Moreover, WM injury precedes formation of plaques and tangles in human Familial Alzheimer's disease (FAD) and precedes plaque formation in rodent AD models. Restoring WM in rodent AD models improves cognition without affecting AD pathology. Thus, we postulate that the amyloid cascade, cholesterol dysmetabolism and white matter injury interact to produce and/or worsen AD pathology. We further postulate that the primary initiating event could be related to any of the three, with age a major factor for WM injury, diet and APOE4 and other genes a factor for cholesterol dysmetabolism, and FAD and other genes for Abeta dysmetabolism.
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Affiliation(s)
- Frank R. Sharp
- Department of Neurology, The MIND Institute, University of California at Davis Medical Center, Sacramento, CA, United States
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29
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Chen Q, Abrigo J, Deng M, Shi L, Wang YX, Chu WCW. Diffusion Changes in Hippocampal Cingulum in Early Biologically Defined Alzheimer's Disease. J Alzheimers Dis 2023; 91:1007-1017. [PMID: 36530082 DOI: 10.3233/jad-220671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Diagnosis of Alzheimer's disease (AD) was recently shifted from clinical to biological construct to reflect underlying neuropathological status, where amyloid deposition designated patients to the Alzheimer's continuum, and additional tau positivity represented AD. OBJECTIVE To investigate white matter (WM) alteration in the brain of patients in the Alzheimer's continuum. METHODS A total of 236 subjects across the clinical and biological spectra of AD were included and stratified by normal/abnormal (-/+) amyloid (A) and tau (T) status based on positron emission tomography results, yielding five groups: A-T-cognitively normal (CN), A+T-CN, A+T+ CN, A+T+ mild cognitive impairment, and A+T+ AD. WM alteration was measured by diffusion tensor imaging (DTI). Group differences, correlation of DTI measures with amyloid and tau, and diagnostic performance of such measures were evaluated. RESULTS Compared with A-T-CN, widespread WM alteration was observed in the Alzheimer's continuum, including hippocampal cingulum (CGH), cingulum of the cingulate gyrus, and uncinate fasciculus. Diffusion changes measured by regional mean fractional anisotropy (FA) in the bilateral CGH were first detected in the A+T+ CN group and associated with tau burden in the Alzheimer's continuum (p < 0.001). For discrimination between A+T+ CN and A-T-CN groups, CGH FA achieved accuracy, sensitivity, and specificity of 74%, 58%, and 78% for right CGH and 57%, 83%, and 47% respectively for left CGH. CONCLUSION WM alteration is widespread in the Alzheimer's continuum. Diffusion alteration in CGH occurred early and was correlated with tau pathology, thus may be a promising biomarker in preclinical AD.
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Affiliation(s)
- Qianyun Chen
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jill Abrigo
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Min Deng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yi-Xiang Wang
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Winnie Chiu Wing Chu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
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30
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Marcolini S, Rojczyk P, Seitz-Holland J, Koerte IK, Alosco ML, Bouix S. Posttraumatic Stress and Traumatic Brain Injury: Cognition, Behavior, and Neuroimaging Markers in Vietnam Veterans. J Alzheimers Dis 2023; 95:1427-1448. [PMID: 37694363 PMCID: PMC10578246 DOI: 10.3233/jad-221304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/24/2023] [Indexed: 09/12/2023]
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) are common in Veterans and linked to behavioral disturbances, increased risk of cognitive decline, and Alzheimer's disease. OBJECTIVE We studied the synergistic effects of PTSD and TBI on behavioral, cognitive, and neuroimaging measures in Vietnam war Veterans. METHODS Data were acquired at baseline and after about one-year from male Veterans categorized into: PTSD, TBI, PTSD+TBI, and Veteran controls without PTSD or TBI. We applied manual tractography to examine white matter microstructure of three fiber tracts: uncinate fasciculus (N = 91), cingulum (N = 87), and inferior longitudinal fasciculus (N = 95). ANCOVAs were used to compare Veterans' baseline behavioral and cognitive functioning (N = 285), white matter microstructure, amyloid-β (N = 230), and tau PET (N = 120). Additional ANCOVAs examined scores' differences from baseline to follow-up. RESULTS Veterans with PTSD and PTSD+TBI, but not Veterans with TBI only, exhibited poorer behavioral and cognitive functioning at baseline than controls. The groups did not differ in baseline white matter, amyloid-β, or tau, nor in behavioral and cognitive functioning, and tau accumulation change. Progression of white matter abnormalities of the uncinate fasciculus in Veterans with PTSD compared to controls was observed; analyses in TBI and PTSD+TBI were not run due to insufficient sample size. CONCLUSIONS PTSD and PTSD+TBI negatively affect behavioral and cognitive functioning, while TBI does not contribute independently. Whether progressive decline in uncinate fasciculus microstructure in Veterans with PTSD might account for cognitive decline should be further studied. Findings did not support an association between PTSD, TBI, and Alzheimer's disease pathology based on amyloid and tau PET.
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Affiliation(s)
- Sofia Marcolini
- Department of Neurology and Alzheimer Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Philine Rojczyk
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital, Ludwig Maximilian University Munich, Germany
| | - Johanna Seitz-Holland
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Inga K. Koerte
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital, Ludwig Maximilian University Munich, Germany
| | - Michael L. Alosco
- Department of Neurology, Boston University Alzheimer’s Disease Research Center, Boston University CTE Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Sylvain Bouix
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Software Engineering and Information Technology, École de Technologie Supe´rieure, Montre´al, Canada
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31
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Han S, Gim Y, Jang EH, Hur EM. Functions and dysfunctions of oligodendrocytes in neurodegenerative diseases. Front Cell Neurosci 2022; 16:1083159. [PMID: 36605616 PMCID: PMC9807813 DOI: 10.3389/fncel.2022.1083159] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
Neurodegenerative diseases (NDDs) are characterized by the progressive loss of selectively vulnerable populations of neurons, which is responsible for the clinical symptoms. Although degeneration of neurons is a prominent feature that undoubtedly contributes to and defines NDD pathology, it is now clear that neuronal cell death is by no means mediated solely by cell-autonomous mechanisms. Oligodendrocytes (OLs), the myelinating cells of the central nervous system (CNS), enable rapid transmission of electrical signals and provide metabolic and trophic support to neurons. Recent evidence suggests that OLs and their progenitor population play a role in the onset and progression of NDDs. In this review, we discuss emerging evidence suggesting a role of OL lineage cells in the pathogenesis of age-related NDDs. We start with multiple system atrophy, an NDD with a well-known oligodendroglial pathology, and then discuss Alzheimer's disease (AD) and Parkinson's disease (PD), NDDs which have been thought of as neuronal origins. Understanding the functions and dysfunctions of OLs might lead to the advent of disease-modifying strategies against NDDs.
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Affiliation(s)
- Seungwan Han
- Laboratory of Neuroscience, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul, South Korea
- BK21 Four Future Veterinary Medicine Leading Education and Research Center, College of Veterinary Medicine, Seoul National University, Seoul, South Korea
| | - Yunho Gim
- Laboratory of Neuroscience, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul, South Korea
- BK21 Four Future Veterinary Medicine Leading Education and Research Center, College of Veterinary Medicine, Seoul National University, Seoul, South Korea
| | - Eun-Hae Jang
- Laboratory of Neuroscience, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul, South Korea
- Comparative Medicine Disease Research Center, Seoul National University, Seoul, South Korea
| | - Eun-Mi Hur
- Laboratory of Neuroscience, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul, South Korea
- BK21 Four Future Veterinary Medicine Leading Education and Research Center, College of Veterinary Medicine, Seoul National University, Seoul, South Korea
- Comparative Medicine Disease Research Center, Seoul National University, Seoul, South Korea
- Interdisciplinary Program in Neuroscience, College of Natural Sciences, Seoul National University, Seoul, South Korea
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32
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Schoemaker D, Zanon Zotin MC, Chen K, Igwe KC, Vila-Castelar C, Martinez J, Baena A, Fox-Fuller JT, Lopera F, Reiman EM, Brickman AM, Quiroz YT. White matter hyperintensities are a prominent feature of autosomal dominant Alzheimer’s disease that emerge prior to dementia. Alzheimers Res Ther 2022; 14:89. [PMID: 35768838 PMCID: PMC9245224 DOI: 10.1186/s13195-022-01030-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/06/2022] [Indexed: 11/10/2022]
Abstract
Abstract
Background
To promote the development of effective therapies, there is an important need to characterize the full spectrum of neuropathological changes associated with Alzheimer’s disease. In line with this need, this study examined white matter abnormalities in individuals with early-onset autosomal dominant Alzheimer’s disease, in relation to age and symptom severity.
Methods
This is a cross-sectional analysis of data collected in members of a large kindred with a PSEN1 E280A mutation. Participants were recruited between September 2011 and July 2012 from the Colombian Alzheimer’s Prevention Initiative registry. The studied cohort comprised 50 participants aged between 20 and 55 years, including 20 cognitively unimpaired mutation carriers, 9 cognitively impaired mutation carriers, and 21 non-carriers. Participants completed an MRI, a lumbar puncture for cerebrospinal fluid collection, a florbetapir PET scan, and neurological and neuropsychological examinations. The volume of white matter hyperintensities (WMH) was compared between cognitively unimpaired carriers, cognitively impaired carriers, and non-carriers. Relationships between WMH, age, and cognitive performance were further examined in mutation carriers.
Results
The mean (SD) age of participants was 35.8 (9.6) years and 64% were women. Cardiovascular risk factors were uncommon and did not differ across groups. Cognitively impaired carriers [median, 6.37; interquartile range (IQR), 9.15] had an increased volume of WMH compared to both cognitively unimpaired carriers [median, 0.85; IQR, 0.79] and non-carriers [median, 1.07; IQR, 0.71]. In mutation carriers, the volume of WMH strongly correlated with cognition and age, with evidence for an accelerated rate of changes after the age of 43 years, 1 year earlier than the estimated median age of symptom onset. In multivariable regression models including cortical amyloid retention, superior parietal lobe cortical thickness, and cerebrospinal fluid phospho-tau, the volume of WMH was the only biomarker independently and significantly contributing to the total explained variance in cognitive performance.
Conclusions
The volume of WMH is increased among individuals with symptomatic autosomal-dominant Alzheimer’s disease, begins to increase prior to clinical symptom onset, and is an independent determinant of cognitive performance in this group. These findings suggest that WMH are a key component of autosomal-dominant Alzheimer’s disease that is closely related to the progression of clinical symptoms.
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33
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Littau JL, Velilla L, Hase Y, Villalba‐Moreno ND, Hagel C, Drexler D, Osorio Restrepo S, Villegas A, Lopera F, Vargas S, Glatzel M, Krasemann S, Quiroz YT, Arboleda‐Velasquez JF, Kalaria R, Sepulveda‐Falla D. Evidence of beta amyloid independent small vessel disease in familial Alzheimer's disease. Brain Pathol 2022; 32:e13097. [PMID: 35695802 PMCID: PMC9616091 DOI: 10.1111/bpa.13097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 05/24/2022] [Indexed: 12/04/2022] Open
Abstract
We studied small vessel disease (SVD) pathology in Familial Alzheimer's disease (FAD) subjects carrying the presenilin 1 (PSEN1) p.Glu280Ala mutation in comparison to those with sporadic Alzheimer's disease (SAD) as a positive control for Alzheimer's pathology and Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) bearing different NOTCH3 mutations, as positive controls for SVD pathology. Upon magnetic resonance imaging (MRI) in life, some FAD showed mild white matter hyperintensities and no further radiologic evidence of SVD. In post-mortem studies, total SVD pathology in cortical areas and basal ganglia was similar in PSEN1 FAD and CADASIL subjects, except for the feature of arteriosclerosis which was higher in CADASIL subjects than in PSEN1 FAD subjects. Further only a few SAD subjects showed a similar degree of SVD pathology as observed in CADASIL. Furthermore, we found significantly enlarged perivascular spaces in vessels devoid of cerebral amyloid angiopathy in FAD compared with SAD and CADASIL subjects. As expected, there was greater fibrinogen-positive perivascular reactivity in CADASIL but similar reactivity in PSEN1 FAD and SAD groups. Fibrinogen immunoreactivity correlated with onset age in the PSEN1 FAD cases, suggesting increased vascular permeability may contribute to cognitive decline. Additionally, we found reduced perivascular expression of PDGFRβ AQP4 in microvessels with enlarged PVS in PSEN1 FAD cases. We demonstrate that there is Aβ-independent SVD pathology in PSEN1 FAD, that was marginally lower than that in CADASIL subjects although not evident by MRI. These observations suggest presence of covert SVD even in PSEN1, contributing to disease progression. As is the case in SAD, these consequences may be preventable by early recognition and actively controlling vascular disease risk, even in familial forms of dementia.
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Affiliation(s)
- Jessica Lisa Littau
- Institute of NeuropathologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Lina Velilla
- Neuroscience Group of AntioquiaUniversity of AntioquiaMedellín
| | - Yoshiki Hase
- Neurovascular Research GroupTranslational and Clinical Research Institute, Newcastle UniversityNewcastle upon Tyne
| | | | - Christian Hagel
- Institute of NeuropathologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Dagmar Drexler
- Institute of NeuropathologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | | | - Andres Villegas
- Neuroscience Group of AntioquiaUniversity of AntioquiaMedellín
| | | | - Sergio Vargas
- Department of Radiology, Neuroradiology SectionUniversidad de AntioquiaMedellínColombia
| | - Markus Glatzel
- Institute of NeuropathologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Susanne Krasemann
- Institute of NeuropathologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Yakeel T. Quiroz
- Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Joseph F. Arboleda‐Velasquez
- Schepens Eye Research Institute of Mass Eye and Ear and the Department of Ophthalmology at Harvard Medical SchoolBostonMassachusetts
| | - Rajesh Kalaria
- Neurovascular Research GroupTranslational and Clinical Research Institute, Newcastle UniversityNewcastle upon Tyne
| | - Diego Sepulveda‐Falla
- Institute of NeuropathologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
- Neuroscience Group of AntioquiaUniversity of AntioquiaMedellín
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34
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Ruiz-Rizzo AL, Viviano RP, Daugherty AM, Finke K, Müller HJ, Damoiseaux JS. Subjective cognitive decline predicts lower cingulo-opercular network functional connectivity in individuals with lower neurite density in the forceps minor: Cingulo-opercular network in SCD. Neuroimage 2022; 263:119662. [PMID: 36198354 DOI: 10.1016/j.neuroimage.2022.119662] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/19/2022] [Accepted: 10/01/2022] [Indexed: 11/18/2022] Open
Abstract
Cognitive complaints of attention/concentration problems are highly frequent in older adults with subjective cognitive decline (SCD). Functional connectivity in the cingulo-opercular network (CON-FC) supports cognitive control, tonic alertness, and visual processing speed. Thus, those complaints in SCD may reflect a decrease in CON-FC. Frontal white-matter tracts such as the forceps minor exhibit age- and SCD-related alterations and, therefore, might influence the CON-FC decrease in SCD. Here, we aimed to determine whether SCD predicts an impairment in CON-FC and whether neurite density in the forceps minor modulates that effect. To do so, we integrated cross-sectional and longitudinal analyses of multimodal data in a latent growth curve modeling approach. Sixty-nine healthy older adults (13 males; 68.33 ± 7.95 years old) underwent resting-state functional and diffusion-weighted magnetic resonance imaging, and the degree of SCD was assessed at baseline with the memory functioning questionnaire (greater score indicating more SCD). Forty-nine of the participants were further enrolled in two follow-ups, each about 18 months apart. Baseline SCD did not predict CON-FC after three years or its rate of change (p-values > 0.092). Notably, however, the forceps minor neurite density did modulate the relation between SCD and CON-FC (intercept; b = 0.21, 95% confidence interval, CI, [0.03, 0.39], p = 0.021), so that SCD predicted a greater CON-FC decrease in older adults with relatively lower neurite density in the forceps minor. The neurite density of the forceps minor, in turn, negatively correlated with age. These results suggest that CON-FC alterations in SCD are dependent upon the forceps minor neurite density. Accordingly, these results imply modifiable age-related factors that could help delay or mitigate both age and SCD-related effects on brain connectivity.
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Affiliation(s)
- Adriana L Ruiz-Rizzo
- Department of Psychology, General and Experimental Psychology Unit, LMU Munich, Munich 80802, Germany; Department of Neurology, Jena University Hospital, Am Klinikum 1, Jena 07747, Germany.
| | - Raymond P Viviano
- Department of Psychology and Institute of Gerontology, Wayne State University, Detroit, MI 48202, USA
| | - Ana M Daugherty
- Department of Psychology and Institute of Gerontology, Wayne State University, Detroit, MI 48202, USA
| | - Kathrin Finke
- Department of Psychology, General and Experimental Psychology Unit, LMU Munich, Munich 80802, Germany; Department of Neurology, Jena University Hospital, Am Klinikum 1, Jena 07747, Germany
| | - Hermann J Müller
- Department of Psychology, General and Experimental Psychology Unit, LMU Munich, Munich 80802, Germany
| | - Jessica S Damoiseaux
- Department of Psychology and Institute of Gerontology, Wayne State University, Detroit, MI 48202, USA
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35
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Xiao D, Wang K, Theriault L, Charbel E. White matter integrity and key structures affected in Alzheimer's disease characterized by diffusion tensor imaging. Eur J Neurosci 2022; 56:5319-5331. [PMID: 36048971 DOI: 10.1111/ejn.15815] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 08/13/2022] [Accepted: 08/22/2022] [Indexed: 12/14/2022]
Abstract
White matter (WM) degeneration is suggested to predict the early signs of Alzheimer's disease (AD). The exact structural regions of brain circuitry involved are not known. This study aims to examine the associations between WM tract integrity, represented by the diffusion tensor imaging (DTI) measures, and AD diagnosis and to denote the key substrates in predicting AD. It included DTI measures of mean diffusivity (MD), fractional anisotropy, radial diffusivity and axial diffusivity of 18 main WM tracts in 84 non-Hispanic white participants from the Alzheimer's Disease Neuroimaging Initiative dataset. The multivariable general linear model was used to examine the association of AD diagnosis with each DTI measure adjusting for age, gender and education. The corpus callosum, fornix, cingulum hippocampus, uncinate fasciculus, sagittal striatum, left posterior thalamic radiation and fornix-stria terminalis showed significant increases in MD, radial and axial diffusivity, whereas the splenium of corpus callosum and the fornix showed significant decreases in fractional anisotropy among AD patients. Variable cluster analysis identified that hippocampus volume, mini-mental state examination (MMSE), cingulate gyrus/hippocampus, inferior fronto-occipital fasciculus and uncinate fasciculus are highly correlated in one cluster with MD measures. In conclusion, there were significant differences in DTI measures between the brain WM of AD patients and controls. Age is the risk factor associated with AD, not gender or education. Right cingulum gyrus and right uncinate fasciculus are particularly affected, correlating well with a cognitive test MMSE and MD measures for dementia in AD patients and could be a region of focus for AD staging.
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Affiliation(s)
- Danqing Xiao
- Department of STEM, School of Arts and Sciences, Regis College, Weston, Massachusetts, USA.,Neuroimaging Center, McLean Hospital, Belmont, Massachusetts, USA
| | - Kesheng Wang
- Department of Family and Community Health, School of Nursing, Health Sciences Center, West Virginia University, Morgantown, West Virginia, USA
| | - Luke Theriault
- Department of STEM, School of Arts and Sciences, Regis College, Weston, Massachusetts, USA.,School of Medicine, St. George's University, Saint George's, Grenada
| | - Elhelou Charbel
- Department of STEM, School of Arts and Sciences, Regis College, Weston, Massachusetts, USA
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Vlegels N, Ossenkoppele R, van der Flier WM, Koek HL, Reijmer YD, Wisse LEM, Biessels GJ. Does Loss of Integrity of the Cingulum Bundle Link Amyloid-β Accumulation and Neurodegeneration in Alzheimer’s Disease? J Alzheimers Dis 2022; 89:39-49. [DOI: 10.3233/jad-220024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Alzheimer’s disease is characterized by the accumulation of amyloid-β (Aβ) into plaques, aggregation of tau into neurofibrillary tangles, and neurodegenerative processes including atrophy. However, there is a poorly understood spatial discordance between initial Aβ deposition and local neurodegeneration. Objective: Here, we test the hypothesis that the cingulum bundle links Aβ deposition in the cingulate cortex to medial temporal lobe (MTL) atrophy. Methods: 21 participants with mild cognitive impairment (MCI) from the UMC Utrecht memory clinic (UMCU, discovery sample) and 37 participants with MCI from Alzheimer’s Disease Neuroimaging Initiative (ADNI, replication sample) with available Aβ-PET scan, T1-weighted and diffusion-weighted MRI were included. Aβ load of the cingulate cortex was measured by the standardized uptake value ratio (SUVR), white matter integrity of the cingulum bundle was assessed by mean diffusivity and atrophy of the MTL by normalized MTL volume. Relationships were tested with linear mixed models, to accommodate multiple measures for each participant. Results: We found at most a weak association between cingulate Aβ and MTL volume (added R2 <0.06), primarily for the posterior hippocampus. In neither sample, white matter integrity of the cingulum bundle was associated with cingulate Aβ or MTL volume (added R2 <0.01). Various sensitivity analyses (Aβ-positive individuals only, posterior cingulate SUVR, MTL sub region volume) provided similar results. Conclusion: These findings, consistent in two independent cohorts, do not support our hypothesis that loss of white matter integrity of the cingulum is a connecting factor between cingulate gyrus Aβ deposition and MTL atrophy.
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Affiliation(s)
- Naomi Vlegels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Epidemiology and Data Science, VU University Medical Center, Amsterdam, The Netherlands
| | - Huiberdina L. Koek
- Department of Geriatrics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D. Reijmer
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Pantazopoulos H, Hossain NM, Chelini G, Durning P, Barbas H, Zikopoulos B, Berretta S. Chondroitin Sulphate Proteoglycan Axonal Coats in the Human Mediodorsal Thalamic Nucleus. Front Integr Neurosci 2022; 16:934764. [PMID: 35875507 PMCID: PMC9298528 DOI: 10.3389/fnint.2022.934764] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/21/2022] [Indexed: 12/21/2022] Open
Abstract
Mounting evidence supports a key involvement of the chondroitin sulfate proteoglycans (CSPGs) NG2 and brevican (BCAN) in the regulation of axonal functions, including axon guidance, fasciculation, conductance, and myelination. Prior work suggested the possibility that these functions may, at least in part, be carried out by specialized CSPG structures surrounding axons, termed axonal coats. However, their existence remains controversial. We tested the hypothesis that NG2 and BCAN, known to be associated with oligodendrocyte precursor cells, form axonal coats enveloping myelinated axons in the human brain. In tissue blocks containing the mediodorsal thalamic nucleus (MD) from healthy donors (n = 5), we used dual immunofluorescence, confocal microscopy, and unbiased stereology to characterize BCAN and NG2 immunoreactive (IR) axonal coats and measure the percentage of myelinated axons associated with them. In a subset of donors (n = 3), we used electron microscopy to analyze the spatial relationship between axons and NG2- and BCAN-IR axonal coats within the human MD. Our results show that a substantial percentage (∼64%) of large and medium myelinated axons in the human MD are surrounded by NG2- and BCAN-IR axonal coats. Electron microscopy studies show NG2- and BCAN-IR axonal coats are interleaved with myelin sheets, with larger axons displaying greater association with axonal coats. These findings represent the first characterization of NG2 and BCAN axonal coats in the human brain. The large percentage of axons surrounded by CSPG coats, and the role of CSPGs in axonal guidance, fasciculation, conductance, and myelination suggest that these structures may contribute to several key axonal properties.
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Affiliation(s)
- Harry Pantazopoulos
- Department of Psychiatry and Program in Neuroscience, University of Mississippi Medical Center, Jackson, MS, United States
| | | | - Gabriele Chelini
- Translational Neuroscience Laboratory, Mclean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Peter Durning
- Translational Neuroscience Laboratory, Mclean Hospital, Belmont, MA, United States
| | - Helen Barbas
- Department of Health Sciences, Boston University, Boston, MA, United States
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
- Neural Systems Laboratory, Boston University, Boston, MA, United States
| | - Basilis Zikopoulos
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
- Neural Systems Laboratory, Boston University, Boston, MA, United States
| | - Sabina Berretta
- Translational Neuroscience Laboratory, Mclean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Program in Neuroscience, Harvard Medical School, Boston, MA, United States
- *Correspondence: Sabina Berretta,
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38
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Wang YL, Sun M, Wang FZ, Wang X, Jia Z, Zhang Y, Li R, Jiang J, Wang L, Li W, Sun Y, Chen J, Zhang C, Shi B, Liu J, Liu X, Xu J. Mediation of the APOE Associations With Cognition Through Cerebral Blood Flow: The CIBL Study. Front Aging Neurosci 2022; 14:928925. [PMID: 35847686 PMCID: PMC9279129 DOI: 10.3389/fnagi.2022.928925] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/02/2022] [Indexed: 12/02/2022] Open
Abstract
Background The ε4 allele of the apolipoprotein E (APOE) gene is a strong genetic risk factor for aging-related cognitive decline. However, the causal connection between ε4 alleles and cognition is not well understood. The objective of this study was to identify the roles of cerebral blood flow (CBF) in cognitive-related brain areas in mediating the associations of APOE with cognition. Methods The multiple linear regression analyses were conducted on 369 subjects (mean age of 68.8 years; 62.9% of women; 29.3% of APOE ε4 allele carriers). Causal mediation analyses with 5,000 bootstrapped iterations were conducted to explore the mediation effects. Result APOE ε4 allele was negatively associated with cognition (P < 0.05) and CBF in the amygdala, hippocampus, middle temporal gyrus, posterior cingulate, and precuneus (all P < 0.05). The effect of the APOE genotype on cognition was partly mediated by the above CBF (all P < 0.05). Conclusion CBF partially mediates the potential links between APOE genotype and cognition. Overall, the APOE ε4 allele may lead to a dysregulation of the vascular structure and function with reduced cerebral perfusion, which in turn leads to cognitive impairment.
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Affiliation(s)
- Yan-Li Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mengfan Sun
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fang-Ze Wang
- Department of Cardiology, Weifang People’s Hospital, Weifang Medical University, Weifang, China
| | - Xiaohong Wang
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
| | - Ziyan Jia
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuan Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Runzhi Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiwei Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Linlin Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenyi Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yongan Sun
- Department of Neurology, Peking University First Hospital, Peking University, Beijing, China
| | - Jinglong Chen
- Division of Neurology, Department of Geriatrics, National Clinical Key Specialty, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Cuicui Zhang
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Baolin Shi
- Department of Neurology, Weifang People’s Hospital, Weifang Medical University, Weifang, China
| | - Jianjian Liu
- Department of Neurology, Fuxing Hospital, Capital Medical University, Beijing, China
| | - Xiangrong Liu
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jun Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- *Correspondence: Jun Xu,
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39
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Sirkis DW, Bonham LW, Johnson TP, La Joie R, Yokoyama JS. Dissecting the clinical heterogeneity of early-onset Alzheimer's disease. Mol Psychiatry 2022; 27:2674-2688. [PMID: 35393555 PMCID: PMC9156414 DOI: 10.1038/s41380-022-01531-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 03/07/2022] [Accepted: 03/16/2022] [Indexed: 12/14/2022]
Abstract
Early-onset Alzheimer's disease (EOAD) is a rare but particularly devastating form of AD. Though notable for its high degree of clinical heterogeneity, EOAD is defined by the same neuropathological hallmarks underlying the more common, late-onset form of AD. In this review, we describe the various clinical syndromes associated with EOAD, including the typical amnestic phenotype as well as atypical variants affecting visuospatial, language, executive, behavioral, and motor functions. We go on to highlight advances in fluid biomarker research and describe how molecular, structural, and functional neuroimaging can be used not only to improve EOAD diagnostic acumen but also enhance our understanding of fundamental pathobiological changes occurring years (and even decades) before the onset of symptoms. In addition, we discuss genetic variation underlying EOAD, including pathogenic variants responsible for the well-known mendelian forms of EOAD as well as variants that may increase risk for the much more common forms of EOAD that are either considered to be sporadic or lack a clear autosomal-dominant inheritance pattern. Intriguingly, specific pathogenic variants in PRNP and MAPT-genes which are more commonly associated with other neurodegenerative diseases-may provide unexpectedly important insights into the formation of AD tau pathology. Genetic analysis of the atypical clinical syndromes associated with EOAD will continue to be challenging given their rarity, but integration of fluid biomarker data, multimodal imaging, and various 'omics techniques and their application to the study of large, multicenter cohorts will enable future discoveries of fundamental mechanisms underlying the development of EOAD and its varied clinical presentations.
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Affiliation(s)
- Daniel W Sirkis
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Luke W Bonham
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94158, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Taylor P Johnson
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Jennifer S Yokoyama
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94158, USA.
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, 94158, USA.
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40
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Taddei RN, Sanchez-Mico MV, Bonnar O, Connors T, Gaona A, Denbow D, Frosch MP, Gómez-Isla T. Changes in glial cell phenotypes precede overt neurofibrillary tangle formation, correlate with markers of cortical cell damage, and predict cognitive status of individuals at Braak III-IV stages. Acta Neuropathol Commun 2022; 10:72. [PMID: 35534858 PMCID: PMC9082857 DOI: 10.1186/s40478-022-01370-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 04/18/2022] [Indexed: 11/10/2022] Open
Abstract
Clinico-pathological correlation studies show that some otherwise healthy elderly individuals who never developed cognitive impairment harbor a burden of Alzheimer's disease lesions (plaques and tangles) that would be expected to result in dementia. In the absence of comorbidities explaining such discrepancies, there is a need to identify other brain changes that meaningfully contribute to the cognitive status of an individual in the face of such burdens of plaques and tangles. Glial inflammatory responses, a universal phenomenon in symptomatic AD, show robust association with degree of cognitive impairment, but their significance in early tau pathology stages and contribution to the trajectory of cognitive decline at an individual level remain widely unexplored. We studied 55 brains from individuals at intermediate stages of tau tangle pathology (Braak III-IV) with diverging antemortem cognition (demented vs. non-demented, here termed `resilient'), and age-matched cognitively normal controls (Braak 0-II). We conducted quantitative assessments of amyloid and tau lesions, cellular vulnerability markers, and glial phenotypes in temporal pole (Braak III-IV region) and visual cortex (Braak V-VI region) using artificial-intelligence based semiautomated quantifications. We found distinct glial responses with increased proinflammatory and decreased homeostatic markers, both in regions with tau tangles (temporal pole) and without overt tau deposits (visual cortex) in demented but not in resilient. These changes were significantly associated with markers of cortical cell damage. Similar phenotypic glial changes were detected in the white matter of demented but not resilient and were associated with higher burden of overlying cortical cellular damage in regions with and without tangles. Our data suggest that changes in glial phenotypes in cortical and subcortical regions represent an early phenomenon that precedes overt tau deposition and likely contributes to cell damage and loss of brain function predicting the cognitive status of individuals at intermediate stages of tau aggregate burden (Braak III-IV).
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Affiliation(s)
- Raquel N Taddei
- Department of Neurology, Massachusetts General Hospital, 15th Parkman St, Boston, MA, 02114, USA
- Massachusetts Alzheimer's Disease Research Center, Boston, MA, USA
- Department of Neurology, Dementia Research Institute, University College London, London, UK
| | - Maria V Sanchez-Mico
- Department of Neurology, Massachusetts General Hospital, 15th Parkman St, Boston, MA, 02114, USA
| | - Orla Bonnar
- Department of Neurology, Massachusetts General Hospital, 15th Parkman St, Boston, MA, 02114, USA
| | - Theresa Connors
- Massachusetts Alzheimer's Disease Research Center, Boston, MA, USA
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, Boston, MA, USA
| | - Angelica Gaona
- Massachusetts Alzheimer's Disease Research Center, Boston, MA, USA
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, Boston, MA, USA
| | - Dominique Denbow
- Department of Neurology, Massachusetts General Hospital, 15th Parkman St, Boston, MA, 02114, USA
| | - Matthew P Frosch
- Massachusetts Alzheimer's Disease Research Center, Boston, MA, USA
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, Boston, MA, USA
| | - Teresa Gómez-Isla
- Department of Neurology, Massachusetts General Hospital, 15th Parkman St, Boston, MA, 02114, USA.
- Massachusetts Alzheimer's Disease Research Center, Boston, MA, USA.
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41
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Li W, Zhao J, Shen C, Zhang J, Hu J, Xiao M, Zhang J, Chen M. Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis. Front Neuroinform 2022; 16:886365. [PMID: 35571869 PMCID: PMC9100702 DOI: 10.3389/fninf.2022.886365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/30/2022] [Indexed: 11/24/2022] Open
Abstract
Alzheimer's disease (AD) has raised extensive concern in healthcare and academia as one of the most prevalent health threats to the elderly. Due to the irreversible nature of AD, early and accurate diagnoses are significant for effective prevention and treatment. However, diverse clinical symptoms and limited neuroimaging accuracy make diagnoses challenging. In this article, we built a brain network for each subject, which assembles several commonly used neuroimaging data simply and reasonably, including structural magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and amyloid positron emission tomography (PET). Based on some existing research results, we applied statistical methods to analyze (i) the distinct affinity of AD burden on each brain region, (ii) the topological lateralization between left and right hemispheric sub-networks, and (iii) the asymmetry of the AD attacks on the left and right hemispheres. In the light of advances in graph convolutional networks for graph classifications and summarized characteristics of brain networks and AD pathologies, we proposed a regional brain fusion-graph convolutional network (RBF-GCN), which is constructed with an RBF framework mainly, including three sub-modules, namely, hemispheric network generation module, multichannel GCN module, and feature fusion module. In the multichannel GCN module, the improved GCN by our proposed adaptive native node attribute (ANNA) unit embeds within each channel independently. We not only fully verified the effectiveness of the RBF framework and ANNA unit but also achieved competitive results in multiple sets of AD stages' classification tasks using hundreds of experiments over the ADNI clinical dataset.
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Affiliation(s)
- Wenchao Li
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Jiaqi Zhao
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Chenyu Shen
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Jingwen Zhang
- Department of Computer Science, Wake Forest University, Winston-Salem, NC, United States
| | - Ji Hu
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Mang Xiao
- Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jiyong Zhang
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
- *Correspondence: Jiyong Zhang
| | - Minghan Chen
- Department of Computer Science, Wake Forest University, Winston-Salem, NC, United States
- Minghan Chen
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42
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Characterizing the Propagation Pathway of Neuropathological Events of Alzheimer's Disease Using Harmonic Wavelet Analysis. Med Image Anal 2022; 79:102446. [DOI: 10.1016/j.media.2022.102446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 03/07/2022] [Accepted: 04/01/2022] [Indexed: 11/19/2022]
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43
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Jünemann K, Marie D, Worschech F, Scholz DS, Grouiller F, Kliegel M, Van De Ville D, James CE, Krüger THC, Altenmüller E, Sinke C. Six Months of Piano Training in Healthy Elderly Stabilizes White Matter Microstructure in the Fornix, Compared to an Active Control Group. Front Aging Neurosci 2022; 14:817889. [PMID: 35242025 PMCID: PMC8886041 DOI: 10.3389/fnagi.2022.817889] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 01/06/2022] [Indexed: 12/31/2022] Open
Abstract
While aging is characterized by neurodegeneration, musical training is associated with experience-driven brain plasticity and protection against age-related cognitive decline. However, evidence for the positive effects of musical training mostly comes from cross-sectional studies while randomized controlled trials with larger sample sizes are rare. The current study compares the influence of six months of piano training with music listening/musical culture lessons in 121 musically naïve healthy elderly individuals with regard to white matter properties using fixel-based analysis. Analyses revealed a significant fiber density decline in the music listening/musical culture group (but not in the piano group), after six months, in the fornix, which is a white matter tract that naturally declines with age. In addition, these changes in fiber density positively correlated to episodic memory task performances and the amount of weekly piano training. These findings not only provide further evidence for the involvement of the fornix in episodic memory encoding but also more importantly show that learning to play the piano at an advanced age may stabilize white matter microstructure of the fornix.
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Affiliation(s)
- Kristin Jünemann
- Division of Clinical Psychology & Sexual Medicine, Department of Psychiatry, Social Psychiatry and Psychotherapy, Hanover Medical School, Hanover, Germany.,Center for Systems Neuroscience, Hanover, Germany
| | - Damien Marie
- Geneva Musical Minds Lab, Geneva School of Health Sciences, University of Applied Sciences and Arts Western Switzerland, Geneva, Switzerland.,Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
| | - Florian Worschech
- Center for Systems Neuroscience, Hanover, Germany.,Institute of Music Physiology and Musicians' Medicine, Hanover University of Music, Drama and Media, Hanover, Germany
| | - Daniel S Scholz
- Center for Systems Neuroscience, Hanover, Germany.,Institute of Music Physiology and Musicians' Medicine, Hanover University of Music, Drama and Media, Hanover, Germany
| | - Frédéric Grouiller
- Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
| | - Matthias Kliegel
- Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland.,Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Geneva, Switzerland
| | - Dimitri Van De Ville
- Ecole Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland.,Faculty of Medicine, University of Geneva, Campus Biotech, Geneva, Switzerland
| | - Clara E James
- Geneva Musical Minds Lab, Geneva School of Health Sciences, University of Applied Sciences and Arts Western Switzerland, Geneva, Switzerland.,Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
| | - Tillmann H C Krüger
- Division of Clinical Psychology & Sexual Medicine, Department of Psychiatry, Social Psychiatry and Psychotherapy, Hanover Medical School, Hanover, Germany.,Center for Systems Neuroscience, Hanover, Germany
| | - Eckart Altenmüller
- Center for Systems Neuroscience, Hanover, Germany.,Institute of Music Physiology and Musicians' Medicine, Hanover University of Music, Drama and Media, Hanover, Germany
| | - Christopher Sinke
- Division of Clinical Psychology & Sexual Medicine, Department of Psychiatry, Social Psychiatry and Psychotherapy, Hanover Medical School, Hanover, Germany
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44
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Moore EE, Khan OA, Shashikumar N, Pechman KR, Liu D, Bell SP, Nair S, Terry JG, Gifford KA, Anderson AW, Landman BA, Blennow K, Zetterberg H, Hohman TJ, Carr JJ, Jefferson AL. Axonal Injury Partially Mediates Associations Between Increased Left Ventricular Mass Index and White Matter Damage. Stroke 2022; 53:808-816. [PMID: 34702069 PMCID: PMC8885768 DOI: 10.1161/strokeaha.121.034349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND PURPOSE Left ventricular (LV) mass index is a marker of subclinical LV remodeling that relates to white matter damage in aging, but molecular pathways underlying this association are unknown. This study assessed if LV mass index related to cerebrospinal fluid (CSF) biomarkers of microglial activation (sTREM2 [soluble triggering receptor expressed on myeloid cells 2]), axonal injury (NFL [neurofilament light]), neurodegeneration (total-tau), and amyloid-β, and whether these biomarkers partially accounted for associations between increased LV mass index and white matter damage. We hypothesized higher LV mass index would relate to greater CSF biomarker levels, and these pathologies would partially mediate associations with cerebral white matter microstructure. METHODS Vanderbilt Memory and Aging Project participants who underwent cardiac magnetic resonance, lumbar puncture, and diffusion tensor imaging (n=142, 72±6 years, 37% mild cognitive impairment [MCI], 32% APOE-ε4 positive, LV mass index 51.4±8.1 g/m2, NFL 1070±588 pg/mL) were included. Linear regressions and voxel-wise analyses related LV mass index to each biomarker and diffusion tensor imaging metrics, respectively. Follow-up models assessed interactions with MCI and APOE-ε4. In models where LV mass index significantly related to a biomarker and white matter microstructure, we assessed if the biomarker mediated white matter associations. RESULTS Among all participants, LV mass index was unrelated to CSF biomarkers (P>0.33). LV mass index interacted with MCI (P=0.01), such that higher LV mass index related to increased NFL among MCI participants. Associations were also present among APOE-ε4 carriers (P=0.02). NFL partially mediated up to 13% of the effect of increased LV mass index on white matter damage. CONCLUSIONS Subclinical cardiovascular remodeling, measured as an increase in LV mass index, is associated with neuroaxonal degeneration among individuals with MCI and APOE-ε4. Neuroaxonal degeneration partially reflects associations between higher LV mass index and white matter damage. Findings highlight neuroaxonal degeneration, rather than amyloidosis or microglia, may be more relevant in pathways between structural cardiovascular remodeling and white matter damage.
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Affiliation(s)
- Elizabeth E. Moore
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Omair A. Khan
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Niranjana Shashikumar
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kimberly R. Pechman
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dandan Liu
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Susan P. Bell
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sangeeta Nair
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - James G. Terry
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Katherine A. Gifford
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam W. Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A. Landman
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden,Clinical Neurochemistry Lab, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden,Clinical Neurochemistry Lab, Sahlgrenska University Hospital, Mölndal, Sweden,Department of Neurodegenerative Disease, University of College London Institute of Neurology, Queen Square, London, UK,United Kingdom Dementia Research Institute at University College London, London, UK
| | - Timothy J. Hohman
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John Jeffrey Carr
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Angela L. Jefferson
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
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Rosenkranz MA, Dean DC, Bendlin BB, Jarjour NN, Esnault S, Zetterberg H, Heslegrave A, Evans MD, Davidson RJ, Busse WW. Neuroimaging and biomarker evidence of neurodegeneration in asthma. J Allergy Clin Immunol 2022; 149:589-598.e6. [PMID: 34536414 PMCID: PMC8821112 DOI: 10.1016/j.jaci.2021.09.010] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 08/19/2021] [Accepted: 09/07/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND Epidemiologic studies have shown that Alzheimer's disease (AD) and related dementias (ADRD) are seen more frequently with asthma, especially with greater asthma severity or exacerbation frequency. OBJECTIVE To examine the changes in brain structure that may underlie this phenomenon, we examined diffusion-weighted magnetic resonance imaging (dMRI) and blood-based biomarkers of AD (phosphorylated tau 181, p-Tau181), neurodegeneration (neurofilament light chain, NfL), and glial activation (glial fibrillary acidic protein, GFAP). METHODS dMRI data were obtained in 111 individuals with asthma, ranging in disease severity from mild to severe, and 135 healthy controls. Regression analyses were used to test the relationships between asthma severity and neuroimaging measures, as well as AD pathology, neurodegeneration, and glial activation, indexed by plasma p-Tau181, NfL, and GFAP, respectively. Additional relationships were tested with cognitive function. RESULTS Asthma participants had widespread and large-magnitude differences in several dMRI metrics, which were indicative of neuroinflammation and neurodegeneration, and which were robustly associated with GFAP and, to a lesser extent, NfL. The AD biomarker p-Tau181 was only minimally associated with neuroimaging outcomes. Further, asthma severity was associated with deleterious changes in neuroimaging outcomes, which in turn were associated with slower processing speed, a test of cognitive performance. CONCLUSIONS Asthma, particularly when severe, is associated with characteristics of neuroinflammation and neurodegeneration, and may be a potential risk factor for neural injury and cognitive dysfunction. There is a need to determine how asthma may affect brain health and whether treatment directed toward characteristics of asthma associated with these risks can mitigate these effects.
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Affiliation(s)
- Melissa A Rosenkranz
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisc; Center for Healthy Minds, University of Wisconsin-Madison, Madison, Wisc.
| | - Douglas C Dean
- Department of Pediatrics, University of Wisconsin-Madison, Madison, Wisc; Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisc; Waisman Center, University of Wisconsin-Madison, Madison, Wisc
| | - Barbara B Bendlin
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisc; Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, Madison, Wisc
| | - Nizar N Jarjour
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisc
| | - Stephane Esnault
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisc
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom; UK Dementia Research Institute at UCL, London, United Kingdom
| | | | - Michael D Evans
- Biostatistical Design and Analysis Center, Clinical and Translational Science Institute, University of Minnesota, Minneapolis, Minn
| | - Richard J Davidson
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisc; Center for Healthy Minds, University of Wisconsin-Madison, Madison, Wisc; Department of Psychology, University of Wisconsin-Madison, Madison, Wisc
| | - William W Busse
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisc
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46
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Celle S, Boutet C, Annweiler C, Ceresetti R, Pichot V, Barthélémy JC, Roche F. Leukoaraiosis and Gray Matter Volume Alteration in Older Adults: The PROOF Study. Front Neurosci 2022; 15:747569. [PMID: 35095388 PMCID: PMC8793339 DOI: 10.3389/fnins.2021.747569] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
Background and Purpose: Leukoaraiosis, also called white matter hyperintensities (WMH), is frequently encountered in the brain of older adults. During aging, gray matter structure is also highly affected. WMH or gray matter defects are commonly associated with a higher prevalence of mild cognitive impairment. However, little is known about the relationship between WMH and gray matter. Our aim was thus to explore the relationship between leukoaraiosis severity and gray matter volume in a cohort of healthy older adults. Methods: Leukoaraiosis was rated in participants from the PROOF cohort using the Fazekas scale. Voxel-based morphometry was performed on brain scans to examine the potential link between WMH and changes of local brain volume. A neuropsychological evaluation including attentional, executive, and memory tests was also performed to explore cognition. Results: Out of 315 75-year-old subjects, 228 had punctuate foci of leukoaraiosis and 62 had begun the confluence of foci. Leukoaraiosis was associated with a decrease of gray matter in the middle temporal gyrus, in the right medial frontal gyrus, and in the left parahippocampal gyrus. It was also associated with decreased performances in memory recall, executive functioning, and depression. Conclusion: In a population of healthy older adults, leukoaraiosis was associated with gray matter defects and reduced cognitive performance. Controlling vascular risk factors and detecting early cerebrovascular disease may prevent, at least in part, dementia onset and progression.
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Affiliation(s)
- Sébastien Celle
- Clinical Physiology, Visas Center, University Hospital, Saint-Etienne, France
- INSERM, U1059, SAINBIOSE, DVH, Saint-Étienne, France
- *Correspondence: Sébastien Celle,
| | - Claire Boutet
- Department of Radiology, University Hospital, Saint Etienne, France
- EA7423 TAPE, UJM, Saint-Étienne, France
| | - Cédric Annweiler
- Department of Geriatric Medicine and Memory Clinic, Research Center on Autonomy and Longevity, University Hospital, Angers, France
- UPRES EA4638, University of Angers, Angers, France
| | - Romain Ceresetti
- Clinical Physiology, Visas Center, University Hospital, Saint-Etienne, France
- INSERM, U1059, SAINBIOSE, DVH, Saint-Étienne, France
| | - Vincent Pichot
- Clinical Physiology, Visas Center, University Hospital, Saint-Etienne, France
- INSERM, U1059, SAINBIOSE, DVH, Saint-Étienne, France
| | - Jean-Claude Barthélémy
- Clinical Physiology, Visas Center, University Hospital, Saint-Etienne, France
- INSERM, U1059, SAINBIOSE, DVH, Saint-Étienne, France
| | - Frédéric Roche
- Clinical Physiology, Visas Center, University Hospital, Saint-Etienne, France
- INSERM, U1059, SAINBIOSE, DVH, Saint-Étienne, France
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47
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Itaguchi Y, Castro-Chavira SA, Waterloo K, Johnsen SH, Rodríguez-Aranda C. Evaluation of Error Production in Animal Fluency and Its Relationship to Frontal Tracts in Normal Aging and Mild Alzheimer's Disease: A Combined LDA and Time-Course Analysis Investigation. Front Aging Neurosci 2022; 13:710938. [PMID: 35095462 PMCID: PMC8790484 DOI: 10.3389/fnagi.2021.710938] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 12/06/2021] [Indexed: 11/13/2022] Open
Abstract
Semantic verbal fluency (VF), assessed by animal category, is a task widely used for early detection of dementia. A feature not regularly assessed is the occurrence of errors such as perseverations and intrusions. So far, no investigation has analyzed the how and when of error occurrence during semantic VF in aging populations, together with their possible neural correlates. The present study aims to address the issue using a combined methodology based on latent Dirichlet allocation (LDA) analysis for word classification together with a time-course analysis identifying exact time of errors' occurrence. LDA is a modeling technique that discloses hidden semantic structures based on a given corpus of documents. We evaluated a sample of 66 participants divided into a healthy young group (n = 24), healthy older adult group (n = 23), and group of patients with mild Alzheimer's disease (AD) (n = 19). We performed DTI analyses to evaluate the white matter integrity of three frontal tracts purportedly underlying error commission: anterior thalamic radiation, frontal aslant tract, and uncinate fasciculus. Contrasts of DTI metrics were performed on the older groups who were further classified into high-error rate and low-error rate subgroups. Results demonstrated a unique deployment of error commission in the patient group characterized by high incidence of intrusions in the first 15 s and higher rate of perseverations toward the end of the trial. Healthy groups predominantly showed very low incidence of perseverations. The DTI analyses revealed that the patients with AD committing high-error rate presented significantly more degenerated frontal tracts in the left hemisphere. Thus, our findings demonstrated that the appearance of intrusions, together with left hemisphere degeneration of frontal tracts, is a pathognomic trait of mild AD. Furthermore, our data suggest that the error commission of patients with AD arises from executive and working memory impairments related partly to deteriorated left frontal tracts.
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Affiliation(s)
| | | | - Knut Waterloo
- Department of Psychology, UiT The Artic University of Norway, Tromsø, Norway
- Department of Neurology, University Hospital North Norway, Tromsø, Norway
| | - Stein Harald Johnsen
- Department of Neurology, University Hospital North Norway, Tromsø, Norway
- Brain and Circulation Research Group, Department of Clinical Medicine, UiT The Artic University of Norway, Tromsø, Norway
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48
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Lin CP, Frigerio I, Boon BDC, Zhou Z, Rozemuller AJM, Bouwman FH, Schoonheim MM, van de Berg WDJ, Jonkman LE. OUP accepted manuscript. Brain 2022; 145:2869-2881. [PMID: 35259207 PMCID: PMC9420016 DOI: 10.1093/brain/awac093] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 01/23/2022] [Accepted: 02/13/2022] [Indexed: 11/24/2022] Open
Abstract
Cognitive deficits in Alzheimer’s disease, specifically amnestic (memory dominant) deficits, are associated with cholinergic degeneration in the basal forebrain. The cholinergic nucleus within the basal forebrain, the nucleus basalis of Meynert, exhibits local atrophy and reduced cortical tract integrity on MRI, and reveals amyloid-β and phosphorylated-tau pathology at autopsy. To understand the pathophysiology of nucleus basalis of Meynert atrophy and its neocortical projections in Alzheimer’s disease, we used a combined post-mortem in situ MRI and histopathology approach. A total of 19 Alzheimer’s disease (10 amnestic and nine non-amnestic) and nine non-neurological control donors underwent 3 T T1-weighted MRI for anatomical delineation and volume assessment of the nucleus basalis of Meynert, and diffusion-weighted imaging for microstructural assessment of the nucleus and its projections. At subsequent brain autopsy, tissue dissection and immunohistochemistry were performed for amyloid-β, phosphorylated-tau and choline acetyltransferase. Compared to controls, we observed an MRI-derived volume reduction and altered microstructural integrity of the nucleus basalis of Meynert in Alzheimer’s disease donors. Furthermore, decreased cholinergic cell density was associated with reduced integrity of the nucleus and its tracts to the temporal lobe, specifically to the temporal pole of the superior temporal gyrus, and the parahippocampal gyrus. Exploratory post hoc subgroup analyses indicated that cholinergic cell density could be associated with cortical tract alterations in amnestic Alzheimer’s disease donors only. Our study illustrates that in Alzheimer’s disease, cholinergic degeneration in the nucleus basalis of Meynert may contribute to damaged cortical projections, specifically to the temporal lobe, leading to cognitive deterioration.
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Affiliation(s)
- Chen Pei Lin
- Correspondence to: Chen-Pei Lin De Boelelaan 1117 1081 HV, Amsterdam, The Netherlands E-mail:
| | - Irene Frigerio
- Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Baayla D C Boon
- Amsterdam UMC, location VUmc, Vrije Universiteit Amsterdam, Department of Pathology, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, Department of Neurology, Alzheimer centrum Amsterdam, Amsterdam, The Netherlands
| | - Zihan Zhou
- Zhejiang University, College of Biomedical Engineering and Instrument Science, Zhejiang, China
| | - Annemieke J M Rozemuller
- Amsterdam UMC, location VUmc, Vrije Universiteit Amsterdam, Department of Pathology, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Femke H Bouwman
- Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, Department of Neurology, Alzheimer centrum Amsterdam, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Wilma D J van de Berg
- Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Laura E Jonkman
- Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
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49
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Late-life depression accentuates cognitive weaknesses in older adults with small vessel disease. Neuropsychopharmacology 2022; 47:580-587. [PMID: 33564103 PMCID: PMC8674355 DOI: 10.1038/s41386-021-00973-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/03/2021] [Accepted: 01/12/2021] [Indexed: 02/08/2023]
Abstract
Neuroimaging features of small vessel disease (SVD) are highly prevalent in older adulthood and associated with significant variability in clinical symptoms, yet the factors predicting these symptom disparities are poorly understood. We employed a novel metric of SVD, peak width of skeletonized mean diffusivity (PSMD), to elucidate the relationship of late-life depression (LLD) to the cognitive presentation of vascular pathology. A total of 109 older adults without a diagnosis of a neurocognitive disorder were enrolled in the study; 44 with major depressive disorder and 65 age-matched controls. Subjects completed neuropsychological testing and magnetic resonance imaging including FLAIR and diffusion tensor imaging sequences, from which white matter hyperintensity volume and diffusion metrics (fractional anisotropy, mean diffusivity, PSMD) were quantified. In hierarchical models, the relationship between vascular burden and cognitive performance varied as a function of diagnostic status, such that the negative association between PSMD and processing speed was significantly stronger in participants with LLD compared to controls. Greater PSMD also predicted poorer performance on delayed memory and executive function tasks specifically among those with LLD, while there were no associations between PSMD and task performance among controls. PSMD outperformed conventional SVD and diffusion markers in predicting cognitive performance and dysexecutive behaviors in participants with LLD. These data suggest that LLD may confer a vulnerability to the cognitive manifestations of white matter abnormalities in older adulthood. PSMD, a novel biomarker of diffuse microstructural changes in SVD, may be a more sensitive marker of subtle cognitive deficits stemming from vascular pathology in LLD.
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50
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McAleese KE, Miah M, Graham S, Hadfield GM, Walker L, Johnson M, Colloby SJ, Thomas AJ, DeCarli C, Koss D, Attems J. Frontal white matter lesions in Alzheimer's disease are associated with both small vessel disease and AD-associated cortical pathology. Acta Neuropathol 2021; 142:937-950. [PMID: 34608542 PMCID: PMC8568857 DOI: 10.1007/s00401-021-02376-2] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 09/26/2021] [Accepted: 09/27/2021] [Indexed: 12/22/2022]
Abstract
Cerebral white matter lesions (WML) encompass axonal loss and demyelination and are assumed to be associated with small vessel disease (SVD)-related ischaemia. However, our previous study in the parietal lobe white matter revealed that WML in Alzheimer's disease (AD) are linked with degenerative axonal loss secondary to the deposition of cortical AD pathology. Furthermore, neuroimaging data suggest that pathomechanisms for the development of WML differ between anterior and posterior lobes with AD-associated degenerative mechanism driving posterior white matter disruption, and both AD-associated degenerative and vascular mechanisms contributed to anterior matter disruption. In this pilot study, we used human post-mortem brain tissue to investigate the composition and aetiology of frontal WML from AD and non-demented controls to determine if frontal WML are SVD-associated and to reveal any regional differences in the pathogenesis of WML. Frontal WML tissue sections from 40 human post-mortem brains (AD, n = 19; controls, n = 21) were quantitatively assessed for demyelination, axonal loss, cortical hyperphosphorylated tau (HPτ) and amyloid-beta (Aβ) burden, and arteriolosclerosis as a measure of SVD. Biochemical assessment included Wallerian degeneration-associated protease calpain and the myelin-associated glycoprotein to proteolipid protein ratio as a measure of ante-mortem ischaemia. Arteriolosclerosis severity was found to be associated with and a significant predictor of frontal WML severity in both AD and non-demented controls. Interesting, frontal axonal loss was also associated with HPτ and calpain levels were associated with increasing Aβ burden in the AD group, suggestive of an additional degenerative influence. To conclude, this pilot data suggest that frontal WML in AD may result from both increased arteriolosclerosis and AD-associated degenerative changes. These preliminary findings in combination with previously published data tentatively indicate regional differences in the aetiology of WML in AD, which should be considered in the clinical diagnosis of dementia subtypes: posterior WML maybe associated with degenerative mechanisms secondary to AD pathology, while anterior WML could be associated with both SVD-associated and degenerative mechanisms.
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Affiliation(s)
- Kirsty E McAleese
- Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK.
| | - Mohi Miah
- Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Sophie Graham
- Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Georgina M Hadfield
- Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Lauren Walker
- Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Mary Johnson
- Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Sean J Colloby
- Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Alan J Thomas
- Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Charles DeCarli
- Department of Neurology, University of California, Davis, CA, USA
| | - David Koss
- Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Johannes Attems
- Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
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