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El Ayoubi NK, Ismail A, Fahd F, Younes L, Chakra NA, Khoury SJ. Retinal optical coherence tomography measures in multiple sclerosis: a systematic review and meta-analysis. Ann Clin Transl Neurol 2024. [PMID: 39073308 DOI: 10.1002/acn3.52165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 07/12/2024] [Accepted: 07/12/2024] [Indexed: 07/30/2024] Open
Abstract
Spectral domain-optical coherence tomography plays a crucial role in the early detection and monitoring of multiple sclerosis (MS) pathophysiology. We aimed to quantify differences in retinal layer measures among different groups of MS and explored different variables that correlate with retinal measures. This study was reported according PRISMA guidelines. A comprehensive search was done across PubMed, Embase, and Google Scholar. The mean difference in thickness of retinal layers and macular volume was assessed. Meta-regression was done to assess the sources of heterogeneity. A total of 100 articles were included in the meta-analyses. The peripapillary retinal nerve fiber layer (pRNFL) thickness significantly decreased in the MSON (MD: -16.44, P < 0.001), MSNON (MD: -6.97, P < 0.001), and PMS (MD: -11.35, P < 0.001) versus HC. The macular RNFL was lower among the MSON (MD: -6.24, P = 0.013) and MSNON (MD: -3.84, P <0.001) versus HC. Macular ganglion cell layer and inner plexiform layer (GCIPL) was thinner among MSON (MD: -14.83, P <0.001), MSNON (MD: -6.38, P < 0.001), and PMS (MD: -11.52, P < 0.001) compared with control eyes. Inner nuclear layer (INL) was higher in the MSON (MD: 0.49, P < 0.001) versus HC. Outer nuclear layer (ONL) thickness significantly lower in the MSNON (MD: -1.15, P = 0.019) versus HC. Meta-regression showed that disease duration, age, EDSS score, and percentage of patients taking DMT are all negatively correlated with pRNFL and GCIPL thickness; however, female gender was correlated with less atrophy. As conclusion, the study highlights substantial thinning in the pRNFL and macular GCIPL between MS versus controls. INL as valuable parameter for capturing inflammatory disease activity.
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Affiliation(s)
- Nabil K El Ayoubi
- Nehme and Therese Tohme Multiple Sclerosis Center, Department of Neurology, American University of Beirut, Beirut, Lebanon
| | - Ali Ismail
- Faculty of Medical Sciences, Lebanese University, Beirut, Lebanon
- Faculty of Medical Sciences, Neuroscience Research Center, Lebanese University, Beirut, Lebanon
| | - Fares Fahd
- Nehme and Therese Tohme Multiple Sclerosis Center, Department of Neurology, American University of Beirut, Beirut, Lebanon
| | - Lama Younes
- Nehme and Therese Tohme Multiple Sclerosis Center, Department of Neurology, American University of Beirut, Beirut, Lebanon
| | - Nour A Chakra
- Nehme and Therese Tohme Multiple Sclerosis Center, Department of Neurology, American University of Beirut, Beirut, Lebanon
| | - Samia J Khoury
- Nehme and Therese Tohme Multiple Sclerosis Center, Department of Neurology, American University of Beirut, Beirut, Lebanon
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Cooper AC, Tchernykh M, Shmuel A, Mendola JD. Diffusion tensor imaging of optic neuropathies: a narrative review. Quant Imaging Med Surg 2024; 14:1086-1107. [PMID: 38223128 PMCID: PMC10784057 DOI: 10.21037/qims-23-779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 11/21/2023] [Indexed: 01/16/2024]
Abstract
Background and Objective Diffusion tensor imaging (DTI) has been implemented in a breadth of scientific investigations of optic neuropathies, though it has yet to be fully adopted for diagnosis or prognosis. This is potentially due to a lack of standardization and weak replication of results. The aim of this investigation was to review DTI results from studies specific to three distinct optic neuropathies in order to probe its current clinical utility. Methods We reviewed the DTI literature specific to primary open-angle glaucoma (POAG), optic neuritis (ON), and traumatic optic neuropathy (TON) by systematically searching the PubMed database on March 1st, 2023. Four distinct DTI metrics are considered: fractional anisotropy (FA), along with mean diffusivity (MD, axial diffusivity (AD), and radial diffusivity (RD). Results from within-group, between-group, and correlational studies were thoroughly assessed. Key Content and Findings POAG studies most consistently report a decrease in FA, especially in the optic radiations, followed in prevalence by an increase in RD and then MD, whilst AD yields conflicting results between studies. It is notable that there is not an equal distribution of investigated DTI metrics, with FA utilized the most, followed by MD, RD, and AD. Studies of ON are similar in that the most consistent findings are specific to FA, RD, and MD. These results are specific to the optic nerve and radiation since only one study measured the intermediary regions. More studies are needed to assess the effect that ON has on the tracts of the visual system. Finally, only three studies assessing DTI of TON have been performed to date, displaying low to moderate replicability of results. To improve the level of agreement between studies assessing each optic neuropathy, an increased level of standardization is recommended. Conclusions Both POAG and ON studies have yielded some prevalent DTI findings, both for contrast and correlation-based assessments. Although the clinical need is high for TON, considering the limitations of the current diagnostic tools, too few studies exist to make confident conclusions. Future use of standardized and longitudinal DTI, along with the foreseen methodological and technical improvements, is warranted to effectively study optic neuropathies.
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Affiliation(s)
- Austin C. Cooper
- McGill Vision Research and Department of Ophthalmology, McGill University, Montréal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Maxim Tchernykh
- McGill Vision Research and Department of Ophthalmology, McGill University, Montréal, QC, Canada
| | - Amir Shmuel
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Departments of Physiology and Biomedical Engineering, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Janine D. Mendola
- McGill Vision Research and Department of Ophthalmology, McGill University, Montréal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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Krijnen EA, Russo AW, Salim Karam E, Lee H, Chiang FL, Schoonheim MM, Huang SY, Klawiter EC. Detection of grey matter microstructural substrates of neurodegeneration in multiple sclerosis. Brain Commun 2023; 5:fcad153. [PMID: 37274832 PMCID: PMC10233898 DOI: 10.1093/braincomms/fcad153] [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: 01/12/2023] [Revised: 02/16/2023] [Accepted: 05/22/2023] [Indexed: 06/07/2023] Open
Abstract
Multiple sclerosis features complex pathological changes in grey matter that begin early and eventually lead to diffuse atrophy. Novel approaches to image grey-matter microstructural alterations in vivo are highly sought after and would enable more sensitive monitoring of disease activity and progression. This cross-sectional study aimed to assess the sensitivity of high-gradient diffusion MRI for microstructural tissue damage in cortical and deep grey matter in people with multiple sclerosis and test the hypothesis that reduced cortical cell body density is associated with cortical and deep grey-matter volume loss. Forty-one people with multiple sclerosis (age 24-72, 14 females) and 37 age- and sex-matched healthy controls were scanned on a 3 T Connectom MRI scanner equipped with 300 mT/m gradients using a multi-shell diffusion MRI protocol. The soma and neurite density imaging model was fitted to high-gradient diffusion MRI data to obtain estimates of intra-neurite, intra-cellular and extra-cellular signal fractions and apparent soma radius. Cortical and deep grey-matter microstructural imaging metrics were compared between multiple sclerosis and healthy controls and correlated with grey-matter volume, clinical disability and cognitive outcomes. People with multiple sclerosis showed significant cortical and deep grey-matter volume loss compared with healthy controls. People with multiple sclerosis showed trends towards lower cortical intra-cellular signal fraction and significantly lower intra-cellular and higher extra-cellular signal fractions in deep grey matter, especially the thalamus and caudate, compared with healthy controls. Changes were most pronounced in progressive disease and correlated with the Expanded Disability Status Scale, but not the Symbol Digit Modalities Test. In multiple sclerosis, normalized thalamic volume was associated with thalamic microstructural imaging metrics. Whereas thalamic volume loss did not correlate with cortical volume loss, cortical microstructural imaging metrics were significantly associated with thalamic volume, and not with cortical volume. Compared with the short diffusion time (Δ = 19 ms) achievable on the Connectom scanner, at the longer diffusion time of Δ = 49 ms attainable on clinical scanners, multiple sclerosis-related changes in imaging metrics were generally less apparent with lower effect sizes in cortical and deep grey matter. Soma and neurite density imaging metrics obtained from high-gradient diffusion MRI data provide detailed grey-matter characterization beyond cortical and thalamic volumes and distinguish multiple sclerosis-related microstructural pathology from healthy controls. Cortical cell body density correlates with thalamic volume, appears sensitive to the microstructural substrate of neurodegeneration and reflects disability status in people with multiple sclerosis, becoming more pronounced as disability worsens.
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Affiliation(s)
- Eva A Krijnen
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
| | - Andrew W Russo
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Elsa Salim Karam
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Hansol Lee
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Florence L Chiang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
| | - Susie Y Huang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Eric C Klawiter
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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Longoni G, Martinez Chavez E, Young K, Brown RA, Bells S, Fetco D, Kim L, Grover SA, Costello F, Reginald A, Bar-Or A, Marrie RA, Arnold DL, Narayanan S, Branson HM, Banwell BL, Sled JG, Mabbott DJ, Yeh EA. Magnetization transfer saturation reveals subclinical optic nerve injury in pediatric-onset multiple sclerosis. Mult Scler 2023; 29:212-220. [PMID: 36545918 PMCID: PMC9925884 DOI: 10.1177/13524585221137500] [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: 12/24/2022]
Abstract
BACKGROUND The presence of subclinical optic nerve (ON) injury in youth living with pediatric-onset MS has not been fully elucidated. Magnetization transfer saturation (MTsat) is an advanced magnetic resonance imaging (MRI) parameter sensitive to myelin density and microstructural integrity, which can be applied to the study of the ON. OBJECTIVE The objective of this study was to investigate the presence of subclinical ON abnormalities in pediatric-onset MS by means of magnetization transfer saturation and evaluate their association with other structural and functional parameters of visual pathway integrity. METHODS Eleven youth living with pediatric-onset MS (ylPOMS) and no previous history of optic neuritis and 18 controls underwent standardized brain MRI, optical coherence tomography (OCT), Magnetoencephalography (MEG)-Visual Evoked Potentials (VEPs), and visual battery. Data were analyzed with mixed effect models. RESULTS While ON volume, OCT parameters, occipital MEG-VEPs outcomes, and visual function did not differ significantly between ylPOMS and controls, ylPOMS had lower MTsat in the supratentorial normal appearing white matter (-0.26 nU, p = 0.0023), and in both in the ON (-0.62 nU, p < 0.001) and in the normal appearing white matter of the optic radiation (-0.56 nU, p = 0.00071), with these being positively correlated (+0.57 nU, p = 0.00037). CONCLUSIONS Subclinical microstructural injury affects the ON of ylPOMS. This may appear as MTsat changes before being detectable by other currently available testing.
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Affiliation(s)
- Giulia Longoni
- Division of Neurology, Department of Pediatrics, University of Toronto, Toronto, ON, Canada/Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Edgar Martinez Chavez
- Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Kimberly Young
- Division of Neurology, Department of Pediatrics, University of Toronto, Toronto, ON, Canada
| | | | - Sonya Bells
- Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Dumitru Fetco
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Laura Kim
- Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Stephanie A Grover
- Division of Neurology, Department of Pediatrics, University of Toronto, Toronto, ON, Canada
| | - Fiona Costello
- Departments of Clinical Neurosciences and Surgery, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Arun Reginald
- Department of Ophthalmology and Visual Sciences, The University of Toronto, Toronto, ON, Canada
| | - Amit Bar-Or
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruth Ann Marrie
- Departments of Internal Medicine and Community Health Sciences, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Douglas L Arnold
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada/Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Sridar Narayanan
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Helen M Branson
- Department of Diagnostic Imaging, University of Toronto, Toronto, ON, Canada
| | - Brenda L Banwell
- Division of Neurology, The Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John G Sled
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Donald J Mabbott
- Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - E Ann Yeh
- Division of Neurology, Department of Pediatrics, University of Toronto, Toronto, ON, Canada/Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
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5
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Krijnen EA, Ngamsombat C, George IC, Yu FF, Fan Q, Tian Q, Huang SY, Klawiter EC. Axonal and myelin changes and their inter-relationship in the optic radiations in people with multiple sclerosis. Mult Scler J Exp Transl Clin 2023; 9:20552173221147620. [PMID: 36814811 PMCID: PMC9940187 DOI: 10.1177/20552173221147620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023] Open
Abstract
Background The imaging g-ratio, estimated from axonal volume fraction (AVF) and myelin volume fraction (MVF), is a novel biomarker of microstructural tissue integrity in multiple sclerosis (MS). Objective To assess axonal and myelin changes and their inter-relationship as measured by g-ratio in the optic radiations (OR) in people with MS (pwMS) with and without previous optic neuritis (ON) compared to healthy controls (HC). Methods Thirty pwMS and 17 HCs were scanned on a 3Tesla Connectom scanner. AVF and MVF, derived from a multi-shell diffusion protocol and macromolecular tissue volume, respectively, were measured in normal-appearing white matter (NAWM) and lesions within the OR and used to calculate imaging g-ratio. Results OR AVF and MVF were decreased in pwMS compared to HC, and in OR lesions compared to NAWM, whereas the g-ratio was not different. Compared to pwMS with previous ON, AVF and g-ratio tended to be higher in pwMS without prior ON. AVF and MVF, particularly in NAWM, were positively correlated with retinal thickness, which was more pronounced in pwMS with prior ON. Conclusion Axonal measures reflect microstructural tissue damage in the OR, particularly in the setting of remote ON, and correlate with established metrics of visual health in MS.
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Affiliation(s)
- Eva A Krijnen
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chanon Ngamsombat
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Ilena C George
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Fang F Yu
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qiuyun Fan
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin, China
| | - Qiyuan Tian
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Susie Y Huang
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Eric C Klawiter
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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6
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3-Dimensional Immunostaining and Automated Deep-Learning Based Analysis of Nerve Degeneration. Int J Mol Sci 2022; 23:ijms232314811. [PMID: 36499143 PMCID: PMC9739543 DOI: 10.3390/ijms232314811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/21/2022] [Accepted: 11/24/2022] [Indexed: 12/03/2022] Open
Abstract
Multiple sclerosis (MS) is an autoimmune and neurodegenerative disease driven by inflammation and demyelination in the brain, spinal cord, and optic nerve. Optic neuritis, characterized by inflammation and demyelination of the optic nerve, is a symptom in many patients with MS. The optic nerve is the highway for visual information transmitted from the retina to the brain. It contains axons from the retinal ganglion cells (RGCs) that reside in the retina, myelin forming oligodendrocytes and resident microglia and astrocytes. Inflammation, demyelination, and axonal degeneration are also present in the optic nerve of mice subjected to experimental autoimmune encephalomyelitis (EAE), a preclinical mouse model of MS. Monitoring the optic nerve in EAE is a useful strategy to study the presentation and progression of pathology in the visual system; however, current approaches have relied on sectioning, staining and manual quantification. Further, information regarding the spatial load of lesions and inflammation is dependent on the area of sectioning. To better characterize cellular pathology in the EAE model, we employed a tissue clearing and 3D immunolabelling and imaging protocol to observe patterns of immune cell infiltration and activation throughout the optic nerve. Increased density of TOPRO staining for nuclei captured immune cell infiltration and Iba1 immunostaining was employed to monitor microglia and macrophages. Axonal degeneration was monitored by neurofilament immunolabelling to reveal axonal swellings throughout the optic nerve. In parallel, we developed a convolutional neural network with a UNet architecture (CNN-UNet) called BlebNet for automated identification and quantification of axonal swellings in whole mount optic nerves. Together this constitutes a toolkit for 3-dimensional immunostaining to monitor general optic nerve pathology and fast automated quantification of axonal defects that could also be adapted to monitor axonal degeneration and inflammation in other neurodegenerative disease models.
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Oladosu O, Liu WQ, Brown L, Pike BG, Metz LM, Zhang Y. Advanced diffusion MRI and image texture analysis detect widespread brain structural differences between relapsing-remitting and secondary progressive multiple sclerosis. Front Hum Neurosci 2022; 16:944908. [PMID: 36034111 PMCID: PMC9413838 DOI: 10.3389/fnhum.2022.944908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/18/2022] [Indexed: 11/29/2022] Open
Abstract
Introduction Disease development in multiple sclerosis (MS) causes dramatic structural changes, but the exact changing patterns are unclear. Our objective is to investigate the differences in brain structure locally and spatially between relapsing-remitting MS (RRMS) and its advanced form, secondary progressive MS (SPMS), through advanced analysis of diffusion magnetic resonance imaging (MRI) and image texture. Methods A total of 20 patients with RRMS and nine patients with SPMS from two datasets underwent 3T anatomical and diffusion tensor imaging (DTI). The DTI was harmonized, augmented, and then modeled, which generated six voxel- and sub-voxel-scale measures. Texture analysis focused on T2 and FLAIR MRI, which produced two phase-based measures, namely, phase congruency and weighted mean phase. Data analysis was 3-fold, i.e., histogram analysis of whole-brain normal appearing white matter (NAWM); region of interest (ROI) analysis of NAWM and lesions within three critical white matter tracts, namely, corpus callosum, corticospinal tract, and optic radiation; and along-tract statistics. Furthermore, by calculating the z-score of core-rim pathology within lesions based on diffusion measures, we developed a novel method to define chronic active lesions and compared them between cohorts. Results Histogram features from diffusion and all but one texture measure differentiated between RRMS and SPMS. Within-tract ROI analysis detected cohort differences in both NAWM and lesions of the corpus callosum body in three measures of neurite orientation and anisotropy. Along-tract statistics detected cohort differences from multiple measures, particularly lesion extent, which increased significantly in SPMS in posterior corpus callosum and optic radiations. The number of chronic active lesions were also significantly higher (by 5-20% over z-scores 0.5 and 1.0) in SPMS than RRMS based on diffusion anisotropy, neurite content, and diameter. Conclusion Advanced diffusion MRI and texture analysis may be promising approaches for thorough understanding of brain structural changes from RRMS to SPMS, thereby providing new insight into disease development mechanisms in MS.
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Affiliation(s)
- Olayinka Oladosu
- Department of Neuroscience, Faculty of Graduate Studies, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Wei-Qiao Liu
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Lenora Brown
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Bruce G. Pike
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Luanne M. Metz
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yunyan Zhang
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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8
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Fan Q, Eichner C, Afzali M, Mueller L, Tax CMW, Davids M, Mahmutovic M, Keil B, Bilgic B, Setsompop K, Lee HH, Tian Q, Maffei C, Ramos-Llordén G, Nummenmaa A, Witzel T, Yendiki A, Song YQ, Huang CC, Lin CP, Weiskopf N, Anwander A, Jones DK, Rosen BR, Wald LL, Huang SY. Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact. Neuroimage 2022; 254:118958. [PMID: 35217204 PMCID: PMC9121330 DOI: 10.1016/j.neuroimage.2022.118958] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 12/20/2022] Open
Abstract
Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength diffusion MRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for diffusion MRI and where the field is headed in the coming years.
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Affiliation(s)
- Qiuyun Fan
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Cornelius Eichner
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK
| | - Lars Mueller
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Image Sciences Institute, University Medical Center (UMC) Utrecht, Utrecht, the Netherlands
| | - Mathias Davids
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mirsad Mahmutovic
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Berkin Bilgic
- 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
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | | | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Yi-Qiao Song
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA USA
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Shanghai Changning Mental Health Center, Shanghai, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - 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
| | - Alfred Anwander
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK
| | - Bruce R Rosen
- 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
| | - Lawrence L Wald
- 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
| | - 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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9
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Bells S, Longoni G, Berenbaum T, de Medeiros CB, Narayanan S, Banwell BL, Arnold DL, Mabbott DJ, Ann Yeh E. Patterns of white and gray structural abnormality associated with paediatric demyelinating disorders. Neuroimage Clin 2022; 34:103001. [PMID: 35381508 PMCID: PMC8980471 DOI: 10.1016/j.nicl.2022.103001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/21/2022] [Accepted: 03/30/2022] [Indexed: 11/26/2022]
Abstract
A multi-modal approach was used to evaluate the visual pathway from anterior (retina) to posterior (visual cortex) in both paediatric MOGAD and MS patients. MS patients exhibited more widespread white matter abnormalities; MOGAD patients exhibited white matter changes primarily within the optic radiation. The pattern of cortical thinning differed in MS and MOGAD patients. Reduced RNFLT was associated with lower axonal density in MOGAD and tortuosity in MS.
The impact of multiple sclerosis (MS) and myelin oligodendrocyte glycoprotein (MOG) - associated disorders (MOGAD) on brain structure in youth remains poorly understood. Reductions in cortical mantle thickness on structural MRI and abnormal diffusion-based white matter metrics (e.g., diffusion tensor parameters) have been well documented in MS but not in MOGAD. Characterizing structural abnormalities found in children with these disorders can help clarify the differences and similarities in their impact on neuroanatomy. Importantly, while MS and MOGAD affect the entire CNS, the visual pathway is of particular interest in both groups, as most patients have evidence for clinical or subclinical involvement of the anterior visual pathway. Thus, the visual pathway is of key interest in analyses of structural abnormalities in these disorders and may distinguish MOGAD from MS patients. In this study we collected MRI data on 18 MS patients, 14 MOGAD patients and 26 age- and sex-matched typically developing children (TDC). Full-brain group differences in fixel diffusion measures (fibre-bundle populations) and cortical thickness measures were tested using age and sex as covariates. Visual pathway analysis was performed by extracting mean diffusion measures within lesion free optic radiations, cortical thickness within the visual cortex, and retinal nerve fibre layer (RNFL) and ganglion cell layer thickness measures from optical coherence tomography (OCT). Fixel based analysis (FBA) revealed MS patients have widespread abnormal white matter within the corticospinal tract, inferior longitudinal fasciculus, and optic radiations, while within MOGAD patients, non-lesional impact on white matter was found primarily in the right optic radiation. Cortical thickness measures were reduced predominately in the temporal and parietal lobes in MS patients and in frontal, cingulate and visual cortices in MOGAD patients. Additionally, our findings of associations between reduced RNFLT and axonal density in MOGAD and TORT in MS patients in the optic radiations imply widespread axonal and myelin damage in the visual pathway, respectively. Overall, our approach of combining FBA, cortical thickness and OCT measures has helped evaluate similarities and differences in brain structure in MS and MOGAD patients in comparison to TDC.
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Affiliation(s)
- Sonya Bells
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Canada; Pediatric Neurology, Spectrum Health Helen Devos Children's Hospital, Grand Rapids, USA; Department of Pediatrics and Human Development, Michigan State University, East Lansing, USA
| | - Giulia Longoni
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Canada; Department of Neurology, Hospital for Sick Children, Toronto, Canada; Department of Paediatrics, University of Toronto, Toronto, Canada
| | - Tara Berenbaum
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Canada
| | - Cynthia B de Medeiros
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Canada
| | - Sridar Narayanan
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Brenda L Banwell
- Division of Child Neurology, Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, USA
| | - Douglas L Arnold
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Donald J Mabbott
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Canada; Department of Psychology, University of Toronto, Toronto, Canada
| | - E Ann Yeh
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Canada; Department of Neurology, Hospital for Sick Children, Toronto, Canada; Department of Paediatrics, University of Toronto, Toronto, Canada.
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10
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Fractional anisotropy helps to differentiate the optic nerve impairment between neuromyelitis optica spectrum disorders and multiple sclerosis. Eur Radiol 2022; 32:6158-6166. [PMID: 35420298 DOI: 10.1007/s00330-022-08779-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 03/18/2022] [Accepted: 03/26/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To assess the characteristics of optic nerve impairment between neuromyelitis optica spectrum disorders (NMOSD) and multiple sclerosis (MS) patients by fractional anisotropy (FA). METHODS Thirty-one NMOSD patients, 25 MS patients, and 17 heathy controls (HC) who underwent optic nerve DTI were included. The optic nerves of the NMOSD and MS patients were divided into vision-impaired (VI) subgroups and normal-appearing (NA) subgroups according to visual status, respectively. FA values were measured in the anterior, middle, and posterior segments of each intraorbital optic nerve. RESULTS FA values in VI NMOSD were significantly decreased in the whole optic nerve, especially the posterior segment of the optic nerve (p < 0.001). FA values measured in the anterior and middle segments of the optic nerve in VI MS were significantly decreased as compared to those in the HC (p < 0.05). Between NMOSD and MS, FA values in the posterior segment of the optic nerve showed significant differences (VI NMOSD vs. VI MS, 0.458 ± 0.097 vs. 0.568 ± 0.098, p < 0.001; NA NMOSD vs. NA MS, 0.568 ± 0.098 vs. 0.600 ± 0.085, p = 0.041, respectively). The signal intensity ratio (SIR) in the posterior segment of the optic nerve was significantly increased in VI NMOSD as compared with VI MS (p = 0.002). The combination of SIR and FA for distinguishing VI NMOSD from VI MS resulted in sensitivity, specificity, and positive and negative predictive values of 86.49%, 80.00%, 88.9%, and 76.2%, respectively. CONCLUSION FA could quantify the characteristics of NMOSD- and MS-related optic nerve impairment. DTI was a simple and effective imaging tool to differentiate between the two. KEY POINTS • NMOSD-related optic nerve impairment is extensive, often greater than half of the optic nerve, with the most significant involvement of the posterior segment of the optic nerve. • MS-related optic nerve impairment is more limited than NMOSD, and anterior and middle optic nerve involvement is common. • Optic nerve DTI is a convenient and effective imaging tool that can help characterize NMOSD and MS.
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11
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Hori M, Maekawa T, Kamiya K, Hagiwara A, Goto M, Takemura MY, Fujita S, Andica C, Kamagata K, Cohen-Adad J, Aoki S. Advanced Diffusion MR Imaging for Multiple Sclerosis in the Brain and Spinal Cord. Magn Reson Med Sci 2022; 21:58-70. [PMID: 35173096 PMCID: PMC9199983 DOI: 10.2463/mrms.rev.2021-0091] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Diffusion tensor imaging (DTI) has been established its usefulness in evaluating normal-appearing white matter (NAWM) and other lesions that are difficult to evaluate with routine clinical MRI in the evaluation of the brain and spinal cord lesions in multiple sclerosis (MS), a demyelinating disease. With the recent advances in the software and hardware of MRI systems, increasingly complex and sophisticated MRI and analysis methods, such as q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, white matter tract integrity, and multiple diffusion encoding, referred to as advanced diffusion MRI, have been proposed. These are capable of capturing in vivo microstructural changes in the brain and spinal cord in normal and pathological states in greater detail than DTI. This paper reviews the current status of recent advanced diffusion MRI for assessing MS in vivo as part of an issue celebrating two decades of magnetic resonance in medical sciences (MRMS), an official journal of the Japanese Society of Magnetic Resonance in Medicine.
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Affiliation(s)
- Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center.,Department of Radiology, Juntendo University School of Medicine
| | - Tomoko Maekawa
- Department of Radiology, Juntendo University School of Medicine
| | - Kouhei Kamiya
- Department of Radiology, Toho University Omori Medical Center.,Department of Radiology, Juntendo University School of Medicine
| | | | - Masami Goto
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
| | | | - Shohei Fujita
- Department of Radiology, Juntendo University School of Medicine
| | | | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine
| | | | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine
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12
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Tian Q, Fan Q, Witzel T, Polackal MN, Ohringer NA, Ngamsombat C, Russo AW, Machado N, Brewer K, Wang F, Setsompop K, Polimeni JR, Keil B, Wald LL, Rosen BR, Klawiter EC, Nummenmaa A, Huang SY. Comprehensive diffusion MRI dataset for in vivo human brain microstructure mapping using 300 mT/m gradients. Sci Data 2022; 9:7. [PMID: 35042861 PMCID: PMC8766594 DOI: 10.1038/s41597-021-01092-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 10/25/2021] [Indexed: 12/27/2022] Open
Abstract
Strong gradient systems can improve the signal-to-noise ratio of diffusion MRI measurements and enable a wider range of acquisition parameters that are beneficial for microstructural imaging. We present a comprehensive diffusion MRI dataset of 26 healthy participants acquired on the MGH-USC 3 T Connectome scanner equipped with 300 mT/m maximum gradient strength and a custom-built 64-channel head coil. For each participant, the one-hour long acquisition systematically sampled the accessible diffusion measurement space, including two diffusion times (19 and 49 ms), eight gradient strengths linearly spaced between 30 mT/m and 290 mT/m for each diffusion time, and 32 or 64 uniformly distributed directions. The diffusion MRI data were preprocessed to correct for gradient nonlinearity, eddy currents, and susceptibility induced distortions. In addition, scan/rescan data from a subset of seven individuals were also acquired and provided. The MGH Connectome Diffusion Microstructure Dataset (CDMD) may serve as a test bed for the development of new data analysis methods, such as fiber orientation estimation, tractography and microstructural modelling.
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Affiliation(s)
- Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Maya N Polackal
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Ned A Ohringer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Andrew W Russo
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Natalya Machado
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Kristina Brewer
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Boris Keil
- Department of Life Science Engineering, Institute of Medical Physics and Radiation Protection, Giessen, Germany
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Eric C Klawiter
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States.
- Harvard Medical School, Boston, Massachusetts, United States.
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States.
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13
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Chen Y, Fallon N, Kreilkamp BAK, Denby C, Bracewell M, Das K, Pegg E, Mohanraj R, Marson AG, Keller SS. Probabilistic mapping of thalamic nuclei and thalamocortical functional connectivity in idiopathic generalised epilepsy. Hum Brain Mapp 2021; 42:5648-5664. [PMID: 34432348 PMCID: PMC8559489 DOI: 10.1002/hbm.25644] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 08/04/2021] [Accepted: 08/16/2021] [Indexed: 02/06/2023] Open
Abstract
It is well established that abnormal thalamocortical systems play an important role in the generation and maintenance of primary generalised seizures. However, it is currently unknown which thalamic nuclei and how nuclear‐specific thalamocortical functional connectivity are differentially impacted in patients with medically refractory and non‐refractory idiopathic generalised epilepsy (IGE). In the present study, we performed structural and resting‐state functional magnetic resonance imaging (MRI) in patients with refractory and non‐refractory IGE, segmented the thalamus into constituent nuclear regions using a probabilistic MRI segmentation method and determined thalamocortical functional connectivity using seed‐to‐voxel connectivity analyses. We report significant volume reduction of the left and right anterior thalamic nuclei only in patients with refractory IGE. Compared to healthy controls, patients with refractory and non‐refractory IGE had significant alterations of functional connectivity between the centromedian nucleus and cortex, but only patients with refractory IGE had altered cortical connectivity with the ventral lateral nuclear group. Patients with refractory IGE had significantly increased functional connectivity between the left and right ventral lateral posterior nuclei and cortical regions compared to patients with non‐refractory IGE. Cortical effects were predominantly located in the frontal lobe. Atrophy of the anterior thalamic nuclei and resting‐state functional hyperconnectivity between ventral lateral nuclei and cerebral cortex may be imaging markers of pharmacoresistance in patients with IGE. These structural and functional abnormalities fit well with the known importance of thalamocortical systems in the generation and maintenance of primary generalised seizures, and the increasing recognition of the importance of limbic pathways in IGE.
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Affiliation(s)
- Yachin Chen
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Nicholas Fallon
- Department of Psychology, University of Liverpool, Liverpool, UK
| | - Barbara A K Kreilkamp
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,Department of Neurology, University Medicine Göttingen, Göttingen, Germany
| | | | - Martyn Bracewell
- The Walton Centre NHS Foundation Trust, Liverpool, UK.,Schools of Medical Sciences and Psychology, Bangor University, Bangor, UK
| | - Kumar Das
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Emily Pegg
- Department of Neurology, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Salford, UK.,Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, School of Biological Sciences, University of Manchester, Manchester, UK
| | - Rajiv Mohanraj
- Department of Neurology, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Salford, UK.,Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, School of Biological Sciences, University of Manchester, Manchester, UK
| | - Anthony G Marson
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,The Walton Centre NHS Foundation Trust, Liverpool, UK
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14
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Xue J, Zhu Y, Liu Z, Lin J, Li Y, Li Y, Zhuo Y. Demyelination of the Optic Nerve: An Underlying Factor in Glaucoma? Front Aging Neurosci 2021; 13:701322. [PMID: 34795572 PMCID: PMC8593209 DOI: 10.3389/fnagi.2021.701322] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 10/13/2021] [Indexed: 11/13/2022] Open
Abstract
Neurodegenerative disorders are characterized by typical neuronal degeneration and axonal loss in the central nervous system (CNS). Demyelination occurs when myelin or oligodendrocytes experience damage. Pathological changes in demyelination contribute to neurodegenerative diseases and worsen clinical symptoms during disease progression. Glaucoma is a neurodegenerative disease characterized by progressive degeneration of retinal ganglion cells (RGCs) and the optic nerve. Since it is not yet well understood, we hypothesized that demyelination could play a significant role in glaucoma. Therefore, this study started with the morphological and functional manifestations of demyelination in the CNS. Then, we discussed the main mechanisms of demyelination in terms of oxidative stress, mitochondrial damage, and immuno-inflammatory responses. Finally, we summarized the existing research on the relationship between optic nerve demyelination and glaucoma, aiming to inspire effective treatment plans for glaucoma in the future.
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Affiliation(s)
- Jingfei Xue
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yingting Zhu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zhe Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jicheng Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yangjiani Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yiqing Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yehong Zhuo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
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15
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Vachha B, Huang SY. MRI with ultrahigh field strength and high-performance gradients: challenges and opportunities for clinical neuroimaging at 7 T and beyond. Eur Radiol Exp 2021; 5:35. [PMID: 34435246 PMCID: PMC8387544 DOI: 10.1186/s41747-021-00216-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 03/30/2021] [Indexed: 12/12/2022] Open
Abstract
Research in ultrahigh magnetic field strength combined with ultrahigh and ultrafast gradient technology has provided enormous gains in sensitivity, resolution, and contrast for neuroimaging. This article provides an overview of the technical advantages and challenges of performing clinical neuroimaging studies at ultrahigh magnetic field strength combined with ultrahigh and ultrafast gradient technology. Emerging clinical applications of 7-T MRI and state-of-the-art gradient systems equipped with up to 300 mT/m gradient strength are reviewed, and the impact and benefits of such advances to anatomical, structural and functional MRI are discussed in a variety of neurological conditions. Finally, an outlook and future directions for ultrahigh field MRI combined with ultrahigh and ultrafast gradient technology in neuroimaging are examined.
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Affiliation(s)
- Behroze Vachha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, 149 13th Street, Room 2301, Charlestown, MA, 02129, USA.
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