Eye movement behavior in a real-world virtual reality task reveals ADHD in children

Peer reviewe

Neural Network Dynamics of Visual Processing in the Higher-Order Visual System

Vision is one of the most important human senses that facilitate rich interaction with the external environment. For example, optimal spatial localization and subsequent motor contact with a specific physical object amongst others requires a combination of visual attention, discrimination, and sensory-motor coordination. The mammalian brain has evolved to elegantly solve this problem of transforming visual input into an efficient motor output to interact with an object of interest. The frontal and parietal cortices are two higher-order (i.e. processes information beyond simple sensory transformations) brain areas that are intimately involved in assessing how an animal’s internal state or prior experiences should influence cognitive-behavioral output. It is well known that activity within each region and functional interactions between both regions are correlated with visual attention, decision-making, and memory performance. Therefore, it is not surprising that impairment in the fronto-parietal circuit is often observed in many psychiatric disorders. Network- and circuit-level fronto-parietal involvement in sensory-based behavior is well studied; however, comparatively less is known about how single neuron activity in each of these areas can give rise to such macroscopic activity. The goal of the studies in this dissertation is to address this gap in knowledge through simultaneous recordings of cellular and population activity during sensory processing and behavioral paradigms. Together, the combined narrative builds on several themes in neuroscience: variability of single cell function, population-level encoding of stimulus properties, and state and context-dependent neural dynamics.Doctor of Philosoph

Eye movement behavior in a real-world virtual reality task reveals ADHD in children

Eye movements and other rich data obtained in virtual reality (VR) environments resembling situations where symptoms are manifested could help in the objective detection of various symptoms in clinical conditions. In the present study, 37 children with attention deficit hyperactivity disorder and 36 typically developing controls (9–13 y.o) played a lifelike prospective memory game using head-mounted display with inbuilt 90 Hz eye tracker. Eye movement patterns had prominent group differences, but they were dispersed across the full performance time rather than associated with specific events or stimulus features. A support vector machine classifier trained on eye movement data showed excellent discrimination ability with 0.92 area under curve, which was significantly higher than for task performance measures or for eye movements obtained in a visual search task. We demonstrated that a naturalistic VR task combined with eye tracking allows accurate prediction of attention deficits, paving the way for precision diagnostics

Linking impulsivity and inhibitory control using manual and oculomotor response inhibition tasks

Separate cognitive processes govern the inhibitory control of manual and oculomotor movements. Despite this fundamental distinction, little is known about how these inhibitory control processes relate to more complex domains of behavioral functioning. This study sought to determine how these inhibitory control mechanisms relate to broadly defined domains of impulsive behavior. Thirty adults with attention-deficit/ hyperactivity disorder (ADHD) and 28 comparison adults performed behavioral measures of inhibitory control and completed impulsivity inventories. Results suggest that oculomotor inhibitory control, but not manual inhibitory control, is related to specific domains of self-reported impulsivity. This finding was limited to the ADHD group; no significant relations between inhibitory control and impulsivity were found in comparison adults. These results highlight the heterogeneity of inhibitory control processes and their differential relations to different facets of impulsivity

Eye Movement Patterns Can Distinguish Schizophrenia From the Major Affective Disorders and Healthy Control Subjects

Open Access under the OUP Agreement Funding This project was supported by the following grants: The Royal Society of London, Chief Scientist Office Scotland (CZB/4/734), NHS Grampian Tenovus Scotland (G12/31), NHS Grampian Endowment Fund, Miller MacKenzie Trust, EU-FP6 (SGENE) and Health Innovation Challenge Fund, jointly from Wellcome Trust and Department of Health (WT-103911/Z/14/Z). The funders had no role in the original study design, the ongoing data collection and analysis, interpretation, or writing of the manuscript. We thank all who helped with clinical aspects of the study including research assistants Barbara Duff, Kate Cotton, Foteini Okonomitsiou, Elizabeth Hannaford, Zsuszanna Nemeth and Joanna Rodzinko Paska as well as the patients and volunteers whose help was indispensable. P Benson and D St Clair are co-founders of SACCADE Diagnostics Ltd a spin out company tasked to develop eye movement technology to assist diagnosis of major mental health disorders. The University of Aberdeen has patents pending in Europe (PCT/GB2013/050016) and USA (14/370,611). The data reported in this paper arose solely from funding by the acknowledged UK research bodies and charities none of whom have vested interests in the company. David St Clair had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Design of study and development of protocols: D St Clair, P Benson and S Beedie. Recruitment of patients, case note review and clinical and eye movement data collection, quality control and feature extraction of eye movement variables: St Clair, Rujescu, MacIntosh, Beedie, Lemmon, Nouzova. Drafting of the manuscript: St Clair and Nath. Critical revision of manuscript for important intellectual content: all authors. Statistical analyses: Nath and Benson. Interpretation of results: Nath, Benson, MacLennan and St Clair. Obtained funding: St Clair, Benson, MacIntosh, Rujescu. Supervision: St Clair, Benson, MacIntosh, Nath, Rujescu.Peer reviewedPublisher PD

Using Gaze for Behavioural Biometrics

A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the unique- ness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The rel- evant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field

Eye movements in patients with post-COVID condition

Eye movement control is impaired in some neurological conditions, but the impact of COVID-19 on eye movements remains unknown. This study aims to investigate differences in oculomotor function and pupil response in individuals who suffer post-COVID-19 condition (PCC) with cognitive deficits. Saccades, smooth pursuit, fixation, vergence and pupillary response were recorded using an eye tracker. Eye movements and pupil response parameters were computed. Data from 16 controls, 38 COVID mild (home recovery) and 19 COVID severe (hospital admission) participants were analyzed. Saccadic latencies were shorter in controls (183¿±¿54 ms) than in COVID mild (236¿±¿83 ms) and COVID severe (227¿±¿42 ms) participants (p¿=¿0.017). Fixation stability was poorer in COVID mild participants (Bivariate Contour Ellipse Area of 0.80¿±¿1.61°2 vs 0.36¿±¿0.65 °2 for controls, p¿=¿0.019), while percentage of pupil area reduction/enlargement was reduced in COVID severe participants (39.7¿±¿12.7%/31.6¿±¿12.7% compared to 51.7¿±¿22.0%/49.1¿±¿20.7% in controls, p¿<¿0.015). The characteristics of oculomotor alterations found in PCC may be useful to understand different pathophysiologic mechanisms.Project PID2020-112527RB-I00 funded by MCIN/AEI/10.13039/501100011033; La Marató de TV3 Foundation (202111-30-31-32).Peer ReviewedPostprint (published version

Digital Oculomotor Biomarkers in Dementia

Dementia is an umbrella term that covers a number of neurodegenerative syndromes featuring gradual disturbance of various cognitive functions that are severe enough to interfere with tasks of daily life. The diagnosis of dementia occurs frequently when pathological changes have been developing for years, symptoms of cognitive impairment are evident and the quality of life of the patients has already been deteriorated significantly. Although brain imaging and fluid biomarkers allow the monitoring of disease progression in vivo, they are expensive, invasive and not necessarily diagnostic in isolation. Recent studies suggest that eye-tracking technology is an innovative tool that holds promise for accelerating early detection of the disease, as well as, supporting the development of strategies that minimise impairment during every day activities. However, the optimal methods for quantitative evaluation of oculomotor behaviour during complex and naturalistic tasks in dementia have yet to be determined. This thesis investigates the development of computational tools and techniques to analyse eye movements of dementia patients and healthy controls under naturalistic and less constrained scenarios to identify novel digital oculomotor biomarkers. Three key contributions are made. First, the evaluation of the role of environment during navigation in patients with typical Alzheimer disease and Posterior Cortical Atrophy compared to a control group using a combination of eye movement and egocentric video analysis. Secondly, the development of a novel method of extracting salient features directly from the raw eye-tracking data of a mixed sample of dementia patients during a novel instruction-less cognitive test to detect oculomotor biomarkers of dementia-related cognitive dysfunction. Third, the application of unsupervised anomaly detection techniques for visualisation of oculomotor anomalies during various cognitive tasks. The work presented in this thesis furthers our understanding of dementia-related oculomotor dysfunction and gives future research direction for the development of computerised cognitive tests and ecological interventions