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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: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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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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Tang ECH, Lau AYL, AU D, Ju Y, Lam BYK, Wong A, Au L, Mok VCT. Effects of Acupuncture upon cerebral hemodynamics in cerebral small vessel disease: A pilot study. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2023; 4:100168. [PMID: 37397268 PMCID: PMC10313857 DOI: 10.1016/j.cccb.2023.100168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 02/20/2023] [Accepted: 05/09/2023] [Indexed: 07/04/2023]
Abstract
Background and aims Recent preclinical studies and meta-analysis of clinical trials suggested that acupuncture may improve cognition in cerebral small vessel disease (CSVD). We investigated the cerebral hemodynamics of acupuncture in subjects with CSVD and compared its impact upon the cerebral hemodynamics in normal elderly subjects. Methods 10 subjects with CSVD (CSVD group) and 10 aged-matched control subjects who had no or insignificant CSVD (control group) were recruited. A single session of acupuncture was applied for 30 min in both groups. We assessed the effect of our acupuncture intervention on cerebral hemodynamics by transcranial Doppler ultrasound (TCD). Peak systolic velocity (PSV) and pulsatility index (PI) of the middle cerebral artery (MCA) were assessed. Results We observed that PSV increased by a maximum of 39% at 20 min (p<0.05), while there was no significant change in PI in the CSVD group during the acupuncture session. In the control group, although we observed no significant change in PSV during the acupuncture session, there was a significant decrease in PI by a maximum of 22% at 20 min (p<0.05). No adverse events were reported during or after the procedure. Conclusion This study suggested that our acupuncture prescription was associated with an increase in cerebral blood flow in subjects with established moderate to severe CSVD yet without apparent impact on distal vascular resistance. While, in subjects with no or insignificant CSVD, it may reduce cerebral small vessel distal vascular resistance. A larger study is needed to confirm our findings.
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Affiliation(s)
- Endy-Chun-hung Tang
- Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Margaret K.L. Cheung Research Centre for Management of Parkinsonism, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Gerald Choa Neuroscience Institute, Lui Che Woo Institute of Innovative Medicine, Li Ka Shing Institute of Health Sciences, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Alexander-Yuk-lun Lau
- Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Margaret K.L. Cheung Research Centre for Management of Parkinsonism, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Gerald Choa Neuroscience Institute, Lui Che Woo Institute of Innovative Medicine, Li Ka Shing Institute of Health Sciences, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
- The Hong Kong Institute of Integrative Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Flat 4L, 4/F, Day Treatment Block, Shatin, Hong Kong SAR, China
| | - David AU
- The Hong Kong Institute of Integrative Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Flat 4L, 4/F, Day Treatment Block, Shatin, Hong Kong SAR, China
| | - Yanli Ju
- The Hong Kong Institute of Integrative Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Flat 4L, 4/F, Day Treatment Block, Shatin, Hong Kong SAR, China
| | - Bonnie-Yin-Ka Lam
- Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Margaret K.L. Cheung Research Centre for Management of Parkinsonism, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Gerald Choa Neuroscience Institute, Lui Che Woo Institute of Innovative Medicine, Li Ka Shing Institute of Health Sciences, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
- The Hong Kong Institute of Integrative Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Flat 4L, 4/F, Day Treatment Block, Shatin, Hong Kong SAR, China
| | - Adrian Wong
- Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Margaret K.L. Cheung Research Centre for Management of Parkinsonism, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Gerald Choa Neuroscience Institute, Lui Che Woo Institute of Innovative Medicine, Li Ka Shing Institute of Health Sciences, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Lisa Au
- Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Margaret K.L. Cheung Research Centre for Management of Parkinsonism, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Gerald Choa Neuroscience Institute, Lui Che Woo Institute of Innovative Medicine, Li Ka Shing Institute of Health Sciences, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Vincent-Chung-tong Mok
- Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Margaret K.L. Cheung Research Centre for Management of Parkinsonism, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Gerald Choa Neuroscience Institute, Lui Che Woo Institute of Innovative Medicine, Li Ka Shing Institute of Health Sciences, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
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4
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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: 12] [Impact Index Per Article: 12.0] [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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5
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Huang P, Zhang M. Magnetic Resonance Imaging Studies of Neurodegenerative Disease: From Methods to Translational Research. Neurosci Bull 2023; 39:99-112. [PMID: 35771383 PMCID: PMC9849544 DOI: 10.1007/s12264-022-00905-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/07/2022] [Indexed: 01/22/2023] Open
Abstract
Neurodegenerative diseases (NDs) have become a significant threat to an aging human society. Numerous studies have been conducted in the past decades to clarify their pathologic mechanisms and search for reliable biomarkers. Magnetic resonance imaging (MRI) is a powerful tool for investigating structural and functional brain alterations in NDs. With the advantages of being non-invasive and non-radioactive, it has been frequently used in both animal research and large-scale clinical investigations. MRI may serve as a bridge connecting micro- and macro-level analysis and promoting bench-to-bed translational research. Nevertheless, due to the abundance and complexity of MRI techniques, exploiting their potential is not always straightforward. This review aims to briefly introduce research progress in clinical imaging studies and discuss possible strategies for applying MRI in translational ND research.
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Affiliation(s)
- Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
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6
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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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7
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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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8
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Mao H, Zhang Y, Zou M, Lv S, Zou J, Huang Y, Zhang M, Zhao Z, Huang P. The interplay between small vessel disease and Parkinson disease pathology: A longitudinal study. Eur J Radiol 2022; 154:110441. [PMID: 35907289 DOI: 10.1016/j.ejrad.2022.110441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/22/2022] [Accepted: 07/09/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Cerebral small vessel disease (SVD) related brain changes have been found associated with various clinical symptoms of Parkinson disease (PD). On the other hand, PD pathology and treatment may also accelerate SVD progression. OBJECTIVE The aim of this study is to explore the interplay between SVD and PD pathology using longitudinal dataset. METHODS We screened 66 healthy controls (HCs) and 114 patients from the Parkinson Progression Markers Initiative (PPMI) database. The peak width of skeletonized mean diffusivity (PSMD) was quantified from diffusion tensor images to reflect vascular pathologies at baseline and 24 months follow-up, and dopamine transporter (DAT) imaging data was used to represent the extent of dopaminergic neuronal degeneration at the same point time. We compared the PSMD between PD patients and HCs, and analyzed whether PSMD and DAT availability could predict each other's progression using multiple regression analyses in PD patients. RESULTS PSMD at baseline had no significant difference between the HCs and patients with PD (P = 0.169). Higher baseline PSMD was associated with less DAT reduction in the caudate (β = 0.216, P = 0.029), but not the putamen (β = 0.058, P = 0.552) in PD patients. Baseline caudate and putamen DAT availability had no significant association with PSMD progression (β = -0.006, P = 0.950; β = 0.017, P = 0.860, respectively). CONCLUSIONS Mild SVD might slow down PD pathology progression, while the effect of PD pathology on the progression of SVD was not significant.
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Affiliation(s)
- Haijia Mao
- Department of Radiology, Shaoxing people's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, China
| | - Yao Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Mingyue Zou
- Department of Radiology, Shaoxing Hospital of Zhejiang University, Shaoxing, China
| | - Sangying Lv
- Department of Radiology, Shaoxing people's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, China
| | - Jiajun Zou
- Department of Radiology, Shaoxing people's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, China
| | - Ya'nan Huang
- Department of Radiology, Shaoxing people's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing people's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, China.
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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9
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Koncz R, Wen W, Makkar SR, Lam BCP, Crawford JD, Rowe CC, Sachdev P. The Interaction Between Vascular Risk Factors, Cerebral Small Vessel Disease, and Amyloid Burden in Older Adults. J Alzheimers Dis 2022; 86:1617-1628. [PMID: 35213365 DOI: 10.3233/jad-210358] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Cerebral small vessel disease (SVD) and Alzheimer's disease pathology, namely amyloid-β (Aβ) deposition, commonly co-occur. Exactly how they interact remains uncertain. OBJECTIVE Using participants from the Alzheimer's Disease Neuroimaging Initiative (n = 216; mean age 73.29±7.08 years, 91 (42.1%) females), we examined whether the presence of vascular risk factors and/or baseline cerebral SVD was related to a greater burden of Aβ cross-sectionally, and at 24 months follow-up. METHOD Amyloid burden, assessed using 18F-florbetapir PET, was quantified as the global standardized uptake value ratio (SUVR). Multimodal imaging was used to strengthen the quantification of baseline SVD as a composite variable, which included white matter hyperintensity volume using MRI, and peak width of skeletonized mean diffusivity using diffusion tensor imaging. Structural equation modelling was used to analyze the associations between demographic factors, Apolipoprotein E ɛ4 carrier status, vascular risk factors, SVD burden and cerebral amyloid. RESULTS SVD burden had a direct association with Aβ burden cross-sectionally (coeff. = 0.229, p = 0.004), and an indirect effect over time (indirect coeff. = 0.235, p = 0.004). Of the vascular risk factors, a history of hypertension (coeff. = 0.094, p = 0.032) and a lower fasting glucose at baseline (coeff. = -0.027, p = 0.014) had a direct effect on Aβ burden at 24 months, but only the direct effect of glucose persisted after regularization. CONCLUSION While Aβ and SVD burden have an association cross-sectionally, SVD does not appear to directly influence the accumulation of Aβ longitudinally. Glucose regulation may be an important modifiable risk factor for Aβ accrual over time.
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Affiliation(s)
- Rebecca Koncz
- Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, UNSW Sydney, NSW, Australia.,The University of Sydney Specialty of Psychiatry, Faculty of Medicine and Health, Concord, NSW, Australia.,Concord Repatriation General Hospital, Sydney Local Health District, Concord, NSW, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, UNSW Sydney, NSW, Australia
| | - Steve R Makkar
- Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, UNSW Sydney, NSW, Australia
| | - Ben C P Lam
- Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, UNSW Sydney, NSW, Australia
| | - John D Crawford
- Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, UNSW Sydney, NSW, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, Victoria, Australia.,Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, UNSW Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW, Australia
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10
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de Brito Robalo BM, Biessels GJ, Chen C, Dewenter A, Duering M, Hilal S, Koek HL, Kopczak A, Yin Ka Lam B, Leemans A, Mok V, Onkenhout LP, van den Brink H, de Luca A. Diffusion MRI harmonization enables joint-analysis of multicentre data of patients with cerebral small vessel disease. Neuroimage Clin 2021; 32:102886. [PMID: 34911192 PMCID: PMC8609094 DOI: 10.1016/j.nicl.2021.102886] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 11/16/2021] [Indexed: 01/10/2023]
Abstract
OBJECTIVES Acquisition-related differences in diffusion magnetic resonance imaging (dMRI) hamper pooling of multicentre data to achieve large sample sizes. A promising solution is to harmonize the raw diffusion signal using rotation invariant spherical harmonic (RISH) features, but this has not been tested in elderly subjects. Here we aimed to establish if RISH harmonization effectively removes acquisition-related differences in multicentre dMRI of elderly subjects with cerebral small vessel disease (SVD), while preserving sensitivity to disease effects. METHODS Five cohorts of patients with SVD (N = 397) and elderly controls (N = 175) with 3 Tesla MRI on different systems were included. First, to establish effectiveness of harmonization, the RISH method was trained with data of 13 to 15 age and sex-matched controls from each site. Fractional anisotropy (FA) and mean diffusivity (MD) were compared in matched controls between sites using tract-based spatial statistics (TBSS) and voxel-wise analysis, before and after harmonization. Second, to assess sensitivity to disease effects, we examined whether the contrast (effect sizes of FA, MD and peak width of skeletonized MD - PSMD) between patients and controls within each site remained unaffected by harmonization. Finally, we evaluated the association between white matter hyperintensity (WMH) burden, FA, MD and PSMD using linear regression analyses both within individual cohorts as well as with pooled scans from multiple sites, before and after harmonization. RESULTS Before harmonization, significant differences in FA and MD were observed between matched controls of different sites (p < 0.05). After harmonization these site-differences were removed. Within each site, RISH harmonization did not alter the effect sizes of FA, MD and PSMD between patients and controls (relative change in Cohen's d = 4 %) nor the strength of association with WMH volume (relative change in R2 = 2.8 %). After harmonization, patient data of all sites could be aggregated in a single analysis to infer the association between WMH volume and FA (R2 = 0.62), MD (R2 = 0.64), and PSMD (R2 = 0.60). CONCLUSIONS We showed that RISH harmonization effectively removes acquisition-related differences in dMRI of elderly subjects while preserving sensitivity to SVD-related effects. This study provides proof of concept for future multicentre SVD studies with pooled datasets.
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Affiliation(s)
- Bruno M de Brito Robalo
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Christopher Chen
- Memory, Aging and Cognition Center, Department of Pharmacology, National University of Singapore, Singapore.
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany; Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
| | - Saima Hilal
- Memory, Aging and Cognition Center, Department of Pharmacology, National University of Singapore, Singapore.
| | - Huiberdina L Koek
- Department of Geriatric Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Anna Kopczak
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.
| | - Bonnie Yin Ka Lam
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region.
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Vincent Mok
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region.
| | - Laurien P Onkenhout
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Hilde van den Brink
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Alberto de Luca
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
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11
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Du J, Koch FC, Xia A, Jiang J, Crawford JD, Lam BCP, Thalamuthu A, Lee T, Kochan N, Fawns-Ritchie C, Brodaty H, Xu Q, Sachdev PS, Wen W. Difference in distribution functions: A new diffusion weighted imaging metric for estimating white matter integrity. Neuroimage 2021; 240:118381. [PMID: 34252528 DOI: 10.1016/j.neuroimage.2021.118381] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/27/2021] [Accepted: 07/08/2021] [Indexed: 11/19/2022] Open
Abstract
Diffusion weighted imaging (DWI) is a widely recognized neuroimaging technique to evaluate the microstructure of brain white matter. The objective of this study is to establish an improved automated DWI marker for estimating white matter integrity and investigating ageing related cognitive decline. The concept of Wasserstein distance was introduced to help establish a new measure: difference in distribution functions (DDF), which captures the difference of reshaping one's mean diffusivity (MD) distribution to a reference MD distribution. This new DWI measure was developed using a population-based cohort (n=19,369) from the UK Biobank. Validation was conducted using the data drawn from two independent cohorts: the Sydney Memory and Ageing Study, a community-dwelling sample (n=402), and the Renji Cerebral Small Vessel Disease Cohort Study (RCCS), which consisted of cerebral small vessel disease (CSVD) patients (n=171) and cognitively normal controls (NC) (n=43). DDF was associated with age across all three samples and better explained the variance of changes than other established DWI measures, such as fractional anisotropy, mean diffusivity and peak width of skeletonized mean diffusivity (PSMD). Significant correlations between DDF and cognition were found in the UK Biobank cohort and the MAS cohort. Binary logistic analysis and receiver operator characteristic curve analysis of RCCS demonstrated that DDF had higher sensitivity in distinguishing CSVD patients from NC than the other DWI measures. To demonstrate the flexibility of DDF, we calculated regional DDF which also showed significant correlation with age and cognition. DDF can be used as a marker for monitoring the white matter microstructural changes and ageing related cognitive decline in the elderly.
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Affiliation(s)
- Jing Du
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia.
| | - Forrest C Koch
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Aihua Xia
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - John D Crawford
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Ben C P Lam
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Teresa Lee
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia; Neuropsychiatric Institute (NPI), Euroa Centre, Prince of Wales Hospital, Randwick, New South Wales 2031, Australia
| | - Nicole Kochan
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Chloe Fawns-Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Henry Brodaty
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia; Dementia Centre for Research Collaboration, School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Qun Xu
- Department of Health Manage Centre, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Perminder S Sachdev
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia; Neuropsychiatric Institute (NPI), Euroa Centre, Prince of Wales Hospital, Randwick, New South Wales 2031, Australia
| | - Wei Wen
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia; Neuropsychiatric Institute (NPI), Euroa Centre, Prince of Wales Hospital, Randwick, New South Wales 2031, Australia
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12
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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: 23] [Impact Index Per Article: 7.7] [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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13
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Magnetic resonance imaging manifestations of cerebral small vessel disease: automated quantification and clinical application. Chin Med J (Engl) 2020; 134:151-160. [PMID: 33443936 PMCID: PMC7817342 DOI: 10.1097/cm9.0000000000001299] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
The common cerebral small vessel disease (CSVD) neuroimaging features visible on conventional structural magnetic resonance imaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. The CSVD neuroimaging features have shared and distinct clinical consequences, and the automatic quantification methods for these features are increasingly used in research and clinical settings. This review article explores the recent progress in CSVD neuroimaging feature quantification and provides an overview of the clinical consequences of these CSVD features as well as the possibilities of using these features as endpoints in clinical trials. The added value of CSVD neuroimaging quantification is also discussed for researches focused on the mechanism of CSVD and the prognosis in subjects with CSVD.
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14
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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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