For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
0
views
71
references
 Top references
cited by
0
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      102
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Contributions of Brain Microstructures and Metabolism to Visual Field Loss Patterns in Glaucoma Using Archetypal and Information Gain Analyses

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Purpose

          To investigate the contributions of the microstructural and metabolic brain environment to glaucoma and their association with visual field (VF) loss patterns by using advanced diffusion magnetic resonance imaging (dMRI), proton magnetic resonance spectroscopy (MRS), and clinical ophthalmic measures.

          Methods

          Sixty-nine glaucoma and healthy subjects underwent dMRI and/or MRS at 3 Tesla. Ophthalmic data were collected from VF perimetry and optical coherence tomography. dMRI parameters of microstructural integrity in the optic radiation and MRS-derived neurochemical levels in the visual cortex were compared among early glaucoma, advanced glaucoma, and healthy controls. Multivariate regression was used to correlate neuroimaging metrics with 16 archetypal VF loss patterns. We also ranked neuroimaging, ophthalmic, and demographic attributes in terms of their information gain to determine their importance to glaucoma.

          Results

          In dMRI, decreasing fractional anisotropy, radial kurtosis, and tortuosity and increasing radial diffusivity correlated with greater overall VF loss bilaterally. Regionally, decreasing intra-axonal space and extra-axonal space diffusivities correlated with greater VF loss in the superior–altitudinal area of the right eye and the inferior–altitudinal area of the left eye. In MRS, both early and advanced glaucoma patients had lower gamma-aminobutyric acid (GABA), glutamate, and choline levels than healthy controls. GABA appeared to associate more with superonasal VF loss, and glutamate and choline more with inferior VF loss. Choline ranked third for importance to early glaucoma, whereas radial kurtosis and GABA ranked fourth and fifth for advanced glaucoma.

          Conclusions

          Our findings highlight the importance of non-invasive neuroimaging biomarkers and analytical modeling for unveiling glaucomatous neurodegeneration and how they reflect complementary VF loss patterns.

          Related collections

          Most cited references71

          • Record: found
          • Abstract: found
          • Article: not found

          Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis.

          Yih-Chung Tham, Xiang Li, Tien Wong … (2014)
          Glaucoma is the leading cause of global irreversible blindness. Present estimates of global glaucoma prevalence are not up-to-date and focused mainly on European ancestry populations. We systematically examined the global prevalence of primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG), and projected the number of affected people in 2020 and 2040. Systematic review and meta-analysis. Data from 50 population-based studies (3770 POAG cases among 140,496 examined individuals and 786 PACG cases among 112 398 examined individuals). We searched PubMed, Medline, and Web of Science for population-based studies of glaucoma prevalence published up to March 25, 2013. Hierarchical Bayesian approach was used to estimate the pooled glaucoma prevalence of the population aged 40-80 years along with 95% credible intervals (CrIs). Projections of glaucoma were estimated based on the United Nations World Population Prospects. Bayesian meta-regression models were performed to assess the association between the prevalence of POAG and the relevant factors. Prevalence and projection numbers of glaucoma cases. The global prevalence of glaucoma for population aged 40-80 years is 3.54% (95% CrI, 2.09-5.82). The prevalence of POAG is highest in Africa (4.20%; 95% CrI, 2.08-7.35), and the prevalence of PACG is highest in Asia (1.09%; 95% CrI, 0.43-2.32). In 2013, the number of people (aged 40-80 years) with glaucoma worldwide was estimated to be 64.3 million, increasing to 76.0 million in 2020 and 111.8 million in 2040. In the Bayesian meta-regression model, men were more likely to have POAG than women (odds ratio [OR], 1.36; 95% CrI, 1.23-1.52), and after adjusting for age, gender, habitation type, response rate, and year of study, people of African ancestry were more likely to have POAG than people of European ancestry (OR, 2.80; 95% CrI, 1.83-4.06), and people living in urban areas were more likely to have POAG than those in rural areas (OR, 1.58; 95% CrI, 1.19-2.04). The number of people with glaucoma worldwide will increase to 111.8 million in 2040, disproportionally affecting people residing in Asia and Africa. These estimates are important in guiding the designs of glaucoma screening, treatment, and related public health strategies. Copyright © 2014 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.
          Article 0 comments Cited 1466 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The pathophysiology and treatment of glaucoma: a review.

            Robert N. Weinreb, Tin Aung, Felipe Medeiros (2014)
            Glaucoma is a worldwide leading cause of irreversible vision loss. Because it may be asymptomatic until a relatively late stage, diagnosis is frequently delayed. A general understanding of the disease pathophysiology, diagnosis, and treatment may assist primary care physicians in referring high-risk patients for comprehensive ophthalmologic examination and in more actively participating in the care of patients affected by this condition. To describe current evidence regarding the pathophysiology and treatment of open-angle glaucoma and angle-closure glaucoma. A literature search was conducted using MEDLINE, the Cochrane Library, and manuscript references for studies published in English between January 2000 and September 2013 on the topics open-angle glaucoma and angle-closure glaucoma. From the 4334 abstracts screened, 210 articles were selected that contained information on pathophysiology and treatment with relevance to primary care physicians. The glaucomas are a group of progressive optic neuropathies characterized by degeneration of retinal ganglion cells and resulting changes in the optic nerve head. Loss of ganglion cells is related to the level of intraocular pressure, but other factors may also play a role. Reduction of intraocular pressure is the only proven method to treat the disease. Although treatment is usually initiated with ocular hypotensive drops, laser trabeculoplasty and surgery may also be used to slow disease progression. Primary care physicians can play an important role in the diagnosis of glaucoma by referring patients with positive family history or with suspicious optic nerve head findings for complete ophthalmologic examination. They can improve treatment outcomes by reinforcing the importance of medication adherence and persistence and by recognizing adverse reactions from glaucoma medications and surgeries.
            Article 0 comments Cited 858 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Estimation of metabolite concentrations from localized in vivo proton NMR spectra.

              Stephen Provencher (1993)
              The LCModel method analyzes an in vivo spectrum as a Linear Combination of Model spectra of metabolite solutions in vitro. By using complete model spectra, rather than just individual resonances, maximum information and uniqueness are incorporated into the analysis. A constrained regularization method accounts for differences in phase, baseline, and lineshapes between the in vitro and in vivo spectra, and estimates the metabolite concentrations and their uncertainties. LCModel is fully automatic in that the only input is the time-domain in vivo data. The lack of subjective interaction should help the exchange and comparison of results. More than 3000 human brain STEAM spectra from patients and healthy volunteers have been analyzed with LCModel. N-acetylaspartate, cholines, creatines, myo-inositol, and glutamate can be reliably determined, and abnormal levels of these or elevated levels of lactate, alanine, scyllo-inositol, glutamine, or glucose clearly indicate numerous pathologies. A computer program will be available.
              Article 0 comments Cited 328 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (nlm-ta): Invest Ophthalmol Vis Sci
                Journal ID (iso-abbrev): Invest Ophthalmol Vis Sci
                Journal ID (publisher-id): IOVS
                Title: Investigative Ophthalmology & Visual Science
                Publisher: The Association for Research in Vision and Ophthalmology
                ISSN (Print): 0146-0404
                ISSN (Electronic): 1552-5783
                Publication date (Electronic): 08 July 2024
                Publication date Collection: July 2024
                Volume: 65
                Issue: 8
                Electronic Location Identifier: 15
                Affiliations
                [1 ]Department of Ophthalmology, New York University Grossman School of Medicine, New York, New York, United States
                [2 ]Department of Radiology, New York University Grossman School of Medicine, New York, New York, United States
                [3 ]Department of Biomedical Engineering, Tandon School of Engineering, New York University, Brooklyn, New York, United States
                [4 ]Center for Neural Science, New York University, New York, New York, United States
                [5 ]Wills Eye Hospital, Philadelphia, Pennsylvania, United States
                [6 ]Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania, United States
                [7 ]Drexel University School of Biomedical Engineering, Science and Health Studies, Philadelphia, Pennsylvania, United States
                [8 ]Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States
                [9 ]Neuroscience Institute and Tech4Health Institute, New York University Grossman School of Medicine, New York, New York, United States
                Author notes
                [* ]Correspondence: Kevin C. Chan, Department of Ophthalmology, New York University Grossman School of Medicine, New York University, 222 East 41st Street, Room 362, New York, NY 10017, USA; chuenwing.chan@ 123456fulbrightmail.org .
                Article
                Publisher ID: IOVS-24-39432
                DOI: 10.1167/iovs.65.8.15
                PMC ID: 11232899
                PubMed ID: 38975942
                SO-VID: 99234337-d218-4a4f-97c9-e34d383bd560
                Copyright © Copyright 2024 The Authors
                License:

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                History
                Date accepted : 13 June 2024
                Date received : 30 January 2024
                Page count
                Pages: 13
                Categories
                Subject: Glaucoma
                Subject: Glaucoma

                Keywords: brain,diffusion magnetic resonance imaging,glaucoma,optic radiation,proton magnetic resonance spectroscopy,visual cortex,visual field
                Data availability:
                Keywords: brain, diffusion magnetic resonance imaging, glaucoma, optic radiation, proton magnetic resonance spectroscopy, visual cortex, visual field

                Comments

                Comment on this article