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Tremblay SA, Nathan Spreng R, Wearn A, Alasmar Z, Pirhadi A, Tardif CL, Chakravarty MM, Villeneuve S, Leppert IR, Carbonell F, Medina YI, Steele CJ, Gauthier CJ. Sex and APOE4-specific links between cardiometabolic risk factors and white matter alterations in individuals with a family history of Alzheimer's disease. Neurobiol Aging 2025; 150:80-96. [PMID: 40086421 DOI: 10.1016/j.neurobiolaging.2025.03.003] [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: 09/25/2024] [Revised: 02/11/2025] [Accepted: 03/02/2025] [Indexed: 03/16/2025]
Abstract
Early detection of pathological changes in Alzheimer's disease (AD) has garnered significant attention in the last few decades as interventions aiming to prevent progression will likely be most effective when initiated early. White matter (WM) alterations are among the earliest changes in AD, yet limited work has comprehensively characterized the effects of AD risk factors on WM. In older adults with a family history of AD, we investigated the sex-specific and APOE genotype-related relationships between WM microstructure and risk factors. Multiple MRI-derived metrics were integrated using a multivariate approach based on the Mahalanobis distance (D2). To uncover the specific biological underpinnings of these WM alterations, we then extracted the contribution of each MRI feature to D2 in significant clusters. Lastly, the links between WM D2 and cognition were explored. WM D2 in several regions was associated with high systolic blood pressure, BMI, and glycated hemoglobin, and low cholesterol, in both males and females. APOE4 + displayed a distinct risk pattern, with LDL-cholesterol having a detrimental effect only in carriers, and this pattern was linked to immediate memory performance. Myelination was the main mechanism underlying WM alterations. Our findings reveal that combined exposure to multiple cardiometabolic risk factors negatively impacts microstructural health, which may subsequently affect cognition. Notably, APOE4 carriers exhibited a different risk pattern, especially in the role of LDL, suggesting distinct underlying mechanisms in this group.
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Affiliation(s)
- Stefanie A Tremblay
- Physics department, Concordia University, 7141 Rue Sherbrooke W, Montréal, QC H4B 1R6, Canada; Montreal Heart Institute, 5000 Rue Bélanger, Montréal, QC H1T 1C8, Canada; School of Health, Concordia University, 7200 Rue Sherbrooke W, Montréal, QC H4B 1R6, Canada.
| | - R Nathan Spreng
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; Department of Psychiatry, McGill University, 845 Rue Sherbrooke W, Montréal, QC H3A 0G4, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, 845 Rue Sherbrooke W, Montréal, QC H3A 0G4, Canada; StoP-AD Centre, Douglas Mental Health Institute Research Centre, 6875 Blvd. LaSalle, Verdun, QC H4H 1R3, Canada
| | - Alfie Wearn
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada
| | - Zaki Alasmar
- School of Health, Concordia University, 7200 Rue Sherbrooke W, Montréal, QC H4B 1R6, Canada; Psychology department, Concordia University, 7141 Rue Sherbrooke W, Montréal, QC H4B 1R6, Canada
| | - Amir Pirhadi
- Electrical Engineering department, Concordia University, 1455 De Maisonneuve Blvd. W, Montreal, QC H3G 1M8, Canada; ViTAA Medical Solutions, 400 Rue Montfort, Montréal, QC H3C 4J9, Canada
| | - Christine L Tardif
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, 845 Rue Sherbrooke W, Montréal, QC H3A 0G4, Canada; Department of Biomedical Engineering, McGill University, 845 Rue Sherbrooke W, Montréal, QC H3A 0G4, Canada
| | - Mallar M Chakravarty
- Department of Psychiatry, McGill University, 845 Rue Sherbrooke W, Montréal, QC H3A 0G4, Canada; Department of Biomedical Engineering, McGill University, 845 Rue Sherbrooke W, Montréal, QC H3A 0G4, Canada; StoP-AD Centre, Douglas Mental Health Institute Research Centre, 6875 Blvd. LaSalle, Verdun, QC H4H 1R3, Canada
| | - Sylvia Villeneuve
- Department of Psychiatry, McGill University, 845 Rue Sherbrooke W, Montréal, QC H3A 0G4, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, 845 Rue Sherbrooke W, Montréal, QC H3A 0G4, Canada; StoP-AD Centre, Douglas Mental Health Institute Research Centre, 6875 Blvd. LaSalle, Verdun, QC H4H 1R3, Canada
| | - Ilana R Leppert
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, 845 Rue Sherbrooke W, Montréal, QC H3A 0G4, Canada
| | | | - Yasser Iturria Medina
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, 845 Rue Sherbrooke W, Montréal, QC H3A 0G4, Canada; Ludmer Center for NeuroInformatics and Mental Health, 1010 rue Sherbrooke W, Montreal, Canada
| | - Christopher J Steele
- School of Health, Concordia University, 7200 Rue Sherbrooke W, Montréal, QC H4B 1R6, Canada; Psychology department, Concordia University, 7141 Rue Sherbrooke W, Montréal, QC H4B 1R6, Canada; Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, Leipzig 04103, Germany
| | - Claudine J Gauthier
- Physics department, Concordia University, 7141 Rue Sherbrooke W, Montréal, QC H4B 1R6, Canada; Montreal Heart Institute, 5000 Rue Bélanger, Montréal, QC H1T 1C8, Canada; School of Health, Concordia University, 7200 Rue Sherbrooke W, Montréal, QC H4B 1R6, Canada.
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2
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Karakuzu A, Blostein N, Caron AV, Boré A, Rheault F, Descoteaux M, Stikov N. Rethinking MRI as a measurement device through modular and portable pipelines. MAGMA (NEW YORK, N.Y.) 2025:10.1007/s10334-025-01245-3. [PMID: 40274699 DOI: 10.1007/s10334-025-01245-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2024] [Revised: 02/27/2025] [Accepted: 03/11/2025] [Indexed: 04/26/2025]
Abstract
The premise of MRI as a reliable measurement device is limited by proprietary barriers and inconsistent implementations, which prevent the establishment of measurement uncertainties. As a result, biomedical studies that rely on these methods are plagued by systematic variance, undermining the perceived promise of quantitative imaging biomarkers (QIBs) and hindering their clinical translation. This review explores the added value of open-source measurement pipelines in minimizing variability sources that would otherwise remain unknown. First, we introduce a tiered benchmarking framework (from black-box to glass-box) that exposes how opacity at different workflow stages propagates measurement uncertainty. Second, we provide a concise glossary to promote consistent terminology for strategies that enhance reproducibility before acquisition or enable valid post-hoc pooling of QIBs. Building on this foundation, we present two illustrative measurement workflows that decouple workflow logic from the orchestration of computational processes in an MRI measurement pipeline, rooted in the core principles of modularity and portability. Designed as accessible entry points for implementation, these examples serve as practical guides, helping users adapt the frameworks to their specific needs and facilitating collaboration. Through critical evaluation of existing approaches, we discuss how standardized workflows can help identify outstanding challenges in translating glass-box frameworks into clinical scanner environments. Ultimately, achieving this goal will require coordinated efforts from QIB developers, regulators, industry partners, and clinicians alike.
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Affiliation(s)
- Agah Karakuzu
- NeuroPoly Lab, Polytechnique Montreal, Montreal, Québec, Canada
- Montreal Heart Institute, University of Montreal, Montreal, Québec, Canada
| | - Nadia Blostein
- School of Medicine, University Collage Cork, Cork, Ireland.
| | - Alex Valcourt Caron
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Arnaud Boré
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - François Rheault
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Nikola Stikov
- NeuroPoly Lab, Polytechnique Montreal, Montreal, Québec, Canada
- Montreal Heart Institute, University of Montreal, Montreal, Québec, Canada
- Center for Advanced Interdisciplinary Research, Ss. Cyril and Methodius University, Skopje, North Macedonia
- NYUAD Research Institute, New York University Abu Dhabi, Abu Dhabi, UAE
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3
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Fields RD, Dutta DJ. G-Ratio Commentary-Why You've Been Doing It Wrong. ASN Neuro 2025; 17:2486962. [PMID: 40193278 DOI: 10.1080/17590914.2025.2486962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2025] [Revised: 03/19/2025] [Accepted: 03/25/2025] [Indexed: 04/09/2025] Open
Affiliation(s)
- R Douglas Fields
- The Nervous System Development and Plasticity Section, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA
| | - Dipankar J Dutta
- The Nervous System Development and Plasticity Section, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA
- Gladstone Institutes, University of California at San Francisco, San Francisco, CA, USA
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4
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Şişman M, Nguyen TD, Roberts AG, Romano DJ, Dimov AV, Kovanlikaya I, Spincemaille P, Wang Y. Microstructure-Informed Myelin Mapping (MIMM) from routine multi-echo gradient echo data using multiscale physics modeling of iron and myelin effects and QSM. Magn Reson Med 2025; 93:1499-1515. [PMID: 39552224 PMCID: PMC11910495 DOI: 10.1002/mrm.30369] [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: 06/27/2024] [Revised: 10/08/2024] [Accepted: 10/21/2024] [Indexed: 11/19/2024]
Abstract
PURPOSE Myelin quantification is used in the study of demyelination in neurodegenerative diseases. A novel noninvasive MRI method, Microstructure-Informed Myelin Mapping (MIMM), is proposed to quantify the myelin volume fraction (MVF) from a routine multi-gradient echo sequence (mGRE) using a multiscale biophysical signal model of the effects of microstructural myelin and iron. THEORY AND METHODS In MIMM, the effects of myelin are modeled based on the Hollow Cylinder Fiber Model accounting for anisotropy, while iron is considered as an isotropic paramagnetic point source. This model is used to create a dictionary of mGRE magnitude signal evolution and total voxel susceptibility using finite elements of size 0.2 μm. Next, voxel-by-voxel stochastic matching pursuit between acquired mGRE data (magnitude+QSM) and the pre-computed dictionary generates quantitative MVF and iron susceptibility maps. Dictionary matching was evaluated under three conditions: (1) without fiber orientation (basic), (2) with fiber orientation obtained using DTI, and (3) with fiber orientation obtained using an atlas (atlas). MIMM was compared with the three-pool complex fitting (3PCF) using T2-relaxometry myelin water fraction (MWF) map as reference. RESULTS The DTI MIMM and atlas MIMM approaches were equally effective in reducing the overestimation of MVF in certain white matter tracts observed in the basic MIMM approach, and they both showed good agreement with T2-relaxometry MWF. MIMM MVF reduced myelin overestimation of globus pallidus observed in 3PCF MWF. CONCLUSION MIMM processing of mGRE data can provide MVF maps from routine clinical scans without requiring special sequences.
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Affiliation(s)
- Mert Şişman
- Department of Electrical and Computer Engineering, Cornell University, Ithaca, New York
- Department of Radiology, Weill Cornel Medicine, New York, New York
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornel Medicine, New York, New York
| | - Alexandra G. Roberts
- Department of Electrical and Computer Engineering, Cornell University, Ithaca, New York
- Department of Radiology, Weill Cornel Medicine, New York, New York
| | - Dominick J. Romano
- Department of Radiology, Weill Cornel Medicine, New York, New York
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York
| | - Alexey V. Dimov
- Department of Radiology, Weill Cornel Medicine, New York, New York
| | | | | | - Yi Wang
- Department of Radiology, Weill Cornel Medicine, New York, New York
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York
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5
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Sharma S, Sundaram S, Kesavadas C, Thomas B. An Algorithmic Approach to MR Imaging of Hypomyelinating Leukodystrophies. J Magn Reson Imaging 2025; 61:1531-1551. [PMID: 39165110 DOI: 10.1002/jmri.29558] [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: 05/03/2024] [Revised: 07/19/2024] [Accepted: 07/19/2024] [Indexed: 08/22/2024] Open
Abstract
Hypomyelinating leukodystrophies (HLDs) are a heterogeneous group of white matter diseases characterized by permanent deficiency of myelin deposition in brain. MRI is instrumental in the diagnosis and recommending genetic analysis, and is especially useful as many patients have a considerable clinical overlap, with the primary presenting complains being global developmental delay with psychomotor regression. Hypomyelination is defined as deficient myelination on two successive MR scans, taken at least 6 months apart, one of which should have been obtained after 1 year of age. Due to subtle differences in MRI features, the need for a systematic imaging approach to diagnose and classify hypomyelinating disorders is reiterated. The presented article provides an explicit review of imaging features of a myriad of primary and secondary HLDs, using state of the art genetically proven MR cases. A systematic pattern-based approach using MR features and specific clinical clues is illustrated for a quick yet optimal diagnosis of common as well as rare hypomyelinating disorders. The major MR features helping to narrow the differential diagnosis include extent of involvement like diffuse or patchy hypomyelination with selective involvement or sparing of certain white matter structures like optic radiations, median lemniscus, posterior limb of internal capsule and periventricular white matter; cerebellar atrophy; brainstem, corpus callosal or basal ganglia involvement; T2 hypointense signal of the thalami; and presence of calcifications. The authors also discuss the genetic and pathophysiologic basis of HLDs and recent methods to quantify myelin in vivo using advanced neuroradiology tools. The proposed algorithmic approach provides an improved understanding of these rare yet important disorders, enhancing diagnostic precision and improving patient outcomes. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 5.
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Affiliation(s)
- Smily Sharma
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India
| | - Soumya Sundaram
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India
| | - Chandrasekharan Kesavadas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India
| | - Bejoy Thomas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India
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Wang Y, Jiang A, Yan J, Wen D, Gu N, Li Z, Sun X, Wu Y, Guo Z. Inhibition of GPR17/ID2 Axis Improve Remyelination and Cognitive Recovery after SAH by Mediating OPC Differentiation in Rat Model. Transl Stroke Res 2025; 16:178-193. [PMID: 37935878 DOI: 10.1007/s12975-023-01201-0] [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] [Received: 08/07/2023] [Revised: 09/21/2023] [Accepted: 10/13/2023] [Indexed: 11/09/2023]
Abstract
Myelin sheath injury contributes to cognitive deficits following subarachnoid hemorrhage (SAH). G protein-coupled receptor 17 (GPR17), a membrane receptor, negatively regulates oligodendrocyte precursor cell (OPC) differentiation in both developmental and pathological contexts. Nonetheless, GPR17's role in modulating OPC differentiation, facilitating remyelination post SAH, and its interaction with downstream molecules remain elusive. In a rat SAH model induced by arterial puncture, OPCs expressing GPR17 proliferated prominently by day 14 post-onset, coinciding with compromised myelin sheath integrity and cognitive deficits. Selective Gpr17 knockdown in oligodendrocytes (OLs) via adeno-associated virus (AAV) administration revealed that reduced GPR17 levels promoted OPC differentiation, restored myelin sheath integrity, and improved cognitive deficits by day 14 post-SAH. Moreover, GPR17 knockdown attenuated the elevated expression of the inhibitor of DNA binding 2 (ID2) post-SAH, suggesting a GPR17-ID2 regulatory axis. Bi-directional modulation of ID2 expression in OLs using AAV unveiled that elevated ID2 counteracted the restorative effects of GPR17 knockdown. This resulted in hindered differentiation, exacerbated myelin sheath impairment, and worsened cognitive deficits. These findings highlight the pivotal roles of GPR17 and ID2 in governing OPC differentiation and axonal remyelination post-SAH. This study positions GPR17 as a potential therapeutic target for SAH intervention. The interplay between GPR17 and ID2 introduces a novel avenue for ameliorating cognitive deficits post-SAH.
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Affiliation(s)
- Yingwen Wang
- Department of Neurosurgery, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, China
| | - Anan Jiang
- Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jin Yan
- Department of Neurosurgery, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, China
| | - Daochen Wen
- Department of Neurosurgery, Xuanhan County People's Hospital, Dazhou, China
| | - Nina Gu
- Department of Neurosurgery, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, China
| | - Zhao Li
- Department of Neurosurgery, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, China
| | - Xiaochuan Sun
- Department of Neurosurgery, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, China
| | - Yue Wu
- Department of Neurosurgery, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, China.
| | - Zongduo Guo
- Department of Neurosurgery, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, China.
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7
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Coleman CR, Nance MG, Jacokes Z, Druzgal TJ, Arutiunian V, Kresse A, Sullivan CA, Santhosh M, Neuhaus E, Borland H, Bernier RA, Bookheimer SY, Dapretto M, Jack A, Jeste S, McPartland JC, Naples A, Geschwind D, Gupta AR, Webb SJ, Pelphrey KA, Van Horn JD, Newman BT, Puglia MH. Structural Determinants of Signal Speed: A Multimodal Investigation of Face Processing in Autism Spectrum Disorder. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.19.644214. [PMID: 40166310 PMCID: PMC11957106 DOI: 10.1101/2025.03.19.644214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
Face perception is fundamental to social cognition and often disrupted in autism. However, the neurological basis for this disrupted face perception and the mechanisms underlying altered electrophysiological signaling in autism, such as increased latency of the N170-an electrophysiological marker of face processing, remain unknown. Here, we leverage multimodal neuroimaging in autistic adolescents to establish a link between MRI-measured axonal microstructure within the face processing network and EEG-measured N170 latency. We demonstrate that a novel metric of axonal signal transit time derived from axonal diameter, myelination, and length-estimated axonal latency (EAL)-predicts N170 latency during face processing. Moreover, we demonstrate that individuals with and without autism rely upon different pathways, providing a structural account for autism-related face processing differences. By establishing this relationship between EEG-based electrical function and MRI-based axonal microstructure, we provide a non-invasive, spatially-detailed estimate of neuronal processing speed that can inform understanding of brain function, development, and disorder.
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Affiliation(s)
| | - Madelyn G. Nance
- Department of Neurology, University of Virginia, Charlottesville, VA
| | - Zachary Jacokes
- School of Data Science, University of Virginia, Charlottesville, VA
| | - T. Jason Druzgal
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA
| | - Vardan Arutiunian
- Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, Seattle, WA
| | - Anna Kresse
- Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, Seattle, WA
- Peabody College of Education and Human Development, Vanderbilt University, Nashville, TN
| | | | - Megha Santhosh
- Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, Seattle, WA
| | - Emily Neuhaus
- Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, Seattle, WA
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA
- Institute on Human Development and Disability, University of Washington, Seattle, WA
| | - Heather Borland
- Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, Seattle, WA
| | - Raphael A. Bernier
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA
| | - Susan Y. Bookheimer
- Center for Autism Research and Treatment, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Mirella Dapretto
- Center for Autism Research and Treatment, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Allison Jack
- Department of Psychology, George Mason University, Fairfax, VA
| | - Shafali Jeste
- Department of Neurology, Children’s Hospital of Los Angeles, Los Angeles, CA
| | | | - Adam Naples
- Yale Child Study Center, Yale School of Medicine, New Haven, CT
| | - Daniel Geschwind
- Center for Autism Research and Treatment, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Abha R. Gupta
- Department of Pediatrics, Yale School of Medicine, New Haven, CT
- Yale Child Study Center, Yale School of Medicine, New Haven, CT
- Department of Neuroscience, Yale School of Medicine, New Haven, CT
| | - Sara Jane Webb
- Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, Seattle, WA
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA
- Institute on Human Development and Disability, University of Washington, Seattle, WA
| | - Kevin A. Pelphrey
- Department of Neurology, University of Virginia, Charlottesville, VA
| | - John Darrell Van Horn
- School of Data Science, University of Virginia, Charlottesville, VA
- Department of Psychology, University of Virginia, Charlottesville, VA
| | - Benjamin T. Newman
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA
- Department of Psychology, University of Virginia, Charlottesville, VA
| | - Meghan H. Puglia
- Department of Neurology, University of Virginia, Charlottesville, VA
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Ressa HJ, Newman BT, Jacokes Z, McPartland JC, Kleinhans NM, Druzgal TJ, Pelphrey KA, Van Horn JD. Widespread Associations between Behavioral Metrics and Brain Microstructure in ASD Suggest Age Mediates Subtypes of ASD. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.04.611183. [PMID: 39282332 PMCID: PMC11398530 DOI: 10.1101/2024.09.04.611183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. Our lab has previously found that g-ratio, the proportion of axon width to myelin diameter, and axonal conduction velocity, which is associated with the capacity of an axon to carry information, are both decreased in ASD individuals. By associating these differences with performance on cognitive and behavioral tests, this study aims to first associate a broad array of behavioral metrics with neuroimaging markers of ASD, and to explore the prevalence of ASD subtypes using a neuroimaging driven perspective. Analyzing 273 participants (148 with ASD) ages 8 to 17 through an NIH-sponsored Autism Centers of Excellence network (MH100028), we observe widespread associations between behavioral and cognitive evaluations of autism and between behavioral and microstructural metrics, alongside different directional correlations between different behavioral metrics. Stronger associations with individual subcategories from each test rather than summary scores suggest that different neuronal profiles may be masked by composite test scores. Machine learning cluster analyses applied to neuroimaging data reinforce the association between neuroimaging and behavioral metrics and suggest that age-related maturation of brain metrics may drive changes in ASD behavior. This suggests that if ASD can be definitively subtyped, these subtypes may show different behavioral trajectories across the developmental period. Clustering identified a pattern of restrictive and repetitive behavior in some participants and a second group that was defined by high sensory sensitivity and language performance.
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Affiliation(s)
- Haylee J Ressa
- Department of Psychology, University of Virginia, Gilmer Hall, Charlottesville, VA 22903
| | - Benjamin T Newman
- Department of Psychology, University of Virginia, Gilmer Hall, Charlottesville, VA 22903
- Department of Neurology, University of Virginia, School of Medicine, Gilmer Hall, Charlottesville, VA 2290
| | - Zachary Jacokes
- School of Data Science, University of Virginia, Elson Building, Charlottesville, VA 22903
| | - James C McPartland
- Yale Child Study Center and the Yale Center for Brain and Mind Health, Yale School of Medicine, Sterling Hall of Medicine, New Haven, CT 06520
| | - Natalia M Kleinhans
- University of Washington Integrated Brain Imaging Center, Eunice Kennedy Shriver Intellectual and Developmental Disabilities Research Center, Seattle, WA 98195
| | - T Jason Druzgal
- Department of Neurology, University of Virginia, School of Medicine, Gilmer Hall, Charlottesville, VA 2290
| | - Kevin A Pelphrey
- Department of Neurology, University of Virginia, School of Medicine, Gilmer Hall, Charlottesville, VA 2290
- Department of Psychology, University of Virginia, Gilmer Hall, 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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9
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Grouza V, Bagheri H, Liu H, Tuznik M, Wu Z, Robinson N, Siminovitch KA, Peterson AC, Rudko DA. Ultra-high-resolution mapping of myelin and g-ratio in a panel of Mbp enhancer-edited mouse strains using microstructural MRI. Neuroimage 2024; 300:120850. [PMID: 39260782 DOI: 10.1016/j.neuroimage.2024.120850] [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: 04/02/2024] [Revised: 08/27/2024] [Accepted: 09/09/2024] [Indexed: 09/13/2024] Open
Abstract
Non-invasive myelin water fraction (MWF) and g-ratio mapping using microstructural MRI have the potential to offer critical insights into brain microstructure and our understanding of neuroplasticity and neuroinflammation. By leveraging a unique panel of variably hypomyelinating mouse strains, we validated a high-resolution, model-free image reconstruction method for whole-brain MWF mapping. Further, by employing a bipolar gradient echo MRI sequence, we achieved high spatial resolution and robust mapping of MWF and g-ratio across the whole mouse brain. Our regional white matter-tract specific analyses demonstrated a graded decrease in MWF in white matter tracts which correlated strongly with myelin basic protein gene (Mbp) mRNA levels. Using these measures, we derived the first sensitive calibrations between MWF and Mbp mRNA in the mouse. Minimal changes in axonal density supported our hypothesis that observed MWF alterations stem from hypomyelination. Overall, our work strongly emphasizes the potential of non-invasive, MRI-derived MWF and g-ratio modeling for both preclinical model validation and ultimately translation to humans.
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Affiliation(s)
- Vladimir Grouza
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Hooman Bagheri
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Hanwen Liu
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Marius Tuznik
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Zhe Wu
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Nicole Robinson
- Histology Innovation Platform, Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada
| | - Katherine A Siminovitch
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Immunology, University of Toronto, Toronto, Ontario, Canada; Mount Sinai Hospital, Lunenfeld-Tanenbaum and Toronto General Hospital Research Institutes, Toronto, Ontario, Canada
| | - Alan C Peterson
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada; Department of Human Genetics, McGill University, Montreal, Quebec, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada
| | - David A Rudko
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada; Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada.
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10
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Sanabria-Diaz G, Cagol A, Lu PJ, Barakovic M, Ocampo-Pineda M, Chen X, Weigel M, Ruberte E, Siebenborn NDOS, Galbusera R, Schädelin S, Benkert P, Kuhle J, Kappos L, Melie-Garcia L, Granziera C. Advanced MRI Measures of Myelin and Axon Volume Identify Repair in Multiple Sclerosis. Ann Neurol 2024. [PMID: 39390658 DOI: 10.1002/ana.27102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 08/10/2024] [Accepted: 09/04/2024] [Indexed: 10/12/2024]
Abstract
OBJECTIVE Pathological studies suggest that multiple sclerosis (MS) lesions endure multiple waves of damage and repair; however, the dynamics and characteristics of these processes are poorly understood in patients living with MS. METHODS We studied 128 MS patients (75 relapsing-remitting, 53 progressive) and 72 healthy controls who underwent advanced magnetic resonance imaging and clinical examination at baseline and 2 years later. Magnetization transfer saturation and multi-shell diffusion imaging were used to quantify longitudinal changes in myelin and axon volumes within MS lesions. Lesions were grouped into 4 classes (repair, damage, mixed repair damage, and stable). The frequency of each class was correlated to clinical measures, demographic characteristics, and levels of serum neurofilament light chain (sNfL). RESULTS Stable lesions were the most frequent (n = 2,276; 44%), followed by lesions with patterns of "repair" (n = 1,352; 26.2%) and damage (n = 1,214; 23.5%). The frequency of "repair" lesion was negatively associated with disability (β = -0.04; p < 0.001) and sNfL (β = -0.16; p < 0.001) at follow-up. The frequency of the "damage" class was higher in progressive than relapsing-remitting patients (p < 0.05) and was related to disability (baseline: β = -0.078; follow-up: β = -0.076; p < 0.001) and age (baseline: β = -0.078; p < 0.001). Stable lesions were more frequent in relapsing-remitting than in progressive patients (p < 0.05), and in younger patients versus older (β = -0.07; p < 0.001) at baseline. Further, "mixed" lesions were most frequent in older patients (β = 0.004; p < 0.001) at baseline. INTERPRETATION These findings show that repair and damage processes within MS lesions occur across the entire disease spectrum and that their frequency correlates with patients disability, age, disease duration, and extent of neuroaxonal damage. ANN NEUROL 2024.
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Affiliation(s)
- Gretel Sanabria-Diaz
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Alessandro Cagol
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Health Sciences, University of Genova, Genoa, Italy
| | - Po-Jui Lu
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Mario Ocampo-Pineda
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Xinjie Chen
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Esther Ruberte
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Medical Image Analysis Center (MIAC), Basel, Switzerland
| | - Nina de Oliveira S Siebenborn
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Medical Image Analysis Center (MIAC), Basel, Switzerland
| | - Riccardo Galbusera
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Sabine Schädelin
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital and University of Basel, Basel, Switzerland
| | - Pascal Benkert
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital and University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Multiple Sclerosis Centre, Department of Neurology, Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Cristina Granziera
- Neurology Clinic and Policlinic, Department of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
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11
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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] [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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12
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Larivière S, Park BY, Royer J, DeKraker J, Ngo A, Sahlas E, Chen J, Rodríguez-Cruces R, Weng Y, Frauscher B, Liu R, Wang Z, Shafiei G, Mišić B, Bernasconi A, Bernasconi N, Fox MD, Zhang Z, Bernhardt BC. Connectome reorganization associated with temporal lobe pathology and its surgical resection. Brain 2024; 147:2483-2495. [PMID: 38701342 PMCID: PMC11224603 DOI: 10.1093/brain/awae141] [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: 11/20/2023] [Revised: 03/23/2024] [Accepted: 04/05/2024] [Indexed: 05/05/2024] Open
Abstract
Network neuroscience offers a unique framework to understand the organizational principles of the human brain. Despite recent progress, our understanding of how the brain is modulated by focal lesions remains incomplete. Resection of the temporal lobe is the most effective treatment to control seizures in pharmaco-resistant temporal lobe epilepsy (TLE), making this syndrome a powerful model to study lesional effects on network organization in young and middle-aged adults. Here, we assessed the downstream consequences of a focal lesion and its surgical resection on the brain's structural connectome, and explored how this reorganization relates to clinical variables at the individual patient level. We included adults with pharmaco-resistant TLE (n = 37) who underwent anterior temporal lobectomy between two imaging time points, as well as age- and sex-matched healthy controls who underwent comparable imaging (n = 31). Core to our analysis was the projection of high-dimensional structural connectome data-derived from diffusion MRI tractography from each subject-into lower-dimensional gradients. We then compared connectome gradients in patients relative to controls before surgery, tracked surgically-induced connectome reconfiguration from pre- to postoperative time points, and examined associations to patient-specific clinical and imaging phenotypes. Before surgery, individuals with TLE presented with marked connectome changes in bilateral temporo-parietal regions, reflecting an increased segregation of the ipsilateral anterior temporal lobe from the rest of the brain. Surgery-induced connectome reorganization was localized to this temporo-parietal subnetwork, but primarily involved postoperative integration of contralateral regions with the rest of the brain. Using a partial least-squares analysis, we uncovered a latent clinical imaging signature underlying this pre- to postoperative connectome reorganization, showing that patients who displayed postoperative integration in bilateral fronto-occipital cortices also had greater preoperative ipsilateral hippocampal atrophy, lower seizure frequency and secondarily generalized seizures. Our results bridge the effects of focal brain lesions and their surgical resections with large-scale network reorganization and interindividual clinical variability, thus offering new avenues to examine the fundamental malleability of the human brain.
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Affiliation(s)
- Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Harvard University, Boston, MA 02115, USA
| | - Bo-yong Park
- Department of Data Science, Inha University, Incheon 22212, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 34126, Republic of Korea
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jordan DeKraker
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Alexander Ngo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Ella Sahlas
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Judy Chen
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Raúl Rodríguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Ruoting Liu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Zhengge Wang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Golia Shafiei
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bratislav Mišić
- Department of Neurology and Neurosurgery, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Harvard University, Boston, MA 02115, USA
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
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13
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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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14
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Papazoglou S, Ashtarayeh M, Oeschger JM, Callaghan MF, Does MD, Mohammadi S. Insights and improvements in correspondence between axonal volume fraction measured with diffusion-weighted MRI and electron microscopy. NMR IN BIOMEDICINE 2024; 37:e5070. [PMID: 38098204 PMCID: PMC11475374 DOI: 10.1002/nbm.5070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 09/25/2023] [Accepted: 10/19/2023] [Indexed: 02/17/2024]
Abstract
Biophysical diffusion-weighted imaging (DWI) models are increasingly used in neuroscience to estimate the axonal water fraction (f AW ), which in turn is key for noninvasive estimation of the axonal volume fraction (f A ). These models require thorough validation by comparison with a reference method, for example, electron microscopy (EM). While EM studies often neglect the unmyelinated axons and solely report the fraction of myelinated axons, in DWI both myelinated and unmyelinated axons contribute to the DWI signal. However, DWI models often include simplifications, for example, the neglect of differences in the compartmental relaxation times or fixed diffusivities, which in turn might affect the estimation off AW . We investigate whether linear calibration parameters (scaling and offset) can improve the comparability between EM- and DWI-based metrics off A . To this end, we (a) used six DWI models based on the so-called standard model of white matter (WM), including two models with fixed compartmental diffusivities (e.g., neurite orientation dispersion and density imaging, NODDI) and four models that fitted the compartmental diffusivities (e.g., white matter tract integrity, WMTI), and (b) used a multimodal data set including ex vivo diffusion DWI and EM data in mice with a broad dynamic range of fibre volume metrics. We demonstrated that the offset is associated with the volume fraction of unmyelinated axons and the scaling factor is associated with different compartmentalT 2 and can substantially enhance the comparability between EM- and DWI-based metrics off A . We found that DWI models that fitted compartmental diffusivities provided the most accurate estimates of the EM-basedf A . Finally, we introduced a more efficient hybrid calibration approach, where only the offset is estimated but the scaling is fixed to a theoretically predicted value. Using this approach, a similar one-to-one correspondence to EM was achieved for WMTI. The method presented can pave the way for use of validated DWI-based models in clinical research and neuroscience.
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Affiliation(s)
- Sebastian Papazoglou
- Department of Systems NeuroscienceUniversity Medical Center Hamburg–EppendorfHamburgGermany
- Max Planck Research Group MR PhysicsMax Planck Institute for Human DevelopmentBerlinGermany
| | - Mohammad Ashtarayeh
- Department of Systems NeuroscienceUniversity Medical Center Hamburg–EppendorfHamburgGermany
| | - Jan Malte Oeschger
- Department of Systems NeuroscienceUniversity Medical Center Hamburg–EppendorfHamburgGermany
| | - Martina F. Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Mark D. Does
- Department of Biomedical EngineeringVanderbilt UniversityNashvilleTennesseeUSA
- Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Radiology and Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Electrical EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Siawoosh Mohammadi
- Department of Systems NeuroscienceUniversity Medical Center Hamburg–EppendorfHamburgGermany
- Max Planck Research Group MR PhysicsMax Planck Institute for Human DevelopmentBerlinGermany
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
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15
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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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16
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Mi X, Ni C, Zhao J, Amin N, Jiao D, Fang M, Ye X. P2Y12 receptor mediates apoptosis and demyelination to affect functional recovery in mice with spinal cord injury. Neurochem Int 2023; 171:105641. [PMID: 37952830 DOI: 10.1016/j.neuint.2023.105641] [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: 03/10/2023] [Revised: 09/28/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023]
Abstract
Among diseases of the central nervous system (CNS), spinal cord injury (SCI) has a high fatality rate. It has been proven that P2Y G protein-coupled purinergic receptors have a neuroprotective role in apoptosis and regeneration inside the damaged spinal cord. The P2Y12 receptor (P2Y12R) has recently been linked to peripheral neuropathy and stroke. However, the role of P2Y12R after SCI remains unclear. Our study randomly divided C57BL/6J female mice into 3 groups: Sham+DMSO, SCI+DMSO, and SCI+MRS2395. MRS2395 as a P2Y12R inhibitor was intraperitoneally injected at a dose of 1.5 mg/kg once daily for 7 days. We showed that the P2Y12R was markedly activated after injury, and it was double labeled with the microglial and neuron. Behavioral tests were employed to assess motor function recovery. By using immunofluorescence staining, the NeuN expression level was detected. The morphology of neurons was observed by hematoxylin-eosin and Nissl staining. P2Y12R, Bax, GFAP, PCNA and calbindin expression levels were detected using Western blot. Meanwhile, mitochondria and myelin sheath were observed by transmission electron microscopy (TEM). Our findings demonstrated that MRS2395 significantly enhanced motor function induced by SCI and that was used to alleviate apoptosis and astrocyte scarring. NeuN positive cells in the SCI group were lower than in the therapy group, although Bax, GFAP, PCNA and calbindin expression levels were considerably higher. Moreover, following MRS2395 therapy, the histological damage was reversed. A notable improvement in myelin sheath and mitochondrial morphology was seen in the therapy group. Together, our findings indicate that activation of P2Y12R in damaged spinal cord may be a critical event and suggest that inhibition of P2Y12R might be a feasible therapeutic strategy for treating SCI.
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Affiliation(s)
- Xiaodan Mi
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - Chengtao Ni
- Graduate School, Bengbu Medical College, Bengbu, Anhui, China
| | - Jingting Zhao
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - Nashwa Amin
- Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China; Institute of System Medicine, Zhejiang University School of Medicine, Hangzhou, China; Department of Zoology, Faculty of Science, Aswan University, Egypt
| | - Dian Jiao
- College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China
| | - Marong Fang
- Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
| | - Xiangming Ye
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China.
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17
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Galbusera R, Bahn E, Weigel M, Schaedelin S, Franz J, Lu P, Barakovic M, Melie‐Garcia L, Dechent P, Lutti A, Sati P, Reich DS, Nair G, Brück W, Kappos L, Stadelmann C, Granziera C. Postmortem quantitative MRI disentangles histological lesion types in multiple sclerosis. Brain Pathol 2023; 33:e13136. [PMID: 36480267 PMCID: PMC10580009 DOI: 10.1111/bpa.13136] [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: 05/09/2022] [Accepted: 11/16/2022] [Indexed: 12/13/2022] Open
Abstract
Quantitative MRI (qMRI) probes the microstructural properties of the central nervous system (CNS) by providing biophysical measures of tissue characteristics. In this work, we aimed to (i) identify qMRI measures that distinguish histological lesion types in postmortem multiple sclerosis (MS) brains, especially the remyelinated ones; and to (ii) investigate the relationship between those measures and quantitative histological markers of myelin, axons, and astrocytes in the same experimental setting. Three fixed MS whole brains were imaged with qMRI at 3T to obtain magnetization transfer ratio (MTR), myelin water fraction (MWF), quantitative T1 (qT1), quantitative susceptibility mapping (QSM), fractional anisotropy (FA) and radial diffusivity (RD) maps. The identification of lesion types (active, inactive, chronic active, or remyelinated) and quantification of tissue components were performed using histological staining methods as well as immunohistochemistry and immunofluorescence. Pairwise logistic and LASSO regression models were used to identify the best qMRI discriminators of lesion types. The association between qMRI and quantitative histological measures was performed using Spearman's correlations and linear mixed-effect models. We identified a total of 65 lesions. MTR and MWF best predicted the chance of a lesion to be remyelinated, whereas RD and QSM were useful in the discrimination of active lesions. The measurement of microstructural properties through qMRI did not show any difference between chronic active and inactive lesions. MWF and RD were associated with myelin content in both lesions and normal-appearing white matter (NAWM), FA was the measure most associated with axon content in both locations, while MWF was associated with astrocyte immunoreactivity only in lesions. Moreover, we provided evidence of extensive astrogliosis in remyelinated lesions. Our study provides new information on the discriminative power of qMRI in differentiating MS lesions -especially remyelinated ones- as well as on the relative association between multiple qMRI measures and myelin, axon and astrocytes.
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Affiliation(s)
- Riccardo Galbusera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of MedicineUniversity Hospital Basel and University of BaselBaselSwitzerland
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB)University Hospital Basel and University of BaselBaselSwitzerland
| | - Erik Bahn
- Institute of NeuropathologyUniversity Medical CenterGöttingenGermany
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of MedicineUniversity Hospital Basel and University of BaselBaselSwitzerland
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB)University Hospital Basel and University of BaselBaselSwitzerland
- Division of Radiological Physics, Department of RadiologyUniversity Hospital BaselBaselSwitzerland
| | - Sabine Schaedelin
- Clinical Trial Unit, Department of Clinical ResearchUniversity Hospital Basel, University of BaselBaselSwitzerland
| | - Jonas Franz
- Institute of NeuropathologyUniversity Medical CenterGöttingenGermany
- Campus Institute for Dynamics of Biological NetworksUniversity of GöttingenGöttingenGermany
- Max Planck Institute for Experimental MedicineGöttingenGermany
| | - Po‐Jui Lu
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of MedicineUniversity Hospital Basel and University of BaselBaselSwitzerland
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB)University Hospital Basel and University of BaselBaselSwitzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of MedicineUniversity Hospital Basel and University of BaselBaselSwitzerland
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB)University Hospital Basel and University of BaselBaselSwitzerland
| | - Lester Melie‐Garcia
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of MedicineUniversity Hospital Basel and University of BaselBaselSwitzerland
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB)University Hospital Basel and University of BaselBaselSwitzerland
| | - Peter Dechent
- Department of Cognitive NeurologyMR‐Research in Neurosciences, University Medical Center GöttingenGöttingenGermany
| | - Antoine Lutti
- Centre for Research in Neuroscience, Department of Clinical NeurosciencesLaboratoire de Recherche en Neuroimagerie (LREN) University Hospital and University of LausanneLausanneSwitzerland
| | - Pascal Sati
- Department of NeurologyCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Daniel S. Reich
- Translational Neuroradiology SectionNational Institute of Neurological Disorders and Stroke, National Institutes of HealthBethesdaMarylandUSA
| | - Govind Nair
- National Institute of Neurological Disorders and StrokeBethesdaMarylandUSA
| | - Wolfgang Brück
- Institute of NeuropathologyUniversity Medical CenterGöttingenGermany
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB)University Hospital Basel and University of BaselBaselSwitzerland
| | - Christine Stadelmann
- Institute of NeuropathologyUniversity Medical CenterGöttingenGermany
- Cluster of Excellence “Multiscale Bioimaging: from Molecular Machines to Network of Excitable Cells (MBExC) ”University of GoettingenGermany
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of MedicineUniversity Hospital Basel and University of BaselBaselSwitzerland
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB)University Hospital Basel and University of BaselBaselSwitzerland
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18
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Chau Loo Kung G, Knowles JK, Batra A, Ni L, Rosenberg J, McNab JA. Quantitative MRI reveals widespread, network-specific myelination change during generalized epilepsy progression. Neuroimage 2023; 280:120312. [PMID: 37574120 PMCID: PMC11095339 DOI: 10.1016/j.neuroimage.2023.120312] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 06/17/2023] [Accepted: 08/04/2023] [Indexed: 08/15/2023] Open
Abstract
Activity-dependent myelination is a fundamental mode of brain plasticity which significantly influences network function. We recently discovered that absence seizures, which occur in multiple forms of generalized epilepsy, can induce activity-dependent myelination, which in turn promotes further progression of epilepsy. Structural alterations of myelin are likely to be widespread, given that absence seizures arise from an extensive thalamocortical network involving frontoparietal regions of the bilateral hemispheres. However, the temporal course and spatial extent of myelin plasticity is unknown, due to limitations of gold-standard histological methods such as electron microscopy (EM). In this study, we leveraged magnetization transfer and diffusion MRI for estimation of g-ratios across major white matter tracts in a mouse model of generalized epilepsy with progressive absence seizures. EM was performed on the same brains after MRI. After seizure progression, we found increased myelination (decreased g-ratios) throughout the anterior portion (genu-to-body) of the corpus callosum but not in the posterior portion (body-splenium) nor in the fornix or the internal capsule. Curves obtained from averaging g-ratio values at every longitudinal point of the corpus callosum were statistically different with p<0.001. Seizure-associated myelin differences found in the corpus callosum body with MRI were statistically significant (p = 0.0027) and were concordant with EM in the same region (p = 0.01). Notably, these differences were not detected by diffusion tensor imaging. This study reveals widespread myelin structural change that is specific to the absence seizure network. Furthermore, our findings demonstrate the potential utility and importance of MRI-based g-ratio estimation to non-invasively detect myelin plasticity.
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Affiliation(s)
- Gustavo Chau Loo Kung
- Bioengineering Department, Stanford University, 443 Via Ortega, Stanford, CA 94305, United States; Radiology Department, Stanford University, 1201 Welch Rd, Stanford, CA 94305, United States.
| | - Juliet K Knowles
- Neurology Department, 1701 Page Mill Road, Palo Alto, CA 94304, United States.
| | - Ankita Batra
- Neurology Department, 1701 Page Mill Road, Palo Alto, CA 94304, United States.
| | - Lijun Ni
- Neurology Department, SIM1 G3035, Stanford, CA 94305, United States.
| | - Jarrett Rosenberg
- Radiology Department, Stanford University, 1201 Welch Rd, Stanford, CA 94305, United States.
| | - Jennifer A McNab
- Radiology Department, Stanford University, 1201 Welch Rd, Stanford, CA 94305, United States.
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19
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Oliveira R, De Lucia M, Lutti A. Single-subject electroencephalography measurement of interhemispheric transfer time for the in-vivo estimation of axonal morphology. Hum Brain Mapp 2023; 44:4859-4874. [PMID: 37470446 PMCID: PMC10472916 DOI: 10.1002/hbm.26420] [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: 12/19/2022] [Revised: 06/12/2023] [Accepted: 06/26/2023] [Indexed: 07/21/2023] Open
Abstract
Assessing axonal morphology in vivo opens new avenues for the combined study of brain structure and function. A novel approach has recently been introduced to estimate the morphology of axonal fibers from the combination of magnetic resonance imaging (MRI) data and electroencephalography (EEG) measures of the interhemispheric transfer time (IHTT). In the original study, the IHTT measures were computed from EEG data averaged across a group, leading to bias of the axonal morphology estimates. Here, we seek to estimate axonal morphology from individual measures of IHTT, obtained from EEG data acquired in a visual evoked potential experiment. Subject-specific IHTTs are computed in a data-driven framework with minimal a priori constraints, based on the maximal peak of neural responses to visual stimuli within periods of statistically significant evoked activity in the inverse solution space. The subject-specific IHTT estimates ranged from 8 to 29 ms except for one participant and the between-session variability was comparable to between-subject variability. The mean radius of the axonal radius distribution, computed from the IHTT estimates and the MRI data, ranged from 0 to 1.09 μm across subjects. The change in axonal g-ratio with axonal radius ranged from 0.62 to 0.81 μm-α . The single-subject measurement of the IHTT yields estimates of axonal morphology that are consistent with histological values. However, improvement of the repeatability of the IHTT estimates is required to improve the specificity of the single-subject axonal morphology estimates.
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Affiliation(s)
- Rita Oliveira
- Laboratory for Research in Neuroimaging, Department of Clinical NeuroscienceLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Marzia De Lucia
- Laboratory for Research in Neuroimaging, Department of Clinical NeuroscienceLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical NeuroscienceLausanne University Hospital and University of LausanneLausanneSwitzerland
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20
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Şişman M, Nguyen TD, Roberts AG, Romano DJ, Dimov AV, Kovanlikaya I, Spincemaille P, Wang Y. Microstructure-Informed Myelin Mapping (MIMM) from Gradient Echo MRI using Stochastic Matching Pursuit. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.22.23295993. [PMID: 37808826 PMCID: PMC10557811 DOI: 10.1101/2023.09.22.23295993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Quantification of the myelin content of the white matter is important for studying demyelination in neurodegenerative diseases such as Multiple Sclerosis (MS), particularly for longitudinal monitoring. A novel noninvasive MRI method, called Microstructure-Informed Myelin Mapping (MIMM), is developed to quantify the myelin volume fraction (MVF) by utilizing a multi gradient echo sequence (mGRE) and a detailed biophysical model of tissue microstructure. Myelin is modeled as anisotropic negative susceptibility source based on the Hollow Cylindrical Fiber Model (HCFM), and iron as isotropic positive susceptibility source in the extracellular region. Voxels with a range of biophysical parameters are simulated to create a dictionary of MR echo time magnitude signals and total susceptibility values. MRI signals measured using a mGRE sequence are then matched voxel-by-voxel to the created dictionary to obtain the spatial distributions of myelin and iron. Three different MIMM versions are presented to deal with the fiber orientation dependent susceptibility effects of the myelin sheaths: a basic variation, which assumes fiber orientation is an unknown to fit, two orientation informed variations, which assume the fiber orientation distribution is available either from a separate diffusion tensor imaging (DTI) acquisition or from a DTI atlas based fiber orientation map. While all showed a significant linear correlation with the reference method based on T2-relaxometry (p < 0.0001), DTI orientation informed and atlas orientation informed variations reduced overestimation at white matter tracts compared to the basic variation. Finally, the implications and usefulness of attaining an additional iron susceptibility distribution map are discussed. Highlights novel stochastic matching pursuit algorithm called microstructure-informed myelin mapping (MIMM) is developed to quantify Myelin Volume Fraction (MVF) using Magnetic Resonance Imaging (MRI) and microstructural modeling.utilizes a detailed biophysical model to capture the susceptibility effects on both magnitude and phase to quantify myelin and iron.matter fiber orientation effects are considered for the improved MVF quantification in the major fiber tracts.acquired myelin and iron maps may be utilized to monitor longitudinal disease progress.
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21
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Deantoni M, Baillet M, Hammad G, Berthomier C, Reyt M, Jaspar M, Meyer C, Van Egroo M, Talwar P, Lambot E, Chellappa SL, Degueldre C, Luxen A, Salmon E, Balteau E, Phillips C, Dijk DJ, Vandewalle G, Collette F, Maquet P, Muto V, Schmidt C. Association between sleep slow-wave activity and in-vivo estimates of myelin in healthy young men. Neuroimage 2023; 272:120045. [PMID: 36997136 PMCID: PMC10112274 DOI: 10.1016/j.neuroimage.2023.120045] [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: 08/04/2022] [Revised: 01/18/2023] [Accepted: 03/17/2023] [Indexed: 03/31/2023] Open
Abstract
Sleep has been suggested to contribute to myelinogenesis and associated structural changes in the brain. As a principal hallmark of sleep, slow-wave activity (SWA) is homeostatically regulated but also differs between individuals. Besides its homeostatic function, SWA topography is suggested to reflect processes of brain maturation. Here, we assessed whether interindividual differences in sleep SWA and its homeostatic response to sleep manipulations are associated with in-vivo myelin estimates in a sample of healthy young men. Two hundred twenty-six participants (18-31 y.) underwent an in-lab protocol in which SWA was assessed at baseline (BAS), after sleep deprivation (high homeostatic sleep pressure, HSP) and after sleep saturation (low homeostatic sleep pressure, LSP). Early-night frontal SWA, the frontal-occipital SWA ratio, as well as the overnight exponential SWA decay were computed over sleep conditions. Semi-quantitative magnetization transfer saturation maps (MTsat), providing markers for myelin content, were acquired during a separate laboratory visit. Early-night frontal SWA was negatively associated with regional myelin estimates in the temporal portion of the inferior longitudinal fasciculus. By contrast, neither the responsiveness of SWA to sleep saturation or deprivation, its overnight dynamics, nor the frontal/occipital SWA ratio were associated with brain structural indices. Our results indicate that frontal SWA generation tracks inter-individual differences in continued structural brain re-organization during early adulthood. This stage of life is not only characterized by ongoing region-specific changes in myelin content, but also by a sharp decrease and a shift towards frontal predominance in SWA generation.
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Affiliation(s)
| | | | | | | | - Mathilde Reyt
- GIGA-CRC in Vivo Imaging, University of Liège, Belgium; Psychology and Neurosciences of Cognition (PsyNCog), Faculty of Psychology, Logopedics and Educational Sciences University of Liège, Belgium
| | - Mathieu Jaspar
- ARCH, Faculty of Psychology, Logopedics and Educational Sciences, University of Liège, Belgium
| | | | - Maxime Van Egroo
- Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, the Netherlands
| | - Puneet Talwar
- GIGA-CRC in Vivo Imaging, University of Liège, Belgium
| | - Eric Lambot
- GIGA-CRC in Vivo Imaging, University of Liège, Belgium
| | - Sarah L Chellappa
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | | | - André Luxen
- GIGA-CRC in Vivo Imaging, University of Liège, Belgium
| | - Eric Salmon
- GIGA-CRC in Vivo Imaging, University of Liège, Belgium
| | | | | | - Derk-Jan Dijk
- Sleep Research Centre, University of Surrey, Guildford, UK; UK Dementia Research Institute, Care Research & Technology Centre at Imperial College London and the University of Surrey, Guildford, UK
| | | | - Fabienne Collette
- GIGA-CRC in Vivo Imaging, University of Liège, Belgium; Psychology and Neurosciences of Cognition (PsyNCog), Faculty of Psychology, Logopedics and Educational Sciences University of Liège, Belgium
| | - Pierre Maquet
- GIGA-CRC in Vivo Imaging, University of Liège, Belgium; Department of Neurology, University Hospital (CHU) of Liège, Liège, Belgium
| | - Vincenzo Muto
- GIGA-CRC in Vivo Imaging, University of Liège, Belgium.
| | - Christina Schmidt
- GIGA-CRC in Vivo Imaging, University of Liège, Belgium; Psychology and Neurosciences of Cognition (PsyNCog), Faculty of Psychology, Logopedics and Educational Sciences University of Liège, Belgium.
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22
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Genc S, Raven EP, Drakesmith M, Blakemore SJ, Jones DK. Novel insights into axon diameter and myelin content in late childhood and adolescence. Cereb Cortex 2023; 33:6435-6448. [PMID: 36610731 PMCID: PMC10183755 DOI: 10.1093/cercor/bhac515] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 01/09/2023] Open
Abstract
White matter microstructural development in late childhood and adolescence is driven predominantly by increasing axon density and myelin thickness. Ex vivo studies suggest that the increase in axon diameter drives developmental increases in axon density observed with pubertal onset. In this cross-sectional study, 50 typically developing participants aged 8-18 years were scanned using an ultra-strong gradient magnetic resonance imaging scanner. Microstructural properties, including apparent axon diameter $({d}_a)$, myelin content, and g-ratio, were estimated in regions of the corpus callosum. We observed age-related differences in ${d}_a$, myelin content, and g-ratio. In early puberty, males had larger ${d}_a$ in the splenium and lower myelin content in the genu and body of the corpus callosum, compared with females. Overall, this work provides novel insights into developmental, pubertal, and cognitive correlates of individual differences in apparent axon diameter and myelin content in the developing human brain.
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Affiliation(s)
- Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Erika P Raven
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
- Department of Radiology, New York University School of Medicine, 550 1st Ave., New York, NY 10016, United States
| | - Mark Drakesmith
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Sarah-Jayne Blakemore
- Department of Psychology, University of Cambridge, Downing Pl, Cambridge CB2 3EB, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
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23
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Soustelle L, Troalen T, Hertanu A, Ranjeva JP, Guye M, Varma G, Alsop DC, Duhamel G, Girard OM. Quantitative magnetization transfer MRI unbiased by on-resonance saturation and dipolar order contributions. Magn Reson Med 2023. [PMID: 37154400 DOI: 10.1002/mrm.29678] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/22/2023] [Accepted: 04/01/2023] [Indexed: 05/10/2023]
Abstract
PURPOSE To demonstrate the bias in quantitative MT (qMT) measures introduced by the presence of dipolar order and on-resonance saturation (ONRS) effects using magnetization transfer (MT) spoiled gradient-recalled (SPGR) acquisitions, and propose changes to the acquisition and analysis strategies to remove these biases. METHODS The proposed framework consists of SPGR sequences prepared with simultaneous dual-offset frequency-saturation pulses to cancel out dipolar order and associated relaxation (T1D ) effects in Z-spectrum acquisitions, and a matched quantitative MT (qMT) mathematical model that includes ONRS effects of readout pulses. Variable flip angle and MT data were fitted jointly to simultaneously estimate qMT parameters (macromolecular proton fraction [MPF], T2,f , T2,b , R, and free pool T1 ). This framework is compared with standard qMT and investigated in terms of reproducibility, and then further developed to follow a joint single-point qMT methodology for combined estimation of MPF and T1 . RESULTS Bland-Altman analyses demonstrated a systematic underestimation of MPF (-2.5% and -1.3%, on average, in white and gray matter, respectively) and overestimation of T1 (47.1 ms and 38.6 ms, on average, in white and gray matter, respectively) if both ONRS and dipolar order effects are ignored. Reproducibility of the proposed framework is excellent (ΔMPF = -0.03% and ΔT1 = -19.0 ms). The single-point methodology yielded consistent MPF and T1 values with respective maximum relative average bias of -0.15% and -3.5 ms found in white matter. CONCLUSION The influence of acquisition strategy and matched mathematical model with regard to ONRS and dipolar order effects in qMT-SPGR frameworks has been investigated. The proposed framework holds promise for improved accuracy with reproducibility.
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Affiliation(s)
- Lucas Soustelle
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | | | - Andreea Hertanu
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Jean-Philippe Ranjeva
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Gopal Varma
- Division of MR Research, Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - David C Alsop
- Division of MR Research, Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Guillaume Duhamel
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Olivier M Girard
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
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Morita T, Takemura H, Naito E. Functional and Structural Properties of Interhemispheric Interaction between Bilateral Precentral Hand Motor Regions in a Top Wheelchair Racing Paralympian. Brain Sci 2023; 13:brainsci13050715. [PMID: 37239187 DOI: 10.3390/brainsci13050715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/14/2023] [Accepted: 04/23/2023] [Indexed: 05/28/2023] Open
Abstract
Long-term motor training can cause functional and structural changes in the human brain. Assessing how the training of specific movements affects specific parts of the neural circuitry is essential to understand better the underlying mechanisms of motor training-induced plasticity in the human brain. We report a single-case neuroimaging study that investigated functional and structural properties in a professional athlete of wheelchair racing. As wheelchair racing requires bilateral synchronization of upper limb movements, we hypothesized that functional and structural properties of interhemispheric interactions in the central motor system might differ between the professional athlete and controls. Functional and diffusion magnetic resonance imaging (fMRI and dMRI) data were obtained from a top Paralympian (P1) in wheelchair racing. With 23 years of wheelchair racing training starting at age eight, she holds an exceptional competitive record. Furthermore, fMRI and dMRI data were collected from three other paraplegic participants (P2-P4) with long-term wheelchair sports training other than wheelchair racing and 37 able-bodied control volunteers. Based on the fMRI data analyses, P1 showed activation in the bilateral precentral hand sections and greater functional connectivity between these sections during a right-hand unimanual task. In contrast, other paraplegic participants and controls showed activation in the contralateral hemisphere and deactivation in the ipsilateral hemisphere. Moreover, dMRI data analysis revealed that P1 exhibited significantly lower mean diffusivity along the transcallosal pathway connecting the bilateral precentral motor regions than control participants, which was not observed in the other paraplegic participants. These results suggest that long-term training with bilaterally synchronized upper-limb movements may promote bilateral recruitment of the precentral hand sections. Such recruitment may affect the structural circuitry involved in the interhemispheric interaction between the bilateral precentral regions. This study provides valuable evidence of the extreme adaptability of the human brain.
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Affiliation(s)
- Tomoyo Morita
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), 2A6 1-4 Yamadaoka, Suita 565-0871, Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita 565-0871, Osaka, Japan
| | - Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), 2A6 1-4 Yamadaoka, Suita 565-0871, Osaka, Japan
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, 38 Nishigonaka Myodaiji, Okazaki 444-8585, Aichi, Japan
- The Graduate Institute for Advanced Studies, SOKENDAI, Shonan Village, Hayama 240-0193, Kanagawa, Japan
| | - Eiichi Naito
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), 2A6 1-4 Yamadaoka, Suita 565-0871, Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita 565-0871, Osaka, Japan
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25
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Laporte JP, Faulkner ME, Gong Z, Palchamy E, Akhonda MA, Bouhrara M. Investigation of the association between central arterial stiffness and aggregate g-ratio in cognitively unimpaired adults. Front Neurol 2023; 14:1170457. [PMID: 37181577 PMCID: PMC10167487 DOI: 10.3389/fneur.2023.1170457] [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: 02/20/2023] [Accepted: 04/03/2023] [Indexed: 05/16/2023] Open
Abstract
Stiffness of the large arteries has been shown to impact cerebral white matter (WM) microstructure in both younger and older adults. However, no study has yet demonstrated an association between arterial stiffness and aggregate g-ratio, a specific magnetic resonance imaging (MRI) measure of axonal myelination that is highly correlated with neuronal signal conduction speed. In a cohort of 38 well-documented cognitively unimpaired adults spanning a wide age range, we investigated the association between central arterial stiffness, measured using pulse wave velocity (PWV), and aggregate g-ratio, measured using our recent advanced quantitative MRI methodology, in several cerebral WM structures. After adjusting for age, sex, smoking status, and systolic blood pressure, our results indicate that higher PWV values, that is, elevated arterial stiffness, were associated with lower aggregate g-ratio values, that is, lower microstructural integrity of WM. Compared to other brain regions, these associations were stronger and highly significant in the splenium of the corpus callosum and the internal capsules, which have been consistently documented as very sensitive to elevated arterial stiffness. Moreover, our detailed analysis indicates that these associations were mainly driven by differences in myelination, measured using myelin volume fraction, rather than axonal density, measured using axonal volume fraction. Our findings suggest that arterial stiffness is associated with myelin degeneration, and encourages further longitudinal studies in larger study cohorts. Controlling arterial stiffness may represent a therapeutic target in maintaining the health of WM tissue in cerebral normative aging.
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Affiliation(s)
| | | | | | | | | | - Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
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26
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Paquola C, Hong SJ. The Potential of Myelin-Sensitive Imaging: Redefining Spatiotemporal Patterns of Myeloarchitecture. Biol Psychiatry 2023; 93:442-454. [PMID: 36481065 DOI: 10.1016/j.biopsych.2022.08.031] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/12/2022] [Accepted: 08/30/2022] [Indexed: 02/07/2023]
Abstract
Recent advances in magnetic resonance imaging (MRI) have paved the way for approximation of myelin content in vivo. In this review, our main goal was to determine how to best capitalize on myelin-sensitive imaging. First, we briefly overview the theoretical and empirical basis for the myelin sensitivity of different MRI markers and, in doing so, highlight how multimodal imaging approaches are important for enhancing specificity to myelin. Then, we discuss recent studies that have probed the nonuniform distribution of myelin across cortical layers and along white matter tracts. These approaches, collectively known as myelin profiling, have provided detailed depictions of myeloarchitecture in both the postmortem and living human brain. Notably, MRI-based profiling studies have recently focused on investigating whether it can capture interindividual variability in myelin characteristics as well as trajectories across the lifespan. Finally, another line of recent evidence emphasizes the contribution of region-specific myelination to large-scale organization, demonstrating the impact of myelination on global brain networks. In conclusion, we suggest that combining well-validated MRI markers with profiling techniques holds strong potential to elucidate individual differences in myeloarchitecture, which has important implications for understanding brain function and disease.
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Affiliation(s)
- Casey Paquola
- Institute of Neuroscience and Medicine, Forschungszentrum Jülich, Jülich, Germany.
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea; Center for the Developing Brain, Child Mind Institute, New York, New York; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
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27
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Wang AYL, Chen KH, Lin HC, Loh CYY, Chang YC, Aviña AE, Lee CM, Chu IM, Wei FC. Sustained Release of Tacrolimus Embedded in a Mixed Thermosensitive Hydrogel for Improving Functional Recovery of Injured Peripheral Nerves in Extremities. Pharmaceutics 2023; 15:508. [PMID: 36839830 PMCID: PMC9960741 DOI: 10.3390/pharmaceutics15020508] [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: 12/31/2022] [Revised: 01/28/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
Vascularized composite allotransplantation is an emerging strategy for the reconstruction of unique defects such as amputated limbs that cannot be repaired with autologous tissues. In order to ensure the function of transplanted limbs, the functional recovery of the anastomosed peripheral nerves must be confirmed. The immunosuppressive drug, tacrolimus, has been reported to promote nerve recovery in animal models. However, its repeated dosing comes with risks of systemic malignancies and opportunistic infections. Therefore, drug delivery approaches for locally sustained release can be designed to overcome this issue and reduce systemic complications. We developed a mixed thermosensitive hydrogel (poloxamer (PLX)-poly(l-alanine-lysine with Pluronic F-127) for the time-dependent sustained release of tacrolimus in our previous study. In this study, we demonstrated that the hydrogel drug degraded in a sustained manner and locally released tacrolimus in mice over one month without affecting the systemic immunity. The hydrogel drug significantly improved the functional recovery of injured sciatic nerves as assessed using five-toe spread and video gait analysis. Neuroregeneration was validated in hydrogel-drug-treated mice using axonal analysis. The hydrogel drug did not cause adverse effects in the mouse model during long-term follow-up. The local injection of encapsulated-tacrolimus mixed thermosensitive hydrogel accelerated peripheral nerve recovery without systemic adverse effects.
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Affiliation(s)
- Aline Yen Ling Wang
- Center for Vascularized Composite Allotransplantation, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
| | - Kuan-Hung Chen
- Department of Physical Medicine & Rehabilitation, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
- College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
| | - Hsiu-Chao Lin
- Department of Chemical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Charles Yuen Yung Loh
- Department of Plastic Surgery, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0SP, UK
| | - Yun-Ching Chang
- Department of Health Industry Technology Management, Chung Shan Medical University, Taichung 40201, Taiwan
- Department of Medical Research, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
| | - Ana Elena Aviña
- Center for Vascularized Composite Allotransplantation, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
| | - Chin-Ming Lee
- Center for Vascularized Composite Allotransplantation, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
| | - I-Ming Chu
- Department of Chemical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Fu-Chan Wei
- Center for Vascularized Composite Allotransplantation, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
- College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
- Department of Plastic Surgery, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
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28
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Berg RC, Menegaux A, Amthor T, Gilbert G, Mora M, Schlaeger S, Pongratz V, Lauerer M, Sorg C, Doneva M, Vavasour I, Mühlau M, Preibisch C. Comparing myelin-sensitive magnetic resonance imaging measures and resulting g-ratios in healthy and multiple sclerosis brains. Neuroimage 2022; 264:119750. [PMID: 36379421 PMCID: PMC9931395 DOI: 10.1016/j.neuroimage.2022.119750] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/11/2022] [Accepted: 11/11/2022] [Indexed: 11/15/2022] Open
Abstract
The myelin concentration and the degree of myelination of nerve fibers can provide valuable information on the integrity of human brain tissue. Magnetic resonance imaging (MRI) of myelin-sensitive parameters can help to non-invasively evaluate demyelinating diseases such as multiple sclerosis (MS). Several different myelin-sensitive MRI methods have been proposed to determine measures of the degree of myelination, in particular the g-ratio. However, variability in underlying physical principles and different biological models influence measured myelin concentrations, and consequently g-ratio values. We therefore investigated similarities and differences between five different myelin-sensitive MRI measures and their effects on g-ratio mapping in the brains of both MS patients and healthy volunteers. We compared two different estimates of the myelin water fraction (MWF) as well as the inhomogeneous magnetization transfer ratio (ihMTR), magnetization transfer saturation (MTsat), and macromolecular tissue volume (MTV) in 13 patients with MS and 14 healthy controls. In combination with diffusion-weighted imaging, we derived g-ratio parameter maps for each of the five different myelin measures. The g-ratio values calculated from different myelin measures varied strongly, especially in MS lesions. While, compared to normal-appearing white matter, MTsat and one estimate of the MWF resulted in higher g-ratio values within lesions, ihMTR, MTV, and the second MWF estimate resulted in lower lesion g-ratio values. As myelin-sensitive measures provide rough estimates of myelin content rather than absolute myelin concentrations, resulting g-ratio values strongly depend on the utilized myelin measure and model used for g-ratio mapping. When comparing g-ratio values, it is, thus, important to utilize the same MRI methods and models or to consider methodological differences. Particular caution is necessary in pathological tissue such as MS lesions.
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Affiliation(s)
- Ronja C. Berg
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany,Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Corresponding author at: Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaninger Str. 22, 81675, München, Germany. (R.C. Berg)
| | - Aurore Menegaux
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
| | | | | | - Maria Mora
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany
| | - Sarah Schlaeger
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany
| | - Viola Pongratz
- Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
| | - Markus Lauerer
- Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
| | - Christian Sorg
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany,Technical University of Munich, School of Medicine, Department of Psychiatry, Munich, Germany
| | | | - Irene Vavasour
- University of British Columbia, Department of Radiology, Vancouver, BC, Canada
| | - Mark Mühlau
- Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
| | - Christine Preibisch
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany,Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
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29
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York EN, Meijboom R, Thrippleton MJ, Bastin ME, Kampaite A, White N, Chandran S, Waldman AD. Longitudinal microstructural MRI markers of demyelination and neurodegeneration in early relapsing-remitting multiple sclerosis: Magnetisation transfer, water diffusion and g-ratio. Neuroimage Clin 2022; 36:103228. [PMID: 36265199 PMCID: PMC9668599 DOI: 10.1016/j.nicl.2022.103228] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 10/07/2022] [Accepted: 10/08/2022] [Indexed: 11/11/2022]
Abstract
INTRODUCTION Quantitative microstructural MRI, such as myelin-sensitive magnetisation transfer ratio (MTR) or saturation (MTsat), axon-sensitive water diffusion Neurite Orientation Dispersion and Density Imaging (NODDI), and the aggregate g-ratio, may provide more specific markers of white matter integrity than conventional MRI for early patient stratification in relapsing-remitting multiple sclerosis (RRMS). The aim of this study was to determine the sensitivity of such markers to longitudinal pathological change within cerebral white matter lesions (WML) and normal-appearing white matter (NAWM) in recently diagnosed RRMS. METHODS Seventy-nine people with recently diagnosed RRMS, from the FutureMS longitudinal cohort, were recruited to an extended MRI protocol at baseline and one year later. Twelve healthy volunteers received the same MRI protocol, repeated within two weeks. Ethics approval and written informed consent were obtained. 3T MRI included magnetisation transfer, and multi-shell diffusion-weighted imaging. NAWM and whole brain were segmented from 3D T1-weighted MPRAGE, and WML from T2-weighted FLAIR. MTR, MTsat, NODDI isotropic (ISOVF) and intracellular (ICVF) volume fractions, and g-ratio (calculated from MTsat and NODDI data) were measured within WML and NAWM. Brain parenchymal fraction (BPF) was also calculated. Longitudinal change in BPF and microstructural metrics was assessed with paired t-tests (α = 0.05) and linear mixed models, adjusted for confounding factors with False Discovery Rate (FDR) correction for multiple comparisons. Longitudinal changes were compared with test-retest Bland-Altman limits of agreement from healthy control white matter. The influence of longitudinal change on g-ratio was explored through post-hoc analysis in silico by computing g-ratio with realistic simulated MTsat and NODDI values. RESULTS In NAWM, g-ratio and ICVF increased, and MTsat decreased over one year (adjusted mean difference = 0.007, 0.005, and -0.057 respectively, all FDR-corrected p < 0.05). There was no significant change in MTR, ISOVF, or BPF. In WML, MTsat, NODDI ICVF and ISOVF increased over time (adjusted mean difference = 0.083, 0.024 and 0.016, respectively, all FDR-corrected p < 0.05). Group-level longitudinal changes exceeded test-retest limits of agreement for NODDI ISOVF and ICVF in WML only. In silico analysis showed g-ratio may increase due to a decrease in MTsat or ISOVF, or an increase in ICVF. DISCUSSION G-ratio and MTsat changes in NAWM over one year may indicate subtle myelin loss in early RRMS, which were not apparent with BPF or NAWM MTR. Increases in NAWM and WML NODDI ICVF were not anticipated, and raise the possibility of axonal swelling or morphological change. Increases in WML MTsat may reflect myelin repair. Changes in NODDI ISOVF are more likely to reflect alterations in water content. Competing MTsat and ICVF changes may account for the absence of g-ratio change in WML. Longitudinal changes in microstructural measures are significant at a group level, however detection in individual patients in early RRMS is limited by technique reproducibility. CONCLUSION MTsat and g-ratio are more sensitive than MTR to early pathological changes in RRMS, but complex dependence of g-ratio on NODDI parameters limit the interpretation of aggregate measures in isolation. Improvements in technique reproducibility and validation of MRI biophysical models across a range of pathological tissue states are needed.
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Affiliation(s)
- Elizabeth N York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom; Anne Rowling Regenerative Neurology Clinic, Edinburgh, United Kingdom.
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Nicole White
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Anne Rowling Regenerative Neurology Clinic, Edinburgh, United Kingdom; UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom.
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30
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Clark IA, Mohammadi S, Callaghan MF, Maguire EA. Conduction velocity along a key white matter tract is associated with autobiographical memory recall ability. eLife 2022; 11:e79303. [PMID: 36166372 PMCID: PMC9514844 DOI: 10.7554/elife.79303] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 09/12/2022] [Indexed: 12/01/2022] Open
Abstract
Conduction velocity is the speed at which electrical signals travel along axons and is a crucial determinant of neural communication. Inferences about conduction velocity can now be made in vivo in humans using a measure called the magnetic resonance (MR) g-ratio. This is the ratio of the inner axon diameter relative to that of the axon plus the myelin sheath that encases it. Here, in the first application to cognition, we found that variations in MR g-ratio, and by inference conduction velocity, of the parahippocampal cingulum bundle were associated with autobiographical memory recall ability in 217 healthy adults. This tract connects the hippocampus with a range of other brain areas. We further observed that the association seemed to be with inner axon diameter rather than myelin content. The extent to which neurites were coherently organised within the parahippocampal cingulum bundle was also linked with autobiographical memory recall ability. Moreover, these findings were specific to autobiographical memory recall and were not apparent for laboratory-based memory tests. Our results offer a new perspective on individual differences in autobiographical memory recall ability, highlighting the possible influence of specific white matter microstructure features on conduction velocity when recalling detailed memories of real-life past experiences.
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Affiliation(s)
- Ian A Clark
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Siawoosh Mohammadi
- Institute of Systems Neuroscience, University Medical Centre Hamburg-EppendorfHamburgGermany
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
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31
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Glasser MF, Coalson TS, Harms MP, Xu J, Baum GL, Autio JA, Auerbach EJ, Greve DN, Yacoub E, Van Essen DC, Bock NA, Hayashi T. Empirical transmit field bias correction of T1w/T2w myelin maps. Neuroimage 2022; 258:119360. [PMID: 35697132 PMCID: PMC9483036 DOI: 10.1016/j.neuroimage.2022.119360] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 06/01/2022] [Accepted: 06/04/2022] [Indexed: 12/30/2022] Open
Abstract
T1-weighted divided by T2-weighted (T1w/T2w) myelin maps were initially developed for neuroanatomical analyses such as identifying cortical areas, but they are increasingly used in statistical comparisons across individuals and groups with other variables of interest. Existing T1w/T2w myelin maps contain radiofrequency transmit field (B1+) biases, which may be correlated with these variables of interest, leading to potentially spurious results. Here we propose two empirical methods for correcting these transmit field biases using either explicit measures of the transmit field or alternatively a 'pseudo-transmit' approach that is highly correlated with the transmit field at 3T. We find that the resulting corrected T1w/T2w myelin maps are both better neuroanatomical measures (e.g., for use in cross-species comparisons), and more appropriate for statistical comparisons of relative T1w/T2w differences across individuals and groups (e.g., sex, age, or body-mass-index) within a consistently acquired study at 3T. We recommend that investigators who use the T1w/T2w approach for mapping cortical myelin use these B1+ transmit field corrected myelin maps going forward.
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Affiliation(s)
| | | | - Michael P Harms
- Psychiatry, Washington University Medical School, St. Louis, MO, United States
| | - Junqian Xu
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States; Departments of Radiology and Psychiatry, Baylor College of Medicine, Houston, TX, United States
| | - Graham L Baum
- Department of Psychology, Harvard University, Cambridge, MA, United States
| | - Joonas A Autio
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Edward J Auerbach
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | | | - Nicholas A Bock
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, ON, Canada
| | - Takuya Hayashi
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
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Zhai Y, Wang Q, Zhu Z, Hao Y, Han F, Hong J, Zheng W, Ma S, Yang L, Cheng G. High-efficiency brain-targeted intranasal delivery of BDNF mediated by engineered exosomes to promote remyelination. Biomater Sci 2022; 10:5707-5718. [PMID: 36039673 DOI: 10.1039/d2bm00518b] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The regeneration of myelin sheaths is the ultimate goal of the treatment of demyelination disease, including multiple sclerosis (MS). However, current drugs for MS mainly target the immune system and can only slow down the disease development and do not promote the differentiation of oligodendrocyte precursor cells (OPCs) abundant in the myelin injury region into mature oligodendrocytes to form a new myelin sheath. Brain-derived neurotrophic factor (BDNF) plays an important role in the regulation of OPC proliferation and differentiation into mature oligodendrocytes. Exosomes, a kind of nanoscale membrane vesicle secreted by cells, can be used as potential therapeutic drug delivery vectors for central nervous system diseases. Here, brain-targeted modification and BDNF intracellular-loaded exosomes were produced through engineering HEK293T cells, which can promote the differentiation of OPCs into mature oligodendrocytes in vitro. The intranasal administration of the brain-targeted engineered exosome-mediated BDNF was a highly effective delivery route to the brain and had a significant therapeutic effect on remyelination and motor coordination ability improvement in demyelination model mice. The combination of intranasal administration with brain-targeted and BDNF-loaded designed exosomes provides a strategy for efficient drug delivery and treatment of central nervous system diseases.
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Affiliation(s)
- Yuanxin Zhai
- School of Nano-Tech and Nano Bionics, University of Science and Technology of China, Hefei Anhui 230026, China. .,CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou Jiangsu 215123, China
| | - Quanwei Wang
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou Jiangsu 215123, China
| | - Zhanchi Zhu
- School of Nano-Tech and Nano Bionics, University of Science and Technology of China, Hefei Anhui 230026, China. .,CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou Jiangsu 215123, China
| | - Ying Hao
- School of Nano-Tech and Nano Bionics, University of Science and Technology of China, Hefei Anhui 230026, China. .,CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou Jiangsu 215123, China.,Guangdong Institute of Semiconductor Micro-Nano Manufacturing Technology, Guangdong 528200, China
| | - Fang Han
- School of Nano-Tech and Nano Bionics, University of Science and Technology of China, Hefei Anhui 230026, China. .,CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou Jiangsu 215123, China
| | - Jing Hong
- School of Nano-Tech and Nano Bionics, University of Science and Technology of China, Hefei Anhui 230026, China. .,CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou Jiangsu 215123, China
| | - Wenlong Zheng
- Suzhou Kowloon Hospital, Shanghai Jiaotong University Medical School, Suzhou Jiangsu 215123, China.
| | - Sancheng Ma
- Suzhou Kowloon Hospital, Shanghai Jiaotong University Medical School, Suzhou Jiangsu 215123, China.
| | - Lingyan Yang
- School of Nano-Tech and Nano Bionics, University of Science and Technology of China, Hefei Anhui 230026, China. .,CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou Jiangsu 215123, China.,Guangdong Institute of Semiconductor Micro-Nano Manufacturing Technology, Guangdong 528200, China
| | - Guosheng Cheng
- School of Nano-Tech and Nano Bionics, University of Science and Technology of China, Hefei Anhui 230026, China. .,CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou Jiangsu 215123, China.,Guangdong Institute of Semiconductor Micro-Nano Manufacturing Technology, Guangdong 528200, China
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Tagge IJ, Leppert IR, Fetco D, Campbell JS, Rudko DA, Brown RA, Stikov N, Pike GB, Giacomini PS, Arnold DL, Narayanan S. Permanent tissue damage in multiple sclerosis lesions is associated with reduced pre-lesion myelin and axon volume fractions. Mult Scler 2022; 28:2027-2037. [PMID: 35903888 PMCID: PMC9574230 DOI: 10.1177/13524585221110585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND The use of advanced magnetic resonance imaging (MRI) techniques in MS research has led to new insights in lesion evolution and disease outcomes. It has not yet been determined if, or how, pre-lesional abnormalities in normal-appearing white matter (NAWM) relate to the long-term evolution of new lesions. OBJECTIVE To investigate the relationship between abnormalities in MRI measures of axonal and myelin volume fractions (AVF and MVF) in NAWM preceding development of black-hole (BH) and non-BH lesions in people with MS. METHODS We obtained magnetization transfer and diffusion MRI at 6-month intervals in patients with MS to estimate MVF and AVF during lesion evolution. Lesions were classified as either BH or non-BH on the final imaging visit using T1 maps. RESULTS Longitudinal data from 97 new T2 lesions from 9 participants were analyzed; 25 lesions in 8 participants were classified as BH 6-12 months after initial appearance. Pre-lesion MVF, AVF, and MVF/AVF were significantly lower, and T1 was significantly higher, in the lesions that later became BHs (p < 0.001) compared to those that did not. No significant pre-lesion abnormalities were found in non-BH lesions (p > 0.05). CONCLUSION The present work demonstrated that pre-lesion abnormalities are associated with worse long-term lesion-level outcome.
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Affiliation(s)
- Ian J Tagge
- McConnell Brain Imaging Center, Montreal Neurological Institute & Hospital, Montreal, QC, Canada
| | - Ilana R Leppert
- McConnell Brain Imaging Center, Montreal Neurological Institute & Hospital, Montreal, QC, Canada
| | - Dumitru Fetco
- McConnell Brain Imaging Center, Montreal Neurological Institute & Hospital, Montreal, QC, Canada
| | - Jennifer Sw Campbell
- McConnell Brain Imaging Center, Montreal Neurological Institute & Hospital, Montreal, QC, Canada
| | - David A Rudko
- McConnell Brain Imaging Center, Montreal Neurological Institute & Hospital, Montreal, QC, Canada
| | - Robert A Brown
- McConnell Brain Imaging Center, Montreal Neurological Institute & Hospital, Montreal, QC, Canada
| | - Nikola Stikov
- Electrical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - G Bruce Pike
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Paul S Giacomini
- Neurology and Neurosurgery, Montreal Neurological Institute & Hospital, Montreal, QC, Canada
| | - Douglas L Arnold
- McConnell Brain Imaging Center, Montreal Neurological Institute & Hospital, Montreal, QC, Canada
| | - Sridar Narayanan
- McConnell Brain Imaging Center, Montreal Neurological Institute & Hospital, Montreal, QC, Canada
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Harris NG, Paydar A, Smith GS, Lepore S. Diffusion MR imaging acquisition and analytics for microstructural delineation in pre-clinical models of TBI. J Neurosci Res 2022; 100:1128-1139. [PMID: 31044457 PMCID: PMC6824967 DOI: 10.1002/jnr.24416] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 02/13/2019] [Accepted: 03/04/2019] [Indexed: 12/31/2022]
Abstract
Significant progress has been made toward improving both the acquisition of clinical diffusion-weighted imaging (DWI) data and its analysis in the uninjured brain, through various techniques including a large number of model-based solutions that have been proposed to fit for multiple tissue compartments, and multiple fibers per voxel. While some of these techniques have been applied to clinical traumatic brain injury (TBI) research, the majority of these technological enhancements have yet to be fully implemented in the preclinical arena of TBI animal model-based research. In this review, we describe the requirement for preclinical, MRI-based efforts to provide systematic confirmation of the applicability of some of these models as indicators of tissue pathology within the injured brain. We review how current DWI techniques are currently being used in animal TBI models, and describe how both acquisition and analytic techniques could be extended to leverage the progress made in clinical work. Finally, we highlight remaining gaps in the preclinical pipeline from data acquisition to final analysis that currently have no real, preclinical-based correlate.
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Affiliation(s)
- N G Harris
- Department of Neurosurgery, UCLA Brain Injury Research Centre, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California
- UCLA Intellectual Development and Disabilities Research Center, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California
| | - A Paydar
- Department of Neurosurgery, UCLA Brain Injury Research Centre, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California
| | - G S Smith
- Department of Neurosurgery, UCLA Brain Injury Research Centre, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California
| | - S Lepore
- Department of Neurosurgery, UCLA Brain Injury Research Centre, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California
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Oliveira R, Pelentritou A, Di Domenicantonio G, De Lucia M, Lutti A. In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data. Front Neurosci 2022; 16:874023. [PMID: 35527816 PMCID: PMC9070985 DOI: 10.3389/fnins.2022.874023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 03/24/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose We present a novel approach that allows the estimation of morphological features of axonal fibers from data acquired in vivo in humans. This approach allows the assessment of white matter microscopic properties non-invasively with improved specificity. Theory The proposed approach is based on a biophysical model of Magnetic Resonance Imaging (MRI) data and of axonal conduction velocity estimates obtained with Electroencephalography (EEG). In a white matter tract of interest, these data depend on (1) the distribution of axonal radius [P(r)] and (2) the g-ratio of the individual axons that compose this tract [g(r)]. P(r) is assumed to follow a Gamma distribution with mode and scale parameters, M and θ, and g(r) is described by a power law with parameters α and β. Methods MRI and EEG data were recorded from 14 healthy volunteers. MRI data were collected with a 3T scanner. MRI-measured g-ratio maps were computed and sampled along the visual transcallosal tract. EEG data were recorded using a 128-lead system with a visual Poffenberg paradigm. The interhemispheric transfer time and axonal conduction velocity were computed from the EEG current density at the group level. Using the MRI and EEG measures and the proposed model, we estimated morphological properties of axons in the visual transcallosal tract. Results The estimated interhemispheric transfer time was 11.72 ± 2.87 ms, leading to an average conduction velocity across subjects of 13.22 ± 1.18 m/s. Out of the 4 free parameters of the proposed model, we estimated θ – the width of the right tail of the axonal radius distribution – and β – the scaling factor of the axonal g-ratio, a measure of fiber myelination. Across subjects, the parameter θ was 0.40 ± 0.07 μm and the parameter β was 0.67 ± 0.02 μm−α. Conclusion The estimates of axonal radius and myelination are consistent with histological findings, illustrating the feasibility of this approach. The proposed method allows the measurement of the distribution of axonal radius and myelination within a white matter tract, opening new avenues for the combined study of brain structure and function, and for in vivo histological studies of the human brain.
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York EN, Thrippleton MJ, Meijboom R, Hunt DPJ, Waldman AD. Quantitative magnetization transfer imaging in relapsing-remitting multiple sclerosis: a systematic review and meta-analysis. Brain Commun 2022; 4:fcac088. [PMID: 35652121 PMCID: PMC9149789 DOI: 10.1093/braincomms/fcac088] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/17/2021] [Accepted: 03/31/2022] [Indexed: 11/28/2022] Open
Abstract
Myelin-sensitive MRI such as magnetization transfer imaging has been widely used in multiple sclerosis. The influence of methodology and differences in disease subtype on imaging findings is, however, not well established. Here, we systematically review magnetization transfer brain imaging findings in relapsing-remitting multiple sclerosis. We examine how methodological differences, disease effects and their interaction influence magnetization transfer imaging measures. Articles published before 06/01/2021 were retrieved from online databases (PubMed, EMBASE and Web of Science) with search terms including 'magnetization transfer' and 'brain' for systematic review, according to a pre-defined protocol. Only studies that used human in vivo quantitative magnetization transfer imaging in adults with relapsing-remitting multiple sclerosis (with or without healthy controls) were included. Additional data from relapsing-remitting multiple sclerosis subjects acquired in other studies comprising mixed disease subtypes were included in meta-analyses. Data including sample size, MRI acquisition protocol parameters, treatments and clinical findings were extracted and qualitatively synthesized. Where possible, effect sizes were calculated for meta-analyses to determine magnetization transfer (i) differences between patients and healthy controls; (ii) longitudinal change and (iii) relationships with clinical disability in relapsing-remitting multiple sclerosis. Eighty-six studies met inclusion criteria. MRI acquisition parameters varied widely, and were also underreported. The majority of studies examined the magnetization transfer ratio in white matter, but magnetization transfer metrics, brain regions examined and results were heterogeneous. The analysis demonstrated a risk of bias due to selective reporting and small sample sizes. The pooled random-effects meta-analysis across all brain compartments revealed magnetization transfer ratio was 1.17 per cent units (95% CI -1.42 to -0.91) lower in relapsing-remitting multiple sclerosis than healthy controls (z-value: -8.99, P < 0.001, 46 studies). Linear mixed-model analysis did not show a significant longitudinal change in magnetization transfer ratio across all brain regions [β = 0.12 (-0.56 to 0.80), t-value = 0.35, P = 0.724, 14 studies] or normal-appearing white matter alone [β = 0.037 (-0.14 to 0.22), t-value = 0.41, P = 0.68, eight studies]. There was a significant negative association between the magnetization transfer ratio and clinical disability, as assessed by the Expanded Disability Status Scale [r = -0.32 (95% CI -0.46 to -0.17); z-value = -4.33, P < 0.001, 13 studies]. Evidence suggests that magnetization transfer imaging metrics are sensitive to pathological brain changes in relapsing-remitting multiple sclerosis, although effect sizes were small in comparison to inter-study variability. Recommendations include: better harmonized magnetization transfer acquisition protocols with detailed methodological reporting standards; larger, well-phenotyped cohorts, including healthy controls; and, further exploration of techniques such as magnetization transfer saturation or inhomogeneous magnetization transfer ratio.
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Affiliation(s)
- Elizabeth N. York
- Centre for Clinical Brain Sciences, University of
Edinburgh, Edinburgh, UK
| | | | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of
Edinburgh, Edinburgh, UK
| | - David P. J. Hunt
- Centre for Clinical Brain Sciences, University of
Edinburgh, Edinburgh, UK
- UK Dementia Research Institute, University of
Edinburgh, Edinburgh, UK
- Anne Rowling Regenerative Neurology Clinic,
University of Edinburgh, Edinburgh, UK
| | - Adam D. Waldman
- Centre for Clinical Brain Sciences, University of
Edinburgh, Edinburgh, UK
- UK Dementia Research Institute, University of
Edinburgh, Edinburgh, UK
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 124] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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Schiavi S, Lu PJ, Weigel M, Lutti A, Jones DK, Kappos L, Granziera C, Daducci A. Bundle myelin fraction (BMF) mapping of different white matter connections using microstructure informed tractography. Neuroimage 2022; 249:118922. [PMID: 35063648 PMCID: PMC7615247 DOI: 10.1016/j.neuroimage.2022.118922] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 01/13/2022] [Accepted: 01/17/2022] [Indexed: 12/13/2022] Open
Abstract
To date, we have scarce information about the relative myelination level of different fiber bundles in the human brain. Indirect evidence comes from postmortem histology data but histological stainings are unable to follow a specific bundle and determine its intrinsic myelination. In this context, quantitative MRI, and diffusion MRI tractography may offer a viable solution by providing, respectively, voxel-wise myelin sensitive maps and the pathways of the major tracts of the brain. Then, "tractometry" can be used to combine these two pieces of information by averaging tissue features (obtained from any voxel-wise map) along the streamlines recovered with diffusion tractography. Although this method has been widely used in the literature, in cases of voxels containing multiple fiber populations (each with different levels of myelination), tractometry provides biased results because the same value will be attributed to any bundle passing through the voxel. To overcome this bias, we propose a new method - named "myelin streamline decomposition" (MySD) - which extends convex optimization modeling for microstructure informed tractography (COMMIT) allowing the actual value measured by a microstructural map to be deconvolved on each individual streamline, thereby recovering unique bundle-specific myelin fractions (BMFs). We demonstrate the advantage of our method with respect to tractometry in well-studied bundles and compare the cortical projection of the obtained bundle-wise myelin values of both methods. We also prove the stability of our approach across different subjects and different MRI sensitive myelin mapping approaches. This work provides a proof-of-concept of in vivo investigations of entire neuronal pathways that, to date, are not possible.
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Affiliation(s)
- Simona Schiavi
- Department of Computer Science, University of Verona, Italy; Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Italy.
| | - Po-Jui Lu
- Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Radiological Physics, Department of Radiology, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, United Kingdom
| | - Ludwig Kappos
- Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Cristina Granziera
- Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
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Wilczynski E, Sasson E, Eliav U, Navon G, Nevo U. An in vivo implementation of the MEX MRI for myelin fraction of mice brain. MAGMA (NEW YORK, N.Y.) 2022; 35:267-276. [PMID: 34357453 DOI: 10.1007/s10334-021-00950-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/11/2021] [Accepted: 07/26/2021] [Indexed: 11/27/2022]
Abstract
OBJECTIVE Magnetization EXchange (MEX) sequence measures a signal linearly dependent on the myelin proton fraction by selective suppression of water magnetization and a recovery period. Varying the recovery period enables extraction of the percentile fraction of myelin bound protons. We aim to demonstrate the MEX sequence sensitivity to the fraction of protons associated with myelin in mice brain, in vivo. METHODS The cuprizone mouse model was used to manipulate the myelin content. Mice fed cuprizone (n = 15) and normal chow (n = 8) were imaged in vivo using MEX sequence. MR images were segmented into corpus callosum and internal capsule (white matter) and cortical gray matter, and fitted to the recovery equation. Results were analyzed with correlation to MWF and histopathology. RESULTS The extracted parameters show significant differences in the corpus callosum between the cuprizone and control groups. The cuprizone group exhibited reduced myelin fraction 26.5% (P < 0.01). The gray matter values were less affected, with 13.5% reduction (P < 0.05); no changes were detected in the internal capsule. Results were validated by MWF scans and good correlation to the histology analysis (R2 = 0.685). CONCLUSION The results of this first in vivo implementation of the MEX sequence provide a quantitative measure of demyelination in brain white matter.
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Affiliation(s)
- Ella Wilczynski
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Efrat Sasson
- School of Chemistry, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Uzi Eliav
- School of Chemistry, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Gil Navon
- School of Chemistry, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Uri Nevo
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel. .,Sagol School of Neuroscience, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.
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Meijboom R, Wiseman SJ, York EN, Bastin ME, Valdés Hernández MDC, Thrippleton MJ, Mollison D, White N, Kampaite A, Ng Kee Kwong K, Rodriguez Gonzalez D, Job D, Weaver C, Kearns PKA, Connick P, Chandran S, Waldman AD. Rationale and design of the brain magnetic resonance imaging protocol for FutureMS: a longitudinal multi-centre study of newly diagnosed patients with relapsing-remitting multiple sclerosis in Scotland. Wellcome Open Res 2022; 7:94. [PMID: 36865371 PMCID: PMC9971644 DOI: 10.12688/wellcomeopenres.17731.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2022] [Indexed: 12/22/2022] Open
Abstract
Introduction: Multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease. MS prevalence varies geographically and is notably high in Scotland. Disease trajectory varies significantly between individuals and the causes for this are largely unclear. Biomarkers predictive of disease course are urgently needed to allow improved stratification for current disease modifying therapies and future targeted treatments aimed at neuroprotection and remyelination. Magnetic resonance imaging (MRI) can detect disease activity and underlying damage non-invasively in vivo at the micro and macrostructural level. FutureMS is a prospective Scottish longitudinal multi-centre cohort study, which focuses on deeply phenotyping patients with recently diagnosed relapsing-remitting MS (RRMS). Neuroimaging is a central component of the study and provides two main primary endpoints for disease activity and neurodegeneration. This paper provides an overview of MRI data acquisition, management and processing in FutureMS. FutureMS is registered with the Integrated Research Application System (IRAS, UK) under reference number 169955. Methods and analysis: MRI is performed at baseline (N=431) and 1-year follow-up, in Dundee, Glasgow and Edinburgh (3T Siemens) and in Aberdeen (3T Philips), and managed and processed in Edinburgh. The core structural MRI protocol comprises T1-weighted, T2-weighted, FLAIR and proton density images. Primary imaging outcome measures are new/enlarging white matter lesions (WML) and reduction in brain volume over one year. Secondary imaging outcome measures comprise WML volume as an additional quantitative structural MRI measure, rim lesions on susceptibility-weighted imaging, and microstructural MRI measures, including diffusion tensor imaging and neurite orientation dispersion and density imaging metrics, relaxometry, magnetisation transfer (MT) ratio, MT saturation and derived g-ratio measures. Conclusions: FutureMS aims to reduce uncertainty around disease course and allow for targeted treatment in RRMS by exploring the role of conventional and advanced MRI measures as biomarkers of disease severity and progression in a large population of RRMS patients in Scotland.
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Affiliation(s)
- Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Stewart J. Wiseman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Elizabeth N. York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Mark E. Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Maria del C. Valdés Hernández
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Michael J. Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Daisy Mollison
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Nicole White
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Koy Ng Kee Kwong
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - David Rodriguez Gonzalez
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Dominic Job
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Christine Weaver
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, UK
| | - Patrick K. A. Kearns
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, UK
| | - Peter Connick
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, UK
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, UK
| | - Adam D. Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
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Takeshige-Amano H, Hatano T, Kamagata K, Andica C, Uchida W, Abe M, Ogawa T, Shimo Y, Oyama G, Umemura A, Ito M, Hori M, Aoki S, Hattori N. White matter microstructures in Parkinson's disease with and without impulse control behaviors. Ann Clin Transl Neurol 2022; 9:253-263. [PMID: 35137566 PMCID: PMC8935280 DOI: 10.1002/acn3.51504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 11/20/2021] [Accepted: 12/28/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Impulse control behaviors (ICBs) in Parkinson's disease (PD) are thought to be caused by an overdose of dopaminergic therapy in the relatively spared ventral striatum, or by hypersensitivity of this region to dopamine. Alterations in brain networks are now also thought to contribute to the development of ICBs. OBJECTIVE To comprehensively assess white matter microstructures in PD patients with ICBs using advanced diffusion MRI and magnetization transfer saturation (MT-sat) imaging. METHODS This study included 19 PD patients with ICBs (PD-ICBs), 18 PD patients without ICBs (PD-nICBs), and 20 healthy controls (HCs). Indices of diffusion tensor imaging (DTI), diffusion kurtosis imaging, neurite orientation dispersion and density imaging, and MT-sat imaging were evaluated using tract-based spatial statistics (TBSS), regions of interest (ROIs), and tract-specific analysis (TSA). RESULTS Compared with HCs, PD-nICBs had significant alterations in many major white matter tracts in most parameters. In contrast, PD-ICBs had only partial changes in several parameters. Compared with PD-ICBs, TBSS, ROI, and TSA analyses revealed that PD-nICBs had lower axial kurtosis, myelin volume fraction, and orientation dispersion index in the uncinate fasciculus and external capsule, as well as in the retrolenticular part of the internal capsule. These are components of the reward system and the visual and emotional perception areas, respectively. INTERPRETATION Myelin and axonal changes in fibers related to the reward system and visual emotional recognition might be more prominent in PD-nICBs than in PD-ICBs.
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Affiliation(s)
- Haruka Takeshige-Amano
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1138421, Japan.,Department of Neurology, Juntendo University Nerima Hospital, 3-1-10 Takanodai Nerima-ku, Tokyo, 1778521, Japan
| | - Taku Hatano
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1138421, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1138421, Japan
| | - Christina Andica
- Department of Radiology, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1138421, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1138421, Japan
| | - Masahiro Abe
- Department of Radiology, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1138421, Japan
| | - Takashi Ogawa
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1138421, Japan
| | - Yasushi Shimo
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1138421, Japan.,Department of Neurology, Juntendo University Nerima Hospital, 3-1-10 Takanodai Nerima-ku, Tokyo, 1778521, Japan
| | - Genko Oyama
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1138421, Japan
| | - Atsushi Umemura
- Department of Neurosurgery, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1138421, Japan
| | - Masanobu Ito
- Department of Psychiatry, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1138421, Japan
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, 6-11-1 Omorinishi, Ota-ku, Tokyo, 1438540, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1138421, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1138421, Japan
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42
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Cortina LE, Kim RW, Kiely M, Triebswetter C, Gong Z, Alsameen MH, Bouhrara M. Cerebral aggregate g-ratio mapping using magnetic resonance relaxometry and diffusion tensor imaging to investigate sex and age-related differences in white matter microstructure. Magn Reson Imaging 2022; 85:87-92. [PMID: 34678436 PMCID: PMC8629921 DOI: 10.1016/j.mri.2021.10.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 10/15/2021] [Accepted: 10/16/2021] [Indexed: 01/03/2023]
Abstract
Axonal demyelination is a cardinal feature of aging and age-related diseases. The g-ratio, mathematically defined as the inner-to-outer diameter of a myelinated axon, is used as a structural index of optimal axonal myelination and has been shown to represent a sensitive imaging biomarker of microstructural integrity. Several magnetic resonance imaging (MRI) methods for whole-brain mapping of aggregate g-ratio have been introduced. Computation of the aggerate g-ratio requires estimates of the myelin volume fraction (MVF) and the axonal volume fraction (AVF). While accurate determinations of MVF and AVF can be obtained through multicomponent relaxometry or diffusion analyses, respectively, these methods require lengthy acquisition times making their implementation challenging in a clinical context. Therefore, any attempt to overcome this drawback is needed. Expanding on our previous work, we introduced a new MRI method for whole-brain mapping of aggregate g-ratio. This new approach is based on the use of a single-shell diffusion for AVF determination, reducing the acquisition time by approximately ~10 min from our recently introduced approach, while offering the possibility to investigate g-ratio differences in previous studies with existing data for MVF mapping and single-shell diffusion data for AVF mapping. Our comparison analysis indicates that our newly derived aggregate g-ratio values were similar to those derived from our previous method, which requires a longer acquisition time. Further, in agreement with our previous observations, we found quadratic U-shaped relationships between aggregate g-ratio and age in this much larger study cohort. However, our results show that sexual dimorphism in g-ratio was not significant in any brain region investigated.
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Affiliation(s)
| | | | | | | | | | | | - Mustapha Bouhrara
- Corresponding author: Mustapha Bouhrara, PhD., MRPAD Unit, National Institute on Aging (NIA), National Institutes of Health (NIH), Intramural Research Program, BRC 05C-222, 251 Bayview Boulevard, Baltimore, MD 21224, USA. Tel: 410-558-8541,
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43
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York EN, Martin SJ, Meijboom R, Thrippleton MJ, Bastin ME, Carter E, Overell J, Connick P, Chandran S, Waldman AD, Hunt DPJ. MRI-derived g-ratio and lesion severity in newly diagnosed multiple sclerosis. Brain Commun 2021; 3:fcab249. [PMID: 34877533 PMCID: PMC8643503 DOI: 10.1093/braincomms/fcab249] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 08/24/2021] [Accepted: 08/30/2021] [Indexed: 01/19/2023] Open
Abstract
Myelin loss is associated with axonal damage in established multiple sclerosis. This relationship is challenging to study in vivo in early disease. Here, we ask whether myelin loss is associated with axonal damage at diagnosis by combining non-invasive neuroimaging and blood biomarkers. We performed quantitative microstructural MRI and single-molecule ELISA plasma neurofilament measurement in 73 patients with newly diagnosed, immunotherapy naïve relapsing-remitting multiple sclerosis. Myelin integrity was evaluated using aggregate g-ratios, derived from magnetization transfer saturation and neurite orientation dispersion and density imaging diffusion data. We found significantly higher g-ratios within cerebral white matter lesions (suggesting myelin loss) compared with normal-appearing white matter (0.61 versus 0.57, difference 0.036, 95% CI: 0.029-0.043, P < 0.001). Lesion volume (Spearman's rho rs= 0.38, P < 0.001) and g-ratio (rs= 0.24, P < 0.05) correlated independently with plasma neurofilament. In patients with substantial lesion load (n = 38), those with higher g-ratio (defined as greater than median) were more likely to have abnormally elevated plasma neurofilament than those with normal g-ratio (defined as less than median) [11/23 (48%) versus 2/15 (13%), P < 0.05]. These data suggest that, even at multiple sclerosis diagnosis, reduced myelin integrity is associated with axonal damage. MRI-derived g-ratio may provide useful additional information regarding lesion severity and help to identify individuals with a high degree of axonal damage at disease onset.
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Affiliation(s)
- Elizabeth N York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Sarah-Jane Martin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
- Department of Neurosciences, University of Glasgow, Glasgow G51 4LB, UK
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | | | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Edwin Carter
- Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - James Overell
- Department of Neurosciences, University of Glasgow, Glasgow G51 4LB, UK
| | - Peter Connick
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh EH16 4SB, UK
- Anne Rowling Clinic, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
- Anne Rowling Clinic, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - David P J Hunt
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh EH16 4SB, UK
- Anne Rowling Clinic, University of Edinburgh, Edinburgh EH16 4SB, UK
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44
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Cottaar M, Wu W, Tendler BC, Nagy Z, Miller K, Jbabdi S. Quantifying myelin in crossing fibers using diffusion-prepared phase imaging: Theory and simulations. Magn Reson Med 2021; 86:2618-2634. [PMID: 34254349 PMCID: PMC8581995 DOI: 10.1002/mrm.28907] [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: 11/10/2020] [Revised: 06/03/2021] [Accepted: 06/08/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE Myelin has long been the target of neuroimaging research. However, most available techniques can only provide a voxel-averaged estimate of myelin content. In the human brain, white matter fiber pathways connecting different brain areas and carrying different functions often cross each other in the same voxel. A measure that can differentiate the degree of myelination of crossing fibers would provide a more specific marker of myelination. THEORY AND METHODS One MRI signal property that is sensitive to myelin is the phase accumulation. This sensitivity is used by measuring the phase accumulation of the signal remaining after diffusion-weighting, which is called diffusion-prepared phase imaging (DIPPI). Including diffusion-weighting before estimating the phase accumulation has two distinct advantages for estimating the degree of myelination: (1) It increases the relative contribution of intra-axonal water, whose phase is related linearly to the thickness of the surrounding myelin (in particular the log g-ratio); and (2) it gives directional information, which can be used to distinguish between crossing fibers. Here the DIPPI sequence is described, an approach is proposed to estimate the log g-ratio, and simulations are used and DIPPI data acquired in an isotropic phantom to quantify other sources of phase accumulation. RESULTS The expected bias is estimated in the log g-ratio for reasonable in vivo acquisition parameters caused by eddy currents (~4%-10%), remaining extra-axonal signal (~15%), and gradients in the bulk off-resonance field (<10% for most of the brain). CONCLUSION This new sequence may provide a g-ratio estimate per fiber population crossing within a voxel.
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Affiliation(s)
- Michiel Cottaar
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
| | - Benjamin C. Tendler
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
| | - Zoltan Nagy
- Laboratory for Social and Neural Systems ResearchUniversity of ZurichZurichSwitzerland
| | - Karla Miller
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging—Centre for Functional Magnetic Resonance Imaging of the BrainJohn Radcliffe HospitalUniversity of OxfordOxfordUnited Kingdom
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45
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Ogawa T, Hatano T, Kamagata K, Andica C, Takeshige-Amano H, Uchida W, Kamiyama D, Shimo Y, Oyama G, Umemura A, Iwamuro H, Ito M, Hori M, Aoki S, Hattori N. White matter and nigral alterations in multiple system atrophy-parkinsonian type. NPJ PARKINSONS DISEASE 2021; 7:96. [PMID: 34716335 PMCID: PMC8556415 DOI: 10.1038/s41531-021-00236-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 09/15/2021] [Indexed: 12/20/2022]
Abstract
Multiple system atrophy (MSA) is classified into two main types: parkinsonian and cerebellar ataxia with oligodendrogliopathy. We examined microstructural alterations in the white matter and the substantia nigra pars compacta (SNc) of patients with MSA of parkinsonian type (MSA-P) using multishell diffusion magnetic resonance imaging (dMRI) and myelin sensitive imaging techniques. Age- and sex-matched patients with MSA-P (n = 21, n = 10 first and second cohorts, respectively), Parkinson’s disease patients (n = 19, 17), and healthy controls (n = 20, 24) were enrolled. Magnetization transfer saturation imaging (MT-sat) and dMRI were obtained using 3-T MRI. Measurements obtained from diffusion tensor imaging (DTI), free-water elimination DTI, neurite orientation dispersion and density imaging (NODDI), and MT-sat were compared between groups. Tract-based spatial statistics analysis revealed differences in diffuse white matter alterations in the free-water fractional volume, myelin volume fraction, and intracellular volume fraction between the patients with MSA-P and healthy controls, whereas free-water and MT-sat differences were limited to the middle cerebellar peduncle in comparison with those with Parkinson’s disease. Region-of-interest analysis of white matter and SNc revealed significant differences in the middle and inferior cerebellar peduncle, pontine crossing tract, corticospinal tract, and SNc between the MSA-P and healthy controls and/or Parkinson’s disease patients. Our results shed light on alterations to brain microstructure in MSA.
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Affiliation(s)
- Takashi Ogawa
- Department of Neurology, Faculty of Medicine, Juntendo University, Tokyo, Japan
| | - Taku Hatano
- Department of Neurology, Faculty of Medicine, Juntendo University, Tokyo, Japan.
| | - Koji Kamagata
- Department of Radiology, Faculty of Medicine, Juntendo University, Tokyo, Japan
| | - Christina Andica
- Department of Radiology, Faculty of Medicine, Juntendo University, Tokyo, Japan
| | | | - Wataru Uchida
- Department of Radiology, Faculty of Medicine, Juntendo University, Tokyo, Japan
| | - Daiki Kamiyama
- Department of Neurology, Faculty of Medicine, Juntendo University, Tokyo, Japan
| | - Yasushi Shimo
- Department of Neurology, Juntendo University Nerima Hospital, Tokyo, Japan
| | - Genko Oyama
- Department of Neurology, Faculty of Medicine, Juntendo University, Tokyo, Japan
| | - Atsushi Umemura
- Department of Neurosurgery, Faculty of Medicine, Juntendo University, Tokyo, Japan
| | - Hirokazu Iwamuro
- Department of Neurosurgery, Faculty of Medicine, Juntendo University, Tokyo, Japan
| | - Masanobu Ito
- Department of Psychiatry, Faculty of Medicine Juntendo University, Tokyo, Japan
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Faculty of Medicine, Juntendo University, Tokyo, Japan
| | - Nobutaka Hattori
- Department of Neurology, Faculty of Medicine, Juntendo University, Tokyo, Japan.
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46
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Balbastre Y, Brudfors M, Azzarito M, Lambert C, Callaghan MF, Ashburner J. Model-based multi-parameter mapping. Med Image Anal 2021; 73:102149. [PMID: 34271531 PMCID: PMC8505752 DOI: 10.1016/j.media.2021.102149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 05/07/2021] [Accepted: 06/24/2021] [Indexed: 12/29/2022]
Abstract
Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, computing quantitative parameter maps, such as those encoding longitudinal relaxation rate (R1), apparent transverse relaxation rate (R2*) or magnetisation-transfer saturation (MTsat), involves inverting a highly non-linear function. Many methods for deriving parameter maps assume perfect measurements and do not consider how noise is propagated through the estimation procedure, resulting in needlessly noisy maps. Instead, we propose a probabilistic generative (forward) model of the entire dataset, which is formulated and inverted to jointly recover (log) parameter maps with a well-defined probabilistic interpretation (e.g., maximum likelihood or maximum a posteriori). The second order optimisation we propose for model fitting achieves rapid and stable convergence thanks to a novel approximate Hessian. We demonstrate the utility of our flexible framework in the context of recovering more accurate maps from data acquired using the popular multi-parameter mapping protocol. We also show how to incorporate a joint total variation prior to further decrease the noise in the maps, noting that the probabilistic formulation allows the uncertainty on the recovered parameter maps to be estimated. Our implementation uses a PyTorch backend and benefits from GPU acceleration. It is available at https://github.com/balbasty/nitorch.
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Affiliation(s)
- Yaël Balbastre
- Wellcome Center for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA.
| | - Mikael Brudfors
- Wellcome Center for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Michela Azzarito
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Switzerland
| | - Christian Lambert
- Wellcome Center for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK
| | - Martina F Callaghan
- Wellcome Center for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK
| | - John Ashburner
- Wellcome Center for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK
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47
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Cohen-Adad J, Alonso-Ortiz E, Abramovic M, Arneitz C, Atcheson N, Barlow L, Barry RL, Barth M, Battiston M, Büchel C, Budde M, Callot V, Combes AJE, De Leener B, Descoteaux M, de Sousa PL, Dostál M, Doyon J, Dvorak A, Eippert F, Epperson KR, Epperson KS, Freund P, Finsterbusch J, Foias A, Fratini M, Fukunaga I, Gandini Wheeler-Kingshott CAM, Germani G, Gilbert G, Giove F, Gros C, Grussu F, Hagiwara A, Henry PG, Horák T, Hori M, Joers J, Kamiya K, Karbasforoushan H, Keřkovský M, Khatibi A, Kim JW, Kinany N, Kitzler HH, Kolind S, Kong Y, Kudlička P, Kuntke P, Kurniawan ND, Kusmia S, Labounek R, Laganà MM, Laule C, Law CS, Lenglet C, Leutritz T, Liu Y, Llufriu S, Mackey S, Martinez-Heras E, Mattera L, Nestrasil I, O'Grady KP, Papinutto N, Papp D, Pareto D, Parrish TB, Pichiecchio A, Prados F, Rovira À, Ruitenberg MJ, Samson RS, Savini G, Seif M, Seifert AC, Smith AK, Smith SA, Smith ZA, Solana E, Suzuki Y, Tackley G, Tinnermann A, Valošek J, Van De Ville D, Yiannakas MC, Weber Ii KA, Weiskopf N, Wise RG, Wyss PO, Xu J. Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers. Sci Data 2021; 8:219. [PMID: 34400655 PMCID: PMC8368310 DOI: 10.1038/s41597-021-00941-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/26/2021] [Indexed: 12/21/2022] Open
Abstract
In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/ . The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord.
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Affiliation(s)
- Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada.
- Mila - Quebec AI Institute, Montreal, QC, Canada.
| | - Eva Alonso-Ortiz
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Mihael Abramovic
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Carina Arneitz
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Nicole Atcheson
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Laura Barlow
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-Massachusetts Institute of Technology Health Sciences & Technology, Cambridge, MA, USA
| | - Markus Barth
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Marco Battiston
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Christian Büchel
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Matthew Budde
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Virginie Callot
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hopital Universitaire Timone, CEMEREM, Marseille, France
| | - Anna J E Combes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benjamin De Leener
- Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, Canada
- CHU Sainte-Justine Research Centre, Montreal, QC, Canada
| | - Maxime Descoteaux
- Centre de Recherche CHUS, CIMS, Sherbrooke, Canada
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science department, Université de Sherbrooke, Sherbrooke, Canada
| | | | - Marek Dostál
- UHB - University Hospital Brno and Masaryk University, Department of Radiology and Nuclear Medicine, Brno, Czech Republic
| | - Julien Doyon
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Adam Dvorak
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Falk Eippert
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Karla R Epperson
- Richard M. Lucas Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Kevin S Epperson
- Richard M. Lucas Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Patrick Freund
- Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexandru Foias
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Michela Fratini
- Institute of Nanotechnology, CNR, Rome, Italy
- IRCCS Santa Lucia Foundation, Rome, Italy
| | - Issei Fukunaga
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Giancarlo Germani
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Federico Giove
- IRCCS Santa Lucia Foundation, Rome, Italy
- CREF - Museo storico della fisica e Centro studi e ricerche Enrico Fermi, Rome, Italy
| | - Charley Gros
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Francesco Grussu
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Pierre-Gilles Henry
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Tomáš Horák
- Multimodal and functional imaging laboratory, Central European Institute of Technology (CEITEC), Brno, Czech Republic
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - James Joers
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Kouhei Kamiya
- Department of Radiology, the University of Tokyo, Tokyo, Japan
| | - Haleh Karbasforoushan
- Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA
| | - Miloš Keřkovský
- UHB - University Hospital Brno and Masaryk University, Department of Radiology and Nuclear Medicine, Brno, Czech Republic
| | - Ali Khatibi
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Joo-Won Kim
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nawal Kinany
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Hagen H Kitzler
- Institute of Diagnostic and Interventional Neuroradiology, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Shannon Kolind
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- Department Of Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Yazhuo Kong
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Petr Kudlička
- Multimodal and functional imaging laboratory, Central European Institute of Technology (CEITEC), Brno, Czech Republic
| | - Paul Kuntke
- Institute of Diagnostic and Interventional Neuroradiology, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Nyoman D Kurniawan
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Slawomir Kusmia
- CUBRIC, Cardiff University, Wales, UK
- Centre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering Department, University College London, London, UK
- Epilepsy Society MRI Unit, Chalfont St Peter, UK
| | - René Labounek
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Departments of Neurology and Biomedical Engineering, University Hospital Olomouc, Olomouc, Czech Republic
| | | | - Cornelia Laule
- Departments of Radiology, Pathology & Laboratory Medicine, Physics & Astronomy; International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada
| | - Christine S Law
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Tobias Leutritz
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Sean Mackey
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Loan Mattera
- Fondation Campus Biotech Genève, 1202, Geneva, Switzerland
| | - Igor Nestrasil
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Kristin P O'Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nico Papinutto
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Daniel Papp
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Deborah Pareto
- Neuroradiology Section, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Todd B Parrish
- Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Anna Pichiecchio
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Ferran Prados
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering Department, University College London, London, UK
- E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Àlex Rovira
- Neuroradiology Section, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Marc J Ruitenberg
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Rebecca S Samson
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Giovanni Savini
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Maryam Seif
- Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Alan C Seifert
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alex K Smith
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Seth A Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zachary A Smith
- University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Y Suzuki
- Department of Radiology, the University of Tokyo, Tokyo, Japan
| | | | - Alexandra Tinnermann
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Valošek
- Department of Neurology, Faculty of Medicine and Dentistry, Palacký University and University Hospital Olomouc, Olomouc, Czech Republic
| | - Dimitri Van De Ville
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Marios C Yiannakas
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Kenneth A Weber Ii
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Richard G Wise
- CUBRIC, Cardiff University, Wales, UK
- Institute for Advanced Biomedical Technologies, Department of Neuroscience, Imaging and Clinical Sciences, "G. D'Annunzio University" of Chieti-Pescara, Chieti-Pescara, Italy
| | - Patrik O Wyss
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Junqian Xu
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Li D, Ji S, Guo Y, Sang N. Ambient NO 2 exposure sex-specifically impairs myelin and contributes to anxiety and depression-like behaviors of C57BL/6J mice. JOURNAL OF HAZARDOUS MATERIALS 2021; 416:125836. [PMID: 34492793 DOI: 10.1016/j.jhazmat.2021.125836] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/27/2021] [Accepted: 04/04/2021] [Indexed: 06/13/2023]
Abstract
NO2 is a common indoor and outdoor air pollutant, but its health effects are still controversial. Beside respiratory injury, more epidemiological studies show that inhalation of NO2 is associated with an increased risk of anxiety and depression. However, the causal relationship at the molecular level remains unclear. In the present study, we exposed adult C57BL/6J mice to NO2 (2.5 ppm, 5 h/day) for four weeks, and found anxiety and depression-like behaviors in male mice, but not female mice. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment indicated that differentially expressed genes (DEGs) in the prefrontal cortex and cerebellum were closely associated with signal transduction pathways, such as axon guidance. Importantly, NO2 inhalation damaged the ultrastructure of myelin sheath and caused the abnormal expression of related genes in males, which partially contributed to mental disorders. We also found that prolactin (Prl), through its anti-inflammatory activity and remyelination, might play a major role in the sex-specific neurobehavioral disorder in male mice caused by NO2 exposure.
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Affiliation(s)
- Dan Li
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Taiyuan, Shanxi 030006, PR China
| | - Shaoyang Ji
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Taiyuan, Shanxi 030006, PR China
| | - Yuqiong Guo
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Taiyuan, Shanxi 030006, PR China
| | - Nan Sang
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Taiyuan, Shanxi 030006, PR China.
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Mancini M, Tian Q, Fan Q, Cercignani M, Huang SY. Dissecting whole-brain conduction delays through MRI microstructural measures. Brain Struct Funct 2021; 226:2651-2663. [PMID: 34390416 PMCID: PMC8448685 DOI: 10.1007/s00429-021-02358-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/28/2021] [Indexed: 01/24/2023]
Abstract
Network models based on structural connectivity have been increasingly used as the blueprint for large-scale simulations of the human brain. As the nodes of this network are distributed through the cortex and interconnected by white matter pathways with different characteristics, modeling the associated conduction delays becomes important. The goal of this study is to estimate and characterize these delays directly from the brain structure. To achieve this, we leveraged microstructural measures from a combination of advanced magnetic resonance imaging acquisitions and computed the main determinants of conduction velocity, namely axonal diameter and myelin content. Using the model proposed by Rushton, we used these measures to calculate the conduction velocity and estimated the associated delays using tractography. We observed that both the axonal diameter and conduction velocity distributions presented a rather constant trend across different connection lengths, with resulting delays that scale linearly with the connection length. Relying on insights from graph theory and Kuramoto simulations, our results support the approximation of constant conduction velocity but also show path- and region-specific differences.
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Affiliation(s)
- Matteo Mancini
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK. .,Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK. .,NeuroPoly Lab, Polytechnique Montréal, Montréal, Canada.
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Mara Cercignani
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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50
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Maekawa T, Hagiwara A, Yokoyama K, Hori M, Andica C, Fujita S, Kamagata K, Wada A, Abe O, Tomizawa Y, Hattori N, Aoki S. Multiple sclerosis plaques may undergo continuous myelin degradation: a cross-sectional study with myelin and axon-related quantitative magnetic resonance imaging metrics. Neuroradiology 2021; 64:465-471. [PMID: 34383123 DOI: 10.1007/s00234-021-02781-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 07/30/2021] [Indexed: 11/30/2022]
Abstract
PURPOSE We hypothesize that myelin is more susceptible to damage over time than axons. We investigated the association between the estimated duration from the onset of multiple sclerosis (MS) plaques and myelin- and axon-related quantitative synthetic magnetic resonance imaging (SyMRI) and neurite orientation dispersion and density imaging (NODDI) metrics. METHODS We analyzed 31 patients with MS with 73 newly appeared plaques. Simple linear regression analysis was performed to assess the association between the estimated duration from the onset of plaques and quantitative MRI metrics. These metrics included the myelin volume fraction (MVF), axon volume fraction, and g-ratio in plaque and normal-appearing white matter. RESULTS MS plaques with a longer estimated duration from onset were significantly correlated with a lower MVF (slope = - 0.0070, R2 = 0.0970), higher g-ratio (slope = 0.0078, R2 = 0.0842) (all P values < 0.05). CONCLUSION These results suggested that myelin in plaques undergoes continuous damage, more so than axons. Myelin imaging with SyMRI and NODDI may be useful for the quantitative assessment of temporal changes in MS plaques.
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Affiliation(s)
- Tomoko Maekawa
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Kazumasa Yokoyama
- Department of Neurology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Masaaki Hori
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
- Department of Diagnostic Radiology, Toho University Omori Medical Center, 6-11-1, Omori-Nishi, Ota-Ku, Tokyo, Japan
| | - Christina Andica
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Shohei Fujita
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
- Departmen of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Osamu Abe
- Departmen of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Yuji Tomizawa
- Department of Neurology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
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