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Rabe F, Smigielski L, Georgiadis F, Kallen N, Omlor W, Kirschner M, Cathomas F, Grünblatt E, Silverstein S, Blose B, Barthelmes D, Schaal K, Rubio J, Lencz T, Homan P. Genetic susceptibility to schizophrenia through neuroinflammatory pathways is associated with retinal thinning: Findings from the UK-Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.05.24305387. [PMID: 38633770 PMCID: PMC11023639 DOI: 10.1101/2024.04.05.24305387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
The human retina is part of the central nervous system and can be easily and non-invasively imaged with optical coherence tomography. While imaging the retina may provide insights on central nervous system-related disorders such as schizophrenia, a typical challenge are confounders often present in schizophrenia which may negatively impact retinal health. Here, we therefore aimed to investigate retinal changes in the context of common genetic variations conveying a risk of schizophrenia as measured by polygenic risk scores. We used population data from the UK Biobank, including White British and Irish individuals without diagnosed schizophrenia, and estimated a polygenic risk score for schizophrenia based on the newest genome-wide association study (PGC release 2022). We hypothesized that greater genetic susceptibility to schizophrenia is associated with retinal thinning, especially within the macula. To gain additional mechanistic insights, we conducted pathway-specific polygenic risk score associations analyses, focusing on gene pathways that are related to schizophrenia. Of 65484 individuals recruited, 48208 participants with available matching imaging-genetic data were included in the analysis of whom 22427 (53.48%) were female and 25781 (46.52%) were male. Our robust principal component regression results showed that polygenic risk scores for schizophrenia were associated with retinal thinning while controlling for confounding factors (b = -0.03, p = 0.007, pFWER = 0.01). Similarly, we found that polygenic risk for schizophrenia specific to neuroinflammation gene sets revealed significant associations with retinal thinning (b = -0.03, self-contained p = 0.041 (reflecting the level of association), competitive p = 0.05 (reflecting the level of enrichment)). These results go beyond previous studies suggesting a relationship between manifested schizophrenia and retinal phenotypes. They indicate that the retina is a mirror reflecting the genetic complexities of schizophrenia and that alterations observed in the retina of individuals with schizophrenia may be connected to an inherent genetic predisposition to neurodegenerative aspects of the condition. These associations also suggest the potential involvement of the neuroinflammatory pathway, with indications of genetic overlap with specific retinal phenotypes. The findings further indicate that this gene pathway in individuals with a high polygenic risk for schizophrenia could contribute through acute-phase proteins to structural changes in the retina.
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Affiliation(s)
- Finn Rabe
- Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland
| | - Lukasz Smigielski
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Zurich, Switzerland
| | - Foivos Georgiadis
- Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland
| | - Nils Kallen
- Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland
| | - Wolfgang Omlor
- Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland
| | - Matthias Kirschner
- Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland
| | - Flurin Cathomas
- Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland
| | - Edna Grünblatt
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
- Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland
| | - Steven Silverstein
- Department of Psychiatry, University of Rochester Medical Center, Rochester, New York, USA
- Department of Ophthalmology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
- Center for Visual Science, University of Rochester, Rochester, New York, USA
| | - Brittany Blose
- Department of Psychiatry, University of Rochester Medical Center, Rochester, New York, USA
- Department of Ophthalmology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
- Center for Visual Science, University of Rochester, Rochester, New York, USA
| | - Daniel Barthelmes
- Department of Ophthalmology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Karen Schaal
- Department of Ophthalmology, Inselspital University Hospital Bern, Bern, Switzerland
| | - Jose Rubio
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Philipp Homan
- Department of Adult Psychiatry and Psychotherapy, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
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Baharlouei Z, Rabbani H, Plonka G. Wavelet scattering transform application in classification of retinal abnormalities using OCT images. Sci Rep 2023; 13:19013. [PMID: 37923770 PMCID: PMC10624695 DOI: 10.1038/s41598-023-46200-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 10/29/2023] [Indexed: 11/06/2023] Open
Abstract
To assist ophthalmologists in diagnosing retinal abnormalities, Computer Aided Diagnosis has played a significant role. In this paper, a particular Convolutional Neural Network based on Wavelet Scattering Transform (WST) is used to detect one to four retinal abnormalities from Optical Coherence Tomography (OCT) images. Predefined wavelet filters in this network decrease the computation complexity and processing time compared to deep learning methods. We use two layers of the WST network to obtain a direct and efficient model. WST generates a sparse representation of the images which is translation-invariant and stable concerning local deformations. Next, a Principal Component Analysis classifies the extracted features. We evaluate the model using four publicly available datasets to have a comprehensive comparison with the literature. The accuracies of classifying the OCT images of the OCTID dataset into two and five classes were [Formula: see text] and [Formula: see text], respectively. We achieved an accuracy of [Formula: see text] in detecting Diabetic Macular Edema from Normal ones using the TOPCON device-based dataset. Heidelberg and Duke datasets contain DME, Age-related Macular Degeneration, and Normal classes, in which we achieved accuracy of [Formula: see text] and [Formula: see text], respectively. A comparison of our results with the state-of-the-art models shows that our model outperforms these models for some assessments or achieves nearly the best results reported so far while having a much smaller computational complexity.
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Affiliation(s)
- Zahra Baharlouei
- Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hossein Rabbani
- Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Gerlind Plonka
- Institute for Numerical and Applied Mathematics, Georg-August-University of Goettingen, Göttingen, Germany
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Berenguer-Vidal R, Verdú-Monedero R, Morales-Sánchez J, Sellés-Navarro I, Kovalyk O, Sancho-Gómez JL. Decision Trees for Glaucoma Screening Based on the Asymmetry of the Retinal Nerve Fiber Layer in Optical Coherence Tomography. SENSORS 2022; 22:s22134842. [PMID: 35808338 PMCID: PMC9269200 DOI: 10.3390/s22134842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 12/10/2022]
Abstract
Purpose: The aim of this study was to analyze the relevance of asymmetry features between both eyes of the same patient for glaucoma screening using optical coherence tomography. Methods: Spectral-domain optical coherence tomography was used to estimate the thickness of the peripapillary retinal nerve fiber layer in both eyes of the patients in the study. These measurements were collected in a dataset from healthy and glaucoma patients. Several metrics for asymmetry in the retinal nerve fiber layer thickness between the two eyes were then proposed. These metrics were evaluated using the dataset by performing a statistical analysis to assess their significance as relevant features in the diagnosis of glaucoma. Finally, the usefulness of these asymmetry features was demonstrated by designing supervised machine learning models that can be used for the early diagnosis of glaucoma. Results: Machine learning models were designed and optimized, specifically decision trees, based on the values of proposed asymmetry metrics. The use of these models on the dataset provided good classification of the patients (accuracy 88%, sensitivity 70%, specificity 93% and precision 75%). Conclusions: The obtained machine learning models based on retinal nerve fiber layer asymmetry are simple but effective methods which offer a good trade-off in classification of patients and simplicity. The fast binary classification relies on a few asymmetry values of the retinal nerve fiber layer thickness, allowing their use in the daily clinical practice for glaucoma screening.
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Affiliation(s)
- Rafael Berenguer-Vidal
- Departamento de Ciencias Politécnicas, Universidad Católica de Murcia UCAM, 30107 Guadalupe, Spain;
| | - Rafael Verdú-Monedero
- Departamento de Tecnologías de la Información y Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain; (J.M.-S.); (O.K.); (J.-L.S.-G.)
- Correspondence:
| | - Juan Morales-Sánchez
- Departamento de Tecnologías de la Información y Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain; (J.M.-S.); (O.K.); (J.-L.S.-G.)
| | | | - Oleksandr Kovalyk
- Departamento de Tecnologías de la Información y Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain; (J.M.-S.); (O.K.); (J.-L.S.-G.)
| | - José-Luis Sancho-Gómez
- Departamento de Tecnologías de la Información y Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain; (J.M.-S.); (O.K.); (J.-L.S.-G.)
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Lu J, Zhou H, Shi Y, Choe J, Shen M, Wang L, Chen K, Zhang Q, Feuer WJ, Gregori G, Rosenfeld PJ, Wang RK. Interocular asymmetry of choroidal thickness and vascularity index measurements in normal eyes assessed by swept-source optical coherence tomography. Quant Imaging Med Surg 2022; 12:781-795. [PMID: 34993118 DOI: 10.21037/qims-21-813] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 08/25/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND To investigate the symmetry of interocular choroidal thickness and vascularity index measurements in normal eyes using swept-source optical coherence tomography (SS-OCT). Cross-sectional and observational study. This study included 244 eyes of 122 normal adults with ages uniformly distributed from 19 to 89 years. METHODS SS-OCT imaging was performed using a scanning pattern of 12×12 mm. Mean choroidal thickness (MCT) and choroidal vascularity index (CVI) measurements in the entire scanning region were obtained using a validated and published automatic method. The correlation and differences (including signed and absolute differences) between bilateral MCT and CVI measurements were analyzed at the following 6 regions: 3 concentric circles centered on the fovea with diameters of 2.5, 5, and 11 mm; the inner rim from 2.5 to 5 mm circle; the outer rim from 5 to 11 mm circle; and the entire 12×12-mm scan region, respectively. Comparison of interocular MCT and CVI measurements. RESULTS MCT measurements in right and left eyes were strongly correlated in all regions [all intraclass correlation (ICC) >0.73], but MCT measurements in right eyes were significantly thicker than in left eyes. CVI measurements in right and left eyes were moderately correlated in all regions (all ICC >0.46), but CVI measurements in right eyes were significantly smaller than that in left eyes in the macular subregions (2.5 mm circle, 5 mm circle, and the inner rim). Neither signed nor absolute interocular differences in MCT were correlated with corresponding CVI interocular differences. CONCLUSIONS Choroidal differences exist between normal fellow eyes in adults in the absence of obvious pathology. This study is useful in assisting clinicians and researchers in distinguishing asymmetric changes that are to be expected in normal eyes versus changes that could be associated with diseases.
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Affiliation(s)
- Jie Lu
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Hao Zhou
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Yingying Shi
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - James Choe
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Mengxi Shen
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Liang Wang
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Kelly Chen
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Qinqin Zhang
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - William J Feuer
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Giovanni Gregori
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Philip J Rosenfeld
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Ruikang K Wang
- Department of Bioengineering, University of Washington, Seattle, WA, USA.,Department of Ophthalmology, University of Washington, Seattle, WA, USA
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Synthetic OCT data in challenging conditions: three-dimensional OCT and presence of abnormalities. Med Biol Eng Comput 2021; 60:189-203. [PMID: 34792759 PMCID: PMC8724113 DOI: 10.1007/s11517-021-02469-w] [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: 04/28/2021] [Accepted: 11/06/2021] [Indexed: 12/09/2022]
Abstract
Nowadays, retinal optical coherence tomography (OCT) plays an important role in ophthalmology and automatic analysis of the OCT is of real importance: image denoising facilitates a better diagnosis and image segmentation and classification are undeniably critical in treatment evaluation. Synthetic OCT was recently considered to provide a benchmark for quantitative comparison of automatic algorithms and to be utilized in the training stage of novel solutions based on deep learning. Due to complicated data structure in retinal OCTs, a limited number of delineated OCT datasets are already available in presence of abnormalities; furthermore, the intrinsic three-dimensional (3D) structure of OCT is ignored in many public 2D datasets. We propose a new synthetic method, applicable to 3D data and feasible in presence of abnormalities like diabetic macular edema (DME). In this method, a limited number of OCT data is used during the training step and the Active Shape Model is used to produce synthetic OCTs plus delineation of retinal boundaries and location of abnormalities. Statistical comparison of thickness maps showed that synthetic dataset can be used as a statistically acceptable representative of the original dataset (p > 0.05). Visual inspection of the synthesized vessels was also promising. Regarding the texture features of the synthesized datasets, Q-Q plots were used, and even in cases that the points have slightly digressed from the straight line, the p-values of the Kolmogorov–Smirnov test rejected the null hypothesis and showed the same distribution in texture features of the real and the synthetic data. The proposed algorithm provides a unique benchmark for comparison of OCT enhancement methods and a tailored augmentation method to overcome the limited number of OCTs in deep learning algorithms.
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