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Abdolahi F, Yu V, Varma R, Zhou X, Wang RK, D'Orazio LM, Zhao C, Jann K, Wang DJ, Kashani AH, Jiang X. Retinal perfusion is linked to cognition and brain MRI biomarkers in Black Americans. Alzheimers Dement 2024; 20:858-868. [PMID: 37800578 PMCID: PMC10917050 DOI: 10.1002/alz.13469] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 08/14/2023] [Accepted: 08/17/2023] [Indexed: 10/07/2023]
Abstract
INTRODUCTION We investigated whether retinal capillary perfusion is a biomarker of cerebral small vessel disease and impaired cognition among Black Americans, an understudied group at higher risk for dementia. METHODS We enrolled 96 Black Americans without known cognitive impairment. Four retinal perfusion measures were derived using optical coherence tomography angiography. Neurocognitive assessment and brain magnetic resonance imaging (MRI) were performed. Multiple linear regression analyses were performed. RESULTS Lower retinal capillary perfusion was correlated with worse Oral Symbol Digit Test (P < = 0.005) and Fluid Cognition Composite scores (P < = 0.02), but not with the Crystallized Cognition Composite score (P > = 0.41). Lower retinal perfusion was also correlated with higher free water and peak width of skeletonized mean diffusivity, and lower fractional anisotropy (all P < 0.05) on MRI (N = 35). DISCUSSION Lower retinal capillary perfusion is associated with worse information processing, fluid cognition, and MRI biomarkers of cerebral small vessel disease, but is not related to crystallized cognition.
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Affiliation(s)
- Farzan Abdolahi
- Department of OphthalmologyUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
| | - Victoria Yu
- Department of OphthalmologyUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
| | - Rohit Varma
- Southern California Eye InstituteCHA Hollywood Presbyterian Medical CenterLos AngelesCaliforniaUSA
| | - Xiao Zhou
- Department of BioengineeringUniversity of WashingtonSeattleWashingtonUSA
| | - Ruikang K. Wang
- Department of BioengineeringUniversity of WashingtonSeattleWashingtonUSA
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
| | - Lina M. D'Orazio
- Department of NeurologyUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
| | - Chenyang Zhao
- Laboratory of FMRI TechnologyStevens Neuroimaging and Informatics InstituteUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
| | - Kay Jann
- Laboratory of FMRI TechnologyStevens Neuroimaging and Informatics InstituteUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
| | - Danny J. Wang
- Department of NeurologyUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
- Laboratory of FMRI TechnologyStevens Neuroimaging and Informatics InstituteUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
| | - Amir H. Kashani
- Department of OphthalmologyWilmer Eye InstituteJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Xuejuan Jiang
- Department of OphthalmologyUniversity of Southern California Keck School of MedicineLos AngelesCaliforniaUSA
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Marawi T, Zhukovsky P, Rashidi-Ranjbar N, Bowie CR, Brooks H, Fischer CE, Flint AJ, Herrmann N, Mah L, Pollock BG, Rajji TK, Tartaglia MC, Voineskos AN, Mulsant BH. Brain-Cognition Associations in Older Patients With Remitted Major Depressive Disorder or Mild Cognitive Impairment: A Multivariate Analysis of Gray and White Matter Integrity. Biol Psychiatry 2023; 94:913-923. [PMID: 37271418 DOI: 10.1016/j.biopsych.2023.05.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 05/10/2023] [Accepted: 05/24/2023] [Indexed: 06/06/2023]
Abstract
BACKGROUND Almost half of older patients with major depressive disorder (MDD) present with cognitive impairment, and one-third meet diagnostic criteria for mild cognitive impairment (MCI). However, mechanisms linking MDD and MCI remain unclear. We investigated multivariate associations between brain structural alterations and cognition in 3 groups of older patients at risk for dementia, remitted MDD (rMDD), MCI, and rMDD+MCI, as well as cognitively healthy nondepressed control participants. METHODS We analyzed magnetic resonance imaging data and cognitive domain scores in participants from the PACt-MD (Prevention of Alzheimer's Disease With Cognitive Remediation Plus Transcranial Direct Current Stimulation in Mild Cognitive Impairment and Depression) study. Following quality control, we measured cortical thickness and subcortical volumes of selected regions from 283 T1-weighted scans and fractional anisotropy of white matter tracts from 226 diffusion-weighted scans. We assessed brain-cognition associations using partial least squares regressions in the whole sample and in each subgroup. RESULTS In the entire sample, atrophy in the medial temporal lobe and subregions of the motor and prefrontal cortex was associated with deficits in verbal and visuospatial memory, language skills, and, to a lesser extent, processing speed (p < .0001; multivariate r = 0.30, 0.34, 0.26, and 0.18, respectively). Widespread reduced white matter integrity was associated with deficits in executive functioning, working memory, and processing speed (p = .008; multivariate r = 0.21, 0.26, 0.35, respectively). Overall, associations remained significant in the MCI and rMDD+MCI groups, but not the rMDD or healthy control groups. CONCLUSIONS We confirm findings of brain-cognition associations previously reported in MCI and extend them to rMDD+MCI, but similar associations in rMDD are not supported. Early-onset and treated MDD might not contribute to structural alterations associated with cognitive impairment.
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Affiliation(s)
- Tulip Marawi
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Peter Zhukovsky
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Neda Rashidi-Ranjbar
- Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Christopher R Bowie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychology, Queen's University, Kingston, Ontario, Canada; Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
| | - Heather Brooks
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Corinne E Fischer
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Alastair J Flint
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
| | - Nathan Herrmann
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Sunnybrook Health Sciences Centre, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Linda Mah
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Baycrest Health Services, Rotman Research Institute, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Bruce G Pollock
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Tarek K Rajji
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Toronto Dementia Research Alliance, University of Toronto, Toronto, Ontario, Canada
| | - Maria Carmela Tartaglia
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada
| | - Aristotle N Voineskos
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Benoit H Mulsant
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Toronto Dementia Research Alliance, University of Toronto, Toronto, Ontario, Canada.
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Fongang B, Satizabal C, Kautz TF, Wadop YN, Muhammad JAS, Vasquez E, Mathews J, Gireud-Goss M, Saklad AR, Himali J, Beiser A, Cavazos JE, Mahaney MC, Maestre G, DeCarli C, Shipp EL, Vasan RS, Seshadri S. Cerebral small vessel disease burden is associated with decreased abundance of gut Barnesiella intestinihominis bacterium in the Framingham Heart Study. Sci Rep 2023; 13:13622. [PMID: 37604954 PMCID: PMC10442369 DOI: 10.1038/s41598-023-40872-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 08/17/2023] [Indexed: 08/23/2023] Open
Abstract
A bidirectional communication exists between the brain and the gut, in which the gut microbiota influences cognitive function and vice-versa. Gut dysbiosis has been linked to several diseases, including Alzheimer's disease and related dementias (ADRD). However, the relationship between gut dysbiosis and markers of cerebral small vessel disease (cSVD), a major contributor to ADRD, is unknown. In this cross-sectional study, we examined the connection between the gut microbiome, cognitive, and neuroimaging markers of cSVD in the Framingham Heart Study (FHS). Markers of cSVD included white matter hyperintensities (WMH), peak width of skeletonized mean diffusivity (PSMD), and executive function (EF), estimated as the difference between the trail-making tests B and A. We included 972 FHS participants with MRI scans, neurocognitive measures, and stool samples and quantified the gut microbiota composition using 16S rRNA sequencing. We used multivariable association and differential abundance analyses adjusting for age, sex, BMI, and education level to estimate the association between gut microbiota and WMH, PSMD, and EF measures. Our results suggest an increased abundance of Pseudobutyrivibrio and Ruminococcus genera was associated with lower WMH and PSMD (p values < 0.001), as well as better executive function (p values < 0.01). In addition, in both differential and multivariable analyses, we found that the gram-negative bacterium Barnesiella intestinihominis was strongly associated with markers indicating a higher cSVD burden. Finally, functional analyses using PICRUSt implicated various KEGG pathways, including microbial quorum sensing, AMP/GMP-activated protein kinase, phenylpyruvate, and β-hydroxybutyrate production previously associated with cognitive performance and dementia. Our study provides important insights into the association between the gut microbiome and cSVD, but further studies are needed to replicate the findings.
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Affiliation(s)
- Bernard Fongang
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
- Department of Biochemistry and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
| | - Claudia Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Tiffany F Kautz
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Yannick N Wadop
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Jazmyn A S Muhammad
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Erin Vasquez
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Julia Mathews
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Monica Gireud-Goss
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Amy R Saklad
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Jayandra Himali
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Alexa Beiser
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Jose E Cavazos
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Michael C Mahaney
- Department of Human Genetics, South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Gladys Maestre
- Department of Neurosciences and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Charles DeCarli
- Department of Neurology, Alzheimer's Disease Center, University of California, Davis, Sacramento, CA, USA
| | - Eric L Shipp
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Ramachandran S Vasan
- Framingham Heart Study, Framingham, MA, USA
- Department of Medicine, Section of Cardiovascular Medicine, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
- Department of Medicine, Section of Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Boston University's Center for Computing and Data Sciences, Boston, MA, USA
- The University of Texas School of Public Health in San Antonio, San Antonio, TX, USA
- The Long School of Medicine, University of Texas Health Science Center, San Antonio, TX, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
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4
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Luo X, Hong H, Li K, Zeng Q, Wang S, Li Z, Fu Y, Liu X, Hong L, Li J, Zhang X, Zhong S, Jiaerken Y, Liu Z, Chen Y, Huang P, Zhang M. Distinct cerebral small vessel disease impairment in early- and late-onset Alzheimer's disease. Ann Clin Transl Neurol 2023; 10:1326-1337. [PMID: 37345812 PMCID: PMC10424647 DOI: 10.1002/acn3.51824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 05/10/2023] [Accepted: 05/26/2023] [Indexed: 06/23/2023] Open
Abstract
OBJECTIVE This study investigated cerebral small vessel disease (CSVD) damage patterns in early-onset and late-onset Alzheimer's disease (EOAD and LOAD) and their effects on cognitive function. METHODS This study included 93 participants, 45 AD patients (14 EOAD and 31 LOAD), and 48 normal controls (13 YNC and 35 ONC) from the ADNI database. All participants had diffusion tensor imaging data; some had amyloid PET and plasma p-tau181 data. The study used peak width of skeletonized mean diffusivity (PSMD) to measure CSVD severity and compared PSMD between patients and age-matched controls. The effect of age on the relationship between PSMD and cognition was also examined. The study also repeated the analysis in amyloid-positive AD patients and amyloid-negative controls in another independent database (31 EOAD and 38 LOAD), and the merged database. RESULTS EOAD and LOAD showed similar cognitive function and disease severity. PSMD was validated as a reliable correlate of cognitive function. In the ADNI database, PSMD was significantly higher for LOAD and showed a tendency to increase for EOAD; in the independent and merged databases, PSMD was significantly higher for both LOAD and EOAD. The impact of PSMD on cognitive function was notably greater in the younger group (YNC and EOAD) than in the older group (ONC and LOAD), as supported by the ADNI and merged databases. INTERPRETATION EOAD has less CSVD burden than LOAD, but has a greater impact on cognition. Proactive cerebrovascular prevention strategies may have potential clinical value for younger older adults with cognitive decline.
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Affiliation(s)
- Xiao Luo
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Hui Hong
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Kaicheng Li
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Qingze Zeng
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Shuyue Wang
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Zheyu Li
- Department of NeurologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Yanv Fu
- Department of NeurologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Xiaocao Liu
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Luwei Hong
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Jixuan Li
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Xinyi Zhang
- Department of NeurologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Siyan Zhong
- Department of NeurologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Yeerfan Jiaerken
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Zhirong Liu
- Department of NeurologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Yanxing Chen
- Department of NeurologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Peiyu Huang
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
| | - Minming Zhang
- Department of RadiologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouChina
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5
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Zanon Zotin MC, Yilmaz P, Sveikata L, Schoemaker D, van Veluw SJ, Etherton MR, Charidimou A, Greenberg SM, Duering M, Viswanathan A. Peak Width of Skeletonized Mean Diffusivity: A Neuroimaging Marker for White Matter Injury. Radiology 2023; 306:e212780. [PMID: 36692402 PMCID: PMC9968775 DOI: 10.1148/radiol.212780] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 10/01/2022] [Accepted: 10/14/2022] [Indexed: 01/25/2023]
Abstract
A leading cause of white matter (WM) injury in older individuals is cerebral small vessel disease (SVD). Cerebral SVD is the most prevalent vascular contributor to cognitive impairment and dementia. Therapeutic progress for cerebral SVD and other WM disorders depends on the development and validation of neuroimaging markers suitable as outcome measures in future interventional trials. Diffusion-tensor imaging (DTI) is one of the best-suited MRI techniques for assessing the extent of WM damage in the brain. But the optimal method to analyze individual DTI data remains hindered by labor-intensive and time-consuming processes. Peak width of skeletonized mean diffusivity (PSMD), a recently developed fast, fully automated DTI marker, was designed to quantify the WM damage secondary to cerebral SVD and reflect related cognitive impairment. Despite its promising results, knowledge about PSMD is still limited in the radiologic community. This focused review provides an overview of the technical details of PSMD while synthesizing the available data on its clinical and neuroimaging associations. From a critical expert viewpoint, the authors discuss the limitations of PSMD and its current validation status as a neuroimaging marker for vascular cognitive impairment. Finally, they point out the gaps to be addressed to further advance the field.
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Affiliation(s)
| | | | - Lukas Sveikata
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Dorothee Schoemaker
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Susanne J. van Veluw
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Mark R. Etherton
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Andreas Charidimou
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Steven M. Greenberg
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Marco Duering
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Anand Viswanathan
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
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6
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Wang S, Zhang F, Huang P, Hong H, Jiaerken Y, Yu X, Zhang R, Zeng Q, Zhang Y, Kikinis R, Rathi Y, Makris N, Lou M, Pasternak O, Zhang M, O'Donnell LJ. Superficial white matter microstructure affects processing speed in cerebral small vessel disease. Hum Brain Mapp 2022; 43:5310-5325. [PMID: 35822593 PMCID: PMC9812245 DOI: 10.1002/hbm.26004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/10/2022] [Accepted: 06/15/2022] [Indexed: 01/15/2023] Open
Abstract
White matter hyperintensities (WMH) are a typical feature of cerebral small vessel disease (CSVD), which contributes to about 50% of dementias worldwide. Microstructural alterations in deep white matter (DWM) have been widely examined in CSVD. However, little is known about abnormalities in superficial white matter (SWM) and their relevance for processing speed, the main cognitive deficit in CSVD. In 141 CSVD patients, processing speed was assessed using Trail Making Test Part A. White matter abnormalities were assessed by WMH burden (volume on T2-FLAIR) and diffusion MRI measures. SWM imaging measures had a large contribution to processing speed, despite a relatively low SWM WMH burden. Across all imaging measures, SWM free water (FW) had the strongest association with processing speed, followed by SWM mean diffusivity (MD). SWM FW was the only marker to significantly increase between two subgroups with the lowest WMH burdens. When comparing two subgroups with the highest WMH burdens, the involvement of WMH in the SWM was accompanied by significant differences in processing speed and white matter microstructure. Mediation analysis revealed that SWM FW fully mediated the association between WMH volume and processing speed, while no mediation effect of MD or DWM FW was observed. Overall, results suggest that the SWM has an important contribution to processing speed, while SWM FW is a sensitive imaging marker associated with cognition in CSVD. This study extends the current understanding of CSVD-related dysfunction and suggests that the SWM, as an understudied region, can be a potential target for monitoring pathophysiological processes.
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Affiliation(s)
- Shuyue Wang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Peiyu Huang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Hui Hong
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Yeerfan Jiaerken
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Xinfeng Yu
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ruiting Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Qingze Zeng
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Yao Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ron Kikinis
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Center for Morphometric AnalysisMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Min Lou
- Department of Neurologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ofer Pasternak
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Minming Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
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7
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Hu Y, Wu Y, Su H, Tu J, Zeng L, Lei J, Xia L. Exploring the relationship between brain white matter change and higher degree of invisible hand tremor with computer technology. Technol Health Care 2022; 31:921-931. [PMID: 36442160 DOI: 10.3233/thc-220361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
BACKGROUND: At present, the clinical diagnosis of white matter change (WMC) patients depends on cranial magnetic resonance imaging (MRI) technology. This diagnostic method is costly and does not allow for large-scale screening, leading to delays in the patient’s condition due to inability to receive timely diagnosis. OBJECTIVE: To evaluate whether the burden of WMC is associated with the degree of invisible hand tremor in humans. METHODS: Previous studies have shown that tremor is associated with WMC, however, tremor does not always have imaging of WMC. Therefore, to confirm that the appearance of WMC causes tremor, which are sometimes invisible to the naked eye, we achieved an optical-based computer-aided diagnostic device by detecting the invisible hand tremor, and we proposed a calculation method of WMC volume by using the characteristics of MRI images. RESULTS: Statistical analysis results further clarified the relationship between WMC and tremor, and our devices are validated for the detection of tremors with WMC. CONCLUSIONS: The burden of WMC volume is positive factor for degree of invisible hand tremor in the participants without visible hand tremor. Detection technology provides a more convenient and low-cost evaluating method before MRI for tremor diseases.
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Affiliation(s)
- Yang Hu
- Department of Cardiovascular Medicine, The Second Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Yanqing Wu
- Department of Cardiovascular Medicine, The Second Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Hai Su
- Department of Cardiovascular Medicine, The Second Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Jianglong Tu
- Department of Nephrology Medicine, The Second Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Luchuan Zeng
- School of Software, Nanchang University, Nanchang, Jiangxi, China
| | - Jie Lei
- School of Software, Nanchang University, Nanchang, Jiangxi, China
| | - Linglin Xia
- School of Software, Nanchang University, Nanchang, Jiangxi, China
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8
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Zanon Zotin MC, Schoemaker D, Raposo N, Perosa V, Bretzner M, Sveikata L, Li Q, van Veluw SJ, Horn MJ, Etherton MR, Charidimou A, Gurol ME, Greenberg SM, Duering M, dos Santos AC, Pontes-Neto OM, Viswanathan A. Peak width of skeletonized mean diffusivity in cerebral amyloid angiopathy: Spatial signature, cognitive, and neuroimaging associations. Front Neurosci 2022; 16:1051038. [PMID: 36440281 PMCID: PMC9693722 DOI: 10.3389/fnins.2022.1051038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 10/25/2022] [Indexed: 11/13/2022] Open
Abstract
Background Peak width of skeletonized mean diffusivity (PSMD) is a promising diffusion tensor imaging (DTI) marker that shows consistent and strong cognitive associations in the context of different cerebral small vessel diseases (cSVD). Purpose Investigate whether PSMD (1) is higher in patients with Cerebral Amyloid Angiopathy (CAA) than those with arteriolosclerosis; (2) can capture the anteroposterior distribution of CAA-related abnormalities; (3) shows similar neuroimaging and cognitive associations in comparison to other classical DTI markers, such as average mean diffusivity (MD) and fractional anisotropy (FA). Materials and methods We analyzed cross-sectional neuroimaging and neuropsychological data from 90 non-demented memory-clinic subjects from a single center. Based on MRI findings, we classified them into probable-CAA (those that fulfilled the modified Boston criteria), subjects with MRI markers of cSVD not attributable to CAA (presumed arteriolosclerosis; cSVD), and subjects without evidence of cSVD on MRI (non-cSVD). We compared total and lobe-specific (frontal and occipital) DTI metrics values across the groups. We used linear regression models to investigate how PSMD, MD, and FA correlate with conventional neuroimaging markers of cSVD and cognitive scores in CAA. Results PSMD was comparable in probable-CAA (median 4.06 × 10–4 mm2/s) and cSVD (4.07 × 10–4 mm2/s) patients, but higher than in non-cSVD (3.30 × 10–4 mm2/s; p < 0.001) subjects. Occipital-frontal PSMD gradients were higher in probable-CAA patients, and we observed a significant interaction between diagnosis and region on PSMD values [F(2, 87) = 3.887, p = 0.024]. PSMD was mainly associated with white matter hyperintensity volume, whereas MD and FA were also associated with other markers, especially with the burden of perivascular spaces. PSMD correlated with worse executive function (β = −0.581, p < 0.001) and processing speed (β = −0.463, p = 0.003), explaining more variance than other MRI markers. MD and FA were not associated with performance in any cognitive domain. Conclusion PSMD is a promising biomarker of cognitive impairment in CAA that outperforms other conventional and DTI-based neuroimaging markers. Although global PSMD is similarly increased in different forms of cSVD, PSMD’s spatial variations could potentially provide insights into the predominant type of underlying microvascular pathology.
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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, MA, 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, Brazil
- *Correspondence: Maria Clara Zanon Zotin, ,
| | - Dorothee Schoemaker
- Department of Psychiatry, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States
| | - Nicolas Raposo
- Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France
| | | | - Martin Bretzner
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog (JPARC) - Lille Neurosciences & Cognition, Lille, France
| | - Lukas Sveikata
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland
- Institute of Cardiology, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Qi Li
- The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Susanne J. van Veluw
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Mitchell J. Horn
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Mark R. Etherton
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Andreas Charidimou
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Department of Neurology, Boston University School of Medicine, Boston University Medical Center, Boston, MA, United States
| | - M. Edip Gurol
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Steven M. Greenberg
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Marco Duering
- Department of Biomedical Engineering, Medical Imaging Analysis Center (MIAC), University of Basel, Basel, Switzerland
| | - Antonio Carlos dos Santos
- 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, Brazil
| | - Octavio M. Pontes-Neto
- Department of Neuroscience and Behavioral Sciences, 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, MA, United States
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9
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Haddad SMH, Scott CJM, Ozzoude M, Berezuk C, Holmes M, Adamo S, Ramirez J, Arnott SR, Nanayakkara ND, Binns M, Beaton D, Lou W, Sunderland K, Sujanthan S, Lawrence J, Kwan D, Tan B, Casaubon L, Mandzia J, Sahlas D, Saposnik G, Hassan A, Levine B, McLaughlin P, Orange JB, Roberts A, Troyer A, Black SE, Dowlatshahi D, Strother SC, Swartz RH, Symons S, Montero-Odasso M, ONDRI Investigators, Bartha R. Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity. Int J Biomed Imaging 2022; 2022:5860364. [PMID: 36313789 PMCID: PMC9616672 DOI: 10.1155/2022/5860364] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 04/21/2022] [Accepted: 06/01/2022] [Indexed: 11/13/2023] Open
Abstract
Alterations in tissue microstructure in normal-appearing white matter (NAWM), specifically measured by diffusion tensor imaging (DTI) fractional anisotropy (FA), have been associated with cognitive outcomes following stroke. The purpose of this study was to comprehensively compare conventional DTI measures of tissue microstructure in NAWM to diverse vascular brain lesions in people with cerebrovascular disease (CVD) and to examine associations between FA in NAWM and cerebrovascular risk factors. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured in cerebral tissues and cerebrovascular anomalies from 152 people with CVD participating in the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Ten cerebral tissue types were segmented including NAWM, and vascular lesions including stroke, periventricular and deep white matter hyperintensities, periventricular and deep lacunar infarcts, and perivascular spaces (PVS) using T1-weighted, proton density-weighted, T2-weighted, and fluid attenuated inversion recovery MRI scans. Mean DTI metrics were measured in each tissue region using a previously developed DTI processing pipeline and compared between tissues using multivariate analysis of covariance. Associations between FA in NAWM and several CVD risk factors were also examined. DTI metrics in vascular lesions differed significantly from healthy tissue. Specifically, all tissue types had significantly different MD values, while FA was also found to be different in most tissue types. FA in NAWM was inversely related to hypertension and modified Rankin scale (mRS). This study demonstrated the differences between conventional DTI metrics, FA, MD, AD, and RD, in cerebral vascular lesions and healthy tissue types. Therefore, incorporating DTI to characterize the integrity of the tissue microstructure could help to define the extent and severity of various brain vascular anomalies. The association between FA within NAWM and clinical evaluation of hypertension and disability provides further evidence that white matter microstructural integrity is impacted by cerebrovascular function.
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Affiliation(s)
- Seyyed M. H. Haddad
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
| | - Christopher J. M. Scott
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | - Miracle Ozzoude
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | | | - Melissa Holmes
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | - Sabrina Adamo
- Clinical Neurosciences, University of Toronto, Toronto, Canada
| | - Joel Ramirez
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | - Stephen R. Arnott
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Nuwan D. Nanayakkara
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
| | - Malcolm Binns
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Wendy Lou
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Kelly Sunderland
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | | | - Jane Lawrence
- Thunder Bay Regional Health Research Institute, Thunder Bay, Canada
| | | | - Brian Tan
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Leanne Casaubon
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Jennifer Mandzia
- Department of Medicine, Division of Neurology, University of Western Ontario, London, Canada
| | - Demetrios Sahlas
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | | | - Ayman Hassan
- Thunder Bay Regional Research Institute, Thunder Bay, Canada
| | - Brian Levine
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | | | - J. B. Orange
- School of Communication Sciences and Disorders, Western University, London, Canada
| | - Angela Roberts
- Roxelyn and Richard Pepper Department of Communication Sciences and Disorder, Northwestern University, Evanston, USA
| | - Angela Troyer
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Sandra E. Black
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
- Sunnybrook Health Sciences Centre, University of Toronto, Stroke Research Program, Toronto, Canada
| | | | - Stephen C. Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Richard H. Swartz
- Sunnybrook Health Sciences Centre, University of Toronto, Stroke Research Program, Toronto, Canada
| | - Sean Symons
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Manuel Montero-Odasso
- Department of Medicine, Division of Geriatric Medicine, Parkwood Hospital, St. Joseph's Health Care London, London, Canada
| | - ONDRI Investigators
- Ontario Neurodegenerative Disease Initiative, Ontario Brain Institute, Toronto, Canada
| | - Robert Bartha
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
- Department of Medical Biophysics, University of Western Ontario, London, Canada
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10
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Associations of Peak-Width Skeletonized Mean Diffusivity and Post-Stroke Cognition. Life (Basel) 2022; 12:life12091362. [PMID: 36143398 PMCID: PMC9504440 DOI: 10.3390/life12091362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 12/02/2022] Open
Abstract
Post-stroke cognitive impairment is common and can have major impact on life after stroke. Peak-width of Skeletonized Mean Diffusivity (PSMD) is a diffusion imaging marker of white matter microstructure and is also associated with cognition. Here, we examined associations between PSMD and post-stroke global cognition in an ongoing study of mild ischemic stroke patients. We studied cross-sectional associations between PSMD and cognition at both 3-months (N = 229) and 1-year (N = 173) post-stroke, adjusted for premorbid IQ, sex, age, stroke severity and disability, as well as the association between baseline PSMD and 1-year cognition. At baseline, (mean age = 65.9 years (SD = 11.1); 34% female), lower Montreal Cognitive Assessment (MoCA) scores were associated with older age, lower premorbid IQ and higher stroke severity, but not with PSMD (βstandardized = −0.116, 95% CI −0.241, 0.009; p = 0.069). At 1-year, premorbid IQ, older age, higher stroke severity and higher PSMD (βstandardized = −0.301, 95% CI −0.434, −0.168; p < 0.001) were associated with lower MoCA. Higher baseline PSMD was associated with lower 1-year MoCA (βstandardized = −0.182, 95% CI −0.308, −0.056; p = 0.005). PSMD becomes more associated with global cognition at 1-year post-stroke, possibly once acute effects have settled. Additionally, PSMD in the subacute phase after a mild stroke could help predict long-term cognitive impairment.
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11
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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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12
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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: 9] [Impact Index Per Article: 4.5] [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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13
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Chylińska M, Karaszewski B, Komendziński J, Wyszomirski A, Sabisz A, Halas M, Szurowska E. Skeletonized mean diffusivity and neuropsychological performance in relapsing-remitting multiple sclerosis. Brain Behav 2022; 12:e2591. [PMID: 35560868 PMCID: PMC9226842 DOI: 10.1002/brb3.2591] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 01/18/2022] [Accepted: 04/10/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Peak width of Skeletonized Mean Diffusivity (PSMD), as a novel marker of white matter (WM) microstructure damage, is associated with cognitive decline in several WM pathologies (i.e., small vessel disorders). We hypothesized that markers combining alterations in whole WM could be associated with cognitive dysfunction in relapsing-remitting multiple sclerosis (RRMS) patients. METHODS We used PSMD based on tract-based spatial statistics (TBSS) of diffusion tensor imaging (DTI) magnetic resonance (MR) scans. We investigated RRMS patients (n = 73) undergoing interferon beta (IFN-β) therapy. In this cross-sectional study, we investigated the association between neuropsychological data and clinical and MRI variables: PSMD, WM hypointensities, and normalized brain volume (NBV). RESULTS In our cohort, 37 (50.7%) patients were recognized as cognitively impaired (CI) and 36 (49.3%) patients were cognitively normal (CN). In regression analysis, PSMD was a statistically significant contributor in the California Verbal Learning Test (CVLT) list A (p = 0.04) and semantic fluency (p = 0.036). PSMD (p < 0.001, r2 = 0.35), NBV (p = 0.002, r2 = 2.6) and WM hypointensities (p < 0.001, r2 = 0.40) were major contributors to upper extremity disability (9HPT) in the CN subgroup. A significant contributor in the majority of neuropsychological measures was education attainment. CONCLUSION We investigated PSMD as a new parameter of WM microstructure damage that is a contributor in complex cognitive tasks, CVLT performance, and semantic fluency. PSMD was a statistically significant contributor to upper extremity disability (9HPT) together with WM hypointensities and NBV. Education attainment proved to be relevant in the majority of cognitive domains. Further studies are needed to estimate PSMD relevance as a marker of CI in MS.
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Affiliation(s)
- Magdalena Chylińska
- Department of Adult NeurologyMedical University of Gdańsk, Faculty of MedicineGdańskPoland
| | - Bartosz Karaszewski
- Department of Adult NeurologyMedical University of Gdańsk, Faculty of MedicineGdańskPoland
| | - Jakub Komendziński
- Department of Adult NeurologyMedical University of Gdańsk, Faculty of MedicineGdańskPoland
| | - Adam Wyszomirski
- Department of Adult NeurologyMedical University of Gdańsk, Faculty of MedicineGdańskPoland
| | - Agnieszka Sabisz
- 2nd Department of RadiologyMedical University of Gdańsk, Faculty of MedicineGdańskPoland
| | - Marek Halas
- Department of Adult NeurologyMedical University of Gdańsk, Faculty of MedicineGdańskPoland
| | - Edyta Szurowska
- 2nd Department of RadiologyMedical University of Gdańsk, Faculty of MedicineGdańskPoland
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14
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Wheater ENW, Galdi P, McCartney DL, Blesa M, Sullivan G, Stoye DQ, Lamb G, Sparrow S, Murphy L, Wrobel N, Quigley AJ, Semple S, Thrippleton MJ, Wardlaw JM, Bastin ME, Marioni RE, Cox SR, Boardman JP. DNA methylation in relation to gestational age and brain dysmaturation in preterm infants. Brain Commun 2022; 4:fcac056. [PMID: 35402911 PMCID: PMC8984700 DOI: 10.1093/braincomms/fcac056] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 12/10/2021] [Accepted: 03/04/2022] [Indexed: 11/14/2022] Open
Abstract
Preterm birth is associated with dysconnectivity of structural brain networks and is a leading cause of neurocognitive impairment in childhood. Variation in DNA methylation is associated with early exposure to extrauterine life but there has been little research exploring its relationship with brain development. Using genome-wide DNA methylation data from the saliva of 258 neonates, we investigated the impact of gestational age on the methylome and performed functional analysis to identify enriched gene sets from probes that contributed to differentially methylated probes or regions. We tested the hypothesis that variation in DNA methylation could underpin the association between low gestational age at birth and atypical brain development by linking differentially methylated probes with measures of white matter connectivity derived from diffusion MRI metrics: peak width skeletonized mean diffusivity, peak width skeletonized fractional anisotropy and peak width skeletonized neurite density index. Gestational age at birth was associated with widespread differential methylation at term equivalent age, with genome-wide significant associations observed for 8870 CpG probes (P < 3.6 × 10-8) and 1767 differentially methylated regions. Functional analysis identified 14 enriched gene ontology terms pertaining to cell-cell contacts and cell-extracellular matrix contacts. Principal component analysis of probes with genome-wide significance revealed a first principal component that explained 23.5% of the variance in DNA methylation, and this was negatively associated with gestational age at birth. The first principal component was associated with peak width of skeletonized mean diffusivity (β = 0.349, P = 8.37 × 10-10) and peak width skeletonized neurite density index (β = 0.364, P = 4.15 × 10-5), but not with peak width skeletonized fraction anisotropy (β = -0.035, P = 0.510); these relationships mirrored the imaging metrics' associations with gestational age at birth. Low gestational age at birth has a profound and widely distributed effect on the neonatal saliva methylome that is apparent at term equivalent age. Enriched gene ontology terms related to cell-cell contacts reveal pathways that could mediate the effect of early life environmental exposures on development. Finally, associations between differential DNA methylation and image markers of white matter tract microstructure suggest that variation in DNA methylation may provide a link between preterm birth and the dysconnectivity of developing brain networks that characterizes atypical brain development in preterm infants.
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Affiliation(s)
- Emily N. W. Wheater
- MRC Centre for Reproductive Health, The University of Edinburgh, Queen’s Medical Research Institute, Edinburgh EH16 4TJ, UK
| | - Paola Galdi
- MRC Centre for Reproductive Health, The University of Edinburgh, Queen’s Medical Research Institute, Edinburgh EH16 4TJ, UK
| | - Daniel L. McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Manuel Blesa
- MRC Centre for Reproductive Health, The University of Edinburgh, Queen’s Medical Research Institute, Edinburgh EH16 4TJ, UK
| | - Gemma Sullivan
- MRC Centre for Reproductive Health, The University of Edinburgh, Queen’s Medical Research Institute, Edinburgh EH16 4TJ, UK
| | - David Q. Stoye
- MRC Centre for Reproductive Health, The University of Edinburgh, Queen’s Medical Research Institute, Edinburgh EH16 4TJ, UK
| | - Gillian Lamb
- MRC Centre for Reproductive Health, The University of Edinburgh, Queen’s Medical Research Institute, Edinburgh EH16 4TJ, UK
| | - Sarah Sparrow
- MRC Centre for Reproductive Health, The University of Edinburgh, Queen’s Medical Research Institute, Edinburgh EH16 4TJ, UK
| | - Lee Murphy
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Nicola Wrobel
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Alan J. Quigley
- Department of Paediatric Radiology, Royal Hospital for Sick Children, NHS Lothian, Edinburgh, UK
| | - Scott Semple
- Edinburgh Imaging, University of Edinburgh, EH16 4SB Edinburgh, UK
- Centre for Cardiovascular Science, The University of Edinburgh, Queen’s Medical Research Institute, Edinburgh EH16 4TJ, UK
| | - Michael J. Thrippleton
- Edinburgh Imaging, University of Edinburgh, EH16 4SB Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Joanna M. Wardlaw
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Mark E. Bastin
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Riccardo E. Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Simon R. Cox
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - James P. Boardman
- MRC Centre for Reproductive Health, The University of Edinburgh, Queen’s Medical Research Institute, Edinburgh EH16 4TJ, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
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15
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Fan L, Ibrahim FEEM, Chu X, Fu Y, Yan H, Wu Z, Tao C, Chen X, Ma Y, Guo Y, Dong Y, Yang C, Ge Y. Altered Microstructural Changes Detected by Diffusion Kurtosis Imaging in Patients With Cognitive Impairment After Acute Cerebral Infarction. Front Neurol 2022; 13:802357. [PMID: 35295835 PMCID: PMC8918512 DOI: 10.3389/fneur.2022.802357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 01/17/2022] [Indexed: 12/02/2022] Open
Abstract
Objective To detect the microstructural changes in patients with cognitive impairment after acute cerebral infarction using diffusion kurtosis imaging (DKI). Materials and Methods A total of 70 patients with acute cerebral infarction were divided into two groups: 35 patients with cognitive impairment (VCI group), and 35 patients without cognitive impairment (N-VCI group), according to mini-mental state examination (MMSE) score. Healthy individuals (n = 36) were selected as the normal control (NORM) group. DKI parameters from 28 different brain regions of interest (ROIs) were selected, measured, and compared. Results VCI group patients had significantly higher mean diffusion (MD) and significantly lower mean kurtosis (MK) values in most ROIs than those in the N-VCI and NORM groups. DKI parameters in some ROIs correlated significantly with MMSE score. The splenium of corpus callosum MD was most correlated with MMSE score, the correlation coefficient was −0.652, and this parameter had good ability to distinguish patients with VCI from healthy controls; at the optimal cut-off MD value (0.9915), sensitivity was 91.4%, specificity 100%, and the area under the curve value 0.964. Conclusions Pathological changes in some brain regions may underlie cognitive impairment after acute cerebral infarction, especially the splenium of corpus callosum. These preliminary results suggest that, in patients with VCI, DKI may be useful for assessing microstructural tissue damage.
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16
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Schulz M, Mayer C, Schlemm E, Frey BM, Malherbe C, Petersen M, Gallinat J, Kühn S, Fiehler J, Hanning U, Twerenbold R, Gerloff C, Cheng B, Thomalla G. Association of Age and Structural Brain Changes With Functional Connectivity and Executive Function in a Middle-Aged to Older Population-Based Cohort. Front Aging Neurosci 2022; 14:782738. [PMID: 35283749 PMCID: PMC8916110 DOI: 10.3389/fnagi.2022.782738] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 01/06/2022] [Indexed: 01/02/2023] Open
Abstract
Aging is accompanied by structural brain changes that are thought to underlie cognitive decline and dementia. Yet little is known regarding the association between increasing age, structural brain damage, and alterations of functional brain connectivity. The aim of this study was to evaluate whether cortical thickness and white matter damage as markers of age-related structural brain changes are associated with alterations in functional connectivity in non-demented healthy middle-aged to older adults. Therefore, we reconstructed functional connectomes from resting-state functional magnetic resonance imaging (MRI) (rsfMRI) data of 976 subjects from the Hamburg City Health Study, a prospective population-based study including participants aged 45-74 years from the metropolitan region Hamburg, Germany. We performed multiple linear regressions to examine the association of age, cortical thickness, and white matter damage quantified by the peak width of skeletonized mean diffusivity (PSMD) from diffusion tensor imaging on whole-brain network connectivity and four predefined resting state networks (default mode, dorsal, salience, and control network). In a second step, we extracted subnetworks with age-related decreased functional connectivity from these networks and conducted a mediation analysis to test whether the effect of age on these networks is mediated by decreased cortical thickness or PSMD. We observed an independent association of higher age with decreased functional connectivity, while there was no significant association of functional connectivity with cortical thickness or PSMD. Mediation analysis identified cortical thickness as a partial mediator between age and default subnetwork connectivity and functional connectivity within the default subnetwork as a partial mediator between age and executive cognitive function. These results indicate that, on a global scale, functional connectivity is not determined by structural damage in healthy middle-aged to older adults. There is a weak association of higher age with decreased functional connectivity which, for specific subnetworks, appears to be mediated by cortical thickness.
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Affiliation(s)
- Maximilian Schulz
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carola Mayer
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Eckhard Schlemm
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Benedikt M. Frey
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Caroline Malherbe
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marvin Petersen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jürgen Gallinat
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Simone Kühn
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Fiehler
- Department of Neuroradiological Diagnostics and Intervention, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Uta Hanning
- Department of Neuroradiological Diagnostics and Intervention, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Raphael Twerenbold
- Department of Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- University Center of Cardiovascular Science, Department of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Gerloff
- 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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17
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Cerebral small vessel disease burden and longitudinal cognitive decline from age 73 to 82: the Lothian Birth Cohort 1936. Transl Psychiatry 2021; 11:376. [PMID: 34226517 PMCID: PMC8257729 DOI: 10.1038/s41398-021-01495-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/14/2021] [Accepted: 06/22/2021] [Indexed: 12/15/2022] Open
Abstract
Slowed processing speed is considered a hallmark feature of cognitive decline in cerebral small vessel disease (SVD); however, it is unclear whether SVD's association with slowed processing might be due to its association with overall declining general cognitive ability. We quantified the total MRI-visible SVD burden of 540 members of the Lothian Birth Cohort 1936 (age: 72.6 ± 0.7 years; 47% female). Using latent growth curve modelling, we tested associations between total SVD burden at mean age 73 and changes in general cognitive ability, processing speed, verbal memory and visuospatial ability, measured at age 73, 76, 79 and 82. Covariates included age, sex, vascular risk and childhood cognitive ability. In the fully adjusted models, greater SVD burden was associated with greater declines in general cognitive ability (standardised β: -0.201; 95% CI: [-0.36, -0.04]; pFDR = 0.022) and processing speed (-0.222; [-0.40, -0.04]; pFDR = 0.022). SVD burden accounted for between 4 and 5% of variance in declines of general cognitive ability and processing speed. After accounting for the covariance between tests of processing speed and general cognitive ability, only SVD's association with greater decline in general cognitive ability remained significant, prior to FDR correction (-0.222; [-0.39, -0.06]; p = 0.008; pFDR = 0.085). Our findings do not support the notion that SVD has a specific association with declining processing speed, independent of decline in general cognitive ability (which captures the variance shared across domains of cognitive ability). The association between SVD burden and declining general cognitive ability supports the notion of SVD as a diffuse, whole-brain disease and suggests that trials monitoring SVD-related cognitive changes should consider domain-specific changes in the context of overall, general cognitive decline.
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18
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Liu Y, Hu A, Chen L, Li B, Zhang M, Xi P, Yang Q, Tang R, Huang Q, He J, Lang Y, Zhang Y. Association between cortical thickness and distinct vascular cognitive impairment and dementia in patients with white matter lesions. Exp Physiol 2021; 106:1612-1620. [PMID: 33866642 DOI: 10.1113/ep089419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 04/08/2021] [Indexed: 12/29/2022]
Abstract
NEW FINDINGS What is the central question of this study? White matter lesions (WMLs) are a brain disease characterized by altered brain structural and functional connectivity, but findings have shown an inconsistent pattern: are there distinct cortical thickness changes in patients with WMLs subtypes? What is the main finding and its importance? Patients with WMLs with non-dementia vascular cognitive impairment and WMLs with vascular dementia showed distinct pathophysiology in cortical thickness. These neural correlates of WMLs should be considered in future treatment. ABSTRACT The effect of cortical thickness on white matter lesions (WMLs) in patients with distinct vascular cognitive impairments is relatively unknown. This study investigated the correlation between cortical thickness and vascular cognitive manifestations. WML patients and healthy controls from Beijing Tiantan Hospital between 2014 and 2018 were included. The patients were further divided into two subgroups, namely WMLs with non-dementia vascular cognitive impairment (WML-VCIND) and WMLs with vascular dementia (WML-VaD) according to the Clinical Dementia Rating (CDR) scale and the Beijing version of the Montreal Cognitive Assessment (MoCA). Changes in cortical thickness were calculated using FreeSurfer. Pearson's correlation analysis was performed to explore the relationship between cognitive manifestations and cortical thickness in WML patients. Forty-five WML patients and 23 healthy controls were recruited. The WML group exhibited significant difference in cortical thickness compared to the control group. Significantly decreased cortical thickness in the middle and superior frontal gyri, middle temporal gyrus, angular gyrus and insula was found in the WML-VaD versus WML-VCIND subgroup. Cortical thickness deficits of the left caudal middle frontal gyrus (r = 0.451, P = 0.002), left rostral middle frontal gyrus (r = 0.514, P < 0.001), left superior frontal gyrus (r = 0.410, P = 0.006), right middle temporal gyrus (r = 0.440, P = 0.003), right pars triangularis (r = 0.462, P = 0.002), right superior frontal gyrus (r = 0.434, P = 0.004) and right insula (r = 0.499, P = 0.001) were positively correlated with the MoCA score in WML patients. The specific pattern of cortical thickness deficits in the WML-VaD subgroup revealed the pathophysiology of WMLs, which should be considered in future treatment of WMLs.
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Affiliation(s)
- Yafei Liu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Anming Hu
- Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Luyao Chen
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China
| | - Bo Li
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Minjian Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Pengcheng Xi
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Qinghu Yang
- College of Life Sciences & Research Center for Resource Peptide Drugs, Shaanxi Engineering & Technological Research Center for Conversation & Utilization of Regional Biological Resources, Yanan University, Yanan, China
| | - Rongyu Tang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China
| | - Qiang Huang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China
| | - Jiping He
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.,Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China
| | - Yiran Lang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China
| | - Yumei Zhang
- Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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19
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Frey BM, Petersen M, Schlemm E, Mayer C, Hanning U, Engelke K, Fiehler J, Borof K, Jagodzinski A, Gerloff C, Thomalla G, Cheng B. White matter integrity and structural brain network topology in cerebral small vessel disease: The Hamburg city health study. Hum Brain Mapp 2021; 42:1406-1415. [PMID: 33289924 PMCID: PMC7927298 DOI: 10.1002/hbm.25301] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 11/08/2020] [Accepted: 11/16/2020] [Indexed: 12/13/2022] Open
Abstract
Cerebral small vessel disease is a common finding in the elderly and associated with various clinical sequelae. Previous studies suggest disturbances in the integration capabilities of structural brain networks as a mediating link between imaging and clinical presentations. To what extent cerebral small vessel disease might interfere with other measures of global network topology is not well understood. Connectomes were reconstructed via diffusion weighted imaging in a sample of 930 participants from a population based epidemiologic study. Linear models were fitted testing for an association of graph-theoretical measures reflecting integration and segregation with both the Peak width of Skeletonized Mean Diffusivity (PSMD) and the load of white matter hyperintensities of presumed vascular origin (WMH). The latter were subdivided in periventricular and deep for an analysis of localisation-dependent correlations of cerebral small vessel disease. The median WMH volume was 0.6 mL (1.4) and the median PSMD 2.18 mm2 /s x 10-4 (0.5). The connectomes showed a median density of 0.880 (0.030), the median values for normalised global efficiency, normalised clustering coefficient, modularity Q and small-world propensity were 0.780 (0.045), 1.182 (0.034), 0.593 (0.026) and 0.876 (0.040) respectively. An increasing burden of cerebral small vessel disease was significantly associated with a decreased integration and increased segregation and thus decreased small-worldness of structural brain networks. Even in rather healthy subjects increased cerebral small vessel disease burden is accompanied by topological brain network disturbances. Segregation parameters and small-worldness might as well contribute to the understanding of the known clinical sequelae of cerebral small vessel disease.
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Affiliation(s)
- Benedikt M. Frey
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Marvin Petersen
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Eckhard Schlemm
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Carola Mayer
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Uta Hanning
- Department of Diagnostic and Interventional NeuroradiologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Kristin Engelke
- Department of Diagnostic and Interventional NeuroradiologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Jens Fiehler
- Department of Diagnostic and Interventional NeuroradiologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Katrin Borof
- Epidemiological study centerUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Annika Jagodzinski
- Epidemiological study centerUniversity Medical Center Hamburg‐EppendorfHamburgGermany
- Department of General and Interventional CardiologyUniversity Heart and Vascular CenterHamburgGermany
| | - Christian Gerloff
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Götz Thomalla
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Bastian Cheng
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
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20
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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: 80] [Impact Index Per Article: 26.7] [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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21
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Raposo N, Zanon Zotin MC, Schoemaker D, Xiong L, Fotiadis P, Charidimou A, Pasi M, Boulouis G, Schwab K, Schirmer MD, Etherton MR, Gurol ME, Greenberg SM, Duering M, Viswanathan A. Peak Width of Skeletonized Mean Diffusivity as Neuroimaging Biomarker in Cerebral Amyloid Angiopathy. AJNR Am J Neuroradiol 2021; 42:875-881. [PMID: 33664113 DOI: 10.3174/ajnr.a7042] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 11/20/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND AND PURPOSE Whole-brain network connectivity has been shown to be a useful biomarker of cerebral amyloid angiopathy and related cognitive impairment. We evaluated an automated DTI-based method, peak width of skeletonized mean diffusivity, in cerebral amyloid angiopathy, together with its association with conventional MRI markers and cognitive functions. MATERIALS AND METHODS We included 24 subjects (mean age, 74.7 [SD, 6.0] years) with probable cerebral amyloid angiopathy and mild cognitive impairment and 62 patients with MCI not attributable to cerebral amyloid angiopathy (non-cerebral amyloid angiopathy-mild cognitive impairment). We compared peak width of skeletonized mean diffusivity between subjects with cerebral amyloid angiopathy-mild cognitive impairment and non-cerebral amyloid angiopathy-mild cognitive impairment and explored its associations with cognitive functions and conventional markers of cerebral small-vessel disease, using linear regression models. RESULTS Subjects with Cerebral amyloid angiopathy-mild cognitive impairment showed increased peak width of skeletonized mean diffusivity in comparison to those with non-cerebral amyloid angiopathy-mild cognitive impairment (P < .001). Peak width of skeletonized mean diffusivity values were correlated with the volume of white matter hyperintensities in both groups. Higher peak width of skeletonized mean diffusivity was associated with worse performance in processing speed among patients with cerebral amyloid angiopathy, after adjusting for other MRI markers of cerebral small vessel disease. The peak width of skeletonized mean diffusivity did not correlate with cognitive functions among those with non-cerebral amyloid angiopathy-mild cognitive impairment. CONCLUSIONS Peak width of skeletonized mean diffusivity is altered in cerebral amyloid angiopathy and is associated with performance in processing speed. This DTI-based method may reflect the degree of white matter structural disruption in cerebral amyloid angiopathy and could be a useful biomarker for cognition in this population.
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Affiliation(s)
- N Raposo
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts .,Department of Neurology (N.R.), Centre Hospitalier Universitaire de Toulouse, Toulouse, France.,Toulouse NeuroImaging Center (N.R.), Université de Toulouse, Institut National de la Santé et de la Recherche Médicale, Toulouse, UPS, France
| | - M C Zanon Zotin
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.,Center for Imaging Sciences and Medical Physics (M.C.Z.Z.). Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil;, Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, São Paulo, Brazil
| | - D Schoemaker
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - L Xiong
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - P Fotiadis
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - A Charidimou
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - M Pasi
- Department of Neurology (M.P.), Centre Hospitalier Universitaire de Lille, Lille, France
| | - G Boulouis
- Department of Neuroradiology (G.B.), Centre Hospitalier Sainte-Anne, Université Paris-Descartes, Paris, France
| | - K Schwab
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - M D Schirmer
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.,Computer Science and Artificial Intelligence Lab (M.D.S.), Massachusetts Institute of Technology, Boston, Massachusetts.,Department of Population Health Sciences (M.D.S.), German Center for Neurodegenerative Diseases, Bonn, Germany
| | - M R Etherton
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - M E Gurol
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - S M Greenberg
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - M Duering
- Medical Image Analysis Center and Quantitative Biomedical Imaging Group (M.D.), Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - A Viswanathan
- From the Stroke Research Center (N.R., M.C.Z.Z., D.S., L.X., P.F., A.C., K.S., M.D.S., M.R.E., M.E.G., S.M.G., A.V.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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Interleukin-8 dysregulation is implicated in brain dysmaturation following preterm birth. Brain Behav Immun 2020; 90:311-318. [PMID: 32920182 DOI: 10.1016/j.bbi.2020.09.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 09/05/2020] [Accepted: 09/05/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Preterm birth is associated with dysconnectivity of structural brain networks, impaired cognition and psychiatric disease. Systemic inflammation contributes to cerebral dysconnectivity, but the immune mediators driving this association are poorly understood. We analysed information from placenta, umbilical cord and neonatal blood, and brain MRI to determine which immune mediators link perinatal systemic inflammation with dysconnectivity of structural brain networks. METHODS Participants were 102 preterm infants (mean gestational age 29+1 weeks, range 23+3-32+0). Placental histopathology identified reaction patterns indicative of histologic chorioamnionitis (HCA), and a customized immunoassay of 24 inflammation-associated proteins selected to reflect the neonatal innate and adaptive immune response was performed from umbilical cord (n = 55) and postnatal day 5 blood samples (n = 71). Brain MRI scans were acquired at term-equivalent age (41+0 weeks [range 38+0-44+4 weeks]) and alterations in white matter connectivity were inferred from mean diffusivity and neurite density index across the white matter skeleton. RESULTS HCA was associated with elevated concentrations of C5a, C9, CRP, IL-1β, IL-6, IL-8 and MCP-1 in cord blood, and IL-8 concentration predicted HCA with an area under the receiver operator curve of 0.917 (95% CI 0.841 - 0.993, p < 0.001). Fourteen analytes explained 66% of the variance in the postnatal profile (BDNF, C3, C5a, C9, CRP, IL-1β, IL-6, IL-8, IL-18, MCP-1, MIP-1β, MMP-9, RANTES and TNF-α). Of these, IL-8 was associated with altered neurite density index across the white matter skeleton after adjustment for gestational age at birth and at scan (β = 0.221, p = 0.037). CONCLUSIONS These findings suggest that IL-8 dysregulation has a role in linking perinatal systemic inflammation and atypical white matter development in preterm infants.
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23
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Peak width of skeletonized mean diffusivity (PSMD) and cognitive functions in relapsing-remitting multiple sclerosis. Brain Imaging Behav 2020; 15:2228-2233. [DOI: 10.1007/s11682-020-00394-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/02/2020] [Indexed: 01/22/2023]
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24
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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: 26] [Impact Index Per Article: 6.5] [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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25
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Low A, Mak E, Stefaniak JD, Malpetti M, Nicastro N, Savulich G, Chouliaras L, Markus HS, Rowe JB, O’Brien JT. Peak Width of Skeletonized Mean Diffusivity as a Marker of Diffuse Cerebrovascular Damage. Front Neurosci 2020; 14:238. [PMID: 32265640 PMCID: PMC7096698 DOI: 10.3389/fnins.2020.00238] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 03/03/2020] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND The peak width of skeletonized mean diffusivity (PSMD) has been proposed as a fully automated imaging marker of relevance to cerebral small vessel disease (SVD). We assessed PSMD in relation to conventional SVD markers, global measures of neurodegeneration, and cognition. METHODS 145 participants underwent 3T brain MRI and cognitive assessment. 112 were patients with mild cognitive impairment, Alzheimer's disease, progressive supranuclear palsy, dementia with Lewy bodies, or frontotemporal dementia. PSMD, SVD burden [white matter hyperintensities (WMH), enlarged perivascular spaces (EPVS), microbleeds, lacunes], average mean diffusivity (MD), gray matter (GM), white matter (WM), and total intracranial volume were quantified. Robust linear regression was conducted to examine associations between variables. Dominance analysis assessed the relative importance of markers in predicting various outcomes. Regional analyses examined spatial overlap between PSMD and WMH. RESULTS PSMD was associated with global and regional SVD measures, especially WMH and microbleeds. Dominance analysis demonstrated that among SVD markers, WMH was the strongest predictor of PSMD. Furthermore, PSMD was more closely associated to WMH than with GM and WM volumes. PSMD was associated with WMH across all regions, and correlations were not significantly stronger in corresponding regions (e.g., frontal PSMD and frontal WMH) compared to non-corresponding regions. PSMD outperformed all four conventional SVD markers and MD in predicting cognition, but was comparable to GM and WM volumes. DISCUSSION PSMD was robustly associated with established SVD markers. This new measure appears to be a marker of diffuse brain injury, largely due to vascular pathology, and may be a useful and convenient metric of overall cerebrovascular burden.
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Affiliation(s)
- Audrey Low
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Elijah Mak
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - James D. Stefaniak
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom
| | - Maura Malpetti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Nicolas Nicastro
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland
| | - George Savulich
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Leonidas Chouliaras
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Hugh S. Markus
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - James B. Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - John T. O’Brien
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
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Beaudet G, Tsuchida A, Petit L, Tzourio C, Caspers S, Schreiber J, Pausova Z, Patel Y, Paus T, Schmidt R, Pirpamer L, Sachdev PS, Brodaty H, Kochan N, Trollor J, Wen W, Armstrong NJ, Deary IJ, Bastin ME, Wardlaw JM, Munõz Maniega S, Witte AV, Villringer A, Duering M, Debette S, Mazoyer B. Age-Related Changes of Peak Width Skeletonized Mean Diffusivity (PSMD) Across the Adult Lifespan: A Multi-Cohort Study. Front Psychiatry 2020; 11:342. [PMID: 32425831 PMCID: PMC7212692 DOI: 10.3389/fpsyt.2020.00342] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 04/06/2020] [Indexed: 12/20/2022] Open
Abstract
Parameters of water diffusion in white matter derived from diffusion-weighted imaging (DWI), such as fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD, and RD), and more recently, peak width of skeletonized mean diffusivity (PSMD), have been proposed as potential markers of normal and pathological brain ageing. However, their relative evolution over the entire adult lifespan in healthy individuals remains partly unknown during early and late adulthood, and particularly for the PSMD index. Here, we gathered and analyzed cross-sectional diffusion tensor imaging (DTI) data from 10 population-based cohort studies in order to establish the time course of white matter water diffusion phenotypes from post-adolescence to late adulthood. DTI data were obtained from a total of 20,005 individuals aged 18.1 to 92.6 years and analyzed with the same pipeline for computing skeletonized DTI metrics from DTI maps. For each individual, MD, AD, RD, and FA mean values were computed over their FA volume skeleton, PSMD being calculated as the 90% peak width of the MD values distribution across the FA skeleton. Mean values of each DTI metric were found to strongly vary across cohorts, most likely due to major differences in DWI acquisition protocols as well as pre-processing and DTI model fitting. However, age effects on each DTI metric were found to be highly consistent across cohorts. RD, MD, and AD variations with age exhibited the same U-shape pattern, first slowly decreasing during post-adolescence until the age of 30, 40, and 50 years, respectively, then progressively increasing until late life. FA showed a reverse profile, initially increasing then continuously decreasing, slowly until the 70s, then sharply declining thereafter. By contrast, PSMD constantly increased, first slowly until the 60s, then more sharply. These results demonstrate that, in the general population, age affects PSMD in a manner different from that of other DTI metrics. The constant increase in PSMD throughout the entire adult life, including during post-adolescence, indicates that PSMD could be an early marker of the ageing process.
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Affiliation(s)
- Grégory Beaudet
- Institute of Neurodegenerative Diseases (IMN), CNRS, CEA, Bordeaux, France.,Institute of Neurodegenerative Diseases (IMN), University of Bordeaux, Bordeaux, France
| | - Ami Tsuchida
- Institute of Neurodegenerative Diseases (IMN), CNRS, CEA, Bordeaux, France.,Institute of Neurodegenerative Diseases (IMN), University of Bordeaux, Bordeaux, France
| | - Laurent Petit
- Institute of Neurodegenerative Diseases (IMN), CNRS, CEA, Bordeaux, France.,Institute of Neurodegenerative Diseases (IMN), University of Bordeaux, Bordeaux, France
| | | | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, Juelich, Germany.,Institute for Anatomy I, Medical Faculty, Heinrich Heine University Dusseldorf, Dusseldorf, Germany
| | - Jan Schreiber
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, Juelich, Germany
| | - Zdenka Pausova
- Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - Yash Patel
- Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - Tomas Paus
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada.,Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Reinhold Schmidt
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Lukas Pirpamer
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Medicine, University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Neuropsychiatric Institute Prince of Wales Hospital, Randwick, NSW, Australia
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Medicine, University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Neuropsychiatric Institute Prince of Wales Hospital, Randwick, NSW, Australia
| | - Nicole Kochan
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Medicine, University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Neuropsychiatric Institute Prince of Wales Hospital, Randwick, NSW, Australia
| | - Julian Trollor
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Medicine, University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Neuropsychiatric Institute Prince of Wales Hospital, Randwick, NSW, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Medicine, University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Neuropsychiatric Institute Prince of Wales Hospital, Randwick, NSW, Australia
| | | | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Neuroimaging Sciences, The University of Edinburgh, Edinburgh, United Kingdom
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Neuroimaging Sciences, The University of Edinburgh, Edinburgh, United Kingdom
| | - Susana Munõz Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Neuroimaging Sciences, The University of Edinburgh, Edinburgh, United Kingdom
| | - A Veronica Witte
- Departmet of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Arno Villringer
- Departmet of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Stéphanie Debette
- Institute of Neurodegenerative Diseases (IMN), University of Bordeaux, Bordeaux, France.,Bordeaux Population Health Research Center, Inserm, Bordeaux, France.,Department of Neurology, Bordeaux University Hospital, Bordeaux, France
| | - Bernard Mazoyer
- Institute of Neurodegenerative Diseases (IMN), CNRS, CEA, Bordeaux, France.,Institute of Neurodegenerative Diseases (IMN), University of Bordeaux, Bordeaux, France
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Lam BYK, Leung KT, Yiu B, Zhao L, Biesbroek JM, Au L, Tang Y, Wang K, Fan Y, Fu JH, Xu Q, Song H, Tian X, Chu WCW, Abrigo J, Shi L, Ko H, Lau A, Duering M, Wong A, Mok VCT. Peak width of skeletonized mean diffusivity and its association with age-related cognitive alterations and vascular risk factors. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2019; 11:721-729. [PMID: 31700990 PMCID: PMC6829102 DOI: 10.1016/j.dadm.2019.09.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Introduction Only two studies investigated the associations between peak width of skeletonized mean diffusivity (PSMD) and age-related cognitive alterations, whereas none of the studies investigated the association with vascular risk factors. Methods We evaluated 801 stroke- and dementia-free elderlies with baseline and 3-year follow-up assessments. Regression analyses were used to assess the association between age-related cognitive functions and PSMD. Simple mediation models were used to study the mediation effect of PSMD between vascular risk factors and age-related cognitive outcomes. Results PSMD was negatively associated with processing speed at baseline and negatively associated with processing and memory scores at 3-year follow-up. The association between vascular risk factors and age-related cognition was mediated by PSMD, as well as other diffusion tensor imaging markers. Discussion PSMD is preferred over other diffusion tensor imaging markers as it is sensitive to age-related cognitive alterations and calculation is fully automated. PSMD is proposed as a research tool to monitor age-related cognitive alterations.
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Affiliation(s)
- Bonnie Yin Ka Lam
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.,Therese Pei Fong Chow Research Center for Prevention of Dementia, Margaret Kam Ling Cheung Research Centre for Management of Parkinsonism, Gerald Choa Neuroscience Centre, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Kam Tat Leung
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Brian Yiu
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.,Therese Pei Fong Chow Research Center for Prevention of Dementia, Margaret Kam Ling Cheung Research Centre for Management of Parkinsonism, Gerald Choa Neuroscience Centre, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Lei Zhao
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.,BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - J Matthijs Biesbroek
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Lisa Au
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.,Therese Pei Fong Chow Research Center for Prevention of Dementia, Margaret Kam Ling Cheung Research Centre for Management of Parkinsonism, Gerald Choa Neuroscience Centre, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Yumi Tang
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Kai Wang
- Department of Neurology, The First Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Yuhua Fan
- Department of Neurology and Stroke Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jian-Hui Fu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Qun Xu
- Department of Neurology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Haiqing Song
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Xiaolin Tian
- Department of Neurology, The Second Affiliated Hospital, Tianjin Medical University, Tianjin, China
| | - Winnie Chiu Wing Chu
- Department of Imaging and Interventional Radiology, Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Jill Abrigo
- Department of Imaging and Interventional Radiology, Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen, Guangdong Province, China.,Department of Imaging and Interventional Radiology, Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.,Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Ho Ko
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.,Therese Pei Fong Chow Research Center for Prevention of Dementia, Margaret Kam Ling Cheung Research Centre for Management of Parkinsonism, Gerald Choa Neuroscience Centre, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.,Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Alexander Lau
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.,Therese Pei Fong Chow Research Center for Prevention of Dementia, Margaret Kam Ling Cheung Research Centre for Management of Parkinsonism, Gerald Choa Neuroscience Centre, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Adrian Wong
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.,Therese Pei Fong Chow Research Center for Prevention of Dementia, Margaret Kam Ling Cheung Research Centre for Management of Parkinsonism, Gerald Choa Neuroscience Centre, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Vincent Chung Tong Mok
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.,Therese Pei Fong Chow Research Center for Prevention of Dementia, Margaret Kam Ling Cheung Research Centre for Management of Parkinsonism, Gerald Choa Neuroscience Centre, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.,Shenzhen Research Institute, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
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Elhfnawy AM, Volkmann J, Schliesser M, Fluri F. Are Cerebral White Matter Lesions Related to the Presence of Bilateral Internal Carotid Artery Stenosis or to the Length of Stenosis Among Patients With Ischemic Cerebrovascular Events? Front Neurol 2019; 10:919. [PMID: 31555196 PMCID: PMC6727787 DOI: 10.3389/fneur.2019.00919] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 08/07/2019] [Indexed: 11/13/2022] Open
Abstract
Background and purpose: Previous studies delivered contradicting results regarding the relation between the presence of an internal carotid artery stenosis (ICAS) and the occurence of white matter lesions (WMLs). We hypothesize that special characteristics related to the ICAS might be related to the WMLs. We examined the relation between the presence of bilateral ICAS, the degree and length of stenosis and ipsi-, contralateral as well as mean white matter lesion load (MWMLL). Methods: In a retrospective cohort, patients with ischemic stroke or transient ischemic attack (TIA) as well as ipsi- and/or contralateral ICAS were identified. The length and degree of ICAS, as well as plaque morphology (hypoechoic, mixed or echogenic), were assessed on ultrasound scans and, if available, the length was also measured on magnetic resonance angiography (MRA) scans, and/or digital subtraction angiography (DSA). The WMLs were assessed in 4 areas separately, (periventricular and deep WMLs on each hemispherer), using the Fazekas scale. The MWMLL was calculated as the mean of these four values. Results: 136 patients with 177 ICAS were identified. A significant correlation between age and MWMLL was observed (Spearman correlation coefficient, ρ = 0.41, p < 0.001). Before adjusting for other risk factors, a significantly positive relation was found between the presence of bilateral ICAS and MWMLL (p = 0.039). The length but not the degree of ICAS showed a very slight trend toward association with ipsilateral WMLs and with MWMLL. In an age-adjusted multivariate logistic regression with MWMLL ≥2 as the outcome measure, atrial fibrillation (OR 3.54, 95% CI 1.12-11.18, p = 0.03), female sex (OR 3.11, 95% CI 1.19-8.11, p = 0.02) and diabetes mellitus (OR 2.76, 95% CI 1.16-6.53, p = 0.02) were significantly related to WMLs, whereas the presence of bilateral stenosis showed a trend toward significance (OR 2.25, 95% CI 0.93-5.45, p = 0.074). No relation was found between plaque morphology and MWMLL, periventricular, or deep WMLs. Conclusion: We have shown a slight correlation between the length of stenosis and the presence of WMLs which might be due to microembolisation originating from the carotid plaque. However, the presence of bilateral ICAS seems also to be related to WMLs which may point to common underlying vascular risk factors contributing to the occurrence of WML.
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Affiliation(s)
- Ahmed Mohamed Elhfnawy
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany.,Department of Neurology, University Hospital of Essen, Essen, Germany.,Department of Neurology, University Hospital of Alexandria, Alexandria, Egypt
| | - Jens Volkmann
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Mira Schliesser
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Felix Fluri
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany.,Department of Neurology, Kantonssptial St. Gallen, St. Gallen, Switzerland
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Deary IJ, Ritchie SJ, Muñoz Maniega S, Cox SR, Valdés Hernández MC, Luciano M, Starr JM, Wardlaw JM, Bastin ME. Brain Peak Width of Skeletonized Mean Diffusivity (PSMD) and Cognitive Function in Later Life. Front Psychiatry 2019; 10:524. [PMID: 31402877 PMCID: PMC6676305 DOI: 10.3389/fpsyt.2019.00524] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 07/03/2019] [Indexed: 11/13/2022] Open
Abstract
It is suggested that the brain's peak width of skeletonized water mean diffusivity (PSMD) is a neuro-biomarker of processing speed, an important aspect of cognitive aging. We tested whether PSMD is more strongly correlated with processing speed than with other cognitive domains, and more strongly than other structural brain MRI indices. Participants were 731 Lothian Birth Cohort 1936 members, mean age = 73 years (SD = 0.7); analytical sample was 656-680. Cognitive domains tested were as follows: processing speed (5 tests), visuospatial (3), memory (3), and verbal (3). Brain-imaging variables included PSMD, white matter diffusion parameters, hyperintensity volumes, gray and white matter volumes, and perivascular spaces. PSMD was significantly associated with processing speed (-0.27), visuospatial ability (-0.23), memory ability (-0.17), and general cognitive ability (-0.25); comparable correlations were found with other brain-imaging measures. In a multivariable model with the other imaging variables, PSMD provided independent prediction of visuospatial ability and general cognitive ability. This incremental prediction, coupled with its ease to compute and possibly better tractability, might make PSMD a useful brain biomarker in studies of cognitive aging.
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Affiliation(s)
- Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Stuart J Ritchie
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Susana Muñoz Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Division of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom
| | - Simon R Cox
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom
| | - Maria C Valdés Hernández
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Division of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom.,Edinburgh Dementia Research Centre, Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Michelle Luciano
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Division of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom.,Edinburgh Dementia Research Centre, Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Division of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom
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