Search Results (83)

Cerebellar ataxias are a group of disorders characterized by clumsy movements because of defective muscle control. In affected individuals, muscular impairment might have an impact on activities like walking, balance, hand coordination, speech, and feeding, as well as eye movements. The development of symptoms typically takes place during the span of adolescence, and it has the potential to cause distress for individuals in many areas of their lives, including professional and interpersonal relationships. Although skeletal muscle is understudied in ataxias, its examination may provide hitherto unexplored details in this family of disorders. Observing muscle involvement can assist in diagnosing conditions where genetic tests alone are inconclusive. Furthermore, it helps determine the stage of progression of a pathology that might otherwise be challenging to assess. In this study, we reviewed the main scientific literature reporting on skeletal muscle examination in autosomal recessive cerebellar ataxias (ARCAs), with a focus on the rare Marinesco–Sjögren syndrome. (MSS). Our aim was to highlight the similarities in muscle alterations observed in ARCA patients while also considering data gathered from preclinical models. Analyzing the similarities among these disorders could enhance our understanding of the unidentified mechanisms underlying the phenotypic evolution of some less common conditions. Full article

Mild cognitive impairment represents a transitional phase between healthy ageing and dementia, including Alzheimer’s disease. Early detection is essential for timely clinical intervention. This study explores the viability of smooth pursuit eye movements (SPEM) as a non-invasive biomarker for cognitive impairment. A total of 115 participants—62 with cognitive impairment and 53 cognitively healthy controls—underwent comprehensive neuropsychological assessments followed by an eye-tracking task involving smooth pursuit of horizontally and vertically moving stimuli at three different speeds. Quantitative metrics such as tracking accuracy were extracted from the eye movement recordings. These features were used to train machine learning models to distinguish cognitively impaired individuals from controls. The best-performing model achieved an area under the ROC curve (AUC) of approximately 68 %, suggesting that SPEM-based assessment has potential as part of an ensemble of eye-tracking based screening methods for early cognitive decline. Of course, additional paradigms or task designs are required to enhance diagnostic performance. Full article

Human–computer interaction (HCI) plays a crucial role across various fields, with eye-tracking technology emerging as a key enabler for intuitive and dynamic control in assistive systems like Assistive Robotic Arms (ARAs). By precisely tracking eye movements, this technology allows for more natural user interaction. However, current systems primarily rely on the single gaze-dependent interaction method, which leads to the “Midas Touch” problem. This highlights the need for real-time eye movement classification in dynamic interactions to ensure accurate and efficient control. This paper proposes a novel Gaussian Mixture Model–Hidden Markov Model (GMM-HMM) classification algorithm aimed at overcoming the limitations of traditional methods in dynamic human–robot interactions. By incorporating sum of squared error (SSE)-based feature extraction and hierarchical training, the proposed algorithm achieves a classification accuracy of 94.39%, significantly outperforming existing approaches. Furthermore, it is integrated with a robotic arm system, enabling gaze trajectory-based dynamic path planning, which reduces the average path planning time to 2.97 milliseconds. The experimental results demonstrate the effectiveness of this approach, offering an efficient and intuitive solution for human–robot interaction in dynamic environments. This work provides a robust framework for future assistive robotic systems, improving interaction intuitiveness and efficiency in complex real-world scenarios. Full article

Visual behaviour in zebrafish, often measured by the optokinetic reflex (OKR), serves as a valuable model for studying aspects of human neurological and ocular diseases and for conducting therapeutic or toxicology assays. Traditional methods for OKR analysis often rely on binarization techniques (threshold-based conversion of images to black and white) or costly software, which limits their utility in low-contrast settings or hypopigmented disease models. Here, we present a novel deep learning pipeline for OKR analysis, using ResNet-50 within the DeepLabCut framework in a Python Version 3.10 environment. Our approach employs object tracking to enable robust eye movement quantification, regardless of variations in contrast or pigmentation. OKR responses were elicited in both wild-type and slc45a2 (albino) mutant zebrafish larvae at 5 days post-fertilisation, using a mini-LED arena with a rotating visual stimulus. Eye movements were recorded and analysed using both conventional software and our deep learning approach. We demonstrate that the deep learning model achieves comparable accuracy to traditional methods, with the added benefits of applicability in diverse lighting conditions and in hypopigmented larvae. Statistical analyses, including Bland–Altman tests, confirmed the reliability of the deep learning model. While this study focuses on 5-day-old zebrafish larvae under controlled conditions, the pipeline is adaptable across developmental stages, pigmentation types, and behavioural assays. With appropriate adjustments to experimental parameters, it could be applied to broader behavioural studies, including social interactions and predator–prey dynamics in ocular and neurological disease models. Full article

With positron emission tomography followed by functional magnetic resonance imaging (fMRI), we demonstrated that rapid eye movements (REMs) in sleep are saccades that scan dream imagery. The brain “sees” essentially the same way while awake and while dreaming in REM sleep. As expected, an event-related fMRI study (events = REMs) showed activation time-locked to REMs in sleep (“REM-locked” activation) in the oculomotor circuit that controls saccadic eye movements and visual attention. More crucially, the fMRI study provided a series of unexpected findings, including REM-locked multisensory integration. REMs in sleep index the processing of endogenous visual information and the hierarchical generation of dream imagery through multisensory integration. The neural processes concurrent with REMs overlap extensively with those reported to be atypical in autism spectrum disorder (ASD). Studies on ASD have shown atypical visual processing and multisensory integration, emerging early in infancy and subsequently developing into autistic symptoms. MRI studies of infants at high risk for ASD are typically conducted during natural sleep. Simply timing REMs may improve the accuracy of early detection and identify markers for stratification in heterogeneous ASD patients. REMs serve as a task-free probe useful for studying both infants and animals, who cannot comply with conventional visual activation tasks. Note that REM-probe studies would be easier to implement in early infancy because REM sleep, which is markedly preponderant in the last trimester of pregnancy, is still pronounced in early infancy. The brain may practice seeing the world during REM sleep in utero before birth. The REM-probe controls the level of attention across both the lifespan and typical-atypical neurodevelopment. Longitudinal REM-probe studies may elucidate how the brain develops the ability to “see” and how this goes awry in autism. REMs in sleep may allow a straightforward comparison of animal and human data. REM-probe studies of animal models of autism have great potential. This narrative review puts forth every reason to believe that employing REMs as a probe into the development of the visual brain will have far-reaching implications. Full article

A multi-order combined diffraction spatial filter, integrated with a set of Zernike phase functions (representing wavefront aberrations) and Zernike polynomials, enables the simultaneous formation of multiple aberration-transformed point spread function (PSF) patterns in a single plane. This is achieved using an optical Fourier correlator and provides significantly more information than a single PSF captured in focal or defocused planes—all without requiring mechanical movement. To analyze the resulting complex intensity patterns, which include 49 diffraction orders, a convolutional neural network based on the Xception architecture is employed. This model effectively identifies wavefront aberrations up to the fourth Zernike order. After 80 training epochs, the model achieved a mean absolute error (MAE) of no more than 0.0028. Additionally, a five-fold cross-validation confirmed the robustness and reliability of the approach. For the experimental validation of the proposed multi-order filter, a liquid crystal spatial light modulator was used. Optical experiments were conducted using a Fourier correlator setup, where aberration fields were generated via a digital micromirror device. The experimental results closely matched the simulation data, confirming the effectiveness of the method. New advanced aberrometers and multichannel diffractive optics technologies can be used in industry for the quality control of optical elements, assessing optical system alignment errors, and the early-stage detection of eye diseases. Full article

Eye-gaze writing technology holds significant promise but faces several limitations. Existing eye-gaze-based systems often suffer from slow performance, particularly under challenging conditions such as low-light environments, user fatigue, or excessive head movement and blinking. These factors negatively impact the accuracy and reliability of eye-tracking technology, limiting the user’s ability to control the cursor or make selections. To address these challenges and enhance accessibility, we created a comprehensive dataset by integrating multiple publicly available datasets, including the Eyes Dataset, Dataset-Pupil, Pupil Detection Computer Vision Project, Pupils Computer Vision Project, and MPIIGaze dataset. This combined dataset provides diverse training data for eye images under various conditions, including open and closed eyes and diverse lighting environments. Using this dataset, we evaluated the performance of several computer vision algorithms across three key areas. For object detection, we implemented YOLOv8, SSD, and Faster R-CNN. For image segmentation, we employed DeepLab and U-Net. Finally, for self-supervised learning, we utilized the SimCLR algorithm. Our results indicate that the Haar classifier achieves the highest accuracy (0.85) with a model size of 97.358 KB, while YOLOv8 demonstrates competitive accuracy (0.83) alongside an exceptional processing speed and the smallest model size (6.083 KB), making it particularly suitable for cost-effective real-time eye-gaze applications. Full article

Color fusion and rivalry are two key information integration mechanisms in binocular vision, representing the visual system’s processing patterns for consistent and conflicting inputs, respectively. This study hypothesizes that there are quantifiable differences in eye movement indicators under states of binocular color fusion and rivalry, which can be verified through multi-paradigm eye movement experiments. The experiment recruited eighteen subjects with normal vision (nine males and nine females), employing the Gaze Stability paradigm, Straight Curve Eye Hopping paradigm, and Smoothed Eye Movement Tracking paradigm for eye movement tracking. Each paradigm included a binocular color rivalry experimental group (R-G) and two binocular color fusion control groups (R-R, G-G). Data analysis indicates significant differences in indicators such as Average Saccade Amplitude, Median Saccade Amplitude, and SD of Saccade Amplitude between binocular color fusion and rivalry states. For instance, through Z-Score normalization and cross-paradigm merged analysis, specific ranges of these indicators were identified to distinguish between the two states. When the Average Saccade Amplitude falls within the range of −0.905–−0.693, it indicates a state of binocular color rivalry; when the range is 0.608–1.294, it reflects a state of binocular color fusion. Subsequently, ROC curve analysis confirmed the effectiveness of the experimental paradigms in analyzing the mechanisms of binocular color fusion and rivalry, with AUC values of 0.990, 0.741, and 0.967, respectively. These results reveal the potential of eye movement behaviors as biomarkers for the dynamic processing of visual conflicts. This finding provides empirical support for understanding the neural computational models of binocular vision and lays a methodological foundation for developing visual impairment assessment tools based on eye movement features. Full article

(1) Background. Eye movement abnormalities are increasingly recognized as early biomarkers of Parkinson’s disease (PD), reflecting both motor and cognitive dysfunction. Advances in eye-tracking technology provide objective, quantifiable measures of saccadic impairments, fixation instability, smooth pursuit deficits, and pupillary changes. These advances offer new opportunities for early diagnosis, disease monitoring, and neurorehabilitation. (2) Objective. This narrative review explores the relationship between oculomotor dysfunction and PD pathophysiology, highlighting the potential applications of eye tracking in clinical and research settings. (3) Methods. A comprehensive literature review was conducted, focusing on peer-reviewed studies examining eye movement dysfunction in PD. Relevant publications were identified through PubMed, Scopus, and Web of Science, using key terms, such as “eye movements in Parkinson’s disease”, “saccadic control and neurodegeneration”, “fixation instability in PD”, and “eye-tracking for cognitive assessment”. Studies integrating machine learning (ML) models and VR-based interventions were also included. (4) Results. Patients with PD exhibit distinct saccadic abnormalities, including hypometric saccades, prolonged saccadic latency, and increased anti-saccade errors. These impairments correlate with executive dysfunction and disease progression. Fixation instability and altered pupillary responses further support the role of oculomotor metrics as non-invasive biomarkers. Emerging AI-driven eye-tracking models show promise for automated PD diagnosis and progression tracking. (5) Conclusions. Eye tracking provides a reliable, cost-effective tool for early PD detection, cognitive assessment, and rehabilitation. Future research should focus on standardizing clinical protocols, validating predictive AI models, and integrating eye tracking into multimodal treatment strategies. Full article

Background: Mild Cognitive Impairment (MCI) is a critical transitional phase between normal aging and dementia, and early detection is essential to mitigate cognitive decline. Traditional cognitive assessment tools, such as the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA), exhibit limitations in feasibility, which potentially and partially affects results for early-stage MCI detection. This study developed and tested a supportive cognitive assessment system for MCI auxiliary identification, leveraging eye-tracking features and convolutional neural network (CNN) analysis. Methods: The system employed eye-tracking technology in conjunction with machine learning to build a multimodal auxiliary identification model. Four eye movement tasks and two cognitive tests were administered to 128 participants (40 MCI patients, 57 elderly controls, 31 young adults as reference). We extracted 31 eye movement and 8 behavioral features to assess their contributions to classification accuracy using CNN analysis. Eye movement features only, behavioral features only, and combined features models were developed and tested respectively, to find out the most effective approach for MCI auxiliary identification. Results: Overall, the combined features model achieved a higher discrimination accuracy than models with single feature sets alone. Specifically, the model’s ability to differentiate MCI from healthy individuals, including young adults, reached an average accuracy of 74.62%. For distinguishing MCI from elderly controls, the model’s accuracy averaged 66.50%. Conclusions: Results show that a multimodal model significantly outperforms single-feature models in identifying MCI, highlighting the potential of eye-tracking for early detection. These findings suggest that integrating multimodal data can enhance the effectiveness of MCI auxiliary identification, providing a novel potential pathway for community-based early detection efforts. Full article

Background/Objectives: Serotonin and the serotonin transporter (SERT) may have a multifaceted, but not fully understood, role in obstructive sleep apnea (OSA) and its impact on mental health in this group of patients. This study aimed to investigate changes in serotonin and the serotonin transporter (SERT) and their association with depressive and insomnia symptoms. Methods: This study included 76 participants (OSA group: n = 36, control group (CG): n = 40) who underwent polysomnography, while venous blood samples (evening and morning) were analyzed for serotonin and the SERT using ELISA. SERT mRNA expression in peripheral leukocytes was measured via quantitative reverse-transcription polymerase chain reaction (qRT-PCR). Participants were evaluated for depression, insomnia, and quality of life (QoL). Results: This study found no significant differences in SERT mRNA or serotonin between the OSA group and CG. In the CG, individuals without mood disorders had higher baseline SERT levels and evening/morning SERT ratios than those with depression. Among the OSA participants, those with good QoL had elevated serotonin levels in the evening (p = 0.028) and morning (p = 0.043) compared to those with poor QoL. Baseline SERT protein levels were higher in the CG than in the OSA group for insomnia, while SERT mRNA expression was higher in the OSA group. Linear regression models showed 13.3% and 13.1% for non-rapid eye movement sleep (NREM) apnea/hypopnea index (AHI) and AHI variability, respectively, which was accounted for by the morning SERT level, while 30.8% of the arousal index variability was explained by the morning serotonin level. Conclusions: Serotonergic signaling may influence quality of life, depression, and insomnia in OSA, as well as the severity of the disease itself. Stratifying patients by clinical and laboratory phenotypes could enable more personalized treatment. Full article

Background/Objectives: Huntington’s disease (HD) is a neurodegenerative movement disorder associated with significant disability and impairment of Activities of Daily Living (ADLs). The impact of upper limb disability on quality of life (QoL) and its influence on ADLs is not well known yet. The aim of this study was to describe the manipulative dexterity, strength, and manual eye coordination of patients with manifest and premanifest-HD compared to healthy individuals and to analyze its influence on ADLs and QoL. Methods: We performed an observational, cross-sectional study including 71 ambulatory participants (27 manifest-HD patients, 15 premanifest-HD, and 29 controls). We gathered sociodemographic data, as well as clinical data, including cognition (MMSE), HD motor severity (Unified HD rating scale, UHDRS-TMS), QoL (Neuro-QoL), and ADLs (HD-ADL). Hand dexterity and strength in the dominant and non-dominant hand were assessed with the Nine Hole Peg Test, Ten Neurotest, Nut and Bolt Test, dynamometry, and Late-Life FDI. Analysis of covariance (ANCOVA) models were performed to investigate differences in hand function between manifest-HD, premanifest-HD, and controls. Results: Manifest-HD patients had significantly worse performance in manual and finger dexterity, fine-motor coordination, and poorer handgrip strength than premanifest-HD and controls. Premanifest-HD required more time to complete the test than controls. Significant correlations were found between hand variables and Late-Life FDI, Neuro-QoL, HD-ADL, and UHDRS-TMS. Conclusions: HD affects manipulative dexterity and hand function in premanifest and manifest patients. Therefore, to prevent disability and decreased QoL, evaluating the progression of upper limb dysfunction in HD is important to offer the best possible therapeutic interventions. Full article

Background/Objectives: Ocular proprioception is implicated in balance control and heterophoria is associated with abnormal posture, though previous research focused mainly on the role of vertical phoria and the use of vertical prisms. This study aims to evaluate whether ocular misalignment and prismatic correction of horizontal phoria affect posture. Methods: Sixty-nine (N = 69) young healthy subjects were included and equally divided by horizontal distance phoria: orthophoria (n = 23), esophoria (n = 23) and exophoria (n = 23). A prism of low power (two-diopter) was placed base out on the non-dominant eye, reducing misalignment in esophorics and increasing it in exophorics more than in orthophorics. Dynamic computerized posturography was performed with the sensory organization test protocol (SOT) of the EquiTest^® NeuroCom^® version 8 platform both without and with prism, always maintaining subjects unaware of prism use. A mixed model for repeated measures analysis of variance was run to evaluate the main effect of prism and the interaction effect of prism with baseline phoria. Results: Composite movement strategy score without prism was 88.1 ± 2.8% (ankle-dominant strategy) and slightly increased to 89.0 ± 3.1% with prism insertion (p = 0.004), further shifting toward ankle strategy. Composite equilibrium score without prism was 80.3 ± 6.5% and remained stable with prism insertion (81.3 ± 8.2%, p = 0.117), medio-lateral and antero-posterior projection of center of gravity did not displace significantly under prism insertion (p = 0.652 and p = 0.270, respectively). At baseline, posturographic parameters were statistically independent of individual phoria, and no significant interaction between prism insertion and individual phoria was documented for any parameters (p > 0.05 for all). Secondary analysis and pairwise comparisons confirmed that the effect of prism was strongly selective on condition SOT 5 (eyes-closed, platform sway-referenced) with improvement of equilibrium (70.4 ± 9.7% with prism vs. 65.7 ± 11.6% without) and more use of ankle strategy (81.6 ± 5.3% with prism vs. 78.2 ± 6.0% without), without any interaction of phoria and ocular dominance, while the other conditions were comparable with and without prism. Conclusions: A two-diopter prism base out on the non-dominant eye induces the body to use the ankle joint more independently of individual phoria, suggesting a small improvement in postural control, while maintaining oscillations of the center of gravity unaltered. Prism seems to enhance the function of vestibular system selectively. Phoria adjustments with prismatic correction enable intervention in postural behavior. Extraocular muscles could act as proprioceptors influencing postural stability. Full article

Background: Hand dexterity is affected by normal aging and neuroinflammatory processes in the brain. Understanding the relationship between hand dexterity and brain structure in neurotypical older adults may be informative about prodromal pathological processes, thus providing an opportunity for earlier diagnosis and intervention to improve functional outcomes. Methods: this study investigates the associations between hand dexterity and brain measures in neurotypical older adults (≥65 years) using the Nine-Hole Peg Test (9HPT) and magnetic resonance imaging (MRI). Results: Elastic net regularized regression revealed that reduced hand dexterity in dominant and non-dominant hands was associated with an enlarged volume of the left choroid plexus, the region implicated in neuroinflammatory and altered myelination processes, and reduced myelin content in the left frontal operculum, the region implicated in motor imagery, action production, and higher-order motor functions. Distinct neural mechanisms underlying hand dexterity in dominant and non-dominant hands included the differences in caudate and thalamic volumes as well as altered cortical myelin patterns in frontal, temporal, parietal, and occipital regions supporting sensorimotor and visual processing and integration, attentional control, and eye movements. Although elastic net identified more predictive features for the dominant vs. non-dominant hand, the feature stability was higher for the latter, thus indicating higher generalizability for the non-dominant hand model. Conclusions: Our findings suggest that the 9HPT for hand dexterity might be a cost-effective screening tool for early detection of neuroinflammatory and neurodegenerative processes. Longitudinal studies are needed to validate our findings in a larger sample and explore the potential of hand dexterity as an early clinical marker. Full article

Assessing handling qualities is crucial for ensuring the safety and operational efficiency of aircraft control characteristics. The growing interest in Urban Air Mobility (UAM) has increased the focus on electric Vertical Takeoff and Landing (eVTOL) aircraft; however, a comprehensive assessment of eVTOL handling qualities remains a challenge. This paper proposed a handling qualities framework to assess eVTOL handling qualities, integrating pilot compensation, task performance, and qualitative comments. An experiment was conducted, where eye-tracking data and subjective ratings from 16 participants as they performed various Mission Task Elements (MTEs) in an eVTOL simulator were analyzed. The relationship between pilot compensation and task workload was investigated based on eye metrics. Data mining results revealed that pilots’ eye movement patterns and workload perception change when performing Mission Task Elements (MTEs) that involve aircraft deficiencies. Additionally, pupil size, pupil diameter, iris diameter, interpupillary distance, iris-to-pupil ratio, and gaze entropy are found to be correlated with both handling qualities and task workload. Furthermore, a handling qualities and pilot workload recognition model is developed based on Long-Short Term Memory (LSTM), which is subsequently trained and evaluated with experimental data, achieving an accuracy of 97%. A case study was conducted to validate the effectiveness of the proposed framework. Overall, the proposed framework addresses the limitations of the existing Handling Qualities Rating Method (HQRM), offering a more comprehensive approach to handling qualities assessment. Full article