A Novel Indicator for Early Detection of Mild Cognitive Impairment: Exploring the Relationship Between Visual Field Characteristics and Response Time

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Gaze behavior analysis has been shown to differentiate individuals with MCI from cognitively healthy older adults. Objective This study aimed to examine visual processing differences between cognitively healthy older adults and those with MCI, focusing on central and useful field of view (UFOV) tasks. Methods Participants completed a central visual field task and a UFOV task. Reaction times, omission and commission errors, and visual orienting frequency were measured. Group comparisons were conducted. For variables showing significant differences, receiver operating characteristic curve analysis evaluated discriminatory accuracy and optimal cutoff values. Results No significant group differences emerged in the central task. In the UFOV task, patients with MCI demonstrated significantly slower reaction times than controls. The optimal UFOV reaction time cutoff was 614.7 ms, with 90.3% sensitivity, 72.1% specificity, and an area under the curve of 0.841. Conclusions Older adults with MCI exhibit delayed visual processing under UFOV conditions. Reaction time in the UFOV task may serve as a sensitive, practical behavioral marker for early MCI detection. Health sciences/Biomarkers Health sciences/Neurology Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Mild cognitive Impairment Peripheral Vision Useful Field Reaction Time Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The global aging of the population has led to a substantial rise in the number of older adults with dementia, placing growing burdens on healthcare systems and social security infrastructures worldwide 1 . To delay or prevent dementia onset, early detection and intervention during the prodromal stage—mild cognitive impairment (MCI)—are essential 2 , 3 . MCI represents an intermediate stage between normal cognitive aging and dementia, marked by measurable deficits in memory or other cognitive domains, while daily functional independence remains largely preserved. Non-pharmacological interventions, including lifestyle modification, aerobic exercise, and cognitive training, have shown promise in slowing progression when applied during the MCI stage 4 – 6 . Recently, disease-modifying therapies such as lecanemab (LEQEMBI) 7 and donanemab 8 have demonstrated efficacy in slowing cognitive decline and, in some cases, improving cognitive function when initiated during MCI. Consequently, timely and accurate identification of individuals with MCI is crucial to optimize the effects of both pharmacological and non-pharmacological treatments. However, conventional screening methods are often influenced by educational background and language proficiency, limiting their sensitivity in detecting early cognitive changes 9 . This limitation highlights the need for practical, scalable, and sensitive tools capable of identifying early cognitive decline before functional impairments appear. Beyond documented deficits in memory 10 , 11 , attention 10 , 12 , 13 , and executive function 14 , 15 , emerging evidence suggests that subtle impairments in visual processing and attentional control may serve as early indicators of cognitive vulnerability 16 – 20 . In visual cognition, the central visual field supports high-resolution object recognition, while the useful field of view (UFOV) enables rapid, accurate processing of peripheral visual information surrounding the fixation point 21 , 22 . These functions are particularly susceptible to aging, and UFOV contraction has been linked to reduced attentional resources and increased cognitive load 23 . Based on these findings, measuring reaction times to stimuli within the central visual field and UFOV has emerged as a promising method for detecting early cognitive decline in aging and MCI. Variations in visual response characteristics may reflect changes in processing speed and attentional resource allocation, offering sensitive behavioral markers of cognitive impairment. Methods Study Design and Participants This cross-sectional study was conducted between 2024 and 2025 and included older adults residing in medical and nursing care facilities. Participants were classified into two groups: those with MCI (MCI group) and cognitively healthy controls (HC group). Inclusion criteria for the HC group were a score of ≥ 26 on the Japanese version of the Montreal Cognitive Assessment (MoCA-J) and a score of 28–30 on the Japanese version of the Mini-Mental State Examination (MMSE-J). The MCI group included patients with MoCA-J scores ≤ 25 and MMSE-J scores 24–27 15 . Exclusion criteria included: presence of visual impairments, cerebrovascular disease, or other neurological or medical conditions likely to affect cognitive or visual performance. A priori power analysis was conducted using G*Power version 3.1.9.7 (Heinrich Heine University Düsseldorf, Germany) 24 to estimate the minimum sample size for logistic regression. Parameters included a two-tailed test, odds ratio of 3.0 (large effect), Pr (Y = 1 | X = 1) = 0.5, α = 0.05, power (1 − β) = 0.80, and two predictor variables. The proportion of variance explained by other predictors (R² other X) was set between 0.0 and 0.1, resulting in a required sample size of approximately 70–80. A total of 79 participants were initially recruited. After excluding five individuals who met exclusion criteria, 74 patients were included in the final analysis: 43 in the HC group (mean age: 72.2 ± 2.9 years; 21 males, 22 females; MoCA-J: 28.1 ± 1.0; MMSE-J: 29.4 ± 0.7) and 31 in the MCI group (mean age: 73.4 ± 3.9 years; 18 males, 13 females; MoCA-J: 22.7 ± 1.3; MMSE-J: 25.7 ± 1.0). Participant characteristics are summarized in Table 1 . The study protocol was approved by the Ethics Committee of Kyoto Tachibana University (Approval Number: 25 − 11). No financial incentives or compensation were provided. All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all individual participants included in the study. Table 1 Participant Characteristics HC- group MCI- group t df p-value Cohen’s d 95% CI Lower upper n 43 31 - - - - - - Age 72.2 ± 2.9 73.4 ± 3.9 -1.57 72 0.12 − .0369 -0.83 0.10 MoCA-J 28.1 ± 1.0 22.7 ± 1.3 19.5 72 <0.001 -1.095 3.71 5.47 MMSE-J 29.4 ± 0.7 25.7 ± 1.0 18.0 72 <0.001 -1.071 3.41 5.08 Note. MoCA-J = Japanese version of the Montreal Cognitive Assessment, MMSE-J = Japanese version of the Mini-Mental State Examination, Statistical analysis was performed using an independent samples t-test. Outcome Measures All assessments were conducted with participants seated in a stable, upright position. A high-precision eye-tracking system (View Tracker III; DITECT Co., Ltd., Tokyo, Japan) 15 was used to measure gaze behavior. Visual stimuli were presented on a 23.8-inch external monitor (model PTFWJA, PRINCETON, Japan) positioned 1 meter in front of the participant’s forehead at eye level. Two visual tasks were administered: a central visual field task and a UFOV task, each designed to assess different aspects of visual processing. The central visual field task evaluated the ability to detect stimuli presented at the fixation point. The UFOV task assessed responses to stimuli located in the peripheral visual field surrounding the fixation point. For each task, the following variables were recorded: (1) reaction time (ms); (2) omission errors (failure to respond to a target stimulus); (3) commission errors (responses to non-target stimuli); and (4) visual orienting frequency (VOF; number of gaze shifts away from the central fixation point toward peripheral stimuli). These variables were compared between the MCI and HC groups to assess group-level differences and evaluate their potential as behavioral markers of cognitive impairment. Stimulus Conditions: Visual Reaction Assessment System (VRAS) Experimental tasks were developed using Microsoft Visual Studio Community 2022 and run on a personal computer (Let's Note CF-FV; Panasonic Corporation, Japan) with Windows 11 Home. Stimulus presentation was controlled by a custom-built VRAS. Condition 1 (central visual field task): A red circular target (4 cm diameter) was displayed 20 times at the center of the screen. Participants were instructed to press a response button as quickly and accurately as possible when the target appeared. Condition 2 (UFOV task): A black fixation point of the same size remained at the center of the screen. While maintaining gaze on the fixation point, red circular targets identical to those in Condition 1 were presented in one of four peripheral locations—upper right (45°), lower right (135°), lower left (225°), or upper left (315°)—each 20 cm from center. Each location included 20 randomized trials, totaling 80. Each stimulus was displayed for 2 seconds. Reaction time was recorded using a USB-connected response button (model USBSWP; TECHNOTOOLS, Japan). Reaction time was defined as the latency between stimulus onset and button press. In addition to reaction time, omission errors, commission errors, and—exclusively for Condition 2—VOF (i.e., number of gaze deviations from the fixation point) were recorded (Fig. 1 ). Eye-Tracking Apparatus and Gaze Data Analysis Gaze behavior was continuously recorded using the View Tracker 3 (DITECT Inc., Tokyo, Japan), a lightweight head-mounted device (~ 40 g) equipped with a 200 Hz infrared eye camera and a 30 Hz scene camera to capture both eye position and the participant’s visual field. The device connected to a computer via USB, allowing synchronized data acquisition. Gaze data were processed using DITECT gaze analysis software (version 1.0518). Before the experimental tasks, standardized calibration was conducted individually to ensure accuracy (Fig. 2 ). Gaze data were analyzed using an area-of-interest (AOI)–based method. AOIs were predefined to match the spatial locations of target stimuli in each condition. The eye-tracking system was integrated with the VRAS platform, enabling real-time gaze behavior analysis under both visual conditions. In Condition 1, participants were required to fixate on the centrally presented target and respond promptly. In Condition 2, gaze accuracy was evaluated based on whether attention was incorrectly directed toward peripheral, non-target stimuli. Incorrect responses were recorded in both conditions and used as quantitative indicators of gaze-related error. Statistical Analysis All statistical analyses were conducted using IBM SPSS Statistics for Windows, version 30.0 (IBM Corp., Armonk, NY, USA). Group comparisons were performed for four primary outcome variables: reaction time, omission errors, commission errors, and VOF. The Shapiro–Wilk test assessed the normality of each variable. Reaction time met normality assumptions and was analyzed using an independent samples t-test. Omission errors, commission errors, and VOF violated normality assumptions and were analyzed using the Mann–Whitney U test. Variables that differed significantly between the MCI and HC groups in preliminary analyses were entered as predictors in a binomial logistic regression model, with group classification (HC = 0, MCI = 1) as the dependent variable. For predictors that were statistically significant in the logistic model, receiver operating characteristic (ROC) curve analysis evaluated classification performance. The area under the curve (AUC) was calculated to assess model accuracy, and optimal cutoff values were identified using the Youden index. A significance level of p < .05 was used for all tests. Results Group differences between older adults with MCI and cognitively healthy controls (HC) were examined using appropriate statistical procedures. Outcome variables included reaction time, omission errors, commission errors, and gaze-related indicators under central visual field and UFOV conditions. Variables showing statistically significant between-group differences were further analyzed using binomial logistic regression to evaluate classification accuracy for MCI. Predictors that reached statistical significance in the regression model underwent ROC curve analysis to assess discriminatory performance. The AUC and optimal cutoff values were calculated using the Youden index. Detailed results for each outcome measure are presented below. 1) Results of Between-Group Analyses ① Reaction time For central reaction time (C-RT), the HC group had a mean of 554.4 ms (SD = 46.6), and the MCI group had a mean of 568.5 ms (SD = 52.6). This difference was not statistically significant (t = −1.22, p = 0.228, Cohen’s d = −0.29). In contrast, for useful reaction time (U-RT), the HC group recorded a mean of 580.3 ms (SD = 44.9), while the MCI group had a significantly slower mean of 638.6 ms (SD = 38.4), indicating a significant group difference (t = −5.84, p < 0.001, Cohen’s d = −1.17) (Table 3, Figure 3). ② Omission Error For central omission errors (C-OE), the median was 0.0 in both groups (interquartile range [IQR]: 0.0–0.0), showing no between-group variability. The difference was not statistically significant ( U = 745.5, Z = 1.276, p = 0.202, r = 0.148). In the UFOV task (U-OE), the median was also 0.0 in both groups, but the MCI group had a wider IQR (0.0–0.5) than the HC group (0.0–0.0), indicating greater performance variability. Although not statistically significant ( U = 790.0, Z = 1.517, p = 0.129, r = 0.185), this trend may suggest increased omission errors among older adults with MCI (Table 3, Figure 3). ③ Commission Error For central commission errors (C-CE), no significant group difference was observed ( U = 705.0, Z = 0.557, p = 0.577, r = 0.065). In the UFOV task (U-CE), the MCI group showed slightly more errors (median = 1.0, IQR: 0.0–1.5) than the HC group (median = 0.0, IQR: 0.0–1.0), but the difference was not statistically significant ( U = 792.0, Z = 1.464, p = 0.143, r = 0.170) (Table 3, Figure 3). ④ UFOV In the UFOV task, the MCI group exhibited a slightly higher VOF (median = 3.0, IQR: 2.0–3.0) compared to the HC group (median = 2.0, IQR: 2.0–3.0). This difference approached but did not reach statistical significance (U = 833.0, p = 0.052, r = 0.230) (Table 3, Figure 3). Table 3. Results of Group Comparisons Between the HC and MCI Groups HC Group Median [25%-75%] Mean± SD MCI Group Median [25%-75] Mean± SD Test Statistic p-value Effect size C-RT - 55.4±46.6 — 568.5±52.6 t -1.22 0.228 -0.29 U-RT - 580.4±44.8 — 638.6±38.4 t -5.84 ＜0.001 -1.17 C-OE 0.0 (0.0–0.0) 0.14±0.35 0.0 (0.0–0.5) 0.26±0.44 U 745.5 0.202 0.148 U-OE 0.0 (0.0–1.0) 0.23±0.43 1.0 (0.0–1.0) 0.29±0.46 U 790.0 0.129 0.185 C-CE 0.0 (0.0–0.0) 0.49±0.59 0.0 (0.0–1.0) 0.71±0.64 U 705.0 0.577 0.065 U-CE 1.0 (0.0–1.5) 1.00±0.79 1.0 (1.0–2.0) 1.29±0.86 U 792.0 0.143 0.17 FOV 2.0 (2.0–3.0) 2.14±0.80 3.0 (2.0–3.0) 2.55±0.85 U 833.0 0.052 0.23 Note. C-RT = Central Reaction Time; U-RT = Useful-field Reaction Time; C-OE = Central Omission Errors; U-OE = Useful-field Omission Errors; C-CE = Central Commission Errors; U-CE = Useful-field Commission Errors; FOV = Visual Orienting Frequency. Median values are presented with interquartile ranges [25%–75%]; mean values are provided with standard deviations (Mean ± SD). Test refers to the statistical method used (t = independent samples t-test; U = Mann–Whitney U test). Statistic indicates the test statistic value (t or U); p-values are two-tailed. Effect size is represented by Cohen’s d for t-tests and by rank-biserial correlation (r) for Mann–Whitney U tests. 2) Logistic Regression Analysis Logistic regression analysis was conducted to determine whether U-RT could distinguish older adults with MCI from those who were cognitively healthy. As shown in Table 4, U-RT was a significant predictor (B = 0.03, SE = 0.01, Wald = 17.55, p < 0.001), with an odds ratio of 1.03 (95% CI: 1.017–1.046). Each 1 ms increase in U-RT was associated with a 3.0% increase in the odds of MCI classification (Table 4). Table 4 Results of logistic regression analysis B SE Wald df p-value Exp(B) EXP（B） 95%CI Lower Upper (Intercept) -19.15 4.53 17.85 1 < 0.001 - - - U-RT 0.03 0.01 17.55 1 < 0.001 1.03 1.017 1.046 Note: SE = Standard Error; CI = Confidence Interval; U-RT = Useful Reaction Time. Diagnostic performance of U-RT for identifying MCI using ROC curve analysis. ROC curve analysis was performed to evaluate the discriminatory ability of U-RT in identifying MCI. The AUC was 0.841 (95% confidence interval [CI]: 0.741–0.916), indicating excellent diagnostic accuracy. The optimal cutoff value, defined as the predicted probability threshold from the logistic model, was 0.331—corresponding to a ≥33.1% likelihood of MCI classification. At this threshold, sensitivity was 90.3%, specificity was 72.1%, and the maximum Youden Index was 0.624 (Figure 4). The ROC curve illustrates the model’s classification performance (AUC = 0.841; 95% CI = 0.741–0.916). The logistic regression model estimating the probability of MCI based on U-RT is defined as: where P represents the predicted probability of being classified as having MCI. Accordingly, the probability can be calculated as: This equation indicates that higher U-RT values are associated with an increased probability of MCI classification. Discussion This study examined visual processing characteristics in older adults with MCI, focusing on central and peripheral (UFOV) visual tasks. We also evaluated the diagnostic utility of reaction time and gaze-related indicators using logistic regression and ROC analysis. Central Visual Processing (C-RT) No significant differences were found in C-RT between the MCI and HC groups. This suggests that visual processing in the foveal region may remain intact in early-stage MCI. These findings align with previous research indicating that lower-level perceptual functions, such as basic visual discrimination and stimulus detection, are largely preserved during the early phases of cognitive decline 25 , 26 . Thus, central visual response latency alone may lack sensitivity for detecting early pathological changes. Peripheral Visual Attention (UFOV Reaction Time) In contrast, patients with MCI exhibited significantly slower reaction times in the UFOV task, which requires broad spatial attention and rapid cognitive resource allocation. This delay likely reflects impairments in divided attention and executive function, both of which are typically affected in early MCI. The large effect size (Cohen’s d = − 1.17) underscores the robustness of this group difference. Moreover, logistic regression and ROC analysis revealed strong diagnostic utility for UFOV reaction time, with an area under the ROC curve (AUC) of 0.841 (95% CI: 0.741–0.916). Using a predicted probability cut-off of 0.331, the model achieved 90.3% sensitivity and 72.1% specificity. As noted by Akobeng (2007), AUC values between 0.7 and 0.9 indicate moderately accurate diagnostic tests 27 . These findings support UFOV reaction time as a moderately accurate and clinically meaningful behavioral marker for detecting MCI. This reinforces earlier studies on the diagnostic relevance of UFOV measures 22 , 28 , demonstrating that even a single behavioral metric, when properly selected, can offer substantial screening power. Neurocognitively, delayed UFOV responses may reflect early disruption in the frontoparietal attention network—particularly the dorsolateral prefrontal cortex and superior parietal lobule—regions critical for visuospatial attention and executive control. Neuroimaging studies have shown hypometabolism and functional decline in these areas in patients with MCI 29 , 30 , consistent with the current behavioral findings. Gaze-Related Indicators Although gaze-related measures such as omission and commission errors, along with visual orienting frequency (VOF), did not differ significantly between groups, upward trends were observed in the MCI group. These patterns may reflect subtle impairments in inhibitory control and attentional regulation. Prior studies suggest that older adults with cognitive vulnerability exhibit greater variability in inhibitory processes and gaze regulation 31 , 32 . While these metrics did not reach statistical significance, they may serve as supplementary indicators if future studies incorporate more detailed eye-tracking parameters (e.g., fixation duration, saccadic latency, scan path entropy). Theoretical Context and Dual-Task Interference These results are interpretable within dual-task interference and attentional bottleneck frameworks. The UFOV task requires concurrent central and peripheral stimulus processing, placing high demand on attentional resources—particularly in those with cognitive impairment. These findings are consistent with dual-task models proposed by Welford (1952) 33 and Pashler (1994) 34 , as well as aging-specific attention theories 32 , which posit that attentional capacity is more severely constrained in individuals with MCI. Clinical Implications Given its simplicity, brevity, and scalability, the UFOV task holds promise for community-based cognitive screening. The rise of disease-modifying therapies for early Alzheimer’s disease highlights the need for early identification tools. UFOV-based assessments, especially when integrated into digital platforms, may help detect cognitive decline before functional impairments emerge. Our findings suggest that UFOV reaction time possesses sufficient sensitivity and specificity to serve as a frontline screening measure. Limitations and Future Directions Several limitations must be acknowledged. First, the cross-sectional design limits causal inference and prevents evaluation of longitudinal change. Second, the modest, relatively homogeneous sample constrains generalizability. Third, gaze behavior metrics were limited in scope; future research should incorporate a wider range of eye-tracking variables. Lastly, tasks were administered on a 2D monitor in a controlled laboratory environment, which may reduce ecological validity. Using immersive platforms, such as virtual reality, may offer more generalizable insights into real-world attentional performance. Conclusion UFOV reaction time appears to be a sensitive and valid behavioral marker for distinguishing older adults with MCI from cognitively healthy peers. Its strong diagnostic performance, confirmed by logistic regression and ROC analysis, and its classification as moderately accurate per Akobeng (2007) 27 , support its use in early detection. Longitudinal studies are warranted to assess its predictive value for dementia progression and responsiveness to intervention. Declarations Data Availability Statement The dataset generated and analyzed during the current study is available in the Mendeley Data repository at https://doi.org/10.17632/hnwrnk8mcw.1. Acknowledgements We would like to thank all participants and their families for their involvement in this study. We also thank Editage (www.editage.jp) for English language editing. Author contributions Y.T. and Y.H. contributed to the conceptualization and methodology of the study; M.I. developed the VRAS program; Y.K. and S.K. performed validation of the results; Y.T., Y.A., and N.I. conducted the statistical analysis; Y.T., Y.H., S.K., and Y.K. participated in data collection. Funding This research was supported by the Individual Research Fund of Kyoto Tachibana University. No external funding was received. 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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-6993172","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":502149868,"identity":"ed2a5f88-8569-4f3e-948e-cf99648d24ac","order_by":0,"name":"Yoshiki Tamaru","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYPACGyDmYTiMJMKGUy0PhEqTIFnLYbAWZqJcZM9+OvFzZdv5Ov72swcPF9TckWeQSGD88IOBLw+nLTy5myXPtt2WkDiTl3B4xrFnhg0SCcySPQxsxbgdlrtBshGoheEGj8FhHrbDjPtvJDBIA/2S2IBLC//bzT8b285JyIO1/DtsD7LlN14tErnbgLYckDAAaeFtO5wI1MKG35Ybb7dZNpxLltx4Jsfg8My+w8kNPA/bLHsMcPuFvT93882GMjt+ueNnjD8XfDts28CefPjGj4pjOEMMG2AEOsngWAIpWsCghnQto2AUjIJRMFwBAJv4Vbu9D3csAAAAAElFTkSuQmCC","orcid":"","institution":"Kyoto Tachibana University","correspondingAuthor":true,"prefix":"","firstName":"Yoshiki","middleName":"","lastName":"Tamaru","suffix":""},{"id":502149869,"identity":"367123d5-ea78-4a1e-84fc-10f860fdbe1b","order_by":1,"name":"Shin Inada","email":"","orcid":"","institution":"Morinomiya University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shin","middleName":"","lastName":"Inada","suffix":""},{"id":502149870,"identity":"ce68e2c8-d1c3-4c81-a3bf-caefcab2144b","order_by":2,"name":"Norio Ideguchi","email":"","orcid":"","institution":"Morinomiya University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Norio","middleName":"","lastName":"Ideguchi","suffix":""},{"id":502149871,"identity":"0c63faf9-b011-447f-a339-2db3d11a58bb","order_by":3,"name":"Shohei Kagino","email":"","orcid":"","institution":"Morinomiya University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shohei","middleName":"","lastName":"Kagino","suffix":""},{"id":502149872,"identity":"5e950062-4366-4541-a2f3-a5651a0812b8","order_by":4,"name":"Yuki Katsuhara","email":"","orcid":"","institution":"Morinomiya University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yuki","middleName":"","lastName":"Katsuhara","suffix":""},{"id":502149873,"identity":"c4706cb3-e0c3-4b97-8b5c-4052550f7a4a","order_by":5,"name":"Yasuhiro Higashi","email":"","orcid":"","institution":"Naragakuen University","correspondingAuthor":false,"prefix":"","firstName":"Yasuhiro","middleName":"","lastName":"Higashi","suffix":""}],"badges":[],"createdAt":"2025-06-27 16:08:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6993172/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6993172/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-32034-6","type":"published","date":"2025-12-11T15:58:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89455121,"identity":"06e8e7e6-61c3-4cf3-80a6-6a771ed19a15","added_by":"auto","created_at":"2025-08-20 06:51:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":214767,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental conditions of the Visual Reaction Assessment System (VRAS).\u003cbr\u003e\nParticipants sat in an upright position and were instructed to press a response button upon detecting a red circular target. In \u003cstrong\u003eCondition 1\u003c/strong\u003e(central visual field task), the target was presented at the center of the screen. In \u003cstrong\u003eCondition 2\u003c/strong\u003e (useful field of view task), while maintaining gaze on a central black fixation point, targets appeared randomly at four peripheral locations (45°, 135°, 225°, and 315°), each positioned 20 cm from the center. All stimuli were 4 cm in diameter and presented for 2 seconds per trial. Each condition consisted of 20 trials with randomized timing.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6993172/v1/d3a8e8eb4d5256bd665e5474.png"},{"id":89453874,"identity":"139bf82f-3485-40e5-9106-6bc223c2406f","added_by":"auto","created_at":"2025-08-20 06:43:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":186851,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental setup for the visual reaction task using the eye-tracking system.\u003cbr\u003e\nThe figure illustrates the two visual task conditions. In the \u003cstrong\u003ecentral visual field task\u003c/strong\u003e (left), a red target appears at the center of the screen, and participants respond by pressing a button as quickly as possible. In the \u003cstrong\u003euseful field of view (UFOV) task\u003c/strong\u003e (right), while maintaining gaze on the central fixation point, red targets are randomly presented in one of four peripheral locations (45°, 135°, 225°, 315°), each located 20 cm from the center. Eye movements are recorded using the View Tracker 3 device, and responses are registered using a USB-connected button box.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6993172/v1/5acee8a61fb87171460b06dd.png"},{"id":89453879,"identity":"50795c0b-a971-4f1a-9d1c-fbad1a76c9c6","added_by":"auto","created_at":"2025-08-20 06:43:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":251424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoxplots comparing central and useful field measures between the MCI and HC groups.\u003c/strong\u003e\u003cbr\u003e\n (A) Central Reaction Time (C-RT); (B) Useful Reaction Time (U-RT); (C) Central Omission Errors (C-OE); (D) Useful Omission Errors (U-OE); (E) Central Commission Errors (C-CE); (F) Useful Commission Errors (U-CE); (G) Useful Visual Orienting Frequency (U-VOF). Each box represents the interquartile range (IQR), with the horizontal line indicating the median and the “×” symbol indicating the mean. Whiskers represent the range of values excluding outliers. The MCI group tended to show slower response times and greater variability in error measures and gaze behavior compared to the HC group.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6993172/v1/9e29cef2396c0205060dc5bf.png"},{"id":89453875,"identity":"72f5d498-ab0c-4f0f-8402-10099825fcc8","added_by":"auto","created_at":"2025-08-20 06:43:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56574,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve of Useful Reaction Time (U-RT) for identifying mild cognitive impairment (MCI).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6993172/v1/7c7c104054e9d23da593c7fa.png"},{"id":98244196,"identity":"423583fb-445b-4785-95af-5c1babb88663","added_by":"auto","created_at":"2025-12-15 16:13:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1724296,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6993172/v1/fba00d45-434b-4836-9231-2df55570cde6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Indicator for Early Detection of Mild Cognitive Impairment: Exploring the Relationship Between Visual Field Characteristics and Response Time","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global aging of the population has led to a substantial rise in the number of older adults with dementia, placing growing burdens on healthcare systems and social security infrastructures worldwide \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. To delay or prevent dementia onset, early detection and intervention during the prodromal stage—mild cognitive impairment (MCI)—are essential \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. MCI represents an intermediate stage between normal cognitive aging and dementia, marked by measurable deficits in memory or other cognitive domains, while daily functional independence remains largely preserved. Non-pharmacological interventions, including lifestyle modification, aerobic exercise, and cognitive training, have shown promise in slowing progression when applied during the MCI stage \u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e–\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRecently, disease-modifying therapies such as lecanemab (LEQEMBI) \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and donanemab \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e have demonstrated efficacy in slowing cognitive decline and, in some cases, improving cognitive function when initiated during MCI. Consequently, timely and accurate identification of individuals with MCI is crucial to optimize the effects of both pharmacological and non-pharmacological treatments. However, conventional screening methods are often influenced by educational background and language proficiency, limiting their sensitivity in detecting early cognitive changes \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This limitation highlights the need for practical, scalable, and sensitive tools capable of identifying early cognitive decline before functional impairments appear.\u003c/p\u003e\u003cp\u003eBeyond documented deficits in memory \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, attention \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and executive function \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, emerging evidence suggests that subtle impairments in visual processing and attentional control may serve as early indicators of cognitive vulnerability \u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e–\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In visual cognition, the central visual field supports high-resolution object recognition, while the useful field of view (UFOV) enables rapid, accurate processing of peripheral visual information surrounding the fixation point \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. These functions are particularly susceptible to aging, and UFOV contraction has been linked to reduced attentional resources and increased cognitive load \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Based on these findings, measuring reaction times to stimuli within the central visual field and UFOV has emerged as a promising method for detecting early cognitive decline in aging and MCI. Variations in visual response characteristics may reflect changes in processing speed and attentional resource allocation, offering sensitive behavioral markers of cognitive impairment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis cross-sectional study was conducted between 2024 and 2025 and included older adults residing in medical and nursing care facilities. Participants were classified into two groups: those with MCI (MCI group) and cognitively healthy controls (HC group). Inclusion criteria for the HC group were a score of ≥ 26 on the Japanese version of the Montreal Cognitive Assessment (MoCA-J) and a score of 28–30 on the Japanese version of the Mini-Mental State Examination (MMSE-J). The MCI group included patients with MoCA-J scores ≤ 25 and MMSE-J scores 24–27 \u003csup\u003e15\u003c/sup\u003e. Exclusion criteria included: presence of visual impairments, cerebrovascular disease, or other neurological or medical conditions likely to affect cognitive or visual performance. A priori power analysis was conducted using G*Power version 3.1.9.7 (Heinrich Heine University Düsseldorf, Germany) \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e to estimate the minimum sample size for logistic regression. Parameters included a two-tailed test, odds ratio of 3.0 (large effect), Pr (Y = 1 | X = 1) = 0.5, α = 0.05, power (1 − β) = 0.80, and two predictor variables. The proportion of variance explained by other predictors (R² other X) was set between 0.0 and 0.1, resulting in a required sample size of approximately 70–80. A total of 79 participants were initially recruited. After excluding five individuals who met exclusion criteria, 74 patients were included in the final analysis: 43 in the HC group (mean age: 72.2 ± 2.9 years; 21 males, 22 females; MoCA-J: 28.1 ± 1.0; MMSE-J: 29.4 ± 0.7) and 31 in the MCI group (mean age: 73.4 ± 3.9 years; 18 males, 13 females; MoCA-J: 22.7 ± 1.3; MMSE-J: 25.7 ± 1.0). Participant characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The study protocol was approved by the Ethics Committee of Kyoto Tachibana University (Approval Number: 25 − 11). No financial incentives or compensation were provided. All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\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\u003eParticipant Characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHC-\u003c/p\u003e\u003cp\u003egroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMCI-\u003c/p\u003e\u003cp\u003egroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCohen’s d\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eupper\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.2 ± 2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.4 ± 3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e− .0369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoCA-J\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.1 ± 1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.7 ± 1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMSE-J\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.4 ± 0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.7 ± 1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNote.\u003c/b\u003e MoCA-J = Japanese version of the Montreal Cognitive Assessment, MMSE-J = Japanese version of the Mini-Mental State Examination, Statistical analysis was performed using an independent samples t-test.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003cb\u003eOutcome Measures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll assessments were conducted with participants seated in a stable, upright position. A high-precision eye-tracking system (View Tracker III; DITECT Co., Ltd., Tokyo, Japan) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e was used to measure gaze behavior. Visual stimuli were presented on a 23.8-inch external monitor (model PTFWJA, PRINCETON, Japan) positioned 1 meter in front of the participant’s forehead at eye level. Two visual tasks were administered: a central visual field task and a UFOV task, each designed to assess different aspects of visual processing.\u003c/p\u003e\u003cp\u003eThe central visual field task evaluated the ability to detect stimuli presented at the fixation point. The UFOV task assessed responses to stimuli located in the peripheral visual field surrounding the fixation point. For each task, the following variables were recorded:\u003c/p\u003e\u003cp\u003e(1) reaction time (ms);\u003c/p\u003e\u003cp\u003e(2) omission errors (failure to respond to a target stimulus);\u003c/p\u003e\u003cp\u003e(3) commission errors (responses to non-target stimuli); and\u003c/p\u003e\u003cp\u003e(4) visual orienting frequency (VOF; number of gaze shifts away from the central fixation point toward peripheral stimuli).\u003c/p\u003e\u003cp\u003eThese variables were compared between the MCI and HC groups to assess group-level differences and evaluate their potential as behavioral markers of cognitive impairment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStimulus Conditions: Visual Reaction Assessment System (VRAS)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eExperimental tasks were developed using Microsoft Visual Studio Community 2022 and run on a personal computer (Let's Note CF-FV; Panasonic Corporation, Japan) with Windows 11 Home. Stimulus presentation was controlled by a custom-built VRAS.\u003c/p\u003e\u003cp\u003eCondition 1 (central visual field task): A red circular target (4 cm diameter) was displayed 20 times at the center of the screen. Participants were instructed to press a response button as quickly and accurately as possible when the target appeared.\u003c/p\u003e\u003cp\u003eCondition 2 (UFOV task): A black fixation point of the same size remained at the center of the screen. While maintaining gaze on the fixation point, red circular targets identical to those in Condition 1 were presented in one of four peripheral locations—upper right (45°), lower right (135°), lower left (225°), or upper left (315°)—each 20 cm from center. Each location included 20 randomized trials, totaling 80. Each stimulus was displayed for 2 seconds.\u003c/p\u003e\u003cp\u003eReaction time was recorded using a USB-connected response button (model USBSWP; TECHNOTOOLS, Japan). Reaction time was defined as the latency between stimulus onset and button press. In addition to reaction time, omission errors, commission errors, and—exclusively for Condition 2—VOF (i.e., number of gaze deviations from the fixation point) were recorded (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEye-Tracking Apparatus and Gaze Data Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGaze behavior was continuously recorded using the View Tracker 3 (DITECT Inc., Tokyo, Japan), a lightweight head-mounted device (~ 40 g) equipped with a 200 Hz infrared eye camera and a 30 Hz scene camera to capture both eye position and the participant’s visual field. The device connected to a computer via USB, allowing synchronized data acquisition. Gaze data were processed using DITECT gaze analysis software (version 1.0518). Before the experimental tasks, standardized calibration was conducted individually to ensure accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Gaze data were analyzed using an area-of-interest (AOI)–based method. AOIs were predefined to match the spatial locations of target stimuli in each condition. The eye-tracking system was integrated with the VRAS platform, enabling real-time gaze behavior analysis under both visual conditions. In Condition 1, participants were required to fixate on the centrally presented target and respond promptly. In Condition 2, gaze accuracy was evaluated based on whether attention was incorrectly directed toward peripheral, non-target stimuli. Incorrect responses were recorded in both conditions and used as quantitative indicators of gaze-related error.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted using IBM SPSS Statistics for Windows, version 30.0 (IBM Corp., Armonk, NY, USA). Group comparisons were performed for four primary outcome variables: reaction time, omission errors, commission errors, and VOF. The Shapiro–Wilk test assessed the normality of each variable. Reaction time met normality assumptions and was analyzed using an independent samples t-test. Omission errors, commission errors, and VOF violated normality assumptions and were analyzed using the Mann–Whitney U test. Variables that differed significantly between the MCI and HC groups in preliminary analyses were entered as predictors in a binomial logistic regression model, with group classification (HC = 0, MCI = 1) as the dependent variable. For predictors that were statistically significant in the logistic model, receiver operating characteristic (ROC) curve analysis evaluated classification performance. The area under the curve (AUC) was calculated to assess model accuracy, and optimal cutoff values were identified using the Youden index. A significance level of p \u0026lt; .05 was used for all tests.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eGroup differences between older adults with MCI and cognitively healthy controls (HC) were examined using appropriate statistical procedures. Outcome variables included reaction time, omission errors, commission errors, and gaze-related indicators under central visual field and UFOV conditions.\u003c/p\u003e\n\u003cp\u003eVariables showing statistically significant between-group differences were further analyzed using binomial logistic regression to evaluate classification accuracy for MCI. Predictors that reached statistical significance in the regression model underwent ROC curve analysis to assess discriminatory performance. The AUC and optimal cutoff values were calculated using the Youden index.\u003c/p\u003e\n\u003cp\u003eDetailed results for each outcome measure are presented below.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1) \u0026nbsp; Results of Between-Group Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e① \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eReaction time\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor central reaction time (C-RT), the HC group had a mean of 554.4 ms (SD = 46.6), and the MCI group had a mean of 568.5 ms (SD = 52.6). This difference was not statistically significant (t = −1.22, p = 0.228, Cohen’s d = −0.29). In contrast, for useful reaction time (U-RT), the HC group recorded a mean of 580.3 ms (SD = 44.9), while the MCI group had a significantly slower mean of 638.6 ms (SD = 38.4), indicating a significant group difference (t = −5.84, p \u0026lt; 0.001, Cohen’s d = −1.17) (Table 3, Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e②\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eOmission Error\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor central omission errors (C-OE), the median was 0.0 in both groups (interquartile range [IQR]: 0.0–0.0), showing no between-group variability. The difference was not statistically significant (\u003cem\u003eU\u003c/em\u003e = 745.5, Z = 1.276, \u003cem\u003ep\u003c/em\u003e = 0.202, \u003cem\u003er\u003c/em\u003e = 0.148). In the UFOV task (U-OE), the median was also 0.0 in both groups, but the MCI group had a wider IQR (0.0–0.5) than the HC group (0.0–0.0), indicating greater performance variability. Although not statistically significant (\u003cem\u003eU\u003c/em\u003e = 790.0, Z = 1.517, \u003cem\u003ep\u003c/em\u003e = 0.129, \u003cem\u003er\u003c/em\u003e = 0.185), this trend may suggest increased omission errors among older adults with MCI (Table 3, Figure 3).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e③\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCommission\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eError\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor central commission errors (C-CE), no significant group difference was observed (\u003cem\u003eU\u003c/em\u003e = 705.0, Z = 0.557, \u003cem\u003ep\u003c/em\u003e = 0.577, \u003cem\u003er\u003c/em\u003e = 0.065).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eIn the UFOV task (U-CE), the MCI group showed slightly more errors (median = 1.0, IQR: 0.0–1.5) than the HC group (median = 0.0, IQR: 0.0–1.0), but the difference was not statistically significant (\u003cem\u003eU\u003c/em\u003e = 792.0, Z = 1.464, \u003cem\u003ep\u003c/em\u003e = 0.143, \u003cem\u003er\u003c/em\u003e = 0.170) (Table 3, Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e④\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eUFOV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the UFOV task, the MCI group exhibited a slightly higher VOF (median = 3.0, IQR: 2.0–3.0) compared to the HC group (median = 2.0, IQR: 2.0–3.0). This difference approached but did not reach statistical significance\u0026nbsp;(U = 833.0, p = 0.052, r = 0.230) (Table 3, Figure 3).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 3. Results of Group Comparisons Between the HC and MCI Groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"532\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHC Group\u003c/p\u003e\n \u003cp\u003eMedian [25%-75%]\u003c/p\u003e\n \u003cp\u003eMean± SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMCI Group\u003c/p\u003e\n \u003cp\u003eMedian [25%-75]\u003c/p\u003e\n \u003cp\u003eMean± SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003cp\u003esize\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eC-RT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e55.4±46.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003cp\u003e568.5±52.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eU-RT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e580.4±44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003cp\u003e638.6±38.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eC-OE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0 (0.0–0.0)\u003c/p\u003e\n \u003cp\u003e0.14±0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0 (0.0–0.5) 0.26±0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e745.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eU-OE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0 (0.0–1.0)\u003c/p\u003e\n \u003cp\u003e0.23±0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0 (0.0–1.0) 0.29±0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e790.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eC-CE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0 (0.0–0.0)\u0026nbsp;0.49±0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0 (0.0–1.0) 0.71±0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e705.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eU-CE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0 (0.0–1.5)\u003c/p\u003e\n \u003cp\u003e1.00±0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0 (1.0–2.0) 1.29±0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e792.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFOV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0 (2.0–3.0) 2.14±0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.0 (2.0–3.0) 2.55±0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e833.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\n \u003cp\u003eNote. C-RT = Central Reaction Time; U-RT = Useful-field Reaction Time; C-OE = Central Omission Errors; U-OE = Useful-field Omission Errors; C-CE = Central Commission Errors; U-CE = Useful-field Commission Errors; FOV = Visual Orienting Frequency. Median values are presented with interquartile ranges [25%–75%]; mean values are provided with standard deviations (Mean ± SD). Test refers to the statistical method used (t = independent samples t-test; U = Mann–Whitney U test). Statistic indicates the test statistic value (t or U); p-values are two-tailed. Effect size is represented by Cohen’s d for t-tests and by rank-biserial correlation (r) for Mann–Whitney U tests.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2) Logistic Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLogistic regression analysis was conducted to determine whether U-RT could distinguish older adults with MCI from those who were cognitively healthy. As shown in Table 4, U-RT was a significant predictor (B = 0.03, SE = 0.01, Wald = 17.55, p \u0026lt; 0.001), with an odds ratio of 1.03 (95% CI: 1.017–1.046). Each 1 ms increase in U-RT was associated with a 3.0% increase in the odds of MCI classification \u003cstrong\u003e(Table 4).\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"573\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\n \u003cp\u003eTable 4 Results of logistic regression analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eExp(B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eEXP（B）\u0026nbsp;95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-19.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eU-RT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eNote: SE = Standard Error; CI = Confidence Interval; U-RT = Useful Reaction Time.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic performance of U-RT for identifying MCI using ROC curve analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curve analysis was performed to evaluate the discriminatory ability of U-RT in identifying MCI. The AUC was 0.841 (95% confidence interval [CI]: 0.741–0.916), indicating excellent diagnostic accuracy. The optimal cutoff value, defined as the predicted probability threshold from the logistic model, was 0.331—corresponding to a ≥33.1% likelihood of MCI classification. At this threshold, sensitivity was 90.3%, specificity was 72.1%, and the maximum Youden Index was 0.624 (Figure 4).\u003c/p\u003e\n\u003cp\u003eThe ROC curve illustrates the model’s classification performance (AUC = 0.841; 95% CI = 0.741–0.916). The logistic regression model estimating the probability of MCI based on U-RT is defined as:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere P represents the predicted probability of being classified as having MCI. Accordingly, the probability can be calculated as:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAANcAAAAwCAYAAACCPO+PAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAX3SURBVHhe7Zw9LzVNHMbH8w3QisLRqChoBIUC8QW8NQoJR6ES5ESi0ZCIzkuno9RIUCIKEaFQcRQiquMl8QHce829c8xZs7uzx85z41y/ZLK7s7Ozs7tzzct/Zrbq3UMQQlLnP39LCEkZiouInZ0d0djYKE5OTnwfkgYUVwVzdXUlNjc3xeTkpMjn874vSQuKq4Jpbm4W4+PjYm5uzvchaUJxEeIIiosQR1BchDiC4iLEERQXIY6guAhxBMVFiCMoLkIcQXER4giKi4iLiwu5vb6+lluSDlxyUsFgom5nZ6d/9AGzRDpY1Vx9fX2iqqrK6AYHBzmb+ofS0dEhhRR0JB2sxLW/vy+Wlpbkfm9vb/EjYCY1HEo/CoyQUqz7XPX19XI7Ojoqt6ChoUGsrq7K/cXFRbklhPzFus+FNT/r6+uypoKoFKrdjhoNNZwJLMbb2tryj5LR3d0tZmZm/CNCfhAQlw3V1dXvra2t/tEH29vbEOf7wMCA7/MZXIcw5TjET8hPxEpcx8fHMqN7/S7f5y+FQqEoHIT5DiwsLJSIk47u/3bPz88yL1qJK5fLyYv0WmRvb68oLNYuhHzGqs+Fn5d4ahQ9PT3i8PBQvLy8CK+ZKM3wWCaO5eKEkFJixXV3dycymYzIZrNibW3N97UDP0BpaWnxj5IB8ULQhPxUYsWFvwNNTEwIr+kna6ok0EpIKplYcWF2xsHBgSgUCqK2ttb3JYTEETmIjCYhhIUxLAqLkGSEiuvp6Unkcjm5rw8aE3fAcBQGmtiYyxkEg/hqnicG+qMIiwPo80eXl5d933hsrwkLp9IEh30bVHg4pFuB96f8g9Px9HO600HaTGHgcA73QoVjDZqFQTCehVNB9xvBMEImk/nn43R4v2FpQBpN3yCfz5f44TmCY5GKsDgA7hv3/KZ49ffmtW5C4wgLh/Rns1m5D79g2sLSCxCPKU0qPhM4p1+DdMEpcB5pUvsqLNKm9pGesOcMYk55BXB5efm+sbEhZ54keWEuwIeMGys0ZT5co2cmVVCEYYoD4Br463EFCWZkxKXfC/dGhg8SFU5lZEXweqTJlF6QhriQlrD4g2EVSHPUO9axnrj72yj3V85oHqS5AgDNDMzZTGqJBff39yXNlLq6On8vGRi79PKC3OrNrChOT09LmrG49+3trX/0QVQ4vbuBJm1wbirSBOeKoaEhOcSUBKQZ4702TdiKFdd34ezsTBqMyqG9vV0anBL1AwyoTI5MD6cKD70fNjs7W9IHAbZ98bhwiA8FTFSfMy305/BqocRjt6Crq0scHR35R+FQXA5B6dbW1iY/ZE1NjbG0Q+1jm0mDYLEjSl6vmSLvgdUJKFW/ApYOPTw8yH3UJKr28JpIxf20axPE5zUfZWa3NWqU+87Uc4DHx0e5LQebAo3icgAsrSj1V1ZW5Ho3fExkejRDdIuUqaSOOx8EJa+e4aenp+X2K9g0L1FrohmpgCBNwrYNpwqKtIh7jxCz6RcHaUJxOWBkZEQ2r1DyI9OA19dXWcNg1okSA8JgEapeCgbP24DrkYmQYcot0RXz8/PFNEehwqgmJK4bHh6W+zq24UCSvieaZmji6e9Or/Xi3iPShVqs3Kao1Xv2bl7RwCKE1wCrVhBYpHDOxinLkrJA6fEpP6wkMIFzUSAudR89rIrXdD2sWvBHGBAWR5i/DeoecOr5QfDeYeHUu1cuKcHrg9ZGHVj4TOGUv8kvLE5YEtWzRZH8iX4Z6gOZxGUC4aPC4sPAIUPhA+DjwNwfJixg+7HI9wDf1wY2C1MEqwA8UUmHfsXu7q7858jNzY3o7+/3Q30G/SY0l9I08RM3oBlpOxmd4kqRt7c3ufVqK9nORx8AfQjMy4SRIwqE13/+Q74fMFLBOGPTJwUUlwNMQoKRI858a2vAIP8GGKiSGIwqXlxp/sq5qalJLvI8Pz+X6+AABIUSb2pq6suWPPKzsP612m8D/RsXv3JGv2tsbEwKTP0KAWNPFFblUbHiIsQ17HMR4giKixBHUFyEOILiIsQJQvwBfGT1w/AGCusAAAAASUVORK5CYII=\"\u003e\u003c/p\u003e\n\u003cp\u003eThis equation indicates that higher U-RT values are associated with an increased probability of MCI classification.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined visual processing characteristics in older adults with MCI, focusing on central and peripheral (UFOV) visual tasks. We also evaluated the diagnostic utility of reaction time and gaze-related indicators using logistic regression and ROC analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCentral Visual Processing (C-RT)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo significant differences were found in C-RT between the MCI and HC groups. This suggests that visual processing in the foveal region may remain intact in early-stage MCI. These findings align with previous research indicating that lower-level perceptual functions, such as basic visual discrimination and stimulus detection, are largely preserved during the early phases of cognitive decline\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Thus, central visual response latency alone may lack sensitivity for detecting early pathological changes.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePeripheral Visual Attention (UFOV Reaction Time)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn contrast, patients with MCI exhibited significantly slower reaction times in the UFOV task, which requires broad spatial attention and rapid cognitive resource allocation. This delay likely reflects impairments in divided attention and executive function, both of which are typically affected in early MCI. The large effect size (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.17) underscores the robustness of this group difference. Moreover, logistic regression and ROC analysis revealed strong diagnostic utility for UFOV reaction time, with an area under the ROC curve (AUC) of 0.841 (95% CI: 0.741\u0026ndash;0.916). Using a predicted probability cut-off of 0.331, the model achieved 90.3% sensitivity and 72.1% specificity. As noted by Akobeng (2007), AUC values between 0.7 and 0.9 indicate moderately accurate diagnostic tests \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. These findings support UFOV reaction time as a moderately accurate and clinically meaningful behavioral marker for detecting MCI. This reinforces earlier studies on the diagnostic relevance of UFOV measures \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, demonstrating that even a single behavioral metric, when properly selected, can offer substantial screening power. Neurocognitively, delayed UFOV responses may reflect early disruption in the frontoparietal attention network\u0026mdash;particularly the dorsolateral prefrontal cortex and superior parietal lobule\u0026mdash;regions critical for visuospatial attention and executive control. Neuroimaging studies have shown hypometabolism and functional decline in these areas in patients with MCI \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, consistent with the current behavioral findings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGaze-Related Indicators\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAlthough gaze-related measures such as omission and commission errors, along with visual orienting frequency (VOF), did not differ significantly between groups, upward trends were observed in the MCI group. These patterns may reflect subtle impairments in inhibitory control and attentional regulation. Prior studies suggest that older adults with cognitive vulnerability exhibit greater variability in inhibitory processes and gaze regulation \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. While these metrics did not reach statistical significance, they may serve as supplementary indicators if future studies incorporate more detailed eye-tracking parameters (e.g., fixation duration, saccadic latency, scan path entropy).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTheoretical Context and Dual-Task Interference\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThese results are interpretable within dual-task interference and attentional bottleneck frameworks. The UFOV task requires concurrent central and peripheral stimulus processing, placing high demand on attentional resources\u0026mdash;particularly in those with cognitive impairment. These findings are consistent with dual-task models proposed by Welford (1952) \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and Pashler (1994) \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, as well as aging-specific attention theories \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, which posit that attentional capacity is more severely constrained in individuals with MCI.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical Implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven its simplicity, brevity, and scalability, the UFOV task holds promise for community-based cognitive screening. The rise of disease-modifying therapies for early Alzheimer\u0026rsquo;s disease highlights the need for early identification tools. UFOV-based assessments, especially when integrated into digital platforms, may help detect cognitive decline before functional impairments emerge. Our findings suggest that UFOV reaction time possesses sufficient sensitivity and specificity to serve as a frontline screening measure.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations and Future Directions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeveral limitations must be acknowledged. First, the cross-sectional design limits causal inference and prevents evaluation of longitudinal change. Second, the modest, relatively homogeneous sample constrains generalizability. Third, gaze behavior metrics were limited in scope; future research should incorporate a wider range of eye-tracking variables. Lastly, tasks were administered on a 2D monitor in a controlled laboratory environment, which may reduce ecological validity. Using immersive platforms, such as virtual reality, may offer more generalizable insights into real-world attentional performance.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUFOV reaction time appears to be a sensitive and valid behavioral marker for distinguishing older adults with MCI from cognitively healthy peers. Its strong diagnostic performance, confirmed by logistic regression and ROC analysis, and its classification as moderately accurate per Akobeng (2007) \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, support its use in early detection. Longitudinal studies are warranted to assess its predictive value for dementia progression and responsiveness to intervention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe dataset generated and analyzed during the current study is available in the Mendeley Data repository at https://doi.org/10.17632/hnwrnk8mcw.1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all participants and their families for their involvement in this study. We also thank Editage (www.editage.jp) for English language editing.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.T. and Y.H. contributed to the conceptualization and methodology of the study; M.I. developed the VRAS program; Y.K. and S.K. performed validation of the results; Y.T., Y.A., and N.I. conducted the statistical analysis; Y.T., Y.H., S.K., and Y.K. participated in data collection.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Individual Research Fund of Kyoto Tachibana University. No external funding was received.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Correspondence and requests for materials should be addressed to Y.T.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health, O. Global status report on the public health response to dementia. 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Bull.\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e, 220-244, doi:10.1037/0033-2909.116.2.220 (1994).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mild cognitive Impairment, Peripheral Vision, Useful Field, Reaction Time","lastPublishedDoi":"10.21203/rs.3.rs-6993172/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6993172/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEarly detection of mild cognitive impairment (MCI) is vital for timely intervention to delay or prevent progression to dementia. Gaze behavior analysis has been shown to differentiate individuals with MCI from cognitively healthy older adults.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study aimed to examine visual processing differences between cognitively healthy older adults and those with MCI, focusing on central and useful field of view (UFOV) tasks.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParticipants completed a central visual field task and a UFOV task. Reaction times, omission and commission errors, and visual orienting frequency were measured. Group comparisons were conducted. For variables showing significant differences, receiver operating characteristic curve analysis evaluated discriminatory accuracy and optimal cutoff values.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo significant group differences emerged in the central task. In the UFOV task, patients with MCI demonstrated significantly slower reaction times than controls. The optimal UFOV reaction time cutoff was 614.7 ms, with 90.3% sensitivity, 72.1% specificity, and an area under the curve of 0.841.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOlder adults with MCI exhibit delayed visual processing under UFOV conditions. Reaction time in the UFOV task may serve as a sensitive, practical behavioral marker for early MCI detection.\u003c/p\u003e","manuscriptTitle":"A Novel Indicator for Early Detection of Mild Cognitive Impairment: Exploring the Relationship Between Visual Field Characteristics and Response Time","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 06:43:24","doi":"10.21203/rs.3.rs-6993172/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-26T21:33:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-12T15:37:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-09T16:17:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230088042296855419797974884590502554768","date":"2025-08-15T08:45:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326304791874316230545411265722762387450","date":"2025-08-13T22:24:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311462330077374393720048374362972706870","date":"2025-08-13T13:08:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150565617649255158143154985489126162378","date":"2025-08-11T22:22:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-11T20:54:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-04T15:55:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-21T11:04:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-07T23:49:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-07T23:46:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3e5c9fcf-1392-45fc-af2b-26bef0dacc04","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53336570,"name":"Health sciences/Biomarkers"},{"id":53336571,"name":"Health sciences/Neurology"},{"id":53336572,"name":"Biological sciences/Neuroscience"},{"id":53336573,"name":"Biological sciences/Psychology"},{"id":53336574,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-12-15T16:06:43+00:00","versionOfRecord":{"articleIdentity":"rs-6993172","link":"https://doi.org/10.1038/s41598-025-32034-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-11 15:58:01","publishedOnDateReadable":"December 11th, 2025"},"versionCreatedAt":"2025-08-20 06:43:24","video":"","vorDoi":"10.1038/s41598-025-32034-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-32034-6","workflowStages":[]},"version":"v1","identity":"rs-6993172","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6993172","identity":"rs-6993172","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}