1
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Yi T, Ji C, Wei W, Wu G, Jin K, Jiang G. Cortical-cerebellar circuits changes in preschool ASD children by multimodal MRI. Cereb Cortex 2024; 34:bhae090. [PMID: 38615243 DOI: 10.1093/cercor/bhae090] [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: 02/02/2024] [Revised: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 04/15/2024] Open
Abstract
OBJECTIVE To investigate the alterations in cortical-cerebellar circuits and assess their diagnostic potential in preschool children with autism spectrum disorder using multimodal magnetic resonance imaging. METHODS We utilized diffusion basis spectrum imaging approaches, namely DBSI_20 and DBSI_combine, alongside 3D structural imaging to examine 31 autism spectrum disorder diagnosed patients and 30 healthy controls. The participants' brains were segmented into 120 anatomical regions for this analysis, and a multimodal strategy was adopted to assess the brain networks using a multi-kernel support vector machine for classification. RESULTS The results revealed consensus connections in the cortical-cerebellar and subcortical-cerebellar circuits, notably in the thalamus and basal ganglia. These connections were predominantly positive in the frontoparietal and subcortical pathways, whereas negative consensus connections were mainly observed in frontotemporal and subcortical pathways. Among the models tested, DBSI_20 showed the highest accuracy rate of 86.88%. In addition, further analysis indicated that combining the 3 models resulted in the most effective performance. CONCLUSION The connectivity network analysis of the multimodal brain data identified significant abnormalities in the cortical-cerebellar circuits in autism spectrum disorder patients. The DBSI_20 model not only provided the highest accuracy but also demonstrated efficiency, suggesting its potential for clinical application in autism spectrum disorder diagnosis.
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Affiliation(s)
- Ting Yi
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510317, China
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou 510317,China
- Department of Radiology, The Affiliated Children's Hospital Of Xiangya School of Medicine, Hunan Children's Hospital, Central South University, Changsha 410007, China
| | - Changquan Ji
- School of Smart City,Chongqing Jiaotong University, Chongqing, 400074,China
| | - Weian Wei
- Department of Radiology, The Affiliated Children's Hospital Of Xiangya School of Medicine, Hunan Children's Hospital, Central South University, Changsha 410007, China
| | - Guangchung Wu
- Department of Radiology, The Affiliated Children's Hospital Of Xiangya School of Medicine, Hunan Children's Hospital, Central South University, Changsha 410007, China
| | - Ke Jin
- Department of Radiology, The Affiliated Children's Hospital Of Xiangya School of Medicine, Hunan Children's Hospital, Central South University, Changsha 410007, China
| | - Guihua Jiang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510317, China
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou 510317,China
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2
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Zhang W, Gorelik AJ, Wang Q, Norton SA, Hershey T, Agrawal A, Bijsterbosch JD, Bogdan R. Associations between COVID-19 and putative markers of neuroinflammation: A diffusion basis spectrum imaging study. Brain Behav Immun Health 2024; 36:100722. [PMID: 38298902 PMCID: PMC10825665 DOI: 10.1016/j.bbih.2023.100722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 12/27/2023] [Accepted: 12/27/2023] [Indexed: 02/02/2024] Open
Abstract
COVID-19 remains a significant international public health concern. Yet, the mechanisms through which symptomatology emerges remain poorly understood. While SARS-CoV-2 infection may induce prolonged inflammation within the central nervous system, the evidence primarily stems from limited small-scale case investigations. To address this gap, our study capitalized on longitudinal UK Biobank neuroimaging data acquired prior to and following COVID-19 testing (N = 416 including n = 224 COVID-19 cases; Mage = 58.6). Putative neuroinflammation was assessed in gray matter structures and white matter tracts using non-invasive Diffusion Basis Spectrum Imaging (DBSI), which estimates inflammation-related cellularity (DBSI-restricted fraction; DBSI-RF) and vasogenic edema (DBSI-hindered fraction; DBSI-HF). We hypothesized that COVID-19 case status would be associated with increases in DBSI markers after accounting for potential confound (age, sex, race, body mass index, smoking frequency, and data acquisition interval) and multiple testing. COVID-19 case status was not significantly associated with DBSI-RF (|β|'s < 0.28, pFDR >0.05), but with greater DBSI-HF in left pre- and post-central gyri and right middle frontal gyrus (β's > 0.3, all pFDR = 0.03). Intriguingly, the brain areas exhibiting increased putative vasogenic edema had previously been linked to COVID-19-related functional and structural alterations, whereas brain regions displaying subtle differences in cellularity between COVID-19 cases and controls included regions within or functionally connected to the olfactory network, which has been implicated in COVID-19 psychopathology. Nevertheless, our study might not have captured acute and transitory neuroinflammatory effects linked to SARS-CoV-2 infection, possibly due to symptom resolution before the imaging scan. Future research is warranted to explore the potential time- and symptom-dependent neuroinflammatory relationship with COVID-19.
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Affiliation(s)
- Wei Zhang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Aaron J. Gorelik
- Department of Psychological & Brain Sciences, Washington University, St. Louis, MO, United States
| | - Qing Wang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Sara A. Norton
- Department of Psychological & Brain Sciences, Washington University, St. Louis, MO, United States
| | - Tamara Hershey
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
- Department of Psychological & Brain Sciences, Washington University, St. Louis, MO, United States
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Janine D. Bijsterbosch
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University, St. Louis, MO, United States
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3
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Zhang W, Gorelik AJ, Wang Q, Norton SA, Hershey T, Agrawal A, Bijsterbosch JD, Bogdan R. Associations between COVID-19 and putative markers of neuroinflammation: A diffusion basis spectrum imaging study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.20.549891. [PMID: 37502886 PMCID: PMC10370178 DOI: 10.1101/2023.07.20.549891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
COVID-19 remains a significant international public health concern. Yet, the mechanisms through which symptomatology emerges remain poorly understood. While SARS-CoV-2 infection may induce prolonged inflammation within the central nervous system, the evidence primarily stems from limited small-scale case investigations. To address this gap, our study capitalized on longitudinal UK Biobank neuroimaging data acquired prior to and following COVID-19 testing (N=416 including n=224 COVID-19 cases; Mage=58.6). Putative neuroinflammation was assessed in gray matter structures and white matter tracts using non-invasive Diffusion Basis Spectrum Imaging (DBSI), which estimates inflammation-related cellularity (DBSI-restricted fraction; DBSI-RF) and vasogenic edema (DBSI-hindered fraction; DBSI-HF).We hypothesized that COVID-19 case status would be associated with increases in DBSI markers after accounting for potential confound (age, sex, race, body mass index, smoking frequency, and data acquisition interval) and multiple testing. COVID-19 case status was not significantly associated with DBSI-RF (|β|'s<0.28, pFDR >0.05), but with greater DBSI-HF in left pre- and post-central gyri and right middle frontal gyrus (β's>0.3, all pFDR=0.03). Intriguingly, the brain areas exhibiting increased putative vasogenic edema had previously been linked to COVID-19-related functional and structural alterations, whereas brain regions displaying subtle differences in cellularity between COVID-19 cases and controls included regions within or functionally connected to the olfactory network, which has been implicated in COVID-19 psychopathology. Nevertheless, our study might not have captured acute and transitory neuroinflammatory effects linked to SARS-CoV-2 infection, possibly due to symptom resolution before the imaging scan. Future research is warranted to explore the potential time- and symptom-dependent neuroinflammatory relationship with COVID-19.
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Affiliation(s)
- Wei Zhang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Aaron J Gorelik
- Department of Psychological & Brain Sciences, Washington University, St. Louis, MO, United States
| | - Qing Wang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Sara A Norton
- Department of Psychological & Brain Sciences, Washington University, St. Louis, MO, United States
| | - Tamara Hershey
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
- Department of Psychological & Brain Sciences, Washington University, St. Louis, MO, United States
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Janine D Bijsterbosch
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University, St. Louis, MO, United States
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4
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Granziera C, Wuerfel J, Barkhof F, Calabrese M, De Stefano N, Enzinger C, Evangelou N, Filippi M, Geurts JJG, Reich DS, Rocca MA, Ropele S, Rovira À, Sati P, Toosy AT, Vrenken H, Gandini Wheeler-Kingshott CAM, Kappos L. Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis. Brain 2021; 144:1296-1311. [PMID: 33970206 PMCID: PMC8219362 DOI: 10.1093/brain/awab029] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 10/25/2020] [Accepted: 11/16/2020] [Indexed: 12/11/2022] Open
Abstract
Quantitative MRI provides biophysical measures of the microstructural integrity of the CNS, which can be compared across CNS regions, patients, and centres. In patients with multiple sclerosis, quantitative MRI techniques such as relaxometry, myelin imaging, magnetization transfer, diffusion MRI, quantitative susceptibility mapping, and perfusion MRI, complement conventional MRI techniques by providing insight into disease mechanisms. These include: (i) presence and extent of diffuse damage in CNS tissue outside lesions (normal-appearing tissue); (ii) heterogeneity of damage and repair in focal lesions; and (iii) specific damage to CNS tissue components. This review summarizes recent technical advances in quantitative MRI, existing pathological validation of quantitative MRI techniques, and emerging applications of quantitative MRI to patients with multiple sclerosis in both research and clinical settings. The current level of clinical maturity of each quantitative MRI technique, especially regarding its integration into clinical routine, is discussed. We aim to provide a better understanding of how quantitative MRI may help clinical practice by improving stratification of patients with multiple sclerosis, and assessment of disease progression, and evaluation of treatment response.
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Affiliation(s)
- Cristina Granziera
- Neurologic Clinic and Policlinic, 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 Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Wuerfel
- Medical Image Analysis Center, Basel, Switzerland
- Quantitative Biomedical Imaging Group (qbig), Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, multiple sclerosis Center Amsterdam, Amsterdam University Medical Center, Amsterdam, The Netherlands
- UCL Institutes of Healthcare Engineering and Neurology, London, UK
| | - Massimiliano Calabrese
- Neurology B, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Nicola De Stefano
- Neurology, Department of Medicine, Surgery and Neuroscience, University of Siena, Italy
| | - Christian Enzinger
- Department of Neurology and Division of Neuroradiology, Medical University of Graz, Graz, Austria
| | - Nikos Evangelou
- Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, multiple sclerosis Center Amsterdam, Neuroscience Amsterdam, Amsterdam University Medical Centers, location VUmc, Amsterdam, The Netherlands
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Stefan Ropele
- Neuroimaging Research Unit, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Àlex Rovira
- Section of Neuroradiology (Department of Radiology), Vall d'Hebron University Hospital and Research Institute, Barcelona, Spain
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), Bethesda, MD, USA
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Ahmed T Toosy
- Queen Square multiple sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, University College London, London, UK
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, multiple sclerosis Center Amsterdam, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square multiple sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, 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
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, 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 Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
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5
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Vavasour IM, Sun P, Graf C, Yik JT, Kolind SH, Li DK, Tam R, Sayao AL, Schabas A, Devonshire V, Carruthers R, Traboulsee A, Moore GW, Song SK, Laule C. Characterization of multiple sclerosis neuroinflammation and neurodegeneration with relaxation and diffusion basis spectrum imaging. Mult Scler 2021; 28:418-428. [PMID: 34132126 DOI: 10.1177/13524585211023345] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Advanced magnetic resonance imaging (MRI) methods can provide more specific information about various microstructural tissue changes in multiple sclerosis (MS) brain. Quantitative measurement of T1 and T2 relaxation, and diffusion basis spectrum imaging (DBSI) yield metrics related to the pathology of neuroinflammation and neurodegeneration that occurs across the spectrum of MS. OBJECTIVE To use relaxation and DBSI MRI metrics to describe measures of neuroinflammation, myelin and axons in different MS subtypes. METHODS 103 participants (20 clinically isolated syndrome (CIS), 33 relapsing-remitting MS (RRMS), 30 secondary progressive MS and 20 primary progressive MS) underwent quantitative T1, T2, DBSI and conventional 3T MRI. Whole brain, normal-appearing white matter, lesion and corpus callosum MRI metrics were compared across MS subtypes. RESULTS A gradation of MRI metric values was seen from CIS to RRMS to progressive MS. RRMS demonstrated large oedema-related differences, while progressive MS had the most extensive abnormalities in myelin and axonal measures. CONCLUSION Relaxation and DBSI-derived MRI measures show differences between MS subtypes related to the severity and composition of underlying tissue damage. RRMS showed oedema, demyelination and axonal loss compared with CIS. Progressive MS had even more evidence of increased oedema, demyelination and axonal loss compared with CIS and RRMS.
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Affiliation(s)
- Irene M Vavasour
- Department of Radiology, The University of British Columbia, UBC Hospital, Vancouver, BC, Canada/International Collaboration on Repair Discoveries (ICORD), The University of British Columbia, Vancouver, BC, Canada
| | - Peng Sun
- Department of Radiology, Washington University, St. Louis, MO, USA
| | - Carina Graf
- Department of Physics & Astronomy, The University of British Columbia, Vancouver, BC, Canada
| | - Jackie T Yik
- Department of Physics & Astronomy, The University of British Columbia, Vancouver, BC, Canada
| | - Shannon H Kolind
- Department of Radiology, The University of British Columbia, Vancouver, BC, Canada/International Collaboration on Repair Discoveries (ICORD), The University of British Columbia, Vancouver, BC, Canada/Department of Physics & Astronomy, The University of British Columbia, Vancouver, BC, Canada/Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - David Kb Li
- Department of Radiology, The University of British Columbia, Vancouver, BC, Canada/Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Roger Tam
- Department of Radiology, The University of British Columbia, Vancouver, BC, Canada/School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Ana-Luiza Sayao
- Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Alice Schabas
- Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Virginia Devonshire
- Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Robert Carruthers
- Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Anthony Traboulsee
- Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Gr Wayne Moore
- Department of Medicine, The University of British Columbia, Vancouver, BC, Canada/Department of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Sheng-Kwei Song
- Department of Radiology, Washington University, St. Louis, MO, USA
| | - Cornelia Laule
- Department of Radiology, The University of British Columbia, Vancouver, BC, Canada/International Collaboration on Repair Discoveries (ICORD), The University of British Columbia, Vancouver, BC, Canada/Department of Physics & Astronomy, The University of British Columbia, Vancouver, BC, Canada/Department of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, BC, Canada
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6
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Schiavi S, Petracca M, Sun P, Fleysher L, Cocozza S, El Mendili MM, Signori A, Babb JS, Podranski K, Song SK, Inglese M. Non-invasive quantification of inflammation, axonal and myelin injury in multiple sclerosis. Brain 2021; 144:213-223. [PMID: 33253366 DOI: 10.1093/brain/awaa381] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 08/12/2020] [Accepted: 08/21/2020] [Indexed: 02/06/2023] Open
Abstract
The aim of this study was to determine the feasibility of diffusion basis spectrum imaging in multiple sclerosis at 7 T and to investigate the pathological substrates of tissue damage in lesions and normal-appearing white matter. To this end, 43 patients with multiple sclerosis (24 relapsing-remitting, 19 progressive), and 21 healthy control subjects were enrolled. White matter lesions were classified in T1-isointense, T1-hypointense and black holes. Mean values of diffusion basis spectrum imaging metrics (fibres, restricted and non-restricted fractions, axial and radial diffusivities and fractional anisotropy) were measured from whole brain white matter lesions and from both lesions and normal appearing white matter of the corpus callosum. Significant differences were found between T1-isointense and black holes (P ranging from 0.005 to <0.001) and between lesions' centre and rim (P < 0.001) for all the metrics. When comparing the three subject groups in terms of metrics derived from corpus callosum normal appearing white matter and T2-hyperintense lesions, a significant difference was found between healthy controls and relapsing-remitting patients for all metrics except restricted fraction and fractional anisotropy; between healthy controls and progressive patients for all metrics except restricted fraction and between relapsing-remitting and progressive multiple sclerosis patients for all metrics except fibres and restricted fractions (P ranging from 0.05 to <0.001 for all). Significant associations were found between corpus callosum normal-appearing white matter fibres fraction/non-restricted fraction and the Symbol Digit Modality Test (respectively, r = 0.35, P = 0.043; r = -0.35, P = 0.046), and between black holes radial diffusivity and Expanded Disability Status Score (r = 0.59, P = 0.002). We showed the feasibility of diffusion basis spectrum imaging metrics at 7 T, confirmed the role of the derived metrics in the characterization of lesions and normal appearing white matter tissue in different stages of the disease and demonstrated their clinical relevance. Thus, suggesting that diffusion basis spectrum imaging is a promising tool to investigate multiple sclerosis pathophysiology, monitor disease progression and treatment response.
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Affiliation(s)
- Simona Schiavi
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Italy.,Ospedale Policlinico San Martino-IRCCS, Genoa, Italy
| | - Maria Petracca
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Peng Sun
- Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Lazar Fleysher
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sirio Cocozza
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | | | - Alessio Signori
- Department of Health Sciences, University of Genoa, Genoa, Italy
| | - James S Babb
- Department of Radiology, Center for Biomedical Imaging, New York University, Langone Medical Center, New York, USA
| | - Kornelius Podranski
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sheng-Kwei Song
- Radiology, Washington University School of Medicine, St. Louis, MO, USA.,Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA.,Biomedical Engineering, Washington University, St. Louis, MO, USA.,Biomedical MR Laboratory, Washington University School of Medicine, St. Louis, MO, USA
| | - Matilde Inglese
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Italy.,Ospedale Policlinico San Martino-IRCCS, Genoa, Italy
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7
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Ye Z, Gary SE, Sun P, Mustafi SM, Glenn GR, Yeh FC, Merisaari H, Song C, Yang R, Huang GS, Kao HW, Lin CY, Wu YC, Jensen JH, Song SK. The impact of edema and fiber crossing on diffusion MRI metrics assessed in an ex vivo nerve phantom: Multi-tensor model vs. diffusion orientation distribution function. NMR IN BIOMEDICINE 2021; 34:e4414. [PMID: 33015890 PMCID: PMC9743958 DOI: 10.1002/nbm.4414] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 08/23/2020] [Accepted: 09/06/2020] [Indexed: 05/30/2023]
Abstract
Diffusion tensor imaging (DTI) has been employed for over 2 decades to noninvasively quantify central nervous system diseases/injuries. However, DTI is an inadequate simplification of diffusion modeling in the presence of coexisting inflammation, edema and crossing nerve fibers. We employed a tissue phantom using fixed mouse trigeminal nerves coated with various amounts of agarose gel to mimic crossing fibers in the presence of vasogenic edema. Diffusivity measures derived by DTI and diffusion basis spectrum imaging (DBSI) were compared at increasing levels of simulated edema and degrees of fiber crossing. Furthermore, we assessed the ability of DBSI, diffusion kurtosis imaging (DKI), generalized q-sampling imaging (GQI), q-ball imaging (QBI) and neurite orientation dispersion and density imaging to resolve fiber crossing, in reference to the gold standard angles measured from structural images. DTI-computed diffusivities and fractional anisotropy were significantly confounded by gel-mimicked edema and crossing fibers. Conversely, DBSI calculated accurate diffusivities of individual fibers regardless of the extent of simulated edema and degrees of fiber crossing angles. Additionally, DBSI accurately and consistently estimated crossing angles in various conditions of gel-mimicked edema when compared with the gold standard (r2 = 0.92, P = 1.9 × 10-9 , bias = 3.9°). Small crossing angles and edema significantly impact the diffusion orientation distribution function, making DKI, GQI and QBI less accurate in detecting and estimating fiber crossing angles. Lastly, we used diffusion tensor ellipsoids to demonstrate that DBSI resolves the confounds of edema and crossing fibers in the peritumoral edema region from a patient with lung cancer metastasis, while DTI failed. In summary, DBSI is able to separate two crossing fibers and accurately recover their diffusivities in a complex environment characterized by increasing crossing angles and amounts of gel-mimicked edema. DBSI also indicated better angular resolution compared with DKI, QBI and GQI.
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Affiliation(s)
- Zezhong Ye
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Sam E. Gary
- Medical Scientist Training Program, University of Alabama at Birmingham, Birmingham, AL 35294
| | - Peng Sun
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Sourajit Mitra Mustafi
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202
| | - George Russell Glenn
- Department of Radiology and Imaging Science, Emory University School of Medicine, Atlanta, GA 30322
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
| | - Harri Merisaari
- Department of Diagnostic Radiology, University of Turku, Turku, Finland 20014
| | - Chunyu Song
- Department of Biomedical Engineering, Washington University, St. Louis, MO 63130
| | - Ruimeng Yang
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510180, China
| | - Guo-Shu Huang
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan 114
| | - Hung-Wen Kao
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan 114
| | | | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202
| | - Jens H. Jensen
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC 29425
| | - Sheng-Kwei Song
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
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8
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Ma YJ, Searleman AC, Jang H, Fan SJ, Wong J, Xue Y, Cai Z, Chang EY, Corey-Bloom J, Du J. Volumetric imaging of myelin in vivo using 3D inversion recovery-prepared ultrashort echo time cones magnetic resonance imaging. NMR IN BIOMEDICINE 2020; 33:e4326. [PMID: 32691472 PMCID: PMC7952008 DOI: 10.1002/nbm.4326] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 03/19/2020] [Accepted: 05/02/2020] [Indexed: 05/28/2023]
Abstract
Direct myelin imaging is promising for characterization of multiple sclerosis (MS) brains at diagnosis and in response to therapy. In this study, a 3D inversion recovery-prepared ultrashort echo time cones (IR-UTE-Cones) sequence was used for both morphological and quantitative imaging of myelin on a clinical 3 T scanner. Myelin powder phantoms with different myelin concentrations were imaged with the 3D UTE-Cones sequence and it showed a strong correlation between concentrations and UTE-Cones signals, demonstrating the ability of the UTE-Cones sequence to directly image myelin in the brain. Quantitative myelin imaging with multi-echo IR-UTE-Cones sequences show similar T2 * values for a D2 O-exchanged myelin phantom (T2 * = 0.33 ± 0.04 ms), ex vivo brain specimens (T2 * = 0.20 ± 0.04 ms) and in vivo healthy volunteers (T2 * = 0.254 ± 0.023 ms), further confirming the feasibility of 3D IR-UTE-Cones sequences for direct myelin imaging in vivo. In ex vivo MS brain study, signal loss is observed in MS lesions, which was confirmed with histology. For the in vivo study, the lesions in MS patients also show myelin signal loss using the proposed direct myelin imaging method, demonstrating the clinical potential for MS diagnosis. Furthermore, the measured IR-UTE-Cones signal intensities show a significant difference between normal-appearing white matter in MS patients and normal white matter in volunteers, which cannot be found in clinical used T2 -FLAIR sequences. Thus, the proposed 3D IR-UTE-Cones sequence showed clinical potential for MS diagnosis with the capability of direct myelin detection of the whole brain.
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Affiliation(s)
- Ya-Jun Ma
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Adam C. Searleman
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Hyungseok Jang
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Shu-Juan Fan
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Jonathan Wong
- Department of Radiology, University of California San Diego, San Diego, CA, USA
- Radiology Service, VA San Diego Healthcare System, San Diego, CA, USA
| | - Yanping Xue
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Zhenyu Cai
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Eric Y. Chang
- Department of Radiology, University of California San Diego, San Diego, CA, USA
- Radiology Service, VA San Diego Healthcare System, San Diego, CA, USA
| | - Jody Corey-Bloom
- Department of Neurosciences, University of California San Diego, San Diego, CA, USA
| | - Jiang Du
- Department of Radiology, University of California San Diego, San Diego, CA, USA
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9
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Ye Z, Price RL, Liu X, Lin J, Yang Q, Sun P, Wu AT, Wang L, Han RH, Song C, Yang R, Gary SE, Mao DD, Wallendorf M, Campian JL, Li JS, Dahiya S, Kim AH, Song SK. Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology. Clin Cancer Res 2020; 26:5388-5399. [PMID: 32694155 DOI: 10.1158/1078-0432.ccr-20-0736] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 06/01/2020] [Accepted: 07/15/2020] [Indexed: 01/10/2023]
Abstract
PURPOSE Glioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted to examine GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation remains an unmet need in the clinical management of GBMs. EXPERIMENTAL DESIGN We employ a novel diffusion histology imaging (DHI) approach, combining diffusion basis spectrum imaging (DBSI) and machine learning, to detect, differentiate, and quantify areas of high cellularity, tumor necrosis, and tumor infiltration in GBM. RESULTS Gadolinium-enhanced T1-weighted or hyperintense fluid-attenuated inversion recovery failed to reflect the morphologic complexity underlying tumor in patients with GBM. Contrary to the conventional wisdom that apparent diffusion coefficient (ADC) negatively correlates with increased tumor cellularity, we demonstrate disagreement between ADC and histologically confirmed tumor cellularity in GBM specimens, whereas DBSI-derived restricted isotropic diffusion fraction positively correlated with tumor cellularity in the same specimens. By incorporating DBSI metrics as classifiers for a supervised machine learning algorithm, we accurately predicted high tumor cellularity, tumor necrosis, and tumor infiltration with 87.5%, 89.0%, and 93.4% accuracy, respectively. CONCLUSIONS Our results suggest that DHI could serve as a favorable alternative to current neuroimaging techniques in guiding biopsy or surgery as well as monitoring therapeutic response in the treatment of GBM.
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Affiliation(s)
- Zezhong Ye
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Richard L Price
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
| | - Xiran Liu
- Department of Electrical & System Engineering, Washington University, St. Louis, Missouri
| | - Joshua Lin
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Qingsong Yang
- Department of Radiology, Changhai Hospital, Yangpu District, Shanghai, China
| | - Peng Sun
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Anthony T Wu
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
| | - Liang Wang
- Department of Electrical & System Engineering, Washington University, St. Louis, Missouri
| | - Rowland H Han
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
| | - Chunyu Song
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
| | - Ruimeng Yang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Sam E Gary
- Medical Scientist Training Program, The University of Alabama at Birmingham, Birmingham, Alabama
| | - Diane D Mao
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
| | - Michael Wallendorf
- Department of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
| | - Jian L Campian
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Jr-Shin Li
- Department of Electrical & System Engineering, Washington University, St. Louis, Missouri
| | - Sonika Dahiya
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri.
| | - Albert H Kim
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri.
| | - Sheng-Kwei Song
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri
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10
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Lakhani DA, Schilling KG, Xu J, Bagnato F. Advanced Multicompartment Diffusion MRI Models and Their Application in Multiple Sclerosis. AJNR Am J Neuroradiol 2020; 41:751-757. [PMID: 32354707 DOI: 10.3174/ajnr.a6484] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 02/03/2020] [Indexed: 01/22/2023]
Abstract
Conventional MR imaging techniques are sensitive to pathologic changes of the brain and spinal cord seen in MS, but they lack specificity for underlying axonal and myelin integrity. By isolating the signal contribution from different tissue compartments, newly developed advanced multicompartment diffusion MR imaging models have the potential to detect specific tissue subtypes and associated injuries with increased pathologic specificity. These models include neurite orientation dispersion and density imaging, diffusion basis spectrum imaging, multicompartment microscopic diffusion MR imaging with the spherical mean technique, and models enabled through high-gradient diffusion MR imaging. In this review, we provide an appraisal of the current literature on the physics principles, histopathologic validation, and clinical applications of each of these techniques in both brains and spinal cords of patients with MS. We discuss limitations of each of the methods and directions that future research could take to provide additional validation of their roles as biomarkers of axonal and myelin injury in MS.
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Affiliation(s)
- D A Lakhani
- From the Neuroimaging Unit (D.A.L., F.B.), Neuroimmunology Division, Department of Neurology
- Division of Internal Medicine (D.A.L.)
- Department of Radiology (D.A.L.), West Virginia University, Morgantown, West Virginia
| | - K G Schilling
- Department of Radiology and Radiological Sciences (K.G.S., J.X.), Vanderbilt University Institute of Imaging Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - J Xu
- Department of Radiology and Radiological Sciences (K.G.S., J.X.), Vanderbilt University Institute of Imaging Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - F Bagnato
- From the Neuroimaging Unit (D.A.L., F.B.), Neuroimmunology Division, Department of Neurology
- Department of Neurology (F.B.), VA Tennessee Valley Healthcare System, Nashville, Tennessee
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11
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Ye Z, George A, Wu AT, Niu X, Lin J, Adusumilli G, Naismith RT, Cross AH, Sun P, Song SK. Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions. Ann Clin Transl Neurol 2020; 7:695-706. [PMID: 32304291 PMCID: PMC7261762 DOI: 10.1002/acn3.51037] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 02/24/2020] [Accepted: 03/13/2020] [Indexed: 11/08/2022] Open
Abstract
OBJECTIVE Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterogeneity. We hypothesized that the profiles of multiple DBSI metrics can identify lesion-defining patterns. Here we test this hypothesis by combining a deep learning algorithm using deep neural network (DNN) with DBSI and other imaging methods. METHODS Thirty-eight MS patients were scanned with diffusion-weighted imaging, magnetization transfer imaging, and standard conventional MRI sequences (cMRI). A total of 499 regions of interest were identified on standard MRI and labeled as persistent black holes (PBH), persistent gray holes (PGH), acute black holes (ABH), acute gray holes (AGH), nonblack or gray holes (NBH), and normal appearing white matter (NAWM). DBSI, diffusion tensor imaging (DTI), and magnetization transfer ratio (MTR) were applied to the 43,261 imaging voxels extracted from these ROIs. The optimized DNN with 10 fully connected hidden layers was trained using the imaging metrics of the lesion subtypes and NAWM. RESULTS Concordance, sensitivity, specificity, and accuracy were determined for the different imaging methods. DBSI-DNN derived lesion classification achieved 93.4% overall concordance with predetermined lesion types, compared with 80.2% for DTI-DNN model, 78.3% for MTR-DNN model, and 74.2% for cMRI-DNN model. DBSI-DNN also produced the highest specificity, sensitivity, and accuracy. CONCLUSIONS DBSI-DNN improves the classification of different MS lesion subtypes, which could aid clinical decision making. The efficacy and efficiency of DBSI-DNN shows great promise for clinical applications in automatic MS lesion detection and classification.
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Affiliation(s)
- Zezhong Ye
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, 63110
| | - Ajit George
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, 63110
| | - Anthony T Wu
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri, 63130
| | - Xuan Niu
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, 63110
| | - Joshua Lin
- Keck School of Medicine, University of Southern California, Los Angeles, California, 90033
| | - Gautam Adusumilli
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, 63110
| | - Robert T Naismith
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, 63110
| | - Anne H Cross
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, 63110
| | - Peng Sun
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, 63110
| | - Sheng-Kwei Song
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, 63110
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12
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Samara A, Murphy T, Strain J, Rutlin J, Sun P, Neyman O, Sreevalsan N, Shimony JS, Ances BM, Song SK, Hershey T, Eisenstein SA. Neuroinflammation and White Matter Alterations in Obesity Assessed by Diffusion Basis Spectrum Imaging. Front Hum Neurosci 2020; 13:464. [PMID: 31992978 PMCID: PMC6971102 DOI: 10.3389/fnhum.2019.00464] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 12/18/2019] [Indexed: 01/06/2023] Open
Abstract
Human obesity is associated with low-grade chronic systemic inflammation, alterations in brain structure and function, and cognitive impairment. Rodent models of obesity show that high-calorie diets cause brain inflammation (neuroinflammation) in multiple regions, including the hippocampus, and impairments in hippocampal-dependent memory tasks. To determine if similar effects exist in humans with obesity, we applied Diffusion Basis Spectrum Imaging (DBSI) to evaluate neuroinflammation and axonal integrity. We examined diffusion-weighted magnetic resonance imaging (MRI) data in two independent cohorts of obese and non-obese individuals (Cohort 1: 25 obese/21 non-obese; Cohort 2: 18 obese/41 non-obese). We applied Tract-based Spatial Statistics (TBSS) to allow whole-brain white matter (WM) analyses and compare DBSI-derived isotropic and anisotropic diffusion measures between the obese and non-obese groups. In both cohorts, the obese group had significantly greater DBSI-derived restricted fraction (DBSI-RF; an indicator of neuroinflammation-related cellularity), and significantly lower DBSI-derived fiber fraction (DBSI-FF; an indicator of apparent axonal density) in several WM tracts (all corrected p < 0.05). Moreover, using region of interest analyses, average DBSI-RF and DBSI-FF values in the hippocampus were significantly greater and lower, respectively, in obese relative to non-obese individuals (Cohort 1: p = 0.045; Cohort 2: p = 0.008). Hippocampal DBSI-FF and DBSI-RF and amygdalar DBSI-FF metrics related to cognitive performance in Cohort 2. In conclusion, these findings suggest that greater neuroinflammation-related cellularity and lower apparent axonal density are associated with human obesity and cognitive performance. Future studies are warranted to determine a potential role for neuroinflammation in obesity-related cognitive impairment.
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Affiliation(s)
- Amjad Samara
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Tatianna Murphy
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Jeremy Strain
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Jerrel Rutlin
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Peng Sun
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Olga Neyman
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Nitya Sreevalsan
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Beau M Ances
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Sheng-Kwei Song
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Tamara Hershey
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States.,Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States.,Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States.,Department of Psychological & Brain Sciences, Washington University School of Medicine, St. Louis, MO, United States
| | - Sarah A Eisenstein
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States.,Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
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13
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Shirani A, Sun P, Trinkaus K, Perantie DC, George A, Naismith RT, Schmidt RE, Song SK, Cross AH. Diffusion basis spectrum imaging for identifying pathologies in MS subtypes. Ann Clin Transl Neurol 2019; 6:2323-2327. [PMID: 31588688 PMCID: PMC6856605 DOI: 10.1002/acn3.50903] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 08/24/2019] [Accepted: 09/04/2019] [Indexed: 11/11/2022] Open
Abstract
Diffusion basis spectrum imaging (DBSI) combines discrete anisotropic diffusion tensors and the spectrum of isotropic diffusion tensors to model the underlying multiple sclerosis (MS) pathologies. We used clinical MS subtypes as a surrogate of underlying pathologies to assess DBSI as a biomarker of pathology in 55 individuals with MS. Restricted isotropic fraction (reflecting cellularity) and fiber fraction (representing apparent axonal density) were the most important DBSI metrics to classify MS using brain white matter lesions. These DBSI metrics outperformed lesion volume. When analyzing the normal‐appearing corpus callosum, the most significant DBSI metrics were fiber fraction, radial diffusivity (reflecting myelination), and nonrestricted isotropic fraction (representing edema). This study provides preliminary evidence supporting the ability of DBSI as a potential noninvasive biomarker of MS neuropathology.
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Affiliation(s)
- Afsaneh Shirani
- The John L. Trotter Multiple Sclerosis Center and Neuroimmunology Section, Department of Neurology, Washington University School of Medicine, St. Louis, Missouri.,Division of Multiple Sclerosis, Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska
| | - Peng Sun
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Kathryn Trinkaus
- Biostatistics Shared Resource and Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
| | - Dana C Perantie
- The John L. Trotter Multiple Sclerosis Center and Neuroimmunology Section, Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Ajit George
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Robert T Naismith
- The John L. Trotter Multiple Sclerosis Center and Neuroimmunology Section, Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Robert E Schmidt
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri
| | - Sheng-Kwei Song
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Anne H Cross
- The John L. Trotter Multiple Sclerosis Center and Neuroimmunology Section, Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
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14
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Kim JW, Andersson JL, Seifert AC, Sun P, Song SK, Dula C, Naismith RT, Xu J. Incorporating non-linear alignment and multi-compartmental modeling for improved human optic nerve diffusion imaging. Neuroimage 2019; 196:102-113. [PMID: 30930313 DOI: 10.1016/j.neuroimage.2019.03.058] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 03/22/2019] [Accepted: 03/25/2019] [Indexed: 12/19/2022] Open
Abstract
In vivo human optic nerve diffusion magnetic resonance imaging (dMRI) is technically challenging with two outstanding issues not yet well addressed: (i) non-linear optic nerve movement, independent of head motion, and (ii) effect from partial-volumed cerebrospinal fluid or interstitial fluid such as in edema. In this work, we developed a non-linear optic nerve registration algorithm for improved volume alignment in axial high resolution optic nerve dMRI. During eyes-closed dMRI data acquisition, optic nerve dMRI measurements by diffusion tensor imaging (DTI) with and without free water elimination (FWE), and by diffusion basis spectrum imaging (DBSI), as well as optic nerve motion, were characterized in healthy adults at various locations along the posterior-to-anterior dimension. Optic nerve DTI results showed consistent trends in microstructural parametric measurements along the posterior-to-anterior direction of the entire intraorbital optic nerve, while the anterior portion of the intraorbital optic nerve exhibited the largest spatial displacement. Multi-compartmental dMRI modeling, such as DTI with FWE or DBSI, was less subject to spatially dependent biases in diffusivity and anisotropy measurements in the optic nerve which corresponded to similar spatial distributions of the estimated fraction of isotropic diffusion components. DBSI results derived from our clinically feasible (∼10 min) optic nerve dMRI protocol in this study are consistent with those from small animal studies, which provides the basis for evaluating the utility of multi-compartmental dMRI modeling in characterizing coexisting pathophysiology in human optic neuropathies.
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Affiliation(s)
- Joo-Won Kim
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Jesper Lr Andersson
- Wellcome Center for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Alan C Seifert
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Peng Sun
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Sheng-Kwei Song
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Courtney Dula
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Robert T Naismith
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Junqian Xu
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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