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Abstract
Vascular contributions to cognitive impairment and dementia (VCID) is an all-encompassing term that describes cognitive impairment due to cerebrovascular origins. With the advancement of imaging and pathological studies, we now understand that VCID is often comorbid with Alzheimer disease. While researchers in the Alzheimer disease field have been working for years to establish and test blood-based biomarkers for Alzheimer disease diagnosis, prognosis, clinical therapy discovery, and early detection, blood-based biomarkers for VCID are in their infancy and also face challenges. VCID is heterogeneous, comprising many different pathological entities (ischemic, or hemorrhagic), and spatial and temporal differences (acute or chronic). This review highlights pathways that are aiding the search for sensitive and specific blood-based cerebrovascular dysfunction markers, describes promising candidates, and explains ongoing initiatives to discover blood-based VCID biomarkers.
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Affiliation(s)
- Kate E. Foley
- Stark Neurosciences Research Institute, Indiana University, Indianapolis IN, USA
- Department of Neurology, School of Medicine, Indiana University, Indianapolis IN, USA
| | - Donna M. Wilcock
- Stark Neurosciences Research Institute, Indiana University, Indianapolis IN, USA
- Department of Neurology, School of Medicine, Indiana University, Indianapolis IN, USA
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Moonen JEF, Haan R, Bos I, Teunissen C, van de Giessen E, Tomassen J, den Braber A, van der Landen SM, de Geus EJC, Legdeur N, van Harten AC, Trieu C, de Boer C, Kroeze L, Barkhof F, Visser PJ, van der Flier WM. Contributions of amyloid beta and cerebral small vessel disease in clinical decline. Alzheimers Dement 2024; 20:1868-1880. [PMID: 38146222 PMCID: PMC10984432 DOI: 10.1002/alz.13607] [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: 06/09/2023] [Revised: 11/21/2023] [Accepted: 11/22/2023] [Indexed: 12/27/2023]
Abstract
INTRODUCTION We assessed whether co-morbid small vessel disease (SVD) has clinical predictive value in preclinical or prodromal Alzheimer's disease. METHODS In 1090 non-demented participants (65.4 ± 10.7 years) SVD was assessed with magnetic resonance imaging and amyloid beta (Aβ) with lumbar puncture and/or positron emission tomography scan (mean follow-up for cognitive function 3.1 ± 2.4 years). RESULTS Thirty-nine percent had neither Aβ nor SVD (A-V-), 21% had SVD only (A-V+), 23% Aβ only (A+V-), and 17% had both (A+V+). Pooled cohort linear mixed model analyses demonstrated that compared to A-V- (reference), A+V- had a faster rate of cognitive decline. Co-morbid SVD (A+V+) did not further increase rate of decline. Cox regression showed that dementia risk was modestly increased in A-V+ (hazard ratio [95% confidence interval: 1.8 [1.0-3.2]) and most strongly in A+ groups. Also, mortality risk was increased in A+ groups. DISCUSSION In non-demented persons Aβ was predictive of cognitive decline, dementia, and mortality. SVD modestly predicts dementia in A-, but did not increase deleterious effects in A+. HIGHLIGHTS Amyloid beta (Aβ; A) was predictive for cognitive decline, dementia, and mortality. Small vessel disease (SVD) had no additional deleterious effects in A+. SVD modestly predicted dementia in A-. Aβ should be assessed even when magnetic resonance imaging indicates vascular cognitive impairment.
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Affiliation(s)
- Justine E. F. Moonen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Renée Haan
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Isabelle Bos
- Nivel, Research Institute for Better CareUtrechtthe Netherlands
| | - Charlotte Teunissen
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
- Neurochemistry LaboratoryDepartment of Clinical ChemistryAmsterdam Neuroscience, Neurodegeneration, Amsterdam UMC, Vrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Elsmarieke van de Giessen
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
- Department of Radiology & Nuclear MedicineVrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
| | - Jori Tomassen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Anouk den Braber
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Sophie M. van der Landen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Eco J. C. de Geus
- Department of Biological PsychologyVU UniversityAmsterdamthe Netherlands
| | - Nienke Legdeur
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Argonde C. van Harten
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Calvin Trieu
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Casper de Boer
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Lior Kroeze
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear MedicineVrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Institute of Healthcare Engineering and the Institute of Neurology, University College LondonLondonUK
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
- Department of Psychiatry and NeuropsychologySchool for Mental Health and Neuroscience (MHeNS), Maastricht UniversityMaastrichtthe Netherlands
- Department of Neurobiology, Care Sciences and Society, Division of NeurogeriatricsKarolinska InstitutetSolnaSweden
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
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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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Zachariou V, Pappas C, Bauer CE, Shao X, Liu P, Lu H, Wang DJJ, Gold BT. Regional differences in the link between water exchange rate across the blood-brain barrier and cognitive performance in normal aging. GeroScience 2024; 46:265-282. [PMID: 37713089 PMCID: PMC10828276 DOI: 10.1007/s11357-023-00930-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023] Open
Abstract
The blood-brain barrier (BBB) undergoes functional changes with aging which may contribute to cognitive decline. A novel, diffusion prepared arterial spin labeling-based MRI technique can measure the rate of water exchange across the BBB (kw) and may thus be sensitive to age-related alterations in water exchange at the BBB. However, studies investigating relationships between kw and cognition have reported different directions of association. Here, we begin to investigate the direction of associations between kw and cognition in different brain regions, and their possible underpinnings, by evaluating links between kw, cognitive performance, and MRI markers of cerebrovascular dysfunction and/or damage. Forty-seven healthy older adults (age range 61-84) underwent neuroimaging to obtain whole-brain measures of kw, cerebrovascular reactivity (CVR), and white matter hyperintensity (WMH) volumes. Additionally, participants completed uniform data set (Version 3) neuropsychological tests of executive function (EF) and episodic memory (MEM). Voxel-wise linear regressions were conducted to test associations between kw and cognitive performance, CVR, and WMH volumes. We found that kw in the frontoparietal brain regions was positively associated with cognitive performance but not with CVR or WMH volumes. Conversely, kw in the basal ganglia was negatively associated with cognitive performance and CVR and positively associated with regional, periventricular WMH volume. These regionally dependent associations may relate to different physiological underpinnings in the relationships between kw and cognition in neocortical versus subcortical brain regions in older adults.
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Affiliation(s)
- Valentinos Zachariou
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA.
| | - Colleen Pappas
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Christopher E Bauer
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Xingfeng Shao
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Peiying Liu
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Danny J J Wang
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Brian T Gold
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA
- Sanders-Brown Center On Aging, University of Kentucky, Lexington, KY, USA
- Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY, USA
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Shou Q, Zhao C, Shao X, Jann K, Kim H, Helmer KG, Lu H, Wang DJJ. Transformer-based deep learning denoising of single and multi-delay 3D arterial spin labeling. Magn Reson Med 2024; 91:803-818. [PMID: 37849048 PMCID: PMC10841192 DOI: 10.1002/mrm.29887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 09/13/2023] [Accepted: 09/21/2023] [Indexed: 10/19/2023]
Abstract
PURPOSE To present a Swin Transformer-based deep learning (DL) model (SwinIR) for denoising single-delay and multi-delay 3D arterial spin labeling (ASL) and compare its performance with convolutional neural network (CNN) and other Transformer-based methods. METHODS SwinIR and CNN-based spatial denoising models were developed for single-delay ASL. The models were trained on 66 subjects (119 scans) and tested on 39 subjects (44 scans) from three different vendors. Spatiotemporal denoising models were developed using another dataset (6 subjects, 10 scans) of multi-delay ASL. A range of input conditions was tested for denoising single and multi-delay ASL, respectively. The performance was evaluated using similarity metrics, spatial SNR and quantification accuracy of cerebral blood flow (CBF), and arterial transit time (ATT). RESULTS SwinIR outperformed CNN and other Transformer-based networks, whereas pseudo-3D models performed better than 2D models for denoising single-delay ASL. The similarity metrics and image quality (SNR) improved with more slices in pseudo-3D models and further improved when using M0 as input, but introduced greater biases for CBF quantification. Pseudo-3D models with three slices achieved optimal balance between SNR and accuracy, which can be generalized to different vendors. For multi-delay ASL, spatiotemporal denoising models had better performance than spatial-only models with reduced biases in fitted CBF and ATT maps. CONCLUSIONS SwinIR provided better performance than CNN and other Transformer-based methods for denoising both single and multi-delay 3D ASL data. The proposed model offers flexibility to improve image quality and/or reduce scan time for 3D ASL to facilitate its clinical use.
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Affiliation(s)
- Qinyang Shou
- Laboratory of Functional MRI Technology (LOFT), Stevens Neuro Imaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Chenyang Zhao
- Laboratory of Functional MRI Technology (LOFT), Stevens Neuro Imaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Xingfeng Shao
- Laboratory of Functional MRI Technology (LOFT), Stevens Neuro Imaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Kay Jann
- Laboratory of Functional MRI Technology (LOFT), Stevens Neuro Imaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Hosung Kim
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Karl G. Helmer
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Danny JJ Wang
- Laboratory of Functional MRI Technology (LOFT), Stevens Neuro Imaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
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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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Masserini F, Baso G, Gendarini C, Pantoni L. Therapeutic strategies in vascular cognitive impairment: A systematic review of population, intervention, comparators, and outcomes. Alzheimers Dement 2023; 19:5795-5804. [PMID: 37539725 DOI: 10.1002/alz.13409] [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: 05/18/2023] [Revised: 06/27/2023] [Accepted: 07/03/2023] [Indexed: 08/05/2023]
Abstract
INTRODUCTION Vascular cognitive impairment (VCI) is a common and heterogeneous condition, clinically and pathophysiologically, that still lacks approved treatment. METHODS We reviewed evidence from randomized and non-randomized clinical trials in VCI to explore whether any therapeutic option warrants further investigation and to assess possible flaws in previous studies. RESULTS We identified 118 studies after searching PubMed and Embase, including 19,223 participants and 5 different VCI subtypes. We found 63 different types of intervention (51 pharmacologic, 5 employing physical agent application, 7 rehabilitation approaches) compared with either placebo, best medical treatment, or other interventions. Treatment efficacy was assessed through 125 outcome measures (with a clearly pre-specified primary outcome in 50.8% of studies). DISCUSSION Therapeutic trials in VCI have been heterogeneous in terms of populations, types of interventions, and outcomes. Overall, a lack of clear pathophysiological rationale for tested interventions seems to emerge, together with the need to homogenize trial study design.
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Affiliation(s)
- Federico Masserini
- Neuroscience Research Center, Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
- Neurology Residency Program, University of Milan, Milan, Italy
| | - Giacomo Baso
- Neuroscience Research Center, Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
- Neurology Residency Program, University of Milan, Milan, Italy
| | - Claudia Gendarini
- Neuroscience Research Center, Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
- Neurology Residency Program, University of Milan, Milan, Italy
| | - Leonardo Pantoni
- Neuroscience Research Center, Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
- Neurology Residency Program, University of Milan, Milan, Italy
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Libecap T, Bauer CE, Zachariou V, Pappas CA, Raslau FD, Liu P, Lu H, Gold BT. Association of Baseline Cerebrovascular Reactivity and Longitudinal Development of Enlarged Perivascular Spaces in the Basal Ganglia. Stroke 2023; 54:2785-2793. [PMID: 37712232 PMCID: PMC10615859 DOI: 10.1161/strokeaha.123.043882] [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: 05/12/2023] [Accepted: 08/24/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Increasing evidence suggests that enlarged perivascular spaces (ePVS) are associated with cognitive dysfunction in aging. However, the pathogenesis of ePVS remains unknown. Here, we tested the possibility that baseline cerebrovascular dysfunction, as measured by a magnetic resonance imaging measure of cerebrovascular reactivity, contributes to the later development of ePVS. METHODS Fifty cognitively unimpaired, older adults (31 women; age range, 60-84 years) underwent magnetic resonance imaging scanning at baseline and follow-up separated by ≈2.5 years. ePVS were counted in the basal ganglia, centrum semiovale, midbrain, and hippocampus. Cerebrovascular reactivity, an index of the vasodilatory capacity of cerebral small vessels, was assessed using carbon dioxide inhalation while acquiring blood oxygen level-dependent magnetic resonance images. RESULTS Low baseline cerebrovascular reactivity values in the basal ganglia were associated with increased follow-up ePVS counts in the basal ganglia after controlling for age, sex, and baseline ePVS values (estimate [SE]=-3.18 [0.96]; P=0.002; [95% CI, -5.11 to -1.24]). This effect remained significant after accounting for self-reported risk factors of cerebral small vessel disease (estimate [SE]=-3.10 [1.00]; P=0.003; [CI, -5.11 to -1.09]) and neuroimaging markers of cerebral small vessel disease (estimate [SE]=-2.72 [0.99]; P=0.009; [CI, -4.71 to -0.73]). CONCLUSIONS Our results demonstrate that low baseline cerebrovascular reactivity is a risk factor for later development of ePVS.
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Affiliation(s)
- T.J. Libecap
- Department of Neuroscience, University of Kentucky College of Medicine, Lexington, Kentucky, USA
| | - Christopher E. Bauer
- Department of Neuroscience, University of Kentucky College of Medicine, Lexington, Kentucky, USA
| | - Valentinos Zachariou
- Department of Neuroscience, University of Kentucky College of Medicine, Lexington, Kentucky, USA
| | - Colleen A. Pappas
- Department of Neuroscience, University of Kentucky College of Medicine, Lexington, Kentucky, USA
| | - Flavius D. Raslau
- Department of Radiology, University of Kentucky College of Medicine, Lexington, Kentucky, USA
| | - Peiying Liu
- Department of Radiology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Brian T. Gold
- Department of Neuroscience, University of Kentucky College of Medicine, Lexington, Kentucky, USA
- Department of Radiology, University of Kentucky College of Medicine, Lexington, Kentucky, USA
- Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, Kentucky, USA
- Sanders-Brown Center on Aging University of Kentucky, Lexington, Kentucky, USA
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Besser LM, Chrisphonte S, Kleiman MJ, O’Shea D, Rosenfeld A, Tolea M, Galvin JE. The Healthy Brain Initiative (HBI): A prospective cohort study protocol. PLoS One 2023; 18:e0293634. [PMID: 37889891 PMCID: PMC10610524 DOI: 10.1371/journal.pone.0293634] [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: 10/06/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND The Health Brain Initiative (HBI), established by University of Miami's Comprehensive Center for Brain Health (CCBH), follows racially/ethnically diverse older adults without dementia living in South Florida. With dementia prevention and brain health promotion as an overarching goal, HBI will advance scientific knowledge by developing novel assessments and non-invasive biomarkers of Alzheimer's disease and related dementias (ADRD), examining additive effects of sociodemographic, lifestyle, neurological and biobehavioral measures, and employing innovative, methodologically advanced modeling methods to characterize ADRD risk and resilience factors and transition of brain aging. METHODS HBI is a longitudinal, observational cohort study that will follow 500 deeply-phenotyped participants annually to collect, analyze, and store clinical, cognitive, behavioral, functional, genetic, and neuroimaging data and biospecimens. Participants are ≥50 years old; have no, subjective, or mild cognitive impairment; have a study partner; and are eligible to undergo magnetic resonance imaging (MRI). Recruitment is community-based including advertisements, word-of-mouth, community events, and physician referrals. At baseline, following informed consent, participants complete detailed web-based surveys (e.g., demographics, health history, risk and resilience factors), followed by two half-day visits which include neurological exams, cognitive and functional assessments, an overnight sleep study, and biospecimen collection. Structural and functional MRI is completed by all participants and a subset also consent to amyloid PET imaging. Annual follow-up visits repeat the same data and biospecimen collection as baseline, except that MRIs are conducted every other year after baseline. ETHICS AND EXPECTED IMPACT HBI has been approved by the University of Miami Miller School of Medicine Institutional Review Board. Participants provide informed consent at baseline and are re-consented as needed with protocol changes. Data collected by HBI will lead to breakthroughs in developing new diagnostics and therapeutics, creating comprehensive diagnostic evaluations, and providing the evidence base for precision medicine approaches to dementia prevention with individualized treatment plans.
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Affiliation(s)
- Lilah M. Besser
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, Florida, United States of America
| | - Stephanie Chrisphonte
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, Florida, United States of America
| | - Michael J. Kleiman
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, Florida, United States of America
| | - Deirdre O’Shea
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, Florida, United States of America
| | - Amie Rosenfeld
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, Florida, United States of America
| | - Magdalena Tolea
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, Florida, United States of America
| | - James E. Galvin
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Boca Raton, Florida, United States of America
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Besser LM, Chrisphonte S, Kleiman MJ, O'Shea D, Rosenfeld A, Tolea M, Galvin JE. The Healthy Brain Initiative (HBI): A prospective cohort study protocol. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.21.23295908. [PMID: 37808766 PMCID: PMC10557773 DOI: 10.1101/2023.09.21.23295908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Background The Health Brain Initiative (HBI), established by University of Miami's Comprehensive Center for Brain Health (CCBH), follows racially/ethnically diverse older adults without dementia living in South Florida. With dementia prevention and brain health promotion as an overarching goal, HBI will advance scientific knowledge by developing novel assessments and non-invasive biomarkers of Alzheimer's disease and related dementias (ADRD), examining additive effects of sociodemographic, lifestyle, neurological and biobehavioral measures, and employing innovative, methodologically advanced modeling methods to characterize ADRD risk and resilience factors and transition of brain aging. Methods HBI is a longitudinal, observational cohort study that will follow 500 deeply-phenotyped participants annually to collect, analyze, and store clinical, cognitive, behavioral, functional, genetic, and neuroimaging data and biospecimens. Participants are ≥50 years old; have no, subjective, or mild cognitive impairment; have a study partner; and are eligible to undergo magnetic resonance imaging (MRI). Recruitment is community-based including advertisements, word-of-mouth, community events, and physician referrals. At baseline, following informed consent, participants complete detailed web-based surveys (e.g., demographics, health history, risk and resilience factors), followed by two half-day visits which include neurological exams, cognitive and functional assessments, an overnight sleep study, and biospecimen collection. Structural and functional MRI is completed by all participants and a subset also consent to amyloid PET imaging. Annual follow-up visits repeat the same data and biospecimen collection as baseline, except that MRIs are conducted every other year after baseline. Ethics and expected impact HBI has been approved by the University of Miami Miller School of Medicine Institutional Review Board. Participants provide informed consent at baseline and are re-consented as needed with protocol changes. Data collected by HBI will lead to breakthroughs in developing new diagnostics and therapeutics, create comprehensive diagnostic evaluations, and provide the evidence base for precision medicine approaches to dementia prevention with individualized treatment plans.
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Badji A, Youwakim J, Cooper A, Westman E, Marseglia A. Vascular cognitive impairment - Past, present, and future challenges. Ageing Res Rev 2023; 90:102042. [PMID: 37634888 DOI: 10.1016/j.arr.2023.102042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 08/29/2023]
Abstract
Vascular cognitive impairment (VCI) is a lifelong process encompassing a broad spectrum of cognitive disorders, ranging from subtle or mild deficits to prodromal and fully developed dementia, originating from cerebrovascular lesions such as large and small vessel disease. Genetic predisposition and environmental exposure to risk factors such as unhealthy lifestyles, hypertension, cardiovascular disease, and metabolic disorders will synergistically interact, yielding biochemical and structural brain changes, ultimately culminating in VCI. However, little is known about the pathological processes underlying VCI and the temporal dynamics between risk factors and disease mechanisms (biochemical and structural brain changes). This narrative review aims to provide an evidence-based summary of the link between individual vascular risk/disorders and cognitive dysfunction and the potential structural and biochemical pathophysiological processes. We also discuss some key challenges for future research on VCI. There is a need to shift from individual risk factors/disorders to comorbid vascular burden, identifying and integrating imaging and fluid biomarkers, implementing a life-course approach, considering possible neuroprotective influences of positive life exposures, and addressing biological sex at birth and gender differences. Finally, this review highlights the need for future researchers to leverage and integrate multidimensional data to advance our understanding of the mechanisms and pathophysiology of VCI.
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Affiliation(s)
- Atef Badji
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Jessica Youwakim
- Department of Pharmacology and Physiology, Université de Montréal, Montreal, QC, Canada; Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Montreal, QC, Canada; Groupe de Recherche sur la Signalisation Neuronal et la Circuiterie (SNC), Montreal, QC, Canada
| | - Alexandra Cooper
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Anna Marseglia
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
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12
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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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13
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Duering M, Biessels GJ, Brodtmann A, Chen C, Cordonnier C, de Leeuw FE, Debette S, Frayne R, Jouvent E, Rost NS, Ter Telgte A, Al-Shahi Salman R, Backes WH, Bae HJ, Brown R, Chabriat H, De Luca A, deCarli C, Dewenter A, Doubal FN, Ewers M, Field TS, Ganesh A, Greenberg S, Helmer KG, Hilal S, Jochems ACC, Jokinen H, Kuijf H, Lam BYK, Lebenberg J, MacIntosh BJ, Maillard P, Mok VCT, Pantoni L, Rudilosso S, Satizabal CL, Schirmer MD, Schmidt R, Smith C, Staals J, Thrippleton MJ, van Veluw SJ, Vemuri P, Wang Y, Werring D, Zedde M, Akinyemi RO, Del Brutto OH, Markus HS, Zhu YC, Smith EE, Dichgans M, Wardlaw JM. Neuroimaging standards for research into small vessel disease-advances since 2013. Lancet Neurol 2023; 22:602-618. [PMID: 37236211 DOI: 10.1016/s1474-4422(23)00131-x] [Citation(s) in RCA: 118] [Impact Index Per Article: 118.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/03/2023] [Accepted: 03/28/2023] [Indexed: 05/28/2023]
Abstract
Cerebral small vessel disease (SVD) is common during ageing and can present as stroke, cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive and other symptoms and affect activities of daily living. Standards for Reporting Vascular Changes on Neuroimaging 1 (STRIVE-1) categorised and standardised the diverse features of SVD that are visible on structural MRI. Since then, new information on these established SVD markers and novel MRI sequences and imaging features have emerged. As the effect of combined SVD imaging features becomes clearer, a key role for quantitative imaging biomarkers to determine sub-visible tissue damage, subtle abnormalities visible at high-field strength MRI, and lesion-symptom patterns, is also apparent. Together with rapidly emerging machine learning methods, these metrics can more comprehensively capture the effect of SVD on the brain than the structural MRI features alone and serve as intermediary outcomes in clinical trials and future routine practice. Using a similar approach to that adopted in STRIVE-1, we updated the guidance on neuroimaging of vascular changes in studies of ageing and neurodegeneration to create STRIVE-2.
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Affiliation(s)
- Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany; Medical Image Analysis Center, University of Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
| | - Geert Jan Biessels
- Department of Neurology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Amy Brodtmann
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia; Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | - Christopher Chen
- Department of Pharmacology, Memory Aging and Cognition Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Psychological Medicine, Memory Aging and Cognition Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Charlotte Cordonnier
- Université de Lille, INSERM, CHU Lille, U1172-Lille Neuroscience and Cognition (LilNCog), Lille, France
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Center for Medical Neuroscience, Radboudumc, Nijmegen, Netherlands
| | - Stéphanie Debette
- Bordeaux Population Health Research Center, University of Bordeaux, INSERM, UMR 1219, Bordeaux, France; Department of Neurology, Institute for Neurodegenerative Diseases, CHU de Bordeaux, Bordeaux, France
| | - Richard Frayne
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Radiology, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, University of Calgary, Calgary, AB, Canada
| | - Eric Jouvent
- AP-HP, Lariboisière Hospital, Translational Neurovascular Centre, FHU NeuroVasc, Université Paris Cité, Paris, France; Université Paris Cité, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Natalia S Rost
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Walter H Backes
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, Netherlands; School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, Netherlands
| | - Hee-Joon Bae
- Department of Neurology, Seoul National University College of Medicine, Seoul, South Korea; Cerebrovascular Disease Center, Seoul National University Bundang Hospital, Seongn-si, South Korea
| | - Rosalind Brown
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Hugues Chabriat
- Centre Neurovasculaire Translationnel, CERVCO, INSERM U1141, FHU NeuroVasc, Université Paris Cité, Paris, France
| | - Alberto De Luca
- Image Sciences Institute, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Charles deCarli
- Department of Neurology and Center for Neuroscience, University of California, Davis, CA, USA
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Fergus N Doubal
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Thalia S Field
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada; Vancouver Stroke Program, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Aravind Ganesh
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
| | - Steven Greenberg
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Karl G Helmer
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Athinoula A Martinos Center for Biomedical Imaging, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Saima Hilal
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Angela C C Jochems
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Hanna Jokinen
- Division of Neuropsychology, HUS Neurocenter, Helsinki University Hospital, University of Helsinki, Helsinki, Finland; Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Hugo Kuijf
- Image Sciences Institute, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Bonnie Y K Lam
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Margaret KL Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Jessica Lebenberg
- AP-HP, Lariboisière Hospital, Translational Neurovascular Centre, FHU NeuroVasc, Université Paris Cité, Paris, France; Université Paris Cité, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Bradley J MacIntosh
- Sandra E Black Centre for Brain Resilience and Repair, Hurvitz Brain Sciences, Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Computational Radiology and Artificial Intelligence Unit, Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Pauline Maillard
- Department of Neurology and Center for Neuroscience, University of California, Davis, CA, USA
| | - Vincent C T Mok
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Margaret KL Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Leonardo Pantoni
- Department of Biomedical and Clinical Science, University of Milan, Milan, Italy
| | - Salvatore Rudilosso
- Comprehensive Stroke Center, Department of Neuroscience, Hospital Clinic and August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain
| | - Claudia L 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; Department of Neurology, Boston University Medical Center, Boston, MA, USA; Framingham Heart Study, Framingham, MA, USA
| | - Markus D Schirmer
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Colin Smith
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Julie Staals
- School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, Netherlands; Department of Neurology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | | | | | - Yilong Wang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - David Werring
- Stroke Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Marialuisa Zedde
- Neurology Unit, Stroke Unit, Department of Neuromotor Physiology and Rehabilitation, Azienda Unità Sanitaria-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Rufus O Akinyemi
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oscar H Del Brutto
- School of Medicine and Research Center, Universidad de Especialidades Espiritu Santo, Ecuador
| | - Hugh S Markus
- Stroke Research Group, Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK
| | - Yi-Cheng Zhu
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Eric E Smith
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada; Department of Radiology, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; German Centre for Cardiovascular Research (DZHK), Munich, Germany
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK.
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14
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Hou X, Guo P, Wang P, Liu P, Lin DDM, Fan H, Li Y, Wei Z, Lin Z, Jiang D, Jin J, Kelly C, Pillai JJ, Huang J, Pinho MC, Thomas BP, Welch BG, Park DC, Patel VM, Hillis AE, Lu H. Deep-learning-enabled brain hemodynamic mapping using resting-state fMRI. NPJ Digit Med 2023; 6:116. [PMID: 37344684 PMCID: PMC10284915 DOI: 10.1038/s41746-023-00859-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 06/09/2023] [Indexed: 06/23/2023] Open
Abstract
Cerebrovascular disease is a leading cause of death globally. Prevention and early intervention are known to be the most effective forms of its management. Non-invasive imaging methods hold great promises for early stratification, but at present lack the sensitivity for personalized prognosis. Resting-state functional magnetic resonance imaging (rs-fMRI), a powerful tool previously used for mapping neural activity, is available in most hospitals. Here we show that rs-fMRI can be used to map cerebral hemodynamic function and delineate impairment. By exploiting time variations in breathing pattern during rs-fMRI, deep learning enables reproducible mapping of cerebrovascular reactivity (CVR) and bolus arrival time (BAT) of the human brain using resting-state CO2 fluctuations as a natural "contrast media". The deep-learning network is trained with CVR and BAT maps obtained with a reference method of CO2-inhalation MRI, which includes data from young and older healthy subjects and patients with Moyamoya disease and brain tumors. We demonstrate the performance of deep-learning cerebrovascular mapping in the detection of vascular abnormalities, evaluation of revascularization effects, and vascular alterations in normal aging. In addition, cerebrovascular maps obtained with the proposed method exhibit excellent reproducibility in both healthy volunteers and stroke patients. Deep-learning resting-state vascular imaging has the potential to become a useful tool in clinical cerebrovascular imaging.
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Affiliation(s)
- Xirui Hou
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Pengfei Guo
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Puyang Wang
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Peiying Liu
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Doris D M Lin
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hongli Fan
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yang Li
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zhiliang Wei
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Zixuan Lin
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dengrong Jiang
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jin Jin
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Catherine Kelly
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jay J Pillai
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Judy Huang
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Marco C Pinho
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Binu P Thomas
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Babu G Welch
- Department of Neurologic Surgery, UT Southwestern Medical Center, Dallas, TX, USA
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Denise C Park
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Vishal M Patel
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Argye E Hillis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hanzhang Lu
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
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15
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Elahi FM, Alladi S, Black SE, Claassen JAHR, DeCarli C, Hughes TM, Moonen J, Pajewski NM, Price BR, Satizabal C, Shaaban CE, Silva NCBS, Snyder HM, Sveikata L, Williamson JD, Wolters FJ, Hainsworth AH. Clinical trials in vascular cognitive impairment following SPRINT-MIND: An international perspective. Cell Rep Med 2023; 4:101089. [PMID: 37343515 PMCID: PMC10314118 DOI: 10.1016/j.xcrm.2023.101089] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 08/19/2022] [Accepted: 05/24/2023] [Indexed: 06/23/2023]
Abstract
A large interventional trial, the Systolic Blood Pressure Intervention Trial sub-study termed Memory and Cognition in Decreased Hypertension (SPRINT-MIND), found reduced risk of cognitive impairment in older adults with intensive, relative to standard, blood-pressure-lowering targets (systolic BP < 120 vs. <140 mm Hg). In this perspective, we discuss key questions and make recommendations for clinical practice and for clinical trials, following SPRINT-MIND. Future trials should embody cognitive endpoints appropriate to the participant group, ideally with adaptive designs that ensure robust answers for cognitive and cardiovascular endpoints. Reliable data from diverse populations, including the oldest-old (age > 80 years), will maximize external validity and global implementation of trial findings. New biomarkers will improve phenotyping to stratify patients to optimal treatments. Currently no antihypertensive drug class stands out for dementia risk reduction. Multi-domain interventions, incorporating lifestyle change (exercise, diet) alongside medications, may maximize global impact. Given the low cost and wide availability of antihypertensive drugs, intensive BP reduction may be a cost-effective means to reduce dementia risk in diverse, aging populations worldwide.
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Affiliation(s)
- Fanny M Elahi
- Friedman Brain Institute, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Suvarna Alladi
- National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka 560030, India
| | - Sandra E Black
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada
| | - Jurgen A H R Claassen
- Department of Geriatric Medicine and Donders Institute for Medical Neuroscience, Radboud University Medical Center, 6525 EN Nijmegen, the Netherlands
| | - Charles DeCarli
- Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA 95817, USA
| | - Timothy M Hughes
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA
| | - Justine Moonen
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, the Netherlands
| | - Nicholas M Pajewski
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC 27154, USA
| | | | - Claudia Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX 78229, USA
| | - C Elizabeth Shaaban
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Nárlon C B S Silva
- Djavad Mowafaghian Centre for Brain Health, Department of Physical Therapy, Faculty of Medicine, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Heather M Snyder
- Alzheimer's Association, 225 N Michigan Avenue, Chicago, IL 60603, USA
| | - Lukas Sveikata
- J.P. Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospitals, 1205 Genève, Switzerland; Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Jeff D Williamson
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA; Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27154, USA
| | - Frank J Wolters
- Departments of Epidemiology and Radiology & Nuclear Medicine, Erasmus MC, University Medical Centre Rotterdam, 3015 GD Rotterdam, the Netherlands
| | - Atticus H Hainsworth
- Neurology, St George's University Hospitals NHS Foundation Trust, London SW17 0QT, UK; Molecular and Clinical Sciences Research Institute, St George's University of London, London SW17 0RE, UK.
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16
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Hillmer L, Erhardt EB, Caprihan A, Adair JC, Knoefel JE, Prestopnik J, Thompson J, Hobson S, Rosenberg GA. Blood-brain barrier disruption measured by albumin index correlates with inflammatory fluid biomarkers. J Cereb Blood Flow Metab 2023; 43:712-721. [PMID: 36522849 PMCID: PMC10108191 DOI: 10.1177/0271678x221146127] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 11/07/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
Blood-brain barrier (BBB) permeability can be measured by the ratio of albumin in cerebrospinal fluid (CSF) and blood and by dynamic contrast-enhanced MRI (DCEMRI). Albumin is a large molecule measured in CSF and blood to form the albumin index (Qalb), which is a global measure of BBB permeability, while the smaller Gadolinium molecule measures regional transfer (Ktrans); few studies have directly compared them in the same patients. We used both methods as part of a study of mechanisms of white matter injury in patients with different forms of dementia. In addition, we also measured biomarkers for inflammation, including proteases, angiogenic growth factors, and cytokines, and correlated them with the BBB results. We found that there was no correlation between Qalb and Ktrans. The Qalb was associated with the matrix metalloproteinases (MMP-2, MMP-3, and MMP-10), the angiogenic factors (VEGF-C and PlGF), and the cytokines (IL-6, IL-8 and TNF-α). On the other hand, Ktrans was associated with the diffusion measures, mean free water and PSMD, which indicate white matter injury. Our results show that the Qalb and Ktrans measure different aspects of BBB permeability, with albumin being a measure of inflammatory BBB opening and Ktrans indicating white matter injury.
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Affiliation(s)
- Laura Hillmer
- Center for Memory and Aging,
University of New Mexico, Albuquerque, New Mexico
| | - Erik B Erhardt
- Department of Mathematics and
Statistics, University of New Mexico, Albuquerque, New Mexico
| | | | - John C Adair
- Center for Memory and Aging,
University of New Mexico, Albuquerque, New Mexico
- Department of Neurology, University
of New Mexico, Albuquerque, New Mexico
| | - Janice E Knoefel
- Center for Memory and Aging,
University of New Mexico, Albuquerque, New Mexico
- Department of Neurology, University
of New Mexico, Albuquerque, New Mexico
| | - Jill Prestopnik
- Center for Memory and Aging,
University of New Mexico, Albuquerque, New Mexico
| | - Jeffrey Thompson
- Center for Memory and Aging,
University of New Mexico, Albuquerque, New Mexico
| | - Sasha Hobson
- Center for Memory and Aging,
University of New Mexico, Albuquerque, New Mexico
| | - Gary A Rosenberg
- Center for Memory and Aging,
University of New Mexico, Albuquerque, New Mexico
- Department of Neurology, University
of New Mexico, Albuquerque, New Mexico
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17
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Shou Q, Zhao C, Shao X, Jann K, Helmer KG, Lu H, Wang DJ. Transformer based deep learning denoising of single and multi-delay 3D Arterial Spin Labeling. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.24.23288718. [PMID: 37162975 PMCID: PMC10168491 DOI: 10.1101/2023.04.24.23288718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Purpose To present a Swin Transformer-based deep learning (DL) model for denoising of single-delay and multi-delay 3D arterial spin labeling (ASL) and compare its performance with convolutional neural network (CNN) methods. Methods Swin Transformer and CNN-based spatial denoising models were developed for single-delay ASL. The models were trained on 59 subjects (104 scans) and tested on 44 subjects (57 scans) from 3 different vendors. Spatiotemporal denoising models were developed using another dataset (6 subjects, 10 scans) of multi-delay ASL. A range of input conditions was tested for denoising single and multi-delay ASL respectively. The performance was evaluated using similarity metrics, spatial signal-to-noise ratio (SNR) and quantification accuracy of cerebral blood flow (CBF) and arterial transit time (ATT). Results Swin Transformer outperformed CNN-based networks, whereas pseudo-3D models showed better performance than 2D models for denoising single-delay ASL. The similarity metrics and image quality (SNR) improved with more slices in pseudo-3D models, and further improved when using M0 as input but introduced greater biases for CBF quantification. Pseudo-3D models with 3 slices as input achieved optimal balance between SNR and accuracy, which can be generalized to different vendors. For multi-delay, spatiotemporal denoising models had better performance than spatial-only models with reduced biases in fitted CBF and ATT maps. Conclusions Swin Transformer DL models provided better performance than CNN methods for denoising both single and multi-delay 3D ASL data. The proposed model offers flexibility to improve image quality and/or reduce scan time for 3D ASL to facilitate its clinical use.
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18
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Sulimai N, Brown J, Lominadze D. Vascular Effects on Cerebrovascular Permeability and Neurodegeneration. Biomolecules 2023; 13:biom13040648. [PMID: 37189395 DOI: 10.3390/biom13040648] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 03/29/2023] [Accepted: 04/02/2023] [Indexed: 05/17/2023] Open
Abstract
Neurons and glial cells in the brain are protected by the blood brain barrier (BBB). The local regulation of blood flow is determined by neurons and signal conducting cells called astrocytes. Although alterations in neurons and glial cells affect the function of neurons, the majority of effects are coming from other cells and organs of the body. Although it seems obvious that effects beginning in brain vasculature would play an important role in the development of various neuroinflammatory and neurodegenerative pathologies, significant interest has only been directed to the possible mechanisms involved in the development of vascular cognitive impairment and dementia (VCID) for the last decade. Presently, the National Institute of Neurological Disorders and Stroke applies considerable attention toward research related to VCID and vascular impairments during Alzheimer's disease. Thus, any changes in cerebral vessels, such as in blood flow, thrombogenesis, permeability, or others, which affect the proper vasculo-neuronal connection and interaction and result in neuronal degeneration that leads to memory decline should be considered as a subject of investigation under the VCID category. Out of several vascular effects that can trigger neurodegeneration, changes in cerebrovascular permeability seem to result in the most devastating effects. The present review emphasizes the importance of changes in the BBB and possible mechanisms primarily involving fibrinogen in the development and/or progression of neuroinflammatory and neurodegenerative diseases resulting in memory decline.
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Affiliation(s)
- Nurul Sulimai
- Department of Surgery, College of Medicine, University of South Florida Morsani, Tampa, FL 33612, USA
| | - Jason Brown
- Department of Surgery, College of Medicine, University of South Florida Morsani, Tampa, FL 33612, USA
| | - David Lominadze
- Department of Surgery, College of Medicine, University of South Florida Morsani, Tampa, FL 33612, USA
- Department of Molecular Pharmacology and Physiology, College of Medicine, University of South Florida Morsani, Tampa, FL 33612, USA
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19
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Eisenmenger LB, Peret A, Famakin BM, Spahic A, Roberts GS, Bockholt JH, Johnson KM, Paulsen JS. Vascular contributions to Alzheimer's disease. Transl Res 2023; 254:41-53. [PMID: 36529160 PMCID: PMC10481451 DOI: 10.1016/j.trsl.2022.12.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/05/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
Alzheimer's disease (AD) is the most common cause of dementia and is characterized by progressive neurodegeneration and cognitive decline. Understanding the pathophysiology underlying AD is paramount for the management of individuals at risk of and suffering from AD. The vascular hypothesis stipulates a relationship between cardiovascular disease and AD-related changes although the nature of this relationship remains unknown. In this review, we discuss several potential pathological pathways of vascular involvement in AD that have been described including dysregulation of neurovascular coupling, disruption of the blood brain barrier, and reduced clearance of metabolite waste such as beta-amyloid, a toxic peptide considered the hallmark of AD. We will also discuss the two-hit hypothesis which proposes a 2-step positive feedback loop in which microvascular insults precede the accumulation of Aß and are thought to be at the origin of the disease development. At neuroimaging, signs of vascular dysfunction such as chronic cerebral hypoperfusion have been demonstrated, appearing early in AD, even before cognitive decline and alteration of traditional biomarkers. Cerebral small vessel disease such as cerebral amyloid angiopathy, characterized by the aggregation of Aß in the vessel wall, is highly prevalent in vascular dementia and AD patients. Current data is unclear whether cardiovascular disease causes, precipitates, amplifies, precedes, or simply coincides with AD. Targeted imaging tools to quantitatively evaluate the intracranial vasculature and longitudinal studies in individuals at risk for or in the early stages of the AD continuum could be critical in disentangling this complex relationship between vascular disease and AD.
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Affiliation(s)
- Laura B Eisenmenger
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Anthony Peret
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Bolanle M Famakin
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Alma Spahic
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Grant S Roberts
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Jeremy H Bockholt
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia
| | - Kevin M Johnson
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Jane S Paulsen
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin.
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20
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Gao KJ, Yin RH, Wang Y, Wang Z, Ma AJ. Exosomal miR-320e as a Novel Potential Biomarker for Cerebral Small Vessel Disease. Int J Gen Med 2023; 16:641-655. [PMID: 36851997 PMCID: PMC9961587 DOI: 10.2147/ijgm.s399338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/20/2023] [Indexed: 02/23/2023] Open
Abstract
Background Cerebral small vessel disease (CSVD) with an insidious onset can cause overall neurological dysfunction and dementia, bringing a massive burden to society. However, the pathogenesis of CSVD is complex and reliable non-invasive biomarkers for diagnosis are still not available at present. Our study aimed to investigate abnormal exosomal miRNA patterns via microarray analysis and identify candidate biomarkers for CSVD. Methods We isolated exosomes from the plasma of all subjects and identified exosomes via currently universally accepted methods. The miRNAs were profiled through microarrays, and then the expression of selected differentially expressed miRNAs was validated through RT-PCR. GO and KEGG analysis predicted possible functions of differentially expressed miRNAs. Receiver operating characteristic (ROC) curve was employed to observe the diagnostic value of selective miRNAs. Finally, the relationship between the expression of miR-320e and the CSVD burden was analyzed. Results A total of 14 miRNAs displayed differential enrichment levels with |fold change|≥1.5 and p<0.05 through miRNA microarray analysis. The RT-PCR analysis validated that exosomal miR-320e was significantly downregulated in CSVD patients (p<0.0001). ROC curve analysis of exosomal miR-320e showed the area under the curve of 0.752. According to the multivariable analysis, miR-320e was an independent predictor of white matter hyperintensity ([aOR]= 0.452, 95% confidence interval [CI]= 0.258-0.792, p=0.006) and exhibited a negative correlation with the load of periventricular white matter hyperintensities (p=0.0021) and deep white matter hyperintensities (p=0.0018), respectively. In addition, it exhibited a negative correlation with total CSVD burden score (r=-0.276, p=0.001). Conclusion In our study, plasma exosomal miR-320e has a certain diagnostic value for CSVD, and a significant correlation with imaging burden of CSVD. Overall, exosomal miR-320e has the potential to be a novel biomarker for CSVD, but further research with a large sample size is necessary to assess its clinical utility.
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Affiliation(s)
- Ke-Jin Gao
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, People's Republic of China
| | - Rui-Hua Yin
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, People's Republic of China
| | - Yuan Wang
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, People's Republic of China
| | - Zheng Wang
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, People's Republic of China
| | - Ai-Jun Ma
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, People's Republic of China.,Institute of Cerebrovascular, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, People's Republic of China
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21
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Abstract
Hypertension affects a significant proportion of the adult and aging population and represents an important risk factor for vascular cognitive impairment and late-life dementia. Chronic high blood pressure continuously challenges the structural and functional integrity of the cerebral vasculature, leading to microvascular rarefaction and dysfunction, and neurovascular uncoupling that typically impairs cerebral blood supply. Hypertension disrupts blood-brain barrier integrity, promotes neuroinflammation, and may contribute to amyloid deposition and Alzheimer pathology. The mechanisms underlying these harmful effects are still a focus of investigation, but studies in animal models have provided significant molecular and cellular mechanistic insights. Remaining questions relate to whether adequate treatment of hypertension may prevent deterioration of cognitive function, the threshold for blood pressure treatment, and the most effective antihypertensive drugs. Recent advances in neurovascular biology, advanced brain imaging, and detection of subtle behavioral phenotypes have begun to provide insights into these critical issues. Importantly, a parallel analysis of these parameters in animal models and humans is feasible, making it possible to foster translational advancements. In this review, we provide a critical evaluation of the evidence available in experimental models and humans to examine the progress made and identify remaining gaps in knowledge.
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Affiliation(s)
| | - Costantino Iadecola
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
| | - Daniela Carnevale
- Department of Molecular Medicine, “Sapienza” University of Rome, Italy
- Research Unit of Neuro and Cardiovascular Pathophysiology, IRCCS Neuromed, Pozzilli, Italy
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22
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Bahrani AA, Abner EL, DeCarli CS, Barber JM, Sutton AC, Maillard P, Sandoval F, Arfanakis K, Yang YC, Evia AM, Schneider JA, Habes M, Franklin CG, Seshadri S, Satizabal CL, Caprihan A, Thompson JF, Rosenberg GA, Wang DJ, Jann K, Zhao C, Lu H, Rosenberg PB, Albert MS, Ali DG, Singh H, Schwab K, Greenberg SM, Helmer KG, Powel DK, Gold BT, Goldstein LB, Wilcock DM, Jicha GA. Multi-Site Cross-Site Inter-Rater and Test-Retest Reliability and Construct Validity of the MarkVCID White Matter Hyperintensity Growth and Regression Protocol. J Alzheimers Dis 2023; 96:683-693. [PMID: 37840499 PMCID: PMC11009792 DOI: 10.3233/jad-230629] [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] [Indexed: 10/17/2023]
Abstract
BACKGROUND White matter hyperintensities (WMH) that occur in the setting of vascular cognitive impairment and dementia (VCID) may be dynamic increasing or decreasing volumes or stable over time. Quantifying such changes may prove useful as a biomarker for clinical trials designed to address vascular cognitive-impairment and dementia and Alzheimer's Disease. OBJECTIVE Conducting multi-site cross-site inter-rater and test-retest reliability of the MarkVCID white matter hyperintensity growth and regression protocol. METHODS The NINDS-supported MarkVCID Consortium evaluated a neuroimaging biomarker developed to track WMH change. Test-retest and cross-site inter-rater reliability of the protocol were assessed. Cognitive test scores were analyzed in relation to WMH changes to explore its construct validity. RESULTS ICC values for test-retest reliability of WMH growth and regression were 0.969 and 0.937 respectively, while for cross-site inter-rater ICC values for WMH growth and regression were 0.995 and 0.990 respectively. Word list long-delay free-recall was negatively associated with WMH growth (p < 0.028) but was not associated with WMH regression. CONCLUSIONS The present data demonstrate robust ICC validity of a WMH growth/regression protocol over a one-year period as measured by cross-site inter-rater and test-retest reliability. These data suggest that this approach may serve an important role in clinical trials of disease-modifying agents for VCID that may preferentially affect WMH growth, stability, or regression.
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Affiliation(s)
- Ahmed A. Bahrani
- Department of Neurology, University of Kentucky, College of Medicine, Lexington, KY, USA
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
| | - Erin L. Abner
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
- Department of Epidemiology & Environmental Health, University of Kentucky, College of Public Health, Lexington, KY, USA
| | | | - Justin M. Barber
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
| | - Abigail C. Sutton
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
| | - Pauline Maillard
- Department of Neurology, University of California, Davis, CA, USA
| | | | - Konstantinos Arfanakis
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Yung-Chuan Yang
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Arnold M. Evia
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Julie A. Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Mohamad Habes
- Research Imaging Institute, University of Texas Health San Antonio, San Antonio, TX, USA
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Crystal G. Franklin
- Research Imaging Institute, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Claudia L. Satizabal
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health San Antonio, San Antonio, TX, USA
| | | | | | - Gary A. Rosenberg
- Center for Memory and Aging, University of New Mexico, Health Sciences Center, Albuquerque, NM, USA
| | - Danny J.J. Wang
- Departments of Neurology and Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kay Jann
- Departments of Neurology and Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chenyang Zhao
- Departments of Neurology and Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hanzhang Lu
- Department of Radiology and Radiological Science, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Paul B. Rosenberg
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Doaa G. Ali
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
| | - Herpreet Singh
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Kristin Schwab
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Karl G. Helmer
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - David K. Powel
- Department of Neuroscience, University of Kentucky, College of Medicine, Lexington, KY, USA
- Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY, USA
| | - Brian T. Gold
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
- Department of Neuroscience, University of Kentucky, College of Medicine, Lexington, KY, USA
- Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY, USA
| | - Larry B. Goldstein
- Department of Neurology, University of Kentucky, College of Medicine, Lexington, KY, USA
| | - Donna M. Wilcock
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
- Department of Physiology, University of Kentucky, College of Medicine, Lexington, KY, USA
| | - Gregory A. Jicha
- Department of Neurology, University of Kentucky, College of Medicine, Lexington, KY, USA
- Sanders-Brown Center on Aging, University of Kentucky, College of Medicine, Lexington, KY, USA
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23
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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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24
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Albuminuria, cognition, and MRI biomarkers of cerebrovascular disease in American Indians of the Zuni Pueblo. eNeurologicalSci 2022; 29:100438. [DOI: 10.1016/j.ensci.2022.100438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 11/14/2022] [Accepted: 11/23/2022] [Indexed: 12/05/2022] Open
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25
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Chandran R, He L, Nie X, Voltin J, Jamil S, Doueiry C, Falangola MF, Ergul A, Li W. Magnetic resonance imaging reveals microemboli-mediated pathological changes in brain microstructure in diabetic rats: relevance to vascular cognitive impairment/dementia. Clin Sci (Lond) 2022; 136:1555-1570. [PMID: 36314470 PMCID: PMC10066787 DOI: 10.1042/cs20220465] [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: 07/06/2022] [Revised: 10/19/2022] [Accepted: 10/28/2022] [Indexed: 11/17/2022]
Abstract
Diabetes doubles the risk of vascular cognitive impairment, but the underlying reasons remain unclear. In the present study, we determined the temporal and spatial changes in the brain structure after microemboli (ME) injection using diffusion MRI (dMRI). Control and diabetic rats received cholesterol crystal ME (40-70 µm) injections. Cognitive tests were followed up to 16 weeks, while dMRI scans were performed at baseline and 12 weeks post-ME. The novel object recognition test had a lower d2 recognition index along with a decrease in spontaneous alternations in the Y maze test in diabetic rats with ME. dMRI showed that ME injection caused infarction in two diabetic animals (n=5) but none in controls (n=6). In diabetes, radial diffusivity (DR) was increased while fractional anisotropy (FA) was decreased in the cortex, indicating loss of tissue integrity and edema. In the dorsal hippocampus, mean diffusivity (MD), axial diffusivity (DA), and DR were significantly increased, indicating loss of axons and myelin damage. Histological analyses confirmed more tissue damage and microglial activation in diabetic rats with ME. These results suggest that ME injury and associated cerebrovascular dysfunction are greater in diabetes, which may cause cognitive deficits. Strategies to improve vascular function can be a preventive and therapeutic approach for vascular cognitive impairment.
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Affiliation(s)
- Raghavendar Chandran
- Ralph H. Johnson VA Medical Center, Charleston, SC
- Department of Pathology & Laboratory Medicine, Medical University of South Carolina, Charleston, SC
| | - Lianying He
- Ralph H. Johnson VA Medical Center, Charleston, SC
- Department of Pathology & Laboratory Medicine, Medical University of South Carolina, Charleston, SC
| | - Xingju Nie
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC
| | - Joshua Voltin
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC
| | - Sarah Jamil
- Ralph H. Johnson VA Medical Center, Charleston, SC
- Department of Pathology & Laboratory Medicine, Medical University of South Carolina, Charleston, SC
| | - Caren Doueiry
- Ralph H. Johnson VA Medical Center, Charleston, SC
- Department of Pathology & Laboratory Medicine, Medical University of South Carolina, Charleston, SC
| | - Maria Fatima Falangola
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC
| | - Adviye Ergul
- Ralph H. Johnson VA Medical Center, Charleston, SC
- Department of Pathology & Laboratory Medicine, Medical University of South Carolina, Charleston, SC
| | - Weiguo Li
- Ralph H. Johnson VA Medical Center, Charleston, SC
- Department of Pathology & Laboratory Medicine, Medical University of South Carolina, Charleston, SC
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Liu P, Baker Z, Li Y, Li Y, Xu J, Park DC, Welch BG, Pinho M, Pillai JJ, Hillis AE, Mori S, Lu H. CVR-MRICloud: An online processing tool for CO2-inhalation and resting-state cerebrovascular reactivity (CVR) MRI data. PLoS One 2022; 17:e0274220. [PMID: 36170233 PMCID: PMC9518872 DOI: 10.1371/journal.pone.0274220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/23/2022] [Indexed: 12/02/2022] Open
Abstract
Cerebrovascular Reactivity (CVR) provides an assessment of the brain’s vascular reserve and has been postulated to be a sensitive marker in cerebrovascular diseases. MRI-based CVR measurement typically employs alterations in arterial carbon dioxide (CO2) level while continuously acquiring Blood-Oxygenation-Level-Dependent (BOLD) images. CO2-inhalation and resting-state methods are two commonly used approaches for CVR MRI. However, processing of CVR MRI data often requires special expertise and may become an obstacle in broad utilization of this promising technique. The aim of this work was to develop CVR-MRICloud, a cloud-based CVR processing pipeline, to enable automated processing of CVR MRI data. The CVR-MRICloud consists of several major steps including extraction of end-tidal CO2 (EtCO2) curve from raw CO2 recording, alignment of EtCO2 curve with BOLD time course, computation of CVR value on a whole-brain, regional, and voxel-wise basis. The pipeline also includes standard BOLD image processing steps such as motion correction, registration between functional and anatomic images, and transformation of the CVR images to canonical space. This paper describes these algorithms and demonstrates the performance of the CVR-MRICloud in lifespan healthy subjects and patients with clinical conditions such as stroke, brain tumor, and Moyamoya disease. CVR-MRICloud has potential to be used as a data processing tool for a variety of basic science and clinical applications.
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Affiliation(s)
- Peiying Liu
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- * E-mail:
| | - Zachary Baker
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Yue Li
- AnatomyWorks, LLC, Baltimore, Maryland, United States of America
| | - Yang Li
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Jiadi Xu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- F.M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States of America
| | - Denise C. Park
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas, United States of America
| | - Babu G. Welch
- Department of Neurological Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Marco Pinho
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Jay J. Pillai
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Argye E. Hillis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Susumu Mori
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- F.M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States of America
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- F.M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States of America
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27
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Liu C, Guo X, Chang X. Intestinal Flora Balance Therapy Based on Probiotic Support Improves Cognitive Function and Symptoms in Patients with Alzheimer's Disease: A Systematic Review and Meta-analysis. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4806163. [PMID: 36017397 PMCID: PMC9398783 DOI: 10.1155/2022/4806163] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/22/2022] [Accepted: 07/27/2022] [Indexed: 11/17/2022]
Abstract
Objective The clinical value of intestinal flora balance therapy based on probiotic support in improving cognitive function and symptoms of patients with Alzheimer's disease was to systematically evaluate, so as to provide evidence-based medicine basis for the promotion and use of this therapy. Methods The randomized controlled trials (RCTs) were searched for the improvement of cognitive function and symptoms of patients with Alzheimer's disease by intestinal flora balance therapy supported mainly by probiotics in PubMed, EMBASE, ScienceDirect, Cochrane Library, China Knowledge Network Database (CNKI), China VIP database, Wanfang database, and China Biomedical Literature Database (CBM) online database (RCT). Data were extracted independently by two researchers, and the literature was assessed for risk of bias according to the Cochrane Handbook 5.1.0 criteria. The data were meta-analyzed using RevMan 5.4 statistical software. Results Finally, 5 randomized controlled trials were included, with a total sample size of 386 cases. The results of meta-analysis showed that Chi2 = 13.14, df = 2, P = 0.001, and I 2 = 85% showed significant heterogeneity in the inclusion of the study data. Probiotic-supported intestinal microflora balance therapy improves cognitive function in patients with Alzheimer's disease. Through meta-analysis of transient memory scores, it is concluded that intestinal flora balance therapy based on probiotic support can improve transient memory in patients with Alzheimer's disease. Meta-analysis of ADAS-COG score showed that intestinal flora balance therapy supported by probiotics could improve the cognitive function of patients with Alzheimer's disease. The ADL score was analyzed by meta, and the heterogeneity test result was Chi2 = 0.79, df = 1, P = 0.37 > 0.05, and I 2 = 0%, indicating that the intestinal flora balance therapy supported by probiotics can improve the ability of daily living of patients with Alzheimer's disease. Conclusion Intestinal flora balance therapy based on probiotic support can effectively improve cognitive function, instantaneous memory, and ability of daily life in patients with Alzheimer's disease. However, more studies and long-term follow-up studies with higher methodological quality are needed to further verify.
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Affiliation(s)
- Changxing Liu
- Shaanxi University of Traditional Chinese Medicine, Xianyang 712046, China
| | - Xinyi Guo
- Shaanxi University of Traditional Chinese Medicine, Xianyang 712046, China
| | - Xiang Chang
- Xi'an Hospital of Traditional Chinese Medicine, Shaanxi Province 710016, China
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28
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da Silva PHR, Paschoal AM, Secchinatto KF, Zotin MCZ, Dos Santos AC, Viswanathan A, Pontes-Neto OM, Leoni RF. Contrast agent-free state-of-the-art magnetic resonance imaging on cerebral small vessel disease - Part 2: Diffusion tensor imaging and functional magnetic resonance imaging. NMR IN BIOMEDICINE 2022; 35:e4743. [PMID: 35429070 DOI: 10.1002/nbm.4743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 04/04/2022] [Indexed: 06/14/2023]
Abstract
Cerebral small vessel disease (cSVD) has been widely studied using conventional magnetic resonance imaging (MRI) methods, although the association between MRI findings and clinical features of cSVD is not always concordant. We assessed the additional contribution of contrast agent-free, state-of-the-art MRI techniques, particularly diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), to understand brain damage and structural and functional connectivity impairment related to cSVD. We performed a review following the PICOS worksheet and Search Strategy, including 152 original papers in English, published from 2000 to 2022. For each MRI method, we extracted information about their contributions regarding the origins, pathology, markers, and clinical outcomes in cSVD. In general, DTI studies have shown that changes in mean, radial, and axial diffusivity measures are related to the presence of cSVD. In addition to the classical deficit in executive functions and processing speed, fMRI studies indicate connectivity dysfunctions in other domains, such as sensorimotor, memory, and attention. Neuroimaging metrics have been correlated with the diagnosis, prognosis, and rehabilitation of patients with cSVD. In short, the application of contrast agent-free, state-of-the-art MRI techniques has provided a complete picture of cSVD markers and tools to explore questions that have not yet been clarified about this clinical condition. Longitudinal studies are desirable to look for causal relationships between image biomarkers and clinical outcomes.
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Affiliation(s)
| | - André Monteiro Paschoal
- Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | | | - Maria Clara Zanon Zotin
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
- J Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Antônio Carlos Dos Santos
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Anand Viswanathan
- J Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Octavio M Pontes-Neto
- Department of Neurosciences and Behavioral Science, Ribeirão Preto Medical School, University of Sao Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Renata Ferranti Leoni
- Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
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29
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Libecap TJ, Zachariou V, Bauer CE, Wilcock DM, Jicha GA, Raslau FD, Gold BT. Enlarged Perivascular Spaces Are Negatively Associated With Montreal Cognitive Assessment Scores in Older Adults. Front Neurol 2022; 13:888511. [PMID: 35847209 PMCID: PMC9283758 DOI: 10.3389/fneur.2022.888511] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 06/06/2022] [Indexed: 11/30/2022] Open
Abstract
Emerging evidence suggests that enlarged perivascular spaces (ePVS) may be a clinically significant neuroimaging marker of global cognitive function related to cerebral small vessel disease (cSVD). We tested this possibility by assessing the relationship between ePVS and both a standardized measure of global cognitive function, the Montreal Cognitive Assessment (MoCA), and an established marker of cSVD, white matter hyperintensity volume (WMH) volume. One hundred and eleven community-dwelling older adults (56-86) underwent neuroimaging and MoCA testing. Quantification of region-specific ePVS burden was performed using a previously validated visual rating method and WMH volumes were computed using the standard ADNI pipeline. Separate linear regression models were run with ePVS as a predictor of MoCA scores and whole brain WMH volume. Results indicated a negative association between MoCA scores and both total ePVS counts (P ≤ 0.001) and centrum semiovale ePVS counts (P ≤ 0.001), after controlling for other relevant cSVD variables. Further, WMH volumes were positively associated with total ePVS (P = 0.010), basal ganglia ePVS (P ≤ 0.001), and centrum semiovale ePVS (P = 0.027). Our results suggest that ePVS burden, particularly in the centrum semiovale, may be a clinically significant neuroimaging marker of global cognitive dysfunction related to cSVD.
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Affiliation(s)
- Timothy J. Libecap
- 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
| | - Christopher E. Bauer
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Donna M. Wilcock
- Department of Physiology, College of Medicine, University of Kentucky, Lexington, KY, United States
- Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Gregory A. Jicha
- Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, United States
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Flavius D. Raslau
- Department of Radiology, College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Brian T. Gold
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, United States
- Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, United States
- Magnetic Resonance Imaging and Spectroscopy Center, College of Medicine, University of Kentucky, Lexington, KY, United States
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30
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Silva NCBS, Bracko O, Nelson AR, de Oliveira FF, Robison LS, Shaaban CE, Hainsworth AH, Price BR. Vascular cognitive impairment and dementia: An early career researcher perspective. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12310. [PMID: 35496373 PMCID: PMC9043906 DOI: 10.1002/dad2.12310] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/16/2022] [Accepted: 03/22/2022] [Indexed: 01/07/2023]
Abstract
The field of vascular contributions to cognitive impairment and dementia (VCID) is evolving rapidly. Research in VCID encompasses topics aiming to understand, prevent, and treat the detrimental effects of vascular disease burden in the human brain. In this perspective piece, early career researchers (ECRs) in the field provide an overview of VCID, discuss past and present efforts, and highlight priorities for future research. We emphasize the following critical points as the field progresses: (a) consolidate existing neuroimaging and fluid biomarkers, and establish their utility for pharmacological and non-pharmacological interventions; (b) develop new biomarkers, and new non-clinical models that better recapitulate vascular pathologies; (c) amplify access to emerging biomarker and imaging techniques; (d) validate findings from previous investigations in diverse populations, including those at higher risk of cognitive impairment (e.g., Black, Hispanic, and Indigenous populations); and (e) conduct randomized controlled trials within diverse populations with well-characterized vascular pathologies emphasizing clinically meaningful outcomes.
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Affiliation(s)
- Nárlon C. Boa Sorte Silva
- Djavad Mowafaghian Centre for Brain HealthDepartment of Physical TherapyFaculty of MedicineThe University of British ColumbiaVancouverBritish ColumbiaCanada
| | - Oliver Bracko
- Department of BiologyThe University of MiamiCoral GablesFloridaUSA
| | - Amy R. Nelson
- Department of Physiology and Cell BiologyUniversity of South AlabamaMobileAlabamaUSA
| | | | - Lisa S. Robison
- Department of Psychology and NeuroscienceNova Southeastern UniversityFort LauderdaleFloridaUSA
| | | | - Atticus H. Hainsworth
- Molecular & Clinical Sciences Research InstituteSt George's University of London, UKDepartment of NeurologySt George's University Hospitals NHS Foundation Trust LondonLondonUK
| | - Brittani R. Price
- Department of NeuroscienceTufts University School of MedicineBostonMassachusettsUSA
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31
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Rost NS, Brodtmann A, Pase MP, van Veluw SJ, Biffi A, Duering M, Hinman JD, Dichgans M. Post-Stroke Cognitive Impairment and Dementia. Circ Res 2022; 130:1252-1271. [PMID: 35420911 DOI: 10.1161/circresaha.122.319951] [Citation(s) in RCA: 191] [Impact Index Per Article: 95.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Poststroke cognitive impairment and dementia (PSCID) is a major source of morbidity and mortality after stroke worldwide. PSCID occurs as a consequence of ischemic stroke, intracerebral hemorrhage, or subarachnoid hemorrhage. Cognitive impairment and dementia manifesting after a clinical stroke is categorized as vascular even in people with comorbid neurodegenerative pathology, which is common in elderly individuals and can contribute to the clinical expression of PSCID. Manifestations of cerebral small vessel disease, such as covert brain infarcts, white matter lesions, microbleeds, and cortical microinfarcts, are also common in patients with stroke and likewise contribute to cognitive outcomes. Although studies of PSCID historically varied in the approach to timing and methods of diagnosis, most of them demonstrate that older age, lower educational status, socioeconomic disparities, premorbid cognitive or functional decline, life-course exposure to vascular risk factors, and a history of prior stroke increase risk of PSCID. Stroke characteristics, in particular stroke severity, lesion volume, lesion location, multiplicity and recurrence, also influence PSCID risk. Understanding the complex interaction between an acute stroke event and preexisting brain pathology remains a priority and will be critical for developing strategies for personalized prediction, prevention, targeted interventions, and rehabilitation. Current challenges in the field relate to a lack of harmonization of definition and classification of PSCID, timing of diagnosis, approaches to neurocognitive assessment, and duration of follow-up after stroke. However, evolving knowledge on pathophysiology, neuroimaging, and biomarkers offers potential for clinical applications and may inform clinical trials. Preventing stroke and PSCID remains a cornerstone of any strategy to achieve optimal brain health. We summarize recent developments in the field and discuss future directions closing with a call for action to systematically include cognitive outcome assessment into any clinical studies of poststroke outcome.
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Affiliation(s)
- Natalia S Rost
- J. Philip Kistler Stroke Research Center (N.S.R., S.J.v.V., A. Biffi), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Amy Brodtmann
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Australia (A. Brodtmann).,Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia (A. Brodtmann. M.P.P.)
| | - Matthew P Pase
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia (A. Brodtmann. M.P.P.).,Harvard T.H. Chan School of Public Health, Boston (M.P.P.)
| | - Susanne J van Veluw
- MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital, Charlestown (S.J.v.V.)
| | - Alessandro Biffi
- J. Philip Kistler Stroke Research Center (N.S.R., S.J.v.V., A. Biffi), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.,Divisions of Memory Disorders and Behavioral Neurology (A. Biffi), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Marco Duering
- J. Philip Kistler Stroke Research Center (N.S.R., S.J.v.V., A. Biffi), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.,Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany (M. Duering, M. Dichgans).,Medical Image Analysis Center and Department of Biomedical Engineering, University of Basel, Switzerland (M. Duering)
| | - Jason D Hinman
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles (J.D.H.).,Department of Neurology, West Los Angeles VA Medical Center, CA (J.D.H.)
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany (M. Duering, M. Dichgans).,German Center for Neurodegenerative Diseases (DZNE), Munich, Germany (M. Dichgans).,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (M. Dichgans)
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32
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Maillard P, Lu H, Arfanakis K, Gold BT, Bauer CE, Zachariou V, Stables L, Wang DJ, Jann K, Seshadri S, Duering M, Hillmer LJ, Rosenberg GA, Snoussi H, Sepehrband F, Habes M, Singh B, Kramer JH, Corriveau RA, Singh H, Schwab K, Helmer KG, Greenberg SM, Caprihan A, DeCarli C, Satizabal CL. Instrumental validation of free water, peak-width of skeletonized mean diffusivity, and white matter hyperintensities: MarkVCID neuroimaging kits. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12261. [PMID: 35382232 PMCID: PMC8959640 DOI: 10.1002/dad2.12261] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Indexed: 11/11/2022]
Abstract
Introduction To describe the protocol and findings of the instrumental validation of three imaging-based biomarker kits selected by the MarkVCID consortium: free water (FW) and peak width of skeletonized mean diffusivity (PSMD), both derived from diffusion tensor imaging (DTI), and white matter hyperintensity (WMH) volume derived from fluid attenuation inversion recovery and T1-weighted imaging. Methods The instrumental validation of imaging-based biomarker kits included inter-rater reliability among participating sites, test-retest repeatability, and inter-scanner reproducibility across three types of magnetic resonance imaging (MRI) scanners using intra-class correlation coefficients (ICC). Results The three biomarkers demonstrated excellent inter-rater reliability (ICC >0.94, P-values < .001), very high agreement between test and retest sessions (ICC >0.98, P-values < .001), and were extremely consistent across the three scanners (ICC >0.98, P-values < .001). Discussion The three biomarker kits demonstrated very high inter-rater reliability, test-retest repeatability, and inter-scanner reproducibility, offering robust biomarkers suitable for future multi-site observational studies and clinical trials in the context of vascular cognitive impairment and dementia (VCID).
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Affiliation(s)
- Pauline Maillard
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Hanzhang Lu
- Department of RadiologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Konstantinos Arfanakis
- Department of Biomedical EngineeringIllinois Institute of TechnologyChicagoIllinoisUSA
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - Brian T. Gold
- Department of NeuroscienceUniversity of KentuckyLexingtonKentuckyUSA
| | | | | | - Lara Stables
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Danny J.J. Wang
- Laboratory of FMRI Technology (LOFT)Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Kay Jann
- 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
| | - Marco Duering
- Department of Biomedical EngineeringMedical Image Analysis Center (MIAC AG)University of BaselBaselSwitzerland
| | - Laura J. Hillmer
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Gary A. Rosenberg
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Haykel Snoussi
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Farshid Sepehrband
- Laboratory of FMRI Technology (LOFT)Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Baljeet Singh
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Joel H. Kramer
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | | | - Herpreet Singh
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Kristin Schwab
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Karl G. Helmer
- Department of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| | | | | | - Charles DeCarli
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - 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
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33
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Rundek T, Tolea M, Ariko T, Fagerli EA, Camargo CJ. Vascular Cognitive Impairment (VCI). Neurotherapeutics 2022; 19:68-88. [PMID: 34939171 PMCID: PMC9130444 DOI: 10.1007/s13311-021-01170-y] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/06/2021] [Indexed: 01/03/2023] Open
Abstract
Vascular cognitive impairment (VCI) is predominately caused by vascular risk factors and cerebrovascular disease. VCI includes a broad spectrum of cognitive disorders, from mild cognitive impairment to vascular dementia caused by ischemic or hemorrhagic stroke, and vascular factors alone or in a combination with neurodegeneration including Alzheimer's disease (AD) and AD-related dementia. VCI accounts for at least 20-40% of all dementia diagnosis. Growing evidence indicates that cerebrovascular pathology is the most important contributor to dementia, with additive or synergistic interactions with neurodegenerative pathology. The most common underlying mechanism of VCI is chronic age-related dysregulation of CBF, although other factors such as inflammation and cardiovascular dysfunction play a role. Vascular risk factors are prevalent in VCI and if measured in midlife they predict cognitive impairment and dementia in later life. Particularly, hypertension, high cholesterol, diabetes, and smoking at midlife are each associated with a 20 to 40% increased risk of dementia. Control of these risk factors including multimodality strategies with an inclusion of lifestyle modification is the most promising strategy for treatment and prevention of VCI. In this review, we present recent developments in age-related VCI, its mechanisms, diagnostic criteria, neuroimaging correlates, vascular risk determinants, and current intervention strategies for prevention and treatment of VCI. We have also summarized the most recent and relevant literature in the field of VCI.
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Affiliation(s)
- Tatjana Rundek
- Department of Neurology and Evelyn F. McKnight Brain Institute, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Magdalena Tolea
- Department of Neurology and Evelyn F. McKnight Brain Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Taylor Ariko
- Department of Neurology and Evelyn F. McKnight Brain Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Eric A Fagerli
- Department of Neurology and Evelyn F. McKnight Brain Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Christian J Camargo
- Department of Neurology and Evelyn F. McKnight Brain Institute, University of Miami Miller School of Medicine, Miami, FL, USA
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Zachariou V, Bauer CE, Powell DK, Gold BT. Ironsmith: An Automated Pipeline for QSM-based Data Analyses. Neuroimage 2021; 249:118835. [PMID: 34936923 PMCID: PMC8935985 DOI: 10.1016/j.neuroimage.2021.118835] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/27/2021] [Accepted: 12/17/2021] [Indexed: 12/13/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) is an MRI-based, computational method for anatomically localizing and measuring concentrations of specific biomarkers in tissue such as iron. Growing research suggests QSM is a viable method for evaluating the impact of iron overload in neurological disorders and on cognitive performance in aging. Several software toolboxes are currently available to reconstruct QSM maps from 3D GRE MR Images. However, few if any software packages currently exist that offer fully automated pipelines for QSM-based data analyses: from DICOM images to region-of-interest (ROI) based QSM values. Even less QSM-based software exist that offer quality control measures for evaluating the QSM output. Here, we address these gaps in the field by introducing and demonstrating the reliability and external validity of Ironsmith; an open-source, fully automated pipeline for creating and processing QSM maps, extracting QSM values from subcortical and cortical brain regions (89 ROIs) and evaluating the quality of QSM data using SNR measures and assessment of outlier regions on phase images. Ironsmith also features automatic filtering of QSM outlier values and precise CSF-only QSM reference masks that minimize partial volume effects. Testing of Ironsmith revealed excellent intra- and inter-rater reliability. Finally, external validity of Ironsmith was demonstrated via an anatomically selective relationship between motor performance and Ironsmith-derived QSM values in motor cortex. In sum, Ironsmith provides a freely-available, reliable, turn-key pipeline for QSM-based data analyses to support research on the impact of brain iron in aging and neurodegenerative disease.
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Affiliation(s)
- Valentinos Zachariou
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY 40536-0298 United States.
| | - Christopher E Bauer
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY 40536-0298 United States
| | - David K Powell
- Department of Neuroscience, Magnetic Resonance Imaging and Spectroscopy Center, College of Medicine, University of Kentucky, Lexington, KY 40536-0298 United States
| | - Brian T Gold
- Department of Neuroscience, Sanders-Brown Center on Aging, Magnetic Resonance Imaging and Spectroscopy Center, College of Medicine, University of Kentucky, Lexington, KY 40536-0298 United States.
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Liu P, Jiang D, Albert M, Bauer CE, Caprihan A, Gold BT, Greenberg SM, Helmer KG, Jann K, Jicha G, Rodriguez P, Satizabal CL, Seshadri S, Singh H, Thompson JF, Wang DJJ, Lu H. Multi-vendor and multisite evaluation of cerebrovascular reactivity mapping using hypercapnia challenge. Neuroimage 2021; 245:118754. [PMID: 34826595 PMCID: PMC8783393 DOI: 10.1016/j.neuroimage.2021.118754] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 11/05/2021] [Accepted: 11/22/2021] [Indexed: 01/22/2023] Open
Abstract
Cerebrovascular reactivity (CVR), which measures the ability of cerebral blood vessels to dilate or constrict in response to vasoactive stimuli such as CO2 inhalation, is an important index of the brain's vascular health. Quantification of CVR using BOLD MRI with hypercapnia challenge has shown great promises in research and clinical studies. However, in order for it to be used as a potential imaging biomarker in large-scale and multi-site studies, the reliability of CO2-CVR quantification across different MRI acquisition platforms and researchers/raters must be examined. The goal of this report from the MarkVCID small vessel disease biomarkers consortium is to evaluate the reliability of CO2-CVR quantification in three studies. First, the inter-rater reliability of CO2-CVR data processing was evaluated by having raters from 5 MarkVCID sites process the same 30 CVR datasets using a cloud-based CVR data processing pipeline. Second, the inter-scanner reproducibility of CO2-CVR quantification was assessed in 10 young subjects across two scanners of different vendors. Third, test-retest repeatability was evaluated in 20 elderly subjects from 4 sites with a scan interval of less than 2 weeks. In all studies, the CO2 CVR measurements were performed using the fixed inspiration method, where the subjects wore a nose clip and a mouthpiece and breathed room air and 5% CO2 air contained in a Douglas bag alternatively through their mouth. The results showed that the inter-rater CoV of CVR processing was 0.08 ± 0.08% for whole-brain CVR values and ranged from 0.16% to 0.88% in major brain regions, with ICC of absolute agreement above 0.9959 for all brain regions. Inter-scanner CoV was found to be 6.90 ± 5.08% for whole-brain CVR values, and ranged from 4.69% to 12.71% in major brain regions, which are comparable to intra-session CoVs obtained from the same scanners on the same day. ICC of consistency between the two scanners was 0.8498 for whole-brain CVR and ranged from 0.8052 to 0.9185 across major brain regions. In the test-retest evaluation, test-retest CoV across different days was found to be 18.29 ± 17.12% for whole-brain CVR values, and ranged from 16.58% to 19.52% in major brain regions, with ICC of absolute agreement ranged from 0.6480 to 0.7785. These results demonstrated good inter-rater, inter-scanner, and test-retest reliability in healthy volunteers, and suggested that CO2-CVR has suitable instrumental properties for use as an imaging biomarker of cerebrovascular function in multi-site and longitudinal observational studies and clinical trials.
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Affiliation(s)
- Peiying Liu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dengrong Jiang
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | - Brian T Gold
- Department of Neuroscience, University of Kentucky, Lexington, KY, USA
| | - Steven M Greenberg
- Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, MA, USA
| | - Karl G Helmer
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Kay Jann
- Laboratory of Functional MRI Technology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Gregory Jicha
- Department of Neurology, University of Kentucky, Lexington, KY, USA
| | - Pavel Rodriguez
- Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA; Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
| | - Herpreet Singh
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Jeffrey F Thompson
- Department of Neurology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
| | - Danny J J Wang
- Laboratory of Functional MRI Technology, Stevens Neuroimaging and Informatics Institute, 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; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore 21287, USA; F.M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, 21205, USA.
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36
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Affiliation(s)
- Christopher L H Chen
- Memory Aging and Cognition Centre, National University Health System, Yong Loo Lin School of Medicine, National University of Singapore (C.L.H.C.)
| | - Tatjana Rundek
- Department of Neurology, Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami (T.R.)
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Palta P, Albert MS, Gottesman RF. Heart health meets cognitive health: evidence on the role of blood pressure. Lancet Neurol 2021; 20:854-867. [PMID: 34536406 DOI: 10.1016/s1474-4422(21)00248-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 05/28/2021] [Accepted: 07/15/2021] [Indexed: 10/20/2022]
Abstract
The enormous societal and financial burden of Alzheimer's disease and related dementias requires the identification of risk factors and pathways to reduce dementia risk. Blood pressure (BP) management and control is one promising area, in which data have been inconclusive. Accumulating evidence over the past 5 years shows the effectiveness of BP management interventions among older individuals at risk, most notably from the SPRINT-MIND trial. These findings have been coupled with longitudinal observational data. However, to date, the results do not concur on the optimal timing and target of BP lowering, and further study in diverse populations is needed. Given the long preclinical phase of dementia and data supporting the importance of BP control earlier in the lifecourse, long-term interventional and observational studies in ethnically and racially diverse populations, with novel imaging and blood-based biomarkers of neurodegeneration and vascular cognitive impairment to understand the pathophysiology, are needed to advance the field.
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Affiliation(s)
- Priya Palta
- Division of General Medicine, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA; Department of Epidemiology, Mailman School of Public Health, New York, NY, USA.
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Rebecca F Gottesman
- Stroke Branch, National Institute of Neurological Disorders and Stroke Intramural Program, National Institutes of Health, Bethesda, MD, USA
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Ge YJ, Xu W, Ou YN, Qu Y, Ma YH, Huang YY, Shen XN, Chen SD, Tan L, Zhao QH, Yu JT. Retinal biomarkers in Alzheimer's disease and mild cognitive impairment: A systematic review and meta-analysis. Ageing Res Rev 2021; 69:101361. [PMID: 34000463 DOI: 10.1016/j.arr.2021.101361] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 04/27/2021] [Accepted: 05/12/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND Retinal changes may reflect the pathophysiological processes in the central nervous system and can be assessed by imaging modalities non-invasively. We aim to localize candidate retinal biomarkers in Alzheimer's disease (AD), mild cognitive impairment (MCI), and preclinical AD. METHODS We systematically searched PubMed, EMBASE, Scopus, and Web of Science from inception to January 2021 for observational studies that investigated retinal imaging and electrophysiological markers in AD, MCI, and preclinical AD. Between-groups standardized mean differences (SMDs) with 95 % confidence intervals were computed using random-effects models. RESULTS Of 19,727 citations identified, 126 articles were eligible for inclusion. Compared with healthy controls, the thickness of peripapillary retinal nerve fiber layer (pRNFL; SMD = -0.723, p < 0.001), total macular (SMD = -0.612, p < 0.001), and subfoveal choroid (SMD = -0.888, p < 0.001) were significantly reduced in patients with AD. Compared with healthy controls, patients with MCI also had lower thickness of pRNFL (SMD = -0.324, p < 0.001), total macular (SMD = -0.302, p < 0.001), and subfoveal choroid (SMD = -0.462, p = 0.020). Other candidate biomarkers included the optic nerve head morphology, retinal amyloid deposition, microvascular morphology and densities, blood flow, and electrophysiological markers. CONCLUSIONS Retinal structural, vascular, and electrophysiological biomarkers hold great potential for the diagnosis, prognosis and risk assessment of AD and MCI. These biomarkers warrant further development in the future, especially in diagnostic test accuracy and longitudinal studies.
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Rost NS, Meschia JF, Gottesman R, Wruck L, Helmer K, Greenberg SM. Cognitive Impairment and Dementia After Stroke: Design and Rationale for the DISCOVERY Study. Stroke 2021; 52:e499-e516. [PMID: 34039035 PMCID: PMC8316324 DOI: 10.1161/strokeaha.120.031611] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Stroke is a leading cause of the adult disability epidemic in the United States, with a major contribution from poststroke cognitive impairment and dementia (PSCID), the rates of which are disproportionally high among the health disparity populations. Despite the PSCID's overwhelming impact on public health, a knowledge gap exists with regard to the complex interaction between the acute stroke event and highly prevalent preexisting brain pathology related to cerebrovascular and Alzheimer disease or related dementia. Understanding the factors that modulate PSCID risk in relation to index stroke event is critically important for developing personalized prognostication of PSCID, targeted interventions to prevent it, and for informing future clinical trial design. The DISCOVERY study (Determinants of Incident Stroke Cognitive Outcomes and Vascular Effects on Recovery), a collaborative network of thirty clinical performance clinical sites with access to acute stroke populations and the expertise and capacity for systematic assessment of PSCID will address this critical challenge. DISCOVERY is a prospective, multicenter, observational, nested-cohort study of 8000 nondemented ischemic and hemorrhagic stroke patients enrolled at the time of index stroke and followed for a minimum of 2 years, with serial cognitive evaluations and assessments of functional outcome, with subsets undergoing research magnetic resonance imaging and positron emission tomography and comprehensive genetic/genomic and fluid biomarker testing. The overall scientific objective of this study is to elucidate mechanisms of brain resilience and susceptibility to PSCID in diverse US populations based on complex interplay between life-course exposure to multiple vascular risk factors, preexisting burden of microvascular and neurodegenerative pathology, the effect of strategic acute stroke lesions, and the mediating effect of genomic and epigenomic variation.
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Affiliation(s)
- Natalia S. Rost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | | | | | | | - Karl Helmer
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA
| | - Steven M. Greenberg
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
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40
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Wilcock D, Jicha G, Blacker D, Albert MS, D’Orazio LM, Elahi FM, Fornage M, Hinman JD, Knoefel J, Kramer J, Kryscio RJ, Lamar M, Moghekar A, Prestopnik J, Ringman JM, Rosenberg G, Sagare A, Satizabal CL, Schneider J, Seshadri S, Sur S, Tracy RP, Yasar S, Williams V, Singh H, Mazina L, Helmer KG, Corriveau RA, Schwab K, Kivisäkk P, Greenberg SM. MarkVCID cerebral small vessel consortium: I. Enrollment, clinical, fluid protocols. Alzheimers Dement 2021; 17:704-715. [PMID: 33480172 PMCID: PMC8122220 DOI: 10.1002/alz.12215] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 09/22/2020] [Indexed: 01/04/2023]
Abstract
The concept of vascular contributions to cognitive impairment and dementia (VCID) derives from more than two decades of research indicating that (1) most older individuals with cognitive impairment have post mortem evidence of multiple contributing pathologies and (2) along with the preeminent role of Alzheimer's disease (AD) pathology, cerebrovascular disease accounts for a substantial proportion of this contribution. Contributing cerebrovascular processes include both overt strokes caused by etiologies such as large vessel occlusion, cardioembolism, and embolic infarcts of unknown source, and frequently asymptomatic brain injuries caused by diseases of the small cerebral vessels. Cerebral small vessel diseases such as arteriolosclerosis and cerebral amyloid angiopathy, when present at moderate or greater pathologic severity, are independently associated with worse cognitive performance and greater likelihood of dementia, particularly in combination with AD and other neurodegenerative pathologies. Based on this evidence, the US National Alzheimer's Project Act explicitly authorized accelerated research in vascular and mixed dementia along with frontotemporal and Lewy body dementia and AD itself. Biomarker development has been consistently identified as a key step toward translating scientific advances in VCID into effective prevention and treatment strategies. Validated biomarkers can serve a range of purposes in trials of candidate interventions, including (1) identifying individuals at increased VCID risk, (2) diagnosing the presence of cerebral small vessel disease or specific small vessel pathologies, (3) stratifying study participants according to their prognosis for VCID progression or treatment response, (4) demonstrating an intervention's target engagement or pharmacodynamic mechanism of action, and (5) monitoring disease progression during treatment. Effective biomarkers allow academic and industry investigators to advance promising interventions at early stages of development and discard interventions with low success likelihood. The MarkVCID consortium was formed in 2016 with the goal of developing and validating fluid- and imaging-based biomarkers for the cerebral small vessel diseases associated with VCID. MarkVCID consists of seven project sites and a central coordinating center, working with the National Institute of Neurologic Diseases and Stroke and National Institute on Aging under cooperative agreements. Through an internal selection process, MarkVCID has identified a panel of 11 candidate biomarker "kits" (consisting of the biomarker measure and the clinical and cognitive data used to validate it) and established a range of harmonized procedures and protocols for participant enrollment, clinical and cognitive evaluation, collection and handling of fluid samples, acquisition of neuroimaging studies, and biomarker validation. The overarching goal of these protocols is to generate rigorous validating data that could be used by investigators throughout the research community in selecting and applying biomarkers to multi-site VCID trials. Key features of MarkVCID participant enrollment, clinical/cognitive testing, and fluid biomarker procedures are summarized here, with full details in the following text, tables, and supplemental material, and a description of the MarkVCID imaging biomarker procedures in a companion paper, "MarkVCID Cerebral small vessel consortium: II. Neuroimaging protocols." The procedures described here address a range of challenges in MarkVCID's design, notably: (1) acquiring all data under informed consent and enrollment procedures that allow unlimited sharing and open-ended analyses without compromising participant privacy rights; (2) acquiring the data in a sufficiently wide range of study participants to allow assessment of candidate biomarkers across the various patient groups who might ultimately be targeted in VCID clinical trials; (3) defining a common dataset of clinical and cognitive elements that contains all the key outcome markers and covariates for VCID studies and is realistically obtainable during a practical study visit; (4) instituting best fluid-handling practices for minimizing avoidable sources of variability; and (5) establishing rigorous procedures for testing the reliability of candidate fluid-based biomarkers across replicates, assay runs, sites, and time intervals (collectively defined as the biomarker's instrumental validity). Participant Enrollment Project sites enroll diverse study cohorts using site-specific inclusion and exclusion criteria so as to provide generalizable validation data across a range of cognitive statuses, risk factor profiles, small vessel disease severities, and racial/ethnic characteristics representative of the diverse patient groups that might be enrolled in a future VCID trial. MarkVCID project sites include both prospectively enrolling centers and centers providing extant data and samples from preexisting community- and population-based studies. With approval of local institutional review boards, all sites incorporate MarkVCID consensus language into their study documents and informed consent agreements. The consensus language asks prospectively enrolled participants to consent to unrestricted access to their data and samples for research analysis within and outside MarkVCID. The data are transferred and stored as a de-identified dataset as defined by the Health Insurance Portability and Accountability Act Privacy Rule. Similar human subject protection and informed consent language serve as the basis for MarkVCID Research Agreements that act as contracts and data/biospecimen sharing agreements across the consortium. Clinical and Cognitive Data Clinical and cognitive data are collected across prospectively enrolling project sites using common MarkVCID instruments. The clinical data elements are modified from study protocols already in use such as the Alzheimer's Disease Center program Uniform Data Set Version 3 (UDS3), with additional focus on VCID-related items such as prior stroke and cardiovascular disease, vascular risk factors, focal neurologic findings, and blood testing for vascular risk markers and kidney function including hemoglobin A1c, cholesterol subtypes, triglycerides, and creatinine. Cognitive assessments and rating instruments include the Clinical Dementia Rating Scale, Geriatric Depression Scale, and most of the UDS3 neuropsychological battery. The cognitive testing requires ≈60 to 90 minutes. Study staff at the prospectively recruiting sites undergo formalized training in all measures and review of their first three UDS3 administrations by the coordinating center. Collection and Handling of Fluid Samples Fluid sample types collected for MarkVCID biomarker kits are serum, ethylenediaminetetraacetic acid-plasma, platelet-poor plasma, and cerebrospinal fluid (CSF) with additional collection of packed cells to allow future DNA extraction and analyses. MarkVCID fluid guidelines to minimize variability include fasting morning fluid collections, rapid processing, standardized handling and storage, and avoidance of CSF contact with polystyrene. Instrumental Validation for Fluid-Based Biomarkers Instrumental validation of MarkVCID fluid-based biomarkers is operationally defined as determination of intra-plate and inter-plate repeatability, inter-site reproducibility, and test-retest repeatability. MarkVCID study participants both with and without advanced small vessel disease are selected for these determinations to assess instrumental validity across the full biomarker assay range. Intra- and inter-plate repeatability is determined by repeat assays of single split fluid samples performed at individual sites. Inter-site reproducibility is determined by assays of split samples distributed to multiple sites. Test-retest repeatability is determined by assay of three samples acquired from the same individual, collected at least 5 days apart over a 30-day period and assayed on a single plate. The MarkVCID protocols are designed to allow direct translation of the biomarker validation results to multicenter trials. They also provide a template for outside groups to perform analyses using identical methods and therefore allow direct comparison of results across studies and centers. All MarkVCID protocols are available to the biomedical community and intended to be shared. In addition to the instrumental validation procedures described here, each of the MarkVCID kits will undergo biological validation to determine whether the candidate biomarker measures important aspects of VCID such as cognitive function. Analytic methods and results of these validation studies for the 11 MarkVCID biomarker kits will be published separately. The results of this rigorous validation process will ultimately determine each kit's potential usefulness for multicenter interventional trials aimed at preventing or treating small vessel disease related VCID.
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Affiliation(s)
- Donna Wilcock
- Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY 40504, USA
| | - Gregory Jicha
- Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY 40504, USA
| | - Deborah Blacker
- Department of Epidemiology, Harvard T.H Chan School of Public Health and Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Lina M. D’Orazio
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Fanny M. Elahi
- Center for Memory and Aging, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School and Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Jason D. Hinman
- David Geffen School of Medicine, Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Janice Knoefel
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Joel Kramer
- David Geffen School of Medicine, Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Richard J. Kryscio
- Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY 40504, USA
| | - Melissa Lamar
- Rush Alzheimer’s Disease Center, Rush University, Chicago, IL, USA
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jillian Prestopnik
- Center for Memory and Aging, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - John M. Ringman
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Gary Rosenberg
- Center for Memory and Aging, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Abhay Sagare
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Claudia L. Satizabal
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Julie Schneider
- Rush Alzheimer’s Disease Center, Rush University, Chicago, IL, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Sandeepa Sur
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Russell P. Tracy
- Department of Pathology and Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, VT 05405, USA
| | - Sevil Yasar
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Victoria Williams
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - Herpreet Singh
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Lidiya Mazina
- Neurological Clinical Research Institute, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Karl G. Helmer
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Kristin Schwab
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Pia Kivisäkk
- Alzheimer’s Clinical and Translational Research Unit, Massachusetts General Hospital, Boston, MA 02129, USA
| | - Steven M. Greenberg
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
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