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Ferris JK, Dahlby J, Rinat S, Greeley B, Ramirez J, Black SE, Boyd LA. Sensitivity of diffusion tensor imaging to regional mixed cerebrovascular pathology. Brain Commun 2025; 7:fcaf193. [PMID: 40443948 PMCID: PMC12120440 DOI: 10.1093/braincomms/fcaf193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2024] [Revised: 04/24/2025] [Accepted: 05/20/2025] [Indexed: 06/02/2025] Open
Abstract
Diffusion tensor imaging is a candidate biomarker in cerebrovascular disease. Yet, little is known about the sensitivity of diffusion tensor imaging to mixed forms of cerebrovascular pathology: stroke and white matter hyperintensities. We evaluated the sensitivity of diffusion tensor imaging to regional lesion load, considering both stroke and white matter hyperintensity lesions. 65 older adults and 39 individuals with chronic stroke underwent diffusion tensor imaging and comprehensive cerebrovascular lesion segmentation. We tested relationships between fractional anisotropy or mean diffusivity and cerebrovascular lesions with linear mixed effects regression. In older adults, tract microstructure related to white matter hyperintensity lesion load (fractional anisotropy: b = -0.003, P = 0.003; mean diffusivity: b = 0.071 × 10-4, P < 0.001). In individuals with chronic stroke, tract microstructure related to stroke lesion load (fractional anisotropy: b = -0.041, P < 0.001; mean diffusivity: b = 1.460 × 10-4, P < 0.001), with a significant interaction between stroke and white matter hyperintensity lesion load (fractional anisotropy: b = 0.019, P < 0.001; mean diffusivity: b = -0.727 × 10-4, P < 0.001). Among both groups, whole-brain normal appearing white matter microstructure did not relate to whole-brain lesion volumes. Our findings provide foundational evidence for the use and interpretation of diffusion tensor imaging as a biomarker in cerebrovascular disease.
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Affiliation(s)
- Jennifer K Ferris
- Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, Canada V6T 1Z3
- Gerontology Research Centre, Simon Fraser University, Vancouver, Canada V6B 5K3
| | - Julia Dahlby
- Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, Canada V6T 1Z3
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada V6T 1Z3
| | - Shie Rinat
- Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, Canada V6T 1Z3
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada V6T 1Z3
| | - Brian Greeley
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada V6T 1Z3
| | - Joel Ramirez
- LC Campbell Cognitive Neurology Research Unit, Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Canada M4N 3M5
- Graduate Department of Clinical Psychological Science, University of Toronto Scarborough, Toronto, Canada M1C 1A4
| | - Sandra E Black
- LC Campbell Cognitive Neurology Research Unit, Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Canada M4N 3M5
- Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada M4N 3M5
| | - Lara A Boyd
- Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, Canada V6T 1Z3
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada V6T 1Z3
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada V6T 1Z3
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Bokulic Panichi L, Stanca S, Dolciotti C, Bongioanni P. The Role of Oligodendrocytes in Neurodegenerative Diseases: Unwrapping the Layers. Int J Mol Sci 2025; 26:4623. [PMID: 40429767 PMCID: PMC12111422 DOI: 10.3390/ijms26104623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2025] [Revised: 05/05/2025] [Accepted: 05/08/2025] [Indexed: 05/29/2025] Open
Abstract
Neurodegenerative diseases (NDs), including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis/motor neuron disease, and multiple sclerosis, are characterized by progressive loss of neuronal structure and function, leading to severe cognitive, motor, and behavioral impairments. They pose a significant and growing challenge due to their rising prevalence and impact on global health systems. The societal and emotional toll on patients, caregivers, and healthcare infrastructures is considerable. While significant progress has been made in elucidating the pathological hallmarks of these disorders, the underlying cellular and molecular mechanisms remain incompletely understood. Increasing evidence implicates oligodendrocytes and their progenitors-oligodendrocyte progenitor cells (OPCs)-in the pathogenesis of several NDs, beyond their traditionally recognized role in demyelinating conditions such as MS. Oligodendrocytes are essential for axonal myelination, metabolic support, and neural circuit modulation in the central nervous system. Disruptions in oligodendrocyte function and myelin integrity-manifesting as demyelination, hypomyelination, or dysmyelination-have been associated with disease progression in various neurodegenerative contexts. This review consolidates recent findings on the role of OPCs in NDs, explores the concept of myelin plasticity, and discusses therapeutic strategies targeting oligodendrocyte dysfunction. By highlighting emerging research in oligodendrocyte biology, this review aims to provide a short overview of its relevance to neurodegenerative disease progression and potential therapeutic advances.
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Affiliation(s)
- Leona Bokulic Panichi
- Neuroscience Department, Azienda Ospedaliero-Universitaria Pisana, 56126 Pisa, Italy
- NeuroCare Onlus, 56124 Pisa, Italy
| | - Stefano Stanca
- NeuroCare Onlus, 56124 Pisa, Italy
- Department of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, 56126 Pisa, Italy
| | - Cristina Dolciotti
- Neuroscience Department, Azienda Ospedaliero-Universitaria Pisana, 56126 Pisa, Italy
| | - Paolo Bongioanni
- Neuroscience Department, Azienda Ospedaliero-Universitaria Pisana, 56126 Pisa, Italy
- NeuroCare Onlus, 56124 Pisa, Italy
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Denecke J, Dewenter A, Lee J, Franzmeier N, Valentim C, Kopczak A, Dichgans M, Pirpamer L, Gesierich B, Duering M, Ewers M. Reduced myelin contributes to cognitive impairment in patients with monogenic small vessel disease. Alzheimers Dement 2025; 21:e70127. [PMID: 40317599 PMCID: PMC12046978 DOI: 10.1002/alz.70127] [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: 12/06/2024] [Revised: 03/03/2025] [Accepted: 03/03/2025] [Indexed: 05/07/2025]
Abstract
INTRODUCTION Myelin is pivotal for signal transfer and thus cognition. Cerebral small vessel disease (cSVD) is primarily associated with white matter (WM) lesions and diffusion changes; however, myelin alterations and related cognitive impairments in cSVD remain unclear. METHODS We included 64 patients with familial cSVD (i.e., cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy [CADASIL]) and 20 cognitively unimpaired individuals. χ separation applied to susceptibility weighted imaging was used to assess myelin and iron within WM hyperintensities, normal appearing WM, and two strategic fiber tracts. Diffusion-based mean diffusivity and free water were analyzed for comparisons. Cognitive impairment was assessed by the Trail Making Test. RESULTS CADASIL patients showed reduced myelin within WM hyperintensities and its penumbra in the normal appearing WM. Myelin was moderately correlated with diffusion and iron changes and associated with slower processing speed controlled for diffusion and iron alterations. DISCUSSION Myelin constitutes WM alterations distinct from diffusion changes and substantially contributes to explaining cognitive impairment in cSVD. HIGHLIGHTS χ-negative magnetic resonance signal was reduced within white matter hyperintensities and normal appearing white matter in patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, suggesting widespread myelin decreases due to cerebral small vessel disease (cSVD). χ-negative values were only moderately associated with diffusion tensor imaging derived indices including free water and mean diffusivity, suggesting that χ separation depicts distinct microstructural changes in cSVD. Alterations in χ-negative values made a unique contribution to explain processing speed impairment, even when controlled for diffusion and iron changes.
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Affiliation(s)
- Jannis Denecke
- Institute for Stroke and Dementia Research (ISD)LMU University HospitalMunichGermany
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD)LMU University HospitalMunichGermany
| | - Jongho Lee
- Laboratory for Imaging Science and TechnologyDepartment of Electrical and Computer EngineeringSeoulRepublic of Korea
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD)LMU University HospitalMunichGermany
- Department of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologyThe Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
- Munich Cluster for Systems Neurology (SyNergy)MunichGermany
| | - Carolina Valentim
- Institute for Stroke and Dementia Research (ISD)LMU University HospitalMunichGermany
| | - Anna Kopczak
- Institute for Stroke and Dementia Research (ISD)LMU University HospitalMunichGermany
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD)LMU University HospitalMunichGermany
- Munich Cluster for Systems Neurology (SyNergy)MunichGermany
- German Center for Neurodegenerative Disease (DZNE)MunichGermany
| | - Lukas Pirpamer
- Medical Image Analysis Center (MIAC) and Department of Biomedical EngineeringUniversity of BaselBaselSwitzerland
| | - Benno Gesierich
- Institute for Stroke and Dementia Research (ISD)LMU University HospitalMunichGermany
- Medical Image Analysis Center (MIAC) and Department of Biomedical EngineeringUniversity of BaselBaselSwitzerland
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD)LMU University HospitalMunichGermany
- Medical Image Analysis Center (MIAC) and Department of Biomedical EngineeringUniversity of BaselBaselSwitzerland
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD)LMU University HospitalMunichGermany
- German Center for Neurodegenerative Disease (DZNE)MunichGermany
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Lao G, Feng R, Qi H, Lv Z, Liu Q, Liu C, Zhang Y, Wei H. Coordinate-based neural representation enabling zero-shot learning for fast 3D multiparametric quantitative MRI. Med Image Anal 2025; 102:103530. [PMID: 40069978 DOI: 10.1016/j.media.2025.103530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2024] [Revised: 01/26/2025] [Accepted: 02/24/2025] [Indexed: 04/15/2025]
Abstract
Quantitative magnetic resonance imaging (qMRI) offers tissue-specific physical parameters with significant potential for neuroscience research and clinical practice. However, lengthy scan times for 3D multiparametric qMRI acquisition limit its clinical utility. Here, we propose SUMMIT, an innovative imaging methodology that includes data acquisition and an unsupervised reconstruction for simultaneous multiparametric qMRI. SUMMIT first encodes multiple important quantitative properties into highly undersampled k-space. It further leverages implicit neural representation incorporated with a dedicated physics model to reconstruct the desired multiparametric maps without needing external training datasets. SUMMIT delivers co-registered T1, T2, T2∗, and subvoxel quantitative susceptibility mapping. Extensive simulations, phantom, and in vivo brain imaging demonstrate SUMMIT's high accuracy. Notably, SUMMIT uniquely unravels microstructural alternations in patients with white matter hyperintense lesions with high sensitivity and specificity. Additionally, the proposed unsupervised approach for qMRI reconstruction also introduces a novel zero-shot learning paradigm for multiparametric imaging applicable to various medical imaging modalities.
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Affiliation(s)
- Guoyan Lao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ruimin Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Haikun Qi
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Zhenfeng Lv
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Qiangqiang Liu
- Department of Neurosurgery, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, Shanghai Jiao Tong University, Shanghai, China.
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Vedaei F, Srinivasan D, Parker D, Erus G, Dolui S, Sorond FA, Jacobs DR, Launer LJ, Lackland DT, Davatzikos C, Bryan RN, Nasrallah IM. Spatial and signal features of white matter integrity and associations with clinical factors: A CARDIA brain MRI study. Neuroimage Clin 2025; 46:103768. [PMID: 40101673 PMCID: PMC11964671 DOI: 10.1016/j.nicl.2025.103768] [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: 12/13/2024] [Revised: 02/23/2025] [Accepted: 03/10/2025] [Indexed: 03/20/2025]
Abstract
White matter hyperintensities (WMH) may be indicative of age-related cerebrovascular diseases and contribute to cognitive and functional decline. Normal appearing WM (NAWM) adjacent to WMH, termed "penumbra," is known to be vulnerable to future WMH pathology. WM integrity can be evaluated using multiple magnetic resonance imaging (MRI) modalities. We aimed to identify MRI features predictive of WMH growth and to compare the implications of these features based on spatial proximity to existing WMH versus signal features in baseline NAWM. We used baseline and 5-year follow-up MRI scans in 485 middle-aged participants form the Coronary Artery Risk Development in Young Adults (CARDIA). Multimodal MRI at baseline, including fluid attenuated inversion recovery (FLAIR), diffusion tensor imaging (DTI), and cerebral blood flow (CBF), was measured within WM ROIs including baseline WMH and regions that later developed into new WMH, within and external to the baseline penumbra. Overall, we found that 80% of new WMH appeared within the baseline penumbra. We also found lower fractional anisotropy (FA) and CBF and higher FLAIR and median diffusivity (MD) in NAWM at baseline in regions with subsequent WMH growth compared to those without WMH growth. For NAWM regions defined by signal features, subthreshold FA and suprathreshold MD and FLAIR abnormality at baseline were the most robust predictors of WMH growth. Baseline systolic blood pressure had significant associations with baseline abnormalities in NAWM and subsequently with cognitive decline, particularly for FA and MD measures. The findings support the use of DTI as the predictor of WMH growth, which is correlated with subtle, adverse WM alterations and cognitive function years before developing to WMH. The results may contribute to future clinical trials aimed at preserving WM integrity.
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Affiliation(s)
- Faezeh Vedaei
- AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
| | - Dhivya Srinivasan
- AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Drew Parker
- Department of Radiology, Diffusion and Connectomics in Precision Healthcare Research Lab, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Sudipto Dolui
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Farzaneh A Sorond
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - David R Jacobs
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, MN, USA
| | - Lenore J Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, MD, USA
| | - Daniel T Lackland
- Division of Translational Neurosciences and Population Studies, Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
| | - Christos Davatzikos
- AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - R Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M Nasrallah
- AI(2)D, Center for AI and Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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Boa Sorte Silva NC, Balbim GM, Stein RG, Gu Y, Tam RC, Dao E, Alkeridy W, Lam K, Kramer AF, Liu‐Ambrose T. Physical activity may protect myelin via modulation of high-density lipoprotein. Alzheimers Dement 2025; 21:e14599. [PMID: 39989020 PMCID: PMC11848041 DOI: 10.1002/alz.14599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2024] [Revised: 12/24/2024] [Accepted: 01/13/2025] [Indexed: 02/25/2025]
Abstract
INTRODUCTION Physical activity is associated with greater myelin content in older individuals with cerebral small vessel disease (CSVD), a condition marked by demyelination. However, potential mechanisms underlying this relationship remain understudied. METHODS We assessed cross-sectionally whether serum high-density lipoprotein (HDL), low-density lipoprotein, and triglycerides moderated the association between physical activity and in vivo myelin in older individuals with CSVD and mild cognitive impairment. RESULTS We included 81 highly educated, community-dwelling older individuals (mean age 74.57 years), 64% of whom were female. Regression models revealed that HDL levels significantly moderated the relationship between physical activity and myelin in the sagittal stratum, wherein higher physical activity levels were linked to greater myelin levels for those with average or high HDL (standardized B [95% CI] = 0.289 [0.087 to 0.491], p = 0.006). DISCUSSION Physical activity may promote myelin health partly through HDL. Data from longitudinal studies are needed to confirm our findings. HIGHLIGHTS Myelin loss is common in individuals with cerebral small vessel disease (CSVD). Physical activity was positively associated with myelin in older adults with CSVD. High-density lipoproteins (HDL) levels were also positively related to myelin. Physical activity effects on myelin were moderated by HDL levels.
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Affiliation(s)
- Nárlon C. Boa Sorte Silva
- Department of HealthKinesiology, and Applied PhysiologyFaculty of Arts and ScienceConcordia UniversityMontréalQuébecCanada
| | - Guilherme M. Balbim
- Department of Physical TherapyFaculty of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Ryan G. Stein
- Department of Physical TherapyFaculty of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Yi Gu
- Department of Physical TherapyFaculty of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Roger C. Tam
- School of Biomedical EngineeringFaculty of Applied Science and Faculty of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Elizabeth Dao
- Department of Physical TherapyFaculty of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Walid Alkeridy
- Department of MedicineKing Saud UniversityCollege of MedicineRiyadhSaudi Arabia
| | - Kevin Lam
- Department of MedicineDivision of NeurologyFaculty of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Arthur F. Kramer
- Department of PsychologyNortheastern UniversityBostonMassachusettsUSA
| | - Teresa Liu‐Ambrose
- Department of Physical TherapyFaculty of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
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Lamar M, Arfanakis K, Kapasi A, Han SD, Bennett DA, Yu L, Boyle PA. Associations between structural neuroimaging markers of Alzheimer's risk and scam susceptibility. Brain Imaging Behav 2024; 18:1491-1498. [PMID: 39347939 PMCID: PMC11752735 DOI: 10.1007/s11682-024-00944-0] [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] [Accepted: 09/19/2024] [Indexed: 10/01/2024]
Abstract
Older adults with greater scam susceptibility are at greater risk for mild cognitive impairment and incident Alzheimer's dementia, regardless of baseline cognition. This, combined with documented associations between scam susceptibility and beta amyloid at death suggests that scam susceptibility may be an earlier indicator of pathological aging than cognition. Little, however, is known about whether in vivo neuroimaging markers of early-stage risk for Alzheimer's dementia are also related to scam susceptibility; such knowledge will inform upon the associations of neurodegenerative processes with scam susceptibility and may help identify vulnerable individuals. Participants were 472 community-based adults without dementia (age ~ 81y; 75% women) from the Rush Memory and Aging Project. Baseline 3T MRI T1-weighted structural and T2-weighted FLAIR data were used to assess the cortical thickness 'signature' of Alzheimer's disease (AD-CT) and white matter hyperintensity (WMH) burden, respectively. Scam susceptibility was measured using a questionnaire that assessed behaviors associated with vulnerability to fraud and scams. Demographically-adjusted linear effects regression models determined the relationship of each neuroimaging measure, first separately and then combined, with scam susceptibility. Reduced AD-CT was associated with higher levels of scam susceptibility (estimate=-0.10, standard error = 0.03, p = 0.002). WMH burden was not associated with scam susceptibility either alone or when combined in the same model as AD-CT (p-values ≥ 0.14). Results for AD-CT persisted after the inclusion of WMH burden. AD-CT was associated with scam susceptibility in older adults without dementia possibly signaling an in vivo profile of this behavior.
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Affiliation(s)
- Melissa Lamar
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750 W Harrison Street, Suite 1000, Chicago, IL, 60612, USA.
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA.
| | - Konstantinos Arfanakis
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750 W Harrison Street, Suite 1000, Chicago, IL, 60612, USA
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Alifiya Kapasi
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750 W Harrison Street, Suite 1000, Chicago, IL, 60612, USA
- Department of Pathology, Rush University Medical Center, Chicago, IL, USA
| | - S Duke Han
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750 W Harrison Street, Suite 1000, Chicago, IL, 60612, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Lei Yu
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750 W Harrison Street, Suite 1000, Chicago, IL, 60612, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Patricia A Boyle
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750 W Harrison Street, Suite 1000, Chicago, IL, 60612, USA
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
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Faulkner ME, Gong Z, Guo A, Laporte JP, Bae J, Bouhrara M. Harnessing myelin water fraction as an imaging biomarker of human cerebral aging, neurodegenerative diseases, and risk factors influencing myelination: A review. J Neurochem 2024; 168:2243-2263. [PMID: 38973579 PMCID: PMC11951035 DOI: 10.1111/jnc.16170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/12/2024] [Accepted: 06/19/2024] [Indexed: 07/09/2024]
Abstract
Myelin water fraction (MWF) imaging has emerged as a promising magnetic resonance imaging (MRI) biomarker for investigating brain function and composition. This comprehensive review synthesizes the current state of knowledge on MWF as a biomarker of human cerebral aging, neurodegenerative diseases, and risk factors influencing myelination. The databases used include Web of Science, Scopus, Science Direct, and PubMed. We begin with a brief discussion of the theoretical foundations of MWF imaging, including its basis in MR physics and the mathematical modeling underlying its calculation, with an overview of the most adopted MRI methods of MWF imaging. Next, we delve into the clinical and research applications that have been explored to date, highlighting its advantages and limitations. Finally, we explore the potential of MWF to serve as a predictive biomarker for neurological disorders and identify future research directions for optimizing MWF imaging protocols and interpreting MWF in various contexts. By harnessing the power of MWF imaging, we may gain new insights into brain health and disease across the human lifespan, ultimately informing novel diagnostic and therapeutic strategies.
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Affiliation(s)
- Mary E Faulkner
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Zhaoyuan Gong
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Alex Guo
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - John P Laporte
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Jonghyun Bae
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
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Drenthen GS, Elschot EP, van der Knaap N, Uher D, Voorter PHM, Backes WH, Jansen JFA, van der Thiel MM. Imaging Interstitial Fluid With MRI: A Narrative Review on the Associations of Altered Interstitial Fluid With Vascular and Neurodegenerative Abnormalities. J Magn Reson Imaging 2024; 60:40-53. [PMID: 37823526 DOI: 10.1002/jmri.29056] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 09/27/2023] [Accepted: 09/27/2023] [Indexed: 10/13/2023] Open
Abstract
Interstitial fluid (ISF) refers to the fluid between the parenchymal cells and along the perivascular spaces (PVS). ISF plays a crucial role in delivering nutrients and clearing waste products from the brain. This narrative review focuses on the use of MRI techniques to measure various ISF characteristics in humans. The complementary value of contrast-enhanced and noncontrast-enhanced techniques is highlighted. While contrast-enhanced MRI methods allow measurement of ISF transport and flow, they lack quantitative assessment of ISF properties. Noninvasive MRI techniques, including multi-b-value diffusion imaging, free-water-imaging, T2-decay imaging, and DTI along the PVS, offer promising alternatives to derive ISF measures, such as ISF volume and diffusivity. The emerging role of these MRI techniques in investigating ISF alterations in neurodegenerative diseases (eg, Alzheimer's disease and Parkinson's disease) and cerebrovascular diseases (eg, cerebral small vessel disease and stroke) is discussed. This review also emphasizes current challenges of ISF imaging, such as the microscopic scale at which ISF has to be measured, and discusses potential focus points for future research to overcome these challenges, for example, the use of high-resolution imaging techniques. Noninvasive MRI methods for measuring ISF characteristics hold significant potential and may have a high clinical impact in understanding the pathophysiology of neurodegenerative and cerebrovascular disorders, as well as in evaluating the efficacy of ISF-targeted therapies in clinical trials. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Gerhard S Drenthen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Elles P Elschot
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Noa van der Knaap
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Daniel Uher
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Paulien H M Voorter
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Walter H Backes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Jacobus F A Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Merel M van der Thiel
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands
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Jochems ACC, Muñoz Maniega S, Clancy U, Arteaga C, Jaime Garcia D, Chappell FM, Hewins W, Locherty R, Backhouse EV, Barclay G, Jardine C, McIntyre D, Gerrish I, Kampaite A, Sakka E, Valdés Hernández M, Wiseman S, Bastin ME, Stringer MS, Thrippleton MJ, Doubal FN, Wardlaw JM. Magnetic Resonance Imaging Tissue Signatures Associated With White Matter Changes Due to Sporadic Cerebral Small Vessel Disease Indicate That White Matter Hyperintensities Can Regress. J Am Heart Assoc 2024; 13:e032259. [PMID: 38293936 PMCID: PMC11056146 DOI: 10.1161/jaha.123.032259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 12/21/2023] [Indexed: 02/01/2024]
Abstract
BACKGROUND White matter hyperintensities (WMHs) might regress and progress contemporaneously, but we know little about underlying mechanisms. We examined WMH change and underlying quantitative magnetic resonance imaging tissue measures over 1 year in patients with minor ischemic stroke with sporadic cerebral small vessel disease. METHODS AND RESULTS We defined areas of stable normal-appearing white matter, stable WMHs, progressing and regressing WMHs based on baseline and 1-year brain magnetic resonance imaging. In these areas we assessed tissue characteristics with quantitative T1, fractional anisotropy (FA), mean diffusivity (MD), and neurite orientation dispersion and density imaging (baseline only). We compared tissue signatures cross-sectionally between areas, and longitudinally within each area. WMH change masks were available for N=197. Participants' mean age was 65.61 years (SD, 11.10), 59% had a lacunar infarct, and 68% were men. FA and MD were available for N=195, quantitative T1 for N=182, and neurite orientation dispersion and density imaging for N=174. Cross-sectionally, all 4 tissue classes differed for FA, MD, T1, and Neurite Density Index. Longitudinally, in regressing WMHs, FA increased with little change in MD and T1 (difference estimate, 0.011 [95% CI, 0.006-0.017]; -0.002 [95% CI, -0.008 to 0.003] and -0.003 [95% CI, -0.009 to 0.004]); in progressing and stable WMHs, FA decreased (-0.022 [95% CI, -0.027 to -0.017] and -0.009 [95% CI, -0.011 to -0.006]), whereas MD and T1 increased (progressing WMHs, 0.057 [95% CI, 0.050-0.063], 0.058 [95% CI, 0.050 -0.066]; stable WMHs, 0.054 [95% CI, 0.045-0.063], 0.049 [95% CI, 0.039-0.058]); and in stable normal-appearing white matter, MD increased (0.004 [95% CI, 0.003-0.005]), whereas FA and T1 slightly decreased and increased (-0.002 [95% CI, -0.004 to -0.000] and 0.005 [95% CI, 0.001-0.009]). CONCLUSIONS Quantitative magnetic resonance imaging shows that WMHs that regress have less abnormal microstructure at baseline than stable WMHs and follow trajectories indicating tissue improvement compared with stable and progressing WMHs.
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Affiliation(s)
- Angela C. C. Jochems
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Susana Muñoz Maniega
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Una Clancy
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Carmen Arteaga
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Daniela Jaime Garcia
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Francesca M. Chappell
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Will Hewins
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Rachel Locherty
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Ellen V. Backhouse
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Gayle Barclay
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Charlotte Jardine
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Donna McIntyre
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Iona Gerrish
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Agniete Kampaite
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Eleni Sakka
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Maria Valdés Hernández
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Stewart Wiseman
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Mark E. Bastin
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Michael S. Stringer
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Michael J. Thrippleton
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Fergus N. Doubal
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Joanna M. Wardlaw
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
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11
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Parent O, Bussy A, Devenyi GA, Dai A, Costantino M, Tullo S, Salaciak A, Bedford S, Farzin S, Béland ML, Valiquette V, Villeneuve S, Poirier J, Tardif CL, Dadar M, Chakravarty MM. Assessment of white matter hyperintensity severity using multimodal magnetic resonance imaging. Brain Commun 2023; 5:fcad279. [PMID: 37953840 PMCID: PMC10636521 DOI: 10.1093/braincomms/fcad279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/05/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023] Open
Abstract
White matter hyperintensities are radiological abnormalities reflecting cerebrovascular dysfunction detectable using MRI. White matter hyperintensities are often present in individuals at the later stages of the lifespan and in prodromal stages in the Alzheimer's disease spectrum. Tissue alterations underlying white matter hyperintensities may include demyelination, inflammation and oedema, but these are highly variable by neuroanatomical location and between individuals. There is a crucial need to characterize these white matter hyperintensity tissue alterations in vivo to improve prognosis and, potentially, treatment outcomes. How different MRI measure(s) of tissue microstructure capture clinically-relevant white matter hyperintensity tissue damage is currently unknown. Here, we compared six MRI signal measures sampled within white matter hyperintensities and their associations with multiple clinically-relevant outcomes, consisting of global and cortical brain morphometry, cognitive function, diagnostic and demographic differences and cardiovascular risk factors. We used cross-sectional data from 118 participants: healthy controls (n = 30), individuals at high risk for Alzheimer's disease due to familial history (n = 47), mild cognitive impairment (n = 32) and clinical Alzheimer's disease dementia (n = 9). We sampled the median signal within white matter hyperintensities on weighted MRI images [T1-weighted (T1w), T2-weighted (T2w), T1w/T2w ratio, fluid-attenuated inversion recovery (FLAIR)] as well as the relaxation times from quantitative T1 (qT1) and T2* (qT2*) images. qT2* and fluid-attenuated inversion recovery signals within white matter hyperintensities displayed different age- and disease-related trends compared to normal-appearing white matter signals, suggesting sensitivity to white matter hyperintensity-specific tissue deterioration. Further, white matter hyperintensity qT2*, particularly in periventricular and occipital white matter regions, was consistently associated with all types of clinically-relevant outcomes in both univariate and multivariate analyses and across two parcellation schemes. qT1 and fluid-attenuated inversion recovery measures showed consistent clinical relationships in multivariate but not univariate analyses, while T1w, T2w and T1w/T2w ratio measures were not consistently associated with clinical variables. We observed that the qT2* signal was sensitive to clinically-relevant microstructural tissue alterations specific to white matter hyperintensities. Our results suggest that combining volumetric and signal measures of white matter hyperintensity should be considered to fully characterize the severity of white matter hyperintensities in vivo. These findings may have implications in determining the reversibility of white matter hyperintensities and the potential efficacy of cardio- and cerebrovascular treatments.
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Affiliation(s)
- Olivier Parent
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Aurélie Bussy
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Gabriel Allan Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Alyssa Dai
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Manuela Costantino
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
| | - Stephanie Tullo
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Alyssa Salaciak
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
| | - Saashi Bedford
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Sarah Farzin
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
| | - Marie-Lise Béland
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
| | - Vanessa Valiquette
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Sylvia Villeneuve
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
- Center for the Studies in the Prevention of Alzheimer's Disease, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Judes Poirier
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
- Center for the Studies in the Prevention of Alzheimer's Disease, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Molecular Neurobiology Unit, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Department of Medicine, McGill University, Montreal, Quebec H4A 3J1, Canada
| | - Christine Lucas Tardif
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Mahsa Dadar
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada
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12
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He Y, Li Z, Shi X, Ding J, Wang X. Roles of NG2 Glia in Cerebral Small Vessel Disease. Neurosci Bull 2023; 39:519-530. [PMID: 36401147 PMCID: PMC10043141 DOI: 10.1007/s12264-022-00976-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 09/02/2022] [Indexed: 11/19/2022] Open
Abstract
Cerebral small vessel disease (CSVD) is one of the most prevalent pathologic processes affecting 5% of people over 50 years of age and contributing to 45% of dementia cases. Increasing evidence has demonstrated the pathological roles of chronic hypoperfusion, impaired cerebral vascular reactivity, and leakage of the blood-brain barrier in CSVD. However, the pathogenesis of CSVD remains elusive thus far, and no radical treatment has been developed. NG2 glia, also known as oligodendrocyte precursor cells, are the fourth type of glial cell in addition to astrocytes, microglia, and oligodendrocytes in the mammalian central nervous system. Many novel functions for NG2 glia in physiological and pathological states have recently been revealed. In this review, we discuss the role of NG2 glia in CSVD and the underlying mechanisms.
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Affiliation(s)
- Yixi He
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, China
| | - Zhenghao Li
- Institute of Neuroscience, MOE Key Laboratory of Molecular Neurobiology, NMU, Shanghai, 200433, China
| | - Xiaoyu Shi
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, China
| | - Jing Ding
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, China.
| | - Xin Wang
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, China.
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13
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Ottoy J, Ozzoude M, Zukotynski K, Kang MS, Adamo S, Scott C, Ramirez J, Swardfager W, Lam B, Bhan A, Mojiri P, Kiss A, Strother S, Bocti C, Borrie M, Chertkow H, Frayne R, Hsiung R, Laforce RJ, Noseworthy MD, Prato FS, Sahlas DJ, Smith EE, Kuo PH, Chad JA, Pasternak O, Sossi V, Thiel A, Soucy JP, Tardif JC, Black SE, Goubran M. Amyloid-PET of the white matter: Relationship to free water, fiber integrity, and cognition in patients with dementia and small vessel disease. J Cereb Blood Flow Metab 2023; 43:921-936. [PMID: 36695071 DOI: 10.1177/0271678x231152001] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
White matter (WM) injury is frequently observed along with dementia. Positron emission tomography with amyloid-ligands (Aβ-PET) recently gained interest for detecting WM injury. Yet, little is understood about the origin of the altered Aβ-PET signal in WM regions. Here, we investigated the relative contributions of diffusion MRI-based microstructural alterations, including free water and tissue-specific properties, to Aβ-PET in WM and to cognition. We included a unique cohort of 115 participants covering the spectrum of low-to-severe white matter hyperintensity (WMH) burden and cognitively normal to dementia. We applied a bi-tensor diffusion-MRI model that differentiates between (i) the extracellular WM compartment (represented via free water), and (ii) the fiber-specific compartment (via free water-adjusted fractional anisotropy [FA]). We observed that, in regions of WMH, a decrease in Aβ-PET related most closely to higher free water and higher WMH volume. In contrast, in normal-appearing WM, an increase in Aβ-PET related more closely to higher cortical Aβ (together with lower free water-adjusted FA). In relation to cognitive impairment, we observed a closer relationship with higher free water than with either free water-adjusted FA or WM PET. Our findings support free water and Aβ-PET as markers of WM abnormalities in patients with mixed dementia, and contribute to a better understanding of processes giving rise to the WM PET signal.
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Affiliation(s)
- Julie Ottoy
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Miracle Ozzoude
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Katherine Zukotynski
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Departments of Medicine and Radiology, McMaster University, Hamilton, ON, Canada.,Department of Medical Imaging, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.,Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Min Su Kang
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sabrina Adamo
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Christopher Scott
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Joel Ramirez
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Walter Swardfager
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada
| | - Benjamin Lam
- Department of Medicine (Division of Neurology), Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Aparna Bhan
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Parisa Mojiri
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Alex Kiss
- Department of Research Design and Biostatistics, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Stephen Strother
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,The Rotman Research Institute Baycrest, University of Toronto, Toronto, ON, Canada
| | - Christian Bocti
- Service de Neurologie, Département de Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Michael Borrie
- Lawson Health Research Institute, Western University, London, ON, Canada
| | - Howard Chertkow
- Jewish General Hospital and Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Richard Frayne
- Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Robin Hsiung
- Physics and Astronomy Department and DM Center for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Robert Jr Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, Université Laval, Québec, QC, Canada
| | - Michael D Noseworthy
- Departments of Medicine and Radiology, McMaster University, Hamilton, ON, Canada.,Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
| | - Frank S Prato
- Lawson Health Research Institute, Western University, London, ON, Canada
| | | | - Eric E Smith
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Phillip H Kuo
- Department of Medical Imaging, Medicine, and Biomedical Engineering, University of Arizona, Tucson, AZ, USA
| | - Jordan A Chad
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,The Rotman Research Institute Baycrest, University of Toronto, Toronto, ON, Canada
| | - Ofer Pasternak
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Vesna Sossi
- Physics and Astronomy Department and DM Center for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Alexander Thiel
- Jewish General Hospital and Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jean-Paul Soucy
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | | | - Sandra E Black
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medicine (Division of Neurology), Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Maged Goubran
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
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