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Zhang X, Pei X, Shi Y, Yang Y, Bai X, Chen T, Zhao Y, Yang Q, Ye J, Leng X, Yang Q, Bai R, Wang Y, Sui B. Unveiling connections between venous disruption and cerebral small vessel disease using diffusion tensor image analysis along perivascular space (DTI-ALPS): A 7-T MRI study. Int J Stroke 2025; 20:497-506. [PMID: 39402900 DOI: 10.1177/17474930241293966] [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] [Indexed: 11/07/2024]
Abstract
BACKGROUND Cerebral venous disruption is one of the characteristic findings in cerebral small vessel disease (CSVD), and its disruption may impede perivascular glymphatic drainage. And lower diffusivity along perivascular space (DTI-ALPS) index has been suggested to be with the presence and severity of CSVD. However, the relationships between venous disruption, DTI-ALPS index, and CSVD neuroimaging features remain unclear. AIMS To investigate the association between venous integrity and perivascular diffusion activity, and explore the mediating role of DTI-ALPS index between venous disruption and CSVD imaging features. METHODS In this cross-sectional study, 31 patients (mean age, 59.0 ± 9.9 years) were prospectively enrolled and underwent 7-T magnetic resonance (MR) imaging. DTI-ALPS index was measured to quantify the perivascular diffusivity. The visibility and continuity of deep medullary veins (DMVs) were evaluated based on a brain region-based visual score on high-resolution susceptibility-weighted imaging. White matter hyperintensity (WMH) and perivascular space (PVS) were assessed using qualitative and quantitative methods. Linear regression and mediation analysis were performed to analyze the relationships among DMV scores, DTI-ALPS index, and CSVD features. RESULTS The DTI-ALPS index was significantly associated with the parietal DMV score (β = -0.573, p corrected = 0.004). Parietal DMV score was associated with WMH volume (β = 0.463, p corrected = 0.013) and PVS volume in basal ganglia (β = 0.415, p corrected = 0.028). Mediation analyses showed that DTI-ALPS index manifested a full mediating effect on the association between parietal DMV score and WMH (indirect effect = 0.115, Pm = 43.1%), as well as between parietal DMV score and PVS volume in basal ganglia (indirect effect = 0.161, Pm = 42.8%). CONCLUSION Cerebral venous disruption is associated with glymphatic activity, and with WMH and PVS volumes. Our results suggest cerebral venous integrity may play a critical role in preserving perivascular glymphatic activity; while disruption of small veins may impair the perivascular diffusivity, thereby contributing to the development of WMH and PVS enlargement.
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Affiliation(s)
- Xue Zhang
- Tiantan Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xun Pei
- Tiantan Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yulu Shi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yingying Yang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaoyan Bai
- Tiantan Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tong Chen
- Tiantan Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuanbin Zhao
- Tiantan Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Qianqian Yang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jinyi Ye
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xinyi Leng
- Department of Medicine & Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Qi Yang
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Key Lab of Medical Engineering for Cardiovascular Disease, Ministry of Education, Beijing, China
| | - Ruiliang Bai
- Key Laboratory of Biomedical Engineering of Education Ministry, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Department of Physical Medicine and Rehabilitation, School of Medicine of the Affiliated Sir Run Shumen Shaw Hospital and Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, China
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Binbin Sui
- Tiantan Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, 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 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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3
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Solé‐Guardia G, Luijten M, Janssen E, Visch R, Geenen B, Küsters B, Claassen JAHR, Litjens G, de Leeuw F, Wiesmann M, Kiliaan AJ. Deep learning-based segmentation in MRI-(immuno)histological examination of myelin and axonal damage in normal-appearing white matter and white matter hyperintensities. Brain Pathol 2025; 35:e13301. [PMID: 39175459 PMCID: PMC11835442 DOI: 10.1111/bpa.13301] [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: 04/12/2024] [Accepted: 07/17/2024] [Indexed: 08/24/2024] Open
Abstract
The major vascular cause of dementia is cerebral small vessel disease (SVD). Its diagnosis relies on imaging hallmarks, such as white matter hyperintensities (WMH). WMH present a heterogenous pathology, including myelin and axonal loss. Yet, these might be only the "tip of the iceberg." Imaging modalities imply that microstructural alterations underlie still normal-appearing white matter (NAWM), preceding the conversion to WMH. Unfortunately, direct pathological characterization of these microstructural alterations affecting myelinated axonal fibers in WMH, and especially NAWM, is still missing. Given that there are no treatments to significantly reduce WMH progression, it is important to extend our knowledge on pathological processes that might already be occurring within NAWM. Staining of myelin with Luxol Fast Blue, while valuable, fails to assess subtle alterations in white matter microstructure. Therefore, we aimed to quantify myelin surrounding axonal fibers and axonal- and microstructural damage in detail by combining (immuno)histochemistry with polarized light imaging (PLI). To study the extent (of early) microstructural damage from periventricular NAWM to the center of WMH, we refined current analysis techniques by using deep learning to define smaller segments of white matter, capturing increasing fluid-attenuated inversion recovery signal. Integration of (immuno)histochemistry and PLI with post-mortem imaging of the brains of individuals with hypertension and normotensive controls enables voxel-wise assessment of the pathology throughout periventricular WMH and NAWM. Myelin loss, axonal integrity, and white matter microstructural damage are not limited to WMH but already occur within NAWM. Notably, we found that axonal damage is higher in individuals with hypertension, particularly in NAWM. These findings highlight the added value of advanced segmentation techniques to visualize subtle changes occurring already in NAWM preceding WMH. By using quantitative MRI and advanced diffusion MRI, future studies may elucidate these very early mechanisms leading to neurodegeneration, which ultimately contribute to the conversion of NAWM to WMH.
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Affiliation(s)
- Gemma Solé‐Guardia
- Department of Medical Imaging, Anatomy, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIMERadboud Alzheimer CenterNijmegenThe Netherlands
| | - Matthijs Luijten
- Department of Medical Imaging, Anatomy, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIMERadboud Alzheimer CenterNijmegenThe Netherlands
| | - Esther Janssen
- Department of Medical Imaging, Anatomy, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIMERadboud Alzheimer CenterNijmegenThe Netherlands
| | - Ruben Visch
- Department of Medical Imaging, Anatomy, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIMERadboud Alzheimer CenterNijmegenThe Netherlands
| | - Bram Geenen
- Department of Medical Imaging, Anatomy, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIMERadboud Alzheimer CenterNijmegenThe Netherlands
| | - Benno Küsters
- Department of Pathology, Research Institute for Medical InnovationRadboud University Medical CenterNijmegenThe Netherlands
| | - Jurgen A. H. R. Claassen
- Department of Geriatrics, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & BehaviorRadboud Alzheimer CenterNijmegenThe Netherlands
- Department of Cardiovascular SciencesUniversity of LeicesterLeicesterUK
| | - Geert Litjens
- Department of Pathology, Research Institute for Medical InnovationRadboud University Medical CenterNijmegenThe Netherlands
- Computational Pathology Group, Research Institute for Medical InnovationRadboud University Medical CenterNijmegenThe Netherlands
| | - Frank‐Erik de Leeuw
- Department of Neurology, Research Institute for Medical Innovation, Radboud University Medical CenterDonders Institute for Brain, Cognition & Behavior, Center for Medical NeuroscienceNijmegenThe Netherlands
| | - Maximilian Wiesmann
- Department of Medical Imaging, Anatomy, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIMERadboud Alzheimer CenterNijmegenThe Netherlands
| | - Amanda J. Kiliaan
- Department of Medical Imaging, Anatomy, Research Institute for Medical Innovation, Radboud University Medical Center, Donders Institute for Brain, Cognition & Behavior, Center for Medical Neuroscience, Preclinical Imaging Center PRIMERadboud Alzheimer CenterNijmegenThe Netherlands
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4
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van de Schraaf S, van Loon AM, Muller M, Hertogh CMPM, Sizoo EM, Rhodius-Meester HFM. Interrelation between domains of functioning and white matter hyperintensities in geriatric memory clinic patients: a holistic approach through network analysis. Aging Ment Health 2025:1-9. [PMID: 39831385 DOI: 10.1080/13607863.2025.2450282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Accepted: 01/02/2025] [Indexed: 01/22/2025]
Abstract
OBJECTIVES To explore interrelations between cognitive, physical, affective, and daily functioning, quality of life and white matter hyperintensities (WMH) in a geriatric memory clinic sample. METHOD Participants received brain imaging, comprehensive geriatric assessment and neuropsychological evaluation including measurements of cognitive, physical, affective, and daily functioning and health-related quality of life. Data was analyzed using multiple linear regressions and network analysis using (moderated) mixed graphical models. RESULTS The total sample included 932 patients (age: 79.6 ± 6.0, 49% women). In regression analyses, severe WMH (Fazekas 3) was associated with decreased cognitive (attention/speed, language) and physical functioning, more apathy symptoms and more (instrumental) activities of daily living dependency (All β's -0.40 to -0.24). Within the network analysis, daily functioning was directly associated with memory, attention/speed, and gait speed, while quality of life was associated with gait speed and affective functioning. WMH had no direct network associations with domains of functioning. CONCLUSION Cognitive, physical, and affective changes associated with severe WMH co-occur with decreased daily functioning and lower quality of life in a geriatric memory clinic sample. However, relationships between domains of functioning are independent of WMH. This warrants a holistic and symptom-based approach in clinical care and post-diagnostic support.
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Affiliation(s)
- Sara van de Schraaf
- Medicine for Older People, Amsterdam UMC, location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Aging & Later Life, Amsterdam, The Netherlands
- Internal Medicine, Geriatric Medicine section, Amsterdam UMC, location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anouk M van Loon
- Medicine for Older People, Amsterdam UMC, location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Aging & Later Life, Amsterdam, The Netherlands
| | - Majon Muller
- Amsterdam Public Health, Aging & Later Life, Amsterdam, The Netherlands
- Internal Medicine, Geriatric Medicine section, Amsterdam UMC, location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Atherosclerosis & Ischemic Syndromes, Amsterdam, The Netherlands
| | - Cees M P M Hertogh
- Medicine for Older People, Amsterdam UMC, location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Aging & Later Life, Amsterdam, The Netherlands
| | - Eefje M Sizoo
- Medicine for Older People, Amsterdam UMC, location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Aging & Later Life, Amsterdam, The Netherlands
| | - Hanneke F M Rhodius-Meester
- Internal Medicine, Geriatric Medicine section, Amsterdam UMC, location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
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5
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Newman BT, Jacokes Z, Venkadesh S, Webb SJ, Kleinhans NM, McPartland JC, Druzgal TJ, Pelphrey KA, Van Horn JD. Conduction velocity, G-ratio, and extracellular water as microstructural characteristics of autism spectrum disorder. PLoS One 2024; 19:e0301964. [PMID: 38630783 PMCID: PMC11023574 DOI: 10.1371/journal.pone.0301964] [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: 11/04/2023] [Accepted: 03/26/2024] [Indexed: 04/19/2024] Open
Abstract
The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a new approach to calculating axonal conduction velocity termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel formulation for calculating aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.
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Affiliation(s)
- Benjamin T. Newman
- Department of Psychology, University of Virginia, Charlottesville, VA, United States of America
- UVA School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Zachary Jacokes
- School of Data Science, University of Virginia, Elson Building, Charlottesville, VA, United States of America
| | - Siva Venkadesh
- Department of Psychology, University of Virginia, Charlottesville, VA, United States of America
| | - Sara J. Webb
- Department of Psychiatry and Behavioral Science, University of Washington, Seattle WA, United States of America
- Seattle Children’s Research Institute, Seattle WA, United States of America
| | - Natalia M. Kleinhans
- Department of Radiology, Integrated Brain Imaging Center, University of Washington, Seattle, WA, United States of America
| | - James C. McPartland
- Yale Child Study Center, New Haven, CT, United States of America
- Yale Center for Brain and Mind Health, New Haven, CT, United States of America
| | - T. Jason Druzgal
- UVA School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Kevin A. Pelphrey
- UVA School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, VA, United States of America
- School of Data Science, University of Virginia, Elson Building, Charlottesville, VA, United States of America
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6
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Newman BT, Jacokes Z, Venkadesh S, Webb SJ, Kleinhans NM, McPartland JC, Druzgal TJ, Pelphrey KA, Van Horn JD. Conduction Velocity, G-ratio, and Extracellular Water as Microstructural Characteristics of Autism Spectrum Disorder. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.23.550166. [PMID: 37546913 PMCID: PMC10402058 DOI: 10.1101/2023.07.23.550166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a novel metric termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel neuroimaging metric, aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.
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Affiliation(s)
- Benjamin T. Newman
- Department of Psychology, University of Virginia, Gilmer Hall, Charlottesville, VA 22903
- UVA School of Medicine, University of Virginia, 560 Ray Hunt Drive, Charlottesville, VA 22903
| | - Zachary Jacokes
- School of Data Science, University of Virginia, Elson Building, Charlottesville, VA 22903
| | - Siva Venkadesh
- Department of Psychology, University of Virginia, Gilmer Hall, Charlottesville, VA 22903
| | - Sara J. Webb
- Department of Psychiatry and Behavioral Science, University of Washington, Seattle WA USA 98195
- Seattle Children’s Research Institute, 1920 Terry Ave, Building Cure-03, Seattle WA 98101
| | - Natalia M. Kleinhans
- Department of Radiology, Integrated Brain Imaging Center, University of Washington, 1959 NE Pacific St Seattle, WA 98195
| | - James C. McPartland
- Yale Child Study Center, 230 South Frontage Road, New Haven, CT 06520
- Yale Center for Brain and Mind Health, 40 Temple Street, Suite 6A, New Haven, CT, 06520
| | - T. Jason Druzgal
- UVA School of Medicine, University of Virginia, 560 Ray Hunt Drive, Charlottesville, VA 22903
| | - Kevin A. Pelphrey
- UVA School of Medicine, University of Virginia, 560 Ray Hunt Drive, Charlottesville, VA 22903
| | - John Darrell Van Horn
- Department of Psychology, University of Virginia, Gilmer Hall, Charlottesville, VA 22903
- School of Data Science, University of Virginia, Elson Building, Charlottesville, VA 22903
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7
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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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8
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Rowsthorn E, Pham W, Nazem-Zadeh MR, Law M, Pase MP, Harding IH. Imaging the neurovascular unit in health and neurodegeneration: a scoping review of interdependencies between MRI measures. Fluids Barriers CNS 2023; 20:97. [PMID: 38129925 PMCID: PMC10734164 DOI: 10.1186/s12987-023-00499-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023] Open
Abstract
The neurovascular unit (NVU) is a complex structure that facilitates nutrient delivery and metabolic waste clearance, forms the blood-brain barrier (BBB), and supports fluid homeostasis in the brain. The integrity of NVU subcomponents can be measured in vivo using magnetic resonance imaging (MRI), including quantification of enlarged perivascular spaces (ePVS), BBB permeability, cerebral perfusion and extracellular free water. The breakdown of NVU subparts is individually associated with aging, pathology, and cognition. However, how these subcomponents interact as a system, and how interdependencies are impacted by pathology remains unclear. This systematic scoping review identified 26 studies that investigated the inter-relationships between multiple subcomponents of the NVU in nonclinical and neurodegenerative populations using MRI. A further 112 studies investigated associations between the NVU and white matter hyperintensities (WMH). We identify two putative clusters of NVU interdependencies: a 'vascular' cluster comprising BBB permeability, perfusion and basal ganglia ePVS; and a 'fluid' cluster comprising ePVS, free water and WMH. Emerging evidence suggests that subcomponent coupling within these clusters may be differentially related to aging, neurovascular injury or neurodegenerative pathology.
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Affiliation(s)
- Ella Rowsthorn
- Department of Neuroscience, Central Clinical School, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia
- Turner Institute for Brain and Mental Health & School of Psychological Sciences, Monash University, 18 Innovation Walk, Clayton, VIC, 3168, Australia
| | - William Pham
- Department of Neuroscience, Central Clinical School, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Mohammad-Reza Nazem-Zadeh
- Department of Neuroscience, Central Clinical School, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Meng Law
- Department of Neuroscience, Central Clinical School, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia
- Department of Radiology, Alfred Health, 99 Commercial Road, Melbourne, VIC, 3004, Australia
- Department of Electrical and Computer Systems Engineering, Monash University, 14 Alliance Lane, Clayton, VIC, 3168, Australia
| | - Matthew P Pase
- Turner Institute for Brain and Mental Health & School of Psychological Sciences, Monash University, 18 Innovation Walk, Clayton, VIC, 3168, Australia
- Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Ian H Harding
- Department of Neuroscience, Central Clinical School, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia.
- Monash Biomedical Imaging, Monash University, 762-772 Blackburn Road, Clayton, VIC, 3168, Australia.
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9
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Kern KC, Zagzoug MS, Gottesman RF, Wright CB, Leigh R. Diffusion tensor free water MRI predicts progression of FLAIR white matter hyperintensities after ischemic stroke. Front Neurol 2023; 14:1172031. [PMID: 37808483 PMCID: PMC10559725 DOI: 10.3389/fneur.2023.1172031] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 08/23/2023] [Indexed: 10/10/2023] Open
Abstract
Background The progression of FLAIR white matter hyperintensities (WMHs) on MRI heralds vascular-mediated cognitive decline. Even before FLAIR WMH progression, adjacent normal appearing white matter (NAWM) already demonstrates microstructural deterioration on diffusion tensor imaging (DTI). We hypothesized that elevated DTI free water (FW) would precede FLAIR WMH progression, implicating interstitial fluid accumulation as a key pathological step in the progression of cerebral small vessel disease. Methods Participants at least 3 months after an ischemic stroke or TIA with WMH on MRI underwent serial brain MRIs every 3 months over the subsequent year. For each participant, the WMHs were automatically segmented, serial MRIs were aligned, and a region of WMH penumbra tissue at risk was defined by dilating lesions at any time point and subtracting baseline lesions. Penumbra voxels were classified as either stable or progressing to WMH if they were segmented as new lesions and demonstrated increasing FLAIR intensity over time. Aligned DTI images included FW and FW-corrected fractional anisotropy (FATissue) and mean diffusivity (MDTissue). Logistic regression and area under the receiver-operator characteristic curve (AUC) were used to test whether baseline DTI predicted voxel-wise classification of stable penumbra or progression to WMH while covarying for clinical risk factors. Results In the included participants (n = 26, mean age 71 ± 9 years, 31% female), we detected a median annual voxel-wise WMH growth of 2.9 ± 2.6 ml. Each baseline DTI metric was associated with lesion progression in the penumbra, but FW had the greatest AUC of 0.732 (0.730 - 0.733) for predicting voxel-wise WMH progression pooled across participants. Discussion Baseline increased interstitial fluid, estimated as FW on DTI, predicted the progression of NAWM to WMH over the following year. These results implicate the presence of FW in the pathogenesis of cerebral small vessel disease progression.
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Affiliation(s)
- Kyle C. Kern
- Stroke Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Marwah S. Zagzoug
- Stroke Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Rebecca F. Gottesman
- Stroke Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Clinton B. Wright
- Stroke Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Richard Leigh
- Stroke Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
- Department of Neurology, Johns Hopkins Medicine, Baltimore, MD, United States
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10
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Lan H, Lei X, Xu Z, Chen S, Gong W, Cai Y. New insights in addressing cerebral small vessel disease: Associated with extracellular fluid in white matter. Front Neurosci 2022; 16:1042824. [PMID: 36340793 PMCID: PMC9631816 DOI: 10.3389/fnins.2022.1042824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022] Open
Abstract
Objective To explore the role of extracellular fluid, assessed by diffusion tensor imaging (DTI) metrics of free water (FW), in the white matter of patients with cerebral small vessel disease (CSVD). Materials and methods The baseline clinical and imaging data of 129 patients with CSVD were collected and reviewed. CSVD MR markers, including periventricular white matter hyperintensity (PWMH), deep white matter hyperintensity (DWMH), cerebral microbleed (CMB), enlarged perivascular space (PVS), and lacunar infarction (LI), were identified, and CSVD burden was calculated. According to total CSVD MR marker score, cases were classified as mild, moderate, or severe. The mean FW and fractional anisotropy (FA) values were calculated using DTI images. Results The mean white matter FW was associated with the CSVD MR markers, including PWMH, DWMH, LI and PVS (P < 0.05). Moreover, age, hypertension, diabetes mellitus, and FW value were associated with total CSVD MR marker score (P < 0.05). Ordinal logistic regression analysis revealed that FW and age were independently associated with CSVD burden (P < 0.05). Finally, FW in white matter was associated with FA (r = –0.334, P < 0.001). Conclusion Extracellular fluid changes, assessed by DTI metrics of FW in white matter, were associated with CSVD markers and burden. An increased extracellular fluid volume in the white matter was associated with lower FA.
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Affiliation(s)
- Haiyuan Lan
- Department of Radiology, Lishui Hospital of Traditional Chinese Medicine affiliated Zhejiang Chinese Medical University, Lishui, China
| | - Xinjun Lei
- Department of Radiology, Lishui Hospital of Traditional Chinese Medicine affiliated Zhejiang Chinese Medical University, Lishui, China
| | - Zhihua Xu
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
- *Correspondence: Zhihua Xu,
| | - Songkuan Chen
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Wanfeng Gong
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Yunqi Cai
- Department of Radiology, Lishui Hospital of Traditional Chinese Medicine affiliated Zhejiang Chinese Medical University, Lishui, China
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11
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Huo Y, Wang Y, Guo C, Liu Q, Shan L, Liu M, Wu H, Li G, Lv H, Lu L, Zhou Y, Feng J, Han Y. Deep white matter hyperintensity is spatially correlated to MRI-visible perivascular spaces in cerebral small vessel disease on 7 Tesla MRI. Stroke Vasc Neurol 2022; 8:144-150. [PMID: 36170993 PMCID: PMC10176991 DOI: 10.1136/svn-2022-001611] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 09/14/2022] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND The association between perivascular space (PVS) and white matter hyperintensity (WMH) has been unclear. Normal-appearing white matter (NAWM) around WMH is also found correlated with the development of focal WMH. This study aims to investigate the topological connections among PVS, deep WMH (dWMH) and NAWM around WMH using 7 Tesla (7T) MRI. METHODS Thirty-two patients with non-confluent WMHs and 16 subjects without WMHs were recruited from our department and clinic. We compared the PVS burden between patients with and without WMHs using a 5-point scale. Then, the dilatation and the number of PVS within a radius of 1 cm around each dWMH were compared with those of a reference site (without WMH) in the contralateral hemisphere. In this study, we define NAWM as an area within the radius of 1 cm around each dWMH. Furthermore, we assessed the spatial relationship between dWMH and PVS. RESULTS Higher PVS scores in the centrum semiovale were found in patients with >5 dWMHs (median 3) than subjects without dWMH (median 2, p = 0.014). We found there was a greater dilatation and a higher number of PVS in NAWM around dWMH than at the reference sites (p<0.001, p<0.001). In addition, 79.59% of the dWMHs were spatially connected with PVS. CONCLUSION dWMH, NAWM surrounding WMH and MRI-visible PVS are spatially correlated in the early stage of cerebral small vessel disease. Future study of WMH and NAWM should not overlook MRI-visible PVS.
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Affiliation(s)
- Yajing Huo
- Department of Neurology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yilin Wang
- Georgetown Preparatory School, North Bethesda, Maryland, USA
| | - Cen Guo
- Department of Neurology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Qianyun Liu
- Department of Neurology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Lili Shan
- Department of Neurology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mingyuan Liu
- Department of Neurology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Haibo Wu
- Department of Neurology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Guanwu Li
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Huihui Lv
- Department of Neurology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Lingdan Lu
- Department of Neurology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yintin Zhou
- Department of Neurology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Yan Han
- Department of Neurology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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12
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Shen X, Raghavan S, Przybelski SA, Lesnick TG, Ma S, Reid RI, Graff-Radford J, Mielke MM, Knopman DS, Petersen RC, Jack CR, Simon GJ, Vemuri P. Causal structure discovery identifies risk factors and early brain markers related to evolution of white matter hyperintensities. Neuroimage Clin 2022; 35:103077. [PMID: 35696810 PMCID: PMC9194644 DOI: 10.1016/j.nicl.2022.103077] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/25/2022] [Accepted: 06/03/2022] [Indexed: 11/25/2022]
Abstract
Our goal was to understand the complex relationship between age, sex, midlife risk factors, and early white matter changes measured by diffusion tensor imaging (DTI) and their role in the evolution of longitudinal white matter hyperintensities (WMH). We identified 1564 participants (1396 cognitively unimpaired, 151 mild cognitive impairment and 17 dementia participants) with age ranges of 30-90 years from the population-based sample of Mayo Clinic Study of Aging. We used computational causal structure discovery and regression analyses to evaluate the predictors of WMH and DTI, and to ascertain the mediating effect of DTI on WMH. We further derived causal graphs to understand the complex interrelationships between midlife protective factors, vascular risk factors, diffusion changes, and WMH. Older age, female sex, and hypertension were associated with higher baseline and progression of WMH as well as DTI measures (P ≤ 0.003). The effects of hypertension and sex on WMH were partially mediated by microstructural changes measured on DTI. Higher midlife physical activity was predictive of lower WMH through a direct impact on better white matter tract integrity as well as an indirect effect through reducing the risk of hypertension by lowering BMI. This study identified key risks factors, early brain changes, and pathways that may lead to the evolution of WMH.
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Affiliation(s)
- Xinpeng Shen
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA; Departments of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Sisi Ma
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Robert I Reid
- Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | - Michelle M Mielke
- Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA; Departments of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - György J Simon
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
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13
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Ferris JK, Greeley B, Vavasour IM, Kraeutner SN, Rinat S, Ramirez J, Black SE, Boyd LA. In vivo myelin imaging and tissue microstructure in white matter hyperintensities and perilesional white matter. Brain Commun 2022; 4:fcac142. [PMID: 35694147 PMCID: PMC9178967 DOI: 10.1093/braincomms/fcac142] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 03/28/2022] [Accepted: 05/26/2022] [Indexed: 11/12/2022] Open
Abstract
White matter hyperintensities negatively impact white matter structure and relate to cognitive decline in aging. Diffusion tensor imaging detects changes to white matter microstructure, both within the white matter hyperintensity and extending into surrounding (perilesional) normal-appearing white matter. However, diffusion tensor imaging markers are not specific to tissue components, complicating the interpretation of previous microstructural findings. Myelin water imaging is a novel imaging technique that provides specific markers of myelin content (myelin water fraction) and interstitial fluid (geometric mean T2). Here we combined diffusion tensor imaging and myelin water imaging to examine tissue characteristics in white matter hyperintensities and perilesional white matter in 80 individuals (47 older adults and 33 individuals with chronic stroke). To measure perilesional normal-appearing white matter, white matter hyperintensity masks were dilated in 2 mm segments up to 10 mm in distance from the white matter hyperintensity. Fractional anisotropy, mean diffusivity, myelin water fraction, and geometric mean T2 were extracted from white matter hyperintensities and perilesional white matter. We observed a spatial gradient of higher mean diffusivity and geometric mean T2, and lower fractional anisotropy, in the white matter hyperintensity and perilesional white matter. In the chronic stroke group, myelin water fraction was reduced in the white matter hyperintensity but did not show a spatial gradient in perilesional white matter. Across the entire sample, white matter metrics within the white matter hyperintensity related to whole-brain white matter hyperintensity volume; with increasing white matter hyperintensity volume there was increased mean diffusivity and geometric mean T2, and decreased myelin water fraction in the white matter hyperintensity. Normal-appearing white matter adjacent to white matter hyperintensities exhibits characteristics of a transitional stage between healthy white matter and white matter hyperintensities. This effect was observed in markers sensitive to interstitial fluid, but not in myelin water fraction, the specific marker of myelin concentration. Within the white matter hyperintensity, interstitial fluid was higher and myelin concentration was lower in individuals with more severe cerebrovascular disease. Our data suggests white matter hyperintensities have penumbra-like effects in perilesional white matter that specifically reflect increased interstitial fluid, with no changes to myelin concentration. In contrast, within the white matter hyperintensity there are varying levels of demyelination, which vary based on the severity of cerebrovascular disease. Diffusion tensor imaging and myelin imaging may be useful clinical markers to predict white matter hyperintensity formation, and to stage neuronal damage within white matter hyperintensities.
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Affiliation(s)
- Jennifer K. Ferris
- University of British Columbia Graduate Programs in Rehabilitation Sciences, , Vancouver, Canada
| | - Brian Greeley
- University of British Columbia Department of Physical Therapy, , Vancouver, Canada
| | - Irene M. Vavasour
- The University of British Columbia Department of Radiology, , Vancouver, Canada
- University of British Columbia UBC MRI Research Centre, Faculty of Medicine, , Vancouver, Canada
| | - Sarah N. Kraeutner
- University of British Columbia Department of Psychology, , Okanagan, Kelowna, Canada
| | - Shie Rinat
- University of British Columbia Graduate Programs in Rehabilitation Sciences, , Vancouver, Canada
| | - Joel Ramirez
- LC Campbell Cognitive Neurology Research Unit, Dr Sandra Black Centre for Brain Resilience and Recovery , Toronto, Canada
- Sunnybrook Research Institute, University of Toronto Hurvitz Brain Sciences Research Program, , Toronto, Canada
| | - Sandra E. Black
- LC Campbell Cognitive Neurology Research Unit, Dr Sandra Black Centre for Brain Resilience and Recovery , Toronto, Canada
- Sunnybrook Research Institute, University of Toronto Hurvitz Brain Sciences Research Program, , Toronto, Canada
| | - Lara A. Boyd
- University of British Columbia Graduate Programs in Rehabilitation Sciences, , Vancouver, Canada
- University of British Columbia Department of Physical Therapy, , Vancouver, Canada
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14
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Kelly C, Dhollander T, Harding IH, Khan W, Beare R, Cheong JL, Doyle LW, Seal M, Thompson DK, Inder TE, Anderson PJ. Brain tissue microstructural and free-water composition 13 years after very preterm birth. Neuroimage 2022; 254:119168. [PMID: 35367651 DOI: 10.1016/j.neuroimage.2022.119168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/27/2022] [Accepted: 03/30/2022] [Indexed: 12/20/2022] Open
Abstract
There have been many studies demonstrating children born very preterm exhibit brain white matter microstructural alterations, which have been related to neurodevelopmental difficulties. These prior studies have often been based on diffusion MRI modelling and analysis techniques, which commonly focussed on white matter microstructural properties in very preterm-born children. However, there have been relatively fewer studies investigating the free-water content of the white matter, and also the microstructure and free-water content of the cortical grey matter, in very preterm-born children. These biophysical properties of the brain change rapidly during fetal and neonatal brain development, and therefore such properties are likely also adversely affected by very preterm birth. In this study, we investigated the relationship of very preterm birth (<30 weeks' gestation) to both white matter and cortical grey matter microstructure and free-water content in childhood using advanced diffusion MRI analyses. A total of 130 very preterm participants and 45 full-term control participants underwent diffusion MRI at age 13 years. Diffusion tissue signal fractions derived by Single-Shell 3-Tissue Constrained Spherical Deconvolution were used to investigate brain tissue microstructural and free-water composition. The tissue microstructural and free-water composition metrics were analysed using a bespoke voxel-based analysis and cortical region-of-interest analysis approach. Very preterm 13-year-olds exhibited reduced white matter microstructural density and increased free-water content across widespread regions of the white matter compared with controls. Additionally, very preterm 13-year-olds exhibited reduced microstructural density and increased free-water content in specific temporal, sensorimotor, occipital and cingulate cortical regions. These brain tissue composition alterations were strongly associated with cerebral white matter abnormalities identified in the neonatal period, and concurrent adverse cognitive and motor outcomes in very preterm children. The findings demonstrate brain microstructural and free-water alterations up to thirteen years from neonatal brain abnormalities in very preterm children that relate to adverse neurodevelopmental outcomes.
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Affiliation(s)
- Claire Kelly
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia; Victorian Infant Brain Studies (VIBeS), Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Developmental Imaging, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia.
| | - Thijs Dhollander
- Developmental Imaging, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia
| | - Ian H Harding
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia; Monash Biomedical Imaging, Monash University, Melbourne, Australia
| | - Wasim Khan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Richard Beare
- Developmental Imaging, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia
| | - Jeanie Ly Cheong
- Victorian Infant Brain Studies (VIBeS), Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; The Royal Women's Hospital, Melbourne, Australia; Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Australia
| | - Lex W Doyle
- Victorian Infant Brain Studies (VIBeS), Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; The Royal Women's Hospital, Melbourne, Australia; Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Marc Seal
- Developmental Imaging, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Deanne K Thompson
- Victorian Infant Brain Studies (VIBeS), Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Developmental Imaging, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Terrie E Inder
- Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter J Anderson
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia; Victorian Infant Brain Studies (VIBeS), Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia
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15
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Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities. Neuroimage 2021; 241:118417. [PMID: 34298083 DOI: 10.1016/j.neuroimage.2021.118417] [Citation(s) in RCA: 127] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 07/11/2021] [Accepted: 07/20/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "Fixel-Based Analysis" (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities.
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16
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Brodtmann A, Veldsman M. Predicting Poststroke Cognitive Impairment: Sharpening the Diffuse? Stroke 2021; 52:1993-1994. [PMID: 33966495 DOI: 10.1161/strokeaha.121.035038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Amy Brodtmann
- The Florey Institute of Neuroscience and Mental Health (A.B., M.V.), University of Melbourne, Australia.,Melbourne Dementia Research Centre, Florey Institute (A.B.), University of Melbourne, Australia
| | - Michele Veldsman
- The Florey Institute of Neuroscience and Mental Health (A.B., M.V.), University of Melbourne, Australia.,Cognitive Neurology Research Group, Kellogg College Department of Experimental Psychology, University of Oxford, United Kingdom (M.V.)
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