Retinal Vascular Hemodynamic Changes in Patients with Ischemic Stroke Investigated by Fundus Laser Speckle Contrast Imaging

Retinal Vascular Hemodynamic Changes in Patients with Ischemic Stroke Investigated by Fundus Laser Speckle Contrast Imaging | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Retinal Vascular Hemodynamic Changes in Patients with Ischemic Stroke Investigated by Fundus Laser Speckle Contrast Imaging Xiao Wu, Yue Yu, Li Hui, Wei Sun, Aini He, Benke Zhao, Xuefan Yao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5377287/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Purpose: To investigate the retinal hemodynamic changes in patients with ischemic stroke using fundus laser speckle contrast imaging (LSCI) and evaluate their microcirculatory impairment. Methods: This cross-sectional study was conducted in the Department of Neurology at Xuanwu Hospital, Capital Medical University. An integrated retinal imaging instrument was employed to collect images of retinal vascular LSCI in patients with ischemic stroke, and the pulse wave waveform parameters were compared with healthy controls. Results: A total of 119 patients with 233 eyes in ischemic stroke group and 67 patients with 113 eyes in the healthy control group were enrolled. Among the fundus vascular waveform parameters, the Flow Acceleration Index (FAI) and Resistivity Index (RI) in ischemic stroke patients were higher than those in the healthy control group ( p = 0.028 and 0.015, respectively), while the Blowout Time (BOT), Blowout Score (BOS) and Rising Rate (RR) were lower than those in the control group ( p values of 0.021, 0.014, and 0.010, respectively). After correcting for confounders by multifactor logistic regression, BOT, RR, and RI ( p values of 0.008, 0.020, and 0.049, respectively) remained statistically significant. Furthermore, most hemodynamic parameters in healthy controls showed significant correlations with age [FAI ( r = 0.200, p = 0.041), BOT ( r = -0.221, p = 0.020), BOS ( r = -0.232, p = 0.014), RI ( r = 0.218, p = 0.020)], whereas few indicators in ischemic stroke patients exhibited a correlation with age. Conclusion: Retinal vascular elasticity in ischemic stroke patients is compromised, and the process of changing microcirculation hemodynamics with aging is disrupted. Retinal hemodynamic parameters may serve as potential indicators for evaluating microcirculatory injury in ischemic stroke patients. Retinal Vessel Hemodynamic Ischemic Stroke Fundus Laser Speckle Contrast Imaging Figures Figure 1 Figure 2 Figure 3 Introduction Stroke is the second leading cause of disability and death worldwide [ 1 ], and the leading cause of disability and death among Chinese adults [ 2 ]. Ischemic stroke (IS) accounts for approximately 80% of all strokes [ 3 ]. IS is characterized by high morbidity, elevated disability rate, significant mortality rates, and high recurrence rates, imposing a substantial burden on individuals and society [ 4 ]. This burden is expected to increase further due to an aging population and the persistent high incidence of risk factors such as hypertension. In recent years, numerous studies have focused on the alteration of retinal neurovascular structures in cerebrovascular diseases. The eye is closely connected to the brain. Anatomically and developmentally, the retina shares similar embryological origins, anatomical features, and physiological characteristics with the central nervous system. Considered an extension of the central nervous system, ocular symptoms often precede the conventional diagnosis of central nervous system disorders [ 5 ]. Furthermore, the retina can be directly observed in the human body, providing a direct, non-invasive method for observing for the central nervous system. Therefore, the evaluation of ocular vascular status serves as an effective entry point for cerebrovascular research [ 6 ]. Retinal vessels share the same risk factors as cerebrovascular vessels, exhibiting similar manifestations of vascular injury and radiographic findings. Additionally, hemodynamic similarities can be observed between the vasculature of the retina and the cerebral penetrating arteries [ 6 ]. Direct observation of retinal vascular through non-invasive techniques allows for the early detection of hemodynamic changes, which may indicate the presence of subclinical brain injury and help mitigate the progression of cerebrovascular disease [ 7 ]. Accumulating evidence suggests that a variety of retinal vascular signs and diseases are associated with cerebrovascular disease [ 7 – 9 ]. The investigation of cerebrovascular disease based on retinal blood vessels can be categorized into studies of static vascular morphology and studies of dynamic blood flow. Previous research has predominantly focused on the morphological changes of retinal blood vessels in IS patient, utilizing static color fundus photography or optical coherence tomography/ optical coherence tomography angiography (OCT/OCTA) [ 10 – 12 ]. These studies have identified various alterations, including narrowing of arterial diameter, thickening of venous diameter, arteriovenous crossing, vascular density. However, there is a scarcity of research examining the dynamic blood flow function of the fundus in relation to cerebrovascular disease [ 13 ], primarily due to limitations in study conditions. The laser speckle contrast imaging (LSCI) system has the capability to measure the correlation index of blood flow fluctuations, utilizing the laser speckle phenomenon to assess ocular blood flow non-invasively [ 14 ]. This system enables the averaging of signal values for blood flow velocity at the optic disc, allowing for the generation of pulse waveforms for each cardiac cycle. Software is then employed to calculate parameters analogous to the pulse wave [ 14 – 16 ]. In this study, we employed LSCI to measure the dynamic blood flow parameters of fundus microcirculation in patients with IS, aiming to assess retinal hemodynamic changes and identify potential microcirculatory impairment. Materials and methods This cross-sectional prospective study was conducted at the Department of Neurology, Xuanwu Hospital, Capital Medical University from October 2020 to December 2022. Fundus LSCI were collected from patients with IS in the outpatient clinic or ward of the Department of Neurology, Xuanwu Hospital, Capital Medical University. This study was approved by the Institutional Ethics Committee of Xuanwu Hospital Capital Medical University (ID: [2020]050). In accordance with the principles of the Declaration of Helsinki, all participants have completed the relevant written informed consent before imaging data collection. Participants The inclusion criteria for patients with ischemic stroke were as follows: (1) a confirmed diagnosis of ischemic stroke within the past 6 months, based on medical history, symptoms, signs, and imaging; (2) the ability to maintain consciousness, understand, and cooperate with relevant examinations, as well as the capacity to sign the informed consent form. The exclusion criteria were: (1) serious ophthalmic diseases that could affect vision, such as glaucoma, cataracts, keratitis, and age-related macular degeneration; (2) the presence of serious life-threatening primary diseases or mental illnesses. For the control group, the inclusion criteria were similar: participants needed to be conscious, understand and cooperate with relevant examinations, as well as sign the informed consent form. The exclusion criteria for the control group were: (1) a previous history of stroke; (2) serious ophthalmic diseases affecting vision, such as glaucoma, cataracts, keratitis, and age-related macular degeneration. Fundus data collection The imaging equipment utilized in this study is a multifunctional fundus optical imaging system developed by the Department of Biomedical Engineering, College of Future Technology, Peking University. This system is based on the optical path platform of a fundus camera, and is designed to create a multi-functional optical system for in vivo retinal imaging. It integrates retinal imaging, retinal oxygen saturation measurement, and retinal hemodynamic measurement [ 17 ]. The device calculates the oxygen saturation distribution from images captured at 550 nm and 600 nm in the multispectral range [ 18 ], while blood flow is assessed using LSCI principle [ 19 ]. The system adheres to the safety standards for lasers employed in ophthalmology clinics concerning the human eye. Notably, participants do not need to dilate their pupils prior to data collection. The acquisition process is as follows: first, multispectral image acquisition is conducted, after which the patient is instructed to close their eyes and rest for 2 minutes, followed by a 5-second acquisition of fundus laser speckle images. If the patient can tolerate the procedure, data from the other eye also be collected. The values for the right and left eyes of the same individual may differ; however, both values reflect the microcirculation of the patient. Therefore, when data from both eyes are collected, both sets are analyzed. This approach has also been adopted by similar studies [ 15 ]. Meaning of the parametric indicator Pulse wave waveform parameters: For LSCI imaging, a speckle image lasting 5 seconds is acquired, comprising approximately 400 frames. The mean blur rate (MBR) cumulative value within the circular region of interest (ROI) that encompasses the optic disc area serves as the baseline for the single frame image, while the fluctuations observed between image sequences represent an approximate pulse wave signal that varies with each heartbeat (Fig. 1 ). In conjunction with prior studies [ 20 ], the following pulse waveform parameters are selected: Flow Acceleration Index (FAI), Acceleration Time Index (ATI), Blowout Time (BOT), Blowout Score (BOS), Rising Rate (RR), Falling Rate (FR), and Resistivity Index (RI), which are defined as follows: FAI is defined as the blood flow acceleration index, as described in Eq. ( 1 ). It represents the maximum increment of the signal value between two frames, reflecting the combined effects of the maximum pumping acceleration of the left ventricle and peripheral resistance. $$\:FAI\:\left[au\right]={max}\left(\frac{\varDelta\:y}{\varDelta\:x}\right),x\:is\:in\:the\:rising\:period$$ 1 ATI is defined as the ratio of the time taken for the signal value to reach its peak to the total duration of the heartbeat, as illustrated in Eq. ( 2 ). $$\:ATI=\frac{The\:duration\:of\:the\:rising\:period}{Full\:heartbeat\:cycle\:duration}$$ 2 BOT is defined as blowout time, as indicated in Eq. (3), represents the proportion of time during which the signal value is at a high level. Specifically, it is the ratio of the duration for which the waveform exceeds half of the average value of the minimum and maximum signals to the overall heartbeat cycle. A higher BOT is considered an indicator of effective perfusion between two heartbeats. \(\:BOT=\text{C}\text{*}\frac{Duration\:of\:signal\:above\:semi-high\:level}{Full\:heartbeat\:cycle\:duration}\) , C is a constant term (3) BOS is defined as blowout score, as indicated in Eq. ( 4 ), is analogous to BOT and serves as an indicator of blood flow sustained between heartbeat cycles. It is calculated based on the difference between the maximum and minimum signal values, as well as the average signal value. A higher BOS indicates a greater constancy of blood flow during the cardiac cycle and improved perfusion. $$\:BOS=\frac{(2-\text{A}\text{m}\text{p}\text{l}\text{i}\text{t}\text{u}\text{d}\text{e}\:\text{o}\text{f}\:\text{w}\text{a}\text{v}\text{e}\text{f}\text{o}\text{r}\text{m}/\text{M}\text{e}\text{a}\text{n}\:\text{v}\text{a}\text{l}\text{u}\text{e}\:\text{o}\text{f}\:\text{w}\text{a}\text{v}\text{e}\text{f}\text{o}\text{r}\text{m})}{2}\text{*}100$$ 4 RR is defined as Eq. ( 5 ), represents the steepness of the ascending portion of the waveform curve of the signal value. A higher RR value corresponds to a more abrupt increase in the signal value. $$\:RR=\frac{Area\:under\:the\:rising\:curve}{Total\:rectangular\:area\:of\:the\:rising\:period}$$ 5 FR is defined as Eq. ( 6 ), represents the steepness of the descending segment of the waveform curve of the signal value. A higher FR value corresponds to a more abrupt decrease in the signal value.. $$\:FR=\frac{Area\:under\:the\:decline\:curve}{Total\:rectangular\:area\:of\:the\:descent\:period}$$ 6 RI is defined as Eq. ( 7 ), represents the ratio of the difference between the maximum and minimum signal values to the maximum signal value. A higher RI indicates greater peripheral circulation resistance. $$\:RI=\frac{Waveform\:peak-Waveform\:valley}{Waveform\:peak}$$ 7 Statistical analysis The Shapiro-Wilk method was employed to assess the normality of the continuous data. Normally distributed continuous data were expressed as mean ± standard deviation (x ± s), with the t-test applied for comparisons between two independent sample groups, while the paired t-test was utilized for paired samples. Non-normally distributed continuous data were expressed as median and interquartile range [M (P25, P75)], using the rank-sum test for comparisons between two independent sample groups and the paired rank-sum test for paired samples. Categorical data were presented as the number of cases and percentage [cases (%)], with chi-square tests employed for group comparisons. Pearson correlation analysis was conducted to evaluate the correlation between the two sets of measures. Binary logistic regression was implemented to control for confounding factors, and values exceeding three standard deviations from the dataset were excluded as outliers. A two-tailed p-value of < 0.05 was deemed statistically significant. Statistical analyses were performed using SPSS version 25.0 (IBM Corporation, Armonk, NY, USA). Results From September 2020 to September 2022, a total of 147 patients (285 eyes) with stroke were enrolled in the Department of Neurology at Xuanwu Hospital, Capital Medical University. Following image screening, 119 patients (233 eyes) were included in the study, all of whom were Han Chinese, with 171 (73.4%) eyes identified as male. Additionally, there were 67 healthy control subjects (113 eyes) without cerebrovascular disease, all of whom were also Han Chinese, comprising 50 (44.2%) eyes identified as male. The baseline data, along with fundus vascular oxygen and blood flow waveform parameters of the patients, are presented in Table 1 . Notably, patients with ischemic stroke were predominantly male ( p < 0.001), older ( p = 0.034), and exhibited higher mean arterial pressure ( p < 0.001). Furthermore, the prevalence of previous hypertension, diabetes, smoking, and alcohol consumption was significantly higher among these patients (all p values < 0.001). Figure 1 presents a typical LSCI pulse waveform plot of a 70-year-old male diagnosed with acute ischemic stroke. Table 1 The baseline data of the patient and the fundus vascular blood oxygen and blood flow waveform parameters Totol Ischemic stroke Control p Eyes 346 233 113 Male [case (%)] 221(63.9) 171(73.4) 50(44.2) < 0.001 ∗ Age [M(P25, P75), years] 58(51,66) 62(52,67) 58(50,64) 0.034 ∗ BMI[M(P25, P75), kg/m2] 25.34(22.44,26.72) 25.39(22.29,27.11) 24.22(22.57,26.43) 0.058 Mean arterial pressure [M(P25, P75), mmHg] 97.00(91.67,105.67) 98.67(93.00,108.33) 94.67(88.67,99.00) < 0.001 ∗ Anamnesis [case (%)] History of hypertension 153(44.2) 121(51.9) 32(28.3) < 0.001 ∗ History of diabetes mellitus 68(19.7) 62(26.6) 6(5.3) < 0.001 ∗ History of smoking 83(24.0) 73(31.3) 10(8.8) < 0.001 ∗ History of alcohol consumption 86(24.9) 76(32.6) 10(8.8) < 0.001 ∗ Fundus blood vessel blood oxygen saturation [M(P25, P75), %] SaO 2 84.64(80.12,88.29) 84.91(80.22,88.84) 84.07(80.06,87.76) 0.329 SvO 2 45.47(40.85,52.03) 45.99(40.77,53.57) 44.61(40.85,50.22) 0.115 Fundus blood vessel waveform parameters [M(P25, P75)] FAI 0.02(0.01,0.02) 0.02(0.01,0.02) 0.02(0.01,0.02) 0.028 ∗ ATI 44.66(40.86,48.38) 47.74(41.18,47.93) 44.10(40.17,49.11) 0.862 BOT 37.84(36.07,39.95) 37.66(35.68,39.66) 38.43(36.52,41.03) 0.021 ∗ BOS 68.61(66.48,70.21) 68.37(65.99,69.96) 69.03(67.30,70.86) 0.014 ∗ RR 10.75(10.42,11.09) 10.71(10.42,11.02) 10.82(10.45,11.27) 0.010 ∗ FR 14.46(13.65,15.11) 14.46(13.64,15.08) 14.47(13.66,15.20) 0.583 RI 0.44(0.35,0.56) 0.45(0.36,0.58) 0.41(0.31,0.51) 0.015 ∗ ∗ The p-value < 0.05 Among the observation indicators, there was no significant difference in the oxygen saturation of fundus vascular arteries and veins between ischemic stroke patients and healthy controls ( p = 0.329 and p = 0.115, respectively). However, in the fundus vascular waveform parameters, the FAI and RI in the cerebrovascular disease group were higher than those in the control group ( p = 0.028 and p = 0.015, respectively), while the BOT, BOS, and RR were lower than those in the control group ( p values = 0.021, 0.014, and 0.010, respectively; Table 1 ). After applying multivariate logistic regression to correct for confounding factors such as gender, age, history of hypertension, diabetes mellitus, smoking, alcohol consumption, BMI, and mean arterial pressure, BOT, RR, and RI remained statistically significant ( p values = 0.008, 0.020, and 0.049, respectively; Table 2 ). Table 2 Univariate and multivariate logistic regression were performed for different groups of outcome measures Univariate regression OR(95%CI) p Multivariate regression # OR(95%CI) p Fundus vascular oxygen saturation SaO 2 0.985(0.947–1.025) 0.466 0.989(0.962–1.1017) 0.441 SvO 2 0.974(0.945–1.004) 0.085 0.976(0.941–1.012) 0.189 Fundus vascular waveform parameters TAI / 0.077 / 0.717 ATI 1.009(0.970–1.050) 0.643 1.042(0.992–1.095) 0.103 BOT 1.087(1.016–1.163) 0.015 ∗ 1.121(1.030–1.220) 0.008 ∗ BOS 1.119(1.033–1.212) 0.006 ∗ 1.101(0.997–1.216) 0.057 RR 1.817(1.198–2.756) 0.005 ∗ 1.927(1.109–3.351) 0.020 ∗ FR 1.022(0.838–1.246) 0.830 1.223(0.947–1.579) 0.123 RI 0.128(0.030–0.557) 0.006 ∗ 0.159(0.025–0.990) 0.049 ∗ # Gender, age, history of hypertension, history of diabetes, history of smoking, history of alcohol use, BMI, and mean arterial pressure were adjusted / The value cannot be displayed because it is too large ∗ The p-value < 0.05 Pearson correlation analysis was conducted on all waveform parameter values (FAI, ATI, BOT, BOS, RR, FR, RI) for both the cerebrovascular disease group and the control group. Scatter plots of the age distribution are presented in Figs. 2 and 3 . The Pearson correlation coefficient ( r ) of each parameter with age distribution was calculated for all groups. In the control group, FAI ( r = 0.200, p = 0.041), BOT ( r = -0.221, p = 0.020), BOS ( r = -0.232, p = 0.014), and RI ( r = 0.218, p = 0.020) showed significant correlations with age, and the correlation magnitudes of these four indicators were similar. However, in ischemic stroke patients, only BOS ( r = -0.154, p = 0.021) was significantly correlated with age. Discussion In this study, LSCI was employed to assess ocular microcirculation parameters in ischemic stroke patients and to investigate the pulse wave shape parameters and age distribution in the optic disc area. Based on a comprehensive literature search and review, this study represents the first application of LSCI, a non-invasive technique, to observe changes in dynamic blood flow function in the fundus related to ischemic stroke. The incidence and prevalence of stroke are notably high in China [ 21 ], with intracranial atherosclerosis identified as a primary cause of ischemic stroke among the Chinese population. Atherosclerosis is characterized by a decline in the elasticity of blood vessels. In healthy blood vessels, the arterial walls possess a degree of elasticity, allowing them to expand as blood is rapidly pumped into the arterial system during cardiac systole. This elasticity facilitates the continuous flow of blood into the microcirculation as the heart contracts. Theoretically, vascular elasticity can be assessed by measuring variations in microcirculatory blood flow [ 15 ]. In the pathophysiology of intracranial arteriosclerosis, this condition represents a roughly linear chronic process that persists throughout an individual's life course [ 22 ]. As we age, the walls of blood vessels gradually lose elasticity and increase in stiffness, a phenomenon observed in every individual, with arteriosclerosis serving as a non-interventionable risk factor. Consequently, when measuring changes in blood flow within microcirculation, the relevant assessment parameters should demonstrate a significant correlation with age. In our study involving healthy subjects, FAI ( r = 0.200, p = 0.041), BOT ( r = -0.221, p = 0.020), BOS ( r = -0.232, p = 0.014), and RI ( r = 0.218, p = 0.020) exhibited significant correlations with age. Specifically, FAI increases with age, indicating a faster rise in systolic blood flow and a diminished capacity to buffer increases in blood flow, which suggests poor arterial elasticity. Conversely, BOT decreases with age, reflecting a shorter time to beat and implying a reduced perfusion time between beats, indicative of arteriosclerosis leading to diminished distal perfusion. Similarly, BOS declines with age, suggesting poor constancy of blood flow and inadequate perfusion during the cardiac cycle. Additionally, RI increases with age, indicating elevated peripheral circulatory resistance. Previous studies involving over 1,000 healthy participants have shown that BOT, FOREST, ATI, RR, and FR are age-related [ 16 ]. However, in our study, no statistically significant correlation was found between ATI, RR, FR, and age, which may suggest that these parameters are not sensitive to changes in vascular wall elasticity, or that the sample size of healthy controls selected in this study was insufficient. Our findings indicate that men are more likely to experience ischemic stroke ( p < 0.001), are older ( p = 0.034), and have higher mean arterial pressure ( p < 0.001), along with a significantly higher prevalence of previous hypertension, diabetes, smoking, and alcohol use ( p -values < 0.001). These results are consistent with established risk factors for the development of arteriosclerosis [ 23 ]. Among the fundus vascular waveform parameters, the FAI and RI in ischemic stroke patients were found to be higher than those in the control group, while the BOT, BOS, and RR were lower. As discussed above, these results suggest poorer arterial elasticity, increased peripheral circulatory resistance, and reduced distal perfusion in ischemic stroke patients. And in the analysis of the correlation between fundus vascular waveform parameters and age performed in IS patients, only the correlation between BOS and age was statistically significant, suggesting that the fundus vasculature of IS patients had been damaged by factors other than the normal aging process. Under the influence of long-term hypertension, diabetes mellitus, and hyperlipidemia, the normal atherosclerotic process of the patients was disturbed, resulting in the loss of age-related correlation of fundus microcirculatory parameters. It is worth noting that intracranial atherosclerosis is categorized as atherosclerosis and vitelliform degeneration. In the large intracranial vessels, atherosclerosis is characterized by lipid infiltration of the arterial intima and a reduction in the elastic tissue of the arterial media, which are the primary pathological changes. In contrast, arterioles lack a continuous elastic layer [ 24 ], and their pathological manifestations include lipid vitreous, fibrinoid necrosis, and arteriolar sclerosis. While cerebral small vessel involvement is noted, atherosclerosis of large vessels is more prevalent in stroke patients [ 25 ]. It is important to note that, except for the central retinal artery, which has a diameter exceeding 100µm in the main trunk of the optic disc, and the large vessels immediately adjacent to the optic disc, the diameters of other branches are less than 100 µm, classifying them as arterioles. Consequently, the parameters representing arteriosclerosis measured in this experiment are exclusively microarteriolar parameters and do not reflect the severity of large vessel arteriosclerosis. However, numerous studies indicate that retinal arterioles are associated with aortic and carotid arteriosclerosis. For instance, a higher degree of aortic sclerosis correlates with stenosis of retinal arterioles and decreased arteriolar pulsatility [ 26 ], which subsequently impacts local metabolism and vasodilation. LSCI parameters can predict carotid intima-media thickness, plaque grade, vascular resistance, and more [ 27 – 29 ]. Furthermore, LSCI can serve as an adjunctive tool to assess tolerance to cerebral ischemia during carotid endarterectomy [ 30 ]. Although we did not measure the degree of sclerosis in large arteries, blood flow to the fundus blood vessels serves as an indicator of the health of intracranial blood vessels, particularly parameters such as FAI, RI, BOT, BOS, and RR. Our research suggests that retinal function parameters may serve as potential indicators for evaluating microcirculatory damage and providing early warnings of stroke and other diseases. Future longitudinal studies should focus on evaluating changes in retinal function parameters to enhance their predictive value. This study has several limitations. First, we conducted only cross-sectional observations and did not implement longitudinal follow-up. Changes in the waveform parameters of fundus blood flow would have been better assessed through longitudinal monitoring. Future studies should establish a cohort to evaluate trends in fundus blood flow over time. Second, we did not categorize stroke severity, primarily due to the variability in stroke cycles among patients; some were already in recovery, which resulted in a lack of representative data regarding stroke severity at the time of onset. Conclusion Retinal vascular elasticity in ischemic stroke patients is compromised, and their microcirculation hemodynamics are disrupted with age. Retinal hemodynamic parameters may serve as potential indicators for evaluating microcirculatory injury in ischemic stroke patients. Abbreviations IS Ischemic Stroke OCT Optical coherence tomography OCTA Optical coherence tomography angiography FFA Fundu fluorescence angiography LSCI Laser speckle contrast imaging ROI Region of interest FAI Flow acceleration index ATI Acceleration time index BOT Blowout time BOS Blowout score RR Rising rate FR Falling rate RI Resistivity index RPC Radial peripapillary capillaries CD Capillary density PI Pulsatility index Declarations Ethics approval and consent to participate This study was approved by the Institutional Ethics Committee of Xuanwu Hospital Capital Medical University (ID: [2020]050). In accordance with the principles of the Declaration of Helsinki, all participants have completed the relevant written informed consent before imaging data collection. Consent for publication Not applicable. Data availability All data supporting our findings are available from the corresponding authors upon reasonable. Competing interests The authors declare no competing interests. Funding This work was supported by National Key Research and Development Program of China (Grant No. 2022YFC3600500 and 2022YFC3600504), Science and Technology Innovation 2030-Major Project (Grant No. 2021ZD0201806) and Xicheng District Finance Science and Technology Program (Grant No. XCSTS-2022-06). Authors’ contributions XW, YY, and LH: writing the manuscript, study concept and design, data collection and management. WS, AH, BZ and XY: analysis and interpretation of data. QR and HS: critical revision of the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Saini V, Guada L, Yavagal DR: Global Epidemiology of Stroke and Access to Acute Ischemic Stroke Interventions . Neurology 2021, 97 (20 Suppl 2):S6-s16. 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Wang YJ, Li ZX, Gu HQ, Zhai Y, Zhou Q, Jiang Y, Zhao XQ, Wang YL, Yang X, Wang CJ et al : China Stroke Statistics: an update on the 2019 report from the National Center for Healthcare Quality Management in Neurological Diseases, China National Clinical Research Center for Neurological Diseases, the Chinese Stroke Association, National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention and Institute for Global Neuroscience and Stroke Collaborations . Stroke and vascular neurology 2022, 7 (5):415-450. Wang Y, Meng R, Liu G, Cao C, Chen F, Jin K, Ji X, Cao G: Intracranial atherosclerotic disease . Neurobiology of disease 2019, 124 :118-132. Holmstedt CA, Turan TN, Chimowitz MI: Atherosclerotic intracranial arterial stenosis: risk factors, diagnosis, and treatment . Lancet Neurol 2013, 12 (11):1106-1114. Pantoni L: Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges . Lancet Neurol 2010, 9 (7):689-701. Barthels D, Das H: Current advances in ischemic stroke research and therapies . Biochimica et biophysica acta Molecular basis of disease 2020, 1866 (4):165260. Holwerda SW, Kardon RH, Hashimoto R, Full JM, Nellis JK, DuBose LE, Fiedorowicz JG, Pierce GL: Aortic stiffness is associated with changes in retinal arteriole flow pulsatility mediated by local vasodilation in healthy young/middle-age adults . Journal of applied physiology (Bethesda, Md : 1985) 2020, 129 (1):84-93. Rina M, Shiba T, Takahashi M, Hori Y, Maeno T: Pulse waveform analysis of optic nerve head circulation for predicting carotid atherosclerotic changes . Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie 2015, 253 (12):2285-2291. Sawada S, Tsuchiya S, Kodama S, Kurosawa S, Endo A, Sugawara H, Hosaka S, Kawana Y, Asai Y, Yamamoto J et al : Vascular resistance of carotid and vertebral arteries is associated with retinal microcirculation measured by laser speckle flowgraphy in patients with type 2 diabetes mellitus . Diabetes research and clinical practice 2020, 165 :108240. Shiba T, Takahashi M, Hori Y, Maeno T: Pulse-wave analysis of optic nerve head circulation is significantly correlated with brachial-ankle pulse-wave velocity, carotid intima-media thickness, and age . Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie 2012, 250 (9):1275-1281. Motoyama Y, Hayashi H, Kawanishi H, Tsubaki K, Takatani T, Takamura Y, Kotsugi M, Kim T, Yamada S, Nakagawa I et al : Ocular blood flow by laser speckle flowgraphy to detect cerebral ischemia during carotid endarterectomy . Journal of clinical monitoring and computing 2021, 35 (2):327-336. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Feb, 2025 Reviews received at journal 18 Jan, 2025 Reviewers agreed at journal 12 Jan, 2025 Reviews received at journal 07 Jan, 2025 Reviewers agreed at journal 03 Jan, 2025 Reviewers agreed at journal 31 Dec, 2024 Reviewers invited by journal 31 Dec, 2024 Editor invited by journal 11 Nov, 2024 Editor assigned by journal 04 Nov, 2024 Submission checks completed at journal 04 Nov, 2024 First submitted to journal 02 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5377287","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376659952,"identity":"70ade23a-edac-42c5-9a13-c17492163e45","order_by":0,"name":"Xiao Wu","email":"","orcid":"","institution":"Xuan Wu Hospital of the Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Wu","suffix":""},{"id":376659954,"identity":"bd5d5d51-e048-4dac-99c3-5db303d59a74","order_by":1,"name":"Yue Yu","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Yu","suffix":""},{"id":376659955,"identity":"bf220a36-a767-4ee2-9ebd-ac789a19dd30","order_by":2,"name":"Li Hui","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Hui","suffix":""},{"id":376659956,"identity":"15ae2bb1-5d4d-44fa-8326-af8a318fb9bc","order_by":3,"name":"Wei Sun","email":"","orcid":"","institution":"Xuan Wu Hospital of the Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Sun","suffix":""},{"id":376659957,"identity":"88055071-03ba-482a-9934-61cc29bccc29","order_by":4,"name":"Aini He","email":"","orcid":"","institution":"Xuan Wu Hospital of the Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Aini","middleName":"","lastName":"He","suffix":""},{"id":376659960,"identity":"1356a177-d1cd-462c-8bf8-95b1f7bc535b","order_by":5,"name":"Benke Zhao","email":"","orcid":"","institution":"Xuan Wu Hospital of the Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Benke","middleName":"","lastName":"Zhao","suffix":""},{"id":376659965,"identity":"8c8dadfc-5e26-47a0-9d55-fa4275e68c6b","order_by":6,"name":"Xuefan Yao","email":"","orcid":"","institution":"Xuan Wu Hospital of the Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuefan","middleName":"","lastName":"Yao","suffix":""},{"id":376659966,"identity":"716aae8f-934a-4874-9b14-ecbf9e52d3b7","order_by":7,"name":"Qiushi Ren","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Qiushi","middleName":"","lastName":"Ren","suffix":""},{"id":376659967,"identity":"faf1b927-10e2-4fc9-8f38-660baec036f6","order_by":8,"name":"Haiqing Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYJACZgYGGwYGCRK1pJGu5TAJWuTbew+/Lig7n7jhdvPhzwUMNvnyDgS0GJw5l2Y949xtY4M7x9KkZzCkWW48QEiLRI6ZMW/bbTmzGzlmzDwMhw0MGwg5bAZYyzkeoBbjz0RpYQCqfMzbdgBki4E0SIs8IR0GZ84A3XMu2dj+RhrQLwZpBgaEtMi39wDdU2aXOHNGMjDEKmwM5Ak6jIGBTYKBDcJiZgBaYXCAsBbmDwgtIHuJsGUUjIJRMApGFgAAL8I8XszQL0QAAAAASUVORK5CYII=","orcid":"","institution":"Xuan Wu Hospital of the Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Haiqing","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2024-11-02 08:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5377287/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5377287/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70953668,"identity":"6b29e0c8-3643-4602-b5c8-ee8a84a92aad","added_by":"auto","created_at":"2024-12-09 13:58:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84387,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe primitive pulse wave waveform of the retinal blood vessels in a 70-year-old male patient with acute ischemic stroke is presented, displaying four complete heart beat cycles.\u003c/strong\u003e The transverse axis represents the number of speckle flow image frames, while the longitudinal axis indicates signal strength. The colors red, purple, and black correspond to the different delineated regions of interest (ROIs).\u003c/p\u003e","description":"","filename":"Figure1Theprimitivepulsewavewaveformoftheretinalbloodvesselsina70yearoldmalepatientwithacuteischemicstroke.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5377287/v1/87b0054be68aa517d3bab9a2.jpg"},{"id":70953670,"identity":"2bea0949-87a0-4f65-84e9-5dad7a119d93","added_by":"auto","created_at":"2024-12-09 13:58:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":198086,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe correlation between pulse waveform parameters of ischemic stroke patients and healthy controls and age distribution scatter plot and Pearson correlation coefficients. FAI (A-B), ATI (C-D), BOT (E-F), BOS (G-H). \u003c/strong\u003eFAI (Flow Acceleration Index) ATI (Acceleration Time Index) BOT (Blowout Time) BOS (Blowout Score)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure2ThecorrelationbetweenpulsewaveformparametersofischemicstrokepatientsandhealthycontrolsandagedistributionscatterplotandPearsoncorrelationcoefficients.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5377287/v1/e265d1483ffc7eed5127f55c.jpg"},{"id":70954091,"identity":"e7137e23-c85a-45f5-8243-d55275b8379e","added_by":"auto","created_at":"2024-12-09 14:06:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":137005,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe correlation between pulse waveform parameters of ischemic stroke patients and healthy controls and age distribution scatter plot and Pearson correlation coefficients. RR (A-B), FR (C-D), RI (E-F). \u003c/strong\u003eRR (Rising Rate) FR (Falling Rate) RI (Resistivity Index)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"Figure3ThecorrelationbetweenpulsewaveformparametersofischemicstrokepatientsandhealthycontrolsandagedistributionscatterplotandPearsoncorrelationcoefficients.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5377287/v1/7ab9c7fca9fc5072e945783e.jpg"},{"id":70955315,"identity":"d202c4ad-7d6f-4678-9183-4af4d87d2ac0","added_by":"auto","created_at":"2024-12-09 14:14:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1987761,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5377287/v1/f55d284c-fe48-4d2c-9d19-27c5c8fc503a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Retinal Vascular Hemodynamic Changes in Patients with Ischemic Stroke Investigated by Fundus Laser Speckle Contrast Imaging","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke is the second leading cause of disability and death worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and the leading cause of disability and death among Chinese adults [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Ischemic stroke (IS) accounts for approximately 80% of all strokes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. IS is characterized by high morbidity, elevated disability rate, significant mortality rates, and high recurrence rates, imposing a substantial burden on individuals and society [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This burden is expected to increase further due to an aging population and the persistent high incidence of risk factors such as hypertension.\u003c/p\u003e \u003cp\u003eIn recent years, numerous studies have focused on the alteration of retinal neurovascular structures in cerebrovascular diseases. The eye is closely connected to the brain. Anatomically and developmentally, the retina shares similar embryological origins, anatomical features, and physiological characteristics with the central nervous system. Considered an extension of the central nervous system, ocular symptoms often precede the conventional diagnosis of central nervous system disorders [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, the retina can be directly observed in the human body, providing a direct, non-invasive method for observing for the central nervous system. Therefore, the evaluation of ocular vascular status serves as an effective entry point for cerebrovascular research [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Retinal vessels share the same risk factors as cerebrovascular vessels, exhibiting similar manifestations of vascular injury and radiographic findings. Additionally, hemodynamic similarities can be observed between the vasculature of the retina and the cerebral penetrating arteries [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Direct observation of retinal vascular through non-invasive techniques allows for the early detection of hemodynamic changes, which may indicate the presence of subclinical brain injury and help mitigate the progression of cerebrovascular disease [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccumulating evidence suggests that a variety of retinal vascular signs and diseases are associated with cerebrovascular disease [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The investigation of cerebrovascular disease based on retinal blood vessels can be categorized into studies of static vascular morphology and studies of dynamic blood flow. Previous research has predominantly focused on the morphological changes of retinal blood vessels in IS patient, utilizing static color fundus photography or optical coherence tomography/ optical coherence tomography angiography (OCT/OCTA) [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These studies have identified various alterations, including narrowing of arterial diameter, thickening of venous diameter, arteriovenous crossing, vascular density. However, there is a scarcity of research examining the dynamic blood flow function of the fundus in relation to cerebrovascular disease [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], primarily due to limitations in study conditions. The laser speckle contrast imaging (LSCI) system has the capability to measure the correlation index of blood flow fluctuations, utilizing the laser speckle phenomenon to assess ocular blood flow non-invasively [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This system enables the averaging of signal values for blood flow velocity at the optic disc, allowing for the generation of pulse waveforms for each cardiac cycle. Software is then employed to calculate parameters analogous to the pulse wave [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this study, we employed LSCI to measure the dynamic blood flow parameters of fundus microcirculation in patients with IS, aiming to assess retinal hemodynamic changes and identify potential microcirculatory impairment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThis cross-sectional prospective study was conducted at the Department of Neurology, Xuanwu Hospital, Capital Medical University from October 2020 to December 2022. Fundus LSCI were collected from patients with IS in the outpatient clinic or ward of the Department of Neurology, Xuanwu Hospital, Capital Medical University. This study was approved by the Institutional Ethics Committee of Xuanwu Hospital Capital Medical University (ID: [2020]050). In accordance with the principles of the Declaration of Helsinki, all participants have completed the relevant written informed consent before imaging data collection.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe inclusion criteria for patients with ischemic stroke were as follows: (1) a confirmed diagnosis of ischemic stroke within the past 6 months, based on medical history, symptoms, signs, and imaging; (2) the ability to maintain consciousness, understand, and cooperate with relevant examinations, as well as the capacity to sign the informed consent form. The exclusion criteria were: (1) serious ophthalmic diseases that could affect vision, such as glaucoma, cataracts, keratitis, and age-related macular degeneration; (2) the presence of serious life-threatening primary diseases or mental illnesses.\u003c/p\u003e \u003cp\u003e For the control group, the inclusion criteria were similar: participants needed to be conscious, understand and cooperate with relevant examinations, as well as sign the informed consent form. The exclusion criteria for the control group were: (1) a previous history of stroke; (2) serious ophthalmic diseases affecting vision, such as glaucoma, cataracts, keratitis, and age-related macular degeneration.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFundus data collection\u003c/h3\u003e\n\u003cp\u003eThe imaging equipment utilized in this study is a multifunctional fundus optical imaging system developed by the Department of Biomedical Engineering, College of Future Technology, Peking University. This system is based on the optical path platform of a fundus camera, and is designed to create a multi-functional optical system for in vivo retinal imaging. It integrates retinal imaging, retinal oxygen saturation measurement, and retinal hemodynamic measurement [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The device calculates the oxygen saturation distribution from images captured at 550 nm and 600 nm in the multispectral range [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], while blood flow is assessed using LSCI principle [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The system adheres to the safety standards for lasers employed in ophthalmology clinics concerning the human eye. Notably, participants do not need to dilate their pupils prior to data collection. The acquisition process is as follows: first, multispectral image acquisition is conducted, after which the patient is instructed to close their eyes and rest for 2 minutes, followed by a 5-second acquisition of fundus laser speckle images. If the patient can tolerate the procedure, data from the other eye also be collected. The values for the right and left eyes of the same individual may differ; however, both values reflect the microcirculation of the patient. Therefore, when data from both eyes are collected, both sets are analyzed. This approach has also been adopted by similar studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eMeaning of the parametric indicator\u003c/h3\u003e\n\u003cp\u003ePulse wave waveform parameters: For LSCI imaging, a speckle image lasting 5 seconds is acquired, comprising approximately 400 frames. The mean blur rate (MBR) cumulative value within the circular region of interest (ROI) that encompasses the optic disc area serves as the baseline for the single frame image, while the fluctuations observed between image sequences represent an approximate pulse wave signal that varies with each heartbeat (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In conjunction with prior studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the following pulse waveform parameters are selected: Flow Acceleration Index (FAI), Acceleration Time Index (ATI), Blowout Time (BOT), Blowout Score (BOS), Rising Rate (RR), Falling Rate (FR), and Resistivity Index (RI), which are defined as follows:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFAI is defined as the blood flow acceleration index, as described in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It represents the maximum increment of the signal value between two frames, reflecting the combined effects of the maximum pumping acceleration of the left ventricle and peripheral resistance.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:FAI\\:\\left[au\\right]={max}\\left(\\frac{\\varDelta\\:y}{\\varDelta\\:x}\\right),x\\:is\\:in\\:the\\:rising\\:period$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eATI is defined as the ratio of the time taken for the signal value to reach its peak to the total duration of the heartbeat, as illustrated in Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:ATI=\\frac{The\\:duration\\:of\\:the\\:rising\\:period}{Full\\:heartbeat\\:cycle\\:duration}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBOT is defined as blowout time, as indicated in Eq.\u0026nbsp;(3), represents the proportion of time during which the signal value is at a high level. Specifically, it is the ratio of the duration for which the waveform exceeds half of the average value of the minimum and maximum signals to the overall heartbeat cycle. A higher BOT is considered an indicator of effective perfusion between two heartbeats.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:BOT=\\text{C}\\text{*}\\frac{Duration\\:of\\:signal\\:above\\:semi-high\\:level}{Full\\:heartbeat\\:cycle\\:duration}\\)\u003c/span\u003e \u003c/span\u003e, C is a constant term (3)\u003c/p\u003e \u003cp\u003eBOS is defined as blowout score, as indicated in Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e4\u003c/span\u003e), is analogous to BOT and serves as an indicator of blood flow sustained between heartbeat cycles. It is calculated based on the difference between the maximum and minimum signal values, as well as the average signal value. A higher BOS indicates a greater constancy of blood flow during the cardiac cycle and improved perfusion.\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:BOS=\\frac{(2-\\text{A}\\text{m}\\text{p}\\text{l}\\text{i}\\text{t}\\text{u}\\text{d}\\text{e}\\:\\text{o}\\text{f}\\:\\text{w}\\text{a}\\text{v}\\text{e}\\text{f}\\text{o}\\text{r}\\text{m}/\\text{M}\\text{e}\\text{a}\\text{n}\\:\\text{v}\\text{a}\\text{l}\\text{u}\\text{e}\\:\\text{o}\\text{f}\\:\\text{w}\\text{a}\\text{v}\\text{e}\\text{f}\\text{o}\\text{r}\\text{m})}{2}\\text{*}100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eRR is defined as Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e5\u003c/span\u003e), represents the steepness of the ascending portion of the waveform curve of the signal value. A higher RR value corresponds to a more abrupt increase in the signal value.\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:RR=\\frac{Area\\:under\\:the\\:rising\\:curve}{Total\\:rectangular\\:area\\:of\\:the\\:rising\\:period}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFR is defined as Eq.\u0026nbsp;(\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e6\u003c/span\u003e), represents the steepness of the descending segment of the waveform curve of the signal value. A higher FR value corresponds to a more abrupt decrease in the signal value..\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:FR=\\frac{Area\\:under\\:the\\:decline\\:curve}{Total\\:rectangular\\:area\\:of\\:the\\:descent\\:period}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eRI is defined as Eq.\u0026nbsp;(\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e7\u003c/span\u003e), represents the ratio of the difference between the maximum and minimum signal values to the maximum signal value. A higher RI indicates greater peripheral circulation resistance.\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:RI=\\frac{Waveform\\:peak-Waveform\\:valley}{Waveform\\:peak}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe Shapiro-Wilk method was employed to assess the normality of the continuous data. Normally distributed continuous data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s), with the t-test applied for comparisons between two independent sample groups, while the paired t-test was utilized for paired samples. Non-normally distributed continuous data were expressed as median and interquartile range [M (P25, P75)], using the rank-sum test for comparisons between two independent sample groups and the paired rank-sum test for paired samples. Categorical data were presented as the number of cases and percentage [cases (%)], with chi-square tests employed for group comparisons. Pearson correlation analysis was conducted to evaluate the correlation between the two sets of measures. Binary logistic regression was implemented to control for confounding factors, and values exceeding three standard deviations from the dataset were excluded as outliers. A two-tailed p-value of \u0026lt;\u0026thinsp;0.05 was deemed statistically significant. Statistical analyses were performed using SPSS version 25.0 (IBM Corporation, Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFrom September 2020 to September 2022, a total of 147 patients (285 eyes) with stroke were enrolled in the Department of Neurology at Xuanwu Hospital, Capital Medical University. Following image screening, 119 patients (233 eyes) were included in the study, all of whom were Han Chinese, with 171 (73.4%) eyes identified as male. Additionally, there were 67 healthy control subjects (113 eyes) without cerebrovascular disease, all of whom were also Han Chinese, comprising 50 (44.2%) eyes identified as male. The baseline data, along with fundus vascular oxygen and blood flow waveform parameters of the patients, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Notably, patients with ischemic stroke were predominantly male (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), older (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), and exhibited higher mean arterial pressure (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, the prevalence of previous hypertension, diabetes, smoking, and alcohol consumption was significantly higher among these patients (all \u003cem\u003ep\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a typical LSCI pulse waveform plot of a 70-year-old male diagnosed with acute ischemic stroke.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eThe baseline data of the patient and the fundus vascular blood oxygen and blood flow waveform parameters\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIschemic stroke\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale [case (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221(63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171(73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge [M(P25, P75), years]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(51,66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(52,67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58(50,64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI[M(P25, P75), kg/m2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.34(22.44,26.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.39(22.29,27.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.22(22.57,26.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean arterial pressure [M(P25, P75), mmHg]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.00(91.67,105.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.67(93.00,108.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.67(88.67,99.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAnamnesis [case (%)]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153(44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121(51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of diabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68(19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of alcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86(24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76(32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFundus blood vessel blood oxygen saturation [M(P25, P75), %]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.64(80.12,88.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.91(80.22,88.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.07(80.06,87.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSvO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.47(40.85,52.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.99(40.77,53.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.61(40.85,50.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFundus blood vessel waveform parameters [M(P25, P75)]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02(0.01,0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02(0.01,0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02(0.01,0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.66(40.86,48.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.74(41.18,47.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.10(40.17,49.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.84(36.07,39.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.66(35.68,39.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.43(36.52,41.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.61(66.48,70.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.37(65.99,69.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.03(67.30,70.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.75(10.42,11.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.71(10.42,11.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.82(10.45,11.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.46(13.65,15.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.46(13.64,15.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.47(13.66,15.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44(0.35,0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45(0.36,0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41(0.31,0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u0026lowast;\u003c/sup\u003e The p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the observation indicators, there was no significant difference in the oxygen saturation of fundus vascular arteries and veins between ischemic stroke patients and healthy controls (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.329 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.115, respectively). However, in the fundus vascular waveform parameters, the FAI and RI in the cerebrovascular disease group were higher than those in the control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, respectively), while the BOT, BOS, and RR were lower than those in the control group (\u003cem\u003ep\u003c/em\u003e values\u0026thinsp;=\u0026thinsp;0.021, 0.014, and 0.010, respectively; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After applying multivariate logistic regression to correct for confounding factors such as gender, age, history of hypertension, diabetes mellitus, smoking, alcohol consumption, BMI, and mean arterial pressure, BOT, RR, and RI remained statistically significant (\u003cem\u003ep\u003c/em\u003e values\u0026thinsp;=\u0026thinsp;0.008, 0.020, and 0.049, respectively; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eUnivariate and multivariate logistic regression were performed for different groups of outcome measures\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate regression\u003c/p\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultivariate regression \u003csup\u003e#\u003c/sup\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFundus vascular oxygen saturation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.985(0.947\u0026ndash;1.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.989(0.962\u0026ndash;1.1017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSvO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.974(0.945\u0026ndash;1.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.976(0.941\u0026ndash;1.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFundus vascular waveform parameters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.009(0.970\u0026ndash;1.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.042(0.992\u0026ndash;1.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.087(1.016\u0026ndash;1.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.121(1.030\u0026ndash;1.220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.119(1.033\u0026ndash;1.212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.101(0.997\u0026ndash;1.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.817(1.198\u0026ndash;2.756)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.927(1.109\u0026ndash;3.351)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.022(0.838\u0026ndash;1.246)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.223(0.947\u0026ndash;1.579)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.128(0.030\u0026ndash;0.557)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.159(0.025\u0026ndash;0.990)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e#\u003c/sup\u003e Gender, age, history of hypertension, history of diabetes, history of smoking, history of alcohol use, BMI, and mean arterial pressure were adjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e/ The value cannot be displayed because it is too large\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u0026lowast;\u003c/sup\u003e The p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePearson correlation analysis was conducted on all waveform parameter values (FAI, ATI, BOT, BOS, RR, FR, RI) for both the cerebrovascular disease group and the control group. Scatter plots of the age distribution are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The Pearson correlation coefficient (\u003cem\u003er\u003c/em\u003e) of each parameter with age distribution was calculated for all groups. In the control group, FAI (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.200, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041), BOT (\u003cem\u003er\u003c/em\u003e = -0.221, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020), BOS (\u003cem\u003er\u003c/em\u003e = -0.232, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), and RI (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.218, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020) showed significant correlations with age, and the correlation magnitudes of these four indicators were similar. However, in ischemic stroke patients, only BOS (\u003cem\u003er\u003c/em\u003e = -0.154, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) was significantly correlated with age.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, LSCI was employed to assess ocular microcirculation parameters in ischemic stroke patients and to investigate the pulse wave shape parameters and age distribution in the optic disc area. Based on a comprehensive literature search and review, this study represents the first application of LSCI, a non-invasive technique, to observe changes in dynamic blood flow function in the fundus related to ischemic stroke.\u003c/p\u003e \u003cp\u003eThe incidence and prevalence of stroke are notably high in China [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], with intracranial atherosclerosis identified as a primary cause of ischemic stroke among the Chinese population. Atherosclerosis is characterized by a decline in the elasticity of blood vessels. In healthy blood vessels, the arterial walls possess a degree of elasticity, allowing them to expand as blood is rapidly pumped into the arterial system during cardiac systole. This elasticity facilitates the continuous flow of blood into the microcirculation as the heart contracts. Theoretically, vascular elasticity can be assessed by measuring variations in microcirculatory blood flow [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In the pathophysiology of intracranial arteriosclerosis, this condition represents a roughly linear chronic process that persists throughout an individual's life course [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. As we age, the walls of blood vessels gradually lose elasticity and increase in stiffness, a phenomenon observed in every individual, with arteriosclerosis serving as a non-interventionable risk factor. Consequently, when measuring changes in blood flow within microcirculation, the relevant assessment parameters should demonstrate a significant correlation with age. In our study involving healthy subjects, FAI (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.200, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041), BOT (\u003cem\u003er\u003c/em\u003e = -0.221, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020), BOS (\u003cem\u003er\u003c/em\u003e = -0.232, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), and RI (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.218, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020) exhibited significant correlations with age. Specifically, FAI increases with age, indicating a faster rise in systolic blood flow and a diminished capacity to buffer increases in blood flow, which suggests poor arterial elasticity. Conversely, BOT decreases with age, reflecting a shorter time to beat and implying a reduced perfusion time between beats, indicative of arteriosclerosis leading to diminished distal perfusion. Similarly, BOS declines with age, suggesting poor constancy of blood flow and inadequate perfusion during the cardiac cycle. Additionally, RI increases with age, indicating elevated peripheral circulatory resistance. Previous studies involving over 1,000 healthy participants have shown that BOT, FOREST, ATI, RR, and FR are age-related [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, in our study, no statistically significant correlation was found between ATI, RR, FR, and age, which may suggest that these parameters are not sensitive to changes in vascular wall elasticity, or that the sample size of healthy controls selected in this study was insufficient.\u003c/p\u003e \u003cp\u003eOur findings indicate that men are more likely to experience ischemic stroke (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), are older (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), and have higher mean arterial pressure (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), along with a significantly higher prevalence of previous hypertension, diabetes, smoking, and alcohol use (\u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results are consistent with established risk factors for the development of arteriosclerosis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Among the fundus vascular waveform parameters, the FAI and RI in ischemic stroke patients were found to be higher than those in the control group, while the BOT, BOS, and RR were lower. As discussed above, these results suggest poorer arterial elasticity, increased peripheral circulatory resistance, and reduced distal perfusion in ischemic stroke patients. And in the analysis of the correlation between fundus vascular waveform parameters and age performed in IS patients, only the correlation between BOS and age was statistically significant, suggesting that the fundus vasculature of IS patients had been damaged by factors other than the normal aging process. Under the influence of long-term hypertension, diabetes mellitus, and hyperlipidemia, the normal atherosclerotic process of the patients was disturbed, resulting in the loss of age-related correlation of fundus microcirculatory parameters.\u003c/p\u003e \u003cp\u003eIt is worth noting that intracranial atherosclerosis is categorized as atherosclerosis and vitelliform degeneration. In the large intracranial vessels, atherosclerosis is characterized by lipid infiltration of the arterial intima and a reduction in the elastic tissue of the arterial media, which are the primary pathological changes. In contrast, arterioles lack a continuous elastic layer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and their pathological manifestations include lipid vitreous, fibrinoid necrosis, and arteriolar sclerosis. While cerebral small vessel involvement is noted, atherosclerosis of large vessels is more prevalent in stroke patients [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It is important to note that, except for the central retinal artery, which has a diameter exceeding 100\u0026micro;m in the main trunk of the optic disc, and the large vessels immediately adjacent to the optic disc, the diameters of other branches are less than 100 \u0026micro;m, classifying them as arterioles. Consequently, the parameters representing arteriosclerosis measured in this experiment are exclusively microarteriolar parameters and do not reflect the severity of large vessel arteriosclerosis. However, numerous studies indicate that retinal arterioles are associated with aortic and carotid arteriosclerosis. For instance, a higher degree of aortic sclerosis correlates with stenosis of retinal arterioles and decreased arteriolar pulsatility [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which subsequently impacts local metabolism and vasodilation. LSCI parameters can predict carotid intima-media thickness, plaque grade, vascular resistance, and more [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, LSCI can serve as an adjunctive tool to assess tolerance to cerebral ischemia during carotid endarterectomy [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Although we did not measure the degree of sclerosis in large arteries, blood flow to the fundus blood vessels serves as an indicator of the health of intracranial blood vessels, particularly parameters such as FAI, RI, BOT, BOS, and RR. Our research suggests that retinal function parameters may serve as potential indicators for evaluating microcirculatory damage and providing early warnings of stroke and other diseases. Future longitudinal studies should focus on evaluating changes in retinal function parameters to enhance their predictive value.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, we conducted only cross-sectional observations and did not implement longitudinal follow-up. Changes in the waveform parameters of fundus blood flow would have been better assessed through longitudinal monitoring. Future studies should establish a cohort to evaluate trends in fundus blood flow over time. Second, we did not categorize stroke severity, primarily due to the variability in stroke cycles among patients; some were already in recovery, which resulted in a lack of representative data regarding stroke severity at the time of onset.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eRetinal vascular elasticity in ischemic stroke patients is compromised, and their microcirculation hemodynamics are disrupted with age. Retinal hemodynamic parameters may serve as potential indicators for evaluating microcirculatory injury in ischemic stroke patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"510\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eIschemic Stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eOCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eOptical coherence tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eOCTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eOptical coherence tomography angiography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eFFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eFundu fluorescence angiography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eLSCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eLaser speckle contrast imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eROI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eRegion of interest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eFAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eFlow acceleration index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eATI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eAcceleration time index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eBOT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eBlowout time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eBOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eBlowout score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eRising rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eFalling rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eResistivity index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eRPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eRadial peripapillary capillaries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003eCapillary density\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.5442%;\"\u003e\n \u003cp\u003ePI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52.4558%;\"\u003e\n \u003cp\u003ePulsatility index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Ethics Committee of Xuanwu Hospital Capital Medical University (ID: [2020]050). In accordance with the principles of the Declaration of Helsinki, all participants have completed the relevant written informed consent before imaging data collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting our findings are available from the corresponding authors upon reasonable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Key Research and Development Program of China (Grant No. 2022YFC3600500 and 2022YFC3600504), Science and Technology Innovation 2030-Major Project (Grant No. 2021ZD0201806) and Xicheng District Finance Science and Technology Program (Grant No. XCSTS-2022-06).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXW, YY, and LH: writing the manuscript, study concept and design, data collection and management. WS, AH, BZ and XY: analysis and interpretation of data. QR and HS: critical revision of the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSaini V, Guada L, Yavagal DR: \u003cstrong\u003eGlobal Epidemiology of Stroke and Access to Acute Ischemic Stroke Interventions\u003c/strong\u003e. \u003cem\u003eNeurology \u003c/em\u003e2021, \u003cstrong\u003e97\u003c/strong\u003e(20 Suppl 2):S6-s16.\u003c/li\u003e\n\u003cli\u003eZhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W, Li X, Wang L, Wang L, Liu Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017\u003c/strong\u003e. \u003cem\u003eLancet (London, England) \u003c/em\u003e2019, \u003cstrong\u003e394\u003c/strong\u003e(10204):1145-1158.\u003c/li\u003e\n\u003cli\u003eWu S, 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\u003c/em\u003e2012, \u003cstrong\u003e250\u003c/strong\u003e(9):1275-1281.\u003c/li\u003e\n\u003cli\u003eMotoyama Y, Hayashi H, Kawanishi H, Tsubaki K, Takatani T, Takamura Y, Kotsugi M, Kim T, Yamada S, Nakagawa I\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eOcular blood flow by laser speckle flowgraphy to detect cerebral ischemia during carotid endarterectomy\u003c/strong\u003e. \u003cem\u003eJournal of clinical monitoring and computing \u003c/em\u003e2021, \u003cstrong\u003e35\u003c/strong\u003e(2):327-336.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Retinal Vessel, Hemodynamic, Ischemic Stroke, Fundus Laser Speckle Contrast Imaging","lastPublishedDoi":"10.21203/rs.3.rs-5377287/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5377287/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eTo investigate the retinal hemodynamic changes in patients with ischemic stroke using fundus laser speckle contrast imaging (LSCI) and evaluate their microcirculatory impairment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis cross-sectional study was conducted in the Department of Neurology at Xuanwu Hospital, Capital Medical University. An integrated retinal imaging instrument was employed to collect images of retinal vascular LSCI in patients with ischemic stroke, and the pulse wave waveform parameters were compared with healthy controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 119 patients with 233 eyes in ischemic stroke group and 67 patients with 113 eyes in the healthy control group were enrolled. Among the fundus vascular waveform parameters, the Flow Acceleration Index (FAI) and Resistivity Index (RI) in ischemic stroke patients were higher than those in the healthy control group (\u003cem\u003ep\u003c/em\u003e = 0.028 and 0.015, respectively), while the Blowout Time (BOT), Blowout Score (BOS) and Rising Rate (RR) were lower than those in the control group (\u003cem\u003ep\u003c/em\u003evalues of 0.021, 0.014, and 0.010, respectively). After correcting for confounders by multifactor logistic regression, BOT, RR, and RI (\u003cem\u003ep\u003c/em\u003e values of 0.008, 0.020, and 0.049, respectively) remained statistically significant. Furthermore, most hemodynamic parameters in healthy controls showed significant correlations with age [FAI (\u003cem\u003er\u003c/em\u003e = 0.200, \u003cem\u003ep\u003c/em\u003e = 0.041), BOT (\u003cem\u003er\u003c/em\u003e = -0.221, \u003cem\u003ep\u003c/em\u003e= 0.020), BOS (\u003cem\u003er \u003c/em\u003e= -0.232, \u003cem\u003ep\u003c/em\u003e = 0.014), RI (\u003cem\u003er\u003c/em\u003e = 0.218, \u003cem\u003ep\u003c/em\u003e= 0.020)], whereas few indicators in ischemic stroke patients exhibited a correlation with age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eRetinal vascular elasticity in ischemic stroke patients is compromised, and the process of changing microcirculation hemodynamics with aging is disrupted. Retinal hemodynamic parameters may serve as potential indicators for evaluating microcirculatory injury in ischemic stroke patients.\u003c/p\u003e","manuscriptTitle":"Retinal Vascular Hemodynamic Changes in Patients with Ischemic Stroke Investigated by Fundus Laser Speckle Contrast Imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-09 13:58:01","doi":"10.21203/rs.3.rs-5377287/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-07T16:08:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-18T12:46:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155998858587192527627131405890015328584","date":"2025-01-12T12:42:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-07T11:45:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331760090132717758226582190864608454123","date":"2025-01-03T23:44:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193363440876529367717512410605641013236","date":"2024-12-31T10:20:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-31T10:15:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-11T12:50:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-04T17:24:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-04T15:46:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2024-11-02T08:40:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ddf8be28-0e34-437c-849b-f623264dc416","owner":[],"postedDate":"December 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-02-24T07:38:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-09 13:58:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5377287","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5377287","identity":"rs-5377287","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}