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Berneshawi AR, Seyedmadani K, Goel R, Anderson MR, Tyson TL, Akay YM, Akay M, Leung LSB, Stone LS. Oculometric biomarkers of visuomotor deficits in clinically asymptomatic patients with systemic lupus erythematosus undergoing long-term hydroxychloroquine treatment. FRONTIERS IN OPHTHALMOLOGY 2024; 4:1354892. [PMID: 39104603 PMCID: PMC11298511 DOI: 10.3389/fopht.2024.1354892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 05/10/2024] [Indexed: 08/07/2024]
Abstract
Introduction This study examines a set of oculomotor measurements, or "oculometric" biomarkers, as potential early indicators of visual and visuomotor deficits due to retinal toxicity in asymptomatic Systemic Lupus Erythematosus (SLE) patients on long-term hydroxychloroquine (HCQ) treatment. The aim is to identify subclinical functional impairments that are otherwise undetectable by standard clinical tests and to link them to structural retinal changes. Methods We measured oculomotor responses in a cohort of SLE patients on chronic HCQ therapy using a previously established behavioral task and analysis technique. We also examined the relationship between oculometrics, OCT measures of retinal thickness, and standard clinical perimetry measures of visual function in our patient group using Bivariate Pearson Correlation and a Linear Mixed-Effects Model (LMM). Results Significant visual and visuomotor deficits were found in 12 asymptomatic SLE patients on long-term HCQ therapy compared to a cohort of 17 age-matched healthy controls. Notably, six oculometrics were significantly different. The median initial pursuit acceleration was 22%, steady-state pursuit gain 16%, proportion smooth 7%, and target speed responsiveness 31% lower, while catch-up saccade amplitude was 46% and fixation error 46% larger. Excluding the two patients with diagnosed mild toxicity, four oculometrics, all but fixation error and proportion smooth, remained significantly impaired compared to controls. Across our population of 12 patients (24 retinae), we found that pursuit latency, initial acceleration, steady-state gain, and fixation error were linearly related to retinal thickness even when age was accounted for, while standard measures of clinical function (Mean Deviation and Pattern Standard Deviation) were not. Discussion Our data show that specific oculometrics are sensitive early biomarkers of functional deficits in SLE patients on HCQ that could be harnessed to assist in the early detection of HCQ-induced retinal toxicity and other visual pathologies, potentially providing early diagnostic value beyond standard visual field and OCT evaluations.
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Affiliation(s)
- Andrew R. Berneshawi
- Ophthalmology Department, Stanford University School of Medicine, Stanford, CA, United States
| | - Kimia Seyedmadani
- Research Operations and Integration Laboratory, Johnson Space Center, National Aeronautics and Space Administration, Houston, TX, United States
- Biomedical Engineering Department, University of Houston, Houston, TX, United States
| | - Rahul Goel
- San Jose State University Foundation, San Jose, CA, United States
- Human Systems Integration Division, Ames Research Center, National Aeronautics and Space Administration, Moffett Field, CA, United States
| | - Mark R. Anderson
- Human Systems Integration Division, Ames Research Center, National Aeronautics and Space Administration, Moffett Field, CA, United States
- Arctic Slope Regional Corporation (ASRC) Federal Data Solutions, Moffett Field, CA, United States
| | - Terence L. Tyson
- Human Systems Integration Division, Ames Research Center, National Aeronautics and Space Administration, Moffett Field, CA, United States
| | - Yasmin M. Akay
- Biomedical Engineering Department, University of Houston, Houston, TX, United States
| | - Metin Akay
- Biomedical Engineering Department, University of Houston, Houston, TX, United States
| | - Loh-Shan B. Leung
- Ophthalmology Department, Stanford University School of Medicine, Stanford, CA, United States
| | - Leland S. Stone
- Human Systems Integration Division, Ames Research Center, National Aeronautics and Space Administration, Moffett Field, CA, United States
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Roberts S, Kufahl PR, Ryznar RJ, Norris T, Patel S, Gubler KD, Paz D, Schwimer G, Besserman R, LaPorta AJ. Start-of-day oculomotor screening demonstrates the effects of fatigue and rest during a total immersion training program. Surgery 2023; 174:1193-1200. [PMID: 37640665 DOI: 10.1016/j.surg.2023.07.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/23/2023] [Accepted: 07/18/2023] [Indexed: 08/31/2023]
Abstract
BACKGROUND Investigating changes in sleep and fatigue metrics during intensive surgical and trauma skills training, this study explored the dynamic association between oculomotor metrics and fatigue. Specifically, alterations in these relations over extended stress exposure, the influence of time of day, and the impact of fatigue exposure on sleep metrics were examined. METHODS Thirty-nine military medical students participated in 6 days of immersion, hyper-realistic, and high-stress experiential casualty training. Participants completed surveys assessing the state of sleepiness with oculomotor tests performed each morning and evening, analyzing eye movement and pupillary change to characterize fatigue. Participants wore Fitbit TM devices to measure overall time asleep and time in each sleep stage during the training. RESULTS Fitbit data showed increased average minutes in rapid eye movement, deep sleep, and less time in light sleep from day 1 to day 4. The microsaccade peak velocity-to-displacement ratio exhibited a morning decrease but not in afternoon sessions, indicating repeated but temporary effects of accumulated fatigue. There were no findings regarding pupil reactivity to illumination changes. CONCLUSION This study describes characteristics of fatigue measured by rapid and individually calibrated oculomotor tests. It demonstrates oculomotor relationships to fatigue in start-of-day testing, providing a direction for timing for optimal fatigue testing. These data suggest that improved sleep could signal resilience to fatigue during afternoon testing. Further investigation with more participants and longer duration is warranted. A deeper understanding of the interrelationships between training, sleep, and fatigue could improve surgical and military fitness.
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Affiliation(s)
- Scott Roberts
- Rocky Vista University College of Osteopathic Medicine, Greenwood Village, CO.
| | | | - Rebecca J Ryznar
- Rocky Vista University College of Osteopathic Medicine, Greenwood Village, CO
| | - Taylor Norris
- Rocky Vista University College of Osteopathic Medicine, Greenwood Village, CO
| | - Sagar Patel
- Rocky Vista University College of Osteopathic Medicine, Greenwood Village, CO. https://twitter.com/SagarPatel98740
| | - K Dean Gubler
- Rocky Vista University College of Osteopathic Medicine, Greenwood Village, CO. https://twitter.com/RFF4Player
| | - Dean Paz
- Rocky Vista University College of Osteopathic Medicine, Greenwood Village, CO. https://twitter.com/StuDoc_DeanPaz
| | | | | | - Anthony J LaPorta
- Rocky Vista University College of Osteopathic Medicine, Greenwood Village, CO
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Fan XR, Wang YS, Chang D, Yang N, Rong MJ, Zhang Z, He Y, Hou X, Zhou Q, Gong ZQ, Cao LZ, Dong HM, Nie JJ, Chen LZ, Zhang Q, Zhang JX, Zhang L, Li HJ, Bao M, Chen A, Chen J, Chen X, Ding J, Dong X, Du Y, Feng C, Feng T, Fu X, Ge LK, Hong B, Hu X, Huang W, Jiang C, Li L, Li Q, Li S, Liu X, Mo F, Qiu J, Su XQ, Wei GX, Wu Y, Xia H, Yan CG, Yan ZX, Yang X, Zhang W, Zhao K, Zhu L, Zuo XN. A longitudinal resource for population neuroscience of school-age children and adolescents in China. Sci Data 2023; 10:545. [PMID: 37604823 PMCID: PMC10442366 DOI: 10.1038/s41597-023-02377-8] [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: 04/14/2023] [Accepted: 07/11/2023] [Indexed: 08/23/2023] Open
Abstract
During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013-2022), the first ten-year stage of the lifespan CCNP (2013-2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0-17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the "Chinese Data-sharing Warehouse for In-vivo Imaging Brain" in the Chinese Color Nest Project (CCNP) - Lifespan Brain-Mind Development Data Community ( https://ccnp.scidb.cn ) at the Science Data Bank.
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Affiliation(s)
- Xue-Ru Fan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Yin-Shan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Da Chang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Ning Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Meng-Jie Rong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Zhe Zhang
- College of Education, Hebei Normal University, Shijiazhuang, 050024, China
| | - Ye He
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Xiaohui Hou
- Laboratory of Cognitive Neuroscience and Education, School of Education Science, Nanning Normal University, Nanning, 530299, China
| | - Quan Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Zhu-Qing Gong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Li-Zhi Cao
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Hao-Ming Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
- Changping Laboratory, Beijing, 102206, China
| | - Jing-Jing Nie
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Li-Zhen Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Qing Zhang
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Jia-Xin Zhang
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Lei Zhang
- School of Government, Shanghai University of Political Science and Law, Shanghai, 201701, China
| | - Hui-Jie Li
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Min Bao
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Antao Chen
- School of Psychology, Research Center for Exercise and Brain Science, Shanghai University of Sport, Shanghai, 200438, China
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Jing Chen
- School of Psychology, Research Center for Exercise and Brain Science, Shanghai University of Sport, Shanghai, 200438, China
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Xu Chen
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Jinfeng Ding
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Xue Dong
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Yi Du
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Chen Feng
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Tingyong Feng
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Xiaolan Fu
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Li-Kun Ge
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Bao Hong
- NYU-ECNU Institute of Brain and Cognitive Science at New York University Shanghai, Shanghai, 200062, China
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China
| | - Xiaomeng Hu
- Department of Psychology, Renmin University of China, Beijing, 100872, China
| | - Wenjun Huang
- NYU-ECNU Institute of Brain and Cognitive Science at New York University Shanghai, Shanghai, 200062, China
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China
| | - Chao Jiang
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Li Li
- NYU-ECNU Institute of Brain and Cognitive Science at New York University Shanghai, Shanghai, 200062, China
- Faculty of Arts and Science, New York University Shanghai, Shanghai, 200122, China
| | - Qi Li
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Su Li
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Xun Liu
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Fan Mo
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jiang Qiu
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Xue-Quan Su
- Laboratory of Cognitive Neuroscience and Education, School of Education Science, Nanning Normal University, Nanning, 530299, China
| | - Gao-Xia Wei
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Yiyang Wu
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Haishuo Xia
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Chao-Gan Yan
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Zhi-Xiong Yan
- Laboratory of Cognitive Neuroscience and Education, School of Education Science, Nanning Normal University, Nanning, 530299, China
| | - Xiaohong Yang
- Department of Psychology, Renmin University of China, Beijing, 100872, China
| | - Wenfang Zhang
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Ke Zhao
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China
| | - Liqi Zhu
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China.
- Laboratory of Cognitive Neuroscience and Education, School of Education Science, Nanning Normal University, Nanning, 530299, China.
- School of Education, Hunan University of Science and Technology, Hunan Xiangtan, 411201, China.
- National Basic Science Data Center, Beijing, 100190, China.
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Chen ST, Su KC, Wang PH, Zhong XY, Cheng CY. Routine binocular examination of young Taiwanese adults as a predictor of visual behavior performance. BMC Ophthalmol 2023; 23:47. [PMID: 36726067 PMCID: PMC9890884 DOI: 10.1186/s12886-022-02731-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 12/09/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Morgan and Scheiman's Optometric Extension Program (OEP) expected binocular vision findings have longstanding use in optometry. With technological advances, the demands and standards of binocular function have changed. This study aimed to investigate which binocular visual functions can effectively predict visual behavior performance. METHODS Participants aged 15-24 years were recruited from two colleges and two universities. After completing the CSMU-Visual Behavioral Performance questionnaire (CSMU-VBP, with four components: near work, visual perception, visual comfort, and whole-body balance), participants were divided into symptomatic and asymptomatic groups based on questionnaire findings (cutoff: < 12 vs. ≥ 12 symptoms). Then a 24-step binocular visual examination was undertaken. Data were analyzed with one-sample, Student's, and paired t-tests. Additionally, receiver operating characteristic analysis was used to determine the predictors of binocular visual function required for near work, visual perception, visual comfort, and body balance dimensions. RESULTS Among 308 participants, 43 (14%) and 265 (86%) were symptomatic and asymptomatic, respectively. Among the 46 participants with abnormal binocular vision, 36 (78%) reported that they had no obvious symptoms. The commonest dysfunctions were accommodative excess and convergence excess. Most of the binocular visual findings significantly diverged from traditional normal values: amplitude of accommodation, as well as base-in prism to break and recovery points at distance were higher than traditional normal values, whereas others were lower than traditional normal values. Total CSMU-VBP scores indicated that the asymptomatic and symptomatic groups had significant differences in DBO recovery (t = 2.334, p = 0.020) and BAF (t = 1.984, p = 0.048). Receiver operating characteristic curve analysis yielded the following binocular visual functional cutoff points: near work (DBO blur < 7, DBO recovery < 5.5), visual perception (MAF < 10.5, BAF < 10.25), visual comfort (DLP < - 2.25, DBI break > 11.5, NBI blur > 15, NBI break > 17.5, NBI recovery > 13, NPC < 5.75), and body balance (NFD_H > - 0.5, gradient AC/A [minus] > 2.25, NPC < 4.75). CONCLUSIONS The mean values of binocular visual function among young Taiwanese adults were statistically different from traditional normative values. Further research is required to confirm whether these findings reflect impaired binocular vision or stringent criteria. Assessments of binocular visual function, especially binocular accommodation sensitivity, are crucial in routine optometric examination.
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Affiliation(s)
- Shyan-Tarng Chen
- grid.411641.70000 0004 0532 2041Department of Optometry, Chung Shan Medical University, Taichung, 402 Taiwan ,grid.411645.30000 0004 0638 9256Department of Ophthalmology, Chung Shan Medical University Hospital, Taichung, 402 Taiwan
| | - Kuo-Chen Su
- grid.411641.70000 0004 0532 2041Department of Optometry, Chung Shan Medical University, Taichung, 402 Taiwan ,grid.411645.30000 0004 0638 9256Department of Ophthalmology, Chung Shan Medical University Hospital, Taichung, 402 Taiwan
| | - Po-Hsin Wang
- grid.411641.70000 0004 0532 2041Department of Optometry, Chung Shan Medical University, Taichung, 402 Taiwan
| | - Xiang-Yin Zhong
- grid.411641.70000 0004 0532 2041Department of Optometry, Chung Shan Medical University, Taichung, 402 Taiwan
| | - Ching-Ying Cheng
- grid.411641.70000 0004 0532 2041Department of Optometry, Chung Shan Medical University, Taichung, 402 Taiwan ,grid.411645.30000 0004 0638 9256Department of Ophthalmology, Chung Shan Medical University Hospital, Taichung, 402 Taiwan
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Zhu M, Tang Y, Wang Z, Shen T, Qiu X, Yan J, Chen J. Clinical characteristics and risk factors of acute acquired concomitant esotropia in last 5 years: a retrospective case-control study. Eye (Lond) 2023; 37:320-324. [PMID: 35075284 PMCID: PMC9873604 DOI: 10.1038/s41433-022-01939-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 12/17/2021] [Accepted: 01/13/2022] [Indexed: 01/28/2023] Open
Abstract
OBJECTIVES A remarkable increase in the number and proportion of surgical patients with acute acquired concomitant esotropia (AACE) has been noted in our hospital in recent years. We aimed to analyse the clinical characteristics and associated risk factors of this increasing number of strabismus in last 5 years. METHODS Medical information was obtained in 62 AACE patients and 73 orthotropic patients as control group completed questionnaires and examination items from March 2017 to May 2020. Data included age at onset, refractive error, angle of deviation, binocular vision, eye care habits, and optical quality of spectacles. RESULTS Of the 62 AACE patients, the mean ± standard deviation age at onset was 25.3 ± 8.5 years, with 47 (75.8%) cases showing myopia, 9 (14.5%) showing emmetropia, and 6 (9.7%) showing hypermetropia. Among the AACE patients, 35 (56.5%) performed >8 h of close work daily and 36 (58.1%) reported late-night use of digital devices. When compared with the control group, the risk factors identified for AACE included long durations of close work (odds ratio [OR], 11.72; 95% confidence interval [CI], 3.53-38.91; P < 0.001) and immoderate late-night use of digital devices (OR, 14.29; 95% CI, 4.10-49.72; P < 0.001). CONCLUSION Our study demonstrated that young adults accounted for the majority of the growing number of individuals affected by AACE in last 5 years, and excessive close visual activities and immoderate late-night use of digital devices were found to be associated with the onset of AACE.
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Affiliation(s)
- Minyi Zhu
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Department of Ophthalmology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Yan Tang
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Department of Ophthalmology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Zhonghao Wang
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Tao Shen
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xuan Qiu
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jianhua Yan
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jingchang Chen
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
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Kayser KC, Puig VA, Estepp JR. Predicting and mitigating fatigue effects due to sleep deprivation: A review. Front Neurosci 2022; 16:930280. [PMID: 35992930 PMCID: PMC9389006 DOI: 10.3389/fnins.2022.930280] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/04/2022] [Indexed: 01/07/2023] Open
Abstract
The deleterious effects of insufficient sleep have been well-established in the literature and can lead to a wide range of adverse health outcomes. Some of the most replicated findings demonstrate significant declines in cognitive functions such as vigilance and executive attention, psychomotor and cognitive speed, and working memory. Consequently, these decrements often lead individuals who are in a fatigued state to engage in substandard performance on everyday tasks. In the interest of curtailing these effects, prior work has attempted to identify mechanisms that predict fatigue onset and develop techniques to mitigate its negative consequences. Nonetheless, these results are often confounded by variables such as an individual’s resistance to fatigue, sleep history, and unclear distinctions about whether certain performance decrements are present due to fatigue or due to other confounding factors. Similar areas of research have provided approaches to produce models for the prediction of cognitive performance decrements due to fatigue through the use of multi-modal recording and analysis of fatigue-related responses. Namely, gathering and combining response information from multiple sources (i.e., physiological and behavioral) at multiple timescales may provide a more comprehensive representation of what constitutes fatigue onset in the individual. Therefore, the purpose of this review is to discuss the relevant literature on the topic of fatigue-related performance effects with a special emphasis on a variety of physiological and behavioral response variables that have shown to be sensitive to changes in fatigue. Furthermore, an increasing reliance on sleep loss, meant to assist in meeting the demands of modern society, has led to an upsurge in the relevance of identifying dependable countermeasures for fatigued states. As such, we will also review methods for the mitigation of performance effects due to fatigue and discuss their usefulness in regulating these effects. In sum, this review aims to inspire future work that will create opportunities to detect fatigue and mitigate its effects prior to the onset of cognitive impairments.
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Affiliation(s)
- Kylie C. Kayser
- Air Force Research Laboratory, Oak Ridge Institute for Science and Education, Wright-Patterson AFB, OH, United States
| | - Vannia A. Puig
- Air Force Research Laboratory, Oak Ridge Institute for Science and Education, Wright-Patterson AFB, OH, United States
| | - Justin R. Estepp
- 711th Human Performance Wing, Air Force Research Laboratory, Wright-Patterson AFB, OH, United States
- *Correspondence: Justin R. Estepp,
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Chen J, Zhou L, Jiang C, Chen Z, Zhang L, Zhou H, Kang W, Jiang X, Li Y, Luo N, Yao M, Niu M, Chen S, Zuo XN, Li L, Liu J. Impaired Ocular Tracking and Cortical Atrophy in Idiopathic Rapid Eye Movement Sleep Behavior Disorder. Mov Disord 2022; 37:972-982. [PMID: 35107831 DOI: 10.1002/mds.28931] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 12/14/2021] [Accepted: 12/17/2021] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of synucleinopathies. Patients with synucleinopathies frequently display eye movement abnormalities. However, whether patients with iRBD have eye movement abnormalities remains unknown. OBJECTIVE The aim of this study was to assess eye movement abnormalities and related gray matter alterations and explore whether such abnormalities can serve as biomarkers to indicate phenoconversion to synucleinopathies in iRBD. METHODS Forty patients with iRBD with early disease progression and 35 healthy control subjects participated in a 15-minute ocular-tracking task that evaluated their control of eye movement abilities. They also underwent clinical assessments for olfactory function, nonmotor symptoms, and autonomic symptoms, all of which are biomarkers to predict phenoconversion to synucleinopathies in iRBD. A subgroup of the participants (20 patients with iRBD and 20 healthy control subjects) also participated in structural magnetic resonance imaging. RESULTS The ocular-tracking ability in patients with iRBD was inferior to that of healthy control subjects in two aspects: pursuit initiation and steady-state tracking. Cortical thinning in the right visual area V4 in patients with iRBD is coupled with impaired pursuit initiation. Furthermore, prolonged pursuit initiation in patients with iRBD exhibits a trend of correlation with olfactory loss, the earliest biomarker that develops prior to other prodromal biomarkers. CONCLUSIONS We found ocular-tracking abnormalities in patients with iRBD even early in their disease progression that have not been reported before. These abnormalities are coupled with atrophy of brain areas involved in the perception of object motion and might indicate phenoconversion to synucleinopathies in iRBD. © 2022 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Jing Chen
- Faculty of Arts and Science, New York University Shanghai, Shanghai, China
- NYU-ECNU Institute of Brain and Cognitive Science at New York University Shanghai, Shanghai, China
- Key Laboratory of Brain Functional Genomics, East China Normal University, Shanghai, China
| | - Liche Zhou
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chao Jiang
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhichun Chen
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lina Zhang
- Department of Biostatistics, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haiyan Zhou
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenyan Kang
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xufeng Jiang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuanyuan Li
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ningdi Luo
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mengsha Yao
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mengyue Niu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shengdi Chen
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xi-Nian Zuo
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Li Li
- Faculty of Arts and Science, New York University Shanghai, Shanghai, China
- NYU-ECNU Institute of Brain and Cognitive Science at New York University Shanghai, Shanghai, China
- Key Laboratory of Brain Functional Genomics, East China Normal University, Shanghai, China
| | - Jun Liu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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8
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Zhu M, Tang Y, Wang Z, Shen T, Qiu X, Yan J, Chen J. Clinical characteristics and risk factors of acute acquired concomitant esotropia in last 5 years: a retrospective case-control study. Eye (Lond) 2022. [PMID: 35075284 DOI: 10.1037/s41433-022-01939-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVES A remarkable increase in the number and proportion of surgical patients with acute acquired concomitant esotropia (AACE) has been noted in our hospital in recent years. We aimed to analyse the clinical characteristics and associated risk factors of this increasing number of strabismus in last 5 years. METHODS Medical information was obtained in 62 AACE patients and 73 orthotropic patients as control group completed questionnaires and examination items from March 2017 to May 2020. Data included age at onset, refractive error, angle of deviation, binocular vision, eye care habits, and optical quality of spectacles. RESULTS Of the 62 AACE patients, the mean ± standard deviation age at onset was 25.3 ± 8.5 years, with 47 (75.8%) cases showing myopia, 9 (14.5%) showing emmetropia, and 6 (9.7%) showing hypermetropia. Among the AACE patients, 35 (56.5%) performed >8 h of close work daily and 36 (58.1%) reported late-night use of digital devices. When compared with the control group, the risk factors identified for AACE included long durations of close work (odds ratio [OR], 11.72; 95% confidence interval [CI], 3.53-38.91; P < 0.001) and immoderate late-night use of digital devices (OR, 14.29; 95% CI, 4.10-49.72; P < 0.001). CONCLUSION Our study demonstrated that young adults accounted for the majority of the growing number of individuals affected by AACE in last 5 years, and excessive close visual activities and immoderate late-night use of digital devices were found to be associated with the onset of AACE.
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Affiliation(s)
- Minyi Zhu
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.,Department of Ophthalmology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Yan Tang
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.,Department of Ophthalmology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Zhonghao Wang
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Tao Shen
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xuan Qiu
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jianhua Yan
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jingchang Chen
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
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9
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Tyson TL, Flynn-Evans EE, Stone LS. Differential saccade-pursuit coordination under sleep loss and low-dose alcohol. Front Neurosci 2022; 16:1067722. [PMID: 36874639 PMCID: PMC9978352 DOI: 10.3389/fnins.2022.1067722] [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: 10/12/2022] [Accepted: 12/07/2022] [Indexed: 02/18/2023] Open
Abstract
Introduction Ocular tracking of a moving object requires tight coordination between smooth pursuit and saccadic eye movements. Normally, pursuit drives gaze velocity to closely match target velocity, with residual position offsets corrected by catch-up saccades. However, how/if common stressors affect this coordination is largely unknown. This study seeks to elucidate the effects of acute and chronic sleep loss, and low-dose alcohol, on saccade-pursuit coordination, as well as that of caffeine. Methods We used an ocular tracking paradigm to assess three metrics of tracking (pursuit gain, saccade rate, saccade amplitude) and to compute "ground lost" (from reductions in steady-state pursuit gain) and "ground recouped" (from increases in steady-state saccade rate and/or amplitude). We emphasize that these are measures of relative changes in positional offsets, and not absolute offset from the fovea. Results Under low-dose alcohol and acute sleep loss, ground lost was similarly large. However, under the former, it was nearly completely recouped by saccades, whereas under the latter, compensation was at best partial. Under chronic sleep restriction and acute sleep loss with a caffeine countermeasure, the pursuit deficit was dramatically smaller, yet saccadic behavior remained altered from baseline. In particular, saccadic rate remained significantly elevated, despite the fact that ground lost was minimal. Discussion This constellation of findings demonstrates differential impacts on saccade-pursuit coordination with low-dose alcohol impacting only pursuit, likely through extrastriate cortical pathways, while acute sleep loss not only disrupts pursuit but also undermines saccadic compensation, likely through midbrain/brainstem pathways. Furthermore, while chronic sleep loss and caffeine-mitigated acute sleep loss show little residual pursuit deficit, consistent with uncompromised cortical visual processing, they nonetheless show an elevated saccade rate, suggesting residual midbrain and/or brainstem impacts.
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Affiliation(s)
- Terence L Tyson
- Visuomotor Control Laboratory, Human Systems Integration Division, NASA Ames Research Center, Moffett Field, CA, United States
| | - Erin E Flynn-Evans
- Fatigue Countermeasures Laboratory, Human Systems Integration Division, NASA Ames Research Center, Moffett Field, CA, United States
| | - Leland S Stone
- Visuomotor Control Laboratory, Human Systems Integration Division, NASA Ames Research Center, Moffett Field, CA, United States
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10
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Rodríguez-Almagro D, Barassi G, Bertollo M, Obrero-Gaitán E, Di Iorio A, Prosperi L, Achalandabaso-Ochoa A, Lomas-Vega R, Ibáñez-Vera AJ. Manual Therapy Approach to the Extraocular Muscles in Migraine Treatment: A Preliminary Study. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1375:29-37. [DOI: 10.1007/5584_2021_704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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11
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Abstract
Hitting a baseball, one of the most difficult skills in all of sports, requires complex hand-eye coordination, but its link with basic visuomotor capabilities remains largely unknown. Here we examined basic visuomotor skills of baseball players and demographically matched nonathletes by measuring their ocular-tracking and manual-control performance. We further investigated how these two capabilities relate to batting performance in baseball players. Compared to nonathletes, baseball players showed better ocular-tracking and manual-control capabilities, which remain unchanged with increasing baseball experience. Both, however, become more correlated with batting accuracy with increasing experience. Ocular-tracking performance is predictive of batting skill, accounting for ≥ 70% of the variance in batting performance across players with ≥ 10 years of experience. A simple linear additive-noise cascade model with shared front-end visual noise that limits batting performance can explain many of our results. Our findings show that fundamental visuomotor capabilities can predict the complex, learned skill of baseball batting.
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Affiliation(s)
- Rongrong Chen
- Department of Psychology, The University of Hong Kong, Hong Kong SAR.,Division of Science & Technology, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, PRC.,
| | - Leland S Stone
- Human Systems Integrations Division, NASA Ames Research Center, Moffett Field, CA, USA.,
| | - Li Li
- Department of Psychology, The University of Hong Kong, Hong Kong SAR.,Faculty of Arts and Science, New York University Shanghai, Shanghai, PRC.,NYU-ECNU Institute of Brain and Cognitive Science at New York University Shanghai, Shanghai, PRC.,
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12
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Kain JN, Sharpp TJ. Addressing sleep deprivation in hospitalized patients. Nursing 2021; 51:11-12. [PMID: 34014868 DOI: 10.1097/01.nurse.0000751736.17544.92] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Jennifer N Kain
- Jennifer N. Kain is an RN at Dignity Health Mercy Foundation in Greater Sacramento, Calif. Tara J. Sharpp is an associate professor at California State University, Sacramento, Calif
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13
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Tseng VWS, Valliappan N, Ramachandran V, Choudhury T, Navalpakkam V. Digital biomarker of mental fatigue. NPJ Digit Med 2021; 4:47. [PMID: 33707736 PMCID: PMC7952693 DOI: 10.1038/s41746-021-00415-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 02/08/2021] [Indexed: 01/28/2023] Open
Abstract
Mental fatigue is an important aspect of alertness and wellbeing. Existing fatigue tests are subjective and/or time-consuming. Here, we show that smartphone-based gaze is significantly impaired with mental fatigue, and tracks the onset and progression of fatigue. A simple model predicts mental fatigue reliably using just a few minutes of gaze data. These results suggest that smartphone-based gaze could provide a scalable, digital biomarker of mental fatigue.
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14
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Tyson TL, Feick NH, Cravalho PF, Flynn-Evans EE, Stone LS. Dose-dependent sensorimotor impairment in human ocular tracking after acute low-dose alcohol administration. J Physiol 2020; 599:1225-1242. [PMID: 33332605 PMCID: PMC7898833 DOI: 10.1113/jp280395] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 10/13/2020] [Indexed: 11/08/2022] Open
Abstract
Key points Oculomotor behaviours are commonly used to evaluate sensorimotor disruption due to ethanol (EtOH). The current study demonstrates the dose‐dependent impairment in oculomotor and ocular behaviours across a range of ultra‐low BACs (<0.035%). Processing of target speed and direction, as well as pursuit eye movements, are significantly impaired at 0.015% BAC, suggesting impaired neural activity within brain regions associated with the visual processing of motion. Catch‐up saccades during steady visual tracking of the moving target compensate for the reduced vigour of smooth eye movements that occurs with the ingestion of low‐dose alcohol. Saccade dynamics start to become ‘sluggish’ at as low as 0.035% BAC. Pupillary light responses appear unaffected at BAC levels up to 0.065%.
Abstract Changes in oculomotor behaviours are often used as metrics of sensorimotor disruption due to ethanol (EtOH); however, previous studies have focused on deficits at blood‐alcohol concentrations (BACs) above about 0.04%. We investigated the dose dependence of the impairment in oculomotor and ocular behaviours caused by EtOH administration across a range of ultra‐low BACs (≤0.035%). We took repeated measures of oculomotor and ocular performance from sixteen participants, both pre‐ and post‐EtOH administration. To assess the neurological impacts across a wide range of brain areas and pathways, our protocol measured 21 largely independent performance metrics extracted from a range of behavioural responses ranging from ocular tracking of radial step‐ramp stimuli, to eccentric gaze holding, to pupillary responses evoked by light flashes. Our results show significant impairment of pursuit and visual motion processing at 0.015% BAC, reflecting degraded neural processing within extrastriate cortical pathways. However, catch‐up saccades largely compensate for the tracking displacement shortfall caused by low pursuit gain, although there still is significant residual retinal slip and thus degraded dynamic acuity. Furthermore, although saccades are more frequent, their dynamics are more sluggish (i.e. show lower peak velocities) starting at BAC levels as low as 0.035%. Small effects in eccentric gaze holding and no effect in pupillary response dynamics were observed at levels below 0.07%, showing the higher sensitivity of the pursuit response to very low levels of blood alcohol, under the conditions of our study. Oculomotor behaviours are commonly used to evaluate sensorimotor disruption due to ethanol (EtOH). The current study demonstrates the dose‐dependent impairment in oculomotor and ocular behaviours across a range of ultra‐low BACs (<0.035%). Processing of target speed and direction, as well as pursuit eye movements, are significantly impaired at 0.015% BAC, suggesting impaired neural activity within brain regions associated with the visual processing of motion. Catch‐up saccades during steady visual tracking of the moving target compensate for the reduced vigour of smooth eye movements that occurs with the ingestion of low‐dose alcohol. Saccade dynamics start to become ‘sluggish’ at as low as 0.035% BAC. Pupillary light responses appear unaffected at BAC levels up to 0.065%.
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Affiliation(s)
- Terence L Tyson
- Visuomotor Control Laboratory, Human Systems Integration Division, NASA Ames Research Center, Moffett Field, CA, USA
| | | | | | - Erin E Flynn-Evans
- Fatigue Countermeasures Laboratory, Human Systems Integration Division, NASA Ames Research Center, Moffett Field, CA, USA
| | - Leland S Stone
- Visuomotor Control Laboratory, Human Systems Integration Division, NASA Ames Research Center, Moffett Field, CA, USA
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15
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Bilbao C, Piñero DP. Clinical Characterization of Oculomotricity in Children with and without Specific Learning Disorders. Brain Sci 2020; 10:brainsci10110836. [PMID: 33187134 PMCID: PMC7697867 DOI: 10.3390/brainsci10110836] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 11/05/2020] [Accepted: 11/08/2020] [Indexed: 11/16/2022] Open
Abstract
Children with specific learning disorders have been associated with oculomotor problems, with their analysis even suggested to be a potential diagnostic tool. A prospective non-randomized comparative study evaluating 59 children (6–13 years old) divided into three groups was conducted: a control group (CG) including 15 healthy emmetropic children; a group of 18 healthy children with oculomotor abnormalities (OAG); and a group of 26 children diagnosed with specific learning disorders (LDG). In all groups, besides a complete eye exam, oculomotricity was characterized with two clinical tests: Northeastern State University College of Optometry’s Oculomotor (NSUCO) and Developmental Eye Movement (DEM) tests. Concerning the NSUCO test, lower ability, precision, and head/body movement associated scorings were obtained for both smooth pursuits and saccades in OAG and LDG when compared to the CG (p < 0.001). Likewise, significantly longer time needed to read the horizontal sheet of the DEM test and a higher DEM ratio were found in OAG and LDG compared to CG (p ≤ 0.003). No differences between LDG and OAG were found in the performance with the two oculomotor tests (p ≥ 0.141). Oculomotor anomalies can be present in children with and without specific learning disorders, and therefore cannot be used as diagnostic criteria of these type of disorders.
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Affiliation(s)
- Carmen Bilbao
- Department of Optometry, Policlínica Alto Aragón, 22003 Huesca, Spain;
| | - David P. Piñero
- Group of Optics and Visual Perception, Department of Optics, Pharmacology and Anatomy, University of Alicante, 03690 San Vicente del Raspeig-Alicante, Spain
- Department of Ophthalmology, Vithas Medimar International Hospital, 03016 Alicante, Spain
- Correspondence: ; Tel.: +34-965-90-34-00
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16
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Valliappan N, Dai N, Steinberg E, He J, Rogers K, Ramachandran V, Xu P, Shojaeizadeh M, Guo L, Kohlhoff K, Navalpakkam V. Accelerating eye movement research via accurate and affordable smartphone eye tracking. Nat Commun 2020; 11:4553. [PMID: 32917902 PMCID: PMC7486382 DOI: 10.1038/s41467-020-18360-5] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 08/13/2020] [Indexed: 11/08/2022] Open
Abstract
Eye tracking has been widely used for decades in vision research, language and usability. However, most prior research has focused on large desktop displays using specialized eye trackers that are expensive and cannot scale. Little is known about eye movement behavior on phones, despite their pervasiveness and large amount of time spent. We leverage machine learning to demonstrate accurate smartphone-based eye tracking without any additional hardware. We show that the accuracy of our method is comparable to state-of-the-art mobile eye trackers that are 100x more expensive. Using data from over 100 opted-in users, we replicate key findings from previous eye movement research on oculomotor tasks and saliency analyses during natural image viewing. In addition, we demonstrate the utility of smartphone-based gaze for detecting reading comprehension difficulty. Our results show the potential for scaling eye movement research by orders-of-magnitude to thousands of participants (with explicit consent), enabling advances in vision research, accessibility and healthcare.
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Affiliation(s)
| | - Na Dai
- Google Research, Mountain View, CA, USA
| | - Ethan Steinberg
- Google Research, Mountain View, CA, USA
- Stanford University, Stanford, CA, USA
| | | | - Kantwon Rogers
- Google Research, Mountain View, CA, USA
- Georgia Institute of Technology, Atlanta, GA, USA
| | | | | | | | - Li Guo
- Google Research, Mountain View, CA, USA
- Johns Hopkins University, Baltimore, MD, USA
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17
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Ghanem M, Mallah SI, Iskandar MA, Tharmaratnam T. Sleep deprivation‐induced oculomotor dysfunction: implications for surgical and medical performance. J Physiol 2020; 598:1437-1439. [DOI: 10.1113/jp279357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- Mohannad Ghanem
- School of Medicine Royal College of Surgeons in Ireland–Bahrain
| | | | | | - Tharmegan Tharmaratnam
- School of Medicine Royal College of Surgeons in Ireland–Bahrain
- School of Medicine Royal College of Surgeons in Ireland‐Dublin Ireland
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