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Xhima K, Ottoy J, Gibson E, Zukotynski K, Scott C, Feliciano GJ, Adamo S, Kuo PH, Borrie MJ, Chertkow H, Frayne R, Laforce R, Noseworthy MD, Prato FS, Sahlas DJ, Smith EE, Sossi V, Thiel A, Soucy J, Tardif J, Goubran M, Black SE, Ramirez J. Distinct spatial contributions of amyloid pathology and cerebral small vessel disease to hippocampal morphology. Alzheimers Dement 2024; 20:3687-3695. [PMID: 38574400 PMCID: PMC11095424 DOI: 10.1002/alz.13791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/22/2024] [Accepted: 02/09/2024] [Indexed: 04/06/2024]
Abstract
INTRODUCTION Cerebral small vessel disease (SVD) and amyloid beta (Aβ) pathology frequently co-exist. The impact of concurrent pathology on the pattern of hippocampal atrophy, a key substrate of memory impacted early and extensively in dementia, remains poorly understood. METHODS In a unique cohort of mixed Alzheimer's disease and moderate-severe SVD, we examined whether total and regional neuroimaging measures of SVD, white matter hyperintensities (WMH), and Aβ, as assessed by 18F-AV45 positron emission tomography, exert additive or synergistic effects on hippocampal volume and shape. RESULTS Frontal WMH, occipital WMH, and Aβ were independently associated with smaller hippocampal volume. Frontal WMH had a spatially distinct impact on hippocampal shape relative to Aβ. In contrast, hippocampal shape alterations associated with occipital WMH spatially overlapped with Aβ-vulnerable subregions. DISCUSSION Hippocampal degeneration is differentially sensitive to SVD and Aβ pathology. The pattern of hippocampal atrophy could serve as a disease-specific biomarker, and thus guide clinical diagnosis and individualized treatment strategies for mixed dementia.
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Affiliation(s)
- Kristiana Xhima
- Dr. Sandra E. Black Centre for Brain Resilience and RecoveryLC Campbell Cognitive Neurology, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
| | - Julie Ottoy
- Dr. Sandra E. Black Centre for Brain Resilience and RecoveryLC Campbell Cognitive Neurology, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
| | - Erin Gibson
- Dr. Sandra E. Black Centre for Brain Resilience and RecoveryLC Campbell Cognitive Neurology, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
| | - Katherine Zukotynski
- Dr. Sandra E. Black Centre for Brain Resilience and RecoveryLC Campbell Cognitive Neurology, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Departments of Medicine and RadiologyMcMaster UniversityHamiltonOntarioCanada
- Department of Medical ImagingSchulich School of Medicine and Dentistry, Western UniversityLondonOntarioCanada
| | - Christopher Scott
- Dr. Sandra E. Black Centre for Brain Resilience and RecoveryLC Campbell Cognitive Neurology, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
| | - Ginelle J. Feliciano
- Dr. Sandra E. Black Centre for Brain Resilience and RecoveryLC Campbell Cognitive Neurology, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
| | - Sabrina Adamo
- Dr. Sandra E. Black Centre for Brain Resilience and RecoveryLC Campbell Cognitive Neurology, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
| | - Phillip H. Kuo
- Departments of Medical Imaging, Medicine, Biomedical EngineeringUniversity of ArizonaTucsonArizonaUSA
| | - Michael J. Borrie
- Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada
| | - Howard Chertkow
- Rotman Research InstituteBaycrest Health SciencesTorontoOntarioCanada
| | - Richard Frayne
- Departments of Radiology and Clinical NeuroscienceHotchkiss Brain Institute, University of CalgaryCalgaryAlbertaCanada
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences NeurologiquesUniversité Laval, Quebec CityQuebecCanada
| | - Michael D. Noseworthy
- Departments of Medicine and RadiologyMcMaster UniversityHamiltonOntarioCanada
- Department of Electrical and Computer EngineeringMcMaster UniversityHamiltonOntarioCanada
| | - Frank S. Prato
- Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada
| | | | - Eric E. Smith
- Department of Clinical Neurosciences and Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
| | - Vesna Sossi
- Physics and Astronomy Department and DM Center for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Alexander Thiel
- Department of Neurology and NeurosurgeryMcGill UniversityMontrealQuebecCanada
| | - Jean‐Paul Soucy
- Montreal Neurological InstituteMcGill UniversityMontrealQuebecCanada
| | | | - Maged Goubran
- Dr. Sandra E. Black Centre for Brain Resilience and RecoveryLC Campbell Cognitive Neurology, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
- Physical Sciences Platform, Sunnybrook Research InstituteUniversity of TorontoTorontoOntarioCanada
| | - Sandra E. Black
- Dr. Sandra E. Black Centre for Brain Resilience and RecoveryLC Campbell Cognitive Neurology, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
- Division of NeurologyDepartment of MedicineUniversity of TorontoTorontoOntarioCanada
| | - Joel Ramirez
- Dr. Sandra E. Black Centre for Brain Resilience and RecoveryLC Campbell Cognitive Neurology, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of TorontoTorontoOntarioCanada
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Chong JR, Chai YL, Yam ATY, Hilal S, Vrooman H, Venketasubramanian N, Blennow K, Zetterberg H, Ashton NJ, Chen CP, Lai MKP. Association of plasma GFAP with elevated brain amyloid is dependent on severity of white matter lesions in an Asian cognitively impaired cohort. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12576. [PMID: 38605996 PMCID: PMC11007806 DOI: 10.1002/dad2.12576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/25/2024] [Accepted: 03/01/2024] [Indexed: 04/13/2024]
Abstract
INTRODUCTION While elevated blood glial fibrillary acidic protein (GFAP) has been associated with brain amyloid pathology, whether this association occurs in populations with high cerebral small vessel disease (CSVD) concomitance remains unclear. METHODS Using a Singapore-based cohort of cognitively impaired subjects, we assessed associations between plasma GFAP and neuroimaging measures of brain amyloid and CSVD, including white matter hyperintensities (WMH). We also examined the diagnostic performance of plasma GFAP in detecting brain amyloid beta positivity (Aβ+). RESULTS When stratified by WMH status, elevated brain amyloid was associated with higher plasma GFAP only in the WMH- group (β = 0.383; P < 0.001). The diagnostic performance of plasma GFAP in identifying Aβ+ was significantly higher in the WMH- group (area under the curve [AUC] = 0.896) than in the WMH+ group (AUC = 0.712, P = 0.008). DISCUSSION The biomarker utility of plasma GFAP in detecting brain amyloid pathology is dependent on the severity of concomitant WMH. Highlight Glial fibrillary acidic protein (GFAP)'s association with brain amyloid is unclear in populations with high cerebral small vessel disease (CSVD).Plasma GFAP was measured in a cohort with CSVD and brain amyloid.Plasma GFAP was better in detecting amyloid in patients with low CSVD versus high CSVD.Biomarker utility of GFAP in detecting brain amyloid depends on the severity of CSVD.
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Affiliation(s)
- Joyce R. Chong
- Department of PharmacologyYong Loo Lin School of MedicineNational University of SingaporeKent RidgeSingapore
- Memory, Aging and Cognition CentreNational University Health SystemsKent RidgeSingapore
| | - Yuek Ling Chai
- Department of PharmacologyYong Loo Lin School of MedicineNational University of SingaporeKent RidgeSingapore
- Memory, Aging and Cognition CentreNational University Health SystemsKent RidgeSingapore
| | - Amelia T. Y. Yam
- Department of PharmacologyYong Loo Lin School of MedicineNational University of SingaporeKent RidgeSingapore
- Memory, Aging and Cognition CentreNational University Health SystemsKent RidgeSingapore
| | - Saima Hilal
- Department of PharmacologyYong Loo Lin School of MedicineNational University of SingaporeKent RidgeSingapore
- Memory, Aging and Cognition CentreNational University Health SystemsKent RidgeSingapore
- Saw Swee Hock School of Public HealthNational University of Singapore and National University Health SystemKent RidgeSingapore
- Department of Radiology and Nuclear MedicineErasmus Medical CenterRotterdamthe Netherlands
| | - Henri Vrooman
- Department of Radiology and Nuclear MedicineErasmus Medical CenterRotterdamthe Netherlands
| | | | - Kaj Blennow
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologySahlgrenska AcademyUniversity of GothenburgGöteborgSweden
| | - Henrik Zetterberg
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologySahlgrenska AcademyUniversity of GothenburgGöteborgSweden
- Department of Neurodegenerative DiseaseThe UCL Queen Square Institute of NeurologyLondonUK
| | - Nicholas J. Ashton
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologySahlgrenska AcademyUniversity of GothenburgGöteborgSweden
| | - Christopher P. Chen
- Department of PharmacologyYong Loo Lin School of MedicineNational University of SingaporeKent RidgeSingapore
- Memory, Aging and Cognition CentreNational University Health SystemsKent RidgeSingapore
| | - Mitchell K. P. Lai
- Department of PharmacologyYong Loo Lin School of MedicineNational University of SingaporeKent RidgeSingapore
- Memory, Aging and Cognition CentreNational University Health SystemsKent RidgeSingapore
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Van Etten EJ, Bharadwaj PK, Grilli MD, Raichlen DA, Hishaw GA, Huentelman MJ, Trouard TP, Alexander GE. Regional covariance of white matter hyperintensity volume patterns associated with hippocampal volume in healthy aging. Front Aging Neurosci 2024; 16:1349449. [PMID: 38524117 PMCID: PMC10957632 DOI: 10.3389/fnagi.2024.1349449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 02/21/2024] [Indexed: 03/26/2024] Open
Abstract
Hippocampal volume is particularly sensitive to the accumulation of total brain white matter hyperintensity volume (WMH) in aging, but how the regional distribution of WMH volume differentially impacts the hippocampus has been less studied. In a cohort of 194 healthy older adults ages 50-89, we used a multivariate statistical method, the Scaled Subprofile Model (SSM), to (1) identify patterns of regional WMH differences related to left and right hippocampal volumes, (2) examine associations between the multimodal neuroimaging covariance patterns and demographic characteristics, and (3) investigate the relation of the patterns to subjective and objective memory in healthy aging. We established network covariance patterns of regional WMH volume differences associated with greater left and right hippocampal volumes, which were characterized by reductions in left temporal and right parietal WMH volumes and relative increases in bilateral occipital WMH volumes. Additionally, we observed lower expression of these hippocampal-related regional WMH patterns were significantly associated with increasing age and greater subjective memory complaints, but not objective memory performance in this healthy older adult cohort. Our findings indicate that, in cognitively healthy older adults, left and right hippocampal volume reductions were associated with differences in the regional distribution of WMH volumes, which were exacerbated by advancing age and related to greater subjective memory complaints. Multivariate network analyses, like SSM, may help elucidate important early effects of regional WMH volume on brain and cognitive aging in healthy older adults.
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Affiliation(s)
- Emily J. Van Etten
- Department of Psychology, University of Arizona, Tucson, AZ, United States
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States
| | - Pradyumna K. Bharadwaj
- Department of Psychology, University of Arizona, Tucson, AZ, United States
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States
| | - Matthew D. Grilli
- Department of Psychology, University of Arizona, Tucson, AZ, United States
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States
- Department of Neurology, University of Arizona, Tucson, AZ, United States
| | - David A. Raichlen
- Human and Evolutionary Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA, United States
- Department of Anthropology, University of Southern California, Los Angeles, CA, United States
| | - Georg A. Hishaw
- Department of Neurology, University of Arizona, Tucson, AZ, United States
| | - Matthew J. Huentelman
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States
- Neurogenomics Division, The Translational Genomics Research Institute (TGen), Phoenix, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
| | - Theodore P. Trouard
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States
| | - Gene E. Alexander
- Department of Psychology, University of Arizona, Tucson, AZ, United States
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States
- Arizona Alzheimer’s Consortium, Phoenix, AZ, United States
- Department of Psychiatry, University of Arizona, Tucson, AZ, United States
- Neuroscience Graduate Interdisciplinary Program, University of Arizona, Tucson, AZ, United States
- Physiological Sciences Graduate Interdisciplinary Program, University of Arizona, Tucson, AZ, United States
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Yu M, Feng L, Zhao X, Huang Q, Xia N, Xia H, Wen C, Wang M, Zhu Z, Yang Y. The interaction of global small vessel disease burden and Alzheimer's disease pathologies do not change the independent association of amyloid-beta with hippocampal volume: A longitudinal study on mild cognitive impairment subjects. Hippocampus 2023; 33:1197-1207. [PMID: 37638636 DOI: 10.1002/hipo.23573] [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: 04/02/2023] [Revised: 08/04/2023] [Accepted: 08/13/2023] [Indexed: 08/29/2023]
Abstract
The purpose of this study was to investigate whether the co-existence of global small vessel disease (SVD) burdens and Alzheimer's disease (AD) pathologies change hippocampal volume (HV) and cognitive function of mild cognitive impairment (MCI) subjects. We obtained MRI images, cerebrospinal fluid biomarkers (Aβ1-42 and p-tau), and neuropsychological tests of 310 MCI subjects from ADNI. The global SVD score was assessed. We used linear regression and linear mixing effect to analyze the effects of global SVD burdens, AD pathologies, and their interactions (SVD*AD) on baseline and longitudinal HV and cognition respectively. We used simple mediation effect to analyze the influencing pathways. After adjusting for global SVD and SVD*AD, Aβ remained independently correlated with baseline and longitudinal HV (std β = 0.294, p = .007; std β = 0.292, p < .001), indicating that global SVD did not affect the correlation between Aβ and HV. Global SVD score was correlated with longitudinal but not baseline HV (std β = 0.470, p = .050), suggesting that global SVD may be more representative of long-term permanent impairment. Global SVD, AD pathologies, and SVD*AD were independently correlated with baseline and longitudinal cognitions, in which the association of Aβ (B = 0.005, 95% CI: 0.005; 0.024) and p-tau (B = -0.002, 95% CI: -0.004; -0.000) with cognition were mediated by HV, suggesting that HV is more likely to explain the progression caused by AD pathology than SVD. The co-existence of global SVD and AD pathologies did not affect the individual association of Aβ on HV; HV played a more important role in the influence of AD pathology on cognition than in SVD.
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Affiliation(s)
- Mengying Yu
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Lufei Feng
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
- Department of Radiology, Zhuji Central Hospital, Zhejiang, China
| | - Xuemiao Zhao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Qun Huang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Nengzhi Xia
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Huwei Xia
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Caiyun Wen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Meihao Wang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Zili Zhu
- Department of Imaging, Ningbo City First Hospital, Zhejiang, China
| | - Yunjun Yang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
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Li JX, Nguyen HL, Qian T, Woodworth DC, Sajjadi SA. Longitudinal hippocampal atrophy in hippocampal sclerosis of aging. AGING BRAIN 2023; 4:100092. [PMID: 37635712 PMCID: PMC10448324 DOI: 10.1016/j.nbas.2023.100092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 08/29/2023] Open
Abstract
Hippocampal sclerosis of aging (HS-A) is a common degenerative neuropathology in older individuals and is associated with dementia. HS-A is characterized by disproportionate hippocampal atrophy at autopsy but cannot be diagnosed during life. Therefore, little is known about the onset and progression of hippocampal atrophy in individuals with HS-A. To better understand the onset and progression of hippocampal atrophy in HS-A, we examined longitudinal hippocampal atrophy using serial MRI in participants with HS-A at autopsy (HS-A+, n = 8) compared to participants with limbic-predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) without HS-A (n = 13), Alzheimer's disease neuropathologic change (ADNC) without HS-A or LATE-NC (n = 16), and those without these pathologies (n = 7). We found that participants with HS-A had lower hippocampal volumes compared to the other groups, and this atrophy preceded the onset of dementia. There was also some evidence that rates of hippocampal volume loss were slightly slower in those with HS-A. Together, these results suggest that the disproportionate hippocampal atrophy seen in HS-A may begin early prior to dementia.
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Affiliation(s)
- Janice X. Li
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - Hannah L. Nguyen
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - Tianchen Qian
- Department of Statistics, University of California, Irvine, Irvine, CA, USA
| | - Davis C. Woodworth
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - S. Ahmad Sajjadi
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
- Department of Pathology, University of California, Irvine, CA, USA
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Alban SL, Lynch KM, Ringman JM, Toga AW, Chui HC, Sepehrband F, Choupan J. The association between white matter hyperintensities and amyloid and tau deposition. Neuroimage Clin 2023; 38:103383. [PMID: 36965457 PMCID: PMC10060905 DOI: 10.1016/j.nicl.2023.103383] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 02/09/2023] [Accepted: 03/16/2023] [Indexed: 03/22/2023]
Abstract
White matter hyperintensities (WMHs) frequently occur in Alzheimer's Disease (AD) and have a contribution from ischemia, though their relationship with β-amyloid and cardiovascular risk factors (CVRFs) is not completely understood. We used AT classification to categorize individuals based on their β-amyloid and tau pathologies, then assessed the effects of β-amyloid and tau on WMH volume and number. We then determined regions in which β-amyloid and WMH accumulation were related. Last, we analyzed the effects of various CVRFs on WMHs. As secondary analyses, we observed effects of age and sex differences, atrophy, cognitive scores, and APOE genotype. PET, MRI, FLAIR, demographic, and cardiovascular health data was collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI-3) (N = 287, 48 % male). Participants were categorized as A + and T + if their Florbetapir SUVR and Flortaucipir SUVR were above 0.79 and 1.25, respectively. WMHs were mapped on MRI using a deep convolutional neural network (Sepehrband et al., 2020). CVRF scores were based on history of hypertension, systolic and diastolic blood pressure, pulse rate, respiration rate, BMI, and a cumulative score with 6 being the maximum score. Regression models and Pearson correlations were used to test associations and correlations between variables, respectively, with age, sex, years of education, and scanner manufacturer as covariates of no interest. WMH volume percent was significantly associated with global β-amyloid (r = 0.28, p < 0.001), but not tau (r = 0.05, p = 0.25). WMH volume percent was higher in individuals with either A + or T + pathology compared to controls, particularly within in the A+/T + group (p = 0.007, Cohen's d = 0.4, t = -2.5). Individual CVRFs nor cumulative CVRF scores were associated with increased WMH volume. Finally, the regions where β-amyloid and WMH count were most positively associated were the middle temporal region in the right hemisphere (r = 0.18, p = 0.002) and the fusiform region in the left hemisphere (r = 0.017, p = 0.005). β-amyloid and WMH have a clear association, though the mechanism facilitating this association is still not fully understood. The associations found between β-amyloid and WMH burden emphasizes the relationship between β-amyloid and vascular lesion formation while factors like CVRFs, age, and sex affect AD development through various mechanisms. These findings highlight potential causes and mechanisms of AD as targets for future preventions and treatments. Going forward, a larger emphasis may be placed on β-amyloid's vascular effects and the implications of impaired brain clearance in AD.
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Affiliation(s)
- Sierra L Alban
- Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kirsten M Lynch
- Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - John M Ringman
- Alzheimer's Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Alzheimer's Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Helena C Chui
- Alzheimer's Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Farshid Sepehrband
- Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jeiran Choupan
- Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; NeuroScope Inc., Scarsdale, NY, USA
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Prosser L, Macdougall A, Sudre CH, Manning EN, Malone IB, Walsh P, Goodkin O, Pemberton H, Barkhof F, Biessels GJ, Cash DM, Barnes J. Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration. Neurology 2023; 100:e834-e845. [PMID: 36357185 PMCID: PMC9984210 DOI: 10.1212/wnl.0000000000201572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 09/28/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Dementia is a growing socioeconomic challenge that requires early intervention. Identifying biomarkers that reliably predict clinical progression early in the disease process would better aid selection of individuals for future trial participation. Here, we compared the ability of baseline, single time-point biomarkers (CSF amyloid 1-42, CSF ptau-181, white matter hyperintensities (WMH), cerebral microbleeds, whole-brain volume, and hippocampal volume) to predict decline in cognitively normal individuals who later converted to mild cognitive impairment (MCI) (CNtoMCI) and those with MCI who later converted to an Alzheimer disease (AD) diagnosis (MCItoAD). METHODS Standardized baseline biomarker data from AD Neuroimaging Initiative 2 (ADNI2)/GO and longitudinal diagnostic data (including ADNI3) were used. Cox regression models assessed biomarkers in relation to time to change in clinical diagnosis using all follow-up time points available. Models were fit for biomarkers univariately and together in a multivariable model. Hazard ratios (HRs) were compared to evaluate biomarkers. Analyses were performed separately in CNtoMCI and MCItoAD groups. RESULTS For CNtoMCI (n = 189), there was strong evidence that higher WMH volume (individual model: HR 1.79, p = 0.002; fully adjusted model: HR 1.98, p = 0.003) and lower hippocampal volume (individual: HR 0.54, p = 0.001; fully adjusted: HR 0.40, p < 0.001) were associated with conversion to MCI individually and independently. For MCItoAD (n = 345), lower hippocampal (individual model: HR 0.45, p < 0.001; fully adjusted model: HR 0.55, p < 0.001) and whole-brain volume (individual: HR 0.31, p < 0.001; fully adjusted: HR 0.48, p = 0.02), increased CSF ptau (individual: HR 1.88, p < 0.001; fully adjusted: HR 1.61, p < 0.001), and lower CSF amyloid (individual: HR 0.37, p < 0.001; fully adjusted: HR 0.62, p = 0.008) were most strongly associated with conversion to AD individually and independently. DISCUSSION Lower hippocampal volume was a consistent predictor of clinical conversion to MCI and AD. CSF and brain volume biomarkers were predictive of conversion to AD from MCI, whereas WMH were predictive of conversion to MCI from cognitively normal. The predictive ability of WMH in the CNtoMCI group may be interpreted as some being on a different pathologic pathway, such as vascular cognitive impairment.
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Affiliation(s)
- Lloyd Prosser
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands.
| | - Amy Macdougall
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Carole H Sudre
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Emily N Manning
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Ian B Malone
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Phoebe Walsh
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Olivia Goodkin
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Hugh Pemberton
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Frederik Barkhof
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Geert Jan Biessels
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - David M Cash
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Josephine Barnes
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
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8
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Wu M, Schweitzer N, Iordanova BE, Halligan-Eddy E, Tudorascu DL, Mathis CA, Lopresti BJ, Kamboh MI, Cohen AD, Snitz BE, Klunk WE, Aizenstein HJ. In Pre-Clinical AD Small Vessel Disease is Associated With Altered Hippocampal Connectivity and Atrophy. Am J Geriatr Psychiatry 2023; 31:112-123. [PMID: 36274019 PMCID: PMC10768933 DOI: 10.1016/j.jagp.2022.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 09/16/2022] [Accepted: 09/20/2022] [Indexed: 01/25/2023]
Abstract
OBJECTIVE Small Vessel Disease (SVD) is known to be associated with higher AD risk, but its relationship to amyloidosis in the progression of AD is unclear. In this cross-sectional study of cognitively normal older adults, we explored the interactive effects of SVD and amyloid-beta (Aβ) pathology on hippocampal functional connectivity during an associative encoding task and on hippocampal volume. METHODS This study included 61 cognitively normal older adults (age range: 65-93 years, age mean ± standard deviation: 75.8 ± 6.4, 41 [67.2%] female). PiB PET, T2-weighted FLAIR, T1-weighted and face-name fMRI images were acquired on each participant to evaluate brain Aβ, white matter hyperintensities (WMH+/- status), gray matter density, and hippocampal functional connectivity. RESULTS We found that, in WMH (+) older adults greater Aβ burden was associated with greater hippocampal local connectivity (i.e., hippocampal-parahippocampal connectivity) and lower gray matter density in medial temporal lobe (MTL), whereas in WMH (-) older adults greater Aβ burden was associated with greater hippocampal distal connectivity (i.e., hippocampal-prefrontal connectivity) and no changes in MTL gray matter density. Moreover, greater hippocampal local connectivity was associated with MTL atrophy. CONCLUSION These observations support a hippocampal excitotoxicity model linking SVD to neurodegeneration in preclinical AD. This may explain how SVD may accelerate the progression from Aβ positivity to neurodegeneration, and subsequent AD.
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Affiliation(s)
- Minjie Wu
- Department of Psychiatry (MW, EHE, DLT, ADC, WEK, HJA), University of Pittsburgh, Pittsburgh, PA.
| | - Noah Schweitzer
- Department of Bioengineering (NS, BEI, HJA), University of Pittsburgh, Pittsburgh, PA
| | - Bistra E Iordanova
- Department of Bioengineering (NS, BEI, HJA), University of Pittsburgh, Pittsburgh, PA
| | - Edythe Halligan-Eddy
- Department of Psychiatry (MW, EHE, DLT, ADC, WEK, HJA), University of Pittsburgh, Pittsburgh, PA
| | - Dana L Tudorascu
- Department of Psychiatry (MW, EHE, DLT, ADC, WEK, HJA), University of Pittsburgh, Pittsburgh, PA; Departments of Medicine and Biostatistics (DLT), University of Pittsburgh, Pittsburgh, PA
| | - Chester A Mathis
- Department of Radiology (CAM, BJL), University of Pittsburgh, Pittsburgh, PA
| | - Brian J Lopresti
- Department of Radiology (CAM, BJL), University of Pittsburgh, Pittsburgh, PA
| | - M Ilyas Kamboh
- Department of Human Genetics (MIK), University of Pittsburgh, Pittsburgh, PA
| | - Ann D Cohen
- Department of Psychiatry (MW, EHE, DLT, ADC, WEK, HJA), University of Pittsburgh, Pittsburgh, PA
| | - Beth E Snitz
- Department of Neurology (BES), University of Pittsburgh, Pittsburgh, PA
| | - William E Klunk
- Department of Psychiatry (MW, EHE, DLT, ADC, WEK, HJA), University of Pittsburgh, Pittsburgh, PA
| | - Howard J Aizenstein
- Department of Psychiatry (MW, EHE, DLT, ADC, WEK, HJA), University of Pittsburgh, Pittsburgh, PA; Department of Bioengineering (NS, BEI, HJA), University of Pittsburgh, Pittsburgh, PA
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9
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Nakazawa T, Ohara T, Hirabayashi N, Furuta Y, Hata J, Shibata M, Honda T, Kitazono T, Nakao T, Ninomiya T. Association of white matter lesions and brain atrophy with the development of dementia in a community: the Hisayama Study. Psychiatry Clin Neurosci 2023. [PMID: 36700514 DOI: 10.1111/pcn.13533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 12/05/2022] [Accepted: 01/19/2023] [Indexed: 01/27/2023]
Abstract
AIM To investigate the association of white matter lesions volume (WMLV) levels with dementia risk and the association between dementia risk and the combined measures of WMLV and either total brain atrophy or dementia-related gray matter atrophy in a general older population. METHODS One thousand one hundred fifty-eight Japanese dementia-free community-residents aged ≥65 years who underwent brain magnetic resonance imaging were followed for 5.0 years. WMLV were segmented using the Lesion Segmentation Toolbox. Total brain volume (TBV) and regional gray matter volume were estimated by voxel-based morphometry. The WMLV-to-intracranial brain volume ratio (WMLV/ICV) was calculated, and its association with dementia risk was estimated using Cox proportional hazard models. Total brain atrophy, defined as the TBV-to-ICV ratio (TBV/ICV), and dementia-related regional brain atrophy defined based on our previous report were calculated. The association between dementia risk and the combined measures of WMLV/ICV and either total brain atrophy or the number of atrophied regions was also tested. RESULTS During the follow-up, 113 participants developed dementia. The risks of dementia increased significantly with higher WMLV/ICV levels. In addition, dementia risk increased additively both in participants with higher WMLV/ICV levels and lower TBV/ICV levels and in those with higher WMLV/ICV levels and a higher number of dementia-related brain regional atrophy. CONCLUSION The risk of dementia increased significantly with higher WMLV/ICV levels. An additive increment in dementia risk was observed with higher WMLV/ICV levels and lower TBV/ICV levels or a higher number of dementia-related brain regional atrophy, suggesting the importance of prevention or control of cardiovascular risk factors.
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Affiliation(s)
- Taro Nakazawa
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tomoyuki Ohara
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Naoki Hirabayashi
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Department of Psychosomatic Medicine, Graduate School of Medical Science, Kyushu University, Fukuoka, Japan
| | - Yoshihiko Furuta
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Jun Hata
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Mao Shibata
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Department of Psychosomatic Medicine, Graduate School of Medical Science, Kyushu University, Fukuoka, Japan.,Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Takanori Honda
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Takanari Kitazono
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Toshiharu Ninomiya
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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10
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Keuss SE, Coath W, Nicholas JM, Poole T, Barnes J, Cash DM, Lane CA, Parker TD, Keshavan A, Buchanan SM, Wagen AZ, Storey M, Harris M, Malone IB, Sudre CH, Lu K, James SN, Street R, Thomas DL, Dickson JC, Murray-Smith H, Wong A, Freiberger T, Crutch S, Richards M, Fox NC, Schott JM. Associations of β-Amyloid and Vascular Burden With Rates of Neurodegeneration in Cognitively Normal Members of the 1946 British Birth Cohort. Neurology 2022; 99:e129-e141. [PMID: 35410910 PMCID: PMC9280996 DOI: 10.1212/wnl.0000000000200524] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 03/01/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The goals of this work were to quantify the independent and interactive associations of β-amyloid (Aβ) and white matter hyperintensity volume (WMHV), a marker of presumed cerebrovascular disease (CVD), with rates of neurodegeneration and to examine the contributions of APOE ε4 and vascular risk measured at different stages of adulthood in cognitively normal members of the 1946 British Birth Cohort. METHODS Participants underwent brain MRI and florbetapir-Aβ PET as part of Insight 46, an observational population-based study. Changes in whole-brain, ventricular, and hippocampal volume were directly measured from baseline and repeat volumetric T1 MRI with the boundary shift integral. Linear regression was used to test associations with baseline Aβ deposition, baseline WMHV, APOE ε4, and office-based Framingham Heart Study Cardiovascular Risk Score (FHS-CVS) and systolic blood pressure (BP) at ages 36, 53, and 69 years. RESULTS Three hundred forty-six cognitively normal participants (mean [SD] age at baseline scan 70.5 [0.6] years; 48% female) had high-quality T1 MRI data from both time points (mean [SD] scan interval 2.4 [0.2] years). Being Aβ positive at baseline was associated with 0.87-mL/y faster whole-brain atrophy (95% CI 0.03, 1.72), 0.39-mL/y greater ventricular expansion (95% CI 0.16, 0.64), and 0.016-mL/y faster hippocampal atrophy (95% CI 0.004, 0.027), while each 10-mL additional WMHV at baseline was associated with 1.07-mL/y faster whole-brain atrophy (95% CI 0.47, 1.67), 0.31-mL/y greater ventricular expansion (95% CI 0.13, 0.60), and 0.014-mL/y faster hippocampal atrophy (95% CI 0.006, 0.022). These contributions were independent, and there was no evidence that Aβ and WMHV interacted in their effects. There were no independent associations of APOE ε4 with rates of neurodegeneration after adjustment for Aβ status and WMHV, no clear relationships between FHS-CVS or systolic BP and rates of neurodegeneration when assessed across the whole sample, and no evidence that FHS-CVS or systolic BP acted synergistically with Aβ. DISCUSSION Aβ and presumed CVD have distinct and additive effects on rates of neurodegeneration in cognitively normal elderly. These findings have implications for the use of MRI measures as biomarkers of neurodegeneration and emphasize the importance of risk management and early intervention targeting both pathways.
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Affiliation(s)
- Sarah E Keuss
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - William Coath
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Jennifer M Nicholas
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Teresa Poole
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Josephine Barnes
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - David M Cash
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Christopher A Lane
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Thomas D Parker
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Ashvini Keshavan
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Sarah M Buchanan
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Aaron Z Wagen
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Mathew Storey
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Matthew Harris
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Ian B Malone
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Carole H Sudre
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Kirsty Lu
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Sarah-Naomi James
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Rebecca Street
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - David L Thomas
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - John C Dickson
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Heidi Murray-Smith
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Andrew Wong
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Tamar Freiberger
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Sebastian Crutch
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Marcus Richards
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Nick C Fox
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK
| | - Jonathan M Schott
- From the Dementia Research Centre (S.E.K., W.C., J.M.N., T.P., J.B., D.M.C., C.A.L., A.K. S.M.B., A.Z.W., M.S., M.H., I.B.M., C.H.S., K.L., R.S., H.M.-S, T.F., S.C., N.C.F., J.M.S.), Dementia Research Institute (D.M.C., N.C.F.), Leonard Wolfson Experimental Neurology Centre (D.L.T.), and Department of Brain Repair and Neurorehabilitation (D.L.T.), UCL Queen Square Institute of Neurology; Department of Medical Statistics (J.M.N., T.P.), London School of Hygiene and Tropical Medicine; 4. Department of Medicine (T.D.P.), Division of Brain Sciences, Imperial College London; MRC Unit for Lifelong Health and Ageing at UCL (C.H.S., S.-N.J., A.W., M.R.); Centre for Medical Image Computing (C.H.S.), University College London; School of Biomedical Engineering & Imaging Sciences (C.H.S.), King's College London; and Institute of Nuclear Medicine (J.C.D.), University College London Hospitals, UK.
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Cao Z, Mai Y, Fang W, Lei M, Luo Y, Zhao L, Liao W, Yu Q, Xu J, Ruan Y, Xiao S, Mok VCT, Shi L, Liu J. The Correlation Between White Matter Hyperintensity Burden and Regional Brain Volumetry in Patients With Alzheimer's Disease. Front Hum Neurosci 2022; 16:760360. [PMID: 35774484 PMCID: PMC9237397 DOI: 10.3389/fnhum.2022.760360] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background White matter hyperintensities (WMHs) and regional brain lobe atrophy coexist in the brain of patients with Alzheimer's disease (AD), but the association between them in patients with AD still lacks comprehensive investigation and solid imaging data support. Objective We explored whether WMHs can promote the pathological process of AD by aggravating atrophy in specific brain regions and tried to explain the regional specificity of these relationships. Methods A sample of 240 adults including 180 normal controls (NCs) and 80 cases with AD were drawn from the ADNI database. T1-weighted magnetic resonance imaging (MRI) and T2-weighted fluid-attenuated MRI of the participants were downloaded and were analyzed using AccuBrain® to generate the quantitative ratio of WMHs (WMHr, WMH volumes corrected by intracranial volume) and regional brain atrophy. We also divided WMHr into periventricular WMHr (PVWMHr) and deep WMHr (DWMHr) for the purpose of this study. The Cholinergic Pathways Hyperintensities Scale (CHIPS) scores were conducted by two evaluators. Independent t-test, Mann–Whitney U test, or χ2 test were used to compare the demographic characteristics, and Spearman correlation coefficient values were used to determine the association between WMHs and different regions of brain atrophy. Results Positive association between WMHr and quantitative medial temporal lobe atrophy (QMTA) (rs = 0.281, p = 0.011), temporal lobe atrophy (rs = 0.285, p = 0.011), and insular atrophy (rs = 0.406, p < 0.001) was found in the AD group before Bonferroni correction. PVWMHr contributed to these correlations. By separately analyzing the relationship between PVWMHr and brain atrophy, we found that there were still positive correlations after correction in QMTA (rs = 0.325, p = 0.003), temporal lobe atrophy (rs = 0.298, p = 0.007), and insular atrophy (rs = 0.429, p < 0.001) in AD group. Conclusion WMH severity tends to be associated with regional brain atrophy in patients with AD, especially with medial temporal lobe, temporal lobe, and insular lobe atrophy. PVWMHs were devoted to these correlations.
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Affiliation(s)
- Zhiyu Cao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yingren Mai
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wenli Fang
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ming Lei
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, China
| | - Lei Zhao
- BrainNow Research Institute, Shenzhen, China
| | - Wang Liao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qun Yu
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiaxin Xu
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yuting Ruan
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Songhua Xiao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Vincent C. T. Mok
- BrainNow Research Institute, Shenzhen, China
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen, China
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- *Correspondence: Lin Shi
| | - Jun Liu
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Jun Liu
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Leifeld J, Förster E, Reiss G, Hamad MIK. Considering the Role of Extracellular Matrix Molecules, in Particular Reelin, in Granule Cell Dispersion Related to Temporal Lobe Epilepsy. Front Cell Dev Biol 2022; 10:917575. [PMID: 35733853 PMCID: PMC9207388 DOI: 10.3389/fcell.2022.917575] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
The extracellular matrix (ECM) of the nervous system can be considered as a dynamically adaptable compartment between neuronal cells, in particular neurons and glial cells, that participates in physiological functions of the nervous system. It is mainly composed of carbohydrates and proteins that are secreted by the different kinds of cell types found in the nervous system, in particular neurons and glial cells, but also other cell types, such as pericytes of capillaries, ependymocytes and meningeal cells. ECM molecules participate in developmental processes, synaptic plasticity, neurodegeneration and regenerative processes. As an example, the ECM of the hippocampal formation is involved in degenerative and adaptive processes related to epilepsy. The role of various components of the ECM has been explored extensively. In particular, the ECM protein reelin, well known for orchestrating the formation of neuronal layer formation in the cerebral cortex, is also considered as a player involved in the occurrence of postnatal granule cell dispersion (GCD), a morphologically peculiar feature frequently observed in hippocampal tissue from epileptic patients. Possible causes and consequences of GCD have been studied in various in vivo and in vitro models. The present review discusses different interpretations of GCD and different views on the role of ECM protein reelin in the formation of this morphological peculiarity.
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Affiliation(s)
- Jennifer Leifeld
- Department of Neuroanatomy and Molecular Brain Research, Medical Faculty, Ruhr University Bochum, Bochum, Germany
- Department of Biochemistry I—Receptor Biochemistry, Faculty of Chemistry and Biochemistry, Ruhr University Bochum, Bochum, Germany
- *Correspondence: Jennifer Leifeld, ; Eckart Förster,
| | - Eckart Förster
- Department of Neuroanatomy and Molecular Brain Research, Medical Faculty, Ruhr University Bochum, Bochum, Germany
- *Correspondence: Jennifer Leifeld, ; Eckart Förster,
| | - Gebhard Reiss
- Institute for Anatomy and Clinical Morphology, School of Medicine, Faculty of Health, Witten/ Herdecke University, Witten, Germany
| | - Mohammad I. K. Hamad
- Institute for Anatomy and Clinical Morphology, School of Medicine, Faculty of Health, Witten/ Herdecke University, Witten, Germany
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13
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APOE-ε4 modulates the association among plasma Aβ 42/Aβ 40, vascular diseases, neurodegeneration and cognitive decline in non-demented elderly adults. Transl Psychiatry 2022; 12:128. [PMID: 35351867 PMCID: PMC8964707 DOI: 10.1038/s41398-022-01899-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 03/03/2022] [Accepted: 03/11/2022] [Indexed: 01/18/2023] Open
Abstract
Including apolipoprotein E-ε4 (APOE-ε4) status and older age into consideration may increase the accuracy of plasma Aβ42/Aβ40 detecting Aβ+ individuals, but the rationale behind this remains to be fully understood. Besides, both Aβ pathology and vascular diseases are related to neurodegeneration and cognitive decline, but it is still not fully understood how APOE-ε4 modulates these relationships. In this study, we examined 241 non-demented Alzheimer's Disease Neuroimaging Initiative participants to investigate the associations among age, white matter hyperintensities (WMH), hypertension, hyperlipidemia, body mass index (BMI), plasma Aβ42/Aβ40 measured by liquid chromatography tandem mass spectrometry, and 18F-florbetapir Aβ PET as well as their prediction of longitudinal adjusted hippocampal volume (aHCV) and cognition in APOE-ε4 carriers and non-carriers. We found older age predicted faster WMH increase (p = 0.024) and cortical Aβ accumulation (p = 0.043) in APOE-ε4 non-carriers only, whereas lower plasma Aβ42/Aβ40 predicted faster cortical Aβ accumulation (p < 0.018) regardless of APOE-ε4 status. While larger WMH and underweight predicted (p < 0.05) faster decreases in aHCV and cognition in APOE-ε4 non-carriers, lower plasma Aβ42/Aβ40 predicted (p < 0.031) faster decreases in aHCV and cognition in APOE-ε4 carriers. Higher Aβ PET also predicted faster rates of aHCV (p = 0.010) in APOE-ε4 carriers only, but was related to faster rates of cognitive decline (p < 0.022) regardless of APOE-ε4 status. These findings may provide novel insights into understanding different mechanisms underlie neurodegeneration and cognitive decline in non-demented elderly adults with and without APOE-ε4 allele, which may help the design of anti-Alzheimer's clinical trials.
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14
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Kok FK, van Leerdam SL, de Lange ECM. Potential Mechanisms Underlying Resistance to Dementia in Non-Demented Individuals with Alzheimer's Disease Neuropathology. J Alzheimers Dis 2022; 87:51-81. [PMID: 35275527 PMCID: PMC9198800 DOI: 10.3233/jad-210607] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Alzheimer’s disease (AD) is the most common form of dementia and typically characterized by the accumulation of amyloid-β plaques and tau tangles. Intriguingly, there also exists a group of elderly which do not develop dementia during their life, despite the AD neuropathology, the so-called non-demented individuals with AD neuropathology (NDAN). In this review, we provide extensive background on AD pathology and normal aging and discuss potential mechanisms that enable these NDAN individuals to remain cognitively intact. Studies presented in this review show that NDAN subjects are generally higher educated and have a larger cognitive reserve. Furthermore, enhanced neural hypertrophy could compensate for hippocampal and cingulate neural atrophy in NDAN individuals. On a cellular level, these individuals show increased levels of neural stem cells and ‘von Economo neurons’. Furthermore, in NDAN brains, binding of Aβ oligomers to synapses is prevented, resulting in decreased glial activation and reduced neuroinflammation. Overall, the evidence stated here strengthens the idea that some individuals are more resistant to AD pathology, or at least show an elongation of the asymptomatic state of the disease compared to others. Insights into the mechanisms underlying this resistance could provide new insight in understanding normal aging and AD itself. Further research should focus on factors and mechanisms that govern the NDAN cognitive resilience in order to find clues on novel biomarkers, targets, and better treatments of AD.
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Affiliation(s)
- Frédérique K Kok
- Predictive Pharmacology, Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre of Drug Research, Leiden University, Leiden, The Netherlands
| | - Suzanne L van Leerdam
- Predictive Pharmacology, Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre of Drug Research, Leiden University, Leiden, The Netherlands
| | - Elizabeth C M de Lange
- Predictive Pharmacology, Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre of Drug Research, Leiden University, Leiden, The Netherlands
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15
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Celle S, Boutet C, Annweiler C, Ceresetti R, Pichot V, Barthélémy JC, Roche F. Leukoaraiosis and Gray Matter Volume Alteration in Older Adults: The PROOF Study. Front Neurosci 2022; 15:747569. [PMID: 35095388 PMCID: PMC8793339 DOI: 10.3389/fnins.2021.747569] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
Background and Purpose: Leukoaraiosis, also called white matter hyperintensities (WMH), is frequently encountered in the brain of older adults. During aging, gray matter structure is also highly affected. WMH or gray matter defects are commonly associated with a higher prevalence of mild cognitive impairment. However, little is known about the relationship between WMH and gray matter. Our aim was thus to explore the relationship between leukoaraiosis severity and gray matter volume in a cohort of healthy older adults. Methods: Leukoaraiosis was rated in participants from the PROOF cohort using the Fazekas scale. Voxel-based morphometry was performed on brain scans to examine the potential link between WMH and changes of local brain volume. A neuropsychological evaluation including attentional, executive, and memory tests was also performed to explore cognition. Results: Out of 315 75-year-old subjects, 228 had punctuate foci of leukoaraiosis and 62 had begun the confluence of foci. Leukoaraiosis was associated with a decrease of gray matter in the middle temporal gyrus, in the right medial frontal gyrus, and in the left parahippocampal gyrus. It was also associated with decreased performances in memory recall, executive functioning, and depression. Conclusion: In a population of healthy older adults, leukoaraiosis was associated with gray matter defects and reduced cognitive performance. Controlling vascular risk factors and detecting early cerebrovascular disease may prevent, at least in part, dementia onset and progression.
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Affiliation(s)
- Sébastien Celle
- Clinical Physiology, Visas Center, University Hospital, Saint-Etienne, France
- INSERM, U1059, SAINBIOSE, DVH, Saint-Étienne, France
- *Correspondence: Sébastien Celle,
| | - Claire Boutet
- Department of Radiology, University Hospital, Saint Etienne, France
- EA7423 TAPE, UJM, Saint-Étienne, France
| | - Cédric Annweiler
- Department of Geriatric Medicine and Memory Clinic, Research Center on Autonomy and Longevity, University Hospital, Angers, France
- UPRES EA4638, University of Angers, Angers, France
| | - Romain Ceresetti
- Clinical Physiology, Visas Center, University Hospital, Saint-Etienne, France
- INSERM, U1059, SAINBIOSE, DVH, Saint-Étienne, France
| | - Vincent Pichot
- Clinical Physiology, Visas Center, University Hospital, Saint-Etienne, France
- INSERM, U1059, SAINBIOSE, DVH, Saint-Étienne, France
| | - Jean-Claude Barthélémy
- Clinical Physiology, Visas Center, University Hospital, Saint-Etienne, France
- INSERM, U1059, SAINBIOSE, DVH, Saint-Étienne, France
| | - Frédéric Roche
- Clinical Physiology, Visas Center, University Hospital, Saint-Etienne, France
- INSERM, U1059, SAINBIOSE, DVH, Saint-Étienne, France
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16
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de Silva E, Sudre CH, Barnes J, Scelsi MA, Altmann A. Polygenic coronary artery disease association with brain atrophy in the cognitively impaired. Brain Commun 2022; 4:fcac314. [PMID: 36523268 PMCID: PMC9746681 DOI: 10.1093/braincomms/fcac314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 09/09/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022] Open
Abstract
While a number of low-frequency genetic variants of large effect size have been shown to underlie both cardiovascular disease and dementia, recent studies have highlighted the importance of common genetic variants of small effect size, which, in aggregate, are embodied by a polygenic risk score. We investigate the effect of polygenic risk for coronary artery disease on brain atrophy in Alzheimer's disease using whole-brain volume and put our findings in context with the polygenic risk for Alzheimer's disease and presumed small vessel disease as quantified by white-matter hyperintensities. We use 730 subjects from the Alzheimer's disease neuroimaging initiative database to investigate polygenic risk score effects (beyond APOE) on whole-brain volumes, total and regional white-matter hyperintensities and amyloid beta across diagnostic groups. In a subset of these subjects (N = 602), we utilized longitudinal changes in whole-brain volume over 24 months using the boundary shift integral approach. Linear regression and linear mixed-effects models were used to investigate the effect of white-matter hyperintensities at baseline as well as Alzheimer's disease-polygenic risk score and coronary artery disease-polygenic risk score on whole-brain atrophy and whole-brain atrophy acceleration, respectively. All genetic associations were examined under the oligogenic (P = 1e-5) and the more variant-inclusive polygenic (P = 0.5) scenarios. Results suggest no evidence for a link between the polygenic risk score and markers of Alzheimer's disease pathology at baseline (when stratified by diagnostic group). However, both Alzheimer's disease-polygenic risk score and coronary artery disease-polygenic risk score were associated with longitudinal decline in whole-brain volume (Alzheimer's disease-polygenic risk score t = 3.3, P FDR = 0.007 over 24 months in healthy controls) and surprisingly, under certain conditions, whole-brain volume atrophy is statistically more correlated with cardiac polygenic risk score than Alzheimer's disease-polygenic risk score (coronary artery disease-polygenic risk score t = 2.1, P FDR = 0.04 over 24 months in the mild cognitive impairment group). Further, in our regional analysis of white-matter hyperintensities, Alzheimer's disease-polygenic risk score beyond APOE is predictive of white-matter volume in the occipital lobe in Alzheimer's disease subjects in the polygenic regime. Finally, the rate of change of brain volume (or atrophy acceleration) may be sensitive to Alzheimer's disease-polygenic risk beyond APOE in healthy individuals (t = 2, P = 0.04). For subjects with mild cognitive impairment, beyond APOE, a more inclusive polygenic risk score including more variants, shows coronary artery disease-polygenic risk score to be more predictive of whole-brain volume atrophy, than an oligogenic approach including fewer larger effect size variants.
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Affiliation(s)
- Eric de Silva
- Centre for Medical Image Computing, University College London, London, UK.,NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Carole H Sudre
- Centre for Medical Image Computing, University College London, London, UK.,MRC Unit for Lifelong Health and Ageing, University College London, London, UK.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Josephine Barnes
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Marzia A Scelsi
- Centre for Medical Image Computing, University College London, London, UK
| | - Andre Altmann
- Centre for Medical Image Computing, University College London, London, UK
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17
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Fiford CM, Sudre CH, Young AL, Macdougall A, Nicholas J, Manning EN, Malone IB, Walsh P, Goodkin O, Pemberton HG, Barkhof F, Alexander DC, Cardoso MJ, Biessels GJ, Barnes J. Presumed small vessel disease, imaging and cognition markers in the Alzheimer's Disease Neuroimaging Initiative. Brain Commun 2021; 3:fcab226. [PMID: 34661106 PMCID: PMC8514859 DOI: 10.1093/braincomms/fcab226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 06/22/2021] [Accepted: 06/25/2021] [Indexed: 01/18/2023] Open
Abstract
MRI-derived features of presumed cerebral small vessel disease are frequently found in Alzheimer's disease. Influences of such markers on disease-progression measures are poorly understood. We measured markers of presumed small vessel disease (white matter hyperintensity volumes; cerebral microbleeds) on baseline images of newly enrolled individuals in the Alzheimer's Disease Neuroimaging Initiative cohort (GO and 2) and used linear mixed models to relate these to subsequent atrophy and neuropsychological score change. We also assessed heterogeneity in white matter hyperintensity positioning within biomarker abnormality sequences, driven by the data, using the Subtype and Stage Inference algorithm. This study recruited both sexes and included: controls: [n = 159, mean(SD) age = 74(6) years]; early and late mild cognitive impairment [ns = 265 and 139, respectively, mean(SD) ages =71(7) and 72(8) years, respectively]; Alzheimer's disease [n = 103, mean(SD) age = 75(8)] and significant memory concern [n = 72, mean(SD) age = 72(6) years]. Baseline demographic and vascular risk-factor data, and longitudinal cognitive scores (Mini-Mental State Examination; logical memory; and Trails A and B) were collected. Whole-brain and hippocampal volume change metrics were calculated. White matter hyperintensity volumes were associated with greater whole-brain and hippocampal volume changes independently of cerebral microbleeds (a doubling of baseline white matter hyperintensity was associated with an increase in atrophy rate of 0.3 ml/year for brain and 0.013 ml/year for hippocampus). Cerebral microbleeds were found in 15% of individuals and the presence of a microbleed, as opposed to none, was associated with increases in atrophy rate of 1.4 ml/year for whole brain and 0.021 ml/year for hippocampus. White matter hyperintensities were predictive of greater decline in all neuropsychological scores, while cerebral microbleeds were predictive of decline in logical memory (immediate recall) and Mini-Mental State Examination scores. We identified distinct groups with specific sequences of biomarker abnormality using continuous baseline measures and brain volume change. Four clusters were found; Group 1 showed early Alzheimer's pathology; Group 2 showed early neurodegeneration; Group 3 had early mixed Alzheimer's and cerebrovascular pathology; Group 4 had early neuropsychological score abnormalities. White matter hyperintensity volumes becoming abnormal was a late event for Groups 1 and 4 and an early event for 2 and 3. In summary, white matter hyperintensities and microbleeds were independently associated with progressive neurodegeneration (brain atrophy rates) and cognitive decline (change in neuropsychological scores). Mechanisms involving white matter hyperintensities and progression and microbleeds and progression may be partially separate. Distinct sequences of biomarker progression were found. White matter hyperintensity development was an early event in two sequences.
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Affiliation(s)
- Cassidy M Fiford
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Carole H Sudre
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Health Sciences, University College London, London WC1E 3HB, UK
| | - Alexandra L Young
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 3AF, UK
| | - Amy Macdougall
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Jennifer Nicholas
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Emily N Manning
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Ian B Malone
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Phoebe Walsh
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Olivia Goodkin
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK
| | - Hugh G Pemberton
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam Neuroscience, 1081 HV Amsterdam, The Netherlands
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- UCL Institute of Healthcare Engineering, London WC1E 6DH, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands
| | - Josephine Barnes
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
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18
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Fan Y, Shen M, Huo Y, Gao X, Li C, Zheng R, Zhang J. Total Cerebral Small Vessel Disease Burden on MRI Correlates With Medial Temporal Lobe Atrophy and Cognitive Performance in Patients of a Memory Clinic. Front Aging Neurosci 2021; 13:698035. [PMID: 34566621 PMCID: PMC8456168 DOI: 10.3389/fnagi.2021.698035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/09/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Cerebral small vessel disease (cSVD) and neurodegeneration are the two main causes of dementia and are considered distinct pathological processes, while studies have shown overlaps and interactions between the two pathological pathways. Medial temporal atrophy (MTA) is considered a classic marker of neurodegeneration. We aimed to investigate the relationship of total cSVD burden and MTA on MRI using a total cSVD score and to explore the impact of the two MRI features on cognition. Methods: Patients in a memory clinic were enrolled, who underwent brain MRI scan and cognitive evaluation within 7 days after the first visit. MTA and total cSVD score were rated using validated visual scales. Cognitive function was assessed by using Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scales. Spearman's correlation and regression models were used to test (i) the association between MTA and total cSVD score as well as each cSVD marker and (ii) the correlation of the MRI features and cognitive status. Results: A total of 312 patients were finally enrolled, with a median age of 75.0 (66.0-80.0) years and 40.7% (127/312) males. All of them finished MRI and MMSE, and 293 subjects finished MoCA. Of note, 71.8% (224/312) of the patients had at least one of the cSVD markers, and 48.7% (152/312) of them had moderate-severe MTA. The total cSVD score was independently associated with MTA levels, after adjusting for age, gender, years of education, and other vascular risk factors (OR 1.191, 95% CI 1.071-1.324, P = 0.001). In regard to individual markers, a significant association existed only between white matter hyperintensities and MTA after adjusting for the factors mentioned above (OR 1.338, 95% CI 1.050-1.704, P = 0.018). Both MTA and total cSVD score were independent risk factors for MMSE ≤ 26 (MTA: OR 1.877, 95% CI 1.407-2.503, P < 0.001; total cSVD score: OR 1.474, 95% CI 1.132-1.921, P = 0.004), and MoCA < 26 (MTA: OR 1.629, 95% CI 1.112-2.388, P = 0.012; total cSVD score: OR 1.520, 95% CI 1.068-2.162, P = 0.020). Among all the cSVD markers, microbleed was found significantly associated with MMSE ≤ 26, while no marker was demonstrated a relationship with MoCA < 26. Conclusion: Cerebral small vessel disease was related to MTA in patients of a memory clinic, and both the MRI features had a significant association with cognitive impairment.
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Affiliation(s)
- Yangyi Fan
- Department of Neurology, Peking University People's Hospital, Beijing, China
| | - Ming Shen
- Department of Neurology, Peking University People's Hospital, Beijing, China
| | - Yang Huo
- Department of Neurology, Peking University People's Hospital, Beijing, China
| | - Xuguang Gao
- Department of Neurology, Peking University People's Hospital, Beijing, China
| | - Chun Li
- Department of Rheumatology and Immunology, Peking University People's Hospital, Beijing, China
| | - Ruimao Zheng
- Neuroscience Research Institute, Peking University, Beijing, China
| | - Jun Zhang
- Department of Neurology, Peking University People's Hospital, Beijing, China
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19
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Lloret A, Esteve D, Lloret MA, Monllor P, López B, León JL, Cervera-Ferri A. Is Oxidative Stress the Link Between Cerebral Small Vessel Disease, Sleep Disruption, and Oligodendrocyte Dysfunction in the Onset of Alzheimer's Disease? Front Physiol 2021; 12:708061. [PMID: 34512381 PMCID: PMC8424010 DOI: 10.3389/fphys.2021.708061] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 07/28/2021] [Indexed: 01/07/2023] Open
Abstract
Oxidative stress is an early occurrence in the development of Alzheimer’s disease (AD) and one of its proposed etiologic hypotheses. There is sufficient experimental evidence supporting the theory that impaired antioxidant enzymatic activity and increased formation of reactive oxygen species (ROS) take place in this disease. However, the antioxidant treatments fail to stop its advancement. Its multifactorial condition and the diverse toxicological cascades that can be initiated by ROS could possibly explain this failure. Recently, it has been suggested that cerebral small vessel disease (CSVD) contributes to the onset of AD. Oxidative stress is a central hallmark of CSVD and is depicted as an early causative factor. Moreover, data from various epidemiological and clinicopathological studies have indicated a relationship between CSVD and AD where endothelial cells are a source of oxidative stress. These cells are also closely related to oligodendrocytes, which are, in particular, sensitive to oxidation and lead to myelination being compromised. The sleep/wake cycle is another important control in the proliferation, migration, and differentiation of oligodendrocytes, and sleep loss reduces myelin thickness. Moreover, sleep plays a crucial role in resistance against CSVD, and poor sleep quality increases the silent markers of this vascular disease. Sleep disruption is another early occurrence in AD and is related to an increase in oxidative stress. In this study, the relationship between CSVD, oligodendrocyte dysfunction, and sleep disorders is discussed while focusing on oxidative stress as a common occurrence and its possible role in the onset of AD.
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Affiliation(s)
- Ana Lloret
- INCLIVA, CIBERFES, Department of Physiology, Faculty of Medicine, University of Valencia, Valencia, Spain
| | - Daniel Esteve
- INCLIVA, CIBERFES, Department of Physiology, Faculty of Medicine, University of Valencia, Valencia, Spain
| | - Maria Angeles Lloret
- Department of Clinical Neurophysiology, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Paloma Monllor
- INCLIVA, CIBERFES, Department of Physiology, Faculty of Medicine, University of Valencia, Valencia, Spain
| | - Begoña López
- Department of Neurology, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - José Luis León
- Departament of Neuroradiology, Ascires Biomedical Group, Hospital Clinico Universitario, Valencia, Spain
| | - Ana Cervera-Ferri
- Department of Anatomy and Human Embryology, University of Valencia, Valencia, Spain
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20
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Barber P, Nestor SM, Wang M, Wu P, Ursenbach J, Munir A, Gupta R, Tariq SS, Smith E, Frayne R, Black SE, Sajobi T, Coutts S. Hippocampal atrophy and cognitive function in transient ischemic attack and minor stroke patients over three years. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2021; 2:100019. [PMID: 36324718 PMCID: PMC9616379 DOI: 10.1016/j.cccb.2021.100019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 06/17/2021] [Accepted: 06/20/2021] [Indexed: 06/16/2023]
Abstract
Introduction Transient ischemic attack (TIA) and minor ischemic stroke (IS) is associated with a increased risk of late life dementia. In this study we aim to study the extent to which the rates of hippocampal atrophy in TIA/IS differ from healthy controls, and how they are correlated to neuropsychological measurements. Methods TIA or minor stroke patients were tested with a neuropsychological battery including tests of executive function, and verbal and non-verbal memory at three time points out to 3 years. Annualized rates of hippocampal atrophy in TIA/IS patients were compared to controls. A linear-mixed regression model was used to assess the difference in rates of hippocampal atrophy after adjusting for time and demographic characteristics. Results TIA/IS patients demonstrated a higher hippocampal atrophy rate than healthy controls over a 3-year interval: the annual percentage change of the left hippocampal volume was 2.5% (78 mm3 per year (SD 60)) for TIA/IS patients compared to 0.9% (29 mm3 per year (SD 32)) for controls (p < 0.01); and the annual percentage change of the right hippocampal volume was 2.5% (80 mm3 per year (SD 46)) for TIA/IS patients compared to 0.5% (17 mm3 per year (SD 33)) for controls (P < 0.01). Patients with higher annual hippocampal atrophy were more likely to report higher TMT B times, but lower ROC total score, lower California Verbal Learning Test-II total recall, and lower ROC Figure recall scores longitudinally. Conclusion TIA/IS patients experience a higher rate of hippocampal atrophy independent of TIA/IS recurrence that are associated with changes in episodic memory and executive function over 3 years.
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Affiliation(s)
- Philip Barber
- Calgary Stroke Program, Department of Clinical Neurosciences, Foothills Medical Centre, 1403 29th Street NW, Calgary AB, Canada
- Cumming School of Medicine, Hotchkiss Brain Institute, 3330 Hospital Drive NW, Calgary AB, Canada
- Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, Canada
| | - Sean M. Nestor
- Hurvitz Brain Sciences Program, Sunnybrook Health Science Centre, University of Toronto, ON, Canada
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Meng Wang
- Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, Canada
| | - Pauline Wu
- Calgary Stroke Program, Department of Clinical Neurosciences, Foothills Medical Centre, 1403 29th Street NW, Calgary AB, Canada
| | - Jake Ursenbach
- Calgary Stroke Program, Department of Clinical Neurosciences, Foothills Medical Centre, 1403 29th Street NW, Calgary AB, Canada
- Seaman Family MR Center, Foothills Medical Centre, 1403 29th Street NW, Calgary AB, Canada
- Cumming School of Medicine, Hotchkiss Brain Institute, 3330 Hospital Drive NW, Calgary AB, Canada
| | - Amlish Munir
- Calgary Stroke Program, Department of Clinical Neurosciences, Foothills Medical Centre, 1403 29th Street NW, Calgary AB, Canada
- Seaman Family MR Center, Foothills Medical Centre, 1403 29th Street NW, Calgary AB, Canada
- Cumming School of Medicine, Hotchkiss Brain Institute, 3330 Hospital Drive NW, Calgary AB, Canada
| | - Rani Gupta
- Calgary Stroke Program, Department of Clinical Neurosciences, Foothills Medical Centre, 1403 29th Street NW, Calgary AB, Canada
- Seaman Family MR Center, Foothills Medical Centre, 1403 29th Street NW, Calgary AB, Canada
- Cumming School of Medicine, Hotchkiss Brain Institute, 3330 Hospital Drive NW, Calgary AB, Canada
| | - Sah Sana Tariq
- Calgary Stroke Program, Department of Clinical Neurosciences, Foothills Medical Centre, 1403 29th Street NW, Calgary AB, Canada
- Cumming School of Medicine, Hotchkiss Brain Institute, 3330 Hospital Drive NW, Calgary AB, Canada
| | - Eric Smith
- Calgary Stroke Program, Department of Clinical Neurosciences, Foothills Medical Centre, 1403 29th Street NW, Calgary AB, Canada
- Cumming School of Medicine, Hotchkiss Brain Institute, 3330 Hospital Drive NW, Calgary AB, Canada
| | - Richard Frayne
- Seaman Family MR Center, Foothills Medical Centre, 1403 29th Street NW, Calgary AB, Canada
- Cumming School of Medicine, Hotchkiss Brain Institute, 3330 Hospital Drive NW, Calgary AB, Canada
- Department of Clinical Neurosciences, University of Calgary, 1403 29th Street NW, Calgary, Canada
- Department of Radiology, University of Calgary, 1403 29th Street NW, Calgary, Canada
| | - Sandra E. Black
- Hurvitz Brain Sciences Program, Sunnybrook Health Science Centre, University of Toronto, ON, Canada
| | - Tolupe Sajobi
- Calgary Stroke Program, Department of Clinical Neurosciences, Foothills Medical Centre, 1403 29th Street NW, Calgary AB, Canada
- Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, Canada
| | - Shelagh Coutts
- Calgary Stroke Program, Department of Clinical Neurosciences, Foothills Medical Centre, 1403 29th Street NW, Calgary AB, Canada
- Cumming School of Medicine, Hotchkiss Brain Institute, 3330 Hospital Drive NW, Calgary AB, Canada
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21
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Xiang Y, Chen S, Lin H, Xiong W, Zheng Z. Cognitive Function and White Matter Lesions in Medication-Overuse Headache. J Pain Res 2021; 14:1845-1853. [PMID: 34168492 PMCID: PMC8216749 DOI: 10.2147/jpr.s310064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/09/2021] [Indexed: 11/23/2022] Open
Abstract
Purpose This study was designed to investigate the cognitive function and the white matter lesions (WMLs) and the relationship between them in medication-overuse headache (MOH) patients. Methods Subjects were enrolled and performed Montreal Cognitive Assessment (MoCA, Chinese-Beijing Version), Hamilton Anxiety Rating Scale (HAMA), Hamilton Depression Rating Scale (HAMD-24), and Pittsburgh Sleep Quality Index (PSQI) to evaluate the general cognitive function, anxiety, depression and sleep quality, and they were divided into three groups according to the MoCA scores: healthy controls, MOH with normal cognition group and MOH with cognitive impairment group. All the participants underwent MRI scans and images were obtained for WML evaluation with Fazekas scale. Results One hundred thirty-four participants were enrolled into this study, 46 of them for healthy controls, and 88 for MOH patients, 40 of the MOH patients for MOH with cognitive impairment group, and 48 for MOH with normal cognition group. MOH patients had significantly lower MoCA scores, including the scores of visuospatial and executive function, attention, and orientation, while they had significantly greater HAMA scores, HAMD-24 scores, PSQI scores, and deep white matter hyperintensity scores compared to healthy controls. And in MOH patients, the age, disease duration, monthly headache days, and periventricular white matter hyperintensity scores in patients with cognitive impairment were greater than those in patients with normal cognition. Moreover, the MoCA scores were negatively related to age, disease duration, monthly headache days, and Fazekas scale scores, and disease duration and monthly headache days were significant predictors of cognitive impairment in MOH patients. Conclusion MOH patients showed cognitive impairment and increased WML burden. And in MOH patients, cognitive function was negatively related to WML burden, and disease duration and monthly headache days were potential predictors of cognitive impairment. Prompt and effective treatment to stop the progression of the disease may alleviate cognitive impairment in MOH patients.
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Affiliation(s)
- Yue Xiang
- Department of Nursing, Fujian Health College, Fuzhou, 350101, People's Republic of China
| | - Shenggen Chen
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001, People's Republic of China
| | - Hanbin Lin
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001, People's Republic of China
| | - Wenting Xiong
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001, People's Republic of China
| | - Zhenyang Zheng
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001, People's Republic of China
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22
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Schellhorn T, Zucknick M, Askim T, Munthe-Kaas R, Ihle-Hansen H, Seljeseth YM, Knapskog AB, Næss H, Ellekjær H, Thingstad P, Wyller TB, Saltvedt I, Beyer MK. Pre-stroke cognitive impairment is associated with vascular imaging pathology: a prospective observational study. BMC Geriatr 2021; 21:362. [PMID: 34126944 PMCID: PMC8201706 DOI: 10.1186/s12877-021-02327-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 06/06/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Chronic brain pathology and pre-stroke cognitive impairment (PCI) is predictive of post-stroke dementia. The aim of the current study was to measure pre-stroke neurodegenerative and vascular disease burden found on brain MRI and to assess the association between pre-stroke imaging pathology and PCI, whilst also looking for potential sex differences. METHODS This prospective brain MRI cohort is part of the multicentre Norwegian cognitive impairment after stroke (Nor-COAST) study. Patients hospitalized with acute ischemic or hemorrhagic stroke were included from five participating stroke units. Visual rating scales were used to categorize baseline MRIs (N = 410) as vascular, neurodegenerative, mixed, or normal, based on the presence of pathological imaging findings. Pre-stroke cognition was assessed by interviews of patients or caregivers using the Global Deterioration Scale (GDS). Stroke severity was assessed with the National Institute of Health Stroke Scale (NIHSS). Univariate and multiple logistic regression analyses were performed to investigate the association between imaging markers, PCI, and sex. RESULTS Patients' (N = 410) mean (SD) age was 73.6 (±11) years; 182 (44%) participants were female, the mean (SD) NIHSS at admittance was 4.1 (±5). In 68% of the participants, at least one pathological imaging marker was found. Medial temporal lobe atrophy (MTA) was present in 30% of patients, white matter hyperintensities (WMH) in 38% of patients and lacunes in 35% of patients. PCI was found in 30% of the patients. PCI was associated with cerebrovascular pathology (OR 2.5; CI = 1.4 to 4.5, p = 0.001) and mixed pathology (OR 3.4; CI = 1.9 to 6.1, p = 0.001) but was not associated with neurodegeneration (OR 1.0; CI = 0.5 to 2.2; p = 0.973). Pathological MRI markers, including MTA and lacunes, were more prevalent among men, as was a history of clinical stroke prior to the index stroke. The OR of PCI for women was not significantly increased (OR 1.2; CI = 0.8 to 1.9; p = 0.3). CONCLUSIONS Pre-stroke chronic brain pathology is common in stroke patients, with a higher prevalence in men. Vascular pathology and mixed pathology are associated with PCI. There were no significant sex differences for the risk of PCI. TRIAL REGISTRATION NCT02650531 , date of registration: 08.01.2016.
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Affiliation(s)
- Till Schellhorn
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
| | - Manuela Zucknick
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Torunn Askim
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Ragnhild Munthe-Kaas
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Medicine, Vestre Viken Hospital Trust, Bærum Hospital, Drammen, Norway
| | - Hege Ihle-Hansen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Yngve M Seljeseth
- Medical Department, Ålesund Hospital, Møre and Romsdal Health Trust, Ålesund, Norway
| | | | - Halvor Næss
- Department of Neurology, Haukeland University Hospital, Bergen, Norway
- Institute of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Hanne Ellekjær
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- Stroke Unit, Department of Internal Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Pernille Thingstad
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Torgeir Bruun Wyller
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Ingvild Saltvedt
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
- Department of Geriatric Medicine, Department of Internal Medicine St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Mona K Beyer
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
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23
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McCollum LE, Das SR, Xie L, de Flores R, Wang J, Xie SX, Wisse LEM, Yushkevich PA, Wolk DA. Oh brother, where art tau? Amyloid, neurodegeneration, and cognitive decline without elevated tau. Neuroimage Clin 2021; 31:102717. [PMID: 34119903 PMCID: PMC8207301 DOI: 10.1016/j.nicl.2021.102717] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 05/21/2021] [Accepted: 06/02/2021] [Indexed: 12/24/2022]
Abstract
Mild cognitive impairment (MCI) can be an early manifestation of Alzheimer's disease (AD) pathology, other pathologic entities [e.g., cerebrovascular disease, Lewy body disease, LATE (limbic-predominant age-related TDP-43 encephalopathy)], or mixed pathologies, with concomitant AD- and non-AD pathology being particularly common, albeit difficult to identify, in living MCI patients. The National Institute on Aging and Alzheimer's Association (NIA-AA) A/T/(N) [β-Amyloid/Tau/(Neurodegeneration)] AD research framework, which classifies research participants according to three binary biomarkers [β-amyloid (A+/A-), tau (T+/T-), and neurodegeneration (N+/N-)], provides an indirect means of identifying such cases. Individuals with A+T-(N+) MCI are thought to have both AD pathologic change, given the presence of β-amyloid, and non-AD pathophysiology, given neurodegeneration without tau, because in typical AD it is tau accumulation that is most tightly linked to neuronal injury and cognitive decline. Thus, in A+T-(N+) MCI (hereafter referred to as "mismatch MCI" for the tau-neurodegeneration mismatch), non-AD pathology is hypothesized to drive neurodegeneration and symptoms, because β-amyloid, in the absence of tau, likely reflects a preclinical stage of AD. We compared a group of individuals with mismatch MCI to groups with A+T+(N+) MCI (or "prodromal AD") and A-T-(N+) MCI (or "neurodegeneration-only MCI") on cross-sectional and longitudinal cognition and neuroimaging characteristics. β-amyloid and tau status were determined by CSF assays, while neurodegeneration status was based on hippocampal volume on MRI. Overall, mismatch MCI was less "AD-like" than prodromal AD and generally, with some exceptions, more closely resembled the neurodegeneration-only group. At baseline, mismatch MCI had less episodic memory loss compared to prodromal AD. Longitudinally, mismatch MCI declined more slowly than prodromal AD across all included cognitive domains, while mismatch MCI and neurodegeneration-only MCI declined at comparable rates. Prodromal AD had smaller baseline posterior hippocampal volume than mismatch MCI, and whole brain analyses demonstrated cortical thinning that was widespread in prodromal AD but largely restricted to the medial temporal lobes (MTLs) for the mismatch and neurodegeneration-only MCI groups. Longitudinally, mismatch MCI had slower rates of volume loss than prodromal AD throughout the MTLs. Differences in cross-sectional and longitudinal cognitive and neuroimaging measures between mismatch MCI and prodromal AD may reflect disparate underlying pathologic processes, with the mismatch group potentially being driven by non-AD pathologies on a background of largely preclinical AD. These findings suggest that β-amyloid status alone in MCI may not reveal the underlying driver of symptoms with important implications for enrollment in clinical trials and prognosis.
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Affiliation(s)
- Lauren E McCollum
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, University of Tennessee Graduate School of Medicine, Knoxville, TN, USA.
| | - Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA
| | - Long Xie
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA
| | - Robin de Flores
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA; INSERM UMR-S U1237, Université de Caen Normandie, Caen, Normandy, USA
| | - Jieqiong Wang
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sharon X Xie
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Laura E M Wisse
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA; Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Paul A Yushkevich
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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24
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Oh KT, Kim D, Ye BS, Lee S, Yun M, Yoo SK. Segmentation of white matter hyperintensities on 18F-FDG PET/CT images with a generative adversarial network. Eur J Nucl Med Mol Imaging 2021; 48:3422-3431. [PMID: 33693968 DOI: 10.1007/s00259-021-05285-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 02/25/2021] [Indexed: 11/28/2022]
Abstract
PURPOSE White matter hyperintensities (WMH) are typically segmented using MRI because WMH are hardly visible on 18F-FDG PET/CT. This retrospective study was conducted to segment WMH and estimate their volumes from 18F-FDG PET with a generative adversarial network (WhyperGAN). METHODS We selected patients whose interval between MRI and FDG PET/CT scans was within 3 months, from January 2017 to December 2018, and classified them into mild, moderate, and severe groups by following the semiquantitative rating method of Fazekas. For each group, 50 patients were selected, and of them, we randomly selected 35 patients for training and 15 for testing. WMH were automatically segmented from FLAIR MRI with manual adjustment. Patches of WMH were extracted from 18F-FDG PET and segmented MRI. WhyperGAN was compared with H-DenseUnet, a deep learning method widely used for segmentation tasks, for segmentation performance based on the dice similarity coefficient (DSC), recall, and average volume differences (AVD). For volume estimation, the predicted WMH volumes from PET were compared with ground truth volumes. RESULTS The DSC values were associated with WMH volumes on MRI. For volumes >60 mL, the DSC values were 0.751 for WhyperGAN and 0.564 for H-DenseUnet. For volumes ≤60 mL, the DSC values rapidly decreased as the volume decreased (0.362 for WhyperGAN vs. 0.237 for H-DenseUnet). For recall, WhyperGAN achieved the highest value in the severe group (0.579 for WhyperGAN vs. 0.509 for H-DenseUnet). For AVD, WhyperGAN achieved the lowest score in the severe group (0.494 for WhyperGAN vs. 0.941 for H-DenseUnet). For the WMH volume estimation, WhyperGAN performed better than H-DenseUnet and yielded excellent correlation coefficients (r = 0.998, 0.983, and 0.908 in the severe, moderate, and mild group). CONCLUSIONS Although limited by visual analysis, the WhyperGAN based can be used to automatically segment and estimate volumes of WMH from 18F-FDG PET/CT. This would increase the usefulness of 18F-FDG PET/CT for the evaluation of WMH in patients with cognitive impairment.
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Affiliation(s)
- Kyeong Taek Oh
- Department of Medical Engineering, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dongwoo Kim
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Byoung Seok Ye
- Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sangwon Lee
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Mijin Yun
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Sun Kook Yoo
- Department of Medical Engineering, Yonsei University College of Medicine, Seoul, Republic of Korea.
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25
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Schellhorn T, Aamodt EB, Lydersen S, Aam S, Wyller TB, Saltvedt I, Beyer MK. Clinically accessible neuroimaging predictors of post-stroke neurocognitive disorder: a prospective observational study. BMC Neurol 2021; 21:89. [PMID: 33632149 PMCID: PMC7905565 DOI: 10.1186/s12883-021-02117-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 02/19/2021] [Indexed: 01/21/2023] Open
Abstract
Background Neurocognitive disorder (NCD) is common in stroke survivors. We aimed to identify clinically accessible imaging markers of stroke and chronic pathology that are associated with early post-stroke NCD. Methods We included 231 stroke survivors from the “Norwegian Cognitive Impairment after Stroke (Nor-COAST)” study who underwent a standardized cognitive assessment 3 months after the stroke. Any NCD (mild cognitive impairment and dementia) and major NCD (dementia) were diagnosed according to “Diagnostic and Statistical Manual of Mental Disorders (DSM-5)” criteria. Clinically accessible imaging findings were analyzed on study-specific brain MRIs in the early phase after stroke. Stroke lesion volumes were semi automatically quantified and strategic stroke locations were determined by an atlas based coregistration. White matter hyperintensities (WMH) and medial temporal lobe atrophy (MTA) were visually scored. Logistic regression was used to identify neuroimaging findings associated with major NCD and any NCD. Results Mean age was 71.8 years (SD 11.1), 101 (43.7%) were females, mean time from stroke to imaging was 8 (SD 16) days. At 3 months 63 (27.3%) had mild NCD and 65 (28.1%) had major NCD. Any NCD was significantly associated with WMH pathology (odds ratio (OR) = 2.73 [1.56 to 4.77], p = 0.001), MTA pathology (OR = 1.95 [1.12 to 3.41], p = 0.019), and left hemispheric stroke (OR = 1.8 [1.05 to 3.09], p = 0.032). Major NCD was significantly associated with WMH pathology (OR = 2.54 [1.33 to 4.84], p = 0.005) and stroke lesion volume (OR (per ml) =1.04 [1.01 to 1.06], p = 0.001). Conclusion WMH pathology, MTA pathology and left hemispheric stroke were associated with the development of any NCD. Stroke lesion volume and WMH pathology were associated with the development of major NCD 3 months after stroke. These imaging findings may be used in the routine clinical setting to identify patients at risk for early post-stroke NCD. Trial registration ClinicalTrials.gov, NCT02650531, Registered 8 January 2016 – Retrospectively registered. Supplementary Information The online version contains supplementary material available at 10.1186/s12883-021-02117-8.
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Affiliation(s)
- Till Schellhorn
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway. .,Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Eva Birgitte Aamodt
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Stian Lydersen
- Regional Centre for Child and Youth Mental Health and Child Welfare, Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Stina Aam
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.,Department of Geriatric Medicine, Clinic of Medicine St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Torgeir Bruun Wyller
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Ingvild Saltvedt
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.,Department of Geriatric Medicine, Clinic of Medicine St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Mona Kristiansen Beyer
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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26
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Van Etten EJ, Bharadwaj PK, Hishaw GA, Huentelman MJ, Trouard TP, Grilli MD, Alexander GE. Influence of regional white matter hyperintensity volume and apolipoprotein E ε4 status on hippocampal volume in healthy older adults. Hippocampus 2021; 31:469-480. [PMID: 33586848 PMCID: PMC9119498 DOI: 10.1002/hipo.23308] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 12/22/2020] [Accepted: 01/23/2021] [Indexed: 11/08/2022]
Abstract
While total white matter hyperintensity (WMH) volume on magnetic resonance imaging (MRI) has been associated with hippocampal atrophy, less is known about how the regional distribution of WMH volume may differentially affect the hippocampus in healthy aging. Additionally, apolipoprotein E (APOE) ε4 carriers may be at an increased risk for greater WMH volumes and hippocampal atrophy in aging. The present study sought to investigate whether regional WMH volume mediates the relationship between age and hippocampal volume and if this association is moderated by APOE ε4 status in a group of 190 cognitively healthy adults (APOE ε4 status [carrier/non-carrier] = 59/131), ages 50-89. Analyses revealed that temporal lobe WMH volume significantly mediated the relationship between age and average bilateral hippocampal volume, and this effect was moderated by APOE ε4 status (-0.020 (SE = 0.009), 95% CI, [-0.039, -0.003]). APOE ε4 carriers, but not non-carriers, showed negative indirect effects of age on hippocampal volume through temporal lobe WMH volume (APOE ε4 carriers: -0.016 (SE = 0.007), 95% CI, [-0.030, -0.003]; APOE ε4 non-carriers: .005 (SE = 0.006), 95% CI, [-0.006, 0.017]). These findings remained significant after additionally adjusting for sex, years of education, hypertension status and duration, cholesterol status, diabetes status, Body Mass Index, history of smoking, and the Wechsler Adult Intelligence Scale-IV Full Scale IQ. There were no significant moderated mediation effects for frontal, parietal, and occipital lobe WMH volumes, with or without covariates. Our findings indicate that in cognitively healthy older adults, elevated WMH volume regionally localized to the temporal lobes in APOE ε4 carriers is associated with reduced hippocampal volume, suggesting greater vulnerability to brain aging and the risk for Alzheimer's disease.
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Affiliation(s)
- Emily J Van Etten
- Department of Psychology, University of Arizona, Tucson, Arizona, USA.,Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, Arizona, USA
| | - Pradyumna K Bharadwaj
- Department of Psychology, University of Arizona, Tucson, Arizona, USA.,Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, Arizona, USA
| | - Georg A Hishaw
- Department of Neurology, University of Arizona, Tucson, Arizona, USA
| | - Matthew J Huentelman
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, Arizona, USA.,Neurogenomics Division, The Translational Genomics Research Institute (TGen), Phoenix, Arizona, USA.,Arizona Alzheimer's Consortium, Phoenix, Arizona, USA
| | - Theodore P Trouard
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, Arizona, USA.,Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, USA.,Arizona Alzheimer's Consortium, Phoenix, Arizona, USA
| | - Matthew D Grilli
- Department of Psychology, University of Arizona, Tucson, Arizona, USA.,Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, Arizona, USA.,Department of Neurology, University of Arizona, Tucson, Arizona, USA
| | - Gene E Alexander
- Department of Psychology, University of Arizona, Tucson, Arizona, USA.,Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, Arizona, USA.,Department of Psychiatry, University of Arizona, Tucson, Arizona, USA.,Neuroscience Graduate Interdisciplinary Program, University of Arizona, Tucson, Arizona, USA.,Physiological Sciences Graduate Interdisciplinary Program, University of Arizona, Tucson, Arizona, USA.,Arizona Alzheimer's Consortium, Phoenix, Arizona, USA
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27
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Xing Y, Yang J, Zhou A, Wang F, Wei C, Tang Y, Jia J. White Matter Fractional Anisotropy Is a Superior Predictor for Cognitive Impairment Than Brain Volumes in Older Adults With Confluent White Matter Hyperintensities. Front Psychiatry 2021; 12:633811. [PMID: 34025467 PMCID: PMC8131652 DOI: 10.3389/fpsyt.2021.633811] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 04/08/2021] [Indexed: 11/13/2022] Open
Abstract
Older patients with confluent white matter hyperintensities (WMHs) on magnetic resonance imaging have an increased risk for the onset of vascular cognitive impairment (VCI). This study investigates the predictive effects of the white matter (WM) fractional anisotropy (FA) and brain volumes on cognitive impairment for those with confluent WMHs. This study enrolled 77 participants with confluent WMHs (Fazekas grade 2 or 3), including 44 with VCI-no dementia (VCIND) and 33 with normal cognition (NC). The mean FA of 20 WM tracts was calculated to evaluate the global WM microstructural integrity, and major WM tracts were reconstructed using probabilistic tractography. Voxel-based morphometry was used to calculate brain volumes for the total gray matter (GM), the hippocampus, and the nucleus basalis of Meynert (NbM). All volumetric assays were corrected for total intracranial volume. All regression analyses were adjusted for age, gender, education, and apolipoprotein E (ApoE) gene ε4 status. Logistic regression analysis revealed that the mean FA value for global WM was the only independent risk factor for VCI (z score of FA: OR = 4.649, 95%CI 1.576-13.712, p = 0.005). The tract-specific FAs were not associated with the risk of cognitive impairment after controlling the mean FA for global WM. The mean FA value was significantly associated with scores of Mini-Mental State Examination (MMSE) and Auditory Verbal Learning Test. A lower FA was also associated with smaller volumes of total GM, hippocampus, and NbM. However, brain volumes were not found to be directly related to cognitive performances, except for an association between the hippocampal volume and MMSE. In conclusion, the mean FA for global WM microstructural integrity is a superior predictor for cognitive impairment than tract-specific FA and brain volumes in people with confluent WMHs.
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Affiliation(s)
- Yi Xing
- Department of Neurology, Innovation Center for Neurological Disorders, National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Neurodegenerative Diseases, Ministry of Education of the People's Republic of China, Beijing, China
| | - Jianwei Yang
- Department of Neurology, Innovation Center for Neurological Disorders, National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Neurodegenerative Diseases, Ministry of Education of the People's Republic of China, Beijing, China
| | - Aihong Zhou
- Department of Neurology, Innovation Center for Neurological Disorders, National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Neurodegenerative Diseases, Ministry of Education of the People's Republic of China, Beijing, China
| | - Fen Wang
- Department of Neurology, Innovation Center for Neurological Disorders, National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Neurodegenerative Diseases, Ministry of Education of the People's Republic of China, Beijing, China
| | - Cuibai Wei
- Department of Neurology, Innovation Center for Neurological Disorders, National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Neurodegenerative Diseases, Ministry of Education of the People's Republic of China, Beijing, China
| | - Yi Tang
- Department of Neurology, Innovation Center for Neurological Disorders, National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Neurodegenerative Diseases, Ministry of Education of the People's Republic of China, Beijing, China
| | - Jianping Jia
- Department of Neurology, Innovation Center for Neurological Disorders, National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Neurodegenerative Diseases, Ministry of Education of the People's Republic of China, Beijing, China
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28
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Eckerström C, Eckerström M, Göthlin M, Molinder A, Jonsson M, Kettunen P, Svensson J, Rolstad S, Wallin A. Characteristic Biomarker and Cognitive Profile in Incipient Mixed Dementia. J Alzheimers Dis 2020; 73:597-607. [PMID: 31815692 PMCID: PMC7029359 DOI: 10.3233/jad-190651] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Background: Research has shown that mixed dementia is more common than previously believed but little is known of its early stages. Objective: To examine if incipient mixed dementia can be differentiated from incipient Alzheimer’s disease (AD) and subcortical ischemic vascular dementia (SVD) using neuropsychological tests, cerebrospinal fluid (CSF) markers, and magnetic resonance imaging markers. Methods: We included 493 patients and controls from the Gothenburg MCI study and used the dementia groups for marker selection (CSF total-tau (T-tau), phospho-tau (P-tau), and amyloid-β42 (Aβ42), 11 neuropsychological tests, and 92 regional brain volumes) and to obtain cut-off values which were then applied to the MCI groups. Results: Incipient mixed dementia was best differentiated from incipient AD by the Word fluency F-A-S test and the Trail making test A. CSF T-tau, P-tau, and Aβ42 differentiated incipient mixed dementia from incipient SVD. Conclusion: Incipient mixed dementia is characterized by an AD-like biomarker profile and an SVD-like cognitive profile. Incipient mixed dementia can be separated from incipient AD and incipient SVD using CSF markers and cognitive testing.
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Affiliation(s)
- Carl Eckerström
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Immunology and Transfusion Medicine, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Marie Eckerström
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Mattias Göthlin
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anna Molinder
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Michael Jonsson
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Petronella Kettunen
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Johan Svensson
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Sindre Rolstad
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anders Wallin
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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29
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Etherton MR, Fotiadis P, Giese AK, Iglesias JE, Wu O, Rost NS. White Matter Hyperintensity Burden Is Associated With Hippocampal Subfield Volume in Stroke. Front Neurol 2020; 11:588883. [PMID: 33193055 PMCID: PMC7649326 DOI: 10.3389/fneur.2020.588883] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 10/05/2020] [Indexed: 12/05/2022] Open
Abstract
White matter hyperintensities of presumed vascular origin (WMH) are a prevalent form of cerebral small-vessel disease and an important risk factor for post-stroke cognitive dysfunction. Despite this prevalence, it is not well understood how WMH contributes to post-stroke cognitive dysfunction. Preliminary findings suggest that increasing WMH volume is associated with total hippocampal volume in chronic stroke patients. The hippocampus, however, is a complex structure with distinct subfields that have varying roles in the function of the hippocampal circuitry and unique anatomical projections to different brain regions. For these reasons, an investigation into the relationship between WMH and hippocampal subfield volume may further delineate how WMH predispose to post-stroke cognitive dysfunction. In a prospective study of acute ischemic stroke patients with moderate/severe WMH burden, we assessed the relationship between quantitative WMH burden and hippocampal subfield volumes. Patients underwent a 3T MRI brain within 2–5 days of stroke onset. Total WMH volume was calculated in a semi-automated manner. Mean cortical thickness and hippocampal volumes were measured in the contralesional hemisphere. Total and subfield hippocampal volumes were measured using an automated, high-resolution, ex vivo computational atlas. Linear regression analyses were performed for predictors of total and subfield hippocampal volumes. Forty patients with acute ischemic stroke and moderate/severe white matter hyperintensity burden were included in this analysis. Median WMH volume was 9.0 cm3. Adjusting for intracranial volume and stroke laterality, age (β = −3.7, P < 0.001), hypertension (β = −44.7, P = 0.04), WMH volume (β = −0.89, P = 0.049), and mean cortical thickness (β = 286.2, P = 0.006) were associated with total hippocampal volume. In multivariable analysis, age (β = −3.3, P < 0.001) and cortical thickness (β = 205.2, P = 0.028) remained independently associated with total hippocampal volume. In linear regression for predictors of hippocampal subfield volume, increasing WMH volume was associated with decreased hippocampal-amygdala transition area volume (β = −0.04, P = 0.001). These finding suggest that in ischemic stroke patients, increased WMH burden is associated with selective hippocampal subfield degeneration in the hippocampal-amygdala transition area.
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Affiliation(s)
- Mark R Etherton
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Panagiotis Fotiadis
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Anne-Katrin Giese
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Juan E Iglesias
- Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Ona Wu
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.,Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Natalia S Rost
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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30
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Busatto Filho G, Duran FLDS, Squarzoni P, Coutinho AMN, Rosa PGP, Torralbo L, Pachi CGDF, da Costa NA, Porto FHDG, Carvalho CL, Brucki SMD, Nitrini R, Forlenza OV, Leite CDC, Buchpiguel CA, de Paula Faria D. Hippocampal subregional volume changes in elders classified using positron emission tomography-based Alzheimer's biomarkers of β-amyloid deposition and neurodegeneration. J Neurosci Res 2020; 99:481-501. [PMID: 33073383 DOI: 10.1002/jnr.24739] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/16/2020] [Accepted: 09/30/2020] [Indexed: 12/14/2022]
Abstract
Changes in hippocampal subfield volumes (HSV) along the Alzheimer's disease (AD) continuum have been scarcely investigated to date in elderly subjects classified based on the presence of β-amyloid aggregation and signs of neurodegeneration. We classified patients (either sex) with mild dementia compatible with AD (n = 35) or amnestic mild cognitive impairment (n = 39), and cognitively unimpaired subjects (either sex; n = 26) using [11 C]PIB-PET to assess β-amyloid aggregation (A+) and [18 F]FDG-PET to account for neurodegeneration ((N)+). Magnetic resonance imaging-based automated methods were used for HSV and white matter hyperintensity (WMH) measurements. Significant HSV reductions were found in A+(N)+ subjects in the presubiculum/subiculum complex and molecular layer, related to worse memory performance. In both the A+(N)+ and A+(N)- categories, subicular volumes were inversely correlated with the degree of Aβ deposition. The A-(N)+ subgroup showed reduced HSV relative to the A-(N)- subgroup also in the subiculum/presubiculum. Combining all (N)- subjects, HSV were lower in subjects presenting significant cognitive decline irrespective of A+/A- classification (controlling for WMH load); these between-group differences were detected again in the presubiculum, but also involved the CA4 and granular layer. These findings demonstrate that differential HSV reductions are detectable both in (N)+ and (N)- categories along the AD continuum, and are directly related to the severity of cognitive deficits. HSV reductions are larger both in A+(N)+ and A+(N)- subjects in direct proportion to the degree of Aβ deposition. The meaningful HSV reductions detected in the A-(N)+ subgroup highlights the strength of biomarker-based classifications outside of the classical AD continuum.
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Affiliation(s)
- Geraldo Busatto Filho
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Fabio Luiz de Souza Duran
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Paula Squarzoni
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Artur Martins Novaes Coutinho
- Laboratory of Nuclear Medicine (LIM43), Department of Radiology and Oncology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Pedro Gomes Penteado Rosa
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Leticia Torralbo
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Clarice Gameiro da Fonseca Pachi
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Naomi Antunes da Costa
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Fabio Henrique de Gobbi Porto
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Cleudiana Lima Carvalho
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Sonia Maria Dozzi Brucki
- Department of Neurology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Ricardo Nitrini
- Department of Neurology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Orestes Vicente Forlenza
- Laboratory of Neuroscience (LIM 27), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Claudia da Costa Leite
- Laboratory of Magnetic Resonance in Neuroradiology (LIM44), Department of Radiology and Oncology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Carlos Alberto Buchpiguel
- Laboratory of Nuclear Medicine (LIM43), Department of Radiology and Oncology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Daniele de Paula Faria
- Laboratory of Nuclear Medicine (LIM43), Department of Radiology and Oncology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
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31
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Spalletta G, Iorio M, Vecchio D, Piras F, Ciullo V, Banaj N, Sensi SL, Gianni W, Assogna F, Caltagirone C, Piras F. Subclinical Cognitive and Neuropsychiatric Correlates and Hippocampal Volume Features of Brain White Matter Hyperintensity in Healthy People. J Pers Med 2020; 10:jpm10040172. [PMID: 33076372 PMCID: PMC7712953 DOI: 10.3390/jpm10040172] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 09/28/2020] [Accepted: 10/12/2020] [Indexed: 12/11/2022] Open
Abstract
White matter hyperintensities (WMH) are associated with brain aging and behavioral symptoms as a possible consequence of disrupted white matter pathways. In this study, we investigated, in a cohort of asymptomatic subjects aged 50 to 80, the relationship between WMH, hippocampal atrophy, and subtle, preclinical cognitive and neuropsychiatric phenomenology. Thirty healthy subjects with WMH (WMH+) and thirty individuals without (WMH−) underwent comprehensive neuropsychological and neuropsychiatric evaluations and 3 Tesla Magnetic Resonance Imaging scan. The presence, degree of severity, and distribution of WMH were evaluated with a semi-automated algorithm. Volumetric analysis of hippocampal structure was performed through voxel-based morphometry. A multivariable logistic regression analysis indicated that phenomenology of subclinical apathy and anxiety was associated with the presence of WMH. ROI-based analyses showed a volume reduction in the right hippocampus of WMH+. In healthy individuals, WMH are associated with significant preclinical neuropsychiatric phenomenology, as well as hippocampal atrophy, which are considered as risk factors to develop cognitive impairment and dementia.
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Affiliation(s)
- Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
- Division of Neuropsychiatry, Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030, USA
- Correspondence: (G.S.); (F.P.); Tel.: +39-06-5150-1575; Fax: +39-06-5150-1575
| | - Mariangela Iorio
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
- Molecular Neurology Unit, Center of Advanced Studies and Technology (CAST), G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy;
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
- Department of Psychology, Sapienza University of Rome, Policlinico Umberto I, 00161 Rome, Italy
| | - Federica Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
| | - Valentina Ciullo
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
| | - Stefano L. Sensi
- Molecular Neurology Unit, Center of Advanced Studies and Technology (CAST), G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy;
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
- Institute for Mind Impairments and Neurological Disorders, University of California-Irvine, Irvine, CA 92697, USA
| | - Walter Gianni
- II Division of Internal Medicine and Geriatrics, Sapienza University of Rome, Policlinico Umberto I, 00161 Rome, Italy;
| | - Francesca Assogna
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
| | - Carlo Caltagirone
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (M.I.); (D.V.); (F.P.); (V.C.); (N.B.); (F.A.); (C.C.)
- Correspondence: (G.S.); (F.P.); Tel.: +39-06-5150-1575; Fax: +39-06-5150-1575
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32
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Porcu M, Operamolla A, Scapin E, Garofalo P, Destro F, Caneglias A, Suri JS, Falini A, Defazio G, Marrosu F, Saba L. Effects of White Matter Hyperintensities on Brain Connectivity and Hippocampal Volume in Healthy Subjects According to Their Localization. Brain Connect 2020; 10:436-447. [DOI: 10.1089/brain.2020.0774] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Michele Porcu
- Department of Radiology, Azienda Ospedaliera Universitaria di Cagliari, University of Cagliari, Cagliari, Italy
| | - Annunziata Operamolla
- Department of Radiology, Azienda Ospedaliera Universitaria di Cagliari, University of Cagliari, Cagliari, Italy
| | - Elisa Scapin
- Department of Radiology, Azienda Ospedaliera Universitaria di Cagliari, University of Cagliari, Cagliari, Italy
| | - Paolo Garofalo
- Department of Radiology, Azienda Ospedaliera Universitaria di Cagliari, University of Cagliari, Cagliari, Italy
| | - Francesco Destro
- Department of Radiology, Azienda Ospedaliera Universitaria di Cagliari, University of Cagliari, Cagliari, Italy
| | - Alessandro Caneglias
- Department of Radiology, Azienda Ospedaliera Universitaria di Cagliari, University of Cagliari, Cagliari, Italy
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™ LLC, Roseville, California, USA
| | - Andrea Falini
- Department of Neuroradiology, Università Vita-Salute San Raffaele, Milan, Italy
| | - Giovanni Defazio
- Department of Neurology, Azienda Ospedaliera Universitaria di Cagliari, University of Cagliari, Cagliari, Italy
| | - Francesco Marrosu
- Stroke Monitoring and Diagnostic Division, AtheroPoint™ LLC, Roseville, California, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliera Universitaria di Cagliari, University of Cagliari, Cagliari, Italy
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33
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The age-dependent associations of white matter hyperintensities and neurofilament light in early- and late-stage Alzheimer's disease. Neurobiol Aging 2020; 97:10-17. [PMID: 33070094 DOI: 10.1016/j.neurobiolaging.2020.09.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 08/28/2020] [Accepted: 09/01/2020] [Indexed: 01/19/2023]
Abstract
Neurofilament light (NFL) is an emerging marker of axonal degeneration. This study investigated the relationship between white matter hyperintensities (WMHs) and plasma NFL in a large elderly cohort with, and without, cognitive impairment. We used the Alzheimer's Disease Neuroimaging Initiative and included 163 controls, 103 participants with a significant memory concern, 279 with early mild cognitive impairment (EMCI), 152 with late mild cognitive impairment (LMCI), and 130 with Alzheimer's disease, with 3T MRI and plasma NFL data. Multiple linear regression models examined the relationship between WMHs and NFL, with and without age adjustment. We used smoking status, history of hypertension, history of diabetes, and BMI as additional covariates to examine the effect of vascular risk. We found increases of between 20% and 41% in WMH volume per 1SD increase in NFL in significant memory concern, early mild cognitive impairment, late mild cognitive impairment, and Alzheimer's disease groups (p < 0.02). Marked attenuation of the positive associations between WMHs and NFL were seen after age adjustment, suggesting that a significant proportion of the association between NFL and WMHs is age-related. No effect of vascular risk was observed. These results are supportive of a link between WMH and axonal degeneration in early to late disease stages, in an age-dependent, but vascular risk-independent manner.
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34
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Legdeur N, Badissi M, Yaqub M, Beker N, Sudre CH, Ten Kate M, Gordon MF, Novak G, Barkhof F, van Berckel BNM, Holstege H, Muller M, Scheltens P, Maier AB, Visser PJ. What determines cognitive functioning in the oldest-old? The EMIF-AD 90+ Study. J Gerontol B Psychol Sci Soc Sci 2020; 76:1499-1511. [PMID: 32898275 DOI: 10.1093/geronb/gbaa152] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVES Determinants of cognitive functioning in individuals aged 90 years and older, the oldest-old, remain poorly understood. We aimed to establish the association of risk factors, white matter hyperintensities (WMH), hippocampal atrophy and amyloid aggregation with cognition in the oldest-old. METHODS We included 84 individuals without cognitive impairment and 38 individuals with cognitive impairment from the EMIF-AD 90+ Study (mean age 92.4 years) and tested cross-sectional associations between risk factors (cognitive activity, physical parameters, nutritional status, inflammatory markers and cardiovascular risk factors), brain pathology biomarkers (WMH and hippocampal volume on MRI, and amyloid binding measured with PET) and cognition. Additionally, we tested whether the brain pathology biomarkers were independently associated with cognition. When applicable, we tested whether the effect of risk factors on cognition was mediated by brain pathology. RESULTS Lower values for handgrip strength, Short Physical Performance Battery (SPPB), nutritional status, HbA1c and hippocampal volume, and higher values for WMH volume and amyloid binding were associated with worse cognition. Higher past cognitive activity and lower BMI were associated with increased amyloid binding, lower muscle mass with more WMH, and lower SPPB scores with more WMH and hippocampal atrophy. The brain pathology markers were independently associated with cognition. The association of SPPB with cognition was partially mediated by hippocampal volume. DISCUSSION In the oldest-old, physical parameters, nutritional status, HbA1c, WMH, hippocampal atrophy and amyloid binding are associated with cognitive impairment. Physical performance may affect cognition through hippocampal atrophy. This study highlights the importance to consider multiple factors when assessing cognition in the oldest-old.
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Affiliation(s)
- Nienke Legdeur
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Maryam Badissi
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Maqsood Yaqub
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Nina Beker
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Carole H Sudre
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Mara Ten Kate
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | | | - Gerald Novak
- Janssen Pharmaceutical Research and Development, Titusville, NJ, USA
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | - Bart N M van Berckel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Henne Holstege
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Clinical Genetics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Majon Muller
- Department of Internal Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Andrea B Maier
- Department of Medicine and Aged Care, @AgeMelbourne, Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia.,Department of Human Movement Sciences, @AgeAmsterdam, Vrije Universiteit Amsterdam, Research Institute Amsterdam Movement Sciences, Amsterdam, The Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Department of Neurobiology, Care Sciences Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden
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35
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Affiliation(s)
- Maximilian Wiesmann
- Department of Anatomy, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
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36
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Fiford CM, Sudre CH, Pemberton H, Walsh P, Manning E, Malone IB, Nicholas J, Bouvy WH, Carmichael OT, Biessels GJ, Cardoso MJ, Barnes J. Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change. Neuroinformatics 2020; 18:429-449. [PMID: 32062817 PMCID: PMC7338814 DOI: 10.1007/s12021-019-09439-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Accurate, automated white matter hyperintensity (WMH) segmentations are needed for large-scale studies to understand contributions of WMH to neurological diseases. We evaluated Bayesian Model Selection (BaMoS), a hierarchical fully-unsupervised model selection framework for WMH segmentation. We compared BaMoS segmentations to semi-automated segmentations, and assessed whether they predicted longitudinal cognitive change in control, early Mild Cognitive Impairment (EMCI), late Mild Cognitive Impairment (LMCI), subjective/significant memory concern (SMC) and Alzheimer's (AD) participants. Data were downloaded from the Alzheimer's disease Neuroimaging Initiative (ADNI). Magnetic resonance images from 30 control and 30 AD participants were selected to incorporate multiple scanners, and were semi-automatically segmented by 4 raters and BaMoS. Segmentations were assessed using volume correlation, Dice score, and other spatial metrics. Linear mixed-effect models were fitted to 180 control, 107 SMC, 320 EMCI, 171 LMCI and 151 AD participants separately in each group, with the outcomes being cognitive change (e.g. mini-mental state examination; MMSE), and BaMoS WMH, age, sex, race and education used as predictors. There was a high level of agreement between BaMoS' WMH segmentation volumes and a consensus of rater segmentations, with a median Dice score of 0.74 and correlation coefficient of 0.96. BaMoS WMH predicted cognitive change in: control, EMCI, and SMC groups using MMSE; LMCI using clinical dementia rating scale; and EMCI using Alzheimer's disease assessment scale-cognitive subscale (p < 0.05, all tests). BaMoS compares well to semi-automated segmentation, is robust to different WMH loads and scanners, and can generate volumes which predict decline. BaMoS can be applicable to further large-scale studies.
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Affiliation(s)
- Cassidy M. Fiford
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Carole H. Sudre
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Hugh Pemberton
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Phoebe Walsh
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Emily Manning
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Ian B. Malone
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | | | - Willem H Bouvy
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - M. Jorge Cardoso
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Josephine Barnes
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- London School of Hygiene and Tropical Medicine, London, UK
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
- Pennington Biomedical Research Center, Baton Rouge, LA USA
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Cabeli V, Verny L, Sella N, Uguzzoni G, Verny M, Isambert H. Learning clinical networks from medical records based on information estimates in mixed-type data. PLoS Comput Biol 2020; 16:e1007866. [PMID: 32421707 PMCID: PMC7259796 DOI: 10.1371/journal.pcbi.1007866] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 05/29/2020] [Accepted: 04/10/2020] [Indexed: 12/13/2022] Open
Abstract
The precise diagnostics of complex diseases require to integrate a large amount of information from heterogeneous clinical and biomedical data, whose direct and indirect interdependences are notoriously difficult to assess. To this end, we propose an efficient computational approach to simultaneously compute and assess the significance of multivariate information between any combination of mixed-type (continuous/categorical) variables. The method is then used to uncover direct, indirect and possibly causal relationships between mixed-type data from medical records, by extending a recent machine learning method to reconstruct graphical models beyond simple categorical datasets. The method is shown to outperform existing tools on benchmark mixed-type datasets, before being applied to analyze the medical records of eldery patients with cognitive disorders from La Pitié-Salpêtrière Hospital, Paris. The resulting clinical network visually captures the global interdependences in these medical records and some facets of clinical diagnosis practice, without specific hypothesis nor prior knowledge on any clinically relevant information. In particular, it provides some physiological insights linking the consequence of cerebrovascular accidents to the atrophy of important brain structures associated to cognitive impairment. We developed a machine learning approach to analyze medical records and help clinicians visualize the direct and indirect interrelations between clinical examinations and the variety of syndromes implicated in complex diseases. The reconstruction of such clinical networks is illustrated on the spectrum of cognitive disorders, originating from either neurodegenerative, cerebrovascular or psychiatric dementias. This global network analysis is also shown to uncover novel direct associations and possible cause-effect relationships between clinically relevant information, such as medical examinations, diagnoses, treatments and personal data from patients’ medical records.
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Affiliation(s)
- Vincent Cabeli
- Institut Curie, PSL Research University, CNRS, UMR168, 26 rue d’Ulm, 75005 Paris, France
- Sorbonne Université, 4, place Jussieu, 75005 Paris, France
| | - Louis Verny
- Institut Curie, PSL Research University, CNRS, UMR168, 26 rue d’Ulm, 75005 Paris, France
- Sorbonne Université, 4, place Jussieu, 75005 Paris, France
| | - Nadir Sella
- Institut Curie, PSL Research University, CNRS, UMR168, 26 rue d’Ulm, 75005 Paris, France
- Sorbonne Université, 4, place Jussieu, 75005 Paris, France
- LIMICS, UMRS 1142, 15 rue de l’école de médecine, 75006 Paris, France
| | - Guido Uguzzoni
- Institut Curie, PSL Research University, CNRS, UMR168, 26 rue d’Ulm, 75005 Paris, France
- Sorbonne Université, 4, place Jussieu, 75005 Paris, France
| | - Marc Verny
- Sorbonne Université, 4, place Jussieu, 75005 Paris, France
- Hôpital La Pitié-Salpêtrière, 47-83 boulevard de l’Hôpital, 75013 Paris, France
- * E-mail: (MV); (HI)
| | - Hervé Isambert
- Institut Curie, PSL Research University, CNRS, UMR168, 26 rue d’Ulm, 75005 Paris, France
- Sorbonne Université, 4, place Jussieu, 75005 Paris, France
- * E-mail: (MV); (HI)
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Klohs J. An Integrated View on Vascular Dysfunction in Alzheimer's Disease. NEURODEGENER DIS 2020; 19:109-127. [PMID: 32062666 DOI: 10.1159/000505625] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 12/23/2019] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Cerebrovascular disease is a common comorbidity in patients with Alzheimer's disease (AD). It is believed to contribute additively to the cognitive impairment and to lower the threshold for the development of dementia. However, accumulating evidence suggests that dysfunction of the cerebral vasculature and AD neuropathology interact in multiple ways. Vascular processes even proceed AD neuropathology, implicating a causal role in the etiology of AD. Thus, the review aims to provide an integrated view on vascular dysfunction in AD. SUMMARY In AD, the cerebral vasculature undergoes pronounced cellular, morphological and structural changes, which alters regulation of blood flow, vascular fluid dynamics and vessel integrity. Stiffening of central blood vessels lead to transmission of excessive pulsatile energy to the brain microvasculature, causing end-organ damage. Moreover, a dysregulated hemostasis and chronic vascular inflammation further impede vascular function, where its mediators interact synergistically. Changes of the cerebral vasculature are triggered and driven by systemic vascular abnormalities that are part of aging, and which can be accelerated and aggravated by cardiovascular diseases. Key Messages: In AD, the cerebral vasculature is the locus where multiple pathogenic processes converge and contribute to cognitive impairment. Understanding the molecular mechanism and pathophysiology of vascular dysfunction in AD and use of vascular blood-based and imaging biomarker in clinical studies may hold promise for future prevention and therapy of the disease.
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Affiliation(s)
- Jan Klohs
- Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland, .,Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland,
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De Guio F, Duering M, Fazekas F, De Leeuw FE, Greenberg SM, Pantoni L, Aghetti A, Smith EE, Wardlaw J, Jouvent E. Brain atrophy in cerebral small vessel diseases: Extent, consequences, technical limitations and perspectives: The HARNESS initiative. J Cereb Blood Flow Metab 2020; 40:231-245. [PMID: 31744377 PMCID: PMC7370623 DOI: 10.1177/0271678x19888967] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Brain atrophy is increasingly evaluated in cerebral small vessel diseases. We aim at systematically reviewing the available data regarding its extent, correlates and cognitive consequences. Given that in this context, brain atrophy measures might be biased, the first part of the review focuses on technical aspects. Thereafter, data from the literature are analyzed in light of these potential limitations, to better understand the relationships between brain atrophy and other MRI markers of cerebral small vessel diseases. In the last part, we review the links between brain atrophy and cognitive alterations in patients with cerebral small vessel diseases.
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Affiliation(s)
- François De Guio
- Department of Neurology and Referral Center for Rare Vascular Diseases of the Brain and Retina (CERVCO), APHP, Lariboisière Hospital, Paris, DHU NeuroVasc, Univ Paris Diderot, and U1141 INSERM, France
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Franz Fazekas
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Frank-Erik De Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Centre for Neuroscience, Radboud University, Nijmegen, The Netherlands
| | - Steven M Greenberg
- Department of Neurology, Stroke Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Leonardo Pantoni
- "Luigi Sacco" Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Agnès Aghetti
- Department of Neurology and Referral Center for Rare Vascular Diseases of the Brain and Retina (CERVCO), APHP, Lariboisière Hospital, Paris, DHU NeuroVasc, Univ Paris Diderot, and U1141 INSERM, France
| | - Eric E Smith
- Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, Edinburgh Imaging, UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Eric Jouvent
- Department of Neurology and Referral Center for Rare Vascular Diseases of the Brain and Retina (CERVCO), APHP, Lariboisière Hospital, Paris, DHU NeuroVasc, Univ Paris Diderot, and U1141 INSERM, France
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A Comprehensive Visual Rating Scale for Predicting Progression from Mild Cognitive Impairment to Dementia in Patients with Alzheimer's Pathology or Suspected Non-Alzheimer's Pathology. Dement Neurocogn Disord 2020; 19:129-139. [PMID: 33377666 PMCID: PMC7781734 DOI: 10.12779/dnd.2020.19.4.129] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/14/2020] [Accepted: 09/29/2020] [Indexed: 11/27/2022] Open
Abstract
Background and Purpose To identify biomarkers for prediction of the progression to dementia in mild cognitive impairment (MCI) patients, evaluation of brain structure changes has been validated by a comprehensive visual grading scale (CVRS) through magnetic resonance imaging (MRI). In this study, we specifically elucidated for the cognitive change of MCI patients classified based on AT(N) pathological status classification during the follow-up period of 3 years through the CVRS. Methods The 301 patients with initial MCI visited at least once for follow-up period. The data used in this study were obtained from the Alzheimer's disease (AD) Neuroimaging Initiative study. Brain atrophy was assessed by CVRS using MRI. AT(N) profiles were classified by cerebrospinal fluid abnormality. Based on the AT(N) assessment, all individuals in this study were divided into 3 groups (normal state biomarker, suspected non-Alzheimer's pathology [SNAP], or Alzheimer's continuum). The cox regression was used to analyze the hazard ratios of CVRS for progression to dementia. Results Sixty-three progressed and 238 remained stable to dementia and the CVRS (mean±standard deviation) had significant difference between progressive MCI and stable MCI (p<0.001). Univariate and multivariate cox regression results (p<0.001) showed the independence of initial CVRS as a predictor for the progression to dementia. Moreover, comparing the classified AT(N) pathology group, SNAP and AD, effectiveness of CVRS as a predictor was verified only in Alzheimer's continuum. Conclusions The initial CVRS score as a predictor of dementia progression was independently validated at the stage of Alzheimer's progression among AT(N) pathologically differentiated MCI.
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Soldan A, Pettigrew C, Zhu Y, Wang MC, Moghekar A, Gottesman RF, Singh B, Martinez O, Fletcher E, DeCarli C, Albert M. White matter hyperintensities and CSF Alzheimer disease biomarkers in preclinical Alzheimer disease. Neurology 2019; 94:e950-e960. [PMID: 31888969 DOI: 10.1212/wnl.0000000000008864] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 08/30/2019] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVE Recent studies suggest that white matter hyperintensities (WMH) on MRI, which primarily reflect small vessel cerebrovascular disease, may play a role in the evolution of Alzheimer disease (AD). In a longitudinal study, we investigated whether WMH promote the progression of AD pathology, or alter the association between AD pathology and risk of progression from normal cognition to mild cognitive impairment (MCI). METHODS Two sets of analyses were conducted. The relationship between whole brain WMH load, based on fluid-attenuated inversion recovery MRI, obtained in initially cognitively normal participants (n = 274) and time to onset of symptoms of MCI (n = 60) was examined using Cox regression models. In a subset of the participants with both MRI and CSF data (n = 204), the interaction of WMH load and CSF AD biomarkers was also evaluated. RESULTS Baseline WMH load interacted with CSF total tau (t-tau) with respect to symptom onset, but not with CSF β-amyloid 1-42 or phosphorylated tau (p-tau) 181. WMH volume was associated with time to symptom onset of MCI among individuals with low t-tau (hazard ratio [HR] 1.35, confidence interval [CI] 1.06-1.73, p = 0.013), but not those with high t-tau (HR 0.86, CI 0.56-1.32, p = 0.47). The rate of change in the CSF biomarkers over time was not associated with the rate of change in WMH volumes. CONCLUSION These results suggest that WMH primarily affect the risk of progression when CSF measures of neurodegeneration or neuronal injury (as reflected by t-tau) are low. However, CSF biomarkers of amyloid and p-tau and WMH appear to have largely independent and nonsynergistic effects on the risk of progression to MCI.
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Affiliation(s)
- Anja Soldan
- From the Department of Neurology (A.S., C.P., A.M., R.F.G., M.A.), The Johns Hopkins University School of Medicine; Department of Biostatistics (Y.Z., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Department of Neurology (B.S., O.M., E.F., C.D.), School of Medicine, University of California, Davis.
| | - Corinne Pettigrew
- From the Department of Neurology (A.S., C.P., A.M., R.F.G., M.A.), The Johns Hopkins University School of Medicine; Department of Biostatistics (Y.Z., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Department of Neurology (B.S., O.M., E.F., C.D.), School of Medicine, University of California, Davis
| | - Yuxin Zhu
- From the Department of Neurology (A.S., C.P., A.M., R.F.G., M.A.), The Johns Hopkins University School of Medicine; Department of Biostatistics (Y.Z., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Department of Neurology (B.S., O.M., E.F., C.D.), School of Medicine, University of California, Davis
| | - Mei-Cheng Wang
- From the Department of Neurology (A.S., C.P., A.M., R.F.G., M.A.), The Johns Hopkins University School of Medicine; Department of Biostatistics (Y.Z., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Department of Neurology (B.S., O.M., E.F., C.D.), School of Medicine, University of California, Davis
| | - Abhay Moghekar
- From the Department of Neurology (A.S., C.P., A.M., R.F.G., M.A.), The Johns Hopkins University School of Medicine; Department of Biostatistics (Y.Z., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Department of Neurology (B.S., O.M., E.F., C.D.), School of Medicine, University of California, Davis
| | - Rebecca F Gottesman
- From the Department of Neurology (A.S., C.P., A.M., R.F.G., M.A.), The Johns Hopkins University School of Medicine; Department of Biostatistics (Y.Z., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Department of Neurology (B.S., O.M., E.F., C.D.), School of Medicine, University of California, Davis
| | - Baljeet Singh
- From the Department of Neurology (A.S., C.P., A.M., R.F.G., M.A.), The Johns Hopkins University School of Medicine; Department of Biostatistics (Y.Z., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Department of Neurology (B.S., O.M., E.F., C.D.), School of Medicine, University of California, Davis
| | - Oliver Martinez
- From the Department of Neurology (A.S., C.P., A.M., R.F.G., M.A.), The Johns Hopkins University School of Medicine; Department of Biostatistics (Y.Z., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Department of Neurology (B.S., O.M., E.F., C.D.), School of Medicine, University of California, Davis
| | - Evan Fletcher
- From the Department of Neurology (A.S., C.P., A.M., R.F.G., M.A.), The Johns Hopkins University School of Medicine; Department of Biostatistics (Y.Z., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Department of Neurology (B.S., O.M., E.F., C.D.), School of Medicine, University of California, Davis
| | - Charles DeCarli
- From the Department of Neurology (A.S., C.P., A.M., R.F.G., M.A.), The Johns Hopkins University School of Medicine; Department of Biostatistics (Y.Z., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Department of Neurology (B.S., O.M., E.F., C.D.), School of Medicine, University of California, Davis
| | - Marilyn Albert
- From the Department of Neurology (A.S., C.P., A.M., R.F.G., M.A.), The Johns Hopkins University School of Medicine; Department of Biostatistics (Y.Z., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and Department of Neurology (B.S., O.M., E.F., C.D.), School of Medicine, University of California, Davis
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Yu L, Boyle PA, Dawe RJ, Bennett DA, Arfanakis K, Schneider JA. Contribution of TDP and hippocampal sclerosis to hippocampal volume loss in older-old persons. Neurology 2019; 94:e142-e152. [PMID: 31757868 DOI: 10.1212/wnl.0000000000008679] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 07/10/2019] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE To investigate the contribution of Alzheimer disease (AD) vs non-AD neuropathologies to hippocampal atrophy. METHODS The Religious Orders Study and Rush Memory and Aging Project are clinicopathologic cohort studies of aging. The current study included 547 participants who had undergone brain autopsy and postmortem hippocampal volume measurement by November 1, 2018. Hippocampal volume was measured with postmortem MRI via a 3D region of interest applied to the hippocampal formation. Neuropathologies were measured via uniform structured evaluations. Linear regression analyses estimated the proportion of variance of hippocampal volume attributable to AD and non-AD neuropathologies. RESULTS The average age at death was 90 years, and the average hippocampal volume was 2.1 mL. AD, transactive response DNA-binding protein 43 (TDP), hippocampal sclerosis (HS), and atherosclerosis were associated with hippocampal volume. After demographics and total hemisphere volume were controlled for, 7.0% of the variance (95% bootstrapped confidence interval [CI] 4.3%-10.5%) of hippocampal volume was attributable to AD pathology. TDP/HS explained an additional 4.5% (95% CI 2.2%-7.6%). Among individuals with Alzheimer dementia (n = 232), 3.1% (95% CI 0.6%-7.7%) of the variance was attributable to AD pathology, and TDP/HS explained an additional 6.1% (95% CI 2.2%-11.6%). Among those without Alzheimer dementia (n = 307), 3.2% (95% CI 0.9%-7.3%) of the variance was attributable to AD pathology, and TDP/HS explained an additional 1.1%, which did not reach statistical significance. Lewy bodies and vascular diseases had modest contribution to the variance of hippocampal volume. CONCLUSIONS Both AD and TDP/HS contribute to hippocampal volume loss in older-old persons, with TDP/HS more strongly associated with hippocampal volume than AD in Alzheimer dementia.
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Affiliation(s)
- Lei Yu
- From the Rush Alzheimer's Disease Center (L.Y., P.A.B., R.J.D., D.A.B., K.A., J.A.S.), Department of Neurological Sciences (L.Y., D.A.B., J.A.S.), Department of Behavioral Sciences (P.A.B.), Department of Diagnostic Radiology and Nuclear Medicine (R.J.D., K.A.), and Department of Pathology (J.A.S.), Rush University Medical Center; and Department of Biomedical Engineering (K.A.), Illinois Institute of Technology, Chicago.
| | - Patricia A Boyle
- From the Rush Alzheimer's Disease Center (L.Y., P.A.B., R.J.D., D.A.B., K.A., J.A.S.), Department of Neurological Sciences (L.Y., D.A.B., J.A.S.), Department of Behavioral Sciences (P.A.B.), Department of Diagnostic Radiology and Nuclear Medicine (R.J.D., K.A.), and Department of Pathology (J.A.S.), Rush University Medical Center; and Department of Biomedical Engineering (K.A.), Illinois Institute of Technology, Chicago
| | - Robert J Dawe
- From the Rush Alzheimer's Disease Center (L.Y., P.A.B., R.J.D., D.A.B., K.A., J.A.S.), Department of Neurological Sciences (L.Y., D.A.B., J.A.S.), Department of Behavioral Sciences (P.A.B.), Department of Diagnostic Radiology and Nuclear Medicine (R.J.D., K.A.), and Department of Pathology (J.A.S.), Rush University Medical Center; and Department of Biomedical Engineering (K.A.), Illinois Institute of Technology, Chicago
| | - David A Bennett
- From the Rush Alzheimer's Disease Center (L.Y., P.A.B., R.J.D., D.A.B., K.A., J.A.S.), Department of Neurological Sciences (L.Y., D.A.B., J.A.S.), Department of Behavioral Sciences (P.A.B.), Department of Diagnostic Radiology and Nuclear Medicine (R.J.D., K.A.), and Department of Pathology (J.A.S.), Rush University Medical Center; and Department of Biomedical Engineering (K.A.), Illinois Institute of Technology, Chicago
| | - Konstantinos Arfanakis
- From the Rush Alzheimer's Disease Center (L.Y., P.A.B., R.J.D., D.A.B., K.A., J.A.S.), Department of Neurological Sciences (L.Y., D.A.B., J.A.S.), Department of Behavioral Sciences (P.A.B.), Department of Diagnostic Radiology and Nuclear Medicine (R.J.D., K.A.), and Department of Pathology (J.A.S.), Rush University Medical Center; and Department of Biomedical Engineering (K.A.), Illinois Institute of Technology, Chicago
| | - Julie A Schneider
- From the Rush Alzheimer's Disease Center (L.Y., P.A.B., R.J.D., D.A.B., K.A., J.A.S.), Department of Neurological Sciences (L.Y., D.A.B., J.A.S.), Department of Behavioral Sciences (P.A.B.), Department of Diagnostic Radiology and Nuclear Medicine (R.J.D., K.A.), and Department of Pathology (J.A.S.), Rush University Medical Center; and Department of Biomedical Engineering (K.A.), Illinois Institute of Technology, Chicago
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Parker TD, Cash DM, Lane CAS, Lu K, Malone IB, Nicholas JM, James SN, Keshavan A, Murray-Smith H, Wong A, Buchanan SM, Keuss SE, Sudre CH, Modat M, Thomas DL, Crutch SJ, Richards M, Fox NC, Schott JM. Hippocampal subfield volumes and pre-clinical Alzheimer's disease in 408 cognitively normal adults born in 1946. PLoS One 2019; 14:e0224030. [PMID: 31622410 PMCID: PMC6797197 DOI: 10.1371/journal.pone.0224030] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 10/03/2019] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The human hippocampus comprises a number of interconnected histologically and functionally distinct subfields, which may be differentially influenced by cerebral pathology. Automated techniques are now available that estimate hippocampal subfield volumes using in vivo structural MRI data. To date, research investigating the influence of cerebral β-amyloid deposition-one of the earliest hypothesised changes in the pathophysiological continuum of Alzheimer's disease-on hippocampal subfield volumes in cognitively normal older individuals, has been limited. METHODS Using cross-sectional data from 408 cognitively normal individuals born in mainland Britain (age range at time of assessment = 69.2-71.9 years) who underwent cognitive assessment, 18F-Florbetapir PET and structural MRI on the same 3 Tesla PET/MR unit (spatial resolution 1.1 x 1.1 x 1.1. mm), we investigated the influences of β-amyloid status, age at scan, and global white matter hyperintensity volume on: CA1, CA2/3, CA4, dentate gyrus, presubiculum and subiculum volumes, adjusting for sex and total intracranial volume. RESULTS Compared to β-amyloid negative participants (n = 334), β-amyloid positive participants (n = 74) had lower volume of the presubiculum (3.4% smaller, p = 0.012). Despite an age range at scanning of just 2.7 years, older age at time of scanning was associated with lower CA1 (p = 0.007), CA4 (p = 0.004), dentate gyrus (p = 0.002), and subiculum (p = 0.035) volumes. There was no evidence that white matter hyperintensity volume was associated with any subfield volumes. CONCLUSION These data provide evidence of differential associations in cognitively normal older adults between hippocampal subfield volumes and β-amyloid deposition and, increasing age at time of scan. The relatively selective effect of lower presubiculum volume in the β-amyloid positive group potentially suggest that the presubiculum may be an area of early and relatively specific volume loss in the pathophysiological continuum of Alzheimer's disease. Future work using higher resolution imaging will be key to exploring these findings further.
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Affiliation(s)
- Thomas D. Parker
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - David M. Cash
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Christopher A. S. Lane
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Kirsty Lu
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Ian B. Malone
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jennifer M. Nicholas
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at University College London, London, United Kingdom
| | - Ashvini Keshavan
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Heidi Murray-Smith
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at University College London, London, United Kingdom
| | - Sarah M. Buchanan
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sarah E. Keuss
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Carole H. Sudre
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Marc Modat
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - David L. Thomas
- Leonard Wolfson Experimental Neurology Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sebastian J. Crutch
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at University College London, London, United Kingdom
| | - Nick C. Fox
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jonathan M. Schott
- The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
- * E-mail:
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Vipin A, Foo HJL, Lim JKW, Chander RJ, Yong TT, Ng ASL, Hameed S, Ting SKS, Zhou J, Kandiah N. Regional White Matter Hyperintensity Influences Grey Matter Atrophy in Mild Cognitive Impairment. J Alzheimers Dis 2019; 66:533-549. [PMID: 30320575 DOI: 10.3233/jad-180280] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
The association between cerebrovascular disease pathology (measured by white matter hyperintensities, WMH) and brain atrophy in early Alzheimer's disease (AD) remain to be elucidated. Thus, we investigated how WMH influence neurodegeneration and cognition in prodromal and clinical AD. We examined 51 healthy controls, 35 subjects with mild cognitive impairment (MCI), and 30 AD patients. We tested how total and regional WMH is related to specific grey matter volume (GMV) reductions in MCI and AD compared to controls. Stepwise regression analysis was further performed to investigate the association of GMV and regional WMH volume with global cognition. We found that total WMH volume was highest in AD but showed the strongest association with lower GMV in MCI. Frontal and parietal WMH had the most extensive influence on GMV loss in MCI. Additionally, parietal lobe WMH volume (but not hippocampal atrophy) was significantly associated with global cognition in MCI while smaller hippocampal volume (but not WMH volume) was associated with lower global cognition in AD. Thus, although WMH volume was highest in AD subjects, it had a more pervasive influence on brain structure and cognitive impairment in MCI. Our study thus highlights the importance of early detection of cerebrovascular disease, as its intervention at the MCI stage might potentially slow down neurodegeneration.
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Affiliation(s)
- Ashwati Vipin
- Center for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-NUS Medical School, Singapore
| | - Heidi Jing Ling Foo
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
| | - Joseph Kai Wei Lim
- Center for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-NUS Medical School, Singapore
| | - Russell Jude Chander
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
| | - Ting Ting Yong
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
| | - Adeline Su Lyn Ng
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
| | - Shahul Hameed
- Department of Neurology, Singapore General Hospital, Singapore
| | | | - Juan Zhou
- Center for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-NUS Medical School, Singapore.,Clinical Imaging Research Centre, The Agency for Science, Technology and Research and National University of Singapore, Singapore
| | - Nagaendran Kandiah
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
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Legdeur N, Visser PJ, Woodworth DC, Muller M, Fletcher E, Maillard P, Scheltens P, DeCarli C, Kawas CH, Corrada MM. White Matter Hyperintensities and Hippocampal Atrophy in Relation to Cognition: The 90+ Study. J Am Geriatr Soc 2019; 67:1827-1834. [PMID: 31169919 PMCID: PMC6732042 DOI: 10.1111/jgs.15990] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 03/21/2019] [Accepted: 04/28/2019] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To study the interactive effect of white matter hyperintensities (WMH) and hippocampal atrophy on cognition in the oldest old. DESIGN Ongoing longitudinal study. SETTING In Southern California, brain magnetic resonance imaging (MRI) scans were conducted between May 2014 and December 2017. PARTICIPANTS Individuals from The 90+ Study with a valid brain MRI scan (N = 141; 94 cognitively normal and 47 with cognitive impairment). MEASUREMENTS Cognitive testing was performed every 6 months with a mean follow-up of 2 years and included these tests: Mini-Mental State Examination (MMSE), modified MMSE (3MS), California Verbal Learning Test (CVLT) immediate recall over four trials and delayed recall, Digit Span Backward, Animal Fluency, and Trail Making Test (TMT) A, B, and C. We used one linear mixed model for each cognitive test to study the baseline and longitudinal association of WMH and hippocampal volume (HV) with cognition. Models were adjusted for age, sex, and education. RESULTS Mean age was 94.3 years (standard deviation [SD] = 3.2 y). At baseline, higher WMH volumes were associated with worse scores on the 3MS, CVLT immediate and delayed recall, and TMT B. Lower HVs were associated with worse baseline scores on all cognitive tests, except for the Digit Span Backward. Longitudinally, higher WMH and lower HVs were associated with faster decline in the 3MS and MMSE, and lower HV was also associated with faster decline in the CVLT immediate recall. No association was observed between WMH and HV and no interaction between WMH and HV in their association with baseline cognition or cognitive decline. CONCLUSION We show that WMH and hippocampal atrophy have an independent, negative effect on cognition that make these biomarkers relevant to evaluate in the diagnostic work-up of the oldest-old individuals with cognitive complaints. However, the predictive value of WMH for cognitive decline seems to be less evident in the oldest-old compared with a younger group of older adults. J Am Geriatr Soc 67:1827-1834, 2019.
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Affiliation(s)
- Nienke Legdeur
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Pieter Jelle Visser
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Davis C. Woodworth
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - Majon Muller
- Department of Internal Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Evan Fletcher
- Department of Neurology, University of California, Davis, CA, USA
| | - Pauline Maillard
- Department of Neurology, University of California, Davis, CA, USA
| | - Philip Scheltens
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Charles DeCarli
- Department of Neurology, University of California, Davis, CA, USA
| | - Claudia H. Kawas
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - María M. Corrada
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
- Department of Epidemiology, University of California, Irvine, CA, USA
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46
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Payton NM, Kalpouzos G, Rizzuto D, Fratiglioni L, Kivipelto M, Bäckman L, Laukka EJ. Combining Cognitive, Genetic, and Structural Neuroimaging Markers to Identify Individuals with Increased Dementia Risk. J Alzheimers Dis 2019; 64:533-542. [PMID: 29889068 PMCID: PMC6027943 DOI: 10.3233/jad-180199] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background: Cognitive and biological markers have shown varying degrees of success in identifying persons who will develop dementia. Objective: To evaluate different combinations of cognitive and biological markers and identify prediction models with the highest accuracy for identifying persons with increased dementia risk. Methods: Neuropsychological assessment, genetic testing (apolipoprotein E –APOE), and structural magnetic resonance imaging (MRI) were performed for 418 older individuals without dementia (60–97 years) from a population-based study (SNAC-K). Participants were followed for six years. Results: Cognitive, genetic, and MRI markers were systematically combined to create prediction models for dementia at six years. The most predictive individual markers were perceptual speed or carrying at least one APOEɛ4 allele (AUC = 0.875). The most predictive model (AUC = 0.924) included variables from all three modalities (category fluency, general knowledge, any ɛ4 allele, hippocampal volume, white matter-hyperintensity volume). Conclusion: This study shows that combining markers within and between modalities leads to increased predictivity for future dementia. However, minor increases in predictive value should be weighed against the cost of additional tests in larger-scale screening.
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Affiliation(s)
- Nicola M Payton
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden
| | - Grégoria Kalpouzos
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden
| | - Debora Rizzuto
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden
| | - Laura Fratiglioni
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden.,Stockholm Gerontology Research Center, Stockholm, Sweden
| | - Miia Kivipelto
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Stockholms Sjukhem, Research and Development Unit, Stockholm, Sweden
| | - Lars Bäckman
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden
| | - Erika J Laukka
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden.,Stockholm Gerontology Research Center, Stockholm, Sweden
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47
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Alosco ML, Sugarman MA, Besser LM, Tripodis Y, Martin B, Palmisano JN, Kowall NW, Au R, Mez J, DeCarli C, Stein TD, McKee AC, Killiany RJ, Stern RA. A Clinicopathological Investigation of White Matter Hyperintensities and Alzheimer's Disease Neuropathology. J Alzheimers Dis 2019; 63:1347-1360. [PMID: 29843242 DOI: 10.3233/jad-180017] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
BACKGROUND White matter hyperintensities (WMH) on magnetic resonance imaging (MRI) have been postulated to be a core feature of Alzheimer's disease. Clinicopathological studies are needed to elucidate and confirm this possibility. OBJECTIVE This study examined: 1) the association between antemortem WMH and autopsy-confirmed Alzheimer's disease neuropathology (ADNP), 2) the relationship between WMH and dementia in participants with ADNP, and 3) the relationships among cerebrovascular disease, WMH, and ADNP. METHODS The sample included 82 participants from the National Alzheimer's Coordinating Center's Data Sets who had quantitated volume of WMH from antemortem FLAIR MRI and available neuropathological data. The Clinical Dementia Rating (CDR) scale (from MRI visit) operationalized dementia status. ADNP+ was defined by moderate to frequent neuritic plaques and Braak stage III-VI at autopsy. Cerebrovascular disease neuropathology included infarcts or lacunes, microinfarcts, arteriolosclerosis, atherosclerosis, and cerebral amyloid angiopathy. RESULTS 60/82 participants were ADNP+. Greater volume of WMH predicted increased odds for ADNP (p = 0.037). In ADNP+ participants, greater WMH corresponded with increased odds for dementia (CDR≥1; p = 0.038). WMH predicted cerebral amyloid angiopathy, microinfarcts, infarcts, and lacunes (ps < 0.04). ADNP+ participants were more likely to have moderate-severe arteriolosclerosis and cerebral amyloid angiopathy compared to ADNP-participants (ps < 0.04). CONCLUSIONS This study found a direct association between total volume of WMH and increased odds for having ADNP. In patients with Alzheimer's disease, FLAIR MRI WMH may be able to provide key insight into disease severity and progression. The association between WMH and ADNP may be explained by underlying cerebrovascular disease.
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Affiliation(s)
- Michael L Alosco
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA, USA.,Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Michael A Sugarman
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA, USA.,Department of Neuropsychology, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, MA, USA
| | - Lilah M Besser
- National Alzheimer's Coordinating Center, University of Washington, Seattle, WA, USA
| | - Yorghos Tripodis
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA, USA.,Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Brett Martin
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA, USA.,Biostatistics and Epidemiology Data Analytics Center, Boston University School of Public Health, Boston, MA, USA
| | - Joseph N Palmisano
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA, USA.,Biostatistics and Epidemiology Data Analytics Center, Boston University School of Public Health, Boston, MA, USA
| | - Neil W Kowall
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA, USA.,Department of Neurology, Boston University School of Medicine, Boston, MA, USA.,Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA, USA.,Neurology Service, VA Boston Healthcare System, Boston, MA, USA
| | - Rhoda Au
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA.,Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA, USA.,Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.,Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Jesse Mez
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA, USA.,Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Charles DeCarli
- Department of Neurology, University of California at Davis Health System, Sacramento, CA, USA
| | - Thor D Stein
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA, USA.,Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA, USA.,U.S. Department of Veteran Affairs, VA Boston Healthcare System, Boston, MA, USA.,Department of Veterans Affairs Medical Center, Bedford, MA, USA
| | - Ann C McKee
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA, USA.,Department of Neurology, Boston University School of Medicine, Boston, MA, USA.,Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA, USA.,U.S. Department of Veteran Affairs, VA Boston Healthcare System, Boston, MA, USA.,Department of Veterans Affairs Medical Center, Bedford, MA, USA
| | - Ronald J Killiany
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA, USA.,Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA.,Center for Biomedical Imaging, Boston University School of Medicine, Boston, MA, USA
| | - Robert A Stern
- Boston University Alzheimer's Disease Center, Boston University School of Medicine, Boston, MA, USA.,Department of Neurology, Boston University School of Medicine, Boston, MA, USA.,Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA.,Department of Neurosurgery, Boston University School of Medicine, Boston, MA, USA
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48
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Whitwell JL, Martin P, Graff-Radford J, Machulda MM, Senjem ML, Schwarz CG, Weigand SD, Spychalla AJ, Drubach DA, Jack CR, Lowe VJ, Josephs KA. The role of age on tau PET uptake and gray matter atrophy in atypical Alzheimer's disease. Alzheimers Dement 2019; 15:675-685. [PMID: 30853465 DOI: 10.1016/j.jalz.2018.12.016] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 11/02/2018] [Accepted: 12/29/2018] [Indexed: 11/28/2022]
Abstract
INTRODUCTION Little is known about the role of age on neurodegeneration and protein deposition in atypical variants of Alzheimer's disease (AD). METHODS Regional tau and β-amyloid positron emission tomography standard uptake value ratios and gray matter volumes were calculated in a cohort of 42 participants with atypical AD. The relationship between regional metrics and age was modeled using a Bayesian hierarchical linear model. RESULTS Age was strongly associated with tau uptake across all cortical regions, particularly parietal, with greater uptake in younger participants. Younger age was associated with smaller parietal and lateral temporal volumes. Regional β-amyloid differed little by age. Age showed a stronger association with tau than volume and β-amyloid in all cortical regions. Age was not associated with cognitive performance. DISCUSSION Age is an important determinant of severity of cortical tau uptake in atypical AD, with young participants more likely to show widespread and severe cortical tau uptake.
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Affiliation(s)
| | - Peter Martin
- Department of Health Science Research, Mayo Clinic, Rochester MN, USA
| | | | - Mary M Machulda
- Department of Psychology and Psychiatry, Mayo Clinic, Rochester MN, USA
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester MN, USA; Department of Information Technology, Mayo Clinic, Rochester MN, USA
| | | | - Stephen D Weigand
- Department of Health Science Research, Mayo Clinic, Rochester MN, USA
| | | | | | | | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester MN, USA
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49
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Abstract
PURPOSE OF REVIEW The aim of this study was to discuss the contribution of neuroimaging studies to our understanding of Alzheimer's disease. We now have the capability of measuring both tau and beta-amyloid (Aβ) proteins in the brain, which together with more traditional neuroimaging modalities, has led the field to focus on using neuroimaging to better characterize disease mechanisms underlying Alzheimer's disease. RECENT FINDINGS Studies have utilized tau and Aβ PET, as well as [18F]fluorodeoxyglucose PET, and structural and functional MRI, to investigate the following topics: phenotypic variability in Alzheimer's disease , including how neuroimaging findings are related to clinical phenotype and age; multimodality analyses to investigate the relationships between different neuroimaging modalities and what that teaches us about disease mechanisms; disease staging by assessing neuroimaging changes in the very earliest phases of the disease in cognitively normal individuals and individuals carrying an autosomal dominant Alzheimer's disease mutation; and influence of other comorbidities and proteins to the disease process. SUMMARY The findings shed light on the role of tau and Aβ, as well as age and other comorbidities, in the neurodegenerative process in Alzheimer's disease. This knowledge will be crucial in the development of better disease biomarkers and targeted therapeutic approaches.
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50
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van Leijsen EMC, Tay J, van Uden IWM, Kooijmans ECM, Bergkamp MI, van der Holst HM, Ghafoorian M, Platel B, Norris DG, Kessels RPC, Markus HS, Tuladhar AM, de Leeuw FE. Memory decline in elderly with cerebral small vessel disease explained by temporal interactions between white matter hyperintensities and hippocampal atrophy. Hippocampus 2018; 29:500-510. [PMID: 30307080 DOI: 10.1002/hipo.23039] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 09/07/2018] [Accepted: 09/29/2018] [Indexed: 11/11/2022]
Abstract
White matter hyperintensities (WMH) constitute the visible spectrum of cerebral small vessel disease (SVD) markers and are associated with cognitive decline, although they do not fully account for memory decline observed in individuals with SVD. We hypothesize that WMH might exert their effect on memory decline indirectly by affecting remote brain structures such as the hippocampus. We investigated the temporal interactions between WMH, hippocampal atrophy and memory decline in older adults with SVD. Five hundred and three participants of the RUNDMC study underwent neuroimaging and cognitive assessments up to 3 times over 8.7 years. We assessed WMH volumes semi-automatically and calculated hippocampal volumes (HV) using FreeSurfer. We used linear mixed effects models and causal mediation analyses to assess both interaction and mediation effects of hippocampal atrophy in the associations between WMH and memory decline, separately for working memory (WM) and episodic memory (EM). Linear mixed effect models revealed that the interaction between WMH and hippocampal volumes explained memory decline (WM: β = .067; 95%CI[.024-0.111]; p < .01; EM: β = .061; 95%CI[.025-.098]; p < .01), with better model fit when the WMH*HV interaction term was added to the model, for both WM (likelihood ratio test, χ2 [1] = 9.3, p < .01) and for EM (likelihood ratio test, χ2 [1] = 10.7, p < .01). Mediation models showed that both baseline WMH volume (β = -.170; p = .001) and hippocampal atrophy (β = 0.126; p = .009) were independently related to EM decline, but the effect of baseline WMH on EM decline was not mediated by hippocampal atrophy (p value indirect effect: 0.572). Memory decline in elderly with SVD was best explained by the interaction of WMH and hippocampal volumes. The relationship between WMH and memory was not causally mediated by hippocampal atrophy, suggesting that memory decline during aging is a heterogeneous condition in which different pathologies contribute to the memory decline observed in elderly with SVD.
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Affiliation(s)
- Esther M C van Leijsen
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Center for Medical Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Jonathan Tay
- Department of Clinical Neurosciences, Neurology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Ingeborg W M van Uden
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Center for Medical Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Eline C M Kooijmans
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Center for Medical Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Mayra I Bergkamp
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Center for Medical Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
| | | | - Mohsen Ghafoorian
- Radboud University Medical Centre, Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Nijmegen, The Netherlands.,Radboud University, Institute for Computing and Information Sciences, Nijmegen, The Netherlands
| | - Bram Platel
- Radboud University Medical Centre, Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Nijmegen, The Netherlands
| | - David G Norris
- Radboud University, Donders Institute for Brain Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands.,Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen, Germany
| | - Roy P C Kessels
- Department of Medical Psychology, Radboud University Medical Centre, Radboud Alzheimer Centre, Nijmegen, The Netherlands.,Radboud University, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognition, Nijmegen, The Netherlands
| | - Hugh S Markus
- Department of Clinical Neurosciences, Neurology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Center for Medical Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Center for Medical Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
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