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Eswaran S, Knopman DS, Koton S, Kucharska-Newton AM, Liu AC, Liu C, Lutsey PL, Mosley TH, Palta P, Sharrett AR, Sullivan KJ, Walker KA, Gottesman RF, Groechel RC. Psychosocial Health and the Association Between Cerebral Small Vessel Disease Markers With Dementia: The ARIC Study. Stroke 2024; 55:2449-2458. [PMID: 39193713 DOI: 10.1161/strokeaha.124.047455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 06/27/2024] [Accepted: 07/10/2024] [Indexed: 08/29/2024]
Abstract
BACKGROUND Associations between magnetic resonance imaging markers of cerebral small vessel disease (CSVD) and dementia risk in older adults have been established, but it remains unclear how lifestyle factors, including psychosocial health, may modify this association. METHODS Social support and social isolation were assessed among participants of the community-based ARIC (Atherosclerosis Risk in Communities) Study, via self-reported questionnaires (1990-1992). Following categorization of both factors, participants were classified as having strong or poor mid-life social relationships. At visit 5 (2011-2013), participants underwent 3T brain magnetic resonance imaging quantifying CSVD measures: white matter hyperintensity volume, microbleeds (subcortical), infarcts (lacunar), and white matter integrity (diffusion tensor imaging). Incident dementia cases were identified from the time of imaging through December 31, 2020 with ongoing surveillance. Associations between CSVD magnetic resonance imaging markers and incident dementia were evaluated using Cox proportional-hazard regressions adjusted for demographic and additional risk factors (from visit 2). Effect modification by mid-life social relationships was evaluated. RESULTS Of the 1977 participants with magnetic resonance imaging, 1617 participants (60.7% women; 26.5% Black participants; mean age at visit 2, 55.4 years) were examined. In this sample, mid-life social relationships significantly modified the association between white matter hyperintensity volume and dementia risk (P interaction=0.001). Greater white matter hyperintensity volume was significantly associated with risk of dementia in all participants, yet, more substantially in those with poor (hazard ratio, 1.84 [95% CI, 1.49-2.27]) versus strong (hazard ratio, 1.26 [95% CI, 1.08-1.47]) mid-life social relationships. Although not statistically significant, subcortical microbleeds in participants with poor mid-life social relationships were associated with a greater risk of dementia, relative to those with strong social relationships, in whom subcortical microbleeds were no longer associated with elevated dementia risk. CONCLUSIONS The elevated risk of dementia associated with CSVD may be reduced in participants with strong mid-life social relationships. Future studies evaluating psychosocial health through the life course and the mechanisms by which they modify the relationship between CSVD and dementia are needed.
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Affiliation(s)
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN (D.S.K.)
| | - Silvia Koton
- Department of Nursing, The Stanley Steyer School of Health Professions, Tel Aviv University, Israel (S.K.)
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD (S.K., A.R.S.)
| | - Anna M Kucharska-Newton
- Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill (A.M.K.-N., A.C.L.)
| | - Albert C Liu
- Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill (A.M.K.-N., A.C.L.)
| | - Chelsea Liu
- Department of Epidemiology, George Washington University-Milken Institute School of Public Health, DC (C.L.)
| | - Pamela L Lutsey
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis (P.L.L.)
| | - Thomas H Mosley
- Department of Medicine, University of Mississippi Medical Center, Jackson (T.H.M., K.J.S.)
| | - Priya Palta
- Department of Neurology, University of North Carolina at Chapel Hill (P.P.)
| | - A Richey Sharrett
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD (S.K., A.R.S.)
| | - Kevin J Sullivan
- Department of Medicine, University of Mississippi Medical Center, Jackson (T.H.M., K.J.S.)
| | - Keenan A Walker
- National Institute on Aging Intramural Research Program, Baltimore, MD (K.A.W.)
| | - Rebecca F Gottesman
- National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD (R.F.G., R.C.G.)
| | - Renee C Groechel
- National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD (R.F.G., R.C.G.)
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Okawa R, Hayashi N, Takahashi T, Atarashi R, Yasui G, Mihara B. Comparison of qualitative and fully automated quantitative tools for classifying severity of white matter hyperintensity. J Stroke Cerebrovasc Dis 2024; 33:107772. [PMID: 38761849 DOI: 10.1016/j.jstrokecerebrovasdis.2024.107772] [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: 08/23/2023] [Accepted: 05/15/2024] [Indexed: 05/20/2024] Open
Abstract
OBJECTIVE In this study, we aimed to compare the Fazekas scoring system and quantitative white matter hyperintensity volume in the classification of white matter hyperintensity severity using a fully automated analysis software to investigate the reliability of quantitative evaluation. MATERIALS AND METHODS Patients with suspected cognitive impairment who underwent medical examinations at our institution between January 2010 and May 2021 were retrospectively examined. White matter hyperintensity volumes were analyzed using fully automated analysis software and Fazekas scoring (scores 0-3). Using one-way analysis of variance, white matter hyperintensity volume differences across Fazekas scores were assessed. We employed post-hoc pairwise comparisons to compare the differences in the mean white matter hyperintensity volume between each Fazekas score. Spearman's rank correlation test was used to investigate the association between Fazekas score and white matter hyperintensity volume. RESULTS Among the 839 patients included in this study, Fazekas scores 0, 1, 2, and 3 were assigned to 68, 198, 217, and 356 patients, respectively. White matter hyperintensity volumes significantly differed according to Fazekas score (F=623.5, p<0.001). Post-hoc pairwise comparisons revealed significant differences in mean white matter hyperintensity volume between all Fazekas scores (p<0.05). We observed a significantly positive correlation between the Fazekas scores and white matter hyperintensity volume (R=0.823, p<0.01). CONCLUSIONS Quantitative white matter hyperintensity volume and the Fazekas scores are highly correlated and may be used as indicators of white matter hyperintensity severity. In addition, quantitative analysis may be more effective in classifying advanced white matter hyperintensity lesions than the Fazekas classification.
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Affiliation(s)
- Ryuya Okawa
- Department of Diagnostic Imaging, Institute of Brain and Blood Vessels Mihara Memorial Hospital; Graduate School of Radiological Technology, Gunma Prefectural College of Health Sciences.
| | - Norio Hayashi
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences.
| | - Tetsuhiko Takahashi
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences.
| | - Ryo Atarashi
- Graduate School of Radiological Technology, Gunma Prefectural College of Health Sciences.
| | - Go Yasui
- Department of Diagnostic Imaging, Institute of Brain and Blood Vessels Mihara Memorial Hospital.
| | - Ban Mihara
- Department of Neurology, Institute of Brain and Blood Vessels Mihara Memorial Hospital.
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Scheppach JB, Wu A, Gottesman RF, Mosley TH, Arsiwala-Scheppach LT, Knopman DS, Grams ME, Sharrett AR, Coresh J, Koton S. Association of Kidney Function Measures With Signs of Neurodegeneration and Small Vessel Disease on Brain Magnetic Resonance Imaging: The Atherosclerosis Risk in Communities (ARIC) Study. Am J Kidney Dis 2023; 81:261-269.e1. [PMID: 36179945 PMCID: PMC9974563 DOI: 10.1053/j.ajkd.2022.07.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 07/21/2022] [Indexed: 11/11/2022]
Abstract
RATIONALE & OBJECTIVE Chronic kidney disease (CKD) is a risk factor for cognitive decline, but evidence is limited on its etiology and morphological manifestation in the brain. We evaluated the association of estimated glomerular filtration rate (eGFR) and urinary albumin-creatinine ratio (UACR) with structural brain abnormalities visible on magnetic resonance imaging (MRI). We also assessed whether this association was altered when different filtration markers were used to estimate GFR. STUDY DESIGN Cross-sectional study nested in a cohort study. SETTING & PARTICIPANTS 1,527 participants in the Atherosclerosis Risk in Communities (ARIC) Study. PREDICTORS Log(UACR) and eGFR based on cystatin C, creatinine, cystatin C and creatinine in combination, or β2-microglobulin (B2M). OUTCOMES Brain volume reduction, infarcts, microhemorrhages, white matter lesions. ANALYTICAL APPROACH Multivariable linear and logistic regression models fit separately for each predictor based on a 1-IQR difference in the predictor value. RESULTS Each 1-IQR lower eGFR was associated with reduced cortex volume (regression coefficient: -0.07 [95% CI, -0.12 to-0.02]), greater white matter hyperintensity volume (logarithmically transformed; regression coefficient: 0.07 [95% CI, 0.01-0.15]), and lower white matter fractional anisotropy (regression coefficient: -0.08 [95% CI, -0.17 to-0.01]). The results were similar when eGFR was estimated with different equations based on cystatin C, creatinine, a combination of cystatin C and creatinine, or B2M. Higher log(UACR) was similarly associated with these outcomes as well as brain infarcts and microhemorrhages (odds ratios per 1-IQR-fold greater UACR of 1.31 [95% CI, 1.13-1.52] and 1.30 [95% CI, 1.12-1.51], respectively). The degree to which brain volume was lower in regions usually susceptible to Alzheimer disease and LATE (limbic-predominant age-related TDP-43 [Tar DNA binding protein 43] encephalopathy) was similar to that seen in the rest of the cortex. LIMITATIONS No inference about longitudinal effects due to cross-sectional design. CONCLUSIONS We found eGFR and UACR are associated with structural brain damage across different domains of etiology, and eGFR- and UACR-related brain atrophy is not selective for regions typically affected by Alzheimer disease and LATE. Hence, Alzheimer disease or LATE may not be leading contributors to neurodegeneration associated with CKD.
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Affiliation(s)
- Johannes B Scheppach
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland; Department of Nephrology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Aozhou Wu
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Rebecca F Gottesman
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland; Current affiliation: National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, Maryland
| | - Thomas H Mosley
- The MIND Center, University of Mississippi Medical Center, Jackson, Mississippi
| | | | | | - Morgan E Grams
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland; Division of Nephrology, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - A Richey Sharrett
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Josef Coresh
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Silvia Koton
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland; Stanley Steyer School of Health Professions, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
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4
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Sedaghat S, Ji Y, Hughes TM, Coresh J, Grams ME, Folsom AR, Sullivan KJ, Murray AM, Gottesman RF, Mosley TH, Lutsey PL. The Association of Kidney Function with Plasma Amyloid-β Levels and Brain Amyloid Deposition. J Alzheimers Dis 2023; 92:229-239. [PMID: 36710673 PMCID: PMC10124796 DOI: 10.3233/jad-220765] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
BACKGROUND Reduced kidney function is related to brain atrophy and higher risk of dementia. It is not known whether kidney impairment is associated with higher levels of circulating amyloid-β and brain amyloid-β deposition, which could contribute to elevated risk of dementia. OBJECTIVE To investigate whether kidney impairment is associated with higher levels of circulating amyloid-β and brain amyloid-β deposition. METHODS This cross-sectional study was performed within the community-based Atherosclerosis Risk in Communities (ARIC) Study cohort. We used estimated glomerular filtration rate (eGFR) based on serum creatinine and cystatin C levels and urine albumin-to-creatinine ratio (ACR) to assess kidney function. Amyloid positivity was defined as a standardized uptake value ratios > 1.2 measured with florbetapir positron emission tomography (PET) (n = 340). Plasma amyloid-β1 - 40 and amyloid-β1 - 42 were measured using a fluorimetric bead-based immunoassay (n = 2,569). RESULTS Independent of demographic and cardiovascular risk factors, a doubling of ACR was associated with 1.10 (95% CI: 1.01,1.20) higher odds of brain amyloid positivity, but not eGFR (odds ratio per 15 ml/min/1.73 m2 lower eGFR: 1.08; 95% CI: 0.95,1.23). A doubling of ACR was associated with a higher level of plasma amyloid-β1 - 40 (standardized difference: 0.12; 95% CI: 0.09,0.14) and higher plasma amyloid-β1 - 42 (0.08; 95% CI: 0.05,0.10). Lower eGFR was associated with higher plasma amyloid-β1 - 40 (0.36; 95% CI: 0.33,0.39) and higher amyloid-β1 - 42 (0.32; 95% CI: 0.29,0.35). CONCLUSION Low clearance of amyloid-β and elevated brain amyloid positivity may link impaired kidney function with elevated risk of dementia. kidney function should be considered in interpreting amyloid biomarker results in clinical and research setting.
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Affiliation(s)
- Sanaz Sedaghat
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, Minnesota
| | - Yuekai Ji
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, Minnesota
| | - Timothy M Hughes
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Aaron R. Folsom
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, Minnesota
| | - Kevin J Sullivan
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi
| | - Anne M Murray
- Department of Medicine, Geriatrics Division, Hennepin HealthCare, and Hennepin HealthCare Institute, Minneapolis, Minnesota
- Department of Medicine, University of Minnesota, Minneapolis, Minnesota
| | - Rebecca F Gottesman
- National Institute of Neurological Disorders and Stroke Intramural Research Program, NIH, Bethesda, Maryland
| | - Thomas H Mosley
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi
| | - Pamela L. Lutsey
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, Minnesota
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Tran P, Thoprakarn U, Gourieux E, Dos Santos CL, Cavedo E, Guizard N, Cotton F, Krolak-Salmon P, Delmaire C, Heidelberg D, Pyatigorskaya N, Ströer S, Dormont D, Martini JB, Chupin M. Automatic segmentation of white matter hyperintensities: validation and comparison with state-of-the-art methods on both Multiple Sclerosis and elderly subjects. Neuroimage Clin 2022; 33:102940. [PMID: 35051744 PMCID: PMC8896108 DOI: 10.1016/j.nicl.2022.102940] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 12/15/2021] [Accepted: 01/06/2022] [Indexed: 11/27/2022]
Abstract
Automatic segmentation of MS lesions and age-related WMH from 3D T1 and T2-FLAIR. Comparison to consensus show improved performance of WHASA-3D compared to WHASA. WHASA-3D outperforms available state-of-the-art methods with their default settings. WHASA-3D could be a useful tool for clinical practice and clinical trials.
Different types of white matter hyperintensities (WMH) can be observed through MRI in the brain and spinal cord, especially Multiple Sclerosis (MS) lesions for patients suffering from MS and age-related WMH for subjects with cognitive disorders and/or elderly people. To better diagnose and monitor the disease progression, the quantitative evaluation of WMH load has proven to be useful for clinical routine and trials. Since manual delineation for WMH segmentation is highly time-consuming and suffers from intra and inter observer variability, several methods have been proposed to automatically segment either MS lesions or age-related WMH, but none is validated on both WMH types. Here, we aim at proposing the White matter Hyperintensities Automatic Segmentation Algorithm adapted to 3D T2-FLAIR datasets (WHASA-3D), a fast and robust automatic segmentation tool designed to be implemented in clinical practice for the detection of both MS lesions and age-related WMH in the brain, using both 3D T1-weighted and T2-FLAIR images. In order to increase its robustness for MS lesions, WHASA-3D expands the original WHASA method, which relies on the coupling of non-linear diffusion framework and watershed parcellation, where regions considered as WMH are selected based on intensity and location characteristics, and finally refined with geodesic dilation. The previous validation was performed on 2D T2-FLAIR and subjects with cognitive disorders and elderly subjects. 60 subjects from a heterogeneous database of dementia patients, multiple sclerosis patients and elderly subjects with multiple MRI scanners and a wide range of lesion loads were used to evaluate WHASA and WHASA-3D through volume and spatial agreement in comparison with consensus reference segmentations. In addition, a direct comparison on the MS database with six available supervised and unsupervised state-of-the-art WMH segmentation methods (LST-LGA and LPA, Lesion-TOADS, lesionBrain, BIANCA and nicMSlesions) with default and optimised settings (when feasible) was conducted. WHASA-3D confirmed an improved performance with respect to WHASA, achieving a better spatial overlap (Dice) (0.67 vs 0.63), a reduced absolute volume error (AVE) (3.11 vs 6.2 mL) and an increased volume agreement (intraclass correlation coefficient, ICC) (0.96 vs 0.78). Compared to available state-of-the-art algorithms on the MS database, WHASA-3D outperformed both unsupervised and supervised methods when used with their default settings, showing the highest volume agreement (ICC = 0.95) as well as the highest average Dice (0.58). Optimising and/or retraining LST-LGA, BIANCA and nicMSlesions, using a subset of the MS database as training set, resulted in improved performances on the remaining testing set (average Dice: LST-LGA default/optimized = 0.41/0.51, BIANCA default/optimized = 0.22/0.39, nicMSlesions default/optimized = 0.17/0.63, WHASA-3D = 0.58). Evaluation and comparison results suggest that WHASA-3D is a reliable and easy-to-use method for the automated segmentation of white matter hyperintensities, for both MS lesions and age-related WMH. Further validation on larger datasets would be useful to confirm these first findings.
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Affiliation(s)
- Philippe Tran
- Qynapse, Paris, France; Equipe-projet ARAMIS, ICM, CNRS UMR 7225, Inserm U1117, Sorbonne Université UMR_S 1127, Centre Inria de Paris, Groupe Hospitalier Pitié-Salpêtrière Charles Foix, Faculté de Médecine Sorbonne Université, Paris, France.
| | | | - Emmanuelle Gourieux
- CATI, ICM, CNRS UMR 7225, Inserm U1117, Sorbonne Université UMR_S 1127, Paris, France; NeuroSpin, CEA, Saclay, France
| | | | | | | | - François Cotton
- Service de Radiologie, Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, Pierre-Bénite, France; Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69495, Pierre-Bénite, France
| | - Pierre Krolak-Salmon
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69495, Pierre-Bénite, France; Clinical and Research Memory Centre of Lyon, Hospices Civils de Lyon, Lyon, France; INSERM, U1028, UMR CNRS 5292, Lyon Neuroscience Research Center, Lyon, France
| | | | - Damien Heidelberg
- Service de Radiologie, Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, Pierre-Bénite, France
| | - Nadya Pyatigorskaya
- Department of Neuroradiology, Groupe Hospitalier Pitié-Salpêtrière, AP-HP, Sorbonne Université UMR_S 1127, Paris, France
| | - Sébastian Ströer
- Department of Neuroradiology, Groupe Hospitalier Pitié-Salpêtrière, AP-HP, Sorbonne Université UMR_S 1127, Paris, France
| | - Didier Dormont
- Equipe-projet ARAMIS, ICM, CNRS UMR 7225, Inserm U1117, Sorbonne Université UMR_S 1127, Centre Inria de Paris, Groupe Hospitalier Pitié-Salpêtrière Charles Foix, Faculté de Médecine Sorbonne Université, Paris, France; Department of Neuroradiology, Groupe Hospitalier Pitié-Salpêtrière, AP-HP, Sorbonne Université UMR_S 1127, Paris, France
| | | | - Marie Chupin
- CATI, ICM, CNRS UMR 7225, Inserm U1117, Sorbonne Université UMR_S 1127, Paris, France
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Gomez GT, Gottesman RF, Gabriel KP, Palta P, Gross AL, Soldan A, Albert MS, Sullivan KJ, Jack CR, Knopman DS, Windham BG, Walker KA. The association of motoric cognitive risk with incident dementia and neuroimaging characteristics: The Atherosclerosis Risk in Communities Study. Alzheimers Dement 2022; 18:434-444. [PMID: 34786837 PMCID: PMC10064850 DOI: 10.1002/alz.12412] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 05/05/2021] [Accepted: 06/01/2021] [Indexed: 11/10/2022]
Abstract
INTRODUCTION Motoric cognitive risk (MCR), a clinical syndrome characterized by slow gait speed and subjective cognitive complaints, has been associated with dementia risk. The neuropathological features underlying MCR remain poorly understood. METHODS The Atherosclerosis Risk in Communities (ARIC) community-based cohort study classified participants using standardized criteria as MCR+/- and mild cognitive impairment (MCI)+/- at study baseline (2011-2013). We examined the 5-year dementia risk and baseline brain structural/molecular abnormalities associated with MCR+ and MCI+ status. RESULTS Of 5023 nondemented participants included, 204 were MCR+ and 1030 were MCI+. Both MCR+ and MCI+ participants demonstrated increased dementia risk. The pattern of structural brain abnormalities associated with MCR+ differed from that of MCI+. Whereas MCI+ was associated with comparatively smaller volumes in brain regions vulnerable to Alzheimer's disease pathology, MCR+ status was associated with smaller volumes in frontoparietal regions and greater white matter abnormalities. DISCUSSION MCR may represent a predementia syndrome characterized by prominent white matter abnormalities and frontoparietal atrophy.
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Affiliation(s)
- Gabriela T. Gomez
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Rebecca F. Gottesman
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | | | - Priya Palta
- Department of Medicine, Columbia University Medical Center, New York, NY
| | - Alden L. Gross
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Anja Soldan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Kevin J. Sullivan
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS
| | | | | | - B. Gwen Windham
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS
| | - Keenan A. Walker
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
- Laboratory of Behavioral Neuroscience, Intramural Research Program, National Institute on Aging, Baltimore, MD
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7
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Igwe KC, Lao PJ, Vorburger RS, Banerjee A, Rivera A, Chesebro A, Laing K, Manly JJ, Brickman AM. Automatic quantification of white matter hyperintensities on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging. Magn Reson Imaging 2022; 85:71-79. [PMID: 34662699 PMCID: PMC8818099 DOI: 10.1016/j.mri.2021.10.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 08/20/2021] [Accepted: 10/12/2021] [Indexed: 01/03/2023]
Abstract
White matter hyperintensities (WMH) are areas of increased signal visualized on T2-weighted fluid attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) sequences. They are typically attributed to small vessel cerebrovascular disease in the context of aging. Among older adults, WMH are associated with risk of cognitive decline and dementia, stroke, and various other health outcomes. There has been increasing interest in incorporating quantitative WMH measurement as outcomes in clinical trials, observational research, and clinical settings. Here, we present a novel, fully automated, unsupervised detection algorithm for WMH segmentation and quantification. The algorithm uses a robust preprocessing pipeline, including brain extraction and a sample-specific mask that incorporates spatial information for automatic false positive reduction, and a half Gaussian mixture model (HGMM). The method was evaluated in 24 participants with varying degrees of WMH (4.9-78.6 cm3) from a community-based study of aging and dementia with dice coefficient, sensitivity, specificity, correlation, and bias relative to the ground truth manual segmentation approach performed by two expert raters. Results were compared with those derived from commonly used available WMH segmentation packages, including SPM lesion probability algorithm (LPA), SPM lesion growing algorithm (LGA), and Brain Intensity AbNormality Classification Algorithm (BIANCA). The HGMM algorithm derived WMH values that had a dice score of 0.87, sensitivity of 0.89, and specificity of 0.99 compared to ground truth. White matter hyperintensity volumes derived with HGMM were strongly correlated with ground truth values (r = 0.97, p = 3.9e-16), with no observable bias (-1.1 [-2.6, 0.44], p-value = 0.16). Our novel algorithm uniquely uses a robust preprocessing pipeline and a half-Gaussian mixture model to segment WMH with high agreement with ground truth for large scale studies of brain aging.
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Affiliation(s)
- Kay C. Igwe
- Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA.,Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA
| | - Patrick J. Lao
- Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA.,Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA.,Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA
| | - Robert S. Vorburger
- Institute of Applied Simulation, School of Life Sciences and Facility Management, Zurich University of Applied Sciences, Wädenswil, 8820, Switzerland
| | - Arit Banerjee
- Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA.,Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA
| | - Andres Rivera
- Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA.,Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA
| | - Anthony Chesebro
- Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA.,Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA
| | - Krystal Laing
- Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA.,Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA
| | - Jennifer J. Manly
- Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA.,Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA.,Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA
| | - Adam M. Brickman
- Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA.,Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA.,Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY, 10032 USA.,Corresponding author Adam M. Brickman, PhD, Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, PS Box 16, 630 West 168th Street, New York, NY 10032, Tel: 212 342 1348, Fax: 212 342 1838,
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Johansen MC, Nyquist P, Sullivan KJ, Fornage M, Gottesman RF, Becker DM. Cerebral Small-Vessel Disease in Individuals with a Family History of Coronary Heart Disease: The Atherosclerosis Risk in Communities Study. Neuroepidemiology 2021; 55:316-322. [PMID: 34139692 PMCID: PMC8371924 DOI: 10.1159/000516428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 04/09/2021] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION The degree to which a family history of coronary heart disease (FHCHD) is associated with silent cerebral small-vessel disease (cSVD) among healthy adults, independent of prevalent CHD and traditional risk factors, is unknown. METHODS The Atherosclerosis Risk in Communities (ARIC) study is a community-based cohort study with self-reported family history data and brain magnetic resonance imaging (ages 68-88). The association between markers of cSVD (lacunar infarcts and cerebral microbleeds), or log-transformed white matter hyperintensity (WMH) volume, and FHCHD, or the number of affected relatives was examined using separate adjusted logistic or linear regression models, respectively. Race interaction terms were evaluated. RESULTS Of 1,639 participants without prevalent CHD (76 ± 5 years, 62% female, 29% black), 686 (42%) had FHCHD. There were higher odds of lacunar infarct (OR 1.40, 95% CI 1.07-1.84) among those with parental FHCHD and higher odds of microhemorrhages (lobar OR 1.86, 95% CI 1.13-3.06; subcortical OR 1.47, 95% CI 1.01-2.15) among those with sibling FHCHD. A greater number of any relative affected was associated with higher odds of lacunar infarct (OR 1.24, 95% CI 1.04-1.47) and lobar microhemorrhages (OR 1.31, 95% CI 1.05-1.64) but not subcortical microhemorrhages (OR 1.09, 95% CI 0.92-1.28). Odds of having a lacunar infarct were higher among blacks (p-interaction 0.04) with paternal FHCHD (OR 2.20, CI 1.35-3.58) than whites with paternal FHCHD (OR 1.17, CI 0.87-1.56). There was no association with WMH. DISCUSSION/CONCLUSION Markers of cSVD, specifically lacunar infarcts and microhemorrhages, appear to be associated with FHCHD, potentially representing shared mechanisms in different vascular beds, and perhaps a genetic propensity for vascular disease.
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Affiliation(s)
- Michelle C. Johansen
- Johns Hopkins University School of Medicine (JHUSOM) Department of Neurology, Baltimore, MD
| | - Paul Nyquist
- Johns Hopkins University School of Medicine (JHUSOM) Department of Neurology, Baltimore, MD
- JHUSOM Department of Medicine, Division of General Internal Medicine, Baltimore, MD
| | - Kevin J. Sullivan
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Myriam Fornage
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX
| | - Rebecca F. Gottesman
- Johns Hopkins University School of Medicine (JHUSOM) Department of Neurology, Baltimore, MD
| | - Diane M. Becker
- JHUSOM Department of Medicine, Division of General Internal Medicine, Baltimore, MD
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9
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Palta P, Sharrett AR, Gabriel KP, Gottesman RF, Folsom AR, Power MC, Evenson KR, Jack CR, Knopman DS, Mosley TH, Heiss G. Prospective Analysis of Leisure-Time Physical Activity in Midlife and Beyond and Brain Damage on MRI in Older Adults. Neurology 2021; 96:e964-e974. [PMID: 33408144 PMCID: PMC8055339 DOI: 10.1212/wnl.0000000000011375] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 10/07/2020] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To test the hypothesis that greater levels of leisure-time moderate to vigorous intensity physical activity (MVPA) in midlife or late life are associated with larger gray matter volumes, less white matter disease, and fewer cerebrovascular lesions measured in late life, we utilized data from 1,604 participants enrolled in the Atherosclerosis Risk in Communities study. METHODS Leisure-time MVPA was quantified using a past-year recall, interviewer-administered questionnaire at baseline and 25 years later and classified as none, low, middle, and high at each time point. The presence of cerebrovascular lesions, white matter hyperintensities (WMH), white matter integrity (mean fractional anisotropy [FA] and mean diffusivity [MD]), and gray matter volumes were quantified with 3T MRI in late life. The odds of cerebrovascular lesions were estimated with logistic regression. Linear regression estimated the mean differences in WMH, mean FA and MD, and gray matter volumes. RESULTS Among 1,604 participants (mean age 53 years, 61% female, 27% Black), 550 (34%), 176 (11%), 250 (16%), and 628 (39%) reported no, low, middle, and high MVPA in midlife, respectively. Compared to no MVPA in midlife, high MVPA was associated with more intact white matter integrity in late life (mean FA difference 0.13 per SD [95% confidence interval (CI) 0.004, 0.26]; mean MD difference -0.11 per SD [95% CI -0.21, -0.004]). High MVPA in midlife was also associated with a lower odds of lacunar infarcts (odds ratio 0.68, 95% CI 0.46, 0.99). High MVPA was not associated with gray matter volumes. High MVPA compared to no MVPA in late life was associated with most brain measures. CONCLUSION Greater levels of physical activity in midlife may protect against cerebrovascular sequelae in late life.
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Affiliation(s)
- Priya Palta
- From the Division of General Medicine, Department of Medicine (P.P.), Columbia University Irving Medical Center, New York, NY; Department of Epidemiology (A.R.S., R.F.G.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Epidemiology, School of Public Health (K.P.G.), The University of Alabama at Birmingham; Department of Neurology (R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Epidemiology and Community Health, School of Public Health (A.R.F.), University of Minnesota, Minneapolis; Department of Epidemiology (M.C.P.), Milken Institute School of Public Health, George Washington University, Washington, DC; Department of Epidemiology (K.R.E., G.H.), Gillings School of Global Public Health, University of North Carolina at Chapel Hill; Departments of Radiology (C.R.J.) and Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and The MIND Center (T.H.M.), University of Mississippi Medical Center, Jackson.
| | - A Richey Sharrett
- From the Division of General Medicine, Department of Medicine (P.P.), Columbia University Irving Medical Center, New York, NY; Department of Epidemiology (A.R.S., R.F.G.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Epidemiology, School of Public Health (K.P.G.), The University of Alabama at Birmingham; Department of Neurology (R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Epidemiology and Community Health, School of Public Health (A.R.F.), University of Minnesota, Minneapolis; Department of Epidemiology (M.C.P.), Milken Institute School of Public Health, George Washington University, Washington, DC; Department of Epidemiology (K.R.E., G.H.), Gillings School of Global Public Health, University of North Carolina at Chapel Hill; Departments of Radiology (C.R.J.) and Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and The MIND Center (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Kelley Pettee Gabriel
- From the Division of General Medicine, Department of Medicine (P.P.), Columbia University Irving Medical Center, New York, NY; Department of Epidemiology (A.R.S., R.F.G.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Epidemiology, School of Public Health (K.P.G.), The University of Alabama at Birmingham; Department of Neurology (R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Epidemiology and Community Health, School of Public Health (A.R.F.), University of Minnesota, Minneapolis; Department of Epidemiology (M.C.P.), Milken Institute School of Public Health, George Washington University, Washington, DC; Department of Epidemiology (K.R.E., G.H.), Gillings School of Global Public Health, University of North Carolina at Chapel Hill; Departments of Radiology (C.R.J.) and Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and The MIND Center (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Rebecca F Gottesman
- From the Division of General Medicine, Department of Medicine (P.P.), Columbia University Irving Medical Center, New York, NY; Department of Epidemiology (A.R.S., R.F.G.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Epidemiology, School of Public Health (K.P.G.), The University of Alabama at Birmingham; Department of Neurology (R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Epidemiology and Community Health, School of Public Health (A.R.F.), University of Minnesota, Minneapolis; Department of Epidemiology (M.C.P.), Milken Institute School of Public Health, George Washington University, Washington, DC; Department of Epidemiology (K.R.E., G.H.), Gillings School of Global Public Health, University of North Carolina at Chapel Hill; Departments of Radiology (C.R.J.) and Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and The MIND Center (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Aaron R Folsom
- From the Division of General Medicine, Department of Medicine (P.P.), Columbia University Irving Medical Center, New York, NY; Department of Epidemiology (A.R.S., R.F.G.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Epidemiology, School of Public Health (K.P.G.), The University of Alabama at Birmingham; Department of Neurology (R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Epidemiology and Community Health, School of Public Health (A.R.F.), University of Minnesota, Minneapolis; Department of Epidemiology (M.C.P.), Milken Institute School of Public Health, George Washington University, Washington, DC; Department of Epidemiology (K.R.E., G.H.), Gillings School of Global Public Health, University of North Carolina at Chapel Hill; Departments of Radiology (C.R.J.) and Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and The MIND Center (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Melinda C Power
- From the Division of General Medicine, Department of Medicine (P.P.), Columbia University Irving Medical Center, New York, NY; Department of Epidemiology (A.R.S., R.F.G.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Epidemiology, School of Public Health (K.P.G.), The University of Alabama at Birmingham; Department of Neurology (R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Epidemiology and Community Health, School of Public Health (A.R.F.), University of Minnesota, Minneapolis; Department of Epidemiology (M.C.P.), Milken Institute School of Public Health, George Washington University, Washington, DC; Department of Epidemiology (K.R.E., G.H.), Gillings School of Global Public Health, University of North Carolina at Chapel Hill; Departments of Radiology (C.R.J.) and Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and The MIND Center (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Kelly R Evenson
- From the Division of General Medicine, Department of Medicine (P.P.), Columbia University Irving Medical Center, New York, NY; Department of Epidemiology (A.R.S., R.F.G.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Epidemiology, School of Public Health (K.P.G.), The University of Alabama at Birmingham; Department of Neurology (R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Epidemiology and Community Health, School of Public Health (A.R.F.), University of Minnesota, Minneapolis; Department of Epidemiology (M.C.P.), Milken Institute School of Public Health, George Washington University, Washington, DC; Department of Epidemiology (K.R.E., G.H.), Gillings School of Global Public Health, University of North Carolina at Chapel Hill; Departments of Radiology (C.R.J.) and Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and The MIND Center (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Clifford R Jack
- From the Division of General Medicine, Department of Medicine (P.P.), Columbia University Irving Medical Center, New York, NY; Department of Epidemiology (A.R.S., R.F.G.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Epidemiology, School of Public Health (K.P.G.), The University of Alabama at Birmingham; Department of Neurology (R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Epidemiology and Community Health, School of Public Health (A.R.F.), University of Minnesota, Minneapolis; Department of Epidemiology (M.C.P.), Milken Institute School of Public Health, George Washington University, Washington, DC; Department of Epidemiology (K.R.E., G.H.), Gillings School of Global Public Health, University of North Carolina at Chapel Hill; Departments of Radiology (C.R.J.) and Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and The MIND Center (T.H.M.), University of Mississippi Medical Center, Jackson
| | - David S Knopman
- From the Division of General Medicine, Department of Medicine (P.P.), Columbia University Irving Medical Center, New York, NY; Department of Epidemiology (A.R.S., R.F.G.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Epidemiology, School of Public Health (K.P.G.), The University of Alabama at Birmingham; Department of Neurology (R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Epidemiology and Community Health, School of Public Health (A.R.F.), University of Minnesota, Minneapolis; Department of Epidemiology (M.C.P.), Milken Institute School of Public Health, George Washington University, Washington, DC; Department of Epidemiology (K.R.E., G.H.), Gillings School of Global Public Health, University of North Carolina at Chapel Hill; Departments of Radiology (C.R.J.) and Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and The MIND Center (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Thomas H Mosley
- From the Division of General Medicine, Department of Medicine (P.P.), Columbia University Irving Medical Center, New York, NY; Department of Epidemiology (A.R.S., R.F.G.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Epidemiology, School of Public Health (K.P.G.), The University of Alabama at Birmingham; Department of Neurology (R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Epidemiology and Community Health, School of Public Health (A.R.F.), University of Minnesota, Minneapolis; Department of Epidemiology (M.C.P.), Milken Institute School of Public Health, George Washington University, Washington, DC; Department of Epidemiology (K.R.E., G.H.), Gillings School of Global Public Health, University of North Carolina at Chapel Hill; Departments of Radiology (C.R.J.) and Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and The MIND Center (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Gerardo Heiss
- From the Division of General Medicine, Department of Medicine (P.P.), Columbia University Irving Medical Center, New York, NY; Department of Epidemiology (A.R.S., R.F.G.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Epidemiology, School of Public Health (K.P.G.), The University of Alabama at Birmingham; Department of Neurology (R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Epidemiology and Community Health, School of Public Health (A.R.F.), University of Minnesota, Minneapolis; Department of Epidemiology (M.C.P.), Milken Institute School of Public Health, George Washington University, Washington, DC; Department of Epidemiology (K.R.E., G.H.), Gillings School of Global Public Health, University of North Carolina at Chapel Hill; Departments of Radiology (C.R.J.) and Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and The MIND Center (T.H.M.), University of Mississippi Medical Center, Jackson
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Ammous F, Zhao W, Ratliff SM, Kho M, Shang L, Jones AC, Chaudhary NS, Tiwari HK, Irvin MR, Arnett DK, Mosley TH, Bielak LF, Kardia SLR, Zhou X, Smith J. Epigenome-wide association study identifies DNA methylation sites associated with target organ damage in older African Americans. Epigenetics 2020; 16:862-875. [PMID: 33100131 DOI: 10.1080/15592294.2020.1827717] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Target organ damage (TOD) manifests as vascular injuries in the body organ systems associated with long-standing hypertension. DNA methylation in peripheral blood leukocytes can capture inflammatory processes and gene expression changes underlying TOD. We investigated the association between epigenome-wide DNA methylation and five measures of TOD (estimated glomerular filtration rate (eGFR), urinary albumin-creatinine ratio (UACR), left ventricular mass index (LVMI), relative wall thickness (RWT), and white matter hyperintensity (WMH)) in 961 African Americans from hypertensive sibships. A multivariate (multi-trait) model of eGFR, UACR, LVMI, and RWT identified seven CpGs associated with at least one of the traits (cg21134922, cg04816311 near C7orf50, cg09155024, cg10254690 near OAT, cg07660512, cg12661888 near IFT43, and cg02264946 near CATSPERD) at FDR q < 0.1. Adjusting for blood pressure, body mass index, and type 2 diabetes attenuated the association for four CpGs. DNA methylation was associated with cis-gene expression for some CpGs, but no significant mediation by gene expression was detected. Mendelian randomization analyses suggested causality between three CpGs and eGFR (cg04816311, cg10254690, and cg07660512). We also assessed whether the identified CpGs were associated with TOD in 614 African Americans in the Hypertension Genetic Epidemiology Network (HyperGEN) study. Out of three CpGs available for replication, cg04816311 was significantly associated with eGFR (p = 0.0003), LVMI (p = 0.0003), and RWT (p = 0.002). This study found evidence of an association between DNA methylation and TOD in African Americans and highlights the utility of using a multivariate-based model that leverages information across related traits in epigenome-wide association studies.
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Affiliation(s)
- Farah Ammous
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Scott M Ratliff
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Minjung Kho
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Lulu Shang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Alana C Jones
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Ninad S Chaudhary
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Hemant K Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Donna K Arnett
- Dean's Office, School of Public Health, University of Kentucky, Lexington, KY, USA
| | - Thomas H Mosley
- Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Xiang Zhou
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Jennifer Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.,Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
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12
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Fujima N, Kameda H, Shimizu Y, Harada T, Tha KK, Yoneyama M, Kudo K. Utility of a diffusion-weighted arterial spin labeling (DW-ASL) technique for evaluating the progression of brain white matter lesions. Magn Reson Imaging 2020; 69:81-87. [PMID: 32217128 DOI: 10.1016/j.mri.2020.03.005] [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: 01/31/2020] [Revised: 03/02/2020] [Accepted: 03/19/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE To investigate the utility of diffusion-weighted arterial spin labeling (DW-ASL) for detecting the progression of brain white matter lesions. MATERIALS AND METHODS A total of 492 regions of interest (ROIs) in 41 patients were prospectively analyzed. DW-ASL was performed using the diffusion gradient prepulse of five b-values (0, 25, 60, 102, and 189) before the ASL readout. We calculated the water exchange rate (Kw) with post-processing using the ASL signal information for each b-value. The cerebral blood flow (CBF) was also calculated using b0 images. Using the signal information in FLAIR (fluid-attenuated inversion recovery) images, we classified the severity of white matter lesions into three grades: non-lesion, moderate, and severe. In addition, the normal Kw level was measured from DW-ASL data of 60 ROIs in five control subjects. The degree of variance of the Kw values (Kw-var) was calculated by squaring the value of the difference between each Kw value and the normal Kw level. All patient's ROIs were divided into non-progressive and progressive white matter lesions by comparing the present FLAIR images with those obtained 2 years before this acquisition. RESULTS Compared to the non-progressive group, the progressive group had significantly lower CBF, significantly higher severity grades in FLAIR, and significantly greater Kw-var values. In a receiver operator characteristic curve analysis, a high area under the curve (AUC) of 0.89 was obtained with the use of Kw-var. In contrast, the AUCs of 0.59 for CBF and 0.72 for severity grades in FLAIR were obtained. CONCLUSIONS The DW-ASL technique can be useful to detect the progression of brain white matter lesions. This technique will become a clinical tool for patients with various degrees of white matter lesions.
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Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan.
| | - Hiroyuki Kameda
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan
| | - Yukie Shimizu
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan
| | - Taisuke Harada
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan
| | - Khin Khin Tha
- Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, N15 W7, Kita-Ku, Sapporo 0608638, Japan; The Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education, N15 W8, Kita-Ku, Sapporo 0608638, Japan
| | - Masami Yoneyama
- Philips Japan, 3-37 Kohnan 2-chome, Minato-ku, Tokyo 108-8507, Japan
| | - Kohsuke Kudo
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan; The Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education, N15 W8, Kita-Ku, Sapporo 0608638, Japan
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13
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Altendahl M, Maillard P, Harvey D, Cotter D, Walters S, Wolf A, Singh B, Kakarla V, Azizkhanian I, Sheth SA, Xiao G, Fox E, You M, Leng M, Elashoff D, Kramer JH, Decarli C, Elahi F, Hinman JD. An IL-18-centered inflammatory network as a biomarker for cerebral white matter injury. PLoS One 2020; 15:e0227835. [PMID: 31978079 PMCID: PMC6980497 DOI: 10.1371/journal.pone.0227835] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 12/30/2019] [Indexed: 12/16/2022] Open
Abstract
Chronic systemic sterile inflammation is implicated in the pathogenesis of cerebrovascular disease and white matter injury. Non-invasive blood markers for risk stratification and dissection of inflammatory molecular substrates in vivo are lacking. We sought to identify whether an interconnected network of inflammatory biomarkers centered on IL-18 and all previously associated with white matter lesions could detect overt and antecedent white matter changes in two populations at risk for cerebral small vessel disease. In a cohort of 167 older adults (mean age: 76, SD 7.1, 83 females) that completed a cognitive battery, physical examination, and blood draw in parallel with MR imaging including DTI, we measured cerebral white matter hyperintensities (WMH) and free water (FW). Concurrently, serum levels of a biologic network of inflammation molecules including MPO, GDF-15, RAGE, ST2, IL-18, and MCP-1 were measured. The ability of a log-transformed population mean-adjusted inflammatory composite score (ICS) to associate with MR variables was demonstrated in an age and total intracranial volume adjusted model. In this cohort, ICS was significantly associated with WMH (β = 0.222, p = 0.013), FW (β = 0.3, p = 0.01), and with the number of vascular risk factor diagnoses (r = 0.36, p<0.001). In a second cohort of 131 subjects presenting for the evaluation of acute neurologic deficits concerning for stroke, we used serum levels of 11 inflammatory biomarkers in an unbiased principal component analysis which identified a single factor significantly associated with WMH. This single factor was strongly correlated with the six component ICS identified in the first cohort and was associated with WMH in a generalized linear regression model adjusted for age and gender (p = 0.027) but not acute stroke. A network of inflammatory molecules driven by IL-18 is associated with overt and antecedent white matter injury resulting from cerebrovascular disease and may be a promising peripheral biomarker for vascular white matter injury.
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Affiliation(s)
- Marie Altendahl
- Memory & Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United States of America
| | - Pauline Maillard
- Department of Neurology and Center for Neurosciences, University of California, Davis, CA, United States of America
| | - Danielle Harvey
- Department of Public Health Sciences, University of California, Davis, CA, United States of America
| | - Devyn Cotter
- Memory & Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United States of America
| | - Samantha Walters
- Memory & Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United States of America
| | - Amy Wolf
- Memory & Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United States of America
| | - Baljeet Singh
- Department of Neurology and Center for Neurosciences, University of California, Davis, CA, United States of America
| | - Visesha Kakarla
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America
| | - Ida Azizkhanian
- School of Medicine, New York Medical College, Vahalla, NY, United States of America
| | - Sunil A. Sheth
- University of Texas Health McGovern School of Medicine, Department of Neurology, Houston, TX, United States of America
| | - Guanxi Xiao
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America
| | - Emily Fox
- Memory & Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United States of America
| | - Michelle You
- Memory & Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United States of America
| | - Mei Leng
- Department of Medicine Statistics Core, Department of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America
| | - David Elashoff
- Department of Medicine Statistics Core, Department of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America
| | - Joel H. Kramer
- Memory & Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United States of America
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, United States of America
| | - Charlie Decarli
- Department of Neurology and Center for Neurosciences, University of California, Davis, CA, United States of America
| | - Fanny Elahi
- Memory & Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United States of America
| | - Jason D. Hinman
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America
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Zhang Y, Wu J, Chen W, Liu Y, Lyu J, Shi H, Chen Y, Wu EX, Tang X. Fully Automatic White Matter Hyperintensity Segmentation using U-net and Skip Connection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:974-977. [PMID: 31946056 DOI: 10.1109/embc.2019.8856913] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
White matter hyperintensity (WMH) is associated with various aging and neurodegenerative diseases. In this paper, we proposed and validated a fully automatic system which integrated classical image processing and deep neural network for segmenting WMH from fluid attenuation inversion recovery (FLAIR) and T1-weighed magnetic resonance (MR) images. A novel skip connection U-net (SC U-net) was proposed and compared with the classical U-net. Experiments were performed on a dataset of 60 images, acquired from three different scanners. Validation analysis and cross-scanner testing were conducted. Compared with U-net, the proposed SC U-net had a faster convergence and higher segmentation accuracy. The software environment and models of the proposed system were made publicly accessible at Dockerhub.
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Bryan RN, Davatzikos C, Herskovits EH, Mohan S, Rudie JD, Rauschecker AM. Medical Image Analysis: Human and Machine. Acad Radiol 2020; 27:76-81. [PMID: 31818388 DOI: 10.1016/j.acra.2019.09.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 09/08/2019] [Indexed: 10/25/2022]
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Smith JA, Raisky J, Ratliff SM, Liu J, Kardia SLR, Turner ST, Mosley TH, Zhao W. Intrinsic and extrinsic epigenetic age acceleration are associated with hypertensive target organ damage in older African Americans. BMC Med Genomics 2019; 12:141. [PMID: 31640709 PMCID: PMC6806502 DOI: 10.1186/s12920-019-0585-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 09/11/2019] [Indexed: 12/22/2022] Open
Abstract
Background Epigenetic age acceleration, a measure of biological aging based on DNA methylation, is associated with cardiovascular mortality. However, little is known about its relationship with hypertensive target organ damage to the heart, kidneys, brain, and peripheral arteries. Methods We investigated associations between intrinsic (IEAA) or extrinsic (EEAA) epigenetic age acceleration, blood pressure, and six types of organ damage in a primarily hypertensive cohort of 1390 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. DNA methylation from peripheral blood leukocytes was collected at baseline (1996–2000), and measures of target organ damage were assessed in a follow-up visit (2000–2004). Linear regression with generalized estimating equations was used to test for associations between epigenetic age acceleration and target organ damage, as well as effect modification of epigenetic age by blood pressure or sex. Sequential Oligogenic Linkage Analysis Routines (SOLAR) was used to test for evidence of shared genetic and/or environmental effects between epigenetic age acceleration and organ damage pairs that were significantly associated. Results After adjustment for sex, chronological age, and time between methylation and organ damage measures, higher IEAA was associated with higher urine albumin to creatinine ratio (UACR, p = 0.004), relative wall thickness (RWT, p = 0.022), and left ventricular mass index (LVMI, p = 0.007), and with lower ankle-brachial index (ABI, p = 0.014). EEAA was associated with higher LVMI (p = 0.005). Target organ damage associations for all but IEAA with LVMI remained significant after further adjustment for blood pressure and antihypertensive use (p < 0.05). Further adjustment for diabetes attenuated the IEAA associations with UACR and RWT, and adjustment for smoking attenuated the IEAA association with ABI. No effect modification by age or sex was observed. Conclusions Measures of epigenetic age acceleration may help to better characterize the functional mechanisms underlying organ damage from cellular aging and/or hypertension. These measures may act as subclinical biomarkers for damage to the kidney, heart, and peripheral vasculature; however more research is needed to determine whether these relationships remain independent of lifestyle factors and comorbidities.
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Affiliation(s)
- Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA. .,Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA.
| | - Jeremy Raisky
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Scott M Ratliff
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jiaxuan Liu
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Stephen T Turner
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, 55905, USA
| | - Thomas H Mosley
- Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, 39126, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
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Maillard P, Fletcher E, Singh B, Martinez O, Johnson DK, Olichney JM, Farias ST, DeCarli C. Cerebral white matter free water: A sensitive biomarker of cognition and function. Neurology 2019; 92:e2221-e2231. [PMID: 30952798 PMCID: PMC6537135 DOI: 10.1212/wnl.0000000000007449] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 01/08/2019] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVE To determine whether free water (FW) content, initially developed to correct metrics derived from diffusion tensor imaging and recently found to be strongly associated with vascular risk factors, may constitute a sensitive biomarker of white matter (WM) microstructural differences associated with cognitive performance but remains unknown. METHODS Five hundred thirty-six cognitively diverse individuals, aged 77 ± 8 years, received yearly comprehensive clinical evaluations and a baseline MRI examination of whom 224 underwent follow-up MRI. WM microstructural measures, including FW, fractional anisotropy, and mean diffusivity corrected for FW and WM hyperintensity burden were computed within WM voxels of each individual. Baseline and change in MRI metrics were then used as independent variables to explain baseline and change in episodic memory (EM), executive function (EF), and Clinical Dementia Rating (CDR) scores using linear, logistic, and Cox proportional-hazards regressions. RESULTS Higher baseline FW and WM hyperintensity were associated with lower baseline EM and EF, higher baseline CDR, accelerated EF and EM decline, and higher probability to transition to a more severe CDR stage (p values <0.01). Annual change in FW was also found to be associated with concomitant change in cognitive and functional performance (p values <0.01). CONCLUSIONS This study finds cross-sectional and longitudinal associations between FW content and trajectory of cognitive and functional performance in a large sample of cognitively diverse individuals. It supports the need to investigate the pathophysiologic process that manifests increased FW, potentially leading to more severe WM territory injury and promoting cognitive and functional decline.
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Affiliation(s)
- Pauline Maillard
- From the Imaging of Dementia and Aging Laboratory and Center for Neurosciences (P.M., E.F., B.S., O.M., C.D.), Davis, CA; and Department of Neurology (D.K.J., J.M.O., S.T.F., C.D.), University of California, Davis.
| | - Evan Fletcher
- From the Imaging of Dementia and Aging Laboratory and Center for Neurosciences (P.M., E.F., B.S., O.M., C.D.), Davis, CA; and Department of Neurology (D.K.J., J.M.O., S.T.F., C.D.), University of California, Davis
| | - Baljeet Singh
- From the Imaging of Dementia and Aging Laboratory and Center for Neurosciences (P.M., E.F., B.S., O.M., C.D.), Davis, CA; and Department of Neurology (D.K.J., J.M.O., S.T.F., C.D.), University of California, Davis
| | - Oliver Martinez
- From the Imaging of Dementia and Aging Laboratory and Center for Neurosciences (P.M., E.F., B.S., O.M., C.D.), Davis, CA; and Department of Neurology (D.K.J., J.M.O., S.T.F., C.D.), University of California, Davis
| | - David K Johnson
- From the Imaging of Dementia and Aging Laboratory and Center for Neurosciences (P.M., E.F., B.S., O.M., C.D.), Davis, CA; and Department of Neurology (D.K.J., J.M.O., S.T.F., C.D.), University of California, Davis
| | - John M Olichney
- From the Imaging of Dementia and Aging Laboratory and Center for Neurosciences (P.M., E.F., B.S., O.M., C.D.), Davis, CA; and Department of Neurology (D.K.J., J.M.O., S.T.F., C.D.), University of California, Davis
| | - Sarah T Farias
- From the Imaging of Dementia and Aging Laboratory and Center for Neurosciences (P.M., E.F., B.S., O.M., C.D.), Davis, CA; and Department of Neurology (D.K.J., J.M.O., S.T.F., C.D.), University of California, Davis
| | - Charles DeCarli
- From the Imaging of Dementia and Aging Laboratory and Center for Neurosciences (P.M., E.F., B.S., O.M., C.D.), Davis, CA; and Department of Neurology (D.K.J., J.M.O., S.T.F., C.D.), University of California, Davis
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Wu A, Sharrett AR, Gottesman RF, Power MC, Mosley TH, Jack CR, Knopman DS, Windham BG, Gross AL, Coresh J. Association of Brain Magnetic Resonance Imaging Signs With Cognitive Outcomes in Persons With Nonimpaired Cognition and Mild Cognitive Impairment. JAMA Netw Open 2019; 2:e193359. [PMID: 31074810 PMCID: PMC6512274 DOI: 10.1001/jamanetworkopen.2019.3359] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
IMPORTANCE Brain atrophy and vascular lesions contribute to dementia and mild cognitive impairment (MCI) in clinical referral populations. Prospective evidence in older general populations is limited. OBJECTIVE To evaluate which magnetic resonance imaging (MRI) signs are independent risk factors for dementia and MCI. DESIGN, SETTING, AND PARTICIPANTS This population-based cohort study included 1553 participants sampled from the Atherosclerosis Risk in Communities Study who had brain MRI scans and were dementia free during visit 5 (June 2011 to September 2013). Participants' cognitive status was evaluated through visit 6 (June 2016 to December 2017). EXPOSURES Brain regional volumes, microhemorrhages, white matter hyperintensity (WMH) volumes, and infarcts measured on 3-T MRI. MAIN OUTCOMES AND MEASURES Cognitive status (dementia, MCI, or nonimpaired cognition) was determined from in-person evaluations. Dementia among participants who missed visit 6 was identified via dementia surveillance and hospital discharge or death certificate codes. Cox proportional hazards models were used to evaluate the risk of dementia in 3 populations: dementia-free participants (N = 1553), participants with nonimpaired cognition (n = 1014), and participants with MCI (n = 539). Complementary log-log models were used for risk of MCI among participants with nonimpaired cognition who also attended visit 6 (n = 767). Models were adjusted for demographic variables, apolipoprotein E ε4 alleles, vascular risk factors, depressive symptoms, and heart failure. RESULTS Overall, 212 incident dementia cases were identified among 1553 participants (mean [SD] age at visit 5, 76 [5.2] years; 946 [60.9%] women; 436 [28.1%] African American) with a median (interquartile range) follow-up period of 4.9 (4.3-5.2) years. Significant risk factors of dementia included lower volumes in the Alzheimer disease (AD) signature region, including hippocampus, entorhinal cortex, and surrounding structures (hazard ratio [HR] per 1-SD decrease, 2.40; 95% CI, 1.89-3.04), lobar microhemorrhages (HR, 1.90; 95% CI, 1.30-2.77), higher volumes of WMH (HR per 1-SD increase, 1.44; 95% CI, 1.23-1.69), and lacunar infarcts (HR, 1.66; 95% CI, 1.20-2.31). The AD signature region volume was also consistently associated with both MCI and progression from MCI to dementia, while subcortical microhemorrhages and infarcts contributed less to the progression from MCI to dementia. CONCLUSIONS AND RELEVANCE In this study, lower AD signature region volumes, brain microhemorrhages, higher WMH volumes, and infarcts were risk factors associated with dementia in older community-based residents. Vascular changes were more important in the development of MCI than in its progression to dementia, while AD-related signs were important in both stages.
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Affiliation(s)
- Aozhou Wu
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | | | | | | | | | | | | | - Alden L. Gross
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Josef Coresh
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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Frey BM, Petersen M, Mayer C, Schulz M, Cheng B, Thomalla G. Characterization of White Matter Hyperintensities in Large-Scale MRI-Studies. Front Neurol 2019; 10:238. [PMID: 30972001 PMCID: PMC6443932 DOI: 10.3389/fneur.2019.00238] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 02/22/2019] [Indexed: 01/18/2023] Open
Abstract
Background: White matter hyperintensities of presumed vascular origin (WMH) are a common finding in elderly people and a growing social malady in the aging western societies. As a manifestation of cerebral small vessel disease, WMH are considered to be a vascular contributor to various sequelae such as cognitive decline, dementia, depression, stroke as well as gait and balance problems. While pathophysiology and therapeutical options remain unclear, large-scale studies have improved the understanding of WMH, particularly by quantitative assessment of WMH. In this review, we aimed to provide an overview of the characteristics, research subjects and segmentation techniques of these studies. Methods: We performed a systematic review according to the PRISMA statement. One thousand one hundred and ninety-six potentially relevant articles were identified via PubMed search. Six further articles classified as relevant were added manually. After applying a catalog of exclusion criteria, remaining articles were read full-text and the following information was extracted into a standardized form: year of publication, sample size, mean age of subjects in the study, the cohort included, and segmentation details like the definition of WMH, the segmentation method, reference to methods papers as well as validation measurements. Results: Our search resulted in the inclusion and full-text review of 137 articles. One hundred and thirty-four of them belonged to 37 prospective cohort studies. Median sample size was 1,030 with no increase over the covered years. Eighty studies investigated in the association of WMH and risk factors. Most of them focussed on arterial hypertension, diabetes mellitus type II and Apo E genotype and inflammatory markers. Sixty-three studies analyzed the association of WMH and secondary conditions like cognitive decline, mood disorder and brain atrophy. Studies applied various methods based on manual (3), semi-automated (57), and automated segmentation techniques (75). Only 18% of the articles referred to an explicit definition of WMH. Discussion: The review yielded a large number of studies engaged in WMH research. A remarkable variety of segmentation techniques was applied, and only a minority referred to a clear definition of WMH. Most addressed topics were risk factors and secondary clinical conditions. In conclusion, WMH research is a vivid field with a need for further standardization regarding definitions and used methods.
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Affiliation(s)
- Benedikt M Frey
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marvin Petersen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carola Mayer
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Maximilian Schulz
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Wu D, Albert M, Soldan A, Pettigrew C, Oishi K, Tomogane Y, Ye C, Ma T, Miller MI, Mori S. Multi-atlas based detection and localization (MADL) for location-dependent quantification of white matter hyperintensities. NEUROIMAGE-CLINICAL 2019; 22:101772. [PMID: 30927606 PMCID: PMC6444296 DOI: 10.1016/j.nicl.2019.101772] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Revised: 02/05/2019] [Accepted: 03/10/2019] [Indexed: 02/07/2023]
Abstract
The extent and spatial location of white matter hyperintensities (WMH) on brain MRI may be relevant to the development of cognitive decline in older persons. Here, we introduce a new method, known as the Multi-atlas based Detection and Localization (MADL), to evaluate WMH on fluid-attenuated inversion recovery (FLAIR) data. This method simultaneously parcellates the whole brain into 143 structures and labels hyperintense areas within each WM structure. First, a multi-atlas library was established with FLAIR data of normal elderly brains; and then a multi-atlas fusion algorithm was developed by which voxels with locally abnormal intensities were detected as WMH. At the same time, brain segmentation maps were generated from the multi-atlas fusion process to determine the anatomical location of WMH. Areas identified using the MADL method agreed well with manual delineation, with an interclass correlation of 0.97 and similarity index (SI) between 0.55 and 0.72, depending on the total WMH load. Performance was compared to other state-of-the-art WMH detection methods, such as BIANCA and LST. MADL-based analyses of WMH in an older population revealed a significant association between age and WMH load in deep WM but not subcortical WM. The findings also suggested increased WMH load in selective brain regions in subjects with mild cognitive impairment compared to controls, including the inferior deep WM and occipital subcortical WM. The proposed MADL approach may facilitate location-dependent characterization of WMH in older individuals with memory impairment. We proposed a multi-atlas based method for simultaneous detection and location of WMH on FLAIR images. The method generates whole-brain segmentation for location-dependent WMH analysis. The method showed reasonably high detection accuracy in comparison with other methods. Results revealed a selective association between deep brain WMH and subject age. Results suggested increased WMH in the inferior white matter in MCI patients.
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Affiliation(s)
- Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China; Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Anja Soldan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Corinne Pettigrew
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kenichi Oishi
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yusuke Tomogane
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Chenfei Ye
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ting Ma
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael I Miller
- Department of Biomedicine Engineering, Johns Hopkins University, Baltimore, MD, USA; Center of Imaging Science, Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Susumu Mori
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
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Johnson EL, Krauss GL, Lee AK, Schneider ALC, Kucharska-Newton AM, Huang J, Jack CR, Gottesman RF. Association between white matter hyperintensities, cortical volumes, and late-onset epilepsy. Neurology 2019; 92:e988-e995. [PMID: 30804067 DOI: 10.1212/wnl.0000000000007010] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 10/25/2018] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE To identify the association between brain vascular changes and cortical volumes on MRI and late-onset epilepsy. METHODS In 1993-1995, 1,920 participants (median age 62.7, 59.9% female) in the community-based Atherosclerosis Risk in Communities (ARIC) Study underwent MRI, and white matter hyperintensities were measured. In addition, in 2011-2013, 1,964 ARIC participants (median age 72.4, 61.1% female) underwent MRI, and cortical volumes and white matter hyperintensities were measured. We identified cases of late-onset epilepsy (starting at age 60 or later) from ARIC hospitalization records and Medicare claims data. Using the 1993-1995 MRI, we evaluated the association between white matter hyperintensities and subsequent epilepsy using survival analysis. We used the 2011-2013 MRI to conduct cross-sectional logistic regression to examine the association of cortical volumes and white matter hyperintensities with late-onset epilepsy. All models were adjusted for demographics, hypertension, diabetes, smoking, and APOE ε4 allele status. RESULTS Ninety-seven ARIC participants developed epilepsy after having an MRI in 1993-1995 (incidence 3.34 per 1,000 person-years). The degree of white matter hyperintensities measured at ages 49-72 years was associated with the risk of late-onset epilepsy (hazard ratio 1.27 per age-adjusted SD, 95% confidence interval [CI] 1.06-1.54). Lower cortical volume scores were associated cross-sectionally with higher odds of late-onset epilepsy (odds ratio 1.87, 95% CI 1.16-3.02) per age-adjusted SD. CONCLUSIONS This study demonstrates associations between earlier-life white matter hyperintensities on MRI and later-life incident epilepsy, and between cortical volumes measured later in life and late-onset epilepsy. These findings may help illuminate the causes of late-onset epilepsy.
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Affiliation(s)
- Emily L Johnson
- From the Department of Neurology (E.L.J., G.L.K., A.L.C.S., R.F.G.), Johns Hopkins University School of Medicine; Department of Epidemiology (A.K.L., R.F.G.), Johns Hopkins School of Public Health, Baltimore, MD; Department of Epidemiology (A.M.K.-N.), University of North Carolina at Chapel Hill; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN.
| | - Gregory L Krauss
- From the Department of Neurology (E.L.J., G.L.K., A.L.C.S., R.F.G.), Johns Hopkins University School of Medicine; Department of Epidemiology (A.K.L., R.F.G.), Johns Hopkins School of Public Health, Baltimore, MD; Department of Epidemiology (A.M.K.-N.), University of North Carolina at Chapel Hill; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN
| | - Alexandra K Lee
- From the Department of Neurology (E.L.J., G.L.K., A.L.C.S., R.F.G.), Johns Hopkins University School of Medicine; Department of Epidemiology (A.K.L., R.F.G.), Johns Hopkins School of Public Health, Baltimore, MD; Department of Epidemiology (A.M.K.-N.), University of North Carolina at Chapel Hill; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN
| | - Andrea L C Schneider
- From the Department of Neurology (E.L.J., G.L.K., A.L.C.S., R.F.G.), Johns Hopkins University School of Medicine; Department of Epidemiology (A.K.L., R.F.G.), Johns Hopkins School of Public Health, Baltimore, MD; Department of Epidemiology (A.M.K.-N.), University of North Carolina at Chapel Hill; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN
| | - Anna M Kucharska-Newton
- From the Department of Neurology (E.L.J., G.L.K., A.L.C.S., R.F.G.), Johns Hopkins University School of Medicine; Department of Epidemiology (A.K.L., R.F.G.), Johns Hopkins School of Public Health, Baltimore, MD; Department of Epidemiology (A.M.K.-N.), University of North Carolina at Chapel Hill; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN
| | - Juebin Huang
- From the Department of Neurology (E.L.J., G.L.K., A.L.C.S., R.F.G.), Johns Hopkins University School of Medicine; Department of Epidemiology (A.K.L., R.F.G.), Johns Hopkins School of Public Health, Baltimore, MD; Department of Epidemiology (A.M.K.-N.), University of North Carolina at Chapel Hill; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN
| | - Clifford R Jack
- From the Department of Neurology (E.L.J., G.L.K., A.L.C.S., R.F.G.), Johns Hopkins University School of Medicine; Department of Epidemiology (A.K.L., R.F.G.), Johns Hopkins School of Public Health, Baltimore, MD; Department of Epidemiology (A.M.K.-N.), University of North Carolina at Chapel Hill; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN
| | - Rebecca F Gottesman
- From the Department of Neurology (E.L.J., G.L.K., A.L.C.S., R.F.G.), Johns Hopkins University School of Medicine; Department of Epidemiology (A.K.L., R.F.G.), Johns Hopkins School of Public Health, Baltimore, MD; Department of Epidemiology (A.M.K.-N.), University of North Carolina at Chapel Hill; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN
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Diniz PHB, Valente TLA, Diniz JOB, Silva AC, Gattass M, Ventura N, Muniz BC, Gasparetto EL. Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 167:49-63. [PMID: 29706405 DOI: 10.1016/j.cmpb.2018.04.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 02/12/2018] [Accepted: 04/17/2018] [Indexed: 05/06/2023]
Abstract
BACKGROUND AND OBJECTIVE White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as possible. Magnetic Resonance Imaging (MRI) provides three-dimensional data with the possibility to detect and emphasize contrast differences in soft tissues, providing rich information about the human soft tissue anatomy. However, the amount of data provided for these images is far too much for manual analysis/interpretation, representing a difficult and time-consuming task for specialists. This work presents a computational methodology capable of detecting regions of white matter lesions of the brain in MRI of FLAIR modality. The techniques highlighted in this methodology are SLIC0 clustering for candidate segmentation and convolutional neural networks for candidate classification. METHODS The methodology proposed here consists of four steps: (1) images acquisition, (2) images preprocessing, (3) candidates segmentation and (4) candidates classification. RESULTS The methodology was applied on 91 magnetic resonance images provided by DASA, and achieved an accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79% with 0.005 of false positives, without any false positives reduction technique, in detection of white matter lesion regions. CONCLUSIONS It is demonstrated the feasibility of the analysis of brain MRI using SLIC0 and convolutional neural network techniques to achieve success in detection of white matter lesions regions.
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Affiliation(s)
- Pedro Henrique Bandeira Diniz
- Pontifical Catholic University of Rio de Janeiro - PUC - RioR. São Vicente, 225, Gávea, RJ, Rio de Janeiro, 22453-900, Brazil.
| | - Thales Levi Azevedo Valente
- Pontifical Catholic University of Rio de Janeiro - PUC - RioR. São Vicente, 225, Gávea, RJ, Rio de Janeiro, 22453-900, Brazil.
| | - João Otávio Bandeira Diniz
- Federal University of Maranhão - UFMA Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, MA, São Luís, 65085-580, Brazil.
| | - Aristófanes Corrêa Silva
- Federal University of Maranhão - UFMA Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, MA, São Luís, 65085-580, Brazil.
| | - Marcelo Gattass
- Pontifical Catholic University of Rio de Janeiro - PUC - RioR. São Vicente, 225, Gávea, RJ, Rio de Janeiro, 22453-900, Brazil.
| | - Nina Ventura
- Paulo Niemeyer State Brain Institute - IECR. Lobo Júnior, 2293, Penha -RJ, 21070-060, Brazil.
| | - Bernardo Carvalho Muniz
- Paulo Niemeyer State Brain Institute - IECR. Lobo Júnior, 2293, Penha -RJ, 21070-060, Brazil.
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Manjón JV, Coupé P, Raniga P, Xia Y, Desmond P, Fripp J, Salvado O. MRI white matter lesion segmentation using an ensemble of neural networks and overcomplete patch-based voting. Comput Med Imaging Graph 2018; 69:43-51. [DOI: 10.1016/j.compmedimag.2018.05.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Revised: 04/21/2018] [Accepted: 05/01/2018] [Indexed: 12/11/2022]
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Knight J, Taylor GW, Khademi A. Voxel-Wise Logistic Regression and Leave-One-Source-Out Cross Validation for white matter hyperintensity segmentation. Magn Reson Imaging 2018; 54:119-136. [PMID: 29932970 DOI: 10.1016/j.mri.2018.06.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 06/11/2018] [Accepted: 06/13/2018] [Indexed: 12/21/2022]
Abstract
Many algorithms have been proposed for automated segmentation of white matter hyperintensities (WMH) in brain MRI. Yet, broad uptake of any particular algorithm has not been observed. In this work, we argue that this may be due to variable and suboptimal validation data and frameworks, precluding direct comparison of methods on heterogeneous data. As a solution, we present Leave-One-Source-Out Cross Validation (LOSO-CV), which leverages all available data for performance estimation, and show that this gives more realistic (lower) estimates of segmentation algorithm performance on data from different scanners. We also develop a FLAIR-only WMH segmentation algorithm: Voxel-Wise Logistic Regression (VLR), inspired by the open-source Lesion Prediction Algorithm (LPA). Our variant facilitates more accurate parameter estimation, and permits intuitive interpretation of model parameters. We illustrate the performance of the VLR algorithm using the LOSO-CV framework with a dataset comprising freely available data from several recent competitions (96 images from 7 scanners). The performance of the VLR algorithm (median Similarity Index of 0.69) is compared to its LPA predecessor (0.58), and the results of the VLR algorithm in the 2017 WMH Segmentation Competition are also presented.
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Affiliation(s)
- Jesse Knight
- University of Guelph, 50 Stone Rd E, Guelph, Canada.
| | - Graham W Taylor
- University of Guelph, 50 Stone Rd E, Guelph, Canada; Vector Institute, 101 College Street, Toronto, Suite HL30B, Canada
| | - April Khademi
- Ryerson University, 350 Victoria St, Toronto, Canada
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Hughes TM, Wagenknecht LE, Craft S, Mintz A, Heiss G, Palta P, Wong D, Zhou Y, Knopman D, Mosley TH, Gottesman RF. Arterial stiffness and dementia pathology: Atherosclerosis Risk in Communities (ARIC)-PET Study. Neurology 2018; 90:e1248-e1256. [PMID: 29549223 PMCID: PMC5890613 DOI: 10.1212/wnl.0000000000005259] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 12/20/2017] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE Arterial stiffness has been associated with evidence of cerebral small vessel disease (cSVD) and fibrillar β-amyloid (Aβ) deposition in the brain. These complex relationships have not been examined in racially and cognitively diverse cohorts. METHODS The Atherosclerosis Risk in Communities (ARIC)-Neurocognitive Study collected detailed cognitive testing for adjudication of dementia and mild cognitive impairment (MCI), brain MRI, and arterial stiffness by pulse wave velocity (PWV, carotid-femoral [cfPWV] and heart-carotid [hcPWV]). The ARIC-PET ancillary study added Aβ imaging using florbetapir ([18F]-AV-45) to obtain standardized uptake volume ratios and defined global Aβ-positivity as standardized uptake volume ratio >1.2. One-SD increase in PWV was related to brain volume, MRI-defined cSVD (e.g., cerebral microbleeds and white matter hyperintensity), and cortical Aβ deposition adjusted for age, body mass index, sex, race, and APOE ε4 status. We examined the cross-sectional relationships including interactions by race, APOE ε4 status, and cognition. RESULTS Among the 320 ARIC-PET participants (76 [5] years, 45% black, 27% MCI), greater central stiffness (hcPWV) was associated with greater Aβ deposition (odds ratio [OR] = 1.31, 95% confidence interval [CI] 1.01-1.71). Greater central stiffness (cfPWV) was significantly associated with having lower brain volumes in Alzheimer disease-susceptible regions (in mm3, β = -1.5 [0.7 SD], p = 0.03) and high white matter hyperintensity burden (OR = 1.6, 95% CI 1.2-2.1). Furthermore, cfPWV was associated with a higher odds of concomitant high white matter hyperintensity and Aβ-positive scans (OR = 1.4, 95% CI 1.1-2.1). These associations were strongest among individuals with MCI and did not differ by race or APOE ε4 status. CONCLUSIONS Arterial stiffness, measured by PWV, is an emerging risk factor for dementia through its repeated relationships with cognition, cSVD, and Aβ deposition.
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Affiliation(s)
- Timothy M Hughes
- From the Departments of Internal Medicine (T.M.H., S.C.) and Radiology (A.M.), and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC; Department of Epidemiology (G.H., P.P.), University of North Carolina at Chapel Hill; Departments of Radiology (D.W., Y.Z.) and Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (D.K.), Mayo Clinic, Rochester, MN; and Department of Medicine (T.H.M.), University of Mississippi Medical Center, Jackson.
| | - Lynne E Wagenknecht
- From the Departments of Internal Medicine (T.M.H., S.C.) and Radiology (A.M.), and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC; Department of Epidemiology (G.H., P.P.), University of North Carolina at Chapel Hill; Departments of Radiology (D.W., Y.Z.) and Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (D.K.), Mayo Clinic, Rochester, MN; and Department of Medicine (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Suzanne Craft
- From the Departments of Internal Medicine (T.M.H., S.C.) and Radiology (A.M.), and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC; Department of Epidemiology (G.H., P.P.), University of North Carolina at Chapel Hill; Departments of Radiology (D.W., Y.Z.) and Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (D.K.), Mayo Clinic, Rochester, MN; and Department of Medicine (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Akiva Mintz
- From the Departments of Internal Medicine (T.M.H., S.C.) and Radiology (A.M.), and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC; Department of Epidemiology (G.H., P.P.), University of North Carolina at Chapel Hill; Departments of Radiology (D.W., Y.Z.) and Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (D.K.), Mayo Clinic, Rochester, MN; and Department of Medicine (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Gerardo Heiss
- From the Departments of Internal Medicine (T.M.H., S.C.) and Radiology (A.M.), and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC; Department of Epidemiology (G.H., P.P.), University of North Carolina at Chapel Hill; Departments of Radiology (D.W., Y.Z.) and Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (D.K.), Mayo Clinic, Rochester, MN; and Department of Medicine (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Priya Palta
- From the Departments of Internal Medicine (T.M.H., S.C.) and Radiology (A.M.), and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC; Department of Epidemiology (G.H., P.P.), University of North Carolina at Chapel Hill; Departments of Radiology (D.W., Y.Z.) and Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (D.K.), Mayo Clinic, Rochester, MN; and Department of Medicine (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Dean Wong
- From the Departments of Internal Medicine (T.M.H., S.C.) and Radiology (A.M.), and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC; Department of Epidemiology (G.H., P.P.), University of North Carolina at Chapel Hill; Departments of Radiology (D.W., Y.Z.) and Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (D.K.), Mayo Clinic, Rochester, MN; and Department of Medicine (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Yun Zhou
- From the Departments of Internal Medicine (T.M.H., S.C.) and Radiology (A.M.), and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC; Department of Epidemiology (G.H., P.P.), University of North Carolina at Chapel Hill; Departments of Radiology (D.W., Y.Z.) and Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (D.K.), Mayo Clinic, Rochester, MN; and Department of Medicine (T.H.M.), University of Mississippi Medical Center, Jackson
| | - David Knopman
- From the Departments of Internal Medicine (T.M.H., S.C.) and Radiology (A.M.), and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC; Department of Epidemiology (G.H., P.P.), University of North Carolina at Chapel Hill; Departments of Radiology (D.W., Y.Z.) and Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (D.K.), Mayo Clinic, Rochester, MN; and Department of Medicine (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Thomas H Mosley
- From the Departments of Internal Medicine (T.M.H., S.C.) and Radiology (A.M.), and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC; Department of Epidemiology (G.H., P.P.), University of North Carolina at Chapel Hill; Departments of Radiology (D.W., Y.Z.) and Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (D.K.), Mayo Clinic, Rochester, MN; and Department of Medicine (T.H.M.), University of Mississippi Medical Center, Jackson
| | - Rebecca F Gottesman
- From the Departments of Internal Medicine (T.M.H., S.C.) and Radiology (A.M.), and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC; Department of Epidemiology (G.H., P.P.), University of North Carolina at Chapel Hill; Departments of Radiology (D.W., Y.Z.) and Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (D.K.), Mayo Clinic, Rochester, MN; and Department of Medicine (T.H.M.), University of Mississippi Medical Center, Jackson
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Walker KA, Power MC, Hoogeveen RC, Folsom AR, Ballantyne CM, Knopman DS, Windham BG, Selvin E, Jack CR, Gottesman RF. Midlife Systemic Inflammation, Late-Life White Matter Integrity, and Cerebral Small Vessel Disease: The Atherosclerosis Risk in Communities Study. Stroke 2017; 48:3196-3202. [PMID: 29101255 PMCID: PMC5705320 DOI: 10.1161/strokeaha.117.018675] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 09/19/2017] [Accepted: 09/25/2017] [Indexed: 12/28/2022]
Abstract
BACKGROUND AND PURPOSE It is currently unclear whether midlife systemic inflammation promotes the development of white matter (WM) abnormalities and small vessel disease in the elderly. We examined the association of midlife systemic inflammation with late-life WM hyperintensity volume, deep and periventricular WM microstructural integrity (fractional anisotropy and mean diffusivity [MD]), cerebral infarcts, and microbleeds in a biracial prospective cohort study. METHODS Linear and logistic regression examined the relation between midlife high-sensitivity C-reactive protein (CRP)-a nonspecific marker of inflammation-and brain magnetic resonance imaging markers assessed 21 years later in the Atherosclerosis Risk in Communities Study. RESULTS We included 1485 participants (baseline age, 56[5]; 28% black). After adjusting for demographic factors and cardiovascular disease, each SD increase in midlife CRP was associated with lower fractional anisotropy (-0.09 SD; 95% confidence interval, -0.15 to -0.02) and greater MD (0.08 SD; 95% confidence interval, 0.03-0.15) in deep WM and lower fractional anisotropy (-0.07 SD; 95% confidence interval, -0.13 to 0.00) in periventricular WM. We found stronger associations between CRP and periventricular WM microstructural integrity among black participants (P interaction=0.011). Although an association between higher CRP levels and greater WM hyperintensity volume was found only among APOE ε4-positive participants in our primary analysis (0.14 SD; 95% confidence interval, 0.01-0.26; P interaction=0.028), this relationship extended to the entire sample after accounting for differential attrition. Midlife CRP was not associated with the presence of cerebral infarcts or microbleeds in late life. CONCLUSIONS Our findings support the hypothesis that midlife systemic inflammation may promote the development of chronic microangiopathic structural WM abnormalities in the elderly.
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Affiliation(s)
- Keenan A Walker
- From the Department of Neurology (K.A.W., R.F.G.) and Department of Internal Medicine (E.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology and Biostatistics, George Washington University Milken Institute School of Public Health (M.C.P.); Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX (R.C.H., C.M.B.); Center for Cardiovascular Disease Prevention, Houston Methodist DeBakey Heart and Vascular Center, TX (R.C.H., C.M.B.); Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.R.F.); Department of Neurology (D.S.K.) and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Medicine, University of Mississippi Medical Center, Jackson (B.G.W.); and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., E.S.).
| | - Melinda C Power
- From the Department of Neurology (K.A.W., R.F.G.) and Department of Internal Medicine (E.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology and Biostatistics, George Washington University Milken Institute School of Public Health (M.C.P.); Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX (R.C.H., C.M.B.); Center for Cardiovascular Disease Prevention, Houston Methodist DeBakey Heart and Vascular Center, TX (R.C.H., C.M.B.); Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.R.F.); Department of Neurology (D.S.K.) and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Medicine, University of Mississippi Medical Center, Jackson (B.G.W.); and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., E.S.)
| | - Ron C Hoogeveen
- From the Department of Neurology (K.A.W., R.F.G.) and Department of Internal Medicine (E.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology and Biostatistics, George Washington University Milken Institute School of Public Health (M.C.P.); Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX (R.C.H., C.M.B.); Center for Cardiovascular Disease Prevention, Houston Methodist DeBakey Heart and Vascular Center, TX (R.C.H., C.M.B.); Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.R.F.); Department of Neurology (D.S.K.) and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Medicine, University of Mississippi Medical Center, Jackson (B.G.W.); and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., E.S.)
| | - Aaron R Folsom
- From the Department of Neurology (K.A.W., R.F.G.) and Department of Internal Medicine (E.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology and Biostatistics, George Washington University Milken Institute School of Public Health (M.C.P.); Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX (R.C.H., C.M.B.); Center for Cardiovascular Disease Prevention, Houston Methodist DeBakey Heart and Vascular Center, TX (R.C.H., C.M.B.); Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.R.F.); Department of Neurology (D.S.K.) and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Medicine, University of Mississippi Medical Center, Jackson (B.G.W.); and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., E.S.)
| | - Christie M Ballantyne
- From the Department of Neurology (K.A.W., R.F.G.) and Department of Internal Medicine (E.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology and Biostatistics, George Washington University Milken Institute School of Public Health (M.C.P.); Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX (R.C.H., C.M.B.); Center for Cardiovascular Disease Prevention, Houston Methodist DeBakey Heart and Vascular Center, TX (R.C.H., C.M.B.); Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.R.F.); Department of Neurology (D.S.K.) and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Medicine, University of Mississippi Medical Center, Jackson (B.G.W.); and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., E.S.)
| | - David S Knopman
- From the Department of Neurology (K.A.W., R.F.G.) and Department of Internal Medicine (E.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology and Biostatistics, George Washington University Milken Institute School of Public Health (M.C.P.); Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX (R.C.H., C.M.B.); Center for Cardiovascular Disease Prevention, Houston Methodist DeBakey Heart and Vascular Center, TX (R.C.H., C.M.B.); Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.R.F.); Department of Neurology (D.S.K.) and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Medicine, University of Mississippi Medical Center, Jackson (B.G.W.); and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., E.S.)
| | - B Gwen Windham
- From the Department of Neurology (K.A.W., R.F.G.) and Department of Internal Medicine (E.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology and Biostatistics, George Washington University Milken Institute School of Public Health (M.C.P.); Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX (R.C.H., C.M.B.); Center for Cardiovascular Disease Prevention, Houston Methodist DeBakey Heart and Vascular Center, TX (R.C.H., C.M.B.); Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.R.F.); Department of Neurology (D.S.K.) and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Medicine, University of Mississippi Medical Center, Jackson (B.G.W.); and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., E.S.)
| | - Elizabeth Selvin
- From the Department of Neurology (K.A.W., R.F.G.) and Department of Internal Medicine (E.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology and Biostatistics, George Washington University Milken Institute School of Public Health (M.C.P.); Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX (R.C.H., C.M.B.); Center for Cardiovascular Disease Prevention, Houston Methodist DeBakey Heart and Vascular Center, TX (R.C.H., C.M.B.); Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.R.F.); Department of Neurology (D.S.K.) and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Medicine, University of Mississippi Medical Center, Jackson (B.G.W.); and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., E.S.)
| | - Clifford R Jack
- From the Department of Neurology (K.A.W., R.F.G.) and Department of Internal Medicine (E.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology and Biostatistics, George Washington University Milken Institute School of Public Health (M.C.P.); Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX (R.C.H., C.M.B.); Center for Cardiovascular Disease Prevention, Houston Methodist DeBakey Heart and Vascular Center, TX (R.C.H., C.M.B.); Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.R.F.); Department of Neurology (D.S.K.) and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Medicine, University of Mississippi Medical Center, Jackson (B.G.W.); and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., E.S.)
| | - Rebecca F Gottesman
- From the Department of Neurology (K.A.W., R.F.G.) and Department of Internal Medicine (E.S.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology and Biostatistics, George Washington University Milken Institute School of Public Health (M.C.P.); Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX (R.C.H., C.M.B.); Center for Cardiovascular Disease Prevention, Houston Methodist DeBakey Heart and Vascular Center, TX (R.C.H., C.M.B.); Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.R.F.); Department of Neurology (D.S.K.) and Department of Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Medicine, University of Mississippi Medical Center, Jackson (B.G.W.); and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., E.S.)
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Schneider ALC, Selvin E, Sharrett AR, Griswold M, Coresh J, Jack CR, Knopman D, Mosley T, Gottesman RF. Diabetes, Prediabetes, and Brain Volumes and Subclinical Cerebrovascular Disease on MRI: The Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS). Diabetes Care 2017; 40:1514-1521. [PMID: 28916531 PMCID: PMC5652590 DOI: 10.2337/dc17-1185] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 08/23/2017] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To examine the associations of prediabetes, diabetes, and diabetes severity (as assessed by HbA1c and diabetes duration) with brain volumes and vascular pathology on brain MRI and to assess whether the associations of diabetes with brain volumes are mediated by brain vascular pathology. RESEARCH DESIGN AND METHODS Cross-sectional study of 1,713 participants in the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS) (mean age 75 years, 60% female, 27% black, 30% prediabetes, and 35% diabetes) who underwent 3T brain MRI scans in 2011-2013. Participants were categorized by diabetes-HbA1c status as without diabetes (<5.7% [reference]), with prediabetes (5.7 to <6.5%), and with diabetes ([defined as prior diagnosis or HbA1c ≥6.5%] <7.0% vs. ≥7.0%), with further stratification by diabetes duration (<10 vs. ≥10 years). RESULTS In adjusted analyses, compared with participants without diabetes and HbA1c <5.7%, participants with prediabetes and those with diabetes and HbA1c <7.0% did not have significantly different brain volumes or vascular pathology (all P > 0.05), but those with diabetes and HbA1c ≥7.0% had smaller total brain volume (β -0.20 SDs, 95% CI -0.31, -0.09), smaller regional brain volumes (including frontal, temporal, occipital, and parietal lobes; deep gray matter; Alzheimer disease signature region; and hippocampus [all P < 0.05]), and increased burden of white matter hyperintensities (WMH) (P = 0.016). Among participants with diabetes, those with HbA1c ≥7.0% had smaller total and regional brain volumes and an increased burden of WMH (all P < 0.05) compared with those with HbA1c <7.0%. Similarly, participants with longer duration of diabetes (≥10 years) had smaller brain volumes and higher burden of lacunes (all P < 0.05) than those with a diabetes duration <10 years. We found no evidence for mediation by WMH in associations of diabetes with smaller brain volumes by structural equation models (all P > 0.05). CONCLUSIONS More-severe diabetes (defined by higher HbA1c and longer disease duration) but not prediabetes or less-severe diabetes was associated with smaller brain volumes and an increased burden of brain vascular pathology. No evidence was found that associations of diabetes with smaller brain volumes are mediated by brain vascular pathology, suggesting that other mechanisms may be responsible for these associations.
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Affiliation(s)
- Andrea L C Schneider
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD .,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | - A Richey Sharrett
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | - Michael Griswold
- Center of Biostatistics and Bioinformatics, University of Mississippi Medical Center, Jackson, MS
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | | | | | - Thomas Mosley
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Rebecca F Gottesman
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD.,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
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White Matter Structure in Older Adults Moderates the Benefit of Sleep Spindles on Motor Memory Consolidation. J Neurosci 2017; 37:11675-11687. [PMID: 29084867 DOI: 10.1523/jneurosci.3033-16.2017] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Revised: 10/16/2017] [Indexed: 11/21/2022] Open
Abstract
Sleep spindles promote the consolidation of motor skill memory in young adults. Older adults, however, exhibit impoverished sleep-dependent motor memory consolidation. The underlying pathophysiological mechanism(s) explaining why motor memory consolidation in older adults fails to benefit from sleep remains unclear. Here, we demonstrate that male and female older adults show impoverished overnight motor skill memory consolidation relative to young adults, with the extent of impairment being associated with the degree of reduced frontal fast sleep spindle density. The magnitude of the loss of frontal fast sleep spindles in older adults was predicted by the degree of reduced white matter integrity throughout multiple white matter tracts known to connect subcortical and cortical brain regions. We further demonstrate that the structural integrity of selective white matter fiber tracts, specifically within right posterior corona radiata, right tapetum, and bilateral corpus callosum, statistically moderates whether sleep spindles promoted overnight consolidation of motor skill memory. Therefore, white matter integrity within tracts known to connect cortical sensorimotor control regions dictates the functional influence of sleep spindles on motor skill memory consolidation in the elderly. The deterioration of white matter fiber tracts associated with human brain aging thus appears to be one pathophysiological mechanism influencing subcortical-cortical propagation of sleep spindles and their related memory benefits.SIGNIFICANCE STATEMENT Numerous studies have shown that sleep spindle expression is reduced and sleep-dependent motor memory is impaired in older adults. However, the mechanisms underlying these alterations have remained unknown. The present study reveals that age-related degeneration of white matter within select fiber tracts is associated with reduced sleep spindles in older adults. We further demonstrate that, within these same fiber tracts, the degree of degeneration determines whether sleep spindles can promote motor memory consolidation. Therefore, white matter integrity in the human brain, more than age per se, determines the magnitude of decline in sleep spindles in later life and, with it, the success (or lack thereof) of sleep-dependent motor memory consolidation in older adults.
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Windham BG, Lirette ST, Fornage M, Benjamin EJ, Parker KG, Turner ST, Jack CR, Griswold ME, Mosley TH. Associations of Brain Structure With Adiposity and Changes in Adiposity in a Middle-Aged and Older Biracial Population. J Gerontol A Biol Sci Med Sci 2017; 72:825-831. [PMID: 27994005 DOI: 10.1093/gerona/glw239] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Accepted: 11/08/2016] [Indexed: 11/14/2022] Open
Abstract
Background Studies of adiposity and brain pathology in African Americans (AA) are sparse despite higher rates of obesity, dementia, and dementia-associated brain pathology in AA. This study examined relations of adiposity to white matter hyperintensities (WMH) and total brain volume (TBV) in AA and non-Hispanic whites (NHW). Methods Waist circumference (WC) and body mass index (BMI) were measured in the Genetic Epidemiology Network of Arteriopathy study at Visits 1 (mean age 57 [±11]) and 2 (mean age 61 [±10], mean 5.2 years later). Brain MRIs were obtained shortly after Visit 2 in 1,702 participants (64% women, 48% AA). Multilevel linear regression using generalized estimating equation estimated associations of adiposity (cross-sectional) or adiposity changes with WMH (accounting for intracranial size) or TBV adjusting for demographics, cardiovascular risk factors, and incorporating adiposity-by-race interactions. Adiposity-by-age interactions were examined. Results Concurrent TBV was inversely associated with BMI (β = -2.76 [95% confidence interval (CI): -4.99, -0.53]) and WC (β = -2.19 [CI: -4.04, -0.34]). Concurrent WMH were negatively associated with BMI (β = -0.04 [CI: -0.06, -0.01]) and, among NHW, with WC (β = -0.04 [CI: -0.06, -0.02]) but not with changes in BMI or WC. BMI increases were associated with lower TBV (β = -16.20, [CI: -30.34, -2.06]) in AA but not in NHW (β = -2.76 [CI: -14.02, 8.51]), although race-by-adiposity interactions were not supported. WC increases were not associated with MRI outcomes. Conclusion Greater measures of obesity and increases in measures of obesity, which are common in mid-life, could be detrimental to brain health, particularly in AA.
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Affiliation(s)
- B Gwen Windham
- Department of Medicine-Geriatrics, University of Mississippi Medical Center, Jackson
| | | | - Myriam Fornage
- Institute of Molecular Medicine, Health Science Center at Houston, University of Texas
| | | | - Kirby G Parker
- Department of Medicine-Geriatrics, University of Mississippi Medical Center, Jackson.,Center of Biostatistics, Jackson, Mississippi
| | | | | | | | - Thomas H Mosley
- Department of Medicine-Geriatrics, University of Mississippi Medical Center, Jackson
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Dadar M, Pascoal TA, Manitsirikul S, Misquitta K, Fonov VS, Tartaglia MC, Breitner J, Rosa-Neto P, Carmichael OT, Decarli C, Collins DL. Validation of a Regression Technique for Segmentation of White Matter Hyperintensities in Alzheimer's Disease. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1758-1768. [PMID: 28422655 DOI: 10.1109/tmi.2017.2693978] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Segmentation and volumetric quantification of white matter hyperintensities (WMHs) is essential in assessment and monitoring of the vascular burden in aging and Alzheimer's disease (AD), especially when considering their effect on cognition. Manually segmenting WMHs in large cohorts is technically unfeasible due to time and accuracy concerns. Automated tools that can detect WMHs robustly and with high accuracy are needed. Here, we present and validate a fully automatic technique for segmentation and volumetric quantification of WMHs in aging and AD. The proposed technique combines intensity and location features frommultiplemagnetic resonance imaging contrasts and manually labeled training data with a linear classifier to perform fast and robust segmentations. It provides both a continuous subject specific WMH map reflecting different levels of tissue damage and binary segmentations. Themethodwas used to detectWMHs in 80 elderly/AD brains (ADC data set) as well as 40 healthy subjects at risk of AD (PREVENT-AD data set). Robustness across different scanners was validated using ten subjects from ADNI2/GO study. Voxel-wise and volumetric agreements were evaluated using Dice similarity index (SI) and intra-class correlation (ICC), yielding ICC=0.96 , SI = 0.62±0.16 for ADC data set and ICC=0.78 , SI=0.51±0.15 for PREVENT-AD data set. The proposed method was robust in the independent sample yielding SI=0.64±0.17 with ICC=0.93 for ADNI2/GO subjects. The proposed method provides fast, accurate, and robust segmentations on previously unseen data from different models of scanners, making it ideal to study WMHs in large scale multi-site studies.
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An energy minimization method for MS lesion segmentation from T1-w and FLAIR images. Magn Reson Imaging 2017; 39:1-6. [DOI: 10.1016/j.mri.2016.04.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 04/12/2016] [Indexed: 11/19/2022]
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Longitudinal segmentation of age-related white matter hyperintensities. Med Image Anal 2017; 38:50-64. [PMID: 28282640 DOI: 10.1016/j.media.2017.02.007] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 02/13/2017] [Accepted: 02/15/2017] [Indexed: 01/18/2023]
Abstract
Although white matter hyperintensities evolve in the course of ageing, few solutions exist to consider the lesion segmentation problem longitudinally. Based on an existing automatic lesion segmentation algorithm, a longitudinal extension is proposed. For evaluation purposes, a longitudinal lesion simulator is created allowing for the comparison between the longitudinal and the cross-sectional version in various situations of lesion load progression. Finally, applied to clinical data, the proposed framework demonstrates an increased robustness compared to available cross-sectional methods and findings are aligned with previously reported clinical patterns.
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Mielke MM, Milic NM, Weissgerber TL, White WM, Kantarci K, Mosley TH, Windham BG, Simpson BN, Turner ST, Garovic VD. Impaired Cognition and Brain Atrophy Decades After Hypertensive Pregnancy Disorders. CIRCULATION-CARDIOVASCULAR QUALITY AND OUTCOMES 2016; 9:S70-6. [PMID: 26908863 DOI: 10.1161/circoutcomes.115.002461] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Hypertensive pregnancy disorders have been associated with subjective cognitive complaints or brain white-matter lesions 5 to 10 years after the hypertensive pregnancy. The long-term effects of hypertensive pregnancies on brain structure and cognitive function remain unknown. METHODS AND RESULTS This study included 1279 women who participated in the Family Blood Pressure Project Genetic Epidemiology Network of Arteriopathy (GENOA) study. As part of the ancillary Genetics of Microangiopathic Brain Injury (GMBI) study, a neurocognitive battery was administered; 1075 also had a brain magnetic resonance imaging. A history of a hypertensive pregnancy disorder was obtained by a self-report using a validated questionnaire. Linear models fit with generalized estimating equations were used to assess the association between hypertensive pregnancy disorders and cognition, adjusting for age, race, education, body mass index, smoking, current hypertension, hypertension duration, and family history of hypertension. Regression models for the brain magnetic resonance imaging outcomes also were adjusted for total intracranial volume. Women with histories of hypertensive pregnancy disorders performed worse on all measures of processing speed (Digital Symbol Substitution Test [mean score, 41.2 versus 43.4; P=0.005], Trail Making Test Part A [mean seconds, 45.1 versus 42.2; P=0.035], and Stroop [mean score, 173.9 versus 181.0; P=0.002]) and had smaller brain volumes compared with women with histories of normotensive pregnancies (286 versus 297; P=0.023). CONCLUSIONS Hypertensive pregnancy disorders are associated with worse performance on tests of processing speed and smaller brain volumes decades later. Population-based studies are needed to provide critical insight as to the contribution of hypertensive pregnancies to risk of cognitive decline and dementia.
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Affiliation(s)
- Michelle M Mielke
- From the Departments of Health Sciences Research and Neurology (M.M.M.), Division of Nephrology and Hypertension (N.M.M., T.L.W., S.T.T., V.D.G.), Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology (W.M.W.), and Department of Radiology (K.K.), Mayo Clinic, Rochester, MN; Department of Biostatistics, Medical Faculty, University of Belgrade, Belgrade, Serbia (N.M.M.); and Department of Medicine, University of Mississippi Medical Center, Jackson (T.H.M., B.G.W., B.N.S.)
| | - Natasa M Milic
- From the Departments of Health Sciences Research and Neurology (M.M.M.), Division of Nephrology and Hypertension (N.M.M., T.L.W., S.T.T., V.D.G.), Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology (W.M.W.), and Department of Radiology (K.K.), Mayo Clinic, Rochester, MN; Department of Biostatistics, Medical Faculty, University of Belgrade, Belgrade, Serbia (N.M.M.); and Department of Medicine, University of Mississippi Medical Center, Jackson (T.H.M., B.G.W., B.N.S.)
| | - Tracey L Weissgerber
- From the Departments of Health Sciences Research and Neurology (M.M.M.), Division of Nephrology and Hypertension (N.M.M., T.L.W., S.T.T., V.D.G.), Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology (W.M.W.), and Department of Radiology (K.K.), Mayo Clinic, Rochester, MN; Department of Biostatistics, Medical Faculty, University of Belgrade, Belgrade, Serbia (N.M.M.); and Department of Medicine, University of Mississippi Medical Center, Jackson (T.H.M., B.G.W., B.N.S.)
| | - Wendy M White
- From the Departments of Health Sciences Research and Neurology (M.M.M.), Division of Nephrology and Hypertension (N.M.M., T.L.W., S.T.T., V.D.G.), Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology (W.M.W.), and Department of Radiology (K.K.), Mayo Clinic, Rochester, MN; Department of Biostatistics, Medical Faculty, University of Belgrade, Belgrade, Serbia (N.M.M.); and Department of Medicine, University of Mississippi Medical Center, Jackson (T.H.M., B.G.W., B.N.S.)
| | - Kejal Kantarci
- From the Departments of Health Sciences Research and Neurology (M.M.M.), Division of Nephrology and Hypertension (N.M.M., T.L.W., S.T.T., V.D.G.), Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology (W.M.W.), and Department of Radiology (K.K.), Mayo Clinic, Rochester, MN; Department of Biostatistics, Medical Faculty, University of Belgrade, Belgrade, Serbia (N.M.M.); and Department of Medicine, University of Mississippi Medical Center, Jackson (T.H.M., B.G.W., B.N.S.)
| | - Thomas H Mosley
- From the Departments of Health Sciences Research and Neurology (M.M.M.), Division of Nephrology and Hypertension (N.M.M., T.L.W., S.T.T., V.D.G.), Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology (W.M.W.), and Department of Radiology (K.K.), Mayo Clinic, Rochester, MN; Department of Biostatistics, Medical Faculty, University of Belgrade, Belgrade, Serbia (N.M.M.); and Department of Medicine, University of Mississippi Medical Center, Jackson (T.H.M., B.G.W., B.N.S.)
| | - B Gwen Windham
- From the Departments of Health Sciences Research and Neurology (M.M.M.), Division of Nephrology and Hypertension (N.M.M., T.L.W., S.T.T., V.D.G.), Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology (W.M.W.), and Department of Radiology (K.K.), Mayo Clinic, Rochester, MN; Department of Biostatistics, Medical Faculty, University of Belgrade, Belgrade, Serbia (N.M.M.); and Department of Medicine, University of Mississippi Medical Center, Jackson (T.H.M., B.G.W., B.N.S.)
| | - Brittany N Simpson
- From the Departments of Health Sciences Research and Neurology (M.M.M.), Division of Nephrology and Hypertension (N.M.M., T.L.W., S.T.T., V.D.G.), Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology (W.M.W.), and Department of Radiology (K.K.), Mayo Clinic, Rochester, MN; Department of Biostatistics, Medical Faculty, University of Belgrade, Belgrade, Serbia (N.M.M.); and Department of Medicine, University of Mississippi Medical Center, Jackson (T.H.M., B.G.W., B.N.S.)
| | - Stephen T Turner
- From the Departments of Health Sciences Research and Neurology (M.M.M.), Division of Nephrology and Hypertension (N.M.M., T.L.W., S.T.T., V.D.G.), Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology (W.M.W.), and Department of Radiology (K.K.), Mayo Clinic, Rochester, MN; Department of Biostatistics, Medical Faculty, University of Belgrade, Belgrade, Serbia (N.M.M.); and Department of Medicine, University of Mississippi Medical Center, Jackson (T.H.M., B.G.W., B.N.S.)
| | - Vesna D Garovic
- From the Departments of Health Sciences Research and Neurology (M.M.M.), Division of Nephrology and Hypertension (N.M.M., T.L.W., S.T.T., V.D.G.), Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology (W.M.W.), and Department of Radiology (K.K.), Mayo Clinic, Rochester, MN; Department of Biostatistics, Medical Faculty, University of Belgrade, Belgrade, Serbia (N.M.M.); and Department of Medicine, University of Mississippi Medical Center, Jackson (T.H.M., B.G.W., B.N.S.).
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Srinath R, Gottesman RF, Hill Golden S, Carson KA, Dobs A. Association Between Endogenous Testosterone and Cerebrovascular Disease in the ARIC Study (Atherosclerosis Risk in Communities). Stroke 2016; 47:2682-2688. [PMID: 27729576 DOI: 10.1161/strokeaha.116.014088] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2016] [Accepted: 08/26/2016] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE Epidemiological studies in men suggest a relationship between endogenous testosterone and ischemic vascular events. We hypothesized that low testosterone is independently associated with ischemic stroke and ischemic brain changes. METHODS In 1558 male participants (mean [SD] age, 63.1 [5.6] years; body mass index, 28.2 [4.3] kg/m2) from visit 4 (1996-1998) of the ARIC study (Atherosclerosis Risk in Communities) without cardiovascular disease, stroke, and previous testosterone therapy, we measured plasma total testosterone by liquid chromatography mass spectrometry using morning samples and divided levels into tertiles (median [25th-75th percentile], 377.6 [288.4-480.1] ng/dL). General linear models, for cross-sectional analyses, and proportional hazards regression, for time-to-event analysis, examined the association of testosterone with participant characteristics and incident stroke through 2011. Linear and logistic regression models examined the association of testosterone with percentage white matter hyperintensities and prevalent infarcts in participants (n=257) who underwent brain magnetic resonance imaging at visit 5 (2011-2013). Analyses were adjusted for age, race, and ARIC center, body mass index, waist circumference, smoking status, diabetes mellitus, hypertension, low-density lipoprotein, and high-density lipoprotein. RESULTS Lower testosterone was significantly associated with higher body mass index, greater waist circumference, diabetes mellitus, hypertension, lower high-density lipoprotein, and never smoking. After adjustment, no association of testosterone with incident stroke was found (hazard ratios [95% confidence intervals] for tertile 1 or 3 versus 2, 1.47 [0.83-2.61], 1.15 [0.62-2.14]; median follow-up, 14.1 years), nor with percentage white matter hyperintensities, cortical infarcts, or subcortical infarcts. CONCLUSIONS After controlling for atherosclerotic risk factors, there was no association between endogenous testosterone and incident clinical stroke or ischemic brain changes in community-dwelling men.
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Affiliation(s)
- Reshmi Srinath
- From the Division of Endocrinology, Metabolism and Bone Diseases, Icahn School of Medicine at Mount Sinai, New York, New York (R.S.); Cerebrovascular Division, Department of Neurology (R.F.G.) and Division of Endocrinology, Diabetes and Metabolism (S.H.G., A.D.), and Division of General Internal Medicine (K.A.C.), Johns Hopkins University School of Medicine, Baltimore, MD; and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., S.H.G., K.A.C.)
| | - Rebecca F Gottesman
- From the Division of Endocrinology, Metabolism and Bone Diseases, Icahn School of Medicine at Mount Sinai, New York, New York (R.S.); Cerebrovascular Division, Department of Neurology (R.F.G.) and Division of Endocrinology, Diabetes and Metabolism (S.H.G., A.D.), and Division of General Internal Medicine (K.A.C.), Johns Hopkins University School of Medicine, Baltimore, MD; and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., S.H.G., K.A.C.)
| | - Sherita Hill Golden
- From the Division of Endocrinology, Metabolism and Bone Diseases, Icahn School of Medicine at Mount Sinai, New York, New York (R.S.); Cerebrovascular Division, Department of Neurology (R.F.G.) and Division of Endocrinology, Diabetes and Metabolism (S.H.G., A.D.), and Division of General Internal Medicine (K.A.C.), Johns Hopkins University School of Medicine, Baltimore, MD; and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., S.H.G., K.A.C.)
| | - Kathryn A Carson
- From the Division of Endocrinology, Metabolism and Bone Diseases, Icahn School of Medicine at Mount Sinai, New York, New York (R.S.); Cerebrovascular Division, Department of Neurology (R.F.G.) and Division of Endocrinology, Diabetes and Metabolism (S.H.G., A.D.), and Division of General Internal Medicine (K.A.C.), Johns Hopkins University School of Medicine, Baltimore, MD; and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., S.H.G., K.A.C.)
| | - Adrian Dobs
- From the Division of Endocrinology, Metabolism and Bone Diseases, Icahn School of Medicine at Mount Sinai, New York, New York (R.S.); Cerebrovascular Division, Department of Neurology (R.F.G.) and Division of Endocrinology, Diabetes and Metabolism (S.H.G., A.D.), and Division of General Internal Medicine (K.A.C.), Johns Hopkins University School of Medicine, Baltimore, MD; and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (R.F.G., S.H.G., K.A.C.).
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Chen Y, Denny KG, Harvey D, Farias ST, Mungas D, DeCarli C, Beckett L. Progression from normal cognition to mild cognitive impairment in a diverse clinic-based and community-based elderly cohort. Alzheimers Dement 2016; 13:399-405. [PMID: 27590706 DOI: 10.1016/j.jalz.2016.07.151] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2016] [Revised: 07/20/2016] [Accepted: 07/28/2016] [Indexed: 12/23/2022]
Abstract
INTRODUCTION Investigation of the conversion rates from normal cognition (NC) to mild cognitive impairment (MCI) is important, as effective early intervention could potentially prevent or substantially delay the onset of dementia. However, reported conversion rates differ across studies and recruitment source. Our study examined predictors of conversion from NC to MCI in a racially and ethnically diverse sample drawn both from community and clinic recruitment sources. METHODS Rates and predictors of conversion were assessed in an ongoing prospective longitudinal study at University of California, Davis, Alzheimer's Disease Center from 2000 to 2015. Participants (n = 254) were recruited through a clinic (5%) and community sample (95%). They were clinically confirmed as cognitively normal at baseline and followed up to seven years. Recruitment source, demographic factors (age, gender, race/ethnicity, year of education, APOE ε4 positive), cognitive measures (SENAS test scores), functional assessments (CDR sum of boxes), and neuroimaging measures (total brain volume, total hippocampal volume, white hyperintensity volume) were assessed as predictors of conversion from cognitively normal to mild cognitive impairment using proportional hazards models. RESULTS Of 254 participants, 62 (11 clinic, 51 community) progressed to MCI. The clinic-based sample showed an annual conversion rate of 30% (95% CI 17%-54%) per person-year, whereas the community-based sample showed a conversion rate of 5% (95% CI 3%-6%) per person-year. Risk factors for conversion include clinic-based recruitment, being older, lower executive function and worse functional assessment at baseline, and smaller total brain volume. DISCUSSION Older adults who sought out a clinical evaluation, even when they are found to have normal cognition, have increased risk of subsequent development of MCI. Results are consistent with other studies showing subjective cognitive complaints are a risk for future cognitive impairment, but extend such findings to show that those who seek evaluation for their complaints are at particularly high risk. Moreover, these individuals have subtle, but significant differences in functional and cognitive abilities that, in the presence of concerns and evidence of atrophy on by brain imaging, warrant continued clinical follow-up. These risk factors could also be used as stratification variables for dementia prevention clinical trial design.
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Affiliation(s)
- Yingjia Chen
- Department of Public Health Sciences, University of California, Davis, Davis, CA, USA
| | - Katherine G Denny
- Department of Neurology, University of California, Davis, Davis, CA, USA
| | - Danielle Harvey
- Department of Public Health Sciences, University of California, Davis, Davis, CA, USA
| | | | - Dan Mungas
- Department of Neurology, University of California, Davis, Davis, CA, USA
| | - Charles DeCarli
- Department of Neurology, University of California, Davis, Davis, CA, USA
| | - Laurel Beckett
- Department of Public Health Sciences, University of California, Davis, Davis, CA, USA.
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Schmidt MF, Freeman KB, Windham BG, Griswold ME, Kullo IJ, Turner ST, Mosley TH. Associations Between Serum Inflammatory Markers and Hippocampal Volume in a Community Sample. J Am Geriatr Soc 2016; 64:1823-9. [PMID: 27549073 DOI: 10.1111/jgs.14283] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVES To quantify associations between inflammatory biomarkers and hippocampal volume (HV) and to examine effect modification according to sex, race, and age. DESIGN Cross-sectional analyses using generalized estimating equations to account for familial clustering; standardized β-coefficients adjusted for age, sex, race, and education. SETTING Community cohorts in Jackson, Mississippi and Rochester, Minnesota. PARTICIPANTS The Genetic Epidemiology Network of Arteriopathy study. MEASUREMENTS C-reactive protein (CRP), interleukin-6 (IL-6), and soluble tumor necrosis factor receptors 1 (sTNFR-1) and 2 (sTNFR-2) from peripheral blood were measured in a sample of 773 non-Hispanic whites (61% women, aged 60.2 ± 9.8) and 514 African Americans (70% women, aged 63.9 ± 8.1) who also underwent brain magnetic resonance imaging. Biomarkers were standardized and compared according to sex, race and age with HV. RESULTS In the full sample, higher sTNFR-1 and sTNFR-2 were associated with smaller HV. Each standard deviation (SD) increase in sTNFR-1 was associated with 59.1 mm(3) (95% confidence interval (CI) = -101.4 to -16.7 mm(3) ) smaller HV and each SD increase in sTNFR-2 associated with 48.8 mm(3) (95% CI = -92.2 to -5.3 mm(3) ) smaller HV. Relationships were stronger for sTNFR-2 in men (HV = -116.6 mm(3) for each SD increase, 95% CI = -201.0 to -32.1) than women (HV = -26.0 per SD increase, 95% CI = -72.4-20.5) and sTNFR-1 in non-Hispanic whites (HV = -84.7 mm(3) per SD increase, 95% CI = -142.2 to -27.1) than African Americans (HV = -14.1 mm(3) per SD increase, 95% CI = -78.3-50.1). Associations between IL-6 or CRP and HV were not supported. CONCLUSION Higher levels of sTNFRs were associated cross-sectionally with smaller hippocampi. Longitudinal data are needed to determine whether these biomarkers may help to identify risk of late-life cognitive impairment.
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Affiliation(s)
- Mike F Schmidt
- Program in Neuroscience, University of Mississippi Medical Center, Jackson, Mississippi.,Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi
| | - Kevin B Freeman
- Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, Mississippi
| | - Beverly G Windham
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi
| | - Michael E Griswold
- Center of Biostatistics and Bioinformatics, University of Mississippi Medical Center, Jackson, Mississippi
| | - Iftikhar J Kullo
- Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Stephen T Turner
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Thomas H Mosley
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi.
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Morel B, Virzi A, Geraud T, Adamsbaum C, Bloch I. A challenging issue: Detection of white matter hyperintensities in neonatal brain MRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:93-96. [PMID: 28268289 DOI: 10.1109/embc.2016.7590648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The progress of magnetic resonance imaging (MRI) allows for a precise exploration of the brain of premature infants at term equivalent age. The so-called DEHSI (diffuse excessive high signal intensity) of the white matter of premature brains remains a challenging issue in terms of definition, and thus of interpretation. We propose a semi-automatic detection and quantification method of white matter hyperintensities in MRI relying on morphological operators and max-tree representations, which constitutes a powerful tool to help radiologists to improve their interpretation. Results show better reproducibility and robustness than interactive segmentation.
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Lutsey PL, Norby FL, Gottesman RF, Mosley T, MacLehose RF, Punjabi NM, Shahar E, Jack CR, Alonso A. Sleep Apnea, Sleep Duration and Brain MRI Markers of Cerebral Vascular Disease and Alzheimer's Disease: The Atherosclerosis Risk in Communities Study (ARIC). PLoS One 2016; 11:e0158758. [PMID: 27415826 PMCID: PMC4944966 DOI: 10.1371/journal.pone.0158758] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 06/21/2016] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND A growing body of literature has suggested that obstructive sleep apnea (OSA) and habitual short sleep duration are linked to poor cognitive function. Neuroimaging studies may provide insight into this relation. OBJECTIVE We tested the hypotheses that OSA and habitual short sleep duration, measured at ages 54-73 years, would be associated with adverse brain morphology at ages 67-89 years. METHODS Included in this analysis are 312 ARIC study participants who underwent in-home overnight polysomnography in 1996-1998 and brain MRI scans about 15 years later (2012-2013). Sleep apnea was quantified by the apnea-hypopnea index and categorized as moderate/severe (≥15.0 events/hour), mild (5.0-14.9 events/hour), or normal (<5.0 events/hour). Habitual sleep duration was categorized, in hours, as <7, 7 to <8, ≥8. MRI outcomes included number of infarcts (total, subcortical, and cortical) and white matter hyperintensity (WMH) and Alzheimer's disease signature region volumes. Multivariable adjusted logistic and linear regression models were used. All models incorporated inverse probability weighting, to adjust for potential selection bias. RESULTS At the time of the sleep study participants were 61.7 (SD: 5.0) years old and 54% female; 19% had moderate/severe sleep apnea. MRI imaging took place 14.8 (SD: 1.0) years later, when participants were 76.5 (SD: 5.2) years old. In multivariable models which accounted for body mass index, neither OSA nor abnormal sleep duration were statistically significantly associated with odds of cerebral infarcts, WMH brain volumes or regional brain volumes. CONCLUSIONS In this community-based sample, mid-life OSA and habitually short sleep duration were not associated with later-life cerebral markers of vascular dementia and Alzheimer's disease. However, selection bias may have influenced our results and the modest sample size led to relatively imprecise associations.
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Affiliation(s)
- Pamela L. Lutsey
- University of Minnesota, Minneapolis, MN, United States of America
| | - Faye L. Norby
- University of Minnesota, Minneapolis, MN, United States of America
| | | | - Thomas Mosley
- University of Mississippi Medical Center, Jackson, MS, United States of America
| | | | | | - Eyal Shahar
- University of Arizona, Tucson, AZ, United States of America
| | | | - Alvaro Alonso
- Emory University, Atlanta, GA, United States of America
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Gottesman RF, Schneider ALC, Zhou Y, Chen X, Green E, Gupta N, Knopman DS, Mintz A, Rahmim A, Sharrett AR, Wagenknecht LE, Wong DF, Mosley TH. The ARIC-PET amyloid imaging study: Brain amyloid differences by age, race, sex, and APOE. Neurology 2016; 87:473-80. [PMID: 27371485 DOI: 10.1212/wnl.0000000000002914] [Citation(s) in RCA: 122] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Accepted: 03/11/2016] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To evaluate differences in amyloid deposition in a community-based cohort without dementia by age, sex, race, education, and APOE ε4 allele status. METHODS Recruited from the longitudinal Atherosclerosis Risk in Communities study, 329 participants without dementia, ages 67-88 years, were imaged using florbetapir PET at 3 US community sites (Washington County, Maryland; Forsyth County, North Carolina; and Jackson, Mississippi). Standardized uptake value ratios (SUVRs) were calculated; global cortical SUVR >1.2 was evaluated as the primary outcome. Age, race, sex, education level, and number of APOE ε4 alleles were evaluated in multivariable models including vascular risk factors, brain white matter hyperintensity and total intracranial volume, and cognitive status. RESULTS A total of 141 of the participants (43%) were black. In multivariable models, odds of elevated SUVR was increased in participants with increasing age (odds ratio [OR] 1.63, 95% confidence interval [CI] 1.01-2.65 per 10 years of age) and black race (OR 2.08, 95% CI 1.23-3.51) but did not differ by educational level. Each ε4 allele was associated with increased odds of elevated SUVR (OR 2.65, 95% CI 1.61-4.39). CONCLUSIONS In this community-based cohort without dementia, florbetapir uptake is associated with older age and APOE genotype. Black race was associated with higher SUVR, after adjusting for demographics, vascular risk factors, cognitive status, white matter hyperintensity volume, and APOE genotype, with effect sizes nearing those seen for APOE ε4. Replication of these findings is needed in other cohorts, and reasons for and consequences of these observed differences by race warrant further study.
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Affiliation(s)
- Rebecca F Gottesman
- From the Department of Neurology (R.F.G., A.L.C.S.), Department of Radiology, Section of High-Resolution Brain PET Imaging (Y.Z., X.C., A.R., D.F.W.), and Departments of Psychiatry (D.F.W.) and Neuroscience (D.F.W.), Johns Hopkins University School of Medicine; Departments of Epidemiology (R.F.G., A.L.C.S., A.R.S.) and Environmental Health Sciences (D.F.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Departments of Radiology (E.G.) and Medicine (T.H.M.), University of Mississippi Medical Center, Jackson; Hagerstown Imaging (N.G.), MD; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Radiology (A.M.) and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC.
| | - Andrea L C Schneider
- From the Department of Neurology (R.F.G., A.L.C.S.), Department of Radiology, Section of High-Resolution Brain PET Imaging (Y.Z., X.C., A.R., D.F.W.), and Departments of Psychiatry (D.F.W.) and Neuroscience (D.F.W.), Johns Hopkins University School of Medicine; Departments of Epidemiology (R.F.G., A.L.C.S., A.R.S.) and Environmental Health Sciences (D.F.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Departments of Radiology (E.G.) and Medicine (T.H.M.), University of Mississippi Medical Center, Jackson; Hagerstown Imaging (N.G.), MD; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Radiology (A.M.) and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Yun Zhou
- From the Department of Neurology (R.F.G., A.L.C.S.), Department of Radiology, Section of High-Resolution Brain PET Imaging (Y.Z., X.C., A.R., D.F.W.), and Departments of Psychiatry (D.F.W.) and Neuroscience (D.F.W.), Johns Hopkins University School of Medicine; Departments of Epidemiology (R.F.G., A.L.C.S., A.R.S.) and Environmental Health Sciences (D.F.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Departments of Radiology (E.G.) and Medicine (T.H.M.), University of Mississippi Medical Center, Jackson; Hagerstown Imaging (N.G.), MD; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Radiology (A.M.) and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Xueqi Chen
- From the Department of Neurology (R.F.G., A.L.C.S.), Department of Radiology, Section of High-Resolution Brain PET Imaging (Y.Z., X.C., A.R., D.F.W.), and Departments of Psychiatry (D.F.W.) and Neuroscience (D.F.W.), Johns Hopkins University School of Medicine; Departments of Epidemiology (R.F.G., A.L.C.S., A.R.S.) and Environmental Health Sciences (D.F.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Departments of Radiology (E.G.) and Medicine (T.H.M.), University of Mississippi Medical Center, Jackson; Hagerstown Imaging (N.G.), MD; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Radiology (A.M.) and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Edward Green
- From the Department of Neurology (R.F.G., A.L.C.S.), Department of Radiology, Section of High-Resolution Brain PET Imaging (Y.Z., X.C., A.R., D.F.W.), and Departments of Psychiatry (D.F.W.) and Neuroscience (D.F.W.), Johns Hopkins University School of Medicine; Departments of Epidemiology (R.F.G., A.L.C.S., A.R.S.) and Environmental Health Sciences (D.F.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Departments of Radiology (E.G.) and Medicine (T.H.M.), University of Mississippi Medical Center, Jackson; Hagerstown Imaging (N.G.), MD; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Radiology (A.M.) and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Naresh Gupta
- From the Department of Neurology (R.F.G., A.L.C.S.), Department of Radiology, Section of High-Resolution Brain PET Imaging (Y.Z., X.C., A.R., D.F.W.), and Departments of Psychiatry (D.F.W.) and Neuroscience (D.F.W.), Johns Hopkins University School of Medicine; Departments of Epidemiology (R.F.G., A.L.C.S., A.R.S.) and Environmental Health Sciences (D.F.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Departments of Radiology (E.G.) and Medicine (T.H.M.), University of Mississippi Medical Center, Jackson; Hagerstown Imaging (N.G.), MD; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Radiology (A.M.) and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC
| | - David S Knopman
- From the Department of Neurology (R.F.G., A.L.C.S.), Department of Radiology, Section of High-Resolution Brain PET Imaging (Y.Z., X.C., A.R., D.F.W.), and Departments of Psychiatry (D.F.W.) and Neuroscience (D.F.W.), Johns Hopkins University School of Medicine; Departments of Epidemiology (R.F.G., A.L.C.S., A.R.S.) and Environmental Health Sciences (D.F.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Departments of Radiology (E.G.) and Medicine (T.H.M.), University of Mississippi Medical Center, Jackson; Hagerstown Imaging (N.G.), MD; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Radiology (A.M.) and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Akiva Mintz
- From the Department of Neurology (R.F.G., A.L.C.S.), Department of Radiology, Section of High-Resolution Brain PET Imaging (Y.Z., X.C., A.R., D.F.W.), and Departments of Psychiatry (D.F.W.) and Neuroscience (D.F.W.), Johns Hopkins University School of Medicine; Departments of Epidemiology (R.F.G., A.L.C.S., A.R.S.) and Environmental Health Sciences (D.F.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Departments of Radiology (E.G.) and Medicine (T.H.M.), University of Mississippi Medical Center, Jackson; Hagerstown Imaging (N.G.), MD; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Radiology (A.M.) and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Arman Rahmim
- From the Department of Neurology (R.F.G., A.L.C.S.), Department of Radiology, Section of High-Resolution Brain PET Imaging (Y.Z., X.C., A.R., D.F.W.), and Departments of Psychiatry (D.F.W.) and Neuroscience (D.F.W.), Johns Hopkins University School of Medicine; Departments of Epidemiology (R.F.G., A.L.C.S., A.R.S.) and Environmental Health Sciences (D.F.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Departments of Radiology (E.G.) and Medicine (T.H.M.), University of Mississippi Medical Center, Jackson; Hagerstown Imaging (N.G.), MD; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Radiology (A.M.) and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC
| | - A Richey Sharrett
- From the Department of Neurology (R.F.G., A.L.C.S.), Department of Radiology, Section of High-Resolution Brain PET Imaging (Y.Z., X.C., A.R., D.F.W.), and Departments of Psychiatry (D.F.W.) and Neuroscience (D.F.W.), Johns Hopkins University School of Medicine; Departments of Epidemiology (R.F.G., A.L.C.S., A.R.S.) and Environmental Health Sciences (D.F.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Departments of Radiology (E.G.) and Medicine (T.H.M.), University of Mississippi Medical Center, Jackson; Hagerstown Imaging (N.G.), MD; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Radiology (A.M.) and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Lynne E Wagenknecht
- From the Department of Neurology (R.F.G., A.L.C.S.), Department of Radiology, Section of High-Resolution Brain PET Imaging (Y.Z., X.C., A.R., D.F.W.), and Departments of Psychiatry (D.F.W.) and Neuroscience (D.F.W.), Johns Hopkins University School of Medicine; Departments of Epidemiology (R.F.G., A.L.C.S., A.R.S.) and Environmental Health Sciences (D.F.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Departments of Radiology (E.G.) and Medicine (T.H.M.), University of Mississippi Medical Center, Jackson; Hagerstown Imaging (N.G.), MD; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Radiology (A.M.) and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Dean F Wong
- From the Department of Neurology (R.F.G., A.L.C.S.), Department of Radiology, Section of High-Resolution Brain PET Imaging (Y.Z., X.C., A.R., D.F.W.), and Departments of Psychiatry (D.F.W.) and Neuroscience (D.F.W.), Johns Hopkins University School of Medicine; Departments of Epidemiology (R.F.G., A.L.C.S., A.R.S.) and Environmental Health Sciences (D.F.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Departments of Radiology (E.G.) and Medicine (T.H.M.), University of Mississippi Medical Center, Jackson; Hagerstown Imaging (N.G.), MD; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Radiology (A.M.) and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Thomas H Mosley
- From the Department of Neurology (R.F.G., A.L.C.S.), Department of Radiology, Section of High-Resolution Brain PET Imaging (Y.Z., X.C., A.R., D.F.W.), and Departments of Psychiatry (D.F.W.) and Neuroscience (D.F.W.), Johns Hopkins University School of Medicine; Departments of Epidemiology (R.F.G., A.L.C.S., A.R.S.) and Environmental Health Sciences (D.F.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Departments of Radiology (E.G.) and Medicine (T.H.M.), University of Mississippi Medical Center, Jackson; Hagerstown Imaging (N.G.), MD; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Radiology (A.M.) and Division of Public Health Sciences (L.E.W.), Wake Forest School of Medicine, Winston-Salem, NC
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Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review. Neuroinformatics 2016; 13:261-76. [PMID: 25649877 PMCID: PMC4468799 DOI: 10.1007/s12021-015-9260-y] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
White matter hyperintensities (WMH) are commonly seen in the brain of healthy elderly subjects and patients with several neurological and vascular disorders. A truly reliable and fully automated method for quantitative assessment of WMH on magnetic resonance imaging (MRI) has not yet been identified. In this paper, we review and compare the large number of automated approaches proposed for segmentation of WMH in the elderly and in patients with vascular risk factors. We conclude that, in order to avoid artifacts and exclude the several sources of bias that may influence the analysis, an optimal method should comprise a careful preprocessing of the images, be based on multimodal, complementary data, take into account spatial information about the lesions and correct for false positives. All these features should not exclude computational leanness and adaptability to available data.
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Dearborn JL, Schneider ALC, Sharrett AR, Mosley TH, Bezerra DC, Knopman DS, Selvin E, Jack CR, Coker LH, Alonso A, Wagenknecht LE, Windham BG, Gottesman RF. Obesity, Insulin Resistance, and Incident Small Vessel Disease on Magnetic Resonance Imaging: Atherosclerosis Risk in Communities Study. Stroke 2015; 46:3131-6. [PMID: 26451022 DOI: 10.1161/strokeaha.115.010060] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2015] [Accepted: 08/26/2015] [Indexed: 12/29/2022]
Abstract
BACKGROUND AND PURPOSE The term metabolic syndrome describes the clustering of risk factors found in many individuals with obesity. Because of their pathophysiology, we hypothesized that 2 features of metabolic syndrome, central obesity and insulin resistance (IR), would be associated with cerebrovascular changes on magnetic resonance imaging, and specifically with incident lacunar disease and not white matter hyperintensity (WMH) progression. METHODS Risk factors were defined at study baseline in 934 participants in the Atherosclerosis Risk in Communities (ARIC) study, who completed 2 brain magnetic resonance imagings≈10 years apart. WMH progression and incident lacunes between the 2 magnetic resonance imagings were determined. An IR score for each participant was created using principal component analysis of 11 risk factors, including (among others): insulin, homeostatic model assessment-IR, body mass index, and waist circumference. Metabolic syndrome (presence/absence), using standard clinical definitions, and IR score at the first magnetic resonance imaging, were independent variables, evaluated in multivariate logistic regression to determine odds of WMH progression (Q5 versus Q1-Q4) and incident lacunes. RESULTS Metabolic syndrome (adjusted odds ratio, 1.98; 95% confidence interval, 1.28-3.05) and IR score (adjusted odds ratio per 1-SD increase, 1.33; 95% confidence interval, 1.05-1.68) were associated with incident lacunes but not with WMH progression. Insulin, homeostatic model assessment-IR, and body mass index were not associated with incident lacunes or WMH progression in separate models. CONCLUSIONS The IR score and central obesity are associated with incident lacunar disease but not WMH progression in individuals. Central obesity and IR may be important risk factors to target to prevent lacunar disease.
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Affiliation(s)
- Jennifer L Dearborn
- From the Department of Neurology, Yale University School of Medicine, New Haven, CT (J.L.D.); Department of Epidemiology, Bloomberg School of Public Health (A.L.C.S., A.R.S., E.S.), Department of Neurology, School of Medicine (A.L.C.S., R.F.G.), and Welch Center for Prevention, Epidemiology and Clinical Research (E.S., R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Geriatrics, Department of Medicine, The University of Mississippi School of Medicine, Jackson (T.H.M., B.G.W.); Department of Neurology, Pro Cardiaco Hospital, Rio de Janeiro, Brazil (D.C.B.); Departments of Neurology (D.S.K.) and Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Division of Public Health Sciences, The Wake Forest University School of Medicine, Winston-Salem, NC (L.H.C., L.E.W.); and Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.A.)
| | - Andrea L C Schneider
- From the Department of Neurology, Yale University School of Medicine, New Haven, CT (J.L.D.); Department of Epidemiology, Bloomberg School of Public Health (A.L.C.S., A.R.S., E.S.), Department of Neurology, School of Medicine (A.L.C.S., R.F.G.), and Welch Center for Prevention, Epidemiology and Clinical Research (E.S., R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Geriatrics, Department of Medicine, The University of Mississippi School of Medicine, Jackson (T.H.M., B.G.W.); Department of Neurology, Pro Cardiaco Hospital, Rio de Janeiro, Brazil (D.C.B.); Departments of Neurology (D.S.K.) and Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Division of Public Health Sciences, The Wake Forest University School of Medicine, Winston-Salem, NC (L.H.C., L.E.W.); and Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.A.)
| | - A Richey Sharrett
- From the Department of Neurology, Yale University School of Medicine, New Haven, CT (J.L.D.); Department of Epidemiology, Bloomberg School of Public Health (A.L.C.S., A.R.S., E.S.), Department of Neurology, School of Medicine (A.L.C.S., R.F.G.), and Welch Center for Prevention, Epidemiology and Clinical Research (E.S., R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Geriatrics, Department of Medicine, The University of Mississippi School of Medicine, Jackson (T.H.M., B.G.W.); Department of Neurology, Pro Cardiaco Hospital, Rio de Janeiro, Brazil (D.C.B.); Departments of Neurology (D.S.K.) and Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Division of Public Health Sciences, The Wake Forest University School of Medicine, Winston-Salem, NC (L.H.C., L.E.W.); and Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.A.)
| | - Thomas H Mosley
- From the Department of Neurology, Yale University School of Medicine, New Haven, CT (J.L.D.); Department of Epidemiology, Bloomberg School of Public Health (A.L.C.S., A.R.S., E.S.), Department of Neurology, School of Medicine (A.L.C.S., R.F.G.), and Welch Center for Prevention, Epidemiology and Clinical Research (E.S., R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Geriatrics, Department of Medicine, The University of Mississippi School of Medicine, Jackson (T.H.M., B.G.W.); Department of Neurology, Pro Cardiaco Hospital, Rio de Janeiro, Brazil (D.C.B.); Departments of Neurology (D.S.K.) and Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Division of Public Health Sciences, The Wake Forest University School of Medicine, Winston-Salem, NC (L.H.C., L.E.W.); and Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.A.)
| | - Daniel C Bezerra
- From the Department of Neurology, Yale University School of Medicine, New Haven, CT (J.L.D.); Department of Epidemiology, Bloomberg School of Public Health (A.L.C.S., A.R.S., E.S.), Department of Neurology, School of Medicine (A.L.C.S., R.F.G.), and Welch Center for Prevention, Epidemiology and Clinical Research (E.S., R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Geriatrics, Department of Medicine, The University of Mississippi School of Medicine, Jackson (T.H.M., B.G.W.); Department of Neurology, Pro Cardiaco Hospital, Rio de Janeiro, Brazil (D.C.B.); Departments of Neurology (D.S.K.) and Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Division of Public Health Sciences, The Wake Forest University School of Medicine, Winston-Salem, NC (L.H.C., L.E.W.); and Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.A.)
| | - David S Knopman
- From the Department of Neurology, Yale University School of Medicine, New Haven, CT (J.L.D.); Department of Epidemiology, Bloomberg School of Public Health (A.L.C.S., A.R.S., E.S.), Department of Neurology, School of Medicine (A.L.C.S., R.F.G.), and Welch Center for Prevention, Epidemiology and Clinical Research (E.S., R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Geriatrics, Department of Medicine, The University of Mississippi School of Medicine, Jackson (T.H.M., B.G.W.); Department of Neurology, Pro Cardiaco Hospital, Rio de Janeiro, Brazil (D.C.B.); Departments of Neurology (D.S.K.) and Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Division of Public Health Sciences, The Wake Forest University School of Medicine, Winston-Salem, NC (L.H.C., L.E.W.); and Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.A.)
| | - Elizabeth Selvin
- From the Department of Neurology, Yale University School of Medicine, New Haven, CT (J.L.D.); Department of Epidemiology, Bloomberg School of Public Health (A.L.C.S., A.R.S., E.S.), Department of Neurology, School of Medicine (A.L.C.S., R.F.G.), and Welch Center for Prevention, Epidemiology and Clinical Research (E.S., R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Geriatrics, Department of Medicine, The University of Mississippi School of Medicine, Jackson (T.H.M., B.G.W.); Department of Neurology, Pro Cardiaco Hospital, Rio de Janeiro, Brazil (D.C.B.); Departments of Neurology (D.S.K.) and Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Division of Public Health Sciences, The Wake Forest University School of Medicine, Winston-Salem, NC (L.H.C., L.E.W.); and Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.A.)
| | - Clifford R Jack
- From the Department of Neurology, Yale University School of Medicine, New Haven, CT (J.L.D.); Department of Epidemiology, Bloomberg School of Public Health (A.L.C.S., A.R.S., E.S.), Department of Neurology, School of Medicine (A.L.C.S., R.F.G.), and Welch Center for Prevention, Epidemiology and Clinical Research (E.S., R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Geriatrics, Department of Medicine, The University of Mississippi School of Medicine, Jackson (T.H.M., B.G.W.); Department of Neurology, Pro Cardiaco Hospital, Rio de Janeiro, Brazil (D.C.B.); Departments of Neurology (D.S.K.) and Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Division of Public Health Sciences, The Wake Forest University School of Medicine, Winston-Salem, NC (L.H.C., L.E.W.); and Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.A.)
| | - Laura H Coker
- From the Department of Neurology, Yale University School of Medicine, New Haven, CT (J.L.D.); Department of Epidemiology, Bloomberg School of Public Health (A.L.C.S., A.R.S., E.S.), Department of Neurology, School of Medicine (A.L.C.S., R.F.G.), and Welch Center for Prevention, Epidemiology and Clinical Research (E.S., R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Geriatrics, Department of Medicine, The University of Mississippi School of Medicine, Jackson (T.H.M., B.G.W.); Department of Neurology, Pro Cardiaco Hospital, Rio de Janeiro, Brazil (D.C.B.); Departments of Neurology (D.S.K.) and Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Division of Public Health Sciences, The Wake Forest University School of Medicine, Winston-Salem, NC (L.H.C., L.E.W.); and Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.A.)
| | - Alvaro Alonso
- From the Department of Neurology, Yale University School of Medicine, New Haven, CT (J.L.D.); Department of Epidemiology, Bloomberg School of Public Health (A.L.C.S., A.R.S., E.S.), Department of Neurology, School of Medicine (A.L.C.S., R.F.G.), and Welch Center for Prevention, Epidemiology and Clinical Research (E.S., R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Geriatrics, Department of Medicine, The University of Mississippi School of Medicine, Jackson (T.H.M., B.G.W.); Department of Neurology, Pro Cardiaco Hospital, Rio de Janeiro, Brazil (D.C.B.); Departments of Neurology (D.S.K.) and Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Division of Public Health Sciences, The Wake Forest University School of Medicine, Winston-Salem, NC (L.H.C., L.E.W.); and Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.A.)
| | - Lynne E Wagenknecht
- From the Department of Neurology, Yale University School of Medicine, New Haven, CT (J.L.D.); Department of Epidemiology, Bloomberg School of Public Health (A.L.C.S., A.R.S., E.S.), Department of Neurology, School of Medicine (A.L.C.S., R.F.G.), and Welch Center for Prevention, Epidemiology and Clinical Research (E.S., R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Geriatrics, Department of Medicine, The University of Mississippi School of Medicine, Jackson (T.H.M., B.G.W.); Department of Neurology, Pro Cardiaco Hospital, Rio de Janeiro, Brazil (D.C.B.); Departments of Neurology (D.S.K.) and Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Division of Public Health Sciences, The Wake Forest University School of Medicine, Winston-Salem, NC (L.H.C., L.E.W.); and Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.A.)
| | - Beverly G Windham
- From the Department of Neurology, Yale University School of Medicine, New Haven, CT (J.L.D.); Department of Epidemiology, Bloomberg School of Public Health (A.L.C.S., A.R.S., E.S.), Department of Neurology, School of Medicine (A.L.C.S., R.F.G.), and Welch Center for Prevention, Epidemiology and Clinical Research (E.S., R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Geriatrics, Department of Medicine, The University of Mississippi School of Medicine, Jackson (T.H.M., B.G.W.); Department of Neurology, Pro Cardiaco Hospital, Rio de Janeiro, Brazil (D.C.B.); Departments of Neurology (D.S.K.) and Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Division of Public Health Sciences, The Wake Forest University School of Medicine, Winston-Salem, NC (L.H.C., L.E.W.); and Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.A.)
| | - Rebecca F Gottesman
- From the Department of Neurology, Yale University School of Medicine, New Haven, CT (J.L.D.); Department of Epidemiology, Bloomberg School of Public Health (A.L.C.S., A.R.S., E.S.), Department of Neurology, School of Medicine (A.L.C.S., R.F.G.), and Welch Center for Prevention, Epidemiology and Clinical Research (E.S., R.F.G.), Johns Hopkins University, Baltimore, MD; Division of Geriatrics, Department of Medicine, The University of Mississippi School of Medicine, Jackson (T.H.M., B.G.W.); Department of Neurology, Pro Cardiaco Hospital, Rio de Janeiro, Brazil (D.C.B.); Departments of Neurology (D.S.K.) and Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Division of Public Health Sciences, The Wake Forest University School of Medicine, Winston-Salem, NC (L.H.C., L.E.W.); and Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis (A.A.).
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Fellgiebel A, Gartenschläger M, Wildberger K, Scheurich A, Desnick RJ, Sims K. Enzyme replacement therapy stabilized white matter lesion progression in Fabry disease. Cerebrovasc Dis 2015; 38:448-56. [PMID: 25502511 DOI: 10.1159/000369293] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2014] [Accepted: 10/21/2014] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND The central nervous system manifestations in Fabry disease (FD) include progressive white matter lesions (WMLs) and stroke. Due to progressive microvascular involvement, men and women with FD over 35 years of age develop WMLs. Moreover, the prevalence of stroke has been estimated to be 12 times higher in FD compared with the general population. Enzyme replacement therapy (ERT) is available and has shown beneficial effects on renal, cardiac, and peripheral nerve function in FD, but the ERT effect on the progression of WMLs, or the reduction in cerebrovascular events, remains unknown. METHODS The WML burden and the effect of agalsidase beta 1 mg/kg biweekly on WML progression were assessed longitudinally in a Phase 4 agalsidase-beta placebo-controlled analysis of untreated and treated FD patients with mild-to-moderate renal involvement (serum creatinine measurements of ≥1.2 mg/dl and <3.0 mg/dl). The primary end point was the difference in the number of patients with increased WML burden between the agalsidase beta and placebo groups at the end of treatment. The diameters of the WMLs were determined manually using axial flow-attenuated-inversion-recovery-weighted magnetic resonance imaging (MRI) scans taken at baseline and follow-up. RESULTS MRI scans from 41 FD patients (mean age 43.9, age range 20-68, 3 females; n=25 on ERT, n=16 on placebo) were analyzed. WML burden was present in 63% of patients at baseline, increased over a mean of 27 months (range 12-33 months) follow-up, and correlated with left ventricular hypertrophy (LVPW). Patients with previous or recent strokes (n=11, 39-68 years) showed an increase in the number of WMLs (p=0.005). A greater proportion of younger patients (≤50 years) on ERT (n=18) had stable WML burden compared with younger patients in the placebo group (n=13): 44% (8 of 18) versus 31% (4 of 13), p=0.014. The number needed to treat was 8. CONCLUSIONS This FD patient cohort, with mild-to-moderate renal involvement, had a significant WML burden and high inter-individual variability associated with the degree of LVPW but not the degree of kidney dysfunction. These advanced patients with increased LVPW and stroke evidence may have had a higher cerebrovascular risk. The WML burden in patients on ERT was more likely to remain stable, compared with patients on placebo. Thus, ERT may reduce the progression of vascular disease, even in advanced FD patients, suggesting that early treatment may stabilize WML progression and stroke risk.
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Affiliation(s)
- Andreas Fellgiebel
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Mainz, Germany
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Knopman DS, Griswold ME, Lirette ST, Gottesman RF, Kantarci K, Sharrett AR, Jack CR, Graff-Radford J, Schneider ALC, Windham BG, Coker LH, Albert MS, Mosley TH. Vascular imaging abnormalities and cognition: mediation by cortical volume in nondemented individuals: atherosclerosis risk in communities-neurocognitive study. Stroke 2015; 46:433-40. [PMID: 25563642 PMCID: PMC4308430 DOI: 10.1161/strokeaha.114.007847] [Citation(s) in RCA: 120] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Accepted: 12/02/2014] [Indexed: 01/18/2023]
Abstract
BACKGROUND AND PURPOSE The relationships between cerebrovascular lesions visible on imaging and cognition are complex. We explored the possibility that the cerebral cortical volume mediated these relationships. METHODS Total of 1906 nondemented participants (59% women; 25% African-American; mean age, 76.6 years) in the Atherosclerosis Risk in Communities (ARIC) study underwent cognitive assessments, risk factor assessments, and quantitative MRI for white matter hyperintensities (WMH) and infarcts. The Freesurfer imaging analysis pipeline was used to determine regional cerebral volumes. We examined the associations of cognitive domain outcomes with cerebral volumes (hippocampus and separate groups of posterior and frontal cortical regions of interest) and cerebrovascular imaging features (presence of large or small cortical/subcortical infarcts and WMH volume). We performed mediation pathway analyses to assess the hypothesis that hippocampal and cortical volumes mediated the associations between cerebrovascular imaging features and cognition. RESULTS In unmediated analyses, WMH and infarcts were both associated with worse psychomotor speed/executive function. In mediation analyses, WMH and infarct associations on psychomotor speed/executive function were significantly attenuated, but not abolished, by the inclusion of the posterior cortical regions of interest volume in the models, and the infarcts on psychomotor speed/executive function association were attenuated, but not abolished, by inclusion of the frontal cortical regions of interest volume. CONCLUSIONS Both WMH and infarcts were associated with cortical volume, and both lesions were also associated with cognitive performance, implying shared pathophysiological mechanisms. Although cross-sectional, our findings suggest that WMH and infarcts could be proxies for clinically covert processes that directly damage cortical regions. Microinfarcts are 1 candidate for such a clinically covert process.
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Affiliation(s)
- David S Knopman
- From the Departments of Neurology (D.S.K., J.G.-R.) and Radiology (K.K., C.R.J.), Mayo Clinic, Rochester, MN; Departments of Biostatistics (M.E.G., S.T.L.) and Medicine (B.G.W., T.H.M.), University of Mississippi Medical Center, Jackson, MS; Departments of Neurology (R.F.G., M.S.A.) and Epidemiology (A.R.S., A.L.C.S.), Johns Hopkins University, Baltimore, MD; and Division of Public Health Sciences, Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC (L.H.C.).
| | - Michael E Griswold
- From the Departments of Neurology (D.S.K., J.G.-R.) and Radiology (K.K., C.R.J.), Mayo Clinic, Rochester, MN; Departments of Biostatistics (M.E.G., S.T.L.) and Medicine (B.G.W., T.H.M.), University of Mississippi Medical Center, Jackson, MS; Departments of Neurology (R.F.G., M.S.A.) and Epidemiology (A.R.S., A.L.C.S.), Johns Hopkins University, Baltimore, MD; and Division of Public Health Sciences, Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC (L.H.C.)
| | - Seth T Lirette
- From the Departments of Neurology (D.S.K., J.G.-R.) and Radiology (K.K., C.R.J.), Mayo Clinic, Rochester, MN; Departments of Biostatistics (M.E.G., S.T.L.) and Medicine (B.G.W., T.H.M.), University of Mississippi Medical Center, Jackson, MS; Departments of Neurology (R.F.G., M.S.A.) and Epidemiology (A.R.S., A.L.C.S.), Johns Hopkins University, Baltimore, MD; and Division of Public Health Sciences, Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC (L.H.C.)
| | - Rebecca F Gottesman
- From the Departments of Neurology (D.S.K., J.G.-R.) and Radiology (K.K., C.R.J.), Mayo Clinic, Rochester, MN; Departments of Biostatistics (M.E.G., S.T.L.) and Medicine (B.G.W., T.H.M.), University of Mississippi Medical Center, Jackson, MS; Departments of Neurology (R.F.G., M.S.A.) and Epidemiology (A.R.S., A.L.C.S.), Johns Hopkins University, Baltimore, MD; and Division of Public Health Sciences, Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC (L.H.C.)
| | - Kejal Kantarci
- From the Departments of Neurology (D.S.K., J.G.-R.) and Radiology (K.K., C.R.J.), Mayo Clinic, Rochester, MN; Departments of Biostatistics (M.E.G., S.T.L.) and Medicine (B.G.W., T.H.M.), University of Mississippi Medical Center, Jackson, MS; Departments of Neurology (R.F.G., M.S.A.) and Epidemiology (A.R.S., A.L.C.S.), Johns Hopkins University, Baltimore, MD; and Division of Public Health Sciences, Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC (L.H.C.)
| | - A Richey Sharrett
- From the Departments of Neurology (D.S.K., J.G.-R.) and Radiology (K.K., C.R.J.), Mayo Clinic, Rochester, MN; Departments of Biostatistics (M.E.G., S.T.L.) and Medicine (B.G.W., T.H.M.), University of Mississippi Medical Center, Jackson, MS; Departments of Neurology (R.F.G., M.S.A.) and Epidemiology (A.R.S., A.L.C.S.), Johns Hopkins University, Baltimore, MD; and Division of Public Health Sciences, Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC (L.H.C.)
| | - Clifford R Jack
- From the Departments of Neurology (D.S.K., J.G.-R.) and Radiology (K.K., C.R.J.), Mayo Clinic, Rochester, MN; Departments of Biostatistics (M.E.G., S.T.L.) and Medicine (B.G.W., T.H.M.), University of Mississippi Medical Center, Jackson, MS; Departments of Neurology (R.F.G., M.S.A.) and Epidemiology (A.R.S., A.L.C.S.), Johns Hopkins University, Baltimore, MD; and Division of Public Health Sciences, Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC (L.H.C.)
| | - Jonathan Graff-Radford
- From the Departments of Neurology (D.S.K., J.G.-R.) and Radiology (K.K., C.R.J.), Mayo Clinic, Rochester, MN; Departments of Biostatistics (M.E.G., S.T.L.) and Medicine (B.G.W., T.H.M.), University of Mississippi Medical Center, Jackson, MS; Departments of Neurology (R.F.G., M.S.A.) and Epidemiology (A.R.S., A.L.C.S.), Johns Hopkins University, Baltimore, MD; and Division of Public Health Sciences, Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC (L.H.C.)
| | - Andrea L C Schneider
- From the Departments of Neurology (D.S.K., J.G.-R.) and Radiology (K.K., C.R.J.), Mayo Clinic, Rochester, MN; Departments of Biostatistics (M.E.G., S.T.L.) and Medicine (B.G.W., T.H.M.), University of Mississippi Medical Center, Jackson, MS; Departments of Neurology (R.F.G., M.S.A.) and Epidemiology (A.R.S., A.L.C.S.), Johns Hopkins University, Baltimore, MD; and Division of Public Health Sciences, Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC (L.H.C.)
| | - B Gwen Windham
- From the Departments of Neurology (D.S.K., J.G.-R.) and Radiology (K.K., C.R.J.), Mayo Clinic, Rochester, MN; Departments of Biostatistics (M.E.G., S.T.L.) and Medicine (B.G.W., T.H.M.), University of Mississippi Medical Center, Jackson, MS; Departments of Neurology (R.F.G., M.S.A.) and Epidemiology (A.R.S., A.L.C.S.), Johns Hopkins University, Baltimore, MD; and Division of Public Health Sciences, Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC (L.H.C.)
| | - Laura H Coker
- From the Departments of Neurology (D.S.K., J.G.-R.) and Radiology (K.K., C.R.J.), Mayo Clinic, Rochester, MN; Departments of Biostatistics (M.E.G., S.T.L.) and Medicine (B.G.W., T.H.M.), University of Mississippi Medical Center, Jackson, MS; Departments of Neurology (R.F.G., M.S.A.) and Epidemiology (A.R.S., A.L.C.S.), Johns Hopkins University, Baltimore, MD; and Division of Public Health Sciences, Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC (L.H.C.)
| | - Marilyn S Albert
- From the Departments of Neurology (D.S.K., J.G.-R.) and Radiology (K.K., C.R.J.), Mayo Clinic, Rochester, MN; Departments of Biostatistics (M.E.G., S.T.L.) and Medicine (B.G.W., T.H.M.), University of Mississippi Medical Center, Jackson, MS; Departments of Neurology (R.F.G., M.S.A.) and Epidemiology (A.R.S., A.L.C.S.), Johns Hopkins University, Baltimore, MD; and Division of Public Health Sciences, Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC (L.H.C.)
| | - Thomas H Mosley
- From the Departments of Neurology (D.S.K., J.G.-R.) and Radiology (K.K., C.R.J.), Mayo Clinic, Rochester, MN; Departments of Biostatistics (M.E.G., S.T.L.) and Medicine (B.G.W., T.H.M.), University of Mississippi Medical Center, Jackson, MS; Departments of Neurology (R.F.G., M.S.A.) and Epidemiology (A.R.S., A.L.C.S.), Johns Hopkins University, Baltimore, MD; and Division of Public Health Sciences, Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC (L.H.C.)
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Age differences in periventricular and deep white matter lesions. Neurobiol Aging 2015; 36:1653-1658. [PMID: 25659858 DOI: 10.1016/j.neurobiolaging.2015.01.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Revised: 12/04/2014] [Accepted: 01/03/2015] [Indexed: 11/23/2022]
Abstract
Deep white matter hyperintensity (DWMH) and periventricular (PV) white matter lesion volumes are associated with age and subsequent stroke. We studied age differences in these volumes accounting for collinearity and risk factors. Subjects were 563 healthy family members of early-onset coronary artery disease patients. Using 3T magnetic resonance imaging, lesions were classified as DWMH or PV. Age association with lesion classification was analyzed using random effects Tobit regression, adjusting for intracranial volume (ICV) and risk factors. Subjects were 60% women, 36% African-American, mean age 51 ± 11 years. In multivariable analysis adjusted for PV and ICV, DWMH was associated with age (p < 0.001) and female sex (p = 0.003). PV, adjusted for DWMH and ICV, was age associated (p < 0.001). For each age decade, DWMH showed 0.07 log units/decade greater volume (95% CI = 0.04-0.11); PV was 0.18 log units/decade greater (95% CI = 0.14-0.23); slope differences (p < 0.001). In people with a family history of coronary artery disease, PV and DWMH are independently and differentially associated with age controlling for traditional risk factors.
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Lesion segmentation from multimodal MRI using random forest following ischemic stroke. Neuroimage 2014; 98:324-35. [DOI: 10.1016/j.neuroimage.2014.04.056] [Citation(s) in RCA: 119] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2013] [Revised: 03/26/2014] [Accepted: 04/21/2014] [Indexed: 11/17/2022] Open
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Setting a gold standard for quantification of leukoaraiosis burden in patients with ischemic stroke: the Atherosclerosis Risk in Communities Study. J Neurosci Methods 2014; 221:196-201. [PMID: 24459720 DOI: 10.1016/j.jneumeth.2013.10.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
BACKGROUND Accurate and reliable measurement of leukoaraiosis, or MR-detected white, matter hyper-intensity (WMH) burden in subjects with acute ischemic stroke (AIS) is important for, ongoing research studies and future models of risk and outcome prediction, but the presence of a, cerebral infarct may complicate measurement. We sought to assess accuracy of a volumetric method, designed to measure WMH in AIS subjects as compared to the previously validated protocol. NEW METHOD We randomly selected and equally sampled 120 brain scans from the Atherosclerosis, Risk in Communities (ARIC) MRI Study individuals within designated mild, moderate, and severe, tertiles of WMH volume (WMHV). T2 FLAIR axial images were analyzed using the AIS WMH volumetric, protocol and compared with the ARIC (gold standard) method. Pearson correlation coefficients, linear, concordance correlation coefficient, and Blant–Altman procedures were used to assess measurement, agreements between the two procedures. RESULTS Median WMHV determined by using the ARIC method was 7.8 cm3 (IQR 5.7–13.55) vs. 3.54 cm3, (IQR 2.1–7.2) using the AIS WMH method. There was good correlation between the two measurements, (r = 0.52, 0.67, and 0.9 for tertiles 1, 2, and 3 respectively) (p < 0.001). COMPARISON WITH EXISTING METHOD The AIS WMH protocol was specific for leukoaraiosis in ischemic, stroke, but it appeared to underestimate WMHV compared to the gold standard method. CONCLUSIONS Estimates of MR-detectable WMH burden using a volumetric protocol designed for, analysis of clinical scans correlate strongly with gold standard measurements. These findings will, facilitate future studies of WMH in normal aging and in patients with stroke and other cerebrovascular, disease.
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Ramirez J, McNeely AA, Scott CJ, Stuss DT, Black SE. Subcortical hyperintensity volumetrics in Alzheimer's disease and normal elderly in the Sunnybrook Dementia Study: correlations with atrophy, executive function, mental processing speed, and verbal memory. ALZHEIMERS RESEARCH & THERAPY 2014; 6:49. [PMID: 25478020 PMCID: PMC4255416 DOI: 10.1186/alzrt279] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2013] [Accepted: 07/15/2014] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Subcortical hyperintensities (SHs) are radiological entities commonly observed on magnetic resonance imaging (MRI) of patients with Alzheimer's disease (AD) and normal elderly controls. Although the presence of SH is believed to indicate some form of subcortical vasculopathy, pathological heterogeneity, methodological differences, and the contribution of brain atrophy associated with AD pathology have yielded inconsistent results in the literature. METHODS Using the Lesion Explorer (LE) MRI processing pipeline for SH quantification and brain atrophy, this study examined SH volumes of interest and cognitive function in a sample of patients with AD (n = 265) and normal elderly controls (n = 100) from the Sunnybrook Dementia Study. RESULTS Compared with healthy controls, patients with AD were found to have less gray matter, less white matter, and more sulcal and ventricular cerebrospinal fluid (all significant, P <0.0001). Additionally, patients with AD had greater volumes of whole-brain SH (P <0.01), periventricular SH (pvSH) (P <0.01), deep white SH (dwSH) (P <0.05), and lacunar lesions (P <0.0001). In patients with AD, regression analyses revealed a significant association between global atrophy and pvSH (P = 0.02) and ventricular atrophy with whole-brain SH (P <0.0001). Regional volumes of interest revealed significant correlations with medial middle frontal SH volume and executive function (P <0.001) in normal controls but not in patients with AD, global pvSH volume and mental processing speed (P <0.01) in patients with AD, and left temporal SH volume and memory (P <0.01) in patients with AD. CONCLUSIONS These brain-behavior relationships and correlations with brain atrophy suggest that subtle, yet measurable, signs of small vessel disease may have potential clinical relevance as targets for treatment in Alzheimer's dementia.
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Affiliation(s)
- Joel Ramirez
- LC Campbell Cognitive Neurology Research Unit, 2075 Bayview Avenue, Room A4 21, Toronto, ON M4N 3M5, Canada ; Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, ON, Canada ; Sunnybrook Health Sciences Centre, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Alicia A McNeely
- LC Campbell Cognitive Neurology Research Unit, 2075 Bayview Avenue, Room A4 21, Toronto, ON M4N 3M5, Canada ; Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, ON, Canada ; Sunnybrook Health Sciences Centre, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Christopher Jm Scott
- LC Campbell Cognitive Neurology Research Unit, 2075 Bayview Avenue, Room A4 21, Toronto, ON M4N 3M5, Canada ; Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, ON, Canada ; Sunnybrook Health Sciences Centre, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Donald T Stuss
- Faculty of Medicine, Institute of Medical Science, University of Toronto, Toronto, ON, Canada ; Rotman Research Institute, Baycrest, Toronto, ON, Canada ; Ontario Brain Institute, Toronto, ON, Canada
| | - Sandra E Black
- LC Campbell Cognitive Neurology Research Unit, 2075 Bayview Avenue, Room A4 21, Toronto, ON M4N 3M5, Canada ; Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, ON, Canada ; Sunnybrook Health Sciences Centre, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada ; Faculty of Medicine, Institute of Medical Science, University of Toronto, Toronto, ON, Canada ; Rotman Research Institute, Baycrest, Toronto, ON, Canada
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Gao J, Li C, Feng C, Xie M, Yin Y, Davatzikos C. Non-locally regularized segmentation of multiple sclerosis lesion from multi-channel MRI data. Magn Reson Imaging 2014; 32:1058-66. [PMID: 24948583 DOI: 10.1016/j.mri.2014.03.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2013] [Revised: 01/20/2014] [Accepted: 03/07/2014] [Indexed: 11/28/2022]
Abstract
Segmentation of multiple sclerosis (MS) lesion is important for many neuroimaging studies. In this paper, we propose a novel algorithm for automatic segmentation of MS lesions from multi-channel MR images (T1W, T2W and FLAIR images). The proposed method is an extension of Li et al.'s algorithm in [1], which only segments the normal tissues from T1W images. The proposed method is aimed to segment MS lesions, while normal tissues are also segmented and bias field is estimated to handle intensity inhomogeneities in the images. Another contribution of this paper is the introduction of a nonlocal means technique to achieve spatially regularized segmentation, which overcomes the influence of noise. Experimental results have demonstrated the effectiveness and advantages of the proposed algorithm.
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Affiliation(s)
- Jingjing Gao
- School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China; Center of Biomedical Image Computing and Analytics, University of PA, Philadelphia 19104, USA
| | - Chunming Li
- Center of Biomedical Image Computing and Analytics, University of PA, Philadelphia 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Chaolu Feng
- Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning 110819, China
| | - Mei Xie
- School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Yilong Yin
- School of Computer Science and Technology, Shandong University, Jinan, Shandong 250100, China
| | - Christos Davatzikos
- Center of Biomedical Image Computing and Analytics, University of PA, Philadelphia 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
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Khademi A, Venetsanopoulos A, Moody AR. Generalized method for partial volume estimation and tissue segmentation in cerebral magnetic resonance images. J Med Imaging (Bellingham) 2014; 1:014002. [PMID: 26158022 DOI: 10.1117/1.jmi.1.1.014002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2013] [Revised: 01/15/2014] [Accepted: 02/25/2014] [Indexed: 11/14/2022] Open
Abstract
An artifact found in magnetic resonance images (MRI) called partial volume averaging (PVA) has received much attention since accurate segmentation of cerebral anatomy and pathology is impeded by this artifact. Traditional neurological segmentation techniques rely on Gaussian mixture models to handle noise and PVA, or high-dimensional feature sets that exploit redundancy in multispectral datasets. Unfortunately, model-based techniques may not be optimal for images with non-Gaussian noise distributions and/or pathology, and multispectral techniques model probabilities instead of the partial volume (PV) fraction. For robust segmentation, a PV fraction estimation approach is developed for cerebral MRI that does not depend on predetermined intensity distribution models or multispectral scans. Instead, the PV fraction is estimated directly from each image using an adaptively defined global edge map constructed by exploiting a relationship between edge content and PVA. The final PVA map is used to segment anatomy and pathology with subvoxel accuracy. Validation on simulated and real, pathology-free T1 MRI (Gaussian noise), as well as pathological fluid attenuation inversion recovery MRI (non-Gaussian noise), demonstrate that the PV fraction is accurately estimated and the resultant segmentation is robust. Comparison to model-based methods further highlight the benefits of the current approach.
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Affiliation(s)
- April Khademi
- University of Guelph , Department of Biomedical Engineering, Guelph, Ontario, N1G 2W1, Canada
| | - Anastasios Venetsanopoulos
- University of Toronto , Department of Electrical and Computer Engineering, Toronto, Ontario, M5S 3G4, Canada ; Ryerson University , Department of Electrical and Computer Engineering, Toronto, Ontario, M5B 2K3, Canada
| | - Alan R Moody
- University of Toronto , Department of Medical Imaging, Toronto, Ontario, M5T 1W7, Canada ; Sunnybrook Research Institute , Department of Medical Imaging, Toronto, Ontario, M4N 3M5, Canada
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Nyquist PA, Bilgel MS, Gottesman R, Yanek LR, Moy TF, Becker LC, Cuzzocreo J, Prince J, Yousem DM, Becker DM, Kral BG, Vaidya D. Extreme deep white matter hyperintensity volumes are associated with African American race. Cerebrovasc Dis 2014; 37:244-50. [PMID: 24686322 DOI: 10.1159/000358117] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2013] [Accepted: 12/17/2013] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND African Americans (AAs) have a higher prevalence of extreme ischemic white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) than do European Americans (EAs) based on the Cardiovascular Health Study (CHS) score. Ischemic white matter disease, limited to the deep white matter, may be biologically distinct from disease in other regions and may reflect a previously observed trend toward an increased risk of subcortical lacunar infarcts in AAs. We hypothesized that extreme deep WMH volume (DWMV) or periventricular volume (PV) may also have a higher prevalence in AAs. Thus, we studied extreme CHS scores and extreme DWMV and PV in a healthy population enriched for cardiovascular disease risk factors. METHODS We imaged the brains of 593 subjects who were first-degree relatives of probands with early onset coronary disease prior to 60 years of age. WMHs were manually delineated on 3-tesla cranial MRI by a trained radiology reader; the location and volume of lesions were characterized using automated software. DWMV and PV were measured directly with automated software, and the CHS score was determined by a neuroradiologist. Volumes were characterized as being in the upper 25% versus lower 75% of total lesion volume. Volumes in the upper versus the remaining quartiles were examined for AA versus EA race using multiple logistic regression (generalized estimating equations adjusted for family relatedness) and adjusted for major vascular disease risk factors including age ≥55 years versus <55, sex, current smoking, obesity, hypertension, diabetes and low-density lipoprotein >160 mg/dl. RESULTS Participants were 58% women and 37% AAs, with a mean age of 51.5 ± 11.0 years (range, 29-74 years). AAs had significantly higher odds of having extreme DWMVs (odds ratio, OR, 1.8; 95% confidence interval, CI, 1.2-2.9; p = 0.0076) independently of age, sex, hypertension and all other risk factors. AAs also had significantly higher odds of having extreme CHS scores ≥3 (OR, 1.3; 95% CI, 1.1-3.6; p = 0.025). Extreme PV was not significantly associated with AA race (OR, 1.3; 95% CI, 0.81-2.1; p = 0.26). CONCLUSIONS AAs from families with early-onset cardiovascular disease are more likely to have extreme DWMVs (a subclinical form of cerebrovascular disease) and an extreme CHS score, but not extreme PV, independently of age and other cardiovascular disease risk factors. These findings suggest that this AA population is at an increased risk for DWMV and may be at an increased risk for future subcortical stroke. Longitudinal studies are required to see if DWMV is predictive of symptomatic subcortical strokes in this population.
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Affiliation(s)
- Paul A Nyquist
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Md., USA
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