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Marzi C, Scheda R, Salvadori E, Giorgio A, De Stefano N, Poggesi A, Inzitari D, Pantoni L, Mascalchi M, Diciotti S. Fractal dimension of the cortical gray matter outweighs other brain MRI features as a predictor of transition to dementia in patients with mild cognitive impairment and leukoaraiosis. Front Hum Neurosci 2023; 17:1231513. [PMID: 37822707 PMCID: PMC10562576 DOI: 10.3389/fnhum.2023.1231513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/31/2023] [Indexed: 10/13/2023] Open
Abstract
Background The relative contribution of changes in the cerebral white matter (WM) and cortical gray matter (GM) to the transition to dementia in patients with mild cognitive impairment (MCI) is not yet established. In this longitudinal study, we aimed to analyze MRI features that may predict the transition to dementia in patients with MCI and T2 hyperintensities in the cerebral WM, also known as leukoaraiosis. Methods Sixty-four participants with MCI and moderate to severe leukoaraiosis underwent baseline MRI examinations and annual neuropsychological testing over a 2 year period. The diagnosis of dementia was based on established criteria. We evaluated demographic, neuropsychological, and several MRI features at baseline as predictors of the clinical transition. The MRI features included visually assessed MRI features, such as the number of lacunes, microbleeds, and dilated perivascular spaces, and quantitative MRI features, such as volumes of the cortical GM, hippocampus, T2 hyperintensities, and diffusion indices of the cerebral WM. Additionally, we examined advanced quantitative features such as the fractal dimension (FD) of cortical GM and WM, which represents an index of tissue structural complexity derived from 3D-T1 weighted images. To assess the prediction of transition to dementia, we employed an XGBoost-based machine learning system using SHapley Additive exPlanations (SHAP) values to provide explainability to the machine learning model. Results After 2 years, 18 (28.1%) participants had transitioned from MCI to dementia. The area under the receiving operator characteristic curve was 0.69 (0.53, 0.85) [mean (90% confidence interval)]. The cortical GM-FD emerged as the top-ranking predictive feature of transition. Furthermore, aggregated quantitative neuroimaging features outperformed visually assessed MRI features in predicting conversion to dementia. Discussion Our findings confirm the complementary roles of cortical GM and WM changes as underlying factors in the development of dementia in subjects with MCI and leukoaraiosis. FD appears to be a biomarker potentially more sensitive than other brain features.
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Affiliation(s)
- Chiara Marzi
- Department of Statistics, Computer Science, Applications "Giuseppe Parenti, " University of Florence, Florence, Italy
| | - Riccardo Scheda
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi, " University of Bologna, Cesena, Italy
| | - Emilia Salvadori
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Antonio Giorgio
- Department of Medicine, Surgery, and Neuroscience, University of Siena, Siena, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery, and Neuroscience, University of Siena, Siena, Italy
| | - Anna Poggesi
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Domenico Inzitari
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Leonardo Pantoni
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio, " University of Florence, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and Network in Oncology (ISPRO), Florence, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi, " University of Bologna, Cesena, Italy
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy
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Trofimova O, Latypova A, DiDomenicantonio G, Lutti A, de Lange AMG, Kliegel M, Stringhini S, Marques-Vidal P, Vaucher J, Vollenweider P, Strippoli MPF, Preisig M, Kherif F, Draganski B. Topography of associations between cardiovascular risk factors and myelin loss in the ageing human brain. Commun Biol 2023; 6:392. [PMID: 37037939 PMCID: PMC10086032 DOI: 10.1038/s42003-023-04741-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/21/2023] [Indexed: 04/12/2023] Open
Abstract
Our knowledge of the mechanisms underlying the vulnerability of the brain's white matter microstructure to cardiovascular risk factors (CVRFs) is still limited. We used a quantitative magnetic resonance imaging (MRI) protocol in a single centre setting to investigate the cross-sectional association between CVRFs and brain tissue properties of white matter tracts in a large community-dwelling cohort (n = 1104, age range 46-87 years). Arterial hypertension was associated with lower myelin and axonal density MRI indices, paralleled by higher extracellular water content. Obesity showed similar associations, though with myelin difference only in male participants. Associations between CVRFs and white matter microstructure were observed predominantly in limbic and prefrontal tracts. Additional genetic, lifestyle and psychiatric factors did not modulate these results, but moderate-to-vigorous physical activity was linked to higher myelin content independently of CVRFs. Our findings complement previously described CVRF-related changes in brain water diffusion properties pointing towards myelin loss and neuroinflammation rather than neurodegeneration.
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Affiliation(s)
- Olga Trofimova
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Adeliya Latypova
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Giulia DiDomenicantonio
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ann-Marie G de Lange
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Matthias Kliegel
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Silvia Stringhini
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
- Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Julien Vaucher
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Marie-Pierre F Strippoli
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Martin Preisig
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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3
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Fouto AR, Nunes RG, Pinto J, Alves L, Calado S, Gonçalves C, Rebolo M, Viana-Baptista M, Vilela P, Figueiredo P. Impact of white-matter mask selection on DTI histogram-based metrics as potential biomarkers in cerebral small vessel disease. MAGMA (NEW YORK, N.Y.) 2022; 35:779-790. [PMID: 34997895 DOI: 10.1007/s10334-021-00991-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 12/09/2021] [Accepted: 12/11/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVE Histogram-based metrics extracted from diffusion-tensor imaging (DTI) have been suggested as potential biomarkers for cerebral small vessel disease (SVD), but methods and results have varied across studies. This work aims to assess the impact of mask selection for extracting histogram-based metrics of fractional anisotropy (FA) and mean diffusivity (MD) on their sensitivity as SVD biomarkers. METHODS DTI data were collected from 17 SVD patients and 12 healthy controls. FA and MD maps were estimated; from these, histograms were computed on two whole-brain white-matter masks: normal-appearing white-matter (NAWM) and mean FA tract skeleton (TBSS). Histogram-based metrics (median, peak height, peak width, peak value) were extracted from the FA and MD maps. These were compared between groups and correlated with the patients' cognitive scores (executive function and processing speed). RESULTS White-matter mask selection significantly impacted FA and MD histogram metrics. In particular, significant interactions were found between Mask and Group for FA peak height (p = 0.027), MD Median (p = 0.035) and MD peak width (p = 0.047); indicating that the mask used affected their ability to discriminate between groups. In fact, MD peak width showed a significant 8.8% increase in patients when using TBSS (p = 0.037), but not when using NAWM (p = 0.69). Moreover, the mask may have an effect on the correlations with cognitive measures. Nevertheless, MD peak width (TBSS: r = - 0.75, NAWM: r = - 0.71) and MD peak height (TBSS: r = 0.65, NAWM: r = 0.62) remained significantly correlated with executive function, regardless of the mask. CONCLUSION The impact of the processing methodology, in particular the choice of white-matter mask, highlights the need for standardized MRI data-processing pipelines.
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Affiliation(s)
- Ana R Fouto
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal.
| | - Rita G Nunes
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal
| | - Joana Pinto
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Luísa Alves
- Neurology Department, Hospital Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal
- CEDOC - NOVA Medical School, NOVA University of Lisbon, Lisbon, Portugal
| | - Sofia Calado
- Neurology Department, Hospital Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal
- CEDOC - NOVA Medical School, NOVA University of Lisbon, Lisbon, Portugal
| | - Carina Gonçalves
- Neurology Department, Hospital Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal
- CEDOC - NOVA Medical School, NOVA University of Lisbon, Lisbon, Portugal
| | | | - Miguel Viana-Baptista
- Neurology Department, Hospital Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal
- CEDOC - NOVA Medical School, NOVA University of Lisbon, Lisbon, Portugal
| | - Pedro Vilela
- Imaging Department, Hospital da Luz, Lisbon, Portugal
| | - Patrícia Figueiredo
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal
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da Silva PHR, Paschoal AM, Secchinatto KF, Zotin MCZ, Dos Santos AC, Viswanathan A, Pontes-Neto OM, Leoni RF. Contrast agent-free state-of-the-art magnetic resonance imaging on cerebral small vessel disease - Part 2: Diffusion tensor imaging and functional magnetic resonance imaging. NMR IN BIOMEDICINE 2022; 35:e4743. [PMID: 35429070 DOI: 10.1002/nbm.4743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 04/04/2022] [Indexed: 06/14/2023]
Abstract
Cerebral small vessel disease (cSVD) has been widely studied using conventional magnetic resonance imaging (MRI) methods, although the association between MRI findings and clinical features of cSVD is not always concordant. We assessed the additional contribution of contrast agent-free, state-of-the-art MRI techniques, particularly diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), to understand brain damage and structural and functional connectivity impairment related to cSVD. We performed a review following the PICOS worksheet and Search Strategy, including 152 original papers in English, published from 2000 to 2022. For each MRI method, we extracted information about their contributions regarding the origins, pathology, markers, and clinical outcomes in cSVD. In general, DTI studies have shown that changes in mean, radial, and axial diffusivity measures are related to the presence of cSVD. In addition to the classical deficit in executive functions and processing speed, fMRI studies indicate connectivity dysfunctions in other domains, such as sensorimotor, memory, and attention. Neuroimaging metrics have been correlated with the diagnosis, prognosis, and rehabilitation of patients with cSVD. In short, the application of contrast agent-free, state-of-the-art MRI techniques has provided a complete picture of cSVD markers and tools to explore questions that have not yet been clarified about this clinical condition. Longitudinal studies are desirable to look for causal relationships between image biomarkers and clinical outcomes.
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Affiliation(s)
| | - André Monteiro Paschoal
- Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | | | - Maria Clara Zanon Zotin
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
- J Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Antônio Carlos Dos Santos
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Anand Viswanathan
- J Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Octavio M Pontes-Neto
- Department of Neurosciences and Behavioral Science, Ribeirão Preto Medical School, University of Sao Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Renata Ferranti Leoni
- Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
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Ren B, Tan L, Song Y, Li D, Xue B, Lai X, Gao Y. Cerebral Small Vessel Disease: Neuroimaging Features, Biochemical Markers, Influencing Factors, Pathological Mechanism and Treatment. Front Neurol 2022; 13:843953. [PMID: 35775047 PMCID: PMC9237477 DOI: 10.3389/fneur.2022.843953] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/12/2022] [Indexed: 01/15/2023] Open
Abstract
Cerebral small vessel disease (CSVD) is the most common chronic vascular disease involving the whole brain. Great progress has been made in clinical imaging, pathological mechanism, and treatment of CSVD, but many problems remain. Clarifying the current research dilemmas and future development direction of CSVD can provide new ideas for both basic and clinical research. In this review, the risk factors, biological markers, pathological mechanisms, and the treatment of CSVD will be systematically illustrated to provide the current research status of CSVD. The future development direction of CSVD will be elucidated by summarizing the research difficulties.
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Affiliation(s)
- Beida Ren
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
- Institute for Brain Disorders, Beijing University of Chinese Medicine, Beijing, China
- Chinese Medicine Key Research Room of Brain Disorders Syndrome and Treatment of the National Administration of Traditonal Chinese Medicine, Beijing, China
| | - Ling Tan
- Department of Cardiology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yuebo Song
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Danxi Li
- Institute for Brain Disorders, Beijing University of Chinese Medicine, Beijing, China
- Chinese Medicine Key Research Room of Brain Disorders Syndrome and Treatment of the National Administration of Traditonal Chinese Medicine, Beijing, China
| | - Bingjie Xue
- Institute for Brain Disorders, Beijing University of Chinese Medicine, Beijing, China
- Chinese Medicine Key Research Room of Brain Disorders Syndrome and Treatment of the National Administration of Traditonal Chinese Medicine, Beijing, China
| | - Xinxing Lai
- Institute for Brain Disorders, Beijing University of Chinese Medicine, Beijing, China
| | - Ying Gao
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
- Institute for Brain Disorders, Beijing University of Chinese Medicine, Beijing, China
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Cerebral small vessel disease alters neurovascular unit regulation of microcirculation integrity involved in vascular cognitive impairment. Neurobiol Dis 2022; 170:105750. [DOI: 10.1016/j.nbd.2022.105750] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 03/09/2022] [Accepted: 05/08/2022] [Indexed: 12/25/2022] Open
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Dobrynina L, Gadzhieva Z, Shamtieva K, Kremneva E, Filatov A, Bitsieva E, Mirokova E, Krotenkova M. Predictors and integrative index of severity of cognitive disorders in cerebral microangiopathy. Zh Nevrol Psikhiatr Im S S Korsakova 2022; 122:52-60. [DOI: 10.17116/jnevro202212204152] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Egle M, Hilal S, Tuladhar AM, Pirpamer L, Bell S, Hofer E, Duering M, Wason J, Morris RG, Dichgans M, Schmidt R, Tozer DJ, Barrick TR, Chen C, de Leeuw FE, Markus HS. Determining the OPTIMAL DTI analysis method for application in cerebral small vessel disease. NEUROIMAGE: CLINICAL 2022; 35:103114. [PMID: 35908307 PMCID: PMC9421487 DOI: 10.1016/j.nicl.2022.103114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 05/24/2022] [Accepted: 07/10/2022] [Indexed: 11/23/2022] Open
Abstract
We were not able to identify a single optimal diffusion-weighted imaging analysis strategy across all 6 cohorts. Diffusion tensor imaging measures at baseline predicted dementia conversion in cerebral small vessel disease and mild cognitive impairment. Diffusion tensor imaging measures at baseline may be sensitive to differentiate between later vascular dementia vs Alzheimer’s disease dementia. Diffusion tensor imaging measures significantly changed over time in cohorts with cerebral small vessel disease and cohorts with mild cognitive impairment. Change in diffusion tensor imaging measures were only consistently associated with dementia conversion in severe SVD. The diffusion tensor imaging measures PSMD and DSEG required the lowest minimum sample sizes for a hypothetical clinical trial in patients with sporadic cerebral small vessel disease and mild cognitive impairment.
Background DTI is sensitive to white matter (WM) microstructural damage and has been suggested as a surrogate marker for phase 2 clinical trials in cerebral small vessel disease (SVD). The study’s objective is to establish the best way to analyse the diffusion-weighted imaging data in SVD for this purpose. The ideal method would be sensitive to change and predict dementia conversion, but also straightforward to implement and ideally automated. As part of the OPTIMAL collaboration, we evaluated five different DTI analysis strategies across six different cohorts with differing SVD severity. Methods Those 5 strategies were: (1) conventional mean diffusivity WM histogram measure (MD median), (2) a principal component-derived measure based on conventional WM histogram measures (PC1), (3) peak width skeletonized mean diffusivity (PSMD), (4) diffusion tensor image segmentation θ (DSEG θ) and (5) a WM measure of global network efficiency (Geff). The association between each measure and cognitive function was tested using a linear regression model adjusted by clinical markers. Changes in the imaging measures over time were determined. In three cohort studies, repeated imaging data together with data on incident dementia were available. The association between the baseline measure, change measure and incident dementia conversion was examined using Cox proportional-hazard regression or logistic regression models. Sample size estimates for a hypothetical clinical trial were furthermore computed for each DTI analysis strategy. Results There was a consistent cross-sectional association between the imaging measures and impaired cognitive function across all cohorts. All baseline measures predicted dementia conversion in severe SVD. In mild SVD, PC1, PSMD and Geff predicted dementia conversion. In MCI, all markers except Geff predicted dementia conversion. Baseline DTI was significantly different in patients converting to vascular dementia than to Alzheimer’ s disease. Significant change in all measures was associated with dementia conversion in severe but not in mild SVD. The automatic and semi-automatic measures PSMD and DSEG θ required the lowest minimum sample sizes for a hypothetical clinical trial in single-centre sporadic SVD cohorts. Conclusion DTI parameters obtained from all analysis methods predicted dementia, and there was no clear winner amongst the different analysis strategies. The fully automated analysis provided by PSMD offers advantages particularly for large datasets.
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Affiliation(s)
- Marco Egle
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.
| | - Saima Hilal
- Department of Pharmacology, National University of Singapore, Singapore; Memory Ageing and Cognition Center, National University Health System, Singapore
| | - Anil M Tuladhar
- Department of Neurology, Donders Center for Medical Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Lukas Pirpamer
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Steven Bell
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Edith Hofer
- Department of Neurology, Medical University of Graz, Graz, Austria; Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Marco Duering
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany; Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - James Wason
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Newcastle Upon Tyne, United Kingdom
| | - Robin G Morris
- Department of Psychology (R.G.M.), King's College, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Reinhold Schmidt
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Daniel J Tozer
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Thomas R Barrick
- Neurosciences Research Centre, Institute for Molecular and Clinical Sciences, St George's, University of London, United Kingdom
| | - Christopher Chen
- Department of Pharmacology, National University of Singapore, Singapore; Memory Ageing and Cognition Center, National University Health System, Singapore
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Center for Medical Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Hugh S Markus
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
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Egle M, Hilal S, Tuladhar AM, Pirpamer L, Hofer E, Duering M, Wason J, Morris RG, Dichgans M, Schmidt R, Tozer D, Chen C, de Leeuw FE, Markus HS. Prediction of dementia using diffusion tensor MRI measures: the OPTIMAL collaboration. J Neurol Neurosurg Psychiatry 2022; 93:14-23. [PMID: 34509999 DOI: 10.1136/jnnp-2021-326571] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 08/21/2021] [Indexed: 11/03/2022]
Abstract
OBJECTIVES It has been suggested that diffusion tensor imaging (DTI) measures sensitive to white matter (WM) damage may predict future dementia risk not only in cerebral small vessel disease (SVD), but also in mild cognitive impairment. To determine whether DTI measures were associated with cognition cross-sectionally and predicted future dementia risk across the full range of SVD severity, we established the International OPtimising mulTImodal MRI markers for use as surrogate markers in trials of Vascular Cognitive Impairment due to cerebrAl small vesseL disease collaboration which included six cohorts. METHODS Among the six cohorts, prospective data with dementia incidences were available for three cohorts. The associations between six different DTI measures and cognition or dementia conversion were tested. The additional contribution to prediction of other MRI markers of SVD was also determined. RESULTS The DTI measure mean diffusivity (MD) median correlated with cognition in all cohorts, demonstrating the contribution of WM damage to cognition. Adding MD median significantly improved the model fit compared to the clinical risk model alone and further increased in all single-centre SVD cohorts when adding conventional MRI measures. Baseline MD median predicted dementia conversion. In a study with severe SVD (SCANS) change in MD median also predicted dementia conversion. The area under the curve was best when employing a multimodal MRI model using both DTI measures and other MRI measures. CONCLUSIONS Our results support a central role for WM alterations in dementia pathogenesis in all cohorts. DTI measures such as MD median may be a useful clinical risk predictor. The contribution of other MRI markers varied according to disease severity.
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Affiliation(s)
- Marco Egle
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Saima Hilal
- Memory Aging and Cognition Centre, Department of Pharmacology, Yong Loo Lim School of Medicine, National University of Singapore, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore and National University Health System of Singapore, Singapore
| | - A M Tuladhar
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Nijmegen, The Netherlands
| | - Lukas Pirpamer
- Department of Neurology, Medical University Graz, Graz, Austria
| | - Edith Hofer
- Department of Neurology, Medical University Graz, Graz, Austria.,Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Marco Duering
- Institute for Stroke and Dementia Research, University Hospital, Ludwig Maximilian University Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - James Wason
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge Institute of Public Health, Cambridge, UK.,Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Newcastle upon Tyne, UK
| | - Robin G Morris
- Department of Psychology (R.G.M), King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, University Hospital, Ludwig Maximilian University Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.,German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | | | - Daniel Tozer
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Christopher Chen
- Memory Aging and Cognition Centre, Department of Pharmacology, Yong Loo Lim School of Medicine, National University of Singapore, Singapore
| | - Frank-Erik de Leeuw
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Nijmegen, The Netherlands
| | - Hugh S Markus
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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Richards E, Tales A, Bayer A, Norris JE, Hanley CJ, Thornton IM. Reaction Time Decomposition as a Tool to Study Subcortical Ischemic Vascular Cognitive Impairment. J Alzheimers Dis Rep 2021; 5:625-636. [PMID: 34632300 PMCID: PMC8461746 DOI: 10.3233/adr-210029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2021] [Indexed: 12/01/2022] Open
Abstract
Background: The study of reaction time (RT) and its intraindividual variability (IIV) in aging, cognitive impairment, and dementia typically fails to investigate the processing stages that contribute to an overall response. Applying “mental chronometry” techniques makes it possible to separately assess the role of processing components during environmental interaction. Objective: To determine whether RT and IIV-decomposition techniques can shed light on the nature of underlying deficits in subcortical ischemic vascular cognitive impairment (VCI). Using a novel iPad task, we examined whether VCI deficits occur during both initiation and movement phases of a response, and whether they are equally reflected in both RT and IIV. Methods: Touch cancellation RT and its IIV were measured in a group of younger adults (n = 22), cognitively healthy older adults (n = 21), and patients with VCI (n = 21) using an iPad task. Results: Whereas cognitively healthy aging affected the speed (RT) of response initiation and movement but not its variability (IIV), VCI resulted in both slowed RT and increased IIV for both response phases. Furthermore, there were group differences with respect to response phase. Conclusion: These results indicate that IIV can be more sensitive than absolute RT in separating VCI from normal aging. Furthermore, compared to cognitively healthy aging, VCI was characterized by significant deficits in planning/initiating action as well as performing movements. Such deficits have important implications for real life actions such as driving safety, employment, and falls risk.
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Affiliation(s)
- Emma Richards
- Centre for Innovative Ageing, Swansea University, Swansea, Wales, UK
| | - Andrea Tales
- Centre for Innovative Ageing, Swansea University, Swansea, Wales, UK
| | - Antony Bayer
- Division of Population Medicine, Cardiff University, Cardiff, Wales, UK
| | - Jade E Norris
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Claire J Hanley
- Department of Psychology, Swansea University, Swansea, Wales, UK
| | - Ian M Thornton
- Department of Cognitive Science, University of Malta, Malta
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11
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Narasimhan M, Schwartz R, Halliday G. Parkinsonism and cerebrovascular disease. J Neurol Sci 2021; 433:120011. [PMID: 34686356 DOI: 10.1016/j.jns.2021.120011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 09/01/2021] [Accepted: 09/29/2021] [Indexed: 11/27/2022]
Abstract
The relationship between cerebrovascular disease and parkinsonism is commonly seen in everyday clinical practice but remains ill-defined and under-recognised with little guidance for the practising neurologist. We attempt to define this association and to illustrate key clinical, radiological and pathological features of the syndrome of Vascular Parkinsonism (VaP). VaP is a major cause of morbidity in the elderly associated with falls, hip fractures and cognitive impairment. Although acute parkinsonism is reported in the context of an acute cerebrovascular event, the vast majority of VaP presents as an insidious syndrome usually in the context of vascular risk factors and radiological evidence of small vessel disease. There may be an anatomic impact on basal ganglia neuronal networks, however the effect of small vessel disease (SVD) on these pathways is not clear. There are now established reporting standards for radiological features of SVD on MRI. White matter hyperintensities and lacunes have been thought to be the representative radiological features of SVD but other features such as the perivascular space are gaining more importance, especially in context of the glymphatic system. It is important to consider VaP in the differential diagnosis of Parkinson disease (PD) and in these situations, neuroimaging may offer diagnostic benefit especially in those patients with atypical presentations or refractoriness to levodopa. Proactive management of vascular risk factors, monitoring of bone density and an exercise program may offer easily attainable therapeutic targets in PD and VaP. Levodopa therapy should be considered in patients with VaP, however the dose and effect may be different from use in PD. This article is part of the Special Issue "Parkinsonism across the spectrum of movement disorders and beyond" edited by Joseph Jankovic, Daniel D. Truong and Matteo Bologna.
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Affiliation(s)
- Manisha Narasimhan
- Brain and Mind Centre and Faculty of Health and Medical Sciences, School of Medical Sciences, University of Sydney, Sydney, NSW, Australia.
| | - Raymond Schwartz
- Brain and Mind Centre and Faculty of Health and Medical Sciences, School of Medical Sciences, University of Sydney, Sydney, NSW, Australia
| | - Glenda Halliday
- Brain and Mind Centre and Faculty of Health and Medical Sciences, School of Medical Sciences, University of Sydney, Sydney, NSW, Australia
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12
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Zanon Zotin MC, Sveikata L, Viswanathan A, Yilmaz P. Cerebral small vessel disease and vascular cognitive impairment: from diagnosis to management. Curr Opin Neurol 2021; 34:246-257. [PMID: 33630769 PMCID: PMC7984766 DOI: 10.1097/wco.0000000000000913] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE OF REVIEW We present recent developments in the field of small vessel disease (SVD)-related vascular cognitive impairment, including pathological mechanisms, updated diagnostic criteria, cognitive profile, neuroimaging markers and risk factors. We further address available management and therapeutic strategies. RECENT FINDINGS Vascular and neurodegenerative pathologies often co-occur and share similar risk factors. The updated consensus criteria aim to standardize vascular cognitive impairment (VCI) diagnosis, relying strongly on cognitive profile and MRI findings. Aggressive blood pressure control and multidomain lifestyle interventions are associated with decreased risk of cognitive impairment, but disease-modifying treatments are still lacking. Recent research has led to a better understanding of mechanisms leading to SVD-related cognitive decline, such as blood-brain barrier dysfunction, reduced cerebrovascular reactivity and impaired perivascular clearance. SUMMARY SVD is the leading cause of VCI and is associated with substantial morbidity. Tackling cardiovascular risk factors is currently the most effective approach to prevent cognitive decline in the elderly. Advanced imaging techniques provide tools for early diagnosis and may play an important role as surrogate markers for cognitive endpoints in clinical trials. Designing and testing disease-modifying interventions for VCI remains a key priority in healthcare.
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Affiliation(s)
- Maria Clara Zanon Zotin
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States
- Center for Imaging Sciences and Medical Physics. Department of Medical Imaging, Hematology and Clinical Oncology. Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Lukas Sveikata
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States
- Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Institute of Cardiology, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Anand Viswanathan
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Pinar Yilmaz
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States
- Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
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13
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Lee YH, Lee WJ, Chung SJ, Yoo HS, Jung JH, Baik K, Sohn YH, Seong JK, Lee PH. Microstructural Connectivity is More Related to Cognition than Conventional MRI in Parkinson's Disease. JOURNAL OF PARKINSONS DISEASE 2021; 11:239-249. [PMID: 33074193 DOI: 10.3233/jpd-202312] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND The different effects of white matter hyperintensity (WMH) severity and WMH-associated microstructural connectivity on cognition in the early stages of Parkinson's disease (PD) have not been investigated. OBJECTIVE To investigate the differential effect of WMH severity and WMH-associated microstructural connectivity on cognition in early stages of PD. METHODS A total of 136 de novo PD patients were enrolled and divided into groups based on total WMH visual rating scores as follows: mild, moderate, and severe. Microstructural connectivity was measured using graph theoretical analysis according to WMH severity. Additionally, correlation coefficients between WMH-associated microstructural connectivity or WMH scores and cognitive performance were assessed. RESULTS Patients with severe WMHs demonstrated poorer performance in language function than those with moderate WMHs, and in frontal/executive and visual memory function than those with mild WMHs. Areas of microstructural connectivity were more extensive in patients with severe WMHs compared to those with mild and moderate WMHs, involving frontal and parieto-temporal regions. WMH-associated right fronto-temporo-parietal microstructural disintegration was correlated with cognitive dysfunction in attention, frontal/executive, and memory domains, whereas there was no correlation between WMH scores and any cognitive domains. CONCLUSION These data suggest that disruption of microstructural networks by WMHs, rather than WMH burden itself, contributed more to cognitive impairment in PD.
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Affiliation(s)
- Yang Hyun Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Wha Jin Lee
- School of Biomedical Engineering, Korea University, Seoul, South Korea
| | - Seok Jong Chung
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Han Soo Yoo
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin Ho Jung
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Kyoungwon Baik
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Young H Sohn
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, Seoul, South Korea.,Department of Artificial Intelligence, Korea University, Seoul, South Korea
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea.,Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, South Korea
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14
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Dobrynina LA, Gadzhieva ZS, Shamtieva KV, Kremneva EI, Akhmetzyanov BM, Kalashnikova LA, Krotenkova MV. Microstructural Predictors of Cognitive Impairment in Cerebral Small Vessel Disease and the Conditions of Their Formation. Diagnostics (Basel) 2020; 10:diagnostics10090720. [PMID: 32961692 PMCID: PMC7554972 DOI: 10.3390/diagnostics10090720] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 09/08/2020] [Accepted: 09/17/2020] [Indexed: 01/10/2023] Open
Abstract
Introduction: Cerebral small vessel disease (CSVD) is the leading cause of vascular and mixed degenerative cognitive impairment (CI). The variability in the rate of progression of CSVD justifies the search for sensitive predictors of CI. Materials: A total of 74 patients (48 women, average age 60.6 ± 6.9 years) with CSVD and CI of varying severity were examined using 3T MRI. The results of diffusion tensor imaging with a region of interest (ROI) analysis were used to construct a predictive model of CI using binary logistic regression, while phase-contrast magnetic resonance imaging and voxel-based morphometry were used to clarify the conditions for the formation of CI predictors. Results: According to the constructed model, the predictors of CI are axial diffusivity (AD) of the posterior frontal periventricular normal-appearing white matter (pvNAWM), right middle cingulum bundle (CB), and mid-posterior corpus callosum (CC). These predictors showed a significant correlation with the volume of white matter hyperintensity; arterial and venous blood flow, pulsatility index, and aqueduct cerebrospinal fluid (CSF) flow; and surface area of the aqueduct, volume of the lateral ventricles and CSF, and gray matter volume. Conclusion: Disturbances in the AD of pvNAWM, CB, and CC, associated with axonal damage, are a predominant factor in the development of CI in CSVD. The relationship between AD predictors and both blood flow and CSF flow indicates a disturbance in their relationship, while their location near the floor of the lateral ventricle and their link with indicators of internal atrophy, CSF volume, and aqueduct CSF flow suggest the importance of transependymal CSF transudation when these regions are damaged.
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15
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Raghavan S, Przybelski SA, Reid RI, Graff-Radford J, Lesnick TG, Zuk SM, Knopman DS, Machulda MM, Mielke MM, Petersen RC, Jack CR, Vemuri P. Reduced fractional anisotropy of the genu of the corpus callosum as a cerebrovascular disease marker and predictor of longitudinal cognition in MCI. Neurobiol Aging 2020; 96:176-183. [PMID: 33022474 DOI: 10.1016/j.neurobiolaging.2020.09.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 08/25/2020] [Accepted: 09/01/2020] [Indexed: 12/29/2022]
Abstract
Our goal was to evaluate the utility of diffusion tensor imaging (DTI) for predicting future cognitive decline in mild cognitive impairment (MCI) in conjunction with Alzheimer's disease (AD) biomarkers (amyloid positron emission tomography and AD signature neurodegeneration) in 132 MCI individuals ≥60 year old with structural magnetic resonance imaging, DTI, amyloid positron emission tomography, and at least one clinical follow-up. We used mixed-effect models to evaluate the prognostic ability of fractional anisotropy of the genu of the corpus callosum (FA-Genu), as a cerebrovascular disease marker, for predicting cognitive decline along with AD biomarkers. We contrasted the value of white matter hyperintensities, a traditional cerebrovascular disease marker as well as FA in the hippocampal cingulum bundle with the FA-Genu models. FA-Genu significantly predicted cognitive decline even after accounting for AD biomarkers. WMH was not associated with cognitive decline in the model with both WMH and FA-Genu. DTI specifically FA-Genu provides unique complementary information to AD biomarkers and has significant utility for prediction of cognitive decline in MCI.
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Affiliation(s)
| | | | - Robert I Reid
- Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Samantha M Zuk
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Michelle M Mielke
- Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Department of Neurology, Mayo Clinic, Rochester, MN, USA
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16
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Dealing with confounders and outliers in classification medical studies: The Autism Spectrum Disorders case study. Artif Intell Med 2020; 108:101926. [DOI: 10.1016/j.artmed.2020.101926] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 12/13/2019] [Accepted: 07/02/2020] [Indexed: 12/21/2022]
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17
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McCreary CR, Beaudin AE, Subotic A, Zwiers AM, Alvarez A, Charlton A, Goodyear BG, Frayne R, Smith EE. Cross-sectional and longitudinal differences in peak skeletonized white matter mean diffusivity in cerebral amyloid angiopathy. NEUROIMAGE-CLINICAL 2020; 27:102280. [PMID: 32521475 PMCID: PMC7284130 DOI: 10.1016/j.nicl.2020.102280] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 04/29/2020] [Accepted: 04/30/2020] [Indexed: 11/13/2022]
Abstract
PSMD, a marker of global white matter microstructure disruption, is increased in CAA. PSMD in CAA participants is associated with processing speed. Changes in PSMD were similar in CAA, NC, MCI, and AD over 1 year.
Objectives To test the hypotheses that peak skeletonized mean diffusivity (PSMD), a measure of cerebral white matter microstructural disruption, is 1) increased in patients with cerebral amyloid angiopathy (CAA) compared to normal control (NC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD); 2) associated with neuropsychological test performance among CAA patients; and 3) increased more quickly over one year in CAA than in AD, MCI, and NC. Methods Ninety-two participants provided a medical history, completed a neuropsychological assessment, and had a magnetic resonance (MR) exam including diffusion tensor imaging (DTI) from which PSMD was calculated. A 75-minute neuropsychological test battery was used to derive domain scores for memory, executive function, and processing speed. Multivariable analyses controlling for age and sex (and education, for cognitive outcomes) were used to test the study hypotheses. Results PSMD was higher in the CAA group (mean 4.97 × 10−4 mm2/s) compared to NC (3.25 × 10−4 mm2/s), MCI (3.62 × 10−4 mm2/s) and AD (3.89 × 10−4 mm2/s) groups (p < .01). Among CAA patients, higher PSMD was associated with slower processing speed (estimated −0.22 standard deviation (SD) change in processing speed z score per SD increase in PSMD, 95% CI −0.42 to −0.03, p = .03), higher WMH volume [β = 0.74, CI 0.48 to 1.00], and higher CAA SVD score [β = 0.68, CI 0.24 to 1.21] but was not associated with MMSE, executive function, memory, CMB count, or cortical thickness. PSMD increased over 1-year in all groups (p < .01) but without rate differences between groups (p = .66). Conclusions PSMD, a simple marker of diffuse global white matter heterogeneity, is increased in CAA. Our findings further support a role for white matter disruption in causing cognitive impairment in CAA.
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Affiliation(s)
- Cheryl R McCreary
- Departments of Clinical Neurosciences and Radiology, Hotchkiss Brain Institute, University of Calgary, Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Sciences, Calgary, Alberta, Canada.
| | - Andrew E Beaudin
- Departments of Clinical Neurosciences and Radiology, Hotchkiss Brain Institute, University of Calgary, Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Sciences, Calgary, Alberta, Canada
| | - Arsenije Subotic
- Departments of Clinical Neurosciences and Radiology, Hotchkiss Brain Institute, University of Calgary, Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Sciences, Calgary, Alberta, Canada
| | - Angela M Zwiers
- Departments of Clinical Neurosciences and Radiology, Hotchkiss Brain Institute, University of Calgary, Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Sciences, Calgary, Alberta, Canada
| | - Ana Alvarez
- Departments of Clinical Neurosciences and Radiology, Hotchkiss Brain Institute, University of Calgary, Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Sciences, Calgary, Alberta, Canada
| | - Anna Charlton
- Departments of Clinical Neurosciences and Radiology, Hotchkiss Brain Institute, University of Calgary, Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Sciences, Calgary, Alberta, Canada
| | - Bradley G Goodyear
- Departments of Clinical Neurosciences and Radiology, Hotchkiss Brain Institute, University of Calgary, Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Sciences, Calgary, Alberta, Canada
| | - Richard Frayne
- Departments of Clinical Neurosciences and Radiology, Hotchkiss Brain Institute, University of Calgary, Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Sciences, Calgary, Alberta, Canada
| | - Eric E Smith
- Departments of Clinical Neurosciences and Radiology, Hotchkiss Brain Institute, University of Calgary, Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Sciences, Calgary, Alberta, Canada
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18
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Williams OA, Zeestraten EA, Benjamin P, Lambert C, Lawrence AJ, Mackinnon AD, Morris RG, Markus HS, Barrick TR, Charlton RA. Predicting Dementia in Cerebral Small Vessel Disease Using an Automatic Diffusion Tensor Image Segmentation Technique. Stroke 2019; 50:2775-2782. [PMID: 31510902 PMCID: PMC6756294 DOI: 10.1161/strokeaha.119.025843] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Supplemental Digital Content is available in the text. Cerebral small vessel disease (SVD) is the most common cause of vascular cognitive impairment, with a significant proportion of cases going on to develop dementia. We explore the extent to which diffusion tensor image segmentation technique (DSEG; which characterizes microstructural damage across the cerebrum) predicts both degree of cognitive decline and conversion to dementia, and hence may provide a useful prognostic procedure.
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Affiliation(s)
- Owen A Williams
- From the Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, United Kingdom (O.A.W., E.A.Z., C.L., T.R.B.)
| | - Eva A Zeestraten
- From the Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, United Kingdom (O.A.W., E.A.Z., C.L., T.R.B.)
| | - Philip Benjamin
- Department of Radiology, Charing Cross Hospital campus, Imperial College NHS Trust, United Kingdom (P.B.)
| | - Christian Lambert
- From the Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, United Kingdom (O.A.W., E.A.Z., C.L., T.R.B.).,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom (C.L.)
| | - Andrew J Lawrence
- Stroke Research Group, Clinical Neurosciences, University of Cambridge, United Kingdom (A.J.L., H.S.M.)
| | - Andrew D Mackinnon
- Atkinson Morley Regional Neuroscience Centre, St George's NHS Healthcare Trust, London, United Kingdom (A.G.M.)
| | - Robin G Morris
- Department of Psychology, King's College Institute of Psychiatry, Psychology, and Neuroscience, London, United Kingdom (R.G.M.)
| | - Hugh S Markus
- Stroke Research Group, Clinical Neurosciences, University of Cambridge, United Kingdom (A.J.L., H.S.M.)
| | - Thomas R Barrick
- From the Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, United Kingdom (O.A.W., E.A.Z., C.L., T.R.B.)
| | - Rebecca A Charlton
- Department of Psychology, Goldsmiths University of London, United Kingdom (R.A.C.)
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19
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Ter Telgte A, van Leijsen EMC, Wiegertjes K, Klijn CJM, Tuladhar AM, de Leeuw FE. Cerebral small vessel disease: from a focal to a global perspective. Nat Rev Neurol 2019; 14:387-398. [PMID: 29802354 DOI: 10.1038/s41582-018-0014-y] [Citation(s) in RCA: 273] [Impact Index Per Article: 54.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Cerebral small vessel disease (SVD) is commonly observed on neuroimaging among elderly individuals and is recognized as a major vascular contributor to dementia, cognitive decline, gait impairment, mood disturbance and stroke. However, clinical symptoms are often highly inconsistent in nature and severity among patients with similar degrees of SVD on brain imaging. Here, we provide a new framework based on new advances in structural and functional neuroimaging that aims to explain the remarkable clinical variation in SVD. First, we discuss the heterogeneous pathology present in SVD lesions despite an identical appearance on imaging and the perilesional and remote effects of these lesions. We review effects of SVD on structural and functional connectivity in the brain, and we discuss how network disruption by SVD can lead to clinical deficits. We address reserve and compensatory mechanisms in SVD and discuss the part played by other age-related pathologies. Finally, we conclude that SVD should be considered a global rather than a focal disease, as the classically recognized focal lesions affect remote brain structures and structural and functional network connections. The large variability in clinical symptoms among patients with SVD can probably be understood by taking into account the heterogeneity of SVD lesions, the effects of SVD beyond the focal lesions, the contribution of neurodegenerative pathologies other than SVD, and the interaction with reserve mechanisms and compensatory mechanisms.
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Affiliation(s)
- Annemieke Ter Telgte
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Esther M C van Leijsen
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Catharina J M Klijn
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands.
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20
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Ramanan VK, Przybelski SA, Graff-Radford J, Castillo AM, Lowe VJ, Mielke MM, Roberts RO, Reid RI, Knopman DS, Jack CR, Petersen RC, Vemuri P. Statins and Brain Health: Alzheimer's Disease and Cerebrovascular Disease Biomarkers in Older Adults. J Alzheimers Dis 2019; 65:1345-1352. [PMID: 30149450 DOI: 10.3233/jad-180446] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Statins have been proposed to reduce the risk of Alzheimer's disease (AD). OBJECTIVE Assess whether long-term statin use was associated with neuroimaging biomarkers of aging and dementia. METHODS Methods: We analyzed neuroimaging biomarkers in 1,160 individuals aged 65+ from the Mayo Clinic Study of Aging, a population-based prospective longitudinal study of cognitive aging. RESULTS Statin-treated (5+ years of therapy) individuals had greater burden of mid-and late-life cardiovascular disease (p < 0.001) than statin-untreated (≤3 months) individuals. Lower fractional anisotropy in the genu of the corpus callosum, an early marker of cerebrovascular disease, was associated with long-term statin exposure (p < 0.035). No significant associations were identified between long-term statin exposure and cerebral amyloid or tau burden, AD pattern neurodegeneration, or white matter hyperintensity burden. CONCLUSIONS Long-term statin therapy was not associated with differences in AD biomarkers. Individuals with long-term statin exposure had worse white matter integrity in the genu of the corpus callosum, consistent with the coexistence of higher cerebrovascular risk factor burden in this group.
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Affiliation(s)
- Vijay K Ramanan
- Department of Neurology, Mayo Clinic-Rochester, Rochester, MN, USA
| | - Scott A Przybelski
- Department of Health Sciences Research, Mayo Clinic-Rochester, Rochester, MN, USA
| | | | - Anna M Castillo
- Department of Health Sciences Research, Mayo Clinic-Rochester, Rochester, MN, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic-Rochester, Rochester, MN, USA
| | - Michelle M Mielke
- Department of Neurology, Mayo Clinic-Rochester, Rochester, MN, USA.,Department of Health Sciences Research, Mayo Clinic-Rochester, Rochester, MN, USA
| | - Rosebud O Roberts
- Department of Neurology, Mayo Clinic-Rochester, Rochester, MN, USA.,Department of Health Sciences Research, Mayo Clinic-Rochester, Rochester, MN, USA
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic-Rochester, Rochester, MN, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic-Rochester, Rochester, MN, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic-Rochester, Rochester, MN, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic-Rochester, Rochester, MN, USA.,Department of Health Sciences Research, Mayo Clinic-Rochester, Rochester, MN, USA
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21
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Faria AV, Crawford J, Ye C, Hsu J, Kenkare A, Scheretlen D, Sawa A. Relationship between neuropsychological behavior and brain white matter in first-episode psychosis. Schizophr Res 2019; 208:49-54. [PMID: 30987924 PMCID: PMC6544495 DOI: 10.1016/j.schres.2019.04.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 03/27/2019] [Accepted: 04/05/2019] [Indexed: 01/14/2023]
Abstract
We addressed the relationship between white matter architecture, represented by MRI fractional anisotropy (FA), and cognition in individuals with first-episode psychosis (FEP) by applying for a new methodology that allows whole brain parcellation of core and peripheral white matter in a biologically meaningful fashion. Regionally specific correlations were found in FEP between three specific domains of cognition (processing speed, attention/working memory, and executive functioning) and FA at the deep (cerebral peduncles, sagittal striatum, uncinate, internal/external capsule, cingulum) and peripheral white matter (adjacent to inferior temporal, angular, supramarginal, insula, occipital, rectus gyrus).
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Affiliation(s)
- Andreia V. Faria
- Department of Radiology, The Johns Hopkins University
School of Medicine, Baltimore, MD, USA;,Correspondence to: Andreia V. Faria, M.D.,PhD.,
Associate Professor, Magnetic Resonance Research Division, Department of
Radiology, The Johns Hopkins University School of Medicine., 217B Traylor Bldg.,
720 Rutland Ave., Baltimore, MD 21205., Phone: (410) 4109554215, Fax: (410)
614-1948,
| | - Jeffrey Crawford
- Department Psychiatry, The Johns Hopkins University School
of Medicine, Baltimore, MD, USA
| | - Chenfei Ye
- Department of Electronics and Information, Harbin Institute
of Technology Shenzhen Graduate School, Guangdong, China, 518055
| | - John Hsu
- Department of Radiology, The Johns Hopkins University
School of Medicine, Baltimore, MD, USA
| | - Anshel Kenkare
- Department Psychiatry, The Johns Hopkins University School
of Medicine, Baltimore, MD, USA
| | - David Scheretlen
- Department Psychiatry, The Johns Hopkins University School
of Medicine, Baltimore, MD, USA
| | - Akira Sawa
- Department Psychiatry, The Johns Hopkins University School
of Medicine, Baltimore, MD, USA;,Department of Biomedical Engineering, The Johns Hopkins
University School of Medicine, Baltimore, MD, USA;,Department of Neuroscience, The Johns Hopkins University
School of Medicine, Baltimore, MD, USA;,Department of Mental Health, The Johns Hopkins University
Bloomberg School of Public Health Baltimore, MD, USA
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22
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Raja R, Rosenberg G, Caprihan A. Review of diffusion MRI studies in chronic white matter diseases. Neurosci Lett 2019; 694:198-207. [PMID: 30528980 PMCID: PMC6380179 DOI: 10.1016/j.neulet.2018.12.007] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 12/03/2018] [Accepted: 12/04/2018] [Indexed: 02/07/2023]
Abstract
Diffusion MRI studies characterizing the changes in white matter (WM) due to vascular cognitive impairment, which includes all forms of small vessel disease are reviewed. We reviewed the usefulness of diffusion methods in discriminating the affected WM regions and its relation to cognitive impairment. These studies were categorized based on the diffusion MRI techniques used. The most common method was the diffusion tensor imaging, whereas other methods included diffusion weighted imaging, diffusion kurtosis imaging, intravoxel incoherent motion, and studies based on diffusion tractography. The diffusion measures showed correlation with cognitive scores and disease progression, with mean diffusivity being the most robust parameter. Future studies should focus on incorporating multi-compartment and higher order diffusion models, which can handle the presence of multiple and crossing fibers inside a voxel.
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Affiliation(s)
- Rajikha Raja
- The MIND Research Network, Albuquerque, NM, United States.
| | - Gary Rosenberg
- Department of Neurology, University of New Mexico Health Sciences Center, Albuquerque, NM, United States
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23
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Zhang X, Su J, Gao C, Ni W, Gao X, Li Y, Zhang J, Lei Y, Gu Y. Progression in Vascular Cognitive Impairment: Pathogenesis, Neuroimaging Evaluation, and Treatment. Cell Transplant 2019; 28:18-25. [PMID: 30488737 PMCID: PMC6322135 DOI: 10.1177/0963689718815820] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Vascular cognitive impairment (VCI) defines an entire spectrum of neurologic disorders from mild cognitive impairment to dementia caused by cerebral vascular disease. The pathogenesis of VCI includes ischemic factors (e.g., large vessel occlusion and small vessel dysfunction); hemorrhagic factors (e.g., intracerebral hemorrhage and subarachnoid hemorrhage); and other factors (combined with Alzheimer's disease). Clinical evaluations of VCI mainly refer to neuropsychological testing and imaging assessments, including structural and functional neuroimaging, with different advantages. At present, the main treatment for VCI focuses on neurological protection, cerebral blood flow reconstruction, and neurological rehabilitation, such as pharmacological treatment, revascularization, and cognitive training. In this review, we discuss the pathogenesis, neuroimaging evaluation, and treatment of VCI.
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Affiliation(s)
- Xin Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiabin Su
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Chao Gao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei Ni
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Xinjie Gao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jun Zhang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yu Lei
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
- Yu Lei and Yuxiang Gu, Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, No. 12 Middle Wulumuqi Road, Shanghai 200040, China. Emails: ;
| | - Yuxiang Gu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
- Yu Lei and Yuxiang Gu, Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, No. 12 Middle Wulumuqi Road, Shanghai 200040, China. Emails: ;
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24
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Vemuri P, Lesnick TG, Przybelski SA, Graff‐Radford J, Reid RI, Lowe VJ, Zuk SM, Senjem ML, Schwarz CG, Gunter JL, Kantarci K, Machulda MM, Mielke MM, Petersen RC, Knopman DS, Jack CR. Development of a cerebrovascular magnetic resonance imaging biomarker for cognitive aging. Ann Neurol 2018; 84:705-716. [PMID: 30264411 PMCID: PMC6282853 DOI: 10.1002/ana.25346] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 09/24/2018] [Accepted: 09/24/2018] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Recent availability of amyloid and tau positron emission tomography (PET) has provided us with a unique opportunity to measure the association of systemic vascular health with brain health after accounting for the impact of Alzheimer disease (AD) pathologies. We wanted to quantify early cerebrovascular health-related magnetic resonance imaging brain measures (structure, perfusion, microstructural integrity) and evaluate their utility as a biomarker for cerebrovascular health. METHODS We used 2 independent samples (discovery, n = 390; validation, n = 1,035) of individuals, aged ≥ 60 years, along the cognitive continuum with imaging from the population-based sample of Mayo Clinic Study of Aging. We ascertained vascular health by summing up recently existing cardiovascular and metabolic conditions (CMC) from health care records (hypertension, hyperlipidemia, cardiac arrhythmias, coronary artery disease, congestive heart failure, diabetes mellitus, and stroke). Using multiple regression models, we quantified associations between CMC and brain health after accounting for age, sex, education/occupation, and AD burden (from amyloid and tau PET). RESULTS Systemic vascular health was associated with medial temporal lobe thinning, widespread cerebral hypoperfusion, and loss of microstructural integrity in several white matter tracts including the corpus callosum and fornix. Further investigations suggested that microstructural integrity of the genu of the corpus callosum was suitable for assessing prodromal cerebrovascular health, had similar distributions in the discovery and independent validation datasets, and predicted cognitive performance above and beyond amyloid deposition. INTERPRETATION Systemic vascular health has significant impact on brain structure and function. Quantifying prodromal cerebrovascular health-related brain measures that are independent of AD pathology-related changes has great utility for cognitive aging. Ann Neurol 2018;84:713-724.
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Affiliation(s)
| | | | | | | | - Robert I. Reid
- Department of Information TechnologyMayo ClinicRochesterMN
| | - Val J. Lowe
- Department of RadiologyMayo ClinicRochesterMN
| | | | - Matthew L. Senjem
- Department of RadiologyMayo ClinicRochesterMN
- Department of Information TechnologyMayo ClinicRochesterMN
| | | | - Jeffrey L. Gunter
- Department of RadiologyMayo ClinicRochesterMN
- Department of Information TechnologyMayo ClinicRochesterMN
| | | | | | - Michelle M. Mielke
- Department of Health Sciences ResearchMayo ClinicRochesterMN
- Department of NeurologyMayo ClinicRochesterMN
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25
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Zhu H, Li Z, Lv J, Zhao R. Effects of cerebral small vessel disease on the outcome of patients with ischemic stroke caused by large artery atherosclerosis. Neurol Res 2018. [PMID: 29543130 DOI: 10.1080/01616412.2018.1446283] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Hui Zhu
- Medical College, Qingdao University, Qingdao, China
| | - Zhixing Li
- Medical College, Qingdao University, Qingdao, China
| | - Jinglei Lv
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Renliang Zhao
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China
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