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Toscano S, Chisari CG, Biondi A, Patti F. Early reduction of retinal thickness predicts physical and cognitive disability in newly diagnosed multiple sclerosis patients: results from a cross-sectional study. Neurol Sci 2024; 45:5385-5394. [PMID: 38951431 PMCID: PMC11470849 DOI: 10.1007/s10072-024-07664-9] [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: 10/18/2023] [Accepted: 06/18/2024] [Indexed: 07/03/2024]
Abstract
INTRODUCTION Retinal nerve fiber layer (RNFL) thickness is a promising biomarker of axonal loss and a potential outcome predictor in Multiple Sclerosis (MS). Cognitive impairment (CoI) exhibits a high prevalence in patients with MS (pwMS), even in the early phases of the disease. Our aim was to explore the role of RNFL thickness as a predictor of physical and cognitive disability in pwMS. METHODS All newly diagnosed pwMS referred to the MS centre of the University-Hospital "Policlinico-San Marco" between 2015-2019 were evaluated at baseline and at 3 years. RNFL and ganglion cell layer (GCL) thickness for right (r.e.) and left eyes (l.e.) were measured with Optical Coherence Tomography (OCT). Disability level and cognitive profile were assessed, using the Expanded Disability Status Scale (EDSS) and the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) battery, respectively. RESULTS We consecutively enrolled 487 pwMS, including 68 (14.0%) with primary progressive MS (PPMS). At baseline, RNFL and GCL were bilaterally thinner in PPMS (r.e. 90.4 ± 12.7; l.e. 90.2 ± 13.5, and r.e. 80.1 ± 11.2; l.e. 80.3 ± 12.6, respectively) compared to relapsing-remitting MS (RRMS) (r.e. 94.6 ± 13.1; l.e. 94.3 ± 14.8, and r.e. 85.1 ± 9.5; l.e. 84.9 ± 9.3, respectively) (p < 0.01). Both groups exhibited reduced RNFL and GCL thickness, worse cognitive performance and higher EDSS scores at 3-years follow-up compared with baseline. RNFL thickness ≤ 88.0 μm was an independent predictor of CoI (OR = 5.32; 95% CI = 1.84-9.12; p = 0.02) and disability worsening (OR = 3.18; 95% CI = 1.21-10.33; p = 0.05). DISCUSSION RNFL thickness, as a biomarker of neurodegeneration, could be considered a predictive biomarker of cognitive degeneration and physical disability in MS.
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Affiliation(s)
- Simona Toscano
- Department "GF Ingrassia", Section of Neurosciences, Neurology Clinic, University of Catania, 9126, Catania, Italy
- Department of Biomedical and Biotechnological Sciences (BIOMETEC), University of Catania, Catania, Italy
| | - Clara Grazia Chisari
- Department "GF Ingrassia", Section of Neurosciences, Neurology Clinic, University of Catania, 9126, Catania, Italy
| | - Alice Biondi
- Department "GF Ingrassia", Section of Neurosciences, Neurology Clinic, University of Catania, 9126, Catania, Italy
| | - Francesco Patti
- Department "GF Ingrassia", Section of Neurosciences, Neurology Clinic, University of Catania, 9126, Catania, Italy.
- Department "GF Ingrassia", Section of Neurosciences, Multiple Sclerosis Center, Neurology Clinic, University of Catania, Via Santa Sofia 78, 95123, Catania, Italy.
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Jellinger KA. Cognitive impairment in multiple sclerosis: from phenomenology to neurobiological mechanisms. J Neural Transm (Vienna) 2024; 131:871-899. [PMID: 38761183 DOI: 10.1007/s00702-024-02786-y] [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: 03/22/2024] [Accepted: 05/08/2024] [Indexed: 05/20/2024]
Abstract
Multiple sclerosis (MS) is an autoimmune-mediated disease of the central nervous system characterized by inflammation, demyelination and chronic progressive neurodegeneration. Among its broad and unpredictable range of clinical symptoms, cognitive impairment (CI) is a common and disabling feature greatly affecting the patients' quality of life. Its prevalence is 20% up to 88% with a wide variety depending on the phenotype of MS, with highest frequency and severity in primary progressive MS. Involving different cognitive domains, CI is often associated with depression and other neuropsychiatric symptoms, but usually not correlated with motor and other deficits, suggesting different pathophysiological mechanisms. While no specific neuropathological data for CI in MS are available, modern research has provided evidence that it arises from the disease-specific brain alterations. Multimodal neuroimaging, besides structural changes of cortical and deep subcortical gray and white matter, exhibited dysfunction of fronto-parietal, thalamo-hippocampal, default mode and cognition-related networks, disruption of inter-network connections and involvement of the γ-aminobutyric acid (GABA) system. This provided a conceptual framework to explain how aberrant pathophysiological processes, including oxidative stress, mitochondrial dysfunction, autoimmune reactions and disruption of essential signaling pathways predict/cause specific disorders of cognition. CI in MS is related to multi-regional patterns of cerebral disturbances, although its complex pathogenic mechanisms await further elucidation. This article, based on systematic analysis of PubMed, Google Scholar and Cochrane Library, reviews current epidemiological, clinical, neuroimaging and pathogenetic evidence that could aid early identification of CI in MS and inform about new therapeutic targets and strategies.
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Affiliation(s)
- Kurt A Jellinger
- Institute of Clinical Neurobiology, Alberichgasse 5/13, Vienna, A-1150, Austria.
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Zhao B, Li Y, Fan Z, Wu Z, Shu J, Yang X, Yang Y, Wang X, Li B, Wang X, Copana C, Yang Y, Lin J, Li Y, Stein JL, O'Brien JM, Li T, Zhu H. Eye-brain connections revealed by multimodal retinal and brain imaging genetics. Nat Commun 2024; 15:6064. [PMID: 39025851 PMCID: PMC11258354 DOI: 10.1038/s41467-024-50309-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
The retina, an anatomical extension of the brain, forms physiological connections with the visual cortex of the brain. Although retinal structures offer a unique opportunity to assess brain disorders, their relationship to brain structure and function is not well understood. In this study, we conducted a systematic cross-organ genetic architecture analysis of eye-brain connections using retinal and brain imaging endophenotypes. We identified novel phenotypic and genetic links between retinal imaging biomarkers and brain structure and function measures from multimodal magnetic resonance imaging (MRI), with many associations involving the primary visual cortex and visual pathways. Retinal imaging biomarkers shared genetic influences with brain diseases and complex traits in 65 genomic regions, with 18 showing genetic overlap with brain MRI traits. Mendelian randomization suggests bidirectional genetic causal links between retinal structures and neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, our findings reveal the genetic basis for eye-brain connections, suggesting that retinal images can help uncover genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA.
- Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Penn Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Population Aging Research Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zhenyi Wu
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Yilin Yang
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Xiyao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Carlos Copana
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jinjie Lin
- Yale School of Management, Yale University, New Haven, CT, 06511, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joan M O'Brien
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Medicine Center for Ophthalmic Genetics in Complex Diseases, Philadelphia, PA, 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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Mirmosayyeb O, Zivadinov R, Weinstock-Guttman B, Benedict RHB, Jakimovski D. Optical coherence tomography (OCT) measurements and cognitive performance in multiple sclerosis: a systematic review and meta-analysis. J Neurol 2023; 270:1266-1285. [PMID: 36396812 DOI: 10.1007/s00415-022-11449-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/21/2022] [Accepted: 10/22/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Several studies report mixed associations between the retinal nerve fiber layer (RNFL) thickness with cognitive and physical disability in persons with multiple sclerosis (PwMS). Systematic synthesis of these findings is crucial in deriving credible conclusions. METHODS Five databases were searched from their inception to March 2022. The inclusion criteria for studies were MS-specific and required RNFL and cognitive performance data in order to be analyzed. The selection processes followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS The systematic review yielded 31 studies that investigated the association between RNFL thickness and cognitive performance. Twenty-two studies reported positive associations, and nine did not. The meta-analysis included 11 studies with a total of 782 PwMS with mean age of 40.5 years, mean Expanded Disability Status Scale (EDSS) of 2.7, and disease duration of 11.3 years. RNFL thickness was significantly associated Symbol Digit Modalities Test (pooled r = 0.306, p < 0.001), Paced Auditory Serial Addition Test (pooled r = 0.374, p < 0.001) and Word List Generation (WLG, pooled r = 0.177, p < 0.001). RNFL was also significantly correlated with visuospatial learning and memory tests (pooled r = 0.148, p = 0.042) and verbal learning and memory tests (pooled r = 0.245, p = 0.005). Within three eligible studies, no significant association between ganglion cell inner-plexiform layer and SDMT 0.083 (95% CI - 0.186, 0.352) was noted. The heterogeneity was high in all correlation studies (I2 > 63% and p < 0.008) except for the WLG and visuospatial memory findings. CONCLUSION RNFL thickness is associated with cognitive processing speed, verbal learning and memory, visual learning and memory, as well as verbal fluency in PwMS. The number of studies included in the meta-analyses were limited due to non-standardized reporting.
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Affiliation(s)
- Omid Mirmosayyeb
- Department of Neurology, Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY, 14203, USA
- Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Bianca Weinstock-Guttman
- Department of Neurology, Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ralph H B Benedict
- Department of Neurology, Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Dejan Jakimovski
- Department of Neurology, Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY, 14203, USA.
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Zhao B, Li Y, Fan Z, Wu Z, Shu J, Yang X, Yang Y, Wang X, Li B, Wang X, Copana C, Yang Y, Lin J, Li Y, Stein JL, O'Brien JM, Li T, Zhu H. Eye-brain connections revealed by multimodal retinal and brain imaging genetics in the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.16.23286035. [PMID: 36824893 PMCID: PMC9949187 DOI: 10.1101/2023.02.16.23286035] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
As an anatomical extension of the brain, the retina of the eye is synaptically connected to the visual cortex, establishing physiological connections between the eye and the brain. Despite the unique opportunity retinal structures offer for assessing brain disorders, less is known about their relationship to brain structure and function. Here we present a systematic cross-organ genetic architecture analysis of eye-brain connections using retina and brain imaging endophenotypes. Novel phenotypic and genetic links were identified between retinal imaging biomarkers and brain structure and function measures derived from multimodal magnetic resonance imaging (MRI), many of which were involved in the visual pathways, including the primary visual cortex. In 65 genomic regions, retinal imaging biomarkers shared genetic influences with brain diseases and complex traits, 18 showing more genetic overlaps with brain MRI traits. Mendelian randomization suggests that retinal structures have bidirectional genetic causal links with neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, cross-organ imaging genetics reveals a genetic basis for eye-brain connections, suggesting that the retinal images can elucidate genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zhenyi Wu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yilin Yang
- Department of Computer and Information Science and Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Xiyao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Carlos Copana
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jinjie Lin
- Yale School of Management, Yale University, New Haven, CT 06511, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joan M. O'Brien
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Medicine Center for Ophthalmic Genetics in Complex Diseases, PA, 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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Davion JB, Jougleux C, Lopes R, Leclerc X, Outteryck O. Relation between retina, cognition and brain volumes in MS: a consequence of asymptomatic optic nerve lesions. J Neurol 2023; 270:240-249. [PMID: 36018381 DOI: 10.1007/s00415-022-11348-9] [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: 07/05/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Asymptomatic optic nerve lesions are frequent in multiple sclerosis (MS) and their impact on cognition and/or brain volume has never been taken into account. PATIENTS AND METHODS We used the data from the cross-sectional Visual Ways in MS (VWIMS) study including relapsing remitting MS. All patients underwent brain and optic nerve Magnetic Resonance Imaging (MRI) including Double Inversion Recuperation (DIR) sequence, retinal OCT, and cognitive evaluation with the Brief International Cognitive Assessment in MS (BICAMS). We measured the association between OCT findings (thickness/volume of retinal layers) and extra-visual parameters (cerebral volumes and BICAMS scores) in optic nerves with and/or without the presence of DIR asymptomatic optic nerve hypersignal. RESULTS Between March and December 2017, we included 98 patients. Two patients were excluded. Over the 192 eyes, 73 had at least one clinical history of optic neuritis (ON-eyes) whereas 119 were asymptomatic (NON-eyes). Among the 119 NON-eyes, 58 had 3D-DIR optic nerve hypersignal (48.7%). We confirmed significant associations between some retinal OCT measures and some extra-visual parameters (cerebral volumes, cognitive scores) in NON-eyes. Unexpectedly, these associations were found when an asymptomatic optic nerve DIR-hypersignal was present on MRI, but not when it was absent. CONCLUSION Our study showed a relation between OCT measures and extra-visual parameters in NON-eyes MS patients. As a confusion factor, asymptomatic optic nerve lesions may be the explanation of the relation between OCT measures and extra-visual parameters. Retinal OCT seems to be far more a "window over the optic nerve" than a "window over the brain".
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Affiliation(s)
- Jean-Baptiste Davion
- Department of Neuroradiology, Univ. Lille, INSERM, CHU Lille, U1172, Degenerative and Vascular Cognitive Disorders, 59000, Lille, France
| | - Caroline Jougleux
- Department of Neurology, Multiple Sclerosis Center of Lille, Univ. Lille, 59000, Lille, France
| | - Renaud Lopes
- Department of Neuroradiology, Univ. Lille, INSERM, CHU Lille, U1172, Degenerative and Vascular Cognitive Disorders, 59000, Lille, France
| | - Xavier Leclerc
- Department of Neuroradiology, Univ. Lille, INSERM, CHU Lille, U1172, Degenerative and Vascular Cognitive Disorders, 59000, Lille, France
| | - Olivier Outteryck
- Department of Neuroradiology, Univ. Lille, INSERM, CHU Lille, U1172, Degenerative and Vascular Cognitive Disorders, 59000, Lille, France.
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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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Geannopoulos K, McMahan C, Maldonado RS, Abbott A, Knickelbein J, Agron E, Wu T, Snow J, Nair G, Horne E, Lau CY, Nath A, Chew EY, Smith BR. Retinal Thinning in People With Well-Controlled HIV Infection. J Acquir Immune Defic Syndr 2022; 91:210-216. [PMID: 36094488 PMCID: PMC9475731 DOI: 10.1097/qai.0000000000003048] [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: 02/14/2022] [Accepted: 05/16/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Retinal measurements correlate with disease progression in patients with multiple sclerosis; however, whether they associate with neurologic disease in people with controlled HIV is unknown. Using spectral domain optical coherence tomography, we evaluated retinal differences between people with HIV and HIV-negative controls and investigated clinical correlates of retinal thinning. METHODS People with HIV on antiretroviral therapy for at least 1 year and HIV-negative controls recruited from the same communities underwent spectral domain optical coherence tomography, ophthalmic examination, brain MRI, and neuropsychological testing. Retinal nerve fiber layer (RNFL) and ganglion cell inner plexiform layer (GC-IPL) thicknesses were compared between groups using analysis of covariance with relevant clinical variables as covariates. Linear regression was used to explore associations of HIV history variables, cognitive domain scores, and MRI volume measurements within the HIV group. RESULTS The HIV group (n = 69), with long-duration HIV infection (median time from diagnosis 19 years) and outstanding viral control have thinner retinal layers than HIV-negative controls (n = 28), after adjusting for covariates (GC-IPL: P = 0.002; RNFL: P = 0.024). The effect of HIV on GC-IPL thickness was stronger in women than in men (Women: P = 0.011; Men: P = 0.126). GC-IPL thickness is associated with information processing speed in the HIV group (P = 0.007, semipartial r = 0.309). No associations were found with retinal thinning and MRI volumes or HIV factors. CONCLUSIONS People with HIV on antiretroviral therapy have thinning of the RNFL and GC-IPL of the retina, and women particularly are affected to a greater degree. This retinal thinning was associated with worse performance on tests of information processing speed.
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Affiliation(s)
- Katrina Geannopoulos
- National Institute of National Disorders and Stroke, National Institutes of Health, Bethesda, MD
- College of Medicine, University of Illinois, Chicago, IL
| | - Cynthia McMahan
- National Institute of National Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Ramiro S Maldonado
- National Eye Institute, National Institutes of Health, Bethesda, MD
- College of Medicine, University of Kentucky, Lexington, KY
| | - Akshar Abbott
- National Eye Institute, National Institutes of Health, Bethesda, MD
- Veterans Affairs Medical Center, University of Minnesota, Minneapolis, MN
| | - Jared Knickelbein
- National Eye Institute, National Institutes of Health, Bethesda, MD
- University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Elvira Agron
- National Eye Institute, National Institutes of Health, Bethesda, MD
| | - Tianxia Wu
- National Institute of National Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Joseph Snow
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD
| | - Govind Nair
- National Institute of National Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Elizabeth Horne
- National Institute of National Disorders and Stroke, National Institutes of Health, Bethesda, MD
- Duke University School of Medicine, Durham, NC; and
| | - Chuen-Yen Lau
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD
| | - Avindra Nath
- National Institute of National Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Emily Y Chew
- National Eye Institute, National Institutes of Health, Bethesda, MD
| | - Bryan R Smith
- National Institute of National Disorders and Stroke, National Institutes of Health, Bethesda, MD
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