Oculomotor deficits in attention deficit hyperactivity disorder: a systematic review and meta-analysis

Development of an innovative approach using portable eye tracking to assist ADHD screening: a machine learning study

Introduction

Attention-deficit/hyperactivity disorder (ADHD) affects a significant proportion of the pediatric population, making early detection crucial for effective intervention. Eye movements are controlled by brain regions associated with neuropsychological functions, such as selective attention, response inhibition, and working memory, and their deficits are related to the core characteristics of ADHD. Herein, we aimed to develop a screening model for ADHD using machine learning (ML) and eye-tracking features from tasks that reflect neuropsychological deficits in ADHD.

Methods

Fifty-six children (mean age 8.38 ± 1.58, 45 males) diagnosed with ADHD based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition were recruited along with seventy-nine typically developing children (TDC) (mean age 8.80 ± 1.82, 33 males). Eye-tracking data were collected using a digital device during the performance of five behavioral tasks measuring selective attention, working memory, and response inhibition (pro-saccade task, anti-saccade task, memory-guided saccade task, change detection task, and Stroop task). ML was employed to select relevant eye-tracking features for ADHD, and to subsequently construct an optimal model classifying ADHD from TDC.

Results

We identified 33 eye-tracking features in the five tasks with the potential to distinguish children with ADHD from TDC. Participants with ADHD showed increased saccade latency and degree, and shorter fixation time in eye-tracking tasks. A soft voting model integrating extra tree and random forest classifiers demonstrated high accuracy (76.3%) at identifying ADHD using eye-tracking features alone. A comparison of the model using only eye-tracking features with models using the Advanced Test of Attention or Stroop test showed no significant difference in the area under the curve (AUC) (p = 0.419 and p=0.235, respectively). Combining demographic, behavioral, and clinical data with eye-tracking features improved accuracy, but did not significantly alter the AUC (p=0.208).

Discussion

Our study suggests that eye-tracking features hold promise as ADHD screening tools, even when obtained using a simple digital device. The current findings emphasize that eye-tracking features could be reliable indicators of impaired neurobiological functioning in individuals with ADHD. To enhance utility as a screening tool, future research should be conducted with a larger sample of participants with a more balanced gender ratio.

Effect of eye-tracking-based attention training for patients with poststroke cognitive impairment: a study protocol for a prospective, single-blinded, single-centre, randomised controlled trial in China

INTRODUCTION

Poststroke cognitive impairment (PSCI) is a common dysfunction that places a great burden on patients who had stroke and their families. Approximately 46%-92% of stroke survivors experience some degree of attention problems after a stroke. Improving attention is considered the core of successfully improving cognitive function and reintegrating patients into daily life. Eye tracking technology provides real-time feedback and accurate monitoring of cognitive processing, and using this technology to introduce attention training may improve patient treatment outcomes. The main purpose of this study was to investigate whether eye-tracking-based attention training has a positive effect on patients with PSCI.

METHODS AND ANALYSIS

This study is a prospective randomised controlled trial. We will recruit 48 patients with PSCI referred to the Department of Rehabilitation Medicine at West China Hospital, Sichuan University, in Southwest China. The participants will be randomly distributed into two groups. Both groups will undergo conventional rehabilitation for 3 weeks, and the intervention group will receive 3 weeks of eye-tracking-based attention training (20-30 min/day). The primary outcome will be the patients' cognitive function, measured by the Montreal Cognitive Assessment. The secondary outcomes will be the patients' attention, independence of daily activities and event-related potential. These outcomes will be assessed at baseline, at the end of treatment (3 weeks) and at follow-up (1 month and 3 months after treatment). We will report the statistics and estimations using 95% CI.

ETHICS AND DISSEMINATION

This trial received ethics approval from the Ethics Committee on Biomedical Research, West China Hospital of Sichuan University (2023 review No. 258). The results from this study will be disseminated via academic publication.

TRIAL REGISTRATION NUMBER

ChiCTR2300068727.

Neurophysiological measures and correlates of cognitive load in attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia: A scoping review and research recommendations

Working memory is integral to a range of critical cognitive functions such as reasoning and decision-making. Although alterations in working memory have been observed in neurodivergent populations, there has been no review mapping how cognitive load is measured in common neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia. This scoping review explores the neurophysiological measures used to study cognitive load in these specific populations. Our findings highlight that electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are the most frequently used methods, with a limited number of studies employing functional near-infrared spectroscopy (fNIRs), magnetoencephalography (MEG) or eye-tracking. Notably, eye-related measures are less commonly used, despite their prominence in cognitive load research among neurotypical individuals. The review also highlights potential correlates of cognitive load, such as neural oscillations in the theta and alpha ranges for EEG studies, blood oxygenation level-dependent (BOLD) responses in lateral and medial frontal brain regions for fMRI and fNIRS studies and eye-related measures such as pupil dilation and blink rate. Finally, critical issues for future studies are discussed, including the technical challenges associated with multimodal approaches, the possible impact of atypical features on cognitive load measures and balancing data richness with participant well-being. These insights contribute to a more nuanced understanding of cognitive load measurement in neurodivergent populations and point to important methodological considerations for future neuroscientific research in this area.