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Wang J, Ackley S, Woodworth DC, Sajjadi SA, Decarli CS, Fletcher EF, Glymour MM, Jiang L, Kawas C, Corrada MM. Associations of Amyloid Burden, White Matter Hyperintensities, and Hippocampal Volume With Cognitive Trajectories in the 90+ Study. Neurology 2024; 103:e209665. [PMID: 39008782 PMCID: PMC11249511 DOI: 10.1212/wnl.0000000000209665] [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: 02/16/2024] [Accepted: 05/10/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Amyloid pathology, vascular disease pathology, and pathologies affecting the medial temporal lobe are associated with cognitive trajectories in older adults. However, only limited evidence exists on how these pathologies influence cognition in the oldest old. We evaluated whether amyloid burden, white matter hyperintensity (WMH) volume, and hippocampal volume (HV) are associated with cognitive level and decline in the oldest old. METHODS This was a longitudinal, observational community-based cohort study. We included participants with 18F-florbetapir PET and MRI data from the 90+ Study. Amyloid load was measured using the standardized uptake value ratio in the precuneus/posterior cingulate with eroded white matter mask as reference. WMH volume was log-transformed. All imaging measures were standardized using sample means and SDs. HV and log-WMH volume were normalized by total intracranial volume using the residual approach. Global cognitive performance was measured by the Mini-Mental State Examination (MMSE) and modified MMSE (3MS) tests, repeated every 6 months. We used linear mixed-effects models with random intercepts; random slopes; and interaction between time, time squared, and imaging variables to estimate the associations of imaging variables with cognitive level and cognitive decline. Models were adjusted for demographics, APOE genotype, and health behaviors. RESULTS The sample included 192 participants. The mean age was 92.9 years, 125 (65.1%) were female, 71 (37.0%) achieved a degree beyond college, and the median follow-up time was 3.0 years. A higher amyloid load was associated with a lower cognitive level (βMMSE = -0.82, 95% CI -1.17 to -0.46; β3MS = -2.77, 95% CI -3.69 to -1.84). A 1-SD decrease in HV was associated with a 0.70-point decrease in the MMSE score (95% CI -1.14 to -0.27) and a 2.27-point decrease in the 3MS score (95% CI -3.40 to -1.14). Clear nonlinear cognitive trajectories were detected. A higher amyloid burden and smaller HV were associated with faster cognitive decline. WMH volume was not significantly associated with cognitive level or decline. DISCUSSION Amyloid burden and hippocampal atrophy are associated with both cognitive level and cognitive decline in the oldest old. Our findings shed light on how different pathologies contributed to driving cognitive function in the oldest old.
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Affiliation(s)
- Jingxuan Wang
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Sarah Ackley
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Davis C Woodworth
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Seyed Ahmad Sajjadi
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Charles S Decarli
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Evan F Fletcher
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - M Maria Glymour
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Luohua Jiang
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Claudia Kawas
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Maria M Corrada
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
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Liu X, Barisano G, Shao X, Jann K, Ringman JM, Lu H, Arfanakis K, Caprihan A, DeCarli C, Gold BT, Maillard P, Satizabal CL, Fadaee E, Habes M, Stables L, Singh H, Fischl B, van der Kouwe A, Schwab K, Helmer KG, Greenberg SM, Wang DJ. Cross-Vendor Test-Retest Validation of Diffusion Tensor Image Analysis along the Perivascular Space (DTI-ALPS) for Evaluating Glymphatic System Function. Aging Dis 2024; 15:1885-1898. [PMID: 37307817 PMCID: PMC11272201 DOI: 10.14336/ad.2023.0321-2] [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: 01/03/2023] [Accepted: 03/21/2023] [Indexed: 06/14/2023] Open
Abstract
The diffusion tensor image analysis along the perivascular space (DTI-ALPS) method was proposed to evaluate glymphatic system (GS) function. However, few studies have validated its reliability and reproducibility. Fifty participants' DTI data from the MarkVCID consortium were included in this study. Two pipelines by using DSI studio and FSL software were developed for data processing and ALPS index calculation. The ALPS index was obtained by the average of bilateral ALPS index and was used for testing the cross-vendor, inter-rater and test-retest reliability by using R studio software. The ALPS index demonstrated favorable inter-scanner reproducibility (ICC=0.77 to 0.95, P< 0.001), inter-rater reliability (ICC=0.96 to 1, P< 0.001) and test-retest repeatability (ICC=0.89 to 0.95, P< 0.001), offering a potential biomarker for in vivo evaluation of GS function.
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Affiliation(s)
- Xiaodan Liu
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA.
- Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, CA, USA.
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
| | | | - Xingfeng Shao
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA.
| | - Kay Jann
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA.
| | - John M Ringman
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA.
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University, Chicago, IL, USA.
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA.
| | | | - Charles DeCarli
- Department of Neurology, University of California, Davis, Davis, CA, USA.
| | - Brian T Gold
- Department of Neuroscience, University of Kentucky, Lexington, KY, USA.
| | - Pauline Maillard
- Department of Neurology, University of California, Davis, Davis, CA, USA.
| | - Claudia L Satizabal
- Population Health Sciences and Glenn Biggs Institute for Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
| | - Elyas Fadaee
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
| | - Lara Stables
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
| | - Herpreet Singh
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
| | - Bruce Fischl
- Department of Radiology, Harvard Medical School, Boston, MA, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
- Division of Health Sciences and Technology, Massachusetts Institute of Technology, Computer Science and AI Lab, Cambridge, Massachusetts, USA.
| | - Andre van der Kouwe
- Department of Radiology, Harvard Medical School, Boston, MA, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
| | - Kristin Schwab
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
| | - Karl G Helmer
- Department of Radiology, Harvard Medical School, Boston, MA, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
| | - Steven M Greenberg
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
| | - Danny J.J Wang
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA.
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Pappas C, Bauer CE, Zachariou V, Maillard P, Caprihan A, Shao X, Wang DJ, Gold BT. MRI free water mediates the association between water exchange rate across the blood brain barrier and executive function among older adults. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2024; 2:1-15. [PMID: 38947942 PMCID: PMC11211995 DOI: 10.1162/imag_a_00183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 03/27/2024] [Accepted: 05/03/2024] [Indexed: 07/02/2024]
Abstract
Vascular risk factors contribute to cognitive aging, with one such risk factor being dysfunction of the blood brain barrier (BBB). Studies using non-invasive magnetic resonance imaging (MRI) techniques, such as diffusion prepared arterial spin labeling (DP-ASL), can estimate BBB function by measuring water exchange rate (kw). DP-ASL kw has been associated with cognition, but the directionality and strength of the relationship is still under investigation. An additional variable that measures water in extracellular space and impacts cognition, MRI free water (FW), may help explain prior findings. A total of 94 older adults without dementia (Mean age = 74.17 years, 59.6% female) underwent MRI (DP-ASL, diffusion weighted imaging (DWI)) and cognitive assessment. Mean kw was computed across the whole brain (WB), and mean white matter FW was computed across all white matter. The relationship between kw and three cognitive domains (executive function, processing speed, memory) was tested using multiple linear regression. FW was tested as a mediator of the kw-cognitive relationship using the PROCESS macro. A positive association was found between WB kw and executive function [F(4,85) = 7.81, p < .001, R2= 0.269; β = .245, p = .014]. Further, this effect was qualified by subsequent results showing that FW was a mediator of the WB kw-executive function relationship (indirect effect results: standardized effect = .060, bootstrap confidence interval = .0006 to .1411). Results suggest that lower water exchange rate (kw) may contribute to greater total white matter (WM) FW which, in turn, may disrupt executive function. Taken together, proper fluid clearance at the BBB contributes to higher-order cognitive abilities.
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Affiliation(s)
- Colleen Pappas
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Christopher E. Bauer
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Valentinos Zachariou
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, United States
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Pauline Maillard
- Department of Neurology, University of California at Davis, Davis, CA, United States
- Center for Neurosciences, University of California at Davis, Davis, CA, United States
| | | | - Xingfeng Shao
- Laboratory of FMRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Danny J.J. Wang
- Laboratory of FMRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Brian T. Gold
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, United States
- Department of Radiology, College of Medicine, University of Kentucky, Lexington, KY, United States
- Sanders Brown Center on Aging, University of Kentucky, Lexington, KY, United States
- Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY, United States
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Li Z, Sang F, Zhang Z, Li X. Effect of the duration of hypertension on white matter structure and its link with cognition. J Cereb Blood Flow Metab 2024; 44:580-594. [PMID: 37950676 PMCID: PMC10981405 DOI: 10.1177/0271678x231214073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 09/22/2023] [Accepted: 10/21/2023] [Indexed: 11/13/2023]
Abstract
The relation between hypertension (HTN) and cognition has been reported inclusive results, which may be affected by disease duration. Our study aimed to examine the influence of HTN duration on cognition and its underlying white matter (WM) changes including macrostructural WM hyperintensities (WMH) and microstructural WM integrity. A total of 1218 patients aged ≥55 years with neuropsychological assessment and a subgroup of 233 people with imaging data were recruited and divided into 3 groups (short duration: <5 years, medium duration: 5-20 years, long duration: >20 years). We found that greater HTN duration was preferentially related to worse executive function (EF), processing speed (PS), and more severe WMH, which became more significant during long duration stage. The reductions in WM integrity were evident at the early stage especially in long-range association fibers and then scattered through the whole brain. Increasing WMH and decreasing integrity of specific tracts consistently undermined EF. Furthermore, free water imaging method greatly enhanced the sensitivity in detecting HTN-related WM alterations. These findings supported that the neurological damaging effects of HTN is cumulative and neuroimaging markers of WM at macro- and microstructural level underlie the progressive effect of HTN on cognition.
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Affiliation(s)
- Zilin Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, China
| | - Feng Sang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, China
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Schmitzer L, Kaczmarz S, Göttler J, Hoffmann G, Kallmayer M, Eckstein HH, Hedderich DM, Kufer J, Zimmer C, Preibisch C, Hyder F, Sollmann N. Macro- and microvascular contributions to cerebral structural alterations in patients with asymptomatic carotid artery stenosis. J Cereb Blood Flow Metab 2024:271678X241238935. [PMID: 38506325 DOI: 10.1177/0271678x241238935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Atherosclerosis can underly internal carotid artery stenosis (ICAS), a major risk factor for ischemic stroke, as well as small vessel disease (SVD). This study aimed to investigate hemodynamics and structural alterations associated with SVD in ICAS patients. 28 patients with unilateral asymptomatic ICAS and 30 age-matched controls underwent structural (T1-/T2-weighted and diffusion tensor imaging [DTI]) and hemodynamic (pseudo-continuous arterial spin labeling and dynamic susceptibility contrast) magnetic resonance imaging. SVD-related alterations were assessed using free water (FW), FW-corrected DTI, and peak-width of skeletonized mean diffusivity (PSMD). Furthermore, cortical thickness, cerebral blood flow (CBF), and capillary transit time heterogeneity (CTH) were analyzed. Ipsilateral to the stenosis, cortical thickness was significantly decreased in the posterior dorsal cingulate cortex (p = 0.024) and temporal pole (p = 0.028). ICAS patients exhibited elevated PSMD (p = 0.005), FW (p < 0.001), and contralateral alterations in FW-corrected DTI metrics. We found significantly lateralized CBF (p = 0.011) and a tendency for lateralized CTH (p = 0.067) in the white matter (WM) related to ICAS. Elevated PSMD and FW may indicate a link between SVD and WM changes. Contralateral alterations were seen in FW-corrected DTI, whereas hemodynamic and cortical changes were mainly ipsilateral, suggesting SVD might influence global brain changes concurrent with ICAS-related hemodynamic alterations.
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Affiliation(s)
- Lena Schmitzer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Radiology & Biomedical Imaging, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephan Kaczmarz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Radiology & Biomedical Imaging, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Philips GmbH Market DACH, Hamburg, Germany
| | - Jens Göttler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Radiology & Biomedical Imaging, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Gabriel Hoffmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Michael Kallmayer
- Department for Vascular and Endovascular Surgery, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Hans-Henning Eckstein
- Department for Vascular and Endovascular Surgery, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Dennis Martin Hedderich
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan Kufer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Radiology & Biomedical Imaging, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Christine Preibisch
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Fahmeed Hyder
- Department of Radiology & Biomedical Imaging, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, USA
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
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Cougo P, Colares H, Farinhas JG, Hämmerle M, Neves P, Bezerra R, Balduino A, Wu O, Pontes-Neto OM. Subtle white matter intensity changes on fluid-attenuated inversion recovery imaging in patients with ischaemic stroke. Brain Commun 2024; 6:fcae089. [PMID: 38529359 PMCID: PMC10963121 DOI: 10.1093/braincomms/fcae089] [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: 06/12/2023] [Revised: 01/12/2024] [Accepted: 03/11/2024] [Indexed: 03/27/2024] Open
Abstract
Leukoaraiosis is a neuroimaging marker of small-vessel disease that is characterized by high signal intensity on fluid-attenuated inversion recovery MRI. There is increasing evidence from pathology and neuroimaging suggesting that the structural abnormalities that characterize leukoaraiosis are actually present within regions of normal-appearing white matter, and that the underlying pathophysiology of white matter damage related to small-vessel disease involves blood-brain barrier damage. In this study, we aim to verify whether leukoaraiosis is associated with elevated signal intensity on fluid-attenuated inversion recovery imaging, a marker of brain tissue free-water accumulation, in normal-appearing white matter. We performed a cross-sectional study of adult patients admitted to our hospital with a diagnosis of acute ischaemic stroke or transient ischaemic attack. Leukoaraiosis was segmented using a semi-automated method involving manual outlining and signal thresholding. White matter regions were segmented based on the probabilistic tissue maps from the International Consortium for Brain Mapping 152 atlas. Also, normal-appearing white matter was further segmented based on voxel distance from leukoaraiosis borders, resulting in five normal-appearing white matter strata at increasing voxel distances from leukoaraiosis. The relationship between mean normalized fluid-attenuated inversion recovery signal intensity on normal-appearing white matter and leukoaraiosis volume was studied in a multivariable statistical analysis using linear mixed modelling, having normal-appearing white matter strata as a clustering variable. One hundred consecutive patients meeting inclusion and exclusion criteria were selected for analysis (53% female, mean age 68 years). Mean normalized fluid-attenuated inversion recovery signal intensity on normal-appearing white matter was higher in the vicinity of leukoaraiosis and progressively lower at increasing distances from leukoaraiosis. In a multivariable analysis, the mean normalized fluid-attenuated inversion recovery signal intensity on normal-appearing white matter was positively associated with leukoaraiosis volume and age (B = 0.025 for each leukoaraiosis quartile increase; 95% confidence interval 0.019-0.030). This association was found similarly across normal-appearing white matter strata. Voxel maps of the mean normalized fluid-attenuated inversion recovery signal intensity on normal-appearing white matter showed an increase in signal intensity that was not adjacent to leukoaraiosis regions. Our results show that normal-appearing white matter exhibits subtle signal intensity changes on fluid-attenuated inversion recovery imaging that are related to leukoaraiosis burden. These results suggest that diffuse free-water accumulation is likely related to the aetiopathogenic processes underlying the development of white matter damage related to small-vessel disease.
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Affiliation(s)
- Pedro Cougo
- Instituto Americas, Neurology Division, Rio de Janeiro 22775-001, Brazil
- Hospital Samaritano Barra, Department of Neurology, Rio de Janeiro 22775-001, Brazil
| | - Heber Colares
- Hospital Samaritano Barra, Department of Radiology, Rio de Janeiro, 22775-001, Brazil
| | - João Gabriel Farinhas
- Instituto Americas, Neurology Division, Rio de Janeiro 22775-001, Brazil
- Hospital Samaritano Barra, Department of Neurology, Rio de Janeiro 22775-001, Brazil
| | - Mariana Hämmerle
- Hospital Samaritano Barra, Department of Neurology, Rio de Janeiro 22775-001, Brazil
| | - Pedro Neves
- Hospital Samaritano Barra, Department of Radiology, Rio de Janeiro, 22775-001, Brazil
| | - Raquel Bezerra
- Hospital Samaritano Barra, Department of Radiology, Rio de Janeiro, 22775-001, Brazil
| | - Alex Balduino
- Instituto Americas, Neurology Division, Rio de Janeiro 22775-001, Brazil
| | - Ona Wu
- Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Octavio M Pontes-Neto
- Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto 14040-900, Brazil
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Shirzadi Z, Boyle R, Yau WYW, Coughlan G, Fu JF, Properzi MJ, Buckley RF, Yang HS, Scanlon CE, Hsieh S, Amariglio RE, Papp K, Rentz D, Price JC, Johnson KA, Sperling RA, Chhatwal JP, Schultz AP. Vascular contributions to cognitive decline: Beyond amyloid and tau in the Harvard aging brain study. J Cereb Blood Flow Metab 2024:271678X241237624. [PMID: 38452039 DOI: 10.1177/0271678x241237624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
In addition to amyloid and tau pathology, elevated systemic vascular risk, white matter injury, and reduced cerebral blood flow contribute to late-life cognitive decline. Given the strong collinearity among these parameters, we proposed a framework to extract the independent latent features underlying cognitive decline using the Harvard Aging Brain Study (N = 166 cognitively unimpaired older adults at baseline). We used the following measures from the baseline visit: cortical amyloid, inferior temporal cortex tau, relative cerebral blood flow, white matter hyperintensities, peak width of skeletonized mean diffusivity, and Framingham Heart Study cardiovascular disease risk. We used exploratory factor analysis to extract orthogonal factors from these variables and their interactions. These factors were used in a regression model to explain longitudinal Preclinical Alzheimer Cognitive Composite-5 (PACC) decline (follow-up = 8.5 ±2.7 years). We next examined whether gray matter volume atrophy acts as a mediator of factors and PACC decline. Latent factors of systemic vascular risk, white matter injury, and relative cerebral blood flow independently explain cognitive decline beyond amyloid and tau. Gray matter volume atrophy mediates these associations with the strongest effect on white matter injury. These results suggest that systemic vascular risk contributes to cognitive decline beyond current markers of cerebrovascular injury, amyloid, and tau.
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Affiliation(s)
- Zahra Shirzadi
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rory Boyle
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Wai-Ying W Yau
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Gillian Coughlan
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jessie Fanglu Fu
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael J Properzi
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hyun-Sik Yang
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Catherine E Scanlon
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Stephanie Hsieh
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rebecca E Amariglio
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kathryn Papp
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Dorene Rentz
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Julie C Price
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Keith A Johnson
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jasmeer P Chhatwal
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Aaron P Schultz
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Landau SM, Lee J, Murphy A, Ward TJ, Harrison TM, Baker SL, DeCarli C, Harvey D, Tosun D, Weiner MW, Koeppe RA, Jagust WJ. Individuals with Alzheimer's disease and low tau burden: Characteristics and implications. Alzheimers Dement 2024; 20:2113-2127. [PMID: 38241084 PMCID: PMC10984443 DOI: 10.1002/alz.13609] [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: 08/08/2023] [Revised: 11/14/2023] [Accepted: 11/20/2023] [Indexed: 01/21/2024]
Abstract
INTRODUCTION Abnormal amyloid-beta (Aβ) and tau deposition define Alzheimer's Disease (AD), but non-elevated tau is relatively frequent in patients on the AD pathway. METHODS We examined characteristics and regional patterns of 397 Aβ+ unimpaired and impaired individuals with low tau (A+T-) in relation to their higher tau counterparts (A+T+). RESULTS Seventy-one percent of Aβ+ unimpaired and 42% of impaired Aβ+ individuals were categorized as A+T- based on global tau. In impaired individuals only, A+T- status was associated with older age, male sex, and greater cardiovascular risk. α-synuclein was linked to poorer cognition, particularly when tau was low. Tau burden was most frequently elevated in a common set of temporal regions regardless of T+/T- status. DISCUSSION Low tau is relatively common in patients on the AD pathway and is linked to comorbidities that contribute to impairment. These findings have implications for the selection of individuals for Aβ- and tau-modifying therapies.
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Affiliation(s)
- Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - JiaQie Lee
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - Alice Murphy
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - Tyler J. Ward
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - Theresa M. Harrison
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - Suzanne L. Baker
- Molecular Biophysics and Integrated BioimagingLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Charles DeCarli
- School of MedicineUniversity of California, DavisSacramentoCaliforniaUSA
| | - Danielle Harvey
- School of MedicineUniversity of California, DavisSacramentoCaliforniaUSA
| | - Duygu Tosun
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)Center for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of MedicineDepartment of Psychiatry and Behavioral SciencesDepartment of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Robert A. Koeppe
- Department of RadiologyUniversity of MichiganAnn ArborMichiganUSA
| | - William J. Jagust
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
- Molecular Biophysics and Integrated BioimagingLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
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Satizabal CL, Beiser AS, Fletcher E, Seshadri S, DeCarli C. A novel neuroimaging signature for ADRD risk stratification in the community. Alzheimers Dement 2024; 20:1881-1893. [PMID: 38147416 PMCID: PMC10984488 DOI: 10.1002/alz.13600] [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: 02/06/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/28/2023]
Abstract
INTRODUCTION Early risk stratification for clinical dementia could lead to preventive therapies. We identified and validated a magnetic resonance imaging (MRI) signature for Alzheimer's disease (AD) and related dementias (ARDR). METHODS An MRI ADRD signature was derived from cortical thickness maps in Framingham Heart Study (FHS) participants with AD dementia and matched controls. The signature was related to the risk of ADRD and cognitive function in FHS. Results were replicated in the University of California Davis Alzheimer's Disease Research Center (UCD-ADRC) cohort. RESULTS Participants in the bottom quartile of the signature had more than three times increased risk for ADRD compared to those in the upper three quartiles (P < 0.001). Greater thickness in the signature was related to better general cognition (P < 0.01) and episodic memory (P = 0.01). Results replicated in UCD-ADRC. DISCUSSION We identified a robust neuroimaging biomarker for persons at increased risk of ADRD. Other cohorts will further test the validity of this biomarker.
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Affiliation(s)
- Claudia L. Satizabal
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health Sciences CenterSan AntonioTexasUSA
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyFraminghamMassachusettsUSA
| | - Alexa S. Beiser
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyFraminghamMassachusettsUSA
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
| | - Evan Fletcher
- IDeA LaboratoryDepartment of NeurologyUniversity of California DavisDavisCaliforniaUSA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health Sciences CenterSan AntonioTexasUSA
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyFraminghamMassachusettsUSA
| | - Charles DeCarli
- IDeA LaboratoryDepartment of NeurologyUniversity of California DavisDavisCaliforniaUSA
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10
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Wu C, Wu H, Zhou C, Guan X, Guo T, Cao Z, Wu J, Liu X, Chen J, Wen J, Qin J, Tan S, Duanmu X, Yuan W, Zheng Q, Zhang B, Huang P, Xu X, Zhang M. Cholinergic basal forebrain system degeneration underlies postural instability/gait difficulty and attention impairment in Parkinson's disease. Eur J Neurol 2024; 31:e16108. [PMID: 37877681 PMCID: PMC11235900 DOI: 10.1111/ene.16108] [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: 06/13/2023] [Revised: 09/11/2023] [Accepted: 10/06/2023] [Indexed: 10/26/2023]
Abstract
BACKGROUND AND PURPOSE The specific pathophysiological mechanisms underlying postural instability/gait difficulty (PIGD) and cognitive function in Parkinson's disease (PD) remain unclear. Both postural and gait control, as well as cognitive function, are associated with the cholinergic basal forebrain (cBF) system. METHODS A total of 84 PD patients and 82 normal controls were enrolled. Each participant underwent motor and cognitive assessments. Diffusion tensor imaging was used to detect structural abnormalities in the cBF system. The cBF was segmented using FreeSurfer, and its fiber tract was traced using probabilistic tractography. To provide information on extracellular water accumulation, free-water fraction (FWf) was quantified. FWf in the cBF and its fiber tract, as well as cortical projection density, were extracted for statistical analyses. RESULTS Patients had significantly higher FWf in the cBF (p < 0.001) and fiber tract (p = 0.021) than normal controls, as well as significantly lower cBF projection in the occipital (p < 0.001), parietal (p < 0.001) and prefrontal cortex (p = 0.005). In patients, a higher FWf in the cBF correlated with worse PIGD score (r = 0.306, p = 0.006) and longer Trail Making Test A time (r = 0.303, p = 0.007). Attentional function (Trail Making Test A) partially mediated the association between FWf in the cBF and PIGD score (indirect effect, a*b = 0.071; total effect, c = 0.256; p = 0.006). CONCLUSIONS Our findings suggest that degeneration of the cBF system in PD, from the cBF to its fiber tract and cortical projection, plays an important role in cognitive-motor interaction.
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Affiliation(s)
- Chenqing Wu
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Haoting Wu
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Cheng Zhou
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Xiaojun Guan
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Tao Guo
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Zhengye Cao
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Jingjing Wu
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Xiaocao Liu
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Jingwen Chen
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Jiaqi Wen
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Jianmei Qin
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Sijia Tan
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Xiaojie Duanmu
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Weijin Yuan
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Qianshi Zheng
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Baorong Zhang
- Department of Neurology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Peiyu Huang
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Xiaojun Xu
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Minming Zhang
- Department of Radiology, Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
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11
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Abdolahi F, Yu V, Varma R, Zhou X, Wang RK, D'Orazio LM, Zhao C, Jann K, Wang DJ, Kashani AH, Jiang X. Retinal perfusion is linked to cognition and brain MRI biomarkers in Black Americans. Alzheimers Dement 2024; 20:858-868. [PMID: 37800578 PMCID: PMC10917050 DOI: 10.1002/alz.13469] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 08/14/2023] [Accepted: 08/17/2023] [Indexed: 10/07/2023]
Abstract
INTRODUCTION We investigated whether retinal capillary perfusion is a biomarker of cerebral small vessel disease and impaired cognition among Black Americans, an understudied group at higher risk for dementia. METHODS We enrolled 96 Black Americans without known cognitive impairment. Four retinal perfusion measures were derived using optical coherence tomography angiography. Neurocognitive assessment and brain magnetic resonance imaging (MRI) were performed. Multiple linear regression analyses were performed. RESULTS Lower retinal capillary perfusion was correlated with worse Oral Symbol Digit Test (P < = 0.005) and Fluid Cognition Composite scores (P < = 0.02), but not with the Crystallized Cognition Composite score (P > = 0.41). Lower retinal perfusion was also correlated with higher free water and peak width of skeletonized mean diffusivity, and lower fractional anisotropy (all P < 0.05) on MRI (N = 35). DISCUSSION Lower retinal capillary perfusion is associated with worse information processing, fluid cognition, and MRI biomarkers of cerebral small vessel disease, but is not related to crystallized cognition.
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Affiliation(s)
- Farzan Abdolahi
- Department of OphthalmologyUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
| | - Victoria Yu
- Department of OphthalmologyUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
| | - Rohit Varma
- Southern California Eye InstituteCHA Hollywood Presbyterian Medical CenterLos AngelesCaliforniaUSA
| | - Xiao Zhou
- Department of BioengineeringUniversity of WashingtonSeattleWashingtonUSA
| | - Ruikang K. Wang
- Department of BioengineeringUniversity of WashingtonSeattleWashingtonUSA
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
| | - Lina M. D'Orazio
- Department of NeurologyUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
| | - Chenyang Zhao
- Laboratory of FMRI TechnologyStevens Neuroimaging and Informatics InstituteUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
| | - Kay Jann
- Laboratory of FMRI TechnologyStevens Neuroimaging and Informatics InstituteUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
| | - Danny J. Wang
- Department of NeurologyUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
- Laboratory of FMRI TechnologyStevens Neuroimaging and Informatics InstituteUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
| | - Amir H. Kashani
- Department of OphthalmologyWilmer Eye InstituteJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Xuejuan Jiang
- Department of OphthalmologyUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
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12
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Strain JF, Rahmani M, Dierker D, Owen C, Jafri H, Vlassenko AG, Womack K, Fripp J, Tosun D, Benzinger TLS, Weiner M, Masters C, Lee JM, Morris JC, Goyal MS. Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study. Neuroimage 2024; 285:120494. [PMID: 38086495 DOI: 10.1016/j.neuroimage.2023.120494] [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: 06/28/2023] [Revised: 10/23/2023] [Accepted: 12/09/2023] [Indexed: 12/17/2023] Open
Abstract
White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation. Here, we compare TrUE-Net to six established automated WMH segmentation tools, including a semi-manual method. We evaluated the techniques at both global and regional level to compare their ability to detect the established relationship between WMH burden and age. We found that TrUE-Net was highly reliable at identifying WMH regions with low false positive rates, when compared to semi-manual segmentation as the reference standard. TrUE-Net performed similarly or favorably when compared to the other automated techniques. Moreover, TrUE-Net was able to detect relationships between WMH and age to a similar degree as the reference standard semi-manual segmentation at both the global and regional level. These results support the use of TrUE-Net for identifying WMH at the global or regional level, including in large, combined datasets.
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Affiliation(s)
- Jeremy F Strain
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA.
| | - Maryam Rahmani
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA
| | - Donna Dierker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA
| | - Christopher Owen
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Hussain Jafri
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrei G Vlassenko
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA
| | - Kyle Womack
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Jurgen Fripp
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, QLD, Australia
| | - Duygu Tosun
- Division of Radiology and Biomedical Imaging, University of California - San Francisco, San Francisco, CA, USA
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Knight Alzheimer Disease Research Center, St. Louis, MO, USA
| | - Michael Weiner
- Division of Radiology and Biomedical Imaging, University of California - San Francisco, San Francisco, CA, USA
| | - Colin Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jin-Moo Lee
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Knight Alzheimer Disease Research Center, St. Louis, MO, USA
| | - Manu S Goyal
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Neuroimaging Labs Research Center, Washington University School of Medicine, St. Louis MO, USA
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13
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Saks DG, Smith EE, Sachdev PS. National and international collaborations to advance research into vascular contributions to cognitive decline. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2023; 6:100195. [PMID: 38226362 PMCID: PMC10788430 DOI: 10.1016/j.cccb.2023.100195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 12/13/2023] [Accepted: 12/13/2023] [Indexed: 01/17/2024]
Abstract
Cerebrovascular disease is the second most common cause of cognitive disorders, usually referred to as vascular contributions to cognitive impairment and dementia (VCID) and makes some contribution to about 70 % of all dementias. Despite its importance, research into VCID has lagged as compared to cognitive impairment due to Alzheimer's disease. There is an increasing appreciation that closing this gap requires large national and international collaborations. This paper highlights 24 notable large-scale national and international efforts to advance research into VCID (MarkVCID, DiverseVCID, DISCOVERY, COMPASS-ND, HBC, RHU SHIVA, UK DRI Vascular Theme, STROKOG, Meta VCI Map, ISGC, ENIGMA-Stroke Recovery, CHARGE, SVDs@target, BRIDGET, CADASIL Consortium, CADREA, AusCADASIL, DPUK, DPAU, STRIVE, HARNESS, FINESSE, VICCCS, VCD-CRE Delphi). These collaborations aim to investigate the effects on cognition from cerebrovascular disease or impaired cerebral blood flow, the mechanisms of action, means of prevention and avenues for treatment. Consensus groups have been developed to harmonise global approaches to VCID, standardise terminology and inform management and treatment, and data sharing is becoming the norm. VCID research is increasingly a global collaborative enterprise which bodes well for rapid advances in this field.
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Affiliation(s)
- Danit G Saks
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Eric E Smith
- Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, New South Wales, Australia
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Caprihan A, Hillmer L, Erhardt EB, Adair JC, Knoefel JE, Prestopnik J, Rosenberg GA. A trichotomy method for defining homogeneous subgroups in a dementia population. Ann Clin Transl Neurol 2023; 10:1802-1815. [PMID: 37602520 PMCID: PMC10578887 DOI: 10.1002/acn3.51869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/11/2023] [Accepted: 07/22/2023] [Indexed: 08/22/2023] Open
Abstract
INTRODUCTION Diagnosis of dementia in the aging brain is confounded by the presence of multiple pathologies. Mixed dementia (MX), a combination of Alzheimer's disease (AD) proteins with vascular disease (VD), is frequently found at autopsy, and has been difficult to diagnose during life. This report develops a method for separating the MX group and defining preclinical AD (presence of AD factors with normal cognition) and preclinical VD subgroups (presence of white matter damage with normal cognition). METHODS Clustering was based on three diagnostic axes: (1) AD factor (ADF) derived from cerebrospinal fluid proteins (Aβ42 and pTau), (2) VD factor (VDF) calculated from mean free water and peak width of skeletonized mean diffusivity in the white matter, and (3) Cognition (Cog) based on memory and executive function. The trichotomy method was applied to an Alzheimer's Disease Neuroimaging Initiative cohort (N = 538). RESULTS Eight biologically defined subgroups were identified which included the MX group with both high ADF and VDF (9.3%) and a preclinical VD group (3.9%), and a preclinical AD group (13.6%). Cog is significantly associated with both ADF and VDF, and the partial-correlation remains significant even when the effect of the other variable is removed (r(Cog, ADF/VDF removed) = 0.46, p < 10-28 and r(Cog, VDF/ADF removed) = 0.24, p < 10-7 ). DISCUSSION The trichotomy method creates eight biologically characterized patient groups, which includes MX, preclinical AD, and preclinical VD subgroups. Further longitudinal studies are needed to determine the utility of the 3-way clustering method with multimodal biological biomarkers.
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Affiliation(s)
| | - Laura Hillmer
- Center for Memory and AgingUniversity of New Mexico School of MedicineAlbuquerqueNew Mexico87106USA
| | - Erik Barry Erhardt
- Departments of Mathematics and StatisticsUniversity of New Mexico College of Arts and SciencesAlbuquerqueNew Mexico87106USA
| | - John C. Adair
- Center for Memory and AgingUniversity of New Mexico School of MedicineAlbuquerqueNew Mexico87106USA
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew Mexico87106USA
| | - Janice E. Knoefel
- Center for Memory and AgingUniversity of New Mexico School of MedicineAlbuquerqueNew Mexico87106USA
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew Mexico87106USA
| | - Jillian Prestopnik
- Center for Memory and AgingUniversity of New Mexico School of MedicineAlbuquerqueNew Mexico87106USA
| | - Gary A. Rosenberg
- Center for Memory and AgingUniversity of New Mexico School of MedicineAlbuquerqueNew Mexico87106USA
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew Mexico87106USA
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15
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Kern KC, Zagzoug MS, Gottesman RF, Wright CB, Leigh R. Diffusion tensor free water MRI predicts progression of FLAIR white matter hyperintensities after ischemic stroke. Front Neurol 2023; 14:1172031. [PMID: 37808483 PMCID: PMC10559725 DOI: 10.3389/fneur.2023.1172031] [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: 02/24/2023] [Accepted: 08/23/2023] [Indexed: 10/10/2023] Open
Abstract
Background The progression of FLAIR white matter hyperintensities (WMHs) on MRI heralds vascular-mediated cognitive decline. Even before FLAIR WMH progression, adjacent normal appearing white matter (NAWM) already demonstrates microstructural deterioration on diffusion tensor imaging (DTI). We hypothesized that elevated DTI free water (FW) would precede FLAIR WMH progression, implicating interstitial fluid accumulation as a key pathological step in the progression of cerebral small vessel disease. Methods Participants at least 3 months after an ischemic stroke or TIA with WMH on MRI underwent serial brain MRIs every 3 months over the subsequent year. For each participant, the WMHs were automatically segmented, serial MRIs were aligned, and a region of WMH penumbra tissue at risk was defined by dilating lesions at any time point and subtracting baseline lesions. Penumbra voxels were classified as either stable or progressing to WMH if they were segmented as new lesions and demonstrated increasing FLAIR intensity over time. Aligned DTI images included FW and FW-corrected fractional anisotropy (FATissue) and mean diffusivity (MDTissue). Logistic regression and area under the receiver-operator characteristic curve (AUC) were used to test whether baseline DTI predicted voxel-wise classification of stable penumbra or progression to WMH while covarying for clinical risk factors. Results In the included participants (n = 26, mean age 71 ± 9 years, 31% female), we detected a median annual voxel-wise WMH growth of 2.9 ± 2.6 ml. Each baseline DTI metric was associated with lesion progression in the penumbra, but FW had the greatest AUC of 0.732 (0.730 - 0.733) for predicting voxel-wise WMH progression pooled across participants. Discussion Baseline increased interstitial fluid, estimated as FW on DTI, predicted the progression of NAWM to WMH over the following year. These results implicate the presence of FW in the pathogenesis of cerebral small vessel disease progression.
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Affiliation(s)
- Kyle C. Kern
- Stroke Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Marwah S. Zagzoug
- Stroke Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Rebecca F. Gottesman
- Stroke Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Clinton B. Wright
- Stroke Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Richard Leigh
- Stroke Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
- Department of Neurology, Johns Hopkins Medicine, Baltimore, MD, United States
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16
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Rodriguez Lara F, Toro AR, Pinheiro A, Demissie S, Ekenze O, Martinez O, Parva P, Charidimou A, Ghosh S, DeCarli C, Seshadri S, Habes M, Maillard P, Romero JR. Relation of MRI-Visible Perivascular Spaces and Other MRI Markers of Cerebral Small Vessel Disease. Brain Sci 2023; 13:1323. [PMID: 37759924 PMCID: PMC10527297 DOI: 10.3390/brainsci13091323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 09/10/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Perivascular spaces (PVS) visible on brain MRI signal cerebral small vessel disease (CSVD). The coexistence of PVS with other CSVD manifestations likely increases the risk of adverse neurological outcomes. We related PVS to other CSVD manifestations and brain volumes that are markers of vascular brain injury and neurodegeneration. Framingham Heart Study (FHS) participants with CSVD ratings on brain MRI were included. PVS were rated in the basal ganglia (BG) and centrum semiovale (CSO) into grades I-IV and a category reflecting high burden in single or mixed CSO-BG regions. We related PVS to covert brain infarcts (CBI), white matter hyperintensities (WMH), cerebral microbleeds (CMB), total brain, hippocampal, and cortical gray matter volumes using adjusted multivariable regression analyses. In 2454 participants (mean age 54 ± 12 years), we observed that higher PVS burden in both BG and CSO was related to CMB in lobar and deep brain regions and increased WMH. Greater CSO PVS burden was associated with decreased total cortical gray volumes. PVS are associated with ischemic markers of CSVD and neurodegeneration markers. Further studies should elucidate the causality between PVS and other CSVD manifestations.
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Affiliation(s)
- Frances Rodriguez Lara
- Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA; (F.R.L.); (A.R.T.)
| | - Arturo Ruben Toro
- Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA; (F.R.L.); (A.R.T.)
| | - Adlin Pinheiro
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA; (A.P.); (S.D.)
- Framingham Heart Study, National Heart Lung and Blood Institute, Framingham, MA 01702, USA; (O.E.); (S.G.); (S.S.)
| | - Serkalem Demissie
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA; (A.P.); (S.D.)
- Framingham Heart Study, National Heart Lung and Blood Institute, Framingham, MA 01702, USA; (O.E.); (S.G.); (S.S.)
| | - Oluchi Ekenze
- Framingham Heart Study, National Heart Lung and Blood Institute, Framingham, MA 01702, USA; (O.E.); (S.G.); (S.S.)
- Graduate Medical Sciences, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA
| | - Oliver Martinez
- Department of Neurology, University of California Davis, Davis, CA 95817, USA; (O.M.); (C.D.); (P.M.)
| | - Pedram Parva
- Department of Radiology, Veterans Affairs Boston Healthcare System, Boston, MA 02118, USA;
- Department of Radiology, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA
| | - Andreas Charidimou
- Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA;
| | - Saptaparni Ghosh
- Framingham Heart Study, National Heart Lung and Blood Institute, Framingham, MA 01702, USA; (O.E.); (S.G.); (S.S.)
- Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA;
| | - Charles DeCarli
- Department of Neurology, University of California Davis, Davis, CA 95817, USA; (O.M.); (C.D.); (P.M.)
| | - Sudha Seshadri
- Framingham Heart Study, National Heart Lung and Blood Institute, Framingham, MA 01702, USA; (O.E.); (S.G.); (S.S.)
- Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA;
- The Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 78229, USA;
| | - Mohamad Habes
- The Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 78229, USA;
| | - Pauline Maillard
- Department of Neurology, University of California Davis, Davis, CA 95817, USA; (O.M.); (C.D.); (P.M.)
| | - Jose Rafael Romero
- Framingham Heart Study, National Heart Lung and Blood Institute, Framingham, MA 01702, USA; (O.E.); (S.G.); (S.S.)
- Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA;
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17
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Fongang B, Satizabal C, Kautz TF, Wadop YN, Muhammad JAS, Vasquez E, Mathews J, Gireud-Goss M, Saklad AR, Himali J, Beiser A, Cavazos JE, Mahaney MC, Maestre G, DeCarli C, Shipp EL, Vasan RS, Seshadri S. Cerebral small vessel disease burden is associated with decreased abundance of gut Barnesiella intestinihominis bacterium in the Framingham Heart Study. Sci Rep 2023; 13:13622. [PMID: 37604954 PMCID: PMC10442369 DOI: 10.1038/s41598-023-40872-5] [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: 02/13/2023] [Accepted: 08/17/2023] [Indexed: 08/23/2023] Open
Abstract
A bidirectional communication exists between the brain and the gut, in which the gut microbiota influences cognitive function and vice-versa. Gut dysbiosis has been linked to several diseases, including Alzheimer's disease and related dementias (ADRD). However, the relationship between gut dysbiosis and markers of cerebral small vessel disease (cSVD), a major contributor to ADRD, is unknown. In this cross-sectional study, we examined the connection between the gut microbiome, cognitive, and neuroimaging markers of cSVD in the Framingham Heart Study (FHS). Markers of cSVD included white matter hyperintensities (WMH), peak width of skeletonized mean diffusivity (PSMD), and executive function (EF), estimated as the difference between the trail-making tests B and A. We included 972 FHS participants with MRI scans, neurocognitive measures, and stool samples and quantified the gut microbiota composition using 16S rRNA sequencing. We used multivariable association and differential abundance analyses adjusting for age, sex, BMI, and education level to estimate the association between gut microbiota and WMH, PSMD, and EF measures. Our results suggest an increased abundance of Pseudobutyrivibrio and Ruminococcus genera was associated with lower WMH and PSMD (p values < 0.001), as well as better executive function (p values < 0.01). In addition, in both differential and multivariable analyses, we found that the gram-negative bacterium Barnesiella intestinihominis was strongly associated with markers indicating a higher cSVD burden. Finally, functional analyses using PICRUSt implicated various KEGG pathways, including microbial quorum sensing, AMP/GMP-activated protein kinase, phenylpyruvate, and β-hydroxybutyrate production previously associated with cognitive performance and dementia. Our study provides important insights into the association between the gut microbiome and cSVD, but further studies are needed to replicate the findings.
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Affiliation(s)
- Bernard Fongang
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
- Department of Biochemistry and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
| | - Claudia Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Tiffany F Kautz
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Yannick N Wadop
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Jazmyn A S Muhammad
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Erin Vasquez
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Julia Mathews
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Monica Gireud-Goss
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Amy R Saklad
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Jayandra Himali
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Alexa Beiser
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Jose E Cavazos
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Michael C Mahaney
- Department of Human Genetics, South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Gladys Maestre
- Department of Neurosciences and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Charles DeCarli
- Department of Neurology, Alzheimer's Disease Center, University of California, Davis, Sacramento, CA, USA
| | - Eric L Shipp
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Ramachandran S Vasan
- Framingham Heart Study, Framingham, MA, USA
- Department of Medicine, Section of Cardiovascular Medicine, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
- Department of Medicine, Section of Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Boston University's Center for Computing and Data Sciences, Boston, MA, USA
- The University of Texas School of Public Health in San Antonio, San Antonio, TX, USA
- The Long School of Medicine, University of Texas Health Science Center, San Antonio, TX, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
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18
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Luo X, Hong H, Li K, Zeng Q, Wang S, Li Z, Fu Y, Liu X, Hong L, Li J, Zhang X, Zhong S, Jiaerken Y, Liu Z, Chen Y, Huang P, Zhang M. Distinct cerebral small vessel disease impairment in early- and late-onset Alzheimer's disease. Ann Clin Transl Neurol 2023; 10:1326-1337. [PMID: 37345812 PMCID: PMC10424647 DOI: 10.1002/acn3.51824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 05/10/2023] [Accepted: 05/26/2023] [Indexed: 06/23/2023] Open
Abstract
OBJECTIVE This study investigated cerebral small vessel disease (CSVD) damage patterns in early-onset and late-onset Alzheimer's disease (EOAD and LOAD) and their effects on cognitive function. METHODS This study included 93 participants, 45 AD patients (14 EOAD and 31 LOAD), and 48 normal controls (13 YNC and 35 ONC) from the ADNI database. All participants had diffusion tensor imaging data; some had amyloid PET and plasma p-tau181 data. The study used peak width of skeletonized mean diffusivity (PSMD) to measure CSVD severity and compared PSMD between patients and age-matched controls. The effect of age on the relationship between PSMD and cognition was also examined. The study also repeated the analysis in amyloid-positive AD patients and amyloid-negative controls in another independent database (31 EOAD and 38 LOAD), and the merged database. RESULTS EOAD and LOAD showed similar cognitive function and disease severity. PSMD was validated as a reliable correlate of cognitive function. In the ADNI database, PSMD was significantly higher for LOAD and showed a tendency to increase for EOAD; in the independent and merged databases, PSMD was significantly higher for both LOAD and EOAD. The impact of PSMD on cognitive function was notably greater in the younger group (YNC and EOAD) than in the older group (ONC and LOAD), as supported by the ADNI and merged databases. INTERPRETATION EOAD has less CSVD burden than LOAD, but has a greater impact on cognition. Proactive cerebrovascular prevention strategies may have potential clinical value for younger older adults with cognitive decline.
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Affiliation(s)
- Xiao Luo
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Hui Hong
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Kaicheng Li
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Qingze Zeng
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Shuyue Wang
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Zheyu Li
- Department of NeurologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Yanv Fu
- Department of NeurologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Xiaocao Liu
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Luwei Hong
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Jixuan Li
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Xinyi Zhang
- Department of NeurologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Siyan Zhong
- Department of NeurologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Yeerfan Jiaerken
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Zhirong Liu
- Department of NeurologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Yanxing Chen
- Department of NeurologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Peiyu Huang
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Minming Zhang
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
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19
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Hinman JD, Elahi F, Chong D, Radabaugh H, Ferguson A, Maillard P, Thompson JF, Rosenberg GA, Sagare A, Moghekar A, Lu H, Lee T, Wilcock D, Satizabal CL, Tracy R, Seshadri S, Schwab K, Helmer K, Singh H, Kivisäkk P, Greenberg S, DeCarli C, Kramer J. Placental growth factor as a sensitive biomarker for vascular cognitive impairment. Alzheimers Dement 2023; 19:3519-3527. [PMID: 36815663 PMCID: PMC10440207 DOI: 10.1002/alz.12974] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 11/14/2022] [Accepted: 12/19/2022] [Indexed: 02/24/2023]
Abstract
INTRODUCTION High-performing biomarkers measuring the vascular contributions to cognitive impairment and dementia are lacking. METHODS Using a multi-site observational cohort study design, we examined the diagnostic accuracy of plasma placental growth factor (PlGF) within the MarkVCID Consortium (n = 335; CDR 0-1). Subjects underwent clinical evaluation, cognitive testing, MRI, and blood sampling as defined by Consortium protocols. RESULTS In the prospective population of 335 subjects (72.2 ± 7.8 years of age, 49.3% female), plasma PlGF (pg/mL) shows an ordinal odds ratio (OR) of 1.16 (1.07-1.25; P = .0003) for increasing Fazekas score and ordinal OR of 1.22 (1.14-1.32; P < .0001) for functional cognitive impairment measured by the Clinical Dementia Rating scale. We achieved the primary study outcome of a site-independent association of plasma PlGF (pg/mL) with white matter injury and cognitive impairment in two of three study cohorts. Secondary outcomes using the full MarkVCID cohort demonstrated that plasma PlGF can significantly discriminate individuals with Fazekas ≥ 2 and CDR = 0.5 (area under the curve [AUC] = 0.74) and CDR = 1 (AUC = 0.89) from individuals with CDR = 0. DISCUSSION Plasma PlGF measured by standardized immunoassay functions as a stable, reliable, diagnostic biomarker for cognitive impairment associated with substantial white matter burden.
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Affiliation(s)
- Jason D. Hinman
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles
- Department of Neurology, West Los Angeles Veterans Association Medical Center, Department of Veterans Affairs
| | - Fanny Elahi
- Memory and Aging Center, Weill Institute for Neuroscience, University of California San Francisco
- Department of Neurology, San Francisco Veterans Association Medical Center, Department of Veterans Affairs
| | - Davis Chong
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles
| | - Hannah Radabaugh
- Department of Neurological Surgery, Weill Institute for Neuroscience, University of California San Francisco
| | - Adam Ferguson
- Department of Neurology, San Francisco Veterans Association Medical Center, Department of Veterans Affairs
- Department of Neurological Surgery, Weill Institute for Neuroscience, University of California San Francisco
| | | | | | | | - Abhay Sagare
- Zilkha Neurogenetic Institute, University of Southern California
| | | | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University
| | - Tiffany Lee
- Sanders-Brown Center on Aging, Department of Physiology, University of Kentucky
| | - Donna Wilcock
- Sanders-Brown Center on Aging, Department of Physiology, University of Kentucky
| | - Claudia L. Satizabal
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, UT Health San Antonio
| | - Russell Tracy
- Department of Pathology & Laboratory Medicine, Larner College of Medicine, University of Vermont
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, UT Health San Antonio
| | - Kristin Schwab
- Department of Neurology, Massachusetts General Hospital, Harvard University
| | - Karl Helmer
- Department of Neurology, Massachusetts General Hospital, Harvard University
| | - Herpreet Singh
- Department of Neurology, Massachusetts General Hospital, Harvard University
| | - Pia Kivisäkk
- Department of Neurology, Massachusetts General Hospital, Harvard University
| | - Steve Greenberg
- Department of Neurology, Massachusetts General Hospital, Harvard University
| | | | - Joel Kramer
- Memory and Aging Center, Weill Institute for Neuroscience, University of California San Francisco
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20
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Campaña Perilla LA, Mahecha Carvajal ME, Cardona Ortegón JD, Barragán Corrales C, Barrera Patiño AM. Diffusion Tensor Imaging Protocol: The Need for Standardization of Measures. Radiology 2023; 308:e230470. [PMID: 37606576 PMCID: PMC10477501 DOI: 10.1148/radiol.230470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
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21
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George KM, Maillard P, Gilsanz P, Fletcher E, Peterson RL, Fong J, Mayeda ER, Mungas DM, Barnes LL, Glymour MM, DeCarli C, Whitmer RA. Association of Early Adulthood Hypertension and Blood Pressure Change With Late-Life Neuroimaging Biomarkers. JAMA Netw Open 2023; 6:e236431. [PMID: 37010868 PMCID: PMC10071343 DOI: 10.1001/jamanetworkopen.2023.6431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 02/19/2023] [Indexed: 04/04/2023] Open
Abstract
Importance The association between hypertension developed before midlife and late-life brain health is understudied and, because of the cardioprotective benefits of estrogen before menopause, may differ by sex. Objective To assess the association of early adulthood hypertension and blood pressure (BP) change with late-life neuroimaging biomarkers and examine potential sex differences. Design, Setting, and Participants This cohort study used data from the Study of Healthy Aging in African Americans (STAR) and Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) study, which were harmonized longitudinal cohorts of racially and ethnically diverse adults aged 50 years and older from the San Francisco Bay area and Sacramento Valley in California. The STAR was conducted from November 6, 2017, to November 5, 2021, and the KHANDLE study was conducted from April 27, 2017, to June 15, 2021. The current study included 427 participants from the KHANDLE and STAR studies who received health assessments between June 1, 1964, and March 31, 1985. Regional brain volumes and white matter (WM) integrity were measured via magnetic resonance imaging between June 1, 2017, and March 1, 2022. Exposures Hypertension status (normotension, transition to hypertension, and hypertension) and BP change (last measure minus first measure) were assessed at 2 multiphasic health checkups (MHCs; 1964-1985) in early adulthood (ages 30-40 years). Main Outcomes and Measures Regional brain volumes and WM integrity were measured using 3T magnetic resonance imaging and z standardized. General linear models adjusted for potential confounders (demographic characteristics and study [KHANDLE or STAR]) were used to assess the association of hypertension and BP change with neuroimaging biomarkers. Sex interactions were tested. Results Among 427 participants, median (SD) ages were 28.9 (7.3) years at the first MHC, 40.3 (9.4) years at the last MHC, and 74.8 (8.0) years at neuroimaging. A total of 263 participants (61.6%) were female and 231 (54.1%) were Black. Overall, 191 participants (44.7%) had normotension, 68 (15.9%) transitioned to hypertension, and 168 (39.3%) had hypertension. Compared with participants who had normotension, those who had hypertension and those who transitioned to hypertension had smaller cerebral volumes (hypertension: β = -0.26 [95% CI, -0.41 to -0.10]; transition to hypertension: β = -0.23 [95% CI, -0.44 to -0.23]), with similar differences in cerebral gray matter volume (hypertension: β = -0.32 [95% CI, -0.52 to -0.13]; transition to hypertension: β = -0.30 [95% CI, -0.56 to -0.05]), frontal cortex volume (hypertension: β = -0.43 [95% CI, -0.63 to -0.23]; transition to hypertension: β = -0.27 [95% CI, -0.53 to 0]), and parietal cortex volume (hypertension: β = -0.22 [95% CI, -0.42 to -0.02]; transition to hypertension: β = -0.29 [95% CI, -0.56 to -0.02]). Participants with hypertension also had smaller hippocampal volume (β = -0.22; 95% CI, -0.42 to -0.02), greater ventricular volumes (lateral ventricle: β = 0.44 [95% CI, 0.25-0.63]; third ventricle: β = 0.20 [95% CI, 0.01-0.39]), larger free water volume (β = 0.35; 95% CI, 0.18-0.52), and lower fractional anisotropy (β = -0.26; 95% CI, -0.45 to -0.08) than those who had normotension. Holding hypertension status constant, a 5-mm Hg increase in systolic BP was associated with smaller temporal cortex volume (β = -0.03; 95% CI, -0.06 to -0.01), while a 5-mm Hg increase in diastolic BP was associated with smaller parietal cortex volume (β = -0.06; 95% CI, -0.10 to -0.02). The negative association of hypertension and BP change with regional brain volumes appeared stronger in men than women for some regions. Conclusions and Relevance In this cohort study, early adulthood hypertension and BP change were associated with late-life volumetric and WM differences implicated in neurodegeneration and dementia. Sex differences were observed for some brain regions whereby hypertension and increasing BP appeared more detrimental for men. These findings suggest that prevention and treatment of hypertension in early adulthood is important for late-life brain health, particularly among men.
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Affiliation(s)
- Kristen M. George
- Department of Public Health Sciences, University of California Davis School of Medicine, Davis
| | - Pauline Maillard
- Department of Neurology, University of California Davis School of Medicine, Sacramento
| | - Paola Gilsanz
- Division of Research, Kaiser Permanente, Oakland, California
| | - Evan Fletcher
- Department of Neurology, University of California Davis School of Medicine, Sacramento
| | - Rachel L. Peterson
- School of Public and Community Health Sciences, University of Montana, Missoula
| | - Joseph Fong
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles
| | - Elizabeth Rose Mayeda
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles
| | - Dan M. Mungas
- Department of Neurology, University of California Davis School of Medicine, Sacramento
| | - Lisa L. Barnes
- Rush Alzheimer’s Disease Center, Rush Medical College, Chicago, Illinois
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco
| | - Charles DeCarli
- Department of Neurology, University of California Davis School of Medicine, Sacramento
| | - Rachel A. Whitmer
- Department of Public Health Sciences, University of California Davis School of Medicine, Davis
- Department of Neurology, University of California Davis School of Medicine, Sacramento
- Division of Research, Kaiser Permanente, Oakland, California
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22
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Maillard P, Hillmer LJ, Lu H, Arfanakis K, Gold BT, Bauer CE, Kramer JH, Staffaroni AM, Stables L, Wang DJ, Seshadri S, Satizabal CL, Beiser A, Habes M, Fornage M, Mosley TH, Rosenberg GA, Singh B, Singh H, Schwab K, Helmer KG, Greenberg SM, DeCarli C, Caprihan A. MRI free water as a biomarker for cognitive performance: Validation in the MarkVCID consortium. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12362. [PMID: 36523847 PMCID: PMC9745638 DOI: 10.1002/dad2.12362] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/11/2022] [Accepted: 08/29/2022] [Indexed: 12/15/2022]
Abstract
Introduction To evaluate the clinical validity of free water (FW), a diffusion tensor imaging-based biomarker kit proposed by the MarkVCID consortium, by investigating the association between mean FW (mFW) and executive function. Methods Baseline mFW was related to a baseline composite measure of executive function (EFC), adjusting for relevant covariates, in three MarkVCID sub-cohorts, and replicated in five, large, independent legacy cohorts. In addition, we tested whether baseline mFW predicted accelerated EFC score decline (mean follow-up time: 1.29 years). Results Higher mFW was found to be associated with lower EFC scores in MarkVCID legacy and sub-cohorts (p-values < 0.05). In addition, higher baseline mFW was associated significantly with accelerated decline in EFC scores (p = 0.0026). Discussion mFW is a sensitive biomarker of cognitive decline, providing a strong clinical rational for its use as a marker of white matter (WM) injury in multi-site observational studies and clinical trials of vascular cognitive impairment and dementia (VCID).
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Affiliation(s)
- Pauline Maillard
- Department of NeurologyUniversity of CaliforniaDavisCaliforniaUSA
| | - Laura J. Hillmer
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Hanzhang Lu
- Department of RadiologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Konstantinos Arfanakis
- Department of Biomedical EngineeringIllinois Institute of TechnologyChicagoIllinoisUSA
- Rush Alzheimer's Disease CenterDepartment of Diagnostic Radiology and Nuclear MedicineRush University Medical CenterChicagoIllinoisUSA
| | - Brian T. Gold
- Department of NeuroscienceUniversity of KentuckyLexingtonKentuckyUSA
| | | | - Joel H. Kramer
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Adam M. Staffaroni
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Lara Stables
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Danny J.J. Wang
- Laboratory of FMRI Technology (LOFT)Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Sudha Seshadri
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Claudia L. Satizabal
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
- Department of Population Health SciencesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Alexa Beiser
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular MedicineMcGovern Medical SchoolSchool of Public HealthThe University of Texas Health Science Center at HoustonHoustonTexasUSA
- Human Genetics CenterSchool of Public HealthThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Thomas H. Mosley
- MIND CenterUniversity of Mississippi Medical CenterJacksonMississippiUSA
| | - Gary A. Rosenberg
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Baljeet Singh
- Department of NeurologyUniversity of CaliforniaDavisCaliforniaUSA
| | - Herpreet Singh
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Kristin Schwab
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Karl G. Helmer
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
| | | | - Charles DeCarli
- Department of NeurologyUniversity of CaliforniaDavisCaliforniaUSA
| | - Arvind Caprihan
- The Mind Research NetworkAlbuquerqueNew MexicoAlbuquerqueNew MexicoUSA
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