Classification and staging of Parkinson's disease using video-based eye tracking

From eyes' microtremors to critical flicker fusion

The critical flicker fusion threshold (CFFT) is the frequency at which a flickering light source becomes indistinguishable from continuous light. The CFFT is an important biomarker of health conditions, such as Alzheimer's disease and epilepsy, and is affected by factors as diverse as fatigue, drug consumption, and oxygen pressure, which make CFFT individual- and context-specific. Other causal factors beyond such biophysical processes are still to be uncovered. We investigate the connection between CFFT and specific eye-movements, called microtremors, which are small oscillatory gaze movements during fixation periods. We present evidence that individual differences in CFFT can be accounted by microtremors, and design an experiment, using a high-frequency monitor and recording the participant's eye-movements with an eye-tracker device, which enables to measure the range of frequencies of a specific individual's CFFT. Additionally, we introduce a classifier that can predict if the CFFT of specific participant lies in the range of high or low frequencies, based on the corresponding range of frequencies of eyes' microtremors. Our results show an accuracy of [Formula: see text] for a frequency threshold of 60 Hz and [Formula: see text] for a threshold of 120 Hz.

Current insights and prospects for the pathogenesis and treatment of clinical manifestations associated with Down syndrome through neurotransmitter, inflammatory, and oxidative stress pathways

Individuals with Down syndrome exhibit various changes in the human body systems, including alterations in the ocular, neurological, and dermatological systems. Especially, preclinical and clinical studies have determined Down syndrome patients to possess reduced intellectual and cognition abilities, which neurobehavioral effects are associated with altered molecular markers in the brain. For instance, neuroinflammation and increased brain oxidative stress are reported in animals models of Down syndrome, and the reversal of those markers lead to positive effects. Dopaminergic and serotonergic neurons are altered in individuals with Down syndrome, with dopamine and serotonin secretion reduced and their transporters upregulated. Hence, blocking reuptake of dopamine and serotonin might improve Down syndrome behavioral impairments. Norepinephrine loss was observed in a mouse model of Down syndrome, and treatment with a β2 adrenergic receptor agonist improved behavioral symptoms. Moreover, targeting certain glutamatergic receptors, particularly in the hippocampus, might correct the glutamatergic dysfunction and altered behaviors. Inverse agonists or antagonists of GABAergic receptors suppress GABA's inhibitory role, an effect associated with improved cognition behaviors in models of Down syndrome. Reports also suggest partial involvement of the histaminergic system in the impairment of memory function observed in Down syndrome. Finally, cholinergic system alteration has been reported, but the therapeutic role of its modulation needs further investigation. This review collects and reports multi-Omics Studies on Down syndrome, the crucial roles of inflammation, oxidative stress independently as well as role of oxidative stress in pregnancies with Down Syndrome and biomarkers of maternal diagnosis of Down syndrome. This review further explained the role of neurotransmitter pathways in Down syndrome pathogenesis, prognosis and therapeutic intervention for Down syndrome and future directions for interventions.

Eye movement and pupillary response abnormalities measured using virtual reality as biomarkers in the diagnosis of early-stage Parkinson's disease

Objective

Characteristic ocular symptoms are expected to serve as potential biomarkers for early diagnosis of Parkinson's disease (PD). However, possible ocular impairments in PD patients are rarely studied. The study aimed to investigate eye movement characteristics and pupil diameter changes in early-stage PD patients using virtual reality (VR)-based system and explore their contribution in the diagnosis of early-stage PD.

Methods

Forty-three early-stage PD patients and 25 healthy controls were included. Eye movements and pupillary response of all subjects were recorded and evaluated by wearing VR glasses. All subjects completed pro-saccade and anti-saccade tasks. Saccadic eye movement and pupillary response parameters were analyzed. Random Forests method was used for classification task, the performance of the classification model in differentiating early-stage PD patients from healthy controls were evaluated.

Results

PD patients exhibited reduced pro-saccade velocity and accuracy, longer average time to complete the pro-saccade, and lower anti-saccade error correction rate than healthy controls (all p < 0.05). Significant differences were found in the trajectories of changes in pupil diameter between the two groups. After extraction of frequency-amplitude features of pupil constriction from the spectra of the eye movement signals of PD patients, it can be seen that the amplitudes of movement signals of both the left and right eyes at different frequencies during pro-saccade and anti-saccade tasks were significant. The number of significant amplitude frequencies in both eyes at low (0-6 Hz), medium (7-12 Hz) and high frequencies (13-19 Hz) was 23, 9, and 16, respectively, during pro-saccade task, which was 10, 29, and 43, respectively, during anti-saccade task. The model with all features achieved an accuracy of up to 79%.

Conclusion

This study presents a non-invasive approach toward the diagnosis of early-stage PD with VR technology. Eye movement and pupillary response abnormalities measured using VR may be used as effective biomarkers for the diagnosis of early-stage PD.

Eye Tracking in Parkinson's Disease: A Review of Oculomotor Markers and Clinical Applications

(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.

Eye Tracking as Biomarker Compared to Neuropsychological Tests in Parkinson Syndromes: An Exploratory Pilot Study Before and After Deep Transcranial Magnetic Stimulation

Background/Objectives: Neurodegenerative diseases such as Parkinson's disease (PD) are becoming increasingly prevalent, necessitating diverse treatment options to manage symptoms. The effectiveness of these treatments depends on accurate and sensitive diagnostic methods. This exploratory pilot study explores the use of eye tracking and compares it to neuropsychological tests on patients treated with deep transcranial magnetic stimulation (dTMS). Methods: We used the HTC Vive Pro Eye VR headset with Tobii eye tracker to measure eye movements in 10 Parkinson syndrome patients while viewing three 360-degree scenes. Eye movements were recorded pre- and post-dTMS, focusing on Fixation Duration, Longest Fixation Period, Saccade Rate, and Total Fixations. Neuropsychological assessments (MoCA, TUG, BDI) were conducted before and after stimulation. dTMS was performed using the Brainsway device with the H5 helmet, targeting the motor cortex (1 Hz) and the prefrontal cortex (10 Hz) for 7-12 sessions. Results: ROC analysis indicated a moderate ability to differentiate between states using eye movement parameters. Significant correlations were found between changes in the longest fixation period and MoCA scores (r = 0.65, p = 0.025), and between fixation durations and BDI scores (r = -0.55, p = 0.043). Paired t-tests showed no significant differences in eye movement parameters, but BDI scores significantly reduced post-dTMS (t(5) = 2.57, p = 0.049). Conclusions: Eye-tracking parameters, particularly the Longest Fixation Duration and Saccade Rate, could serve as sensitive and feasible biomarkers for cognitive changes in Parkinson's Syndrome, offering a quick alternative to traditional methods. Traditional neuropsychological tests showed a significant improvement in depressive symptoms after dTMS. Further research with larger sample sizes is necessary to validate these findings and explore the diagnostic utility of eye tracking.

A study on the exploration of mild cognitive impairment in Parkinson's disease based on decision-making cognitive computing

Mild cognitive impairment in Parkinson's disease (PD-MCI) as an independent risk factor for dementia in Parkinson's disease has prognostic value in predicting dementia in PD patients. It was found that the calculation of cognitive function decision-making could better evaluate the cognitive function of PD-MCI. Therefore, this study explored deficits in decision-making cognitive function in PD-MCI population, and mined novel digital biomarkers for recognizing early cognitive decline in PD-MCI through an independently designed maze decision-making digital assessment paradigm. This study included 30 healthy controls 37 PD with normal cognition (PD-NC) and 40 PD-MCI patients. Through difference comparison and stepwise regression analysis, two digital decision-making biomarkers, total decision time and performance average acceleration, were screened, and their joint area under curve for the ability to discriminate between PD-MCI and PD-NC was 0.909, and for the ability to discriminate between PD-MCI and NC was 0.942. In addition, it was found that maze digital decision-making biomarkers had greater early warning efficacy in men than in women. Unlike traditional methods, this study used digital dynamic assessment to reveal possible decision-making cognitive deficits in the PD-MCI populations, which provides new ideas for effective screening for PD-MCI.

Age- and Sex-Based Developmental Biomarkers in Eye Movements

BACKGROUND

Eye movement research serves as a critical tool for assessing brain function, diagnosing neurological and psychiatric disorders, and understanding cognition and behavior. Sex differences have largely been under reported or ignored in neurological research. However, eye movement features provide biomarkers that are useful for disease classification with superior accuracy and robustness compared to previous classifiers for neurological diseases. Neurological diseases have a sex specificity, yet eye movement analysis has not been specific to our understanding of sex differences.

METHODS

The study involved subjects recruited from 804 sites equipped with RightEye Vision Systems, primarily located in optometry practices across the United States. Subjects completed six eye movement assessments: circular smooth pursuit (CSP), horizontal smooth pursuit (HSP), vertical smooth pursuit (VSP), horizontal saccades (HS), vertical saccades (VS), and fixation stability (FS). Eye movements were analyzed and classified in accordance with age and sex by multiple t-tests and linear regression models.

RESULTS

This study represented a large sample size of 23,557 subjects, with 11,871 males and 11,686 females representing ages from birth through 80 years of age. We observed statistically significant differences for all eye movement functions between males and females.

CONCLUSIONS

We demonstrate that eye movements are sex-specific and offer normative data to compare sex-specific eye movement function by age. Novel baseline metrics can be compared to individual performance, regardless of sex. This study represents significant progress in linking eye movements with brain function and clinical syndromes, allowing researchers and clinicians to stratify individuals by age and sex.

Multimodal oculomotor assessment reveals prodromal markers of Parkinson's disease in non-manifesting LRRK2 G2019S mutation carriers

Oculomotor behaviour changes in patients with Parkinson's disease (PD) are a promising source of prodromal disease markers. Capitalizing on this phenomenon to facilitate early diagnosis requires oculomotor assessment in prodromal cohorts. We examined oculomotor behaviour in non-manifesting LRRK2 G2019S mutation carriers (LRRK2-NM), who have heightened PD risk.Seventeen LRRK2-NM participants, 47 patients with idiopathic PD, and 63 healthy age-matched control participants completed an interleaved pro- and antisaccade task while undergoing video-based eye-tracking. We analyzed between-group differences in saccade, pupil, blink, and fixation acquisition behaviour. Patients with PD showed previously demonstrated abnormalities (saccade hypometria, antisaccade errors). Relative to controls, LRRK2-NM participants and patients with PD both displayed increased short-latency prosaccades and reduced pupil velocity, plus altered fixation acquisition-less preemptive returning of gaze to the future fixation point location. Interestingly, the effect on blink probability was opposite-higher than controls in LRRK2-NM participants but lower in patients with PD. Future longitudinal studies must confirm the viability of these features as prodromal PD markers.

Noradrenergic modulation of saccades in Parkinson's disease

Noradrenaline is a powerful modulator of cognitive processes, including action decisions underlying saccadic control. Changes in saccadic eye movements are common across neurodegenerative diseases of ageing, including Parkinson's disease. With growing interest in noradrenergic treatment potential for non-motor symptoms in Parkinson's disease, the temporal precision of oculomotor function is advantageous to assess the effects of this modulation. Here, we studied the effect of 40 mg atomoxetine, a noradrenaline reuptake inhibitor, in 19 people with idiopathic Parkinson's disease using a single dose, randomized double-blind, crossover, placebo-controlled design. Twenty-five healthy adult participants completed the assessments to provide normative data. Participants performed prosaccade and antisaccade tasks. The latency, velocity and accuracy of saccades, and resting pupil diameter, were measured. Increased pupil diameter on the drug confirmed its expected effect on the locus coeruleus ascending arousal system. Atomoxetine altered key aspects of saccade performance: prosaccade latencies were faster and the saccadic main sequence was normalized. These changes were accompanied by increased antisaccade error rates on the drug. Together, these findings suggest a shift in the speed-accuracy trade-off for visuomotor decisions in response to noradrenergic treatment. Our results provide new evidence to substantiate a role for noradrenergic modulation of saccades, and based on known circuitry, we advance the hypothesis that this reflects modulation at the level of the locus coeruleus-superior colliculus pathway. Given the potential for noradrenergic treatment of non-motor symptoms of Parkinson's disease and related conditions, the oculomotor system can support the assessment of cognitive effects without limb-motor confounds on task performance.

Automated analysis of spontaneous eye blinking in patients with acute facial palsy or facial synkinesis

Although patients with facial palsy often complain of disturbed eye blinking which may lead to visual impairment, a blinking analysis is not part of routine grading of facial palsy. Twenty minutes of spontaneous eye blinking at rest of 30 patients with facial palsy (6 with acute palsy; 24 patients with facial synkinesis; median age: 58 years, 67% female), and 30 matched healthy probands (median age: 57 years; 67% female) was smart phone video recorded. A custom computer program automatically extracted eye measures and determined the eye closure rate (eye aspect ratio [EAR]), blink frequency, and blink duration. Facial Clinimetric Evaluation (FaCE), Facial Disability Index (FDI) were assessed as patient-reported outcome measures. The minimal EAR, i.e., minimal visible eye surface during blinking, was significantly higher on the paretic side in patients with acute facial palsy than in patients with synkinesis or in healthy controls. The blinking frequency on the affected side was significantly lower in both patient groups compared to healthy controls. Vice versa, blink duration was longer in both patient groups. There was no clear correlation between the blinking values and FaCE and FDI. Blinking parameters are easy to estimate automatically and add a functionally important parameter to facial grading.

Customized treatment for Parkinson’s disease: extending lifespan and improving symptoms

Background

Parkinson's disease (PD) is the second most prevalent neurodegenerative ailment affecting aged people. Several motor and non-motor symptoms appearing with this disease are linked to reduction in dopamine levels. Parkinson’s disease manifested by a wide range of symptoms, including gait instability, voice impairments, bradykinesia, hypomimia, tremors, and cognitive dysfunctions, where the concern is its steady global progression. Therefore, by evaluating the stage-specific retention and transition thresholds, the suggested work attempts to detect the progression of PD. Similarly, the assessment of the annual rate of deterioration of symptoms aids in assessing the influence of medications on four stages and six prime symptoms.

Results

During the 9-year period following clinical diagnosis, a symptomatic deterioration of 34.995% is observed in untreated patients compared to treated patients. Furthermore, the experimental analysis illustrates that medicines assist in regulating only bradykinesia, tremors, hypomimia, and speech impairment, which lasts for 4.8 years on average. A saturation period is also identified during the moderate phase, where the computed average depreciation in the Unified Parkinson’s Disease Rating Scale (UPDRS) is the least. Similarly, from stages 1 to 4, medicines might improve stage retention by 4.44, 3.5, 2.72, and 1.6 years, respectively. It demonstrates that, despite transient advantages and adverse effects, medications also have an optimistic impact on PD patients.

Conclusions

Here, we show that medication can extend the lifespan of PD patients up to 12 years cumulatively. Furthermore, the proposed work suggests that stagewise alternative remedies for improving quality of life, boosting the benefits of medicines, and eliminating their side effects. These include counselling, diet changes, yoga asanas, herbal medicines, bio-cleansing and other therapies. The current work does not consider the temporary transition among stages; nonetheless, further investigation regarding PD could open avenues for more worthy treatments with fewer side effects.

ATST-Net: A method to identify early symptoms in the upper and lower extremities of PD

Bradykinesia, a core symptom of motor disorders in Parkinson's disease (PD), is a major criterion for screening early PD patients in clinical practice. Currently, many studies have proposed automatic assessment schemes for bradykinesia in PD. However, existing schemes suffer from problems such as dependence on professional equipment, single evaluation tasks, difficulty in obtaining samples and low accuracy. This paper proposes a manual feature extraction- and neural network-based method to evaluate bradykinesia, effectively solving the problem of a small sample size. This method can automatically assess finger tapping (FT), hand movement (HM), toe tapping (TT) and bilateral foot sensitivity tasks (LA) through a unified model. Data were obtained from 120 individuals, including 93 patients with Parkinson's disease and 27 age- and sex-matched normal controls (NCs). Manual feature extraction and Attention Time Series Two-stream Networks (ATST-Net) were used for classification. Accuracy rates of 0.844, 0.819, 0.728, and 0.768 were achieved for FT, HM, TT, and LA, respectively. To our knowledge, this study is the first to simultaneously evaluate the upper and lower limbs using a unified model that has significant advantages in both model training and transfer learning.

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review

BACKGROUND

Parkinson's Disease is the second most common neurological disease in over 60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to 20 years post-diagnosis. Processes for detection and diagnosis of cognitive impairments are not sufficient to predict decline at an early stage for significant impact. Ageing populations, neurologist shortages and subjective interpretations reduce the effectiveness of decisions and diagnoses. Researchers are now utilising machine learning for detection and diagnosis of cognitive impairment based on symptom presentation and clinical investigation. This work aims to provide an overview of published studies applying machine learning to detecting and diagnosing cognitive impairment, evaluate the feasibility of implemented methods, their impacts, and provide suitable recommendations for methods, modalities and outcomes.

METHODS

To provide an overview of the machine learning techniques, data sources and modalities used for detection and diagnosis of cognitive impairment in Parkinson's Disease, we conducted a review of studies published on the PubMed, IEEE Xplore, Scopus and ScienceDirect databases. 70 studies were included in this review, with the most relevant information extracted from each. From each study, strategy, modalities, sources, methods and outcomes were extracted.

RESULTS

Literatures demonstrate that machine learning techniques have potential to provide considerable insight into investigation of cognitive impairment in Parkinson's Disease. Our review demonstrates the versatility of machine learning in analysing a wide range of different modalities for the detection and diagnosis of cognitive impairment in Parkinson's Disease, including imaging, EEG, speech and more, yielding notable diagnostic accuracy.

CONCLUSIONS

Machine learning based interventions have the potential to glean meaningful insight from data, and may offer non-invasive means of enhancing cognitive impairment assessment, providing clear and formidable potential for implementation of machine learning into clinical practice.

Eye movement function captured via an electronic tablet informs on cognition and disease severity in Parkinson's disease

Studying the oculomotor system provides a unique window to assess brain health and function in various clinical populations. Although the use of detailed oculomotor parameters in clinical research has been limited due to the scalability of the required equipment, the development of novel tablet-based technologies has created opportunities for fast, easy, cost-effective, and reliable eye tracking. Oculomotor measures captured via a mobile tablet-based technology have previously been shown to reliably discriminate between Parkinson's Disease (PD) patients and healthy controls. Here we further investigate the use of oculomotor measures from tablet-based eye-tracking to inform on various cognitive abilities and disease severity in PD patients. When combined using partial least square regression, the extracted oculomotor parameters can explain up to 71% of the variance in cognitive test scores (e.g. Trail Making Test). Moreover, using a receiver operating characteristics (ROC) analysis we show that eye-tracking parameters can be used in a support vector classifier to discriminate between individuals with mild PD from those with moderate PD (based on UPDRS cut-off scores) with an accuracy of 90%. Taken together, our findings highlight the potential usefulness of mobile tablet-based technology to rapidly scale eye-tracking use and usefulness in both research and clinical settings by informing on disease stage and cognitive outcomes.

Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases

Neurodegenerative diseases (NDs) such as Alzheimer's Disease (AD) and Parkinson's Disease (PD) are devastating conditions that can develop without noticeable symptoms, causing irreversible damage to neurons before any signs become clinically evident. NDs are a major cause of disability and mortality worldwide. Currently, there are no cures or treatments to halt their progression. Therefore, the development of early detection methods is urgently needed to delay neuronal loss as soon as possible. Despite advancements in Medtech, the early diagnosis of NDs remains a challenge at the intersection of medical, IT, and regulatory fields. Thus, this review explores "digital biomarkers" (tools designed for remote neurocognitive data collection and AI analysis) as a potential solution. The review summarizes that recent studies combining AI with digital biomarkers suggest the possibility of identifying pre-symptomatic indicators of NDs. For instance, research utilizing convolutional neural networks for eye tracking has achieved significant diagnostic accuracies. ROC-AUC scores reached up to 0.88, indicating high model performance in differentiating between PD patients and healthy controls. Similarly, advancements in facial expression analysis through tools have demonstrated significant potential in detecting emotional changes in ND patients, with some models reaching an accuracy of 0.89 and a precision of 0.85. This review follows a structured approach to article selection, starting with a comprehensive database search and culminating in a rigorous quality assessment and meaning for NDs of the different methods. The process is visualized in 10 tables with 54 parameters describing different approaches and their consequences for understanding various mechanisms in ND changes. However, these methods also face challenges related to data accuracy and privacy concerns. To address these issues, this review proposes strategies that emphasize the need for rigorous validation and rapid integration into clinical practice. Such integration could transform ND diagnostics, making early detection tools more cost-effective and globally accessible. In conclusion, this review underscores the urgent need to incorporate validated digital health tools into mainstream medical practice. This integration could indicate a new era in the early diagnosis of neurodegenerative diseases, potentially altering the trajectory of these conditions for millions worldwide. Thus, by highlighting specific and statistically significant findings, this review demonstrates the current progress in this field and the potential impact of these advancements on the global management of NDs.

Attentional modulation of eye blinking is altered by sex, age, and task structure

Spontaneous eye blinking is gaining popularity as a proxy for higher cognitive functions, as it is readily modulated by both environmental demands and internal processes. Prior studies were impoverished in sample size, sex representation and age distribution, making it difficult to establish a complete picture of the behavior. Here we present eye-tracking data from a large cohort of normative participants (n=604, 393 F, aged 5-93 years) performing two tasks: one with structured, discrete trials (interleaved pro/anti-saccade task; IPAST) and one with a less structured, continuous organization in which participants watch movies (free-viewing; FV). Sex- and age-based analyses revealed that females had higher blink rates between the ages of 22 and 58 years in the IPAST, and 22 and 34 years in FV. We derived a continuous measure of blink probability to reveal behavioral changes driven by stimulus appearance in both paradigms. In the IPAST, blinks were suppressed near stimulus appearance, particularly on correct anti-saccade trials, which we attribute to the stronger inhibitory control required for anti-saccades compared to pro-saccades. In FV, blink suppression occurred immediately after scene changes, and the effect was sustained on scenes where gaze clustered among participants (indicating engagement of attention). Females were more likely than males to blink during appearance of novel stimuli in both tasks, but only within the age bin of 18-44 years. The consistency of blink patterns in each paradigm endorses blinking as a sensitive index for changes in visual processing and attention, while sex and age differences drive interindividual variability.Significance Statement Eye-tracking is becoming useful as a non-invasive tool for detecting preclinical markers of neurological and psychiatric disease. Blinks are understudied despite being an important supplement to saccade and pupil eye-tracking metrics. The present study is a crucial step in developing a healthy baseline for blink behavior to compare to clinical groups. While many prior blink studies suffered from small sample sizes with relatively low age- and sex-diversity (review by Jongkees & Colzato, 2016), our large cohort of healthy participants has permitted a more detailed analysis of sex and age effects in blink behavior. Furthermore, our analysis techniques are robust to temporal changes in blink probability, greatly clarifying the relationship between blinking, visual processing, and inhibitory control mechanisms on visual tasks.

Eye movements in Parkinson's disease: from neurophysiological mechanisms to diagnostic tools

Movement disorders such as Parkinson's disease (PD) impact oculomotor function - the ability to move the eyes accurately and purposefully to serve a multitude of sensory, cognitive, and secondary motor tasks. Decades of neurophysiological research in monkeys and behavioral studies in humans have characterized the neural basis of healthy oculomotor control. This review links eye movement abnormalities in persons living with PD to the underlying neurophysiological mechanisms and pathways. Building on this foundation, we highlight recent progress in using eye movements to gauge symptom severity, assess treatment effects, and serve as potential precision biomarkers. We conclude that whereas eye movements provide insights into PD mechanisms, based on current evidence they appear to lack sufficient sensitivity and specificity to serve as a standalone diagnostic tool. Their full potential may be realized when combined with other disease indicators in big datasets.

The Selective Social Attention task in children with autism spectrum disorder: Results from the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) feasibility study

The Selective Social Attention (SSA) task is a brief eye-tracking task involving experimental conditions varying along socio-communicative axes. Traditionally the SSA has been used to probe socially-specific attentional patterns in infants and toddlers who develop autism spectrum disorder (ASD). This current work extends these findings to preschool and school-age children. Children 4- to 12-years-old with ASD (N = 23) and a typically-developing comparison group (TD; N = 25) completed the SSA task as well as standardized clinical assessments. Linear mixed models examined group and condition effects on two outcome variables: percent of time spent looking at the scene relative to scene presentation time (%Valid), and percent of time looking at the face relative to time spent looking at the scene (%Face). Age and IQ were included as covariates. Outcome variables' relationships to clinical data were assessed via correlation analysis. The ASD group, compared to the TD group, looked less at the scene and focused less on the actress' face during the most socially-engaging experimental conditions. Additionally, within the ASD group, %Face negatively correlated with SRS total T-scores with a particularly strong negative correlation with the Autistic Mannerism subscale T-score. These results highlight the extensibility of the SSA to older children with ASD, including replication of between-group differences previously seen in infants and toddlers, as well as its ability to capture meaningful clinical variation within the autism spectrum across a wide developmental span inclusive of preschool and school-aged children. The properties suggest that the SSA may have broad potential as a biomarker for ASD.

Oculometric measures as a tool for assessment of clinical symptoms and severity of Parkinson's disease

Abnormalities of oculometric measures (OM) are widely described in people with Parkinson's disease (PD). However, knowledge of correlations between abnormal OM, disease severity and clinical assessment in PD patients is still lacking. To evaluate these correlations, PD patients (215 patients, mean age 69 ± 9.1 years, 79 females) with severe (H&Y > 3) and mild to moderate (H&Y ≤ 2) disease, and 215 age-matched healthy subjects were enrolled. All patients were evaluated using MDS-UPDRS and an oculometric test using computer vision and deep learning algorithms. Comparisons of OM between groups and correlations between OM and MDS-UPDRS scores were calculated. Saccadic latency (ms) was prolonged in patients with severe compared with mild to moderate disease (pro-saccades: 267 ± 69 vs. 238 ± 53, p = 0.0011; anti-saccades: 386 ± 119 vs. 352 ± 106, p = 0.0393) and in patients with mild to moderate disease versus healthy subjects (pro-saccades: 238 ± 53 vs. 220 ± 45, p = 0.0003; anti-saccades: 352 ± 106 vs. 289 ± 71, p < 0.0001). Error rate (%) was higher among patients with severe (64.06 ± 23.08) versus mild to moderate disease (49.84 ± 24.81, p = 0.0001), and versus healthy subjects (49.84 ± 24.81 vs. 28.31 ± 21.72, p = 0.00001). Response accuracy (%) was lower for patients with severe (75.66 ± 13.11) versus mild to moderate disease (79.66 ± 13.56, p = 0.0462), and versus healthy subjects (79.66 ± 13.56 vs. 90.27 ± 8.79, p < 0.0001). Pro- and anti-saccadic latency, error rate and accuracy were correlated with MDS-UPDRS scores (r = 0.32, 0.28, 0.36 and -0.30, respectively, p < 0.0001) and similar correlations were found with its axial subscore (R = 0.38, 0.29, 0.44, and -0.30, respectively, p < 0.0001). Several OM were different in patients under levodopa treatment. OM worsened as PD severity increases, and were correlated with MDS-UPDRS scores. Using OM can be implemented for PD patients' assessment as a tool to follow disease progression.

Motor synchronization and impulsivity in pediatric borderline personality disorder with and without attention-deficit hyperactivity disorder: an eye-tracking study of saccade, blink and pupil behavior

Shifting motor actions from reflexively reacting to an environmental stimulus to predicting it allows for smooth synchronization of behavior with the outside world. This shift relies on the identification of patterns within the stimulus - knowing when a stimulus is predictable and when it is not - and launching motor actions accordingly. Failure to identify predictable stimuli results in movement delays whereas failure to recognize unpredictable stimuli results in early movements with incomplete information that can result in errors. Here we used a metronome task, combined with video-based eye-tracking, to quantify temporal predictive learning and performance to regularly paced visual targets at 5 different interstimulus intervals (ISIs). We compared these results to the random task where the timing of the target was randomized at each target step. We completed these tasks in female pediatric psychiatry patients (age range: 11-18 years) with borderline personality disorder (BPD) symptoms, with (n = 22) and without (n = 23) a comorbid attention-deficit hyperactivity disorder (ADHD) diagnosis, against controls (n = 35). Compared to controls, BPD and ADHD/BPD cohorts showed no differences in their predictive saccade performance to metronome targets, however, when targets were random ADHD/BPD participants made significantly more anticipatory saccades (i.e., guesses of target arrival). The ADHD/BPD group also significantly increased their blink rate and pupil size when initiating movements to predictable versus unpredictable targets, likely a reflection of increased neural effort for motor synchronization. BPD and ADHD/BPD groups showed increased sympathetic tone evidenced by larger pupil sizes than controls. Together, these results support normal temporal motor prediction in BPD with and without ADHD, reduced response inhibition in BPD with comorbid ADHD, and increased pupil sizes in BPD patients. Further these results emphasize the importance of controlling for comorbid ADHD when querying BPD pathology.